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TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| import base64
def base85_encode(string: str) -> bytes:
"""
>>> base85_encode("")
b''
>>> base85_encode("12345")
b'0etOA2#'
>>> base85_encode("base 85")
b'@UX=h+?24'
"""
# encoded the input to a bytes-like object and then a85encode that
return base64.a85encode(string.encode("utf-8"))
def base85_decode(a85encoded: bytes) -> str:
"""
>>> base85_decode(b"")
''
>>> base85_decode(b"0etOA2#")
'12345'
>>> base85_decode(b"@UX=h+?24")
'base 85'
"""
# a85decode the input into bytes and decode that into a human readable string
return base64.a85decode(a85encoded).decode("utf-8")
if __name__ == "__main__":
import doctest
doctest.testmod()
| import base64
def base85_encode(string: str) -> bytes:
"""
>>> base85_encode("")
b''
>>> base85_encode("12345")
b'0etOA2#'
>>> base85_encode("base 85")
b'@UX=h+?24'
"""
# encoded the input to a bytes-like object and then a85encode that
return base64.a85encode(string.encode("utf-8"))
def base85_decode(a85encoded: bytes) -> str:
"""
>>> base85_decode(b"")
''
>>> base85_decode(b"0etOA2#")
'12345'
>>> base85_decode(b"@UX=h+?24")
'base 85'
"""
# a85decode the input into bytes and decode that into a human readable string
return base64.a85decode(a85encoded).decode("utf-8")
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| """
Project Euler Problem 89: https://projecteuler.net/problem=89
For a number written in Roman numerals to be considered valid there are basic rules
which must be followed. Even though the rules allow some numbers to be expressed in
more than one way there is always a "best" way of writing a particular number.
For example, it would appear that there are at least six ways of writing the number
sixteen:
IIIIIIIIIIIIIIII
VIIIIIIIIIII
VVIIIIII
XIIIIII
VVVI
XVI
However, according to the rules only XIIIIII and XVI are valid, and the last example
is considered to be the most efficient, as it uses the least number of numerals.
The 11K text file, roman.txt (right click and 'Save Link/Target As...'), contains one
thousand numbers written in valid, but not necessarily minimal, Roman numerals; see
About... Roman Numerals for the definitive rules for this problem.
Find the number of characters saved by writing each of these in their minimal form.
Note: You can assume that all the Roman numerals in the file contain no more than four
consecutive identical units.
"""
import os
SYMBOLS = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1000}
def parse_roman_numerals(numerals: str) -> int:
"""
Converts a string of roman numerals to an integer.
e.g.
>>> parse_roman_numerals("LXXXIX")
89
>>> parse_roman_numerals("IIII")
4
"""
total_value = 0
index = 0
while index < len(numerals) - 1:
current_value = SYMBOLS[numerals[index]]
next_value = SYMBOLS[numerals[index + 1]]
if current_value < next_value:
total_value -= current_value
else:
total_value += current_value
index += 1
total_value += SYMBOLS[numerals[index]]
return total_value
def generate_roman_numerals(num: int) -> str:
"""
Generates a string of roman numerals for a given integer.
e.g.
>>> generate_roman_numerals(89)
'LXXXIX'
>>> generate_roman_numerals(4)
'IV'
"""
numerals = ""
m_count = num // 1000
numerals += m_count * "M"
num %= 1000
c_count = num // 100
if c_count == 9:
numerals += "CM"
c_count -= 9
elif c_count == 4:
numerals += "CD"
c_count -= 4
if c_count >= 5:
numerals += "D"
c_count -= 5
numerals += c_count * "C"
num %= 100
x_count = num // 10
if x_count == 9:
numerals += "XC"
x_count -= 9
elif x_count == 4:
numerals += "XL"
x_count -= 4
if x_count >= 5:
numerals += "L"
x_count -= 5
numerals += x_count * "X"
num %= 10
if num == 9:
numerals += "IX"
num -= 9
elif num == 4:
numerals += "IV"
num -= 4
if num >= 5:
numerals += "V"
num -= 5
numerals += num * "I"
return numerals
def solution(roman_numerals_filename: str = "/p089_roman.txt") -> int:
"""
Calculates and returns the answer to project euler problem 89.
>>> solution("/numeralcleanup_test.txt")
16
"""
savings = 0
with open(os.path.dirname(__file__) + roman_numerals_filename) as file1:
lines = file1.readlines()
for line in lines:
original = line.strip()
num = parse_roman_numerals(original)
shortened = generate_roman_numerals(num)
savings += len(original) - len(shortened)
return savings
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 89: https://projecteuler.net/problem=89
For a number written in Roman numerals to be considered valid there are basic rules
which must be followed. Even though the rules allow some numbers to be expressed in
more than one way there is always a "best" way of writing a particular number.
For example, it would appear that there are at least six ways of writing the number
sixteen:
IIIIIIIIIIIIIIII
VIIIIIIIIIII
VVIIIIII
XIIIIII
VVVI
XVI
However, according to the rules only XIIIIII and XVI are valid, and the last example
is considered to be the most efficient, as it uses the least number of numerals.
The 11K text file, roman.txt (right click and 'Save Link/Target As...'), contains one
thousand numbers written in valid, but not necessarily minimal, Roman numerals; see
About... Roman Numerals for the definitive rules for this problem.
Find the number of characters saved by writing each of these in their minimal form.
Note: You can assume that all the Roman numerals in the file contain no more than four
consecutive identical units.
"""
import os
SYMBOLS = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1000}
def parse_roman_numerals(numerals: str) -> int:
"""
Converts a string of roman numerals to an integer.
e.g.
>>> parse_roman_numerals("LXXXIX")
89
>>> parse_roman_numerals("IIII")
4
"""
total_value = 0
index = 0
while index < len(numerals) - 1:
current_value = SYMBOLS[numerals[index]]
next_value = SYMBOLS[numerals[index + 1]]
if current_value < next_value:
total_value -= current_value
else:
total_value += current_value
index += 1
total_value += SYMBOLS[numerals[index]]
return total_value
def generate_roman_numerals(num: int) -> str:
"""
Generates a string of roman numerals for a given integer.
e.g.
>>> generate_roman_numerals(89)
'LXXXIX'
>>> generate_roman_numerals(4)
'IV'
"""
numerals = ""
m_count = num // 1000
numerals += m_count * "M"
num %= 1000
c_count = num // 100
if c_count == 9:
numerals += "CM"
c_count -= 9
elif c_count == 4:
numerals += "CD"
c_count -= 4
if c_count >= 5:
numerals += "D"
c_count -= 5
numerals += c_count * "C"
num %= 100
x_count = num // 10
if x_count == 9:
numerals += "XC"
x_count -= 9
elif x_count == 4:
numerals += "XL"
x_count -= 4
if x_count >= 5:
numerals += "L"
x_count -= 5
numerals += x_count * "X"
num %= 10
if num == 9:
numerals += "IX"
num -= 9
elif num == 4:
numerals += "IV"
num -= 4
if num >= 5:
numerals += "V"
num -= 5
numerals += num * "I"
return numerals
def solution(roman_numerals_filename: str = "/p089_roman.txt") -> int:
"""
Calculates and returns the answer to project euler problem 89.
>>> solution("/numeralcleanup_test.txt")
16
"""
savings = 0
with open(os.path.dirname(__file__) + roman_numerals_filename) as file1:
lines = file1.readlines()
for line in lines:
original = line.strip()
num = parse_roman_numerals(original)
shortened = generate_roman_numerals(num)
savings += len(original) - len(shortened)
return savings
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| # Information on binary shifts:
# https://docs.python.org/3/library/stdtypes.html#bitwise-operations-on-integer-types
# https://www.interviewcake.com/concept/java/bit-shift
def logical_left_shift(number: int, shift_amount: int) -> str:
"""
Take in 2 positive integers.
'number' is the integer to be logically left shifted 'shift_amount' times.
i.e. (number << shift_amount)
Return the shifted binary representation.
>>> logical_left_shift(0, 1)
'0b00'
>>> logical_left_shift(1, 1)
'0b10'
>>> logical_left_shift(1, 5)
'0b100000'
>>> logical_left_shift(17, 2)
'0b1000100'
>>> logical_left_shift(1983, 4)
'0b111101111110000'
>>> logical_left_shift(1, -1)
Traceback (most recent call last):
...
ValueError: both inputs must be positive integers
"""
if number < 0 or shift_amount < 0:
raise ValueError("both inputs must be positive integers")
binary_number = str(bin(number))
binary_number += "0" * shift_amount
return binary_number
def logical_right_shift(number: int, shift_amount: int) -> str:
"""
Take in positive 2 integers.
'number' is the integer to be logically right shifted 'shift_amount' times.
i.e. (number >>> shift_amount)
Return the shifted binary representation.
>>> logical_right_shift(0, 1)
'0b0'
>>> logical_right_shift(1, 1)
'0b0'
>>> logical_right_shift(1, 5)
'0b0'
>>> logical_right_shift(17, 2)
'0b100'
>>> logical_right_shift(1983, 4)
'0b1111011'
>>> logical_right_shift(1, -1)
Traceback (most recent call last):
...
ValueError: both inputs must be positive integers
"""
if number < 0 or shift_amount < 0:
raise ValueError("both inputs must be positive integers")
binary_number = str(bin(number))[2:]
if shift_amount >= len(binary_number):
return "0b0"
shifted_binary_number = binary_number[: len(binary_number) - shift_amount]
return "0b" + shifted_binary_number
def arithmetic_right_shift(number: int, shift_amount: int) -> str:
"""
Take in 2 integers.
'number' is the integer to be arithmetically right shifted 'shift_amount' times.
i.e. (number >> shift_amount)
Return the shifted binary representation.
>>> arithmetic_right_shift(0, 1)
'0b00'
>>> arithmetic_right_shift(1, 1)
'0b00'
>>> arithmetic_right_shift(-1, 1)
'0b11'
>>> arithmetic_right_shift(17, 2)
'0b000100'
>>> arithmetic_right_shift(-17, 2)
'0b111011'
>>> arithmetic_right_shift(-1983, 4)
'0b111110000100'
"""
if number >= 0: # Get binary representation of positive number
binary_number = "0" + str(bin(number)).strip("-")[2:]
else: # Get binary (2's complement) representation of negative number
binary_number_length = len(bin(number)[3:]) # Find 2's complement of number
binary_number = bin(abs(number) - (1 << binary_number_length))[3:]
binary_number = (
"1" + "0" * (binary_number_length - len(binary_number)) + binary_number
)
if shift_amount >= len(binary_number):
return "0b" + binary_number[0] * len(binary_number)
return (
"0b"
+ binary_number[0] * shift_amount
+ binary_number[: len(binary_number) - shift_amount]
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| # Information on binary shifts:
# https://docs.python.org/3/library/stdtypes.html#bitwise-operations-on-integer-types
# https://www.interviewcake.com/concept/java/bit-shift
def logical_left_shift(number: int, shift_amount: int) -> str:
"""
Take in 2 positive integers.
'number' is the integer to be logically left shifted 'shift_amount' times.
i.e. (number << shift_amount)
Return the shifted binary representation.
>>> logical_left_shift(0, 1)
'0b00'
>>> logical_left_shift(1, 1)
'0b10'
>>> logical_left_shift(1, 5)
'0b100000'
>>> logical_left_shift(17, 2)
'0b1000100'
>>> logical_left_shift(1983, 4)
'0b111101111110000'
>>> logical_left_shift(1, -1)
Traceback (most recent call last):
...
ValueError: both inputs must be positive integers
"""
if number < 0 or shift_amount < 0:
raise ValueError("both inputs must be positive integers")
binary_number = str(bin(number))
binary_number += "0" * shift_amount
return binary_number
def logical_right_shift(number: int, shift_amount: int) -> str:
"""
Take in positive 2 integers.
'number' is the integer to be logically right shifted 'shift_amount' times.
i.e. (number >>> shift_amount)
Return the shifted binary representation.
>>> logical_right_shift(0, 1)
'0b0'
>>> logical_right_shift(1, 1)
'0b0'
>>> logical_right_shift(1, 5)
'0b0'
>>> logical_right_shift(17, 2)
'0b100'
>>> logical_right_shift(1983, 4)
'0b1111011'
>>> logical_right_shift(1, -1)
Traceback (most recent call last):
...
ValueError: both inputs must be positive integers
"""
if number < 0 or shift_amount < 0:
raise ValueError("both inputs must be positive integers")
binary_number = str(bin(number))[2:]
if shift_amount >= len(binary_number):
return "0b0"
shifted_binary_number = binary_number[: len(binary_number) - shift_amount]
return "0b" + shifted_binary_number
def arithmetic_right_shift(number: int, shift_amount: int) -> str:
"""
Take in 2 integers.
'number' is the integer to be arithmetically right shifted 'shift_amount' times.
i.e. (number >> shift_amount)
Return the shifted binary representation.
>>> arithmetic_right_shift(0, 1)
'0b00'
>>> arithmetic_right_shift(1, 1)
'0b00'
>>> arithmetic_right_shift(-1, 1)
'0b11'
>>> arithmetic_right_shift(17, 2)
'0b000100'
>>> arithmetic_right_shift(-17, 2)
'0b111011'
>>> arithmetic_right_shift(-1983, 4)
'0b111110000100'
"""
if number >= 0: # Get binary representation of positive number
binary_number = "0" + str(bin(number)).strip("-")[2:]
else: # Get binary (2's complement) representation of negative number
binary_number_length = len(bin(number)[3:]) # Find 2's complement of number
binary_number = bin(abs(number) - (1 << binary_number_length))[3:]
binary_number = (
"1" + "0" * (binary_number_length - len(binary_number)) + binary_number
)
if shift_amount >= len(binary_number):
return "0b" + binary_number[0] * len(binary_number)
return (
"0b"
+ binary_number[0] * shift_amount
+ binary_number[: len(binary_number) - shift_amount]
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| NUMBERS_PLUS_LETTER = "Input must be a string of 8 numbers plus letter"
LOOKUP_LETTERS = "TRWAGMYFPDXBNJZSQVHLCKE"
def is_spain_national_id(spanish_id: str) -> bool:
"""
Spain National Id is a string composed by 8 numbers plus a letter
The letter in fact is not part of the ID, it acts as a validator,
checking you didn't do a mistake when entering it on a system or
are giving a fake one.
https://en.wikipedia.org/wiki/Documento_Nacional_de_Identidad_(Spain)#Number
>>> is_spain_national_id("12345678Z")
True
>>> is_spain_national_id("12345678z") # It is case-insensitive
True
>>> is_spain_national_id("12345678x")
False
>>> is_spain_national_id("12345678I")
False
>>> is_spain_national_id("12345678-Z") # Some systems add a dash
True
>>> is_spain_national_id("12345678")
Traceback (most recent call last):
...
ValueError: Input must be a string of 8 numbers plus letter
>>> is_spain_national_id("123456709")
Traceback (most recent call last):
...
ValueError: Input must be a string of 8 numbers plus letter
>>> is_spain_national_id("1234567--Z")
Traceback (most recent call last):
...
ValueError: Input must be a string of 8 numbers plus letter
>>> is_spain_national_id("1234Z")
Traceback (most recent call last):
...
ValueError: Input must be a string of 8 numbers plus letter
>>> is_spain_national_id("1234ZzZZ")
Traceback (most recent call last):
...
ValueError: Input must be a string of 8 numbers plus letter
>>> is_spain_national_id(12345678)
Traceback (most recent call last):
...
TypeError: Expected string as input, found int
"""
if not isinstance(spanish_id, str):
msg = f"Expected string as input, found {type(spanish_id).__name__}"
raise TypeError(msg)
spanish_id_clean = spanish_id.replace("-", "").upper()
if len(spanish_id_clean) != 9:
raise ValueError(NUMBERS_PLUS_LETTER)
try:
number = int(spanish_id_clean[0:8])
letter = spanish_id_clean[8]
except ValueError as ex:
raise ValueError(NUMBERS_PLUS_LETTER) from ex
if letter.isdigit():
raise ValueError(NUMBERS_PLUS_LETTER)
return letter == LOOKUP_LETTERS[number % 23]
if __name__ == "__main__":
import doctest
doctest.testmod()
| NUMBERS_PLUS_LETTER = "Input must be a string of 8 numbers plus letter"
LOOKUP_LETTERS = "TRWAGMYFPDXBNJZSQVHLCKE"
def is_spain_national_id(spanish_id: str) -> bool:
"""
Spain National Id is a string composed by 8 numbers plus a letter
The letter in fact is not part of the ID, it acts as a validator,
checking you didn't do a mistake when entering it on a system or
are giving a fake one.
https://en.wikipedia.org/wiki/Documento_Nacional_de_Identidad_(Spain)#Number
>>> is_spain_national_id("12345678Z")
True
>>> is_spain_national_id("12345678z") # It is case-insensitive
True
>>> is_spain_national_id("12345678x")
False
>>> is_spain_national_id("12345678I")
False
>>> is_spain_national_id("12345678-Z") # Some systems add a dash
True
>>> is_spain_national_id("12345678")
Traceback (most recent call last):
...
ValueError: Input must be a string of 8 numbers plus letter
>>> is_spain_national_id("123456709")
Traceback (most recent call last):
...
ValueError: Input must be a string of 8 numbers plus letter
>>> is_spain_national_id("1234567--Z")
Traceback (most recent call last):
...
ValueError: Input must be a string of 8 numbers plus letter
>>> is_spain_national_id("1234Z")
Traceback (most recent call last):
...
ValueError: Input must be a string of 8 numbers plus letter
>>> is_spain_national_id("1234ZzZZ")
Traceback (most recent call last):
...
ValueError: Input must be a string of 8 numbers plus letter
>>> is_spain_national_id(12345678)
Traceback (most recent call last):
...
TypeError: Expected string as input, found int
"""
if not isinstance(spanish_id, str):
msg = f"Expected string as input, found {type(spanish_id).__name__}"
raise TypeError(msg)
spanish_id_clean = spanish_id.replace("-", "").upper()
if len(spanish_id_clean) != 9:
raise ValueError(NUMBERS_PLUS_LETTER)
try:
number = int(spanish_id_clean[0:8])
letter = spanish_id_clean[8]
except ValueError as ex:
raise ValueError(NUMBERS_PLUS_LETTER) from ex
if letter.isdigit():
raise ValueError(NUMBERS_PLUS_LETTER)
return letter == LOOKUP_LETTERS[number % 23]
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| -1 |
||
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| B64_CHARSET = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"
def base64_encode(data: bytes) -> bytes:
"""Encodes data according to RFC4648.
The data is first transformed to binary and appended with binary digits so that its
length becomes a multiple of 6, then each 6 binary digits will match a character in
the B64_CHARSET string. The number of appended binary digits would later determine
how many "=" signs should be added, the padding.
For every 2 binary digits added, a "=" sign is added in the output.
We can add any binary digits to make it a multiple of 6, for instance, consider the
following example:
"AA" -> 0010100100101001 -> 001010 010010 1001
As can be seen above, 2 more binary digits should be added, so there's 4
possibilities here: 00, 01, 10 or 11.
That being said, Base64 encoding can be used in Steganography to hide data in these
appended digits.
>>> from base64 import b64encode
>>> a = b"This pull request is part of Hacktoberfest20!"
>>> b = b"https://tools.ietf.org/html/rfc4648"
>>> c = b"A"
>>> base64_encode(a) == b64encode(a)
True
>>> base64_encode(b) == b64encode(b)
True
>>> base64_encode(c) == b64encode(c)
True
>>> base64_encode("abc")
Traceback (most recent call last):
...
TypeError: a bytes-like object is required, not 'str'
"""
# Make sure the supplied data is a bytes-like object
if not isinstance(data, bytes):
msg = f"a bytes-like object is required, not '{data.__class__.__name__}'"
raise TypeError(msg)
binary_stream = "".join(bin(byte)[2:].zfill(8) for byte in data)
padding_needed = len(binary_stream) % 6 != 0
if padding_needed:
# The padding that will be added later
padding = b"=" * ((6 - len(binary_stream) % 6) // 2)
# Append binary_stream with arbitrary binary digits (0's by default) to make its
# length a multiple of 6.
binary_stream += "0" * (6 - len(binary_stream) % 6)
else:
padding = b""
# Encode every 6 binary digits to their corresponding Base64 character
return (
"".join(
B64_CHARSET[int(binary_stream[index : index + 6], 2)]
for index in range(0, len(binary_stream), 6)
).encode()
+ padding
)
def base64_decode(encoded_data: str) -> bytes:
"""Decodes data according to RFC4648.
This does the reverse operation of base64_encode.
We first transform the encoded data back to a binary stream, take off the
previously appended binary digits according to the padding, at this point we
would have a binary stream whose length is multiple of 8, the last step is
to convert every 8 bits to a byte.
>>> from base64 import b64decode
>>> a = "VGhpcyBwdWxsIHJlcXVlc3QgaXMgcGFydCBvZiBIYWNrdG9iZXJmZXN0MjAh"
>>> b = "aHR0cHM6Ly90b29scy5pZXRmLm9yZy9odG1sL3JmYzQ2NDg="
>>> c = "QQ=="
>>> base64_decode(a) == b64decode(a)
True
>>> base64_decode(b) == b64decode(b)
True
>>> base64_decode(c) == b64decode(c)
True
>>> base64_decode("abc")
Traceback (most recent call last):
...
AssertionError: Incorrect padding
"""
# Make sure encoded_data is either a string or a bytes-like object
if not isinstance(encoded_data, bytes) and not isinstance(encoded_data, str):
msg = (
"argument should be a bytes-like object or ASCII string, "
f"not '{encoded_data.__class__.__name__}'"
)
raise TypeError(msg)
# In case encoded_data is a bytes-like object, make sure it contains only
# ASCII characters so we convert it to a string object
if isinstance(encoded_data, bytes):
try:
encoded_data = encoded_data.decode("utf-8")
except UnicodeDecodeError:
raise ValueError("base64 encoded data should only contain ASCII characters")
padding = encoded_data.count("=")
# Check if the encoded string contains non base64 characters
if padding:
assert all(
char in B64_CHARSET for char in encoded_data[:-padding]
), "Invalid base64 character(s) found."
else:
assert all(
char in B64_CHARSET for char in encoded_data
), "Invalid base64 character(s) found."
# Check the padding
assert len(encoded_data) % 4 == 0 and padding < 3, "Incorrect padding"
if padding:
# Remove padding if there is one
encoded_data = encoded_data[:-padding]
binary_stream = "".join(
bin(B64_CHARSET.index(char))[2:].zfill(6) for char in encoded_data
)[: -padding * 2]
else:
binary_stream = "".join(
bin(B64_CHARSET.index(char))[2:].zfill(6) for char in encoded_data
)
data = [
int(binary_stream[index : index + 8], 2)
for index in range(0, len(binary_stream), 8)
]
return bytes(data)
if __name__ == "__main__":
import doctest
doctest.testmod()
| B64_CHARSET = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"
def base64_encode(data: bytes) -> bytes:
"""Encodes data according to RFC4648.
The data is first transformed to binary and appended with binary digits so that its
length becomes a multiple of 6, then each 6 binary digits will match a character in
the B64_CHARSET string. The number of appended binary digits would later determine
how many "=" signs should be added, the padding.
For every 2 binary digits added, a "=" sign is added in the output.
We can add any binary digits to make it a multiple of 6, for instance, consider the
following example:
"AA" -> 0010100100101001 -> 001010 010010 1001
As can be seen above, 2 more binary digits should be added, so there's 4
possibilities here: 00, 01, 10 or 11.
That being said, Base64 encoding can be used in Steganography to hide data in these
appended digits.
>>> from base64 import b64encode
>>> a = b"This pull request is part of Hacktoberfest20!"
>>> b = b"https://tools.ietf.org/html/rfc4648"
>>> c = b"A"
>>> base64_encode(a) == b64encode(a)
True
>>> base64_encode(b) == b64encode(b)
True
>>> base64_encode(c) == b64encode(c)
True
>>> base64_encode("abc")
Traceback (most recent call last):
...
TypeError: a bytes-like object is required, not 'str'
"""
# Make sure the supplied data is a bytes-like object
if not isinstance(data, bytes):
msg = f"a bytes-like object is required, not '{data.__class__.__name__}'"
raise TypeError(msg)
binary_stream = "".join(bin(byte)[2:].zfill(8) for byte in data)
padding_needed = len(binary_stream) % 6 != 0
if padding_needed:
# The padding that will be added later
padding = b"=" * ((6 - len(binary_stream) % 6) // 2)
# Append binary_stream with arbitrary binary digits (0's by default) to make its
# length a multiple of 6.
binary_stream += "0" * (6 - len(binary_stream) % 6)
else:
padding = b""
# Encode every 6 binary digits to their corresponding Base64 character
return (
"".join(
B64_CHARSET[int(binary_stream[index : index + 6], 2)]
for index in range(0, len(binary_stream), 6)
).encode()
+ padding
)
def base64_decode(encoded_data: str) -> bytes:
"""Decodes data according to RFC4648.
This does the reverse operation of base64_encode.
We first transform the encoded data back to a binary stream, take off the
previously appended binary digits according to the padding, at this point we
would have a binary stream whose length is multiple of 8, the last step is
to convert every 8 bits to a byte.
>>> from base64 import b64decode
>>> a = "VGhpcyBwdWxsIHJlcXVlc3QgaXMgcGFydCBvZiBIYWNrdG9iZXJmZXN0MjAh"
>>> b = "aHR0cHM6Ly90b29scy5pZXRmLm9yZy9odG1sL3JmYzQ2NDg="
>>> c = "QQ=="
>>> base64_decode(a) == b64decode(a)
True
>>> base64_decode(b) == b64decode(b)
True
>>> base64_decode(c) == b64decode(c)
True
>>> base64_decode("abc")
Traceback (most recent call last):
...
AssertionError: Incorrect padding
"""
# Make sure encoded_data is either a string or a bytes-like object
if not isinstance(encoded_data, bytes) and not isinstance(encoded_data, str):
msg = (
"argument should be a bytes-like object or ASCII string, "
f"not '{encoded_data.__class__.__name__}'"
)
raise TypeError(msg)
# In case encoded_data is a bytes-like object, make sure it contains only
# ASCII characters so we convert it to a string object
if isinstance(encoded_data, bytes):
try:
encoded_data = encoded_data.decode("utf-8")
except UnicodeDecodeError:
raise ValueError("base64 encoded data should only contain ASCII characters")
padding = encoded_data.count("=")
# Check if the encoded string contains non base64 characters
if padding:
assert all(
char in B64_CHARSET for char in encoded_data[:-padding]
), "Invalid base64 character(s) found."
else:
assert all(
char in B64_CHARSET for char in encoded_data
), "Invalid base64 character(s) found."
# Check the padding
assert len(encoded_data) % 4 == 0 and padding < 3, "Incorrect padding"
if padding:
# Remove padding if there is one
encoded_data = encoded_data[:-padding]
binary_stream = "".join(
bin(B64_CHARSET.index(char))[2:].zfill(6) for char in encoded_data
)[: -padding * 2]
else:
binary_stream = "".join(
bin(B64_CHARSET.index(char))[2:].zfill(6) for char in encoded_data
)
data = [
int(binary_stream[index : index + 8], 2)
for index in range(0, len(binary_stream), 8)
]
return bytes(data)
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| import math
"""
In cryptography, the TRANSPOSITION cipher is a method of encryption where the
positions of plaintext are shifted a certain number(determined by the key) that
follows a regular system that results in the permuted text, known as the encrypted
text. The type of transposition cipher demonstrated under is the ROUTE cipher.
"""
def main() -> None:
message = input("Enter message: ")
key = int(input(f"Enter key [2-{len(message) - 1}]: "))
mode = input("Encryption/Decryption [e/d]: ")
if mode.lower().startswith("e"):
text = encrypt_message(key, message)
elif mode.lower().startswith("d"):
text = decrypt_message(key, message)
# Append pipe symbol (vertical bar) to identify spaces at the end.
print(f"Output:\n{text + '|'}")
def encrypt_message(key: int, message: str) -> str:
"""
>>> encrypt_message(6, 'Harshil Darji')
'Hlia rDsahrij'
"""
cipher_text = [""] * key
for col in range(key):
pointer = col
while pointer < len(message):
cipher_text[col] += message[pointer]
pointer += key
return "".join(cipher_text)
def decrypt_message(key: int, message: str) -> str:
"""
>>> decrypt_message(6, 'Hlia rDsahrij')
'Harshil Darji'
"""
num_cols = math.ceil(len(message) / key)
num_rows = key
num_shaded_boxes = (num_cols * num_rows) - len(message)
plain_text = [""] * num_cols
col = 0
row = 0
for symbol in message:
plain_text[col] += symbol
col += 1
if (
(col == num_cols)
or (col == num_cols - 1)
and (row >= num_rows - num_shaded_boxes)
):
col = 0
row += 1
return "".join(plain_text)
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| import math
"""
In cryptography, the TRANSPOSITION cipher is a method of encryption where the
positions of plaintext are shifted a certain number(determined by the key) that
follows a regular system that results in the permuted text, known as the encrypted
text. The type of transposition cipher demonstrated under is the ROUTE cipher.
"""
def main() -> None:
message = input("Enter message: ")
key = int(input(f"Enter key [2-{len(message) - 1}]: "))
mode = input("Encryption/Decryption [e/d]: ")
if mode.lower().startswith("e"):
text = encrypt_message(key, message)
elif mode.lower().startswith("d"):
text = decrypt_message(key, message)
# Append pipe symbol (vertical bar) to identify spaces at the end.
print(f"Output:\n{text + '|'}")
def encrypt_message(key: int, message: str) -> str:
"""
>>> encrypt_message(6, 'Harshil Darji')
'Hlia rDsahrij'
"""
cipher_text = [""] * key
for col in range(key):
pointer = col
while pointer < len(message):
cipher_text[col] += message[pointer]
pointer += key
return "".join(cipher_text)
def decrypt_message(key: int, message: str) -> str:
"""
>>> decrypt_message(6, 'Hlia rDsahrij')
'Harshil Darji'
"""
num_cols = math.ceil(len(message) / key)
num_rows = key
num_shaded_boxes = (num_cols * num_rows) - len(message)
plain_text = [""] * num_cols
col = 0
row = 0
for symbol in message:
plain_text[col] += symbol
col += 1
if (
(col == num_cols)
or (col == num_cols - 1)
and (row >= num_rows - num_shaded_boxes)
):
col = 0
row += 1
return "".join(plain_text)
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| """
This is used to convert the currency using the Amdoren Currency API
https://www.amdoren.com
"""
import os
import requests
URL_BASE = "https://www.amdoren.com/api/currency.php"
# Currency and their description
list_of_currencies = """
AED United Arab Emirates Dirham
AFN Afghan Afghani
ALL Albanian Lek
AMD Armenian Dram
ANG Netherlands Antillean Guilder
AOA Angolan Kwanza
ARS Argentine Peso
AUD Australian Dollar
AWG Aruban Florin
AZN Azerbaijani Manat
BAM Bosnia & Herzegovina Convertible Mark
BBD Barbadian Dollar
BDT Bangladeshi Taka
BGN Bulgarian Lev
BHD Bahraini Dinar
BIF Burundian Franc
BMD Bermudian Dollar
BND Brunei Dollar
BOB Bolivian Boliviano
BRL Brazilian Real
BSD Bahamian Dollar
BTN Bhutanese Ngultrum
BWP Botswana Pula
BYN Belarus Ruble
BZD Belize Dollar
CAD Canadian Dollar
CDF Congolese Franc
CHF Swiss Franc
CLP Chilean Peso
CNY Chinese Yuan
COP Colombian Peso
CRC Costa Rican Colon
CUC Cuban Convertible Peso
CVE Cape Verdean Escudo
CZK Czech Republic Koruna
DJF Djiboutian Franc
DKK Danish Krone
DOP Dominican Peso
DZD Algerian Dinar
EGP Egyptian Pound
ERN Eritrean Nakfa
ETB Ethiopian Birr
EUR Euro
FJD Fiji Dollar
GBP British Pound Sterling
GEL Georgian Lari
GHS Ghanaian Cedi
GIP Gibraltar Pound
GMD Gambian Dalasi
GNF Guinea Franc
GTQ Guatemalan Quetzal
GYD Guyanaese Dollar
HKD Hong Kong Dollar
HNL Honduran Lempira
HRK Croatian Kuna
HTG Haiti Gourde
HUF Hungarian Forint
IDR Indonesian Rupiah
ILS Israeli Shekel
INR Indian Rupee
IQD Iraqi Dinar
IRR Iranian Rial
ISK Icelandic Krona
JMD Jamaican Dollar
JOD Jordanian Dinar
JPY Japanese Yen
KES Kenyan Shilling
KGS Kyrgystani Som
KHR Cambodian Riel
KMF Comorian Franc
KPW North Korean Won
KRW South Korean Won
KWD Kuwaiti Dinar
KYD Cayman Islands Dollar
KZT Kazakhstan Tenge
LAK Laotian Kip
LBP Lebanese Pound
LKR Sri Lankan Rupee
LRD Liberian Dollar
LSL Lesotho Loti
LYD Libyan Dinar
MAD Moroccan Dirham
MDL Moldovan Leu
MGA Malagasy Ariary
MKD Macedonian Denar
MMK Myanma Kyat
MNT Mongolian Tugrik
MOP Macau Pataca
MRO Mauritanian Ouguiya
MUR Mauritian Rupee
MVR Maldivian Rufiyaa
MWK Malawi Kwacha
MXN Mexican Peso
MYR Malaysian Ringgit
MZN Mozambican Metical
NAD Namibian Dollar
NGN Nigerian Naira
NIO Nicaragua Cordoba
NOK Norwegian Krone
NPR Nepalese Rupee
NZD New Zealand Dollar
OMR Omani Rial
PAB Panamanian Balboa
PEN Peruvian Nuevo Sol
PGK Papua New Guinean Kina
PHP Philippine Peso
PKR Pakistani Rupee
PLN Polish Zloty
PYG Paraguayan Guarani
QAR Qatari Riyal
RON Romanian Leu
RSD Serbian Dinar
RUB Russian Ruble
RWF Rwanda Franc
SAR Saudi Riyal
SBD Solomon Islands Dollar
SCR Seychellois Rupee
SDG Sudanese Pound
SEK Swedish Krona
SGD Singapore Dollar
SHP Saint Helena Pound
SLL Sierra Leonean Leone
SOS Somali Shilling
SRD Surinamese Dollar
SSP South Sudanese Pound
STD Sao Tome and Principe Dobra
SYP Syrian Pound
SZL Swazi Lilangeni
THB Thai Baht
TJS Tajikistan Somoni
TMT Turkmenistani Manat
TND Tunisian Dinar
TOP Tonga Paanga
TRY Turkish Lira
TTD Trinidad and Tobago Dollar
TWD New Taiwan Dollar
TZS Tanzanian Shilling
UAH Ukrainian Hryvnia
UGX Ugandan Shilling
USD United States Dollar
UYU Uruguayan Peso
UZS Uzbekistan Som
VEF Venezuelan Bolivar
VND Vietnamese Dong
VUV Vanuatu Vatu
WST Samoan Tala
XAF Central African CFA franc
XCD East Caribbean Dollar
XOF West African CFA franc
XPF CFP Franc
YER Yemeni Rial
ZAR South African Rand
ZMW Zambian Kwacha
"""
def convert_currency(
from_: str = "USD", to: str = "INR", amount: float = 1.0, api_key: str = ""
) -> str:
"""https://www.amdoren.com/currency-api/"""
# Instead of manually generating parameters
params = locals()
# from is a reserved keyword
params["from"] = params.pop("from_")
res = requests.get(URL_BASE, params=params).json()
return str(res["amount"]) if res["error"] == 0 else res["error_message"]
if __name__ == "__main__":
TESTING = os.getenv("CI", "")
API_KEY = os.getenv("AMDOREN_API_KEY", "")
if not API_KEY and not TESTING:
raise KeyError(
"API key must be provided in the 'AMDOREN_API_KEY' environment variable."
)
print(
convert_currency(
input("Enter from currency: ").strip(),
input("Enter to currency: ").strip(),
float(input("Enter the amount: ").strip()),
API_KEY,
)
)
| """
This is used to convert the currency using the Amdoren Currency API
https://www.amdoren.com
"""
import os
import requests
URL_BASE = "https://www.amdoren.com/api/currency.php"
# Currency and their description
list_of_currencies = """
AED United Arab Emirates Dirham
AFN Afghan Afghani
ALL Albanian Lek
AMD Armenian Dram
ANG Netherlands Antillean Guilder
AOA Angolan Kwanza
ARS Argentine Peso
AUD Australian Dollar
AWG Aruban Florin
AZN Azerbaijani Manat
BAM Bosnia & Herzegovina Convertible Mark
BBD Barbadian Dollar
BDT Bangladeshi Taka
BGN Bulgarian Lev
BHD Bahraini Dinar
BIF Burundian Franc
BMD Bermudian Dollar
BND Brunei Dollar
BOB Bolivian Boliviano
BRL Brazilian Real
BSD Bahamian Dollar
BTN Bhutanese Ngultrum
BWP Botswana Pula
BYN Belarus Ruble
BZD Belize Dollar
CAD Canadian Dollar
CDF Congolese Franc
CHF Swiss Franc
CLP Chilean Peso
CNY Chinese Yuan
COP Colombian Peso
CRC Costa Rican Colon
CUC Cuban Convertible Peso
CVE Cape Verdean Escudo
CZK Czech Republic Koruna
DJF Djiboutian Franc
DKK Danish Krone
DOP Dominican Peso
DZD Algerian Dinar
EGP Egyptian Pound
ERN Eritrean Nakfa
ETB Ethiopian Birr
EUR Euro
FJD Fiji Dollar
GBP British Pound Sterling
GEL Georgian Lari
GHS Ghanaian Cedi
GIP Gibraltar Pound
GMD Gambian Dalasi
GNF Guinea Franc
GTQ Guatemalan Quetzal
GYD Guyanaese Dollar
HKD Hong Kong Dollar
HNL Honduran Lempira
HRK Croatian Kuna
HTG Haiti Gourde
HUF Hungarian Forint
IDR Indonesian Rupiah
ILS Israeli Shekel
INR Indian Rupee
IQD Iraqi Dinar
IRR Iranian Rial
ISK Icelandic Krona
JMD Jamaican Dollar
JOD Jordanian Dinar
JPY Japanese Yen
KES Kenyan Shilling
KGS Kyrgystani Som
KHR Cambodian Riel
KMF Comorian Franc
KPW North Korean Won
KRW South Korean Won
KWD Kuwaiti Dinar
KYD Cayman Islands Dollar
KZT Kazakhstan Tenge
LAK Laotian Kip
LBP Lebanese Pound
LKR Sri Lankan Rupee
LRD Liberian Dollar
LSL Lesotho Loti
LYD Libyan Dinar
MAD Moroccan Dirham
MDL Moldovan Leu
MGA Malagasy Ariary
MKD Macedonian Denar
MMK Myanma Kyat
MNT Mongolian Tugrik
MOP Macau Pataca
MRO Mauritanian Ouguiya
MUR Mauritian Rupee
MVR Maldivian Rufiyaa
MWK Malawi Kwacha
MXN Mexican Peso
MYR Malaysian Ringgit
MZN Mozambican Metical
NAD Namibian Dollar
NGN Nigerian Naira
NIO Nicaragua Cordoba
NOK Norwegian Krone
NPR Nepalese Rupee
NZD New Zealand Dollar
OMR Omani Rial
PAB Panamanian Balboa
PEN Peruvian Nuevo Sol
PGK Papua New Guinean Kina
PHP Philippine Peso
PKR Pakistani Rupee
PLN Polish Zloty
PYG Paraguayan Guarani
QAR Qatari Riyal
RON Romanian Leu
RSD Serbian Dinar
RUB Russian Ruble
RWF Rwanda Franc
SAR Saudi Riyal
SBD Solomon Islands Dollar
SCR Seychellois Rupee
SDG Sudanese Pound
SEK Swedish Krona
SGD Singapore Dollar
SHP Saint Helena Pound
SLL Sierra Leonean Leone
SOS Somali Shilling
SRD Surinamese Dollar
SSP South Sudanese Pound
STD Sao Tome and Principe Dobra
SYP Syrian Pound
SZL Swazi Lilangeni
THB Thai Baht
TJS Tajikistan Somoni
TMT Turkmenistani Manat
TND Tunisian Dinar
TOP Tonga Paanga
TRY Turkish Lira
TTD Trinidad and Tobago Dollar
TWD New Taiwan Dollar
TZS Tanzanian Shilling
UAH Ukrainian Hryvnia
UGX Ugandan Shilling
USD United States Dollar
UYU Uruguayan Peso
UZS Uzbekistan Som
VEF Venezuelan Bolivar
VND Vietnamese Dong
VUV Vanuatu Vatu
WST Samoan Tala
XAF Central African CFA franc
XCD East Caribbean Dollar
XOF West African CFA franc
XPF CFP Franc
YER Yemeni Rial
ZAR South African Rand
ZMW Zambian Kwacha
"""
def convert_currency(
from_: str = "USD", to: str = "INR", amount: float = 1.0, api_key: str = ""
) -> str:
"""https://www.amdoren.com/currency-api/"""
# Instead of manually generating parameters
params = locals()
# from is a reserved keyword
params["from"] = params.pop("from_")
res = requests.get(URL_BASE, params=params).json()
return str(res["amount"]) if res["error"] == 0 else res["error_message"]
if __name__ == "__main__":
TESTING = os.getenv("CI", "")
API_KEY = os.getenv("AMDOREN_API_KEY", "")
if not API_KEY and not TESTING:
raise KeyError(
"API key must be provided in the 'AMDOREN_API_KEY' environment variable."
)
print(
convert_currency(
input("Enter from currency: ").strip(),
input("Enter to currency: ").strip(),
float(input("Enter the amount: ").strip()),
API_KEY,
)
)
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| """
See https://en.wikipedia.org/wiki/Bloom_filter
The use of this data structure is to test membership in a set.
Compared to Python's built-in set() it is more space-efficient.
In the following example, only 8 bits of memory will be used:
>>> bloom = Bloom(size=8)
Initially, the filter contains all zeros:
>>> bloom.bitstring
'00000000'
When an element is added, two bits are set to 1
since there are 2 hash functions in this implementation:
>>> "Titanic" in bloom
False
>>> bloom.add("Titanic")
>>> bloom.bitstring
'01100000'
>>> "Titanic" in bloom
True
However, sometimes only one bit is added
because both hash functions return the same value
>>> bloom.add("Avatar")
>>> "Avatar" in bloom
True
>>> bloom.format_hash("Avatar")
'00000100'
>>> bloom.bitstring
'01100100'
Not added elements should return False ...
>>> not_present_films = ("The Godfather", "Interstellar", "Parasite", "Pulp Fiction")
>>> {
... film: bloom.format_hash(film) for film in not_present_films
... } # doctest: +NORMALIZE_WHITESPACE
{'The Godfather': '00000101',
'Interstellar': '00000011',
'Parasite': '00010010',
'Pulp Fiction': '10000100'}
>>> any(film in bloom for film in not_present_films)
False
but sometimes there are false positives:
>>> "Ratatouille" in bloom
True
>>> bloom.format_hash("Ratatouille")
'01100000'
The probability increases with the number of elements added.
The probability decreases with the number of bits in the bitarray.
>>> bloom.estimated_error_rate
0.140625
>>> bloom.add("The Godfather")
>>> bloom.estimated_error_rate
0.25
>>> bloom.bitstring
'01100101'
"""
from hashlib import md5, sha256
HASH_FUNCTIONS = (sha256, md5)
class Bloom:
def __init__(self, size: int = 8) -> None:
self.bitarray = 0b0
self.size = size
def add(self, value: str) -> None:
h = self.hash_(value)
self.bitarray |= h
def exists(self, value: str) -> bool:
h = self.hash_(value)
return (h & self.bitarray) == h
def __contains__(self, other: str) -> bool:
return self.exists(other)
def format_bin(self, bitarray: int) -> str:
res = bin(bitarray)[2:]
return res.zfill(self.size)
@property
def bitstring(self) -> str:
return self.format_bin(self.bitarray)
def hash_(self, value: str) -> int:
res = 0b0
for func in HASH_FUNCTIONS:
position = (
int.from_bytes(func(value.encode()).digest(), "little") % self.size
)
res |= 2**position
return res
def format_hash(self, value: str) -> str:
return self.format_bin(self.hash_(value))
@property
def estimated_error_rate(self) -> float:
n_ones = bin(self.bitarray).count("1")
return (n_ones / self.size) ** len(HASH_FUNCTIONS)
| """
See https://en.wikipedia.org/wiki/Bloom_filter
The use of this data structure is to test membership in a set.
Compared to Python's built-in set() it is more space-efficient.
In the following example, only 8 bits of memory will be used:
>>> bloom = Bloom(size=8)
Initially, the filter contains all zeros:
>>> bloom.bitstring
'00000000'
When an element is added, two bits are set to 1
since there are 2 hash functions in this implementation:
>>> "Titanic" in bloom
False
>>> bloom.add("Titanic")
>>> bloom.bitstring
'01100000'
>>> "Titanic" in bloom
True
However, sometimes only one bit is added
because both hash functions return the same value
>>> bloom.add("Avatar")
>>> "Avatar" in bloom
True
>>> bloom.format_hash("Avatar")
'00000100'
>>> bloom.bitstring
'01100100'
Not added elements should return False ...
>>> not_present_films = ("The Godfather", "Interstellar", "Parasite", "Pulp Fiction")
>>> {
... film: bloom.format_hash(film) for film in not_present_films
... } # doctest: +NORMALIZE_WHITESPACE
{'The Godfather': '00000101',
'Interstellar': '00000011',
'Parasite': '00010010',
'Pulp Fiction': '10000100'}
>>> any(film in bloom for film in not_present_films)
False
but sometimes there are false positives:
>>> "Ratatouille" in bloom
True
>>> bloom.format_hash("Ratatouille")
'01100000'
The probability increases with the number of elements added.
The probability decreases with the number of bits in the bitarray.
>>> bloom.estimated_error_rate
0.140625
>>> bloom.add("The Godfather")
>>> bloom.estimated_error_rate
0.25
>>> bloom.bitstring
'01100101'
"""
from hashlib import md5, sha256
HASH_FUNCTIONS = (sha256, md5)
class Bloom:
def __init__(self, size: int = 8) -> None:
self.bitarray = 0b0
self.size = size
def add(self, value: str) -> None:
h = self.hash_(value)
self.bitarray |= h
def exists(self, value: str) -> bool:
h = self.hash_(value)
return (h & self.bitarray) == h
def __contains__(self, other: str) -> bool:
return self.exists(other)
def format_bin(self, bitarray: int) -> str:
res = bin(bitarray)[2:]
return res.zfill(self.size)
@property
def bitstring(self) -> str:
return self.format_bin(self.bitarray)
def hash_(self, value: str) -> int:
res = 0b0
for func in HASH_FUNCTIONS:
position = (
int.from_bytes(func(value.encode()).digest(), "little") % self.size
)
res |= 2**position
return res
def format_hash(self, value: str) -> str:
return self.format_bin(self.hash_(value))
@property
def estimated_error_rate(self) -> float:
n_ones = bin(self.bitarray).count("1")
return (n_ones / self.size) ** len(HASH_FUNCTIONS)
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| """
Created on Fri Oct 16 09:31:07 2020
@author: Dr. Tobias Schröder
@license: MIT-license
This file contains the test-suite for the knapsack problem.
"""
import unittest
from knapsack import knapsack as k
class Test(unittest.TestCase):
def test_base_case(self):
"""
test for the base case
"""
cap = 0
val = [0]
w = [0]
c = len(val)
self.assertEqual(k.knapsack(cap, w, val, c), 0)
val = [60]
w = [10]
c = len(val)
self.assertEqual(k.knapsack(cap, w, val, c), 0)
def test_easy_case(self):
"""
test for the base case
"""
cap = 3
val = [1, 2, 3]
w = [3, 2, 1]
c = len(val)
self.assertEqual(k.knapsack(cap, w, val, c), 5)
def test_knapsack(self):
"""
test for the knapsack
"""
cap = 50
val = [60, 100, 120]
w = [10, 20, 30]
c = len(val)
self.assertEqual(k.knapsack(cap, w, val, c), 220)
if __name__ == "__main__":
unittest.main()
| """
Created on Fri Oct 16 09:31:07 2020
@author: Dr. Tobias Schröder
@license: MIT-license
This file contains the test-suite for the knapsack problem.
"""
import unittest
from knapsack import knapsack as k
class Test(unittest.TestCase):
def test_base_case(self):
"""
test for the base case
"""
cap = 0
val = [0]
w = [0]
c = len(val)
self.assertEqual(k.knapsack(cap, w, val, c), 0)
val = [60]
w = [10]
c = len(val)
self.assertEqual(k.knapsack(cap, w, val, c), 0)
def test_easy_case(self):
"""
test for the base case
"""
cap = 3
val = [1, 2, 3]
w = [3, 2, 1]
c = len(val)
self.assertEqual(k.knapsack(cap, w, val, c), 5)
def test_knapsack(self):
"""
test for the knapsack
"""
cap = 50
val = [60, 100, 120]
w = [10, 20, 30]
c = len(val)
self.assertEqual(k.knapsack(cap, w, val, c), 220)
if __name__ == "__main__":
unittest.main()
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| # A Python implementation of the Banker's Algorithm in Operating Systems using
# Processes and Resources
# {
# "Author: "Biney Kingsley ([email protected]), [email protected]",
# "Date": 28-10-2018
# }
"""
The Banker's algorithm is a resource allocation and deadlock avoidance algorithm
developed by Edsger Dijkstra that tests for safety by simulating the allocation of
predetermined maximum possible amounts of all resources, and then makes a "s-state"
check to test for possible deadlock conditions for all other pending activities,
before deciding whether allocation should be allowed to continue.
[Source] Wikipedia
[Credit] Rosetta Code C implementation helped very much.
(https://rosettacode.org/wiki/Banker%27s_algorithm)
"""
from __future__ import annotations
import time
import numpy as np
test_claim_vector = [8, 5, 9, 7]
test_allocated_res_table = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
test_maximum_claim_table = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5],
[1, 5, 3, 0],
[3, 0, 3, 3],
]
class BankersAlgorithm:
def __init__(
self,
claim_vector: list[int],
allocated_resources_table: list[list[int]],
maximum_claim_table: list[list[int]],
) -> None:
"""
:param claim_vector: A nxn/nxm list depicting the amount of each resources
(eg. memory, interface, semaphores, etc.) available.
:param allocated_resources_table: A nxn/nxm list depicting the amount of each
resource each process is currently holding
:param maximum_claim_table: A nxn/nxm list depicting how much of each resource
the system currently has available
"""
self.__claim_vector = claim_vector
self.__allocated_resources_table = allocated_resources_table
self.__maximum_claim_table = maximum_claim_table
def __processes_resource_summation(self) -> list[int]:
"""
Check for allocated resources in line with each resource in the claim vector
"""
return [
sum(p_item[i] for p_item in self.__allocated_resources_table)
for i in range(len(self.__allocated_resources_table[0]))
]
def __available_resources(self) -> list[int]:
"""
Check for available resources in line with each resource in the claim vector
"""
return np.array(self.__claim_vector) - np.array(
self.__processes_resource_summation()
)
def __need(self) -> list[list[int]]:
"""
Implement safety checker that calculates the needs by ensuring that
max_claim[i][j] - alloc_table[i][j] <= avail[j]
"""
return [
list(np.array(self.__maximum_claim_table[i]) - np.array(allocated_resource))
for i, allocated_resource in enumerate(self.__allocated_resources_table)
]
def __need_index_manager(self) -> dict[int, list[int]]:
"""
This function builds an index control dictionary to track original ids/indices
of processes when altered during execution of method "main"
Return: {0: [a: int, b: int], 1: [c: int, d: int]}
>>> (BankersAlgorithm(test_claim_vector, test_allocated_res_table,
... test_maximum_claim_table)._BankersAlgorithm__need_index_manager()
... ) # doctest: +NORMALIZE_WHITESPACE
{0: [1, 2, 0, 3], 1: [0, 1, 3, 1], 2: [1, 1, 0, 2], 3: [1, 3, 2, 0],
4: [2, 0, 0, 3]}
"""
return {self.__need().index(i): i for i in self.__need()}
def main(self, **kwargs) -> None:
"""
Utilize various methods in this class to simulate the Banker's algorithm
Return: None
>>> BankersAlgorithm(test_claim_vector, test_allocated_res_table,
... test_maximum_claim_table).main(describe=True)
Allocated Resource Table
P1 2 0 1 1
<BLANKLINE>
P2 0 1 2 1
<BLANKLINE>
P3 4 0 0 3
<BLANKLINE>
P4 0 2 1 0
<BLANKLINE>
P5 1 0 3 0
<BLANKLINE>
System Resource Table
P1 3 2 1 4
<BLANKLINE>
P2 0 2 5 2
<BLANKLINE>
P3 5 1 0 5
<BLANKLINE>
P4 1 5 3 0
<BLANKLINE>
P5 3 0 3 3
<BLANKLINE>
Current Usage by Active Processes: 8 5 9 7
Initial Available Resources: 1 2 2 2
__________________________________________________
<BLANKLINE>
Process 3 is executing.
Updated available resource stack for processes: 5 2 2 5
The process is in a safe state.
<BLANKLINE>
Process 1 is executing.
Updated available resource stack for processes: 7 2 3 6
The process is in a safe state.
<BLANKLINE>
Process 2 is executing.
Updated available resource stack for processes: 7 3 5 7
The process is in a safe state.
<BLANKLINE>
Process 4 is executing.
Updated available resource stack for processes: 7 5 6 7
The process is in a safe state.
<BLANKLINE>
Process 5 is executing.
Updated available resource stack for processes: 8 5 9 7
The process is in a safe state.
<BLANKLINE>
"""
need_list = self.__need()
alloc_resources_table = self.__allocated_resources_table
available_resources = self.__available_resources()
need_index_manager = self.__need_index_manager()
for kw, val in kwargs.items():
if kw and val is True:
self.__pretty_data()
print("_" * 50 + "\n")
while need_list:
safe = False
for each_need in need_list:
execution = True
for index, need in enumerate(each_need):
if need > available_resources[index]:
execution = False
break
if execution:
safe = True
# get the original index of the process from ind_ctrl db
for original_need_index, need_clone in need_index_manager.items():
if each_need == need_clone:
process_number = original_need_index
print(f"Process {process_number + 1} is executing.")
# remove the process run from stack
need_list.remove(each_need)
# update available/freed resources stack
available_resources = np.array(available_resources) + np.array(
alloc_resources_table[process_number]
)
print(
"Updated available resource stack for processes: "
+ " ".join([str(x) for x in available_resources])
)
break
if safe:
print("The process is in a safe state.\n")
else:
print("System in unsafe state. Aborting...\n")
break
def __pretty_data(self):
"""
Properly align display of the algorithm's solution
"""
print(" " * 9 + "Allocated Resource Table")
for item in self.__allocated_resources_table:
print(
f"P{self.__allocated_resources_table.index(item) + 1}"
+ " ".join(f"{it:>8}" for it in item)
+ "\n"
)
print(" " * 9 + "System Resource Table")
for item in self.__maximum_claim_table:
print(
f"P{self.__maximum_claim_table.index(item) + 1}"
+ " ".join(f"{it:>8}" for it in item)
+ "\n"
)
print(
"Current Usage by Active Processes: "
+ " ".join(str(x) for x in self.__claim_vector)
)
print(
"Initial Available Resources: "
+ " ".join(str(x) for x in self.__available_resources())
)
time.sleep(1)
if __name__ == "__main__":
import doctest
doctest.testmod()
| # A Python implementation of the Banker's Algorithm in Operating Systems using
# Processes and Resources
# {
# "Author: "Biney Kingsley ([email protected]), [email protected]",
# "Date": 28-10-2018
# }
"""
The Banker's algorithm is a resource allocation and deadlock avoidance algorithm
developed by Edsger Dijkstra that tests for safety by simulating the allocation of
predetermined maximum possible amounts of all resources, and then makes a "s-state"
check to test for possible deadlock conditions for all other pending activities,
before deciding whether allocation should be allowed to continue.
[Source] Wikipedia
[Credit] Rosetta Code C implementation helped very much.
(https://rosettacode.org/wiki/Banker%27s_algorithm)
"""
from __future__ import annotations
import time
import numpy as np
test_claim_vector = [8, 5, 9, 7]
test_allocated_res_table = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
test_maximum_claim_table = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5],
[1, 5, 3, 0],
[3, 0, 3, 3],
]
class BankersAlgorithm:
def __init__(
self,
claim_vector: list[int],
allocated_resources_table: list[list[int]],
maximum_claim_table: list[list[int]],
) -> None:
"""
:param claim_vector: A nxn/nxm list depicting the amount of each resources
(eg. memory, interface, semaphores, etc.) available.
:param allocated_resources_table: A nxn/nxm list depicting the amount of each
resource each process is currently holding
:param maximum_claim_table: A nxn/nxm list depicting how much of each resource
the system currently has available
"""
self.__claim_vector = claim_vector
self.__allocated_resources_table = allocated_resources_table
self.__maximum_claim_table = maximum_claim_table
def __processes_resource_summation(self) -> list[int]:
"""
Check for allocated resources in line with each resource in the claim vector
"""
return [
sum(p_item[i] for p_item in self.__allocated_resources_table)
for i in range(len(self.__allocated_resources_table[0]))
]
def __available_resources(self) -> list[int]:
"""
Check for available resources in line with each resource in the claim vector
"""
return np.array(self.__claim_vector) - np.array(
self.__processes_resource_summation()
)
def __need(self) -> list[list[int]]:
"""
Implement safety checker that calculates the needs by ensuring that
max_claim[i][j] - alloc_table[i][j] <= avail[j]
"""
return [
list(np.array(self.__maximum_claim_table[i]) - np.array(allocated_resource))
for i, allocated_resource in enumerate(self.__allocated_resources_table)
]
def __need_index_manager(self) -> dict[int, list[int]]:
"""
This function builds an index control dictionary to track original ids/indices
of processes when altered during execution of method "main"
Return: {0: [a: int, b: int], 1: [c: int, d: int]}
>>> (BankersAlgorithm(test_claim_vector, test_allocated_res_table,
... test_maximum_claim_table)._BankersAlgorithm__need_index_manager()
... ) # doctest: +NORMALIZE_WHITESPACE
{0: [1, 2, 0, 3], 1: [0, 1, 3, 1], 2: [1, 1, 0, 2], 3: [1, 3, 2, 0],
4: [2, 0, 0, 3]}
"""
return {self.__need().index(i): i for i in self.__need()}
def main(self, **kwargs) -> None:
"""
Utilize various methods in this class to simulate the Banker's algorithm
Return: None
>>> BankersAlgorithm(test_claim_vector, test_allocated_res_table,
... test_maximum_claim_table).main(describe=True)
Allocated Resource Table
P1 2 0 1 1
<BLANKLINE>
P2 0 1 2 1
<BLANKLINE>
P3 4 0 0 3
<BLANKLINE>
P4 0 2 1 0
<BLANKLINE>
P5 1 0 3 0
<BLANKLINE>
System Resource Table
P1 3 2 1 4
<BLANKLINE>
P2 0 2 5 2
<BLANKLINE>
P3 5 1 0 5
<BLANKLINE>
P4 1 5 3 0
<BLANKLINE>
P5 3 0 3 3
<BLANKLINE>
Current Usage by Active Processes: 8 5 9 7
Initial Available Resources: 1 2 2 2
__________________________________________________
<BLANKLINE>
Process 3 is executing.
Updated available resource stack for processes: 5 2 2 5
The process is in a safe state.
<BLANKLINE>
Process 1 is executing.
Updated available resource stack for processes: 7 2 3 6
The process is in a safe state.
<BLANKLINE>
Process 2 is executing.
Updated available resource stack for processes: 7 3 5 7
The process is in a safe state.
<BLANKLINE>
Process 4 is executing.
Updated available resource stack for processes: 7 5 6 7
The process is in a safe state.
<BLANKLINE>
Process 5 is executing.
Updated available resource stack for processes: 8 5 9 7
The process is in a safe state.
<BLANKLINE>
"""
need_list = self.__need()
alloc_resources_table = self.__allocated_resources_table
available_resources = self.__available_resources()
need_index_manager = self.__need_index_manager()
for kw, val in kwargs.items():
if kw and val is True:
self.__pretty_data()
print("_" * 50 + "\n")
while need_list:
safe = False
for each_need in need_list:
execution = True
for index, need in enumerate(each_need):
if need > available_resources[index]:
execution = False
break
if execution:
safe = True
# get the original index of the process from ind_ctrl db
for original_need_index, need_clone in need_index_manager.items():
if each_need == need_clone:
process_number = original_need_index
print(f"Process {process_number + 1} is executing.")
# remove the process run from stack
need_list.remove(each_need)
# update available/freed resources stack
available_resources = np.array(available_resources) + np.array(
alloc_resources_table[process_number]
)
print(
"Updated available resource stack for processes: "
+ " ".join([str(x) for x in available_resources])
)
break
if safe:
print("The process is in a safe state.\n")
else:
print("System in unsafe state. Aborting...\n")
break
def __pretty_data(self):
"""
Properly align display of the algorithm's solution
"""
print(" " * 9 + "Allocated Resource Table")
for item in self.__allocated_resources_table:
print(
f"P{self.__allocated_resources_table.index(item) + 1}"
+ " ".join(f"{it:>8}" for it in item)
+ "\n"
)
print(" " * 9 + "System Resource Table")
for item in self.__maximum_claim_table:
print(
f"P{self.__maximum_claim_table.index(item) + 1}"
+ " ".join(f"{it:>8}" for it in item)
+ "\n"
)
print(
"Current Usage by Active Processes: "
+ " ".join(str(x) for x in self.__claim_vector)
)
print(
"Initial Available Resources: "
+ " ".join(str(x) for x in self.__available_resources())
)
time.sleep(1)
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| from __future__ import annotations
import math
def default_matrix_multiplication(a: list, b: list) -> list:
"""
Multiplication only for 2x2 matrices
"""
if len(a) != 2 or len(a[0]) != 2 or len(b) != 2 or len(b[0]) != 2:
raise Exception("Matrices are not 2x2")
new_matrix = [
[a[0][0] * b[0][0] + a[0][1] * b[1][0], a[0][0] * b[0][1] + a[0][1] * b[1][1]],
[a[1][0] * b[0][0] + a[1][1] * b[1][0], a[1][0] * b[0][1] + a[1][1] * b[1][1]],
]
return new_matrix
def matrix_addition(matrix_a: list, matrix_b: list):
return [
[matrix_a[row][col] + matrix_b[row][col] for col in range(len(matrix_a[row]))]
for row in range(len(matrix_a))
]
def matrix_subtraction(matrix_a: list, matrix_b: list):
return [
[matrix_a[row][col] - matrix_b[row][col] for col in range(len(matrix_a[row]))]
for row in range(len(matrix_a))
]
def split_matrix(a: list) -> tuple[list, list, list, list]:
"""
Given an even length matrix, returns the top_left, top_right, bot_left, bot_right
quadrant.
>>> split_matrix([[4,3,2,4],[2,3,1,1],[6,5,4,3],[8,4,1,6]])
([[4, 3], [2, 3]], [[2, 4], [1, 1]], [[6, 5], [8, 4]], [[4, 3], [1, 6]])
>>> split_matrix([
... [4,3,2,4,4,3,2,4],[2,3,1,1,2,3,1,1],[6,5,4,3,6,5,4,3],[8,4,1,6,8,4,1,6],
... [4,3,2,4,4,3,2,4],[2,3,1,1,2,3,1,1],[6,5,4,3,6,5,4,3],[8,4,1,6,8,4,1,6]
... ]) # doctest: +NORMALIZE_WHITESPACE
([[4, 3, 2, 4], [2, 3, 1, 1], [6, 5, 4, 3], [8, 4, 1, 6]], [[4, 3, 2, 4],
[2, 3, 1, 1], [6, 5, 4, 3], [8, 4, 1, 6]], [[4, 3, 2, 4], [2, 3, 1, 1],
[6, 5, 4, 3], [8, 4, 1, 6]], [[4, 3, 2, 4], [2, 3, 1, 1], [6, 5, 4, 3],
[8, 4, 1, 6]])
"""
if len(a) % 2 != 0 or len(a[0]) % 2 != 0:
raise Exception("Odd matrices are not supported!")
matrix_length = len(a)
mid = matrix_length // 2
top_right = [[a[i][j] for j in range(mid, matrix_length)] for i in range(mid)]
bot_right = [
[a[i][j] for j in range(mid, matrix_length)] for i in range(mid, matrix_length)
]
top_left = [[a[i][j] for j in range(mid)] for i in range(mid)]
bot_left = [[a[i][j] for j in range(mid)] for i in range(mid, matrix_length)]
return top_left, top_right, bot_left, bot_right
def matrix_dimensions(matrix: list) -> tuple[int, int]:
return len(matrix), len(matrix[0])
def print_matrix(matrix: list) -> None:
print("\n".join(str(line) for line in matrix))
def actual_strassen(matrix_a: list, matrix_b: list) -> list:
"""
Recursive function to calculate the product of two matrices, using the Strassen
Algorithm. It only supports even length matrices.
"""
if matrix_dimensions(matrix_a) == (2, 2):
return default_matrix_multiplication(matrix_a, matrix_b)
a, b, c, d = split_matrix(matrix_a)
e, f, g, h = split_matrix(matrix_b)
t1 = actual_strassen(a, matrix_subtraction(f, h))
t2 = actual_strassen(matrix_addition(a, b), h)
t3 = actual_strassen(matrix_addition(c, d), e)
t4 = actual_strassen(d, matrix_subtraction(g, e))
t5 = actual_strassen(matrix_addition(a, d), matrix_addition(e, h))
t6 = actual_strassen(matrix_subtraction(b, d), matrix_addition(g, h))
t7 = actual_strassen(matrix_subtraction(a, c), matrix_addition(e, f))
top_left = matrix_addition(matrix_subtraction(matrix_addition(t5, t4), t2), t6)
top_right = matrix_addition(t1, t2)
bot_left = matrix_addition(t3, t4)
bot_right = matrix_subtraction(matrix_subtraction(matrix_addition(t1, t5), t3), t7)
# construct the new matrix from our 4 quadrants
new_matrix = []
for i in range(len(top_right)):
new_matrix.append(top_left[i] + top_right[i])
for i in range(len(bot_right)):
new_matrix.append(bot_left[i] + bot_right[i])
return new_matrix
def strassen(matrix1: list, matrix2: list) -> list:
"""
>>> strassen([[2,1,3],[3,4,6],[1,4,2],[7,6,7]], [[4,2,3,4],[2,1,1,1],[8,6,4,2]])
[[34, 23, 19, 15], [68, 46, 37, 28], [28, 18, 15, 12], [96, 62, 55, 48]]
>>> strassen([[3,7,5,6,9],[1,5,3,7,8],[1,4,4,5,7]], [[2,4],[5,2],[1,7],[5,5],[7,8]])
[[139, 163], [121, 134], [100, 121]]
"""
if matrix_dimensions(matrix1)[1] != matrix_dimensions(matrix2)[0]:
msg = (
"Unable to multiply these matrices, please check the dimensions.\n"
f"Matrix A: {matrix1}\n"
f"Matrix B: {matrix2}"
)
raise Exception(msg)
dimension1 = matrix_dimensions(matrix1)
dimension2 = matrix_dimensions(matrix2)
if dimension1[0] == dimension1[1] and dimension2[0] == dimension2[1]:
return [matrix1, matrix2]
maximum = max(*dimension1, *dimension2)
maxim = int(math.pow(2, math.ceil(math.log2(maximum))))
new_matrix1 = matrix1
new_matrix2 = matrix2
# Adding zeros to the matrices so that the arrays dimensions are the same and also
# power of 2
for i in range(0, maxim):
if i < dimension1[0]:
for _ in range(dimension1[1], maxim):
new_matrix1[i].append(0)
else:
new_matrix1.append([0] * maxim)
if i < dimension2[0]:
for _ in range(dimension2[1], maxim):
new_matrix2[i].append(0)
else:
new_matrix2.append([0] * maxim)
final_matrix = actual_strassen(new_matrix1, new_matrix2)
# Removing the additional zeros
for i in range(0, maxim):
if i < dimension1[0]:
for _ in range(dimension2[1], maxim):
final_matrix[i].pop()
else:
final_matrix.pop()
return final_matrix
if __name__ == "__main__":
matrix1 = [
[2, 3, 4, 5],
[6, 4, 3, 1],
[2, 3, 6, 7],
[3, 1, 2, 4],
[2, 3, 4, 5],
[6, 4, 3, 1],
[2, 3, 6, 7],
[3, 1, 2, 4],
[2, 3, 4, 5],
[6, 2, 3, 1],
]
matrix2 = [[0, 2, 1, 1], [16, 2, 3, 3], [2, 2, 7, 7], [13, 11, 22, 4]]
print(strassen(matrix1, matrix2))
| from __future__ import annotations
import math
def default_matrix_multiplication(a: list, b: list) -> list:
"""
Multiplication only for 2x2 matrices
"""
if len(a) != 2 or len(a[0]) != 2 or len(b) != 2 or len(b[0]) != 2:
raise Exception("Matrices are not 2x2")
new_matrix = [
[a[0][0] * b[0][0] + a[0][1] * b[1][0], a[0][0] * b[0][1] + a[0][1] * b[1][1]],
[a[1][0] * b[0][0] + a[1][1] * b[1][0], a[1][0] * b[0][1] + a[1][1] * b[1][1]],
]
return new_matrix
def matrix_addition(matrix_a: list, matrix_b: list):
return [
[matrix_a[row][col] + matrix_b[row][col] for col in range(len(matrix_a[row]))]
for row in range(len(matrix_a))
]
def matrix_subtraction(matrix_a: list, matrix_b: list):
return [
[matrix_a[row][col] - matrix_b[row][col] for col in range(len(matrix_a[row]))]
for row in range(len(matrix_a))
]
def split_matrix(a: list) -> tuple[list, list, list, list]:
"""
Given an even length matrix, returns the top_left, top_right, bot_left, bot_right
quadrant.
>>> split_matrix([[4,3,2,4],[2,3,1,1],[6,5,4,3],[8,4,1,6]])
([[4, 3], [2, 3]], [[2, 4], [1, 1]], [[6, 5], [8, 4]], [[4, 3], [1, 6]])
>>> split_matrix([
... [4,3,2,4,4,3,2,4],[2,3,1,1,2,3,1,1],[6,5,4,3,6,5,4,3],[8,4,1,6,8,4,1,6],
... [4,3,2,4,4,3,2,4],[2,3,1,1,2,3,1,1],[6,5,4,3,6,5,4,3],[8,4,1,6,8,4,1,6]
... ]) # doctest: +NORMALIZE_WHITESPACE
([[4, 3, 2, 4], [2, 3, 1, 1], [6, 5, 4, 3], [8, 4, 1, 6]], [[4, 3, 2, 4],
[2, 3, 1, 1], [6, 5, 4, 3], [8, 4, 1, 6]], [[4, 3, 2, 4], [2, 3, 1, 1],
[6, 5, 4, 3], [8, 4, 1, 6]], [[4, 3, 2, 4], [2, 3, 1, 1], [6, 5, 4, 3],
[8, 4, 1, 6]])
"""
if len(a) % 2 != 0 or len(a[0]) % 2 != 0:
raise Exception("Odd matrices are not supported!")
matrix_length = len(a)
mid = matrix_length // 2
top_right = [[a[i][j] for j in range(mid, matrix_length)] for i in range(mid)]
bot_right = [
[a[i][j] for j in range(mid, matrix_length)] for i in range(mid, matrix_length)
]
top_left = [[a[i][j] for j in range(mid)] for i in range(mid)]
bot_left = [[a[i][j] for j in range(mid)] for i in range(mid, matrix_length)]
return top_left, top_right, bot_left, bot_right
def matrix_dimensions(matrix: list) -> tuple[int, int]:
return len(matrix), len(matrix[0])
def print_matrix(matrix: list) -> None:
print("\n".join(str(line) for line in matrix))
def actual_strassen(matrix_a: list, matrix_b: list) -> list:
"""
Recursive function to calculate the product of two matrices, using the Strassen
Algorithm. It only supports even length matrices.
"""
if matrix_dimensions(matrix_a) == (2, 2):
return default_matrix_multiplication(matrix_a, matrix_b)
a, b, c, d = split_matrix(matrix_a)
e, f, g, h = split_matrix(matrix_b)
t1 = actual_strassen(a, matrix_subtraction(f, h))
t2 = actual_strassen(matrix_addition(a, b), h)
t3 = actual_strassen(matrix_addition(c, d), e)
t4 = actual_strassen(d, matrix_subtraction(g, e))
t5 = actual_strassen(matrix_addition(a, d), matrix_addition(e, h))
t6 = actual_strassen(matrix_subtraction(b, d), matrix_addition(g, h))
t7 = actual_strassen(matrix_subtraction(a, c), matrix_addition(e, f))
top_left = matrix_addition(matrix_subtraction(matrix_addition(t5, t4), t2), t6)
top_right = matrix_addition(t1, t2)
bot_left = matrix_addition(t3, t4)
bot_right = matrix_subtraction(matrix_subtraction(matrix_addition(t1, t5), t3), t7)
# construct the new matrix from our 4 quadrants
new_matrix = []
for i in range(len(top_right)):
new_matrix.append(top_left[i] + top_right[i])
for i in range(len(bot_right)):
new_matrix.append(bot_left[i] + bot_right[i])
return new_matrix
def strassen(matrix1: list, matrix2: list) -> list:
"""
>>> strassen([[2,1,3],[3,4,6],[1,4,2],[7,6,7]], [[4,2,3,4],[2,1,1,1],[8,6,4,2]])
[[34, 23, 19, 15], [68, 46, 37, 28], [28, 18, 15, 12], [96, 62, 55, 48]]
>>> strassen([[3,7,5,6,9],[1,5,3,7,8],[1,4,4,5,7]], [[2,4],[5,2],[1,7],[5,5],[7,8]])
[[139, 163], [121, 134], [100, 121]]
"""
if matrix_dimensions(matrix1)[1] != matrix_dimensions(matrix2)[0]:
msg = (
"Unable to multiply these matrices, please check the dimensions.\n"
f"Matrix A: {matrix1}\n"
f"Matrix B: {matrix2}"
)
raise Exception(msg)
dimension1 = matrix_dimensions(matrix1)
dimension2 = matrix_dimensions(matrix2)
if dimension1[0] == dimension1[1] and dimension2[0] == dimension2[1]:
return [matrix1, matrix2]
maximum = max(*dimension1, *dimension2)
maxim = int(math.pow(2, math.ceil(math.log2(maximum))))
new_matrix1 = matrix1
new_matrix2 = matrix2
# Adding zeros to the matrices so that the arrays dimensions are the same and also
# power of 2
for i in range(0, maxim):
if i < dimension1[0]:
for _ in range(dimension1[1], maxim):
new_matrix1[i].append(0)
else:
new_matrix1.append([0] * maxim)
if i < dimension2[0]:
for _ in range(dimension2[1], maxim):
new_matrix2[i].append(0)
else:
new_matrix2.append([0] * maxim)
final_matrix = actual_strassen(new_matrix1, new_matrix2)
# Removing the additional zeros
for i in range(0, maxim):
if i < dimension1[0]:
for _ in range(dimension2[1], maxim):
final_matrix[i].pop()
else:
final_matrix.pop()
return final_matrix
if __name__ == "__main__":
matrix1 = [
[2, 3, 4, 5],
[6, 4, 3, 1],
[2, 3, 6, 7],
[3, 1, 2, 4],
[2, 3, 4, 5],
[6, 4, 3, 1],
[2, 3, 6, 7],
[3, 1, 2, 4],
[2, 3, 4, 5],
[6, 2, 3, 1],
]
matrix2 = [[0, 2, 1, 1], [16, 2, 3, 3], [2, 2, 7, 7], [13, 11, 22, 4]]
print(strassen(matrix1, matrix2))
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| # Python program to show the usage of Fermat's little theorem in a division
# According to Fermat's little theorem, (a / b) mod p always equals
# a * (b ^ (p - 2)) mod p
# Here we assume that p is a prime number, b divides a, and p doesn't divide b
# Wikipedia reference: https://en.wikipedia.org/wiki/Fermat%27s_little_theorem
def binary_exponentiation(a, n, mod):
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(a, n - 1, mod) * a) % mod
else:
b = binary_exponentiation(a, n / 2, mod)
return (b * b) % mod
# a prime number
p = 701
a = 1000000000
b = 10
# using binary exponentiation function, O(log(p)):
print((a / b) % p == (a * binary_exponentiation(b, p - 2, p)) % p)
# using Python operators:
print((a / b) % p == (a * b ** (p - 2)) % p)
| # Python program to show the usage of Fermat's little theorem in a division
# According to Fermat's little theorem, (a / b) mod p always equals
# a * (b ^ (p - 2)) mod p
# Here we assume that p is a prime number, b divides a, and p doesn't divide b
# Wikipedia reference: https://en.wikipedia.org/wiki/Fermat%27s_little_theorem
def binary_exponentiation(a, n, mod):
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(a, n - 1, mod) * a) % mod
else:
b = binary_exponentiation(a, n / 2, mod)
return (b * b) % mod
# a prime number
p = 701
a = 1000000000
b = 10
# using binary exponentiation function, O(log(p)):
print((a / b) % p == (a * binary_exponentiation(b, p - 2, p)) % p)
# using Python operators:
print((a / b) % p == (a * b ** (p - 2)) % p)
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| # Welcome to Quantum Algorithms
Started at https://github.com/TheAlgorithms/Python/issues/1831
* D-Wave: https://www.dwavesys.com and https://github.com/dwavesystems
* Google: https://research.google/teams/applied-science/quantum
* IBM: https://qiskit.org and https://github.com/Qiskit
* Rigetti: https://rigetti.com and https://github.com/rigetti
* Zapata: https://www.zapatacomputing.com and https://github.com/zapatacomputing
## IBM Qiskit
- Start using by installing `pip install qiskit`, refer the [docs](https://qiskit.org/documentation/install.html) for more info.
- Tutorials & References
- https://github.com/Qiskit/qiskit-tutorials
- https://quantum-computing.ibm.com/docs/iql/first-circuit
- https://medium.com/qiskit/how-to-program-a-quantum-computer-982a9329ed02
## Google Cirq
- Start using by installing `python -m pip install cirq`, refer the [docs](https://quantumai.google/cirq/start/install) for more info.
- Tutorials & references
- https://github.com/quantumlib/cirq
- https://quantumai.google/cirq/experiments
- https://tanishabassan.medium.com/quantum-programming-with-google-cirq-3209805279bc
| # Welcome to Quantum Algorithms
Started at https://github.com/TheAlgorithms/Python/issues/1831
* D-Wave: https://www.dwavesys.com and https://github.com/dwavesystems
* Google: https://research.google/teams/applied-science/quantum
* IBM: https://qiskit.org and https://github.com/Qiskit
* Rigetti: https://rigetti.com and https://github.com/rigetti
* Zapata: https://www.zapatacomputing.com and https://github.com/zapatacomputing
## IBM Qiskit
- Start using by installing `pip install qiskit`, refer the [docs](https://qiskit.org/documentation/install.html) for more info.
- Tutorials & References
- https://github.com/Qiskit/qiskit-tutorials
- https://quantum-computing.ibm.com/docs/iql/first-circuit
- https://medium.com/qiskit/how-to-program-a-quantum-computer-982a9329ed02
## Google Cirq
- Start using by installing `python -m pip install cirq`, refer the [docs](https://quantumai.google/cirq/start/install) for more info.
- Tutorials & references
- https://github.com/quantumlib/cirq
- https://quantumai.google/cirq/experiments
- https://tanishabassan.medium.com/quantum-programming-with-google-cirq-3209805279bc
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| def reverse_long_words(sentence: str) -> str:
"""
Reverse all words that are longer than 4 characters in a sentence.
>>> reverse_long_words("Hey wollef sroirraw")
'Hey fellow warriors'
>>> reverse_long_words("nohtyP is nohtyP")
'Python is Python'
>>> reverse_long_words("1 12 123 1234 54321 654321")
'1 12 123 1234 12345 123456'
"""
return " ".join(
"".join(word[::-1]) if len(word) > 4 else word for word in sentence.split()
)
if __name__ == "__main__":
import doctest
doctest.testmod()
print(reverse_long_words("Hey wollef sroirraw"))
| def reverse_long_words(sentence: str) -> str:
"""
Reverse all words that are longer than 4 characters in a sentence.
>>> reverse_long_words("Hey wollef sroirraw")
'Hey fellow warriors'
>>> reverse_long_words("nohtyP is nohtyP")
'Python is Python'
>>> reverse_long_words("1 12 123 1234 54321 654321")
'1 12 123 1234 12345 123456'
"""
return " ".join(
"".join(word[::-1]) if len(word) > 4 else word for word in sentence.split()
)
if __name__ == "__main__":
import doctest
doctest.testmod()
print(reverse_long_words("Hey wollef sroirraw"))
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| """
Project Euler Problem 8: https://projecteuler.net/problem=8
Largest product in a series
The four adjacent digits in the 1000-digit number that have the greatest
product are 9 × 9 × 8 × 9 = 5832.
73167176531330624919225119674426574742355349194934
96983520312774506326239578318016984801869478851843
85861560789112949495459501737958331952853208805511
12540698747158523863050715693290963295227443043557
66896648950445244523161731856403098711121722383113
62229893423380308135336276614282806444486645238749
30358907296290491560440772390713810515859307960866
70172427121883998797908792274921901699720888093776
65727333001053367881220235421809751254540594752243
52584907711670556013604839586446706324415722155397
53697817977846174064955149290862569321978468622482
83972241375657056057490261407972968652414535100474
82166370484403199890008895243450658541227588666881
16427171479924442928230863465674813919123162824586
17866458359124566529476545682848912883142607690042
24219022671055626321111109370544217506941658960408
07198403850962455444362981230987879927244284909188
84580156166097919133875499200524063689912560717606
05886116467109405077541002256983155200055935729725
71636269561882670428252483600823257530420752963450
Find the thirteen adjacent digits in the 1000-digit number that have the
greatest product. What is the value of this product?
"""
import sys
N = (
"73167176531330624919225119674426574742355349194934"
"96983520312774506326239578318016984801869478851843"
"85861560789112949495459501737958331952853208805511"
"12540698747158523863050715693290963295227443043557"
"66896648950445244523161731856403098711121722383113"
"62229893423380308135336276614282806444486645238749"
"30358907296290491560440772390713810515859307960866"
"70172427121883998797908792274921901699720888093776"
"65727333001053367881220235421809751254540594752243"
"52584907711670556013604839586446706324415722155397"
"53697817977846174064955149290862569321978468622482"
"83972241375657056057490261407972968652414535100474"
"82166370484403199890008895243450658541227588666881"
"16427171479924442928230863465674813919123162824586"
"17866458359124566529476545682848912883142607690042"
"24219022671055626321111109370544217506941658960408"
"07198403850962455444362981230987879927244284909188"
"84580156166097919133875499200524063689912560717606"
"05886116467109405077541002256983155200055935729725"
"71636269561882670428252483600823257530420752963450"
)
def str_eval(s: str) -> int:
"""
Returns product of digits in given string n
>>> str_eval("987654321")
362880
>>> str_eval("22222222")
256
"""
product = 1
for digit in s:
product *= int(digit)
return product
def solution(n: str = N) -> int:
"""
Find the thirteen adjacent digits in the 1000-digit number n that have
the greatest product and returns it.
"""
largest_product = -sys.maxsize - 1
substr = n[:13]
cur_index = 13
while cur_index < len(n) - 13:
if int(n[cur_index]) >= int(substr[0]):
substr = substr[1:] + n[cur_index]
cur_index += 1
else:
largest_product = max(largest_product, str_eval(substr))
substr = n[cur_index : cur_index + 13]
cur_index += 13
return largest_product
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 8: https://projecteuler.net/problem=8
Largest product in a series
The four adjacent digits in the 1000-digit number that have the greatest
product are 9 × 9 × 8 × 9 = 5832.
73167176531330624919225119674426574742355349194934
96983520312774506326239578318016984801869478851843
85861560789112949495459501737958331952853208805511
12540698747158523863050715693290963295227443043557
66896648950445244523161731856403098711121722383113
62229893423380308135336276614282806444486645238749
30358907296290491560440772390713810515859307960866
70172427121883998797908792274921901699720888093776
65727333001053367881220235421809751254540594752243
52584907711670556013604839586446706324415722155397
53697817977846174064955149290862569321978468622482
83972241375657056057490261407972968652414535100474
82166370484403199890008895243450658541227588666881
16427171479924442928230863465674813919123162824586
17866458359124566529476545682848912883142607690042
24219022671055626321111109370544217506941658960408
07198403850962455444362981230987879927244284909188
84580156166097919133875499200524063689912560717606
05886116467109405077541002256983155200055935729725
71636269561882670428252483600823257530420752963450
Find the thirteen adjacent digits in the 1000-digit number that have the
greatest product. What is the value of this product?
"""
import sys
N = (
"73167176531330624919225119674426574742355349194934"
"96983520312774506326239578318016984801869478851843"
"85861560789112949495459501737958331952853208805511"
"12540698747158523863050715693290963295227443043557"
"66896648950445244523161731856403098711121722383113"
"62229893423380308135336276614282806444486645238749"
"30358907296290491560440772390713810515859307960866"
"70172427121883998797908792274921901699720888093776"
"65727333001053367881220235421809751254540594752243"
"52584907711670556013604839586446706324415722155397"
"53697817977846174064955149290862569321978468622482"
"83972241375657056057490261407972968652414535100474"
"82166370484403199890008895243450658541227588666881"
"16427171479924442928230863465674813919123162824586"
"17866458359124566529476545682848912883142607690042"
"24219022671055626321111109370544217506941658960408"
"07198403850962455444362981230987879927244284909188"
"84580156166097919133875499200524063689912560717606"
"05886116467109405077541002256983155200055935729725"
"71636269561882670428252483600823257530420752963450"
)
def str_eval(s: str) -> int:
"""
Returns product of digits in given string n
>>> str_eval("987654321")
362880
>>> str_eval("22222222")
256
"""
product = 1
for digit in s:
product *= int(digit)
return product
def solution(n: str = N) -> int:
"""
Find the thirteen adjacent digits in the 1000-digit number n that have
the greatest product and returns it.
"""
largest_product = -sys.maxsize - 1
substr = n[:13]
cur_index = 13
while cur_index < len(n) - 13:
if int(n[cur_index]) >= int(substr[0]):
substr = substr[1:] + n[cur_index]
cur_index += 1
else:
largest_product = max(largest_product, str_eval(substr))
substr = n[cur_index : cur_index + 13]
cur_index += 13
return largest_product
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| """
== Hexagonal Number ==
The nth hexagonal number hn is the number of distinct dots
in a pattern of dots consisting of the outlines of regular
hexagons with sides up to n dots, when the hexagons are
overlaid so that they share one vertex.
https://en.wikipedia.org/wiki/Hexagonal_number
"""
# Author : Akshay Dubey (https://github.com/itsAkshayDubey)
def hexagonal(number: int) -> int:
"""
:param number: nth hexagonal number to calculate
:return: the nth hexagonal number
Note: A hexagonal number is only defined for positive integers
>>> hexagonal(4)
28
>>> hexagonal(11)
231
>>> hexagonal(22)
946
>>> hexagonal(0)
Traceback (most recent call last):
...
ValueError: Input must be a positive integer
>>> hexagonal(-1)
Traceback (most recent call last):
...
ValueError: Input must be a positive integer
>>> hexagonal(11.0)
Traceback (most recent call last):
...
TypeError: Input value of [number=11.0] must be an integer
"""
if not isinstance(number, int):
msg = f"Input value of [number={number}] must be an integer"
raise TypeError(msg)
if number < 1:
raise ValueError("Input must be a positive integer")
return number * (2 * number - 1)
if __name__ == "__main__":
import doctest
doctest.testmod()
| """
== Hexagonal Number ==
The nth hexagonal number hn is the number of distinct dots
in a pattern of dots consisting of the outlines of regular
hexagons with sides up to n dots, when the hexagons are
overlaid so that they share one vertex.
https://en.wikipedia.org/wiki/Hexagonal_number
"""
# Author : Akshay Dubey (https://github.com/itsAkshayDubey)
def hexagonal(number: int) -> int:
"""
:param number: nth hexagonal number to calculate
:return: the nth hexagonal number
Note: A hexagonal number is only defined for positive integers
>>> hexagonal(4)
28
>>> hexagonal(11)
231
>>> hexagonal(22)
946
>>> hexagonal(0)
Traceback (most recent call last):
...
ValueError: Input must be a positive integer
>>> hexagonal(-1)
Traceback (most recent call last):
...
ValueError: Input must be a positive integer
>>> hexagonal(11.0)
Traceback (most recent call last):
...
TypeError: Input value of [number=11.0] must be an integer
"""
if not isinstance(number, int):
msg = f"Input value of [number={number}] must be an integer"
raise TypeError(msg)
if number < 1:
raise ValueError("Input must be a positive integer")
return number * (2 * number - 1)
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| import random
import sys
LETTERS = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
def main() -> None:
message = input("Enter message: ")
key = "LFWOAYUISVKMNXPBDCRJTQEGHZ"
resp = input("Encrypt/Decrypt [e/d]: ")
check_valid_key(key)
if resp.lower().startswith("e"):
mode = "encrypt"
translated = encrypt_message(key, message)
elif resp.lower().startswith("d"):
mode = "decrypt"
translated = decrypt_message(key, message)
print(f"\n{mode.title()}ion: \n{translated}")
def check_valid_key(key: str) -> None:
key_list = list(key)
letters_list = list(LETTERS)
key_list.sort()
letters_list.sort()
if key_list != letters_list:
sys.exit("Error in the key or symbol set.")
def encrypt_message(key: str, message: str) -> str:
"""
>>> encrypt_message('LFWOAYUISVKMNXPBDCRJTQEGHZ', 'Harshil Darji')
'Ilcrism Olcvs'
"""
return translate_message(key, message, "encrypt")
def decrypt_message(key: str, message: str) -> str:
"""
>>> decrypt_message('LFWOAYUISVKMNXPBDCRJTQEGHZ', 'Ilcrism Olcvs')
'Harshil Darji'
"""
return translate_message(key, message, "decrypt")
def translate_message(key: str, message: str, mode: str) -> str:
translated = ""
chars_a = LETTERS
chars_b = key
if mode == "decrypt":
chars_a, chars_b = chars_b, chars_a
for symbol in message:
if symbol.upper() in chars_a:
sym_index = chars_a.find(symbol.upper())
if symbol.isupper():
translated += chars_b[sym_index].upper()
else:
translated += chars_b[sym_index].lower()
else:
translated += symbol
return translated
def get_random_key() -> str:
key = list(LETTERS)
random.shuffle(key)
return "".join(key)
if __name__ == "__main__":
main()
| import random
import sys
LETTERS = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
def main() -> None:
message = input("Enter message: ")
key = "LFWOAYUISVKMNXPBDCRJTQEGHZ"
resp = input("Encrypt/Decrypt [e/d]: ")
check_valid_key(key)
if resp.lower().startswith("e"):
mode = "encrypt"
translated = encrypt_message(key, message)
elif resp.lower().startswith("d"):
mode = "decrypt"
translated = decrypt_message(key, message)
print(f"\n{mode.title()}ion: \n{translated}")
def check_valid_key(key: str) -> None:
key_list = list(key)
letters_list = list(LETTERS)
key_list.sort()
letters_list.sort()
if key_list != letters_list:
sys.exit("Error in the key or symbol set.")
def encrypt_message(key: str, message: str) -> str:
"""
>>> encrypt_message('LFWOAYUISVKMNXPBDCRJTQEGHZ', 'Harshil Darji')
'Ilcrism Olcvs'
"""
return translate_message(key, message, "encrypt")
def decrypt_message(key: str, message: str) -> str:
"""
>>> decrypt_message('LFWOAYUISVKMNXPBDCRJTQEGHZ', 'Ilcrism Olcvs')
'Harshil Darji'
"""
return translate_message(key, message, "decrypt")
def translate_message(key: str, message: str, mode: str) -> str:
translated = ""
chars_a = LETTERS
chars_b = key
if mode == "decrypt":
chars_a, chars_b = chars_b, chars_a
for symbol in message:
if symbol.upper() in chars_a:
sym_index = chars_a.find(symbol.upper())
if symbol.isupper():
translated += chars_b[sym_index].upper()
else:
translated += chars_b[sym_index].lower()
else:
translated += symbol
return translated
def get_random_key() -> str:
key = list(LETTERS)
random.shuffle(key)
return "".join(key)
if __name__ == "__main__":
main()
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def password_generator(length: int = 8) -> str:
"""
Password Generator allows you to generate a random password of length N.
>>> len(password_generator())
8
>>> len(password_generator(length=16))
16
>>> len(password_generator(257))
257
>>> len(password_generator(length=0))
0
>>> len(password_generator(-1))
0
"""
chars = ascii_letters + digits + punctuation
return "".join(secrets.choice(chars) for _ in range(length))
# ALTERNATIVE METHODS
# chars_incl= characters that must be in password
# i= how many letters or characters the password length will be
def alternative_password_generator(chars_incl: str, i: int) -> str:
# Password Generator = full boot with random_number, random_letters, and
# random_character FUNCTIONS
# Put your code here...
i -= len(chars_incl)
quotient = i // 3
remainder = i % 3
# chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
# random_number(digits, i / 3) + random_characters(punctuation, i / 3)
chars = (
chars_incl
+ random(ascii_letters, quotient + remainder)
+ random(digits, quotient)
+ random(punctuation, quotient)
)
list_of_chars = list(chars)
shuffle(list_of_chars)
return "".join(list_of_chars)
# random is a generalised function for letters, characters and numbers
def random(chars_incl: str, i: int) -> str:
return "".join(secrets.choice(chars_incl) for _ in range(i))
def random_number(chars_incl, i):
pass # Put your code here...
def random_letters(chars_incl, i):
pass # Put your code here...
def random_characters(chars_incl, i):
pass # Put your code here...
# This Will Check Whether A Given Password Is Strong Or Not
# It Follows The Rule that Length Of Password Should Be At Least 8 Characters
# And At Least 1 Lower, 1 Upper, 1 Number And 1 Special Character
def is_strong_password(password: str, min_length: int = 8) -> bool:
"""
>>> is_strong_password('Hwea7$2!')
True
>>> is_strong_password('Sh0r1')
False
>>> is_strong_password('Hello123')
False
>>> is_strong_password('Hello1238udfhiaf038fajdvjjf!jaiuFhkqi1')
True
>>> is_strong_password('0')
False
"""
if len(password) < min_length:
# Your Password must be at least 8 characters long
return False
upper = any(char in ascii_uppercase for char in password)
lower = any(char in ascii_lowercase for char in password)
num = any(char in digits for char in password)
spec_char = any(char in punctuation for char in password)
return upper and lower and num and spec_char
# Passwords should contain UPPERCASE, lowerase
# numbers, and special characters
def main():
length = int(input("Please indicate the max length of your password: ").strip())
chars_incl = input(
"Please indicate the characters that must be in your password: "
).strip()
print("Password generated:", password_generator(length))
print(
"Alternative Password generated:",
alternative_password_generator(chars_incl, length),
)
print("[If you are thinking of using this passsword, You better save it.]")
if __name__ == "__main__":
main()
| import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def password_generator(length: int = 8) -> str:
"""
Password Generator allows you to generate a random password of length N.
>>> len(password_generator())
8
>>> len(password_generator(length=16))
16
>>> len(password_generator(257))
257
>>> len(password_generator(length=0))
0
>>> len(password_generator(-1))
0
"""
chars = ascii_letters + digits + punctuation
return "".join(secrets.choice(chars) for _ in range(length))
# ALTERNATIVE METHODS
# chars_incl= characters that must be in password
# i= how many letters or characters the password length will be
def alternative_password_generator(chars_incl: str, i: int) -> str:
# Password Generator = full boot with random_number, random_letters, and
# random_character FUNCTIONS
# Put your code here...
i -= len(chars_incl)
quotient = i // 3
remainder = i % 3
# chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
# random_number(digits, i / 3) + random_characters(punctuation, i / 3)
chars = (
chars_incl
+ random(ascii_letters, quotient + remainder)
+ random(digits, quotient)
+ random(punctuation, quotient)
)
list_of_chars = list(chars)
shuffle(list_of_chars)
return "".join(list_of_chars)
# random is a generalised function for letters, characters and numbers
def random(chars_incl: str, i: int) -> str:
return "".join(secrets.choice(chars_incl) for _ in range(i))
def random_number(chars_incl, i):
pass # Put your code here...
def random_letters(chars_incl, i):
pass # Put your code here...
def random_characters(chars_incl, i):
pass # Put your code here...
# This Will Check Whether A Given Password Is Strong Or Not
# It Follows The Rule that Length Of Password Should Be At Least 8 Characters
# And At Least 1 Lower, 1 Upper, 1 Number And 1 Special Character
def is_strong_password(password: str, min_length: int = 8) -> bool:
"""
>>> is_strong_password('Hwea7$2!')
True
>>> is_strong_password('Sh0r1')
False
>>> is_strong_password('Hello123')
False
>>> is_strong_password('Hello1238udfhiaf038fajdvjjf!jaiuFhkqi1')
True
>>> is_strong_password('0')
False
"""
if len(password) < min_length:
# Your Password must be at least 8 characters long
return False
upper = any(char in ascii_uppercase for char in password)
lower = any(char in ascii_lowercase for char in password)
num = any(char in digits for char in password)
spec_char = any(char in punctuation for char in password)
return upper and lower and num and spec_char
# Passwords should contain UPPERCASE, lowerase
# numbers, and special characters
def main():
length = int(input("Please indicate the max length of your password: ").strip())
chars_incl = input(
"Please indicate the characters that must be in your password: "
).strip()
print("Password generated:", password_generator(length))
print(
"Alternative Password generated:",
alternative_password_generator(chars_incl, length),
)
print("[If you are thinking of using this passsword, You better save it.]")
if __name__ == "__main__":
main()
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| name: Bug report
description: Create a bug report to help us address errors in the repository
labels: [bug]
body:
- type: markdown
attributes:
value: >
Before requesting please search [existing issues](https://github.com/TheAlgorithms/Python/labels/bug).
Usage questions such as "How do I...?" belong on the
[Discord](https://discord.gg/c7MnfGFGa6) and will be closed.
- type: input
attributes:
label: "Repository commit"
description: >
The commit hash for `TheAlgorithms/Python` repository. You can get this
by running the command `git rev-parse HEAD` locally.
placeholder: "a0b0f414ae134aa1772d33bb930e5a960f9979e8"
validations:
required: true
- type: input
attributes:
label: "Python version (python --version)"
placeholder: "Python 3.10.7"
validations:
required: true
- type: textarea
attributes:
label: "Dependencies version (pip freeze)"
description: >
This is the output of the command `pip freeze --all`. Note that the
actual output might be different as compared to the placeholder text.
placeholder: |
appnope==0.1.3
asttokens==2.0.8
backcall==0.2.0
...
validations:
required: true
- type: textarea
attributes:
label: "Expected behavior"
description: "Describe the behavior you expect. May include images or videos."
validations:
required: true
- type: textarea
attributes:
label: "Actual behavior"
validations:
required: true
| name: Bug report
description: Create a bug report to help us address errors in the repository
labels: [bug]
body:
- type: markdown
attributes:
value: >
Before requesting please search [existing issues](https://github.com/TheAlgorithms/Python/labels/bug).
Usage questions such as "How do I...?" belong on the
[Discord](https://discord.gg/c7MnfGFGa6) and will be closed.
- type: input
attributes:
label: "Repository commit"
description: >
The commit hash for `TheAlgorithms/Python` repository. You can get this
by running the command `git rev-parse HEAD` locally.
placeholder: "a0b0f414ae134aa1772d33bb930e5a960f9979e8"
validations:
required: true
- type: input
attributes:
label: "Python version (python --version)"
placeholder: "Python 3.10.7"
validations:
required: true
- type: textarea
attributes:
label: "Dependencies version (pip freeze)"
description: >
This is the output of the command `pip freeze --all`. Note that the
actual output might be different as compared to the placeholder text.
placeholder: |
appnope==0.1.3
asttokens==2.0.8
backcall==0.2.0
...
validations:
required: true
- type: textarea
attributes:
label: "Expected behavior"
description: "Describe the behavior you expect. May include images or videos."
validations:
required: true
- type: textarea
attributes:
label: "Actual behavior"
validations:
required: true
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| """
Project Euler Problem 205: https://projecteuler.net/problem=205
Peter has nine four-sided (pyramidal) dice, each with faces numbered 1, 2, 3, 4.
Colin has six six-sided (cubic) dice, each with faces numbered 1, 2, 3, 4, 5, 6.
Peter and Colin roll their dice and compare totals: the highest total wins.
The result is a draw if the totals are equal.
What is the probability that Pyramidal Peter beats Cubic Colin?
Give your answer rounded to seven decimal places in the form 0.abcdefg
"""
from itertools import product
def total_frequency_distribution(sides_number: int, dice_number: int) -> list[int]:
"""
Returns frequency distribution of total
>>> total_frequency_distribution(sides_number=6, dice_number=1)
[0, 1, 1, 1, 1, 1, 1]
>>> total_frequency_distribution(sides_number=4, dice_number=2)
[0, 0, 1, 2, 3, 4, 3, 2, 1]
"""
max_face_number = sides_number
max_total = max_face_number * dice_number
totals_frequencies = [0] * (max_total + 1)
min_face_number = 1
faces_numbers = range(min_face_number, max_face_number + 1)
for dice_numbers in product(faces_numbers, repeat=dice_number):
total = sum(dice_numbers)
totals_frequencies[total] += 1
return totals_frequencies
def solution() -> float:
"""
Returns probability that Pyramidal Peter beats Cubic Colin
rounded to seven decimal places in the form 0.abcdefg
>>> solution()
0.5731441
"""
peter_totals_frequencies = total_frequency_distribution(
sides_number=4, dice_number=9
)
colin_totals_frequencies = total_frequency_distribution(
sides_number=6, dice_number=6
)
peter_wins_count = 0
min_peter_total = 9
max_peter_total = 4 * 9
min_colin_total = 6
for peter_total in range(min_peter_total, max_peter_total + 1):
peter_wins_count += peter_totals_frequencies[peter_total] * sum(
colin_totals_frequencies[min_colin_total:peter_total]
)
total_games_number = (4**9) * (6**6)
peter_win_probability = peter_wins_count / total_games_number
rounded_peter_win_probability = round(peter_win_probability, ndigits=7)
return rounded_peter_win_probability
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 205: https://projecteuler.net/problem=205
Peter has nine four-sided (pyramidal) dice, each with faces numbered 1, 2, 3, 4.
Colin has six six-sided (cubic) dice, each with faces numbered 1, 2, 3, 4, 5, 6.
Peter and Colin roll their dice and compare totals: the highest total wins.
The result is a draw if the totals are equal.
What is the probability that Pyramidal Peter beats Cubic Colin?
Give your answer rounded to seven decimal places in the form 0.abcdefg
"""
from itertools import product
def total_frequency_distribution(sides_number: int, dice_number: int) -> list[int]:
"""
Returns frequency distribution of total
>>> total_frequency_distribution(sides_number=6, dice_number=1)
[0, 1, 1, 1, 1, 1, 1]
>>> total_frequency_distribution(sides_number=4, dice_number=2)
[0, 0, 1, 2, 3, 4, 3, 2, 1]
"""
max_face_number = sides_number
max_total = max_face_number * dice_number
totals_frequencies = [0] * (max_total + 1)
min_face_number = 1
faces_numbers = range(min_face_number, max_face_number + 1)
for dice_numbers in product(faces_numbers, repeat=dice_number):
total = sum(dice_numbers)
totals_frequencies[total] += 1
return totals_frequencies
def solution() -> float:
"""
Returns probability that Pyramidal Peter beats Cubic Colin
rounded to seven decimal places in the form 0.abcdefg
>>> solution()
0.5731441
"""
peter_totals_frequencies = total_frequency_distribution(
sides_number=4, dice_number=9
)
colin_totals_frequencies = total_frequency_distribution(
sides_number=6, dice_number=6
)
peter_wins_count = 0
min_peter_total = 9
max_peter_total = 4 * 9
min_colin_total = 6
for peter_total in range(min_peter_total, max_peter_total + 1):
peter_wins_count += peter_totals_frequencies[peter_total] * sum(
colin_totals_frequencies[min_colin_total:peter_total]
)
total_games_number = (4**9) * (6**6)
peter_win_probability = peter_wins_count / total_games_number
rounded_peter_win_probability = round(peter_win_probability, ndigits=7)
return rounded_peter_win_probability
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| -1 |
||
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| # Linear algebra library for Python
This module contains classes and functions for doing linear algebra.
---
## Overview
### class Vector
-
- This class represents a vector of arbitrary size and related operations.
**Overview of the methods:**
- constructor(components) : init the vector
- set(components) : changes the vector components.
- \_\_str\_\_() : toString method
- component(i): gets the i-th component (0-indexed)
- \_\_len\_\_() : gets the size / length of the vector (number of components)
- euclidean_length() : returns the eulidean length of the vector
- operator + : vector addition
- operator - : vector subtraction
- operator * : scalar multiplication and dot product
- copy() : copies this vector and returns it
- change_component(pos,value) : changes the specified component
- function zero_vector(dimension)
- returns a zero vector of 'dimension'
- function unit_basis_vector(dimension, pos)
- returns a unit basis vector with a one at index 'pos' (0-indexed)
- function axpy(scalar, vector1, vector2)
- computes the axpy operation
- function random_vector(N, a, b)
- returns a random vector of size N, with random integer components between 'a' and 'b' inclusive
### class Matrix
-
- This class represents a matrix of arbitrary size and operations on it.
**Overview of the methods:**
- \_\_str\_\_() : returns a string representation
- operator * : implements the matrix vector multiplication
implements the matrix-scalar multiplication.
- change_component(x, y, value) : changes the specified component.
- component(x, y) : returns the specified component.
- width() : returns the width of the matrix
- height() : returns the height of the matrix
- determinant() : returns the determinant of the matrix if it is square
- operator + : implements the matrix-addition.
- operator - : implements the matrix-subtraction
- function square_zero_matrix(N)
- returns a square zero-matrix of dimension NxN
- function random_matrix(W, H, a, b)
- returns a random matrix WxH with integer components between 'a' and 'b' inclusive
---
## Documentation
This module uses docstrings to enable the use of Python's in-built `help(...)` function.
For instance, try `help(Vector)`, `help(unit_basis_vector)`, and `help(CLASSNAME.METHODNAME)`.
---
## Usage
Import the module `lib.py` from the **src** directory into your project.
Alternatively, you can directly use the Python bytecode file `lib.pyc`.
---
## Tests
`src/tests.py` contains Python unit tests which can be run with `python3 -m unittest -v`.
| # Linear algebra library for Python
This module contains classes and functions for doing linear algebra.
---
## Overview
### class Vector
-
- This class represents a vector of arbitrary size and related operations.
**Overview of the methods:**
- constructor(components) : init the vector
- set(components) : changes the vector components.
- \_\_str\_\_() : toString method
- component(i): gets the i-th component (0-indexed)
- \_\_len\_\_() : gets the size / length of the vector (number of components)
- euclidean_length() : returns the eulidean length of the vector
- operator + : vector addition
- operator - : vector subtraction
- operator * : scalar multiplication and dot product
- copy() : copies this vector and returns it
- change_component(pos,value) : changes the specified component
- function zero_vector(dimension)
- returns a zero vector of 'dimension'
- function unit_basis_vector(dimension, pos)
- returns a unit basis vector with a one at index 'pos' (0-indexed)
- function axpy(scalar, vector1, vector2)
- computes the axpy operation
- function random_vector(N, a, b)
- returns a random vector of size N, with random integer components between 'a' and 'b' inclusive
### class Matrix
-
- This class represents a matrix of arbitrary size and operations on it.
**Overview of the methods:**
- \_\_str\_\_() : returns a string representation
- operator * : implements the matrix vector multiplication
implements the matrix-scalar multiplication.
- change_component(x, y, value) : changes the specified component.
- component(x, y) : returns the specified component.
- width() : returns the width of the matrix
- height() : returns the height of the matrix
- determinant() : returns the determinant of the matrix if it is square
- operator + : implements the matrix-addition.
- operator - : implements the matrix-subtraction
- function square_zero_matrix(N)
- returns a square zero-matrix of dimension NxN
- function random_matrix(W, H, a, b)
- returns a random matrix WxH with integer components between 'a' and 'b' inclusive
---
## Documentation
This module uses docstrings to enable the use of Python's in-built `help(...)` function.
For instance, try `help(Vector)`, `help(unit_basis_vector)`, and `help(CLASSNAME.METHODNAME)`.
---
## Usage
Import the module `lib.py` from the **src** directory into your project.
Alternatively, you can directly use the Python bytecode file `lib.pyc`.
---
## Tests
`src/tests.py` contains Python unit tests which can be run with `python3 -m unittest -v`.
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| -1 |
||
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| """
This is a pure Python implementation of the greedy-merge-sort algorithm
reference: https://www.geeksforgeeks.org/optimal-file-merge-patterns/
For doctests run following command:
python3 -m doctest -v greedy_merge_sort.py
Objective
Merge a set of sorted files of different length into a single sorted file.
We need to find an optimal solution, where the resultant file
will be generated in minimum time.
Approach
If the number of sorted files are given, there are many ways
to merge them into a single sorted file.
This merge can be performed pair wise.
To merge a m-record file and a n-record file requires possibly m+n record moves
the optimal choice being,
merge the two smallest files together at each step (greedy approach).
"""
def optimal_merge_pattern(files: list) -> float:
"""Function to merge all the files with optimum cost
Args:
files [list]: A list of sizes of different files to be merged
Returns:
optimal_merge_cost [int]: Optimal cost to merge all those files
Examples:
>>> optimal_merge_pattern([2, 3, 4])
14
>>> optimal_merge_pattern([5, 10, 20, 30, 30])
205
>>> optimal_merge_pattern([8, 8, 8, 8, 8])
96
"""
optimal_merge_cost = 0
while len(files) > 1:
temp = 0
# Consider two files with minimum cost to be merged
for _ in range(2):
min_index = files.index(min(files))
temp += files[min_index]
files.pop(min_index)
files.append(temp)
optimal_merge_cost += temp
return optimal_merge_cost
if __name__ == "__main__":
import doctest
doctest.testmod()
| """
This is a pure Python implementation of the greedy-merge-sort algorithm
reference: https://www.geeksforgeeks.org/optimal-file-merge-patterns/
For doctests run following command:
python3 -m doctest -v greedy_merge_sort.py
Objective
Merge a set of sorted files of different length into a single sorted file.
We need to find an optimal solution, where the resultant file
will be generated in minimum time.
Approach
If the number of sorted files are given, there are many ways
to merge them into a single sorted file.
This merge can be performed pair wise.
To merge a m-record file and a n-record file requires possibly m+n record moves
the optimal choice being,
merge the two smallest files together at each step (greedy approach).
"""
def optimal_merge_pattern(files: list) -> float:
"""Function to merge all the files with optimum cost
Args:
files [list]: A list of sizes of different files to be merged
Returns:
optimal_merge_cost [int]: Optimal cost to merge all those files
Examples:
>>> optimal_merge_pattern([2, 3, 4])
14
>>> optimal_merge_pattern([5, 10, 20, 30, 30])
205
>>> optimal_merge_pattern([8, 8, 8, 8, 8])
96
"""
optimal_merge_cost = 0
while len(files) > 1:
temp = 0
# Consider two files with minimum cost to be merged
for _ in range(2):
min_index = files.index(min(files))
temp += files[min_index]
files.pop(min_index)
files.append(temp)
optimal_merge_cost += temp
return optimal_merge_cost
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| """
* Author: Manuel Di Lullo (https://github.com/manueldilullo)
* Description: Approximization algorithm for minimum vertex cover problem.
Matching Approach. Uses graphs represented with an adjacency list
URL: https://mathworld.wolfram.com/MinimumVertexCover.html
URL: https://www.princeton.edu/~aaa/Public/Teaching/ORF523/ORF523_Lec6.pdf
"""
def matching_min_vertex_cover(graph: dict) -> set:
"""
APX Algorithm for min Vertex Cover using Matching Approach
@input: graph (graph stored in an adjacency list where each vertex
is represented as an integer)
@example:
>>> graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
>>> matching_min_vertex_cover(graph)
{0, 1, 2, 4}
"""
# chosen_vertices = set of chosen vertices
chosen_vertices = set()
# edges = list of graph's edges
edges = get_edges(graph)
# While there are still elements in edges list, take an arbitrary edge
# (from_node, to_node) and add his extremity to chosen_vertices and then
# remove all arcs adjacent to the from_node and to_node
while edges:
from_node, to_node = edges.pop()
chosen_vertices.add(from_node)
chosen_vertices.add(to_node)
for edge in edges.copy():
if from_node in edge or to_node in edge:
edges.discard(edge)
return chosen_vertices
def get_edges(graph: dict) -> set:
"""
Return a set of couples that represents all of the edges.
@input: graph (graph stored in an adjacency list where each vertex is
represented as an integer)
@example:
>>> graph = {0: [1, 3], 1: [0, 3], 2: [0, 3], 3: [0, 1, 2]}
>>> get_edges(graph)
{(0, 1), (3, 1), (0, 3), (2, 0), (3, 0), (2, 3), (1, 0), (3, 2), (1, 3)}
"""
edges = set()
for from_node, to_nodes in graph.items():
for to_node in to_nodes:
edges.add((from_node, to_node))
return edges
if __name__ == "__main__":
import doctest
doctest.testmod()
# graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
# print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")
| """
* Author: Manuel Di Lullo (https://github.com/manueldilullo)
* Description: Approximization algorithm for minimum vertex cover problem.
Matching Approach. Uses graphs represented with an adjacency list
URL: https://mathworld.wolfram.com/MinimumVertexCover.html
URL: https://www.princeton.edu/~aaa/Public/Teaching/ORF523/ORF523_Lec6.pdf
"""
def matching_min_vertex_cover(graph: dict) -> set:
"""
APX Algorithm for min Vertex Cover using Matching Approach
@input: graph (graph stored in an adjacency list where each vertex
is represented as an integer)
@example:
>>> graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
>>> matching_min_vertex_cover(graph)
{0, 1, 2, 4}
"""
# chosen_vertices = set of chosen vertices
chosen_vertices = set()
# edges = list of graph's edges
edges = get_edges(graph)
# While there are still elements in edges list, take an arbitrary edge
# (from_node, to_node) and add his extremity to chosen_vertices and then
# remove all arcs adjacent to the from_node and to_node
while edges:
from_node, to_node = edges.pop()
chosen_vertices.add(from_node)
chosen_vertices.add(to_node)
for edge in edges.copy():
if from_node in edge or to_node in edge:
edges.discard(edge)
return chosen_vertices
def get_edges(graph: dict) -> set:
"""
Return a set of couples that represents all of the edges.
@input: graph (graph stored in an adjacency list where each vertex is
represented as an integer)
@example:
>>> graph = {0: [1, 3], 1: [0, 3], 2: [0, 3], 3: [0, 1, 2]}
>>> get_edges(graph)
{(0, 1), (3, 1), (0, 3), (2, 0), (3, 0), (2, 3), (1, 0), (3, 2), (1, 3)}
"""
edges = set()
for from_node, to_nodes in graph.items():
for to_node in to_nodes:
edges.add((from_node, to_node))
return edges
if __name__ == "__main__":
import doctest
doctest.testmod()
# graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
# print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| """
This script demonstrates the implementation of the Sigmoid Linear Unit (SiLU)
or swish function.
* https://en.wikipedia.org/wiki/Rectifier_(neural_networks)
* https://en.wikipedia.org/wiki/Swish_function
The function takes a vector x of K real numbers as input and returns x * sigmoid(x).
Swish is a smooth, non-monotonic function defined as f(x) = x * sigmoid(x).
Extensive experiments shows that Swish consistently matches or outperforms ReLU
on deep networks applied to a variety of challenging domains such as
image classification and machine translation.
This script is inspired by a corresponding research paper.
* https://arxiv.org/abs/1710.05941
"""
import numpy as np
def sigmoid(vector: np.ndarray) -> np.ndarray:
"""
Mathematical function sigmoid takes a vector x of K real numbers as input and
returns 1/ (1 + e^-x).
https://en.wikipedia.org/wiki/Sigmoid_function
>>> sigmoid(np.array([-1.0, 1.0, 2.0]))
array([0.26894142, 0.73105858, 0.88079708])
"""
return 1 / (1 + np.exp(-vector))
def sigmoid_linear_unit(vector: np.ndarray) -> np.ndarray:
"""
Implements the Sigmoid Linear Unit (SiLU) or swish function
Parameters:
vector (np.ndarray): A numpy array consisting of real values
Returns:
swish_vec (np.ndarray): The input numpy array, after applying swish
Examples:
>>> sigmoid_linear_unit(np.array([-1.0, 1.0, 2.0]))
array([-0.26894142, 0.73105858, 1.76159416])
>>> sigmoid_linear_unit(np.array([-2]))
array([-0.23840584])
"""
return vector * sigmoid(vector)
if __name__ == "__main__":
import doctest
doctest.testmod()
| """
This script demonstrates the implementation of the Sigmoid Linear Unit (SiLU)
or swish function.
* https://en.wikipedia.org/wiki/Rectifier_(neural_networks)
* https://en.wikipedia.org/wiki/Swish_function
The function takes a vector x of K real numbers as input and returns x * sigmoid(x).
Swish is a smooth, non-monotonic function defined as f(x) = x * sigmoid(x).
Extensive experiments shows that Swish consistently matches or outperforms ReLU
on deep networks applied to a variety of challenging domains such as
image classification and machine translation.
This script is inspired by a corresponding research paper.
* https://arxiv.org/abs/1710.05941
"""
import numpy as np
def sigmoid(vector: np.ndarray) -> np.ndarray:
"""
Mathematical function sigmoid takes a vector x of K real numbers as input and
returns 1/ (1 + e^-x).
https://en.wikipedia.org/wiki/Sigmoid_function
>>> sigmoid(np.array([-1.0, 1.0, 2.0]))
array([0.26894142, 0.73105858, 0.88079708])
"""
return 1 / (1 + np.exp(-vector))
def sigmoid_linear_unit(vector: np.ndarray) -> np.ndarray:
"""
Implements the Sigmoid Linear Unit (SiLU) or swish function
Parameters:
vector (np.ndarray): A numpy array consisting of real values
Returns:
swish_vec (np.ndarray): The input numpy array, after applying swish
Examples:
>>> sigmoid_linear_unit(np.array([-1.0, 1.0, 2.0]))
array([-0.26894142, 0.73105858, 1.76159416])
>>> sigmoid_linear_unit(np.array([-2]))
array([-0.23840584])
"""
return vector * sigmoid(vector)
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| -1 |
||
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| from math import pi
def radians(degree: float) -> float:
"""
Coverts the given angle from degrees to radians
https://en.wikipedia.org/wiki/Radian
>>> radians(180)
3.141592653589793
>>> radians(92)
1.6057029118347832
>>> radians(274)
4.782202150464463
>>> radians(109.82)
1.9167205845401725
>>> from math import radians as math_radians
>>> all(abs(radians(i)-math_radians(i)) <= 0.00000001 for i in range(-2, 361))
True
"""
return degree / (180 / pi)
if __name__ == "__main__":
from doctest import testmod
testmod()
| from math import pi
def radians(degree: float) -> float:
"""
Coverts the given angle from degrees to radians
https://en.wikipedia.org/wiki/Radian
>>> radians(180)
3.141592653589793
>>> radians(92)
1.6057029118347832
>>> radians(274)
4.782202150464463
>>> radians(109.82)
1.9167205845401725
>>> from math import radians as math_radians
>>> all(abs(radians(i)-math_radians(i)) <= 0.00000001 for i in range(-2, 361))
True
"""
return degree / (180 / pi)
if __name__ == "__main__":
from doctest import testmod
testmod()
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| -1 |
||
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
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TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| # https://en.wikipedia.org/wiki/LC_circuit
"""An LC circuit, also called a resonant circuit, tank circuit, or tuned circuit,
is an electric circuit consisting of an inductor, represented by the letter L,
and a capacitor, represented by the letter C, connected together.
The circuit can act as an electrical resonator, an electrical analogue of a
tuning fork, storing energy oscillating at the circuit's resonant frequency.
Source: https://en.wikipedia.org/wiki/LC_circuit
"""
from __future__ import annotations
from math import pi, sqrt
def resonant_frequency(inductance: float, capacitance: float) -> tuple:
"""
This function can calculate the resonant frequency of LC circuit,
for the given value of inductance and capacitnace.
Examples are given below:
>>> resonant_frequency(inductance=10, capacitance=5)
('Resonant frequency', 0.022507907903927652)
>>> resonant_frequency(inductance=0, capacitance=5)
Traceback (most recent call last):
...
ValueError: Inductance cannot be 0 or negative
>>> resonant_frequency(inductance=10, capacitance=0)
Traceback (most recent call last):
...
ValueError: Capacitance cannot be 0 or negative
"""
if inductance <= 0:
raise ValueError("Inductance cannot be 0 or negative")
elif capacitance <= 0:
raise ValueError("Capacitance cannot be 0 or negative")
else:
return (
"Resonant frequency",
float(1 / (2 * pi * (sqrt(inductance * capacitance)))),
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| # https://en.wikipedia.org/wiki/LC_circuit
"""An LC circuit, also called a resonant circuit, tank circuit, or tuned circuit,
is an electric circuit consisting of an inductor, represented by the letter L,
and a capacitor, represented by the letter C, connected together.
The circuit can act as an electrical resonator, an electrical analogue of a
tuning fork, storing energy oscillating at the circuit's resonant frequency.
Source: https://en.wikipedia.org/wiki/LC_circuit
"""
from __future__ import annotations
from math import pi, sqrt
def resonant_frequency(inductance: float, capacitance: float) -> tuple:
"""
This function can calculate the resonant frequency of LC circuit,
for the given value of inductance and capacitnace.
Examples are given below:
>>> resonant_frequency(inductance=10, capacitance=5)
('Resonant frequency', 0.022507907903927652)
>>> resonant_frequency(inductance=0, capacitance=5)
Traceback (most recent call last):
...
ValueError: Inductance cannot be 0 or negative
>>> resonant_frequency(inductance=10, capacitance=0)
Traceback (most recent call last):
...
ValueError: Capacitance cannot be 0 or negative
"""
if inductance <= 0:
raise ValueError("Inductance cannot be 0 or negative")
elif capacitance <= 0:
raise ValueError("Capacitance cannot be 0 or negative")
else:
return (
"Resonant frequency",
float(1 / (2 * pi * (sqrt(inductance * capacitance)))),
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| """
One of the several implementations of Lempel–Ziv–Welch decompression algorithm
https://en.wikipedia.org/wiki/Lempel%E2%80%93Ziv%E2%80%93Welch
"""
import math
import sys
def read_file_binary(file_path: str) -> str:
"""
Reads given file as bytes and returns them as a long string
"""
result = ""
try:
with open(file_path, "rb") as binary_file:
data = binary_file.read()
for dat in data:
curr_byte = f"{dat:08b}"
result += curr_byte
return result
except OSError:
print("File not accessible")
sys.exit()
def decompress_data(data_bits: str) -> str:
"""
Decompresses given data_bits using Lempel–Ziv–Welch compression algorithm
and returns the result as a string
"""
lexicon = {"0": "0", "1": "1"}
result, curr_string = "", ""
index = len(lexicon)
for i in range(len(data_bits)):
curr_string += data_bits[i]
if curr_string not in lexicon:
continue
last_match_id = lexicon[curr_string]
result += last_match_id
lexicon[curr_string] = last_match_id + "0"
if math.log2(index).is_integer():
new_lex = {}
for curr_key in list(lexicon):
new_lex["0" + curr_key] = lexicon.pop(curr_key)
lexicon = new_lex
lexicon[bin(index)[2:]] = last_match_id + "1"
index += 1
curr_string = ""
return result
def write_file_binary(file_path: str, to_write: str) -> None:
"""
Writes given to_write string (should only consist of 0's and 1's) as bytes in the
file
"""
byte_length = 8
try:
with open(file_path, "wb") as opened_file:
result_byte_array = [
to_write[i : i + byte_length]
for i in range(0, len(to_write), byte_length)
]
if len(result_byte_array[-1]) % byte_length == 0:
result_byte_array.append("10000000")
else:
result_byte_array[-1] += "1" + "0" * (
byte_length - len(result_byte_array[-1]) - 1
)
for elem in result_byte_array[:-1]:
opened_file.write(int(elem, 2).to_bytes(1, byteorder="big"))
except OSError:
print("File not accessible")
sys.exit()
def remove_prefix(data_bits: str) -> str:
"""
Removes size prefix, that compressed file should have
Returns the result
"""
counter = 0
for letter in data_bits:
if letter == "1":
break
counter += 1
data_bits = data_bits[counter:]
data_bits = data_bits[counter + 1 :]
return data_bits
def compress(source_path: str, destination_path: str) -> None:
"""
Reads source file, decompresses it and writes the result in destination file
"""
data_bits = read_file_binary(source_path)
data_bits = remove_prefix(data_bits)
decompressed = decompress_data(data_bits)
write_file_binary(destination_path, decompressed)
if __name__ == "__main__":
compress(sys.argv[1], sys.argv[2])
| """
One of the several implementations of Lempel–Ziv–Welch decompression algorithm
https://en.wikipedia.org/wiki/Lempel%E2%80%93Ziv%E2%80%93Welch
"""
import math
import sys
def read_file_binary(file_path: str) -> str:
"""
Reads given file as bytes and returns them as a long string
"""
result = ""
try:
with open(file_path, "rb") as binary_file:
data = binary_file.read()
for dat in data:
curr_byte = f"{dat:08b}"
result += curr_byte
return result
except OSError:
print("File not accessible")
sys.exit()
def decompress_data(data_bits: str) -> str:
"""
Decompresses given data_bits using Lempel–Ziv–Welch compression algorithm
and returns the result as a string
"""
lexicon = {"0": "0", "1": "1"}
result, curr_string = "", ""
index = len(lexicon)
for i in range(len(data_bits)):
curr_string += data_bits[i]
if curr_string not in lexicon:
continue
last_match_id = lexicon[curr_string]
result += last_match_id
lexicon[curr_string] = last_match_id + "0"
if math.log2(index).is_integer():
new_lex = {}
for curr_key in list(lexicon):
new_lex["0" + curr_key] = lexicon.pop(curr_key)
lexicon = new_lex
lexicon[bin(index)[2:]] = last_match_id + "1"
index += 1
curr_string = ""
return result
def write_file_binary(file_path: str, to_write: str) -> None:
"""
Writes given to_write string (should only consist of 0's and 1's) as bytes in the
file
"""
byte_length = 8
try:
with open(file_path, "wb") as opened_file:
result_byte_array = [
to_write[i : i + byte_length]
for i in range(0, len(to_write), byte_length)
]
if len(result_byte_array[-1]) % byte_length == 0:
result_byte_array.append("10000000")
else:
result_byte_array[-1] += "1" + "0" * (
byte_length - len(result_byte_array[-1]) - 1
)
for elem in result_byte_array[:-1]:
opened_file.write(int(elem, 2).to_bytes(1, byteorder="big"))
except OSError:
print("File not accessible")
sys.exit()
def remove_prefix(data_bits: str) -> str:
"""
Removes size prefix, that compressed file should have
Returns the result
"""
counter = 0
for letter in data_bits:
if letter == "1":
break
counter += 1
data_bits = data_bits[counter:]
data_bits = data_bits[counter + 1 :]
return data_bits
def compress(source_path: str, destination_path: str) -> None:
"""
Reads source file, decompresses it and writes the result in destination file
"""
data_bits = read_file_binary(source_path)
data_bits = remove_prefix(data_bits)
decompressed = decompress_data(data_bits)
write_file_binary(destination_path, decompressed)
if __name__ == "__main__":
compress(sys.argv[1], sys.argv[2])
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch("socket.socket")
@patch("builtins.open")
def test_send_file_running_as_expected(file, sock):
# ===== initialization =====
conn = Mock()
sock.return_value.accept.return_value = conn, Mock()
f = iter([1, None])
file.return_value.__enter__.return_value.read.side_effect = lambda _: next(f)
# ===== invoke =====
send_file(filename="mytext.txt", testing=True)
# ===== ensurance =====
sock.assert_called_once()
sock.return_value.bind.assert_called_once()
sock.return_value.listen.assert_called_once()
sock.return_value.accept.assert_called_once()
conn.recv.assert_called_once()
file.return_value.__enter__.assert_called_once()
file.return_value.__enter__.return_value.read.assert_called()
conn.send.assert_called_once()
conn.close.assert_called_once()
sock.return_value.shutdown.assert_called_once()
sock.return_value.close.assert_called_once()
| from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch("socket.socket")
@patch("builtins.open")
def test_send_file_running_as_expected(file, sock):
# ===== initialization =====
conn = Mock()
sock.return_value.accept.return_value = conn, Mock()
f = iter([1, None])
file.return_value.__enter__.return_value.read.side_effect = lambda _: next(f)
# ===== invoke =====
send_file(filename="mytext.txt", testing=True)
# ===== ensurance =====
sock.assert_called_once()
sock.return_value.bind.assert_called_once()
sock.return_value.listen.assert_called_once()
sock.return_value.accept.assert_called_once()
conn.recv.assert_called_once()
file.return_value.__enter__.assert_called_once()
file.return_value.__enter__.return_value.read.assert_called()
conn.send.assert_called_once()
conn.close.assert_called_once()
sock.return_value.shutdown.assert_called_once()
sock.return_value.close.assert_called_once()
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| -1 |
||
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| # Author: João Gustavo A. Amorim & Gabriel Kunz
# Author email: [email protected] and [email protected]
# Coding date: apr 2019
# Black: True
"""
* This code implement the Hamming code:
https://en.wikipedia.org/wiki/Hamming_code - In telecommunication,
Hamming codes are a family of linear error-correcting codes. Hamming
codes can detect up to two-bit errors or correct one-bit errors
without detection of uncorrected errors. By contrast, the simple
parity code cannot correct errors, and can detect only an odd number
of bits in error. Hamming codes are perfect codes, that is, they
achieve the highest possible rate for codes with their block length
and minimum distance of three.
* the implemented code consists of:
* a function responsible for encoding the message (emitterConverter)
* return the encoded message
* a function responsible for decoding the message (receptorConverter)
* return the decoded message and a ack of data integrity
* how to use:
to be used you must declare how many parity bits (sizePari)
you want to include in the message.
it is desired (for test purposes) to select a bit to be set
as an error. This serves to check whether the code is working correctly.
Lastly, the variable of the message/word that must be desired to be
encoded (text).
* how this work:
declaration of variables (sizePari, be, text)
converts the message/word (text) to binary using the
text_to_bits function
encodes the message using the rules of hamming encoding
decodes the message using the rules of hamming encoding
print the original message, the encoded message and the
decoded message
forces an error in the coded text variable
decodes the message that was forced the error
print the original message, the encoded message, the bit changed
message and the decoded message
"""
# Imports
import numpy as np
# Functions of binary conversion--------------------------------------
def text_to_bits(text, encoding="utf-8", errors="surrogatepass"):
"""
>>> text_to_bits("msg")
'011011010111001101100111'
"""
bits = bin(int.from_bytes(text.encode(encoding, errors), "big"))[2:]
return bits.zfill(8 * ((len(bits) + 7) // 8))
def text_from_bits(bits, encoding="utf-8", errors="surrogatepass"):
"""
>>> text_from_bits('011011010111001101100111')
'msg'
"""
n = int(bits, 2)
return n.to_bytes((n.bit_length() + 7) // 8, "big").decode(encoding, errors) or "\0"
# Functions of hamming code-------------------------------------------
def emitter_converter(size_par, data):
"""
:param size_par: how many parity bits the message must have
:param data: information bits
:return: message to be transmitted by unreliable medium
- bits of information merged with parity bits
>>> emitter_converter(4, "101010111111")
['1', '1', '1', '1', '0', '1', '0', '0', '1', '0', '1', '1', '1', '1', '1', '1']
"""
if size_par + len(data) <= 2**size_par - (len(data) - 1):
raise ValueError("size of parity don't match with size of data")
data_out = []
parity = []
bin_pos = [bin(x)[2:] for x in range(1, size_par + len(data) + 1)]
# sorted information data for the size of the output data
data_ord = []
# data position template + parity
data_out_gab = []
# parity bit counter
qtd_bp = 0
# counter position of data bits
cont_data = 0
for x in range(1, size_par + len(data) + 1):
# Performs a template of bit positions - who should be given,
# and who should be parity
if qtd_bp < size_par:
if (np.log(x) / np.log(2)).is_integer():
data_out_gab.append("P")
qtd_bp = qtd_bp + 1
else:
data_out_gab.append("D")
else:
data_out_gab.append("D")
# Sorts the data to the new output size
if data_out_gab[-1] == "D":
data_ord.append(data[cont_data])
cont_data += 1
else:
data_ord.append(None)
# Calculates parity
qtd_bp = 0 # parity bit counter
for bp in range(1, size_par + 1):
# Bit counter one for a given parity
cont_bo = 0
# counter to control the loop reading
cont_loop = 0
for x in data_ord:
if x is not None:
try:
aux = (bin_pos[cont_loop])[-1 * (bp)]
except IndexError:
aux = "0"
if aux == "1" and x == "1":
cont_bo += 1
cont_loop += 1
parity.append(cont_bo % 2)
qtd_bp += 1
# Mount the message
cont_bp = 0 # parity bit counter
for x in range(0, size_par + len(data)):
if data_ord[x] is None:
data_out.append(str(parity[cont_bp]))
cont_bp += 1
else:
data_out.append(data_ord[x])
return data_out
def receptor_converter(size_par, data):
"""
>>> receptor_converter(4, "1111010010111111")
(['1', '0', '1', '0', '1', '0', '1', '1', '1', '1', '1', '1'], True)
"""
# data position template + parity
data_out_gab = []
# Parity bit counter
qtd_bp = 0
# Counter p data bit reading
cont_data = 0
# list of parity received
parity_received = []
data_output = []
for x in range(1, len(data) + 1):
# Performs a template of bit positions - who should be given,
# and who should be parity
if qtd_bp < size_par and (np.log(x) / np.log(2)).is_integer():
data_out_gab.append("P")
qtd_bp = qtd_bp + 1
else:
data_out_gab.append("D")
# Sorts the data to the new output size
if data_out_gab[-1] == "D":
data_output.append(data[cont_data])
else:
parity_received.append(data[cont_data])
cont_data += 1
# -----------calculates the parity with the data
data_out = []
parity = []
bin_pos = [bin(x)[2:] for x in range(1, size_par + len(data_output) + 1)]
# sorted information data for the size of the output data
data_ord = []
# Data position feedback + parity
data_out_gab = []
# Parity bit counter
qtd_bp = 0
# Counter p data bit reading
cont_data = 0
for x in range(1, size_par + len(data_output) + 1):
# Performs a template position of bits - who should be given,
# and who should be parity
if qtd_bp < size_par and (np.log(x) / np.log(2)).is_integer():
data_out_gab.append("P")
qtd_bp = qtd_bp + 1
else:
data_out_gab.append("D")
# Sorts the data to the new output size
if data_out_gab[-1] == "D":
data_ord.append(data_output[cont_data])
cont_data += 1
else:
data_ord.append(None)
# Calculates parity
qtd_bp = 0 # parity bit counter
for bp in range(1, size_par + 1):
# Bit counter one for a certain parity
cont_bo = 0
# Counter to control loop reading
cont_loop = 0
for x in data_ord:
if x is not None:
try:
aux = (bin_pos[cont_loop])[-1 * (bp)]
except IndexError:
aux = "0"
if aux == "1" and x == "1":
cont_bo += 1
cont_loop += 1
parity.append(str(cont_bo % 2))
qtd_bp += 1
# Mount the message
cont_bp = 0 # Parity bit counter
for x in range(0, size_par + len(data_output)):
if data_ord[x] is None:
data_out.append(str(parity[cont_bp]))
cont_bp += 1
else:
data_out.append(data_ord[x])
ack = parity_received == parity
return data_output, ack
# ---------------------------------------------------------------------
"""
# Example how to use
# number of parity bits
sizePari = 4
# location of the bit that will be forced an error
be = 2
# Message/word to be encoded and decoded with hamming
# text = input("Enter the word to be read: ")
text = "Message01"
# Convert the message to binary
binaryText = text_to_bits(text)
# Prints the binary of the string
print("Text input in binary is '" + binaryText + "'")
# total transmitted bits
totalBits = len(binaryText) + sizePari
print("Size of data is " + str(totalBits))
print("\n --Message exchange--")
print("Data to send ------------> " + binaryText)
dataOut = emitterConverter(sizePari, binaryText)
print("Data converted ----------> " + "".join(dataOut))
dataReceiv, ack = receptorConverter(sizePari, dataOut)
print(
"Data receive ------------> "
+ "".join(dataReceiv)
+ "\t\t -- Data integrity: "
+ str(ack)
)
print("\n --Force error--")
print("Data to send ------------> " + binaryText)
dataOut = emitterConverter(sizePari, binaryText)
print("Data converted ----------> " + "".join(dataOut))
# forces error
dataOut[-be] = "1" * (dataOut[-be] == "0") + "0" * (dataOut[-be] == "1")
print("Data after transmission -> " + "".join(dataOut))
dataReceiv, ack = receptorConverter(sizePari, dataOut)
print(
"Data receive ------------> "
+ "".join(dataReceiv)
+ "\t\t -- Data integrity: "
+ str(ack)
)
"""
| # Author: João Gustavo A. Amorim & Gabriel Kunz
# Author email: [email protected] and [email protected]
# Coding date: apr 2019
# Black: True
"""
* This code implement the Hamming code:
https://en.wikipedia.org/wiki/Hamming_code - In telecommunication,
Hamming codes are a family of linear error-correcting codes. Hamming
codes can detect up to two-bit errors or correct one-bit errors
without detection of uncorrected errors. By contrast, the simple
parity code cannot correct errors, and can detect only an odd number
of bits in error. Hamming codes are perfect codes, that is, they
achieve the highest possible rate for codes with their block length
and minimum distance of three.
* the implemented code consists of:
* a function responsible for encoding the message (emitterConverter)
* return the encoded message
* a function responsible for decoding the message (receptorConverter)
* return the decoded message and a ack of data integrity
* how to use:
to be used you must declare how many parity bits (sizePari)
you want to include in the message.
it is desired (for test purposes) to select a bit to be set
as an error. This serves to check whether the code is working correctly.
Lastly, the variable of the message/word that must be desired to be
encoded (text).
* how this work:
declaration of variables (sizePari, be, text)
converts the message/word (text) to binary using the
text_to_bits function
encodes the message using the rules of hamming encoding
decodes the message using the rules of hamming encoding
print the original message, the encoded message and the
decoded message
forces an error in the coded text variable
decodes the message that was forced the error
print the original message, the encoded message, the bit changed
message and the decoded message
"""
# Imports
import numpy as np
# Functions of binary conversion--------------------------------------
def text_to_bits(text, encoding="utf-8", errors="surrogatepass"):
"""
>>> text_to_bits("msg")
'011011010111001101100111'
"""
bits = bin(int.from_bytes(text.encode(encoding, errors), "big"))[2:]
return bits.zfill(8 * ((len(bits) + 7) // 8))
def text_from_bits(bits, encoding="utf-8", errors="surrogatepass"):
"""
>>> text_from_bits('011011010111001101100111')
'msg'
"""
n = int(bits, 2)
return n.to_bytes((n.bit_length() + 7) // 8, "big").decode(encoding, errors) or "\0"
# Functions of hamming code-------------------------------------------
def emitter_converter(size_par, data):
"""
:param size_par: how many parity bits the message must have
:param data: information bits
:return: message to be transmitted by unreliable medium
- bits of information merged with parity bits
>>> emitter_converter(4, "101010111111")
['1', '1', '1', '1', '0', '1', '0', '0', '1', '0', '1', '1', '1', '1', '1', '1']
"""
if size_par + len(data) <= 2**size_par - (len(data) - 1):
raise ValueError("size of parity don't match with size of data")
data_out = []
parity = []
bin_pos = [bin(x)[2:] for x in range(1, size_par + len(data) + 1)]
# sorted information data for the size of the output data
data_ord = []
# data position template + parity
data_out_gab = []
# parity bit counter
qtd_bp = 0
# counter position of data bits
cont_data = 0
for x in range(1, size_par + len(data) + 1):
# Performs a template of bit positions - who should be given,
# and who should be parity
if qtd_bp < size_par:
if (np.log(x) / np.log(2)).is_integer():
data_out_gab.append("P")
qtd_bp = qtd_bp + 1
else:
data_out_gab.append("D")
else:
data_out_gab.append("D")
# Sorts the data to the new output size
if data_out_gab[-1] == "D":
data_ord.append(data[cont_data])
cont_data += 1
else:
data_ord.append(None)
# Calculates parity
qtd_bp = 0 # parity bit counter
for bp in range(1, size_par + 1):
# Bit counter one for a given parity
cont_bo = 0
# counter to control the loop reading
cont_loop = 0
for x in data_ord:
if x is not None:
try:
aux = (bin_pos[cont_loop])[-1 * (bp)]
except IndexError:
aux = "0"
if aux == "1" and x == "1":
cont_bo += 1
cont_loop += 1
parity.append(cont_bo % 2)
qtd_bp += 1
# Mount the message
cont_bp = 0 # parity bit counter
for x in range(0, size_par + len(data)):
if data_ord[x] is None:
data_out.append(str(parity[cont_bp]))
cont_bp += 1
else:
data_out.append(data_ord[x])
return data_out
def receptor_converter(size_par, data):
"""
>>> receptor_converter(4, "1111010010111111")
(['1', '0', '1', '0', '1', '0', '1', '1', '1', '1', '1', '1'], True)
"""
# data position template + parity
data_out_gab = []
# Parity bit counter
qtd_bp = 0
# Counter p data bit reading
cont_data = 0
# list of parity received
parity_received = []
data_output = []
for x in range(1, len(data) + 1):
# Performs a template of bit positions - who should be given,
# and who should be parity
if qtd_bp < size_par and (np.log(x) / np.log(2)).is_integer():
data_out_gab.append("P")
qtd_bp = qtd_bp + 1
else:
data_out_gab.append("D")
# Sorts the data to the new output size
if data_out_gab[-1] == "D":
data_output.append(data[cont_data])
else:
parity_received.append(data[cont_data])
cont_data += 1
# -----------calculates the parity with the data
data_out = []
parity = []
bin_pos = [bin(x)[2:] for x in range(1, size_par + len(data_output) + 1)]
# sorted information data for the size of the output data
data_ord = []
# Data position feedback + parity
data_out_gab = []
# Parity bit counter
qtd_bp = 0
# Counter p data bit reading
cont_data = 0
for x in range(1, size_par + len(data_output) + 1):
# Performs a template position of bits - who should be given,
# and who should be parity
if qtd_bp < size_par and (np.log(x) / np.log(2)).is_integer():
data_out_gab.append("P")
qtd_bp = qtd_bp + 1
else:
data_out_gab.append("D")
# Sorts the data to the new output size
if data_out_gab[-1] == "D":
data_ord.append(data_output[cont_data])
cont_data += 1
else:
data_ord.append(None)
# Calculates parity
qtd_bp = 0 # parity bit counter
for bp in range(1, size_par + 1):
# Bit counter one for a certain parity
cont_bo = 0
# Counter to control loop reading
cont_loop = 0
for x in data_ord:
if x is not None:
try:
aux = (bin_pos[cont_loop])[-1 * (bp)]
except IndexError:
aux = "0"
if aux == "1" and x == "1":
cont_bo += 1
cont_loop += 1
parity.append(str(cont_bo % 2))
qtd_bp += 1
# Mount the message
cont_bp = 0 # Parity bit counter
for x in range(0, size_par + len(data_output)):
if data_ord[x] is None:
data_out.append(str(parity[cont_bp]))
cont_bp += 1
else:
data_out.append(data_ord[x])
ack = parity_received == parity
return data_output, ack
# ---------------------------------------------------------------------
"""
# Example how to use
# number of parity bits
sizePari = 4
# location of the bit that will be forced an error
be = 2
# Message/word to be encoded and decoded with hamming
# text = input("Enter the word to be read: ")
text = "Message01"
# Convert the message to binary
binaryText = text_to_bits(text)
# Prints the binary of the string
print("Text input in binary is '" + binaryText + "'")
# total transmitted bits
totalBits = len(binaryText) + sizePari
print("Size of data is " + str(totalBits))
print("\n --Message exchange--")
print("Data to send ------------> " + binaryText)
dataOut = emitterConverter(sizePari, binaryText)
print("Data converted ----------> " + "".join(dataOut))
dataReceiv, ack = receptorConverter(sizePari, dataOut)
print(
"Data receive ------------> "
+ "".join(dataReceiv)
+ "\t\t -- Data integrity: "
+ str(ack)
)
print("\n --Force error--")
print("Data to send ------------> " + binaryText)
dataOut = emitterConverter(sizePari, binaryText)
print("Data converted ----------> " + "".join(dataOut))
# forces error
dataOut[-be] = "1" * (dataOut[-be] == "0") + "0" * (dataOut[-be] == "1")
print("Data after transmission -> " + "".join(dataOut))
dataReceiv, ack = receptorConverter(sizePari, dataOut)
print(
"Data receive ------------> "
+ "".join(dataReceiv)
+ "\t\t -- Data integrity: "
+ str(ack)
)
"""
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| """
Problem Statement:
By starting at the top of the triangle below and moving to adjacent numbers on
the row below, the maximum total from top to bottom is 23.
3
7 4
2 4 6
8 5 9 3
That is, 3 + 7 + 4 + 9 = 23.
Find the maximum total from top to bottom in triangle.txt (right click and
'Save Link/Target As...'), a 15K text file containing a triangle with
one-hundred rows.
"""
import os
def solution():
"""
Finds the maximum total in a triangle as described by the problem statement
above.
>>> solution()
7273
"""
script_dir = os.path.dirname(os.path.realpath(__file__))
triangle = os.path.join(script_dir, "triangle.txt")
with open(triangle) as f:
triangle = f.readlines()
a = []
for line in triangle:
numbers_from_line = []
for number in line.strip().split(" "):
numbers_from_line.append(int(number))
a.append(numbers_from_line)
for i in range(1, len(a)):
for j in range(len(a[i])):
number1 = a[i - 1][j] if j != len(a[i - 1]) else 0
number2 = a[i - 1][j - 1] if j > 0 else 0
a[i][j] += max(number1, number2)
return max(a[-1])
if __name__ == "__main__":
print(solution())
| """
Problem Statement:
By starting at the top of the triangle below and moving to adjacent numbers on
the row below, the maximum total from top to bottom is 23.
3
7 4
2 4 6
8 5 9 3
That is, 3 + 7 + 4 + 9 = 23.
Find the maximum total from top to bottom in triangle.txt (right click and
'Save Link/Target As...'), a 15K text file containing a triangle with
one-hundred rows.
"""
import os
def solution():
"""
Finds the maximum total in a triangle as described by the problem statement
above.
>>> solution()
7273
"""
script_dir = os.path.dirname(os.path.realpath(__file__))
triangle = os.path.join(script_dir, "triangle.txt")
with open(triangle) as f:
triangle = f.readlines()
a = []
for line in triangle:
numbers_from_line = []
for number in line.strip().split(" "):
numbers_from_line.append(int(number))
a.append(numbers_from_line)
for i in range(1, len(a)):
for j in range(len(a[i])):
number1 = a[i - 1][j] if j != len(a[i - 1]) else 0
number2 = a[i - 1][j - 1] if j > 0 else 0
a[i][j] += max(number1, number2)
return max(a[-1])
if __name__ == "__main__":
print(solution())
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| -1 |
||
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| def lower(word: str) -> str:
"""
Will convert the entire string to lowercase letters
>>> lower("wow")
'wow'
>>> lower("HellZo")
'hellzo'
>>> lower("WHAT")
'what'
>>> lower("wh[]32")
'wh[]32'
>>> lower("whAT")
'what'
"""
# converting to ascii value int value and checking to see if char is a capital
# letter if it is a capital letter it is getting shift by 32 which makes it a lower
# case letter
return "".join(chr(ord(char) + 32) if "A" <= char <= "Z" else char for char in word)
if __name__ == "__main__":
from doctest import testmod
testmod()
| def lower(word: str) -> str:
"""
Will convert the entire string to lowercase letters
>>> lower("wow")
'wow'
>>> lower("HellZo")
'hellzo'
>>> lower("WHAT")
'what'
>>> lower("wh[]32")
'wh[]32'
>>> lower("whAT")
'what'
"""
# converting to ascii value int value and checking to see if char is a capital
# letter if it is a capital letter it is getting shift by 32 which makes it a lower
# case letter
return "".join(chr(ord(char) + 32) if "A" <= char <= "Z" else char for char in word)
if __name__ == "__main__":
from doctest import testmod
testmod()
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| -1 |
||
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| """Newton's Method."""
# Newton's Method - https://en.wikipedia.org/wiki/Newton%27s_method
from collections.abc import Callable
RealFunc = Callable[[float], float] # type alias for a real -> real function
# function is the f(x) and derivative is the f'(x)
def newton(
function: RealFunc,
derivative: RealFunc,
starting_int: int,
) -> float:
"""
>>> newton(lambda x: x ** 3 - 2 * x - 5, lambda x: 3 * x ** 2 - 2, 3)
2.0945514815423474
>>> newton(lambda x: x ** 3 - 1, lambda x: 3 * x ** 2, -2)
1.0
>>> newton(lambda x: x ** 3 - 1, lambda x: 3 * x ** 2, -4)
1.0000000000000102
>>> import math
>>> newton(math.sin, math.cos, 1)
0.0
>>> newton(math.sin, math.cos, 2)
3.141592653589793
>>> newton(math.cos, lambda x: -math.sin(x), 2)
1.5707963267948966
>>> newton(math.cos, lambda x: -math.sin(x), 0)
Traceback (most recent call last):
...
ZeroDivisionError: Could not find root
"""
prev_guess = float(starting_int)
while True:
try:
next_guess = prev_guess - function(prev_guess) / derivative(prev_guess)
except ZeroDivisionError:
raise ZeroDivisionError("Could not find root") from None
if abs(prev_guess - next_guess) < 10**-5:
return next_guess
prev_guess = next_guess
def f(x: float) -> float:
return (x**3) - (2 * x) - 5
def f1(x: float) -> float:
return 3 * (x**2) - 2
if __name__ == "__main__":
print(newton(f, f1, 3))
| """Newton's Method."""
# Newton's Method - https://en.wikipedia.org/wiki/Newton%27s_method
from collections.abc import Callable
RealFunc = Callable[[float], float] # type alias for a real -> real function
# function is the f(x) and derivative is the f'(x)
def newton(
function: RealFunc,
derivative: RealFunc,
starting_int: int,
) -> float:
"""
>>> newton(lambda x: x ** 3 - 2 * x - 5, lambda x: 3 * x ** 2 - 2, 3)
2.0945514815423474
>>> newton(lambda x: x ** 3 - 1, lambda x: 3 * x ** 2, -2)
1.0
>>> newton(lambda x: x ** 3 - 1, lambda x: 3 * x ** 2, -4)
1.0000000000000102
>>> import math
>>> newton(math.sin, math.cos, 1)
0.0
>>> newton(math.sin, math.cos, 2)
3.141592653589793
>>> newton(math.cos, lambda x: -math.sin(x), 2)
1.5707963267948966
>>> newton(math.cos, lambda x: -math.sin(x), 0)
Traceback (most recent call last):
...
ZeroDivisionError: Could not find root
"""
prev_guess = float(starting_int)
while True:
try:
next_guess = prev_guess - function(prev_guess) / derivative(prev_guess)
except ZeroDivisionError:
raise ZeroDivisionError("Could not find root") from None
if abs(prev_guess - next_guess) < 10**-5:
return next_guess
prev_guess = next_guess
def f(x: float) -> float:
return (x**3) - (2 * x) - 5
def f1(x: float) -> float:
return 3 * (x**2) - 2
if __name__ == "__main__":
print(newton(f, f1, 3))
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| #!/usr/bin/perl
use strict;
use warnings;
use IPC::Open2;
# An example hook script to integrate Watchman
# (https://facebook.github.io/watchman/) with git to speed up detecting
# new and modified files.
#
# The hook is passed a version (currently 1) and a time in nanoseconds
# formatted as a string and outputs to stdout all files that have been
# modified since the given time. Paths must be relative to the root of
# the working tree and separated by a single NUL.
#
# To enable this hook, rename this file to "query-watchman" and set
# 'git config core.fsmonitor .git/hooks/query-watchman'
#
my ($version, $time) = @ARGV;
# Check the hook interface version
if ($version == 1) {
# convert nanoseconds to seconds
# subtract one second to make sure watchman will return all changes
$time = int ($time / 1000000000) - 1;
} else {
die "Unsupported query-fsmonitor hook version '$version'.\n" .
"Falling back to scanning...\n";
}
my $git_work_tree;
if ($^O =~ 'msys' || $^O =~ 'cygwin') {
$git_work_tree = Win32::GetCwd();
$git_work_tree =~ tr/\\/\//;
} else {
require Cwd;
$git_work_tree = Cwd::cwd();
}
my $retry = 1;
launch_watchman();
sub launch_watchman {
my $pid = open2(\*CHLD_OUT, \*CHLD_IN, 'watchman -j --no-pretty')
or die "open2() failed: $!\n" .
"Falling back to scanning...\n";
# In the query expression below we're asking for names of files that
# changed since $time but were not transient (ie created after
# $time but no longer exist).
#
# To accomplish this, we're using the "since" generator to use the
# recency index to select candidate nodes and "fields" to limit the
# output to file names only.
my $query = <<" END";
["query", "$git_work_tree", {
"since": $time,
"fields": ["name"]
}]
END
print CHLD_IN $query;
close CHLD_IN;
my $response = do {local $/; <CHLD_OUT>};
die "Watchman: command returned no output.\n" .
"Falling back to scanning...\n" if $response eq "";
die "Watchman: command returned invalid output: $response\n" .
"Falling back to scanning...\n" unless $response =~ /^\{/;
my $json_pkg;
eval {
require JSON::XS;
$json_pkg = "JSON::XS";
1;
} or do {
require JSON::PP;
$json_pkg = "JSON::PP";
};
my $o = $json_pkg->new->utf8->decode($response);
if ($retry > 0 and $o->{error} and $o->{error} =~ m/unable to resolve root .* directory (.*) is not watched/) {
print STDERR "Adding '$git_work_tree' to watchman's watch list.\n";
$retry--;
qx/watchman watch "$git_work_tree"/;
die "Failed to make watchman watch '$git_work_tree'.\n" .
"Falling back to scanning...\n" if $? != 0;
# Watchman will always return all files on the first query so
# return the fast "everything is dirty" flag to git and do the
# Watchman query just to get it over with now so we won't pay
# the cost in git to look up each individual file.
print "/\0";
eval { launch_watchman() };
exit 0;
}
die "Watchman: $o->{error}.\n" .
"Falling back to scanning...\n" if $o->{error};
binmode STDOUT, ":utf8";
local $, = "\0";
print @{$o->{files}};
}
| #!/usr/bin/perl
use strict;
use warnings;
use IPC::Open2;
# An example hook script to integrate Watchman
# (https://facebook.github.io/watchman/) with git to speed up detecting
# new and modified files.
#
# The hook is passed a version (currently 1) and a time in nanoseconds
# formatted as a string and outputs to stdout all files that have been
# modified since the given time. Paths must be relative to the root of
# the working tree and separated by a single NUL.
#
# To enable this hook, rename this file to "query-watchman" and set
# 'git config core.fsmonitor .git/hooks/query-watchman'
#
my ($version, $time) = @ARGV;
# Check the hook interface version
if ($version == 1) {
# convert nanoseconds to seconds
# subtract one second to make sure watchman will return all changes
$time = int ($time / 1000000000) - 1;
} else {
die "Unsupported query-fsmonitor hook version '$version'.\n" .
"Falling back to scanning...\n";
}
my $git_work_tree;
if ($^O =~ 'msys' || $^O =~ 'cygwin') {
$git_work_tree = Win32::GetCwd();
$git_work_tree =~ tr/\\/\//;
} else {
require Cwd;
$git_work_tree = Cwd::cwd();
}
my $retry = 1;
launch_watchman();
sub launch_watchman {
my $pid = open2(\*CHLD_OUT, \*CHLD_IN, 'watchman -j --no-pretty')
or die "open2() failed: $!\n" .
"Falling back to scanning...\n";
# In the query expression below we're asking for names of files that
# changed since $time but were not transient (ie created after
# $time but no longer exist).
#
# To accomplish this, we're using the "since" generator to use the
# recency index to select candidate nodes and "fields" to limit the
# output to file names only.
my $query = <<" END";
["query", "$git_work_tree", {
"since": $time,
"fields": ["name"]
}]
END
print CHLD_IN $query;
close CHLD_IN;
my $response = do {local $/; <CHLD_OUT>};
die "Watchman: command returned no output.\n" .
"Falling back to scanning...\n" if $response eq "";
die "Watchman: command returned invalid output: $response\n" .
"Falling back to scanning...\n" unless $response =~ /^\{/;
my $json_pkg;
eval {
require JSON::XS;
$json_pkg = "JSON::XS";
1;
} or do {
require JSON::PP;
$json_pkg = "JSON::PP";
};
my $o = $json_pkg->new->utf8->decode($response);
if ($retry > 0 and $o->{error} and $o->{error} =~ m/unable to resolve root .* directory (.*) is not watched/) {
print STDERR "Adding '$git_work_tree' to watchman's watch list.\n";
$retry--;
qx/watchman watch "$git_work_tree"/;
die "Failed to make watchman watch '$git_work_tree'.\n" .
"Falling back to scanning...\n" if $? != 0;
# Watchman will always return all files on the first query so
# return the fast "everything is dirty" flag to git and do the
# Watchman query just to get it over with now so we won't pay
# the cost in git to look up each individual file.
print "/\0";
eval { launch_watchman() };
exit 0;
}
die "Watchman: $o->{error}.\n" .
"Falling back to scanning...\n" if $o->{error};
binmode STDOUT, ":utf8";
local $, = "\0";
print @{$o->{files}};
}
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| # floyd_warshall.py
"""
The problem is to find the shortest distance between all pairs of vertices in a
weighted directed graph that can have negative edge weights.
"""
def _print_dist(dist, v):
print("\nThe shortest path matrix using Floyd Warshall algorithm\n")
for i in range(v):
for j in range(v):
if dist[i][j] != float("inf"):
print(int(dist[i][j]), end="\t")
else:
print("INF", end="\t")
print()
def floyd_warshall(graph, v):
"""
:param graph: 2D array calculated from weight[edge[i, j]]
:type graph: List[List[float]]
:param v: number of vertices
:type v: int
:return: shortest distance between all vertex pairs
distance[u][v] will contain the shortest distance from vertex u to v.
1. For all edges from v to n, distance[i][j] = weight(edge(i, j)).
3. The algorithm then performs distance[i][j] = min(distance[i][j], distance[i][k] +
distance[k][j]) for each possible pair i, j of vertices.
4. The above is repeated for each vertex k in the graph.
5. Whenever distance[i][j] is given a new minimum value, next vertex[i][j] is
updated to the next vertex[i][k].
"""
dist = [[float("inf") for _ in range(v)] for _ in range(v)]
for i in range(v):
for j in range(v):
dist[i][j] = graph[i][j]
# check vertex k against all other vertices (i, j)
for k in range(v):
# looping through rows of graph array
for i in range(v):
# looping through columns of graph array
for j in range(v):
if (
dist[i][k] != float("inf")
and dist[k][j] != float("inf")
and dist[i][k] + dist[k][j] < dist[i][j]
):
dist[i][j] = dist[i][k] + dist[k][j]
_print_dist(dist, v)
return dist, v
if __name__ == "__main__":
v = int(input("Enter number of vertices: "))
e = int(input("Enter number of edges: "))
graph = [[float("inf") for i in range(v)] for j in range(v)]
for i in range(v):
graph[i][i] = 0.0
# src and dst are indices that must be within the array size graph[e][v]
# failure to follow this will result in an error
for i in range(e):
print("\nEdge ", i + 1)
src = int(input("Enter source:"))
dst = int(input("Enter destination:"))
weight = float(input("Enter weight:"))
graph[src][dst] = weight
floyd_warshall(graph, v)
# Example Input
# Enter number of vertices: 3
# Enter number of edges: 2
# # generated graph from vertex and edge inputs
# [[inf, inf, inf], [inf, inf, inf], [inf, inf, inf]]
# [[0.0, inf, inf], [inf, 0.0, inf], [inf, inf, 0.0]]
# specify source, destination and weight for edge #1
# Edge 1
# Enter source:1
# Enter destination:2
# Enter weight:2
# specify source, destination and weight for edge #2
# Edge 2
# Enter source:2
# Enter destination:1
# Enter weight:1
# # Expected Output from the vertice, edge and src, dst, weight inputs!!
# 0 INF INF
# INF 0 2
# INF 1 0
| # floyd_warshall.py
"""
The problem is to find the shortest distance between all pairs of vertices in a
weighted directed graph that can have negative edge weights.
"""
def _print_dist(dist, v):
print("\nThe shortest path matrix using Floyd Warshall algorithm\n")
for i in range(v):
for j in range(v):
if dist[i][j] != float("inf"):
print(int(dist[i][j]), end="\t")
else:
print("INF", end="\t")
print()
def floyd_warshall(graph, v):
"""
:param graph: 2D array calculated from weight[edge[i, j]]
:type graph: List[List[float]]
:param v: number of vertices
:type v: int
:return: shortest distance between all vertex pairs
distance[u][v] will contain the shortest distance from vertex u to v.
1. For all edges from v to n, distance[i][j] = weight(edge(i, j)).
3. The algorithm then performs distance[i][j] = min(distance[i][j], distance[i][k] +
distance[k][j]) for each possible pair i, j of vertices.
4. The above is repeated for each vertex k in the graph.
5. Whenever distance[i][j] is given a new minimum value, next vertex[i][j] is
updated to the next vertex[i][k].
"""
dist = [[float("inf") for _ in range(v)] for _ in range(v)]
for i in range(v):
for j in range(v):
dist[i][j] = graph[i][j]
# check vertex k against all other vertices (i, j)
for k in range(v):
# looping through rows of graph array
for i in range(v):
# looping through columns of graph array
for j in range(v):
if (
dist[i][k] != float("inf")
and dist[k][j] != float("inf")
and dist[i][k] + dist[k][j] < dist[i][j]
):
dist[i][j] = dist[i][k] + dist[k][j]
_print_dist(dist, v)
return dist, v
if __name__ == "__main__":
v = int(input("Enter number of vertices: "))
e = int(input("Enter number of edges: "))
graph = [[float("inf") for i in range(v)] for j in range(v)]
for i in range(v):
graph[i][i] = 0.0
# src and dst are indices that must be within the array size graph[e][v]
# failure to follow this will result in an error
for i in range(e):
print("\nEdge ", i + 1)
src = int(input("Enter source:"))
dst = int(input("Enter destination:"))
weight = float(input("Enter weight:"))
graph[src][dst] = weight
floyd_warshall(graph, v)
# Example Input
# Enter number of vertices: 3
# Enter number of edges: 2
# # generated graph from vertex and edge inputs
# [[inf, inf, inf], [inf, inf, inf], [inf, inf, inf]]
# [[0.0, inf, inf], [inf, 0.0, inf], [inf, inf, 0.0]]
# specify source, destination and weight for edge #1
# Edge 1
# Enter source:1
# Enter destination:2
# Enter weight:2
# specify source, destination and weight for edge #2
# Edge 2
# Enter source:2
# Enter destination:1
# Enter weight:1
# # Expected Output from the vertice, edge and src, dst, weight inputs!!
# 0 INF INF
# INF 0 2
# INF 1 0
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| """
Code contributed by Honey Sharma
Source: https://en.wikipedia.org/wiki/Cycle_sort
"""
def cycle_sort(array: list) -> list:
"""
>>> cycle_sort([4, 3, 2, 1])
[1, 2, 3, 4]
>>> cycle_sort([-4, 20, 0, -50, 100, -1])
[-50, -4, -1, 0, 20, 100]
>>> cycle_sort([-.1, -.2, 1.3, -.8])
[-0.8, -0.2, -0.1, 1.3]
>>> cycle_sort([])
[]
"""
array_len = len(array)
for cycle_start in range(0, array_len - 1):
item = array[cycle_start]
pos = cycle_start
for i in range(cycle_start + 1, array_len):
if array[i] < item:
pos += 1
if pos == cycle_start:
continue
while item == array[pos]:
pos += 1
array[pos], item = item, array[pos]
while pos != cycle_start:
pos = cycle_start
for i in range(cycle_start + 1, array_len):
if array[i] < item:
pos += 1
while item == array[pos]:
pos += 1
array[pos], item = item, array[pos]
return array
if __name__ == "__main__":
assert cycle_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert cycle_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]
| """
Code contributed by Honey Sharma
Source: https://en.wikipedia.org/wiki/Cycle_sort
"""
def cycle_sort(array: list) -> list:
"""
>>> cycle_sort([4, 3, 2, 1])
[1, 2, 3, 4]
>>> cycle_sort([-4, 20, 0, -50, 100, -1])
[-50, -4, -1, 0, 20, 100]
>>> cycle_sort([-.1, -.2, 1.3, -.8])
[-0.8, -0.2, -0.1, 1.3]
>>> cycle_sort([])
[]
"""
array_len = len(array)
for cycle_start in range(0, array_len - 1):
item = array[cycle_start]
pos = cycle_start
for i in range(cycle_start + 1, array_len):
if array[i] < item:
pos += 1
if pos == cycle_start:
continue
while item == array[pos]:
pos += 1
array[pos], item = item, array[pos]
while pos != cycle_start:
pos = cycle_start
for i in range(cycle_start + 1, array_len):
if array[i] < item:
pos += 1
while item == array[pos]:
pos += 1
array[pos], item = item, array[pos]
return array
if __name__ == "__main__":
assert cycle_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert cycle_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| """
Illustrate how to implement inorder traversal in binary search tree.
Author: Gurneet Singh
https://www.geeksforgeeks.org/tree-traversals-inorder-preorder-and-postorder/
"""
class BinaryTreeNode:
"""Defining the structure of BinaryTreeNode"""
def __init__(self, data: int) -> None:
self.data = data
self.left_child: BinaryTreeNode | None = None
self.right_child: BinaryTreeNode | None = None
def insert(node: BinaryTreeNode | None, new_value: int) -> BinaryTreeNode | None:
"""
If the binary search tree is empty, make a new node and declare it as root.
>>> node_a = BinaryTreeNode(12345)
>>> node_b = insert(node_a, 67890)
>>> node_a.left_child == node_b.left_child
True
>>> node_a.right_child == node_b.right_child
True
>>> node_a.data == node_b.data
True
"""
if node is None:
node = BinaryTreeNode(new_value)
return node
# binary search tree is not empty,
# so we will insert it into the tree
# if new_value is less than value of data in node,
# add it to left subtree and proceed recursively
if new_value < node.data:
node.left_child = insert(node.left_child, new_value)
else:
# if new_value is greater than value of data in node,
# add it to right subtree and proceed recursively
node.right_child = insert(node.right_child, new_value)
return node
def inorder(node: None | BinaryTreeNode) -> list[int]: # if node is None,return
"""
>>> inorder(make_tree())
[6, 10, 14, 15, 20, 25, 60]
"""
if node:
inorder_array = inorder(node.left_child)
inorder_array = [*inorder_array, node.data]
inorder_array = inorder_array + inorder(node.right_child)
else:
inorder_array = []
return inorder_array
def make_tree() -> BinaryTreeNode | None:
root = insert(None, 15)
insert(root, 10)
insert(root, 25)
insert(root, 6)
insert(root, 14)
insert(root, 20)
insert(root, 60)
return root
def main() -> None:
# main function
root = make_tree()
print("Printing values of binary search tree in Inorder Traversal.")
inorder(root)
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| """
Illustrate how to implement inorder traversal in binary search tree.
Author: Gurneet Singh
https://www.geeksforgeeks.org/tree-traversals-inorder-preorder-and-postorder/
"""
class BinaryTreeNode:
"""Defining the structure of BinaryTreeNode"""
def __init__(self, data: int) -> None:
self.data = data
self.left_child: BinaryTreeNode | None = None
self.right_child: BinaryTreeNode | None = None
def insert(node: BinaryTreeNode | None, new_value: int) -> BinaryTreeNode | None:
"""
If the binary search tree is empty, make a new node and declare it as root.
>>> node_a = BinaryTreeNode(12345)
>>> node_b = insert(node_a, 67890)
>>> node_a.left_child == node_b.left_child
True
>>> node_a.right_child == node_b.right_child
True
>>> node_a.data == node_b.data
True
"""
if node is None:
node = BinaryTreeNode(new_value)
return node
# binary search tree is not empty,
# so we will insert it into the tree
# if new_value is less than value of data in node,
# add it to left subtree and proceed recursively
if new_value < node.data:
node.left_child = insert(node.left_child, new_value)
else:
# if new_value is greater than value of data in node,
# add it to right subtree and proceed recursively
node.right_child = insert(node.right_child, new_value)
return node
def inorder(node: None | BinaryTreeNode) -> list[int]: # if node is None,return
"""
>>> inorder(make_tree())
[6, 10, 14, 15, 20, 25, 60]
"""
if node:
inorder_array = inorder(node.left_child)
inorder_array = [*inorder_array, node.data]
inorder_array = inorder_array + inorder(node.right_child)
else:
inorder_array = []
return inorder_array
def make_tree() -> BinaryTreeNode | None:
root = insert(None, 15)
insert(root, 10)
insert(root, 25)
insert(root, 6)
insert(root, 14)
insert(root, 20)
insert(root, 60)
return root
def main() -> None:
# main function
root = make_tree()
print("Printing values of binary search tree in Inorder Traversal.")
inorder(root)
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| """
Wavelet tree is a data-structure designed to efficiently answer various range queries
for arrays. Wavelets trees are different from other binary trees in the sense that
the nodes are split based on the actual values of the elements and not on indices,
such as the with segment trees or fenwick trees. You can read more about them here:
1. https://users.dcc.uchile.cl/~jperez/papers/ioiconf16.pdf
2. https://www.youtube.com/watch?v=4aSv9PcecDw&t=811s
3. https://www.youtube.com/watch?v=CybAgVF-MMc&t=1178s
"""
from __future__ import annotations
test_array = [2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]
class Node:
def __init__(self, length: int) -> None:
self.minn: int = -1
self.maxx: int = -1
self.map_left: list[int] = [-1] * length
self.left: Node | None = None
self.right: Node | None = None
def __repr__(self) -> str:
"""
>>> node = Node(length=27)
>>> repr(node)
'Node(min_value=-1 max_value=-1)'
>>> repr(node) == str(node)
True
"""
return f"Node(min_value={self.minn} max_value={self.maxx})"
def build_tree(arr: list[int]) -> Node | None:
"""
Builds the tree for arr and returns the root
of the constructed tree
>>> build_tree(test_array)
Node(min_value=0 max_value=9)
"""
root = Node(len(arr))
root.minn, root.maxx = min(arr), max(arr)
# Leaf node case where the node contains only one unique value
if root.minn == root.maxx:
return root
"""
Take the mean of min and max element of arr as the pivot and
partition arr into left_arr and right_arr with all elements <= pivot in the
left_arr and the rest in right_arr, maintaining the order of the elements,
then recursively build trees for left_arr and right_arr
"""
pivot = (root.minn + root.maxx) // 2
left_arr: list[int] = []
right_arr: list[int] = []
for index, num in enumerate(arr):
if num <= pivot:
left_arr.append(num)
else:
right_arr.append(num)
root.map_left[index] = len(left_arr)
root.left = build_tree(left_arr)
root.right = build_tree(right_arr)
return root
def rank_till_index(node: Node | None, num: int, index: int) -> int:
"""
Returns the number of occurrences of num in interval [0, index] in the list
>>> root = build_tree(test_array)
>>> rank_till_index(root, 6, 6)
1
>>> rank_till_index(root, 2, 0)
1
>>> rank_till_index(root, 1, 10)
2
>>> rank_till_index(root, 17, 7)
0
>>> rank_till_index(root, 0, 9)
1
"""
if index < 0 or node is None:
return 0
# Leaf node cases
if node.minn == node.maxx:
return index + 1 if node.minn == num else 0
pivot = (node.minn + node.maxx) // 2
if num <= pivot:
# go the left subtree and map index to the left subtree
return rank_till_index(node.left, num, node.map_left[index] - 1)
else:
# go to the right subtree and map index to the right subtree
return rank_till_index(node.right, num, index - node.map_left[index])
def rank(node: Node | None, num: int, start: int, end: int) -> int:
"""
Returns the number of occurrences of num in interval [start, end] in the list
>>> root = build_tree(test_array)
>>> rank(root, 6, 3, 13)
2
>>> rank(root, 2, 0, 19)
4
>>> rank(root, 9, 2 ,2)
0
>>> rank(root, 0, 5, 10)
2
"""
if start > end:
return 0
rank_till_end = rank_till_index(node, num, end)
rank_before_start = rank_till_index(node, num, start - 1)
return rank_till_end - rank_before_start
def quantile(node: Node | None, index: int, start: int, end: int) -> int:
"""
Returns the index'th smallest element in interval [start, end] in the list
index is 0-indexed
>>> root = build_tree(test_array)
>>> quantile(root, 2, 2, 5)
5
>>> quantile(root, 5, 2, 13)
4
>>> quantile(root, 0, 6, 6)
8
>>> quantile(root, 4, 2, 5)
-1
"""
if index > (end - start) or start > end or node is None:
return -1
# Leaf node case
if node.minn == node.maxx:
return node.minn
# Number of elements in the left subtree in interval [start, end]
num_elements_in_left_tree = node.map_left[end] - (
node.map_left[start - 1] if start else 0
)
if num_elements_in_left_tree > index:
return quantile(
node.left,
index,
(node.map_left[start - 1] if start else 0),
node.map_left[end] - 1,
)
else:
return quantile(
node.right,
index - num_elements_in_left_tree,
start - (node.map_left[start - 1] if start else 0),
end - node.map_left[end],
)
def range_counting(
node: Node | None, start: int, end: int, start_num: int, end_num: int
) -> int:
"""
Returns the number of elements in range [start_num, end_num]
in interval [start, end] in the list
>>> root = build_tree(test_array)
>>> range_counting(root, 1, 10, 3, 7)
3
>>> range_counting(root, 2, 2, 1, 4)
1
>>> range_counting(root, 0, 19, 0, 100)
20
>>> range_counting(root, 1, 0, 1, 100)
0
>>> range_counting(root, 0, 17, 100, 1)
0
"""
if (
start > end
or node is None
or start_num > end_num
or node.minn > end_num
or node.maxx < start_num
):
return 0
if start_num <= node.minn and node.maxx <= end_num:
return end - start + 1
left = range_counting(
node.left,
(node.map_left[start - 1] if start else 0),
node.map_left[end] - 1,
start_num,
end_num,
)
right = range_counting(
node.right,
start - (node.map_left[start - 1] if start else 0),
end - node.map_left[end],
start_num,
end_num,
)
return left + right
if __name__ == "__main__":
import doctest
doctest.testmod()
| """
Wavelet tree is a data-structure designed to efficiently answer various range queries
for arrays. Wavelets trees are different from other binary trees in the sense that
the nodes are split based on the actual values of the elements and not on indices,
such as the with segment trees or fenwick trees. You can read more about them here:
1. https://users.dcc.uchile.cl/~jperez/papers/ioiconf16.pdf
2. https://www.youtube.com/watch?v=4aSv9PcecDw&t=811s
3. https://www.youtube.com/watch?v=CybAgVF-MMc&t=1178s
"""
from __future__ import annotations
test_array = [2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]
class Node:
def __init__(self, length: int) -> None:
self.minn: int = -1
self.maxx: int = -1
self.map_left: list[int] = [-1] * length
self.left: Node | None = None
self.right: Node | None = None
def __repr__(self) -> str:
"""
>>> node = Node(length=27)
>>> repr(node)
'Node(min_value=-1 max_value=-1)'
>>> repr(node) == str(node)
True
"""
return f"Node(min_value={self.minn} max_value={self.maxx})"
def build_tree(arr: list[int]) -> Node | None:
"""
Builds the tree for arr and returns the root
of the constructed tree
>>> build_tree(test_array)
Node(min_value=0 max_value=9)
"""
root = Node(len(arr))
root.minn, root.maxx = min(arr), max(arr)
# Leaf node case where the node contains only one unique value
if root.minn == root.maxx:
return root
"""
Take the mean of min and max element of arr as the pivot and
partition arr into left_arr and right_arr with all elements <= pivot in the
left_arr and the rest in right_arr, maintaining the order of the elements,
then recursively build trees for left_arr and right_arr
"""
pivot = (root.minn + root.maxx) // 2
left_arr: list[int] = []
right_arr: list[int] = []
for index, num in enumerate(arr):
if num <= pivot:
left_arr.append(num)
else:
right_arr.append(num)
root.map_left[index] = len(left_arr)
root.left = build_tree(left_arr)
root.right = build_tree(right_arr)
return root
def rank_till_index(node: Node | None, num: int, index: int) -> int:
"""
Returns the number of occurrences of num in interval [0, index] in the list
>>> root = build_tree(test_array)
>>> rank_till_index(root, 6, 6)
1
>>> rank_till_index(root, 2, 0)
1
>>> rank_till_index(root, 1, 10)
2
>>> rank_till_index(root, 17, 7)
0
>>> rank_till_index(root, 0, 9)
1
"""
if index < 0 or node is None:
return 0
# Leaf node cases
if node.minn == node.maxx:
return index + 1 if node.minn == num else 0
pivot = (node.minn + node.maxx) // 2
if num <= pivot:
# go the left subtree and map index to the left subtree
return rank_till_index(node.left, num, node.map_left[index] - 1)
else:
# go to the right subtree and map index to the right subtree
return rank_till_index(node.right, num, index - node.map_left[index])
def rank(node: Node | None, num: int, start: int, end: int) -> int:
"""
Returns the number of occurrences of num in interval [start, end] in the list
>>> root = build_tree(test_array)
>>> rank(root, 6, 3, 13)
2
>>> rank(root, 2, 0, 19)
4
>>> rank(root, 9, 2 ,2)
0
>>> rank(root, 0, 5, 10)
2
"""
if start > end:
return 0
rank_till_end = rank_till_index(node, num, end)
rank_before_start = rank_till_index(node, num, start - 1)
return rank_till_end - rank_before_start
def quantile(node: Node | None, index: int, start: int, end: int) -> int:
"""
Returns the index'th smallest element in interval [start, end] in the list
index is 0-indexed
>>> root = build_tree(test_array)
>>> quantile(root, 2, 2, 5)
5
>>> quantile(root, 5, 2, 13)
4
>>> quantile(root, 0, 6, 6)
8
>>> quantile(root, 4, 2, 5)
-1
"""
if index > (end - start) or start > end or node is None:
return -1
# Leaf node case
if node.minn == node.maxx:
return node.minn
# Number of elements in the left subtree in interval [start, end]
num_elements_in_left_tree = node.map_left[end] - (
node.map_left[start - 1] if start else 0
)
if num_elements_in_left_tree > index:
return quantile(
node.left,
index,
(node.map_left[start - 1] if start else 0),
node.map_left[end] - 1,
)
else:
return quantile(
node.right,
index - num_elements_in_left_tree,
start - (node.map_left[start - 1] if start else 0),
end - node.map_left[end],
)
def range_counting(
node: Node | None, start: int, end: int, start_num: int, end_num: int
) -> int:
"""
Returns the number of elements in range [start_num, end_num]
in interval [start, end] in the list
>>> root = build_tree(test_array)
>>> range_counting(root, 1, 10, 3, 7)
3
>>> range_counting(root, 2, 2, 1, 4)
1
>>> range_counting(root, 0, 19, 0, 100)
20
>>> range_counting(root, 1, 0, 1, 100)
0
>>> range_counting(root, 0, 17, 100, 1)
0
"""
if (
start > end
or node is None
or start_num > end_num
or node.minn > end_num
or node.maxx < start_num
):
return 0
if start_num <= node.minn and node.maxx <= end_num:
return end - start + 1
left = range_counting(
node.left,
(node.map_left[start - 1] if start else 0),
node.map_left[end] - 1,
start_num,
end_num,
)
right = range_counting(
node.right,
start - (node.map_left[start - 1] if start else 0),
end - node.map_left[end],
start_num,
end_num,
)
return left + right
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| """
A Hamming number is a positive integer of the form 2^i*3^j*5^k, for some
non-negative integers i, j, and k. They are often referred to as regular numbers.
More info at: https://en.wikipedia.org/wiki/Regular_number.
"""
def hamming(n_element: int) -> list:
"""
This function creates an ordered list of n length as requested, and afterwards
returns the last value of the list. It must be given a positive integer.
:param n_element: The number of elements on the list
:return: The nth element of the list
>>> hamming(5)
[1, 2, 3, 4, 5]
>>> hamming(10)
[1, 2, 3, 4, 5, 6, 8, 9, 10, 12]
>>> hamming(15)
[1, 2, 3, 4, 5, 6, 8, 9, 10, 12, 15, 16, 18, 20, 24]
"""
n_element = int(n_element)
if n_element < 1:
my_error = ValueError("a should be a positive number")
raise my_error
hamming_list = [1]
i, j, k = (0, 0, 0)
index = 1
while index < n_element:
while hamming_list[i] * 2 <= hamming_list[-1]:
i += 1
while hamming_list[j] * 3 <= hamming_list[-1]:
j += 1
while hamming_list[k] * 5 <= hamming_list[-1]:
k += 1
hamming_list.append(
min(hamming_list[i] * 2, hamming_list[j] * 3, hamming_list[k] * 5)
)
index += 1
return hamming_list
if __name__ == "__main__":
n = input("Enter the last number (nth term) of the Hamming Number Series: ")
print("Formula of Hamming Number Series => 2^i * 3^j * 5^k")
hamming_numbers = hamming(int(n))
print("-----------------------------------------------------")
print(f"The list with nth numbers is: {hamming_numbers}")
print("-----------------------------------------------------")
| """
A Hamming number is a positive integer of the form 2^i*3^j*5^k, for some
non-negative integers i, j, and k. They are often referred to as regular numbers.
More info at: https://en.wikipedia.org/wiki/Regular_number.
"""
def hamming(n_element: int) -> list:
"""
This function creates an ordered list of n length as requested, and afterwards
returns the last value of the list. It must be given a positive integer.
:param n_element: The number of elements on the list
:return: The nth element of the list
>>> hamming(5)
[1, 2, 3, 4, 5]
>>> hamming(10)
[1, 2, 3, 4, 5, 6, 8, 9, 10, 12]
>>> hamming(15)
[1, 2, 3, 4, 5, 6, 8, 9, 10, 12, 15, 16, 18, 20, 24]
"""
n_element = int(n_element)
if n_element < 1:
my_error = ValueError("a should be a positive number")
raise my_error
hamming_list = [1]
i, j, k = (0, 0, 0)
index = 1
while index < n_element:
while hamming_list[i] * 2 <= hamming_list[-1]:
i += 1
while hamming_list[j] * 3 <= hamming_list[-1]:
j += 1
while hamming_list[k] * 5 <= hamming_list[-1]:
k += 1
hamming_list.append(
min(hamming_list[i] * 2, hamming_list[j] * 3, hamming_list[k] * 5)
)
index += 1
return hamming_list
if __name__ == "__main__":
n = input("Enter the last number (nth term) of the Hamming Number Series: ")
print("Formula of Hamming Number Series => 2^i * 3^j * 5^k")
hamming_numbers = hamming(int(n))
print("-----------------------------------------------------")
print(f"The list with nth numbers is: {hamming_numbers}")
print("-----------------------------------------------------")
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| from doctest import testmod
from math import sqrt
def factors_of_a_number(num: int) -> list:
"""
>>> factors_of_a_number(1)
[1]
>>> factors_of_a_number(5)
[1, 5]
>>> factors_of_a_number(24)
[1, 2, 3, 4, 6, 8, 12, 24]
>>> factors_of_a_number(-24)
[]
"""
facs: list[int] = []
if num < 1:
return facs
facs.append(1)
if num == 1:
return facs
facs.append(num)
for i in range(2, int(sqrt(num)) + 1):
if num % i == 0: # If i is a factor of num
facs.append(i)
d = num // i # num//i is the other factor of num
if d != i: # If d and i are distinct
facs.append(d) # we have found another factor
facs.sort()
return facs
if __name__ == "__main__":
testmod(name="factors_of_a_number", verbose=True)
| from doctest import testmod
from math import sqrt
def factors_of_a_number(num: int) -> list:
"""
>>> factors_of_a_number(1)
[1]
>>> factors_of_a_number(5)
[1, 5]
>>> factors_of_a_number(24)
[1, 2, 3, 4, 6, 8, 12, 24]
>>> factors_of_a_number(-24)
[]
"""
facs: list[int] = []
if num < 1:
return facs
facs.append(1)
if num == 1:
return facs
facs.append(num)
for i in range(2, int(sqrt(num)) + 1):
if num % i == 0: # If i is a factor of num
facs.append(i)
d = num // i # num//i is the other factor of num
if d != i: # If d and i are distinct
facs.append(d) # we have found another factor
facs.sort()
return facs
if __name__ == "__main__":
testmod(name="factors_of_a_number", verbose=True)
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| # Project Euler
Problems are taken from https://projecteuler.net/, the Project Euler. [Problems are licensed under CC BY-NC-SA 4.0](https://projecteuler.net/copyright).
Project Euler is a series of challenging mathematical/computer programming problems that require more than just mathematical
insights to solve. Project Euler is ideal for mathematicians who are learning to code.
The solutions will be checked by our [automated testing on GitHub Actions](https://github.com/TheAlgorithms/Python/actions) with the help of [this script](https://github.com/TheAlgorithms/Python/blob/master/scripts/validate_solutions.py). The efficiency of your code is also checked. You can view the top 10 slowest solutions on GitHub Actions logs (under `slowest 10 durations`) and open a pull request to improve those solutions.
## Solution Guidelines
Welcome to [TheAlgorithms/Python](https://github.com/TheAlgorithms/Python)! Before reading the solution guidelines, make sure you read the whole [Contributing Guidelines](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md) as it won't be repeated in here. If you have any doubt on the guidelines, please feel free to [state it clearly in an issue](https://github.com/TheAlgorithms/Python/issues/new) or ask the community in [Gitter](https://gitter.im/TheAlgorithms/community). You can use the [template](https://github.com/TheAlgorithms/Python/blob/master/project_euler/README.md#solution-template) we have provided below as your starting point but be sure to read the [Coding Style](https://github.com/TheAlgorithms/Python/blob/master/project_euler/README.md#coding-style) part first.
### Coding Style
* Please maintain consistency in project directory and solution file names. Keep the following points in mind:
* Create a new directory only for the problems which do not exist yet.
* If you create a new directory, please create an empty `__init__.py` file inside it as well.
* Please name the project **directory** as `problem_<problem_number>` where `problem_number` should be filled with 0s so as to occupy 3 digits. Example: `problem_001`, `problem_002`, `problem_067`, `problem_145`, and so on.
* Please provide a link to the problem and other references, if used, in the **module-level docstring**.
* All imports should come ***after*** the module-level docstring.
* You can have as many helper functions as you want but there should be one main function called `solution` which should satisfy the conditions as stated below:
* It should contain positional argument(s) whose default value is the question input. Example: Please take a look at [Problem 1](https://projecteuler.net/problem=1) where the question is to *Find the sum of all the multiples of 3 or 5 below 1000.* In this case the main solution function will be `solution(limit: int = 1000)`.
* When the `solution` function is called without any arguments like so: `solution()`, it should return the answer to the problem.
* Every function, which includes all the helper functions, if any, and the main solution function, should have `doctest` in the function docstring along with a brief statement mentioning what the function is about.
* There should not be a `doctest` for testing the answer as that is done by our GitHub Actions build using this [script](https://github.com/TheAlgorithms/Python/blob/master/scripts/validate_solutions.py). Keeping in mind the above example of [Problem 1](https://projecteuler.net/problem=1):
```python
def solution(limit: int = 1000):
"""
A brief statement mentioning what the function is about.
You can have a detailed explanation about the solution method in the
module-level docstring.
>>> solution(1)
...
>>> solution(16)
...
>>> solution(100)
...
"""
```
### Solution Template
You can use the below template as your starting point but please read the [Coding Style](https://github.com/TheAlgorithms/Python/blob/master/project_euler/README.md#coding-style) first to understand how the template works.
Please change the name of the helper functions accordingly, change the parameter names with a descriptive one, replace the content within `[square brackets]` (including the brackets) with the appropriate content.
```python
"""
Project Euler Problem [problem number]: [link to the original problem]
... [Entire problem statement] ...
... [Solution explanation - Optional] ...
References [Optional]:
- [Wikipedia link to the topic]
- [Stackoverflow link]
...
"""
import module1
import module2
...
def helper1(arg1: [type hint], arg2: [type hint], ...) -> [Return type hint]:
"""
A brief statement explaining what the function is about.
... A more elaborate description ... [Optional]
...
[Doctest]
...
"""
...
# calculations
...
return
# You can have multiple helper functions but the solution function should be
# after all the helper functions ...
def solution(arg1: [type hint], arg2: [type hint], ...) -> [Return type hint]:
"""
A brief statement mentioning what the function is about.
You can have a detailed explanation about the solution in the
module-level docstring.
...
[Doctest as mentioned above]
...
"""
...
# calculations
...
return answer
if __name__ == "__main__":
print(f"{solution() = }")
```
| # Project Euler
Problems are taken from https://projecteuler.net/, the Project Euler. [Problems are licensed under CC BY-NC-SA 4.0](https://projecteuler.net/copyright).
Project Euler is a series of challenging mathematical/computer programming problems that require more than just mathematical
insights to solve. Project Euler is ideal for mathematicians who are learning to code.
The solutions will be checked by our [automated testing on GitHub Actions](https://github.com/TheAlgorithms/Python/actions) with the help of [this script](https://github.com/TheAlgorithms/Python/blob/master/scripts/validate_solutions.py). The efficiency of your code is also checked. You can view the top 10 slowest solutions on GitHub Actions logs (under `slowest 10 durations`) and open a pull request to improve those solutions.
## Solution Guidelines
Welcome to [TheAlgorithms/Python](https://github.com/TheAlgorithms/Python)! Before reading the solution guidelines, make sure you read the whole [Contributing Guidelines](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md) as it won't be repeated in here. If you have any doubt on the guidelines, please feel free to [state it clearly in an issue](https://github.com/TheAlgorithms/Python/issues/new) or ask the community in [Gitter](https://gitter.im/TheAlgorithms/community). You can use the [template](https://github.com/TheAlgorithms/Python/blob/master/project_euler/README.md#solution-template) we have provided below as your starting point but be sure to read the [Coding Style](https://github.com/TheAlgorithms/Python/blob/master/project_euler/README.md#coding-style) part first.
### Coding Style
* Please maintain consistency in project directory and solution file names. Keep the following points in mind:
* Create a new directory only for the problems which do not exist yet.
* If you create a new directory, please create an empty `__init__.py` file inside it as well.
* Please name the project **directory** as `problem_<problem_number>` where `problem_number` should be filled with 0s so as to occupy 3 digits. Example: `problem_001`, `problem_002`, `problem_067`, `problem_145`, and so on.
* Please provide a link to the problem and other references, if used, in the **module-level docstring**.
* All imports should come ***after*** the module-level docstring.
* You can have as many helper functions as you want but there should be one main function called `solution` which should satisfy the conditions as stated below:
* It should contain positional argument(s) whose default value is the question input. Example: Please take a look at [Problem 1](https://projecteuler.net/problem=1) where the question is to *Find the sum of all the multiples of 3 or 5 below 1000.* In this case the main solution function will be `solution(limit: int = 1000)`.
* When the `solution` function is called without any arguments like so: `solution()`, it should return the answer to the problem.
* Every function, which includes all the helper functions, if any, and the main solution function, should have `doctest` in the function docstring along with a brief statement mentioning what the function is about.
* There should not be a `doctest` for testing the answer as that is done by our GitHub Actions build using this [script](https://github.com/TheAlgorithms/Python/blob/master/scripts/validate_solutions.py). Keeping in mind the above example of [Problem 1](https://projecteuler.net/problem=1):
```python
def solution(limit: int = 1000):
"""
A brief statement mentioning what the function is about.
You can have a detailed explanation about the solution method in the
module-level docstring.
>>> solution(1)
...
>>> solution(16)
...
>>> solution(100)
...
"""
```
### Solution Template
You can use the below template as your starting point but please read the [Coding Style](https://github.com/TheAlgorithms/Python/blob/master/project_euler/README.md#coding-style) first to understand how the template works.
Please change the name of the helper functions accordingly, change the parameter names with a descriptive one, replace the content within `[square brackets]` (including the brackets) with the appropriate content.
```python
"""
Project Euler Problem [problem number]: [link to the original problem]
... [Entire problem statement] ...
... [Solution explanation - Optional] ...
References [Optional]:
- [Wikipedia link to the topic]
- [Stackoverflow link]
...
"""
import module1
import module2
...
def helper1(arg1: [type hint], arg2: [type hint], ...) -> [Return type hint]:
"""
A brief statement explaining what the function is about.
... A more elaborate description ... [Optional]
...
[Doctest]
...
"""
...
# calculations
...
return
# You can have multiple helper functions but the solution function should be
# after all the helper functions ...
def solution(arg1: [type hint], arg2: [type hint], ...) -> [Return type hint]:
"""
A brief statement mentioning what the function is about.
You can have a detailed explanation about the solution in the
module-level docstring.
...
[Doctest as mentioned above]
...
"""
...
# calculations
...
return answer
if __name__ == "__main__":
print(f"{solution() = }")
```
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| """
Problem 45: https://projecteuler.net/problem=45
Triangle, pentagonal, and hexagonal numbers are generated by the following formulae:
Triangle T(n) = (n * (n + 1)) / 2 1, 3, 6, 10, 15, ...
Pentagonal P(n) = (n * (3 * n − 1)) / 2 1, 5, 12, 22, 35, ...
Hexagonal H(n) = n * (2 * n − 1) 1, 6, 15, 28, 45, ...
It can be verified that T(285) = P(165) = H(143) = 40755.
Find the next triangle number that is also pentagonal and hexagonal.
All triangle numbers are hexagonal numbers.
T(2n-1) = n * (2 * n - 1) = H(n)
So we shall check only for hexagonal numbers which are also pentagonal.
"""
def hexagonal_num(n: int) -> int:
"""
Returns nth hexagonal number
>>> hexagonal_num(143)
40755
>>> hexagonal_num(21)
861
>>> hexagonal_num(10)
190
"""
return n * (2 * n - 1)
def is_pentagonal(n: int) -> bool:
"""
Returns True if n is pentagonal, False otherwise.
>>> is_pentagonal(330)
True
>>> is_pentagonal(7683)
False
>>> is_pentagonal(2380)
True
"""
root = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def solution(start: int = 144) -> int:
"""
Returns the next number which is triangular, pentagonal and hexagonal.
>>> solution(144)
1533776805
"""
n = start
num = hexagonal_num(n)
while not is_pentagonal(num):
n += 1
num = hexagonal_num(n)
return num
if __name__ == "__main__":
print(f"{solution()} = ")
| """
Problem 45: https://projecteuler.net/problem=45
Triangle, pentagonal, and hexagonal numbers are generated by the following formulae:
Triangle T(n) = (n * (n + 1)) / 2 1, 3, 6, 10, 15, ...
Pentagonal P(n) = (n * (3 * n − 1)) / 2 1, 5, 12, 22, 35, ...
Hexagonal H(n) = n * (2 * n − 1) 1, 6, 15, 28, 45, ...
It can be verified that T(285) = P(165) = H(143) = 40755.
Find the next triangle number that is also pentagonal and hexagonal.
All triangle numbers are hexagonal numbers.
T(2n-1) = n * (2 * n - 1) = H(n)
So we shall check only for hexagonal numbers which are also pentagonal.
"""
def hexagonal_num(n: int) -> int:
"""
Returns nth hexagonal number
>>> hexagonal_num(143)
40755
>>> hexagonal_num(21)
861
>>> hexagonal_num(10)
190
"""
return n * (2 * n - 1)
def is_pentagonal(n: int) -> bool:
"""
Returns True if n is pentagonal, False otherwise.
>>> is_pentagonal(330)
True
>>> is_pentagonal(7683)
False
>>> is_pentagonal(2380)
True
"""
root = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def solution(start: int = 144) -> int:
"""
Returns the next number which is triangular, pentagonal and hexagonal.
>>> solution(144)
1533776805
"""
n = start
num = hexagonal_num(n)
while not is_pentagonal(num):
n += 1
num = hexagonal_num(n)
return num
if __name__ == "__main__":
print(f"{solution()} = ")
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| class Things:
def __init__(self, name, value, weight):
self.name = name
self.value = value
self.weight = weight
def __repr__(self):
return f"{self.__class__.__name__}({self.name}, {self.value}, {self.weight})"
def get_value(self):
return self.value
def get_name(self):
return self.name
def get_weight(self):
return self.weight
def value_weight(self):
return self.value / self.weight
def build_menu(name, value, weight):
menu = []
for i in range(len(value)):
menu.append(Things(name[i], value[i], weight[i]))
return menu
def greedy(item, max_cost, key_func):
items_copy = sorted(item, key=key_func, reverse=True)
result = []
total_value, total_cost = 0.0, 0.0
for i in range(len(items_copy)):
if (total_cost + items_copy[i].get_weight()) <= max_cost:
result.append(items_copy[i])
total_cost += items_copy[i].get_weight()
total_value += items_copy[i].get_value()
return (result, total_value)
def test_greedy():
"""
>>> food = ["Burger", "Pizza", "Coca Cola", "Rice",
... "Sambhar", "Chicken", "Fries", "Milk"]
>>> value = [80, 100, 60, 70, 50, 110, 90, 60]
>>> weight = [40, 60, 40, 70, 100, 85, 55, 70]
>>> foods = build_menu(food, value, weight)
>>> foods # doctest: +NORMALIZE_WHITESPACE
[Things(Burger, 80, 40), Things(Pizza, 100, 60), Things(Coca Cola, 60, 40),
Things(Rice, 70, 70), Things(Sambhar, 50, 100), Things(Chicken, 110, 85),
Things(Fries, 90, 55), Things(Milk, 60, 70)]
>>> greedy(foods, 500, Things.get_value) # doctest: +NORMALIZE_WHITESPACE
([Things(Chicken, 110, 85), Things(Pizza, 100, 60), Things(Fries, 90, 55),
Things(Burger, 80, 40), Things(Rice, 70, 70), Things(Coca Cola, 60, 40),
Things(Milk, 60, 70)], 570.0)
"""
if __name__ == "__main__":
import doctest
doctest.testmod()
| class Things:
def __init__(self, name, value, weight):
self.name = name
self.value = value
self.weight = weight
def __repr__(self):
return f"{self.__class__.__name__}({self.name}, {self.value}, {self.weight})"
def get_value(self):
return self.value
def get_name(self):
return self.name
def get_weight(self):
return self.weight
def value_weight(self):
return self.value / self.weight
def build_menu(name, value, weight):
menu = []
for i in range(len(value)):
menu.append(Things(name[i], value[i], weight[i]))
return menu
def greedy(item, max_cost, key_func):
items_copy = sorted(item, key=key_func, reverse=True)
result = []
total_value, total_cost = 0.0, 0.0
for i in range(len(items_copy)):
if (total_cost + items_copy[i].get_weight()) <= max_cost:
result.append(items_copy[i])
total_cost += items_copy[i].get_weight()
total_value += items_copy[i].get_value()
return (result, total_value)
def test_greedy():
"""
>>> food = ["Burger", "Pizza", "Coca Cola", "Rice",
... "Sambhar", "Chicken", "Fries", "Milk"]
>>> value = [80, 100, 60, 70, 50, 110, 90, 60]
>>> weight = [40, 60, 40, 70, 100, 85, 55, 70]
>>> foods = build_menu(food, value, weight)
>>> foods # doctest: +NORMALIZE_WHITESPACE
[Things(Burger, 80, 40), Things(Pizza, 100, 60), Things(Coca Cola, 60, 40),
Things(Rice, 70, 70), Things(Sambhar, 50, 100), Things(Chicken, 110, 85),
Things(Fries, 90, 55), Things(Milk, 60, 70)]
>>> greedy(foods, 500, Things.get_value) # doctest: +NORMALIZE_WHITESPACE
([Things(Chicken, 110, 85), Things(Pizza, 100, 60), Things(Fries, 90, 55),
Things(Burger, 80, 40), Things(Rice, 70, 70), Things(Coca Cola, 60, 40),
Things(Milk, 60, 70)], 570.0)
"""
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| import itertools
import string
from collections.abc import Generator, Iterable
def chunker(seq: Iterable[str], size: int) -> Generator[tuple[str, ...], None, None]:
it = iter(seq)
while True:
chunk = tuple(itertools.islice(it, size))
if not chunk:
return
yield chunk
def prepare_input(dirty: str) -> str:
"""
Prepare the plaintext by up-casing it
and separating repeated letters with X's
"""
dirty = "".join([c.upper() for c in dirty if c in string.ascii_letters])
clean = ""
if len(dirty) < 2:
return dirty
for i in range(len(dirty) - 1):
clean += dirty[i]
if dirty[i] == dirty[i + 1]:
clean += "X"
clean += dirty[-1]
if len(clean) & 1:
clean += "X"
return clean
def generate_table(key: str) -> list[str]:
# I and J are used interchangeably to allow
# us to use a 5x5 table (25 letters)
alphabet = "ABCDEFGHIKLMNOPQRSTUVWXYZ"
# we're using a list instead of a '2d' array because it makes the math
# for setting up the table and doing the actual encoding/decoding simpler
table = []
# copy key chars into the table if they are in `alphabet` ignoring duplicates
for char in key.upper():
if char not in table and char in alphabet:
table.append(char)
# fill the rest of the table in with the remaining alphabet chars
for char in alphabet:
if char not in table:
table.append(char)
return table
def encode(plaintext: str, key: str) -> str:
table = generate_table(key)
plaintext = prepare_input(plaintext)
ciphertext = ""
# https://en.wikipedia.org/wiki/Playfair_cipher#Description
for char1, char2 in chunker(plaintext, 2):
row1, col1 = divmod(table.index(char1), 5)
row2, col2 = divmod(table.index(char2), 5)
if row1 == row2:
ciphertext += table[row1 * 5 + (col1 + 1) % 5]
ciphertext += table[row2 * 5 + (col2 + 1) % 5]
elif col1 == col2:
ciphertext += table[((row1 + 1) % 5) * 5 + col1]
ciphertext += table[((row2 + 1) % 5) * 5 + col2]
else: # rectangle
ciphertext += table[row1 * 5 + col2]
ciphertext += table[row2 * 5 + col1]
return ciphertext
def decode(ciphertext: str, key: str) -> str:
table = generate_table(key)
plaintext = ""
# https://en.wikipedia.org/wiki/Playfair_cipher#Description
for char1, char2 in chunker(ciphertext, 2):
row1, col1 = divmod(table.index(char1), 5)
row2, col2 = divmod(table.index(char2), 5)
if row1 == row2:
plaintext += table[row1 * 5 + (col1 - 1) % 5]
plaintext += table[row2 * 5 + (col2 - 1) % 5]
elif col1 == col2:
plaintext += table[((row1 - 1) % 5) * 5 + col1]
plaintext += table[((row2 - 1) % 5) * 5 + col2]
else: # rectangle
plaintext += table[row1 * 5 + col2]
plaintext += table[row2 * 5 + col1]
return plaintext
| import itertools
import string
from collections.abc import Generator, Iterable
def chunker(seq: Iterable[str], size: int) -> Generator[tuple[str, ...], None, None]:
it = iter(seq)
while True:
chunk = tuple(itertools.islice(it, size))
if not chunk:
return
yield chunk
def prepare_input(dirty: str) -> str:
"""
Prepare the plaintext by up-casing it
and separating repeated letters with X's
"""
dirty = "".join([c.upper() for c in dirty if c in string.ascii_letters])
clean = ""
if len(dirty) < 2:
return dirty
for i in range(len(dirty) - 1):
clean += dirty[i]
if dirty[i] == dirty[i + 1]:
clean += "X"
clean += dirty[-1]
if len(clean) & 1:
clean += "X"
return clean
def generate_table(key: str) -> list[str]:
# I and J are used interchangeably to allow
# us to use a 5x5 table (25 letters)
alphabet = "ABCDEFGHIKLMNOPQRSTUVWXYZ"
# we're using a list instead of a '2d' array because it makes the math
# for setting up the table and doing the actual encoding/decoding simpler
table = []
# copy key chars into the table if they are in `alphabet` ignoring duplicates
for char in key.upper():
if char not in table and char in alphabet:
table.append(char)
# fill the rest of the table in with the remaining alphabet chars
for char in alphabet:
if char not in table:
table.append(char)
return table
def encode(plaintext: str, key: str) -> str:
table = generate_table(key)
plaintext = prepare_input(plaintext)
ciphertext = ""
# https://en.wikipedia.org/wiki/Playfair_cipher#Description
for char1, char2 in chunker(plaintext, 2):
row1, col1 = divmod(table.index(char1), 5)
row2, col2 = divmod(table.index(char2), 5)
if row1 == row2:
ciphertext += table[row1 * 5 + (col1 + 1) % 5]
ciphertext += table[row2 * 5 + (col2 + 1) % 5]
elif col1 == col2:
ciphertext += table[((row1 + 1) % 5) * 5 + col1]
ciphertext += table[((row2 + 1) % 5) * 5 + col2]
else: # rectangle
ciphertext += table[row1 * 5 + col2]
ciphertext += table[row2 * 5 + col1]
return ciphertext
def decode(ciphertext: str, key: str) -> str:
table = generate_table(key)
plaintext = ""
# https://en.wikipedia.org/wiki/Playfair_cipher#Description
for char1, char2 in chunker(ciphertext, 2):
row1, col1 = divmod(table.index(char1), 5)
row2, col2 = divmod(table.index(char2), 5)
if row1 == row2:
plaintext += table[row1 * 5 + (col1 - 1) % 5]
plaintext += table[row2 * 5 + (col2 - 1) % 5]
elif col1 == col2:
plaintext += table[((row1 - 1) % 5) * 5 + col1]
plaintext += table[((row2 - 1) % 5) * 5 + col2]
else: # rectangle
plaintext += table[row1 * 5 + col2]
plaintext += table[row2 * 5 + col1]
return plaintext
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| -1 |
||
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| # Normal Distribution QuickSort
QuickSort Algorithm where the pivot element is chosen randomly between first and last elements of the array, and the array elements are taken from Standard Normal Distribution.
## Array elements
The array elements are taken from a Standard Normal Distribution, having mean = 0 and standard deviation = 1.
### The code
```python
>>> import numpy as np
>>> from tempfile import TemporaryFile
>>> outfile = TemporaryFile()
>>> p = 100 # 100 elements are to be sorted
>>> mu, sigma = 0, 1 # mean and standard deviation
>>> X = np.random.normal(mu, sigma, p)
>>> np.save(outfile, X)
>>> 'The array is'
>>> X
```
------
#### The distribution of the array elements
```python
>>> mu, sigma = 0, 1 # mean and standard deviation
>>> s = np.random.normal(mu, sigma, p)
>>> count, bins, ignored = plt.hist(s, 30, normed=True)
>>> plt.plot(bins , 1/(sigma * np.sqrt(2 * np.pi)) *np.exp( - (bins - mu)**2 / (2 * sigma**2) ),linewidth=2, color='r')
>>> plt.show()
```
------
------
## Comparing the numbers of comparisons
We can plot the function for Checking 'The Number of Comparisons' taking place between Normal Distribution QuickSort and Ordinary QuickSort:
```python
>>> import matplotlib.pyplot as plt
# Normal Distribution QuickSort is red
>>> plt.plot([1,2,4,16,32,64,128,256,512,1024,2048],[1,1,6,15,43,136,340,800,2156,6821,16325],linewidth=2, color='r')
# Ordinary QuickSort is green
>>> plt.plot([1,2,4,16,32,64,128,256,512,1024,2048],[1,1,4,16,67,122,362,949,2131,5086,12866],linewidth=2, color='g')
>>> plt.show()
```
| # Normal Distribution QuickSort
QuickSort Algorithm where the pivot element is chosen randomly between first and last elements of the array, and the array elements are taken from Standard Normal Distribution.
## Array elements
The array elements are taken from a Standard Normal Distribution, having mean = 0 and standard deviation = 1.
### The code
```python
>>> import numpy as np
>>> from tempfile import TemporaryFile
>>> outfile = TemporaryFile()
>>> p = 100 # 100 elements are to be sorted
>>> mu, sigma = 0, 1 # mean and standard deviation
>>> X = np.random.normal(mu, sigma, p)
>>> np.save(outfile, X)
>>> 'The array is'
>>> X
```
------
#### The distribution of the array elements
```python
>>> mu, sigma = 0, 1 # mean and standard deviation
>>> s = np.random.normal(mu, sigma, p)
>>> count, bins, ignored = plt.hist(s, 30, normed=True)
>>> plt.plot(bins , 1/(sigma * np.sqrt(2 * np.pi)) *np.exp( - (bins - mu)**2 / (2 * sigma**2) ),linewidth=2, color='r')
>>> plt.show()
```
------
------
## Comparing the numbers of comparisons
We can plot the function for Checking 'The Number of Comparisons' taking place between Normal Distribution QuickSort and Ordinary QuickSort:
```python
>>> import matplotlib.pyplot as plt
# Normal Distribution QuickSort is red
>>> plt.plot([1,2,4,16,32,64,128,256,512,1024,2048],[1,1,6,15,43,136,340,800,2156,6821,16325],linewidth=2, color='r')
# Ordinary QuickSort is green
>>> plt.plot([1,2,4,16,32,64,128,256,512,1024,2048],[1,1,4,16,67,122,362,949,2131,5086,12866],linewidth=2, color='g')
>>> plt.show()
```
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| def elf_hash(data: str) -> int:
"""
Implementation of ElfHash Algorithm, a variant of PJW hash function.
>>> elf_hash('lorem ipsum')
253956621
"""
hash_ = x = 0
for letter in data:
hash_ = (hash_ << 4) + ord(letter)
x = hash_ & 0xF0000000
if x != 0:
hash_ ^= x >> 24
hash_ &= ~x
return hash_
if __name__ == "__main__":
import doctest
doctest.testmod()
| def elf_hash(data: str) -> int:
"""
Implementation of ElfHash Algorithm, a variant of PJW hash function.
>>> elf_hash('lorem ipsum')
253956621
"""
hash_ = x = 0
for letter in data:
hash_ = (hash_ << 4) + ord(letter)
x = hash_ & 0xF0000000
if x != 0:
hash_ ^= x >> 24
hash_ &= ~x
return hash_
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| -1 |
||
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| """
https://en.wikipedia.org/wiki/Lucas_number
"""
def recursive_lucas_number(n_th_number: int) -> int:
"""
Returns the nth lucas number
>>> recursive_lucas_number(1)
1
>>> recursive_lucas_number(20)
15127
>>> recursive_lucas_number(0)
2
>>> recursive_lucas_number(25)
167761
>>> recursive_lucas_number(-1.5)
Traceback (most recent call last):
...
TypeError: recursive_lucas_number accepts only integer arguments.
"""
if not isinstance(n_th_number, int):
raise TypeError("recursive_lucas_number accepts only integer arguments.")
if n_th_number == 0:
return 2
if n_th_number == 1:
return 1
return recursive_lucas_number(n_th_number - 1) + recursive_lucas_number(
n_th_number - 2
)
def dynamic_lucas_number(n_th_number: int) -> int:
"""
Returns the nth lucas number
>>> dynamic_lucas_number(1)
1
>>> dynamic_lucas_number(20)
15127
>>> dynamic_lucas_number(0)
2
>>> dynamic_lucas_number(25)
167761
>>> dynamic_lucas_number(-1.5)
Traceback (most recent call last):
...
TypeError: dynamic_lucas_number accepts only integer arguments.
"""
if not isinstance(n_th_number, int):
raise TypeError("dynamic_lucas_number accepts only integer arguments.")
a, b = 2, 1
for _ in range(n_th_number):
a, b = b, a + b
return a
if __name__ == "__main__":
from doctest import testmod
testmod()
n = int(input("Enter the number of terms in lucas series:\n").strip())
print("Using recursive function to calculate lucas series:")
print(" ".join(str(recursive_lucas_number(i)) for i in range(n)))
print("\nUsing dynamic function to calculate lucas series:")
print(" ".join(str(dynamic_lucas_number(i)) for i in range(n)))
| """
https://en.wikipedia.org/wiki/Lucas_number
"""
def recursive_lucas_number(n_th_number: int) -> int:
"""
Returns the nth lucas number
>>> recursive_lucas_number(1)
1
>>> recursive_lucas_number(20)
15127
>>> recursive_lucas_number(0)
2
>>> recursive_lucas_number(25)
167761
>>> recursive_lucas_number(-1.5)
Traceback (most recent call last):
...
TypeError: recursive_lucas_number accepts only integer arguments.
"""
if not isinstance(n_th_number, int):
raise TypeError("recursive_lucas_number accepts only integer arguments.")
if n_th_number == 0:
return 2
if n_th_number == 1:
return 1
return recursive_lucas_number(n_th_number - 1) + recursive_lucas_number(
n_th_number - 2
)
def dynamic_lucas_number(n_th_number: int) -> int:
"""
Returns the nth lucas number
>>> dynamic_lucas_number(1)
1
>>> dynamic_lucas_number(20)
15127
>>> dynamic_lucas_number(0)
2
>>> dynamic_lucas_number(25)
167761
>>> dynamic_lucas_number(-1.5)
Traceback (most recent call last):
...
TypeError: dynamic_lucas_number accepts only integer arguments.
"""
if not isinstance(n_th_number, int):
raise TypeError("dynamic_lucas_number accepts only integer arguments.")
a, b = 2, 1
for _ in range(n_th_number):
a, b = b, a + b
return a
if __name__ == "__main__":
from doctest import testmod
testmod()
n = int(input("Enter the number of terms in lucas series:\n").strip())
print("Using recursive function to calculate lucas series:")
print(" ".join(str(recursive_lucas_number(i)) for i in range(n)))
print("\nUsing dynamic function to calculate lucas series:")
print(" ".join(str(dynamic_lucas_number(i)) for i in range(n)))
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| ### Interest
* Compound Interest: "Compound interest is calculated by multiplying the initial principal amount by one plus the annual interest rate raised to the number of compound periods minus one." [Compound Interest](https://www.investopedia.com/)
* Simple Interest: "Simple interest paid or received over a certain period is a fixed percentage of the principal amount that was borrowed or lent. " [Simple Interest](https://www.investopedia.com/)
| ### Interest
* Compound Interest: "Compound interest is calculated by multiplying the initial principal amount by one plus the annual interest rate raised to the number of compound periods minus one." [Compound Interest](https://www.investopedia.com/)
* Simple Interest: "Simple interest paid or received over a certain period is a fixed percentage of the principal amount that was borrowed or lent. " [Simple Interest](https://www.investopedia.com/)
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| """
Minimax helps to achieve maximum score in a game by checking all possible moves
depth is current depth in game tree.
nodeIndex is index of current node in scores[].
if move is of maximizer return true else false
leaves of game tree is stored in scores[]
height is maximum height of Game tree
"""
from __future__ import annotations
import math
def minimax(
depth: int, node_index: int, is_max: bool, scores: list[int], height: float
) -> int:
"""
>>> import math
>>> scores = [90, 23, 6, 33, 21, 65, 123, 34423]
>>> height = math.log(len(scores), 2)
>>> minimax(0, 0, True, scores, height)
65
>>> minimax(-1, 0, True, scores, height)
Traceback (most recent call last):
...
ValueError: Depth cannot be less than 0
>>> minimax(0, 0, True, [], 2)
Traceback (most recent call last):
...
ValueError: Scores cannot be empty
>>> scores = [3, 5, 2, 9, 12, 5, 23, 23]
>>> height = math.log(len(scores), 2)
>>> minimax(0, 0, True, scores, height)
12
"""
if depth < 0:
raise ValueError("Depth cannot be less than 0")
if len(scores) == 0:
raise ValueError("Scores cannot be empty")
if depth == height:
return scores[node_index]
if is_max:
return max(
minimax(depth + 1, node_index * 2, False, scores, height),
minimax(depth + 1, node_index * 2 + 1, False, scores, height),
)
return min(
minimax(depth + 1, node_index * 2, True, scores, height),
minimax(depth + 1, node_index * 2 + 1, True, scores, height),
)
def main() -> None:
scores = [90, 23, 6, 33, 21, 65, 123, 34423]
height = math.log(len(scores), 2)
print("Optimal value : ", end="")
print(minimax(0, 0, True, scores, height))
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| """
Minimax helps to achieve maximum score in a game by checking all possible moves
depth is current depth in game tree.
nodeIndex is index of current node in scores[].
if move is of maximizer return true else false
leaves of game tree is stored in scores[]
height is maximum height of Game tree
"""
from __future__ import annotations
import math
def minimax(
depth: int, node_index: int, is_max: bool, scores: list[int], height: float
) -> int:
"""
>>> import math
>>> scores = [90, 23, 6, 33, 21, 65, 123, 34423]
>>> height = math.log(len(scores), 2)
>>> minimax(0, 0, True, scores, height)
65
>>> minimax(-1, 0, True, scores, height)
Traceback (most recent call last):
...
ValueError: Depth cannot be less than 0
>>> minimax(0, 0, True, [], 2)
Traceback (most recent call last):
...
ValueError: Scores cannot be empty
>>> scores = [3, 5, 2, 9, 12, 5, 23, 23]
>>> height = math.log(len(scores), 2)
>>> minimax(0, 0, True, scores, height)
12
"""
if depth < 0:
raise ValueError("Depth cannot be less than 0")
if len(scores) == 0:
raise ValueError("Scores cannot be empty")
if depth == height:
return scores[node_index]
if is_max:
return max(
minimax(depth + 1, node_index * 2, False, scores, height),
minimax(depth + 1, node_index * 2 + 1, False, scores, height),
)
return min(
minimax(depth + 1, node_index * 2, True, scores, height),
minimax(depth + 1, node_index * 2 + 1, True, scores, height),
)
def main() -> None:
scores = [90, 23, 6, 33, 21, 65, 123, 34423]
height = math.log(len(scores), 2)
print("Optimal value : ", end="")
print(minimax(0, 0, True, scores, height))
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| """
A pure Python implementation of the quick sort algorithm
For doctests run following command:
python3 -m doctest -v quick_sort.py
For manual testing run:
python3 quick_sort.py
"""
from __future__ import annotations
from random import randrange
def quick_sort(collection: list) -> list:
"""A pure Python implementation of quick sort algorithm
:param collection: a mutable collection of comparable items
:return: the same collection ordered by ascending
Examples:
>>> quick_sort([0, 5, 3, 2, 2])
[0, 2, 2, 3, 5]
>>> quick_sort([])
[]
>>> quick_sort([-2, 5, 0, -45])
[-45, -2, 0, 5]
"""
if len(collection) < 2:
return collection
pivot_index = randrange(len(collection)) # Use random element as pivot
pivot = collection[pivot_index]
greater: list[int] = [] # All elements greater than pivot
lesser: list[int] = [] # All elements less than or equal to pivot
for element in collection[:pivot_index]:
(greater if element > pivot else lesser).append(element)
for element in collection[pivot_index + 1 :]:
(greater if element > pivot else lesser).append(element)
return [*quick_sort(lesser), pivot, *quick_sort(greater)]
if __name__ == "__main__":
user_input = input("Enter numbers separated by a comma:\n").strip()
unsorted = [int(item) for item in user_input.split(",")]
print(quick_sort(unsorted))
| """
A pure Python implementation of the quick sort algorithm
For doctests run following command:
python3 -m doctest -v quick_sort.py
For manual testing run:
python3 quick_sort.py
"""
from __future__ import annotations
from random import randrange
def quick_sort(collection: list) -> list:
"""A pure Python implementation of quick sort algorithm
:param collection: a mutable collection of comparable items
:return: the same collection ordered by ascending
Examples:
>>> quick_sort([0, 5, 3, 2, 2])
[0, 2, 2, 3, 5]
>>> quick_sort([])
[]
>>> quick_sort([-2, 5, 0, -45])
[-45, -2, 0, 5]
"""
if len(collection) < 2:
return collection
pivot_index = randrange(len(collection)) # Use random element as pivot
pivot = collection[pivot_index]
greater: list[int] = [] # All elements greater than pivot
lesser: list[int] = [] # All elements less than or equal to pivot
for element in collection[:pivot_index]:
(greater if element > pivot else lesser).append(element)
for element in collection[pivot_index + 1 :]:
(greater if element > pivot else lesser).append(element)
return [*quick_sort(lesser), pivot, *quick_sort(greater)]
if __name__ == "__main__":
user_input = input("Enter numbers separated by a comma:\n").strip()
unsorted = [int(item) for item in user_input.split(",")]
print(quick_sort(unsorted))
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| """
Project Euler Problem 6: https://projecteuler.net/problem=6
Sum square difference
The sum of the squares of the first ten natural numbers is,
1^2 + 2^2 + ... + 10^2 = 385
The square of the sum of the first ten natural numbers is,
(1 + 2 + ... + 10)^2 = 55^2 = 3025
Hence the difference between the sum of the squares of the first ten
natural numbers and the square of the sum is 3025 - 385 = 2640.
Find the difference between the sum of the squares of the first one
hundred natural numbers and the square of the sum.
"""
def solution(n: int = 100) -> int:
"""
Returns the difference between the sum of the squares of the first n
natural numbers and the square of the sum.
>>> solution(10)
2640
>>> solution(15)
13160
>>> solution(20)
41230
>>> solution(50)
1582700
"""
sum_of_squares = n * (n + 1) * (2 * n + 1) / 6
square_of_sum = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares)
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 6: https://projecteuler.net/problem=6
Sum square difference
The sum of the squares of the first ten natural numbers is,
1^2 + 2^2 + ... + 10^2 = 385
The square of the sum of the first ten natural numbers is,
(1 + 2 + ... + 10)^2 = 55^2 = 3025
Hence the difference between the sum of the squares of the first ten
natural numbers and the square of the sum is 3025 - 385 = 2640.
Find the difference between the sum of the squares of the first one
hundred natural numbers and the square of the sum.
"""
def solution(n: int = 100) -> int:
"""
Returns the difference between the sum of the squares of the first n
natural numbers and the square of the sum.
>>> solution(10)
2640
>>> solution(15)
13160
>>> solution(20)
41230
>>> solution(50)
1582700
"""
sum_of_squares = n * (n + 1) * (2 * n + 1) / 6
square_of_sum = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares)
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| import numpy as np
def runge_kutta(f, y0, x0, h, x_end):
"""
Calculate the numeric solution at each step to the ODE f(x, y) using RK4
https://en.wikipedia.org/wiki/Runge-Kutta_methods
Arguments:
f -- The ode as a function of x and y
y0 -- the initial value for y
x0 -- the initial value for x
h -- the stepsize
x_end -- the end value for x
>>> # the exact solution is math.exp(x)
>>> def f(x, y):
... return y
>>> y0 = 1
>>> y = runge_kutta(f, y0, 0.0, 0.01, 5)
>>> y[-1]
148.41315904125113
"""
n = int(np.ceil((x_end - x0) / h))
y = np.zeros((n + 1,))
y[0] = y0
x = x0
for k in range(n):
k1 = f(x, y[k])
k2 = f(x + 0.5 * h, y[k] + 0.5 * h * k1)
k3 = f(x + 0.5 * h, y[k] + 0.5 * h * k2)
k4 = f(x + h, y[k] + h * k3)
y[k + 1] = y[k] + (1 / 6) * h * (k1 + 2 * k2 + 2 * k3 + k4)
x += h
return y
if __name__ == "__main__":
import doctest
doctest.testmod()
| import numpy as np
def runge_kutta(f, y0, x0, h, x_end):
"""
Calculate the numeric solution at each step to the ODE f(x, y) using RK4
https://en.wikipedia.org/wiki/Runge-Kutta_methods
Arguments:
f -- The ode as a function of x and y
y0 -- the initial value for y
x0 -- the initial value for x
h -- the stepsize
x_end -- the end value for x
>>> # the exact solution is math.exp(x)
>>> def f(x, y):
... return y
>>> y0 = 1
>>> y = runge_kutta(f, y0, 0.0, 0.01, 5)
>>> y[-1]
148.41315904125113
"""
n = int(np.ceil((x_end - x0) / h))
y = np.zeros((n + 1,))
y[0] = y0
x = x0
for k in range(n):
k1 = f(x, y[k])
k2 = f(x + 0.5 * h, y[k] + 0.5 * h * k1)
k3 = f(x + 0.5 * h, y[k] + 0.5 * h * k2)
k4 = f(x + h, y[k] + h * k3)
y[k + 1] = y[k] + (1 / 6) * h * (k1 + 2 * k2 + 2 * k3 + k4)
x += h
return y
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| """Breath First Search (BFS) can be used when finding the shortest path
from a given source node to a target node in an unweighted graph.
"""
from __future__ import annotations
graph = {
"A": ["B", "C", "E"],
"B": ["A", "D", "E"],
"C": ["A", "F", "G"],
"D": ["B"],
"E": ["A", "B", "D"],
"F": ["C"],
"G": ["C"],
}
class Graph:
def __init__(self, graph: dict[str, list[str]], source_vertex: str) -> None:
"""
Graph is implemented as dictionary of adjacency lists. Also,
Source vertex have to be defined upon initialization.
"""
self.graph = graph
# mapping node to its parent in resulting breadth first tree
self.parent: dict[str, str | None] = {}
self.source_vertex = source_vertex
def breath_first_search(self) -> None:
"""
This function is a helper for running breath first search on this graph.
>>> g = Graph(graph, "G")
>>> g.breath_first_search()
>>> g.parent
{'G': None, 'C': 'G', 'A': 'C', 'F': 'C', 'B': 'A', 'E': 'A', 'D': 'B'}
"""
visited = {self.source_vertex}
self.parent[self.source_vertex] = None
queue = [self.source_vertex] # first in first out queue
while queue:
vertex = queue.pop(0)
for adjacent_vertex in self.graph[vertex]:
if adjacent_vertex not in visited:
visited.add(adjacent_vertex)
self.parent[adjacent_vertex] = vertex
queue.append(adjacent_vertex)
def shortest_path(self, target_vertex: str) -> str:
"""
This shortest path function returns a string, describing the result:
1.) No path is found. The string is a human readable message to indicate this.
2.) The shortest path is found. The string is in the form
`v1(->v2->v3->...->vn)`, where v1 is the source vertex and vn is the target
vertex, if it exists separately.
>>> g = Graph(graph, "G")
>>> g.breath_first_search()
Case 1 - No path is found.
>>> g.shortest_path("Foo")
Traceback (most recent call last):
...
ValueError: No path from vertex: G to vertex: Foo
Case 2 - The path is found.
>>> g.shortest_path("D")
'G->C->A->B->D'
>>> g.shortest_path("G")
'G'
"""
if target_vertex == self.source_vertex:
return self.source_vertex
target_vertex_parent = self.parent.get(target_vertex)
if target_vertex_parent is None:
msg = (
f"No path from vertex: {self.source_vertex} to vertex: {target_vertex}"
)
raise ValueError(msg)
return self.shortest_path(target_vertex_parent) + f"->{target_vertex}"
if __name__ == "__main__":
g = Graph(graph, "G")
g.breath_first_search()
print(g.shortest_path("D"))
print(g.shortest_path("G"))
print(g.shortest_path("Foo"))
| """Breath First Search (BFS) can be used when finding the shortest path
from a given source node to a target node in an unweighted graph.
"""
from __future__ import annotations
graph = {
"A": ["B", "C", "E"],
"B": ["A", "D", "E"],
"C": ["A", "F", "G"],
"D": ["B"],
"E": ["A", "B", "D"],
"F": ["C"],
"G": ["C"],
}
class Graph:
def __init__(self, graph: dict[str, list[str]], source_vertex: str) -> None:
"""
Graph is implemented as dictionary of adjacency lists. Also,
Source vertex have to be defined upon initialization.
"""
self.graph = graph
# mapping node to its parent in resulting breadth first tree
self.parent: dict[str, str | None] = {}
self.source_vertex = source_vertex
def breath_first_search(self) -> None:
"""
This function is a helper for running breath first search on this graph.
>>> g = Graph(graph, "G")
>>> g.breath_first_search()
>>> g.parent
{'G': None, 'C': 'G', 'A': 'C', 'F': 'C', 'B': 'A', 'E': 'A', 'D': 'B'}
"""
visited = {self.source_vertex}
self.parent[self.source_vertex] = None
queue = [self.source_vertex] # first in first out queue
while queue:
vertex = queue.pop(0)
for adjacent_vertex in self.graph[vertex]:
if adjacent_vertex not in visited:
visited.add(adjacent_vertex)
self.parent[adjacent_vertex] = vertex
queue.append(adjacent_vertex)
def shortest_path(self, target_vertex: str) -> str:
"""
This shortest path function returns a string, describing the result:
1.) No path is found. The string is a human readable message to indicate this.
2.) The shortest path is found. The string is in the form
`v1(->v2->v3->...->vn)`, where v1 is the source vertex and vn is the target
vertex, if it exists separately.
>>> g = Graph(graph, "G")
>>> g.breath_first_search()
Case 1 - No path is found.
>>> g.shortest_path("Foo")
Traceback (most recent call last):
...
ValueError: No path from vertex: G to vertex: Foo
Case 2 - The path is found.
>>> g.shortest_path("D")
'G->C->A->B->D'
>>> g.shortest_path("G")
'G'
"""
if target_vertex == self.source_vertex:
return self.source_vertex
target_vertex_parent = self.parent.get(target_vertex)
if target_vertex_parent is None:
msg = (
f"No path from vertex: {self.source_vertex} to vertex: {target_vertex}"
)
raise ValueError(msg)
return self.shortest_path(target_vertex_parent) + f"->{target_vertex}"
if __name__ == "__main__":
g = Graph(graph, "G")
g.breath_first_search()
print(g.shortest_path("D"))
print(g.shortest_path("G"))
print(g.shortest_path("Foo"))
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| """
https://en.wikipedia.org/wiki/Burrows%E2%80%93Wheeler_transform
The Burrows–Wheeler transform (BWT, also called block-sorting compression)
rearranges a character string into runs of similar characters. This is useful
for compression, since it tends to be easy to compress a string that has runs
of repeated characters by techniques such as move-to-front transform and
run-length encoding. More importantly, the transformation is reversible,
without needing to store any additional data except the position of the first
original character. The BWT is thus a "free" method of improving the efficiency
of text compression algorithms, costing only some extra computation.
"""
from __future__ import annotations
from typing import TypedDict
class BWTTransformDict(TypedDict):
bwt_string: str
idx_original_string: int
def all_rotations(s: str) -> list[str]:
"""
:param s: The string that will be rotated len(s) times.
:return: A list with the rotations.
:raises TypeError: If s is not an instance of str.
Examples:
>>> all_rotations("^BANANA|") # doctest: +NORMALIZE_WHITESPACE
['^BANANA|', 'BANANA|^', 'ANANA|^B', 'NANA|^BA', 'ANA|^BAN', 'NA|^BANA',
'A|^BANAN', '|^BANANA']
>>> all_rotations("a_asa_da_casa") # doctest: +NORMALIZE_WHITESPACE
['a_asa_da_casa', '_asa_da_casaa', 'asa_da_casaa_', 'sa_da_casaa_a',
'a_da_casaa_as', '_da_casaa_asa', 'da_casaa_asa_', 'a_casaa_asa_d',
'_casaa_asa_da', 'casaa_asa_da_', 'asaa_asa_da_c', 'saa_asa_da_ca',
'aa_asa_da_cas']
>>> all_rotations("panamabanana") # doctest: +NORMALIZE_WHITESPACE
['panamabanana', 'anamabananap', 'namabananapa', 'amabananapan',
'mabananapana', 'abananapanam', 'bananapanama', 'ananapanamab',
'nanapanamaba', 'anapanamaban', 'napanamabana', 'apanamabanan']
>>> all_rotations(5)
Traceback (most recent call last):
...
TypeError: The parameter s type must be str.
"""
if not isinstance(s, str):
raise TypeError("The parameter s type must be str.")
return [s[i:] + s[:i] for i in range(len(s))]
def bwt_transform(s: str) -> BWTTransformDict:
"""
:param s: The string that will be used at bwt algorithm
:return: the string composed of the last char of each row of the ordered
rotations and the index of the original string at ordered rotations list
:raises TypeError: If the s parameter type is not str
:raises ValueError: If the s parameter is empty
Examples:
>>> bwt_transform("^BANANA")
{'bwt_string': 'BNN^AAA', 'idx_original_string': 6}
>>> bwt_transform("a_asa_da_casa")
{'bwt_string': 'aaaadss_c__aa', 'idx_original_string': 3}
>>> bwt_transform("panamabanana")
{'bwt_string': 'mnpbnnaaaaaa', 'idx_original_string': 11}
>>> bwt_transform(4)
Traceback (most recent call last):
...
TypeError: The parameter s type must be str.
>>> bwt_transform('')
Traceback (most recent call last):
...
ValueError: The parameter s must not be empty.
"""
if not isinstance(s, str):
raise TypeError("The parameter s type must be str.")
if not s:
raise ValueError("The parameter s must not be empty.")
rotations = all_rotations(s)
rotations.sort() # sort the list of rotations in alphabetically order
# make a string composed of the last char of each rotation
response: BWTTransformDict = {
"bwt_string": "".join([word[-1] for word in rotations]),
"idx_original_string": rotations.index(s),
}
return response
def reverse_bwt(bwt_string: str, idx_original_string: int) -> str:
"""
:param bwt_string: The string returned from bwt algorithm execution
:param idx_original_string: A 0-based index of the string that was used to
generate bwt_string at ordered rotations list
:return: The string used to generate bwt_string when bwt was executed
:raises TypeError: If the bwt_string parameter type is not str
:raises ValueError: If the bwt_string parameter is empty
:raises TypeError: If the idx_original_string type is not int or if not
possible to cast it to int
:raises ValueError: If the idx_original_string value is lower than 0 or
greater than len(bwt_string) - 1
>>> reverse_bwt("BNN^AAA", 6)
'^BANANA'
>>> reverse_bwt("aaaadss_c__aa", 3)
'a_asa_da_casa'
>>> reverse_bwt("mnpbnnaaaaaa", 11)
'panamabanana'
>>> reverse_bwt(4, 11)
Traceback (most recent call last):
...
TypeError: The parameter bwt_string type must be str.
>>> reverse_bwt("", 11)
Traceback (most recent call last):
...
ValueError: The parameter bwt_string must not be empty.
>>> reverse_bwt("mnpbnnaaaaaa", "asd") # doctest: +NORMALIZE_WHITESPACE
Traceback (most recent call last):
...
TypeError: The parameter idx_original_string type must be int or passive
of cast to int.
>>> reverse_bwt("mnpbnnaaaaaa", -1)
Traceback (most recent call last):
...
ValueError: The parameter idx_original_string must not be lower than 0.
>>> reverse_bwt("mnpbnnaaaaaa", 12) # doctest: +NORMALIZE_WHITESPACE
Traceback (most recent call last):
...
ValueError: The parameter idx_original_string must be lower than
len(bwt_string).
>>> reverse_bwt("mnpbnnaaaaaa", 11.0)
'panamabanana'
>>> reverse_bwt("mnpbnnaaaaaa", 11.4)
'panamabanana'
"""
if not isinstance(bwt_string, str):
raise TypeError("The parameter bwt_string type must be str.")
if not bwt_string:
raise ValueError("The parameter bwt_string must not be empty.")
try:
idx_original_string = int(idx_original_string)
except ValueError:
raise TypeError(
"The parameter idx_original_string type must be int or passive"
" of cast to int."
)
if idx_original_string < 0:
raise ValueError("The parameter idx_original_string must not be lower than 0.")
if idx_original_string >= len(bwt_string):
raise ValueError(
"The parameter idx_original_string must be lower than len(bwt_string)."
)
ordered_rotations = [""] * len(bwt_string)
for _ in range(len(bwt_string)):
for i in range(len(bwt_string)):
ordered_rotations[i] = bwt_string[i] + ordered_rotations[i]
ordered_rotations.sort()
return ordered_rotations[idx_original_string]
if __name__ == "__main__":
entry_msg = "Provide a string that I will generate its BWT transform: "
s = input(entry_msg).strip()
result = bwt_transform(s)
print(
f"Burrows Wheeler transform for string '{s}' results "
f"in '{result['bwt_string']}'"
)
original_string = reverse_bwt(result["bwt_string"], result["idx_original_string"])
print(
f"Reversing Burrows Wheeler transform for entry '{result['bwt_string']}' "
f"we get original string '{original_string}'"
)
| """
https://en.wikipedia.org/wiki/Burrows%E2%80%93Wheeler_transform
The Burrows–Wheeler transform (BWT, also called block-sorting compression)
rearranges a character string into runs of similar characters. This is useful
for compression, since it tends to be easy to compress a string that has runs
of repeated characters by techniques such as move-to-front transform and
run-length encoding. More importantly, the transformation is reversible,
without needing to store any additional data except the position of the first
original character. The BWT is thus a "free" method of improving the efficiency
of text compression algorithms, costing only some extra computation.
"""
from __future__ import annotations
from typing import TypedDict
class BWTTransformDict(TypedDict):
bwt_string: str
idx_original_string: int
def all_rotations(s: str) -> list[str]:
"""
:param s: The string that will be rotated len(s) times.
:return: A list with the rotations.
:raises TypeError: If s is not an instance of str.
Examples:
>>> all_rotations("^BANANA|") # doctest: +NORMALIZE_WHITESPACE
['^BANANA|', 'BANANA|^', 'ANANA|^B', 'NANA|^BA', 'ANA|^BAN', 'NA|^BANA',
'A|^BANAN', '|^BANANA']
>>> all_rotations("a_asa_da_casa") # doctest: +NORMALIZE_WHITESPACE
['a_asa_da_casa', '_asa_da_casaa', 'asa_da_casaa_', 'sa_da_casaa_a',
'a_da_casaa_as', '_da_casaa_asa', 'da_casaa_asa_', 'a_casaa_asa_d',
'_casaa_asa_da', 'casaa_asa_da_', 'asaa_asa_da_c', 'saa_asa_da_ca',
'aa_asa_da_cas']
>>> all_rotations("panamabanana") # doctest: +NORMALIZE_WHITESPACE
['panamabanana', 'anamabananap', 'namabananapa', 'amabananapan',
'mabananapana', 'abananapanam', 'bananapanama', 'ananapanamab',
'nanapanamaba', 'anapanamaban', 'napanamabana', 'apanamabanan']
>>> all_rotations(5)
Traceback (most recent call last):
...
TypeError: The parameter s type must be str.
"""
if not isinstance(s, str):
raise TypeError("The parameter s type must be str.")
return [s[i:] + s[:i] for i in range(len(s))]
def bwt_transform(s: str) -> BWTTransformDict:
"""
:param s: The string that will be used at bwt algorithm
:return: the string composed of the last char of each row of the ordered
rotations and the index of the original string at ordered rotations list
:raises TypeError: If the s parameter type is not str
:raises ValueError: If the s parameter is empty
Examples:
>>> bwt_transform("^BANANA")
{'bwt_string': 'BNN^AAA', 'idx_original_string': 6}
>>> bwt_transform("a_asa_da_casa")
{'bwt_string': 'aaaadss_c__aa', 'idx_original_string': 3}
>>> bwt_transform("panamabanana")
{'bwt_string': 'mnpbnnaaaaaa', 'idx_original_string': 11}
>>> bwt_transform(4)
Traceback (most recent call last):
...
TypeError: The parameter s type must be str.
>>> bwt_transform('')
Traceback (most recent call last):
...
ValueError: The parameter s must not be empty.
"""
if not isinstance(s, str):
raise TypeError("The parameter s type must be str.")
if not s:
raise ValueError("The parameter s must not be empty.")
rotations = all_rotations(s)
rotations.sort() # sort the list of rotations in alphabetically order
# make a string composed of the last char of each rotation
response: BWTTransformDict = {
"bwt_string": "".join([word[-1] for word in rotations]),
"idx_original_string": rotations.index(s),
}
return response
def reverse_bwt(bwt_string: str, idx_original_string: int) -> str:
"""
:param bwt_string: The string returned from bwt algorithm execution
:param idx_original_string: A 0-based index of the string that was used to
generate bwt_string at ordered rotations list
:return: The string used to generate bwt_string when bwt was executed
:raises TypeError: If the bwt_string parameter type is not str
:raises ValueError: If the bwt_string parameter is empty
:raises TypeError: If the idx_original_string type is not int or if not
possible to cast it to int
:raises ValueError: If the idx_original_string value is lower than 0 or
greater than len(bwt_string) - 1
>>> reverse_bwt("BNN^AAA", 6)
'^BANANA'
>>> reverse_bwt("aaaadss_c__aa", 3)
'a_asa_da_casa'
>>> reverse_bwt("mnpbnnaaaaaa", 11)
'panamabanana'
>>> reverse_bwt(4, 11)
Traceback (most recent call last):
...
TypeError: The parameter bwt_string type must be str.
>>> reverse_bwt("", 11)
Traceback (most recent call last):
...
ValueError: The parameter bwt_string must not be empty.
>>> reverse_bwt("mnpbnnaaaaaa", "asd") # doctest: +NORMALIZE_WHITESPACE
Traceback (most recent call last):
...
TypeError: The parameter idx_original_string type must be int or passive
of cast to int.
>>> reverse_bwt("mnpbnnaaaaaa", -1)
Traceback (most recent call last):
...
ValueError: The parameter idx_original_string must not be lower than 0.
>>> reverse_bwt("mnpbnnaaaaaa", 12) # doctest: +NORMALIZE_WHITESPACE
Traceback (most recent call last):
...
ValueError: The parameter idx_original_string must be lower than
len(bwt_string).
>>> reverse_bwt("mnpbnnaaaaaa", 11.0)
'panamabanana'
>>> reverse_bwt("mnpbnnaaaaaa", 11.4)
'panamabanana'
"""
if not isinstance(bwt_string, str):
raise TypeError("The parameter bwt_string type must be str.")
if not bwt_string:
raise ValueError("The parameter bwt_string must not be empty.")
try:
idx_original_string = int(idx_original_string)
except ValueError:
raise TypeError(
"The parameter idx_original_string type must be int or passive"
" of cast to int."
)
if idx_original_string < 0:
raise ValueError("The parameter idx_original_string must not be lower than 0.")
if idx_original_string >= len(bwt_string):
raise ValueError(
"The parameter idx_original_string must be lower than len(bwt_string)."
)
ordered_rotations = [""] * len(bwt_string)
for _ in range(len(bwt_string)):
for i in range(len(bwt_string)):
ordered_rotations[i] = bwt_string[i] + ordered_rotations[i]
ordered_rotations.sort()
return ordered_rotations[idx_original_string]
if __name__ == "__main__":
entry_msg = "Provide a string that I will generate its BWT transform: "
s = input(entry_msg).strip()
result = bwt_transform(s)
print(
f"Burrows Wheeler transform for string '{s}' results "
f"in '{result['bwt_string']}'"
)
original_string = reverse_bwt(result["bwt_string"], result["idx_original_string"])
print(
f"Reversing Burrows Wheeler transform for entry '{result['bwt_string']}' "
f"we get original string '{original_string}'"
)
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| -1 |
||
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| """
Checks if a system of forces is in static equilibrium.
"""
from __future__ import annotations
from numpy import array, cos, cross, float64, radians, sin
from numpy.typing import NDArray
def polar_force(
magnitude: float, angle: float, radian_mode: bool = False
) -> list[float]:
"""
Resolves force along rectangular components.
(force, angle) => (force_x, force_y)
>>> import math
>>> force = polar_force(10, 45)
>>> math.isclose(force[0], 7.071067811865477)
True
>>> math.isclose(force[1], 7.0710678118654755)
True
>>> force = polar_force(10, 3.14, radian_mode=True)
>>> math.isclose(force[0], -9.999987317275396)
True
>>> math.isclose(force[1], 0.01592652916486828)
True
"""
if radian_mode:
return [magnitude * cos(angle), magnitude * sin(angle)]
return [magnitude * cos(radians(angle)), magnitude * sin(radians(angle))]
def in_static_equilibrium(
forces: NDArray[float64], location: NDArray[float64], eps: float = 10**-1
) -> bool:
"""
Check if a system is in equilibrium.
It takes two numpy.array objects.
forces ==> [
[force1_x, force1_y],
[force2_x, force2_y],
....]
location ==> [
[x1, y1],
[x2, y2],
....]
>>> force = array([[1, 1], [-1, 2]])
>>> location = array([[1, 0], [10, 0]])
>>> in_static_equilibrium(force, location)
False
"""
# summation of moments is zero
moments: NDArray[float64] = cross(location, forces)
sum_moments: float = sum(moments)
return abs(sum_moments) < eps
if __name__ == "__main__":
# Test to check if it works
forces = array(
[
polar_force(718.4, 180 - 30),
polar_force(879.54, 45),
polar_force(100, -90),
]
)
location: NDArray[float64] = array([[0, 0], [0, 0], [0, 0]])
assert in_static_equilibrium(forces, location)
# Problem 1 in image_data/2D_problems.jpg
forces = array(
[
polar_force(30 * 9.81, 15),
polar_force(215, 180 - 45),
polar_force(264, 90 - 30),
]
)
location = array([[0, 0], [0, 0], [0, 0]])
assert in_static_equilibrium(forces, location)
# Problem in image_data/2D_problems_1.jpg
forces = array([[0, -2000], [0, -1200], [0, 15600], [0, -12400]])
location = array([[0, 0], [6, 0], [10, 0], [12, 0]])
assert in_static_equilibrium(forces, location)
import doctest
doctest.testmod()
| """
Checks if a system of forces is in static equilibrium.
"""
from __future__ import annotations
from numpy import array, cos, cross, float64, radians, sin
from numpy.typing import NDArray
def polar_force(
magnitude: float, angle: float, radian_mode: bool = False
) -> list[float]:
"""
Resolves force along rectangular components.
(force, angle) => (force_x, force_y)
>>> import math
>>> force = polar_force(10, 45)
>>> math.isclose(force[0], 7.071067811865477)
True
>>> math.isclose(force[1], 7.0710678118654755)
True
>>> force = polar_force(10, 3.14, radian_mode=True)
>>> math.isclose(force[0], -9.999987317275396)
True
>>> math.isclose(force[1], 0.01592652916486828)
True
"""
if radian_mode:
return [magnitude * cos(angle), magnitude * sin(angle)]
return [magnitude * cos(radians(angle)), magnitude * sin(radians(angle))]
def in_static_equilibrium(
forces: NDArray[float64], location: NDArray[float64], eps: float = 10**-1
) -> bool:
"""
Check if a system is in equilibrium.
It takes two numpy.array objects.
forces ==> [
[force1_x, force1_y],
[force2_x, force2_y],
....]
location ==> [
[x1, y1],
[x2, y2],
....]
>>> force = array([[1, 1], [-1, 2]])
>>> location = array([[1, 0], [10, 0]])
>>> in_static_equilibrium(force, location)
False
"""
# summation of moments is zero
moments: NDArray[float64] = cross(location, forces)
sum_moments: float = sum(moments)
return abs(sum_moments) < eps
if __name__ == "__main__":
# Test to check if it works
forces = array(
[
polar_force(718.4, 180 - 30),
polar_force(879.54, 45),
polar_force(100, -90),
]
)
location: NDArray[float64] = array([[0, 0], [0, 0], [0, 0]])
assert in_static_equilibrium(forces, location)
# Problem 1 in image_data/2D_problems.jpg
forces = array(
[
polar_force(30 * 9.81, 15),
polar_force(215, 180 - 45),
polar_force(264, 90 - 30),
]
)
location = array([[0, 0], [0, 0], [0, 0]])
assert in_static_equilibrium(forces, location)
# Problem in image_data/2D_problems_1.jpg
forces = array([[0, -2000], [0, -1200], [0, 15600], [0, -12400]])
location = array([[0, 0], [6, 0], [10, 0], [12, 0]])
assert in_static_equilibrium(forces, location)
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| # A complete working Python program to demonstrate all
# stack operations using a doubly linked list
from __future__ import annotations
from typing import Generic, TypeVar
T = TypeVar("T")
class Node(Generic[T]):
def __init__(self, data: T):
self.data = data # Assign data
self.next: Node[T] | None = None # Initialize next as null
self.prev: Node[T] | None = None # Initialize prev as null
class Stack(Generic[T]):
"""
>>> stack = Stack()
>>> stack.is_empty()
True
>>> stack.print_stack()
stack elements are:
>>> for i in range(4):
... stack.push(i)
...
>>> stack.is_empty()
False
>>> stack.print_stack()
stack elements are:
3->2->1->0->
>>> stack.top()
3
>>> len(stack)
4
>>> stack.pop()
3
>>> stack.print_stack()
stack elements are:
2->1->0->
"""
def __init__(self) -> None:
self.head: Node[T] | None = None
def push(self, data: T) -> None:
"""add a Node to the stack"""
if self.head is None:
self.head = Node(data)
else:
new_node = Node(data)
self.head.prev = new_node
new_node.next = self.head
new_node.prev = None
self.head = new_node
def pop(self) -> T | None:
"""pop the top element off the stack"""
if self.head is None:
return None
else:
assert self.head is not None
temp = self.head.data
self.head = self.head.next
if self.head is not None:
self.head.prev = None
return temp
def top(self) -> T | None:
"""return the top element of the stack"""
return self.head.data if self.head is not None else None
def __len__(self) -> int:
temp = self.head
count = 0
while temp is not None:
count += 1
temp = temp.next
return count
def is_empty(self) -> bool:
return self.head is None
def print_stack(self) -> None:
print("stack elements are:")
temp = self.head
while temp is not None:
print(temp.data, end="->")
temp = temp.next
# Code execution starts here
if __name__ == "__main__":
# Start with the empty stack
stack: Stack[int] = Stack()
# Insert 4 at the beginning. So stack becomes 4->None
print("Stack operations using Doubly LinkedList")
stack.push(4)
# Insert 5 at the beginning. So stack becomes 4->5->None
stack.push(5)
# Insert 6 at the beginning. So stack becomes 4->5->6->None
stack.push(6)
# Insert 7 at the beginning. So stack becomes 4->5->6->7->None
stack.push(7)
# Print the stack
stack.print_stack()
# Print the top element
print("\nTop element is ", stack.top())
# Print the stack size
print("Size of the stack is ", len(stack))
# pop the top element
stack.pop()
# pop the top element
stack.pop()
# two elements have now been popped off
stack.print_stack()
# Print True if the stack is empty else False
print("\nstack is empty:", stack.is_empty())
| # A complete working Python program to demonstrate all
# stack operations using a doubly linked list
from __future__ import annotations
from typing import Generic, TypeVar
T = TypeVar("T")
class Node(Generic[T]):
def __init__(self, data: T):
self.data = data # Assign data
self.next: Node[T] | None = None # Initialize next as null
self.prev: Node[T] | None = None # Initialize prev as null
class Stack(Generic[T]):
"""
>>> stack = Stack()
>>> stack.is_empty()
True
>>> stack.print_stack()
stack elements are:
>>> for i in range(4):
... stack.push(i)
...
>>> stack.is_empty()
False
>>> stack.print_stack()
stack elements are:
3->2->1->0->
>>> stack.top()
3
>>> len(stack)
4
>>> stack.pop()
3
>>> stack.print_stack()
stack elements are:
2->1->0->
"""
def __init__(self) -> None:
self.head: Node[T] | None = None
def push(self, data: T) -> None:
"""add a Node to the stack"""
if self.head is None:
self.head = Node(data)
else:
new_node = Node(data)
self.head.prev = new_node
new_node.next = self.head
new_node.prev = None
self.head = new_node
def pop(self) -> T | None:
"""pop the top element off the stack"""
if self.head is None:
return None
else:
assert self.head is not None
temp = self.head.data
self.head = self.head.next
if self.head is not None:
self.head.prev = None
return temp
def top(self) -> T | None:
"""return the top element of the stack"""
return self.head.data if self.head is not None else None
def __len__(self) -> int:
temp = self.head
count = 0
while temp is not None:
count += 1
temp = temp.next
return count
def is_empty(self) -> bool:
return self.head is None
def print_stack(self) -> None:
print("stack elements are:")
temp = self.head
while temp is not None:
print(temp.data, end="->")
temp = temp.next
# Code execution starts here
if __name__ == "__main__":
# Start with the empty stack
stack: Stack[int] = Stack()
# Insert 4 at the beginning. So stack becomes 4->None
print("Stack operations using Doubly LinkedList")
stack.push(4)
# Insert 5 at the beginning. So stack becomes 4->5->None
stack.push(5)
# Insert 6 at the beginning. So stack becomes 4->5->6->None
stack.push(6)
# Insert 7 at the beginning. So stack becomes 4->5->6->7->None
stack.push(7)
# Print the stack
stack.print_stack()
# Print the top element
print("\nTop element is ", stack.top())
# Print the stack size
print("Size of the stack is ", len(stack))
# pop the top element
stack.pop()
# pop the top element
stack.pop()
# two elements have now been popped off
stack.print_stack()
# Print True if the stack is empty else False
print("\nstack is empty:", stack.is_empty())
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| """
Project Euler Problem 301: https://projecteuler.net/problem=301
Problem Statement:
Nim is a game played with heaps of stones, where two players take
it in turn to remove any number of stones from any heap until no stones remain.
We'll consider the three-heap normal-play version of
Nim, which works as follows:
- At the start of the game there are three heaps of stones.
- On each player's turn, the player may remove any positive
number of stones from any single heap.
- The first player unable to move (because no stones remain) loses.
If (n1, n2, n3) indicates a Nim position consisting of heaps of size
n1, n2, and n3, then there is a simple function, which you may look up
or attempt to deduce for yourself, X(n1, n2, n3) that returns:
- zero if, with perfect strategy, the player about to
move will eventually lose; or
- non-zero if, with perfect strategy, the player about
to move will eventually win.
For example X(1,2,3) = 0 because, no matter what the current player does,
the opponent can respond with a move that leaves two heaps of equal size,
at which point every move by the current player can be mirrored by the
opponent until no stones remain; so the current player loses. To illustrate:
- current player moves to (1,2,1)
- opponent moves to (1,0,1)
- current player moves to (0,0,1)
- opponent moves to (0,0,0), and so wins.
For how many positive integers n <= 2^30 does X(n,2n,3n) = 0?
"""
def solution(exponent: int = 30) -> int:
"""
For any given exponent x >= 0, 1 <= n <= 2^x.
This function returns how many Nim games are lost given that
each Nim game has three heaps of the form (n, 2*n, 3*n).
>>> solution(0)
1
>>> solution(2)
3
>>> solution(10)
144
"""
# To find how many total games were lost for a given exponent x,
# we need to find the Fibonacci number F(x+2).
fibonacci_index = exponent + 2
phi = (1 + 5**0.5) / 2
fibonacci = (phi**fibonacci_index - (phi - 1) ** fibonacci_index) / 5**0.5
return int(fibonacci)
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 301: https://projecteuler.net/problem=301
Problem Statement:
Nim is a game played with heaps of stones, where two players take
it in turn to remove any number of stones from any heap until no stones remain.
We'll consider the three-heap normal-play version of
Nim, which works as follows:
- At the start of the game there are three heaps of stones.
- On each player's turn, the player may remove any positive
number of stones from any single heap.
- The first player unable to move (because no stones remain) loses.
If (n1, n2, n3) indicates a Nim position consisting of heaps of size
n1, n2, and n3, then there is a simple function, which you may look up
or attempt to deduce for yourself, X(n1, n2, n3) that returns:
- zero if, with perfect strategy, the player about to
move will eventually lose; or
- non-zero if, with perfect strategy, the player about
to move will eventually win.
For example X(1,2,3) = 0 because, no matter what the current player does,
the opponent can respond with a move that leaves two heaps of equal size,
at which point every move by the current player can be mirrored by the
opponent until no stones remain; so the current player loses. To illustrate:
- current player moves to (1,2,1)
- opponent moves to (1,0,1)
- current player moves to (0,0,1)
- opponent moves to (0,0,0), and so wins.
For how many positive integers n <= 2^30 does X(n,2n,3n) = 0?
"""
def solution(exponent: int = 30) -> int:
"""
For any given exponent x >= 0, 1 <= n <= 2^x.
This function returns how many Nim games are lost given that
each Nim game has three heaps of the form (n, 2*n, 3*n).
>>> solution(0)
1
>>> solution(2)
3
>>> solution(10)
144
"""
# To find how many total games were lost for a given exponent x,
# we need to find the Fibonacci number F(x+2).
fibonacci_index = exponent + 2
phi = (1 + 5**0.5) / 2
fibonacci = (phi**fibonacci_index - (phi - 1) ** fibonacci_index) / 5**0.5
return int(fibonacci)
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| """
This is pure Python implementation of comb sort algorithm.
Comb sort is a relatively simple sorting algorithm originally designed by Wlodzimierz
Dobosiewicz in 1980. It was rediscovered by Stephen Lacey and Richard Box in 1991.
Comb sort improves on bubble sort algorithm.
In bubble sort, distance (or gap) between two compared elements is always one.
Comb sort improvement is that gap can be much more than 1, in order to prevent slowing
down by small values
at the end of a list.
More info on: https://en.wikipedia.org/wiki/Comb_sort
For doctests run following command:
python -m doctest -v comb_sort.py
or
python3 -m doctest -v comb_sort.py
For manual testing run:
python comb_sort.py
"""
def comb_sort(data: list) -> list:
"""Pure implementation of comb sort algorithm in Python
:param data: mutable collection with comparable items
:return: the same collection in ascending order
Examples:
>>> comb_sort([0, 5, 3, 2, 2])
[0, 2, 2, 3, 5]
>>> comb_sort([])
[]
>>> comb_sort([99, 45, -7, 8, 2, 0, -15, 3])
[-15, -7, 0, 2, 3, 8, 45, 99]
"""
shrink_factor = 1.3
gap = len(data)
completed = False
while not completed:
# Update the gap value for a next comb
gap = int(gap / shrink_factor)
if gap <= 1:
completed = True
index = 0
while index + gap < len(data):
if data[index] > data[index + gap]:
# Swap values
data[index], data[index + gap] = data[index + gap], data[index]
completed = False
index += 1
return data
if __name__ == "__main__":
import doctest
doctest.testmod()
user_input = input("Enter numbers separated by a comma:\n").strip()
unsorted = [int(item) for item in user_input.split(",")]
print(comb_sort(unsorted))
| """
This is pure Python implementation of comb sort algorithm.
Comb sort is a relatively simple sorting algorithm originally designed by Wlodzimierz
Dobosiewicz in 1980. It was rediscovered by Stephen Lacey and Richard Box in 1991.
Comb sort improves on bubble sort algorithm.
In bubble sort, distance (or gap) between two compared elements is always one.
Comb sort improvement is that gap can be much more than 1, in order to prevent slowing
down by small values
at the end of a list.
More info on: https://en.wikipedia.org/wiki/Comb_sort
For doctests run following command:
python -m doctest -v comb_sort.py
or
python3 -m doctest -v comb_sort.py
For manual testing run:
python comb_sort.py
"""
def comb_sort(data: list) -> list:
"""Pure implementation of comb sort algorithm in Python
:param data: mutable collection with comparable items
:return: the same collection in ascending order
Examples:
>>> comb_sort([0, 5, 3, 2, 2])
[0, 2, 2, 3, 5]
>>> comb_sort([])
[]
>>> comb_sort([99, 45, -7, 8, 2, 0, -15, 3])
[-15, -7, 0, 2, 3, 8, 45, 99]
"""
shrink_factor = 1.3
gap = len(data)
completed = False
while not completed:
# Update the gap value for a next comb
gap = int(gap / shrink_factor)
if gap <= 1:
completed = True
index = 0
while index + gap < len(data):
if data[index] > data[index + gap]:
# Swap values
data[index], data[index + gap] = data[index + gap], data[index]
completed = False
index += 1
return data
if __name__ == "__main__":
import doctest
doctest.testmod()
user_input = input("Enter numbers separated by a comma:\n").strip()
unsorted = [int(item) for item in user_input.split(",")]
print(comb_sort(unsorted))
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| def bin_to_decimal(bin_string: str) -> int:
"""
Convert a binary value to its decimal equivalent
>>> bin_to_decimal("101")
5
>>> bin_to_decimal(" 1010 ")
10
>>> bin_to_decimal("-11101")
-29
>>> bin_to_decimal("0")
0
>>> bin_to_decimal("a")
Traceback (most recent call last):
...
ValueError: Non-binary value was passed to the function
>>> bin_to_decimal("")
Traceback (most recent call last):
...
ValueError: Empty string was passed to the function
>>> bin_to_decimal("39")
Traceback (most recent call last):
...
ValueError: Non-binary value was passed to the function
"""
bin_string = str(bin_string).strip()
if not bin_string:
raise ValueError("Empty string was passed to the function")
is_negative = bin_string[0] == "-"
if is_negative:
bin_string = bin_string[1:]
if not all(char in "01" for char in bin_string):
raise ValueError("Non-binary value was passed to the function")
decimal_number = 0
for char in bin_string:
decimal_number = 2 * decimal_number + int(char)
return -decimal_number if is_negative else decimal_number
if __name__ == "__main__":
from doctest import testmod
testmod()
| def bin_to_decimal(bin_string: str) -> int:
"""
Convert a binary value to its decimal equivalent
>>> bin_to_decimal("101")
5
>>> bin_to_decimal(" 1010 ")
10
>>> bin_to_decimal("-11101")
-29
>>> bin_to_decimal("0")
0
>>> bin_to_decimal("a")
Traceback (most recent call last):
...
ValueError: Non-binary value was passed to the function
>>> bin_to_decimal("")
Traceback (most recent call last):
...
ValueError: Empty string was passed to the function
>>> bin_to_decimal("39")
Traceback (most recent call last):
...
ValueError: Non-binary value was passed to the function
"""
bin_string = str(bin_string).strip()
if not bin_string:
raise ValueError("Empty string was passed to the function")
is_negative = bin_string[0] == "-"
if is_negative:
bin_string = bin_string[1:]
if not all(char in "01" for char in bin_string):
raise ValueError("Non-binary value was passed to the function")
decimal_number = 0
for char in bin_string:
decimal_number = 2 * decimal_number + int(char)
return -decimal_number if is_negative else decimal_number
if __name__ == "__main__":
from doctest import testmod
testmod()
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| """
Program to list all the ways a target string can be
constructed from the given list of substrings
"""
from __future__ import annotations
def all_construct(target: str, word_bank: list[str] | None = None) -> list[list[str]]:
"""
returns the list containing all the possible
combinations a string(target) can be constructed from
the given list of substrings(word_bank)
>>> all_construct("hello", ["he", "l", "o"])
[['he', 'l', 'l', 'o']]
>>> all_construct("purple",["purp","p","ur","le","purpl"])
[['purp', 'le'], ['p', 'ur', 'p', 'le']]
"""
word_bank = word_bank or []
# create a table
table_size: int = len(target) + 1
table: list[list[list[str]]] = []
for _ in range(table_size):
table.append([])
# seed value
table[0] = [[]] # because empty string has empty combination
# iterate through the indices
for i in range(table_size):
# condition
if table[i] != []:
for word in word_bank:
# slice condition
if target[i : i + len(word)] == word:
new_combinations: list[list[str]] = [
[word, *way] for way in table[i]
]
# adds the word to every combination the current position holds
# now,push that combination to the table[i+len(word)]
table[i + len(word)] += new_combinations
# combinations are in reverse order so reverse for better output
for combination in table[len(target)]:
combination.reverse()
return table[len(target)]
if __name__ == "__main__":
print(all_construct("jwajalapa", ["jwa", "j", "w", "a", "la", "lapa"]))
print(all_construct("rajamati", ["s", "raj", "amat", "raja", "ma", "i", "t"]))
print(
all_construct(
"hexagonosaurus",
["h", "ex", "hex", "ag", "ago", "ru", "auru", "rus", "go", "no", "o", "s"],
)
)
| """
Program to list all the ways a target string can be
constructed from the given list of substrings
"""
from __future__ import annotations
def all_construct(target: str, word_bank: list[str] | None = None) -> list[list[str]]:
"""
returns the list containing all the possible
combinations a string(target) can be constructed from
the given list of substrings(word_bank)
>>> all_construct("hello", ["he", "l", "o"])
[['he', 'l', 'l', 'o']]
>>> all_construct("purple",["purp","p","ur","le","purpl"])
[['purp', 'le'], ['p', 'ur', 'p', 'le']]
"""
word_bank = word_bank or []
# create a table
table_size: int = len(target) + 1
table: list[list[list[str]]] = []
for _ in range(table_size):
table.append([])
# seed value
table[0] = [[]] # because empty string has empty combination
# iterate through the indices
for i in range(table_size):
# condition
if table[i] != []:
for word in word_bank:
# slice condition
if target[i : i + len(word)] == word:
new_combinations: list[list[str]] = [
[word, *way] for way in table[i]
]
# adds the word to every combination the current position holds
# now,push that combination to the table[i+len(word)]
table[i + len(word)] += new_combinations
# combinations are in reverse order so reverse for better output
for combination in table[len(target)]:
combination.reverse()
return table[len(target)]
if __name__ == "__main__":
print(all_construct("jwajalapa", ["jwa", "j", "w", "a", "la", "lapa"]))
print(all_construct("rajamati", ["s", "raj", "amat", "raja", "ma", "i", "t"]))
print(
all_construct(
"hexagonosaurus",
["h", "ex", "hex", "ag", "ago", "ru", "auru", "rus", "go", "no", "o", "s"],
)
)
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| from typing import Any
def viterbi(
observations_space: list,
states_space: list,
initial_probabilities: dict,
transition_probabilities: dict,
emission_probabilities: dict,
) -> list:
"""
Viterbi Algorithm, to find the most likely path of
states from the start and the expected output.
https://en.wikipedia.org/wiki/Viterbi_algorithm
sdafads
Wikipedia example
>>> observations = ["normal", "cold", "dizzy"]
>>> states = ["Healthy", "Fever"]
>>> start_p = {"Healthy": 0.6, "Fever": 0.4}
>>> trans_p = {
... "Healthy": {"Healthy": 0.7, "Fever": 0.3},
... "Fever": {"Healthy": 0.4, "Fever": 0.6},
... }
>>> emit_p = {
... "Healthy": {"normal": 0.5, "cold": 0.4, "dizzy": 0.1},
... "Fever": {"normal": 0.1, "cold": 0.3, "dizzy": 0.6},
... }
>>> viterbi(observations, states, start_p, trans_p, emit_p)
['Healthy', 'Healthy', 'Fever']
>>> viterbi((), states, start_p, trans_p, emit_p)
Traceback (most recent call last):
...
ValueError: There's an empty parameter
>>> viterbi(observations, (), start_p, trans_p, emit_p)
Traceback (most recent call last):
...
ValueError: There's an empty parameter
>>> viterbi(observations, states, {}, trans_p, emit_p)
Traceback (most recent call last):
...
ValueError: There's an empty parameter
>>> viterbi(observations, states, start_p, {}, emit_p)
Traceback (most recent call last):
...
ValueError: There's an empty parameter
>>> viterbi(observations, states, start_p, trans_p, {})
Traceback (most recent call last):
...
ValueError: There's an empty parameter
>>> viterbi("invalid", states, start_p, trans_p, emit_p)
Traceback (most recent call last):
...
ValueError: observations_space must be a list
>>> viterbi(["valid", 123], states, start_p, trans_p, emit_p)
Traceback (most recent call last):
...
ValueError: observations_space must be a list of strings
>>> viterbi(observations, "invalid", start_p, trans_p, emit_p)
Traceback (most recent call last):
...
ValueError: states_space must be a list
>>> viterbi(observations, ["valid", 123], start_p, trans_p, emit_p)
Traceback (most recent call last):
...
ValueError: states_space must be a list of strings
>>> viterbi(observations, states, "invalid", trans_p, emit_p)
Traceback (most recent call last):
...
ValueError: initial_probabilities must be a dict
>>> viterbi(observations, states, {2:2}, trans_p, emit_p)
Traceback (most recent call last):
...
ValueError: initial_probabilities all keys must be strings
>>> viterbi(observations, states, {"a":2}, trans_p, emit_p)
Traceback (most recent call last):
...
ValueError: initial_probabilities all values must be float
>>> viterbi(observations, states, start_p, "invalid", emit_p)
Traceback (most recent call last):
...
ValueError: transition_probabilities must be a dict
>>> viterbi(observations, states, start_p, {"a":2}, emit_p)
Traceback (most recent call last):
...
ValueError: transition_probabilities all values must be dict
>>> viterbi(observations, states, start_p, {2:{2:2}}, emit_p)
Traceback (most recent call last):
...
ValueError: transition_probabilities all keys must be strings
>>> viterbi(observations, states, start_p, {"a":{2:2}}, emit_p)
Traceback (most recent call last):
...
ValueError: transition_probabilities all keys must be strings
>>> viterbi(observations, states, start_p, {"a":{"b":2}}, emit_p)
Traceback (most recent call last):
...
ValueError: transition_probabilities nested dictionary all values must be float
>>> viterbi(observations, states, start_p, trans_p, "invalid")
Traceback (most recent call last):
...
ValueError: emission_probabilities must be a dict
>>> viterbi(observations, states, start_p, trans_p, None)
Traceback (most recent call last):
...
ValueError: There's an empty parameter
"""
_validation(
observations_space,
states_space,
initial_probabilities,
transition_probabilities,
emission_probabilities,
)
# Creates data structures and fill initial step
probabilities: dict = {}
pointers: dict = {}
for state in states_space:
observation = observations_space[0]
probabilities[(state, observation)] = (
initial_probabilities[state] * emission_probabilities[state][observation]
)
pointers[(state, observation)] = None
# Fills the data structure with the probabilities of
# different transitions and pointers to previous states
for o in range(1, len(observations_space)):
observation = observations_space[o]
prior_observation = observations_space[o - 1]
for state in states_space:
# Calculates the argmax for probability function
arg_max = ""
max_probability = -1
for k_state in states_space:
probability = (
probabilities[(k_state, prior_observation)]
* transition_probabilities[k_state][state]
* emission_probabilities[state][observation]
)
if probability > max_probability:
max_probability = probability
arg_max = k_state
# Update probabilities and pointers dicts
probabilities[(state, observation)] = (
probabilities[(arg_max, prior_observation)]
* transition_probabilities[arg_max][state]
* emission_probabilities[state][observation]
)
pointers[(state, observation)] = arg_max
# The final observation
final_observation = observations_space[len(observations_space) - 1]
# argmax for given final observation
arg_max = ""
max_probability = -1
for k_state in states_space:
probability = probabilities[(k_state, final_observation)]
if probability > max_probability:
max_probability = probability
arg_max = k_state
last_state = arg_max
# Process pointers backwards
previous = last_state
result = []
for o in range(len(observations_space) - 1, -1, -1):
result.append(previous)
previous = pointers[previous, observations_space[o]]
result.reverse()
return result
def _validation(
observations_space: Any,
states_space: Any,
initial_probabilities: Any,
transition_probabilities: Any,
emission_probabilities: Any,
) -> None:
"""
>>> observations = ["normal", "cold", "dizzy"]
>>> states = ["Healthy", "Fever"]
>>> start_p = {"Healthy": 0.6, "Fever": 0.4}
>>> trans_p = {
... "Healthy": {"Healthy": 0.7, "Fever": 0.3},
... "Fever": {"Healthy": 0.4, "Fever": 0.6},
... }
>>> emit_p = {
... "Healthy": {"normal": 0.5, "cold": 0.4, "dizzy": 0.1},
... "Fever": {"normal": 0.1, "cold": 0.3, "dizzy": 0.6},
... }
>>> _validation(observations, states, start_p, trans_p, emit_p)
>>> _validation([], states, start_p, trans_p, emit_p)
Traceback (most recent call last):
...
ValueError: There's an empty parameter
"""
_validate_not_empty(
observations_space,
states_space,
initial_probabilities,
transition_probabilities,
emission_probabilities,
)
_validate_lists(observations_space, states_space)
_validate_dicts(
initial_probabilities, transition_probabilities, emission_probabilities
)
def _validate_not_empty(
observations_space: Any,
states_space: Any,
initial_probabilities: Any,
transition_probabilities: Any,
emission_probabilities: Any,
) -> None:
"""
>>> _validate_not_empty(["a"], ["b"], {"c":0.5},
... {"d": {"e": 0.6}}, {"f": {"g": 0.7}})
>>> _validate_not_empty(["a"], ["b"], {"c":0.5}, {}, {"f": {"g": 0.7}})
Traceback (most recent call last):
...
ValueError: There's an empty parameter
>>> _validate_not_empty(["a"], ["b"], None, {"d": {"e": 0.6}}, {"f": {"g": 0.7}})
Traceback (most recent call last):
...
ValueError: There's an empty parameter
"""
if not all(
[
observations_space,
states_space,
initial_probabilities,
transition_probabilities,
emission_probabilities,
]
):
raise ValueError("There's an empty parameter")
def _validate_lists(observations_space: Any, states_space: Any) -> None:
"""
>>> _validate_lists(["a"], ["b"])
>>> _validate_lists(1234, ["b"])
Traceback (most recent call last):
...
ValueError: observations_space must be a list
>>> _validate_lists(["a"], [3])
Traceback (most recent call last):
...
ValueError: states_space must be a list of strings
"""
_validate_list(observations_space, "observations_space")
_validate_list(states_space, "states_space")
def _validate_list(_object: Any, var_name: str) -> None:
"""
>>> _validate_list(["a"], "mock_name")
>>> _validate_list("a", "mock_name")
Traceback (most recent call last):
...
ValueError: mock_name must be a list
>>> _validate_list([0.5], "mock_name")
Traceback (most recent call last):
...
ValueError: mock_name must be a list of strings
"""
if not isinstance(_object, list):
msg = f"{var_name} must be a list"
raise ValueError(msg)
else:
for x in _object:
if not isinstance(x, str):
msg = f"{var_name} must be a list of strings"
raise ValueError(msg)
def _validate_dicts(
initial_probabilities: Any,
transition_probabilities: Any,
emission_probabilities: Any,
) -> None:
"""
>>> _validate_dicts({"c":0.5}, {"d": {"e": 0.6}}, {"f": {"g": 0.7}})
>>> _validate_dicts("invalid", {"d": {"e": 0.6}}, {"f": {"g": 0.7}})
Traceback (most recent call last):
...
ValueError: initial_probabilities must be a dict
>>> _validate_dicts({"c":0.5}, {2: {"e": 0.6}}, {"f": {"g": 0.7}})
Traceback (most recent call last):
...
ValueError: transition_probabilities all keys must be strings
>>> _validate_dicts({"c":0.5}, {"d": {"e": 0.6}}, {"f": {2: 0.7}})
Traceback (most recent call last):
...
ValueError: emission_probabilities all keys must be strings
>>> _validate_dicts({"c":0.5}, {"d": {"e": 0.6}}, {"f": {"g": "h"}})
Traceback (most recent call last):
...
ValueError: emission_probabilities nested dictionary all values must be float
"""
_validate_dict(initial_probabilities, "initial_probabilities", float)
_validate_nested_dict(transition_probabilities, "transition_probabilities")
_validate_nested_dict(emission_probabilities, "emission_probabilities")
def _validate_nested_dict(_object: Any, var_name: str) -> None:
"""
>>> _validate_nested_dict({"a":{"b": 0.5}}, "mock_name")
>>> _validate_nested_dict("invalid", "mock_name")
Traceback (most recent call last):
...
ValueError: mock_name must be a dict
>>> _validate_nested_dict({"a": 8}, "mock_name")
Traceback (most recent call last):
...
ValueError: mock_name all values must be dict
>>> _validate_nested_dict({"a":{2: 0.5}}, "mock_name")
Traceback (most recent call last):
...
ValueError: mock_name all keys must be strings
>>> _validate_nested_dict({"a":{"b": 4}}, "mock_name")
Traceback (most recent call last):
...
ValueError: mock_name nested dictionary all values must be float
"""
_validate_dict(_object, var_name, dict)
for x in _object.values():
_validate_dict(x, var_name, float, True)
def _validate_dict(
_object: Any, var_name: str, value_type: type, nested: bool = False
) -> None:
"""
>>> _validate_dict({"b": 0.5}, "mock_name", float)
>>> _validate_dict("invalid", "mock_name", float)
Traceback (most recent call last):
...
ValueError: mock_name must be a dict
>>> _validate_dict({"a": 8}, "mock_name", dict)
Traceback (most recent call last):
...
ValueError: mock_name all values must be dict
>>> _validate_dict({2: 0.5}, "mock_name",float, True)
Traceback (most recent call last):
...
ValueError: mock_name all keys must be strings
>>> _validate_dict({"b": 4}, "mock_name", float,True)
Traceback (most recent call last):
...
ValueError: mock_name nested dictionary all values must be float
"""
if not isinstance(_object, dict):
msg = f"{var_name} must be a dict"
raise ValueError(msg)
if not all(isinstance(x, str) for x in _object):
msg = f"{var_name} all keys must be strings"
raise ValueError(msg)
if not all(isinstance(x, value_type) for x in _object.values()):
nested_text = "nested dictionary " if nested else ""
msg = f"{var_name} {nested_text}all values must be {value_type.__name__}"
raise ValueError(msg)
if __name__ == "__main__":
from doctest import testmod
testmod()
| from typing import Any
def viterbi(
observations_space: list,
states_space: list,
initial_probabilities: dict,
transition_probabilities: dict,
emission_probabilities: dict,
) -> list:
"""
Viterbi Algorithm, to find the most likely path of
states from the start and the expected output.
https://en.wikipedia.org/wiki/Viterbi_algorithm
sdafads
Wikipedia example
>>> observations = ["normal", "cold", "dizzy"]
>>> states = ["Healthy", "Fever"]
>>> start_p = {"Healthy": 0.6, "Fever": 0.4}
>>> trans_p = {
... "Healthy": {"Healthy": 0.7, "Fever": 0.3},
... "Fever": {"Healthy": 0.4, "Fever": 0.6},
... }
>>> emit_p = {
... "Healthy": {"normal": 0.5, "cold": 0.4, "dizzy": 0.1},
... "Fever": {"normal": 0.1, "cold": 0.3, "dizzy": 0.6},
... }
>>> viterbi(observations, states, start_p, trans_p, emit_p)
['Healthy', 'Healthy', 'Fever']
>>> viterbi((), states, start_p, trans_p, emit_p)
Traceback (most recent call last):
...
ValueError: There's an empty parameter
>>> viterbi(observations, (), start_p, trans_p, emit_p)
Traceback (most recent call last):
...
ValueError: There's an empty parameter
>>> viterbi(observations, states, {}, trans_p, emit_p)
Traceback (most recent call last):
...
ValueError: There's an empty parameter
>>> viterbi(observations, states, start_p, {}, emit_p)
Traceback (most recent call last):
...
ValueError: There's an empty parameter
>>> viterbi(observations, states, start_p, trans_p, {})
Traceback (most recent call last):
...
ValueError: There's an empty parameter
>>> viterbi("invalid", states, start_p, trans_p, emit_p)
Traceback (most recent call last):
...
ValueError: observations_space must be a list
>>> viterbi(["valid", 123], states, start_p, trans_p, emit_p)
Traceback (most recent call last):
...
ValueError: observations_space must be a list of strings
>>> viterbi(observations, "invalid", start_p, trans_p, emit_p)
Traceback (most recent call last):
...
ValueError: states_space must be a list
>>> viterbi(observations, ["valid", 123], start_p, trans_p, emit_p)
Traceback (most recent call last):
...
ValueError: states_space must be a list of strings
>>> viterbi(observations, states, "invalid", trans_p, emit_p)
Traceback (most recent call last):
...
ValueError: initial_probabilities must be a dict
>>> viterbi(observations, states, {2:2}, trans_p, emit_p)
Traceback (most recent call last):
...
ValueError: initial_probabilities all keys must be strings
>>> viterbi(observations, states, {"a":2}, trans_p, emit_p)
Traceback (most recent call last):
...
ValueError: initial_probabilities all values must be float
>>> viterbi(observations, states, start_p, "invalid", emit_p)
Traceback (most recent call last):
...
ValueError: transition_probabilities must be a dict
>>> viterbi(observations, states, start_p, {"a":2}, emit_p)
Traceback (most recent call last):
...
ValueError: transition_probabilities all values must be dict
>>> viterbi(observations, states, start_p, {2:{2:2}}, emit_p)
Traceback (most recent call last):
...
ValueError: transition_probabilities all keys must be strings
>>> viterbi(observations, states, start_p, {"a":{2:2}}, emit_p)
Traceback (most recent call last):
...
ValueError: transition_probabilities all keys must be strings
>>> viterbi(observations, states, start_p, {"a":{"b":2}}, emit_p)
Traceback (most recent call last):
...
ValueError: transition_probabilities nested dictionary all values must be float
>>> viterbi(observations, states, start_p, trans_p, "invalid")
Traceback (most recent call last):
...
ValueError: emission_probabilities must be a dict
>>> viterbi(observations, states, start_p, trans_p, None)
Traceback (most recent call last):
...
ValueError: There's an empty parameter
"""
_validation(
observations_space,
states_space,
initial_probabilities,
transition_probabilities,
emission_probabilities,
)
# Creates data structures and fill initial step
probabilities: dict = {}
pointers: dict = {}
for state in states_space:
observation = observations_space[0]
probabilities[(state, observation)] = (
initial_probabilities[state] * emission_probabilities[state][observation]
)
pointers[(state, observation)] = None
# Fills the data structure with the probabilities of
# different transitions and pointers to previous states
for o in range(1, len(observations_space)):
observation = observations_space[o]
prior_observation = observations_space[o - 1]
for state in states_space:
# Calculates the argmax for probability function
arg_max = ""
max_probability = -1
for k_state in states_space:
probability = (
probabilities[(k_state, prior_observation)]
* transition_probabilities[k_state][state]
* emission_probabilities[state][observation]
)
if probability > max_probability:
max_probability = probability
arg_max = k_state
# Update probabilities and pointers dicts
probabilities[(state, observation)] = (
probabilities[(arg_max, prior_observation)]
* transition_probabilities[arg_max][state]
* emission_probabilities[state][observation]
)
pointers[(state, observation)] = arg_max
# The final observation
final_observation = observations_space[len(observations_space) - 1]
# argmax for given final observation
arg_max = ""
max_probability = -1
for k_state in states_space:
probability = probabilities[(k_state, final_observation)]
if probability > max_probability:
max_probability = probability
arg_max = k_state
last_state = arg_max
# Process pointers backwards
previous = last_state
result = []
for o in range(len(observations_space) - 1, -1, -1):
result.append(previous)
previous = pointers[previous, observations_space[o]]
result.reverse()
return result
def _validation(
observations_space: Any,
states_space: Any,
initial_probabilities: Any,
transition_probabilities: Any,
emission_probabilities: Any,
) -> None:
"""
>>> observations = ["normal", "cold", "dizzy"]
>>> states = ["Healthy", "Fever"]
>>> start_p = {"Healthy": 0.6, "Fever": 0.4}
>>> trans_p = {
... "Healthy": {"Healthy": 0.7, "Fever": 0.3},
... "Fever": {"Healthy": 0.4, "Fever": 0.6},
... }
>>> emit_p = {
... "Healthy": {"normal": 0.5, "cold": 0.4, "dizzy": 0.1},
... "Fever": {"normal": 0.1, "cold": 0.3, "dizzy": 0.6},
... }
>>> _validation(observations, states, start_p, trans_p, emit_p)
>>> _validation([], states, start_p, trans_p, emit_p)
Traceback (most recent call last):
...
ValueError: There's an empty parameter
"""
_validate_not_empty(
observations_space,
states_space,
initial_probabilities,
transition_probabilities,
emission_probabilities,
)
_validate_lists(observations_space, states_space)
_validate_dicts(
initial_probabilities, transition_probabilities, emission_probabilities
)
def _validate_not_empty(
observations_space: Any,
states_space: Any,
initial_probabilities: Any,
transition_probabilities: Any,
emission_probabilities: Any,
) -> None:
"""
>>> _validate_not_empty(["a"], ["b"], {"c":0.5},
... {"d": {"e": 0.6}}, {"f": {"g": 0.7}})
>>> _validate_not_empty(["a"], ["b"], {"c":0.5}, {}, {"f": {"g": 0.7}})
Traceback (most recent call last):
...
ValueError: There's an empty parameter
>>> _validate_not_empty(["a"], ["b"], None, {"d": {"e": 0.6}}, {"f": {"g": 0.7}})
Traceback (most recent call last):
...
ValueError: There's an empty parameter
"""
if not all(
[
observations_space,
states_space,
initial_probabilities,
transition_probabilities,
emission_probabilities,
]
):
raise ValueError("There's an empty parameter")
def _validate_lists(observations_space: Any, states_space: Any) -> None:
"""
>>> _validate_lists(["a"], ["b"])
>>> _validate_lists(1234, ["b"])
Traceback (most recent call last):
...
ValueError: observations_space must be a list
>>> _validate_lists(["a"], [3])
Traceback (most recent call last):
...
ValueError: states_space must be a list of strings
"""
_validate_list(observations_space, "observations_space")
_validate_list(states_space, "states_space")
def _validate_list(_object: Any, var_name: str) -> None:
"""
>>> _validate_list(["a"], "mock_name")
>>> _validate_list("a", "mock_name")
Traceback (most recent call last):
...
ValueError: mock_name must be a list
>>> _validate_list([0.5], "mock_name")
Traceback (most recent call last):
...
ValueError: mock_name must be a list of strings
"""
if not isinstance(_object, list):
msg = f"{var_name} must be a list"
raise ValueError(msg)
else:
for x in _object:
if not isinstance(x, str):
msg = f"{var_name} must be a list of strings"
raise ValueError(msg)
def _validate_dicts(
initial_probabilities: Any,
transition_probabilities: Any,
emission_probabilities: Any,
) -> None:
"""
>>> _validate_dicts({"c":0.5}, {"d": {"e": 0.6}}, {"f": {"g": 0.7}})
>>> _validate_dicts("invalid", {"d": {"e": 0.6}}, {"f": {"g": 0.7}})
Traceback (most recent call last):
...
ValueError: initial_probabilities must be a dict
>>> _validate_dicts({"c":0.5}, {2: {"e": 0.6}}, {"f": {"g": 0.7}})
Traceback (most recent call last):
...
ValueError: transition_probabilities all keys must be strings
>>> _validate_dicts({"c":0.5}, {"d": {"e": 0.6}}, {"f": {2: 0.7}})
Traceback (most recent call last):
...
ValueError: emission_probabilities all keys must be strings
>>> _validate_dicts({"c":0.5}, {"d": {"e": 0.6}}, {"f": {"g": "h"}})
Traceback (most recent call last):
...
ValueError: emission_probabilities nested dictionary all values must be float
"""
_validate_dict(initial_probabilities, "initial_probabilities", float)
_validate_nested_dict(transition_probabilities, "transition_probabilities")
_validate_nested_dict(emission_probabilities, "emission_probabilities")
def _validate_nested_dict(_object: Any, var_name: str) -> None:
"""
>>> _validate_nested_dict({"a":{"b": 0.5}}, "mock_name")
>>> _validate_nested_dict("invalid", "mock_name")
Traceback (most recent call last):
...
ValueError: mock_name must be a dict
>>> _validate_nested_dict({"a": 8}, "mock_name")
Traceback (most recent call last):
...
ValueError: mock_name all values must be dict
>>> _validate_nested_dict({"a":{2: 0.5}}, "mock_name")
Traceback (most recent call last):
...
ValueError: mock_name all keys must be strings
>>> _validate_nested_dict({"a":{"b": 4}}, "mock_name")
Traceback (most recent call last):
...
ValueError: mock_name nested dictionary all values must be float
"""
_validate_dict(_object, var_name, dict)
for x in _object.values():
_validate_dict(x, var_name, float, True)
def _validate_dict(
_object: Any, var_name: str, value_type: type, nested: bool = False
) -> None:
"""
>>> _validate_dict({"b": 0.5}, "mock_name", float)
>>> _validate_dict("invalid", "mock_name", float)
Traceback (most recent call last):
...
ValueError: mock_name must be a dict
>>> _validate_dict({"a": 8}, "mock_name", dict)
Traceback (most recent call last):
...
ValueError: mock_name all values must be dict
>>> _validate_dict({2: 0.5}, "mock_name",float, True)
Traceback (most recent call last):
...
ValueError: mock_name all keys must be strings
>>> _validate_dict({"b": 4}, "mock_name", float,True)
Traceback (most recent call last):
...
ValueError: mock_name nested dictionary all values must be float
"""
if not isinstance(_object, dict):
msg = f"{var_name} must be a dict"
raise ValueError(msg)
if not all(isinstance(x, str) for x in _object):
msg = f"{var_name} all keys must be strings"
raise ValueError(msg)
if not all(isinstance(x, value_type) for x in _object.values()):
nested_text = "nested dictionary " if nested else ""
msg = f"{var_name} {nested_text}all values must be {value_type.__name__}"
raise ValueError(msg)
if __name__ == "__main__":
from doctest import testmod
testmod()
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| """For more information about the Binomial Distribution -
https://en.wikipedia.org/wiki/Binomial_distribution"""
from math import factorial
def binomial_distribution(successes: int, trials: int, prob: float) -> float:
"""
Return probability of k successes out of n tries, with p probability for one
success
The function uses the factorial function in order to calculate the binomial
coefficient
>>> binomial_distribution(3, 5, 0.7)
0.30870000000000003
>>> binomial_distribution (2, 4, 0.5)
0.375
"""
if successes > trials:
raise ValueError("""successes must be lower or equal to trials""")
if trials < 0 or successes < 0:
raise ValueError("the function is defined for non-negative integers")
if not isinstance(successes, int) or not isinstance(trials, int):
raise ValueError("the function is defined for non-negative integers")
if not 0 < prob < 1:
raise ValueError("prob has to be in range of 1 - 0")
probability = (prob**successes) * ((1 - prob) ** (trials - successes))
# Calculate the binomial coefficient: n! / k!(n-k)!
coefficient = float(factorial(trials))
coefficient /= factorial(successes) * factorial(trials - successes)
return probability * coefficient
if __name__ == "__main__":
from doctest import testmod
testmod()
print("Probability of 2 successes out of 4 trails")
print("with probability of 0.75 is:", end=" ")
print(binomial_distribution(2, 4, 0.75))
| """For more information about the Binomial Distribution -
https://en.wikipedia.org/wiki/Binomial_distribution"""
from math import factorial
def binomial_distribution(successes: int, trials: int, prob: float) -> float:
"""
Return probability of k successes out of n tries, with p probability for one
success
The function uses the factorial function in order to calculate the binomial
coefficient
>>> binomial_distribution(3, 5, 0.7)
0.30870000000000003
>>> binomial_distribution (2, 4, 0.5)
0.375
"""
if successes > trials:
raise ValueError("""successes must be lower or equal to trials""")
if trials < 0 or successes < 0:
raise ValueError("the function is defined for non-negative integers")
if not isinstance(successes, int) or not isinstance(trials, int):
raise ValueError("the function is defined for non-negative integers")
if not 0 < prob < 1:
raise ValueError("prob has to be in range of 1 - 0")
probability = (prob**successes) * ((1 - prob) ** (trials - successes))
# Calculate the binomial coefficient: n! / k!(n-k)!
coefficient = float(factorial(trials))
coefficient /= factorial(successes) * factorial(trials - successes)
return probability * coefficient
if __name__ == "__main__":
from doctest import testmod
testmod()
print("Probability of 2 successes out of 4 trails")
print("with probability of 0.75 is:", end=" ")
print(binomial_distribution(2, 4, 0.75))
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| from collections.abc import Sequence
def max_subsequence_sum(nums: Sequence[int] | None = None) -> int:
"""Return the maximum possible sum amongst all non - empty subsequences.
Raises:
ValueError: when nums is empty.
>>> max_subsequence_sum([1,2,3,4,-2])
10
>>> max_subsequence_sum([-2, -3, -1, -4, -6])
-1
>>> max_subsequence_sum([])
Traceback (most recent call last):
. . .
ValueError: Input sequence should not be empty
>>> max_subsequence_sum()
Traceback (most recent call last):
. . .
ValueError: Input sequence should not be empty
"""
if nums is None or not nums:
raise ValueError("Input sequence should not be empty")
ans = nums[0]
for i in range(1, len(nums)):
num = nums[i]
ans = max(ans, ans + num, num)
return ans
if __name__ == "__main__":
import doctest
doctest.testmod()
# Try on a sample input from the user
n = int(input("Enter number of elements : ").strip())
array = list(map(int, input("\nEnter the numbers : ").strip().split()))[:n]
print(max_subsequence_sum(array))
| from collections.abc import Sequence
def max_subsequence_sum(nums: Sequence[int] | None = None) -> int:
"""Return the maximum possible sum amongst all non - empty subsequences.
Raises:
ValueError: when nums is empty.
>>> max_subsequence_sum([1,2,3,4,-2])
10
>>> max_subsequence_sum([-2, -3, -1, -4, -6])
-1
>>> max_subsequence_sum([])
Traceback (most recent call last):
. . .
ValueError: Input sequence should not be empty
>>> max_subsequence_sum()
Traceback (most recent call last):
. . .
ValueError: Input sequence should not be empty
"""
if nums is None or not nums:
raise ValueError("Input sequence should not be empty")
ans = nums[0]
for i in range(1, len(nums)):
num = nums[i]
ans = max(ans, ans + num, num)
return ans
if __name__ == "__main__":
import doctest
doctest.testmod()
# Try on a sample input from the user
n = int(input("Enter number of elements : ").strip())
array = list(map(int, input("\nEnter the numbers : ").strip().split()))[:n]
print(max_subsequence_sum(array))
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| """
Given a sorted array of integers, return indices of the two numbers such
that they add up to a specific target using the two pointers technique.
You may assume that each input would have exactly one solution, and you
may not use the same element twice.
This is an alternative solution of the two-sum problem, which uses a
map to solve the problem. Hence can not solve the issue if there is a
constraint not use the same index twice. [1]
Example:
Given nums = [2, 7, 11, 15], target = 9,
Because nums[0] + nums[1] = 2 + 7 = 9,
return [0, 1].
[1]: https://github.com/TheAlgorithms/Python/blob/master/other/two_sum.py
"""
from __future__ import annotations
def two_pointer(nums: list[int], target: int) -> list[int]:
"""
>>> two_pointer([2, 7, 11, 15], 9)
[0, 1]
>>> two_pointer([2, 7, 11, 15], 17)
[0, 3]
>>> two_pointer([2, 7, 11, 15], 18)
[1, 2]
>>> two_pointer([2, 7, 11, 15], 26)
[2, 3]
>>> two_pointer([1, 3, 3], 6)
[1, 2]
>>> two_pointer([2, 7, 11, 15], 8)
[]
>>> two_pointer([3 * i for i in range(10)], 19)
[]
>>> two_pointer([1, 2, 3], 6)
[]
"""
i = 0
j = len(nums) - 1
while i < j:
if nums[i] + nums[j] == target:
return [i, j]
elif nums[i] + nums[j] < target:
i = i + 1
else:
j = j - 1
return []
if __name__ == "__main__":
import doctest
doctest.testmod()
print(f"{two_pointer([2, 7, 11, 15], 9) = }")
| """
Given a sorted array of integers, return indices of the two numbers such
that they add up to a specific target using the two pointers technique.
You may assume that each input would have exactly one solution, and you
may not use the same element twice.
This is an alternative solution of the two-sum problem, which uses a
map to solve the problem. Hence can not solve the issue if there is a
constraint not use the same index twice. [1]
Example:
Given nums = [2, 7, 11, 15], target = 9,
Because nums[0] + nums[1] = 2 + 7 = 9,
return [0, 1].
[1]: https://github.com/TheAlgorithms/Python/blob/master/other/two_sum.py
"""
from __future__ import annotations
def two_pointer(nums: list[int], target: int) -> list[int]:
"""
>>> two_pointer([2, 7, 11, 15], 9)
[0, 1]
>>> two_pointer([2, 7, 11, 15], 17)
[0, 3]
>>> two_pointer([2, 7, 11, 15], 18)
[1, 2]
>>> two_pointer([2, 7, 11, 15], 26)
[2, 3]
>>> two_pointer([1, 3, 3], 6)
[1, 2]
>>> two_pointer([2, 7, 11, 15], 8)
[]
>>> two_pointer([3 * i for i in range(10)], 19)
[]
>>> two_pointer([1, 2, 3], 6)
[]
"""
i = 0
j = len(nums) - 1
while i < j:
if nums[i] + nums[j] == target:
return [i, j]
elif nums[i] + nums[j] < target:
i = i + 1
else:
j = j - 1
return []
if __name__ == "__main__":
import doctest
doctest.testmod()
print(f"{two_pointer([2, 7, 11, 15], 9) = }")
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| """
Author: Alexander Joslin
GitHub: github.com/echoaj
Explanation: https://medium.com/@haleesammar/implemented-in-js-dijkstras-2-stack-
algorithm-for-evaluating-mathematical-expressions-fc0837dae1ea
We can use Dijkstra's two stack algorithm to solve an equation
such as: (5 + ((4 * 2) * (2 + 3)))
THESE ARE THE ALGORITHM'S RULES:
RULE 1: Scan the expression from left to right. When an operand is encountered,
push it onto the operand stack.
RULE 2: When an operator is encountered in the expression,
push it onto the operator stack.
RULE 3: When a left parenthesis is encountered in the expression, ignore it.
RULE 4: When a right parenthesis is encountered in the expression,
pop an operator off the operator stack. The two operands it must
operate on must be the last two operands pushed onto the operand stack.
We therefore pop the operand stack twice, perform the operation,
and push the result back onto the operand stack so it will be available
for use as an operand of the next operator popped off the operator stack.
RULE 5: When the entire infix expression has been scanned, the value left on
the operand stack represents the value of the expression.
NOTE: It only works with whole numbers.
"""
__author__ = "Alexander Joslin"
import operator as op
from .stack import Stack
def dijkstras_two_stack_algorithm(equation: str) -> int:
"""
DocTests
>>> dijkstras_two_stack_algorithm("(5 + 3)")
8
>>> dijkstras_two_stack_algorithm("((9 - (2 + 9)) + (8 - 1))")
5
>>> dijkstras_two_stack_algorithm("((((3 - 2) - (2 + 3)) + (2 - 4)) + 3)")
-3
:param equation: a string
:return: result: an integer
"""
operators = {"*": op.mul, "/": op.truediv, "+": op.add, "-": op.sub}
operand_stack: Stack[int] = Stack()
operator_stack: Stack[str] = Stack()
for i in equation:
if i.isdigit():
# RULE 1
operand_stack.push(int(i))
elif i in operators:
# RULE 2
operator_stack.push(i)
elif i == ")":
# RULE 4
opr = operator_stack.peek()
operator_stack.pop()
num1 = operand_stack.peek()
operand_stack.pop()
num2 = operand_stack.peek()
operand_stack.pop()
total = operators[opr](num2, num1)
operand_stack.push(total)
# RULE 5
return operand_stack.peek()
if __name__ == "__main__":
equation = "(5 + ((4 * 2) * (2 + 3)))"
# answer = 45
print(f"{equation} = {dijkstras_two_stack_algorithm(equation)}")
| """
Author: Alexander Joslin
GitHub: github.com/echoaj
Explanation: https://medium.com/@haleesammar/implemented-in-js-dijkstras-2-stack-
algorithm-for-evaluating-mathematical-expressions-fc0837dae1ea
We can use Dijkstra's two stack algorithm to solve an equation
such as: (5 + ((4 * 2) * (2 + 3)))
THESE ARE THE ALGORITHM'S RULES:
RULE 1: Scan the expression from left to right. When an operand is encountered,
push it onto the operand stack.
RULE 2: When an operator is encountered in the expression,
push it onto the operator stack.
RULE 3: When a left parenthesis is encountered in the expression, ignore it.
RULE 4: When a right parenthesis is encountered in the expression,
pop an operator off the operator stack. The two operands it must
operate on must be the last two operands pushed onto the operand stack.
We therefore pop the operand stack twice, perform the operation,
and push the result back onto the operand stack so it will be available
for use as an operand of the next operator popped off the operator stack.
RULE 5: When the entire infix expression has been scanned, the value left on
the operand stack represents the value of the expression.
NOTE: It only works with whole numbers.
"""
__author__ = "Alexander Joslin"
import operator as op
from .stack import Stack
def dijkstras_two_stack_algorithm(equation: str) -> int:
"""
DocTests
>>> dijkstras_two_stack_algorithm("(5 + 3)")
8
>>> dijkstras_two_stack_algorithm("((9 - (2 + 9)) + (8 - 1))")
5
>>> dijkstras_two_stack_algorithm("((((3 - 2) - (2 + 3)) + (2 - 4)) + 3)")
-3
:param equation: a string
:return: result: an integer
"""
operators = {"*": op.mul, "/": op.truediv, "+": op.add, "-": op.sub}
operand_stack: Stack[int] = Stack()
operator_stack: Stack[str] = Stack()
for i in equation:
if i.isdigit():
# RULE 1
operand_stack.push(int(i))
elif i in operators:
# RULE 2
operator_stack.push(i)
elif i == ")":
# RULE 4
opr = operator_stack.peek()
operator_stack.pop()
num1 = operand_stack.peek()
operand_stack.pop()
num2 = operand_stack.peek()
operand_stack.pop()
total = operators[opr](num2, num1)
operand_stack.push(total)
# RULE 5
return operand_stack.peek()
if __name__ == "__main__":
equation = "(5 + ((4 * 2) * (2 + 3)))"
# answer = 45
print(f"{equation} = {dijkstras_two_stack_algorithm(equation)}")
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| -1 |
||
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| """
Lychrel numbers
Problem 55: https://projecteuler.net/problem=55
If we take 47, reverse and add, 47 + 74 = 121, which is palindromic.
Not all numbers produce palindromes so quickly. For example,
349 + 943 = 1292,
1292 + 2921 = 4213
4213 + 3124 = 7337
That is, 349 took three iterations to arrive at a palindrome.
Although no one has proved it yet, it is thought that some numbers, like 196,
never produce a palindrome. A number that never forms a palindrome through the
reverse and add process is called a Lychrel number. Due to the theoretical nature
of these numbers, and for the purpose of this problem, we shall assume that a number
is Lychrel until proven otherwise. In addition you are given that for every number
below ten-thousand, it will either (i) become a palindrome in less than fifty
iterations, or, (ii) no one, with all the computing power that exists, has managed
so far to map it to a palindrome. In fact, 10677 is the first number to be shown
to require over fifty iterations before producing a palindrome:
4668731596684224866951378664 (53 iterations, 28-digits).
Surprisingly, there are palindromic numbers that are themselves Lychrel numbers;
the first example is 4994.
How many Lychrel numbers are there below ten-thousand?
"""
def is_palindrome(n: int) -> bool:
"""
Returns True if a number is palindrome.
>>> is_palindrome(12567321)
False
>>> is_palindrome(1221)
True
>>> is_palindrome(9876789)
True
"""
return str(n) == str(n)[::-1]
def sum_reverse(n: int) -> int:
"""
Returns the sum of n and reverse of n.
>>> sum_reverse(123)
444
>>> sum_reverse(3478)
12221
>>> sum_reverse(12)
33
"""
return int(n) + int(str(n)[::-1])
def solution(limit: int = 10000) -> int:
"""
Returns the count of all lychrel numbers below limit.
>>> solution(10000)
249
>>> solution(5000)
76
>>> solution(1000)
13
"""
lychrel_nums = []
for num in range(1, limit):
iterations = 0
a = num
while iterations < 50:
num = sum_reverse(num)
iterations += 1
if is_palindrome(num):
break
else:
lychrel_nums.append(a)
return len(lychrel_nums)
if __name__ == "__main__":
print(f"{solution() = }")
| """
Lychrel numbers
Problem 55: https://projecteuler.net/problem=55
If we take 47, reverse and add, 47 + 74 = 121, which is palindromic.
Not all numbers produce palindromes so quickly. For example,
349 + 943 = 1292,
1292 + 2921 = 4213
4213 + 3124 = 7337
That is, 349 took three iterations to arrive at a palindrome.
Although no one has proved it yet, it is thought that some numbers, like 196,
never produce a palindrome. A number that never forms a palindrome through the
reverse and add process is called a Lychrel number. Due to the theoretical nature
of these numbers, and for the purpose of this problem, we shall assume that a number
is Lychrel until proven otherwise. In addition you are given that for every number
below ten-thousand, it will either (i) become a palindrome in less than fifty
iterations, or, (ii) no one, with all the computing power that exists, has managed
so far to map it to a palindrome. In fact, 10677 is the first number to be shown
to require over fifty iterations before producing a palindrome:
4668731596684224866951378664 (53 iterations, 28-digits).
Surprisingly, there are palindromic numbers that are themselves Lychrel numbers;
the first example is 4994.
How many Lychrel numbers are there below ten-thousand?
"""
def is_palindrome(n: int) -> bool:
"""
Returns True if a number is palindrome.
>>> is_palindrome(12567321)
False
>>> is_palindrome(1221)
True
>>> is_palindrome(9876789)
True
"""
return str(n) == str(n)[::-1]
def sum_reverse(n: int) -> int:
"""
Returns the sum of n and reverse of n.
>>> sum_reverse(123)
444
>>> sum_reverse(3478)
12221
>>> sum_reverse(12)
33
"""
return int(n) + int(str(n)[::-1])
def solution(limit: int = 10000) -> int:
"""
Returns the count of all lychrel numbers below limit.
>>> solution(10000)
249
>>> solution(5000)
76
>>> solution(1000)
13
"""
lychrel_nums = []
for num in range(1, limit):
iterations = 0
a = num
while iterations < 50:
num = sum_reverse(num)
iterations += 1
if is_palindrome(num):
break
else:
lychrel_nums.append(a)
return len(lychrel_nums)
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| """
This script demonstrates the implementation of the ReLU function.
It's a kind of activation function defined as the positive part of its argument in the
context of neural network.
The function takes a vector of K real numbers as input and then argmax(x, 0).
After through ReLU, the element of the vector always 0 or real number.
Script inspired from its corresponding Wikipedia article
https://en.wikipedia.org/wiki/Rectifier_(neural_networks)
"""
from __future__ import annotations
import numpy as np
def relu(vector: list[float]):
"""
Implements the relu function
Parameters:
vector (np.array,list,tuple): A numpy array of shape (1,n)
consisting of real values or a similar list,tuple
Returns:
relu_vec (np.array): The input numpy array, after applying
relu.
>>> vec = np.array([-1, 0, 5])
>>> relu(vec)
array([0, 0, 5])
"""
# compare two arrays and then return element-wise maxima.
return np.maximum(0, vector)
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| """
This script demonstrates the implementation of the ReLU function.
It's a kind of activation function defined as the positive part of its argument in the
context of neural network.
The function takes a vector of K real numbers as input and then argmax(x, 0).
After through ReLU, the element of the vector always 0 or real number.
Script inspired from its corresponding Wikipedia article
https://en.wikipedia.org/wiki/Rectifier_(neural_networks)
"""
from __future__ import annotations
import numpy as np
def relu(vector: list[float]):
"""
Implements the relu function
Parameters:
vector (np.array,list,tuple): A numpy array of shape (1,n)
consisting of real values or a similar list,tuple
Returns:
relu_vec (np.array): The input numpy array, after applying
relu.
>>> vec = np.array([-1, 0, 5])
>>> relu(vec)
array([0, 0, 5])
"""
# compare two arrays and then return element-wise maxima.
return np.maximum(0, vector)
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| # https://en.wikipedia.org/wiki/Fizz_buzz#Programming
def fizz_buzz(number: int, iterations: int) -> str:
"""
Plays FizzBuzz.
Prints Fizz if number is a multiple of 3.
Prints Buzz if its a multiple of 5.
Prints FizzBuzz if its a multiple of both 3 and 5 or 15.
Else Prints The Number Itself.
>>> fizz_buzz(1,7)
'1 2 Fizz 4 Buzz Fizz 7 '
>>> fizz_buzz(1,0)
Traceback (most recent call last):
...
ValueError: Iterations must be done more than 0 times to play FizzBuzz
>>> fizz_buzz(-5,5)
Traceback (most recent call last):
...
ValueError: starting number must be
and integer and be more than 0
>>> fizz_buzz(10,-5)
Traceback (most recent call last):
...
ValueError: Iterations must be done more than 0 times to play FizzBuzz
>>> fizz_buzz(1.5,5)
Traceback (most recent call last):
...
ValueError: starting number must be
and integer and be more than 0
>>> fizz_buzz(1,5.5)
Traceback (most recent call last):
...
ValueError: iterations must be defined as integers
"""
if not isinstance(iterations, int):
raise ValueError("iterations must be defined as integers")
if not isinstance(number, int) or not number >= 1:
raise ValueError(
"""starting number must be
and integer and be more than 0"""
)
if not iterations >= 1:
raise ValueError("Iterations must be done more than 0 times to play FizzBuzz")
out = ""
while number <= iterations:
if number % 3 == 0:
out += "Fizz"
if number % 5 == 0:
out += "Buzz"
if 0 not in (number % 3, number % 5):
out += str(number)
# print(out)
number += 1
out += " "
return out
if __name__ == "__main__":
import doctest
doctest.testmod()
| # https://en.wikipedia.org/wiki/Fizz_buzz#Programming
def fizz_buzz(number: int, iterations: int) -> str:
"""
Plays FizzBuzz.
Prints Fizz if number is a multiple of 3.
Prints Buzz if its a multiple of 5.
Prints FizzBuzz if its a multiple of both 3 and 5 or 15.
Else Prints The Number Itself.
>>> fizz_buzz(1,7)
'1 2 Fizz 4 Buzz Fizz 7 '
>>> fizz_buzz(1,0)
Traceback (most recent call last):
...
ValueError: Iterations must be done more than 0 times to play FizzBuzz
>>> fizz_buzz(-5,5)
Traceback (most recent call last):
...
ValueError: starting number must be
and integer and be more than 0
>>> fizz_buzz(10,-5)
Traceback (most recent call last):
...
ValueError: Iterations must be done more than 0 times to play FizzBuzz
>>> fizz_buzz(1.5,5)
Traceback (most recent call last):
...
ValueError: starting number must be
and integer and be more than 0
>>> fizz_buzz(1,5.5)
Traceback (most recent call last):
...
ValueError: iterations must be defined as integers
"""
if not isinstance(iterations, int):
raise ValueError("iterations must be defined as integers")
if not isinstance(number, int) or not number >= 1:
raise ValueError(
"""starting number must be
and integer and be more than 0"""
)
if not iterations >= 1:
raise ValueError("Iterations must be done more than 0 times to play FizzBuzz")
out = ""
while number <= iterations:
if number % 3 == 0:
out += "Fizz"
if number % 5 == 0:
out += "Buzz"
if 0 not in (number % 3, number % 5):
out += str(number)
# print(out)
number += 1
out += " "
return out
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| """
Harmonic mean
Reference: https://en.wikipedia.org/wiki/Harmonic_mean
Harmonic series
Reference: https://en.wikipedia.org/wiki/Harmonic_series(mathematics)
"""
def is_harmonic_series(series: list) -> bool:
"""
checking whether the input series is arithmetic series or not
>>> is_harmonic_series([ 1, 2/3, 1/2, 2/5, 1/3])
True
>>> is_harmonic_series([ 1, 2/3, 2/5, 1/3])
False
>>> is_harmonic_series([1, 2, 3])
False
>>> is_harmonic_series([1/2, 1/3, 1/4])
True
>>> is_harmonic_series([2/5, 2/10, 2/15, 2/20, 2/25])
True
>>> is_harmonic_series(4)
Traceback (most recent call last):
...
ValueError: Input series is not valid, valid series - [1, 2/3, 2]
>>> is_harmonic_series([])
Traceback (most recent call last):
...
ValueError: Input list must be a non empty list
>>> is_harmonic_series([0])
Traceback (most recent call last):
...
ValueError: Input series cannot have 0 as an element
>>> is_harmonic_series([1,2,0,6])
Traceback (most recent call last):
...
ValueError: Input series cannot have 0 as an element
"""
if not isinstance(series, list):
raise ValueError("Input series is not valid, valid series - [1, 2/3, 2]")
if len(series) == 0:
raise ValueError("Input list must be a non empty list")
if len(series) == 1 and series[0] != 0:
return True
rec_series = []
series_len = len(series)
for i in range(0, series_len):
if series[i] == 0:
raise ValueError("Input series cannot have 0 as an element")
rec_series.append(1 / series[i])
common_diff = rec_series[1] - rec_series[0]
for index in range(2, series_len):
if rec_series[index] - rec_series[index - 1] != common_diff:
return False
return True
def harmonic_mean(series: list) -> float:
"""
return the harmonic mean of series
>>> harmonic_mean([1, 4, 4])
2.0
>>> harmonic_mean([3, 6, 9, 12])
5.759999999999999
>>> harmonic_mean(4)
Traceback (most recent call last):
...
ValueError: Input series is not valid, valid series - [2, 4, 6]
>>> harmonic_mean([1, 2, 3])
1.6363636363636365
>>> harmonic_mean([])
Traceback (most recent call last):
...
ValueError: Input list must be a non empty list
"""
if not isinstance(series, list):
raise ValueError("Input series is not valid, valid series - [2, 4, 6]")
if len(series) == 0:
raise ValueError("Input list must be a non empty list")
answer = 0
for val in series:
answer += 1 / val
return len(series) / answer
if __name__ == "__main__":
import doctest
doctest.testmod()
| """
Harmonic mean
Reference: https://en.wikipedia.org/wiki/Harmonic_mean
Harmonic series
Reference: https://en.wikipedia.org/wiki/Harmonic_series(mathematics)
"""
def is_harmonic_series(series: list) -> bool:
"""
checking whether the input series is arithmetic series or not
>>> is_harmonic_series([ 1, 2/3, 1/2, 2/5, 1/3])
True
>>> is_harmonic_series([ 1, 2/3, 2/5, 1/3])
False
>>> is_harmonic_series([1, 2, 3])
False
>>> is_harmonic_series([1/2, 1/3, 1/4])
True
>>> is_harmonic_series([2/5, 2/10, 2/15, 2/20, 2/25])
True
>>> is_harmonic_series(4)
Traceback (most recent call last):
...
ValueError: Input series is not valid, valid series - [1, 2/3, 2]
>>> is_harmonic_series([])
Traceback (most recent call last):
...
ValueError: Input list must be a non empty list
>>> is_harmonic_series([0])
Traceback (most recent call last):
...
ValueError: Input series cannot have 0 as an element
>>> is_harmonic_series([1,2,0,6])
Traceback (most recent call last):
...
ValueError: Input series cannot have 0 as an element
"""
if not isinstance(series, list):
raise ValueError("Input series is not valid, valid series - [1, 2/3, 2]")
if len(series) == 0:
raise ValueError("Input list must be a non empty list")
if len(series) == 1 and series[0] != 0:
return True
rec_series = []
series_len = len(series)
for i in range(0, series_len):
if series[i] == 0:
raise ValueError("Input series cannot have 0 as an element")
rec_series.append(1 / series[i])
common_diff = rec_series[1] - rec_series[0]
for index in range(2, series_len):
if rec_series[index] - rec_series[index - 1] != common_diff:
return False
return True
def harmonic_mean(series: list) -> float:
"""
return the harmonic mean of series
>>> harmonic_mean([1, 4, 4])
2.0
>>> harmonic_mean([3, 6, 9, 12])
5.759999999999999
>>> harmonic_mean(4)
Traceback (most recent call last):
...
ValueError: Input series is not valid, valid series - [2, 4, 6]
>>> harmonic_mean([1, 2, 3])
1.6363636363636365
>>> harmonic_mean([])
Traceback (most recent call last):
...
ValueError: Input list must be a non empty list
"""
if not isinstance(series, list):
raise ValueError("Input series is not valid, valid series - [2, 4, 6]")
if len(series) == 0:
raise ValueError("Input list must be a non empty list")
answer = 0
for val in series:
answer += 1 / val
return len(series) / answer
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| # Min heap data structure
# with decrease key functionality - in O(log(n)) time
class Node:
def __init__(self, name, val):
self.name = name
self.val = val
def __str__(self):
return f"{self.__class__.__name__}({self.name}, {self.val})"
def __lt__(self, other):
return self.val < other.val
class MinHeap:
"""
>>> r = Node("R", -1)
>>> b = Node("B", 6)
>>> a = Node("A", 3)
>>> x = Node("X", 1)
>>> e = Node("E", 4)
>>> print(b)
Node(B, 6)
>>> myMinHeap = MinHeap([r, b, a, x, e])
>>> myMinHeap.decrease_key(b, -17)
>>> print(b)
Node(B, -17)
>>> myMinHeap["B"]
-17
"""
def __init__(self, array):
self.idx_of_element = {}
self.heap_dict = {}
self.heap = self.build_heap(array)
def __getitem__(self, key):
return self.get_value(key)
def get_parent_idx(self, idx):
return (idx - 1) // 2
def get_left_child_idx(self, idx):
return idx * 2 + 1
def get_right_child_idx(self, idx):
return idx * 2 + 2
def get_value(self, key):
return self.heap_dict[key]
def build_heap(self, array):
last_idx = len(array) - 1
start_from = self.get_parent_idx(last_idx)
for idx, i in enumerate(array):
self.idx_of_element[i] = idx
self.heap_dict[i.name] = i.val
for i in range(start_from, -1, -1):
self.sift_down(i, array)
return array
# this is min-heapify method
def sift_down(self, idx, array):
while True:
l = self.get_left_child_idx(idx) # noqa: E741
r = self.get_right_child_idx(idx)
smallest = idx
if l < len(array) and array[l] < array[idx]:
smallest = l
if r < len(array) and array[r] < array[smallest]:
smallest = r
if smallest != idx:
array[idx], array[smallest] = array[smallest], array[idx]
(
self.idx_of_element[array[idx]],
self.idx_of_element[array[smallest]],
) = (
self.idx_of_element[array[smallest]],
self.idx_of_element[array[idx]],
)
idx = smallest
else:
break
def sift_up(self, idx):
p = self.get_parent_idx(idx)
while p >= 0 and self.heap[p] > self.heap[idx]:
self.heap[p], self.heap[idx] = self.heap[idx], self.heap[p]
self.idx_of_element[self.heap[p]], self.idx_of_element[self.heap[idx]] = (
self.idx_of_element[self.heap[idx]],
self.idx_of_element[self.heap[p]],
)
idx = p
p = self.get_parent_idx(idx)
def peek(self):
return self.heap[0]
def remove(self):
self.heap[0], self.heap[-1] = self.heap[-1], self.heap[0]
self.idx_of_element[self.heap[0]], self.idx_of_element[self.heap[-1]] = (
self.idx_of_element[self.heap[-1]],
self.idx_of_element[self.heap[0]],
)
x = self.heap.pop()
del self.idx_of_element[x]
self.sift_down(0, self.heap)
return x
def insert(self, node):
self.heap.append(node)
self.idx_of_element[node] = len(self.heap) - 1
self.heap_dict[node.name] = node.val
self.sift_up(len(self.heap) - 1)
def is_empty(self):
return len(self.heap) == 0
def decrease_key(self, node, new_value):
assert (
self.heap[self.idx_of_element[node]].val > new_value
), "newValue must be less that current value"
node.val = new_value
self.heap_dict[node.name] = new_value
self.sift_up(self.idx_of_element[node])
# USAGE
r = Node("R", -1)
b = Node("B", 6)
a = Node("A", 3)
x = Node("X", 1)
e = Node("E", 4)
# Use one of these two ways to generate Min-Heap
# Generating Min-Heap from array
my_min_heap = MinHeap([r, b, a, x, e])
# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)
# Before
print("Min Heap - before decrease key")
for i in my_min_heap.heap:
print(i)
print("Min Heap - After decrease key of node [B -> -17]")
my_min_heap.decrease_key(b, -17)
# After
for i in my_min_heap.heap:
print(i)
if __name__ == "__main__":
import doctest
doctest.testmod()
| # Min heap data structure
# with decrease key functionality - in O(log(n)) time
class Node:
def __init__(self, name, val):
self.name = name
self.val = val
def __str__(self):
return f"{self.__class__.__name__}({self.name}, {self.val})"
def __lt__(self, other):
return self.val < other.val
class MinHeap:
"""
>>> r = Node("R", -1)
>>> b = Node("B", 6)
>>> a = Node("A", 3)
>>> x = Node("X", 1)
>>> e = Node("E", 4)
>>> print(b)
Node(B, 6)
>>> myMinHeap = MinHeap([r, b, a, x, e])
>>> myMinHeap.decrease_key(b, -17)
>>> print(b)
Node(B, -17)
>>> myMinHeap["B"]
-17
"""
def __init__(self, array):
self.idx_of_element = {}
self.heap_dict = {}
self.heap = self.build_heap(array)
def __getitem__(self, key):
return self.get_value(key)
def get_parent_idx(self, idx):
return (idx - 1) // 2
def get_left_child_idx(self, idx):
return idx * 2 + 1
def get_right_child_idx(self, idx):
return idx * 2 + 2
def get_value(self, key):
return self.heap_dict[key]
def build_heap(self, array):
last_idx = len(array) - 1
start_from = self.get_parent_idx(last_idx)
for idx, i in enumerate(array):
self.idx_of_element[i] = idx
self.heap_dict[i.name] = i.val
for i in range(start_from, -1, -1):
self.sift_down(i, array)
return array
# this is min-heapify method
def sift_down(self, idx, array):
while True:
l = self.get_left_child_idx(idx) # noqa: E741
r = self.get_right_child_idx(idx)
smallest = idx
if l < len(array) and array[l] < array[idx]:
smallest = l
if r < len(array) and array[r] < array[smallest]:
smallest = r
if smallest != idx:
array[idx], array[smallest] = array[smallest], array[idx]
(
self.idx_of_element[array[idx]],
self.idx_of_element[array[smallest]],
) = (
self.idx_of_element[array[smallest]],
self.idx_of_element[array[idx]],
)
idx = smallest
else:
break
def sift_up(self, idx):
p = self.get_parent_idx(idx)
while p >= 0 and self.heap[p] > self.heap[idx]:
self.heap[p], self.heap[idx] = self.heap[idx], self.heap[p]
self.idx_of_element[self.heap[p]], self.idx_of_element[self.heap[idx]] = (
self.idx_of_element[self.heap[idx]],
self.idx_of_element[self.heap[p]],
)
idx = p
p = self.get_parent_idx(idx)
def peek(self):
return self.heap[0]
def remove(self):
self.heap[0], self.heap[-1] = self.heap[-1], self.heap[0]
self.idx_of_element[self.heap[0]], self.idx_of_element[self.heap[-1]] = (
self.idx_of_element[self.heap[-1]],
self.idx_of_element[self.heap[0]],
)
x = self.heap.pop()
del self.idx_of_element[x]
self.sift_down(0, self.heap)
return x
def insert(self, node):
self.heap.append(node)
self.idx_of_element[node] = len(self.heap) - 1
self.heap_dict[node.name] = node.val
self.sift_up(len(self.heap) - 1)
def is_empty(self):
return len(self.heap) == 0
def decrease_key(self, node, new_value):
assert (
self.heap[self.idx_of_element[node]].val > new_value
), "newValue must be less that current value"
node.val = new_value
self.heap_dict[node.name] = new_value
self.sift_up(self.idx_of_element[node])
# USAGE
r = Node("R", -1)
b = Node("B", 6)
a = Node("A", 3)
x = Node("X", 1)
e = Node("E", 4)
# Use one of these two ways to generate Min-Heap
# Generating Min-Heap from array
my_min_heap = MinHeap([r, b, a, x, e])
# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)
# Before
print("Min Heap - before decrease key")
for i in my_min_heap.heap:
print(i)
print("Min Heap - After decrease key of node [B -> -17]")
my_min_heap.decrease_key(b, -17)
# After
for i in my_min_heap.heap:
print(i)
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| """
- - - - - -- - - - - - - - - - - - - - - - - - - - - - -
Name - - CNN - Convolution Neural Network For Photo Recognizing
Goal - - Recognize Handing Writing Word Photo
Detail：Total 5 layers neural network
* Convolution layer
* Pooling layer
* Input layer layer of BP
* Hidden layer of BP
* Output layer of BP
Author: Stephen Lee
Github: [email protected]
Date: 2017.9.20
- - - - - -- - - - - - - - - - - - - - - - - - - - - - -
"""
import pickle
import numpy as np
from matplotlib import pyplot as plt
class CNN:
def __init__(
self, conv1_get, size_p1, bp_num1, bp_num2, bp_num3, rate_w=0.2, rate_t=0.2
):
"""
:param conv1_get: [a,c,d]，size, number, step of convolution kernel
:param size_p1: pooling size
:param bp_num1: units number of flatten layer
:param bp_num2: units number of hidden layer
:param bp_num3: units number of output layer
:param rate_w: rate of weight learning
:param rate_t: rate of threshold learning
"""
self.num_bp1 = bp_num1
self.num_bp2 = bp_num2
self.num_bp3 = bp_num3
self.conv1 = conv1_get[:2]
self.step_conv1 = conv1_get[2]
self.size_pooling1 = size_p1
self.rate_weight = rate_w
self.rate_thre = rate_t
self.w_conv1 = [
np.mat(-1 * np.random.rand(self.conv1[0], self.conv1[0]) + 0.5)
for i in range(self.conv1[1])
]
self.wkj = np.mat(-1 * np.random.rand(self.num_bp3, self.num_bp2) + 0.5)
self.vji = np.mat(-1 * np.random.rand(self.num_bp2, self.num_bp1) + 0.5)
self.thre_conv1 = -2 * np.random.rand(self.conv1[1]) + 1
self.thre_bp2 = -2 * np.random.rand(self.num_bp2) + 1
self.thre_bp3 = -2 * np.random.rand(self.num_bp3) + 1
def save_model(self, save_path):
# save model dict with pickle
model_dic = {
"num_bp1": self.num_bp1,
"num_bp2": self.num_bp2,
"num_bp3": self.num_bp3,
"conv1": self.conv1,
"step_conv1": self.step_conv1,
"size_pooling1": self.size_pooling1,
"rate_weight": self.rate_weight,
"rate_thre": self.rate_thre,
"w_conv1": self.w_conv1,
"wkj": self.wkj,
"vji": self.vji,
"thre_conv1": self.thre_conv1,
"thre_bp2": self.thre_bp2,
"thre_bp3": self.thre_bp3,
}
with open(save_path, "wb") as f:
pickle.dump(model_dic, f)
print(f"Model saved： {save_path}")
@classmethod
def read_model(cls, model_path):
# read saved model
with open(model_path, "rb") as f:
model_dic = pickle.load(f) # noqa: S301
conv_get = model_dic.get("conv1")
conv_get.append(model_dic.get("step_conv1"))
size_p1 = model_dic.get("size_pooling1")
bp1 = model_dic.get("num_bp1")
bp2 = model_dic.get("num_bp2")
bp3 = model_dic.get("num_bp3")
r_w = model_dic.get("rate_weight")
r_t = model_dic.get("rate_thre")
# create model instance
conv_ins = CNN(conv_get, size_p1, bp1, bp2, bp3, r_w, r_t)
# modify model parameter
conv_ins.w_conv1 = model_dic.get("w_conv1")
conv_ins.wkj = model_dic.get("wkj")
conv_ins.vji = model_dic.get("vji")
conv_ins.thre_conv1 = model_dic.get("thre_conv1")
conv_ins.thre_bp2 = model_dic.get("thre_bp2")
conv_ins.thre_bp3 = model_dic.get("thre_bp3")
return conv_ins
def sig(self, x):
return 1 / (1 + np.exp(-1 * x))
def do_round(self, x):
return round(x, 3)
def convolute(self, data, convs, w_convs, thre_convs, conv_step):
# convolution process
size_conv = convs[0]
num_conv = convs[1]
size_data = np.shape(data)[0]
# get the data slice of original image data, data_focus
data_focus = []
for i_focus in range(0, size_data - size_conv + 1, conv_step):
for j_focus in range(0, size_data - size_conv + 1, conv_step):
focus = data[
i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
]
data_focus.append(focus)
# calculate the feature map of every single kernel, and saved as list of matrix
data_featuremap = []
size_feature_map = int((size_data - size_conv) / conv_step + 1)
for i_map in range(num_conv):
featuremap = []
for i_focus in range(len(data_focus)):
net_focus = (
np.sum(np.multiply(data_focus[i_focus], w_convs[i_map]))
- thre_convs[i_map]
)
featuremap.append(self.sig(net_focus))
featuremap = np.asmatrix(featuremap).reshape(
size_feature_map, size_feature_map
)
data_featuremap.append(featuremap)
# expanding the data slice to One dimenssion
focus1_list = []
for each_focus in data_focus:
focus1_list.extend(self.Expand_Mat(each_focus))
focus_list = np.asarray(focus1_list)
return focus_list, data_featuremap
def pooling(self, featuremaps, size_pooling, pooling_type="average_pool"):
# pooling process
size_map = len(featuremaps[0])
size_pooled = int(size_map / size_pooling)
featuremap_pooled = []
for i_map in range(len(featuremaps)):
feature_map = featuremaps[i_map]
map_pooled = []
for i_focus in range(0, size_map, size_pooling):
for j_focus in range(0, size_map, size_pooling):
focus = feature_map[
i_focus : i_focus + size_pooling,
j_focus : j_focus + size_pooling,
]
if pooling_type == "average_pool":
# average pooling
map_pooled.append(np.average(focus))
elif pooling_type == "max_pooling":
# max pooling
map_pooled.append(np.max(focus))
map_pooled = np.asmatrix(map_pooled).reshape(size_pooled, size_pooled)
featuremap_pooled.append(map_pooled)
return featuremap_pooled
def _expand(self, data):
# expanding three dimension data to one dimension list
data_expanded = []
for i in range(len(data)):
shapes = np.shape(data[i])
data_listed = data[i].reshape(1, shapes[0] * shapes[1])
data_listed = data_listed.getA().tolist()[0]
data_expanded.extend(data_listed)
data_expanded = np.asarray(data_expanded)
return data_expanded
def _expand_mat(self, data_mat):
# expanding matrix to one dimension list
data_mat = np.asarray(data_mat)
shapes = np.shape(data_mat)
data_expanded = data_mat.reshape(1, shapes[0] * shapes[1])
return data_expanded
def _calculate_gradient_from_pool(
self, out_map, pd_pool, num_map, size_map, size_pooling
):
"""
calculate the gradient from the data slice of pool layer
pd_pool: list of matrix
out_map: the shape of data slice(size_map*size_map)
return: pd_all: list of matrix, [num, size_map, size_map]
"""
pd_all = []
i_pool = 0
for i_map in range(num_map):
pd_conv1 = np.ones((size_map, size_map))
for i in range(0, size_map, size_pooling):
for j in range(0, size_map, size_pooling):
pd_conv1[i : i + size_pooling, j : j + size_pooling] = pd_pool[
i_pool
]
i_pool = i_pool + 1
pd_conv2 = np.multiply(
pd_conv1, np.multiply(out_map[i_map], (1 - out_map[i_map]))
)
pd_all.append(pd_conv2)
return pd_all
def train(
self, patterns, datas_train, datas_teach, n_repeat, error_accuracy, draw_e=bool
):
# model traning
print("----------------------Start Training-------------------------")
print((" - - Shape: Train_Data ", np.shape(datas_train)))
print((" - - Shape: Teach_Data ", np.shape(datas_teach)))
rp = 0
all_mse = []
mse = 10000
while rp < n_repeat and mse >= error_accuracy:
error_count = 0
print(f"-------------Learning Time {rp}--------------")
for p in range(len(datas_train)):
# print('------------Learning Image: %d--------------'%p)
data_train = np.asmatrix(datas_train[p])
data_teach = np.asarray(datas_teach[p])
data_focus1, data_conved1 = self.convolute(
data_train,
self.conv1,
self.w_conv1,
self.thre_conv1,
conv_step=self.step_conv1,
)
data_pooled1 = self.pooling(data_conved1, self.size_pooling1)
shape_featuremap1 = np.shape(data_conved1)
"""
print(' -----original shape ', np.shape(data_train))
print(' ---- after convolution ',np.shape(data_conv1))
print(' -----after pooling ',np.shape(data_pooled1))
"""
data_bp_input = self._expand(data_pooled1)
bp_out1 = data_bp_input
bp_net_j = np.dot(bp_out1, self.vji.T) - self.thre_bp2
bp_out2 = self.sig(bp_net_j)
bp_net_k = np.dot(bp_out2, self.wkj.T) - self.thre_bp3
bp_out3 = self.sig(bp_net_k)
# --------------Model Leaning ------------------------
# calculate error and gradient---------------
pd_k_all = np.multiply(
(data_teach - bp_out3), np.multiply(bp_out3, (1 - bp_out3))
)
pd_j_all = np.multiply(
np.dot(pd_k_all, self.wkj), np.multiply(bp_out2, (1 - bp_out2))
)
pd_i_all = np.dot(pd_j_all, self.vji)
pd_conv1_pooled = pd_i_all / (self.size_pooling1 * self.size_pooling1)
pd_conv1_pooled = pd_conv1_pooled.T.getA().tolist()
pd_conv1_all = self._calculate_gradient_from_pool(
data_conved1,
pd_conv1_pooled,
shape_featuremap1[0],
shape_featuremap1[1],
self.size_pooling1,
)
# weight and threshold learning process---------
# convolution layer
for k_conv in range(self.conv1[1]):
pd_conv_list = self._expand_mat(pd_conv1_all[k_conv])
delta_w = self.rate_weight * np.dot(pd_conv_list, data_focus1)
self.w_conv1[k_conv] = self.w_conv1[k_conv] + delta_w.reshape(
(self.conv1[0], self.conv1[0])
)
self.thre_conv1[k_conv] = (
self.thre_conv1[k_conv]
- np.sum(pd_conv1_all[k_conv]) * self.rate_thre
)
# all connected layer
self.wkj = self.wkj + pd_k_all.T * bp_out2 * self.rate_weight
self.vji = self.vji + pd_j_all.T * bp_out1 * self.rate_weight
self.thre_bp3 = self.thre_bp3 - pd_k_all * self.rate_thre
self.thre_bp2 = self.thre_bp2 - pd_j_all * self.rate_thre
# calculate the sum error of all single image
errors = np.sum(abs(data_teach - bp_out3))
error_count += errors
# print(' ----Teach ',data_teach)
# print(' ----BP_output ',bp_out3)
rp = rp + 1
mse = error_count / patterns
all_mse.append(mse)
def draw_error():
yplot = [error_accuracy for i in range(int(n_repeat * 1.2))]
plt.plot(all_mse, "+-")
plt.plot(yplot, "r--")
plt.xlabel("Learning Times")
plt.ylabel("All_mse")
plt.grid(True, alpha=0.5)
plt.show()
print("------------------Training Complished---------------------")
print((" - - Training epoch: ", rp, f" - - Mse: {mse:.6f}"))
if draw_e:
draw_error()
return mse
def predict(self, datas_test):
# model predict
produce_out = []
print("-------------------Start Testing-------------------------")
print((" - - Shape: Test_Data ", np.shape(datas_test)))
for p in range(len(datas_test)):
data_test = np.asmatrix(datas_test[p])
data_focus1, data_conved1 = self.convolute(
data_test,
self.conv1,
self.w_conv1,
self.thre_conv1,
conv_step=self.step_conv1,
)
data_pooled1 = self.pooling(data_conved1, self.size_pooling1)
data_bp_input = self._expand(data_pooled1)
bp_out1 = data_bp_input
bp_net_j = bp_out1 * self.vji.T - self.thre_bp2
bp_out2 = self.sig(bp_net_j)
bp_net_k = bp_out2 * self.wkj.T - self.thre_bp3
bp_out3 = self.sig(bp_net_k)
produce_out.extend(bp_out3.getA().tolist())
res = [list(map(self.do_round, each)) for each in produce_out]
return np.asarray(res)
def convolution(self, data):
# return the data of image after convoluting process so we can check it out
data_test = np.asmatrix(data)
data_focus1, data_conved1 = self.convolute(
data_test,
self.conv1,
self.w_conv1,
self.thre_conv1,
conv_step=self.step_conv1,
)
data_pooled1 = self.pooling(data_conved1, self.size_pooling1)
return data_conved1, data_pooled1
if __name__ == "__main__":
"""
I will put the example on other file
"""
| """
- - - - - -- - - - - - - - - - - - - - - - - - - - - - -
Name - - CNN - Convolution Neural Network For Photo Recognizing
Goal - - Recognize Handing Writing Word Photo
Detail：Total 5 layers neural network
* Convolution layer
* Pooling layer
* Input layer layer of BP
* Hidden layer of BP
* Output layer of BP
Author: Stephen Lee
Github: [email protected]
Date: 2017.9.20
- - - - - -- - - - - - - - - - - - - - - - - - - - - - -
"""
import pickle
import numpy as np
from matplotlib import pyplot as plt
class CNN:
def __init__(
self, conv1_get, size_p1, bp_num1, bp_num2, bp_num3, rate_w=0.2, rate_t=0.2
):
"""
:param conv1_get: [a,c,d]，size, number, step of convolution kernel
:param size_p1: pooling size
:param bp_num1: units number of flatten layer
:param bp_num2: units number of hidden layer
:param bp_num3: units number of output layer
:param rate_w: rate of weight learning
:param rate_t: rate of threshold learning
"""
self.num_bp1 = bp_num1
self.num_bp2 = bp_num2
self.num_bp3 = bp_num3
self.conv1 = conv1_get[:2]
self.step_conv1 = conv1_get[2]
self.size_pooling1 = size_p1
self.rate_weight = rate_w
self.rate_thre = rate_t
self.w_conv1 = [
np.mat(-1 * np.random.rand(self.conv1[0], self.conv1[0]) + 0.5)
for i in range(self.conv1[1])
]
self.wkj = np.mat(-1 * np.random.rand(self.num_bp3, self.num_bp2) + 0.5)
self.vji = np.mat(-1 * np.random.rand(self.num_bp2, self.num_bp1) + 0.5)
self.thre_conv1 = -2 * np.random.rand(self.conv1[1]) + 1
self.thre_bp2 = -2 * np.random.rand(self.num_bp2) + 1
self.thre_bp3 = -2 * np.random.rand(self.num_bp3) + 1
def save_model(self, save_path):
# save model dict with pickle
model_dic = {
"num_bp1": self.num_bp1,
"num_bp2": self.num_bp2,
"num_bp3": self.num_bp3,
"conv1": self.conv1,
"step_conv1": self.step_conv1,
"size_pooling1": self.size_pooling1,
"rate_weight": self.rate_weight,
"rate_thre": self.rate_thre,
"w_conv1": self.w_conv1,
"wkj": self.wkj,
"vji": self.vji,
"thre_conv1": self.thre_conv1,
"thre_bp2": self.thre_bp2,
"thre_bp3": self.thre_bp3,
}
with open(save_path, "wb") as f:
pickle.dump(model_dic, f)
print(f"Model saved： {save_path}")
@classmethod
def read_model(cls, model_path):
# read saved model
with open(model_path, "rb") as f:
model_dic = pickle.load(f) # noqa: S301
conv_get = model_dic.get("conv1")
conv_get.append(model_dic.get("step_conv1"))
size_p1 = model_dic.get("size_pooling1")
bp1 = model_dic.get("num_bp1")
bp2 = model_dic.get("num_bp2")
bp3 = model_dic.get("num_bp3")
r_w = model_dic.get("rate_weight")
r_t = model_dic.get("rate_thre")
# create model instance
conv_ins = CNN(conv_get, size_p1, bp1, bp2, bp3, r_w, r_t)
# modify model parameter
conv_ins.w_conv1 = model_dic.get("w_conv1")
conv_ins.wkj = model_dic.get("wkj")
conv_ins.vji = model_dic.get("vji")
conv_ins.thre_conv1 = model_dic.get("thre_conv1")
conv_ins.thre_bp2 = model_dic.get("thre_bp2")
conv_ins.thre_bp3 = model_dic.get("thre_bp3")
return conv_ins
def sig(self, x):
return 1 / (1 + np.exp(-1 * x))
def do_round(self, x):
return round(x, 3)
def convolute(self, data, convs, w_convs, thre_convs, conv_step):
# convolution process
size_conv = convs[0]
num_conv = convs[1]
size_data = np.shape(data)[0]
# get the data slice of original image data, data_focus
data_focus = []
for i_focus in range(0, size_data - size_conv + 1, conv_step):
for j_focus in range(0, size_data - size_conv + 1, conv_step):
focus = data[
i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
]
data_focus.append(focus)
# calculate the feature map of every single kernel, and saved as list of matrix
data_featuremap = []
size_feature_map = int((size_data - size_conv) / conv_step + 1)
for i_map in range(num_conv):
featuremap = []
for i_focus in range(len(data_focus)):
net_focus = (
np.sum(np.multiply(data_focus[i_focus], w_convs[i_map]))
- thre_convs[i_map]
)
featuremap.append(self.sig(net_focus))
featuremap = np.asmatrix(featuremap).reshape(
size_feature_map, size_feature_map
)
data_featuremap.append(featuremap)
# expanding the data slice to One dimenssion
focus1_list = []
for each_focus in data_focus:
focus1_list.extend(self.Expand_Mat(each_focus))
focus_list = np.asarray(focus1_list)
return focus_list, data_featuremap
def pooling(self, featuremaps, size_pooling, pooling_type="average_pool"):
# pooling process
size_map = len(featuremaps[0])
size_pooled = int(size_map / size_pooling)
featuremap_pooled = []
for i_map in range(len(featuremaps)):
feature_map = featuremaps[i_map]
map_pooled = []
for i_focus in range(0, size_map, size_pooling):
for j_focus in range(0, size_map, size_pooling):
focus = feature_map[
i_focus : i_focus + size_pooling,
j_focus : j_focus + size_pooling,
]
if pooling_type == "average_pool":
# average pooling
map_pooled.append(np.average(focus))
elif pooling_type == "max_pooling":
# max pooling
map_pooled.append(np.max(focus))
map_pooled = np.asmatrix(map_pooled).reshape(size_pooled, size_pooled)
featuremap_pooled.append(map_pooled)
return featuremap_pooled
def _expand(self, data):
# expanding three dimension data to one dimension list
data_expanded = []
for i in range(len(data)):
shapes = np.shape(data[i])
data_listed = data[i].reshape(1, shapes[0] * shapes[1])
data_listed = data_listed.getA().tolist()[0]
data_expanded.extend(data_listed)
data_expanded = np.asarray(data_expanded)
return data_expanded
def _expand_mat(self, data_mat):
# expanding matrix to one dimension list
data_mat = np.asarray(data_mat)
shapes = np.shape(data_mat)
data_expanded = data_mat.reshape(1, shapes[0] * shapes[1])
return data_expanded
def _calculate_gradient_from_pool(
self, out_map, pd_pool, num_map, size_map, size_pooling
):
"""
calculate the gradient from the data slice of pool layer
pd_pool: list of matrix
out_map: the shape of data slice(size_map*size_map)
return: pd_all: list of matrix, [num, size_map, size_map]
"""
pd_all = []
i_pool = 0
for i_map in range(num_map):
pd_conv1 = np.ones((size_map, size_map))
for i in range(0, size_map, size_pooling):
for j in range(0, size_map, size_pooling):
pd_conv1[i : i + size_pooling, j : j + size_pooling] = pd_pool[
i_pool
]
i_pool = i_pool + 1
pd_conv2 = np.multiply(
pd_conv1, np.multiply(out_map[i_map], (1 - out_map[i_map]))
)
pd_all.append(pd_conv2)
return pd_all
def train(
self, patterns, datas_train, datas_teach, n_repeat, error_accuracy, draw_e=bool
):
# model traning
print("----------------------Start Training-------------------------")
print((" - - Shape: Train_Data ", np.shape(datas_train)))
print((" - - Shape: Teach_Data ", np.shape(datas_teach)))
rp = 0
all_mse = []
mse = 10000
while rp < n_repeat and mse >= error_accuracy:
error_count = 0
print(f"-------------Learning Time {rp}--------------")
for p in range(len(datas_train)):
# print('------------Learning Image: %d--------------'%p)
data_train = np.asmatrix(datas_train[p])
data_teach = np.asarray(datas_teach[p])
data_focus1, data_conved1 = self.convolute(
data_train,
self.conv1,
self.w_conv1,
self.thre_conv1,
conv_step=self.step_conv1,
)
data_pooled1 = self.pooling(data_conved1, self.size_pooling1)
shape_featuremap1 = np.shape(data_conved1)
"""
print(' -----original shape ', np.shape(data_train))
print(' ---- after convolution ',np.shape(data_conv1))
print(' -----after pooling ',np.shape(data_pooled1))
"""
data_bp_input = self._expand(data_pooled1)
bp_out1 = data_bp_input
bp_net_j = np.dot(bp_out1, self.vji.T) - self.thre_bp2
bp_out2 = self.sig(bp_net_j)
bp_net_k = np.dot(bp_out2, self.wkj.T) - self.thre_bp3
bp_out3 = self.sig(bp_net_k)
# --------------Model Leaning ------------------------
# calculate error and gradient---------------
pd_k_all = np.multiply(
(data_teach - bp_out3), np.multiply(bp_out3, (1 - bp_out3))
)
pd_j_all = np.multiply(
np.dot(pd_k_all, self.wkj), np.multiply(bp_out2, (1 - bp_out2))
)
pd_i_all = np.dot(pd_j_all, self.vji)
pd_conv1_pooled = pd_i_all / (self.size_pooling1 * self.size_pooling1)
pd_conv1_pooled = pd_conv1_pooled.T.getA().tolist()
pd_conv1_all = self._calculate_gradient_from_pool(
data_conved1,
pd_conv1_pooled,
shape_featuremap1[0],
shape_featuremap1[1],
self.size_pooling1,
)
# weight and threshold learning process---------
# convolution layer
for k_conv in range(self.conv1[1]):
pd_conv_list = self._expand_mat(pd_conv1_all[k_conv])
delta_w = self.rate_weight * np.dot(pd_conv_list, data_focus1)
self.w_conv1[k_conv] = self.w_conv1[k_conv] + delta_w.reshape(
(self.conv1[0], self.conv1[0])
)
self.thre_conv1[k_conv] = (
self.thre_conv1[k_conv]
- np.sum(pd_conv1_all[k_conv]) * self.rate_thre
)
# all connected layer
self.wkj = self.wkj + pd_k_all.T * bp_out2 * self.rate_weight
self.vji = self.vji + pd_j_all.T * bp_out1 * self.rate_weight
self.thre_bp3 = self.thre_bp3 - pd_k_all * self.rate_thre
self.thre_bp2 = self.thre_bp2 - pd_j_all * self.rate_thre
# calculate the sum error of all single image
errors = np.sum(abs(data_teach - bp_out3))
error_count += errors
# print(' ----Teach ',data_teach)
# print(' ----BP_output ',bp_out3)
rp = rp + 1
mse = error_count / patterns
all_mse.append(mse)
def draw_error():
yplot = [error_accuracy for i in range(int(n_repeat * 1.2))]
plt.plot(all_mse, "+-")
plt.plot(yplot, "r--")
plt.xlabel("Learning Times")
plt.ylabel("All_mse")
plt.grid(True, alpha=0.5)
plt.show()
print("------------------Training Complished---------------------")
print((" - - Training epoch: ", rp, f" - - Mse: {mse:.6f}"))
if draw_e:
draw_error()
return mse
def predict(self, datas_test):
# model predict
produce_out = []
print("-------------------Start Testing-------------------------")
print((" - - Shape: Test_Data ", np.shape(datas_test)))
for p in range(len(datas_test)):
data_test = np.asmatrix(datas_test[p])
data_focus1, data_conved1 = self.convolute(
data_test,
self.conv1,
self.w_conv1,
self.thre_conv1,
conv_step=self.step_conv1,
)
data_pooled1 = self.pooling(data_conved1, self.size_pooling1)
data_bp_input = self._expand(data_pooled1)
bp_out1 = data_bp_input
bp_net_j = bp_out1 * self.vji.T - self.thre_bp2
bp_out2 = self.sig(bp_net_j)
bp_net_k = bp_out2 * self.wkj.T - self.thre_bp3
bp_out3 = self.sig(bp_net_k)
produce_out.extend(bp_out3.getA().tolist())
res = [list(map(self.do_round, each)) for each in produce_out]
return np.asarray(res)
def convolution(self, data):
# return the data of image after convoluting process so we can check it out
data_test = np.asmatrix(data)
data_focus1, data_conved1 = self.convolute(
data_test,
self.conv1,
self.w_conv1,
self.thre_conv1,
conv_step=self.step_conv1,
)
data_pooled1 = self.pooling(data_conved1, self.size_pooling1)
return data_conved1, data_pooled1
if __name__ == "__main__":
"""
I will put the example on other file
"""
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| #!/bin/python3
# Doomsday algorithm info: https://en.wikipedia.org/wiki/Doomsday_rule
DOOMSDAY_LEAP = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
DOOMSDAY_NOT_LEAP = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
WEEK_DAY_NAMES = {
0: "Sunday",
1: "Monday",
2: "Tuesday",
3: "Wednesday",
4: "Thursday",
5: "Friday",
6: "Saturday",
}
def get_week_day(year: int, month: int, day: int) -> str:
"""Returns the week-day name out of a given date.
>>> get_week_day(2020, 10, 24)
'Saturday'
>>> get_week_day(2017, 10, 24)
'Tuesday'
>>> get_week_day(2019, 5, 3)
'Friday'
>>> get_week_day(1970, 9, 16)
'Wednesday'
>>> get_week_day(1870, 8, 13)
'Saturday'
>>> get_week_day(2040, 3, 14)
'Wednesday'
"""
# minimal input check:
assert len(str(year)) > 2, "year should be in YYYY format"
assert 1 <= month <= 12, "month should be between 1 to 12"
assert 1 <= day <= 31, "day should be between 1 to 31"
# Doomsday algorithm:
century = year // 100
century_anchor = (5 * (century % 4) + 2) % 7
centurian = year % 100
centurian_m = centurian % 12
dooms_day = (
(centurian // 12) + centurian_m + (centurian_m // 4) + century_anchor
) % 7
day_anchor = (
DOOMSDAY_NOT_LEAP[month - 1]
if (year % 4 != 0) or (centurian == 0 and (year % 400) == 0)
else DOOMSDAY_LEAP[month - 1]
)
week_day = (dooms_day + day - day_anchor) % 7
return WEEK_DAY_NAMES[week_day]
if __name__ == "__main__":
import doctest
doctest.testmod()
| #!/bin/python3
# Doomsday algorithm info: https://en.wikipedia.org/wiki/Doomsday_rule
DOOMSDAY_LEAP = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
DOOMSDAY_NOT_LEAP = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
WEEK_DAY_NAMES = {
0: "Sunday",
1: "Monday",
2: "Tuesday",
3: "Wednesday",
4: "Thursday",
5: "Friday",
6: "Saturday",
}
def get_week_day(year: int, month: int, day: int) -> str:
"""Returns the week-day name out of a given date.
>>> get_week_day(2020, 10, 24)
'Saturday'
>>> get_week_day(2017, 10, 24)
'Tuesday'
>>> get_week_day(2019, 5, 3)
'Friday'
>>> get_week_day(1970, 9, 16)
'Wednesday'
>>> get_week_day(1870, 8, 13)
'Saturday'
>>> get_week_day(2040, 3, 14)
'Wednesday'
"""
# minimal input check:
assert len(str(year)) > 2, "year should be in YYYY format"
assert 1 <= month <= 12, "month should be between 1 to 12"
assert 1 <= day <= 31, "day should be between 1 to 31"
# Doomsday algorithm:
century = year // 100
century_anchor = (5 * (century % 4) + 2) % 7
centurian = year % 100
centurian_m = centurian % 12
dooms_day = (
(centurian // 12) + centurian_m + (centurian_m // 4) + century_anchor
) % 7
day_anchor = (
DOOMSDAY_NOT_LEAP[month - 1]
if (year % 4 != 0) or (centurian == 0 and (year % 400) == 0)
else DOOMSDAY_LEAP[month - 1]
)
week_day = (dooms_day + day - day_anchor) % 7
return WEEK_DAY_NAMES[week_day]
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| def max_difference(a: list[int]) -> tuple[int, int]:
"""
We are given an array A[1..n] of integers, n >= 1. We want to
find a pair of indices (i, j) such that
1 <= i <= j <= n and A[j] - A[i] is as large as possible.
Explanation:
https://www.geeksforgeeks.org/maximum-difference-between-two-elements/
>>> max_difference([5, 11, 2, 1, 7, 9, 0, 7])
(1, 9)
"""
# base case
if len(a) == 1:
return a[0], a[0]
else:
# split A into half.
first = a[: len(a) // 2]
second = a[len(a) // 2 :]
# 2 sub problems, 1/2 of original size.
small1, big1 = max_difference(first)
small2, big2 = max_difference(second)
# get min of first and max of second
# linear time
min_first = min(first)
max_second = max(second)
# 3 cases, either (small1, big1),
# (min_first, max_second), (small2, big2)
# constant comparisons
if big2 - small2 > max_second - min_first and big2 - small2 > big1 - small1:
return small2, big2
elif big1 - small1 > max_second - min_first:
return small1, big1
else:
return min_first, max_second
if __name__ == "__main__":
import doctest
doctest.testmod()
| def max_difference(a: list[int]) -> tuple[int, int]:
"""
We are given an array A[1..n] of integers, n >= 1. We want to
find a pair of indices (i, j) such that
1 <= i <= j <= n and A[j] - A[i] is as large as possible.
Explanation:
https://www.geeksforgeeks.org/maximum-difference-between-two-elements/
>>> max_difference([5, 11, 2, 1, 7, 9, 0, 7])
(1, 9)
"""
# base case
if len(a) == 1:
return a[0], a[0]
else:
# split A into half.
first = a[: len(a) // 2]
second = a[len(a) // 2 :]
# 2 sub problems, 1/2 of original size.
small1, big1 = max_difference(first)
small2, big2 = max_difference(second)
# get min of first and max of second
# linear time
min_first = min(first)
max_second = max(second)
# 3 cases, either (small1, big1),
# (min_first, max_second), (small2, big2)
# constant comparisons
if big2 - small2 > max_second - min_first and big2 - small2 > big1 - small1:
return small2, big2
elif big1 - small1 > max_second - min_first:
return small1, big1
else:
return min_first, max_second
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| -1 |
||
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| """
Calculates buoyant force on object submerged within static fluid.
Discovered by greek mathematician, Archimedes. The principle is named after him.
Equation for calculating buoyant force:
Fb = ρ * V * g
Source:
- https://en.wikipedia.org/wiki/Archimedes%27_principle
"""
# Acceleration Constant on Earth (unit m/s^2)
g = 9.80665
def archimedes_principle(
fluid_density: float, volume: float, gravity: float = g
) -> float:
"""
Args:
fluid_density: density of fluid (kg/m^3)
volume: volume of object / liquid being displaced by object
gravity: Acceleration from gravity. Gravitational force on system,
Default is Earth Gravity
returns:
buoyant force on object in Newtons
>>> archimedes_principle(fluid_density=997, volume=0.5, gravity=9.8)
4885.3
>>> archimedes_principle(fluid_density=997, volume=0.7)
6844.061035
"""
if fluid_density <= 0:
raise ValueError("Impossible fluid density")
if volume < 0:
raise ValueError("Impossible Object volume")
if gravity <= 0:
raise ValueError("Impossible Gravity")
return fluid_density * gravity * volume
if __name__ == "__main__":
import doctest
# run doctest
doctest.testmod()
| """
Calculates buoyant force on object submerged within static fluid.
Discovered by greek mathematician, Archimedes. The principle is named after him.
Equation for calculating buoyant force:
Fb = ρ * V * g
Source:
- https://en.wikipedia.org/wiki/Archimedes%27_principle
"""
# Acceleration Constant on Earth (unit m/s^2)
g = 9.80665
def archimedes_principle(
fluid_density: float, volume: float, gravity: float = g
) -> float:
"""
Args:
fluid_density: density of fluid (kg/m^3)
volume: volume of object / liquid being displaced by object
gravity: Acceleration from gravity. Gravitational force on system,
Default is Earth Gravity
returns:
buoyant force on object in Newtons
>>> archimedes_principle(fluid_density=997, volume=0.5, gravity=9.8)
4885.3
>>> archimedes_principle(fluid_density=997, volume=0.7)
6844.061035
"""
if fluid_density <= 0:
raise ValueError("Impossible fluid density")
if volume < 0:
raise ValueError("Impossible Object volume")
if gravity <= 0:
raise ValueError("Impossible Gravity")
return fluid_density * gravity * volume
if __name__ == "__main__":
import doctest
# run doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| """
Project Euler Problem 3: https://projecteuler.net/problem=3
Largest prime factor
The prime factors of 13195 are 5, 7, 13 and 29.
What is the largest prime factor of the number 600851475143?
References:
- https://en.wikipedia.org/wiki/Prime_number#Unique_factorization
"""
def solution(n: int = 600851475143) -> int:
"""
Returns the largest prime factor of a given number n.
>>> solution(13195)
29
>>> solution(10)
5
>>> solution(17)
17
>>> solution(3.4)
3
>>> solution(0)
Traceback (most recent call last):
...
ValueError: Parameter n must be greater than or equal to one.
>>> solution(-17)
Traceback (most recent call last):
...
ValueError: Parameter n must be greater than or equal to one.
>>> solution([])
Traceback (most recent call last):
...
TypeError: Parameter n must be int or castable to int.
>>> solution("asd")
Traceback (most recent call last):
...
TypeError: Parameter n must be int or castable to int.
"""
try:
n = int(n)
except (TypeError, ValueError):
raise TypeError("Parameter n must be int or castable to int.")
if n <= 0:
raise ValueError("Parameter n must be greater than or equal to one.")
i = 2
ans = 0
if n == 2:
return 2
while n > 2:
while n % i != 0:
i += 1
ans = i
while n % i == 0:
n = n // i
i += 1
return int(ans)
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 3: https://projecteuler.net/problem=3
Largest prime factor
The prime factors of 13195 are 5, 7, 13 and 29.
What is the largest prime factor of the number 600851475143?
References:
- https://en.wikipedia.org/wiki/Prime_number#Unique_factorization
"""
def solution(n: int = 600851475143) -> int:
"""
Returns the largest prime factor of a given number n.
>>> solution(13195)
29
>>> solution(10)
5
>>> solution(17)
17
>>> solution(3.4)
3
>>> solution(0)
Traceback (most recent call last):
...
ValueError: Parameter n must be greater than or equal to one.
>>> solution(-17)
Traceback (most recent call last):
...
ValueError: Parameter n must be greater than or equal to one.
>>> solution([])
Traceback (most recent call last):
...
TypeError: Parameter n must be int or castable to int.
>>> solution("asd")
Traceback (most recent call last):
...
TypeError: Parameter n must be int or castable to int.
"""
try:
n = int(n)
except (TypeError, ValueError):
raise TypeError("Parameter n must be int or castable to int.")
if n <= 0:
raise ValueError("Parameter n must be greater than or equal to one.")
i = 2
ans = 0
if n == 2:
return 2
while n > 2:
while n % i != 0:
i += 1
ans = i
while n % i == 0:
n = n // i
i += 1
return int(ans)
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 8,936 | Fix ruff errors | ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-08-09T07:13:45Z" | "2023-08-09T07:55:31Z" | 842d03fb2ab7d83e4d4081c248d71e89bb520809 | ae0fc85401efd9816193a06e554a66600cc09a97 | Fix ruff errors. ### Describe your change:
Fixes #8935
Fixing ruff errors again due to the recent version update
Notably, I didn't fix the ruff error in [neural_network/input_data.py](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/input_data.py) because it appears that the file was taken directly from TensorFlow's codebase, so I don't want to modify it _just_ yet. Instead, I renamed it to neural_network/input_data.py_tf because it should be left out of the directory for the following reasons:
1. Its sole purpose is to be used by [neural_network/gan.py_tf](https://github.com/TheAlgorithms/Python/blob/842d03fb2ab7d83e4d4081c248d71e89bb520809/neural_network/gan.py_tf), which is itself left out of the directory because of issues with TensorFlow.
2. All of it's actually deprecated—TensorFlow explicitly says so in the code and recommends getting the necessary input data using a different function. If/when neural_network/gan.py_tf is eventually added back to the directory, its implementation should be changed to not use neural_network/input_data.py anyway.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| # Hashes
Hashing is the process of mapping any amount of data to a specified size using an algorithm. This is known as a hash value (or, if you're feeling fancy, a hash code, hash sums, or even a hash digest). Hashing is a one-way function, whereas encryption is a two-way function. While it is functionally conceivable to reverse-hash stuff, the required computing power makes it impractical. Hashing is a one-way street.
Unlike encryption, which is intended to protect data in transit, hashing is intended to authenticate that a file or piece of data has not been altered—that it is authentic. In other words, it functions as a checksum.
## Common hashing algorithms
### MD5
This is one of the first algorithms that has gained widespread acceptance. MD5 is hashing algorithm made by Ray Rivest that is known to suffer vulnerabilities. It was created in 1992 as the successor to MD4. Currently MD6 is in the works, but as of 2009 Rivest had removed it from NIST consideration for SHA-3.
### SHA
SHA stands for Security Hashing Algorithm and it’s probably best known as the hashing algorithm used in most SSL/TLS cipher suites. A cipher suite is a collection of ciphers and algorithms that are used for SSL/TLS connections. SHA handles the hashing aspects. SHA-1, as we mentioned earlier, is now deprecated. SHA-2 is now mandatory. SHA-2 is sometimes known has SHA-256, though variants with longer bit lengths are also available.
### SHA256
SHA 256 is a member of the SHA 2 algorithm family, under which SHA stands for Secure Hash Algorithm. It was a collaborative effort between both the NSA and NIST to implement a successor to the SHA 1 family, which was beginning to lose potency against brute force attacks. It was published in 2001.
The importance of the 256 in the name refers to the final hash digest value, i.e. the hash value will remain 256 bits regardless of the size of the plaintext/cleartext. Other algorithms in the SHA family are similar to SHA 256 in some ways.
### Luhn
The Luhn algorithm, also renowned as the modulus 10 or mod 10 algorithm, is a straightforward checksum formula used to validate a wide range of identification numbers, including credit card numbers, IMEI numbers, and Canadian Social Insurance Numbers. A community of mathematicians developed the LUHN formula in the late 1960s. Companies offering credit cards quickly followed suit. Since the algorithm is in the public interest, anyone can use it. The algorithm is used by most credit cards and many government identification numbers as a simple method of differentiating valid figures from mistyped or otherwise incorrect numbers. It was created to guard against unintentional errors, not malicious attacks. | # Hashes
Hashing is the process of mapping any amount of data to a specified size using an algorithm. This is known as a hash value (or, if you're feeling fancy, a hash code, hash sums, or even a hash digest). Hashing is a one-way function, whereas encryption is a two-way function. While it is functionally conceivable to reverse-hash stuff, the required computing power makes it impractical. Hashing is a one-way street.
Unlike encryption, which is intended to protect data in transit, hashing is intended to authenticate that a file or piece of data has not been altered—that it is authentic. In other words, it functions as a checksum.
## Common hashing algorithms
### MD5
This is one of the first algorithms that has gained widespread acceptance. MD5 is hashing algorithm made by Ray Rivest that is known to suffer vulnerabilities. It was created in 1992 as the successor to MD4. Currently MD6 is in the works, but as of 2009 Rivest had removed it from NIST consideration for SHA-3.
### SHA
SHA stands for Security Hashing Algorithm and it’s probably best known as the hashing algorithm used in most SSL/TLS cipher suites. A cipher suite is a collection of ciphers and algorithms that are used for SSL/TLS connections. SHA handles the hashing aspects. SHA-1, as we mentioned earlier, is now deprecated. SHA-2 is now mandatory. SHA-2 is sometimes known has SHA-256, though variants with longer bit lengths are also available.
### SHA256
SHA 256 is a member of the SHA 2 algorithm family, under which SHA stands for Secure Hash Algorithm. It was a collaborative effort between both the NSA and NIST to implement a successor to the SHA 1 family, which was beginning to lose potency against brute force attacks. It was published in 2001.
The importance of the 256 in the name refers to the final hash digest value, i.e. the hash value will remain 256 bits regardless of the size of the plaintext/cleartext. Other algorithms in the SHA family are similar to SHA 256 in some ways.
### Luhn
The Luhn algorithm, also renowned as the modulus 10 or mod 10 algorithm, is a straightforward checksum formula used to validate a wide range of identification numbers, including credit card numbers, IMEI numbers, and Canadian Social Insurance Numbers. A community of mathematicians developed the LUHN formula in the late 1960s. Companies offering credit cards quickly followed suit. Since the algorithm is in the public interest, anyone can use it. The algorithm is used by most credit cards and many government identification numbers as a simple method of differentiating valid figures from mistyped or otherwise incorrect numbers. It was created to guard against unintentional errors, not malicious attacks. | -1 |
TheAlgorithms/Python | 8,913 | Ruff fixes | ### Describe your change:
Fix graphs/eulerian_path_and_circuit_for_undirected_graph.py and physics/newtons_second_law_of_motion.py, which are causing ruff to fail
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-07-31T19:13:04Z" | "2023-07-31T20:53:26Z" | 90a8e6e0d210a5c526c8f485fa825e1649d217e2 | 5cf34d901e32b65425103309bbad0068b1851238 | Ruff fixes. ### Describe your change:
Fix graphs/eulerian_path_and_circuit_for_undirected_graph.py and physics/newtons_second_law_of_motion.py, which are causing ruff to fail
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
|
## Arithmetic Analysis
* [Bisection](arithmetic_analysis/bisection.py)
* [Gaussian Elimination](arithmetic_analysis/gaussian_elimination.py)
* [In Static Equilibrium](arithmetic_analysis/in_static_equilibrium.py)
* [Intersection](arithmetic_analysis/intersection.py)
* [Jacobi Iteration Method](arithmetic_analysis/jacobi_iteration_method.py)
* [Lu Decomposition](arithmetic_analysis/lu_decomposition.py)
* [Newton Forward Interpolation](arithmetic_analysis/newton_forward_interpolation.py)
* [Newton Method](arithmetic_analysis/newton_method.py)
* [Newton Raphson](arithmetic_analysis/newton_raphson.py)
* [Newton Raphson New](arithmetic_analysis/newton_raphson_new.py)
* [Secant Method](arithmetic_analysis/secant_method.py)
## Audio Filters
* [Butterworth Filter](audio_filters/butterworth_filter.py)
* [Iir Filter](audio_filters/iir_filter.py)
* [Show Response](audio_filters/show_response.py)
## Backtracking
* [All Combinations](backtracking/all_combinations.py)
* [All Permutations](backtracking/all_permutations.py)
* [All Subsequences](backtracking/all_subsequences.py)
* [Coloring](backtracking/coloring.py)
* [Combination Sum](backtracking/combination_sum.py)
* [Hamiltonian Cycle](backtracking/hamiltonian_cycle.py)
* [Knight Tour](backtracking/knight_tour.py)
* [Minimax](backtracking/minimax.py)
* [Minmax](backtracking/minmax.py)
* [N Queens](backtracking/n_queens.py)
* [N Queens Math](backtracking/n_queens_math.py)
* [Power Sum](backtracking/power_sum.py)
* [Rat In Maze](backtracking/rat_in_maze.py)
* [Sudoku](backtracking/sudoku.py)
* [Sum Of Subsets](backtracking/sum_of_subsets.py)
* [Word Search](backtracking/word_search.py)
## Bit Manipulation
* [Binary And Operator](bit_manipulation/binary_and_operator.py)
* [Binary Count Setbits](bit_manipulation/binary_count_setbits.py)
* [Binary Count Trailing Zeros](bit_manipulation/binary_count_trailing_zeros.py)
* [Binary Or Operator](bit_manipulation/binary_or_operator.py)
* [Binary Shifts](bit_manipulation/binary_shifts.py)
* [Binary Twos Complement](bit_manipulation/binary_twos_complement.py)
* [Binary Xor Operator](bit_manipulation/binary_xor_operator.py)
* [Count 1S Brian Kernighan Method](bit_manipulation/count_1s_brian_kernighan_method.py)
* [Count Number Of One Bits](bit_manipulation/count_number_of_one_bits.py)
* [Gray Code Sequence](bit_manipulation/gray_code_sequence.py)
* [Highest Set Bit](bit_manipulation/highest_set_bit.py)
* [Index Of Rightmost Set Bit](bit_manipulation/index_of_rightmost_set_bit.py)
* [Is Even](bit_manipulation/is_even.py)
* [Is Power Of Two](bit_manipulation/is_power_of_two.py)
* [Numbers Different Signs](bit_manipulation/numbers_different_signs.py)
* [Reverse Bits](bit_manipulation/reverse_bits.py)
* [Single Bit Manipulation Operations](bit_manipulation/single_bit_manipulation_operations.py)
## Blockchain
* [Chinese Remainder Theorem](blockchain/chinese_remainder_theorem.py)
* [Diophantine Equation](blockchain/diophantine_equation.py)
* [Modular Division](blockchain/modular_division.py)
## Boolean Algebra
* [And Gate](boolean_algebra/and_gate.py)
* [Nand Gate](boolean_algebra/nand_gate.py)
* [Norgate](boolean_algebra/norgate.py)
* [Not Gate](boolean_algebra/not_gate.py)
* [Or Gate](boolean_algebra/or_gate.py)
* [Quine Mc Cluskey](boolean_algebra/quine_mc_cluskey.py)
* [Xnor Gate](boolean_algebra/xnor_gate.py)
* [Xor Gate](boolean_algebra/xor_gate.py)
## Cellular Automata
* [Conways Game Of Life](cellular_automata/conways_game_of_life.py)
* [Game Of Life](cellular_automata/game_of_life.py)
* [Nagel Schrekenberg](cellular_automata/nagel_schrekenberg.py)
* [One Dimensional](cellular_automata/one_dimensional.py)
## Ciphers
* [A1Z26](ciphers/a1z26.py)
* [Affine Cipher](ciphers/affine_cipher.py)
* [Atbash](ciphers/atbash.py)
* [Autokey](ciphers/autokey.py)
* [Baconian Cipher](ciphers/baconian_cipher.py)
* [Base16](ciphers/base16.py)
* [Base32](ciphers/base32.py)
* [Base64](ciphers/base64.py)
* [Base85](ciphers/base85.py)
* [Beaufort Cipher](ciphers/beaufort_cipher.py)
* [Bifid](ciphers/bifid.py)
* [Brute Force Caesar Cipher](ciphers/brute_force_caesar_cipher.py)
* [Caesar Cipher](ciphers/caesar_cipher.py)
* [Cryptomath Module](ciphers/cryptomath_module.py)
* [Decrypt Caesar With Chi Squared](ciphers/decrypt_caesar_with_chi_squared.py)
* [Deterministic Miller Rabin](ciphers/deterministic_miller_rabin.py)
* [Diffie](ciphers/diffie.py)
* [Diffie Hellman](ciphers/diffie_hellman.py)
* [Elgamal Key Generator](ciphers/elgamal_key_generator.py)
* [Enigma Machine2](ciphers/enigma_machine2.py)
* [Hill Cipher](ciphers/hill_cipher.py)
* [Mixed Keyword Cypher](ciphers/mixed_keyword_cypher.py)
* [Mono Alphabetic Ciphers](ciphers/mono_alphabetic_ciphers.py)
* [Morse Code](ciphers/morse_code.py)
* [Onepad Cipher](ciphers/onepad_cipher.py)
* [Playfair Cipher](ciphers/playfair_cipher.py)
* [Polybius](ciphers/polybius.py)
* [Porta Cipher](ciphers/porta_cipher.py)
* [Rabin Miller](ciphers/rabin_miller.py)
* [Rail Fence Cipher](ciphers/rail_fence_cipher.py)
* [Rot13](ciphers/rot13.py)
* [Rsa Cipher](ciphers/rsa_cipher.py)
* [Rsa Factorization](ciphers/rsa_factorization.py)
* [Rsa Key Generator](ciphers/rsa_key_generator.py)
* [Shuffled Shift Cipher](ciphers/shuffled_shift_cipher.py)
* [Simple Keyword Cypher](ciphers/simple_keyword_cypher.py)
* [Simple Substitution Cipher](ciphers/simple_substitution_cipher.py)
* [Trafid Cipher](ciphers/trafid_cipher.py)
* [Transposition Cipher](ciphers/transposition_cipher.py)
* [Transposition Cipher Encrypt Decrypt File](ciphers/transposition_cipher_encrypt_decrypt_file.py)
* [Vigenere Cipher](ciphers/vigenere_cipher.py)
* [Xor Cipher](ciphers/xor_cipher.py)
## Compression
* [Burrows Wheeler](compression/burrows_wheeler.py)
* [Huffman](compression/huffman.py)
* [Lempel Ziv](compression/lempel_ziv.py)
* [Lempel Ziv Decompress](compression/lempel_ziv_decompress.py)
* [Lz77](compression/lz77.py)
* [Peak Signal To Noise Ratio](compression/peak_signal_to_noise_ratio.py)
* [Run Length Encoding](compression/run_length_encoding.py)
## Computer Vision
* [Cnn Classification](computer_vision/cnn_classification.py)
* [Flip Augmentation](computer_vision/flip_augmentation.py)
* [Harris Corner](computer_vision/harris_corner.py)
* [Horn Schunck](computer_vision/horn_schunck.py)
* [Mean Threshold](computer_vision/mean_threshold.py)
* [Mosaic Augmentation](computer_vision/mosaic_augmentation.py)
* [Pooling Functions](computer_vision/pooling_functions.py)
## Conversions
* [Astronomical Length Scale Conversion](conversions/astronomical_length_scale_conversion.py)
* [Binary To Decimal](conversions/binary_to_decimal.py)
* [Binary To Hexadecimal](conversions/binary_to_hexadecimal.py)
* [Binary To Octal](conversions/binary_to_octal.py)
* [Decimal To Any](conversions/decimal_to_any.py)
* [Decimal To Binary](conversions/decimal_to_binary.py)
* [Decimal To Binary Recursion](conversions/decimal_to_binary_recursion.py)
* [Decimal To Hexadecimal](conversions/decimal_to_hexadecimal.py)
* [Decimal To Octal](conversions/decimal_to_octal.py)
* [Energy Conversions](conversions/energy_conversions.py)
* [Excel Title To Column](conversions/excel_title_to_column.py)
* [Hex To Bin](conversions/hex_to_bin.py)
* [Hexadecimal To Decimal](conversions/hexadecimal_to_decimal.py)
* [Length Conversion](conversions/length_conversion.py)
* [Molecular Chemistry](conversions/molecular_chemistry.py)
* [Octal To Decimal](conversions/octal_to_decimal.py)
* [Prefix Conversions](conversions/prefix_conversions.py)
* [Prefix Conversions String](conversions/prefix_conversions_string.py)
* [Pressure Conversions](conversions/pressure_conversions.py)
* [Rgb Hsv Conversion](conversions/rgb_hsv_conversion.py)
* [Roman Numerals](conversions/roman_numerals.py)
* [Speed Conversions](conversions/speed_conversions.py)
* [Temperature Conversions](conversions/temperature_conversions.py)
* [Volume Conversions](conversions/volume_conversions.py)
* [Weight Conversion](conversions/weight_conversion.py)
## Data Structures
* Arrays
* [Permutations](data_structures/arrays/permutations.py)
* [Prefix Sum](data_structures/arrays/prefix_sum.py)
* [Product Sum](data_structures/arrays/product_sum.py)
* Binary Tree
* [Avl Tree](data_structures/binary_tree/avl_tree.py)
* [Basic Binary Tree](data_structures/binary_tree/basic_binary_tree.py)
* [Binary Search Tree](data_structures/binary_tree/binary_search_tree.py)
* [Binary Search Tree Recursive](data_structures/binary_tree/binary_search_tree_recursive.py)
* [Binary Tree Mirror](data_structures/binary_tree/binary_tree_mirror.py)
* [Binary Tree Node Sum](data_structures/binary_tree/binary_tree_node_sum.py)
* [Binary Tree Path Sum](data_structures/binary_tree/binary_tree_path_sum.py)
* [Binary Tree Traversals](data_structures/binary_tree/binary_tree_traversals.py)
* [Diff Views Of Binary Tree](data_structures/binary_tree/diff_views_of_binary_tree.py)
* [Distribute Coins](data_structures/binary_tree/distribute_coins.py)
* [Fenwick Tree](data_structures/binary_tree/fenwick_tree.py)
* [Inorder Tree Traversal 2022](data_structures/binary_tree/inorder_tree_traversal_2022.py)
* [Is Bst](data_structures/binary_tree/is_bst.py)
* [Lazy Segment Tree](data_structures/binary_tree/lazy_segment_tree.py)
* [Lowest Common Ancestor](data_structures/binary_tree/lowest_common_ancestor.py)
* [Maximum Fenwick Tree](data_structures/binary_tree/maximum_fenwick_tree.py)
* [Merge Two Binary Trees](data_structures/binary_tree/merge_two_binary_trees.py)
* [Non Recursive Segment Tree](data_structures/binary_tree/non_recursive_segment_tree.py)
* [Number Of Possible Binary Trees](data_structures/binary_tree/number_of_possible_binary_trees.py)
* [Red Black Tree](data_structures/binary_tree/red_black_tree.py)
* [Segment Tree](data_structures/binary_tree/segment_tree.py)
* [Segment Tree Other](data_structures/binary_tree/segment_tree_other.py)
* [Treap](data_structures/binary_tree/treap.py)
* [Wavelet Tree](data_structures/binary_tree/wavelet_tree.py)
* Disjoint Set
* [Alternate Disjoint Set](data_structures/disjoint_set/alternate_disjoint_set.py)
* [Disjoint Set](data_structures/disjoint_set/disjoint_set.py)
* Hashing
* [Bloom Filter](data_structures/hashing/bloom_filter.py)
* [Double Hash](data_structures/hashing/double_hash.py)
* [Hash Map](data_structures/hashing/hash_map.py)
* [Hash Table](data_structures/hashing/hash_table.py)
* [Hash Table With Linked List](data_structures/hashing/hash_table_with_linked_list.py)
* Number Theory
* [Prime Numbers](data_structures/hashing/number_theory/prime_numbers.py)
* [Quadratic Probing](data_structures/hashing/quadratic_probing.py)
* Tests
* [Test Hash Map](data_structures/hashing/tests/test_hash_map.py)
* Heap
* [Binomial Heap](data_structures/heap/binomial_heap.py)
* [Heap](data_structures/heap/heap.py)
* [Heap Generic](data_structures/heap/heap_generic.py)
* [Max Heap](data_structures/heap/max_heap.py)
* [Min Heap](data_structures/heap/min_heap.py)
* [Randomized Heap](data_structures/heap/randomized_heap.py)
* [Skew Heap](data_structures/heap/skew_heap.py)
* Linked List
* [Circular Linked List](data_structures/linked_list/circular_linked_list.py)
* [Deque Doubly](data_structures/linked_list/deque_doubly.py)
* [Doubly Linked List](data_structures/linked_list/doubly_linked_list.py)
* [Doubly Linked List Two](data_structures/linked_list/doubly_linked_list_two.py)
* [From Sequence](data_structures/linked_list/from_sequence.py)
* [Has Loop](data_structures/linked_list/has_loop.py)
* [Is Palindrome](data_structures/linked_list/is_palindrome.py)
* [Merge Two Lists](data_structures/linked_list/merge_two_lists.py)
* [Middle Element Of Linked List](data_structures/linked_list/middle_element_of_linked_list.py)
* [Print Reverse](data_structures/linked_list/print_reverse.py)
* [Singly Linked List](data_structures/linked_list/singly_linked_list.py)
* [Skip List](data_structures/linked_list/skip_list.py)
* [Swap Nodes](data_structures/linked_list/swap_nodes.py)
* Queue
* [Circular Queue](data_structures/queue/circular_queue.py)
* [Circular Queue Linked List](data_structures/queue/circular_queue_linked_list.py)
* [Double Ended Queue](data_structures/queue/double_ended_queue.py)
* [Linked Queue](data_structures/queue/linked_queue.py)
* [Priority Queue Using List](data_structures/queue/priority_queue_using_list.py)
* [Queue By Two Stacks](data_structures/queue/queue_by_two_stacks.py)
* [Queue On List](data_structures/queue/queue_on_list.py)
* [Queue On Pseudo Stack](data_structures/queue/queue_on_pseudo_stack.py)
* Stacks
* [Balanced Parentheses](data_structures/stacks/balanced_parentheses.py)
* [Dijkstras Two Stack Algorithm](data_structures/stacks/dijkstras_two_stack_algorithm.py)
* [Evaluate Postfix Notations](data_structures/stacks/evaluate_postfix_notations.py)
* [Infix To Postfix Conversion](data_structures/stacks/infix_to_postfix_conversion.py)
* [Infix To Prefix Conversion](data_structures/stacks/infix_to_prefix_conversion.py)
* [Next Greater Element](data_structures/stacks/next_greater_element.py)
* [Postfix Evaluation](data_structures/stacks/postfix_evaluation.py)
* [Prefix Evaluation](data_structures/stacks/prefix_evaluation.py)
* [Stack](data_structures/stacks/stack.py)
* [Stack With Doubly Linked List](data_structures/stacks/stack_with_doubly_linked_list.py)
* [Stack With Singly Linked List](data_structures/stacks/stack_with_singly_linked_list.py)
* [Stock Span Problem](data_structures/stacks/stock_span_problem.py)
* Trie
* [Radix Tree](data_structures/trie/radix_tree.py)
* [Trie](data_structures/trie/trie.py)
## Digital Image Processing
* [Change Brightness](digital_image_processing/change_brightness.py)
* [Change Contrast](digital_image_processing/change_contrast.py)
* [Convert To Negative](digital_image_processing/convert_to_negative.py)
* Dithering
* [Burkes](digital_image_processing/dithering/burkes.py)
* Edge Detection
* [Canny](digital_image_processing/edge_detection/canny.py)
* Filters
* [Bilateral Filter](digital_image_processing/filters/bilateral_filter.py)
* [Convolve](digital_image_processing/filters/convolve.py)
* [Gabor Filter](digital_image_processing/filters/gabor_filter.py)
* [Gaussian Filter](digital_image_processing/filters/gaussian_filter.py)
* [Local Binary Pattern](digital_image_processing/filters/local_binary_pattern.py)
* [Median Filter](digital_image_processing/filters/median_filter.py)
* [Sobel Filter](digital_image_processing/filters/sobel_filter.py)
* Histogram Equalization
* [Histogram Stretch](digital_image_processing/histogram_equalization/histogram_stretch.py)
* [Index Calculation](digital_image_processing/index_calculation.py)
* Morphological Operations
* [Dilation Operation](digital_image_processing/morphological_operations/dilation_operation.py)
* [Erosion Operation](digital_image_processing/morphological_operations/erosion_operation.py)
* Resize
* [Resize](digital_image_processing/resize/resize.py)
* Rotation
* [Rotation](digital_image_processing/rotation/rotation.py)
* [Sepia](digital_image_processing/sepia.py)
* [Test Digital Image Processing](digital_image_processing/test_digital_image_processing.py)
## Divide And Conquer
* [Closest Pair Of Points](divide_and_conquer/closest_pair_of_points.py)
* [Convex Hull](divide_and_conquer/convex_hull.py)
* [Heaps Algorithm](divide_and_conquer/heaps_algorithm.py)
* [Heaps Algorithm Iterative](divide_and_conquer/heaps_algorithm_iterative.py)
* [Inversions](divide_and_conquer/inversions.py)
* [Kth Order Statistic](divide_and_conquer/kth_order_statistic.py)
* [Max Difference Pair](divide_and_conquer/max_difference_pair.py)
* [Max Subarray](divide_and_conquer/max_subarray.py)
* [Mergesort](divide_and_conquer/mergesort.py)
* [Peak](divide_and_conquer/peak.py)
* [Power](divide_and_conquer/power.py)
* [Strassen Matrix Multiplication](divide_and_conquer/strassen_matrix_multiplication.py)
## Dynamic Programming
* [Abbreviation](dynamic_programming/abbreviation.py)
* [All Construct](dynamic_programming/all_construct.py)
* [Bitmask](dynamic_programming/bitmask.py)
* [Catalan Numbers](dynamic_programming/catalan_numbers.py)
* [Climbing Stairs](dynamic_programming/climbing_stairs.py)
* [Combination Sum Iv](dynamic_programming/combination_sum_iv.py)
* [Edit Distance](dynamic_programming/edit_distance.py)
* [Factorial](dynamic_programming/factorial.py)
* [Fast Fibonacci](dynamic_programming/fast_fibonacci.py)
* [Fibonacci](dynamic_programming/fibonacci.py)
* [Fizz Buzz](dynamic_programming/fizz_buzz.py)
* [Floyd Warshall](dynamic_programming/floyd_warshall.py)
* [Integer Partition](dynamic_programming/integer_partition.py)
* [Iterating Through Submasks](dynamic_programming/iterating_through_submasks.py)
* [K Means Clustering Tensorflow](dynamic_programming/k_means_clustering_tensorflow.py)
* [Knapsack](dynamic_programming/knapsack.py)
* [Longest Common Subsequence](dynamic_programming/longest_common_subsequence.py)
* [Longest Common Substring](dynamic_programming/longest_common_substring.py)
* [Longest Increasing Subsequence](dynamic_programming/longest_increasing_subsequence.py)
* [Longest Increasing Subsequence O(Nlogn)](dynamic_programming/longest_increasing_subsequence_o(nlogn).py)
* [Longest Sub Array](dynamic_programming/longest_sub_array.py)
* [Matrix Chain Order](dynamic_programming/matrix_chain_order.py)
* [Max Non Adjacent Sum](dynamic_programming/max_non_adjacent_sum.py)
* [Max Product Subarray](dynamic_programming/max_product_subarray.py)
* [Max Subarray Sum](dynamic_programming/max_subarray_sum.py)
* [Min Distance Up Bottom](dynamic_programming/min_distance_up_bottom.py)
* [Minimum Coin Change](dynamic_programming/minimum_coin_change.py)
* [Minimum Cost Path](dynamic_programming/minimum_cost_path.py)
* [Minimum Partition](dynamic_programming/minimum_partition.py)
* [Minimum Size Subarray Sum](dynamic_programming/minimum_size_subarray_sum.py)
* [Minimum Squares To Represent A Number](dynamic_programming/minimum_squares_to_represent_a_number.py)
* [Minimum Steps To One](dynamic_programming/minimum_steps_to_one.py)
* [Minimum Tickets Cost](dynamic_programming/minimum_tickets_cost.py)
* [Optimal Binary Search Tree](dynamic_programming/optimal_binary_search_tree.py)
* [Palindrome Partitioning](dynamic_programming/palindrome_partitioning.py)
* [Rod Cutting](dynamic_programming/rod_cutting.py)
* [Subset Generation](dynamic_programming/subset_generation.py)
* [Sum Of Subset](dynamic_programming/sum_of_subset.py)
* [Viterbi](dynamic_programming/viterbi.py)
* [Word Break](dynamic_programming/word_break.py)
## Electronics
* [Apparent Power](electronics/apparent_power.py)
* [Builtin Voltage](electronics/builtin_voltage.py)
* [Carrier Concentration](electronics/carrier_concentration.py)
* [Circular Convolution](electronics/circular_convolution.py)
* [Coulombs Law](electronics/coulombs_law.py)
* [Electric Conductivity](electronics/electric_conductivity.py)
* [Electric Power](electronics/electric_power.py)
* [Electrical Impedance](electronics/electrical_impedance.py)
* [Ind Reactance](electronics/ind_reactance.py)
* [Ohms Law](electronics/ohms_law.py)
* [Real And Reactive Power](electronics/real_and_reactive_power.py)
* [Resistor Equivalence](electronics/resistor_equivalence.py)
* [Resonant Frequency](electronics/resonant_frequency.py)
## File Transfer
* [Receive File](file_transfer/receive_file.py)
* [Send File](file_transfer/send_file.py)
* Tests
* [Test Send File](file_transfer/tests/test_send_file.py)
## Financial
* [Equated Monthly Installments](financial/equated_monthly_installments.py)
* [Interest](financial/interest.py)
* [Present Value](financial/present_value.py)
* [Price Plus Tax](financial/price_plus_tax.py)
## Fractals
* [Julia Sets](fractals/julia_sets.py)
* [Koch Snowflake](fractals/koch_snowflake.py)
* [Mandelbrot](fractals/mandelbrot.py)
* [Sierpinski Triangle](fractals/sierpinski_triangle.py)
## Fuzzy Logic
* [Fuzzy Operations](fuzzy_logic/fuzzy_operations.py)
## Genetic Algorithm
* [Basic String](genetic_algorithm/basic_string.py)
## Geodesy
* [Haversine Distance](geodesy/haversine_distance.py)
* [Lamberts Ellipsoidal Distance](geodesy/lamberts_ellipsoidal_distance.py)
## Graphics
* [Bezier Curve](graphics/bezier_curve.py)
* [Vector3 For 2D Rendering](graphics/vector3_for_2d_rendering.py)
## Graphs
* [A Star](graphs/a_star.py)
* [Articulation Points](graphs/articulation_points.py)
* [Basic Graphs](graphs/basic_graphs.py)
* [Bellman Ford](graphs/bellman_ford.py)
* [Bi Directional Dijkstra](graphs/bi_directional_dijkstra.py)
* [Bidirectional A Star](graphs/bidirectional_a_star.py)
* [Bidirectional Breadth First Search](graphs/bidirectional_breadth_first_search.py)
* [Boruvka](graphs/boruvka.py)
* [Breadth First Search](graphs/breadth_first_search.py)
* [Breadth First Search 2](graphs/breadth_first_search_2.py)
* [Breadth First Search Shortest Path](graphs/breadth_first_search_shortest_path.py)
* [Breadth First Search Shortest Path 2](graphs/breadth_first_search_shortest_path_2.py)
* [Breadth First Search Zero One Shortest Path](graphs/breadth_first_search_zero_one_shortest_path.py)
* [Check Bipartite Graph Bfs](graphs/check_bipartite_graph_bfs.py)
* [Check Bipartite Graph Dfs](graphs/check_bipartite_graph_dfs.py)
* [Check Cycle](graphs/check_cycle.py)
* [Connected Components](graphs/connected_components.py)
* [Depth First Search](graphs/depth_first_search.py)
* [Depth First Search 2](graphs/depth_first_search_2.py)
* [Dijkstra](graphs/dijkstra.py)
* [Dijkstra 2](graphs/dijkstra_2.py)
* [Dijkstra Algorithm](graphs/dijkstra_algorithm.py)
* [Dijkstra Alternate](graphs/dijkstra_alternate.py)
* [Dijkstra Binary Grid](graphs/dijkstra_binary_grid.py)
* [Dinic](graphs/dinic.py)
* [Directed And Undirected (Weighted) Graph](graphs/directed_and_undirected_(weighted)_graph.py)
* [Edmonds Karp Multiple Source And Sink](graphs/edmonds_karp_multiple_source_and_sink.py)
* [Eulerian Path And Circuit For Undirected Graph](graphs/eulerian_path_and_circuit_for_undirected_graph.py)
* [Even Tree](graphs/even_tree.py)
* [Finding Bridges](graphs/finding_bridges.py)
* [Frequent Pattern Graph Miner](graphs/frequent_pattern_graph_miner.py)
* [G Topological Sort](graphs/g_topological_sort.py)
* [Gale Shapley Bigraph](graphs/gale_shapley_bigraph.py)
* [Graph Adjacency List](graphs/graph_adjacency_list.py)
* [Graph Adjacency Matrix](graphs/graph_adjacency_matrix.py)
* [Graph List](graphs/graph_list.py)
* [Graphs Floyd Warshall](graphs/graphs_floyd_warshall.py)
* [Greedy Best First](graphs/greedy_best_first.py)
* [Greedy Min Vertex Cover](graphs/greedy_min_vertex_cover.py)
* [Kahns Algorithm Long](graphs/kahns_algorithm_long.py)
* [Kahns Algorithm Topo](graphs/kahns_algorithm_topo.py)
* [Karger](graphs/karger.py)
* [Markov Chain](graphs/markov_chain.py)
* [Matching Min Vertex Cover](graphs/matching_min_vertex_cover.py)
* [Minimum Path Sum](graphs/minimum_path_sum.py)
* [Minimum Spanning Tree Boruvka](graphs/minimum_spanning_tree_boruvka.py)
* [Minimum Spanning Tree Kruskal](graphs/minimum_spanning_tree_kruskal.py)
* [Minimum Spanning Tree Kruskal2](graphs/minimum_spanning_tree_kruskal2.py)
* [Minimum Spanning Tree Prims](graphs/minimum_spanning_tree_prims.py)
* [Minimum Spanning Tree Prims2](graphs/minimum_spanning_tree_prims2.py)
* [Multi Heuristic Astar](graphs/multi_heuristic_astar.py)
* [Page Rank](graphs/page_rank.py)
* [Prim](graphs/prim.py)
* [Random Graph Generator](graphs/random_graph_generator.py)
* [Scc Kosaraju](graphs/scc_kosaraju.py)
* [Strongly Connected Components](graphs/strongly_connected_components.py)
* [Tarjans Scc](graphs/tarjans_scc.py)
* Tests
* [Test Min Spanning Tree Kruskal](graphs/tests/test_min_spanning_tree_kruskal.py)
* [Test Min Spanning Tree Prim](graphs/tests/test_min_spanning_tree_prim.py)
## Greedy Methods
* [Fractional Knapsack](greedy_methods/fractional_knapsack.py)
* [Fractional Knapsack 2](greedy_methods/fractional_knapsack_2.py)
* [Minimum Waiting Time](greedy_methods/minimum_waiting_time.py)
* [Optimal Merge Pattern](greedy_methods/optimal_merge_pattern.py)
## Hashes
* [Adler32](hashes/adler32.py)
* [Chaos Machine](hashes/chaos_machine.py)
* [Djb2](hashes/djb2.py)
* [Elf](hashes/elf.py)
* [Enigma Machine](hashes/enigma_machine.py)
* [Hamming Code](hashes/hamming_code.py)
* [Luhn](hashes/luhn.py)
* [Md5](hashes/md5.py)
* [Sdbm](hashes/sdbm.py)
* [Sha1](hashes/sha1.py)
* [Sha256](hashes/sha256.py)
## Knapsack
* [Greedy Knapsack](knapsack/greedy_knapsack.py)
* [Knapsack](knapsack/knapsack.py)
* [Recursive Approach Knapsack](knapsack/recursive_approach_knapsack.py)
* Tests
* [Test Greedy Knapsack](knapsack/tests/test_greedy_knapsack.py)
* [Test Knapsack](knapsack/tests/test_knapsack.py)
## Linear Algebra
* Src
* [Conjugate Gradient](linear_algebra/src/conjugate_gradient.py)
* [Lib](linear_algebra/src/lib.py)
* [Polynom For Points](linear_algebra/src/polynom_for_points.py)
* [Power Iteration](linear_algebra/src/power_iteration.py)
* [Rank Of Matrix](linear_algebra/src/rank_of_matrix.py)
* [Rayleigh Quotient](linear_algebra/src/rayleigh_quotient.py)
* [Schur Complement](linear_algebra/src/schur_complement.py)
* [Test Linear Algebra](linear_algebra/src/test_linear_algebra.py)
* [Transformations 2D](linear_algebra/src/transformations_2d.py)
## Linear Programming
* [Simplex](linear_programming/simplex.py)
## Machine Learning
* [Astar](machine_learning/astar.py)
* [Data Transformations](machine_learning/data_transformations.py)
* [Decision Tree](machine_learning/decision_tree.py)
* [Dimensionality Reduction](machine_learning/dimensionality_reduction.py)
* Forecasting
* [Run](machine_learning/forecasting/run.py)
* [Gradient Descent](machine_learning/gradient_descent.py)
* [K Means Clust](machine_learning/k_means_clust.py)
* [K Nearest Neighbours](machine_learning/k_nearest_neighbours.py)
* [Knn Sklearn](machine_learning/knn_sklearn.py)
* [Linear Discriminant Analysis](machine_learning/linear_discriminant_analysis.py)
* [Linear Regression](machine_learning/linear_regression.py)
* Local Weighted Learning
* [Local Weighted Learning](machine_learning/local_weighted_learning/local_weighted_learning.py)
* [Logistic Regression](machine_learning/logistic_regression.py)
* Lstm
* [Lstm Prediction](machine_learning/lstm/lstm_prediction.py)
* [Multilayer Perceptron Classifier](machine_learning/multilayer_perceptron_classifier.py)
* [Polynomial Regression](machine_learning/polynomial_regression.py)
* [Scoring Functions](machine_learning/scoring_functions.py)
* [Self Organizing Map](machine_learning/self_organizing_map.py)
* [Sequential Minimum Optimization](machine_learning/sequential_minimum_optimization.py)
* [Similarity Search](machine_learning/similarity_search.py)
* [Support Vector Machines](machine_learning/support_vector_machines.py)
* [Word Frequency Functions](machine_learning/word_frequency_functions.py)
* [Xgboost Classifier](machine_learning/xgboost_classifier.py)
* [Xgboost Regressor](machine_learning/xgboost_regressor.py)
## Maths
* [Abs](maths/abs.py)
* [Add](maths/add.py)
* [Addition Without Arithmetic](maths/addition_without_arithmetic.py)
* [Aliquot Sum](maths/aliquot_sum.py)
* [Allocation Number](maths/allocation_number.py)
* [Arc Length](maths/arc_length.py)
* [Area](maths/area.py)
* [Area Under Curve](maths/area_under_curve.py)
* [Armstrong Numbers](maths/armstrong_numbers.py)
* [Automorphic Number](maths/automorphic_number.py)
* [Average Absolute Deviation](maths/average_absolute_deviation.py)
* [Average Mean](maths/average_mean.py)
* [Average Median](maths/average_median.py)
* [Average Mode](maths/average_mode.py)
* [Bailey Borwein Plouffe](maths/bailey_borwein_plouffe.py)
* [Basic Maths](maths/basic_maths.py)
* [Binary Exp Mod](maths/binary_exp_mod.py)
* [Binary Exponentiation](maths/binary_exponentiation.py)
* [Binary Exponentiation 2](maths/binary_exponentiation_2.py)
* [Binary Exponentiation 3](maths/binary_exponentiation_3.py)
* [Binomial Coefficient](maths/binomial_coefficient.py)
* [Binomial Distribution](maths/binomial_distribution.py)
* [Bisection](maths/bisection.py)
* [Carmichael Number](maths/carmichael_number.py)
* [Catalan Number](maths/catalan_number.py)
* [Ceil](maths/ceil.py)
* [Check Polygon](maths/check_polygon.py)
* [Chudnovsky Algorithm](maths/chudnovsky_algorithm.py)
* [Collatz Sequence](maths/collatz_sequence.py)
* [Combinations](maths/combinations.py)
* [Decimal Isolate](maths/decimal_isolate.py)
* [Decimal To Fraction](maths/decimal_to_fraction.py)
* [Dodecahedron](maths/dodecahedron.py)
* [Double Factorial Iterative](maths/double_factorial_iterative.py)
* [Double Factorial Recursive](maths/double_factorial_recursive.py)
* [Dual Number Automatic Differentiation](maths/dual_number_automatic_differentiation.py)
* [Entropy](maths/entropy.py)
* [Euclidean Distance](maths/euclidean_distance.py)
* [Euclidean Gcd](maths/euclidean_gcd.py)
* [Euler Method](maths/euler_method.py)
* [Euler Modified](maths/euler_modified.py)
* [Eulers Totient](maths/eulers_totient.py)
* [Extended Euclidean Algorithm](maths/extended_euclidean_algorithm.py)
* [Factorial](maths/factorial.py)
* [Factors](maths/factors.py)
* [Fermat Little Theorem](maths/fermat_little_theorem.py)
* [Fibonacci](maths/fibonacci.py)
* [Find Max](maths/find_max.py)
* [Find Max Recursion](maths/find_max_recursion.py)
* [Find Min](maths/find_min.py)
* [Find Min Recursion](maths/find_min_recursion.py)
* [Floor](maths/floor.py)
* [Gamma](maths/gamma.py)
* [Gamma Recursive](maths/gamma_recursive.py)
* [Gaussian](maths/gaussian.py)
* [Gaussian Error Linear Unit](maths/gaussian_error_linear_unit.py)
* [Gcd Of N Numbers](maths/gcd_of_n_numbers.py)
* [Greatest Common Divisor](maths/greatest_common_divisor.py)
* [Greedy Coin Change](maths/greedy_coin_change.py)
* [Hamming Numbers](maths/hamming_numbers.py)
* [Hardy Ramanujanalgo](maths/hardy_ramanujanalgo.py)
* [Hexagonal Number](maths/hexagonal_number.py)
* [Integration By Simpson Approx](maths/integration_by_simpson_approx.py)
* [Is Int Palindrome](maths/is_int_palindrome.py)
* [Is Ip V4 Address Valid](maths/is_ip_v4_address_valid.py)
* [Is Square Free](maths/is_square_free.py)
* [Jaccard Similarity](maths/jaccard_similarity.py)
* [Juggler Sequence](maths/juggler_sequence.py)
* [Karatsuba](maths/karatsuba.py)
* [Krishnamurthy Number](maths/krishnamurthy_number.py)
* [Kth Lexicographic Permutation](maths/kth_lexicographic_permutation.py)
* [Largest Of Very Large Numbers](maths/largest_of_very_large_numbers.py)
* [Least Common Multiple](maths/least_common_multiple.py)
* [Line Length](maths/line_length.py)
* [Liouville Lambda](maths/liouville_lambda.py)
* [Lucas Lehmer Primality Test](maths/lucas_lehmer_primality_test.py)
* [Lucas Series](maths/lucas_series.py)
* [Maclaurin Series](maths/maclaurin_series.py)
* [Manhattan Distance](maths/manhattan_distance.py)
* [Matrix Exponentiation](maths/matrix_exponentiation.py)
* [Max Sum Sliding Window](maths/max_sum_sliding_window.py)
* [Median Of Two Arrays](maths/median_of_two_arrays.py)
* [Miller Rabin](maths/miller_rabin.py)
* [Mobius Function](maths/mobius_function.py)
* [Modular Exponential](maths/modular_exponential.py)
* [Monte Carlo](maths/monte_carlo.py)
* [Monte Carlo Dice](maths/monte_carlo_dice.py)
* [Nevilles Method](maths/nevilles_method.py)
* [Newton Raphson](maths/newton_raphson.py)
* [Number Of Digits](maths/number_of_digits.py)
* [Numerical Integration](maths/numerical_integration.py)
* [Odd Sieve](maths/odd_sieve.py)
* [Perfect Cube](maths/perfect_cube.py)
* [Perfect Number](maths/perfect_number.py)
* [Perfect Square](maths/perfect_square.py)
* [Persistence](maths/persistence.py)
* [Pi Generator](maths/pi_generator.py)
* [Pi Monte Carlo Estimation](maths/pi_monte_carlo_estimation.py)
* [Points Are Collinear 3D](maths/points_are_collinear_3d.py)
* [Pollard Rho](maths/pollard_rho.py)
* [Polynomial Evaluation](maths/polynomial_evaluation.py)
* Polynomials
* [Single Indeterminate Operations](maths/polynomials/single_indeterminate_operations.py)
* [Power Using Recursion](maths/power_using_recursion.py)
* [Prime Check](maths/prime_check.py)
* [Prime Factors](maths/prime_factors.py)
* [Prime Numbers](maths/prime_numbers.py)
* [Prime Sieve Eratosthenes](maths/prime_sieve_eratosthenes.py)
* [Primelib](maths/primelib.py)
* [Print Multiplication Table](maths/print_multiplication_table.py)
* [Pronic Number](maths/pronic_number.py)
* [Proth Number](maths/proth_number.py)
* [Pythagoras](maths/pythagoras.py)
* [Qr Decomposition](maths/qr_decomposition.py)
* [Quadratic Equations Complex Numbers](maths/quadratic_equations_complex_numbers.py)
* [Radians](maths/radians.py)
* [Radix2 Fft](maths/radix2_fft.py)
* [Relu](maths/relu.py)
* [Remove Digit](maths/remove_digit.py)
* [Runge Kutta](maths/runge_kutta.py)
* [Segmented Sieve](maths/segmented_sieve.py)
* Series
* [Arithmetic](maths/series/arithmetic.py)
* [Geometric](maths/series/geometric.py)
* [Geometric Series](maths/series/geometric_series.py)
* [Harmonic](maths/series/harmonic.py)
* [Harmonic Series](maths/series/harmonic_series.py)
* [Hexagonal Numbers](maths/series/hexagonal_numbers.py)
* [P Series](maths/series/p_series.py)
* [Sieve Of Eratosthenes](maths/sieve_of_eratosthenes.py)
* [Sigmoid](maths/sigmoid.py)
* [Sigmoid Linear Unit](maths/sigmoid_linear_unit.py)
* [Signum](maths/signum.py)
* [Simpson Rule](maths/simpson_rule.py)
* [Simultaneous Linear Equation Solver](maths/simultaneous_linear_equation_solver.py)
* [Sin](maths/sin.py)
* [Sock Merchant](maths/sock_merchant.py)
* [Softmax](maths/softmax.py)
* [Square Root](maths/square_root.py)
* [Sum Of Arithmetic Series](maths/sum_of_arithmetic_series.py)
* [Sum Of Digits](maths/sum_of_digits.py)
* [Sum Of Geometric Progression](maths/sum_of_geometric_progression.py)
* [Sum Of Harmonic Series](maths/sum_of_harmonic_series.py)
* [Sumset](maths/sumset.py)
* [Sylvester Sequence](maths/sylvester_sequence.py)
* [Tanh](maths/tanh.py)
* [Test Prime Check](maths/test_prime_check.py)
* [Trapezoidal Rule](maths/trapezoidal_rule.py)
* [Triplet Sum](maths/triplet_sum.py)
* [Twin Prime](maths/twin_prime.py)
* [Two Pointer](maths/two_pointer.py)
* [Two Sum](maths/two_sum.py)
* [Ugly Numbers](maths/ugly_numbers.py)
* [Volume](maths/volume.py)
* [Weird Number](maths/weird_number.py)
* [Zellers Congruence](maths/zellers_congruence.py)
## Matrix
* [Binary Search Matrix](matrix/binary_search_matrix.py)
* [Count Islands In Matrix](matrix/count_islands_in_matrix.py)
* [Count Negative Numbers In Sorted Matrix](matrix/count_negative_numbers_in_sorted_matrix.py)
* [Count Paths](matrix/count_paths.py)
* [Cramers Rule 2X2](matrix/cramers_rule_2x2.py)
* [Inverse Of Matrix](matrix/inverse_of_matrix.py)
* [Largest Square Area In Matrix](matrix/largest_square_area_in_matrix.py)
* [Matrix Class](matrix/matrix_class.py)
* [Matrix Operation](matrix/matrix_operation.py)
* [Max Area Of Island](matrix/max_area_of_island.py)
* [Nth Fibonacci Using Matrix Exponentiation](matrix/nth_fibonacci_using_matrix_exponentiation.py)
* [Pascal Triangle](matrix/pascal_triangle.py)
* [Rotate Matrix](matrix/rotate_matrix.py)
* [Searching In Sorted Matrix](matrix/searching_in_sorted_matrix.py)
* [Sherman Morrison](matrix/sherman_morrison.py)
* [Spiral Print](matrix/spiral_print.py)
* Tests
* [Test Matrix Operation](matrix/tests/test_matrix_operation.py)
## Networking Flow
* [Ford Fulkerson](networking_flow/ford_fulkerson.py)
* [Minimum Cut](networking_flow/minimum_cut.py)
## Neural Network
* [2 Hidden Layers Neural Network](neural_network/2_hidden_layers_neural_network.py)
* Activation Functions
* [Exponential Linear Unit](neural_network/activation_functions/exponential_linear_unit.py)
* [Back Propagation Neural Network](neural_network/back_propagation_neural_network.py)
* [Convolution Neural Network](neural_network/convolution_neural_network.py)
* [Input Data](neural_network/input_data.py)
* [Perceptron](neural_network/perceptron.py)
* [Simple Neural Network](neural_network/simple_neural_network.py)
## Other
* [Activity Selection](other/activity_selection.py)
* [Alternative List Arrange](other/alternative_list_arrange.py)
* [Davisb Putnamb Logemannb Loveland](other/davisb_putnamb_logemannb_loveland.py)
* [Dijkstra Bankers Algorithm](other/dijkstra_bankers_algorithm.py)
* [Doomsday](other/doomsday.py)
* [Fischer Yates Shuffle](other/fischer_yates_shuffle.py)
* [Gauss Easter](other/gauss_easter.py)
* [Graham Scan](other/graham_scan.py)
* [Greedy](other/greedy.py)
* [Guess The Number Search](other/guess_the_number_search.py)
* [H Index](other/h_index.py)
* [Least Recently Used](other/least_recently_used.py)
* [Lfu Cache](other/lfu_cache.py)
* [Linear Congruential Generator](other/linear_congruential_generator.py)
* [Lru Cache](other/lru_cache.py)
* [Magicdiamondpattern](other/magicdiamondpattern.py)
* [Maximum Subsequence](other/maximum_subsequence.py)
* [Nested Brackets](other/nested_brackets.py)
* [Number Container System](other/number_container_system.py)
* [Password](other/password.py)
* [Quine](other/quine.py)
* [Scoring Algorithm](other/scoring_algorithm.py)
* [Sdes](other/sdes.py)
* [Tower Of Hanoi](other/tower_of_hanoi.py)
## Physics
* [Altitude Pressure](physics/altitude_pressure.py)
* [Archimedes Principle](physics/archimedes_principle.py)
* [Basic Orbital Capture](physics/basic_orbital_capture.py)
* [Casimir Effect](physics/casimir_effect.py)
* [Centripetal Force](physics/centripetal_force.py)
* [Grahams Law](physics/grahams_law.py)
* [Horizontal Projectile Motion](physics/horizontal_projectile_motion.py)
* [Hubble Parameter](physics/hubble_parameter.py)
* [Ideal Gas Law](physics/ideal_gas_law.py)
* [Kinetic Energy](physics/kinetic_energy.py)
* [Lorentz Transformation Four Vector](physics/lorentz_transformation_four_vector.py)
* [Malus Law](physics/malus_law.py)
* [N Body Simulation](physics/n_body_simulation.py)
* [Newtons Law Of Gravitation](physics/newtons_law_of_gravitation.py)
* [Newtons Second Law Of Motion](physics/newtons_second_law_of_motion.py)
* [Potential Energy](physics/potential_energy.py)
* [Rms Speed Of Molecule](physics/rms_speed_of_molecule.py)
* [Shear Stress](physics/shear_stress.py)
* [Speed Of Sound](physics/speed_of_sound.py)
## Project Euler
* Problem 001
* [Sol1](project_euler/problem_001/sol1.py)
* [Sol2](project_euler/problem_001/sol2.py)
* [Sol3](project_euler/problem_001/sol3.py)
* [Sol4](project_euler/problem_001/sol4.py)
* [Sol5](project_euler/problem_001/sol5.py)
* [Sol6](project_euler/problem_001/sol6.py)
* [Sol7](project_euler/problem_001/sol7.py)
* Problem 002
* [Sol1](project_euler/problem_002/sol1.py)
* [Sol2](project_euler/problem_002/sol2.py)
* [Sol3](project_euler/problem_002/sol3.py)
* [Sol4](project_euler/problem_002/sol4.py)
* [Sol5](project_euler/problem_002/sol5.py)
* Problem 003
* [Sol1](project_euler/problem_003/sol1.py)
* [Sol2](project_euler/problem_003/sol2.py)
* [Sol3](project_euler/problem_003/sol3.py)
* Problem 004
* [Sol1](project_euler/problem_004/sol1.py)
* [Sol2](project_euler/problem_004/sol2.py)
* Problem 005
* [Sol1](project_euler/problem_005/sol1.py)
* [Sol2](project_euler/problem_005/sol2.py)
* Problem 006
* [Sol1](project_euler/problem_006/sol1.py)
* [Sol2](project_euler/problem_006/sol2.py)
* [Sol3](project_euler/problem_006/sol3.py)
* [Sol4](project_euler/problem_006/sol4.py)
* Problem 007
* [Sol1](project_euler/problem_007/sol1.py)
* [Sol2](project_euler/problem_007/sol2.py)
* [Sol3](project_euler/problem_007/sol3.py)
* Problem 008
* [Sol1](project_euler/problem_008/sol1.py)
* [Sol2](project_euler/problem_008/sol2.py)
* [Sol3](project_euler/problem_008/sol3.py)
* Problem 009
* [Sol1](project_euler/problem_009/sol1.py)
* [Sol2](project_euler/problem_009/sol2.py)
* [Sol3](project_euler/problem_009/sol3.py)
* Problem 010
* [Sol1](project_euler/problem_010/sol1.py)
* [Sol2](project_euler/problem_010/sol2.py)
* [Sol3](project_euler/problem_010/sol3.py)
* Problem 011
* [Sol1](project_euler/problem_011/sol1.py)
* [Sol2](project_euler/problem_011/sol2.py)
* Problem 012
* [Sol1](project_euler/problem_012/sol1.py)
* [Sol2](project_euler/problem_012/sol2.py)
* Problem 013
* [Sol1](project_euler/problem_013/sol1.py)
* Problem 014
* [Sol1](project_euler/problem_014/sol1.py)
* [Sol2](project_euler/problem_014/sol2.py)
* Problem 015
* [Sol1](project_euler/problem_015/sol1.py)
* Problem 016
* [Sol1](project_euler/problem_016/sol1.py)
* [Sol2](project_euler/problem_016/sol2.py)
* Problem 017
* [Sol1](project_euler/problem_017/sol1.py)
* Problem 018
* [Solution](project_euler/problem_018/solution.py)
* Problem 019
* [Sol1](project_euler/problem_019/sol1.py)
* Problem 020
* [Sol1](project_euler/problem_020/sol1.py)
* [Sol2](project_euler/problem_020/sol2.py)
* [Sol3](project_euler/problem_020/sol3.py)
* [Sol4](project_euler/problem_020/sol4.py)
* Problem 021
* [Sol1](project_euler/problem_021/sol1.py)
* Problem 022
* [Sol1](project_euler/problem_022/sol1.py)
* [Sol2](project_euler/problem_022/sol2.py)
* Problem 023
* [Sol1](project_euler/problem_023/sol1.py)
* Problem 024
* [Sol1](project_euler/problem_024/sol1.py)
* Problem 025
* [Sol1](project_euler/problem_025/sol1.py)
* [Sol2](project_euler/problem_025/sol2.py)
* [Sol3](project_euler/problem_025/sol3.py)
* Problem 026
* [Sol1](project_euler/problem_026/sol1.py)
* Problem 027
* [Sol1](project_euler/problem_027/sol1.py)
* Problem 028
* [Sol1](project_euler/problem_028/sol1.py)
* Problem 029
* [Sol1](project_euler/problem_029/sol1.py)
* Problem 030
* [Sol1](project_euler/problem_030/sol1.py)
* Problem 031
* [Sol1](project_euler/problem_031/sol1.py)
* [Sol2](project_euler/problem_031/sol2.py)
* Problem 032
* [Sol32](project_euler/problem_032/sol32.py)
* Problem 033
* [Sol1](project_euler/problem_033/sol1.py)
* Problem 034
* [Sol1](project_euler/problem_034/sol1.py)
* Problem 035
* [Sol1](project_euler/problem_035/sol1.py)
* Problem 036
* [Sol1](project_euler/problem_036/sol1.py)
* Problem 037
* [Sol1](project_euler/problem_037/sol1.py)
* Problem 038
* [Sol1](project_euler/problem_038/sol1.py)
* Problem 039
* [Sol1](project_euler/problem_039/sol1.py)
* Problem 040
* [Sol1](project_euler/problem_040/sol1.py)
* Problem 041
* [Sol1](project_euler/problem_041/sol1.py)
* Problem 042
* [Solution42](project_euler/problem_042/solution42.py)
* Problem 043
* [Sol1](project_euler/problem_043/sol1.py)
* Problem 044
* [Sol1](project_euler/problem_044/sol1.py)
* Problem 045
* [Sol1](project_euler/problem_045/sol1.py)
* Problem 046
* [Sol1](project_euler/problem_046/sol1.py)
* Problem 047
* [Sol1](project_euler/problem_047/sol1.py)
* Problem 048
* [Sol1](project_euler/problem_048/sol1.py)
* Problem 049
* [Sol1](project_euler/problem_049/sol1.py)
* Problem 050
* [Sol1](project_euler/problem_050/sol1.py)
* Problem 051
* [Sol1](project_euler/problem_051/sol1.py)
* Problem 052
* [Sol1](project_euler/problem_052/sol1.py)
* Problem 053
* [Sol1](project_euler/problem_053/sol1.py)
* Problem 054
* [Sol1](project_euler/problem_054/sol1.py)
* [Test Poker Hand](project_euler/problem_054/test_poker_hand.py)
* Problem 055
* [Sol1](project_euler/problem_055/sol1.py)
* Problem 056
* [Sol1](project_euler/problem_056/sol1.py)
* Problem 057
* [Sol1](project_euler/problem_057/sol1.py)
* Problem 058
* [Sol1](project_euler/problem_058/sol1.py)
* Problem 059
* [Sol1](project_euler/problem_059/sol1.py)
* Problem 062
* [Sol1](project_euler/problem_062/sol1.py)
* Problem 063
* [Sol1](project_euler/problem_063/sol1.py)
* Problem 064
* [Sol1](project_euler/problem_064/sol1.py)
* Problem 065
* [Sol1](project_euler/problem_065/sol1.py)
* Problem 067
* [Sol1](project_euler/problem_067/sol1.py)
* [Sol2](project_euler/problem_067/sol2.py)
* Problem 068
* [Sol1](project_euler/problem_068/sol1.py)
* Problem 069
* [Sol1](project_euler/problem_069/sol1.py)
* Problem 070
* [Sol1](project_euler/problem_070/sol1.py)
* Problem 071
* [Sol1](project_euler/problem_071/sol1.py)
* Problem 072
* [Sol1](project_euler/problem_072/sol1.py)
* [Sol2](project_euler/problem_072/sol2.py)
* Problem 073
* [Sol1](project_euler/problem_073/sol1.py)
* Problem 074
* [Sol1](project_euler/problem_074/sol1.py)
* [Sol2](project_euler/problem_074/sol2.py)
* Problem 075
* [Sol1](project_euler/problem_075/sol1.py)
* Problem 076
* [Sol1](project_euler/problem_076/sol1.py)
* Problem 077
* [Sol1](project_euler/problem_077/sol1.py)
* Problem 078
* [Sol1](project_euler/problem_078/sol1.py)
* Problem 079
* [Sol1](project_euler/problem_079/sol1.py)
* Problem 080
* [Sol1](project_euler/problem_080/sol1.py)
* Problem 081
* [Sol1](project_euler/problem_081/sol1.py)
* Problem 082
* [Sol1](project_euler/problem_082/sol1.py)
* Problem 085
* [Sol1](project_euler/problem_085/sol1.py)
* Problem 086
* [Sol1](project_euler/problem_086/sol1.py)
* Problem 087
* [Sol1](project_euler/problem_087/sol1.py)
* Problem 089
* [Sol1](project_euler/problem_089/sol1.py)
* Problem 091
* [Sol1](project_euler/problem_091/sol1.py)
* Problem 092
* [Sol1](project_euler/problem_092/sol1.py)
* Problem 094
* [Sol1](project_euler/problem_094/sol1.py)
* Problem 097
* [Sol1](project_euler/problem_097/sol1.py)
* Problem 099
* [Sol1](project_euler/problem_099/sol1.py)
* Problem 100
* [Sol1](project_euler/problem_100/sol1.py)
* Problem 101
* [Sol1](project_euler/problem_101/sol1.py)
* Problem 102
* [Sol1](project_euler/problem_102/sol1.py)
* Problem 104
* [Sol1](project_euler/problem_104/sol1.py)
* Problem 107
* [Sol1](project_euler/problem_107/sol1.py)
* Problem 109
* [Sol1](project_euler/problem_109/sol1.py)
* Problem 112
* [Sol1](project_euler/problem_112/sol1.py)
* Problem 113
* [Sol1](project_euler/problem_113/sol1.py)
* Problem 114
* [Sol1](project_euler/problem_114/sol1.py)
* Problem 115
* [Sol1](project_euler/problem_115/sol1.py)
* Problem 116
* [Sol1](project_euler/problem_116/sol1.py)
* Problem 117
* [Sol1](project_euler/problem_117/sol1.py)
* Problem 119
* [Sol1](project_euler/problem_119/sol1.py)
* Problem 120
* [Sol1](project_euler/problem_120/sol1.py)
* Problem 121
* [Sol1](project_euler/problem_121/sol1.py)
* Problem 123
* [Sol1](project_euler/problem_123/sol1.py)
* Problem 125
* [Sol1](project_euler/problem_125/sol1.py)
* Problem 129
* [Sol1](project_euler/problem_129/sol1.py)
* Problem 131
* [Sol1](project_euler/problem_131/sol1.py)
* Problem 135
* [Sol1](project_euler/problem_135/sol1.py)
* Problem 144
* [Sol1](project_euler/problem_144/sol1.py)
* Problem 145
* [Sol1](project_euler/problem_145/sol1.py)
* Problem 173
* [Sol1](project_euler/problem_173/sol1.py)
* Problem 174
* [Sol1](project_euler/problem_174/sol1.py)
* Problem 180
* [Sol1](project_euler/problem_180/sol1.py)
* Problem 187
* [Sol1](project_euler/problem_187/sol1.py)
* Problem 188
* [Sol1](project_euler/problem_188/sol1.py)
* Problem 191
* [Sol1](project_euler/problem_191/sol1.py)
* Problem 203
* [Sol1](project_euler/problem_203/sol1.py)
* Problem 205
* [Sol1](project_euler/problem_205/sol1.py)
* Problem 206
* [Sol1](project_euler/problem_206/sol1.py)
* Problem 207
* [Sol1](project_euler/problem_207/sol1.py)
* Problem 234
* [Sol1](project_euler/problem_234/sol1.py)
* Problem 301
* [Sol1](project_euler/problem_301/sol1.py)
* Problem 493
* [Sol1](project_euler/problem_493/sol1.py)
* Problem 551
* [Sol1](project_euler/problem_551/sol1.py)
* Problem 587
* [Sol1](project_euler/problem_587/sol1.py)
* Problem 686
* [Sol1](project_euler/problem_686/sol1.py)
* Problem 800
* [Sol1](project_euler/problem_800/sol1.py)
## Quantum
* [Bb84](quantum/bb84.py)
* [Deutsch Jozsa](quantum/deutsch_jozsa.py)
* [Half Adder](quantum/half_adder.py)
* [Not Gate](quantum/not_gate.py)
* [Q Fourier Transform](quantum/q_fourier_transform.py)
* [Q Full Adder](quantum/q_full_adder.py)
* [Quantum Entanglement](quantum/quantum_entanglement.py)
* [Quantum Teleportation](quantum/quantum_teleportation.py)
* [Ripple Adder Classic](quantum/ripple_adder_classic.py)
* [Single Qubit Measure](quantum/single_qubit_measure.py)
* [Superdense Coding](quantum/superdense_coding.py)
## Scheduling
* [First Come First Served](scheduling/first_come_first_served.py)
* [Highest Response Ratio Next](scheduling/highest_response_ratio_next.py)
* [Job Sequencing With Deadline](scheduling/job_sequencing_with_deadline.py)
* [Multi Level Feedback Queue](scheduling/multi_level_feedback_queue.py)
* [Non Preemptive Shortest Job First](scheduling/non_preemptive_shortest_job_first.py)
* [Round Robin](scheduling/round_robin.py)
* [Shortest Job First](scheduling/shortest_job_first.py)
## Searches
* [Binary Search](searches/binary_search.py)
* [Binary Tree Traversal](searches/binary_tree_traversal.py)
* [Double Linear Search](searches/double_linear_search.py)
* [Double Linear Search Recursion](searches/double_linear_search_recursion.py)
* [Fibonacci Search](searches/fibonacci_search.py)
* [Hill Climbing](searches/hill_climbing.py)
* [Interpolation Search](searches/interpolation_search.py)
* [Jump Search](searches/jump_search.py)
* [Linear Search](searches/linear_search.py)
* [Quick Select](searches/quick_select.py)
* [Sentinel Linear Search](searches/sentinel_linear_search.py)
* [Simple Binary Search](searches/simple_binary_search.py)
* [Simulated Annealing](searches/simulated_annealing.py)
* [Tabu Search](searches/tabu_search.py)
* [Ternary Search](searches/ternary_search.py)
## Sorts
* [Bead Sort](sorts/bead_sort.py)
* [Binary Insertion Sort](sorts/binary_insertion_sort.py)
* [Bitonic Sort](sorts/bitonic_sort.py)
* [Bogo Sort](sorts/bogo_sort.py)
* [Bubble Sort](sorts/bubble_sort.py)
* [Bucket Sort](sorts/bucket_sort.py)
* [Circle Sort](sorts/circle_sort.py)
* [Cocktail Shaker Sort](sorts/cocktail_shaker_sort.py)
* [Comb Sort](sorts/comb_sort.py)
* [Counting Sort](sorts/counting_sort.py)
* [Cycle Sort](sorts/cycle_sort.py)
* [Double Sort](sorts/double_sort.py)
* [Dutch National Flag Sort](sorts/dutch_national_flag_sort.py)
* [Exchange Sort](sorts/exchange_sort.py)
* [External Sort](sorts/external_sort.py)
* [Gnome Sort](sorts/gnome_sort.py)
* [Heap Sort](sorts/heap_sort.py)
* [Insertion Sort](sorts/insertion_sort.py)
* [Intro Sort](sorts/intro_sort.py)
* [Iterative Merge Sort](sorts/iterative_merge_sort.py)
* [Merge Insertion Sort](sorts/merge_insertion_sort.py)
* [Merge Sort](sorts/merge_sort.py)
* [Msd Radix Sort](sorts/msd_radix_sort.py)
* [Natural Sort](sorts/natural_sort.py)
* [Odd Even Sort](sorts/odd_even_sort.py)
* [Odd Even Transposition Parallel](sorts/odd_even_transposition_parallel.py)
* [Odd Even Transposition Single Threaded](sorts/odd_even_transposition_single_threaded.py)
* [Pancake Sort](sorts/pancake_sort.py)
* [Patience Sort](sorts/patience_sort.py)
* [Pigeon Sort](sorts/pigeon_sort.py)
* [Pigeonhole Sort](sorts/pigeonhole_sort.py)
* [Quick Sort](sorts/quick_sort.py)
* [Quick Sort 3 Partition](sorts/quick_sort_3_partition.py)
* [Radix Sort](sorts/radix_sort.py)
* [Random Normal Distribution Quicksort](sorts/random_normal_distribution_quicksort.py)
* [Random Pivot Quick Sort](sorts/random_pivot_quick_sort.py)
* [Recursive Bubble Sort](sorts/recursive_bubble_sort.py)
* [Recursive Insertion Sort](sorts/recursive_insertion_sort.py)
* [Recursive Mergesort Array](sorts/recursive_mergesort_array.py)
* [Recursive Quick Sort](sorts/recursive_quick_sort.py)
* [Selection Sort](sorts/selection_sort.py)
* [Shell Sort](sorts/shell_sort.py)
* [Shrink Shell Sort](sorts/shrink_shell_sort.py)
* [Slowsort](sorts/slowsort.py)
* [Stooge Sort](sorts/stooge_sort.py)
* [Strand Sort](sorts/strand_sort.py)
* [Tim Sort](sorts/tim_sort.py)
* [Topological Sort](sorts/topological_sort.py)
* [Tree Sort](sorts/tree_sort.py)
* [Unknown Sort](sorts/unknown_sort.py)
* [Wiggle Sort](sorts/wiggle_sort.py)
## Strings
* [Aho Corasick](strings/aho_corasick.py)
* [Alternative String Arrange](strings/alternative_string_arrange.py)
* [Anagrams](strings/anagrams.py)
* [Autocomplete Using Trie](strings/autocomplete_using_trie.py)
* [Barcode Validator](strings/barcode_validator.py)
* [Boyer Moore Search](strings/boyer_moore_search.py)
* [Can String Be Rearranged As Palindrome](strings/can_string_be_rearranged_as_palindrome.py)
* [Capitalize](strings/capitalize.py)
* [Check Anagrams](strings/check_anagrams.py)
* [Credit Card Validator](strings/credit_card_validator.py)
* [Detecting English Programmatically](strings/detecting_english_programmatically.py)
* [Dna](strings/dna.py)
* [Frequency Finder](strings/frequency_finder.py)
* [Hamming Distance](strings/hamming_distance.py)
* [Indian Phone Validator](strings/indian_phone_validator.py)
* [Is Contains Unique Chars](strings/is_contains_unique_chars.py)
* [Is Isogram](strings/is_isogram.py)
* [Is Pangram](strings/is_pangram.py)
* [Is Spain National Id](strings/is_spain_national_id.py)
* [Is Srilankan Phone Number](strings/is_srilankan_phone_number.py)
* [Jaro Winkler](strings/jaro_winkler.py)
* [Join](strings/join.py)
* [Knuth Morris Pratt](strings/knuth_morris_pratt.py)
* [Levenshtein Distance](strings/levenshtein_distance.py)
* [Lower](strings/lower.py)
* [Manacher](strings/manacher.py)
* [Min Cost String Conversion](strings/min_cost_string_conversion.py)
* [Naive String Search](strings/naive_string_search.py)
* [Ngram](strings/ngram.py)
* [Palindrome](strings/palindrome.py)
* [Prefix Function](strings/prefix_function.py)
* [Rabin Karp](strings/rabin_karp.py)
* [Remove Duplicate](strings/remove_duplicate.py)
* [Reverse Letters](strings/reverse_letters.py)
* [Reverse Long Words](strings/reverse_long_words.py)
* [Reverse Words](strings/reverse_words.py)
* [Snake Case To Camel Pascal Case](strings/snake_case_to_camel_pascal_case.py)
* [Split](strings/split.py)
* [String Switch Case](strings/string_switch_case.py)
* [Text Justification](strings/text_justification.py)
* [Top K Frequent Words](strings/top_k_frequent_words.py)
* [Upper](strings/upper.py)
* [Wave](strings/wave.py)
* [Wildcard Pattern Matching](strings/wildcard_pattern_matching.py)
* [Word Occurrence](strings/word_occurrence.py)
* [Word Patterns](strings/word_patterns.py)
* [Z Function](strings/z_function.py)
## Web Programming
* [Co2 Emission](web_programming/co2_emission.py)
* [Convert Number To Words](web_programming/convert_number_to_words.py)
* [Covid Stats Via Xpath](web_programming/covid_stats_via_xpath.py)
* [Crawl Google Results](web_programming/crawl_google_results.py)
* [Crawl Google Scholar Citation](web_programming/crawl_google_scholar_citation.py)
* [Currency Converter](web_programming/currency_converter.py)
* [Current Stock Price](web_programming/current_stock_price.py)
* [Current Weather](web_programming/current_weather.py)
* [Daily Horoscope](web_programming/daily_horoscope.py)
* [Download Images From Google Query](web_programming/download_images_from_google_query.py)
* [Emails From Url](web_programming/emails_from_url.py)
* [Fetch Bbc News](web_programming/fetch_bbc_news.py)
* [Fetch Github Info](web_programming/fetch_github_info.py)
* [Fetch Jobs](web_programming/fetch_jobs.py)
* [Fetch Quotes](web_programming/fetch_quotes.py)
* [Fetch Well Rx Price](web_programming/fetch_well_rx_price.py)
* [Get Amazon Product Data](web_programming/get_amazon_product_data.py)
* [Get Imdb Top 250 Movies Csv](web_programming/get_imdb_top_250_movies_csv.py)
* [Get Imdbtop](web_programming/get_imdbtop.py)
* [Get Top Hn Posts](web_programming/get_top_hn_posts.py)
* [Get User Tweets](web_programming/get_user_tweets.py)
* [Giphy](web_programming/giphy.py)
* [Instagram Crawler](web_programming/instagram_crawler.py)
* [Instagram Pic](web_programming/instagram_pic.py)
* [Instagram Video](web_programming/instagram_video.py)
* [Nasa Data](web_programming/nasa_data.py)
* [Open Google Results](web_programming/open_google_results.py)
* [Random Anime Character](web_programming/random_anime_character.py)
* [Recaptcha Verification](web_programming/recaptcha_verification.py)
* [Reddit](web_programming/reddit.py)
* [Search Books By Isbn](web_programming/search_books_by_isbn.py)
* [Slack Message](web_programming/slack_message.py)
* [Test Fetch Github Info](web_programming/test_fetch_github_info.py)
* [World Covid19 Stats](web_programming/world_covid19_stats.py)
|
## Arithmetic Analysis
* [Bisection](arithmetic_analysis/bisection.py)
* [Gaussian Elimination](arithmetic_analysis/gaussian_elimination.py)
* [In Static Equilibrium](arithmetic_analysis/in_static_equilibrium.py)
* [Intersection](arithmetic_analysis/intersection.py)
* [Jacobi Iteration Method](arithmetic_analysis/jacobi_iteration_method.py)
* [Lu Decomposition](arithmetic_analysis/lu_decomposition.py)
* [Newton Forward Interpolation](arithmetic_analysis/newton_forward_interpolation.py)
* [Newton Method](arithmetic_analysis/newton_method.py)
* [Newton Raphson](arithmetic_analysis/newton_raphson.py)
* [Newton Raphson New](arithmetic_analysis/newton_raphson_new.py)
* [Secant Method](arithmetic_analysis/secant_method.py)
## Audio Filters
* [Butterworth Filter](audio_filters/butterworth_filter.py)
* [Iir Filter](audio_filters/iir_filter.py)
* [Show Response](audio_filters/show_response.py)
## Backtracking
* [All Combinations](backtracking/all_combinations.py)
* [All Permutations](backtracking/all_permutations.py)
* [All Subsequences](backtracking/all_subsequences.py)
* [Coloring](backtracking/coloring.py)
* [Combination Sum](backtracking/combination_sum.py)
* [Hamiltonian Cycle](backtracking/hamiltonian_cycle.py)
* [Knight Tour](backtracking/knight_tour.py)
* [Minimax](backtracking/minimax.py)
* [Minmax](backtracking/minmax.py)
* [N Queens](backtracking/n_queens.py)
* [N Queens Math](backtracking/n_queens_math.py)
* [Power Sum](backtracking/power_sum.py)
* [Rat In Maze](backtracking/rat_in_maze.py)
* [Sudoku](backtracking/sudoku.py)
* [Sum Of Subsets](backtracking/sum_of_subsets.py)
* [Word Search](backtracking/word_search.py)
## Bit Manipulation
* [Binary And Operator](bit_manipulation/binary_and_operator.py)
* [Binary Count Setbits](bit_manipulation/binary_count_setbits.py)
* [Binary Count Trailing Zeros](bit_manipulation/binary_count_trailing_zeros.py)
* [Binary Or Operator](bit_manipulation/binary_or_operator.py)
* [Binary Shifts](bit_manipulation/binary_shifts.py)
* [Binary Twos Complement](bit_manipulation/binary_twos_complement.py)
* [Binary Xor Operator](bit_manipulation/binary_xor_operator.py)
* [Count 1S Brian Kernighan Method](bit_manipulation/count_1s_brian_kernighan_method.py)
* [Count Number Of One Bits](bit_manipulation/count_number_of_one_bits.py)
* [Gray Code Sequence](bit_manipulation/gray_code_sequence.py)
* [Highest Set Bit](bit_manipulation/highest_set_bit.py)
* [Index Of Rightmost Set Bit](bit_manipulation/index_of_rightmost_set_bit.py)
* [Is Even](bit_manipulation/is_even.py)
* [Is Power Of Two](bit_manipulation/is_power_of_two.py)
* [Numbers Different Signs](bit_manipulation/numbers_different_signs.py)
* [Reverse Bits](bit_manipulation/reverse_bits.py)
* [Single Bit Manipulation Operations](bit_manipulation/single_bit_manipulation_operations.py)
## Blockchain
* [Chinese Remainder Theorem](blockchain/chinese_remainder_theorem.py)
* [Diophantine Equation](blockchain/diophantine_equation.py)
* [Modular Division](blockchain/modular_division.py)
## Boolean Algebra
* [And Gate](boolean_algebra/and_gate.py)
* [Nand Gate](boolean_algebra/nand_gate.py)
* [Norgate](boolean_algebra/norgate.py)
* [Not Gate](boolean_algebra/not_gate.py)
* [Or Gate](boolean_algebra/or_gate.py)
* [Quine Mc Cluskey](boolean_algebra/quine_mc_cluskey.py)
* [Xnor Gate](boolean_algebra/xnor_gate.py)
* [Xor Gate](boolean_algebra/xor_gate.py)
## Cellular Automata
* [Conways Game Of Life](cellular_automata/conways_game_of_life.py)
* [Game Of Life](cellular_automata/game_of_life.py)
* [Nagel Schrekenberg](cellular_automata/nagel_schrekenberg.py)
* [One Dimensional](cellular_automata/one_dimensional.py)
## Ciphers
* [A1Z26](ciphers/a1z26.py)
* [Affine Cipher](ciphers/affine_cipher.py)
* [Atbash](ciphers/atbash.py)
* [Autokey](ciphers/autokey.py)
* [Baconian Cipher](ciphers/baconian_cipher.py)
* [Base16](ciphers/base16.py)
* [Base32](ciphers/base32.py)
* [Base64](ciphers/base64.py)
* [Base85](ciphers/base85.py)
* [Beaufort Cipher](ciphers/beaufort_cipher.py)
* [Bifid](ciphers/bifid.py)
* [Brute Force Caesar Cipher](ciphers/brute_force_caesar_cipher.py)
* [Caesar Cipher](ciphers/caesar_cipher.py)
* [Cryptomath Module](ciphers/cryptomath_module.py)
* [Decrypt Caesar With Chi Squared](ciphers/decrypt_caesar_with_chi_squared.py)
* [Deterministic Miller Rabin](ciphers/deterministic_miller_rabin.py)
* [Diffie](ciphers/diffie.py)
* [Diffie Hellman](ciphers/diffie_hellman.py)
* [Elgamal Key Generator](ciphers/elgamal_key_generator.py)
* [Enigma Machine2](ciphers/enigma_machine2.py)
* [Hill Cipher](ciphers/hill_cipher.py)
* [Mixed Keyword Cypher](ciphers/mixed_keyword_cypher.py)
* [Mono Alphabetic Ciphers](ciphers/mono_alphabetic_ciphers.py)
* [Morse Code](ciphers/morse_code.py)
* [Onepad Cipher](ciphers/onepad_cipher.py)
* [Playfair Cipher](ciphers/playfair_cipher.py)
* [Polybius](ciphers/polybius.py)
* [Porta Cipher](ciphers/porta_cipher.py)
* [Rabin Miller](ciphers/rabin_miller.py)
* [Rail Fence Cipher](ciphers/rail_fence_cipher.py)
* [Rot13](ciphers/rot13.py)
* [Rsa Cipher](ciphers/rsa_cipher.py)
* [Rsa Factorization](ciphers/rsa_factorization.py)
* [Rsa Key Generator](ciphers/rsa_key_generator.py)
* [Shuffled Shift Cipher](ciphers/shuffled_shift_cipher.py)
* [Simple Keyword Cypher](ciphers/simple_keyword_cypher.py)
* [Simple Substitution Cipher](ciphers/simple_substitution_cipher.py)
* [Trafid Cipher](ciphers/trafid_cipher.py)
* [Transposition Cipher](ciphers/transposition_cipher.py)
* [Transposition Cipher Encrypt Decrypt File](ciphers/transposition_cipher_encrypt_decrypt_file.py)
* [Vigenere Cipher](ciphers/vigenere_cipher.py)
* [Xor Cipher](ciphers/xor_cipher.py)
## Compression
* [Burrows Wheeler](compression/burrows_wheeler.py)
* [Huffman](compression/huffman.py)
* [Lempel Ziv](compression/lempel_ziv.py)
* [Lempel Ziv Decompress](compression/lempel_ziv_decompress.py)
* [Lz77](compression/lz77.py)
* [Peak Signal To Noise Ratio](compression/peak_signal_to_noise_ratio.py)
* [Run Length Encoding](compression/run_length_encoding.py)
## Computer Vision
* [Cnn Classification](computer_vision/cnn_classification.py)
* [Flip Augmentation](computer_vision/flip_augmentation.py)
* [Harris Corner](computer_vision/harris_corner.py)
* [Horn Schunck](computer_vision/horn_schunck.py)
* [Mean Threshold](computer_vision/mean_threshold.py)
* [Mosaic Augmentation](computer_vision/mosaic_augmentation.py)
* [Pooling Functions](computer_vision/pooling_functions.py)
## Conversions
* [Astronomical Length Scale Conversion](conversions/astronomical_length_scale_conversion.py)
* [Binary To Decimal](conversions/binary_to_decimal.py)
* [Binary To Hexadecimal](conversions/binary_to_hexadecimal.py)
* [Binary To Octal](conversions/binary_to_octal.py)
* [Decimal To Any](conversions/decimal_to_any.py)
* [Decimal To Binary](conversions/decimal_to_binary.py)
* [Decimal To Binary Recursion](conversions/decimal_to_binary_recursion.py)
* [Decimal To Hexadecimal](conversions/decimal_to_hexadecimal.py)
* [Decimal To Octal](conversions/decimal_to_octal.py)
* [Energy Conversions](conversions/energy_conversions.py)
* [Excel Title To Column](conversions/excel_title_to_column.py)
* [Hex To Bin](conversions/hex_to_bin.py)
* [Hexadecimal To Decimal](conversions/hexadecimal_to_decimal.py)
* [Length Conversion](conversions/length_conversion.py)
* [Molecular Chemistry](conversions/molecular_chemistry.py)
* [Octal To Decimal](conversions/octal_to_decimal.py)
* [Prefix Conversions](conversions/prefix_conversions.py)
* [Prefix Conversions String](conversions/prefix_conversions_string.py)
* [Pressure Conversions](conversions/pressure_conversions.py)
* [Rgb Hsv Conversion](conversions/rgb_hsv_conversion.py)
* [Roman Numerals](conversions/roman_numerals.py)
* [Speed Conversions](conversions/speed_conversions.py)
* [Temperature Conversions](conversions/temperature_conversions.py)
* [Volume Conversions](conversions/volume_conversions.py)
* [Weight Conversion](conversions/weight_conversion.py)
## Data Structures
* Arrays
* [Permutations](data_structures/arrays/permutations.py)
* [Prefix Sum](data_structures/arrays/prefix_sum.py)
* [Product Sum](data_structures/arrays/product_sum.py)
* Binary Tree
* [Avl Tree](data_structures/binary_tree/avl_tree.py)
* [Basic Binary Tree](data_structures/binary_tree/basic_binary_tree.py)
* [Binary Search Tree](data_structures/binary_tree/binary_search_tree.py)
* [Binary Search Tree Recursive](data_structures/binary_tree/binary_search_tree_recursive.py)
* [Binary Tree Mirror](data_structures/binary_tree/binary_tree_mirror.py)
* [Binary Tree Node Sum](data_structures/binary_tree/binary_tree_node_sum.py)
* [Binary Tree Path Sum](data_structures/binary_tree/binary_tree_path_sum.py)
* [Binary Tree Traversals](data_structures/binary_tree/binary_tree_traversals.py)
* [Diff Views Of Binary Tree](data_structures/binary_tree/diff_views_of_binary_tree.py)
* [Distribute Coins](data_structures/binary_tree/distribute_coins.py)
* [Fenwick Tree](data_structures/binary_tree/fenwick_tree.py)
* [Inorder Tree Traversal 2022](data_structures/binary_tree/inorder_tree_traversal_2022.py)
* [Is Bst](data_structures/binary_tree/is_bst.py)
* [Lazy Segment Tree](data_structures/binary_tree/lazy_segment_tree.py)
* [Lowest Common Ancestor](data_structures/binary_tree/lowest_common_ancestor.py)
* [Maximum Fenwick Tree](data_structures/binary_tree/maximum_fenwick_tree.py)
* [Merge Two Binary Trees](data_structures/binary_tree/merge_two_binary_trees.py)
* [Non Recursive Segment Tree](data_structures/binary_tree/non_recursive_segment_tree.py)
* [Number Of Possible Binary Trees](data_structures/binary_tree/number_of_possible_binary_trees.py)
* [Red Black Tree](data_structures/binary_tree/red_black_tree.py)
* [Segment Tree](data_structures/binary_tree/segment_tree.py)
* [Segment Tree Other](data_structures/binary_tree/segment_tree_other.py)
* [Treap](data_structures/binary_tree/treap.py)
* [Wavelet Tree](data_structures/binary_tree/wavelet_tree.py)
* Disjoint Set
* [Alternate Disjoint Set](data_structures/disjoint_set/alternate_disjoint_set.py)
* [Disjoint Set](data_structures/disjoint_set/disjoint_set.py)
* Hashing
* [Bloom Filter](data_structures/hashing/bloom_filter.py)
* [Double Hash](data_structures/hashing/double_hash.py)
* [Hash Map](data_structures/hashing/hash_map.py)
* [Hash Table](data_structures/hashing/hash_table.py)
* [Hash Table With Linked List](data_structures/hashing/hash_table_with_linked_list.py)
* Number Theory
* [Prime Numbers](data_structures/hashing/number_theory/prime_numbers.py)
* [Quadratic Probing](data_structures/hashing/quadratic_probing.py)
* Tests
* [Test Hash Map](data_structures/hashing/tests/test_hash_map.py)
* Heap
* [Binomial Heap](data_structures/heap/binomial_heap.py)
* [Heap](data_structures/heap/heap.py)
* [Heap Generic](data_structures/heap/heap_generic.py)
* [Max Heap](data_structures/heap/max_heap.py)
* [Min Heap](data_structures/heap/min_heap.py)
* [Randomized Heap](data_structures/heap/randomized_heap.py)
* [Skew Heap](data_structures/heap/skew_heap.py)
* Linked List
* [Circular Linked List](data_structures/linked_list/circular_linked_list.py)
* [Deque Doubly](data_structures/linked_list/deque_doubly.py)
* [Doubly Linked List](data_structures/linked_list/doubly_linked_list.py)
* [Doubly Linked List Two](data_structures/linked_list/doubly_linked_list_two.py)
* [From Sequence](data_structures/linked_list/from_sequence.py)
* [Has Loop](data_structures/linked_list/has_loop.py)
* [Is Palindrome](data_structures/linked_list/is_palindrome.py)
* [Merge Two Lists](data_structures/linked_list/merge_two_lists.py)
* [Middle Element Of Linked List](data_structures/linked_list/middle_element_of_linked_list.py)
* [Print Reverse](data_structures/linked_list/print_reverse.py)
* [Singly Linked List](data_structures/linked_list/singly_linked_list.py)
* [Skip List](data_structures/linked_list/skip_list.py)
* [Swap Nodes](data_structures/linked_list/swap_nodes.py)
* Queue
* [Circular Queue](data_structures/queue/circular_queue.py)
* [Circular Queue Linked List](data_structures/queue/circular_queue_linked_list.py)
* [Double Ended Queue](data_structures/queue/double_ended_queue.py)
* [Linked Queue](data_structures/queue/linked_queue.py)
* [Priority Queue Using List](data_structures/queue/priority_queue_using_list.py)
* [Queue By List](data_structures/queue/queue_by_list.py)
* [Queue By Two Stacks](data_structures/queue/queue_by_two_stacks.py)
* [Queue On Pseudo Stack](data_structures/queue/queue_on_pseudo_stack.py)
* Stacks
* [Balanced Parentheses](data_structures/stacks/balanced_parentheses.py)
* [Dijkstras Two Stack Algorithm](data_structures/stacks/dijkstras_two_stack_algorithm.py)
* [Evaluate Postfix Notations](data_structures/stacks/evaluate_postfix_notations.py)
* [Infix To Postfix Conversion](data_structures/stacks/infix_to_postfix_conversion.py)
* [Infix To Prefix Conversion](data_structures/stacks/infix_to_prefix_conversion.py)
* [Next Greater Element](data_structures/stacks/next_greater_element.py)
* [Postfix Evaluation](data_structures/stacks/postfix_evaluation.py)
* [Prefix Evaluation](data_structures/stacks/prefix_evaluation.py)
* [Stack](data_structures/stacks/stack.py)
* [Stack With Doubly Linked List](data_structures/stacks/stack_with_doubly_linked_list.py)
* [Stack With Singly Linked List](data_structures/stacks/stack_with_singly_linked_list.py)
* [Stock Span Problem](data_structures/stacks/stock_span_problem.py)
* Trie
* [Radix Tree](data_structures/trie/radix_tree.py)
* [Trie](data_structures/trie/trie.py)
## Digital Image Processing
* [Change Brightness](digital_image_processing/change_brightness.py)
* [Change Contrast](digital_image_processing/change_contrast.py)
* [Convert To Negative](digital_image_processing/convert_to_negative.py)
* Dithering
* [Burkes](digital_image_processing/dithering/burkes.py)
* Edge Detection
* [Canny](digital_image_processing/edge_detection/canny.py)
* Filters
* [Bilateral Filter](digital_image_processing/filters/bilateral_filter.py)
* [Convolve](digital_image_processing/filters/convolve.py)
* [Gabor Filter](digital_image_processing/filters/gabor_filter.py)
* [Gaussian Filter](digital_image_processing/filters/gaussian_filter.py)
* [Local Binary Pattern](digital_image_processing/filters/local_binary_pattern.py)
* [Median Filter](digital_image_processing/filters/median_filter.py)
* [Sobel Filter](digital_image_processing/filters/sobel_filter.py)
* Histogram Equalization
* [Histogram Stretch](digital_image_processing/histogram_equalization/histogram_stretch.py)
* [Index Calculation](digital_image_processing/index_calculation.py)
* Morphological Operations
* [Dilation Operation](digital_image_processing/morphological_operations/dilation_operation.py)
* [Erosion Operation](digital_image_processing/morphological_operations/erosion_operation.py)
* Resize
* [Resize](digital_image_processing/resize/resize.py)
* Rotation
* [Rotation](digital_image_processing/rotation/rotation.py)
* [Sepia](digital_image_processing/sepia.py)
* [Test Digital Image Processing](digital_image_processing/test_digital_image_processing.py)
## Divide And Conquer
* [Closest Pair Of Points](divide_and_conquer/closest_pair_of_points.py)
* [Convex Hull](divide_and_conquer/convex_hull.py)
* [Heaps Algorithm](divide_and_conquer/heaps_algorithm.py)
* [Heaps Algorithm Iterative](divide_and_conquer/heaps_algorithm_iterative.py)
* [Inversions](divide_and_conquer/inversions.py)
* [Kth Order Statistic](divide_and_conquer/kth_order_statistic.py)
* [Max Difference Pair](divide_and_conquer/max_difference_pair.py)
* [Max Subarray](divide_and_conquer/max_subarray.py)
* [Mergesort](divide_and_conquer/mergesort.py)
* [Peak](divide_and_conquer/peak.py)
* [Power](divide_and_conquer/power.py)
* [Strassen Matrix Multiplication](divide_and_conquer/strassen_matrix_multiplication.py)
## Dynamic Programming
* [Abbreviation](dynamic_programming/abbreviation.py)
* [All Construct](dynamic_programming/all_construct.py)
* [Bitmask](dynamic_programming/bitmask.py)
* [Catalan Numbers](dynamic_programming/catalan_numbers.py)
* [Climbing Stairs](dynamic_programming/climbing_stairs.py)
* [Combination Sum Iv](dynamic_programming/combination_sum_iv.py)
* [Edit Distance](dynamic_programming/edit_distance.py)
* [Factorial](dynamic_programming/factorial.py)
* [Fast Fibonacci](dynamic_programming/fast_fibonacci.py)
* [Fibonacci](dynamic_programming/fibonacci.py)
* [Fizz Buzz](dynamic_programming/fizz_buzz.py)
* [Floyd Warshall](dynamic_programming/floyd_warshall.py)
* [Integer Partition](dynamic_programming/integer_partition.py)
* [Iterating Through Submasks](dynamic_programming/iterating_through_submasks.py)
* [K Means Clustering Tensorflow](dynamic_programming/k_means_clustering_tensorflow.py)
* [Knapsack](dynamic_programming/knapsack.py)
* [Longest Common Subsequence](dynamic_programming/longest_common_subsequence.py)
* [Longest Common Substring](dynamic_programming/longest_common_substring.py)
* [Longest Increasing Subsequence](dynamic_programming/longest_increasing_subsequence.py)
* [Longest Increasing Subsequence O(Nlogn)](dynamic_programming/longest_increasing_subsequence_o(nlogn).py)
* [Longest Sub Array](dynamic_programming/longest_sub_array.py)
* [Matrix Chain Order](dynamic_programming/matrix_chain_order.py)
* [Max Non Adjacent Sum](dynamic_programming/max_non_adjacent_sum.py)
* [Max Product Subarray](dynamic_programming/max_product_subarray.py)
* [Max Subarray Sum](dynamic_programming/max_subarray_sum.py)
* [Min Distance Up Bottom](dynamic_programming/min_distance_up_bottom.py)
* [Minimum Coin Change](dynamic_programming/minimum_coin_change.py)
* [Minimum Cost Path](dynamic_programming/minimum_cost_path.py)
* [Minimum Partition](dynamic_programming/minimum_partition.py)
* [Minimum Size Subarray Sum](dynamic_programming/minimum_size_subarray_sum.py)
* [Minimum Squares To Represent A Number](dynamic_programming/minimum_squares_to_represent_a_number.py)
* [Minimum Steps To One](dynamic_programming/minimum_steps_to_one.py)
* [Minimum Tickets Cost](dynamic_programming/minimum_tickets_cost.py)
* [Optimal Binary Search Tree](dynamic_programming/optimal_binary_search_tree.py)
* [Palindrome Partitioning](dynamic_programming/palindrome_partitioning.py)
* [Rod Cutting](dynamic_programming/rod_cutting.py)
* [Subset Generation](dynamic_programming/subset_generation.py)
* [Sum Of Subset](dynamic_programming/sum_of_subset.py)
* [Viterbi](dynamic_programming/viterbi.py)
* [Word Break](dynamic_programming/word_break.py)
## Electronics
* [Apparent Power](electronics/apparent_power.py)
* [Builtin Voltage](electronics/builtin_voltage.py)
* [Carrier Concentration](electronics/carrier_concentration.py)
* [Circular Convolution](electronics/circular_convolution.py)
* [Coulombs Law](electronics/coulombs_law.py)
* [Electric Conductivity](electronics/electric_conductivity.py)
* [Electric Power](electronics/electric_power.py)
* [Electrical Impedance](electronics/electrical_impedance.py)
* [Ind Reactance](electronics/ind_reactance.py)
* [Ohms Law](electronics/ohms_law.py)
* [Real And Reactive Power](electronics/real_and_reactive_power.py)
* [Resistor Equivalence](electronics/resistor_equivalence.py)
* [Resonant Frequency](electronics/resonant_frequency.py)
## File Transfer
* [Receive File](file_transfer/receive_file.py)
* [Send File](file_transfer/send_file.py)
* Tests
* [Test Send File](file_transfer/tests/test_send_file.py)
## Financial
* [Equated Monthly Installments](financial/equated_monthly_installments.py)
* [Interest](financial/interest.py)
* [Present Value](financial/present_value.py)
* [Price Plus Tax](financial/price_plus_tax.py)
## Fractals
* [Julia Sets](fractals/julia_sets.py)
* [Koch Snowflake](fractals/koch_snowflake.py)
* [Mandelbrot](fractals/mandelbrot.py)
* [Sierpinski Triangle](fractals/sierpinski_triangle.py)
## Fuzzy Logic
* [Fuzzy Operations](fuzzy_logic/fuzzy_operations.py)
## Genetic Algorithm
* [Basic String](genetic_algorithm/basic_string.py)
## Geodesy
* [Haversine Distance](geodesy/haversine_distance.py)
* [Lamberts Ellipsoidal Distance](geodesy/lamberts_ellipsoidal_distance.py)
## Graphics
* [Bezier Curve](graphics/bezier_curve.py)
* [Vector3 For 2D Rendering](graphics/vector3_for_2d_rendering.py)
## Graphs
* [A Star](graphs/a_star.py)
* [Articulation Points](graphs/articulation_points.py)
* [Basic Graphs](graphs/basic_graphs.py)
* [Bellman Ford](graphs/bellman_ford.py)
* [Bi Directional Dijkstra](graphs/bi_directional_dijkstra.py)
* [Bidirectional A Star](graphs/bidirectional_a_star.py)
* [Bidirectional Breadth First Search](graphs/bidirectional_breadth_first_search.py)
* [Boruvka](graphs/boruvka.py)
* [Breadth First Search](graphs/breadth_first_search.py)
* [Breadth First Search 2](graphs/breadth_first_search_2.py)
* [Breadth First Search Shortest Path](graphs/breadth_first_search_shortest_path.py)
* [Breadth First Search Shortest Path 2](graphs/breadth_first_search_shortest_path_2.py)
* [Breadth First Search Zero One Shortest Path](graphs/breadth_first_search_zero_one_shortest_path.py)
* [Check Bipartite Graph Bfs](graphs/check_bipartite_graph_bfs.py)
* [Check Bipartite Graph Dfs](graphs/check_bipartite_graph_dfs.py)
* [Check Cycle](graphs/check_cycle.py)
* [Connected Components](graphs/connected_components.py)
* [Depth First Search](graphs/depth_first_search.py)
* [Depth First Search 2](graphs/depth_first_search_2.py)
* [Dijkstra](graphs/dijkstra.py)
* [Dijkstra 2](graphs/dijkstra_2.py)
* [Dijkstra Algorithm](graphs/dijkstra_algorithm.py)
* [Dijkstra Alternate](graphs/dijkstra_alternate.py)
* [Dijkstra Binary Grid](graphs/dijkstra_binary_grid.py)
* [Dinic](graphs/dinic.py)
* [Directed And Undirected (Weighted) Graph](graphs/directed_and_undirected_(weighted)_graph.py)
* [Edmonds Karp Multiple Source And Sink](graphs/edmonds_karp_multiple_source_and_sink.py)
* [Eulerian Path And Circuit For Undirected Graph](graphs/eulerian_path_and_circuit_for_undirected_graph.py)
* [Even Tree](graphs/even_tree.py)
* [Finding Bridges](graphs/finding_bridges.py)
* [Frequent Pattern Graph Miner](graphs/frequent_pattern_graph_miner.py)
* [G Topological Sort](graphs/g_topological_sort.py)
* [Gale Shapley Bigraph](graphs/gale_shapley_bigraph.py)
* [Graph Adjacency List](graphs/graph_adjacency_list.py)
* [Graph Adjacency Matrix](graphs/graph_adjacency_matrix.py)
* [Graph List](graphs/graph_list.py)
* [Graphs Floyd Warshall](graphs/graphs_floyd_warshall.py)
* [Greedy Best First](graphs/greedy_best_first.py)
* [Greedy Min Vertex Cover](graphs/greedy_min_vertex_cover.py)
* [Kahns Algorithm Long](graphs/kahns_algorithm_long.py)
* [Kahns Algorithm Topo](graphs/kahns_algorithm_topo.py)
* [Karger](graphs/karger.py)
* [Markov Chain](graphs/markov_chain.py)
* [Matching Min Vertex Cover](graphs/matching_min_vertex_cover.py)
* [Minimum Path Sum](graphs/minimum_path_sum.py)
* [Minimum Spanning Tree Boruvka](graphs/minimum_spanning_tree_boruvka.py)
* [Minimum Spanning Tree Kruskal](graphs/minimum_spanning_tree_kruskal.py)
* [Minimum Spanning Tree Kruskal2](graphs/minimum_spanning_tree_kruskal2.py)
* [Minimum Spanning Tree Prims](graphs/minimum_spanning_tree_prims.py)
* [Minimum Spanning Tree Prims2](graphs/minimum_spanning_tree_prims2.py)
* [Multi Heuristic Astar](graphs/multi_heuristic_astar.py)
* [Page Rank](graphs/page_rank.py)
* [Prim](graphs/prim.py)
* [Random Graph Generator](graphs/random_graph_generator.py)
* [Scc Kosaraju](graphs/scc_kosaraju.py)
* [Strongly Connected Components](graphs/strongly_connected_components.py)
* [Tarjans Scc](graphs/tarjans_scc.py)
* Tests
* [Test Min Spanning Tree Kruskal](graphs/tests/test_min_spanning_tree_kruskal.py)
* [Test Min Spanning Tree Prim](graphs/tests/test_min_spanning_tree_prim.py)
## Greedy Methods
* [Fractional Knapsack](greedy_methods/fractional_knapsack.py)
* [Fractional Knapsack 2](greedy_methods/fractional_knapsack_2.py)
* [Minimum Waiting Time](greedy_methods/minimum_waiting_time.py)
* [Optimal Merge Pattern](greedy_methods/optimal_merge_pattern.py)
## Hashes
* [Adler32](hashes/adler32.py)
* [Chaos Machine](hashes/chaos_machine.py)
* [Djb2](hashes/djb2.py)
* [Elf](hashes/elf.py)
* [Enigma Machine](hashes/enigma_machine.py)
* [Hamming Code](hashes/hamming_code.py)
* [Luhn](hashes/luhn.py)
* [Md5](hashes/md5.py)
* [Sdbm](hashes/sdbm.py)
* [Sha1](hashes/sha1.py)
* [Sha256](hashes/sha256.py)
## Knapsack
* [Greedy Knapsack](knapsack/greedy_knapsack.py)
* [Knapsack](knapsack/knapsack.py)
* [Recursive Approach Knapsack](knapsack/recursive_approach_knapsack.py)
* Tests
* [Test Greedy Knapsack](knapsack/tests/test_greedy_knapsack.py)
* [Test Knapsack](knapsack/tests/test_knapsack.py)
## Linear Algebra
* Src
* [Conjugate Gradient](linear_algebra/src/conjugate_gradient.py)
* [Lib](linear_algebra/src/lib.py)
* [Polynom For Points](linear_algebra/src/polynom_for_points.py)
* [Power Iteration](linear_algebra/src/power_iteration.py)
* [Rank Of Matrix](linear_algebra/src/rank_of_matrix.py)
* [Rayleigh Quotient](linear_algebra/src/rayleigh_quotient.py)
* [Schur Complement](linear_algebra/src/schur_complement.py)
* [Test Linear Algebra](linear_algebra/src/test_linear_algebra.py)
* [Transformations 2D](linear_algebra/src/transformations_2d.py)
## Linear Programming
* [Simplex](linear_programming/simplex.py)
## Machine Learning
* [Astar](machine_learning/astar.py)
* [Data Transformations](machine_learning/data_transformations.py)
* [Decision Tree](machine_learning/decision_tree.py)
* [Dimensionality Reduction](machine_learning/dimensionality_reduction.py)
* Forecasting
* [Run](machine_learning/forecasting/run.py)
* [Gradient Descent](machine_learning/gradient_descent.py)
* [K Means Clust](machine_learning/k_means_clust.py)
* [K Nearest Neighbours](machine_learning/k_nearest_neighbours.py)
* [Knn Sklearn](machine_learning/knn_sklearn.py)
* [Linear Discriminant Analysis](machine_learning/linear_discriminant_analysis.py)
* [Linear Regression](machine_learning/linear_regression.py)
* Local Weighted Learning
* [Local Weighted Learning](machine_learning/local_weighted_learning/local_weighted_learning.py)
* [Logistic Regression](machine_learning/logistic_regression.py)
* Lstm
* [Lstm Prediction](machine_learning/lstm/lstm_prediction.py)
* [Multilayer Perceptron Classifier](machine_learning/multilayer_perceptron_classifier.py)
* [Polynomial Regression](machine_learning/polynomial_regression.py)
* [Scoring Functions](machine_learning/scoring_functions.py)
* [Self Organizing Map](machine_learning/self_organizing_map.py)
* [Sequential Minimum Optimization](machine_learning/sequential_minimum_optimization.py)
* [Similarity Search](machine_learning/similarity_search.py)
* [Support Vector Machines](machine_learning/support_vector_machines.py)
* [Word Frequency Functions](machine_learning/word_frequency_functions.py)
* [Xgboost Classifier](machine_learning/xgboost_classifier.py)
* [Xgboost Regressor](machine_learning/xgboost_regressor.py)
## Maths
* [Abs](maths/abs.py)
* [Add](maths/add.py)
* [Addition Without Arithmetic](maths/addition_without_arithmetic.py)
* [Aliquot Sum](maths/aliquot_sum.py)
* [Allocation Number](maths/allocation_number.py)
* [Arc Length](maths/arc_length.py)
* [Area](maths/area.py)
* [Area Under Curve](maths/area_under_curve.py)
* [Armstrong Numbers](maths/armstrong_numbers.py)
* [Automorphic Number](maths/automorphic_number.py)
* [Average Absolute Deviation](maths/average_absolute_deviation.py)
* [Average Mean](maths/average_mean.py)
* [Average Median](maths/average_median.py)
* [Average Mode](maths/average_mode.py)
* [Bailey Borwein Plouffe](maths/bailey_borwein_plouffe.py)
* [Basic Maths](maths/basic_maths.py)
* [Binary Exp Mod](maths/binary_exp_mod.py)
* [Binary Exponentiation](maths/binary_exponentiation.py)
* [Binary Exponentiation 2](maths/binary_exponentiation_2.py)
* [Binary Exponentiation 3](maths/binary_exponentiation_3.py)
* [Binomial Coefficient](maths/binomial_coefficient.py)
* [Binomial Distribution](maths/binomial_distribution.py)
* [Bisection](maths/bisection.py)
* [Carmichael Number](maths/carmichael_number.py)
* [Catalan Number](maths/catalan_number.py)
* [Ceil](maths/ceil.py)
* [Check Polygon](maths/check_polygon.py)
* [Chudnovsky Algorithm](maths/chudnovsky_algorithm.py)
* [Collatz Sequence](maths/collatz_sequence.py)
* [Combinations](maths/combinations.py)
* [Decimal Isolate](maths/decimal_isolate.py)
* [Decimal To Fraction](maths/decimal_to_fraction.py)
* [Dodecahedron](maths/dodecahedron.py)
* [Double Factorial Iterative](maths/double_factorial_iterative.py)
* [Double Factorial Recursive](maths/double_factorial_recursive.py)
* [Dual Number Automatic Differentiation](maths/dual_number_automatic_differentiation.py)
* [Entropy](maths/entropy.py)
* [Euclidean Distance](maths/euclidean_distance.py)
* [Euclidean Gcd](maths/euclidean_gcd.py)
* [Euler Method](maths/euler_method.py)
* [Euler Modified](maths/euler_modified.py)
* [Eulers Totient](maths/eulers_totient.py)
* [Extended Euclidean Algorithm](maths/extended_euclidean_algorithm.py)
* [Factorial](maths/factorial.py)
* [Factors](maths/factors.py)
* [Fermat Little Theorem](maths/fermat_little_theorem.py)
* [Fibonacci](maths/fibonacci.py)
* [Find Max](maths/find_max.py)
* [Find Max Recursion](maths/find_max_recursion.py)
* [Find Min](maths/find_min.py)
* [Find Min Recursion](maths/find_min_recursion.py)
* [Floor](maths/floor.py)
* [Gamma](maths/gamma.py)
* [Gamma Recursive](maths/gamma_recursive.py)
* [Gaussian](maths/gaussian.py)
* [Gaussian Error Linear Unit](maths/gaussian_error_linear_unit.py)
* [Gcd Of N Numbers](maths/gcd_of_n_numbers.py)
* [Greatest Common Divisor](maths/greatest_common_divisor.py)
* [Greedy Coin Change](maths/greedy_coin_change.py)
* [Hamming Numbers](maths/hamming_numbers.py)
* [Hardy Ramanujanalgo](maths/hardy_ramanujanalgo.py)
* [Hexagonal Number](maths/hexagonal_number.py)
* [Integration By Simpson Approx](maths/integration_by_simpson_approx.py)
* [Is Int Palindrome](maths/is_int_palindrome.py)
* [Is Ip V4 Address Valid](maths/is_ip_v4_address_valid.py)
* [Is Square Free](maths/is_square_free.py)
* [Jaccard Similarity](maths/jaccard_similarity.py)
* [Juggler Sequence](maths/juggler_sequence.py)
* [Karatsuba](maths/karatsuba.py)
* [Krishnamurthy Number](maths/krishnamurthy_number.py)
* [Kth Lexicographic Permutation](maths/kth_lexicographic_permutation.py)
* [Largest Of Very Large Numbers](maths/largest_of_very_large_numbers.py)
* [Least Common Multiple](maths/least_common_multiple.py)
* [Line Length](maths/line_length.py)
* [Liouville Lambda](maths/liouville_lambda.py)
* [Lucas Lehmer Primality Test](maths/lucas_lehmer_primality_test.py)
* [Lucas Series](maths/lucas_series.py)
* [Maclaurin Series](maths/maclaurin_series.py)
* [Manhattan Distance](maths/manhattan_distance.py)
* [Matrix Exponentiation](maths/matrix_exponentiation.py)
* [Max Sum Sliding Window](maths/max_sum_sliding_window.py)
* [Median Of Two Arrays](maths/median_of_two_arrays.py)
* [Miller Rabin](maths/miller_rabin.py)
* [Mobius Function](maths/mobius_function.py)
* [Modular Exponential](maths/modular_exponential.py)
* [Monte Carlo](maths/monte_carlo.py)
* [Monte Carlo Dice](maths/monte_carlo_dice.py)
* [Nevilles Method](maths/nevilles_method.py)
* [Newton Raphson](maths/newton_raphson.py)
* [Number Of Digits](maths/number_of_digits.py)
* [Numerical Integration](maths/numerical_integration.py)
* [Odd Sieve](maths/odd_sieve.py)
* [Perfect Cube](maths/perfect_cube.py)
* [Perfect Number](maths/perfect_number.py)
* [Perfect Square](maths/perfect_square.py)
* [Persistence](maths/persistence.py)
* [Pi Generator](maths/pi_generator.py)
* [Pi Monte Carlo Estimation](maths/pi_monte_carlo_estimation.py)
* [Points Are Collinear 3D](maths/points_are_collinear_3d.py)
* [Pollard Rho](maths/pollard_rho.py)
* [Polynomial Evaluation](maths/polynomial_evaluation.py)
* Polynomials
* [Single Indeterminate Operations](maths/polynomials/single_indeterminate_operations.py)
* [Power Using Recursion](maths/power_using_recursion.py)
* [Prime Check](maths/prime_check.py)
* [Prime Factors](maths/prime_factors.py)
* [Prime Numbers](maths/prime_numbers.py)
* [Prime Sieve Eratosthenes](maths/prime_sieve_eratosthenes.py)
* [Primelib](maths/primelib.py)
* [Print Multiplication Table](maths/print_multiplication_table.py)
* [Pronic Number](maths/pronic_number.py)
* [Proth Number](maths/proth_number.py)
* [Pythagoras](maths/pythagoras.py)
* [Qr Decomposition](maths/qr_decomposition.py)
* [Quadratic Equations Complex Numbers](maths/quadratic_equations_complex_numbers.py)
* [Radians](maths/radians.py)
* [Radix2 Fft](maths/radix2_fft.py)
* [Relu](maths/relu.py)
* [Remove Digit](maths/remove_digit.py)
* [Runge Kutta](maths/runge_kutta.py)
* [Segmented Sieve](maths/segmented_sieve.py)
* Series
* [Arithmetic](maths/series/arithmetic.py)
* [Geometric](maths/series/geometric.py)
* [Geometric Series](maths/series/geometric_series.py)
* [Harmonic](maths/series/harmonic.py)
* [Harmonic Series](maths/series/harmonic_series.py)
* [Hexagonal Numbers](maths/series/hexagonal_numbers.py)
* [P Series](maths/series/p_series.py)
* [Sieve Of Eratosthenes](maths/sieve_of_eratosthenes.py)
* [Sigmoid](maths/sigmoid.py)
* [Sigmoid Linear Unit](maths/sigmoid_linear_unit.py)
* [Signum](maths/signum.py)
* [Simpson Rule](maths/simpson_rule.py)
* [Simultaneous Linear Equation Solver](maths/simultaneous_linear_equation_solver.py)
* [Sin](maths/sin.py)
* [Sock Merchant](maths/sock_merchant.py)
* [Softmax](maths/softmax.py)
* [Square Root](maths/square_root.py)
* [Sum Of Arithmetic Series](maths/sum_of_arithmetic_series.py)
* [Sum Of Digits](maths/sum_of_digits.py)
* [Sum Of Geometric Progression](maths/sum_of_geometric_progression.py)
* [Sum Of Harmonic Series](maths/sum_of_harmonic_series.py)
* [Sumset](maths/sumset.py)
* [Sylvester Sequence](maths/sylvester_sequence.py)
* [Tanh](maths/tanh.py)
* [Test Prime Check](maths/test_prime_check.py)
* [Trapezoidal Rule](maths/trapezoidal_rule.py)
* [Triplet Sum](maths/triplet_sum.py)
* [Twin Prime](maths/twin_prime.py)
* [Two Pointer](maths/two_pointer.py)
* [Two Sum](maths/two_sum.py)
* [Ugly Numbers](maths/ugly_numbers.py)
* [Volume](maths/volume.py)
* [Weird Number](maths/weird_number.py)
* [Zellers Congruence](maths/zellers_congruence.py)
## Matrix
* [Binary Search Matrix](matrix/binary_search_matrix.py)
* [Count Islands In Matrix](matrix/count_islands_in_matrix.py)
* [Count Negative Numbers In Sorted Matrix](matrix/count_negative_numbers_in_sorted_matrix.py)
* [Count Paths](matrix/count_paths.py)
* [Cramers Rule 2X2](matrix/cramers_rule_2x2.py)
* [Inverse Of Matrix](matrix/inverse_of_matrix.py)
* [Largest Square Area In Matrix](matrix/largest_square_area_in_matrix.py)
* [Matrix Class](matrix/matrix_class.py)
* [Matrix Operation](matrix/matrix_operation.py)
* [Max Area Of Island](matrix/max_area_of_island.py)
* [Nth Fibonacci Using Matrix Exponentiation](matrix/nth_fibonacci_using_matrix_exponentiation.py)
* [Pascal Triangle](matrix/pascal_triangle.py)
* [Rotate Matrix](matrix/rotate_matrix.py)
* [Searching In Sorted Matrix](matrix/searching_in_sorted_matrix.py)
* [Sherman Morrison](matrix/sherman_morrison.py)
* [Spiral Print](matrix/spiral_print.py)
* Tests
* [Test Matrix Operation](matrix/tests/test_matrix_operation.py)
## Networking Flow
* [Ford Fulkerson](networking_flow/ford_fulkerson.py)
* [Minimum Cut](networking_flow/minimum_cut.py)
## Neural Network
* [2 Hidden Layers Neural Network](neural_network/2_hidden_layers_neural_network.py)
* Activation Functions
* [Exponential Linear Unit](neural_network/activation_functions/exponential_linear_unit.py)
* [Back Propagation Neural Network](neural_network/back_propagation_neural_network.py)
* [Convolution Neural Network](neural_network/convolution_neural_network.py)
* [Input Data](neural_network/input_data.py)
* [Perceptron](neural_network/perceptron.py)
* [Simple Neural Network](neural_network/simple_neural_network.py)
## Other
* [Activity Selection](other/activity_selection.py)
* [Alternative List Arrange](other/alternative_list_arrange.py)
* [Davisb Putnamb Logemannb Loveland](other/davisb_putnamb_logemannb_loveland.py)
* [Dijkstra Bankers Algorithm](other/dijkstra_bankers_algorithm.py)
* [Doomsday](other/doomsday.py)
* [Fischer Yates Shuffle](other/fischer_yates_shuffle.py)
* [Gauss Easter](other/gauss_easter.py)
* [Graham Scan](other/graham_scan.py)
* [Greedy](other/greedy.py)
* [Guess The Number Search](other/guess_the_number_search.py)
* [H Index](other/h_index.py)
* [Least Recently Used](other/least_recently_used.py)
* [Lfu Cache](other/lfu_cache.py)
* [Linear Congruential Generator](other/linear_congruential_generator.py)
* [Lru Cache](other/lru_cache.py)
* [Magicdiamondpattern](other/magicdiamondpattern.py)
* [Maximum Subsequence](other/maximum_subsequence.py)
* [Nested Brackets](other/nested_brackets.py)
* [Number Container System](other/number_container_system.py)
* [Password](other/password.py)
* [Quine](other/quine.py)
* [Scoring Algorithm](other/scoring_algorithm.py)
* [Sdes](other/sdes.py)
* [Tower Of Hanoi](other/tower_of_hanoi.py)
## Physics
* [Altitude Pressure](physics/altitude_pressure.py)
* [Archimedes Principle](physics/archimedes_principle.py)
* [Basic Orbital Capture](physics/basic_orbital_capture.py)
* [Casimir Effect](physics/casimir_effect.py)
* [Centripetal Force](physics/centripetal_force.py)
* [Grahams Law](physics/grahams_law.py)
* [Horizontal Projectile Motion](physics/horizontal_projectile_motion.py)
* [Hubble Parameter](physics/hubble_parameter.py)
* [Ideal Gas Law](physics/ideal_gas_law.py)
* [Kinetic Energy](physics/kinetic_energy.py)
* [Lorentz Transformation Four Vector](physics/lorentz_transformation_four_vector.py)
* [Malus Law](physics/malus_law.py)
* [N Body Simulation](physics/n_body_simulation.py)
* [Newtons Law Of Gravitation](physics/newtons_law_of_gravitation.py)
* [Newtons Second Law Of Motion](physics/newtons_second_law_of_motion.py)
* [Potential Energy](physics/potential_energy.py)
* [Rms Speed Of Molecule](physics/rms_speed_of_molecule.py)
* [Shear Stress](physics/shear_stress.py)
* [Speed Of Sound](physics/speed_of_sound.py)
## Project Euler
* Problem 001
* [Sol1](project_euler/problem_001/sol1.py)
* [Sol2](project_euler/problem_001/sol2.py)
* [Sol3](project_euler/problem_001/sol3.py)
* [Sol4](project_euler/problem_001/sol4.py)
* [Sol5](project_euler/problem_001/sol5.py)
* [Sol6](project_euler/problem_001/sol6.py)
* [Sol7](project_euler/problem_001/sol7.py)
* Problem 002
* [Sol1](project_euler/problem_002/sol1.py)
* [Sol2](project_euler/problem_002/sol2.py)
* [Sol3](project_euler/problem_002/sol3.py)
* [Sol4](project_euler/problem_002/sol4.py)
* [Sol5](project_euler/problem_002/sol5.py)
* Problem 003
* [Sol1](project_euler/problem_003/sol1.py)
* [Sol2](project_euler/problem_003/sol2.py)
* [Sol3](project_euler/problem_003/sol3.py)
* Problem 004
* [Sol1](project_euler/problem_004/sol1.py)
* [Sol2](project_euler/problem_004/sol2.py)
* Problem 005
* [Sol1](project_euler/problem_005/sol1.py)
* [Sol2](project_euler/problem_005/sol2.py)
* Problem 006
* [Sol1](project_euler/problem_006/sol1.py)
* [Sol2](project_euler/problem_006/sol2.py)
* [Sol3](project_euler/problem_006/sol3.py)
* [Sol4](project_euler/problem_006/sol4.py)
* Problem 007
* [Sol1](project_euler/problem_007/sol1.py)
* [Sol2](project_euler/problem_007/sol2.py)
* [Sol3](project_euler/problem_007/sol3.py)
* Problem 008
* [Sol1](project_euler/problem_008/sol1.py)
* [Sol2](project_euler/problem_008/sol2.py)
* [Sol3](project_euler/problem_008/sol3.py)
* Problem 009
* [Sol1](project_euler/problem_009/sol1.py)
* [Sol2](project_euler/problem_009/sol2.py)
* [Sol3](project_euler/problem_009/sol3.py)
* Problem 010
* [Sol1](project_euler/problem_010/sol1.py)
* [Sol2](project_euler/problem_010/sol2.py)
* [Sol3](project_euler/problem_010/sol3.py)
* Problem 011
* [Sol1](project_euler/problem_011/sol1.py)
* [Sol2](project_euler/problem_011/sol2.py)
* Problem 012
* [Sol1](project_euler/problem_012/sol1.py)
* [Sol2](project_euler/problem_012/sol2.py)
* Problem 013
* [Sol1](project_euler/problem_013/sol1.py)
* Problem 014
* [Sol1](project_euler/problem_014/sol1.py)
* [Sol2](project_euler/problem_014/sol2.py)
* Problem 015
* [Sol1](project_euler/problem_015/sol1.py)
* Problem 016
* [Sol1](project_euler/problem_016/sol1.py)
* [Sol2](project_euler/problem_016/sol2.py)
* Problem 017
* [Sol1](project_euler/problem_017/sol1.py)
* Problem 018
* [Solution](project_euler/problem_018/solution.py)
* Problem 019
* [Sol1](project_euler/problem_019/sol1.py)
* Problem 020
* [Sol1](project_euler/problem_020/sol1.py)
* [Sol2](project_euler/problem_020/sol2.py)
* [Sol3](project_euler/problem_020/sol3.py)
* [Sol4](project_euler/problem_020/sol4.py)
* Problem 021
* [Sol1](project_euler/problem_021/sol1.py)
* Problem 022
* [Sol1](project_euler/problem_022/sol1.py)
* [Sol2](project_euler/problem_022/sol2.py)
* Problem 023
* [Sol1](project_euler/problem_023/sol1.py)
* Problem 024
* [Sol1](project_euler/problem_024/sol1.py)
* Problem 025
* [Sol1](project_euler/problem_025/sol1.py)
* [Sol2](project_euler/problem_025/sol2.py)
* [Sol3](project_euler/problem_025/sol3.py)
* Problem 026
* [Sol1](project_euler/problem_026/sol1.py)
* Problem 027
* [Sol1](project_euler/problem_027/sol1.py)
* Problem 028
* [Sol1](project_euler/problem_028/sol1.py)
* Problem 029
* [Sol1](project_euler/problem_029/sol1.py)
* Problem 030
* [Sol1](project_euler/problem_030/sol1.py)
* Problem 031
* [Sol1](project_euler/problem_031/sol1.py)
* [Sol2](project_euler/problem_031/sol2.py)
* Problem 032
* [Sol32](project_euler/problem_032/sol32.py)
* Problem 033
* [Sol1](project_euler/problem_033/sol1.py)
* Problem 034
* [Sol1](project_euler/problem_034/sol1.py)
* Problem 035
* [Sol1](project_euler/problem_035/sol1.py)
* Problem 036
* [Sol1](project_euler/problem_036/sol1.py)
* Problem 037
* [Sol1](project_euler/problem_037/sol1.py)
* Problem 038
* [Sol1](project_euler/problem_038/sol1.py)
* Problem 039
* [Sol1](project_euler/problem_039/sol1.py)
* Problem 040
* [Sol1](project_euler/problem_040/sol1.py)
* Problem 041
* [Sol1](project_euler/problem_041/sol1.py)
* Problem 042
* [Solution42](project_euler/problem_042/solution42.py)
* Problem 043
* [Sol1](project_euler/problem_043/sol1.py)
* Problem 044
* [Sol1](project_euler/problem_044/sol1.py)
* Problem 045
* [Sol1](project_euler/problem_045/sol1.py)
* Problem 046
* [Sol1](project_euler/problem_046/sol1.py)
* Problem 047
* [Sol1](project_euler/problem_047/sol1.py)
* Problem 048
* [Sol1](project_euler/problem_048/sol1.py)
* Problem 049
* [Sol1](project_euler/problem_049/sol1.py)
* Problem 050
* [Sol1](project_euler/problem_050/sol1.py)
* Problem 051
* [Sol1](project_euler/problem_051/sol1.py)
* Problem 052
* [Sol1](project_euler/problem_052/sol1.py)
* Problem 053
* [Sol1](project_euler/problem_053/sol1.py)
* Problem 054
* [Sol1](project_euler/problem_054/sol1.py)
* [Test Poker Hand](project_euler/problem_054/test_poker_hand.py)
* Problem 055
* [Sol1](project_euler/problem_055/sol1.py)
* Problem 056
* [Sol1](project_euler/problem_056/sol1.py)
* Problem 057
* [Sol1](project_euler/problem_057/sol1.py)
* Problem 058
* [Sol1](project_euler/problem_058/sol1.py)
* Problem 059
* [Sol1](project_euler/problem_059/sol1.py)
* Problem 062
* [Sol1](project_euler/problem_062/sol1.py)
* Problem 063
* [Sol1](project_euler/problem_063/sol1.py)
* Problem 064
* [Sol1](project_euler/problem_064/sol1.py)
* Problem 065
* [Sol1](project_euler/problem_065/sol1.py)
* Problem 067
* [Sol1](project_euler/problem_067/sol1.py)
* [Sol2](project_euler/problem_067/sol2.py)
* Problem 068
* [Sol1](project_euler/problem_068/sol1.py)
* Problem 069
* [Sol1](project_euler/problem_069/sol1.py)
* Problem 070
* [Sol1](project_euler/problem_070/sol1.py)
* Problem 071
* [Sol1](project_euler/problem_071/sol1.py)
* Problem 072
* [Sol1](project_euler/problem_072/sol1.py)
* [Sol2](project_euler/problem_072/sol2.py)
* Problem 073
* [Sol1](project_euler/problem_073/sol1.py)
* Problem 074
* [Sol1](project_euler/problem_074/sol1.py)
* [Sol2](project_euler/problem_074/sol2.py)
* Problem 075
* [Sol1](project_euler/problem_075/sol1.py)
* Problem 076
* [Sol1](project_euler/problem_076/sol1.py)
* Problem 077
* [Sol1](project_euler/problem_077/sol1.py)
* Problem 078
* [Sol1](project_euler/problem_078/sol1.py)
* Problem 079
* [Sol1](project_euler/problem_079/sol1.py)
* Problem 080
* [Sol1](project_euler/problem_080/sol1.py)
* Problem 081
* [Sol1](project_euler/problem_081/sol1.py)
* Problem 082
* [Sol1](project_euler/problem_082/sol1.py)
* Problem 085
* [Sol1](project_euler/problem_085/sol1.py)
* Problem 086
* [Sol1](project_euler/problem_086/sol1.py)
* Problem 087
* [Sol1](project_euler/problem_087/sol1.py)
* Problem 089
* [Sol1](project_euler/problem_089/sol1.py)
* Problem 091
* [Sol1](project_euler/problem_091/sol1.py)
* Problem 092
* [Sol1](project_euler/problem_092/sol1.py)
* Problem 094
* [Sol1](project_euler/problem_094/sol1.py)
* Problem 097
* [Sol1](project_euler/problem_097/sol1.py)
* Problem 099
* [Sol1](project_euler/problem_099/sol1.py)
* Problem 100
* [Sol1](project_euler/problem_100/sol1.py)
* Problem 101
* [Sol1](project_euler/problem_101/sol1.py)
* Problem 102
* [Sol1](project_euler/problem_102/sol1.py)
* Problem 104
* [Sol1](project_euler/problem_104/sol1.py)
* Problem 107
* [Sol1](project_euler/problem_107/sol1.py)
* Problem 109
* [Sol1](project_euler/problem_109/sol1.py)
* Problem 112
* [Sol1](project_euler/problem_112/sol1.py)
* Problem 113
* [Sol1](project_euler/problem_113/sol1.py)
* Problem 114
* [Sol1](project_euler/problem_114/sol1.py)
* Problem 115
* [Sol1](project_euler/problem_115/sol1.py)
* Problem 116
* [Sol1](project_euler/problem_116/sol1.py)
* Problem 117
* [Sol1](project_euler/problem_117/sol1.py)
* Problem 119
* [Sol1](project_euler/problem_119/sol1.py)
* Problem 120
* [Sol1](project_euler/problem_120/sol1.py)
* Problem 121
* [Sol1](project_euler/problem_121/sol1.py)
* Problem 123
* [Sol1](project_euler/problem_123/sol1.py)
* Problem 125
* [Sol1](project_euler/problem_125/sol1.py)
* Problem 129
* [Sol1](project_euler/problem_129/sol1.py)
* Problem 131
* [Sol1](project_euler/problem_131/sol1.py)
* Problem 135
* [Sol1](project_euler/problem_135/sol1.py)
* Problem 144
* [Sol1](project_euler/problem_144/sol1.py)
* Problem 145
* [Sol1](project_euler/problem_145/sol1.py)
* Problem 173
* [Sol1](project_euler/problem_173/sol1.py)
* Problem 174
* [Sol1](project_euler/problem_174/sol1.py)
* Problem 180
* [Sol1](project_euler/problem_180/sol1.py)
* Problem 187
* [Sol1](project_euler/problem_187/sol1.py)
* Problem 188
* [Sol1](project_euler/problem_188/sol1.py)
* Problem 191
* [Sol1](project_euler/problem_191/sol1.py)
* Problem 203
* [Sol1](project_euler/problem_203/sol1.py)
* Problem 205
* [Sol1](project_euler/problem_205/sol1.py)
* Problem 206
* [Sol1](project_euler/problem_206/sol1.py)
* Problem 207
* [Sol1](project_euler/problem_207/sol1.py)
* Problem 234
* [Sol1](project_euler/problem_234/sol1.py)
* Problem 301
* [Sol1](project_euler/problem_301/sol1.py)
* Problem 493
* [Sol1](project_euler/problem_493/sol1.py)
* Problem 551
* [Sol1](project_euler/problem_551/sol1.py)
* Problem 587
* [Sol1](project_euler/problem_587/sol1.py)
* Problem 686
* [Sol1](project_euler/problem_686/sol1.py)
* Problem 800
* [Sol1](project_euler/problem_800/sol1.py)
## Quantum
* [Bb84](quantum/bb84.py)
* [Deutsch Jozsa](quantum/deutsch_jozsa.py)
* [Half Adder](quantum/half_adder.py)
* [Not Gate](quantum/not_gate.py)
* [Q Fourier Transform](quantum/q_fourier_transform.py)
* [Q Full Adder](quantum/q_full_adder.py)
* [Quantum Entanglement](quantum/quantum_entanglement.py)
* [Quantum Teleportation](quantum/quantum_teleportation.py)
* [Ripple Adder Classic](quantum/ripple_adder_classic.py)
* [Single Qubit Measure](quantum/single_qubit_measure.py)
* [Superdense Coding](quantum/superdense_coding.py)
## Scheduling
* [First Come First Served](scheduling/first_come_first_served.py)
* [Highest Response Ratio Next](scheduling/highest_response_ratio_next.py)
* [Job Sequencing With Deadline](scheduling/job_sequencing_with_deadline.py)
* [Multi Level Feedback Queue](scheduling/multi_level_feedback_queue.py)
* [Non Preemptive Shortest Job First](scheduling/non_preemptive_shortest_job_first.py)
* [Round Robin](scheduling/round_robin.py)
* [Shortest Job First](scheduling/shortest_job_first.py)
## Searches
* [Binary Search](searches/binary_search.py)
* [Binary Tree Traversal](searches/binary_tree_traversal.py)
* [Double Linear Search](searches/double_linear_search.py)
* [Double Linear Search Recursion](searches/double_linear_search_recursion.py)
* [Fibonacci Search](searches/fibonacci_search.py)
* [Hill Climbing](searches/hill_climbing.py)
* [Interpolation Search](searches/interpolation_search.py)
* [Jump Search](searches/jump_search.py)
* [Linear Search](searches/linear_search.py)
* [Quick Select](searches/quick_select.py)
* [Sentinel Linear Search](searches/sentinel_linear_search.py)
* [Simple Binary Search](searches/simple_binary_search.py)
* [Simulated Annealing](searches/simulated_annealing.py)
* [Tabu Search](searches/tabu_search.py)
* [Ternary Search](searches/ternary_search.py)
## Sorts
* [Bead Sort](sorts/bead_sort.py)
* [Binary Insertion Sort](sorts/binary_insertion_sort.py)
* [Bitonic Sort](sorts/bitonic_sort.py)
* [Bogo Sort](sorts/bogo_sort.py)
* [Bubble Sort](sorts/bubble_sort.py)
* [Bucket Sort](sorts/bucket_sort.py)
* [Circle Sort](sorts/circle_sort.py)
* [Cocktail Shaker Sort](sorts/cocktail_shaker_sort.py)
* [Comb Sort](sorts/comb_sort.py)
* [Counting Sort](sorts/counting_sort.py)
* [Cycle Sort](sorts/cycle_sort.py)
* [Double Sort](sorts/double_sort.py)
* [Dutch National Flag Sort](sorts/dutch_national_flag_sort.py)
* [Exchange Sort](sorts/exchange_sort.py)
* [External Sort](sorts/external_sort.py)
* [Gnome Sort](sorts/gnome_sort.py)
* [Heap Sort](sorts/heap_sort.py)
* [Insertion Sort](sorts/insertion_sort.py)
* [Intro Sort](sorts/intro_sort.py)
* [Iterative Merge Sort](sorts/iterative_merge_sort.py)
* [Merge Insertion Sort](sorts/merge_insertion_sort.py)
* [Merge Sort](sorts/merge_sort.py)
* [Msd Radix Sort](sorts/msd_radix_sort.py)
* [Natural Sort](sorts/natural_sort.py)
* [Odd Even Sort](sorts/odd_even_sort.py)
* [Odd Even Transposition Parallel](sorts/odd_even_transposition_parallel.py)
* [Odd Even Transposition Single Threaded](sorts/odd_even_transposition_single_threaded.py)
* [Pancake Sort](sorts/pancake_sort.py)
* [Patience Sort](sorts/patience_sort.py)
* [Pigeon Sort](sorts/pigeon_sort.py)
* [Pigeonhole Sort](sorts/pigeonhole_sort.py)
* [Quick Sort](sorts/quick_sort.py)
* [Quick Sort 3 Partition](sorts/quick_sort_3_partition.py)
* [Radix Sort](sorts/radix_sort.py)
* [Random Normal Distribution Quicksort](sorts/random_normal_distribution_quicksort.py)
* [Random Pivot Quick Sort](sorts/random_pivot_quick_sort.py)
* [Recursive Bubble Sort](sorts/recursive_bubble_sort.py)
* [Recursive Insertion Sort](sorts/recursive_insertion_sort.py)
* [Recursive Mergesort Array](sorts/recursive_mergesort_array.py)
* [Recursive Quick Sort](sorts/recursive_quick_sort.py)
* [Selection Sort](sorts/selection_sort.py)
* [Shell Sort](sorts/shell_sort.py)
* [Shrink Shell Sort](sorts/shrink_shell_sort.py)
* [Slowsort](sorts/slowsort.py)
* [Stooge Sort](sorts/stooge_sort.py)
* [Strand Sort](sorts/strand_sort.py)
* [Tim Sort](sorts/tim_sort.py)
* [Topological Sort](sorts/topological_sort.py)
* [Tree Sort](sorts/tree_sort.py)
* [Unknown Sort](sorts/unknown_sort.py)
* [Wiggle Sort](sorts/wiggle_sort.py)
## Strings
* [Aho Corasick](strings/aho_corasick.py)
* [Alternative String Arrange](strings/alternative_string_arrange.py)
* [Anagrams](strings/anagrams.py)
* [Autocomplete Using Trie](strings/autocomplete_using_trie.py)
* [Barcode Validator](strings/barcode_validator.py)
* [Boyer Moore Search](strings/boyer_moore_search.py)
* [Can String Be Rearranged As Palindrome](strings/can_string_be_rearranged_as_palindrome.py)
* [Capitalize](strings/capitalize.py)
* [Check Anagrams](strings/check_anagrams.py)
* [Credit Card Validator](strings/credit_card_validator.py)
* [Detecting English Programmatically](strings/detecting_english_programmatically.py)
* [Dna](strings/dna.py)
* [Frequency Finder](strings/frequency_finder.py)
* [Hamming Distance](strings/hamming_distance.py)
* [Indian Phone Validator](strings/indian_phone_validator.py)
* [Is Contains Unique Chars](strings/is_contains_unique_chars.py)
* [Is Isogram](strings/is_isogram.py)
* [Is Pangram](strings/is_pangram.py)
* [Is Spain National Id](strings/is_spain_national_id.py)
* [Is Srilankan Phone Number](strings/is_srilankan_phone_number.py)
* [Jaro Winkler](strings/jaro_winkler.py)
* [Join](strings/join.py)
* [Knuth Morris Pratt](strings/knuth_morris_pratt.py)
* [Levenshtein Distance](strings/levenshtein_distance.py)
* [Lower](strings/lower.py)
* [Manacher](strings/manacher.py)
* [Min Cost String Conversion](strings/min_cost_string_conversion.py)
* [Naive String Search](strings/naive_string_search.py)
* [Ngram](strings/ngram.py)
* [Palindrome](strings/palindrome.py)
* [Prefix Function](strings/prefix_function.py)
* [Rabin Karp](strings/rabin_karp.py)
* [Remove Duplicate](strings/remove_duplicate.py)
* [Reverse Letters](strings/reverse_letters.py)
* [Reverse Long Words](strings/reverse_long_words.py)
* [Reverse Words](strings/reverse_words.py)
* [Snake Case To Camel Pascal Case](strings/snake_case_to_camel_pascal_case.py)
* [Split](strings/split.py)
* [String Switch Case](strings/string_switch_case.py)
* [Text Justification](strings/text_justification.py)
* [Top K Frequent Words](strings/top_k_frequent_words.py)
* [Upper](strings/upper.py)
* [Wave](strings/wave.py)
* [Wildcard Pattern Matching](strings/wildcard_pattern_matching.py)
* [Word Occurrence](strings/word_occurrence.py)
* [Word Patterns](strings/word_patterns.py)
* [Z Function](strings/z_function.py)
## Web Programming
* [Co2 Emission](web_programming/co2_emission.py)
* [Convert Number To Words](web_programming/convert_number_to_words.py)
* [Covid Stats Via Xpath](web_programming/covid_stats_via_xpath.py)
* [Crawl Google Results](web_programming/crawl_google_results.py)
* [Crawl Google Scholar Citation](web_programming/crawl_google_scholar_citation.py)
* [Currency Converter](web_programming/currency_converter.py)
* [Current Stock Price](web_programming/current_stock_price.py)
* [Current Weather](web_programming/current_weather.py)
* [Daily Horoscope](web_programming/daily_horoscope.py)
* [Download Images From Google Query](web_programming/download_images_from_google_query.py)
* [Emails From Url](web_programming/emails_from_url.py)
* [Fetch Bbc News](web_programming/fetch_bbc_news.py)
* [Fetch Github Info](web_programming/fetch_github_info.py)
* [Fetch Jobs](web_programming/fetch_jobs.py)
* [Fetch Quotes](web_programming/fetch_quotes.py)
* [Fetch Well Rx Price](web_programming/fetch_well_rx_price.py)
* [Get Amazon Product Data](web_programming/get_amazon_product_data.py)
* [Get Imdb Top 250 Movies Csv](web_programming/get_imdb_top_250_movies_csv.py)
* [Get Imdbtop](web_programming/get_imdbtop.py)
* [Get Top Hn Posts](web_programming/get_top_hn_posts.py)
* [Get User Tweets](web_programming/get_user_tweets.py)
* [Giphy](web_programming/giphy.py)
* [Instagram Crawler](web_programming/instagram_crawler.py)
* [Instagram Pic](web_programming/instagram_pic.py)
* [Instagram Video](web_programming/instagram_video.py)
* [Nasa Data](web_programming/nasa_data.py)
* [Open Google Results](web_programming/open_google_results.py)
* [Random Anime Character](web_programming/random_anime_character.py)
* [Recaptcha Verification](web_programming/recaptcha_verification.py)
* [Reddit](web_programming/reddit.py)
* [Search Books By Isbn](web_programming/search_books_by_isbn.py)
* [Slack Message](web_programming/slack_message.py)
* [Test Fetch Github Info](web_programming/test_fetch_github_info.py)
* [World Covid19 Stats](web_programming/world_covid19_stats.py)
| 1 |
TheAlgorithms/Python | 8,913 | Ruff fixes | ### Describe your change:
Fix graphs/eulerian_path_and_circuit_for_undirected_graph.py and physics/newtons_second_law_of_motion.py, which are causing ruff to fail
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-07-31T19:13:04Z" | "2023-07-31T20:53:26Z" | 90a8e6e0d210a5c526c8f485fa825e1649d217e2 | 5cf34d901e32b65425103309bbad0068b1851238 | Ruff fixes. ### Describe your change:
Fix graphs/eulerian_path_and_circuit_for_undirected_graph.py and physics/newtons_second_law_of_motion.py, which are causing ruff to fail
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| # Eulerian Path is a path in graph that visits every edge exactly once.
# Eulerian Circuit is an Eulerian Path which starts and ends on the same
# vertex.
# time complexity is O(V+E)
# space complexity is O(VE)
# using dfs for finding eulerian path traversal
def dfs(u, graph, visited_edge, path=None):
path = (path or []) + [u]
for v in graph[u]:
if visited_edge[u][v] is False:
visited_edge[u][v], visited_edge[v][u] = True, True
path = dfs(v, graph, visited_edge, path)
return path
# for checking in graph has euler path or circuit
def check_circuit_or_path(graph, max_node):
odd_degree_nodes = 0
odd_node = -1
for i in range(max_node):
if i not in graph.keys():
continue
if len(graph[i]) % 2 == 1:
odd_degree_nodes += 1
odd_node = i
if odd_degree_nodes == 0:
return 1, odd_node
if odd_degree_nodes == 2:
return 2, odd_node
return 3, odd_node
def check_euler(graph, max_node):
visited_edge = [[False for _ in range(max_node + 1)] for _ in range(max_node + 1)]
check, odd_node = check_circuit_or_path(graph, max_node)
if check == 3:
print("graph is not Eulerian")
print("no path")
return
start_node = 1
if check == 2:
start_node = odd_node
print("graph has a Euler path")
if check == 1:
print("graph has a Euler cycle")
path = dfs(start_node, graph, visited_edge)
print(path)
def main():
g1 = {1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
g2 = {1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
g3 = {1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
g4 = {1: [2, 3], 2: [1, 3], 3: [1, 2]}
g5 = {
1: [],
2: []
# all degree is zero
}
max_node = 10
check_euler(g1, max_node)
check_euler(g2, max_node)
check_euler(g3, max_node)
check_euler(g4, max_node)
check_euler(g5, max_node)
if __name__ == "__main__":
main()
| # Eulerian Path is a path in graph that visits every edge exactly once.
# Eulerian Circuit is an Eulerian Path which starts and ends on the same
# vertex.
# time complexity is O(V+E)
# space complexity is O(VE)
# using dfs for finding eulerian path traversal
def dfs(u, graph, visited_edge, path=None):
path = (path or []) + [u]
for v in graph[u]:
if visited_edge[u][v] is False:
visited_edge[u][v], visited_edge[v][u] = True, True
path = dfs(v, graph, visited_edge, path)
return path
# for checking in graph has euler path or circuit
def check_circuit_or_path(graph, max_node):
odd_degree_nodes = 0
odd_node = -1
for i in range(max_node):
if i not in graph:
continue
if len(graph[i]) % 2 == 1:
odd_degree_nodes += 1
odd_node = i
if odd_degree_nodes == 0:
return 1, odd_node
if odd_degree_nodes == 2:
return 2, odd_node
return 3, odd_node
def check_euler(graph, max_node):
visited_edge = [[False for _ in range(max_node + 1)] for _ in range(max_node + 1)]
check, odd_node = check_circuit_or_path(graph, max_node)
if check == 3:
print("graph is not Eulerian")
print("no path")
return
start_node = 1
if check == 2:
start_node = odd_node
print("graph has a Euler path")
if check == 1:
print("graph has a Euler cycle")
path = dfs(start_node, graph, visited_edge)
print(path)
def main():
g1 = {1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
g2 = {1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
g3 = {1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
g4 = {1: [2, 3], 2: [1, 3], 3: [1, 2]}
g5 = {
1: [],
2: []
# all degree is zero
}
max_node = 10
check_euler(g1, max_node)
check_euler(g2, max_node)
check_euler(g3, max_node)
check_euler(g4, max_node)
check_euler(g5, max_node)
if __name__ == "__main__":
main()
| 1 |
TheAlgorithms/Python | 8,913 | Ruff fixes | ### Describe your change:
Fix graphs/eulerian_path_and_circuit_for_undirected_graph.py and physics/newtons_second_law_of_motion.py, which are causing ruff to fail
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-07-31T19:13:04Z" | "2023-07-31T20:53:26Z" | 90a8e6e0d210a5c526c8f485fa825e1649d217e2 | 5cf34d901e32b65425103309bbad0068b1851238 | Ruff fixes. ### Describe your change:
Fix graphs/eulerian_path_and_circuit_for_undirected_graph.py and physics/newtons_second_law_of_motion.py, which are causing ruff to fail
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| """
Description :
Newton's second law of motion pertains to the behavior of objects for which
all existing forces are not balanced.
The second law states that the acceleration of an object is dependent upon two variables
- the net force acting upon the object and the mass of the object.
The acceleration of an object depends directly
upon the net force acting upon the object,
and inversely upon the mass of the object.
As the force acting upon an object is increased,
the acceleration of the object is increased.
As the mass of an object is increased, the acceleration of the object is decreased.
Source: https://www.physicsclassroom.com/class/newtlaws/Lesson-3/Newton-s-Second-Law
Formulation: Fnet = m • a
Diagrammatic Explanation:
Forces are unbalanced
|
|
|
V
There is acceleration
/\
/ \
/ \
/ \
/ \
/ \
/ \
__________________ ____ ________________
|The acceleration | |The acceleration |
|depends directly | |depends inversely |
|on the net Force | |upon the object's |
|_________________| |mass_______________|
Units:
1 Newton = 1 kg X meters / (seconds^2)
How to use?
Inputs:
___________________________________________________
|Name | Units | Type |
|-------------|-------------------------|-----------|
|mass | (in kgs) | float |
|-------------|-------------------------|-----------|
|acceleration | (in meters/(seconds^2)) | float |
|_____________|_________________________|___________|
Output:
___________________________________________________
|Name | Units | Type |
|-------------|-------------------------|-----------|
|force | (in Newtons) | float |
|_____________|_________________________|___________|
"""
def newtons_second_law_of_motion(mass: float, acceleration: float) -> float:
"""
>>> newtons_second_law_of_motion(10, 10)
100
>>> newtons_second_law_of_motion(2.0, 1)
2.0
"""
force = float()
try:
force = mass * acceleration
except Exception:
return -0.0
return force
if __name__ == "__main__":
import doctest
# run doctest
doctest.testmod()
# demo
mass = 12.5
acceleration = 10
force = newtons_second_law_of_motion(mass, acceleration)
print("The force is ", force, "N")
| """
Description :
Newton's second law of motion pertains to the behavior of objects for which
all existing forces are not balanced.
The second law states that the acceleration of an object is dependent upon two variables
- the net force acting upon the object and the mass of the object.
The acceleration of an object depends directly
upon the net force acting upon the object,
and inversely upon the mass of the object.
As the force acting upon an object is increased,
the acceleration of the object is increased.
As the mass of an object is increased, the acceleration of the object is decreased.
Source: https://www.physicsclassroom.com/class/newtlaws/Lesson-3/Newton-s-Second-Law
Formulation: Fnet = m • a
Diagrammatic Explanation:
Forces are unbalanced
|
|
|
V
There is acceleration
/\
/ \
/ \
/ \
/ \
/ \
/ \
__________________ ____ ________________
|The acceleration | |The acceleration |
|depends directly | |depends inversely |
|on the net Force | |upon the object's |
|_________________| |mass_______________|
Units:
1 Newton = 1 kg X meters / (seconds^2)
How to use?
Inputs:
___________________________________________________
|Name | Units | Type |
|-------------|-------------------------|-----------|
|mass | (in kgs) | float |
|-------------|-------------------------|-----------|
|acceleration | (in meters/(seconds^2)) | float |
|_____________|_________________________|___________|
Output:
___________________________________________________
|Name | Units | Type |
|-------------|-------------------------|-----------|
|force | (in Newtons) | float |
|_____________|_________________________|___________|
"""
def newtons_second_law_of_motion(mass: float, acceleration: float) -> float:
"""
>>> newtons_second_law_of_motion(10, 10)
100
>>> newtons_second_law_of_motion(2.0, 1)
2.0
"""
force = 0.0
try:
force = mass * acceleration
except Exception:
return -0.0
return force
if __name__ == "__main__":
import doctest
# run doctest
doctest.testmod()
# demo
mass = 12.5
acceleration = 10
force = newtons_second_law_of_motion(mass, acceleration)
print("The force is ", force, "N")
| 1 |
TheAlgorithms/Python | 8,913 | Ruff fixes | ### Describe your change:
Fix graphs/eulerian_path_and_circuit_for_undirected_graph.py and physics/newtons_second_law_of_motion.py, which are causing ruff to fail
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-07-31T19:13:04Z" | "2023-07-31T20:53:26Z" | 90a8e6e0d210a5c526c8f485fa825e1649d217e2 | 5cf34d901e32b65425103309bbad0068b1851238 | Ruff fixes. ### Describe your change:
Fix graphs/eulerian_path_and_circuit_for_undirected_graph.py and physics/newtons_second_law_of_motion.py, which are causing ruff to fail
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| -1 |
||
TheAlgorithms/Python | 8,913 | Ruff fixes | ### Describe your change:
Fix graphs/eulerian_path_and_circuit_for_undirected_graph.py and physics/newtons_second_law_of_motion.py, which are causing ruff to fail
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-07-31T19:13:04Z" | "2023-07-31T20:53:26Z" | 90a8e6e0d210a5c526c8f485fa825e1649d217e2 | 5cf34d901e32b65425103309bbad0068b1851238 | Ruff fixes. ### Describe your change:
Fix graphs/eulerian_path_and_circuit_for_undirected_graph.py and physics/newtons_second_law_of_motion.py, which are causing ruff to fail
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| #!/usr/bin/env python3
import requests
giphy_api_key = "YOUR API KEY"
# Can be fetched from https://developers.giphy.com/dashboard/
def get_gifs(query: str, api_key: str = giphy_api_key) -> list:
"""
Get a list of URLs of GIFs based on a given query..
"""
formatted_query = "+".join(query.split())
url = f"https://api.giphy.com/v1/gifs/search?q={formatted_query}&api_key={api_key}"
gifs = requests.get(url).json()["data"]
return [gif["url"] for gif in gifs]
if __name__ == "__main__":
print("\n".join(get_gifs("space ship")))
| #!/usr/bin/env python3
import requests
giphy_api_key = "YOUR API KEY"
# Can be fetched from https://developers.giphy.com/dashboard/
def get_gifs(query: str, api_key: str = giphy_api_key) -> list:
"""
Get a list of URLs of GIFs based on a given query..
"""
formatted_query = "+".join(query.split())
url = f"https://api.giphy.com/v1/gifs/search?q={formatted_query}&api_key={api_key}"
gifs = requests.get(url).json()["data"]
return [gif["url"] for gif in gifs]
if __name__ == "__main__":
print("\n".join(get_gifs("space ship")))
| -1 |
TheAlgorithms/Python | 8,913 | Ruff fixes | ### Describe your change:
Fix graphs/eulerian_path_and_circuit_for_undirected_graph.py and physics/newtons_second_law_of_motion.py, which are causing ruff to fail
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-07-31T19:13:04Z" | "2023-07-31T20:53:26Z" | 90a8e6e0d210a5c526c8f485fa825e1649d217e2 | 5cf34d901e32b65425103309bbad0068b1851238 | Ruff fixes. ### Describe your change:
Fix graphs/eulerian_path_and_circuit_for_undirected_graph.py and physics/newtons_second_law_of_motion.py, which are causing ruff to fail
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| """
Author : Alexander Pantyukhin
Date : October 14, 2022
This is implementation Dynamic Programming up bottom approach
to find edit distance.
The aim is to demonstate up bottom approach for solving the task.
The implementation was tested on the
leetcode: https://leetcode.com/problems/edit-distance/
Levinstein distance
Dynamic Programming: up -> down.
"""
import functools
def min_distance_up_bottom(word1: str, word2: str) -> int:
"""
>>> min_distance_up_bottom("intention", "execution")
5
>>> min_distance_up_bottom("intention", "")
9
>>> min_distance_up_bottom("", "")
0
>>> min_distance_up_bottom("zooicoarchaeologist", "zoologist")
10
"""
len_word1 = len(word1)
len_word2 = len(word2)
@functools.cache
def min_distance(index1: int, index2: int) -> int:
# if first word index is overflow - delete all from the second word
if index1 >= len_word1:
return len_word2 - index2
# if second word index is overflow - delete all from the first word
if index2 >= len_word2:
return len_word1 - index1
diff = int(word1[index1] != word2[index2]) # current letters not identical
return min(
1 + min_distance(index1 + 1, index2),
1 + min_distance(index1, index2 + 1),
diff + min_distance(index1 + 1, index2 + 1),
)
return min_distance(0, 0)
if __name__ == "__main__":
import doctest
doctest.testmod()
| """
Author : Alexander Pantyukhin
Date : October 14, 2022
This is implementation Dynamic Programming up bottom approach
to find edit distance.
The aim is to demonstate up bottom approach for solving the task.
The implementation was tested on the
leetcode: https://leetcode.com/problems/edit-distance/
Levinstein distance
Dynamic Programming: up -> down.
"""
import functools
def min_distance_up_bottom(word1: str, word2: str) -> int:
"""
>>> min_distance_up_bottom("intention", "execution")
5
>>> min_distance_up_bottom("intention", "")
9
>>> min_distance_up_bottom("", "")
0
>>> min_distance_up_bottom("zooicoarchaeologist", "zoologist")
10
"""
len_word1 = len(word1)
len_word2 = len(word2)
@functools.cache
def min_distance(index1: int, index2: int) -> int:
# if first word index is overflow - delete all from the second word
if index1 >= len_word1:
return len_word2 - index2
# if second word index is overflow - delete all from the first word
if index2 >= len_word2:
return len_word1 - index1
diff = int(word1[index1] != word2[index2]) # current letters not identical
return min(
1 + min_distance(index1 + 1, index2),
1 + min_distance(index1, index2 + 1),
diff + min_distance(index1 + 1, index2 + 1),
)
return min_distance(0, 0)
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 8,913 | Ruff fixes | ### Describe your change:
Fix graphs/eulerian_path_and_circuit_for_undirected_graph.py and physics/newtons_second_law_of_motion.py, which are causing ruff to fail
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-07-31T19:13:04Z" | "2023-07-31T20:53:26Z" | 90a8e6e0d210a5c526c8f485fa825e1649d217e2 | 5cf34d901e32b65425103309bbad0068b1851238 | Ruff fixes. ### Describe your change:
Fix graphs/eulerian_path_and_circuit_for_undirected_graph.py and physics/newtons_second_law_of_motion.py, which are causing ruff to fail
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| """
Harmonic mean
Reference: https://en.wikipedia.org/wiki/Harmonic_mean
Harmonic series
Reference: https://en.wikipedia.org/wiki/Harmonic_series(mathematics)
"""
def is_harmonic_series(series: list) -> bool:
"""
checking whether the input series is arithmetic series or not
>>> is_harmonic_series([ 1, 2/3, 1/2, 2/5, 1/3])
True
>>> is_harmonic_series([ 1, 2/3, 2/5, 1/3])
False
>>> is_harmonic_series([1, 2, 3])
False
>>> is_harmonic_series([1/2, 1/3, 1/4])
True
>>> is_harmonic_series([2/5, 2/10, 2/15, 2/20, 2/25])
True
>>> is_harmonic_series(4)
Traceback (most recent call last):
...
ValueError: Input series is not valid, valid series - [1, 2/3, 2]
>>> is_harmonic_series([])
Traceback (most recent call last):
...
ValueError: Input list must be a non empty list
>>> is_harmonic_series([0])
Traceback (most recent call last):
...
ValueError: Input series cannot have 0 as an element
>>> is_harmonic_series([1,2,0,6])
Traceback (most recent call last):
...
ValueError: Input series cannot have 0 as an element
"""
if not isinstance(series, list):
raise ValueError("Input series is not valid, valid series - [1, 2/3, 2]")
if len(series) == 0:
raise ValueError("Input list must be a non empty list")
if len(series) == 1 and series[0] != 0:
return True
rec_series = []
series_len = len(series)
for i in range(0, series_len):
if series[i] == 0:
raise ValueError("Input series cannot have 0 as an element")
rec_series.append(1 / series[i])
common_diff = rec_series[1] - rec_series[0]
for index in range(2, series_len):
if rec_series[index] - rec_series[index - 1] != common_diff:
return False
return True
def harmonic_mean(series: list) -> float:
"""
return the harmonic mean of series
>>> harmonic_mean([1, 4, 4])
2.0
>>> harmonic_mean([3, 6, 9, 12])
5.759999999999999
>>> harmonic_mean(4)
Traceback (most recent call last):
...
ValueError: Input series is not valid, valid series - [2, 4, 6]
>>> harmonic_mean([1, 2, 3])
1.6363636363636365
>>> harmonic_mean([])
Traceback (most recent call last):
...
ValueError: Input list must be a non empty list
"""
if not isinstance(series, list):
raise ValueError("Input series is not valid, valid series - [2, 4, 6]")
if len(series) == 0:
raise ValueError("Input list must be a non empty list")
answer = 0
for val in series:
answer += 1 / val
return len(series) / answer
if __name__ == "__main__":
import doctest
doctest.testmod()
| """
Harmonic mean
Reference: https://en.wikipedia.org/wiki/Harmonic_mean
Harmonic series
Reference: https://en.wikipedia.org/wiki/Harmonic_series(mathematics)
"""
def is_harmonic_series(series: list) -> bool:
"""
checking whether the input series is arithmetic series or not
>>> is_harmonic_series([ 1, 2/3, 1/2, 2/5, 1/3])
True
>>> is_harmonic_series([ 1, 2/3, 2/5, 1/3])
False
>>> is_harmonic_series([1, 2, 3])
False
>>> is_harmonic_series([1/2, 1/3, 1/4])
True
>>> is_harmonic_series([2/5, 2/10, 2/15, 2/20, 2/25])
True
>>> is_harmonic_series(4)
Traceback (most recent call last):
...
ValueError: Input series is not valid, valid series - [1, 2/3, 2]
>>> is_harmonic_series([])
Traceback (most recent call last):
...
ValueError: Input list must be a non empty list
>>> is_harmonic_series([0])
Traceback (most recent call last):
...
ValueError: Input series cannot have 0 as an element
>>> is_harmonic_series([1,2,0,6])
Traceback (most recent call last):
...
ValueError: Input series cannot have 0 as an element
"""
if not isinstance(series, list):
raise ValueError("Input series is not valid, valid series - [1, 2/3, 2]")
if len(series) == 0:
raise ValueError("Input list must be a non empty list")
if len(series) == 1 and series[0] != 0:
return True
rec_series = []
series_len = len(series)
for i in range(0, series_len):
if series[i] == 0:
raise ValueError("Input series cannot have 0 as an element")
rec_series.append(1 / series[i])
common_diff = rec_series[1] - rec_series[0]
for index in range(2, series_len):
if rec_series[index] - rec_series[index - 1] != common_diff:
return False
return True
def harmonic_mean(series: list) -> float:
"""
return the harmonic mean of series
>>> harmonic_mean([1, 4, 4])
2.0
>>> harmonic_mean([3, 6, 9, 12])
5.759999999999999
>>> harmonic_mean(4)
Traceback (most recent call last):
...
ValueError: Input series is not valid, valid series - [2, 4, 6]
>>> harmonic_mean([1, 2, 3])
1.6363636363636365
>>> harmonic_mean([])
Traceback (most recent call last):
...
ValueError: Input list must be a non empty list
"""
if not isinstance(series, list):
raise ValueError("Input series is not valid, valid series - [2, 4, 6]")
if len(series) == 0:
raise ValueError("Input list must be a non empty list")
answer = 0
for val in series:
answer += 1 / val
return len(series) / answer
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 8,913 | Ruff fixes | ### Describe your change:
Fix graphs/eulerian_path_and_circuit_for_undirected_graph.py and physics/newtons_second_law_of_motion.py, which are causing ruff to fail
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-07-31T19:13:04Z" | "2023-07-31T20:53:26Z" | 90a8e6e0d210a5c526c8f485fa825e1649d217e2 | 5cf34d901e32b65425103309bbad0068b1851238 | Ruff fixes. ### Describe your change:
Fix graphs/eulerian_path_and_circuit_for_undirected_graph.py and physics/newtons_second_law_of_motion.py, which are causing ruff to fail
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
min_primitive_root = 3
# I have written my code naively same as definition of primitive root
# however every time I run this program, memory exceeded...
# so I used 4.80 Algorithm in
# Handbook of Applied Cryptography(CRC Press, ISBN : 0-8493-8523-7, October 1996)
# and it seems to run nicely!
def primitive_root(p_val: int) -> int:
print("Generating primitive root of p")
while True:
g = random.randrange(3, p_val)
if pow(g, 2, p_val) == 1:
continue
if pow(g, p_val, p_val) == 1:
continue
return g
def generate_key(key_size: int) -> tuple[tuple[int, int, int, int], tuple[int, int]]:
print("Generating prime p...")
p = rabin_miller.generate_large_prime(key_size) # select large prime number.
e_1 = primitive_root(p) # one primitive root on modulo p.
d = random.randrange(3, p) # private_key -> have to be greater than 2 for safety.
e_2 = cryptomath.find_mod_inverse(pow(e_1, d, p), p)
public_key = (key_size, e_1, e_2, p)
private_key = (key_size, d)
return public_key, private_key
def make_key_files(name: str, key_size: int) -> None:
if os.path.exists(f"{name}_pubkey.txt") or os.path.exists(f"{name}_privkey.txt"):
print("\nWARNING:")
print(
f'"{name}_pubkey.txt" or "{name}_privkey.txt" already exists. \n'
"Use a different name or delete these files and re-run this program."
)
sys.exit()
public_key, private_key = generate_key(key_size)
print(f"\nWriting public key to file {name}_pubkey.txt...")
with open(f"{name}_pubkey.txt", "w") as fo:
fo.write(f"{public_key[0]},{public_key[1]},{public_key[2]},{public_key[3]}")
print(f"Writing private key to file {name}_privkey.txt...")
with open(f"{name}_privkey.txt", "w") as fo:
fo.write(f"{private_key[0]},{private_key[1]}")
def main() -> None:
print("Making key files...")
make_key_files("elgamal", 2048)
print("Key files generation successful")
if __name__ == "__main__":
main()
| import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
min_primitive_root = 3
# I have written my code naively same as definition of primitive root
# however every time I run this program, memory exceeded...
# so I used 4.80 Algorithm in
# Handbook of Applied Cryptography(CRC Press, ISBN : 0-8493-8523-7, October 1996)
# and it seems to run nicely!
def primitive_root(p_val: int) -> int:
print("Generating primitive root of p")
while True:
g = random.randrange(3, p_val)
if pow(g, 2, p_val) == 1:
continue
if pow(g, p_val, p_val) == 1:
continue
return g
def generate_key(key_size: int) -> tuple[tuple[int, int, int, int], tuple[int, int]]:
print("Generating prime p...")
p = rabin_miller.generate_large_prime(key_size) # select large prime number.
e_1 = primitive_root(p) # one primitive root on modulo p.
d = random.randrange(3, p) # private_key -> have to be greater than 2 for safety.
e_2 = cryptomath.find_mod_inverse(pow(e_1, d, p), p)
public_key = (key_size, e_1, e_2, p)
private_key = (key_size, d)
return public_key, private_key
def make_key_files(name: str, key_size: int) -> None:
if os.path.exists(f"{name}_pubkey.txt") or os.path.exists(f"{name}_privkey.txt"):
print("\nWARNING:")
print(
f'"{name}_pubkey.txt" or "{name}_privkey.txt" already exists. \n'
"Use a different name or delete these files and re-run this program."
)
sys.exit()
public_key, private_key = generate_key(key_size)
print(f"\nWriting public key to file {name}_pubkey.txt...")
with open(f"{name}_pubkey.txt", "w") as fo:
fo.write(f"{public_key[0]},{public_key[1]},{public_key[2]},{public_key[3]}")
print(f"Writing private key to file {name}_privkey.txt...")
with open(f"{name}_privkey.txt", "w") as fo:
fo.write(f"{private_key[0]},{private_key[1]}")
def main() -> None:
print("Making key files...")
make_key_files("elgamal", 2048)
print("Key files generation successful")
if __name__ == "__main__":
main()
| -1 |
TheAlgorithms/Python | 8,913 | Ruff fixes | ### Describe your change:
Fix graphs/eulerian_path_and_circuit_for_undirected_graph.py and physics/newtons_second_law_of_motion.py, which are causing ruff to fail
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-07-31T19:13:04Z" | "2023-07-31T20:53:26Z" | 90a8e6e0d210a5c526c8f485fa825e1649d217e2 | 5cf34d901e32b65425103309bbad0068b1851238 | Ruff fixes. ### Describe your change:
Fix graphs/eulerian_path_and_circuit_for_undirected_graph.py and physics/newtons_second_law_of_motion.py, which are causing ruff to fail
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| def multiplicative_persistence(num: int) -> int:
"""
Return the persistence of a given number.
https://en.wikipedia.org/wiki/Persistence_of_a_number
>>> multiplicative_persistence(217)
2
>>> multiplicative_persistence(-1)
Traceback (most recent call last):
...
ValueError: multiplicative_persistence() does not accept negative values
>>> multiplicative_persistence("long number")
Traceback (most recent call last):
...
ValueError: multiplicative_persistence() only accepts integral values
"""
if not isinstance(num, int):
raise ValueError("multiplicative_persistence() only accepts integral values")
if num < 0:
raise ValueError("multiplicative_persistence() does not accept negative values")
steps = 0
num_string = str(num)
while len(num_string) != 1:
numbers = [int(i) for i in num_string]
total = 1
for i in range(0, len(numbers)):
total *= numbers[i]
num_string = str(total)
steps += 1
return steps
def additive_persistence(num: int) -> int:
"""
Return the persistence of a given number.
https://en.wikipedia.org/wiki/Persistence_of_a_number
>>> additive_persistence(199)
3
>>> additive_persistence(-1)
Traceback (most recent call last):
...
ValueError: additive_persistence() does not accept negative values
>>> additive_persistence("long number")
Traceback (most recent call last):
...
ValueError: additive_persistence() only accepts integral values
"""
if not isinstance(num, int):
raise ValueError("additive_persistence() only accepts integral values")
if num < 0:
raise ValueError("additive_persistence() does not accept negative values")
steps = 0
num_string = str(num)
while len(num_string) != 1:
numbers = [int(i) for i in num_string]
total = 0
for i in range(0, len(numbers)):
total += numbers[i]
num_string = str(total)
steps += 1
return steps
if __name__ == "__main__":
import doctest
doctest.testmod()
| def multiplicative_persistence(num: int) -> int:
"""
Return the persistence of a given number.
https://en.wikipedia.org/wiki/Persistence_of_a_number
>>> multiplicative_persistence(217)
2
>>> multiplicative_persistence(-1)
Traceback (most recent call last):
...
ValueError: multiplicative_persistence() does not accept negative values
>>> multiplicative_persistence("long number")
Traceback (most recent call last):
...
ValueError: multiplicative_persistence() only accepts integral values
"""
if not isinstance(num, int):
raise ValueError("multiplicative_persistence() only accepts integral values")
if num < 0:
raise ValueError("multiplicative_persistence() does not accept negative values")
steps = 0
num_string = str(num)
while len(num_string) != 1:
numbers = [int(i) for i in num_string]
total = 1
for i in range(0, len(numbers)):
total *= numbers[i]
num_string = str(total)
steps += 1
return steps
def additive_persistence(num: int) -> int:
"""
Return the persistence of a given number.
https://en.wikipedia.org/wiki/Persistence_of_a_number
>>> additive_persistence(199)
3
>>> additive_persistence(-1)
Traceback (most recent call last):
...
ValueError: additive_persistence() does not accept negative values
>>> additive_persistence("long number")
Traceback (most recent call last):
...
ValueError: additive_persistence() only accepts integral values
"""
if not isinstance(num, int):
raise ValueError("additive_persistence() only accepts integral values")
if num < 0:
raise ValueError("additive_persistence() does not accept negative values")
steps = 0
num_string = str(num)
while len(num_string) != 1:
numbers = [int(i) for i in num_string]
total = 0
for i in range(0, len(numbers)):
total += numbers[i]
num_string = str(total)
steps += 1
return steps
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 8,913 | Ruff fixes | ### Describe your change:
Fix graphs/eulerian_path_and_circuit_for_undirected_graph.py and physics/newtons_second_law_of_motion.py, which are causing ruff to fail
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-07-31T19:13:04Z" | "2023-07-31T20:53:26Z" | 90a8e6e0d210a5c526c8f485fa825e1649d217e2 | 5cf34d901e32b65425103309bbad0068b1851238 | Ruff fixes. ### Describe your change:
Fix graphs/eulerian_path_and_circuit_for_undirected_graph.py and physics/newtons_second_law_of_motion.py, which are causing ruff to fail
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| #!/usr/bin/env python3
"""
Implementation of entropy of information
https://en.wikipedia.org/wiki/Entropy_(information_theory)
"""
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def calculate_prob(text: str) -> None:
"""
This method takes path and two dict as argument
and than calculates entropy of them.
:param dict:
:param dict:
:return: Prints
1) Entropy of information based on 1 alphabet
2) Entropy of information based on couples of 2 alphabet
3) print Entropy of H(X n∣Xn−1)
Text from random books. Also, random quotes.
>>> text = ("Behind Winston’s back the voice "
... "from the telescreen was still "
... "babbling and the overfulfilment")
>>> calculate_prob(text)
4.0
6.0
2.0
>>> text = ("The Ministry of Truth—Minitrue, in Newspeak [Newspeak was the official"
... "face in elegant lettering, the three")
>>> calculate_prob(text)
4.0
5.0
1.0
>>> text = ("Had repulsive dashwoods suspicion sincerity but advantage now him. "
... "Remark easily garret nor nay. Civil those mrs enjoy shy fat merry. "
... "You greatest jointure saw horrible. He private he on be imagine "
... "suppose. Fertile beloved evident through no service elderly is. Blind "
... "there if every no so at. Own neglected you preferred way sincerity "
... "delivered his attempted. To of message cottage windows do besides "
... "against uncivil. Delightful unreserved impossible few estimating "
... "men favourable see entreaties. She propriety immediate was improving. "
... "He or entrance humoured likewise moderate. Much nor game son say "
... "feel. Fat make met can must form into gate. Me we offending prevailed "
... "discovery.")
>>> calculate_prob(text)
4.0
7.0
3.0
"""
single_char_strings, two_char_strings = analyze_text(text)
my_alphas = list(" " + ascii_lowercase)
# what is our total sum of probabilities.
all_sum = sum(single_char_strings.values())
# one length string
my_fir_sum = 0
# for each alpha we go in our dict and if it is in it we calculate entropy
for ch in my_alphas:
if ch in single_char_strings:
my_str = single_char_strings[ch]
prob = my_str / all_sum
my_fir_sum += prob * math.log2(prob) # entropy formula.
# print entropy
print(f"{round(-1 * my_fir_sum):.1f}")
# two len string
all_sum = sum(two_char_strings.values())
my_sec_sum = 0
# for each alpha (two in size) calculate entropy.
for ch0 in my_alphas:
for ch1 in my_alphas:
sequence = ch0 + ch1
if sequence in two_char_strings:
my_str = two_char_strings[sequence]
prob = int(my_str) / all_sum
my_sec_sum += prob * math.log2(prob)
# print second entropy
print(f"{round(-1 * my_sec_sum):.1f}")
# print the difference between them
print(f"{round((-1 * my_sec_sum) - (-1 * my_fir_sum)):.1f}")
def analyze_text(text: str) -> tuple[dict, dict]:
"""
Convert text input into two dicts of counts.
The first dictionary stores the frequency of single character strings.
The second dictionary stores the frequency of two character strings.
"""
single_char_strings = Counter() # type: ignore
two_char_strings = Counter() # type: ignore
single_char_strings[text[-1]] += 1
# first case when we have space at start.
two_char_strings[" " + text[0]] += 1
for i in range(0, len(text) - 1):
single_char_strings[text[i]] += 1
two_char_strings[text[i : i + 2]] += 1
return single_char_strings, two_char_strings
def main():
import doctest
doctest.testmod()
# text = (
# "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
# "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
# "jointure saw horrible. He private he on be imagine suppose. Fertile "
# "beloved evident through no service elderly is. Blind there if every no so "
# "at. Own neglected you preferred way sincerity delivered his attempted. To "
# "of message cottage windows do besides against uncivil. Delightful "
# "unreserved impossible few estimating men favourable see entreaties. She "
# "propriety immediate was improving. He or entrance humoured likewise "
# "moderate. Much nor game son say feel. Fat make met can must form into "
# "gate. Me we offending prevailed discovery. "
# )
# calculate_prob(text)
if __name__ == "__main__":
main()
| #!/usr/bin/env python3
"""
Implementation of entropy of information
https://en.wikipedia.org/wiki/Entropy_(information_theory)
"""
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def calculate_prob(text: str) -> None:
"""
This method takes path and two dict as argument
and than calculates entropy of them.
:param dict:
:param dict:
:return: Prints
1) Entropy of information based on 1 alphabet
2) Entropy of information based on couples of 2 alphabet
3) print Entropy of H(X n∣Xn−1)
Text from random books. Also, random quotes.
>>> text = ("Behind Winston’s back the voice "
... "from the telescreen was still "
... "babbling and the overfulfilment")
>>> calculate_prob(text)
4.0
6.0
2.0
>>> text = ("The Ministry of Truth—Minitrue, in Newspeak [Newspeak was the official"
... "face in elegant lettering, the three")
>>> calculate_prob(text)
4.0
5.0
1.0
>>> text = ("Had repulsive dashwoods suspicion sincerity but advantage now him. "
... "Remark easily garret nor nay. Civil those mrs enjoy shy fat merry. "
... "You greatest jointure saw horrible. He private he on be imagine "
... "suppose. Fertile beloved evident through no service elderly is. Blind "
... "there if every no so at. Own neglected you preferred way sincerity "
... "delivered his attempted. To of message cottage windows do besides "
... "against uncivil. Delightful unreserved impossible few estimating "
... "men favourable see entreaties. She propriety immediate was improving. "
... "He or entrance humoured likewise moderate. Much nor game son say "
... "feel. Fat make met can must form into gate. Me we offending prevailed "
... "discovery.")
>>> calculate_prob(text)
4.0
7.0
3.0
"""
single_char_strings, two_char_strings = analyze_text(text)
my_alphas = list(" " + ascii_lowercase)
# what is our total sum of probabilities.
all_sum = sum(single_char_strings.values())
# one length string
my_fir_sum = 0
# for each alpha we go in our dict and if it is in it we calculate entropy
for ch in my_alphas:
if ch in single_char_strings:
my_str = single_char_strings[ch]
prob = my_str / all_sum
my_fir_sum += prob * math.log2(prob) # entropy formula.
# print entropy
print(f"{round(-1 * my_fir_sum):.1f}")
# two len string
all_sum = sum(two_char_strings.values())
my_sec_sum = 0
# for each alpha (two in size) calculate entropy.
for ch0 in my_alphas:
for ch1 in my_alphas:
sequence = ch0 + ch1
if sequence in two_char_strings:
my_str = two_char_strings[sequence]
prob = int(my_str) / all_sum
my_sec_sum += prob * math.log2(prob)
# print second entropy
print(f"{round(-1 * my_sec_sum):.1f}")
# print the difference between them
print(f"{round((-1 * my_sec_sum) - (-1 * my_fir_sum)):.1f}")
def analyze_text(text: str) -> tuple[dict, dict]:
"""
Convert text input into two dicts of counts.
The first dictionary stores the frequency of single character strings.
The second dictionary stores the frequency of two character strings.
"""
single_char_strings = Counter() # type: ignore
two_char_strings = Counter() # type: ignore
single_char_strings[text[-1]] += 1
# first case when we have space at start.
two_char_strings[" " + text[0]] += 1
for i in range(0, len(text) - 1):
single_char_strings[text[i]] += 1
two_char_strings[text[i : i + 2]] += 1
return single_char_strings, two_char_strings
def main():
import doctest
doctest.testmod()
# text = (
# "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
# "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
# "jointure saw horrible. He private he on be imagine suppose. Fertile "
# "beloved evident through no service elderly is. Blind there if every no so "
# "at. Own neglected you preferred way sincerity delivered his attempted. To "
# "of message cottage windows do besides against uncivil. Delightful "
# "unreserved impossible few estimating men favourable see entreaties. She "
# "propriety immediate was improving. He or entrance humoured likewise "
# "moderate. Much nor game son say feel. Fat make met can must form into "
# "gate. Me we offending prevailed discovery. "
# )
# calculate_prob(text)
if __name__ == "__main__":
main()
| -1 |
TheAlgorithms/Python | 8,913 | Ruff fixes | ### Describe your change:
Fix graphs/eulerian_path_and_circuit_for_undirected_graph.py and physics/newtons_second_law_of_motion.py, which are causing ruff to fail
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| tianyizheng02 | "2023-07-31T19:13:04Z" | "2023-07-31T20:53:26Z" | 90a8e6e0d210a5c526c8f485fa825e1649d217e2 | 5cf34d901e32b65425103309bbad0068b1851238 | Ruff fixes. ### Describe your change:
Fix graphs/eulerian_path_and_circuit_for_undirected_graph.py and physics/newtons_second_law_of_motion.py, which are causing ruff to fail
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [ ] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* [ ] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue): "Fixes #ISSUE-NUMBER".
| # Implementation of First Come First Served scheduling algorithm
# In this Algorithm we just care about the order that the processes arrived
# without carring about their duration time
# https://en.wikipedia.org/wiki/Scheduling_(computing)#First_come,_first_served
from __future__ import annotations
def calculate_waiting_times(duration_times: list[int]) -> list[int]:
"""
This function calculates the waiting time of some processes that have a
specified duration time.
Return: The waiting time for each process.
>>> calculate_waiting_times([5, 10, 15])
[0, 5, 15]
>>> calculate_waiting_times([1, 2, 3, 4, 5])
[0, 1, 3, 6, 10]
>>> calculate_waiting_times([10, 3])
[0, 10]
"""
waiting_times = [0] * len(duration_times)
for i in range(1, len(duration_times)):
waiting_times[i] = duration_times[i - 1] + waiting_times[i - 1]
return waiting_times
def calculate_turnaround_times(
duration_times: list[int], waiting_times: list[int]
) -> list[int]:
"""
This function calculates the turnaround time of some processes.
Return: The time difference between the completion time and the
arrival time.
Practically waiting_time + duration_time
>>> calculate_turnaround_times([5, 10, 15], [0, 5, 15])
[5, 15, 30]
>>> calculate_turnaround_times([1, 2, 3, 4, 5], [0, 1, 3, 6, 10])
[1, 3, 6, 10, 15]
>>> calculate_turnaround_times([10, 3], [0, 10])
[10, 13]
"""
return [
duration_time + waiting_times[i]
for i, duration_time in enumerate(duration_times)
]
def calculate_average_turnaround_time(turnaround_times: list[int]) -> float:
"""
This function calculates the average of the turnaround times
Return: The average of the turnaround times.
>>> calculate_average_turnaround_time([0, 5, 16])
7.0
>>> calculate_average_turnaround_time([1, 5, 8, 12])
6.5
>>> calculate_average_turnaround_time([10, 24])
17.0
"""
return sum(turnaround_times) / len(turnaround_times)
def calculate_average_waiting_time(waiting_times: list[int]) -> float:
"""
This function calculates the average of the waiting times
Return: The average of the waiting times.
>>> calculate_average_waiting_time([0, 5, 16])
7.0
>>> calculate_average_waiting_time([1, 5, 8, 12])
6.5
>>> calculate_average_waiting_time([10, 24])
17.0
"""
return sum(waiting_times) / len(waiting_times)
if __name__ == "__main__":
# process id's
processes = [1, 2, 3]
# ensure that we actually have processes
if len(processes) == 0:
print("Zero amount of processes")
raise SystemExit(0)
# duration time of all processes
duration_times = [19, 8, 9]
# ensure we can match each id to a duration time
if len(duration_times) != len(processes):
print("Unable to match all id's with their duration time")
raise SystemExit(0)
# get the waiting times and the turnaround times
waiting_times = calculate_waiting_times(duration_times)
turnaround_times = calculate_turnaround_times(duration_times, waiting_times)
# get the average times
average_waiting_time = calculate_average_waiting_time(waiting_times)
average_turnaround_time = calculate_average_turnaround_time(turnaround_times)
# print all the results
print("Process ID\tDuration Time\tWaiting Time\tTurnaround Time")
for i, process in enumerate(processes):
print(
f"{process}\t\t{duration_times[i]}\t\t{waiting_times[i]}\t\t"
f"{turnaround_times[i]}"
)
print(f"Average waiting time = {average_waiting_time}")
print(f"Average turn around time = {average_turnaround_time}")
| # Implementation of First Come First Served scheduling algorithm
# In this Algorithm we just care about the order that the processes arrived
# without carring about their duration time
# https://en.wikipedia.org/wiki/Scheduling_(computing)#First_come,_first_served
from __future__ import annotations
def calculate_waiting_times(duration_times: list[int]) -> list[int]:
"""
This function calculates the waiting time of some processes that have a
specified duration time.
Return: The waiting time for each process.
>>> calculate_waiting_times([5, 10, 15])
[0, 5, 15]
>>> calculate_waiting_times([1, 2, 3, 4, 5])
[0, 1, 3, 6, 10]
>>> calculate_waiting_times([10, 3])
[0, 10]
"""
waiting_times = [0] * len(duration_times)
for i in range(1, len(duration_times)):
waiting_times[i] = duration_times[i - 1] + waiting_times[i - 1]
return waiting_times
def calculate_turnaround_times(
duration_times: list[int], waiting_times: list[int]
) -> list[int]:
"""
This function calculates the turnaround time of some processes.
Return: The time difference between the completion time and the
arrival time.
Practically waiting_time + duration_time
>>> calculate_turnaround_times([5, 10, 15], [0, 5, 15])
[5, 15, 30]
>>> calculate_turnaround_times([1, 2, 3, 4, 5], [0, 1, 3, 6, 10])
[1, 3, 6, 10, 15]
>>> calculate_turnaround_times([10, 3], [0, 10])
[10, 13]
"""
return [
duration_time + waiting_times[i]
for i, duration_time in enumerate(duration_times)
]
def calculate_average_turnaround_time(turnaround_times: list[int]) -> float:
"""
This function calculates the average of the turnaround times
Return: The average of the turnaround times.
>>> calculate_average_turnaround_time([0, 5, 16])
7.0
>>> calculate_average_turnaround_time([1, 5, 8, 12])
6.5
>>> calculate_average_turnaround_time([10, 24])
17.0
"""
return sum(turnaround_times) / len(turnaround_times)
def calculate_average_waiting_time(waiting_times: list[int]) -> float:
"""
This function calculates the average of the waiting times
Return: The average of the waiting times.
>>> calculate_average_waiting_time([0, 5, 16])
7.0
>>> calculate_average_waiting_time([1, 5, 8, 12])
6.5
>>> calculate_average_waiting_time([10, 24])
17.0
"""
return sum(waiting_times) / len(waiting_times)
if __name__ == "__main__":
# process id's
processes = [1, 2, 3]
# ensure that we actually have processes
if len(processes) == 0:
print("Zero amount of processes")
raise SystemExit(0)
# duration time of all processes
duration_times = [19, 8, 9]
# ensure we can match each id to a duration time
if len(duration_times) != len(processes):
print("Unable to match all id's with their duration time")
raise SystemExit(0)
# get the waiting times and the turnaround times
waiting_times = calculate_waiting_times(duration_times)
turnaround_times = calculate_turnaround_times(duration_times, waiting_times)
# get the average times
average_waiting_time = calculate_average_waiting_time(waiting_times)
average_turnaround_time = calculate_average_turnaround_time(turnaround_times)
# print all the results
print("Process ID\tDuration Time\tWaiting Time\tTurnaround Time")
for i, process in enumerate(processes):
print(
f"{process}\t\t{duration_times[i]}\t\t{waiting_times[i]}\t\t"
f"{turnaround_times[i]}"
)
print(f"Average waiting time = {average_waiting_time}")
print(f"Average turn around time = {average_turnaround_time}")
| -1 |
Subsets and Splits