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TheAlgorithms/Python | 9,358 | Removed redundant greatest_common_divisor code | ### Describe your change:
Previously all the files that needed greatest_common_divisor (aka gcd), used to define the method instead of just importing from Maths directory's greatest_common_divisor.py file. I removed thos definitions and imported gcd method from Maths folder. Fixes #8098
* [ ] Add an algorithm?
* [ ] Fix a bug or typo in an existing algorithm?
* [x] 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.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] 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".
| Siddikpatel | "2023-10-02T05:03:50Z" | "2023-10-09T12:19:12Z" | 876087be998d5b366d68cbb9394b6b92b7f619f6 | 583a614fefaa9c932e6d650abfea2eaa75a93b05 | Removed redundant greatest_common_divisor code. ### Describe your change:
Previously all the files that needed greatest_common_divisor (aka gcd), used to define the method instead of just importing from Maths directory's greatest_common_divisor.py file. I removed thos definitions and imported gcd method from Maths folder. Fixes #8098
* [ ] Add an algorithm?
* [ ] Fix a bug or typo in an existing algorithm?
* [x] 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.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] 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".
| """
Reference: https://www.investopedia.com/terms/p/presentvalue.asp
An algorithm that calculates the present value of a stream of yearly cash flows given...
1. The discount rate (as a decimal, not a percent)
2. An array of cash flows, with the index of the cash flow being the associated year
Note: This algorithm assumes that cash flows are paid at the end of the specified year
"""
def present_value(discount_rate: float, cash_flows: list[float]) -> float:
"""
>>> present_value(0.13, [10, 20.70, -293, 297])
4.69
>>> present_value(0.07, [-109129.39, 30923.23, 15098.93, 29734,39])
-42739.63
>>> present_value(0.07, [109129.39, 30923.23, 15098.93, 29734,39])
175519.15
>>> present_value(-1, [109129.39, 30923.23, 15098.93, 29734,39])
Traceback (most recent call last):
...
ValueError: Discount rate cannot be negative
>>> present_value(0.03, [])
Traceback (most recent call last):
...
ValueError: Cash flows list cannot be empty
"""
if discount_rate < 0:
raise ValueError("Discount rate cannot be negative")
if not cash_flows:
raise ValueError("Cash flows list cannot be empty")
present_value = sum(
cash_flow / ((1 + discount_rate) ** i) for i, cash_flow in enumerate(cash_flows)
)
return round(present_value, ndigits=2)
if __name__ == "__main__":
import doctest
doctest.testmod()
| """
Reference: https://www.investopedia.com/terms/p/presentvalue.asp
An algorithm that calculates the present value of a stream of yearly cash flows given...
1. The discount rate (as a decimal, not a percent)
2. An array of cash flows, with the index of the cash flow being the associated year
Note: This algorithm assumes that cash flows are paid at the end of the specified year
"""
def present_value(discount_rate: float, cash_flows: list[float]) -> float:
"""
>>> present_value(0.13, [10, 20.70, -293, 297])
4.69
>>> present_value(0.07, [-109129.39, 30923.23, 15098.93, 29734,39])
-42739.63
>>> present_value(0.07, [109129.39, 30923.23, 15098.93, 29734,39])
175519.15
>>> present_value(-1, [109129.39, 30923.23, 15098.93, 29734,39])
Traceback (most recent call last):
...
ValueError: Discount rate cannot be negative
>>> present_value(0.03, [])
Traceback (most recent call last):
...
ValueError: Cash flows list cannot be empty
"""
if discount_rate < 0:
raise ValueError("Discount rate cannot be negative")
if not cash_flows:
raise ValueError("Cash flows list cannot be empty")
present_value = sum(
cash_flow / ((1 + discount_rate) ** i) for i, cash_flow in enumerate(cash_flows)
)
return round(present_value, ndigits=2)
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 9,358 | Removed redundant greatest_common_divisor code | ### Describe your change:
Previously all the files that needed greatest_common_divisor (aka gcd), used to define the method instead of just importing from Maths directory's greatest_common_divisor.py file. I removed thos definitions and imported gcd method from Maths folder. Fixes #8098
* [ ] Add an algorithm?
* [ ] Fix a bug or typo in an existing algorithm?
* [x] 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.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] 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".
| Siddikpatel | "2023-10-02T05:03:50Z" | "2023-10-09T12:19:12Z" | 876087be998d5b366d68cbb9394b6b92b7f619f6 | 583a614fefaa9c932e6d650abfea2eaa75a93b05 | Removed redundant greatest_common_divisor code. ### Describe your change:
Previously all the files that needed greatest_common_divisor (aka gcd), used to define the method instead of just importing from Maths directory's greatest_common_divisor.py file. I removed thos definitions and imported gcd method from Maths folder. Fixes #8098
* [ ] Add an algorithm?
* [ ] Fix a bug or typo in an existing algorithm?
* [x] 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.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] 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".
| """Prime Check."""
import math
import unittest
def is_prime(number: int) -> bool:
"""Checks to see if a number is a prime in O(sqrt(n)).
A number is prime if it has exactly two factors: 1 and itself.
>>> is_prime(0)
False
>>> is_prime(1)
False
>>> is_prime(2)
True
>>> is_prime(3)
True
>>> is_prime(27)
False
>>> is_prime(87)
False
>>> is_prime(563)
True
>>> is_prime(2999)
True
>>> is_prime(67483)
False
"""
# precondition
assert isinstance(number, int) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5, int(math.sqrt(number) + 1), 6):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
class Test(unittest.TestCase):
def test_primes(self):
self.assertTrue(is_prime(2))
self.assertTrue(is_prime(3))
self.assertTrue(is_prime(5))
self.assertTrue(is_prime(7))
self.assertTrue(is_prime(11))
self.assertTrue(is_prime(13))
self.assertTrue(is_prime(17))
self.assertTrue(is_prime(19))
self.assertTrue(is_prime(23))
self.assertTrue(is_prime(29))
def test_not_primes(self):
with self.assertRaises(AssertionError):
is_prime(-19)
self.assertFalse(
is_prime(0),
"Zero doesn't have any positive factors, primes must have exactly two.",
)
self.assertFalse(
is_prime(1),
"One only has 1 positive factor, primes must have exactly two.",
)
self.assertFalse(is_prime(2 * 2))
self.assertFalse(is_prime(2 * 3))
self.assertFalse(is_prime(3 * 3))
self.assertFalse(is_prime(3 * 5))
self.assertFalse(is_prime(3 * 5 * 7))
if __name__ == "__main__":
unittest.main()
| """Prime Check."""
import math
import unittest
def is_prime(number: int) -> bool:
"""Checks to see if a number is a prime in O(sqrt(n)).
A number is prime if it has exactly two factors: 1 and itself.
>>> is_prime(0)
False
>>> is_prime(1)
False
>>> is_prime(2)
True
>>> is_prime(3)
True
>>> is_prime(27)
False
>>> is_prime(87)
False
>>> is_prime(563)
True
>>> is_prime(2999)
True
>>> is_prime(67483)
False
"""
# precondition
assert isinstance(number, int) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5, int(math.sqrt(number) + 1), 6):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
class Test(unittest.TestCase):
def test_primes(self):
self.assertTrue(is_prime(2))
self.assertTrue(is_prime(3))
self.assertTrue(is_prime(5))
self.assertTrue(is_prime(7))
self.assertTrue(is_prime(11))
self.assertTrue(is_prime(13))
self.assertTrue(is_prime(17))
self.assertTrue(is_prime(19))
self.assertTrue(is_prime(23))
self.assertTrue(is_prime(29))
def test_not_primes(self):
with self.assertRaises(AssertionError):
is_prime(-19)
self.assertFalse(
is_prime(0),
"Zero doesn't have any positive factors, primes must have exactly two.",
)
self.assertFalse(
is_prime(1),
"One only has 1 positive factor, primes must have exactly two.",
)
self.assertFalse(is_prime(2 * 2))
self.assertFalse(is_prime(2 * 3))
self.assertFalse(is_prime(3 * 3))
self.assertFalse(is_prime(3 * 5))
self.assertFalse(is_prime(3 * 5 * 7))
if __name__ == "__main__":
unittest.main()
| -1 |
TheAlgorithms/Python | 9,358 | Removed redundant greatest_common_divisor code | ### Describe your change:
Previously all the files that needed greatest_common_divisor (aka gcd), used to define the method instead of just importing from Maths directory's greatest_common_divisor.py file. I removed thos definitions and imported gcd method from Maths folder. Fixes #8098
* [ ] Add an algorithm?
* [ ] Fix a bug or typo in an existing algorithm?
* [x] 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.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] 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".
| Siddikpatel | "2023-10-02T05:03:50Z" | "2023-10-09T12:19:12Z" | 876087be998d5b366d68cbb9394b6b92b7f619f6 | 583a614fefaa9c932e6d650abfea2eaa75a93b05 | Removed redundant greatest_common_divisor code. ### Describe your change:
Previously all the files that needed greatest_common_divisor (aka gcd), used to define the method instead of just importing from Maths directory's greatest_common_divisor.py file. I removed thos definitions and imported gcd method from Maths folder. Fixes #8098
* [ ] Add an algorithm?
* [ ] Fix a bug or typo in an existing algorithm?
* [x] 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.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] 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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WVL | -1 |
TheAlgorithms/Python | 9,358 | Removed redundant greatest_common_divisor code | ### Describe your change:
Previously all the files that needed greatest_common_divisor (aka gcd), used to define the method instead of just importing from Maths directory's greatest_common_divisor.py file. I removed thos definitions and imported gcd method from Maths folder. Fixes #8098
* [ ] Add an algorithm?
* [ ] Fix a bug or typo in an existing algorithm?
* [x] 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.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] 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".
| Siddikpatel | "2023-10-02T05:03:50Z" | "2023-10-09T12:19:12Z" | 876087be998d5b366d68cbb9394b6b92b7f619f6 | 583a614fefaa9c932e6d650abfea2eaa75a93b05 | Removed redundant greatest_common_divisor code. ### Describe your change:
Previously all the files that needed greatest_common_divisor (aka gcd), used to define the method instead of just importing from Maths directory's greatest_common_divisor.py file. I removed thos definitions and imported gcd method from Maths folder. Fixes #8098
* [ ] Add an algorithm?
* [ ] Fix a bug or typo in an existing algorithm?
* [x] 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.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] 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 | 9,358 | Removed redundant greatest_common_divisor code | ### Describe your change:
Previously all the files that needed greatest_common_divisor (aka gcd), used to define the method instead of just importing from Maths directory's greatest_common_divisor.py file. I removed thos definitions and imported gcd method from Maths folder. Fixes #8098
* [ ] Add an algorithm?
* [ ] Fix a bug or typo in an existing algorithm?
* [x] 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.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] 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".
| Siddikpatel | "2023-10-02T05:03:50Z" | "2023-10-09T12:19:12Z" | 876087be998d5b366d68cbb9394b6b92b7f619f6 | 583a614fefaa9c932e6d650abfea2eaa75a93b05 | Removed redundant greatest_common_divisor code. ### Describe your change:
Previously all the files that needed greatest_common_divisor (aka gcd), used to define the method instead of just importing from Maths directory's greatest_common_divisor.py file. I removed thos definitions and imported gcd method from Maths folder. Fixes #8098
* [ ] Add an algorithm?
* [ ] Fix a bug or typo in an existing algorithm?
* [x] 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.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] 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 quick_sort(data: list) -> list:
"""
>>> for data in ([2, 1, 0], [2.2, 1.1, 0], "quick_sort"):
... quick_sort(data) == sorted(data)
True
True
True
"""
if len(data) <= 1:
return data
else:
return [
*quick_sort([e for e in data[1:] if e <= data[0]]),
data[0],
*quick_sort([e for e in data[1:] if e > data[0]]),
]
if __name__ == "__main__":
import doctest
doctest.testmod()
| def quick_sort(data: list) -> list:
"""
>>> for data in ([2, 1, 0], [2.2, 1.1, 0], "quick_sort"):
... quick_sort(data) == sorted(data)
True
True
True
"""
if len(data) <= 1:
return data
else:
return [
*quick_sort([e for e in data[1:] if e <= data[0]]),
data[0],
*quick_sort([e for e in data[1:] if e > data[0]]),
]
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 9,076 | Fix typos |
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| omahs | "2023-09-22T13:22:15Z" | "2023-09-23T08:53:09Z" | dc50add8a78ebf34bc7bb050c1a0e61d207b9544 | b203150ac481743a6d8c1ef01091712a54dfbf6c | Fix typos.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| """
Langton's ant
@ https://en.wikipedia.org/wiki/Langton%27s_ant
@ https://upload.wikimedia.org/wikipedia/commons/0/09/LangtonsAntAnimated.gif
"""
from functools import partial
from matplotlib import pyplot as plt
from matplotlib.animation import FuncAnimation
WIDTH = 80
HEIGHT = 80
class LangtonsAnt:
"""
Represents the main LangonsAnt algorithm.
>>> la = LangtonsAnt(2, 2)
>>> la.board
[[True, True], [True, True]]
>>> la.ant_position
(1, 1)
"""
def __init__(self, width: int, height: int) -> None:
# Each square is either True or False where True is white and False is black
self.board = [[True] * width for _ in range(height)]
self.ant_position: tuple[int, int] = (width // 2, height // 2)
# Initially pointing left (similar to the the wikipedia image)
# (0 = 0° | 1 = 90° | 2 = 180 ° | 3 = 270°)
self.ant_direction: int = 3
def move_ant(self, axes: plt.Axes | None, display: bool, _frame: int) -> None:
"""
Performs three tasks:
1. The ant turns either clockwise or anti-clockwise according to the colour
of the square that it is currently on. If the square is white, the ant
turns clockwise, and if the square is black the ant turns anti-clockwise
2. The ant moves one square in the direction that it is currently facing
3. The square the ant was previously on is inverted (White -> Black and
Black -> White)
If display is True, the board will also be displayed on the axes
>>> la = LangtonsAnt(2, 2)
>>> la.move_ant(None, True, 0)
>>> la.board
[[True, True], [True, False]]
>>> la.move_ant(None, True, 0)
>>> la.board
[[True, False], [True, False]]
"""
directions = {
0: (-1, 0), # 0°
1: (0, 1), # 90°
2: (1, 0), # 180°
3: (0, -1), # 270°
}
x, y = self.ant_position
# Turn clockwise or anti-clockwise according to colour of square
if self.board[x][y] is True:
# The square is white so turn 90° clockwise
self.ant_direction = (self.ant_direction + 1) % 4
else:
# The square is black so turn 90° anti-clockwise
self.ant_direction = (self.ant_direction - 1) % 4
# Move ant
move_x, move_y = directions[self.ant_direction]
self.ant_position = (x + move_x, y + move_y)
# Flip colour of square
self.board[x][y] = not self.board[x][y]
if display and axes:
# Display the board on the axes
axes.get_xaxis().set_ticks([])
axes.get_yaxis().set_ticks([])
axes.imshow(self.board, cmap="gray", interpolation="nearest")
def display(self, frames: int = 100_000) -> None:
"""
Displays the board without delay in a matplotlib plot
to visually understand and track the ant.
>>> _ = LangtonsAnt(WIDTH, HEIGHT)
"""
fig, ax = plt.subplots()
# Assign animation to a variable to prevent it from getting garbage collected
self.animation = FuncAnimation(
fig, partial(self.move_ant, ax, True), frames=frames, interval=1
)
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod()
LangtonsAnt(WIDTH, HEIGHT).display()
| """
Langton's ant
@ https://en.wikipedia.org/wiki/Langton%27s_ant
@ https://upload.wikimedia.org/wikipedia/commons/0/09/LangtonsAntAnimated.gif
"""
from functools import partial
from matplotlib import pyplot as plt
from matplotlib.animation import FuncAnimation
WIDTH = 80
HEIGHT = 80
class LangtonsAnt:
"""
Represents the main LangonsAnt algorithm.
>>> la = LangtonsAnt(2, 2)
>>> la.board
[[True, True], [True, True]]
>>> la.ant_position
(1, 1)
"""
def __init__(self, width: int, height: int) -> None:
# Each square is either True or False where True is white and False is black
self.board = [[True] * width for _ in range(height)]
self.ant_position: tuple[int, int] = (width // 2, height // 2)
# Initially pointing left (similar to the wikipedia image)
# (0 = 0° | 1 = 90° | 2 = 180 ° | 3 = 270°)
self.ant_direction: int = 3
def move_ant(self, axes: plt.Axes | None, display: bool, _frame: int) -> None:
"""
Performs three tasks:
1. The ant turns either clockwise or anti-clockwise according to the colour
of the square that it is currently on. If the square is white, the ant
turns clockwise, and if the square is black the ant turns anti-clockwise
2. The ant moves one square in the direction that it is currently facing
3. The square the ant was previously on is inverted (White -> Black and
Black -> White)
If display is True, the board will also be displayed on the axes
>>> la = LangtonsAnt(2, 2)
>>> la.move_ant(None, True, 0)
>>> la.board
[[True, True], [True, False]]
>>> la.move_ant(None, True, 0)
>>> la.board
[[True, False], [True, False]]
"""
directions = {
0: (-1, 0), # 0°
1: (0, 1), # 90°
2: (1, 0), # 180°
3: (0, -1), # 270°
}
x, y = self.ant_position
# Turn clockwise or anti-clockwise according to colour of square
if self.board[x][y] is True:
# The square is white so turn 90° clockwise
self.ant_direction = (self.ant_direction + 1) % 4
else:
# The square is black so turn 90° anti-clockwise
self.ant_direction = (self.ant_direction - 1) % 4
# Move ant
move_x, move_y = directions[self.ant_direction]
self.ant_position = (x + move_x, y + move_y)
# Flip colour of square
self.board[x][y] = not self.board[x][y]
if display and axes:
# Display the board on the axes
axes.get_xaxis().set_ticks([])
axes.get_yaxis().set_ticks([])
axes.imshow(self.board, cmap="gray", interpolation="nearest")
def display(self, frames: int = 100_000) -> None:
"""
Displays the board without delay in a matplotlib plot
to visually understand and track the ant.
>>> _ = LangtonsAnt(WIDTH, HEIGHT)
"""
fig, ax = plt.subplots()
# Assign animation to a variable to prevent it from getting garbage collected
self.animation = FuncAnimation(
fig, partial(self.move_ant, ax, True), frames=frames, interval=1
)
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod()
LangtonsAnt(WIDTH, HEIGHT).display()
| 1 |
TheAlgorithms/Python | 9,076 | Fix typos |
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| omahs | "2023-09-22T13:22:15Z" | "2023-09-23T08:53:09Z" | dc50add8a78ebf34bc7bb050c1a0e61d207b9544 | b203150ac481743a6d8c1ef01091712a54dfbf6c | Fix typos.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| # Compression
Data compression is everywhere, you need it to store data without taking too much space.
Either the compression lose some data (then we talk about lossy compression, such as .jpg) or it does not (and then it is lossless compression, such as .png)
Lossless compression is mainly used for archive purpose as it allow storing data without losing information about the file archived. On the other hand, lossy compression is used for transfer of file where quality isn't necessarily what is required (i.e: images on Twitter).
* <https://www.sciencedirect.com/topics/computer-science/compression-algorithm>
* <https://en.wikipedia.org/wiki/Data_compression>
* <https://en.wikipedia.org/wiki/Pigeonhole_principle>
| # Compression
Data compression is everywhere, you need it to store data without taking too much space.
Either the compression loses some data (then we talk about lossy compression, such as .jpg) or it does not (and then it is lossless compression, such as .png)
Lossless compression is mainly used for archive purpose as it allows storing data without losing information about the file archived. On the other hand, lossy compression is used for transfer of file where quality isn't necessarily what is required (i.e: images on Twitter).
* <https://www.sciencedirect.com/topics/computer-science/compression-algorithm>
* <https://en.wikipedia.org/wiki/Data_compression>
* <https://en.wikipedia.org/wiki/Pigeonhole_principle>
| 1 |
TheAlgorithms/Python | 9,076 | Fix typos |
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| omahs | "2023-09-22T13:22:15Z" | "2023-09-23T08:53:09Z" | dc50add8a78ebf34bc7bb050c1a0e61d207b9544 | b203150ac481743a6d8c1ef01091712a54dfbf6c | Fix typos.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| # 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 as 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 | 9,076 | Fix typos |
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| omahs | "2023-09-22T13:22:15Z" | "2023-09-23T08:53:09Z" | dc50add8a78ebf34bc7bb050c1a0e61d207b9544 | b203150ac481743a6d8c1ef01091712a54dfbf6c | Fix typos.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| # Sorting Algorithms
Sorting is the process of putting data in a specific order. The way to arrange data in a specific order
is specified by the sorting algorithm. The most typical orders are lexical or numerical. The significance
of sorting lies in the fact that, if data is stored in a sorted manner, data searching can be highly optimised.
Another use for sorting is to represent data in a more readable manner.
This section contains a lot of important algorithms that helps us to use sorting algorithms in various scenarios.
## References
* <https://www.tutorialspoint.com/python_data_structure/python_sorting_algorithms.htm>
* <https://www.geeksforgeeks.org/sorting-algorithms-in-python>
* <https://realpython.com/sorting-algorithms-python>
| # Sorting Algorithms
Sorting is the process of putting data in a specific order. The way to arrange data in a specific order
is specified by the sorting algorithm. The most typical orders are lexical or numerical. The significance
of sorting lies in the fact that, if data is stored in a sorted manner, data searching can be highly optimised.
Another use for sorting is to represent data in a more readable manner.
This section contains a lot of important algorithms that help us to use sorting algorithms in various scenarios.
## References
* <https://www.tutorialspoint.com/python_data_structure/python_sorting_algorithms.htm>
* <https://www.geeksforgeeks.org/sorting-algorithms-in-python>
* <https://realpython.com/sorting-algorithms-python>
| 1 |
TheAlgorithms/Python | 9,076 | Fix typos |
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| omahs | "2023-09-22T13:22:15Z" | "2023-09-23T08:53:09Z" | dc50add8a78ebf34bc7bb050c1a0e61d207b9544 | b203150ac481743a6d8c1ef01091712a54dfbf6c | Fix typos.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| -1 |
||
TheAlgorithms/Python | 9,076 | Fix typos |
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| omahs | "2023-09-22T13:22:15Z" | "2023-09-23T08:53:09Z" | dc50add8a78ebf34bc7bb050c1a0e61d207b9544 | b203150ac481743a6d8c1ef01091712a54dfbf6c | Fix typos.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| def remove_duplicates(key: str) -> str:
"""
Removes duplicate alphabetic characters in a keyword (letter is ignored after its
first appearance).
:param key: Keyword to use
:return: String with duplicates removed
>>> remove_duplicates('Hello World!!')
'Helo Wrd'
"""
key_no_dups = ""
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def create_cipher_map(key: str) -> dict[str, str]:
"""
Returns a cipher map given a keyword.
:param key: keyword to use
:return: dictionary cipher map
"""
# Create a list of the letters in the alphabet
alphabet = [chr(i + 65) for i in range(26)]
# Remove duplicate characters from key
key = remove_duplicates(key.upper())
offset = len(key)
# First fill cipher with key characters
cipher_alphabet = {alphabet[i]: char for i, char in enumerate(key)}
# Then map remaining characters in alphabet to
# the alphabet from the beginning
for i in range(len(cipher_alphabet), 26):
char = alphabet[i - offset]
# Ensure we are not mapping letters to letters previously mapped
while char in key:
offset -= 1
char = alphabet[i - offset]
cipher_alphabet[alphabet[i]] = char
return cipher_alphabet
def encipher(message: str, cipher_map: dict[str, str]) -> str:
"""
Enciphers a message given a cipher map.
:param message: Message to encipher
:param cipher_map: Cipher map
:return: enciphered string
>>> encipher('Hello World!!', create_cipher_map('Goodbye!!'))
'CYJJM VMQJB!!'
"""
return "".join(cipher_map.get(ch, ch) for ch in message.upper())
def decipher(message: str, cipher_map: dict[str, str]) -> str:
"""
Deciphers a message given a cipher map
:param message: Message to decipher
:param cipher_map: Dictionary mapping to use
:return: Deciphered string
>>> cipher_map = create_cipher_map('Goodbye!!')
>>> decipher(encipher('Hello World!!', cipher_map), cipher_map)
'HELLO WORLD!!'
"""
# Reverse our cipher mappings
rev_cipher_map = {v: k for k, v in cipher_map.items()}
return "".join(rev_cipher_map.get(ch, ch) for ch in message.upper())
def main() -> None:
"""
Handles I/O
:return: void
"""
message = input("Enter message to encode or decode: ").strip()
key = input("Enter keyword: ").strip()
option = input("Encipher or decipher? E/D:").strip()[0].lower()
try:
func = {"e": encipher, "d": decipher}[option]
except KeyError:
raise KeyError("invalid input option")
cipher_map = create_cipher_map(key)
print(func(message, cipher_map))
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| def remove_duplicates(key: str) -> str:
"""
Removes duplicate alphabetic characters in a keyword (letter is ignored after its
first appearance).
:param key: Keyword to use
:return: String with duplicates removed
>>> remove_duplicates('Hello World!!')
'Helo Wrd'
"""
key_no_dups = ""
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def create_cipher_map(key: str) -> dict[str, str]:
"""
Returns a cipher map given a keyword.
:param key: keyword to use
:return: dictionary cipher map
"""
# Create a list of the letters in the alphabet
alphabet = [chr(i + 65) for i in range(26)]
# Remove duplicate characters from key
key = remove_duplicates(key.upper())
offset = len(key)
# First fill cipher with key characters
cipher_alphabet = {alphabet[i]: char for i, char in enumerate(key)}
# Then map remaining characters in alphabet to
# the alphabet from the beginning
for i in range(len(cipher_alphabet), 26):
char = alphabet[i - offset]
# Ensure we are not mapping letters to letters previously mapped
while char in key:
offset -= 1
char = alphabet[i - offset]
cipher_alphabet[alphabet[i]] = char
return cipher_alphabet
def encipher(message: str, cipher_map: dict[str, str]) -> str:
"""
Enciphers a message given a cipher map.
:param message: Message to encipher
:param cipher_map: Cipher map
:return: enciphered string
>>> encipher('Hello World!!', create_cipher_map('Goodbye!!'))
'CYJJM VMQJB!!'
"""
return "".join(cipher_map.get(ch, ch) for ch in message.upper())
def decipher(message: str, cipher_map: dict[str, str]) -> str:
"""
Deciphers a message given a cipher map
:param message: Message to decipher
:param cipher_map: Dictionary mapping to use
:return: Deciphered string
>>> cipher_map = create_cipher_map('Goodbye!!')
>>> decipher(encipher('Hello World!!', cipher_map), cipher_map)
'HELLO WORLD!!'
"""
# Reverse our cipher mappings
rev_cipher_map = {v: k for k, v in cipher_map.items()}
return "".join(rev_cipher_map.get(ch, ch) for ch in message.upper())
def main() -> None:
"""
Handles I/O
:return: void
"""
message = input("Enter message to encode or decode: ").strip()
key = input("Enter keyword: ").strip()
option = input("Encipher or decipher? E/D:").strip()[0].lower()
try:
func = {"e": encipher, "d": decipher}[option]
except KeyError:
raise KeyError("invalid input option")
cipher_map = create_cipher_map(key)
print(func(message, cipher_map))
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| -1 |
TheAlgorithms/Python | 9,076 | Fix typos |
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| omahs | "2023-09-22T13:22:15Z" | "2023-09-23T08:53:09Z" | dc50add8a78ebf34bc7bb050c1a0e61d207b9544 | b203150ac481743a6d8c1ef01091712a54dfbf6c | Fix typos.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| """
Implemented an algorithm using opencv to convert a colored image into its negative
"""
from cv2 import destroyAllWindows, imread, imshow, waitKey
def convert_to_negative(img):
# getting number of pixels in the image
pixel_h, pixel_v = img.shape[0], img.shape[1]
# converting each pixel's color to its negative
for i in range(pixel_h):
for j in range(pixel_v):
img[i][j] = [255, 255, 255] - img[i][j]
return img
if __name__ == "__main__":
# read original image
img = imread("image_data/lena.jpg", 1)
# convert to its negative
neg = convert_to_negative(img)
# show result image
imshow("negative of original image", img)
waitKey(0)
destroyAllWindows()
| """
Implemented an algorithm using opencv to convert a colored image into its negative
"""
from cv2 import destroyAllWindows, imread, imshow, waitKey
def convert_to_negative(img):
# getting number of pixels in the image
pixel_h, pixel_v = img.shape[0], img.shape[1]
# converting each pixel's color to its negative
for i in range(pixel_h):
for j in range(pixel_v):
img[i][j] = [255, 255, 255] - img[i][j]
return img
if __name__ == "__main__":
# read original image
img = imread("image_data/lena.jpg", 1)
# convert to its negative
neg = convert_to_negative(img)
# show result image
imshow("negative of original image", img)
waitKey(0)
destroyAllWindows()
| -1 |
TheAlgorithms/Python | 9,076 | Fix typos |
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| omahs | "2023-09-22T13:22:15Z" | "2023-09-23T08:53:09Z" | dc50add8a78ebf34bc7bb050c1a0e61d207b9544 | b203150ac481743a6d8c1ef01091712a54dfbf6c | Fix typos.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| """
This function implements the shell sort algorithm
which is slightly faster than its pure implementation.
This shell sort is implemented using a gap, which
shrinks by a certain factor each iteration. In this
implementation, the gap is initially set to the
length of the collection. The gap is then reduced by
a certain factor (1.3) each iteration.
For each iteration, the algorithm compares elements
that are a certain number of positions apart
(determined by the gap). If the element at the higher
position is greater than the element at the lower
position, the two elements are swapped. The process
is repeated until the gap is equal to 1.
The reason this is more efficient is that it reduces
the number of comparisons that need to be made. By
using a smaller gap, the list is sorted more quickly.
"""
def shell_sort(collection: list) -> list:
"""Implementation of shell sort algorithm in Python
:param collection: Some mutable ordered collection with heterogeneous
comparable items inside
:return: the same collection ordered by ascending
>>> shell_sort([3, 2, 1])
[1, 2, 3]
>>> shell_sort([])
[]
>>> shell_sort([1])
[1]
"""
# Choose an initial gap value
gap = len(collection)
# Set the gap value to be decreased by a factor of 1.3
# after each iteration
shrink = 1.3
# Continue sorting until the gap is 1
while gap > 1:
# Decrease the gap value
gap = int(gap / shrink)
# Sort the elements using insertion sort
for i in range(gap, len(collection)):
temp = collection[i]
j = i
while j >= gap and collection[j - gap] > temp:
collection[j] = collection[j - gap]
j -= gap
collection[j] = temp
return collection
if __name__ == "__main__":
import doctest
doctest.testmod()
| """
This function implements the shell sort algorithm
which is slightly faster than its pure implementation.
This shell sort is implemented using a gap, which
shrinks by a certain factor each iteration. In this
implementation, the gap is initially set to the
length of the collection. The gap is then reduced by
a certain factor (1.3) each iteration.
For each iteration, the algorithm compares elements
that are a certain number of positions apart
(determined by the gap). If the element at the higher
position is greater than the element at the lower
position, the two elements are swapped. The process
is repeated until the gap is equal to 1.
The reason this is more efficient is that it reduces
the number of comparisons that need to be made. By
using a smaller gap, the list is sorted more quickly.
"""
def shell_sort(collection: list) -> list:
"""Implementation of shell sort algorithm in Python
:param collection: Some mutable ordered collection with heterogeneous
comparable items inside
:return: the same collection ordered by ascending
>>> shell_sort([3, 2, 1])
[1, 2, 3]
>>> shell_sort([])
[]
>>> shell_sort([1])
[1]
"""
# Choose an initial gap value
gap = len(collection)
# Set the gap value to be decreased by a factor of 1.3
# after each iteration
shrink = 1.3
# Continue sorting until the gap is 1
while gap > 1:
# Decrease the gap value
gap = int(gap / shrink)
# Sort the elements using insertion sort
for i in range(gap, len(collection)):
temp = collection[i]
j = i
while j >= gap and collection[j - gap] > temp:
collection[j] = collection[j - gap]
j -= gap
collection[j] = temp
return collection
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 9,076 | Fix typos |
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| omahs | "2023-09-22T13:22:15Z" | "2023-09-23T08:53:09Z" | dc50add8a78ebf34bc7bb050c1a0e61d207b9544 | b203150ac481743a6d8c1ef01091712a54dfbf6c | Fix typos.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| """
Problem 78
Url: https://projecteuler.net/problem=78
Statement:
Let p(n) represent the number of different ways in which n coins
can be separated into piles. For example, five coins can be separated
into piles in exactly seven different ways, so p(5)=7.
OOOOO
OOOO O
OOO OO
OOO O O
OO OO O
OO O O O
O O O O O
Find the least value of n for which p(n) is divisible by one million.
"""
import itertools
def solution(number: int = 1000000) -> int:
"""
>>> solution(1)
1
>>> solution(9)
14
>>> solution()
55374
"""
partitions = [1]
for i in itertools.count(len(partitions)):
item = 0
for j in itertools.count(1):
sign = -1 if j % 2 == 0 else +1
index = (j * j * 3 - j) // 2
if index > i:
break
item += partitions[i - index] * sign
item %= number
index += j
if index > i:
break
item += partitions[i - index] * sign
item %= number
if item == 0:
return i
partitions.append(item)
return 0
if __name__ == "__main__":
import doctest
doctest.testmod()
print(f"{solution() = }")
| """
Problem 78
Url: https://projecteuler.net/problem=78
Statement:
Let p(n) represent the number of different ways in which n coins
can be separated into piles. For example, five coins can be separated
into piles in exactly seven different ways, so p(5)=7.
OOOOO
OOOO O
OOO OO
OOO O O
OO OO O
OO O O O
O O O O O
Find the least value of n for which p(n) is divisible by one million.
"""
import itertools
def solution(number: int = 1000000) -> int:
"""
>>> solution(1)
1
>>> solution(9)
14
>>> solution()
55374
"""
partitions = [1]
for i in itertools.count(len(partitions)):
item = 0
for j in itertools.count(1):
sign = -1 if j % 2 == 0 else +1
index = (j * j * 3 - j) // 2
if index > i:
break
item += partitions[i - index] * sign
item %= number
index += j
if index > i:
break
item += partitions[i - index] * sign
item %= number
if item == 0:
return i
partitions.append(item)
return 0
if __name__ == "__main__":
import doctest
doctest.testmod()
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 9,076 | Fix typos |
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| omahs | "2023-09-22T13:22:15Z" | "2023-09-23T08:53:09Z" | dc50add8a78ebf34bc7bb050c1a0e61d207b9544 | b203150ac481743a6d8c1ef01091712a54dfbf6c | Fix typos.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| """
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 | 9,076 | Fix typos |
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| omahs | "2023-09-22T13:22:15Z" | "2023-09-23T08:53:09Z" | dc50add8a78ebf34bc7bb050c1a0e61d207b9544 | b203150ac481743a6d8c1ef01091712a54dfbf6c | Fix typos.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| from bisect import bisect
from itertools import accumulate
def frac_knapsack(vl, wt, w, n):
"""
>>> frac_knapsack([60, 100, 120], [10, 20, 30], 50, 3)
240.0
"""
r = sorted(zip(vl, wt), key=lambda x: x[0] / x[1], reverse=True)
vl, wt = [i[0] for i in r], [i[1] for i in r]
acc = list(accumulate(wt))
k = bisect(acc, w)
return (
0
if k == 0
else sum(vl[:k]) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
if k != n
else sum(vl[:k])
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| from bisect import bisect
from itertools import accumulate
def frac_knapsack(vl, wt, w, n):
"""
>>> frac_knapsack([60, 100, 120], [10, 20, 30], 50, 3)
240.0
"""
r = sorted(zip(vl, wt), key=lambda x: x[0] / x[1], reverse=True)
vl, wt = [i[0] for i in r], [i[1] for i in r]
acc = list(accumulate(wt))
k = bisect(acc, w)
return (
0
if k == 0
else sum(vl[:k]) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
if k != n
else sum(vl[:k])
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 9,076 | Fix typos |
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| omahs | "2023-09-22T13:22:15Z" | "2023-09-23T08:53:09Z" | dc50add8a78ebf34bc7bb050c1a0e61d207b9544 | b203150ac481743a6d8c1ef01091712a54dfbf6c | Fix typos.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| def alternative_string_arrange(first_str: str, second_str: str) -> str:
"""
Return the alternative arrangements of the two strings.
:param first_str:
:param second_str:
:return: String
>>> alternative_string_arrange("ABCD", "XY")
'AXBYCD'
>>> alternative_string_arrange("XY", "ABCD")
'XAYBCD'
>>> alternative_string_arrange("AB", "XYZ")
'AXBYZ'
>>> alternative_string_arrange("ABC", "")
'ABC'
"""
first_str_length: int = len(first_str)
second_str_length: int = len(second_str)
abs_length: int = (
first_str_length if first_str_length > second_str_length else second_str_length
)
output_list: list = []
for char_count in range(abs_length):
if char_count < first_str_length:
output_list.append(first_str[char_count])
if char_count < second_str_length:
output_list.append(second_str[char_count])
return "".join(output_list)
if __name__ == "__main__":
print(alternative_string_arrange("AB", "XYZ"), end=" ")
| def alternative_string_arrange(first_str: str, second_str: str) -> str:
"""
Return the alternative arrangements of the two strings.
:param first_str:
:param second_str:
:return: String
>>> alternative_string_arrange("ABCD", "XY")
'AXBYCD'
>>> alternative_string_arrange("XY", "ABCD")
'XAYBCD'
>>> alternative_string_arrange("AB", "XYZ")
'AXBYZ'
>>> alternative_string_arrange("ABC", "")
'ABC'
"""
first_str_length: int = len(first_str)
second_str_length: int = len(second_str)
abs_length: int = (
first_str_length if first_str_length > second_str_length else second_str_length
)
output_list: list = []
for char_count in range(abs_length):
if char_count < first_str_length:
output_list.append(first_str[char_count])
if char_count < second_str_length:
output_list.append(second_str[char_count])
return "".join(output_list)
if __name__ == "__main__":
print(alternative_string_arrange("AB", "XYZ"), end=" ")
| -1 |
TheAlgorithms/Python | 9,076 | Fix typos |
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| omahs | "2023-09-22T13:22:15Z" | "2023-09-23T08:53:09Z" | dc50add8a78ebf34bc7bb050c1a0e61d207b9544 | b203150ac481743a6d8c1ef01091712a54dfbf6c | Fix typos.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| """
The sum-of-subsetsproblem states that a set of non-negative integers, and a
value M, determine all possible subsets of the given set whose summation sum
equal to given M.
Summation of the chosen numbers must be equal to given number M and one number
can be used only once.
"""
from __future__ import annotations
def generate_sum_of_subsets_soln(nums: list[int], max_sum: int) -> list[list[int]]:
result: list[list[int]] = []
path: list[int] = []
num_index = 0
remaining_nums_sum = sum(nums)
create_state_space_tree(nums, max_sum, num_index, path, result, remaining_nums_sum)
return result
def create_state_space_tree(
nums: list[int],
max_sum: int,
num_index: int,
path: list[int],
result: list[list[int]],
remaining_nums_sum: int,
) -> None:
"""
Creates a state space tree to iterate through each branch using DFS.
It terminates the branching of a node when any of the two conditions
given below satisfy.
This algorithm follows depth-fist-search and backtracks when the node is not
branchable.
"""
if sum(path) > max_sum or (remaining_nums_sum + sum(path)) < max_sum:
return
if sum(path) == max_sum:
result.append(path)
return
for index in range(num_index, len(nums)):
create_state_space_tree(
nums,
max_sum,
index + 1,
[*path, nums[index]],
result,
remaining_nums_sum - nums[index],
)
"""
remove the comment to take an input from the user
print("Enter the elements")
nums = list(map(int, input().split()))
print("Enter max_sum sum")
max_sum = int(input())
"""
nums = [3, 34, 4, 12, 5, 2]
max_sum = 9
result = generate_sum_of_subsets_soln(nums, max_sum)
print(*result)
| """
The sum-of-subsetsproblem states that a set of non-negative integers, and a
value M, determine all possible subsets of the given set whose summation sum
equal to given M.
Summation of the chosen numbers must be equal to given number M and one number
can be used only once.
"""
from __future__ import annotations
def generate_sum_of_subsets_soln(nums: list[int], max_sum: int) -> list[list[int]]:
result: list[list[int]] = []
path: list[int] = []
num_index = 0
remaining_nums_sum = sum(nums)
create_state_space_tree(nums, max_sum, num_index, path, result, remaining_nums_sum)
return result
def create_state_space_tree(
nums: list[int],
max_sum: int,
num_index: int,
path: list[int],
result: list[list[int]],
remaining_nums_sum: int,
) -> None:
"""
Creates a state space tree to iterate through each branch using DFS.
It terminates the branching of a node when any of the two conditions
given below satisfy.
This algorithm follows depth-fist-search and backtracks when the node is not
branchable.
"""
if sum(path) > max_sum or (remaining_nums_sum + sum(path)) < max_sum:
return
if sum(path) == max_sum:
result.append(path)
return
for index in range(num_index, len(nums)):
create_state_space_tree(
nums,
max_sum,
index + 1,
[*path, nums[index]],
result,
remaining_nums_sum - nums[index],
)
"""
remove the comment to take an input from the user
print("Enter the elements")
nums = list(map(int, input().split()))
print("Enter max_sum sum")
max_sum = int(input())
"""
nums = [3, 34, 4, 12, 5, 2]
max_sum = 9
result = generate_sum_of_subsets_soln(nums, max_sum)
print(*result)
| -1 |
TheAlgorithms/Python | 9,076 | Fix typos |
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| omahs | "2023-09-22T13:22:15Z" | "2023-09-23T08:53:09Z" | dc50add8a78ebf34bc7bb050c1a0e61d207b9544 | b203150ac481743a6d8c1ef01091712a54dfbf6c | Fix typos.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| """
Pure Python implementation of a binary search algorithm.
For doctests run following command:
python3 -m doctest -v simple_binary_search.py
For manual testing run:
python3 simple_binary_search.py
"""
from __future__ import annotations
def binary_search(a_list: list[int], item: int) -> bool:
"""
>>> test_list = [0, 1, 2, 8, 13, 17, 19, 32, 42]
>>> binary_search(test_list, 3)
False
>>> binary_search(test_list, 13)
True
>>> binary_search([4, 4, 5, 6, 7], 4)
True
>>> binary_search([4, 4, 5, 6, 7], -10)
False
>>> binary_search([-18, 2], -18)
True
>>> binary_search([5], 5)
True
>>> binary_search(['a', 'c', 'd'], 'c')
True
>>> binary_search(['a', 'c', 'd'], 'f')
False
>>> binary_search([], 1)
False
>>> binary_search([-.1, .1 , .8], .1)
True
>>> binary_search(range(-5000, 5000, 10), 80)
True
>>> binary_search(range(-5000, 5000, 10), 1255)
False
>>> binary_search(range(0, 10000, 5), 2)
False
"""
if len(a_list) == 0:
return False
midpoint = len(a_list) // 2
if a_list[midpoint] == item:
return True
if item < a_list[midpoint]:
return binary_search(a_list[:midpoint], item)
else:
return binary_search(a_list[midpoint + 1 :], item)
if __name__ == "__main__":
user_input = input("Enter numbers separated by comma:\n").strip()
sequence = [int(item.strip()) for item in user_input.split(",")]
target = int(input("Enter the number to be found in the list:\n").strip())
not_str = "" if binary_search(sequence, target) else "not "
print(f"{target} was {not_str}found in {sequence}")
| """
Pure Python implementation of a binary search algorithm.
For doctests run following command:
python3 -m doctest -v simple_binary_search.py
For manual testing run:
python3 simple_binary_search.py
"""
from __future__ import annotations
def binary_search(a_list: list[int], item: int) -> bool:
"""
>>> test_list = [0, 1, 2, 8, 13, 17, 19, 32, 42]
>>> binary_search(test_list, 3)
False
>>> binary_search(test_list, 13)
True
>>> binary_search([4, 4, 5, 6, 7], 4)
True
>>> binary_search([4, 4, 5, 6, 7], -10)
False
>>> binary_search([-18, 2], -18)
True
>>> binary_search([5], 5)
True
>>> binary_search(['a', 'c', 'd'], 'c')
True
>>> binary_search(['a', 'c', 'd'], 'f')
False
>>> binary_search([], 1)
False
>>> binary_search([-.1, .1 , .8], .1)
True
>>> binary_search(range(-5000, 5000, 10), 80)
True
>>> binary_search(range(-5000, 5000, 10), 1255)
False
>>> binary_search(range(0, 10000, 5), 2)
False
"""
if len(a_list) == 0:
return False
midpoint = len(a_list) // 2
if a_list[midpoint] == item:
return True
if item < a_list[midpoint]:
return binary_search(a_list[:midpoint], item)
else:
return binary_search(a_list[midpoint + 1 :], item)
if __name__ == "__main__":
user_input = input("Enter numbers separated by comma:\n").strip()
sequence = [int(item.strip()) for item in user_input.split(",")]
target = int(input("Enter the number to be found in the list:\n").strip())
not_str = "" if binary_search(sequence, target) else "not "
print(f"{target} was {not_str}found in {sequence}")
| -1 |
TheAlgorithms/Python | 9,076 | Fix typos |
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| omahs | "2023-09-22T13:22:15Z" | "2023-09-23T08:53:09Z" | dc50add8a78ebf34bc7bb050c1a0e61d207b9544 | b203150ac481743a6d8c1ef01091712a54dfbf6c | Fix typos.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| -1 |
||
TheAlgorithms/Python | 9,076 | Fix typos |
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| omahs | "2023-09-22T13:22:15Z" | "2023-09-23T08:53:09Z" | dc50add8a78ebf34bc7bb050c1a0e61d207b9544 | b203150ac481743a6d8c1ef01091712a54dfbf6c | Fix typos.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| # https://en.wikipedia.org/wiki/Simulated_annealing
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def simulated_annealing(
search_prob,
find_max: bool = True,
max_x: float = math.inf,
min_x: float = -math.inf,
max_y: float = math.inf,
min_y: float = -math.inf,
visualization: bool = False,
start_temperate: float = 100,
rate_of_decrease: float = 0.01,
threshold_temp: float = 1,
) -> Any:
"""
Implementation of the simulated annealing algorithm. We start with a given state,
find all its neighbors. Pick a random neighbor, if that neighbor improves the
solution, we move in that direction, if that neighbor does not improve the solution,
we generate a random real number between 0 and 1, if the number is within a certain
range (calculated using temperature) we move in that direction, else we pick
another neighbor randomly and repeat the process.
Args:
search_prob: The search state at the start.
find_max: If True, the algorithm should find the minimum else the minimum.
max_x, min_x, max_y, min_y: the maximum and minimum bounds of x and y.
visualization: If True, a matplotlib graph is displayed.
start_temperate: the initial temperate of the system when the program starts.
rate_of_decrease: the rate at which the temperate decreases in each iteration.
threshold_temp: the threshold temperature below which we end the search
Returns a search state having the maximum (or minimum) score.
"""
search_end = False
current_state = search_prob
current_temp = start_temperate
scores = []
iterations = 0
best_state = None
while not search_end:
current_score = current_state.score()
if best_state is None or current_score > best_state.score():
best_state = current_state
scores.append(current_score)
iterations += 1
next_state = None
neighbors = current_state.get_neighbors()
while (
next_state is None and neighbors
): # till we do not find a neighbor that we can move to
index = random.randint(0, len(neighbors) - 1) # picking a random neighbor
picked_neighbor = neighbors.pop(index)
change = picked_neighbor.score() - current_score
if (
picked_neighbor.x > max_x
or picked_neighbor.x < min_x
or picked_neighbor.y > max_y
or picked_neighbor.y < min_y
):
continue # neighbor outside our bounds
if not find_max:
change = change * -1 # in case we are finding minimum
if change > 0: # improves the solution
next_state = picked_neighbor
else:
probability = (math.e) ** (
change / current_temp
) # probability generation function
if random.random() < probability: # random number within probability
next_state = picked_neighbor
current_temp = current_temp - (current_temp * rate_of_decrease)
if current_temp < threshold_temp or next_state is None:
# temperature below threshold, or could not find a suitable neighbor
search_end = True
else:
current_state = next_state
if visualization:
from matplotlib import pyplot as plt
plt.plot(range(iterations), scores)
plt.xlabel("Iterations")
plt.ylabel("Function values")
plt.show()
return best_state
if __name__ == "__main__":
def test_f1(x, y):
return (x**2) + (y**2)
# starting the problem with initial coordinates (12, 47)
prob = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_f1)
local_min = simulated_annealing(
prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
)
print(
"The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 "
f"and 50 > y > - 5 found via hill climbing: {local_min.score()}"
)
# starting the problem with initial coordinates (12, 47)
prob = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_f1)
local_min = simulated_annealing(
prob, find_max=True, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
)
print(
"The maximum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 "
f"and 50 > y > - 5 found via hill climbing: {local_min.score()}"
)
def test_f2(x, y):
return (3 * x**2) - (6 * y)
prob = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_f1)
local_min = simulated_annealing(prob, find_max=False, visualization=True)
print(
"The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: "
f"{local_min.score()}"
)
prob = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_f1)
local_min = simulated_annealing(prob, find_max=True, visualization=True)
print(
"The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: "
f"{local_min.score()}"
)
| # https://en.wikipedia.org/wiki/Simulated_annealing
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def simulated_annealing(
search_prob,
find_max: bool = True,
max_x: float = math.inf,
min_x: float = -math.inf,
max_y: float = math.inf,
min_y: float = -math.inf,
visualization: bool = False,
start_temperate: float = 100,
rate_of_decrease: float = 0.01,
threshold_temp: float = 1,
) -> Any:
"""
Implementation of the simulated annealing algorithm. We start with a given state,
find all its neighbors. Pick a random neighbor, if that neighbor improves the
solution, we move in that direction, if that neighbor does not improve the solution,
we generate a random real number between 0 and 1, if the number is within a certain
range (calculated using temperature) we move in that direction, else we pick
another neighbor randomly and repeat the process.
Args:
search_prob: The search state at the start.
find_max: If True, the algorithm should find the minimum else the minimum.
max_x, min_x, max_y, min_y: the maximum and minimum bounds of x and y.
visualization: If True, a matplotlib graph is displayed.
start_temperate: the initial temperate of the system when the program starts.
rate_of_decrease: the rate at which the temperate decreases in each iteration.
threshold_temp: the threshold temperature below which we end the search
Returns a search state having the maximum (or minimum) score.
"""
search_end = False
current_state = search_prob
current_temp = start_temperate
scores = []
iterations = 0
best_state = None
while not search_end:
current_score = current_state.score()
if best_state is None or current_score > best_state.score():
best_state = current_state
scores.append(current_score)
iterations += 1
next_state = None
neighbors = current_state.get_neighbors()
while (
next_state is None and neighbors
): # till we do not find a neighbor that we can move to
index = random.randint(0, len(neighbors) - 1) # picking a random neighbor
picked_neighbor = neighbors.pop(index)
change = picked_neighbor.score() - current_score
if (
picked_neighbor.x > max_x
or picked_neighbor.x < min_x
or picked_neighbor.y > max_y
or picked_neighbor.y < min_y
):
continue # neighbor outside our bounds
if not find_max:
change = change * -1 # in case we are finding minimum
if change > 0: # improves the solution
next_state = picked_neighbor
else:
probability = (math.e) ** (
change / current_temp
) # probability generation function
if random.random() < probability: # random number within probability
next_state = picked_neighbor
current_temp = current_temp - (current_temp * rate_of_decrease)
if current_temp < threshold_temp or next_state is None:
# temperature below threshold, or could not find a suitable neighbor
search_end = True
else:
current_state = next_state
if visualization:
from matplotlib import pyplot as plt
plt.plot(range(iterations), scores)
plt.xlabel("Iterations")
plt.ylabel("Function values")
plt.show()
return best_state
if __name__ == "__main__":
def test_f1(x, y):
return (x**2) + (y**2)
# starting the problem with initial coordinates (12, 47)
prob = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_f1)
local_min = simulated_annealing(
prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
)
print(
"The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 "
f"and 50 > y > - 5 found via hill climbing: {local_min.score()}"
)
# starting the problem with initial coordinates (12, 47)
prob = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_f1)
local_min = simulated_annealing(
prob, find_max=True, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
)
print(
"The maximum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 "
f"and 50 > y > - 5 found via hill climbing: {local_min.score()}"
)
def test_f2(x, y):
return (3 * x**2) - (6 * y)
prob = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_f1)
local_min = simulated_annealing(prob, find_max=False, visualization=True)
print(
"The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: "
f"{local_min.score()}"
)
prob = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_f1)
local_min = simulated_annealing(prob, find_max=True, visualization=True)
print(
"The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: "
f"{local_min.score()}"
)
| -1 |
TheAlgorithms/Python | 9,076 | Fix typos |
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| omahs | "2023-09-22T13:22:15Z" | "2023-09-23T08:53:09Z" | dc50add8a78ebf34bc7bb050c1a0e61d207b9544 | b203150ac481743a6d8c1ef01091712a54dfbf6c | Fix typos.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| -1 |
||
TheAlgorithms/Python | 9,076 | Fix typos |
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| omahs | "2023-09-22T13:22:15Z" | "2023-09-23T08:53:09Z" | dc50add8a78ebf34bc7bb050c1a0e61d207b9544 | b203150ac481743a6d8c1ef01091712a54dfbf6c | Fix typos.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| #!/usr/bin/env python3
"""
Simulation of the Quantum Key Distribution (QKD) protocol called BB84,
created by Charles Bennett and Gilles Brassard in 1984.
BB84 is a key-distribution protocol that ensures secure key distribution
using qubits instead of classical bits. The generated key is the result
of simulating a quantum circuit. Our algorithm to construct the circuit
is as follows:
Alice generates two binary strings. One encodes the basis for each qubit:
- 0 -> {0,1} basis.
- 1 -> {+,-} basis.
The other encodes the state:
- 0 -> |0> or |+>.
- 1 -> |1> or |->.
Bob also generates a binary string and uses the same convention to choose
a basis for measurement. Based on the following results, we follow the
algorithm below:
X|0> = |1>
H|0> = |+>
HX|0> = |->
1. Whenever Alice wants to encode 1 in a qubit, she applies an
X (NOT) gate to the qubit. To encode 0, no action is needed.
2. Wherever she wants to encode it in the {+,-} basis, she applies
an H (Hadamard) gate. No action is necessary to encode a qubit in
the {0,1} basis.
3. She then sends the qubits to Bob (symbolically represented in
this circuit using wires).
4. Bob measures the qubits according to his binary string for
measurement. To measure a qubit in the {+,-} basis, he applies
an H gate to the corresponding qubit and then performs a measurement.
References:
https://en.wikipedia.org/wiki/BB84
https://qiskit.org/textbook/ch-algorithms/quantum-key-distribution.html
"""
import numpy as np
import qiskit
def bb84(key_len: int = 8, seed: int | None = None) -> str:
"""
Performs the BB84 protocol using a key made of `key_len` bits.
The two parties in the key distribution are called Alice and Bob.
Args:
key_len: The length of the generated key in bits. The default is 8.
seed: Seed for the random number generator.
Mostly used for testing. Default is None.
Returns:
key: The key generated using BB84 protocol.
>>> bb84(16, seed=0)
'0111110111010010'
>>> bb84(8, seed=0)
'10110001'
"""
# Set up the random number generator.
rng = np.random.default_rng(seed=seed)
# Roughly 25% of the qubits will contribute to the key.
# So we take more than we need.
num_qubits = 6 * key_len
# Measurement basis for Alice's qubits.
alice_basis = rng.integers(2, size=num_qubits)
# The set of states Alice will prepare.
alice_state = rng.integers(2, size=num_qubits)
# Measurement basis for Bob's qubits.
bob_basis = rng.integers(2, size=num_qubits)
# Quantum Circuit to simulate BB84
bb84_circ = qiskit.QuantumCircuit(num_qubits, name="BB84")
# Alice prepares her qubits according to rules above.
for index, _ in enumerate(alice_basis):
if alice_state[index] == 1:
bb84_circ.x(index)
if alice_basis[index] == 1:
bb84_circ.h(index)
bb84_circ.barrier()
# Bob measures the received qubits according to rules above.
for index, _ in enumerate(bob_basis):
if bob_basis[index] == 1:
bb84_circ.h(index)
bb84_circ.barrier()
bb84_circ.measure_all()
# Simulate the quantum circuit.
sim = qiskit.Aer.get_backend("aer_simulator")
# We only need to run one shot because the key is unique.
# Multiple shots will produce the same key.
job = qiskit.execute(bb84_circ, sim, shots=1, seed_simulator=seed)
# Returns the result of measurement.
result = job.result().get_counts(bb84_circ).most_frequent()
# Extracting the generated key from the simulation results.
# Only keep measurement results where Alice and Bob chose the same basis.
gen_key = "".join(
[
result_bit
for alice_basis_bit, bob_basis_bit, result_bit in zip(
alice_basis, bob_basis, result
)
if alice_basis_bit == bob_basis_bit
]
)
# Get final key. Pad with 0 if too short, otherwise truncate.
key = gen_key[:key_len] if len(gen_key) >= key_len else gen_key.ljust(key_len, "0")
return key
if __name__ == "__main__":
print(f"The generated key is : {bb84(8, seed=0)}")
from doctest import testmod
testmod()
| #!/usr/bin/env python3
"""
Simulation of the Quantum Key Distribution (QKD) protocol called BB84,
created by Charles Bennett and Gilles Brassard in 1984.
BB84 is a key-distribution protocol that ensures secure key distribution
using qubits instead of classical bits. The generated key is the result
of simulating a quantum circuit. Our algorithm to construct the circuit
is as follows:
Alice generates two binary strings. One encodes the basis for each qubit:
- 0 -> {0,1} basis.
- 1 -> {+,-} basis.
The other encodes the state:
- 0 -> |0> or |+>.
- 1 -> |1> or |->.
Bob also generates a binary string and uses the same convention to choose
a basis for measurement. Based on the following results, we follow the
algorithm below:
X|0> = |1>
H|0> = |+>
HX|0> = |->
1. Whenever Alice wants to encode 1 in a qubit, she applies an
X (NOT) gate to the qubit. To encode 0, no action is needed.
2. Wherever she wants to encode it in the {+,-} basis, she applies
an H (Hadamard) gate. No action is necessary to encode a qubit in
the {0,1} basis.
3. She then sends the qubits to Bob (symbolically represented in
this circuit using wires).
4. Bob measures the qubits according to his binary string for
measurement. To measure a qubit in the {+,-} basis, he applies
an H gate to the corresponding qubit and then performs a measurement.
References:
https://en.wikipedia.org/wiki/BB84
https://qiskit.org/textbook/ch-algorithms/quantum-key-distribution.html
"""
import numpy as np
import qiskit
def bb84(key_len: int = 8, seed: int | None = None) -> str:
"""
Performs the BB84 protocol using a key made of `key_len` bits.
The two parties in the key distribution are called Alice and Bob.
Args:
key_len: The length of the generated key in bits. The default is 8.
seed: Seed for the random number generator.
Mostly used for testing. Default is None.
Returns:
key: The key generated using BB84 protocol.
>>> bb84(16, seed=0)
'0111110111010010'
>>> bb84(8, seed=0)
'10110001'
"""
# Set up the random number generator.
rng = np.random.default_rng(seed=seed)
# Roughly 25% of the qubits will contribute to the key.
# So we take more than we need.
num_qubits = 6 * key_len
# Measurement basis for Alice's qubits.
alice_basis = rng.integers(2, size=num_qubits)
# The set of states Alice will prepare.
alice_state = rng.integers(2, size=num_qubits)
# Measurement basis for Bob's qubits.
bob_basis = rng.integers(2, size=num_qubits)
# Quantum Circuit to simulate BB84
bb84_circ = qiskit.QuantumCircuit(num_qubits, name="BB84")
# Alice prepares her qubits according to rules above.
for index, _ in enumerate(alice_basis):
if alice_state[index] == 1:
bb84_circ.x(index)
if alice_basis[index] == 1:
bb84_circ.h(index)
bb84_circ.barrier()
# Bob measures the received qubits according to rules above.
for index, _ in enumerate(bob_basis):
if bob_basis[index] == 1:
bb84_circ.h(index)
bb84_circ.barrier()
bb84_circ.measure_all()
# Simulate the quantum circuit.
sim = qiskit.Aer.get_backend("aer_simulator")
# We only need to run one shot because the key is unique.
# Multiple shots will produce the same key.
job = qiskit.execute(bb84_circ, sim, shots=1, seed_simulator=seed)
# Returns the result of measurement.
result = job.result().get_counts(bb84_circ).most_frequent()
# Extracting the generated key from the simulation results.
# Only keep measurement results where Alice and Bob chose the same basis.
gen_key = "".join(
[
result_bit
for alice_basis_bit, bob_basis_bit, result_bit in zip(
alice_basis, bob_basis, result
)
if alice_basis_bit == bob_basis_bit
]
)
# Get final key. Pad with 0 if too short, otherwise truncate.
key = gen_key[:key_len] if len(gen_key) >= key_len else gen_key.ljust(key_len, "0")
return key
if __name__ == "__main__":
print(f"The generated key is : {bb84(8, seed=0)}")
from doctest import testmod
testmod()
| -1 |
TheAlgorithms/Python | 9,076 | Fix typos |
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| omahs | "2023-09-22T13:22:15Z" | "2023-09-23T08:53:09Z" | dc50add8a78ebf34bc7bb050c1a0e61d207b9544 | b203150ac481743a6d8c1ef01091712a54dfbf6c | Fix typos.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| """
Signum function -- https://en.wikipedia.org/wiki/Sign_function
"""
def signum(num: float) -> int:
"""
Applies signum function on the number
>>> signum(-10)
-1
>>> signum(10)
1
>>> signum(0)
0
"""
if num < 0:
return -1
return 1 if num else 0
def test_signum() -> None:
"""
Tests the signum function
"""
assert signum(5) == 1
assert signum(-5) == -1
assert signum(0) == 0
if __name__ == "__main__":
print(signum(12))
print(signum(-12))
print(signum(0))
| """
Signum function -- https://en.wikipedia.org/wiki/Sign_function
"""
def signum(num: float) -> int:
"""
Applies signum function on the number
>>> signum(-10)
-1
>>> signum(10)
1
>>> signum(0)
0
"""
if num < 0:
return -1
return 1 if num else 0
def test_signum() -> None:
"""
Tests the signum function
"""
assert signum(5) == 1
assert signum(-5) == -1
assert signum(0) == 0
if __name__ == "__main__":
print(signum(12))
print(signum(-12))
print(signum(0))
| -1 |
TheAlgorithms/Python | 9,076 | Fix typos |
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| omahs | "2023-09-22T13:22:15Z" | "2023-09-23T08:53:09Z" | dc50add8a78ebf34bc7bb050c1a0e61d207b9544 | b203150ac481743a6d8c1ef01091712a54dfbf6c | Fix typos.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| def is_int_palindrome(num: int) -> bool:
"""
Returns whether `num` is a palindrome or not
(see for reference https://en.wikipedia.org/wiki/Palindromic_number).
>>> is_int_palindrome(-121)
False
>>> is_int_palindrome(0)
True
>>> is_int_palindrome(10)
False
>>> is_int_palindrome(11)
True
>>> is_int_palindrome(101)
True
>>> is_int_palindrome(120)
False
"""
if num < 0:
return False
num_copy: int = num
rev_num: int = 0
while num > 0:
rev_num = rev_num * 10 + (num % 10)
num //= 10
return num_copy == rev_num
if __name__ == "__main__":
import doctest
doctest.testmod()
| def is_int_palindrome(num: int) -> bool:
"""
Returns whether `num` is a palindrome or not
(see for reference https://en.wikipedia.org/wiki/Palindromic_number).
>>> is_int_palindrome(-121)
False
>>> is_int_palindrome(0)
True
>>> is_int_palindrome(10)
False
>>> is_int_palindrome(11)
True
>>> is_int_palindrome(101)
True
>>> is_int_palindrome(120)
False
"""
if num < 0:
return False
num_copy: int = num
rev_num: int = 0
while num > 0:
rev_num = rev_num * 10 + (num % 10)
num //= 10
return num_copy == rev_num
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 9,076 | Fix typos |
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| omahs | "2023-09-22T13:22:15Z" | "2023-09-23T08:53:09Z" | dc50add8a78ebf34bc7bb050c1a0e61d207b9544 | b203150ac481743a6d8c1ef01091712a54dfbf6c | Fix typos.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| #!/usr/bin/env python3
"""
Davis–Putnam–Logemann–Loveland (DPLL) algorithm is a complete, backtracking-based
search algorithm for deciding the satisfiability of propositional logic formulae in
conjunctive normal form, i.e, for solving the Conjunctive Normal Form SATisfiability
(CNF-SAT) problem.
For more information about the algorithm: https://en.wikipedia.org/wiki/DPLL_algorithm
"""
from __future__ import annotations
import random
from collections.abc import Iterable
class Clause:
"""
A clause represented in Conjunctive Normal Form.
A clause is a set of literals, either complemented or otherwise.
For example:
{A1, A2, A3'} is the clause (A1 v A2 v A3')
{A5', A2', A1} is the clause (A5' v A2' v A1)
Create model
>>> clause = Clause(["A1", "A2'", "A3"])
>>> clause.evaluate({"A1": True})
True
"""
def __init__(self, literals: list[str]) -> None:
"""
Represent the literals and an assignment in a clause."
"""
# Assign all literals to None initially
self.literals: dict[str, bool | None] = {literal: None for literal in literals}
def __str__(self) -> str:
"""
To print a clause as in Conjunctive Normal Form.
>>> str(Clause(["A1", "A2'", "A3"]))
"{A1 , A2' , A3}"
"""
return "{" + " , ".join(self.literals) + "}"
def __len__(self) -> int:
"""
To print a clause as in Conjunctive Normal Form.
>>> len(Clause([]))
0
>>> len(Clause(["A1", "A2'", "A3"]))
3
"""
return len(self.literals)
def assign(self, model: dict[str, bool | None]) -> None:
"""
Assign values to literals of the clause as given by model.
"""
for literal in self.literals:
symbol = literal[:2]
if symbol in model:
value = model[symbol]
else:
continue
if value is not None:
# Complement assignment if literal is in complemented form
if literal.endswith("'"):
value = not value
self.literals[literal] = value
def evaluate(self, model: dict[str, bool | None]) -> bool | None:
"""
Evaluates the clause with the assignments in model.
This has the following steps:
1. Return True if both a literal and its complement exist in the clause.
2. Return True if a single literal has the assignment True.
3. Return None(unable to complete evaluation) if a literal has no assignment.
4. Compute disjunction of all values assigned in clause.
"""
for literal in self.literals:
symbol = literal.rstrip("'") if literal.endswith("'") else literal + "'"
if symbol in self.literals:
return True
self.assign(model)
for value in self.literals.values():
if value in (True, None):
return value
return any(self.literals.values())
class Formula:
"""
A formula represented in Conjunctive Normal Form.
A formula is a set of clauses.
For example,
{{A1, A2, A3'}, {A5', A2', A1}} is ((A1 v A2 v A3') and (A5' v A2' v A1))
"""
def __init__(self, clauses: Iterable[Clause]) -> None:
"""
Represent the number of clauses and the clauses themselves.
"""
self.clauses = list(clauses)
def __str__(self) -> str:
"""
To print a formula as in Conjunctive Normal Form.
str(Formula([Clause(["A1", "A2'", "A3"]), Clause(["A5'", "A2'", "A1"])]))
"{{A1 , A2' , A3} , {A5' , A2' , A1}}"
"""
return "{" + " , ".join(str(clause) for clause in self.clauses) + "}"
def generate_clause() -> Clause:
"""
Randomly generate a clause.
All literals have the name Ax, where x is an integer from 1 to 5.
"""
literals = []
no_of_literals = random.randint(1, 5)
base_var = "A"
i = 0
while i < no_of_literals:
var_no = random.randint(1, 5)
var_name = base_var + str(var_no)
var_complement = random.randint(0, 1)
if var_complement == 1:
var_name += "'"
if var_name in literals:
i -= 1
else:
literals.append(var_name)
i += 1
return Clause(literals)
def generate_formula() -> Formula:
"""
Randomly generate a formula.
"""
clauses: set[Clause] = set()
no_of_clauses = random.randint(1, 10)
while len(clauses) < no_of_clauses:
clauses.add(generate_clause())
return Formula(clauses)
def generate_parameters(formula: Formula) -> tuple[list[Clause], list[str]]:
"""
Return the clauses and symbols from a formula.
A symbol is the uncomplemented form of a literal.
For example,
Symbol of A3 is A3.
Symbol of A5' is A5.
>>> formula = Formula([Clause(["A1", "A2'", "A3"]), Clause(["A5'", "A2'", "A1"])])
>>> clauses, symbols = generate_parameters(formula)
>>> clauses_list = [str(i) for i in clauses]
>>> clauses_list
["{A1 , A2' , A3}", "{A5' , A2' , A1}"]
>>> symbols
['A1', 'A2', 'A3', 'A5']
"""
clauses = formula.clauses
symbols_set = []
for clause in formula.clauses:
for literal in clause.literals:
symbol = literal[:2]
if symbol not in symbols_set:
symbols_set.append(symbol)
return clauses, symbols_set
def find_pure_symbols(
clauses: list[Clause], symbols: list[str], model: dict[str, bool | None]
) -> tuple[list[str], dict[str, bool | None]]:
"""
Return pure symbols and their values to satisfy clause.
Pure symbols are symbols in a formula that exist only
in one form, either complemented or otherwise.
For example,
{ { A4 , A3 , A5' , A1 , A3' } , { A4 } , { A3 } } has
pure symbols A4, A5' and A1.
This has the following steps:
1. Ignore clauses that have already evaluated to be True.
2. Find symbols that occur only in one form in the rest of the clauses.
3. Assign value True or False depending on whether the symbols occurs
in normal or complemented form respectively.
>>> formula = Formula([Clause(["A1", "A2'", "A3"]), Clause(["A5'", "A2'", "A1"])])
>>> clauses, symbols = generate_parameters(formula)
>>> pure_symbols, values = find_pure_symbols(clauses, symbols, {})
>>> pure_symbols
['A1', 'A2', 'A3', 'A5']
>>> values
{'A1': True, 'A2': False, 'A3': True, 'A5': False}
"""
pure_symbols = []
assignment: dict[str, bool | None] = {}
literals = []
for clause in clauses:
if clause.evaluate(model):
continue
for literal in clause.literals:
literals.append(literal)
for s in symbols:
sym = s + "'"
if (s in literals and sym not in literals) or (
s not in literals and sym in literals
):
pure_symbols.append(s)
for p in pure_symbols:
assignment[p] = None
for s in pure_symbols:
sym = s + "'"
if s in literals:
assignment[s] = True
elif sym in literals:
assignment[s] = False
return pure_symbols, assignment
def find_unit_clauses(
clauses: list[Clause], model: dict[str, bool | None]
) -> tuple[list[str], dict[str, bool | None]]:
"""
Returns the unit symbols and their values to satisfy clause.
Unit symbols are symbols in a formula that are:
- Either the only symbol in a clause
- Or all other literals in that clause have been assigned False
This has the following steps:
1. Find symbols that are the only occurrences in a clause.
2. Find symbols in a clause where all other literals are assigned False.
3. Assign True or False depending on whether the symbols occurs in
normal or complemented form respectively.
>>> clause1 = Clause(["A4", "A3", "A5'", "A1", "A3'"])
>>> clause2 = Clause(["A4"])
>>> clause3 = Clause(["A3"])
>>> clauses, symbols = generate_parameters(Formula([clause1, clause2, clause3]))
>>> unit_clauses, values = find_unit_clauses(clauses, {})
>>> unit_clauses
['A4', 'A3']
>>> values
{'A4': True, 'A3': True}
"""
unit_symbols = []
for clause in clauses:
if len(clause) == 1:
unit_symbols.append(next(iter(clause.literals.keys())))
else:
f_count, n_count = 0, 0
for literal, value in clause.literals.items():
if value is False:
f_count += 1
elif value is None:
sym = literal
n_count += 1
if f_count == len(clause) - 1 and n_count == 1:
unit_symbols.append(sym)
assignment: dict[str, bool | None] = {}
for i in unit_symbols:
symbol = i[:2]
assignment[symbol] = len(i) == 2
unit_symbols = [i[:2] for i in unit_symbols]
return unit_symbols, assignment
def dpll_algorithm(
clauses: list[Clause], symbols: list[str], model: dict[str, bool | None]
) -> tuple[bool | None, dict[str, bool | None] | None]:
"""
Returns the model if the formula is satisfiable, else None
This has the following steps:
1. If every clause in clauses is True, return True.
2. If some clause in clauses is False, return False.
3. Find pure symbols.
4. Find unit symbols.
>>> formula = Formula([Clause(["A4", "A3", "A5'", "A1", "A3'"]), Clause(["A4"])])
>>> clauses, symbols = generate_parameters(formula)
>>> soln, model = dpll_algorithm(clauses, symbols, {})
>>> soln
True
>>> model
{'A4': True}
"""
check_clause_all_true = True
for clause in clauses:
clause_check = clause.evaluate(model)
if clause_check is False:
return False, None
elif clause_check is None:
check_clause_all_true = False
continue
if check_clause_all_true:
return True, model
try:
pure_symbols, assignment = find_pure_symbols(clauses, symbols, model)
except RecursionError:
print("raises a RecursionError and is")
return None, {}
p = None
if len(pure_symbols) > 0:
p, value = pure_symbols[0], assignment[pure_symbols[0]]
if p:
tmp_model = model
tmp_model[p] = value
tmp_symbols = list(symbols)
if p in tmp_symbols:
tmp_symbols.remove(p)
return dpll_algorithm(clauses, tmp_symbols, tmp_model)
unit_symbols, assignment = find_unit_clauses(clauses, model)
p = None
if len(unit_symbols) > 0:
p, value = unit_symbols[0], assignment[unit_symbols[0]]
if p:
tmp_model = model
tmp_model[p] = value
tmp_symbols = list(symbols)
if p in tmp_symbols:
tmp_symbols.remove(p)
return dpll_algorithm(clauses, tmp_symbols, tmp_model)
p = symbols[0]
rest = symbols[1:]
tmp1, tmp2 = model, model
tmp1[p], tmp2[p] = True, False
return dpll_algorithm(clauses, rest, tmp1) or dpll_algorithm(clauses, rest, tmp2)
if __name__ == "__main__":
import doctest
doctest.testmod()
formula = generate_formula()
print(f"The formula {formula} is", end=" ")
clauses, symbols = generate_parameters(formula)
solution, model = dpll_algorithm(clauses, symbols, {})
if solution:
print(f"satisfiable with the assignment {model}.")
else:
print("not satisfiable.")
| #!/usr/bin/env python3
"""
Davis–Putnam–Logemann–Loveland (DPLL) algorithm is a complete, backtracking-based
search algorithm for deciding the satisfiability of propositional logic formulae in
conjunctive normal form, i.e, for solving the Conjunctive Normal Form SATisfiability
(CNF-SAT) problem.
For more information about the algorithm: https://en.wikipedia.org/wiki/DPLL_algorithm
"""
from __future__ import annotations
import random
from collections.abc import Iterable
class Clause:
"""
A clause represented in Conjunctive Normal Form.
A clause is a set of literals, either complemented or otherwise.
For example:
{A1, A2, A3'} is the clause (A1 v A2 v A3')
{A5', A2', A1} is the clause (A5' v A2' v A1)
Create model
>>> clause = Clause(["A1", "A2'", "A3"])
>>> clause.evaluate({"A1": True})
True
"""
def __init__(self, literals: list[str]) -> None:
"""
Represent the literals and an assignment in a clause."
"""
# Assign all literals to None initially
self.literals: dict[str, bool | None] = {literal: None for literal in literals}
def __str__(self) -> str:
"""
To print a clause as in Conjunctive Normal Form.
>>> str(Clause(["A1", "A2'", "A3"]))
"{A1 , A2' , A3}"
"""
return "{" + " , ".join(self.literals) + "}"
def __len__(self) -> int:
"""
To print a clause as in Conjunctive Normal Form.
>>> len(Clause([]))
0
>>> len(Clause(["A1", "A2'", "A3"]))
3
"""
return len(self.literals)
def assign(self, model: dict[str, bool | None]) -> None:
"""
Assign values to literals of the clause as given by model.
"""
for literal in self.literals:
symbol = literal[:2]
if symbol in model:
value = model[symbol]
else:
continue
if value is not None:
# Complement assignment if literal is in complemented form
if literal.endswith("'"):
value = not value
self.literals[literal] = value
def evaluate(self, model: dict[str, bool | None]) -> bool | None:
"""
Evaluates the clause with the assignments in model.
This has the following steps:
1. Return True if both a literal and its complement exist in the clause.
2. Return True if a single literal has the assignment True.
3. Return None(unable to complete evaluation) if a literal has no assignment.
4. Compute disjunction of all values assigned in clause.
"""
for literal in self.literals:
symbol = literal.rstrip("'") if literal.endswith("'") else literal + "'"
if symbol in self.literals:
return True
self.assign(model)
for value in self.literals.values():
if value in (True, None):
return value
return any(self.literals.values())
class Formula:
"""
A formula represented in Conjunctive Normal Form.
A formula is a set of clauses.
For example,
{{A1, A2, A3'}, {A5', A2', A1}} is ((A1 v A2 v A3') and (A5' v A2' v A1))
"""
def __init__(self, clauses: Iterable[Clause]) -> None:
"""
Represent the number of clauses and the clauses themselves.
"""
self.clauses = list(clauses)
def __str__(self) -> str:
"""
To print a formula as in Conjunctive Normal Form.
str(Formula([Clause(["A1", "A2'", "A3"]), Clause(["A5'", "A2'", "A1"])]))
"{{A1 , A2' , A3} , {A5' , A2' , A1}}"
"""
return "{" + " , ".join(str(clause) for clause in self.clauses) + "}"
def generate_clause() -> Clause:
"""
Randomly generate a clause.
All literals have the name Ax, where x is an integer from 1 to 5.
"""
literals = []
no_of_literals = random.randint(1, 5)
base_var = "A"
i = 0
while i < no_of_literals:
var_no = random.randint(1, 5)
var_name = base_var + str(var_no)
var_complement = random.randint(0, 1)
if var_complement == 1:
var_name += "'"
if var_name in literals:
i -= 1
else:
literals.append(var_name)
i += 1
return Clause(literals)
def generate_formula() -> Formula:
"""
Randomly generate a formula.
"""
clauses: set[Clause] = set()
no_of_clauses = random.randint(1, 10)
while len(clauses) < no_of_clauses:
clauses.add(generate_clause())
return Formula(clauses)
def generate_parameters(formula: Formula) -> tuple[list[Clause], list[str]]:
"""
Return the clauses and symbols from a formula.
A symbol is the uncomplemented form of a literal.
For example,
Symbol of A3 is A3.
Symbol of A5' is A5.
>>> formula = Formula([Clause(["A1", "A2'", "A3"]), Clause(["A5'", "A2'", "A1"])])
>>> clauses, symbols = generate_parameters(formula)
>>> clauses_list = [str(i) for i in clauses]
>>> clauses_list
["{A1 , A2' , A3}", "{A5' , A2' , A1}"]
>>> symbols
['A1', 'A2', 'A3', 'A5']
"""
clauses = formula.clauses
symbols_set = []
for clause in formula.clauses:
for literal in clause.literals:
symbol = literal[:2]
if symbol not in symbols_set:
symbols_set.append(symbol)
return clauses, symbols_set
def find_pure_symbols(
clauses: list[Clause], symbols: list[str], model: dict[str, bool | None]
) -> tuple[list[str], dict[str, bool | None]]:
"""
Return pure symbols and their values to satisfy clause.
Pure symbols are symbols in a formula that exist only
in one form, either complemented or otherwise.
For example,
{ { A4 , A3 , A5' , A1 , A3' } , { A4 } , { A3 } } has
pure symbols A4, A5' and A1.
This has the following steps:
1. Ignore clauses that have already evaluated to be True.
2. Find symbols that occur only in one form in the rest of the clauses.
3. Assign value True or False depending on whether the symbols occurs
in normal or complemented form respectively.
>>> formula = Formula([Clause(["A1", "A2'", "A3"]), Clause(["A5'", "A2'", "A1"])])
>>> clauses, symbols = generate_parameters(formula)
>>> pure_symbols, values = find_pure_symbols(clauses, symbols, {})
>>> pure_symbols
['A1', 'A2', 'A3', 'A5']
>>> values
{'A1': True, 'A2': False, 'A3': True, 'A5': False}
"""
pure_symbols = []
assignment: dict[str, bool | None] = {}
literals = []
for clause in clauses:
if clause.evaluate(model):
continue
for literal in clause.literals:
literals.append(literal)
for s in symbols:
sym = s + "'"
if (s in literals and sym not in literals) or (
s not in literals and sym in literals
):
pure_symbols.append(s)
for p in pure_symbols:
assignment[p] = None
for s in pure_symbols:
sym = s + "'"
if s in literals:
assignment[s] = True
elif sym in literals:
assignment[s] = False
return pure_symbols, assignment
def find_unit_clauses(
clauses: list[Clause], model: dict[str, bool | None]
) -> tuple[list[str], dict[str, bool | None]]:
"""
Returns the unit symbols and their values to satisfy clause.
Unit symbols are symbols in a formula that are:
- Either the only symbol in a clause
- Or all other literals in that clause have been assigned False
This has the following steps:
1. Find symbols that are the only occurrences in a clause.
2. Find symbols in a clause where all other literals are assigned False.
3. Assign True or False depending on whether the symbols occurs in
normal or complemented form respectively.
>>> clause1 = Clause(["A4", "A3", "A5'", "A1", "A3'"])
>>> clause2 = Clause(["A4"])
>>> clause3 = Clause(["A3"])
>>> clauses, symbols = generate_parameters(Formula([clause1, clause2, clause3]))
>>> unit_clauses, values = find_unit_clauses(clauses, {})
>>> unit_clauses
['A4', 'A3']
>>> values
{'A4': True, 'A3': True}
"""
unit_symbols = []
for clause in clauses:
if len(clause) == 1:
unit_symbols.append(next(iter(clause.literals.keys())))
else:
f_count, n_count = 0, 0
for literal, value in clause.literals.items():
if value is False:
f_count += 1
elif value is None:
sym = literal
n_count += 1
if f_count == len(clause) - 1 and n_count == 1:
unit_symbols.append(sym)
assignment: dict[str, bool | None] = {}
for i in unit_symbols:
symbol = i[:2]
assignment[symbol] = len(i) == 2
unit_symbols = [i[:2] for i in unit_symbols]
return unit_symbols, assignment
def dpll_algorithm(
clauses: list[Clause], symbols: list[str], model: dict[str, bool | None]
) -> tuple[bool | None, dict[str, bool | None] | None]:
"""
Returns the model if the formula is satisfiable, else None
This has the following steps:
1. If every clause in clauses is True, return True.
2. If some clause in clauses is False, return False.
3. Find pure symbols.
4. Find unit symbols.
>>> formula = Formula([Clause(["A4", "A3", "A5'", "A1", "A3'"]), Clause(["A4"])])
>>> clauses, symbols = generate_parameters(formula)
>>> soln, model = dpll_algorithm(clauses, symbols, {})
>>> soln
True
>>> model
{'A4': True}
"""
check_clause_all_true = True
for clause in clauses:
clause_check = clause.evaluate(model)
if clause_check is False:
return False, None
elif clause_check is None:
check_clause_all_true = False
continue
if check_clause_all_true:
return True, model
try:
pure_symbols, assignment = find_pure_symbols(clauses, symbols, model)
except RecursionError:
print("raises a RecursionError and is")
return None, {}
p = None
if len(pure_symbols) > 0:
p, value = pure_symbols[0], assignment[pure_symbols[0]]
if p:
tmp_model = model
tmp_model[p] = value
tmp_symbols = list(symbols)
if p in tmp_symbols:
tmp_symbols.remove(p)
return dpll_algorithm(clauses, tmp_symbols, tmp_model)
unit_symbols, assignment = find_unit_clauses(clauses, model)
p = None
if len(unit_symbols) > 0:
p, value = unit_symbols[0], assignment[unit_symbols[0]]
if p:
tmp_model = model
tmp_model[p] = value
tmp_symbols = list(symbols)
if p in tmp_symbols:
tmp_symbols.remove(p)
return dpll_algorithm(clauses, tmp_symbols, tmp_model)
p = symbols[0]
rest = symbols[1:]
tmp1, tmp2 = model, model
tmp1[p], tmp2[p] = True, False
return dpll_algorithm(clauses, rest, tmp1) or dpll_algorithm(clauses, rest, tmp2)
if __name__ == "__main__":
import doctest
doctest.testmod()
formula = generate_formula()
print(f"The formula {formula} is", end=" ")
clauses, symbols = generate_parameters(formula)
solution, model = dpll_algorithm(clauses, symbols, {})
if solution:
print(f"satisfiable with the assignment {model}.")
else:
print("not satisfiable.")
| -1 |
TheAlgorithms/Python | 9,076 | Fix typos |
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| omahs | "2023-09-22T13:22:15Z" | "2023-09-23T08:53:09Z" | dc50add8a78ebf34bc7bb050c1a0e61d207b9544 | b203150ac481743a6d8c1ef01091712a54dfbf6c | Fix typos.
* [ ] Add an algorithm?
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| from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def test_prim_successful_result():
num_nodes, num_edges = 9, 14 # noqa: F841
edges = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
[8, 6, 6],
[2, 3, 7],
[2, 5, 4],
[6, 5, 2],
[3, 5, 14],
[3, 4, 9],
[5, 4, 10],
[1, 7, 11],
]
adjancency = defaultdict(list)
for node1, node2, cost in edges:
adjancency[node1].append([node2, cost])
adjancency[node2].append([node1, cost])
result = mst(adjancency)
expected = [
[7, 6, 1],
[2, 8, 2],
[6, 5, 2],
[0, 1, 4],
[2, 5, 4],
[2, 3, 7],
[0, 7, 8],
[3, 4, 9],
]
for answer in expected:
edge = tuple(answer[:2])
reverse = tuple(edge[::-1])
assert edge in result or reverse in result
| from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def test_prim_successful_result():
num_nodes, num_edges = 9, 14 # noqa: F841
edges = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
[8, 6, 6],
[2, 3, 7],
[2, 5, 4],
[6, 5, 2],
[3, 5, 14],
[3, 4, 9],
[5, 4, 10],
[1, 7, 11],
]
adjancency = defaultdict(list)
for node1, node2, cost in edges:
adjancency[node1].append([node2, cost])
adjancency[node2].append([node1, cost])
result = mst(adjancency)
expected = [
[7, 6, 1],
[2, 8, 2],
[6, 5, 2],
[0, 1, 4],
[2, 5, 4],
[2, 3, 7],
[0, 7, 8],
[3, 4, 9],
]
for answer in expected:
edge = tuple(answer[:2])
reverse = tuple(edge[::-1])
assert edge in result or reverse in result
| -1 |
TheAlgorithms/Python | 9,076 | Fix typos |
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| omahs | "2023-09-22T13:22:15Z" | "2023-09-23T08:53:09Z" | dc50add8a78ebf34bc7bb050c1a0e61d207b9544 | b203150ac481743a6d8c1ef01091712a54dfbf6c | Fix typos.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| from __future__ import annotations
def find_max_iterative(nums: list[int | float]) -> int | float:
"""
>>> for nums in ([3, 2, 1], [-3, -2, -1], [3, -3, 0], [3.0, 3.1, 2.9]):
... find_max_iterative(nums) == max(nums)
True
True
True
True
>>> find_max_iterative([2, 4, 9, 7, 19, 94, 5])
94
>>> find_max_iterative([])
Traceback (most recent call last):
...
ValueError: find_max_iterative() arg is an empty sequence
"""
if len(nums) == 0:
raise ValueError("find_max_iterative() arg is an empty sequence")
max_num = nums[0]
for x in nums:
if x > max_num:
max_num = x
return max_num
# Divide and Conquer algorithm
def find_max_recursive(nums: list[int | float], left: int, right: int) -> int | float:
"""
find max value in list
:param nums: contains elements
:param left: index of first element
:param right: index of last element
:return: max in nums
>>> for nums in ([3, 2, 1], [-3, -2, -1], [3, -3, 0], [3.0, 3.1, 2.9]):
... find_max_recursive(nums, 0, len(nums) - 1) == max(nums)
True
True
True
True
>>> nums = [1, 3, 5, 7, 9, 2, 4, 6, 8, 10]
>>> find_max_recursive(nums, 0, len(nums) - 1) == max(nums)
True
>>> find_max_recursive([], 0, 0)
Traceback (most recent call last):
...
ValueError: find_max_recursive() arg is an empty sequence
>>> find_max_recursive(nums, 0, len(nums)) == max(nums)
Traceback (most recent call last):
...
IndexError: list index out of range
>>> find_max_recursive(nums, -len(nums), -1) == max(nums)
True
>>> find_max_recursive(nums, -len(nums) - 1, -1) == max(nums)
Traceback (most recent call last):
...
IndexError: list index out of range
"""
if len(nums) == 0:
raise ValueError("find_max_recursive() arg is an empty sequence")
if (
left >= len(nums)
or left < -len(nums)
or right >= len(nums)
or right < -len(nums)
):
raise IndexError("list index out of range")
if left == right:
return nums[left]
mid = (left + right) >> 1 # the middle
left_max = find_max_recursive(nums, left, mid) # find max in range[left, mid]
right_max = find_max_recursive(
nums, mid + 1, right
) # find max in range[mid + 1, right]
return left_max if left_max >= right_max else right_max
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| from __future__ import annotations
def find_max_iterative(nums: list[int | float]) -> int | float:
"""
>>> for nums in ([3, 2, 1], [-3, -2, -1], [3, -3, 0], [3.0, 3.1, 2.9]):
... find_max_iterative(nums) == max(nums)
True
True
True
True
>>> find_max_iterative([2, 4, 9, 7, 19, 94, 5])
94
>>> find_max_iterative([])
Traceback (most recent call last):
...
ValueError: find_max_iterative() arg is an empty sequence
"""
if len(nums) == 0:
raise ValueError("find_max_iterative() arg is an empty sequence")
max_num = nums[0]
for x in nums:
if x > max_num:
max_num = x
return max_num
# Divide and Conquer algorithm
def find_max_recursive(nums: list[int | float], left: int, right: int) -> int | float:
"""
find max value in list
:param nums: contains elements
:param left: index of first element
:param right: index of last element
:return: max in nums
>>> for nums in ([3, 2, 1], [-3, -2, -1], [3, -3, 0], [3.0, 3.1, 2.9]):
... find_max_recursive(nums, 0, len(nums) - 1) == max(nums)
True
True
True
True
>>> nums = [1, 3, 5, 7, 9, 2, 4, 6, 8, 10]
>>> find_max_recursive(nums, 0, len(nums) - 1) == max(nums)
True
>>> find_max_recursive([], 0, 0)
Traceback (most recent call last):
...
ValueError: find_max_recursive() arg is an empty sequence
>>> find_max_recursive(nums, 0, len(nums)) == max(nums)
Traceback (most recent call last):
...
IndexError: list index out of range
>>> find_max_recursive(nums, -len(nums), -1) == max(nums)
True
>>> find_max_recursive(nums, -len(nums) - 1, -1) == max(nums)
Traceback (most recent call last):
...
IndexError: list index out of range
"""
if len(nums) == 0:
raise ValueError("find_max_recursive() arg is an empty sequence")
if (
left >= len(nums)
or left < -len(nums)
or right >= len(nums)
or right < -len(nums)
):
raise IndexError("list index out of range")
if left == right:
return nums[left]
mid = (left + right) >> 1 # the middle
left_max = find_max_recursive(nums, left, mid) # find max in range[left, mid]
right_max = find_max_recursive(
nums, mid + 1, right
) # find max in range[mid + 1, right]
return left_max if left_max >= right_max else right_max
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| -1 |
TheAlgorithms/Python | 9,076 | Fix typos |
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| omahs | "2023-09-22T13:22:15Z" | "2023-09-23T08:53:09Z" | dc50add8a78ebf34bc7bb050c1a0e61d207b9544 | b203150ac481743a6d8c1ef01091712a54dfbf6c | Fix typos.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| """
Project Euler Problem 10: https://projecteuler.net/problem=10
Summation of primes
The sum of the primes below 10 is 2 + 3 + 5 + 7 = 17.
Find the sum of all the primes below two million.
References:
- https://en.wikipedia.org/wiki/Prime_number
"""
import math
from collections.abc import Iterator
from itertools import takewhile
def is_prime(number: int) -> bool:
"""Checks to see if a number is a prime in O(sqrt(n)).
A number is prime if it has exactly two factors: 1 and itself.
Returns boolean representing primality of given number num (i.e., if the
result is true, then the number is indeed prime else it is not).
>>> is_prime(2)
True
>>> is_prime(3)
True
>>> is_prime(27)
False
>>> is_prime(2999)
True
>>> is_prime(0)
False
>>> is_prime(1)
False
"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5, int(math.sqrt(number) + 1), 6):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def prime_generator() -> Iterator[int]:
"""
Generate a list sequence of prime numbers
"""
num = 2
while True:
if is_prime(num):
yield num
num += 1
def solution(n: int = 2000000) -> int:
"""
Returns the sum of all the primes below n.
>>> solution(1000)
76127
>>> solution(5000)
1548136
>>> solution(10000)
5736396
>>> solution(7)
10
"""
return sum(takewhile(lambda x: x < n, prime_generator()))
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 10: https://projecteuler.net/problem=10
Summation of primes
The sum of the primes below 10 is 2 + 3 + 5 + 7 = 17.
Find the sum of all the primes below two million.
References:
- https://en.wikipedia.org/wiki/Prime_number
"""
import math
from collections.abc import Iterator
from itertools import takewhile
def is_prime(number: int) -> bool:
"""Checks to see if a number is a prime in O(sqrt(n)).
A number is prime if it has exactly two factors: 1 and itself.
Returns boolean representing primality of given number num (i.e., if the
result is true, then the number is indeed prime else it is not).
>>> is_prime(2)
True
>>> is_prime(3)
True
>>> is_prime(27)
False
>>> is_prime(2999)
True
>>> is_prime(0)
False
>>> is_prime(1)
False
"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5, int(math.sqrt(number) + 1), 6):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def prime_generator() -> Iterator[int]:
"""
Generate a list sequence of prime numbers
"""
num = 2
while True:
if is_prime(num):
yield num
num += 1
def solution(n: int = 2000000) -> int:
"""
Returns the sum of all the primes below n.
>>> solution(1000)
76127
>>> solution(5000)
1548136
>>> solution(10000)
5736396
>>> solution(7)
10
"""
return sum(takewhile(lambda x: x < n, prime_generator()))
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 9,076 | Fix typos |
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| omahs | "2023-09-22T13:22:15Z" | "2023-09-23T08:53:09Z" | dc50add8a78ebf34bc7bb050c1a0e61d207b9544 | b203150ac481743a6d8c1ef01091712a54dfbf6c | Fix typos.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
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| # Conversion
Conversion programs convert a type of data, a number from a numerical base or unit into one of another type, base or unit, e.g. binary to decimal, integer to string or foot to meters.
* <https://en.wikipedia.org/wiki/Data_conversion>
* <https://en.wikipedia.org/wiki/Transcoding>
| # Conversion
Conversion programs convert a type of data, a number from a numerical base or unit into one of another type, base or unit, e.g. binary to decimal, integer to string or foot to meters.
* <https://en.wikipedia.org/wiki/Data_conversion>
* <https://en.wikipedia.org/wiki/Transcoding>
| -1 |
TheAlgorithms/Python | 9,076 | Fix typos |
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| omahs | "2023-09-22T13:22:15Z" | "2023-09-23T08:53:09Z" | dc50add8a78ebf34bc7bb050c1a0e61d207b9544 | b203150ac481743a6d8c1ef01091712a54dfbf6c | Fix typos.
* [ ] Add an algorithm?
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| # https://en.wikipedia.org/wiki/Charge_carrier_density
# https://www.pveducation.org/pvcdrom/pn-junctions/equilibrium-carrier-concentration
# http://www.ece.utep.edu/courses/ee3329/ee3329/Studyguide/ToC/Fundamentals/Carriers/concentrations.html
from __future__ import annotations
def carrier_concentration(
electron_conc: float,
hole_conc: float,
intrinsic_conc: float,
) -> tuple:
"""
This function can calculate any one of the three -
1. Electron Concentration
2, Hole Concentration
3. Intrinsic Concentration
given the other two.
Examples -
>>> carrier_concentration(electron_conc=25, hole_conc=100, intrinsic_conc=0)
('intrinsic_conc', 50.0)
>>> carrier_concentration(electron_conc=0, hole_conc=1600, intrinsic_conc=200)
('electron_conc', 25.0)
>>> carrier_concentration(electron_conc=1000, hole_conc=0, intrinsic_conc=1200)
('hole_conc', 1440.0)
>>> carrier_concentration(electron_conc=1000, hole_conc=400, intrinsic_conc=1200)
Traceback (most recent call last):
...
ValueError: You cannot supply more or less than 2 values
>>> carrier_concentration(electron_conc=-1000, hole_conc=0, intrinsic_conc=1200)
Traceback (most recent call last):
...
ValueError: Electron concentration cannot be negative in a semiconductor
>>> carrier_concentration(electron_conc=0, hole_conc=-400, intrinsic_conc=1200)
Traceback (most recent call last):
...
ValueError: Hole concentration cannot be negative in a semiconductor
>>> carrier_concentration(electron_conc=0, hole_conc=400, intrinsic_conc=-1200)
Traceback (most recent call last):
...
ValueError: Intrinsic concentration cannot be negative in a semiconductor
"""
if (electron_conc, hole_conc, intrinsic_conc).count(0) != 1:
raise ValueError("You cannot supply more or less than 2 values")
elif electron_conc < 0:
raise ValueError("Electron concentration cannot be negative in a semiconductor")
elif hole_conc < 0:
raise ValueError("Hole concentration cannot be negative in a semiconductor")
elif intrinsic_conc < 0:
raise ValueError(
"Intrinsic concentration cannot be negative in a semiconductor"
)
elif electron_conc == 0:
return (
"electron_conc",
intrinsic_conc**2 / hole_conc,
)
elif hole_conc == 0:
return (
"hole_conc",
intrinsic_conc**2 / electron_conc,
)
elif intrinsic_conc == 0:
return (
"intrinsic_conc",
(electron_conc * hole_conc) ** 0.5,
)
else:
return (-1, -1)
if __name__ == "__main__":
import doctest
doctest.testmod()
| # https://en.wikipedia.org/wiki/Charge_carrier_density
# https://www.pveducation.org/pvcdrom/pn-junctions/equilibrium-carrier-concentration
# http://www.ece.utep.edu/courses/ee3329/ee3329/Studyguide/ToC/Fundamentals/Carriers/concentrations.html
from __future__ import annotations
def carrier_concentration(
electron_conc: float,
hole_conc: float,
intrinsic_conc: float,
) -> tuple:
"""
This function can calculate any one of the three -
1. Electron Concentration
2, Hole Concentration
3. Intrinsic Concentration
given the other two.
Examples -
>>> carrier_concentration(electron_conc=25, hole_conc=100, intrinsic_conc=0)
('intrinsic_conc', 50.0)
>>> carrier_concentration(electron_conc=0, hole_conc=1600, intrinsic_conc=200)
('electron_conc', 25.0)
>>> carrier_concentration(electron_conc=1000, hole_conc=0, intrinsic_conc=1200)
('hole_conc', 1440.0)
>>> carrier_concentration(electron_conc=1000, hole_conc=400, intrinsic_conc=1200)
Traceback (most recent call last):
...
ValueError: You cannot supply more or less than 2 values
>>> carrier_concentration(electron_conc=-1000, hole_conc=0, intrinsic_conc=1200)
Traceback (most recent call last):
...
ValueError: Electron concentration cannot be negative in a semiconductor
>>> carrier_concentration(electron_conc=0, hole_conc=-400, intrinsic_conc=1200)
Traceback (most recent call last):
...
ValueError: Hole concentration cannot be negative in a semiconductor
>>> carrier_concentration(electron_conc=0, hole_conc=400, intrinsic_conc=-1200)
Traceback (most recent call last):
...
ValueError: Intrinsic concentration cannot be negative in a semiconductor
"""
if (electron_conc, hole_conc, intrinsic_conc).count(0) != 1:
raise ValueError("You cannot supply more or less than 2 values")
elif electron_conc < 0:
raise ValueError("Electron concentration cannot be negative in a semiconductor")
elif hole_conc < 0:
raise ValueError("Hole concentration cannot be negative in a semiconductor")
elif intrinsic_conc < 0:
raise ValueError(
"Intrinsic concentration cannot be negative in a semiconductor"
)
elif electron_conc == 0:
return (
"electron_conc",
intrinsic_conc**2 / hole_conc,
)
elif hole_conc == 0:
return (
"hole_conc",
intrinsic_conc**2 / electron_conc,
)
elif intrinsic_conc == 0:
return (
"intrinsic_conc",
(electron_conc * hole_conc) ** 0.5,
)
else:
return (-1, -1)
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 9,076 | Fix typos |
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| omahs | "2023-09-22T13:22:15Z" | "2023-09-23T08:53:09Z" | dc50add8a78ebf34bc7bb050c1a0e61d207b9544 | b203150ac481743a6d8c1ef01091712a54dfbf6c | Fix typos.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| # Binary Tree Traversal
## Overview
The combination of binary trees being data structures and traversal being an algorithm relates to classic problems, either directly or indirectly.
> If you can grasp the traversal of binary trees, the traversal of other complicated trees will be easy for you.
The following are some common ways to traverse trees.
- Depth First Traversals (DFS): In-order, Pre-order, Post-order
- Level Order Traversal or Breadth First or Traversal (BFS)
There are applications for both DFS and BFS.
Stack can be used to simplify the process of DFS traversal. Besides, since tree is a recursive data structure, recursion and stack are two key points for DFS.
Graph for DFS:
The key point of BFS is how to determine whether the traversal of each level has been completed. The answer is to use a variable as a flag to represent the end of the traversal of current level.
## Pre-order Traversal
The traversal order of pre-order traversal is `root-left-right`.
Algorithm Pre-order
1. Visit the root node and push it into a stack.
2. Pop a node from the stack, and push its right and left child node into the stack respectively.
3. Repeat step 2.
Conclusion: This problem involves the classic recursive data structure (i.e. a binary tree), and the algorithm above demonstrates how a simplified solution can be reached by using a stack.
If you look at the bigger picture, you'll find that the process of traversal is as followed. `Visit the left subtrees respectively from top to bottom, and visit the right subtrees respectively from bottom to top`. If we are to implement it from this perspective, things will be somewhat different. For the `top to bottom` part we can simply use recursion, and for the `bottom to top` part we can turn to stack.
## In-order Traversal
The traversal order of in-order traversal is `left-root-right`.
So the root node is not printed first. Things are getting a bit complicated here.
Algorithm In-order
1. Visit the root and push it into a stack.
2. If there is a left child node, push it into the stack. Repeat this process until a leaf node reached.
> At this point the root node and all the left nodes are in the stack.
3. Start popping nodes from the stack. If a node has a right child node, push the child node into the stack. Repeat step 2.
It's worth pointing out that the in-order traversal of a binary search tree (BST) is a sorted array, which is helpful for coming up simplified solutions for some problems.
## Post-order Traversal
The traversal order of post-order traversal is `left-right-root`.
This one is a bit of a challenge. It deserves the `hard` tag of LeetCode.
In this case, the root node is printed not as the first but the last one. A cunning way to do it is to:
Record whether the current node has been visited. If 1) it's a leaf node or 2) both its left and right subtrees have been traversed, then it can be popped from the stack.
As for `1) it's a leaf node`, you can easily tell whether a node is a leaf if both its left and right are `null`.
As for `2) both its left and right subtrees have been traversed`, we only need a variable to record whether a node has been visited or not. In the worst case, we need to record the status for every single node and the space complexity is `O(n)`. But if you come to think about it, as we are using a stack and start printing the result from the leaf nodes, it makes sense that we only record the status for the current node popping from the stack, reducing the space complexity to `O(1)`.
## Level Order Traversal
The key point of level order traversal is how do we know whether the traversal of each level is done. The answer is that we use a variable as a flag representing the end of the traversal of the current level.
Algorithm Level-order
1. Visit the root node, put it in a FIFO queue, put in the queue a special flag (we are using `null` here).
2. Dequeue a node.
3. If the node equals `null`, it means that all nodes of the current level have been visited. If the queue is empty, we do nothing. Or else we put in another `null`.
4. If the node is not `null`, meaning the traversal of current level has not finished yet, we enqueue its left subtree and right subtree respectively.
## Bi-color marking
We know that there is a tri-color marking in garbage collection algorithm, which works as described below.
- The white color represents "not visited".
- The gray color represents "not all child nodes visited".
- The black color represents "all child nodes visited".
Enlightened by tri-color marking, a bi-color marking method can be invented to solve all three traversal problems with one solution.
The core idea is as follow.
- Use a color to mark whether a node has been visited or not. Nodes yet to be visited are marked as white and visited nodes are marked as gray.
- If we are visiting a white node, turn it into gray, and push its right child node, itself, and it's left child node into the stack respectively.
- If we are visiting a gray node, print it.
Implementation of pre-order and post-order traversal algorithms can be easily done by changing the order of pushing the child nodes into the stack.
Reference: [LeetCode](https://github.com/azl397985856/leetcode/blob/master/thinkings/binary-tree-traversal.en.md)
| # Binary Tree Traversal
## Overview
The combination of binary trees being data structures and traversal being an algorithm relates to classic problems, either directly or indirectly.
> If you can grasp the traversal of binary trees, the traversal of other complicated trees will be easy for you.
The following are some common ways to traverse trees.
- Depth First Traversals (DFS): In-order, Pre-order, Post-order
- Level Order Traversal or Breadth First or Traversal (BFS)
There are applications for both DFS and BFS.
Stack can be used to simplify the process of DFS traversal. Besides, since tree is a recursive data structure, recursion and stack are two key points for DFS.
Graph for DFS:
The key point of BFS is how to determine whether the traversal of each level has been completed. The answer is to use a variable as a flag to represent the end of the traversal of current level.
## Pre-order Traversal
The traversal order of pre-order traversal is `root-left-right`.
Algorithm Pre-order
1. Visit the root node and push it into a stack.
2. Pop a node from the stack, and push its right and left child node into the stack respectively.
3. Repeat step 2.
Conclusion: This problem involves the classic recursive data structure (i.e. a binary tree), and the algorithm above demonstrates how a simplified solution can be reached by using a stack.
If you look at the bigger picture, you'll find that the process of traversal is as followed. `Visit the left subtrees respectively from top to bottom, and visit the right subtrees respectively from bottom to top`. If we are to implement it from this perspective, things will be somewhat different. For the `top to bottom` part we can simply use recursion, and for the `bottom to top` part we can turn to stack.
## In-order Traversal
The traversal order of in-order traversal is `left-root-right`.
So the root node is not printed first. Things are getting a bit complicated here.
Algorithm In-order
1. Visit the root and push it into a stack.
2. If there is a left child node, push it into the stack. Repeat this process until a leaf node reached.
> At this point the root node and all the left nodes are in the stack.
3. Start popping nodes from the stack. If a node has a right child node, push the child node into the stack. Repeat step 2.
It's worth pointing out that the in-order traversal of a binary search tree (BST) is a sorted array, which is helpful for coming up simplified solutions for some problems.
## Post-order Traversal
The traversal order of post-order traversal is `left-right-root`.
This one is a bit of a challenge. It deserves the `hard` tag of LeetCode.
In this case, the root node is printed not as the first but the last one. A cunning way to do it is to:
Record whether the current node has been visited. If 1) it's a leaf node or 2) both its left and right subtrees have been traversed, then it can be popped from the stack.
As for `1) it's a leaf node`, you can easily tell whether a node is a leaf if both its left and right are `null`.
As for `2) both its left and right subtrees have been traversed`, we only need a variable to record whether a node has been visited or not. In the worst case, we need to record the status for every single node and the space complexity is `O(n)`. But if you come to think about it, as we are using a stack and start printing the result from the leaf nodes, it makes sense that we only record the status for the current node popping from the stack, reducing the space complexity to `O(1)`.
## Level Order Traversal
The key point of level order traversal is how do we know whether the traversal of each level is done. The answer is that we use a variable as a flag representing the end of the traversal of the current level.
Algorithm Level-order
1. Visit the root node, put it in a FIFO queue, put in the queue a special flag (we are using `null` here).
2. Dequeue a node.
3. If the node equals `null`, it means that all nodes of the current level have been visited. If the queue is empty, we do nothing. Or else we put in another `null`.
4. If the node is not `null`, meaning the traversal of current level has not finished yet, we enqueue its left subtree and right subtree respectively.
## Bi-color marking
We know that there is a tri-color marking in garbage collection algorithm, which works as described below.
- The white color represents "not visited".
- The gray color represents "not all child nodes visited".
- The black color represents "all child nodes visited".
Enlightened by tri-color marking, a bi-color marking method can be invented to solve all three traversal problems with one solution.
The core idea is as follow.
- Use a color to mark whether a node has been visited or not. Nodes yet to be visited are marked as white and visited nodes are marked as gray.
- If we are visiting a white node, turn it into gray, and push its right child node, itself, and it's left child node into the stack respectively.
- If we are visiting a gray node, print it.
Implementation of pre-order and post-order traversal algorithms can be easily done by changing the order of pushing the child nodes into the stack.
Reference: [LeetCode](https://github.com/azl397985856/leetcode/blob/master/thinkings/binary-tree-traversal.en.md)
| -1 |
TheAlgorithms/Python | 9,076 | Fix typos |
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| omahs | "2023-09-22T13:22:15Z" | "2023-09-23T08:53:09Z" | dc50add8a78ebf34bc7bb050c1a0e61d207b9544 | b203150ac481743a6d8c1ef01091712a54dfbf6c | Fix typos.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| from typing import Literal
LETTERS = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
def translate_message(
key: str, message: str, mode: Literal["encrypt", "decrypt"]
) -> str:
"""
>>> translate_message("QWERTYUIOPASDFGHJKLZXCVBNM","Hello World","encrypt")
'Pcssi Bidsm'
"""
chars_a = LETTERS if mode == "decrypt" else key
chars_b = key if mode == "decrypt" else LETTERS
translated = ""
# loop through each symbol in the message
for symbol in message:
if symbol.upper() in chars_a:
# encrypt/decrypt the symbol
sym_index = chars_a.find(symbol.upper())
if symbol.isupper():
translated += chars_b[sym_index].upper()
else:
translated += chars_b[sym_index].lower()
else:
# symbol is not in LETTERS, just add it
translated += symbol
return translated
def encrypt_message(key: str, message: str) -> str:
"""
>>> encrypt_message("QWERTYUIOPASDFGHJKLZXCVBNM", "Hello World")
'Pcssi Bidsm'
"""
return translate_message(key, message, "encrypt")
def decrypt_message(key: str, message: str) -> str:
"""
>>> decrypt_message("QWERTYUIOPASDFGHJKLZXCVBNM", "Hello World")
'Itssg Vgksr'
"""
return translate_message(key, message, "decrypt")
def main() -> None:
message = "Hello World"
key = "QWERTYUIOPASDFGHJKLZXCVBNM"
mode = "decrypt" # set to 'encrypt' or 'decrypt'
if mode == "encrypt":
translated = encrypt_message(key, message)
elif mode == "decrypt":
translated = decrypt_message(key, message)
print(f"Using the key {key}, the {mode}ed message is: {translated}")
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| from typing import Literal
LETTERS = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
def translate_message(
key: str, message: str, mode: Literal["encrypt", "decrypt"]
) -> str:
"""
>>> translate_message("QWERTYUIOPASDFGHJKLZXCVBNM","Hello World","encrypt")
'Pcssi Bidsm'
"""
chars_a = LETTERS if mode == "decrypt" else key
chars_b = key if mode == "decrypt" else LETTERS
translated = ""
# loop through each symbol in the message
for symbol in message:
if symbol.upper() in chars_a:
# encrypt/decrypt the symbol
sym_index = chars_a.find(symbol.upper())
if symbol.isupper():
translated += chars_b[sym_index].upper()
else:
translated += chars_b[sym_index].lower()
else:
# symbol is not in LETTERS, just add it
translated += symbol
return translated
def encrypt_message(key: str, message: str) -> str:
"""
>>> encrypt_message("QWERTYUIOPASDFGHJKLZXCVBNM", "Hello World")
'Pcssi Bidsm'
"""
return translate_message(key, message, "encrypt")
def decrypt_message(key: str, message: str) -> str:
"""
>>> decrypt_message("QWERTYUIOPASDFGHJKLZXCVBNM", "Hello World")
'Itssg Vgksr'
"""
return translate_message(key, message, "decrypt")
def main() -> None:
message = "Hello World"
key = "QWERTYUIOPASDFGHJKLZXCVBNM"
mode = "decrypt" # set to 'encrypt' or 'decrypt'
if mode == "encrypt":
translated = encrypt_message(key, message)
elif mode == "decrypt":
translated = decrypt_message(key, message)
print(f"Using the key {key}, the {mode}ed message is: {translated}")
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| -1 |
TheAlgorithms/Python | 9,076 | Fix typos |
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| omahs | "2023-09-22T13:22:15Z" | "2023-09-23T08:53:09Z" | dc50add8a78ebf34bc7bb050c1a0e61d207b9544 | b203150ac481743a6d8c1ef01091712a54dfbf6c | Fix typos.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| """
Minimax helps to achieve maximum score in a game by checking all possible moves.
"""
from __future__ import annotations
import math
def minimax(
depth: int, node_index: int, is_max: bool, scores: list[int], height: float
) -> int:
"""
depth is current depth in game tree.
node_index is index of current node in scores[].
scores[] contains the leaves of game tree.
height is maximum height of game tree.
>>> 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 not scores:
raise ValueError("Scores cannot be empty")
if depth == height:
return scores[node_index]
return (
max(
minimax(depth + 1, node_index * 2, False, scores, height),
minimax(depth + 1, node_index * 2 + 1, False, scores, height),
)
if is_max
else 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(f"Optimal value : {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.
"""
from __future__ import annotations
import math
def minimax(
depth: int, node_index: int, is_max: bool, scores: list[int], height: float
) -> int:
"""
depth is current depth in game tree.
node_index is index of current node in scores[].
scores[] contains the leaves of game tree.
height is maximum height of game tree.
>>> 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 not scores:
raise ValueError("Scores cannot be empty")
if depth == height:
return scores[node_index]
return (
max(
minimax(depth + 1, node_index * 2, False, scores, height),
minimax(depth + 1, node_index * 2 + 1, False, scores, height),
)
if is_max
else 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(f"Optimal value : {minimax(0, 0, True, scores, height)}")
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| -1 |
TheAlgorithms/Python | 9,076 | Fix typos |
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| omahs | "2023-09-22T13:22:15Z" | "2023-09-23T08:53:09Z" | dc50add8a78ebf34bc7bb050c1a0e61d207b9544 | b203150ac481743a6d8c1ef01091712a54dfbf6c | Fix typos.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| -1 |
||
TheAlgorithms/Python | 9,076 | Fix typos |
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| omahs | "2023-09-22T13:22:15Z" | "2023-09-23T08:53:09Z" | dc50add8a78ebf34bc7bb050c1a0e61d207b9544 | b203150ac481743a6d8c1ef01091712a54dfbf6c | Fix typos.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| import argparse
import datetime
def zeller(date_input: str) -> str:
"""
Zellers Congruence Algorithm
Find the day of the week for nearly any Gregorian or Julian calendar date
>>> zeller('01-31-2010')
'Your date 01-31-2010, is a Sunday!'
Validate out of range month
>>> zeller('13-31-2010')
Traceback (most recent call last):
...
ValueError: Month must be between 1 - 12
>>> zeller('.2-31-2010')
Traceback (most recent call last):
...
ValueError: invalid literal for int() with base 10: '.2'
Validate out of range date:
>>> zeller('01-33-2010')
Traceback (most recent call last):
...
ValueError: Date must be between 1 - 31
>>> zeller('01-.4-2010')
Traceback (most recent call last):
...
ValueError: invalid literal for int() with base 10: '.4'
Validate second separator:
>>> zeller('01-31*2010')
Traceback (most recent call last):
...
ValueError: Date separator must be '-' or '/'
Validate first separator:
>>> zeller('01^31-2010')
Traceback (most recent call last):
...
ValueError: Date separator must be '-' or '/'
Validate out of range year:
>>> zeller('01-31-8999')
Traceback (most recent call last):
...
ValueError: Year out of range. There has to be some sort of limit...right?
Test null input:
>>> zeller()
Traceback (most recent call last):
...
TypeError: zeller() missing 1 required positional argument: 'date_input'
Test length of date_input:
>>> zeller('')
Traceback (most recent call last):
...
ValueError: Must be 10 characters long
>>> zeller('01-31-19082939')
Traceback (most recent call last):
...
ValueError: Must be 10 characters long"""
# Days of the week for response
days = {
"0": "Sunday",
"1": "Monday",
"2": "Tuesday",
"3": "Wednesday",
"4": "Thursday",
"5": "Friday",
"6": "Saturday",
}
convert_datetime_days = {0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6, 6: 0}
# Validate
if not 0 < len(date_input) < 11:
raise ValueError("Must be 10 characters long")
# Get month
m: int = int(date_input[0] + date_input[1])
# Validate
if not 0 < m < 13:
raise ValueError("Month must be between 1 - 12")
sep_1: str = date_input[2]
# Validate
if sep_1 not in ["-", "/"]:
raise ValueError("Date separator must be '-' or '/'")
# Get day
d: int = int(date_input[3] + date_input[4])
# Validate
if not 0 < d < 32:
raise ValueError("Date must be between 1 - 31")
# Get second separator
sep_2: str = date_input[5]
# Validate
if sep_2 not in ["-", "/"]:
raise ValueError("Date separator must be '-' or '/'")
# Get year
y: int = int(date_input[6] + date_input[7] + date_input[8] + date_input[9])
# Arbitrary year range
if not 45 < y < 8500:
raise ValueError(
"Year out of range. There has to be some sort of limit...right?"
)
# Get datetime obj for validation
dt_ck = datetime.date(int(y), int(m), int(d))
# Start math
if m <= 2:
y = y - 1
m = m + 12
# maths var
c: int = int(str(y)[:2])
k: int = int(str(y)[2:])
t: int = int(2.6 * m - 5.39)
u: int = int(c / 4)
v: int = int(k / 4)
x: int = int(d + k)
z: int = int(t + u + v + x)
w: int = int(z - (2 * c))
f: int = round(w % 7)
# End math
# Validate math
if f != convert_datetime_days[dt_ck.weekday()]:
raise AssertionError("The date was evaluated incorrectly. Contact developer.")
# Response
response: str = f"Your date {date_input}, is a {days[str(f)]}!"
return response
if __name__ == "__main__":
import doctest
doctest.testmod()
parser = argparse.ArgumentParser(
description=(
"Find out what day of the week nearly any date is or was. Enter "
"date as a string in the mm-dd-yyyy or mm/dd/yyyy format"
)
)
parser.add_argument(
"date_input", type=str, help="Date as a string (mm-dd-yyyy or mm/dd/yyyy)"
)
args = parser.parse_args()
zeller(args.date_input)
| import argparse
import datetime
def zeller(date_input: str) -> str:
"""
Zellers Congruence Algorithm
Find the day of the week for nearly any Gregorian or Julian calendar date
>>> zeller('01-31-2010')
'Your date 01-31-2010, is a Sunday!'
Validate out of range month
>>> zeller('13-31-2010')
Traceback (most recent call last):
...
ValueError: Month must be between 1 - 12
>>> zeller('.2-31-2010')
Traceback (most recent call last):
...
ValueError: invalid literal for int() with base 10: '.2'
Validate out of range date:
>>> zeller('01-33-2010')
Traceback (most recent call last):
...
ValueError: Date must be between 1 - 31
>>> zeller('01-.4-2010')
Traceback (most recent call last):
...
ValueError: invalid literal for int() with base 10: '.4'
Validate second separator:
>>> zeller('01-31*2010')
Traceback (most recent call last):
...
ValueError: Date separator must be '-' or '/'
Validate first separator:
>>> zeller('01^31-2010')
Traceback (most recent call last):
...
ValueError: Date separator must be '-' or '/'
Validate out of range year:
>>> zeller('01-31-8999')
Traceback (most recent call last):
...
ValueError: Year out of range. There has to be some sort of limit...right?
Test null input:
>>> zeller()
Traceback (most recent call last):
...
TypeError: zeller() missing 1 required positional argument: 'date_input'
Test length of date_input:
>>> zeller('')
Traceback (most recent call last):
...
ValueError: Must be 10 characters long
>>> zeller('01-31-19082939')
Traceback (most recent call last):
...
ValueError: Must be 10 characters long"""
# Days of the week for response
days = {
"0": "Sunday",
"1": "Monday",
"2": "Tuesday",
"3": "Wednesday",
"4": "Thursday",
"5": "Friday",
"6": "Saturday",
}
convert_datetime_days = {0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6, 6: 0}
# Validate
if not 0 < len(date_input) < 11:
raise ValueError("Must be 10 characters long")
# Get month
m: int = int(date_input[0] + date_input[1])
# Validate
if not 0 < m < 13:
raise ValueError("Month must be between 1 - 12")
sep_1: str = date_input[2]
# Validate
if sep_1 not in ["-", "/"]:
raise ValueError("Date separator must be '-' or '/'")
# Get day
d: int = int(date_input[3] + date_input[4])
# Validate
if not 0 < d < 32:
raise ValueError("Date must be between 1 - 31")
# Get second separator
sep_2: str = date_input[5]
# Validate
if sep_2 not in ["-", "/"]:
raise ValueError("Date separator must be '-' or '/'")
# Get year
y: int = int(date_input[6] + date_input[7] + date_input[8] + date_input[9])
# Arbitrary year range
if not 45 < y < 8500:
raise ValueError(
"Year out of range. There has to be some sort of limit...right?"
)
# Get datetime obj for validation
dt_ck = datetime.date(int(y), int(m), int(d))
# Start math
if m <= 2:
y = y - 1
m = m + 12
# maths var
c: int = int(str(y)[:2])
k: int = int(str(y)[2:])
t: int = int(2.6 * m - 5.39)
u: int = int(c / 4)
v: int = int(k / 4)
x: int = int(d + k)
z: int = int(t + u + v + x)
w: int = int(z - (2 * c))
f: int = round(w % 7)
# End math
# Validate math
if f != convert_datetime_days[dt_ck.weekday()]:
raise AssertionError("The date was evaluated incorrectly. Contact developer.")
# Response
response: str = f"Your date {date_input}, is a {days[str(f)]}!"
return response
if __name__ == "__main__":
import doctest
doctest.testmod()
parser = argparse.ArgumentParser(
description=(
"Find out what day of the week nearly any date is or was. Enter "
"date as a string in the mm-dd-yyyy or mm/dd/yyyy format"
)
)
parser.add_argument(
"date_input", type=str, help="Date as a string (mm-dd-yyyy or mm/dd/yyyy)"
)
args = parser.parse_args()
zeller(args.date_input)
| -1 |
TheAlgorithms/Python | 9,076 | Fix typos |
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| omahs | "2023-09-22T13:22:15Z" | "2023-09-23T08:53:09Z" | dc50add8a78ebf34bc7bb050c1a0e61d207b9544 | b203150ac481743a6d8c1ef01091712a54dfbf6c | Fix typos.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| """
Project Euler Problem 100: https://projecteuler.net/problem=100
If a box contains twenty-one coloured discs, composed of fifteen blue discs and
six red discs, and two discs were taken at random, it can be seen that
the probability of taking two blue discs, P(BB) = (15/21) x (14/20) = 1/2.
The next such arrangement, for which there is exactly 50% chance of taking two blue
discs at random, is a box containing eighty-five blue discs and thirty-five red discs.
By finding the first arrangement to contain over 10^12 = 1,000,000,000,000 discs
in total, determine the number of blue discs that the box would contain.
"""
def solution(min_total: int = 10**12) -> int:
"""
Returns the number of blue discs for the first arrangement to contain
over min_total discs in total
>>> solution(2)
3
>>> solution(4)
15
>>> solution(21)
85
"""
prev_numerator = 1
prev_denominator = 0
numerator = 1
denominator = 1
while numerator <= 2 * min_total - 1:
prev_numerator += 2 * numerator
numerator += 2 * prev_numerator
prev_denominator += 2 * denominator
denominator += 2 * prev_denominator
return (denominator + 1) // 2
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 100: https://projecteuler.net/problem=100
If a box contains twenty-one coloured discs, composed of fifteen blue discs and
six red discs, and two discs were taken at random, it can be seen that
the probability of taking two blue discs, P(BB) = (15/21) x (14/20) = 1/2.
The next such arrangement, for which there is exactly 50% chance of taking two blue
discs at random, is a box containing eighty-five blue discs and thirty-five red discs.
By finding the first arrangement to contain over 10^12 = 1,000,000,000,000 discs
in total, determine the number of blue discs that the box would contain.
"""
def solution(min_total: int = 10**12) -> int:
"""
Returns the number of blue discs for the first arrangement to contain
over min_total discs in total
>>> solution(2)
3
>>> solution(4)
15
>>> solution(21)
85
"""
prev_numerator = 1
prev_denominator = 0
numerator = 1
denominator = 1
while numerator <= 2 * min_total - 1:
prev_numerator += 2 * numerator
numerator += 2 * prev_numerator
prev_denominator += 2 * denominator
denominator += 2 * prev_denominator
return (denominator + 1) // 2
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 9,076 | Fix typos |
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| omahs | "2023-09-22T13:22:15Z" | "2023-09-23T08:53:09Z" | dc50add8a78ebf34bc7bb050c1a0e61d207b9544 | b203150ac481743a6d8c1ef01091712a54dfbf6c | Fix typos.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| -1 |
||
TheAlgorithms/Python | 9,076 | Fix typos |
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| omahs | "2023-09-22T13:22:15Z" | "2023-09-23T08:53:09Z" | dc50add8a78ebf34bc7bb050c1a0e61d207b9544 | b203150ac481743a6d8c1ef01091712a54dfbf6c | Fix typos.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| """
Given the root of a binary tree and an integer target,
find the number of paths where the sum of the values
along the path equals target.
Leetcode reference: https://leetcode.com/problems/path-sum-iii/
"""
from __future__ import annotations
class Node:
"""
A Node has value variable and pointers to Nodes to its left and right.
"""
def __init__(self, value: int) -> None:
self.value = value
self.left: Node | None = None
self.right: Node | None = None
class BinaryTreePathSum:
r"""
The below tree looks like this
10
/ \
5 -3
/ \ \
3 2 11
/ \ \
3 -2 1
>>> tree = Node(10)
>>> tree.left = Node(5)
>>> tree.right = Node(-3)
>>> tree.left.left = Node(3)
>>> tree.left.right = Node(2)
>>> tree.right.right = Node(11)
>>> tree.left.left.left = Node(3)
>>> tree.left.left.right = Node(-2)
>>> tree.left.right.right = Node(1)
>>> BinaryTreePathSum().path_sum(tree, 8)
3
>>> BinaryTreePathSum().path_sum(tree, 7)
2
>>> tree.right.right = Node(10)
>>> BinaryTreePathSum().path_sum(tree, 8)
2
"""
target: int
def __init__(self) -> None:
self.paths = 0
def depth_first_search(self, node: Node | None, path_sum: int) -> None:
if node is None:
return
if path_sum == self.target:
self.paths += 1
if node.left:
self.depth_first_search(node.left, path_sum + node.left.value)
if node.right:
self.depth_first_search(node.right, path_sum + node.right.value)
def path_sum(self, node: Node | None, target: int | None = None) -> int:
if node is None:
return 0
if target is not None:
self.target = target
self.depth_first_search(node, node.value)
self.path_sum(node.left)
self.path_sum(node.right)
return self.paths
if __name__ == "__main__":
import doctest
doctest.testmod()
| """
Given the root of a binary tree and an integer target,
find the number of paths where the sum of the values
along the path equals target.
Leetcode reference: https://leetcode.com/problems/path-sum-iii/
"""
from __future__ import annotations
class Node:
"""
A Node has value variable and pointers to Nodes to its left and right.
"""
def __init__(self, value: int) -> None:
self.value = value
self.left: Node | None = None
self.right: Node | None = None
class BinaryTreePathSum:
r"""
The below tree looks like this
10
/ \
5 -3
/ \ \
3 2 11
/ \ \
3 -2 1
>>> tree = Node(10)
>>> tree.left = Node(5)
>>> tree.right = Node(-3)
>>> tree.left.left = Node(3)
>>> tree.left.right = Node(2)
>>> tree.right.right = Node(11)
>>> tree.left.left.left = Node(3)
>>> tree.left.left.right = Node(-2)
>>> tree.left.right.right = Node(1)
>>> BinaryTreePathSum().path_sum(tree, 8)
3
>>> BinaryTreePathSum().path_sum(tree, 7)
2
>>> tree.right.right = Node(10)
>>> BinaryTreePathSum().path_sum(tree, 8)
2
"""
target: int
def __init__(self) -> None:
self.paths = 0
def depth_first_search(self, node: Node | None, path_sum: int) -> None:
if node is None:
return
if path_sum == self.target:
self.paths += 1
if node.left:
self.depth_first_search(node.left, path_sum + node.left.value)
if node.right:
self.depth_first_search(node.right, path_sum + node.right.value)
def path_sum(self, node: Node | None, target: int | None = None) -> int:
if node is None:
return 0
if target is not None:
self.target = target
self.depth_first_search(node, node.value)
self.path_sum(node.left)
self.path_sum(node.right)
return self.paths
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 9,076 | Fix typos |
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| omahs | "2023-09-22T13:22:15Z" | "2023-09-23T08:53:09Z" | dc50add8a78ebf34bc7bb050c1a0e61d207b9544 | b203150ac481743a6d8c1ef01091712a54dfbf6c | Fix typos.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| """
Shortest job remaining first
Please note arrival time and burst
Please use spaces to separate times entered.
"""
from __future__ import annotations
import pandas as pd
def calculate_waitingtime(
arrival_time: list[int], burst_time: list[int], no_of_processes: int
) -> list[int]:
"""
Calculate the waiting time of each processes
Return: List of waiting times.
>>> calculate_waitingtime([1,2,3,4],[3,3,5,1],4)
[0, 3, 5, 0]
>>> calculate_waitingtime([1,2,3],[2,5,1],3)
[0, 2, 0]
>>> calculate_waitingtime([2,3],[5,1],2)
[1, 0]
"""
remaining_time = [0] * no_of_processes
waiting_time = [0] * no_of_processes
# Copy the burst time into remaining_time[]
for i in range(no_of_processes):
remaining_time[i] = burst_time[i]
complete = 0
increment_time = 0
minm = 999999999
short = 0
check = False
# Process until all processes are completed
while complete != no_of_processes:
for j in range(no_of_processes):
if arrival_time[j] <= increment_time and remaining_time[j] > 0:
if remaining_time[j] < minm:
minm = remaining_time[j]
short = j
check = True
if not check:
increment_time += 1
continue
remaining_time[short] -= 1
minm = remaining_time[short]
if minm == 0:
minm = 999999999
if remaining_time[short] == 0:
complete += 1
check = False
# Find finish time of current process
finish_time = increment_time + 1
# Calculate waiting time
finar = finish_time - arrival_time[short]
waiting_time[short] = finar - burst_time[short]
if waiting_time[short] < 0:
waiting_time[short] = 0
# Increment time
increment_time += 1
return waiting_time
def calculate_turnaroundtime(
burst_time: list[int], no_of_processes: int, waiting_time: list[int]
) -> list[int]:
"""
Calculate the turn around time of each Processes
Return: list of turn around times.
>>> calculate_turnaroundtime([3,3,5,1], 4, [0,3,5,0])
[3, 6, 10, 1]
>>> calculate_turnaroundtime([3,3], 2, [0,3])
[3, 6]
>>> calculate_turnaroundtime([8,10,1], 3, [1,0,3])
[9, 10, 4]
"""
turn_around_time = [0] * no_of_processes
for i in range(no_of_processes):
turn_around_time[i] = burst_time[i] + waiting_time[i]
return turn_around_time
def calculate_average_times(
waiting_time: list[int], turn_around_time: list[int], no_of_processes: int
) -> None:
"""
This function calculates the average of the waiting & turnaround times
Prints: Average Waiting time & Average Turn Around Time
>>> calculate_average_times([0,3,5,0],[3,6,10,1],4)
Average waiting time = 2.00000
Average turn around time = 5.0
>>> calculate_average_times([2,3],[3,6],2)
Average waiting time = 2.50000
Average turn around time = 4.5
>>> calculate_average_times([10,4,3],[2,7,6],3)
Average waiting time = 5.66667
Average turn around time = 5.0
"""
total_waiting_time = 0
total_turn_around_time = 0
for i in range(no_of_processes):
total_waiting_time = total_waiting_time + waiting_time[i]
total_turn_around_time = total_turn_around_time + turn_around_time[i]
print(f"Average waiting time = {total_waiting_time / no_of_processes:.5f}")
print("Average turn around time =", total_turn_around_time / no_of_processes)
if __name__ == "__main__":
print("Enter how many process you want to analyze")
no_of_processes = int(input())
burst_time = [0] * no_of_processes
arrival_time = [0] * no_of_processes
processes = list(range(1, no_of_processes + 1))
for i in range(no_of_processes):
print("Enter the arrival time and burst time for process:--" + str(i + 1))
arrival_time[i], burst_time[i] = map(int, input().split())
waiting_time = calculate_waitingtime(arrival_time, burst_time, no_of_processes)
bt = burst_time
n = no_of_processes
wt = waiting_time
turn_around_time = calculate_turnaroundtime(bt, n, wt)
calculate_average_times(waiting_time, turn_around_time, no_of_processes)
fcfs = pd.DataFrame(
list(zip(processes, burst_time, arrival_time, waiting_time, turn_around_time)),
columns=[
"Process",
"BurstTime",
"ArrivalTime",
"WaitingTime",
"TurnAroundTime",
],
)
# Printing the dataFrame
pd.set_option("display.max_rows", fcfs.shape[0] + 1)
print(fcfs)
| """
Shortest job remaining first
Please note arrival time and burst
Please use spaces to separate times entered.
"""
from __future__ import annotations
import pandas as pd
def calculate_waitingtime(
arrival_time: list[int], burst_time: list[int], no_of_processes: int
) -> list[int]:
"""
Calculate the waiting time of each processes
Return: List of waiting times.
>>> calculate_waitingtime([1,2,3,4],[3,3,5,1],4)
[0, 3, 5, 0]
>>> calculate_waitingtime([1,2,3],[2,5,1],3)
[0, 2, 0]
>>> calculate_waitingtime([2,3],[5,1],2)
[1, 0]
"""
remaining_time = [0] * no_of_processes
waiting_time = [0] * no_of_processes
# Copy the burst time into remaining_time[]
for i in range(no_of_processes):
remaining_time[i] = burst_time[i]
complete = 0
increment_time = 0
minm = 999999999
short = 0
check = False
# Process until all processes are completed
while complete != no_of_processes:
for j in range(no_of_processes):
if arrival_time[j] <= increment_time and remaining_time[j] > 0:
if remaining_time[j] < minm:
minm = remaining_time[j]
short = j
check = True
if not check:
increment_time += 1
continue
remaining_time[short] -= 1
minm = remaining_time[short]
if minm == 0:
minm = 999999999
if remaining_time[short] == 0:
complete += 1
check = False
# Find finish time of current process
finish_time = increment_time + 1
# Calculate waiting time
finar = finish_time - arrival_time[short]
waiting_time[short] = finar - burst_time[short]
if waiting_time[short] < 0:
waiting_time[short] = 0
# Increment time
increment_time += 1
return waiting_time
def calculate_turnaroundtime(
burst_time: list[int], no_of_processes: int, waiting_time: list[int]
) -> list[int]:
"""
Calculate the turn around time of each Processes
Return: list of turn around times.
>>> calculate_turnaroundtime([3,3,5,1], 4, [0,3,5,0])
[3, 6, 10, 1]
>>> calculate_turnaroundtime([3,3], 2, [0,3])
[3, 6]
>>> calculate_turnaroundtime([8,10,1], 3, [1,0,3])
[9, 10, 4]
"""
turn_around_time = [0] * no_of_processes
for i in range(no_of_processes):
turn_around_time[i] = burst_time[i] + waiting_time[i]
return turn_around_time
def calculate_average_times(
waiting_time: list[int], turn_around_time: list[int], no_of_processes: int
) -> None:
"""
This function calculates the average of the waiting & turnaround times
Prints: Average Waiting time & Average Turn Around Time
>>> calculate_average_times([0,3,5,0],[3,6,10,1],4)
Average waiting time = 2.00000
Average turn around time = 5.0
>>> calculate_average_times([2,3],[3,6],2)
Average waiting time = 2.50000
Average turn around time = 4.5
>>> calculate_average_times([10,4,3],[2,7,6],3)
Average waiting time = 5.66667
Average turn around time = 5.0
"""
total_waiting_time = 0
total_turn_around_time = 0
for i in range(no_of_processes):
total_waiting_time = total_waiting_time + waiting_time[i]
total_turn_around_time = total_turn_around_time + turn_around_time[i]
print(f"Average waiting time = {total_waiting_time / no_of_processes:.5f}")
print("Average turn around time =", total_turn_around_time / no_of_processes)
if __name__ == "__main__":
print("Enter how many process you want to analyze")
no_of_processes = int(input())
burst_time = [0] * no_of_processes
arrival_time = [0] * no_of_processes
processes = list(range(1, no_of_processes + 1))
for i in range(no_of_processes):
print("Enter the arrival time and burst time for process:--" + str(i + 1))
arrival_time[i], burst_time[i] = map(int, input().split())
waiting_time = calculate_waitingtime(arrival_time, burst_time, no_of_processes)
bt = burst_time
n = no_of_processes
wt = waiting_time
turn_around_time = calculate_turnaroundtime(bt, n, wt)
calculate_average_times(waiting_time, turn_around_time, no_of_processes)
fcfs = pd.DataFrame(
list(zip(processes, burst_time, arrival_time, waiting_time, turn_around_time)),
columns=[
"Process",
"BurstTime",
"ArrivalTime",
"WaitingTime",
"TurnAroundTime",
],
)
# Printing the dataFrame
pd.set_option("display.max_rows", fcfs.shape[0] + 1)
print(fcfs)
| -1 |
TheAlgorithms/Python | 9,076 | Fix typos |
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| omahs | "2023-09-22T13:22:15Z" | "2023-09-23T08:53:09Z" | dc50add8a78ebf34bc7bb050c1a0e61d207b9544 | b203150ac481743a6d8c1ef01091712a54dfbf6c | Fix typos.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| def reverse_letters(input_str: str) -> str:
"""
Reverses letters in a given string without adjusting the position of the words
>>> reverse_letters('The cat in the hat')
'ehT tac ni eht tah'
>>> reverse_letters('The quick brown fox jumped over the lazy dog.')
'ehT kciuq nworb xof depmuj revo eht yzal .god'
>>> reverse_letters('Is this true?')
'sI siht ?eurt'
>>> reverse_letters("I love Python")
'I evol nohtyP'
"""
return " ".join([word[::-1] for word in input_str.split()])
if __name__ == "__main__":
import doctest
doctest.testmod()
| def reverse_letters(input_str: str) -> str:
"""
Reverses letters in a given string without adjusting the position of the words
>>> reverse_letters('The cat in the hat')
'ehT tac ni eht tah'
>>> reverse_letters('The quick brown fox jumped over the lazy dog.')
'ehT kciuq nworb xof depmuj revo eht yzal .god'
>>> reverse_letters('Is this true?')
'sI siht ?eurt'
>>> reverse_letters("I love Python")
'I evol nohtyP'
"""
return " ".join([word[::-1] for word in input_str.split()])
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 9,076 | Fix typos |
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| omahs | "2023-09-22T13:22:15Z" | "2023-09-23T08:53:09Z" | dc50add8a78ebf34bc7bb050c1a0e61d207b9544 | b203150ac481743a6d8c1ef01091712a54dfbf6c | Fix typos.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| def perfect_cube(n: int) -> bool:
"""
Check if a number is a perfect cube or not.
>>> perfect_cube(27)
True
>>> perfect_cube(4)
False
"""
val = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(27))
print(perfect_cube(4))
| def perfect_cube(n: int) -> bool:
"""
Check if a number is a perfect cube or not.
>>> perfect_cube(27)
True
>>> perfect_cube(4)
False
"""
val = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(27))
print(perfect_cube(4))
| -1 |
TheAlgorithms/Python | 9,076 | Fix typos |
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| omahs | "2023-09-22T13:22:15Z" | "2023-09-23T08:53:09Z" | dc50add8a78ebf34bc7bb050c1a0e61d207b9544 | b203150ac481743a6d8c1ef01091712a54dfbf6c | Fix typos.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| """
Project Euler Problem 493: https://projecteuler.net/problem=493
70 coloured balls are placed in an urn, 10 for each of the seven rainbow colours.
What is the expected number of distinct colours in 20 randomly picked balls?
Give your answer with nine digits after the decimal point (a.bcdefghij).
-----
This combinatorial problem can be solved by decomposing the problem into the
following steps:
1. Calculate the total number of possible picking cominations
[combinations := binom_coeff(70, 20)]
2. Calculate the number of combinations with one colour missing
[missing := binom_coeff(60, 20)]
3. Calculate the probability of one colour missing
[missing_prob := missing / combinations]
4. Calculate the probability of no colour missing
[no_missing_prob := 1 - missing_prob]
5. Calculate the expected number of distinct colours
[expected = 7 * no_missing_prob]
References:
- https://en.wikipedia.org/wiki/Binomial_coefficient
"""
import math
BALLS_PER_COLOUR = 10
NUM_COLOURS = 7
NUM_BALLS = BALLS_PER_COLOUR * NUM_COLOURS
def solution(num_picks: int = 20) -> str:
"""
Calculates the expected number of distinct colours
>>> solution(10)
'5.669644129'
>>> solution(30)
'6.985042712'
"""
total = math.comb(NUM_BALLS, num_picks)
missing_colour = math.comb(NUM_BALLS - BALLS_PER_COLOUR, num_picks)
result = NUM_COLOURS * (1 - missing_colour / total)
return f"{result:.9f}"
if __name__ == "__main__":
print(solution(20))
| """
Project Euler Problem 493: https://projecteuler.net/problem=493
70 coloured balls are placed in an urn, 10 for each of the seven rainbow colours.
What is the expected number of distinct colours in 20 randomly picked balls?
Give your answer with nine digits after the decimal point (a.bcdefghij).
-----
This combinatorial problem can be solved by decomposing the problem into the
following steps:
1. Calculate the total number of possible picking cominations
[combinations := binom_coeff(70, 20)]
2. Calculate the number of combinations with one colour missing
[missing := binom_coeff(60, 20)]
3. Calculate the probability of one colour missing
[missing_prob := missing / combinations]
4. Calculate the probability of no colour missing
[no_missing_prob := 1 - missing_prob]
5. Calculate the expected number of distinct colours
[expected = 7 * no_missing_prob]
References:
- https://en.wikipedia.org/wiki/Binomial_coefficient
"""
import math
BALLS_PER_COLOUR = 10
NUM_COLOURS = 7
NUM_BALLS = BALLS_PER_COLOUR * NUM_COLOURS
def solution(num_picks: int = 20) -> str:
"""
Calculates the expected number of distinct colours
>>> solution(10)
'5.669644129'
>>> solution(30)
'6.985042712'
"""
total = math.comb(NUM_BALLS, num_picks)
missing_colour = math.comb(NUM_BALLS - BALLS_PER_COLOUR, num_picks)
result = NUM_COLOURS * (1 - missing_colour / total)
return f"{result:.9f}"
if __name__ == "__main__":
print(solution(20))
| -1 |
TheAlgorithms/Python | 9,076 | Fix typos |
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| omahs | "2023-09-22T13:22:15Z" | "2023-09-23T08:53:09Z" | dc50add8a78ebf34bc7bb050c1a0e61d207b9544 | b203150ac481743a6d8c1ef01091712a54dfbf6c | Fix typos.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| total_users,total_events,days
18231,0.0,1
22621,1.0,2
15675,0.0,3
23583,1.0,4
68351,5.0,5
34338,3.0,6
19238,0.0,0
24192,0.0,1
70349,0.0,2
103510,0.0,3
128355,1.0,4
148484,6.0,5
153489,3.0,6
162667,1.0,0
311430,3.0,1
435663,7.0,2
273526,0.0,3
628588,2.0,4
454989,13.0,5
539040,3.0,6
52974,1.0,0
103451,2.0,1
810020,5.0,2
580982,3.0,3
216515,0.0,4
134694,10.0,5
93563,1.0,6
55432,1.0,0
169634,1.0,1
254908,4.0,2
315285,3.0,3
191764,0.0,4
514284,7.0,5
181214,4.0,6
78459,2.0,0
161620,3.0,1
245610,4.0,2
326722,5.0,3
214578,0.0,4
312365,5.0,5
232454,4.0,6
178368,1.0,0
97152,1.0,1
222813,4.0,2
285852,4.0,3
192149,1.0,4
142241,1.0,5
173011,2.0,6
56488,3.0,0
89572,2.0,1
356082,2.0,2
172799,0.0,3
142300,1.0,4
78432,2.0,5
539023,9.0,6
62389,1.0,0
70247,1.0,1
89229,0.0,2
94583,1.0,3
102455,0.0,4
129270,0.0,5
311409,1.0,6
1837026,0.0,0
361824,0.0,1
111379,2.0,2
76337,2.0,3
96747,0.0,4
92058,0.0,5
81929,2.0,6
143423,0.0,0
82939,0.0,1
74403,1.0,2
68234,0.0,3
94556,1.0,4
80311,0.0,5
75283,3.0,6
77724,0.0,0
49229,2.0,1
65708,2.0,2
273864,1.0,3
1711281,0.0,4
1900253,5.0,5
343071,1.0,6
1551326,0.0,0
56636,1.0,1
272782,2.0,2
1785678,0.0,3
241866,0.0,4
461904,0.0,5
2191901,2.0,6
102925,0.0,0
242778,1.0,1
298608,0.0,2
322458,10.0,3
216027,9.0,4
916052,12.0,5
193278,12.0,6
263207,8.0,0
672948,10.0,1
281909,1.0,2
384562,1.0,3
1027375,2.0,4
828905,9.0,5
624188,22.0,6
392218,8.0,0
292581,10.0,1
299869,12.0,2
769455,20.0,3
316443,8.0,4
1212864,24.0,5
1397338,28.0,6
223249,8.0,0
191264,14.0,1
| total_users,total_events,days
18231,0.0,1
22621,1.0,2
15675,0.0,3
23583,1.0,4
68351,5.0,5
34338,3.0,6
19238,0.0,0
24192,0.0,1
70349,0.0,2
103510,0.0,3
128355,1.0,4
148484,6.0,5
153489,3.0,6
162667,1.0,0
311430,3.0,1
435663,7.0,2
273526,0.0,3
628588,2.0,4
454989,13.0,5
539040,3.0,6
52974,1.0,0
103451,2.0,1
810020,5.0,2
580982,3.0,3
216515,0.0,4
134694,10.0,5
93563,1.0,6
55432,1.0,0
169634,1.0,1
254908,4.0,2
315285,3.0,3
191764,0.0,4
514284,7.0,5
181214,4.0,6
78459,2.0,0
161620,3.0,1
245610,4.0,2
326722,5.0,3
214578,0.0,4
312365,5.0,5
232454,4.0,6
178368,1.0,0
97152,1.0,1
222813,4.0,2
285852,4.0,3
192149,1.0,4
142241,1.0,5
173011,2.0,6
56488,3.0,0
89572,2.0,1
356082,2.0,2
172799,0.0,3
142300,1.0,4
78432,2.0,5
539023,9.0,6
62389,1.0,0
70247,1.0,1
89229,0.0,2
94583,1.0,3
102455,0.0,4
129270,0.0,5
311409,1.0,6
1837026,0.0,0
361824,0.0,1
111379,2.0,2
76337,2.0,3
96747,0.0,4
92058,0.0,5
81929,2.0,6
143423,0.0,0
82939,0.0,1
74403,1.0,2
68234,0.0,3
94556,1.0,4
80311,0.0,5
75283,3.0,6
77724,0.0,0
49229,2.0,1
65708,2.0,2
273864,1.0,3
1711281,0.0,4
1900253,5.0,5
343071,1.0,6
1551326,0.0,0
56636,1.0,1
272782,2.0,2
1785678,0.0,3
241866,0.0,4
461904,0.0,5
2191901,2.0,6
102925,0.0,0
242778,1.0,1
298608,0.0,2
322458,10.0,3
216027,9.0,4
916052,12.0,5
193278,12.0,6
263207,8.0,0
672948,10.0,1
281909,1.0,2
384562,1.0,3
1027375,2.0,4
828905,9.0,5
624188,22.0,6
392218,8.0,0
292581,10.0,1
299869,12.0,2
769455,20.0,3
316443,8.0,4
1212864,24.0,5
1397338,28.0,6
223249,8.0,0
191264,14.0,1
| -1 |
TheAlgorithms/Python | 9,076 | Fix typos |
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| omahs | "2023-09-22T13:22:15Z" | "2023-09-23T08:53:09Z" | dc50add8a78ebf34bc7bb050c1a0e61d207b9544 | b203150ac481743a6d8c1ef01091712a54dfbf6c | Fix typos.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| from __future__ import annotations
class Node:
"""
A Node has data variable and pointers to Nodes to its left and right.
"""
def __init__(self, data: int) -> None:
self.data = data
self.left: Node | None = None
self.right: Node | None = None
def display(tree: Node | None) -> None: # In Order traversal of the tree
"""
>>> root = Node(1)
>>> root.left = Node(0)
>>> root.right = Node(2)
>>> display(root)
0
1
2
>>> display(root.right)
2
"""
if tree:
display(tree.left)
print(tree.data)
display(tree.right)
def depth_of_tree(tree: Node | None) -> int:
"""
Recursive function that returns the depth of a binary tree.
>>> root = Node(0)
>>> depth_of_tree(root)
1
>>> root.left = Node(0)
>>> depth_of_tree(root)
2
>>> root.right = Node(0)
>>> depth_of_tree(root)
2
>>> root.left.right = Node(0)
>>> depth_of_tree(root)
3
>>> depth_of_tree(root.left)
2
"""
return 1 + max(depth_of_tree(tree.left), depth_of_tree(tree.right)) if tree else 0
def is_full_binary_tree(tree: Node) -> bool:
"""
Returns True if this is a full binary tree
>>> root = Node(0)
>>> is_full_binary_tree(root)
True
>>> root.left = Node(0)
>>> is_full_binary_tree(root)
False
>>> root.right = Node(0)
>>> is_full_binary_tree(root)
True
>>> root.left.left = Node(0)
>>> is_full_binary_tree(root)
False
>>> root.right.right = Node(0)
>>> is_full_binary_tree(root)
False
"""
if not tree:
return True
if tree.left and tree.right:
return is_full_binary_tree(tree.left) and is_full_binary_tree(tree.right)
else:
return not tree.left and not tree.right
def main() -> None: # Main function for testing.
tree = Node(1)
tree.left = Node(2)
tree.right = Node(3)
tree.left.left = Node(4)
tree.left.right = Node(5)
tree.left.right.left = Node(6)
tree.right.left = Node(7)
tree.right.left.left = Node(8)
tree.right.left.left.right = Node(9)
print(is_full_binary_tree(tree))
print(depth_of_tree(tree))
print("Tree is: ")
display(tree)
if __name__ == "__main__":
main()
| from __future__ import annotations
class Node:
"""
A Node has data variable and pointers to Nodes to its left and right.
"""
def __init__(self, data: int) -> None:
self.data = data
self.left: Node | None = None
self.right: Node | None = None
def display(tree: Node | None) -> None: # In Order traversal of the tree
"""
>>> root = Node(1)
>>> root.left = Node(0)
>>> root.right = Node(2)
>>> display(root)
0
1
2
>>> display(root.right)
2
"""
if tree:
display(tree.left)
print(tree.data)
display(tree.right)
def depth_of_tree(tree: Node | None) -> int:
"""
Recursive function that returns the depth of a binary tree.
>>> root = Node(0)
>>> depth_of_tree(root)
1
>>> root.left = Node(0)
>>> depth_of_tree(root)
2
>>> root.right = Node(0)
>>> depth_of_tree(root)
2
>>> root.left.right = Node(0)
>>> depth_of_tree(root)
3
>>> depth_of_tree(root.left)
2
"""
return 1 + max(depth_of_tree(tree.left), depth_of_tree(tree.right)) if tree else 0
def is_full_binary_tree(tree: Node) -> bool:
"""
Returns True if this is a full binary tree
>>> root = Node(0)
>>> is_full_binary_tree(root)
True
>>> root.left = Node(0)
>>> is_full_binary_tree(root)
False
>>> root.right = Node(0)
>>> is_full_binary_tree(root)
True
>>> root.left.left = Node(0)
>>> is_full_binary_tree(root)
False
>>> root.right.right = Node(0)
>>> is_full_binary_tree(root)
False
"""
if not tree:
return True
if tree.left and tree.right:
return is_full_binary_tree(tree.left) and is_full_binary_tree(tree.right)
else:
return not tree.left and not tree.right
def main() -> None: # Main function for testing.
tree = Node(1)
tree.left = Node(2)
tree.right = Node(3)
tree.left.left = Node(4)
tree.left.right = Node(5)
tree.left.right.left = Node(6)
tree.right.left = Node(7)
tree.right.left.left = Node(8)
tree.right.left.left.right = Node(9)
print(is_full_binary_tree(tree))
print(depth_of_tree(tree))
print("Tree is: ")
display(tree)
if __name__ == "__main__":
main()
| -1 |
TheAlgorithms/Python | 9,076 | Fix typos |
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| omahs | "2023-09-22T13:22:15Z" | "2023-09-23T08:53:09Z" | dc50add8a78ebf34bc7bb050c1a0e61d207b9544 | b203150ac481743a6d8c1ef01091712a54dfbf6c | Fix typos.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| def bin_exp_mod(a, n, b):
"""
>>> bin_exp_mod(3, 4, 5)
1
>>> bin_exp_mod(7, 13, 10)
7
"""
# mod b
assert b != 0, "This cannot accept modulo that is == 0"
if n == 0:
return 1
if n % 2 == 1:
return (bin_exp_mod(a, n - 1, b) * a) % b
r = bin_exp_mod(a, n / 2, b)
return (r * r) % b
if __name__ == "__main__":
try:
BASE = int(input("Enter Base : ").strip())
POWER = int(input("Enter Power : ").strip())
MODULO = int(input("Enter Modulo : ").strip())
except ValueError:
print("Invalid literal for integer")
print(bin_exp_mod(BASE, POWER, MODULO))
| def bin_exp_mod(a, n, b):
"""
>>> bin_exp_mod(3, 4, 5)
1
>>> bin_exp_mod(7, 13, 10)
7
"""
# mod b
assert b != 0, "This cannot accept modulo that is == 0"
if n == 0:
return 1
if n % 2 == 1:
return (bin_exp_mod(a, n - 1, b) * a) % b
r = bin_exp_mod(a, n / 2, b)
return (r * r) % b
if __name__ == "__main__":
try:
BASE = int(input("Enter Base : ").strip())
POWER = int(input("Enter Power : ").strip())
MODULO = int(input("Enter Modulo : ").strip())
except ValueError:
print("Invalid literal for integer")
print(bin_exp_mod(BASE, POWER, MODULO))
| -1 |
TheAlgorithms/Python | 9,076 | Fix typos |
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| omahs | "2023-09-22T13:22:15Z" | "2023-09-23T08:53:09Z" | dc50add8a78ebf34bc7bb050c1a0e61d207b9544 | b203150ac481743a6d8c1ef01091712a54dfbf6c | Fix typos.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| """
A perfect number is a number for which the sum of its proper divisors is exactly
equal to the number. For example, the sum of the proper divisors of 28 would be
1 + 2 + 4 + 7 + 14 = 28, which means that 28 is a perfect number.
A number n is called deficient if the sum of its proper divisors is less than n
and it is called abundant if this sum exceeds n.
As 12 is the smallest abundant number, 1 + 2 + 3 + 4 + 6 = 16, the smallest
number that can be written as the sum of two abundant numbers is 24. By
mathematical analysis, it can be shown that all integers greater than 28123
can be written as the sum of two abundant numbers. However, this upper limit
cannot be reduced any further by analysis even though it is known that the
greatest number that cannot be expressed as the sum of two abundant numbers
is less than this limit.
Find the sum of all the positive integers which cannot be written as the sum
of two abundant numbers.
"""
def solution(limit=28123):
"""
Finds the sum of all the positive integers which cannot be written as
the sum of two abundant numbers
as described by the statement above.
>>> solution()
4179871
"""
sum_divs = [1] * (limit + 1)
for i in range(2, int(limit**0.5) + 1):
sum_divs[i * i] += i
for k in range(i + 1, limit // i + 1):
sum_divs[k * i] += k + i
abundants = set()
res = 0
for n in range(1, limit + 1):
if sum_divs[n] > n:
abundants.add(n)
if not any((n - a in abundants) for a in abundants):
res += n
return res
if __name__ == "__main__":
print(solution())
| """
A perfect number is a number for which the sum of its proper divisors is exactly
equal to the number. For example, the sum of the proper divisors of 28 would be
1 + 2 + 4 + 7 + 14 = 28, which means that 28 is a perfect number.
A number n is called deficient if the sum of its proper divisors is less than n
and it is called abundant if this sum exceeds n.
As 12 is the smallest abundant number, 1 + 2 + 3 + 4 + 6 = 16, the smallest
number that can be written as the sum of two abundant numbers is 24. By
mathematical analysis, it can be shown that all integers greater than 28123
can be written as the sum of two abundant numbers. However, this upper limit
cannot be reduced any further by analysis even though it is known that the
greatest number that cannot be expressed as the sum of two abundant numbers
is less than this limit.
Find the sum of all the positive integers which cannot be written as the sum
of two abundant numbers.
"""
def solution(limit=28123):
"""
Finds the sum of all the positive integers which cannot be written as
the sum of two abundant numbers
as described by the statement above.
>>> solution()
4179871
"""
sum_divs = [1] * (limit + 1)
for i in range(2, int(limit**0.5) + 1):
sum_divs[i * i] += i
for k in range(i + 1, limit // i + 1):
sum_divs[k * i] += k + i
abundants = set()
res = 0
for n in range(1, limit + 1):
if sum_divs[n] > n:
abundants.add(n)
if not any((n - a in abundants) for a in abundants):
res += n
return res
if __name__ == "__main__":
print(solution())
| -1 |
TheAlgorithms/Python | 9,076 | Fix typos |
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| omahs | "2023-09-22T13:22:15Z" | "2023-09-23T08:53:09Z" | dc50add8a78ebf34bc7bb050c1a0e61d207b9544 | b203150ac481743a6d8c1ef01091712a54dfbf6c | Fix typos.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| from math import factorial
"""
https://en.wikipedia.org/wiki/Automatic_differentiation#Automatic_differentiation_using_dual_numbers
https://blog.jliszka.org/2013/10/24/exact-numeric-nth-derivatives.html
Note this only works for basic functions, f(x) where the power of x is positive.
"""
class Dual:
def __init__(self, real, rank):
self.real = real
if isinstance(rank, int):
self.duals = [1] * rank
else:
self.duals = rank
def __repr__(self):
return (
f"{self.real}+"
f"{'+'.join(str(dual)+'E'+str(n+1)for n,dual in enumerate(self.duals))}"
)
def reduce(self):
cur = self.duals.copy()
while cur[-1] == 0:
cur.pop(-1)
return Dual(self.real, cur)
def __add__(self, other):
if not isinstance(other, Dual):
return Dual(self.real + other, self.duals)
s_dual = self.duals.copy()
o_dual = other.duals.copy()
if len(s_dual) > len(o_dual):
o_dual.extend([1] * (len(s_dual) - len(o_dual)))
elif len(s_dual) < len(o_dual):
s_dual.extend([1] * (len(o_dual) - len(s_dual)))
new_duals = []
for i in range(len(s_dual)):
new_duals.append(s_dual[i] + o_dual[i])
return Dual(self.real + other.real, new_duals)
__radd__ = __add__
def __sub__(self, other):
return self + other * -1
def __mul__(self, other):
if not isinstance(other, Dual):
new_duals = []
for i in self.duals:
new_duals.append(i * other)
return Dual(self.real * other, new_duals)
new_duals = [0] * (len(self.duals) + len(other.duals) + 1)
for i, item in enumerate(self.duals):
for j, jtem in enumerate(other.duals):
new_duals[i + j + 1] += item * jtem
for k in range(len(self.duals)):
new_duals[k] += self.duals[k] * other.real
for index in range(len(other.duals)):
new_duals[index] += other.duals[index] * self.real
return Dual(self.real * other.real, new_duals)
__rmul__ = __mul__
def __truediv__(self, other):
if not isinstance(other, Dual):
new_duals = []
for i in self.duals:
new_duals.append(i / other)
return Dual(self.real / other, new_duals)
raise ValueError
def __floordiv__(self, other):
if not isinstance(other, Dual):
new_duals = []
for i in self.duals:
new_duals.append(i // other)
return Dual(self.real // other, new_duals)
raise ValueError
def __pow__(self, n):
if n < 0 or isinstance(n, float):
raise ValueError("power must be a positive integer")
if n == 0:
return 1
if n == 1:
return self
x = self
for _ in range(n - 1):
x *= self
return x
def differentiate(func, position, order):
"""
>>> differentiate(lambda x: x**2, 2, 2)
2
>>> differentiate(lambda x: x**2 * x**4, 9, 2)
196830
>>> differentiate(lambda y: 0.5 * (y + 3) ** 6, 3.5, 4)
7605.0
>>> differentiate(lambda y: y ** 2, 4, 3)
0
>>> differentiate(8, 8, 8)
Traceback (most recent call last):
...
ValueError: differentiate() requires a function as input for func
>>> differentiate(lambda x: x **2, "", 1)
Traceback (most recent call last):
...
ValueError: differentiate() requires a float as input for position
>>> differentiate(lambda x: x**2, 3, "")
Traceback (most recent call last):
...
ValueError: differentiate() requires an int as input for order
"""
if not callable(func):
raise ValueError("differentiate() requires a function as input for func")
if not isinstance(position, (float, int)):
raise ValueError("differentiate() requires a float as input for position")
if not isinstance(order, int):
raise ValueError("differentiate() requires an int as input for order")
d = Dual(position, 1)
result = func(d)
if order == 0:
return result.real
return result.duals[order - 1] * factorial(order)
if __name__ == "__main__":
import doctest
doctest.testmod()
def f(y):
return y**2 * y**4
print(differentiate(f, 9, 2))
| from math import factorial
"""
https://en.wikipedia.org/wiki/Automatic_differentiation#Automatic_differentiation_using_dual_numbers
https://blog.jliszka.org/2013/10/24/exact-numeric-nth-derivatives.html
Note this only works for basic functions, f(x) where the power of x is positive.
"""
class Dual:
def __init__(self, real, rank):
self.real = real
if isinstance(rank, int):
self.duals = [1] * rank
else:
self.duals = rank
def __repr__(self):
return (
f"{self.real}+"
f"{'+'.join(str(dual)+'E'+str(n+1)for n,dual in enumerate(self.duals))}"
)
def reduce(self):
cur = self.duals.copy()
while cur[-1] == 0:
cur.pop(-1)
return Dual(self.real, cur)
def __add__(self, other):
if not isinstance(other, Dual):
return Dual(self.real + other, self.duals)
s_dual = self.duals.copy()
o_dual = other.duals.copy()
if len(s_dual) > len(o_dual):
o_dual.extend([1] * (len(s_dual) - len(o_dual)))
elif len(s_dual) < len(o_dual):
s_dual.extend([1] * (len(o_dual) - len(s_dual)))
new_duals = []
for i in range(len(s_dual)):
new_duals.append(s_dual[i] + o_dual[i])
return Dual(self.real + other.real, new_duals)
__radd__ = __add__
def __sub__(self, other):
return self + other * -1
def __mul__(self, other):
if not isinstance(other, Dual):
new_duals = []
for i in self.duals:
new_duals.append(i * other)
return Dual(self.real * other, new_duals)
new_duals = [0] * (len(self.duals) + len(other.duals) + 1)
for i, item in enumerate(self.duals):
for j, jtem in enumerate(other.duals):
new_duals[i + j + 1] += item * jtem
for k in range(len(self.duals)):
new_duals[k] += self.duals[k] * other.real
for index in range(len(other.duals)):
new_duals[index] += other.duals[index] * self.real
return Dual(self.real * other.real, new_duals)
__rmul__ = __mul__
def __truediv__(self, other):
if not isinstance(other, Dual):
new_duals = []
for i in self.duals:
new_duals.append(i / other)
return Dual(self.real / other, new_duals)
raise ValueError
def __floordiv__(self, other):
if not isinstance(other, Dual):
new_duals = []
for i in self.duals:
new_duals.append(i // other)
return Dual(self.real // other, new_duals)
raise ValueError
def __pow__(self, n):
if n < 0 or isinstance(n, float):
raise ValueError("power must be a positive integer")
if n == 0:
return 1
if n == 1:
return self
x = self
for _ in range(n - 1):
x *= self
return x
def differentiate(func, position, order):
"""
>>> differentiate(lambda x: x**2, 2, 2)
2
>>> differentiate(lambda x: x**2 * x**4, 9, 2)
196830
>>> differentiate(lambda y: 0.5 * (y + 3) ** 6, 3.5, 4)
7605.0
>>> differentiate(lambda y: y ** 2, 4, 3)
0
>>> differentiate(8, 8, 8)
Traceback (most recent call last):
...
ValueError: differentiate() requires a function as input for func
>>> differentiate(lambda x: x **2, "", 1)
Traceback (most recent call last):
...
ValueError: differentiate() requires a float as input for position
>>> differentiate(lambda x: x**2, 3, "")
Traceback (most recent call last):
...
ValueError: differentiate() requires an int as input for order
"""
if not callable(func):
raise ValueError("differentiate() requires a function as input for func")
if not isinstance(position, (float, int)):
raise ValueError("differentiate() requires a float as input for position")
if not isinstance(order, int):
raise ValueError("differentiate() requires an int as input for order")
d = Dual(position, 1)
result = func(d)
if order == 0:
return result.real
return result.duals[order - 1] * factorial(order)
if __name__ == "__main__":
import doctest
doctest.testmod()
def f(y):
return y**2 * y**4
print(differentiate(f, 9, 2))
| -1 |
TheAlgorithms/Python | 9,076 | Fix typos |
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| omahs | "2023-09-22T13:22:15Z" | "2023-09-23T08:53:09Z" | dc50add8a78ebf34bc7bb050c1a0e61d207b9544 | b203150ac481743a6d8c1ef01091712a54dfbf6c | Fix typos.
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
| """
Project Euler Problem 234: https://projecteuler.net/problem=234
For any integer n, consider the three functions
f1,n(x,y,z) = x^(n+1) + y^(n+1) - z^(n+1)
f2,n(x,y,z) = (xy + yz + zx)*(x^(n-1) + y^(n-1) - z^(n-1))
f3,n(x,y,z) = xyz*(xn-2 + yn-2 - zn-2)
and their combination
fn(x,y,z) = f1,n(x,y,z) + f2,n(x,y,z) - f3,n(x,y,z)
We call (x,y,z) a golden triple of order k if x, y, and z are all rational numbers
of the form a / b with 0 < a < b ≤ k and there is (at least) one integer n,
so that fn(x,y,z) = 0.
Let s(x,y,z) = x + y + z.
Let t = u / v be the sum of all distinct s(x,y,z) for all golden triples
(x,y,z) of order 35.
All the s(x,y,z) and t must be in reduced form.
Find u + v.
Solution:
By expanding the brackets it is easy to show that
fn(x, y, z) = (x + y + z) * (x^n + y^n - z^n).
Since x,y,z are positive, the requirement fn(x, y, z) = 0 is fulfilled if and
only if x^n + y^n = z^n.
By Fermat's Last Theorem, this means that the absolute value of n can not
exceed 2, i.e. n is in {-2, -1, 0, 1, 2}. We can eliminate n = 0 since then the
equation would reduce to 1 + 1 = 1, for which there are no solutions.
So all we have to do is iterate through the possible numerators and denominators
of x and y, calculate the corresponding z, and check if the corresponding numerator and
denominator are integer and satisfy 0 < z_num < z_den <= 0. We use a set "uniquq_s"
to make sure there are no duplicates, and the fractions.Fraction class to make sure
we get the right numerator and denominator.
Reference:
https://en.wikipedia.org/wiki/Fermat%27s_Last_Theorem
"""
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def is_sq(number: int) -> bool:
"""
Check if number is a perfect square.
>>> is_sq(1)
True
>>> is_sq(1000001)
False
>>> is_sq(1000000)
True
"""
sq: int = int(number**0.5)
return number == sq * sq
def add_three(
x_num: int, x_den: int, y_num: int, y_den: int, z_num: int, z_den: int
) -> tuple[int, int]:
"""
Given the numerators and denominators of three fractions, return the
numerator and denominator of their sum in lowest form.
>>> add_three(1, 3, 1, 3, 1, 3)
(1, 1)
>>> add_three(2, 5, 4, 11, 12, 3)
(262, 55)
"""
top: int = x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
bottom: int = x_den * y_den * z_den
hcf: int = gcd(top, bottom)
top //= hcf
bottom //= hcf
return top, bottom
def solution(order: int = 35) -> int:
"""
Find the sum of the numerator and denominator of the sum of all s(x,y,z) for
golden triples (x,y,z) of the given order.
>>> solution(5)
296
>>> solution(10)
12519
>>> solution(20)
19408891927
"""
unique_s: set = set()
hcf: int
total: Fraction = Fraction(0)
fraction_sum: tuple[int, int]
for x_num in range(1, order + 1):
for x_den in range(x_num + 1, order + 1):
for y_num in range(1, order + 1):
for y_den in range(y_num + 1, order + 1):
# n=1
z_num = x_num * y_den + x_den * y_num
z_den = x_den * y_den
hcf = gcd(z_num, z_den)
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
fraction_sum = add_three(
x_num, x_den, y_num, y_den, z_num, z_den
)
unique_s.add(fraction_sum)
# n=2
z_num = (
x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
)
z_den = x_den * x_den * y_den * y_den
if is_sq(z_num) and is_sq(z_den):
z_num = int(sqrt(z_num))
z_den = int(sqrt(z_den))
hcf = gcd(z_num, z_den)
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
fraction_sum = add_three(
x_num, x_den, y_num, y_den, z_num, z_den
)
unique_s.add(fraction_sum)
# n=-1
z_num = x_num * y_num
z_den = x_den * y_num + x_num * y_den
hcf = gcd(z_num, z_den)
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
fraction_sum = add_three(
x_num, x_den, y_num, y_den, z_num, z_den
)
unique_s.add(fraction_sum)
# n=2
z_num = x_num * x_num * y_num * y_num
z_den = (
x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
)
if is_sq(z_num) and is_sq(z_den):
z_num = int(sqrt(z_num))
z_den = int(sqrt(z_den))
hcf = gcd(z_num, z_den)
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
fraction_sum = add_three(
x_num, x_den, y_num, y_den, z_num, z_den
)
unique_s.add(fraction_sum)
for num, den in unique_s:
total += Fraction(num, den)
return total.denominator + total.numerator
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 234: https://projecteuler.net/problem=234
For any integer n, consider the three functions
f1,n(x,y,z) = x^(n+1) + y^(n+1) - z^(n+1)
f2,n(x,y,z) = (xy + yz + zx)*(x^(n-1) + y^(n-1) - z^(n-1))
f3,n(x,y,z) = xyz*(xn-2 + yn-2 - zn-2)
and their combination
fn(x,y,z) = f1,n(x,y,z) + f2,n(x,y,z) - f3,n(x,y,z)
We call (x,y,z) a golden triple of order k if x, y, and z are all rational numbers
of the form a / b with 0 < a < b ≤ k and there is (at least) one integer n,
so that fn(x,y,z) = 0.
Let s(x,y,z) = x + y + z.
Let t = u / v be the sum of all distinct s(x,y,z) for all golden triples
(x,y,z) of order 35.
All the s(x,y,z) and t must be in reduced form.
Find u + v.
Solution:
By expanding the brackets it is easy to show that
fn(x, y, z) = (x + y + z) * (x^n + y^n - z^n).
Since x,y,z are positive, the requirement fn(x, y, z) = 0 is fulfilled if and
only if x^n + y^n = z^n.
By Fermat's Last Theorem, this means that the absolute value of n can not
exceed 2, i.e. n is in {-2, -1, 0, 1, 2}. We can eliminate n = 0 since then the
equation would reduce to 1 + 1 = 1, for which there are no solutions.
So all we have to do is iterate through the possible numerators and denominators
of x and y, calculate the corresponding z, and check if the corresponding numerator and
denominator are integer and satisfy 0 < z_num < z_den <= 0. We use a set "uniquq_s"
to make sure there are no duplicates, and the fractions.Fraction class to make sure
we get the right numerator and denominator.
Reference:
https://en.wikipedia.org/wiki/Fermat%27s_Last_Theorem
"""
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def is_sq(number: int) -> bool:
"""
Check if number is a perfect square.
>>> is_sq(1)
True
>>> is_sq(1000001)
False
>>> is_sq(1000000)
True
"""
sq: int = int(number**0.5)
return number == sq * sq
def add_three(
x_num: int, x_den: int, y_num: int, y_den: int, z_num: int, z_den: int
) -> tuple[int, int]:
"""
Given the numerators and denominators of three fractions, return the
numerator and denominator of their sum in lowest form.
>>> add_three(1, 3, 1, 3, 1, 3)
(1, 1)
>>> add_three(2, 5, 4, 11, 12, 3)
(262, 55)
"""
top: int = x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
bottom: int = x_den * y_den * z_den
hcf: int = gcd(top, bottom)
top //= hcf
bottom //= hcf
return top, bottom
def solution(order: int = 35) -> int:
"""
Find the sum of the numerator and denominator of the sum of all s(x,y,z) for
golden triples (x,y,z) of the given order.
>>> solution(5)
296
>>> solution(10)
12519
>>> solution(20)
19408891927
"""
unique_s: set = set()
hcf: int
total: Fraction = Fraction(0)
fraction_sum: tuple[int, int]
for x_num in range(1, order + 1):
for x_den in range(x_num + 1, order + 1):
for y_num in range(1, order + 1):
for y_den in range(y_num + 1, order + 1):
# n=1
z_num = x_num * y_den + x_den * y_num
z_den = x_den * y_den
hcf = gcd(z_num, z_den)
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
fraction_sum = add_three(
x_num, x_den, y_num, y_den, z_num, z_den
)
unique_s.add(fraction_sum)
# n=2
z_num = (
x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
)
z_den = x_den * x_den * y_den * y_den
if is_sq(z_num) and is_sq(z_den):
z_num = int(sqrt(z_num))
z_den = int(sqrt(z_den))
hcf = gcd(z_num, z_den)
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
fraction_sum = add_three(
x_num, x_den, y_num, y_den, z_num, z_den
)
unique_s.add(fraction_sum)
# n=-1
z_num = x_num * y_num
z_den = x_den * y_num + x_num * y_den
hcf = gcd(z_num, z_den)
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
fraction_sum = add_three(
x_num, x_den, y_num, y_den, z_num, z_den
)
unique_s.add(fraction_sum)
# n=2
z_num = x_num * x_num * y_num * y_num
z_den = (
x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
)
if is_sq(z_num) and is_sq(z_den):
z_num = int(sqrt(z_num))
z_den = int(sqrt(z_den))
hcf = gcd(z_num, z_den)
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
fraction_sum = add_three(
x_num, x_den, y_num, y_den, z_num, z_den
)
unique_s.add(fraction_sum)
for num, den in unique_s:
total += Fraction(num, den)
return total.denominator + total.numerator
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 9,062 | Delete empty junk file | ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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 | "2023-09-16T18:59:42Z" | "2023-09-16T22:12:31Z" | 1488cdea708485eb1d81c73126eab13cb9b04a47 | fbad85d3ecbbb826a5891807c823149d38bbaed3 | Delete empty junk file. ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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)
* [Junk](arithmetic_analysis/junk.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)
* [Nor Gate](boolean_algebra/nor_gate.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)
* [Langtons Ant](cellular_automata/langtons_ant.py)
* [Nagel Schrekenberg](cellular_automata/nagel_schrekenberg.py)
* [One Dimensional](cellular_automata/one_dimensional.py)
* [Wa Tor](cellular_automata/wa_tor.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)
* [Haralick Descriptors](computer_vision/haralick_descriptors.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)
* [Convert Number To Words](conversions/convert_number_to_words.py)
* [Decimal To Any](conversions/decimal_to_any.py)
* [Decimal To Binary](conversions/decimal_to_binary.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 Binary](conversions/octal_to_binary.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)
* [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)
* [Regex Match](dynamic_programming/regex_match.py)
* [Rod Cutting](dynamic_programming/rod_cutting.py)
* [Subset Generation](dynamic_programming/subset_generation.py)
* [Sum Of Subset](dynamic_programming/sum_of_subset.py)
* [Tribonacci](dynamic_programming/tribonacci.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)
* [Continued Fraction](maths/continued_fraction.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 Min](maths/find_min.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)
* [Harshad Numbers](maths/harshad_numbers.py)
* [Hexagonal Number](maths/hexagonal_number.py)
* [Integration By Simpson Approx](maths/integration_by_simpson_approx.py)
* [Interquartile Range](maths/interquartile_range.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)
* [Polygonal Numbers](maths/polygonal_numbers.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)
* [Leaky Rectified Linear Unit](neural_network/activation_functions/leaky_rectified_linear_unit.py)
* [Scaled Exponential Linear Unit](neural_network/activation_functions/scaled_exponential_linear_unit.py)
* [Back Propagation Neural Network](neural_network/back_propagation_neural_network.py)
* [Convolution Neural Network](neural_network/convolution_neural_network.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)
* [Is Valid Email Address](strings/is_valid_email_address.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)
* [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 Anime And Play](web_programming/fetch_anime_and_play.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 Billionaires](web_programming/get_top_billionaires.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)
* [Nor Gate](boolean_algebra/nor_gate.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)
* [Langtons Ant](cellular_automata/langtons_ant.py)
* [Nagel Schrekenberg](cellular_automata/nagel_schrekenberg.py)
* [One Dimensional](cellular_automata/one_dimensional.py)
* [Wa Tor](cellular_automata/wa_tor.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)
* [Haralick Descriptors](computer_vision/haralick_descriptors.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)
* [Convert Number To Words](conversions/convert_number_to_words.py)
* [Decimal To Any](conversions/decimal_to_any.py)
* [Decimal To Binary](conversions/decimal_to_binary.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 Binary](conversions/octal_to_binary.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)
* [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)
* [Regex Match](dynamic_programming/regex_match.py)
* [Rod Cutting](dynamic_programming/rod_cutting.py)
* [Subset Generation](dynamic_programming/subset_generation.py)
* [Sum Of Subset](dynamic_programming/sum_of_subset.py)
* [Tribonacci](dynamic_programming/tribonacci.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)
* [Continued Fraction](maths/continued_fraction.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 Min](maths/find_min.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)
* [Harshad Numbers](maths/harshad_numbers.py)
* [Hexagonal Number](maths/hexagonal_number.py)
* [Integration By Simpson Approx](maths/integration_by_simpson_approx.py)
* [Interquartile Range](maths/interquartile_range.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)
* [Polygonal Numbers](maths/polygonal_numbers.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)
* [Leaky Rectified Linear Unit](neural_network/activation_functions/leaky_rectified_linear_unit.py)
* [Scaled Exponential Linear Unit](neural_network/activation_functions/scaled_exponential_linear_unit.py)
* [Back Propagation Neural Network](neural_network/back_propagation_neural_network.py)
* [Convolution Neural Network](neural_network/convolution_neural_network.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)
* [Is Valid Email Address](strings/is_valid_email_address.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)
* [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 Anime And Play](web_programming/fetch_anime_and_play.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 Billionaires](web_programming/get_top_billionaires.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 | 9,062 | Delete empty junk file | ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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 | "2023-09-16T18:59:42Z" | "2023-09-16T22:12:31Z" | 1488cdea708485eb1d81c73126eab13cb9b04a47 | fbad85d3ecbbb826a5891807c823149d38bbaed3 | Delete empty junk file. ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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".
| """
https://en.wikipedia.org/wiki/Image_texture
https://en.wikipedia.org/wiki/Co-occurrence_matrix#Application_to_image_analysis
"""
import imageio.v2 as imageio
import numpy as np
def root_mean_square_error(original: np.ndarray, reference: np.ndarray) -> float:
"""Simple implementation of Root Mean Squared Error
for two N dimensional numpy arrays.
Examples:
>>> root_mean_square_error(np.array([1, 2, 3]), np.array([1, 2, 3]))
0.0
>>> root_mean_square_error(np.array([1, 2, 3]), np.array([2, 2, 2]))
0.816496580927726
>>> root_mean_square_error(np.array([1, 2, 3]), np.array([6, 4, 2]))
3.1622776601683795
"""
return np.sqrt(((original - reference) ** 2).mean())
def normalize_image(
image: np.ndarray, cap: float = 255.0, data_type: np.dtype = np.uint8
) -> np.ndarray:
"""
Normalizes image in Numpy 2D array format, between ranges 0-cap,
as to fit uint8 type.
Args:
image: 2D numpy array representing image as matrix, with values in any range
cap: Maximum cap amount for normalization
data_type: numpy data type to set output variable to
Returns:
return 2D numpy array of type uint8, corresponding to limited range matrix
Examples:
>>> normalize_image(np.array([[1, 2, 3], [4, 5, 10]]),
... cap=1.0, data_type=np.float64)
array([[0. , 0.11111111, 0.22222222],
[0.33333333, 0.44444444, 1. ]])
>>> normalize_image(np.array([[4, 4, 3], [1, 7, 2]]))
array([[127, 127, 85],
[ 0, 255, 42]], dtype=uint8)
"""
normalized = (image - np.min(image)) / (np.max(image) - np.min(image)) * cap
return normalized.astype(data_type)
def normalize_array(array: np.ndarray, cap: float = 1) -> np.ndarray:
"""Normalizes a 1D array, between ranges 0-cap.
Args:
array: List containing values to be normalized between cap range.
cap: Maximum cap amount for normalization.
Returns:
return 1D numpy array, corresponding to limited range array
Examples:
>>> normalize_array(np.array([2, 3, 5, 7]))
array([0. , 0.2, 0.6, 1. ])
>>> normalize_array(np.array([[5], [7], [11], [13]]))
array([[0. ],
[0.25],
[0.75],
[1. ]])
"""
diff = np.max(array) - np.min(array)
return (array - np.min(array)) / (1 if diff == 0 else diff) * cap
def grayscale(image: np.ndarray) -> np.ndarray:
"""
Uses luminance weights to transform RGB channel to greyscale, by
taking the dot product between the channel and the weights.
Example:
>>> grayscale(np.array([[[108, 201, 72], [255, 11, 127]],
... [[56, 56, 56], [128, 255, 107]]]))
array([[158, 97],
[ 56, 200]], dtype=uint8)
"""
return np.dot(image[:, :, 0:3], [0.299, 0.587, 0.114]).astype(np.uint8)
def binarize(image: np.ndarray, threshold: float = 127.0) -> np.ndarray:
"""
Binarizes a grayscale image based on a given threshold value,
setting values to 1 or 0 accordingly.
Examples:
>>> binarize(np.array([[128, 255], [101, 156]]))
array([[1, 1],
[0, 1]])
>>> binarize(np.array([[0.07, 1], [0.51, 0.3]]), threshold=0.5)
array([[0, 1],
[1, 0]])
"""
return np.where(image > threshold, 1, 0)
def transform(image: np.ndarray, kind: str, kernel: np.ndarray = None) -> np.ndarray:
"""
Simple image transformation using one of two available filter functions:
Erosion and Dilation.
Args:
image: binarized input image, onto which to apply transformation
kind: Can be either 'erosion', in which case the :func:np.max
function is called, or 'dilation', when :func:np.min is used instead.
kernel: n x n kernel with shape < :attr:image.shape,
to be used when applying convolution to original image
Returns:
returns a numpy array with same shape as input image,
corresponding to applied binary transformation.
Examples:
>>> img = np.array([[1, 0.5], [0.2, 0.7]])
>>> img = binarize(img, threshold=0.5)
>>> transform(img, 'erosion')
array([[1, 1],
[1, 1]], dtype=uint8)
>>> transform(img, 'dilation')
array([[0, 0],
[0, 0]], dtype=uint8)
"""
if kernel is None:
kernel = np.ones((3, 3))
if kind == "erosion":
constant = 1
apply = np.max
else:
constant = 0
apply = np.min
center_x, center_y = (x // 2 for x in kernel.shape)
# Use padded image when applying convolotion
# to not go out of bounds of the original the image
transformed = np.zeros(image.shape, dtype=np.uint8)
padded = np.pad(image, 1, "constant", constant_values=constant)
for x in range(center_x, padded.shape[0] - center_x):
for y in range(center_y, padded.shape[1] - center_y):
center = padded[
x - center_x : x + center_x + 1, y - center_y : y + center_y + 1
]
# Apply transformation method to the centered section of the image
transformed[x - center_x, y - center_y] = apply(center[kernel == 1])
return transformed
def opening_filter(image: np.ndarray, kernel: np.ndarray = None) -> np.ndarray:
"""
Opening filter, defined as the sequence of
erosion and then a dilation filter on the same image.
Examples:
>>> img = np.array([[1, 0.5], [0.2, 0.7]])
>>> img = binarize(img, threshold=0.5)
>>> opening_filter(img)
array([[1, 1],
[1, 1]], dtype=uint8)
"""
if kernel is None:
np.ones((3, 3))
return transform(transform(image, "dilation", kernel), "erosion", kernel)
def closing_filter(image: np.ndarray, kernel: np.ndarray = None) -> np.ndarray:
"""
Opening filter, defined as the sequence of
dilation and then erosion filter on the same image.
Examples:
>>> img = np.array([[1, 0.5], [0.2, 0.7]])
>>> img = binarize(img, threshold=0.5)
>>> closing_filter(img)
array([[0, 0],
[0, 0]], dtype=uint8)
"""
if kernel is None:
kernel = np.ones((3, 3))
return transform(transform(image, "erosion", kernel), "dilation", kernel)
def binary_mask(
image_gray: np.ndarray, image_map: np.ndarray
) -> tuple[np.ndarray, np.ndarray]:
"""
Apply binary mask, or thresholding based
on bit mask value (mapping mask is binary).
Returns the mapped true value mask and its complementary false value mask.
Example:
>>> img = np.array([[[108, 201, 72], [255, 11, 127]],
... [[56, 56, 56], [128, 255, 107]]])
>>> gray = grayscale(img)
>>> binary = binarize(gray)
>>> morphological = opening_filter(binary)
>>> binary_mask(gray, morphological)
(array([[1, 1],
[1, 1]], dtype=uint8), array([[158, 97],
[ 56, 200]], dtype=uint8))
"""
true_mask, false_mask = image_gray.copy(), image_gray.copy()
true_mask[image_map == 1] = 1
false_mask[image_map == 0] = 0
return true_mask, false_mask
def matrix_concurrency(image: np.ndarray, coordinate: tuple[int, int]) -> np.ndarray:
"""
Calculate sample co-occurrence matrix based on input image
as well as selected coordinates on image.
Implementation is made using basic iteration,
as function to be performed (np.max) is non-linear and therefore
not callable on the frequency domain.
Example:
>>> img = np.array([[[108, 201, 72], [255, 11, 127]],
... [[56, 56, 56], [128, 255, 107]]])
>>> gray = grayscale(img)
>>> binary = binarize(gray)
>>> morphological = opening_filter(binary)
>>> mask_1 = binary_mask(gray, morphological)[0]
>>> matrix_concurrency(mask_1, (0, 1))
array([[0., 0.],
[0., 0.]])
"""
matrix = np.zeros([np.max(image) + 1, np.max(image) + 1])
offset_x, offset_y = coordinate
for x in range(1, image.shape[0] - 1):
for y in range(1, image.shape[1] - 1):
base_pixel = image[x, y]
offset_pixel = image[x + offset_x, y + offset_y]
matrix[base_pixel, offset_pixel] += 1
matrix_sum = np.sum(matrix)
return matrix / (1 if matrix_sum == 0 else matrix_sum)
def haralick_descriptors(matrix: np.ndarray) -> list[float]:
"""Calculates all 8 Haralick descriptors based on co-occurence input matrix.
All descriptors are as follows:
Maximum probability, Inverse Difference, Homogeneity, Entropy,
Energy, Dissimilarity, Contrast and Correlation
Args:
matrix: Co-occurence matrix to use as base for calculating descriptors.
Returns:
Reverse ordered list of resulting descriptors
Example:
>>> img = np.array([[[108, 201, 72], [255, 11, 127]],
... [[56, 56, 56], [128, 255, 107]]])
>>> gray = grayscale(img)
>>> binary = binarize(gray)
>>> morphological = opening_filter(binary)
>>> mask_1 = binary_mask(gray, morphological)[0]
>>> concurrency = matrix_concurrency(mask_1, (0, 1))
>>> haralick_descriptors(concurrency)
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
"""
# Function np.indices could be used for bigger input types,
# but np.ogrid works just fine
i, j = np.ogrid[0 : matrix.shape[0], 0 : matrix.shape[1]] # np.indices()
# Pre-calculate frequent multiplication and subtraction
prod = np.multiply(i, j)
sub = np.subtract(i, j)
# Calculate numerical value of Maximum Probability
maximum_prob = np.max(matrix)
# Using the definition for each descriptor individually to calculate its matrix
correlation = prod * matrix
energy = np.power(matrix, 2)
contrast = matrix * np.power(sub, 2)
dissimilarity = matrix * np.abs(sub)
inverse_difference = matrix / (1 + np.abs(sub))
homogeneity = matrix / (1 + np.power(sub, 2))
entropy = -(matrix[matrix > 0] * np.log(matrix[matrix > 0]))
# Sum values for descriptors ranging from the first one to the last,
# as all are their respective origin matrix and not the resulting value yet.
return [
maximum_prob,
correlation.sum(),
energy.sum(),
contrast.sum(),
dissimilarity.sum(),
inverse_difference.sum(),
homogeneity.sum(),
entropy.sum(),
]
def get_descriptors(
masks: tuple[np.ndarray, np.ndarray], coordinate: tuple[int, int]
) -> np.ndarray:
"""
Calculate all Haralick descriptors for a sequence of
different co-occurrence matrices, given input masks and coordinates.
Example:
>>> img = np.array([[[108, 201, 72], [255, 11, 127]],
... [[56, 56, 56], [128, 255, 107]]])
>>> gray = grayscale(img)
>>> binary = binarize(gray)
>>> morphological = opening_filter(binary)
>>> get_descriptors(binary_mask(gray, morphological), (0, 1))
array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])
"""
descriptors = np.array(
[haralick_descriptors(matrix_concurrency(mask, coordinate)) for mask in masks]
)
# Concatenate each individual descriptor into
# one single list containing sequence of descriptors
return np.concatenate(descriptors, axis=None)
def euclidean(point_1: np.ndarray, point_2: np.ndarray) -> np.float32:
"""
Simple method for calculating the euclidean distance between two points,
with type np.ndarray.
Example:
>>> a = np.array([1, 0, -2])
>>> b = np.array([2, -1, 1])
>>> euclidean(a, b)
3.3166247903554
"""
return np.sqrt(np.sum(np.square(point_1 - point_2)))
def get_distances(descriptors: np.ndarray, base: int) -> list[tuple[int, float]]:
"""
Calculate all Euclidean distances between a selected base descriptor
and all other Haralick descriptors
The resulting comparison is return in decreasing order,
showing which descriptor is the most similar to the selected base.
Args:
descriptors: Haralick descriptors to compare with base index
base: Haralick descriptor index to use as base when calculating respective
euclidean distance to other descriptors.
Returns:
Ordered distances between descriptors
Example:
>>> index = 1
>>> img = np.array([[[108, 201, 72], [255, 11, 127]],
... [[56, 56, 56], [128, 255, 107]]])
>>> gray = grayscale(img)
>>> binary = binarize(gray)
>>> morphological = opening_filter(binary)
>>> get_distances(get_descriptors(
... binary_mask(gray, morphological), (0, 1)),
... index)
[(0, 0.0), (1, 0.0), (2, 0.0), (3, 0.0), (4, 0.0), (5, 0.0), \
(6, 0.0), (7, 0.0), (8, 0.0), (9, 0.0), (10, 0.0), (11, 0.0), (12, 0.0), \
(13, 0.0), (14, 0.0), (15, 0.0)]
"""
distances = np.array(
[euclidean(descriptor, descriptors[base]) for descriptor in descriptors]
)
# Normalize distances between range [0, 1]
normalized_distances: list[float] = normalize_array(distances, 1).tolist()
enum_distances = list(enumerate(normalized_distances))
enum_distances.sort(key=lambda tup: tup[1], reverse=True)
return enum_distances
if __name__ == "__main__":
# Index to compare haralick descriptors to
index = int(input())
q_value_list = [int(value) for value in input().split()]
q_value = (q_value_list[0], q_value_list[1])
# Format is the respective filter to apply,
# can be either 1 for the opening filter or else for the closing
parameters = {"format": int(input()), "threshold": int(input())}
# Number of images to perform methods on
b_number = int(input())
files, descriptors = [], []
for _ in range(b_number):
file = input().rstrip()
files.append(file)
# Open given image and calculate morphological filter,
# respective masks and correspondent Harralick Descriptors.
image = imageio.imread(file).astype(np.float32)
gray = grayscale(image)
threshold = binarize(gray, parameters["threshold"])
morphological = (
opening_filter(threshold)
if parameters["format"] == 1
else closing_filter(threshold)
)
masks = binary_mask(gray, morphological)
descriptors.append(get_descriptors(masks, q_value))
# Transform ordered distances array into a sequence of indexes
# corresponding to original file position
distances = get_distances(np.array(descriptors), index)
indexed_distances = np.array(distances).astype(np.uint8)[:, 0]
# Finally, print distances considering the Haralick descriptions from the base
# file to all other images using the morphology method of choice.
print(f"Query: {files[index]}")
print("Ranking:")
for idx, file_idx in enumerate(indexed_distances):
print(f"({idx}) {files[file_idx]}", end="\n")
| """
https://en.wikipedia.org/wiki/Image_texture
https://en.wikipedia.org/wiki/Co-occurrence_matrix#Application_to_image_analysis
"""
import imageio.v2 as imageio
import numpy as np
def root_mean_square_error(original: np.ndarray, reference: np.ndarray) -> float:
"""Simple implementation of Root Mean Squared Error
for two N dimensional numpy arrays.
Examples:
>>> root_mean_square_error(np.array([1, 2, 3]), np.array([1, 2, 3]))
0.0
>>> root_mean_square_error(np.array([1, 2, 3]), np.array([2, 2, 2]))
0.816496580927726
>>> root_mean_square_error(np.array([1, 2, 3]), np.array([6, 4, 2]))
3.1622776601683795
"""
return np.sqrt(((original - reference) ** 2).mean())
def normalize_image(
image: np.ndarray, cap: float = 255.0, data_type: np.dtype = np.uint8
) -> np.ndarray:
"""
Normalizes image in Numpy 2D array format, between ranges 0-cap,
as to fit uint8 type.
Args:
image: 2D numpy array representing image as matrix, with values in any range
cap: Maximum cap amount for normalization
data_type: numpy data type to set output variable to
Returns:
return 2D numpy array of type uint8, corresponding to limited range matrix
Examples:
>>> normalize_image(np.array([[1, 2, 3], [4, 5, 10]]),
... cap=1.0, data_type=np.float64)
array([[0. , 0.11111111, 0.22222222],
[0.33333333, 0.44444444, 1. ]])
>>> normalize_image(np.array([[4, 4, 3], [1, 7, 2]]))
array([[127, 127, 85],
[ 0, 255, 42]], dtype=uint8)
"""
normalized = (image - np.min(image)) / (np.max(image) - np.min(image)) * cap
return normalized.astype(data_type)
def normalize_array(array: np.ndarray, cap: float = 1) -> np.ndarray:
"""Normalizes a 1D array, between ranges 0-cap.
Args:
array: List containing values to be normalized between cap range.
cap: Maximum cap amount for normalization.
Returns:
return 1D numpy array, corresponding to limited range array
Examples:
>>> normalize_array(np.array([2, 3, 5, 7]))
array([0. , 0.2, 0.6, 1. ])
>>> normalize_array(np.array([[5], [7], [11], [13]]))
array([[0. ],
[0.25],
[0.75],
[1. ]])
"""
diff = np.max(array) - np.min(array)
return (array - np.min(array)) / (1 if diff == 0 else diff) * cap
def grayscale(image: np.ndarray) -> np.ndarray:
"""
Uses luminance weights to transform RGB channel to greyscale, by
taking the dot product between the channel and the weights.
Example:
>>> grayscale(np.array([[[108, 201, 72], [255, 11, 127]],
... [[56, 56, 56], [128, 255, 107]]]))
array([[158, 97],
[ 56, 200]], dtype=uint8)
"""
return np.dot(image[:, :, 0:3], [0.299, 0.587, 0.114]).astype(np.uint8)
def binarize(image: np.ndarray, threshold: float = 127.0) -> np.ndarray:
"""
Binarizes a grayscale image based on a given threshold value,
setting values to 1 or 0 accordingly.
Examples:
>>> binarize(np.array([[128, 255], [101, 156]]))
array([[1, 1],
[0, 1]])
>>> binarize(np.array([[0.07, 1], [0.51, 0.3]]), threshold=0.5)
array([[0, 1],
[1, 0]])
"""
return np.where(image > threshold, 1, 0)
def transform(
image: np.ndarray, kind: str, kernel: np.ndarray | None = None
) -> np.ndarray:
"""
Simple image transformation using one of two available filter functions:
Erosion and Dilation.
Args:
image: binarized input image, onto which to apply transformation
kind: Can be either 'erosion', in which case the :func:np.max
function is called, or 'dilation', when :func:np.min is used instead.
kernel: n x n kernel with shape < :attr:image.shape,
to be used when applying convolution to original image
Returns:
returns a numpy array with same shape as input image,
corresponding to applied binary transformation.
Examples:
>>> img = np.array([[1, 0.5], [0.2, 0.7]])
>>> img = binarize(img, threshold=0.5)
>>> transform(img, 'erosion')
array([[1, 1],
[1, 1]], dtype=uint8)
>>> transform(img, 'dilation')
array([[0, 0],
[0, 0]], dtype=uint8)
"""
if kernel is None:
kernel = np.ones((3, 3))
if kind == "erosion":
constant = 1
apply = np.max
else:
constant = 0
apply = np.min
center_x, center_y = (x // 2 for x in kernel.shape)
# Use padded image when applying convolotion
# to not go out of bounds of the original the image
transformed = np.zeros(image.shape, dtype=np.uint8)
padded = np.pad(image, 1, "constant", constant_values=constant)
for x in range(center_x, padded.shape[0] - center_x):
for y in range(center_y, padded.shape[1] - center_y):
center = padded[
x - center_x : x + center_x + 1, y - center_y : y + center_y + 1
]
# Apply transformation method to the centered section of the image
transformed[x - center_x, y - center_y] = apply(center[kernel == 1])
return transformed
def opening_filter(image: np.ndarray, kernel: np.ndarray | None = None) -> np.ndarray:
"""
Opening filter, defined as the sequence of
erosion and then a dilation filter on the same image.
Examples:
>>> img = np.array([[1, 0.5], [0.2, 0.7]])
>>> img = binarize(img, threshold=0.5)
>>> opening_filter(img)
array([[1, 1],
[1, 1]], dtype=uint8)
"""
if kernel is None:
np.ones((3, 3))
return transform(transform(image, "dilation", kernel), "erosion", kernel)
def closing_filter(image: np.ndarray, kernel: np.ndarray | None = None) -> np.ndarray:
"""
Opening filter, defined as the sequence of
dilation and then erosion filter on the same image.
Examples:
>>> img = np.array([[1, 0.5], [0.2, 0.7]])
>>> img = binarize(img, threshold=0.5)
>>> closing_filter(img)
array([[0, 0],
[0, 0]], dtype=uint8)
"""
if kernel is None:
kernel = np.ones((3, 3))
return transform(transform(image, "erosion", kernel), "dilation", kernel)
def binary_mask(
image_gray: np.ndarray, image_map: np.ndarray
) -> tuple[np.ndarray, np.ndarray]:
"""
Apply binary mask, or thresholding based
on bit mask value (mapping mask is binary).
Returns the mapped true value mask and its complementary false value mask.
Example:
>>> img = np.array([[[108, 201, 72], [255, 11, 127]],
... [[56, 56, 56], [128, 255, 107]]])
>>> gray = grayscale(img)
>>> binary = binarize(gray)
>>> morphological = opening_filter(binary)
>>> binary_mask(gray, morphological)
(array([[1, 1],
[1, 1]], dtype=uint8), array([[158, 97],
[ 56, 200]], dtype=uint8))
"""
true_mask, false_mask = image_gray.copy(), image_gray.copy()
true_mask[image_map == 1] = 1
false_mask[image_map == 0] = 0
return true_mask, false_mask
def matrix_concurrency(image: np.ndarray, coordinate: tuple[int, int]) -> np.ndarray:
"""
Calculate sample co-occurrence matrix based on input image
as well as selected coordinates on image.
Implementation is made using basic iteration,
as function to be performed (np.max) is non-linear and therefore
not callable on the frequency domain.
Example:
>>> img = np.array([[[108, 201, 72], [255, 11, 127]],
... [[56, 56, 56], [128, 255, 107]]])
>>> gray = grayscale(img)
>>> binary = binarize(gray)
>>> morphological = opening_filter(binary)
>>> mask_1 = binary_mask(gray, morphological)[0]
>>> matrix_concurrency(mask_1, (0, 1))
array([[0., 0.],
[0., 0.]])
"""
matrix = np.zeros([np.max(image) + 1, np.max(image) + 1])
offset_x, offset_y = coordinate
for x in range(1, image.shape[0] - 1):
for y in range(1, image.shape[1] - 1):
base_pixel = image[x, y]
offset_pixel = image[x + offset_x, y + offset_y]
matrix[base_pixel, offset_pixel] += 1
matrix_sum = np.sum(matrix)
return matrix / (1 if matrix_sum == 0 else matrix_sum)
def haralick_descriptors(matrix: np.ndarray) -> list[float]:
"""Calculates all 8 Haralick descriptors based on co-occurence input matrix.
All descriptors are as follows:
Maximum probability, Inverse Difference, Homogeneity, Entropy,
Energy, Dissimilarity, Contrast and Correlation
Args:
matrix: Co-occurence matrix to use as base for calculating descriptors.
Returns:
Reverse ordered list of resulting descriptors
Example:
>>> img = np.array([[[108, 201, 72], [255, 11, 127]],
... [[56, 56, 56], [128, 255, 107]]])
>>> gray = grayscale(img)
>>> binary = binarize(gray)
>>> morphological = opening_filter(binary)
>>> mask_1 = binary_mask(gray, morphological)[0]
>>> concurrency = matrix_concurrency(mask_1, (0, 1))
>>> haralick_descriptors(concurrency)
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
"""
# Function np.indices could be used for bigger input types,
# but np.ogrid works just fine
i, j = np.ogrid[0 : matrix.shape[0], 0 : matrix.shape[1]] # np.indices()
# Pre-calculate frequent multiplication and subtraction
prod = np.multiply(i, j)
sub = np.subtract(i, j)
# Calculate numerical value of Maximum Probability
maximum_prob = np.max(matrix)
# Using the definition for each descriptor individually to calculate its matrix
correlation = prod * matrix
energy = np.power(matrix, 2)
contrast = matrix * np.power(sub, 2)
dissimilarity = matrix * np.abs(sub)
inverse_difference = matrix / (1 + np.abs(sub))
homogeneity = matrix / (1 + np.power(sub, 2))
entropy = -(matrix[matrix > 0] * np.log(matrix[matrix > 0]))
# Sum values for descriptors ranging from the first one to the last,
# as all are their respective origin matrix and not the resulting value yet.
return [
maximum_prob,
correlation.sum(),
energy.sum(),
contrast.sum(),
dissimilarity.sum(),
inverse_difference.sum(),
homogeneity.sum(),
entropy.sum(),
]
def get_descriptors(
masks: tuple[np.ndarray, np.ndarray], coordinate: tuple[int, int]
) -> np.ndarray:
"""
Calculate all Haralick descriptors for a sequence of
different co-occurrence matrices, given input masks and coordinates.
Example:
>>> img = np.array([[[108, 201, 72], [255, 11, 127]],
... [[56, 56, 56], [128, 255, 107]]])
>>> gray = grayscale(img)
>>> binary = binarize(gray)
>>> morphological = opening_filter(binary)
>>> get_descriptors(binary_mask(gray, morphological), (0, 1))
array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])
"""
descriptors = np.array(
[haralick_descriptors(matrix_concurrency(mask, coordinate)) for mask in masks]
)
# Concatenate each individual descriptor into
# one single list containing sequence of descriptors
return np.concatenate(descriptors, axis=None)
def euclidean(point_1: np.ndarray, point_2: np.ndarray) -> np.float32:
"""
Simple method for calculating the euclidean distance between two points,
with type np.ndarray.
Example:
>>> a = np.array([1, 0, -2])
>>> b = np.array([2, -1, 1])
>>> euclidean(a, b)
3.3166247903554
"""
return np.sqrt(np.sum(np.square(point_1 - point_2)))
def get_distances(descriptors: np.ndarray, base: int) -> list[tuple[int, float]]:
"""
Calculate all Euclidean distances between a selected base descriptor
and all other Haralick descriptors
The resulting comparison is return in decreasing order,
showing which descriptor is the most similar to the selected base.
Args:
descriptors: Haralick descriptors to compare with base index
base: Haralick descriptor index to use as base when calculating respective
euclidean distance to other descriptors.
Returns:
Ordered distances between descriptors
Example:
>>> index = 1
>>> img = np.array([[[108, 201, 72], [255, 11, 127]],
... [[56, 56, 56], [128, 255, 107]]])
>>> gray = grayscale(img)
>>> binary = binarize(gray)
>>> morphological = opening_filter(binary)
>>> get_distances(get_descriptors(
... binary_mask(gray, morphological), (0, 1)),
... index)
[(0, 0.0), (1, 0.0), (2, 0.0), (3, 0.0), (4, 0.0), (5, 0.0), \
(6, 0.0), (7, 0.0), (8, 0.0), (9, 0.0), (10, 0.0), (11, 0.0), (12, 0.0), \
(13, 0.0), (14, 0.0), (15, 0.0)]
"""
distances = np.array(
[euclidean(descriptor, descriptors[base]) for descriptor in descriptors]
)
# Normalize distances between range [0, 1]
normalized_distances: list[float] = normalize_array(distances, 1).tolist()
enum_distances = list(enumerate(normalized_distances))
enum_distances.sort(key=lambda tup: tup[1], reverse=True)
return enum_distances
if __name__ == "__main__":
# Index to compare haralick descriptors to
index = int(input())
q_value_list = [int(value) for value in input().split()]
q_value = (q_value_list[0], q_value_list[1])
# Format is the respective filter to apply,
# can be either 1 for the opening filter or else for the closing
parameters = {"format": int(input()), "threshold": int(input())}
# Number of images to perform methods on
b_number = int(input())
files, descriptors = [], []
for _ in range(b_number):
file = input().rstrip()
files.append(file)
# Open given image and calculate morphological filter,
# respective masks and correspondent Harralick Descriptors.
image = imageio.imread(file).astype(np.float32)
gray = grayscale(image)
threshold = binarize(gray, parameters["threshold"])
morphological = (
opening_filter(threshold)
if parameters["format"] == 1
else closing_filter(threshold)
)
masks = binary_mask(gray, morphological)
descriptors.append(get_descriptors(masks, q_value))
# Transform ordered distances array into a sequence of indexes
# corresponding to original file position
distances = get_distances(np.array(descriptors), index)
indexed_distances = np.array(distances).astype(np.uint8)[:, 0]
# Finally, print distances considering the Haralick descriptions from the base
# file to all other images using the morphology method of choice.
print(f"Query: {files[index]}")
print("Ranking:")
for idx, file_idx in enumerate(indexed_distances):
print(f"({idx}) {files[file_idx]}", end="\n")
| 1 |
TheAlgorithms/Python | 9,062 | Delete empty junk file | ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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 | "2023-09-16T18:59:42Z" | "2023-09-16T22:12:31Z" | 1488cdea708485eb1d81c73126eab13cb9b04a47 | fbad85d3ecbbb826a5891807c823149d38bbaed3 | Delete empty junk file. ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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".
| from enum import Enum
from typing import ClassVar, Literal
class NumberingSystem(Enum):
SHORT = (
(15, "quadrillion"),
(12, "trillion"),
(9, "billion"),
(6, "million"),
(3, "thousand"),
(2, "hundred"),
)
LONG = (
(15, "billiard"),
(9, "milliard"),
(6, "million"),
(3, "thousand"),
(2, "hundred"),
)
INDIAN = (
(14, "crore crore"),
(12, "lakh crore"),
(7, "crore"),
(5, "lakh"),
(3, "thousand"),
(2, "hundred"),
)
@classmethod
def max_value(cls, system: str) -> int:
"""
Gets the max value supported by the given number system.
>>> NumberingSystem.max_value("short") == 10**18 - 1
True
>>> NumberingSystem.max_value("long") == 10**21 - 1
True
>>> NumberingSystem.max_value("indian") == 10**19 - 1
True
"""
match (system_enum := cls[system.upper()]):
case cls.SHORT:
max_exp = system_enum.value[0][0] + 3
case cls.LONG:
max_exp = system_enum.value[0][0] + 6
case cls.INDIAN:
max_exp = 19
case _:
raise ValueError("Invalid numbering system")
return 10**max_exp - 1
class NumberWords(Enum):
ONES: ClassVar = {
0: "",
1: "one",
2: "two",
3: "three",
4: "four",
5: "five",
6: "six",
7: "seven",
8: "eight",
9: "nine",
}
TEENS: ClassVar = {
0: "ten",
1: "eleven",
2: "twelve",
3: "thirteen",
4: "fourteen",
5: "fifteen",
6: "sixteen",
7: "seventeen",
8: "eighteen",
9: "nineteen",
}
TENS: ClassVar = {
2: "twenty",
3: "thirty",
4: "forty",
5: "fifty",
6: "sixty",
7: "seventy",
8: "eighty",
9: "ninety",
}
def convert_small_number(num: int) -> str:
"""
Converts small, non-negative integers with irregular constructions in English (i.e.,
numbers under 100) into words.
>>> convert_small_number(0)
'zero'
>>> convert_small_number(5)
'five'
>>> convert_small_number(10)
'ten'
>>> convert_small_number(15)
'fifteen'
>>> convert_small_number(20)
'twenty'
>>> convert_small_number(25)
'twenty-five'
>>> convert_small_number(-1)
Traceback (most recent call last):
...
ValueError: This function only accepts non-negative integers
>>> convert_small_number(123)
Traceback (most recent call last):
...
ValueError: This function only converts numbers less than 100
"""
if num < 0:
raise ValueError("This function only accepts non-negative integers")
if num >= 100:
raise ValueError("This function only converts numbers less than 100")
tens, ones = divmod(num, 10)
if tens == 0:
return NumberWords.ONES.value[ones] or "zero"
if tens == 1:
return NumberWords.TEENS.value[ones]
return (
NumberWords.TENS.value[tens]
+ ("-" if NumberWords.ONES.value[ones] else "")
+ NumberWords.ONES.value[ones]
)
def convert_number(
num: int, system: Literal["short", "long", "indian"] = "short"
) -> str:
"""
Converts an integer to English words.
:param num: The integer to be converted
:param system: The numbering system (short, long, or Indian)
>>> convert_number(0)
'zero'
>>> convert_number(1)
'one'
>>> convert_number(100)
'one hundred'
>>> convert_number(-100)
'negative one hundred'
>>> convert_number(123_456_789_012_345) # doctest: +NORMALIZE_WHITESPACE
'one hundred twenty-three trillion four hundred fifty-six billion
seven hundred eighty-nine million twelve thousand three hundred forty-five'
>>> convert_number(123_456_789_012_345, "long") # doctest: +NORMALIZE_WHITESPACE
'one hundred twenty-three thousand four hundred fifty-six milliard
seven hundred eighty-nine million twelve thousand three hundred forty-five'
>>> convert_number(12_34_56_78_90_12_345, "indian") # doctest: +NORMALIZE_WHITESPACE
'one crore crore twenty-three lakh crore
forty-five thousand six hundred seventy-eight crore
ninety lakh twelve thousand three hundred forty-five'
>>> convert_number(10**18)
Traceback (most recent call last):
...
ValueError: Input number is too large
>>> convert_number(10**21, "long")
Traceback (most recent call last):
...
ValueError: Input number is too large
>>> convert_number(10**19, "indian")
Traceback (most recent call last):
...
ValueError: Input number is too large
"""
word_groups = []
if num < 0:
word_groups.append("negative")
num *= -1
if num > NumberingSystem.max_value(system):
raise ValueError("Input number is too large")
for power, unit in NumberingSystem[system.upper()].value:
digit_group, num = divmod(num, 10**power)
if digit_group > 0:
word_group = (
convert_number(digit_group, system)
if digit_group >= 100
else convert_small_number(digit_group)
)
word_groups.append(f"{word_group} {unit}")
if num > 0 or not word_groups: # word_groups is only empty if input num was 0
word_groups.append(convert_small_number(num))
return " ".join(word_groups)
if __name__ == "__main__":
import doctest
doctest.testmod()
print(f"{convert_number(123456789) = }")
| from enum import Enum
from typing import ClassVar, Literal
class NumberingSystem(Enum):
SHORT = (
(15, "quadrillion"),
(12, "trillion"),
(9, "billion"),
(6, "million"),
(3, "thousand"),
(2, "hundred"),
)
LONG = (
(15, "billiard"),
(9, "milliard"),
(6, "million"),
(3, "thousand"),
(2, "hundred"),
)
INDIAN = (
(14, "crore crore"),
(12, "lakh crore"),
(7, "crore"),
(5, "lakh"),
(3, "thousand"),
(2, "hundred"),
)
@classmethod
def max_value(cls, system: str) -> int:
"""
Gets the max value supported by the given number system.
>>> NumberingSystem.max_value("short") == 10**18 - 1
True
>>> NumberingSystem.max_value("long") == 10**21 - 1
True
>>> NumberingSystem.max_value("indian") == 10**19 - 1
True
"""
match (system_enum := cls[system.upper()]):
case cls.SHORT:
max_exp = system_enum.value[0][0] + 3
case cls.LONG:
max_exp = system_enum.value[0][0] + 6
case cls.INDIAN:
max_exp = 19
case _:
raise ValueError("Invalid numbering system")
return 10**max_exp - 1
class NumberWords(Enum):
ONES: ClassVar[dict[int, str]] = {
0: "",
1: "one",
2: "two",
3: "three",
4: "four",
5: "five",
6: "six",
7: "seven",
8: "eight",
9: "nine",
}
TEENS: ClassVar[dict[int, str]] = {
0: "ten",
1: "eleven",
2: "twelve",
3: "thirteen",
4: "fourteen",
5: "fifteen",
6: "sixteen",
7: "seventeen",
8: "eighteen",
9: "nineteen",
}
TENS: ClassVar[dict[int, str]] = {
2: "twenty",
3: "thirty",
4: "forty",
5: "fifty",
6: "sixty",
7: "seventy",
8: "eighty",
9: "ninety",
}
def convert_small_number(num: int) -> str:
"""
Converts small, non-negative integers with irregular constructions in English (i.e.,
numbers under 100) into words.
>>> convert_small_number(0)
'zero'
>>> convert_small_number(5)
'five'
>>> convert_small_number(10)
'ten'
>>> convert_small_number(15)
'fifteen'
>>> convert_small_number(20)
'twenty'
>>> convert_small_number(25)
'twenty-five'
>>> convert_small_number(-1)
Traceback (most recent call last):
...
ValueError: This function only accepts non-negative integers
>>> convert_small_number(123)
Traceback (most recent call last):
...
ValueError: This function only converts numbers less than 100
"""
if num < 0:
raise ValueError("This function only accepts non-negative integers")
if num >= 100:
raise ValueError("This function only converts numbers less than 100")
tens, ones = divmod(num, 10)
if tens == 0:
return NumberWords.ONES.value[ones] or "zero"
if tens == 1:
return NumberWords.TEENS.value[ones]
return (
NumberWords.TENS.value[tens]
+ ("-" if NumberWords.ONES.value[ones] else "")
+ NumberWords.ONES.value[ones]
)
def convert_number(
num: int, system: Literal["short", "long", "indian"] = "short"
) -> str:
"""
Converts an integer to English words.
:param num: The integer to be converted
:param system: The numbering system (short, long, or Indian)
>>> convert_number(0)
'zero'
>>> convert_number(1)
'one'
>>> convert_number(100)
'one hundred'
>>> convert_number(-100)
'negative one hundred'
>>> convert_number(123_456_789_012_345) # doctest: +NORMALIZE_WHITESPACE
'one hundred twenty-three trillion four hundred fifty-six billion
seven hundred eighty-nine million twelve thousand three hundred forty-five'
>>> convert_number(123_456_789_012_345, "long") # doctest: +NORMALIZE_WHITESPACE
'one hundred twenty-three thousand four hundred fifty-six milliard
seven hundred eighty-nine million twelve thousand three hundred forty-five'
>>> convert_number(12_34_56_78_90_12_345, "indian") # doctest: +NORMALIZE_WHITESPACE
'one crore crore twenty-three lakh crore
forty-five thousand six hundred seventy-eight crore
ninety lakh twelve thousand three hundred forty-five'
>>> convert_number(10**18)
Traceback (most recent call last):
...
ValueError: Input number is too large
>>> convert_number(10**21, "long")
Traceback (most recent call last):
...
ValueError: Input number is too large
>>> convert_number(10**19, "indian")
Traceback (most recent call last):
...
ValueError: Input number is too large
"""
word_groups = []
if num < 0:
word_groups.append("negative")
num *= -1
if num > NumberingSystem.max_value(system):
raise ValueError("Input number is too large")
for power, unit in NumberingSystem[system.upper()].value:
digit_group, num = divmod(num, 10**power)
if digit_group > 0:
word_group = (
convert_number(digit_group, system)
if digit_group >= 100
else convert_small_number(digit_group)
)
word_groups.append(f"{word_group} {unit}")
if num > 0 or not word_groups: # word_groups is only empty if input num was 0
word_groups.append(convert_small_number(num))
return " ".join(word_groups)
if __name__ == "__main__":
import doctest
doctest.testmod()
print(f"{convert_number(123456789) = }")
| 1 |
TheAlgorithms/Python | 9,062 | Delete empty junk file | ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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 | "2023-09-16T18:59:42Z" | "2023-09-16T22:12:31Z" | 1488cdea708485eb1d81c73126eab13cb9b04a47 | fbad85d3ecbbb826a5891807c823149d38bbaed3 | Delete empty junk file. ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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".
| from collections import deque
def tarjan(g):
"""
Tarjan's algo for finding strongly connected components in a directed graph
Uses two main attributes of each node to track reachability, the index of that node
within a component(index), and the lowest index reachable from that node(lowlink).
We then perform a dfs of the each component making sure to update these parameters
for each node and saving the nodes we visit on the way.
If ever we find that the lowest reachable node from a current node is equal to the
index of the current node then it must be the root of a strongly connected
component and so we save it and it's equireachable vertices as a strongly
connected component.
Complexity: strong_connect() is called at most once for each node and has a
complexity of O(|E|) as it is DFS.
Therefore this has complexity O(|V| + |E|) for a graph G = (V, E)
"""
n = len(g)
stack = deque()
on_stack = [False for _ in range(n)]
index_of = [-1 for _ in range(n)]
lowlink_of = index_of[:]
def strong_connect(v, index, components):
index_of[v] = index # the number when this node is seen
lowlink_of[v] = index # lowest rank node reachable from here
index += 1
stack.append(v)
on_stack[v] = True
for w in g[v]:
if index_of[w] == -1:
index = strong_connect(w, index, components)
lowlink_of[v] = (
lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
)
elif on_stack[w]:
lowlink_of[v] = (
lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
)
if lowlink_of[v] == index_of[v]:
component = []
w = stack.pop()
on_stack[w] = False
component.append(w)
while w != v:
w = stack.pop()
on_stack[w] = False
component.append(w)
components.append(component)
return index
components = []
for v in range(n):
if index_of[v] == -1:
strong_connect(v, 0, components)
return components
def create_graph(n, edges):
g = [[] for _ in range(n)]
for u, v in edges:
g[u].append(v)
return g
if __name__ == "__main__":
# Test
n_vertices = 7
source = [0, 0, 1, 2, 3, 3, 4, 4, 6]
target = [1, 3, 2, 0, 1, 4, 5, 6, 5]
edges = [(u, v) for u, v in zip(source, target)]
g = create_graph(n_vertices, edges)
assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)
| from collections import deque
def tarjan(g):
"""
Tarjan's algo for finding strongly connected components in a directed graph
Uses two main attributes of each node to track reachability, the index of that node
within a component(index), and the lowest index reachable from that node(lowlink).
We then perform a dfs of the each component making sure to update these parameters
for each node and saving the nodes we visit on the way.
If ever we find that the lowest reachable node from a current node is equal to the
index of the current node then it must be the root of a strongly connected
component and so we save it and it's equireachable vertices as a strongly
connected component.
Complexity: strong_connect() is called at most once for each node and has a
complexity of O(|E|) as it is DFS.
Therefore this has complexity O(|V| + |E|) for a graph G = (V, E)
"""
n = len(g)
stack = deque()
on_stack = [False for _ in range(n)]
index_of = [-1 for _ in range(n)]
lowlink_of = index_of[:]
def strong_connect(v, index, components):
index_of[v] = index # the number when this node is seen
lowlink_of[v] = index # lowest rank node reachable from here
index += 1
stack.append(v)
on_stack[v] = True
for w in g[v]:
if index_of[w] == -1:
index = strong_connect(w, index, components)
lowlink_of[v] = (
lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
)
elif on_stack[w]:
lowlink_of[v] = (
lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
)
if lowlink_of[v] == index_of[v]:
component = []
w = stack.pop()
on_stack[w] = False
component.append(w)
while w != v:
w = stack.pop()
on_stack[w] = False
component.append(w)
components.append(component)
return index
components = []
for v in range(n):
if index_of[v] == -1:
strong_connect(v, 0, components)
return components
def create_graph(n, edges):
g = [[] for _ in range(n)]
for u, v in edges:
g[u].append(v)
return g
if __name__ == "__main__":
# Test
n_vertices = 7
source = [0, 0, 1, 2, 3, 3, 4, 4, 6]
target = [1, 3, 2, 0, 1, 4, 5, 6, 5]
edges = list(zip(source, target))
g = create_graph(n_vertices, edges)
assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)
| 1 |
TheAlgorithms/Python | 9,062 | Delete empty junk file | ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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 | "2023-09-16T18:59:42Z" | "2023-09-16T22:12:31Z" | 1488cdea708485eb1d81c73126eab13cb9b04a47 | fbad85d3ecbbb826a5891807c823149d38bbaed3 | Delete empty junk file. ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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".
| """
Ugly numbers are numbers whose only prime factors are 2, 3 or 5. The sequence
1, 2, 3, 4, 5, 6, 8, 9, 10, 12, 15, … shows the first 11 ugly numbers. By convention,
1 is included.
Given an integer n, we have to find the nth ugly number.
For more details, refer this article
https://www.geeksforgeeks.org/ugly-numbers/
"""
def ugly_numbers(n: int) -> int:
"""
Returns the nth ugly number.
>>> ugly_numbers(100)
1536
>>> ugly_numbers(0)
1
>>> ugly_numbers(20)
36
>>> ugly_numbers(-5)
1
>>> ugly_numbers(-5.5)
Traceback (most recent call last):
...
TypeError: 'float' object cannot be interpreted as an integer
"""
ugly_nums = [1]
i2, i3, i5 = 0, 0, 0
next_2 = ugly_nums[i2] * 2
next_3 = ugly_nums[i3] * 3
next_5 = ugly_nums[i5] * 5
for _ in range(1, n):
next_num = min(next_2, next_3, next_5)
ugly_nums.append(next_num)
if next_num == next_2:
i2 += 1
next_2 = ugly_nums[i2] * 2
if next_num == next_3:
i3 += 1
next_3 = ugly_nums[i3] * 3
if next_num == next_5:
i5 += 1
next_5 = ugly_nums[i5] * 5
return ugly_nums[-1]
if __name__ == "__main__":
from doctest import testmod
testmod(verbose=True)
print(f"{ugly_numbers(200) = }")
| """
Ugly numbers are numbers whose only prime factors are 2, 3 or 5. The sequence
1, 2, 3, 4, 5, 6, 8, 9, 10, 12, 15, … shows the first 11 ugly numbers. By convention,
1 is included.
Given an integer n, we have to find the nth ugly number.
For more details, refer this article
https://www.geeksforgeeks.org/ugly-numbers/
"""
def ugly_numbers(n: int) -> int:
"""
Returns the nth ugly number.
>>> ugly_numbers(100)
1536
>>> ugly_numbers(0)
1
>>> ugly_numbers(20)
36
>>> ugly_numbers(-5)
1
>>> ugly_numbers(-5.5)
Traceback (most recent call last):
...
TypeError: 'float' object cannot be interpreted as an integer
"""
ugly_nums = [1]
i2, i3, i5 = 0, 0, 0
next_2 = ugly_nums[i2] * 2
next_3 = ugly_nums[i3] * 3
next_5 = ugly_nums[i5] * 5
for _ in range(1, n):
next_num = min(next_2, next_3, next_5)
ugly_nums.append(next_num)
if next_num == next_2:
i2 += 1
next_2 = ugly_nums[i2] * 2
if next_num == next_3:
i3 += 1
next_3 = ugly_nums[i3] * 3
if next_num == next_5:
i5 += 1
next_5 = ugly_nums[i5] * 5
return ugly_nums[-1]
if __name__ == "__main__":
from doctest import testmod
testmod(verbose=True)
print(f"{ugly_numbers(200) = }")
| -1 |
TheAlgorithms/Python | 9,062 | Delete empty junk file | ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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 | "2023-09-16T18:59:42Z" | "2023-09-16T22:12:31Z" | 1488cdea708485eb1d81c73126eab13cb9b04a47 | fbad85d3ecbbb826a5891807c823149d38bbaed3 | Delete empty junk file. ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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".
| """
Peak signal-to-noise ratio - PSNR
https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio
Source:
https://tutorials.techonical.com/how-to-calculate-psnr-value-of-two-images-using-python
"""
import math
import os
import cv2
import numpy as np
PIXEL_MAX = 255.0
def peak_signal_to_noise_ratio(original: float, contrast: float) -> float:
mse = np.mean((original - contrast) ** 2)
if mse == 0:
return 100
return 20 * math.log10(PIXEL_MAX / math.sqrt(mse))
def main() -> None:
dir_path = os.path.dirname(os.path.realpath(__file__))
# Loading images (original image and compressed image)
original = cv2.imread(os.path.join(dir_path, "image_data/original_image.png"))
contrast = cv2.imread(os.path.join(dir_path, "image_data/compressed_image.png"), 1)
original2 = cv2.imread(os.path.join(dir_path, "image_data/PSNR-example-base.png"))
contrast2 = cv2.imread(
os.path.join(dir_path, "image_data/PSNR-example-comp-10.jpg"), 1
)
# Value expected: 29.73dB
print("-- First Test --")
print(f"PSNR value is {peak_signal_to_noise_ratio(original, contrast)} dB")
# # Value expected: 31.53dB (Wikipedia Example)
print("\n-- Second Test --")
print(f"PSNR value is {peak_signal_to_noise_ratio(original2, contrast2)} dB")
if __name__ == "__main__":
main()
| """
Peak signal-to-noise ratio - PSNR
https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio
Source:
https://tutorials.techonical.com/how-to-calculate-psnr-value-of-two-images-using-python
"""
import math
import os
import cv2
import numpy as np
PIXEL_MAX = 255.0
def peak_signal_to_noise_ratio(original: float, contrast: float) -> float:
mse = np.mean((original - contrast) ** 2)
if mse == 0:
return 100
return 20 * math.log10(PIXEL_MAX / math.sqrt(mse))
def main() -> None:
dir_path = os.path.dirname(os.path.realpath(__file__))
# Loading images (original image and compressed image)
original = cv2.imread(os.path.join(dir_path, "image_data/original_image.png"))
contrast = cv2.imread(os.path.join(dir_path, "image_data/compressed_image.png"), 1)
original2 = cv2.imread(os.path.join(dir_path, "image_data/PSNR-example-base.png"))
contrast2 = cv2.imread(
os.path.join(dir_path, "image_data/PSNR-example-comp-10.jpg"), 1
)
# Value expected: 29.73dB
print("-- First Test --")
print(f"PSNR value is {peak_signal_to_noise_ratio(original, contrast)} dB")
# # Value expected: 31.53dB (Wikipedia Example)
print("\n-- Second Test --")
print(f"PSNR value is {peak_signal_to_noise_ratio(original2, contrast2)} dB")
if __name__ == "__main__":
main()
| -1 |
TheAlgorithms/Python | 9,062 | Delete empty junk file | ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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 | "2023-09-16T18:59:42Z" | "2023-09-16T22:12:31Z" | 1488cdea708485eb1d81c73126eab13cb9b04a47 | fbad85d3ecbbb826a5891807c823149d38bbaed3 | Delete empty junk file. ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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".
| """
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 | 9,062 | Delete empty junk file | ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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 | "2023-09-16T18:59:42Z" | "2023-09-16T22:12:31Z" | 1488cdea708485eb1d81c73126eab13cb9b04a47 | fbad85d3ecbbb826a5891807c823149d38bbaed3 | Delete empty junk file. ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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 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 | 9,062 | Delete empty junk file | ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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 | "2023-09-16T18:59:42Z" | "2023-09-16T22:12:31Z" | 1488cdea708485eb1d81c73126eab13cb9b04a47 | fbad85d3ecbbb826a5891807c823149d38bbaed3 | Delete empty junk file. ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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 | 9,062 | Delete empty junk file | ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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 | "2023-09-16T18:59:42Z" | "2023-09-16T22:12:31Z" | 1488cdea708485eb1d81c73126eab13cb9b04a47 | fbad85d3ecbbb826a5891807c823149d38bbaed3 | Delete empty junk file. ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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 sys
def minimum_subarray_sum(target: int, numbers: list[int]) -> int:
"""
Return the length of the shortest contiguous subarray in a list of numbers whose sum
is at least target. Reference: https://stackoverflow.com/questions/8269916
>>> minimum_subarray_sum(7, [2, 3, 1, 2, 4, 3])
2
>>> minimum_subarray_sum(7, [2, 3, -1, 2, 4, -3])
4
>>> minimum_subarray_sum(11, [1, 1, 1, 1, 1, 1, 1, 1])
0
>>> minimum_subarray_sum(10, [1, 2, 3, 4, 5, 6, 7])
2
>>> minimum_subarray_sum(5, [1, 1, 1, 1, 1, 5])
1
>>> minimum_subarray_sum(0, [])
0
>>> minimum_subarray_sum(0, [1, 2, 3])
1
>>> minimum_subarray_sum(10, [10, 20, 30])
1
>>> minimum_subarray_sum(7, [1, 1, 1, 1, 1, 1, 10])
1
>>> minimum_subarray_sum(6, [])
0
>>> minimum_subarray_sum(2, [1, 2, 3])
1
>>> minimum_subarray_sum(-6, [])
0
>>> minimum_subarray_sum(-6, [3, 4, 5])
1
>>> minimum_subarray_sum(8, None)
0
>>> minimum_subarray_sum(2, "ABC")
Traceback (most recent call last):
...
ValueError: numbers must be an iterable of integers
"""
if not numbers:
return 0
if target == 0 and target in numbers:
return 0
if not isinstance(numbers, (list, tuple)) or not all(
isinstance(number, int) for number in numbers
):
raise ValueError("numbers must be an iterable of integers")
left = right = curr_sum = 0
min_len = sys.maxsize
while right < len(numbers):
curr_sum += numbers[right]
while curr_sum >= target and left <= right:
min_len = min(min_len, right - left + 1)
curr_sum -= numbers[left]
left += 1
right += 1
return 0 if min_len == sys.maxsize else min_len
| import sys
def minimum_subarray_sum(target: int, numbers: list[int]) -> int:
"""
Return the length of the shortest contiguous subarray in a list of numbers whose sum
is at least target. Reference: https://stackoverflow.com/questions/8269916
>>> minimum_subarray_sum(7, [2, 3, 1, 2, 4, 3])
2
>>> minimum_subarray_sum(7, [2, 3, -1, 2, 4, -3])
4
>>> minimum_subarray_sum(11, [1, 1, 1, 1, 1, 1, 1, 1])
0
>>> minimum_subarray_sum(10, [1, 2, 3, 4, 5, 6, 7])
2
>>> minimum_subarray_sum(5, [1, 1, 1, 1, 1, 5])
1
>>> minimum_subarray_sum(0, [])
0
>>> minimum_subarray_sum(0, [1, 2, 3])
1
>>> minimum_subarray_sum(10, [10, 20, 30])
1
>>> minimum_subarray_sum(7, [1, 1, 1, 1, 1, 1, 10])
1
>>> minimum_subarray_sum(6, [])
0
>>> minimum_subarray_sum(2, [1, 2, 3])
1
>>> minimum_subarray_sum(-6, [])
0
>>> minimum_subarray_sum(-6, [3, 4, 5])
1
>>> minimum_subarray_sum(8, None)
0
>>> minimum_subarray_sum(2, "ABC")
Traceback (most recent call last):
...
ValueError: numbers must be an iterable of integers
"""
if not numbers:
return 0
if target == 0 and target in numbers:
return 0
if not isinstance(numbers, (list, tuple)) or not all(
isinstance(number, int) for number in numbers
):
raise ValueError("numbers must be an iterable of integers")
left = right = curr_sum = 0
min_len = sys.maxsize
while right < len(numbers):
curr_sum += numbers[right]
while curr_sum >= target and left <= right:
min_len = min(min_len, right - left + 1)
curr_sum -= numbers[left]
left += 1
right += 1
return 0 if min_len == sys.maxsize else min_len
| -1 |
TheAlgorithms/Python | 9,062 | Delete empty junk file | ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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 | "2023-09-16T18:59:42Z" | "2023-09-16T22:12:31Z" | 1488cdea708485eb1d81c73126eab13cb9b04a47 | fbad85d3ecbbb826a5891807c823149d38bbaed3 | Delete empty junk file. ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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".
| """
Project Euler Problem 73: https://projecteuler.net/problem=73
Consider the fraction, n/d, where n and d are positive integers.
If n<d and HCF(n,d)=1, it is called a reduced proper fraction.
If we list the set of reduced proper fractions for d ≤ 8 in ascending order of size,
we get:
1/8, 1/7, 1/6, 1/5, 1/4, 2/7, 1/3, 3/8, 2/5, 3/7, 1/2, 4/7, 3/5, 5/8, 2/3,
5/7, 3/4, 4/5, 5/6, 6/7, 7/8
It can be seen that there are 3 fractions between 1/3 and 1/2.
How many fractions lie between 1/3 and 1/2 in the sorted set
of reduced proper fractions for d ≤ 12,000?
"""
from math import gcd
def solution(max_d: int = 12_000) -> int:
"""
Returns number of fractions lie between 1/3 and 1/2 in the sorted set
of reduced proper fractions for d ≤ max_d
>>> solution(4)
0
>>> solution(5)
1
>>> solution(8)
3
"""
fractions_number = 0
for d in range(max_d + 1):
for n in range(d // 3 + 1, (d + 1) // 2):
if gcd(n, d) == 1:
fractions_number += 1
return fractions_number
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 73: https://projecteuler.net/problem=73
Consider the fraction, n/d, where n and d are positive integers.
If n<d and HCF(n,d)=1, it is called a reduced proper fraction.
If we list the set of reduced proper fractions for d ≤ 8 in ascending order of size,
we get:
1/8, 1/7, 1/6, 1/5, 1/4, 2/7, 1/3, 3/8, 2/5, 3/7, 1/2, 4/7, 3/5, 5/8, 2/3,
5/7, 3/4, 4/5, 5/6, 6/7, 7/8
It can be seen that there are 3 fractions between 1/3 and 1/2.
How many fractions lie between 1/3 and 1/2 in the sorted set
of reduced proper fractions for d ≤ 12,000?
"""
from math import gcd
def solution(max_d: int = 12_000) -> int:
"""
Returns number of fractions lie between 1/3 and 1/2 in the sorted set
of reduced proper fractions for d ≤ max_d
>>> solution(4)
0
>>> solution(5)
1
>>> solution(8)
3
"""
fractions_number = 0
for d in range(max_d + 1):
for n in range(d // 3 + 1, (d + 1) // 2):
if gcd(n, d) == 1:
fractions_number += 1
return fractions_number
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 9,062 | Delete empty junk file | ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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 | "2023-09-16T18:59:42Z" | "2023-09-16T22:12:31Z" | 1488cdea708485eb1d81c73126eab13cb9b04a47 | fbad85d3ecbbb826a5891807c823149d38bbaed3 | Delete empty junk file. ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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".
| """
What is the greatest product of four adjacent numbers (horizontally,
vertically, or diagonally) in this 20x20 array?
08 02 22 97 38 15 00 40 00 75 04 05 07 78 52 12 50 77 91 08
49 49 99 40 17 81 18 57 60 87 17 40 98 43 69 48 04 56 62 00
81 49 31 73 55 79 14 29 93 71 40 67 53 88 30 03 49 13 36 65
52 70 95 23 04 60 11 42 69 24 68 56 01 32 56 71 37 02 36 91
22 31 16 71 51 67 63 89 41 92 36 54 22 40 40 28 66 33 13 80
24 47 32 60 99 03 45 02 44 75 33 53 78 36 84 20 35 17 12 50
32 98 81 28 64 23 67 10 26 38 40 67 59 54 70 66 18 38 64 70
67 26 20 68 02 62 12 20 95 63 94 39 63 08 40 91 66 49 94 21
24 55 58 05 66 73 99 26 97 17 78 78 96 83 14 88 34 89 63 72
21 36 23 09 75 00 76 44 20 45 35 14 00 61 33 97 34 31 33 95
78 17 53 28 22 75 31 67 15 94 03 80 04 62 16 14 09 53 56 92
16 39 05 42 96 35 31 47 55 58 88 24 00 17 54 24 36 29 85 57
86 56 00 48 35 71 89 07 05 44 44 37 44 60 21 58 51 54 17 58
19 80 81 68 05 94 47 69 28 73 92 13 86 52 17 77 04 89 55 40
04 52 08 83 97 35 99 16 07 97 57 32 16 26 26 79 33 27 98 66
88 36 68 87 57 62 20 72 03 46 33 67 46 55 12 32 63 93 53 69
04 42 16 73 38 25 39 11 24 94 72 18 08 46 29 32 40 62 76 36
20 69 36 41 72 30 23 88 34 62 99 69 82 67 59 85 74 04 36 16
20 73 35 29 78 31 90 01 74 31 49 71 48 86 81 16 23 57 05 54
01 70 54 71 83 51 54 69 16 92 33 48 61 43 52 01 89 19 67 48
"""
import os
def solution():
"""Returns the greatest product of four adjacent numbers (horizontally,
vertically, or diagonally).
>>> solution()
70600674
"""
with open(os.path.dirname(__file__) + "/grid.txt") as f:
l = [] # noqa: E741
for _ in range(20):
l.append([int(x) for x in f.readline().split()])
maximum = 0
# right
for i in range(20):
for j in range(17):
temp = l[i][j] * l[i][j + 1] * l[i][j + 2] * l[i][j + 3]
if temp > maximum:
maximum = temp
# down
for i in range(17):
for j in range(20):
temp = l[i][j] * l[i + 1][j] * l[i + 2][j] * l[i + 3][j]
if temp > maximum:
maximum = temp
# diagonal 1
for i in range(17):
for j in range(17):
temp = l[i][j] * l[i + 1][j + 1] * l[i + 2][j + 2] * l[i + 3][j + 3]
if temp > maximum:
maximum = temp
# diagonal 2
for i in range(17):
for j in range(3, 20):
temp = l[i][j] * l[i + 1][j - 1] * l[i + 2][j - 2] * l[i + 3][j - 3]
if temp > maximum:
maximum = temp
return maximum
if __name__ == "__main__":
print(solution())
| """
What is the greatest product of four adjacent numbers (horizontally,
vertically, or diagonally) in this 20x20 array?
08 02 22 97 38 15 00 40 00 75 04 05 07 78 52 12 50 77 91 08
49 49 99 40 17 81 18 57 60 87 17 40 98 43 69 48 04 56 62 00
81 49 31 73 55 79 14 29 93 71 40 67 53 88 30 03 49 13 36 65
52 70 95 23 04 60 11 42 69 24 68 56 01 32 56 71 37 02 36 91
22 31 16 71 51 67 63 89 41 92 36 54 22 40 40 28 66 33 13 80
24 47 32 60 99 03 45 02 44 75 33 53 78 36 84 20 35 17 12 50
32 98 81 28 64 23 67 10 26 38 40 67 59 54 70 66 18 38 64 70
67 26 20 68 02 62 12 20 95 63 94 39 63 08 40 91 66 49 94 21
24 55 58 05 66 73 99 26 97 17 78 78 96 83 14 88 34 89 63 72
21 36 23 09 75 00 76 44 20 45 35 14 00 61 33 97 34 31 33 95
78 17 53 28 22 75 31 67 15 94 03 80 04 62 16 14 09 53 56 92
16 39 05 42 96 35 31 47 55 58 88 24 00 17 54 24 36 29 85 57
86 56 00 48 35 71 89 07 05 44 44 37 44 60 21 58 51 54 17 58
19 80 81 68 05 94 47 69 28 73 92 13 86 52 17 77 04 89 55 40
04 52 08 83 97 35 99 16 07 97 57 32 16 26 26 79 33 27 98 66
88 36 68 87 57 62 20 72 03 46 33 67 46 55 12 32 63 93 53 69
04 42 16 73 38 25 39 11 24 94 72 18 08 46 29 32 40 62 76 36
20 69 36 41 72 30 23 88 34 62 99 69 82 67 59 85 74 04 36 16
20 73 35 29 78 31 90 01 74 31 49 71 48 86 81 16 23 57 05 54
01 70 54 71 83 51 54 69 16 92 33 48 61 43 52 01 89 19 67 48
"""
import os
def solution():
"""Returns the greatest product of four adjacent numbers (horizontally,
vertically, or diagonally).
>>> solution()
70600674
"""
with open(os.path.dirname(__file__) + "/grid.txt") as f:
l = [] # noqa: E741
for _ in range(20):
l.append([int(x) for x in f.readline().split()])
maximum = 0
# right
for i in range(20):
for j in range(17):
temp = l[i][j] * l[i][j + 1] * l[i][j + 2] * l[i][j + 3]
if temp > maximum:
maximum = temp
# down
for i in range(17):
for j in range(20):
temp = l[i][j] * l[i + 1][j] * l[i + 2][j] * l[i + 3][j]
if temp > maximum:
maximum = temp
# diagonal 1
for i in range(17):
for j in range(17):
temp = l[i][j] * l[i + 1][j + 1] * l[i + 2][j + 2] * l[i + 3][j + 3]
if temp > maximum:
maximum = temp
# diagonal 2
for i in range(17):
for j in range(3, 20):
temp = l[i][j] * l[i + 1][j - 1] * l[i + 2][j - 2] * l[i + 3][j - 3]
if temp > maximum:
maximum = temp
return maximum
if __name__ == "__main__":
print(solution())
| -1 |
TheAlgorithms/Python | 9,062 | Delete empty junk file | ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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 | "2023-09-16T18:59:42Z" | "2023-09-16T22:12:31Z" | 1488cdea708485eb1d81c73126eab13cb9b04a47 | fbad85d3ecbbb826a5891807c823149d38bbaed3 | Delete empty junk file. ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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".
| from graphs.minimum_spanning_tree_kruskal import kruskal
def test_kruskal_successful_result():
num_nodes = 9
edges = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
[8, 6, 6],
[2, 3, 7],
[2, 5, 4],
[6, 5, 2],
[3, 5, 14],
[3, 4, 9],
[5, 4, 10],
[1, 7, 11],
]
result = kruskal(num_nodes, edges)
expected = [
[7, 6, 1],
[2, 8, 2],
[6, 5, 2],
[0, 1, 4],
[2, 5, 4],
[2, 3, 7],
[0, 7, 8],
[3, 4, 9],
]
assert sorted(expected) == sorted(result)
| from graphs.minimum_spanning_tree_kruskal import kruskal
def test_kruskal_successful_result():
num_nodes = 9
edges = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
[8, 6, 6],
[2, 3, 7],
[2, 5, 4],
[6, 5, 2],
[3, 5, 14],
[3, 4, 9],
[5, 4, 10],
[1, 7, 11],
]
result = kruskal(num_nodes, edges)
expected = [
[7, 6, 1],
[2, 8, 2],
[6, 5, 2],
[0, 1, 4],
[2, 5, 4],
[2, 3, 7],
[0, 7, 8],
[3, 4, 9],
]
assert sorted(expected) == sorted(result)
| -1 |
TheAlgorithms/Python | 9,062 | Delete empty junk file | ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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 | "2023-09-16T18:59:42Z" | "2023-09-16T22:12:31Z" | 1488cdea708485eb1d81c73126eab13cb9b04a47 | fbad85d3ecbbb826a5891807c823149d38bbaed3 | Delete empty junk file. ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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".
| """
Project Euler Problem 82: https://projecteuler.net/problem=82
The minimal path sum in the 5 by 5 matrix below, by starting in any cell
in the left column and finishing in any cell in the right column,
and only moving up, down, and right, is indicated in red and bold;
the sum is equal to 994.
131 673 [234] [103] [18]
[201] [96] [342] 965 150
630 803 746 422 111
537 699 497 121 956
805 732 524 37 331
Find the minimal path sum from the left column to the right column in matrix.txt
(https://projecteuler.net/project/resources/p082_matrix.txt)
(right click and "Save Link/Target As..."),
a 31K text file containing an 80 by 80 matrix.
"""
import os
def solution(filename: str = "input.txt") -> int:
"""
Returns the minimal path sum in the matrix from the file, by starting in any cell
in the left column and finishing in any cell in the right column,
and only moving up, down, and right
>>> solution("test_matrix.txt")
994
"""
with open(os.path.join(os.path.dirname(__file__), filename)) as input_file:
matrix = [
[int(element) for element in line.split(",")]
for line in input_file.readlines()
]
rows = len(matrix)
cols = len(matrix[0])
minimal_path_sums = [[-1 for _ in range(cols)] for _ in range(rows)]
for i in range(rows):
minimal_path_sums[i][0] = matrix[i][0]
for j in range(1, cols):
for i in range(rows):
minimal_path_sums[i][j] = minimal_path_sums[i][j - 1] + matrix[i][j]
for i in range(1, rows):
minimal_path_sums[i][j] = min(
minimal_path_sums[i][j], minimal_path_sums[i - 1][j] + matrix[i][j]
)
for i in range(rows - 2, -1, -1):
minimal_path_sums[i][j] = min(
minimal_path_sums[i][j], minimal_path_sums[i + 1][j] + matrix[i][j]
)
return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums)
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 82: https://projecteuler.net/problem=82
The minimal path sum in the 5 by 5 matrix below, by starting in any cell
in the left column and finishing in any cell in the right column,
and only moving up, down, and right, is indicated in red and bold;
the sum is equal to 994.
131 673 [234] [103] [18]
[201] [96] [342] 965 150
630 803 746 422 111
537 699 497 121 956
805 732 524 37 331
Find the minimal path sum from the left column to the right column in matrix.txt
(https://projecteuler.net/project/resources/p082_matrix.txt)
(right click and "Save Link/Target As..."),
a 31K text file containing an 80 by 80 matrix.
"""
import os
def solution(filename: str = "input.txt") -> int:
"""
Returns the minimal path sum in the matrix from the file, by starting in any cell
in the left column and finishing in any cell in the right column,
and only moving up, down, and right
>>> solution("test_matrix.txt")
994
"""
with open(os.path.join(os.path.dirname(__file__), filename)) as input_file:
matrix = [
[int(element) for element in line.split(",")]
for line in input_file.readlines()
]
rows = len(matrix)
cols = len(matrix[0])
minimal_path_sums = [[-1 for _ in range(cols)] for _ in range(rows)]
for i in range(rows):
minimal_path_sums[i][0] = matrix[i][0]
for j in range(1, cols):
for i in range(rows):
minimal_path_sums[i][j] = minimal_path_sums[i][j - 1] + matrix[i][j]
for i in range(1, rows):
minimal_path_sums[i][j] = min(
minimal_path_sums[i][j], minimal_path_sums[i - 1][j] + matrix[i][j]
)
for i in range(rows - 2, -1, -1):
minimal_path_sums[i][j] = min(
minimal_path_sums[i][j], minimal_path_sums[i + 1][j] + matrix[i][j]
)
return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums)
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 9,062 | Delete empty junk file | ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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 | "2023-09-16T18:59:42Z" | "2023-09-16T22:12:31Z" | 1488cdea708485eb1d81c73126eab13cb9b04a47 | fbad85d3ecbbb826a5891807c823149d38bbaed3 | Delete empty junk file. ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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".
| """
The first known prime found to exceed one million digits was discovered in 1999,
and is a Mersenne prime of the form 2**6972593 − 1; it contains exactly 2,098,960
digits. Subsequently other Mersenne primes, of the form 2**p − 1, have been found
which contain more digits.
However, in 2004 there was found a massive non-Mersenne prime which contains
2,357,207 digits: (28433 * (2 ** 7830457 + 1)).
Find the last ten digits of this prime number.
"""
def solution(n: int = 10) -> str:
"""
Returns the last n digits of NUMBER.
>>> solution()
'8739992577'
>>> solution(8)
'39992577'
>>> solution(1)
'7'
>>> solution(-1)
Traceback (most recent call last):
...
ValueError: Invalid input
>>> solution(8.3)
Traceback (most recent call last):
...
ValueError: Invalid input
>>> solution("a")
Traceback (most recent call last):
...
ValueError: Invalid input
"""
if not isinstance(n, int) or n < 0:
raise ValueError("Invalid input")
modulus = 10**n
number = 28433 * (pow(2, 7830457, modulus)) + 1
return str(number % modulus)
if __name__ == "__main__":
from doctest import testmod
testmod()
print(f"{solution(10) = }")
| """
The first known prime found to exceed one million digits was discovered in 1999,
and is a Mersenne prime of the form 2**6972593 − 1; it contains exactly 2,098,960
digits. Subsequently other Mersenne primes, of the form 2**p − 1, have been found
which contain more digits.
However, in 2004 there was found a massive non-Mersenne prime which contains
2,357,207 digits: (28433 * (2 ** 7830457 + 1)).
Find the last ten digits of this prime number.
"""
def solution(n: int = 10) -> str:
"""
Returns the last n digits of NUMBER.
>>> solution()
'8739992577'
>>> solution(8)
'39992577'
>>> solution(1)
'7'
>>> solution(-1)
Traceback (most recent call last):
...
ValueError: Invalid input
>>> solution(8.3)
Traceback (most recent call last):
...
ValueError: Invalid input
>>> solution("a")
Traceback (most recent call last):
...
ValueError: Invalid input
"""
if not isinstance(n, int) or n < 0:
raise ValueError("Invalid input")
modulus = 10**n
number = 28433 * (pow(2, 7830457, modulus)) + 1
return str(number % modulus)
if __name__ == "__main__":
from doctest import testmod
testmod()
print(f"{solution(10) = }")
| -1 |
TheAlgorithms/Python | 9,062 | Delete empty junk file | ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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 | "2023-09-16T18:59:42Z" | "2023-09-16T22:12:31Z" | 1488cdea708485eb1d81c73126eab13cb9b04a47 | fbad85d3ecbbb826a5891807c823149d38bbaed3 | Delete empty junk file. ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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".
| """
A NOT Gate is a logic gate in boolean algebra which results to 0 (False) if the
input is high, and 1 (True) if the input is low.
Following is the truth table of a XOR Gate:
------------------------------
| Input | Output |
------------------------------
| 0 | 1 |
| 1 | 0 |
------------------------------
Refer - https://www.geeksforgeeks.org/logic-gates-in-python/
"""
def not_gate(input_1: int) -> int:
"""
Calculate NOT of the input values
>>> not_gate(0)
1
>>> not_gate(1)
0
"""
return 1 if input_1 == 0 else 0
def test_not_gate() -> None:
"""
Tests the not_gate function
"""
assert not_gate(0) == 1
assert not_gate(1) == 0
if __name__ == "__main__":
print(not_gate(0))
print(not_gate(1))
| """
A NOT Gate is a logic gate in boolean algebra which results to 0 (False) if the
input is high, and 1 (True) if the input is low.
Following is the truth table of a XOR Gate:
------------------------------
| Input | Output |
------------------------------
| 0 | 1 |
| 1 | 0 |
------------------------------
Refer - https://www.geeksforgeeks.org/logic-gates-in-python/
"""
def not_gate(input_1: int) -> int:
"""
Calculate NOT of the input values
>>> not_gate(0)
1
>>> not_gate(1)
0
"""
return 1 if input_1 == 0 else 0
def test_not_gate() -> None:
"""
Tests the not_gate function
"""
assert not_gate(0) == 1
assert not_gate(1) == 0
if __name__ == "__main__":
print(not_gate(0))
print(not_gate(1))
| -1 |
TheAlgorithms/Python | 9,062 | Delete empty junk file | ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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 | "2023-09-16T18:59:42Z" | "2023-09-16T22:12:31Z" | 1488cdea708485eb1d81c73126eab13cb9b04a47 | fbad85d3ecbbb826a5891807c823149d38bbaed3 | Delete empty junk file. ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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".
| """
Project Euler Problem 75: https://projecteuler.net/problem=75
It turns out that 12 cm is the smallest length of wire that can be bent to form an
integer sided right angle triangle in exactly one way, but there are many more examples.
12 cm: (3,4,5)
24 cm: (6,8,10)
30 cm: (5,12,13)
36 cm: (9,12,15)
40 cm: (8,15,17)
48 cm: (12,16,20)
In contrast, some lengths of wire, like 20 cm, cannot be bent to form an integer sided
right angle triangle, and other lengths allow more than one solution to be found; for
example, using 120 cm it is possible to form exactly three different integer sided
right angle triangles.
120 cm: (30,40,50), (20,48,52), (24,45,51)
Given that L is the length of the wire, for how many values of L ≤ 1,500,000 can
exactly one integer sided right angle triangle be formed?
Solution: we generate all pythagorean triples using Euclid's formula and
keep track of the frequencies of the perimeters.
Reference: https://en.wikipedia.org/wiki/Pythagorean_triple#Generating_a_triple
"""
from collections import defaultdict
from math import gcd
def solution(limit: int = 1500000) -> int:
"""
Return the number of values of L <= limit such that a wire of length L can be
formmed into an integer sided right angle triangle in exactly one way.
>>> solution(50)
6
>>> solution(1000)
112
>>> solution(50000)
5502
"""
frequencies: defaultdict = defaultdict(int)
euclid_m = 2
while 2 * euclid_m * (euclid_m + 1) <= limit:
for euclid_n in range((euclid_m % 2) + 1, euclid_m, 2):
if gcd(euclid_m, euclid_n) > 1:
continue
primitive_perimeter = 2 * euclid_m * (euclid_m + euclid_n)
for perimeter in range(primitive_perimeter, limit + 1, primitive_perimeter):
frequencies[perimeter] += 1
euclid_m += 1
return sum(1 for frequency in frequencies.values() if frequency == 1)
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 75: https://projecteuler.net/problem=75
It turns out that 12 cm is the smallest length of wire that can be bent to form an
integer sided right angle triangle in exactly one way, but there are many more examples.
12 cm: (3,4,5)
24 cm: (6,8,10)
30 cm: (5,12,13)
36 cm: (9,12,15)
40 cm: (8,15,17)
48 cm: (12,16,20)
In contrast, some lengths of wire, like 20 cm, cannot be bent to form an integer sided
right angle triangle, and other lengths allow more than one solution to be found; for
example, using 120 cm it is possible to form exactly three different integer sided
right angle triangles.
120 cm: (30,40,50), (20,48,52), (24,45,51)
Given that L is the length of the wire, for how many values of L ≤ 1,500,000 can
exactly one integer sided right angle triangle be formed?
Solution: we generate all pythagorean triples using Euclid's formula and
keep track of the frequencies of the perimeters.
Reference: https://en.wikipedia.org/wiki/Pythagorean_triple#Generating_a_triple
"""
from collections import defaultdict
from math import gcd
def solution(limit: int = 1500000) -> int:
"""
Return the number of values of L <= limit such that a wire of length L can be
formmed into an integer sided right angle triangle in exactly one way.
>>> solution(50)
6
>>> solution(1000)
112
>>> solution(50000)
5502
"""
frequencies: defaultdict = defaultdict(int)
euclid_m = 2
while 2 * euclid_m * (euclid_m + 1) <= limit:
for euclid_n in range((euclid_m % 2) + 1, euclid_m, 2):
if gcd(euclid_m, euclid_n) > 1:
continue
primitive_perimeter = 2 * euclid_m * (euclid_m + euclid_n)
for perimeter in range(primitive_perimeter, limit + 1, primitive_perimeter):
frequencies[perimeter] += 1
euclid_m += 1
return sum(1 for frequency in frequencies.values() if frequency == 1)
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 9,062 | Delete empty junk file | ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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 | "2023-09-16T18:59:42Z" | "2023-09-16T22:12:31Z" | 1488cdea708485eb1d81c73126eab13cb9b04a47 | fbad85d3ecbbb826a5891807c823149d38bbaed3 | Delete empty junk file. ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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: Phyllipe Bezerra (https://github.com/pmba)
clothes = {
0: "underwear",
1: "pants",
2: "belt",
3: "suit",
4: "shoe",
5: "socks",
6: "shirt",
7: "tie",
8: "watch",
}
graph = [[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]
visited = [0 for x in range(len(graph))]
stack = []
def print_stack(stack, clothes):
order = 1
while stack:
current_clothing = stack.pop()
print(order, clothes[current_clothing])
order += 1
def depth_first_search(u, visited, graph):
visited[u] = 1
for v in graph[u]:
if not visited[v]:
depth_first_search(v, visited, graph)
stack.append(u)
def topological_sort(graph, visited):
for v in range(len(graph)):
if not visited[v]:
depth_first_search(v, visited, graph)
if __name__ == "__main__":
topological_sort(graph, visited)
print(stack)
print_stack(stack, clothes)
| # Author: Phyllipe Bezerra (https://github.com/pmba)
clothes = {
0: "underwear",
1: "pants",
2: "belt",
3: "suit",
4: "shoe",
5: "socks",
6: "shirt",
7: "tie",
8: "watch",
}
graph = [[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]
visited = [0 for x in range(len(graph))]
stack = []
def print_stack(stack, clothes):
order = 1
while stack:
current_clothing = stack.pop()
print(order, clothes[current_clothing])
order += 1
def depth_first_search(u, visited, graph):
visited[u] = 1
for v in graph[u]:
if not visited[v]:
depth_first_search(v, visited, graph)
stack.append(u)
def topological_sort(graph, visited):
for v in range(len(graph)):
if not visited[v]:
depth_first_search(v, visited, graph)
if __name__ == "__main__":
topological_sort(graph, visited)
print(stack)
print_stack(stack, clothes)
| -1 |
TheAlgorithms/Python | 9,062 | Delete empty junk file | ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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 | "2023-09-16T18:59:42Z" | "2023-09-16T22:12:31Z" | 1488cdea708485eb1d81c73126eab13cb9b04a47 | fbad85d3ecbbb826a5891807c823149d38bbaed3 | Delete empty junk file. ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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".
| """
An implementation of Karger's Algorithm for partitioning a graph.
"""
from __future__ import annotations
import random
# Adjacency list representation of this graph:
# https://en.wikipedia.org/wiki/File:Single_run_of_Karger%E2%80%99s_Mincut_algorithm.svg
TEST_GRAPH = {
"1": ["2", "3", "4", "5"],
"2": ["1", "3", "4", "5"],
"3": ["1", "2", "4", "5", "10"],
"4": ["1", "2", "3", "5", "6"],
"5": ["1", "2", "3", "4", "7"],
"6": ["7", "8", "9", "10", "4"],
"7": ["6", "8", "9", "10", "5"],
"8": ["6", "7", "9", "10"],
"9": ["6", "7", "8", "10"],
"10": ["6", "7", "8", "9", "3"],
}
def partition_graph(graph: dict[str, list[str]]) -> set[tuple[str, str]]:
"""
Partitions a graph using Karger's Algorithm. Implemented from
pseudocode found here:
https://en.wikipedia.org/wiki/Karger%27s_algorithm.
This function involves random choices, meaning it will not give
consistent outputs.
Args:
graph: A dictionary containing adacency lists for the graph.
Nodes must be strings.
Returns:
The cutset of the cut found by Karger's Algorithm.
>>> graph = {'0':['1'], '1':['0']}
>>> partition_graph(graph)
{('0', '1')}
"""
# Dict that maps contracted nodes to a list of all the nodes it "contains."
contracted_nodes = {node: {node} for node in graph}
graph_copy = {node: graph[node][:] for node in graph}
while len(graph_copy) > 2:
# Choose a random edge.
u = random.choice(list(graph_copy.keys()))
v = random.choice(graph_copy[u])
# Contract edge (u, v) to new node uv
uv = u + v
uv_neighbors = list(set(graph_copy[u] + graph_copy[v]))
uv_neighbors.remove(u)
uv_neighbors.remove(v)
graph_copy[uv] = uv_neighbors
for neighbor in uv_neighbors:
graph_copy[neighbor].append(uv)
contracted_nodes[uv] = set(contracted_nodes[u].union(contracted_nodes[v]))
# Remove nodes u and v.
del graph_copy[u]
del graph_copy[v]
for neighbor in uv_neighbors:
if u in graph_copy[neighbor]:
graph_copy[neighbor].remove(u)
if v in graph_copy[neighbor]:
graph_copy[neighbor].remove(v)
# Find cutset.
groups = [contracted_nodes[node] for node in graph_copy]
return {
(node, neighbor)
for node in groups[0]
for neighbor in graph[node]
if neighbor in groups[1]
}
if __name__ == "__main__":
print(partition_graph(TEST_GRAPH))
| """
An implementation of Karger's Algorithm for partitioning a graph.
"""
from __future__ import annotations
import random
# Adjacency list representation of this graph:
# https://en.wikipedia.org/wiki/File:Single_run_of_Karger%E2%80%99s_Mincut_algorithm.svg
TEST_GRAPH = {
"1": ["2", "3", "4", "5"],
"2": ["1", "3", "4", "5"],
"3": ["1", "2", "4", "5", "10"],
"4": ["1", "2", "3", "5", "6"],
"5": ["1", "2", "3", "4", "7"],
"6": ["7", "8", "9", "10", "4"],
"7": ["6", "8", "9", "10", "5"],
"8": ["6", "7", "9", "10"],
"9": ["6", "7", "8", "10"],
"10": ["6", "7", "8", "9", "3"],
}
def partition_graph(graph: dict[str, list[str]]) -> set[tuple[str, str]]:
"""
Partitions a graph using Karger's Algorithm. Implemented from
pseudocode found here:
https://en.wikipedia.org/wiki/Karger%27s_algorithm.
This function involves random choices, meaning it will not give
consistent outputs.
Args:
graph: A dictionary containing adacency lists for the graph.
Nodes must be strings.
Returns:
The cutset of the cut found by Karger's Algorithm.
>>> graph = {'0':['1'], '1':['0']}
>>> partition_graph(graph)
{('0', '1')}
"""
# Dict that maps contracted nodes to a list of all the nodes it "contains."
contracted_nodes = {node: {node} for node in graph}
graph_copy = {node: graph[node][:] for node in graph}
while len(graph_copy) > 2:
# Choose a random edge.
u = random.choice(list(graph_copy.keys()))
v = random.choice(graph_copy[u])
# Contract edge (u, v) to new node uv
uv = u + v
uv_neighbors = list(set(graph_copy[u] + graph_copy[v]))
uv_neighbors.remove(u)
uv_neighbors.remove(v)
graph_copy[uv] = uv_neighbors
for neighbor in uv_neighbors:
graph_copy[neighbor].append(uv)
contracted_nodes[uv] = set(contracted_nodes[u].union(contracted_nodes[v]))
# Remove nodes u and v.
del graph_copy[u]
del graph_copy[v]
for neighbor in uv_neighbors:
if u in graph_copy[neighbor]:
graph_copy[neighbor].remove(u)
if v in graph_copy[neighbor]:
graph_copy[neighbor].remove(v)
# Find cutset.
groups = [contracted_nodes[node] for node in graph_copy]
return {
(node, neighbor)
for node in groups[0]
for neighbor in graph[node]
if neighbor in groups[1]
}
if __name__ == "__main__":
print(partition_graph(TEST_GRAPH))
| -1 |
TheAlgorithms/Python | 9,062 | Delete empty junk file | ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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 | "2023-09-16T18:59:42Z" | "2023-09-16T22:12:31Z" | 1488cdea708485eb1d81c73126eab13cb9b04a47 | fbad85d3ecbbb826a5891807c823149d38bbaed3 | Delete empty junk file. ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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".
| """ Convert Base 10 (Decimal) Values to Hexadecimal Representations """
# set decimal value for each hexadecimal digit
values = {
0: "0",
1: "1",
2: "2",
3: "3",
4: "4",
5: "5",
6: "6",
7: "7",
8: "8",
9: "9",
10: "a",
11: "b",
12: "c",
13: "d",
14: "e",
15: "f",
}
def decimal_to_hexadecimal(decimal: float) -> str:
"""
take integer decimal value, return hexadecimal representation as str beginning
with 0x
>>> decimal_to_hexadecimal(5)
'0x5'
>>> decimal_to_hexadecimal(15)
'0xf'
>>> decimal_to_hexadecimal(37)
'0x25'
>>> decimal_to_hexadecimal(255)
'0xff'
>>> decimal_to_hexadecimal(4096)
'0x1000'
>>> decimal_to_hexadecimal(999098)
'0xf3eba'
>>> # negatives work too
>>> decimal_to_hexadecimal(-256)
'-0x100'
>>> # floats are acceptable if equivalent to an int
>>> decimal_to_hexadecimal(17.0)
'0x11'
>>> # other floats will error
>>> decimal_to_hexadecimal(16.16) # doctest: +ELLIPSIS
Traceback (most recent call last):
...
AssertionError
>>> # strings will error as well
>>> decimal_to_hexadecimal('0xfffff') # doctest: +ELLIPSIS
Traceback (most recent call last):
...
AssertionError
>>> # results are the same when compared to Python's default hex function
>>> decimal_to_hexadecimal(-256) == hex(-256)
True
"""
assert type(decimal) in (int, float) and decimal == int(decimal)
decimal = int(decimal)
hexadecimal = ""
negative = False
if decimal < 0:
negative = True
decimal *= -1
while decimal > 0:
decimal, remainder = divmod(decimal, 16)
hexadecimal = values[remainder] + hexadecimal
hexadecimal = "0x" + hexadecimal
if negative:
hexadecimal = "-" + hexadecimal
return hexadecimal
if __name__ == "__main__":
import doctest
doctest.testmod()
| """ Convert Base 10 (Decimal) Values to Hexadecimal Representations """
# set decimal value for each hexadecimal digit
values = {
0: "0",
1: "1",
2: "2",
3: "3",
4: "4",
5: "5",
6: "6",
7: "7",
8: "8",
9: "9",
10: "a",
11: "b",
12: "c",
13: "d",
14: "e",
15: "f",
}
def decimal_to_hexadecimal(decimal: float) -> str:
"""
take integer decimal value, return hexadecimal representation as str beginning
with 0x
>>> decimal_to_hexadecimal(5)
'0x5'
>>> decimal_to_hexadecimal(15)
'0xf'
>>> decimal_to_hexadecimal(37)
'0x25'
>>> decimal_to_hexadecimal(255)
'0xff'
>>> decimal_to_hexadecimal(4096)
'0x1000'
>>> decimal_to_hexadecimal(999098)
'0xf3eba'
>>> # negatives work too
>>> decimal_to_hexadecimal(-256)
'-0x100'
>>> # floats are acceptable if equivalent to an int
>>> decimal_to_hexadecimal(17.0)
'0x11'
>>> # other floats will error
>>> decimal_to_hexadecimal(16.16) # doctest: +ELLIPSIS
Traceback (most recent call last):
...
AssertionError
>>> # strings will error as well
>>> decimal_to_hexadecimal('0xfffff') # doctest: +ELLIPSIS
Traceback (most recent call last):
...
AssertionError
>>> # results are the same when compared to Python's default hex function
>>> decimal_to_hexadecimal(-256) == hex(-256)
True
"""
assert type(decimal) in (int, float) and decimal == int(decimal)
decimal = int(decimal)
hexadecimal = ""
negative = False
if decimal < 0:
negative = True
decimal *= -1
while decimal > 0:
decimal, remainder = divmod(decimal, 16)
hexadecimal = values[remainder] + hexadecimal
hexadecimal = "0x" + hexadecimal
if negative:
hexadecimal = "-" + hexadecimal
return hexadecimal
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 9,062 | Delete empty junk file | ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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 | "2023-09-16T18:59:42Z" | "2023-09-16T22:12:31Z" | 1488cdea708485eb1d81c73126eab13cb9b04a47 | fbad85d3ecbbb826a5891807c823149d38bbaed3 | Delete empty junk file. ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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".
| """
Implements an is valid email address algorithm
@ https://en.wikipedia.org/wiki/Email_address
"""
import string
email_tests: tuple[tuple[str, bool], ...] = (
("[email protected]", True),
("[email protected]", True),
("[email protected]", True),
("[email protected]", True),
("[email protected]", True),
("[email protected]", True),
("[email protected]", True),
("[email protected]", True),
("test/[email protected]", True),
(
"123456789012345678901234567890123456789012345678901234567890123@example.com",
True,
),
("admin@mailserver1", True),
("[email protected]", True),
("Abc.example.com", False),
("A@b@[email protected]", False),
("[email protected]", False),
("a(c)d,e:f;g<h>i[j\\k][email protected]", False),
(
"12345678901234567890123456789012345678901234567890123456789012345@example.com",
False,
),
("i.like.underscores@but_its_not_allowed_in_this_part", False),
("", False),
)
# The maximum octets (one character as a standard unicode character is one byte)
# that the local part and the domain part can have
MAX_LOCAL_PART_OCTETS = 64
MAX_DOMAIN_OCTETS = 255
def is_valid_email_address(email: str) -> bool:
"""
Returns True if the passed email address is valid.
The local part of the email precedes the singular @ symbol and
is associated with a display-name. For example, "john.smith"
The domain is stricter than the local part and follows the @ symbol.
Global email checks:
1. There can only be one @ symbol in the email address. Technically if the
@ symbol is quoted in the local-part, then it is valid, however this
implementation ignores "" for now.
(See https://en.wikipedia.org/wiki/Email_address#:~:text=If%20quoted,)
2. The local-part and the domain are limited to a certain number of octets. With
unicode storing a single character in one byte, each octet is equivalent to
a character. Hence, we can just check the length of the string.
Checks for the local-part:
3. The local-part may contain: upper and lowercase latin letters, digits 0 to 9,
and printable characters (!#$%&'*+-/=?^_`{|}~)
4. The local-part may also contain a "." in any place that is not the first or
last character, and may not have more than one "." consecutively.
Checks for the domain:
5. The domain may contain: upper and lowercase latin letters and digits 0 to 9
6. Hyphen "-", provided that it is not the first or last character
7. The domain may also contain a "." in any place that is not the first or
last character, and may not have more than one "." consecutively.
>>> for email, valid in email_tests:
... assert is_valid_email_address(email) == valid
"""
# (1.) Make sure that there is only one @ symbol in the email address
if email.count("@") != 1:
return False
local_part, domain = email.split("@")
# (2.) Check octet length of the local part and domain
if len(local_part) > MAX_LOCAL_PART_OCTETS or len(domain) > MAX_DOMAIN_OCTETS:
return False
# (3.) Validate the characters in the local-part
if any(
char not in string.ascii_letters + string.digits + ".(!#$%&'*+-/=?^_`{|}~)"
for char in local_part
):
return False
# (4.) Validate the placement of "." characters in the local-part
if local_part.startswith(".") or local_part.endswith(".") or ".." in local_part:
return False
# (5.) Validate the characters in the domain
if any(char not in string.ascii_letters + string.digits + ".-" for char in domain):
return False
# (6.) Validate the placement of "-" characters
if domain.startswith("-") or domain.endswith("."):
return False
# (7.) Validate the placement of "." characters
if domain.startswith(".") or domain.endswith(".") or ".." in domain:
return False
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
for email, valid in email_tests:
is_valid = is_valid_email_address(email)
assert is_valid == valid, f"{email} is {is_valid}"
print(f"Email address {email} is {'not ' if not is_valid else ''}valid")
| """
Implements an is valid email address algorithm
@ https://en.wikipedia.org/wiki/Email_address
"""
import string
email_tests: tuple[tuple[str, bool], ...] = (
("[email protected]", True),
("[email protected]", True),
("[email protected]", True),
("[email protected]", True),
("[email protected]", True),
("[email protected]", True),
("[email protected]", True),
("[email protected]", True),
("test/[email protected]", True),
(
"123456789012345678901234567890123456789012345678901234567890123@example.com",
True,
),
("admin@mailserver1", True),
("[email protected]", True),
("Abc.example.com", False),
("A@b@[email protected]", False),
("[email protected]", False),
("a(c)d,e:f;g<h>i[j\\k][email protected]", False),
(
"12345678901234567890123456789012345678901234567890123456789012345@example.com",
False,
),
("i.like.underscores@but_its_not_allowed_in_this_part", False),
("", False),
)
# The maximum octets (one character as a standard unicode character is one byte)
# that the local part and the domain part can have
MAX_LOCAL_PART_OCTETS = 64
MAX_DOMAIN_OCTETS = 255
def is_valid_email_address(email: str) -> bool:
"""
Returns True if the passed email address is valid.
The local part of the email precedes the singular @ symbol and
is associated with a display-name. For example, "john.smith"
The domain is stricter than the local part and follows the @ symbol.
Global email checks:
1. There can only be one @ symbol in the email address. Technically if the
@ symbol is quoted in the local-part, then it is valid, however this
implementation ignores "" for now.
(See https://en.wikipedia.org/wiki/Email_address#:~:text=If%20quoted,)
2. The local-part and the domain are limited to a certain number of octets. With
unicode storing a single character in one byte, each octet is equivalent to
a character. Hence, we can just check the length of the string.
Checks for the local-part:
3. The local-part may contain: upper and lowercase latin letters, digits 0 to 9,
and printable characters (!#$%&'*+-/=?^_`{|}~)
4. The local-part may also contain a "." in any place that is not the first or
last character, and may not have more than one "." consecutively.
Checks for the domain:
5. The domain may contain: upper and lowercase latin letters and digits 0 to 9
6. Hyphen "-", provided that it is not the first or last character
7. The domain may also contain a "." in any place that is not the first or
last character, and may not have more than one "." consecutively.
>>> for email, valid in email_tests:
... assert is_valid_email_address(email) == valid
"""
# (1.) Make sure that there is only one @ symbol in the email address
if email.count("@") != 1:
return False
local_part, domain = email.split("@")
# (2.) Check octet length of the local part and domain
if len(local_part) > MAX_LOCAL_PART_OCTETS or len(domain) > MAX_DOMAIN_OCTETS:
return False
# (3.) Validate the characters in the local-part
if any(
char not in string.ascii_letters + string.digits + ".(!#$%&'*+-/=?^_`{|}~)"
for char in local_part
):
return False
# (4.) Validate the placement of "." characters in the local-part
if local_part.startswith(".") or local_part.endswith(".") or ".." in local_part:
return False
# (5.) Validate the characters in the domain
if any(char not in string.ascii_letters + string.digits + ".-" for char in domain):
return False
# (6.) Validate the placement of "-" characters
if domain.startswith("-") or domain.endswith("."):
return False
# (7.) Validate the placement of "." characters
if domain.startswith(".") or domain.endswith(".") or ".." in domain:
return False
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
for email, valid in email_tests:
is_valid = is_valid_email_address(email)
assert is_valid == valid, f"{email} is {is_valid}"
print(f"Email address {email} is {'not ' if not is_valid else ''}valid")
| -1 |
TheAlgorithms/Python | 9,062 | Delete empty junk file | ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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 | "2023-09-16T18:59:42Z" | "2023-09-16T22:12:31Z" | 1488cdea708485eb1d81c73126eab13cb9b04a47 | fbad85d3ecbbb826a5891807c823149d38bbaed3 | Delete empty junk file. ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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 | 9,062 | Delete empty junk file | ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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 | "2023-09-16T18:59:42Z" | "2023-09-16T22:12:31Z" | 1488cdea708485eb1d81c73126eab13cb9b04a47 | fbad85d3ecbbb826a5891807c823149d38bbaed3 | Delete empty junk file. ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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".
| """
guess the number using lower,higher and the value to find or guess
solution works by dividing lower and higher of number guessed
suppose lower is 0, higher is 1000 and the number to guess is 355
>>> guess_the_number(10, 1000, 17)
started...
guess the number : 17
details : [505, 257, 133, 71, 40, 25, 17]
"""
def temp_input_value(
min_val: int = 10, max_val: int = 1000, option: bool = True
) -> int:
"""
Temporary input values for tests
>>> temp_input_value(option=True)
10
>>> temp_input_value(option=False)
1000
>>> temp_input_value(min_val=100, option=True)
100
>>> temp_input_value(min_val=100, max_val=50)
Traceback (most recent call last):
...
ValueError: Invalid value for min_val or max_val (min_value < max_value)
>>> temp_input_value("ten","fifty",1)
Traceback (most recent call last):
...
AssertionError: Invalid type of value(s) specified to function!
>>> temp_input_value(min_val=-100, max_val=500)
-100
>>> temp_input_value(min_val=-5100, max_val=-100)
-5100
"""
assert (
isinstance(min_val, int)
and isinstance(max_val, int)
and isinstance(option, bool)
), "Invalid type of value(s) specified to function!"
if min_val > max_val:
raise ValueError("Invalid value for min_val or max_val (min_value < max_value)")
return min_val if option else max_val
def get_avg(number_1: int, number_2: int) -> int:
"""
Return the mid-number(whole) of two integers a and b
>>> get_avg(10, 15)
12
>>> get_avg(20, 300)
160
>>> get_avg("abcd", 300)
Traceback (most recent call last):
...
TypeError: can only concatenate str (not "int") to str
>>> get_avg(10.5,50.25)
30
"""
return int((number_1 + number_2) / 2)
def guess_the_number(lower: int, higher: int, to_guess: int) -> None:
"""
The `guess_the_number` function that guess the number by some operations
and using inner functions
>>> guess_the_number(10, 1000, 17)
started...
guess the number : 17
details : [505, 257, 133, 71, 40, 25, 17]
>>> guess_the_number(-10000, 10000, 7)
started...
guess the number : 7
details : [0, 5000, 2500, 1250, 625, 312, 156, 78, 39, 19, 9, 4, 6, 7]
>>> guess_the_number(10, 1000, "a")
Traceback (most recent call last):
...
AssertionError: argument values must be type of "int"
>>> guess_the_number(10, 1000, 5)
Traceback (most recent call last):
...
ValueError: guess value must be within the range of lower and higher value
>>> guess_the_number(10000, 100, 5)
Traceback (most recent call last):
...
ValueError: argument value for lower and higher must be(lower > higher)
"""
assert (
isinstance(lower, int) and isinstance(higher, int) and isinstance(to_guess, int)
), 'argument values must be type of "int"'
if lower > higher:
raise ValueError("argument value for lower and higher must be(lower > higher)")
if not lower < to_guess < higher:
raise ValueError(
"guess value must be within the range of lower and higher value"
)
def answer(number: int) -> str:
"""
Returns value by comparing with entered `to_guess` number
"""
if number > to_guess:
return "high"
elif number < to_guess:
return "low"
else:
return "same"
print("started...")
last_lowest = lower
last_highest = higher
last_numbers = []
while True:
number = get_avg(last_lowest, last_highest)
last_numbers.append(number)
if answer(number) == "low":
last_lowest = number
elif answer(number) == "high":
last_highest = number
else:
break
print(f"guess the number : {last_numbers[-1]}")
print(f"details : {last_numbers!s}")
def main() -> None:
"""
starting point or function of script
"""
lower = int(input("Enter lower value : ").strip())
higher = int(input("Enter high value : ").strip())
guess = int(input("Enter value to guess : ").strip())
guess_the_number(lower, higher, guess)
if __name__ == "__main__":
main()
| """
guess the number using lower,higher and the value to find or guess
solution works by dividing lower and higher of number guessed
suppose lower is 0, higher is 1000 and the number to guess is 355
>>> guess_the_number(10, 1000, 17)
started...
guess the number : 17
details : [505, 257, 133, 71, 40, 25, 17]
"""
def temp_input_value(
min_val: int = 10, max_val: int = 1000, option: bool = True
) -> int:
"""
Temporary input values for tests
>>> temp_input_value(option=True)
10
>>> temp_input_value(option=False)
1000
>>> temp_input_value(min_val=100, option=True)
100
>>> temp_input_value(min_val=100, max_val=50)
Traceback (most recent call last):
...
ValueError: Invalid value for min_val or max_val (min_value < max_value)
>>> temp_input_value("ten","fifty",1)
Traceback (most recent call last):
...
AssertionError: Invalid type of value(s) specified to function!
>>> temp_input_value(min_val=-100, max_val=500)
-100
>>> temp_input_value(min_val=-5100, max_val=-100)
-5100
"""
assert (
isinstance(min_val, int)
and isinstance(max_val, int)
and isinstance(option, bool)
), "Invalid type of value(s) specified to function!"
if min_val > max_val:
raise ValueError("Invalid value for min_val or max_val (min_value < max_value)")
return min_val if option else max_val
def get_avg(number_1: int, number_2: int) -> int:
"""
Return the mid-number(whole) of two integers a and b
>>> get_avg(10, 15)
12
>>> get_avg(20, 300)
160
>>> get_avg("abcd", 300)
Traceback (most recent call last):
...
TypeError: can only concatenate str (not "int") to str
>>> get_avg(10.5,50.25)
30
"""
return int((number_1 + number_2) / 2)
def guess_the_number(lower: int, higher: int, to_guess: int) -> None:
"""
The `guess_the_number` function that guess the number by some operations
and using inner functions
>>> guess_the_number(10, 1000, 17)
started...
guess the number : 17
details : [505, 257, 133, 71, 40, 25, 17]
>>> guess_the_number(-10000, 10000, 7)
started...
guess the number : 7
details : [0, 5000, 2500, 1250, 625, 312, 156, 78, 39, 19, 9, 4, 6, 7]
>>> guess_the_number(10, 1000, "a")
Traceback (most recent call last):
...
AssertionError: argument values must be type of "int"
>>> guess_the_number(10, 1000, 5)
Traceback (most recent call last):
...
ValueError: guess value must be within the range of lower and higher value
>>> guess_the_number(10000, 100, 5)
Traceback (most recent call last):
...
ValueError: argument value for lower and higher must be(lower > higher)
"""
assert (
isinstance(lower, int) and isinstance(higher, int) and isinstance(to_guess, int)
), 'argument values must be type of "int"'
if lower > higher:
raise ValueError("argument value for lower and higher must be(lower > higher)")
if not lower < to_guess < higher:
raise ValueError(
"guess value must be within the range of lower and higher value"
)
def answer(number: int) -> str:
"""
Returns value by comparing with entered `to_guess` number
"""
if number > to_guess:
return "high"
elif number < to_guess:
return "low"
else:
return "same"
print("started...")
last_lowest = lower
last_highest = higher
last_numbers = []
while True:
number = get_avg(last_lowest, last_highest)
last_numbers.append(number)
if answer(number) == "low":
last_lowest = number
elif answer(number) == "high":
last_highest = number
else:
break
print(f"guess the number : {last_numbers[-1]}")
print(f"details : {last_numbers!s}")
def main() -> None:
"""
starting point or function of script
"""
lower = int(input("Enter lower value : ").strip())
higher = int(input("Enter high value : ").strip())
guess = int(input("Enter value to guess : ").strip())
guess_the_number(lower, higher, guess)
if __name__ == "__main__":
main()
| -1 |
TheAlgorithms/Python | 9,062 | Delete empty junk file | ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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 | "2023-09-16T18:59:42Z" | "2023-09-16T22:12:31Z" | 1488cdea708485eb1d81c73126eab13cb9b04a47 | fbad85d3ecbbb826a5891807c823149d38bbaed3 | Delete empty junk file. ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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".
| """
This is pure Python implementation of fibonacci search.
Resources used:
https://en.wikipedia.org/wiki/Fibonacci_search_technique
For doctests run following command:
python3 -m doctest -v fibonacci_search.py
For manual testing run:
python3 fibonacci_search.py
"""
from functools import lru_cache
@lru_cache
def fibonacci(k: int) -> int:
"""Finds fibonacci number in index k.
Parameters
----------
k :
Index of fibonacci.
Returns
-------
int
Fibonacci number in position k.
>>> fibonacci(0)
0
>>> fibonacci(2)
1
>>> fibonacci(5)
5
>>> fibonacci(15)
610
>>> fibonacci('a')
Traceback (most recent call last):
TypeError: k must be an integer.
>>> fibonacci(-5)
Traceback (most recent call last):
ValueError: k integer must be greater or equal to zero.
"""
if not isinstance(k, int):
raise TypeError("k must be an integer.")
if k < 0:
raise ValueError("k integer must be greater or equal to zero.")
if k == 0:
return 0
elif k == 1:
return 1
else:
return fibonacci(k - 1) + fibonacci(k - 2)
def fibonacci_search(arr: list, val: int) -> int:
"""A pure Python implementation of a fibonacci search algorithm.
Parameters
----------
arr
List of sorted elements.
val
Element to search in list.
Returns
-------
int
The index of the element in the array.
-1 if the element is not found.
>>> fibonacci_search([4, 5, 6, 7], 4)
0
>>> fibonacci_search([4, 5, 6, 7], -10)
-1
>>> fibonacci_search([-18, 2], -18)
0
>>> fibonacci_search([5], 5)
0
>>> fibonacci_search(['a', 'c', 'd'], 'c')
1
>>> fibonacci_search(['a', 'c', 'd'], 'f')
-1
>>> fibonacci_search([], 1)
-1
>>> fibonacci_search([.1, .4 , 7], .4)
1
>>> fibonacci_search([], 9)
-1
>>> fibonacci_search(list(range(100)), 63)
63
>>> fibonacci_search(list(range(100)), 99)
99
>>> fibonacci_search(list(range(-100, 100, 3)), -97)
1
>>> fibonacci_search(list(range(-100, 100, 3)), 0)
-1
>>> fibonacci_search(list(range(-100, 100, 5)), 0)
20
>>> fibonacci_search(list(range(-100, 100, 5)), 95)
39
"""
len_list = len(arr)
# Find m such that F_m >= n where F_i is the i_th fibonacci number.
i = 0
while True:
if fibonacci(i) >= len_list:
fibb_k = i
break
i += 1
offset = 0
while fibb_k > 0:
index_k = min(
offset + fibonacci(fibb_k - 1), len_list - 1
) # Prevent out of range
item_k_1 = arr[index_k]
if item_k_1 == val:
return index_k
elif val < item_k_1:
fibb_k -= 1
elif val > item_k_1:
offset += fibonacci(fibb_k - 1)
fibb_k -= 2
else:
return -1
if __name__ == "__main__":
import doctest
doctest.testmod()
| """
This is pure Python implementation of fibonacci search.
Resources used:
https://en.wikipedia.org/wiki/Fibonacci_search_technique
For doctests run following command:
python3 -m doctest -v fibonacci_search.py
For manual testing run:
python3 fibonacci_search.py
"""
from functools import lru_cache
@lru_cache
def fibonacci(k: int) -> int:
"""Finds fibonacci number in index k.
Parameters
----------
k :
Index of fibonacci.
Returns
-------
int
Fibonacci number in position k.
>>> fibonacci(0)
0
>>> fibonacci(2)
1
>>> fibonacci(5)
5
>>> fibonacci(15)
610
>>> fibonacci('a')
Traceback (most recent call last):
TypeError: k must be an integer.
>>> fibonacci(-5)
Traceback (most recent call last):
ValueError: k integer must be greater or equal to zero.
"""
if not isinstance(k, int):
raise TypeError("k must be an integer.")
if k < 0:
raise ValueError("k integer must be greater or equal to zero.")
if k == 0:
return 0
elif k == 1:
return 1
else:
return fibonacci(k - 1) + fibonacci(k - 2)
def fibonacci_search(arr: list, val: int) -> int:
"""A pure Python implementation of a fibonacci search algorithm.
Parameters
----------
arr
List of sorted elements.
val
Element to search in list.
Returns
-------
int
The index of the element in the array.
-1 if the element is not found.
>>> fibonacci_search([4, 5, 6, 7], 4)
0
>>> fibonacci_search([4, 5, 6, 7], -10)
-1
>>> fibonacci_search([-18, 2], -18)
0
>>> fibonacci_search([5], 5)
0
>>> fibonacci_search(['a', 'c', 'd'], 'c')
1
>>> fibonacci_search(['a', 'c', 'd'], 'f')
-1
>>> fibonacci_search([], 1)
-1
>>> fibonacci_search([.1, .4 , 7], .4)
1
>>> fibonacci_search([], 9)
-1
>>> fibonacci_search(list(range(100)), 63)
63
>>> fibonacci_search(list(range(100)), 99)
99
>>> fibonacci_search(list(range(-100, 100, 3)), -97)
1
>>> fibonacci_search(list(range(-100, 100, 3)), 0)
-1
>>> fibonacci_search(list(range(-100, 100, 5)), 0)
20
>>> fibonacci_search(list(range(-100, 100, 5)), 95)
39
"""
len_list = len(arr)
# Find m such that F_m >= n where F_i is the i_th fibonacci number.
i = 0
while True:
if fibonacci(i) >= len_list:
fibb_k = i
break
i += 1
offset = 0
while fibb_k > 0:
index_k = min(
offset + fibonacci(fibb_k - 1), len_list - 1
) # Prevent out of range
item_k_1 = arr[index_k]
if item_k_1 == val:
return index_k
elif val < item_k_1:
fibb_k -= 1
elif val > item_k_1:
offset += fibonacci(fibb_k - 1)
fibb_k -= 2
else:
return -1
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 9,062 | Delete empty junk file | ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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 | "2023-09-16T18:59:42Z" | "2023-09-16T22:12:31Z" | 1488cdea708485eb1d81c73126eab13cb9b04a47 | fbad85d3ecbbb826a5891807c823149d38bbaed3 | Delete empty junk file. ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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 | 9,062 | Delete empty junk file | ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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 | "2023-09-16T18:59:42Z" | "2023-09-16T22:12:31Z" | 1488cdea708485eb1d81c73126eab13cb9b04a47 | fbad85d3ecbbb826a5891807c823149d38bbaed3 | Delete empty junk file. ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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 | 9,062 | Delete empty junk file | ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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 | "2023-09-16T18:59:42Z" | "2023-09-16T22:12:31Z" | 1488cdea708485eb1d81c73126eab13cb9b04a47 | fbad85d3ecbbb826a5891807c823149d38bbaed3 | Delete empty junk file. ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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".
| # https://en.wikipedia.org/wiki/Lowest_common_ancestor
# https://en.wikipedia.org/wiki/Breadth-first_search
from __future__ import annotations
from queue import Queue
def swap(a: int, b: int) -> tuple[int, int]:
"""
Return a tuple (b, a) when given two integers a and b
>>> swap(2,3)
(3, 2)
>>> swap(3,4)
(4, 3)
>>> swap(67, 12)
(12, 67)
"""
a ^= b
b ^= a
a ^= b
return a, b
def create_sparse(max_node: int, parent: list[list[int]]) -> list[list[int]]:
"""
creating sparse table which saves each nodes 2^i-th parent
"""
j = 1
while (1 << j) < max_node:
for i in range(1, max_node + 1):
parent[j][i] = parent[j - 1][parent[j - 1][i]]
j += 1
return parent
# returns lca of node u,v
def lowest_common_ancestor(
u: int, v: int, level: list[int], parent: list[list[int]]
) -> int:
# u must be deeper in the tree than v
if level[u] < level[v]:
u, v = swap(u, v)
# making depth of u same as depth of v
for i in range(18, -1, -1):
if level[u] - (1 << i) >= level[v]:
u = parent[i][u]
# at the same depth if u==v that mean lca is found
if u == v:
return u
# moving both nodes upwards till lca in found
for i in range(18, -1, -1):
if parent[i][u] not in [0, parent[i][v]]:
u, v = parent[i][u], parent[i][v]
# returning longest common ancestor of u,v
return parent[0][u]
# runs a breadth first search from root node of the tree
def breadth_first_search(
level: list[int],
parent: list[list[int]],
max_node: int,
graph: dict[int, list[int]],
root: int = 1,
) -> tuple[list[int], list[list[int]]]:
"""
sets every nodes direct parent
parent of root node is set to 0
calculates depth of each node from root node
"""
level[root] = 0
q: Queue[int] = Queue(maxsize=max_node)
q.put(root)
while q.qsize() != 0:
u = q.get()
for v in graph[u]:
if level[v] == -1:
level[v] = level[u] + 1
q.put(v)
parent[0][v] = u
return level, parent
def main() -> None:
max_node = 13
# initializing with 0
parent = [[0 for _ in range(max_node + 10)] for _ in range(20)]
# initializing with -1 which means every node is unvisited
level = [-1 for _ in range(max_node + 10)]
graph: dict[int, list[int]] = {
1: [2, 3, 4],
2: [5],
3: [6, 7],
4: [8],
5: [9, 10],
6: [11],
7: [],
8: [12, 13],
9: [],
10: [],
11: [],
12: [],
13: [],
}
level, parent = breadth_first_search(level, parent, max_node, graph, 1)
parent = create_sparse(max_node, parent)
print("LCA of node 1 and 3 is: ", lowest_common_ancestor(1, 3, level, parent))
print("LCA of node 5 and 6 is: ", lowest_common_ancestor(5, 6, level, parent))
print("LCA of node 7 and 11 is: ", lowest_common_ancestor(7, 11, level, parent))
print("LCA of node 6 and 7 is: ", lowest_common_ancestor(6, 7, level, parent))
print("LCA of node 4 and 12 is: ", lowest_common_ancestor(4, 12, level, parent))
print("LCA of node 8 and 8 is: ", lowest_common_ancestor(8, 8, level, parent))
if __name__ == "__main__":
main()
| # https://en.wikipedia.org/wiki/Lowest_common_ancestor
# https://en.wikipedia.org/wiki/Breadth-first_search
from __future__ import annotations
from queue import Queue
def swap(a: int, b: int) -> tuple[int, int]:
"""
Return a tuple (b, a) when given two integers a and b
>>> swap(2,3)
(3, 2)
>>> swap(3,4)
(4, 3)
>>> swap(67, 12)
(12, 67)
"""
a ^= b
b ^= a
a ^= b
return a, b
def create_sparse(max_node: int, parent: list[list[int]]) -> list[list[int]]:
"""
creating sparse table which saves each nodes 2^i-th parent
"""
j = 1
while (1 << j) < max_node:
for i in range(1, max_node + 1):
parent[j][i] = parent[j - 1][parent[j - 1][i]]
j += 1
return parent
# returns lca of node u,v
def lowest_common_ancestor(
u: int, v: int, level: list[int], parent: list[list[int]]
) -> int:
# u must be deeper in the tree than v
if level[u] < level[v]:
u, v = swap(u, v)
# making depth of u same as depth of v
for i in range(18, -1, -1):
if level[u] - (1 << i) >= level[v]:
u = parent[i][u]
# at the same depth if u==v that mean lca is found
if u == v:
return u
# moving both nodes upwards till lca in found
for i in range(18, -1, -1):
if parent[i][u] not in [0, parent[i][v]]:
u, v = parent[i][u], parent[i][v]
# returning longest common ancestor of u,v
return parent[0][u]
# runs a breadth first search from root node of the tree
def breadth_first_search(
level: list[int],
parent: list[list[int]],
max_node: int,
graph: dict[int, list[int]],
root: int = 1,
) -> tuple[list[int], list[list[int]]]:
"""
sets every nodes direct parent
parent of root node is set to 0
calculates depth of each node from root node
"""
level[root] = 0
q: Queue[int] = Queue(maxsize=max_node)
q.put(root)
while q.qsize() != 0:
u = q.get()
for v in graph[u]:
if level[v] == -1:
level[v] = level[u] + 1
q.put(v)
parent[0][v] = u
return level, parent
def main() -> None:
max_node = 13
# initializing with 0
parent = [[0 for _ in range(max_node + 10)] for _ in range(20)]
# initializing with -1 which means every node is unvisited
level = [-1 for _ in range(max_node + 10)]
graph: dict[int, list[int]] = {
1: [2, 3, 4],
2: [5],
3: [6, 7],
4: [8],
5: [9, 10],
6: [11],
7: [],
8: [12, 13],
9: [],
10: [],
11: [],
12: [],
13: [],
}
level, parent = breadth_first_search(level, parent, max_node, graph, 1)
parent = create_sparse(max_node, parent)
print("LCA of node 1 and 3 is: ", lowest_common_ancestor(1, 3, level, parent))
print("LCA of node 5 and 6 is: ", lowest_common_ancestor(5, 6, level, parent))
print("LCA of node 7 and 11 is: ", lowest_common_ancestor(7, 11, level, parent))
print("LCA of node 6 and 7 is: ", lowest_common_ancestor(6, 7, level, parent))
print("LCA of node 4 and 12 is: ", lowest_common_ancestor(4, 12, level, parent))
print("LCA of node 8 and 8 is: ", lowest_common_ancestor(8, 8, level, parent))
if __name__ == "__main__":
main()
| -1 |
TheAlgorithms/Python | 9,062 | Delete empty junk file | ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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 | "2023-09-16T18:59:42Z" | "2023-09-16T22:12:31Z" | 1488cdea708485eb1d81c73126eab13cb9b04a47 | fbad85d3ecbbb826a5891807c823149d38bbaed3 | Delete empty junk file. ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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 | 9,062 | Delete empty junk file | ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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 | "2023-09-16T18:59:42Z" | "2023-09-16T22:12:31Z" | 1488cdea708485eb1d81c73126eab13cb9b04a47 | fbad85d3ecbbb826a5891807c823149d38bbaed3 | Delete empty junk file. ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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 | 9,062 | Delete empty junk file | ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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 | "2023-09-16T18:59:42Z" | "2023-09-16T22:12:31Z" | 1488cdea708485eb1d81c73126eab13cb9b04a47 | fbad85d3ecbbb826a5891807c823149d38bbaed3 | Delete empty junk file. ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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 alternative_list_arrange(first_input_list: list, second_input_list: list) -> list:
"""
The method arranges two lists as one list in alternative forms of the list elements.
:param first_input_list:
:param second_input_list:
:return: List
>>> alternative_list_arrange([1, 2, 3, 4, 5], ["A", "B", "C"])
[1, 'A', 2, 'B', 3, 'C', 4, 5]
>>> alternative_list_arrange(["A", "B", "C"], [1, 2, 3, 4, 5])
['A', 1, 'B', 2, 'C', 3, 4, 5]
>>> alternative_list_arrange(["X", "Y", "Z"], [9, 8, 7, 6])
['X', 9, 'Y', 8, 'Z', 7, 6]
>>> alternative_list_arrange([1, 2, 3, 4, 5], [])
[1, 2, 3, 4, 5]
"""
first_input_list_length: int = len(first_input_list)
second_input_list_length: int = len(second_input_list)
abs_length: int = (
first_input_list_length
if first_input_list_length > second_input_list_length
else second_input_list_length
)
output_result_list: list = []
for char_count in range(abs_length):
if char_count < first_input_list_length:
output_result_list.append(first_input_list[char_count])
if char_count < second_input_list_length:
output_result_list.append(second_input_list[char_count])
return output_result_list
if __name__ == "__main__":
print(alternative_list_arrange(["A", "B", "C"], [1, 2, 3, 4, 5]), end=" ")
| def alternative_list_arrange(first_input_list: list, second_input_list: list) -> list:
"""
The method arranges two lists as one list in alternative forms of the list elements.
:param first_input_list:
:param second_input_list:
:return: List
>>> alternative_list_arrange([1, 2, 3, 4, 5], ["A", "B", "C"])
[1, 'A', 2, 'B', 3, 'C', 4, 5]
>>> alternative_list_arrange(["A", "B", "C"], [1, 2, 3, 4, 5])
['A', 1, 'B', 2, 'C', 3, 4, 5]
>>> alternative_list_arrange(["X", "Y", "Z"], [9, 8, 7, 6])
['X', 9, 'Y', 8, 'Z', 7, 6]
>>> alternative_list_arrange([1, 2, 3, 4, 5], [])
[1, 2, 3, 4, 5]
"""
first_input_list_length: int = len(first_input_list)
second_input_list_length: int = len(second_input_list)
abs_length: int = (
first_input_list_length
if first_input_list_length > second_input_list_length
else second_input_list_length
)
output_result_list: list = []
for char_count in range(abs_length):
if char_count < first_input_list_length:
output_result_list.append(first_input_list[char_count])
if char_count < second_input_list_length:
output_result_list.append(second_input_list[char_count])
return output_result_list
if __name__ == "__main__":
print(alternative_list_arrange(["A", "B", "C"], [1, 2, 3, 4, 5]), end=" ")
| -1 |
TheAlgorithms/Python | 9,062 | Delete empty junk file | ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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 | "2023-09-16T18:59:42Z" | "2023-09-16T22:12:31Z" | 1488cdea708485eb1d81c73126eab13cb9b04a47 | fbad85d3ecbbb826a5891807c823149d38bbaed3 | Delete empty junk file. ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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".
| """
This script demonstrates an implementation of the Gaussian Error Linear Unit function.
* https://en.wikipedia.org/wiki/Activation_function#Comparison_of_activation_functions
The function takes a vector of K real numbers as input and returns x * sigmoid(1.702*x).
Gaussian Error Linear Unit (GELU) is a high-performing neural network activation
function.
This script is inspired by a corresponding research paper.
* https://arxiv.org/abs/1606.08415
"""
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 gaussian_error_linear_unit(vector: np.ndarray) -> np.ndarray:
"""
Implements the Gaussian Error Linear Unit (GELU) function
Parameters:
vector (np.ndarray): A numpy array of shape (1, n) consisting of real values
Returns:
gelu_vec (np.ndarray): The input numpy array, after applying gelu
Examples:
>>> gaussian_error_linear_unit(np.array([-1.0, 1.0, 2.0]))
array([-0.15420423, 0.84579577, 1.93565862])
>>> gaussian_error_linear_unit(np.array([-3]))
array([-0.01807131])
"""
return vector * sigmoid(1.702 * vector)
if __name__ == "__main__":
import doctest
doctest.testmod()
| """
This script demonstrates an implementation of the Gaussian Error Linear Unit function.
* https://en.wikipedia.org/wiki/Activation_function#Comparison_of_activation_functions
The function takes a vector of K real numbers as input and returns x * sigmoid(1.702*x).
Gaussian Error Linear Unit (GELU) is a high-performing neural network activation
function.
This script is inspired by a corresponding research paper.
* https://arxiv.org/abs/1606.08415
"""
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 gaussian_error_linear_unit(vector: np.ndarray) -> np.ndarray:
"""
Implements the Gaussian Error Linear Unit (GELU) function
Parameters:
vector (np.ndarray): A numpy array of shape (1, n) consisting of real values
Returns:
gelu_vec (np.ndarray): The input numpy array, after applying gelu
Examples:
>>> gaussian_error_linear_unit(np.array([-1.0, 1.0, 2.0]))
array([-0.15420423, 0.84579577, 1.93565862])
>>> gaussian_error_linear_unit(np.array([-3]))
array([-0.01807131])
"""
return vector * sigmoid(1.702 * vector)
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 9,062 | Delete empty junk file | ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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 | "2023-09-16T18:59:42Z" | "2023-09-16T22:12:31Z" | 1488cdea708485eb1d81c73126eab13cb9b04a47 | fbad85d3ecbbb826a5891807c823149d38bbaed3 | Delete empty junk file. ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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: Christian Bender
date: 21.12.2017
class: XORCipher
This class implements the XOR-cipher algorithm and provides
some useful methods for encrypting and decrypting strings and
files.
Overview about methods
- encrypt : list of char
- decrypt : list of char
- encrypt_string : str
- decrypt_string : str
- encrypt_file : boolean
- decrypt_file : boolean
"""
from __future__ import annotations
class XORCipher:
def __init__(self, key: int = 0):
"""
simple constructor that receives a key or uses
default key = 0
"""
# private field
self.__key = key
def encrypt(self, content: str, key: int) -> list[str]:
"""
input: 'content' of type string and 'key' of type int
output: encrypted string 'content' as a list of chars
if key not passed the method uses the key by the constructor.
otherwise key = 1
"""
# precondition
assert isinstance(key, int) and isinstance(content, str)
key = key or self.__key or 1
# make sure key is an appropriate size
key %= 255
return [chr(ord(ch) ^ key) for ch in content]
def decrypt(self, content: str, key: int) -> list[str]:
"""
input: 'content' of type list and 'key' of type int
output: decrypted string 'content' as a list of chars
if key not passed the method uses the key by the constructor.
otherwise key = 1
"""
# precondition
assert isinstance(key, int) and isinstance(content, list)
key = key or self.__key or 1
# make sure key is an appropriate size
key %= 255
return [chr(ord(ch) ^ key) for ch in content]
def encrypt_string(self, content: str, key: int = 0) -> str:
"""
input: 'content' of type string and 'key' of type int
output: encrypted string 'content'
if key not passed the method uses the key by the constructor.
otherwise key = 1
"""
# precondition
assert isinstance(key, int) and isinstance(content, str)
key = key or self.__key or 1
# make sure key can be any size
while key > 255:
key -= 255
# This will be returned
ans = ""
for ch in content:
ans += chr(ord(ch) ^ key)
return ans
def decrypt_string(self, content: str, key: int = 0) -> str:
"""
input: 'content' of type string and 'key' of type int
output: decrypted string 'content'
if key not passed the method uses the key by the constructor.
otherwise key = 1
"""
# precondition
assert isinstance(key, int) and isinstance(content, str)
key = key or self.__key or 1
# make sure key can be any size
while key > 255:
key -= 255
# This will be returned
ans = ""
for ch in content:
ans += chr(ord(ch) ^ key)
return ans
def encrypt_file(self, file: str, key: int = 0) -> bool:
"""
input: filename (str) and a key (int)
output: returns true if encrypt process was
successful otherwise false
if key not passed the method uses the key by the constructor.
otherwise key = 1
"""
# precondition
assert isinstance(file, str) and isinstance(key, int)
try:
with open(file) as fin, open("encrypt.out", "w+") as fout:
# actual encrypt-process
for line in fin:
fout.write(self.encrypt_string(line, key))
except OSError:
return False
return True
def decrypt_file(self, file: str, key: int) -> bool:
"""
input: filename (str) and a key (int)
output: returns true if decrypt process was
successful otherwise false
if key not passed the method uses the key by the constructor.
otherwise key = 1
"""
# precondition
assert isinstance(file, str) and isinstance(key, int)
try:
with open(file) as fin, open("decrypt.out", "w+") as fout:
# actual encrypt-process
for line in fin:
fout.write(self.decrypt_string(line, key))
except OSError:
return False
return True
# Tests
# crypt = XORCipher()
# key = 67
# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))
# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))
# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))
# if (crypt.encrypt_file("test.txt",key)):
# print("encrypt successful")
# else:
# print("encrypt unsuccessful")
# if (crypt.decrypt_file("encrypt.out",key)):
# print("decrypt successful")
# else:
# print("decrypt unsuccessful")
| """
author: Christian Bender
date: 21.12.2017
class: XORCipher
This class implements the XOR-cipher algorithm and provides
some useful methods for encrypting and decrypting strings and
files.
Overview about methods
- encrypt : list of char
- decrypt : list of char
- encrypt_string : str
- decrypt_string : str
- encrypt_file : boolean
- decrypt_file : boolean
"""
from __future__ import annotations
class XORCipher:
def __init__(self, key: int = 0):
"""
simple constructor that receives a key or uses
default key = 0
"""
# private field
self.__key = key
def encrypt(self, content: str, key: int) -> list[str]:
"""
input: 'content' of type string and 'key' of type int
output: encrypted string 'content' as a list of chars
if key not passed the method uses the key by the constructor.
otherwise key = 1
"""
# precondition
assert isinstance(key, int) and isinstance(content, str)
key = key or self.__key or 1
# make sure key is an appropriate size
key %= 255
return [chr(ord(ch) ^ key) for ch in content]
def decrypt(self, content: str, key: int) -> list[str]:
"""
input: 'content' of type list and 'key' of type int
output: decrypted string 'content' as a list of chars
if key not passed the method uses the key by the constructor.
otherwise key = 1
"""
# precondition
assert isinstance(key, int) and isinstance(content, list)
key = key or self.__key or 1
# make sure key is an appropriate size
key %= 255
return [chr(ord(ch) ^ key) for ch in content]
def encrypt_string(self, content: str, key: int = 0) -> str:
"""
input: 'content' of type string and 'key' of type int
output: encrypted string 'content'
if key not passed the method uses the key by the constructor.
otherwise key = 1
"""
# precondition
assert isinstance(key, int) and isinstance(content, str)
key = key or self.__key or 1
# make sure key can be any size
while key > 255:
key -= 255
# This will be returned
ans = ""
for ch in content:
ans += chr(ord(ch) ^ key)
return ans
def decrypt_string(self, content: str, key: int = 0) -> str:
"""
input: 'content' of type string and 'key' of type int
output: decrypted string 'content'
if key not passed the method uses the key by the constructor.
otherwise key = 1
"""
# precondition
assert isinstance(key, int) and isinstance(content, str)
key = key or self.__key or 1
# make sure key can be any size
while key > 255:
key -= 255
# This will be returned
ans = ""
for ch in content:
ans += chr(ord(ch) ^ key)
return ans
def encrypt_file(self, file: str, key: int = 0) -> bool:
"""
input: filename (str) and a key (int)
output: returns true if encrypt process was
successful otherwise false
if key not passed the method uses the key by the constructor.
otherwise key = 1
"""
# precondition
assert isinstance(file, str) and isinstance(key, int)
try:
with open(file) as fin, open("encrypt.out", "w+") as fout:
# actual encrypt-process
for line in fin:
fout.write(self.encrypt_string(line, key))
except OSError:
return False
return True
def decrypt_file(self, file: str, key: int) -> bool:
"""
input: filename (str) and a key (int)
output: returns true if decrypt process was
successful otherwise false
if key not passed the method uses the key by the constructor.
otherwise key = 1
"""
# precondition
assert isinstance(file, str) and isinstance(key, int)
try:
with open(file) as fin, open("decrypt.out", "w+") as fout:
# actual encrypt-process
for line in fin:
fout.write(self.decrypt_string(line, key))
except OSError:
return False
return True
# Tests
# crypt = XORCipher()
# key = 67
# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))
# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))
# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))
# if (crypt.encrypt_file("test.txt",key)):
# print("encrypt successful")
# else:
# print("encrypt unsuccessful")
# if (crypt.decrypt_file("encrypt.out",key)):
# print("decrypt successful")
# else:
# print("decrypt unsuccessful")
| -1 |
TheAlgorithms/Python | 9,062 | Delete empty junk file | ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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 | "2023-09-16T18:59:42Z" | "2023-09-16T22:12:31Z" | 1488cdea708485eb1d81c73126eab13cb9b04a47 | fbad85d3ecbbb826a5891807c823149d38bbaed3 | Delete empty junk file. ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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".
| """
Problem 20: https://projecteuler.net/problem=20
n! means n × (n − 1) × ... × 3 × 2 × 1
For example, 10! = 10 × 9 × ... × 3 × 2 × 1 = 3628800,
and the sum of the digits in the number 10! is 3 + 6 + 2 + 8 + 8 + 0 + 0 = 27.
Find the sum of the digits in the number 100!
"""
def factorial(num: int) -> int:
"""Find the factorial of a given number n"""
fact = 1
for i in range(1, num + 1):
fact *= i
return fact
def split_and_add(number: int) -> int:
"""Split number digits and add them."""
sum_of_digits = 0
while number > 0:
last_digit = number % 10
sum_of_digits += last_digit
number = number // 10 # Removing the last_digit from the given number
return sum_of_digits
def solution(num: int = 100) -> int:
"""Returns the sum of the digits in the factorial of num
>>> solution(100)
648
>>> solution(50)
216
>>> solution(10)
27
>>> solution(5)
3
>>> solution(3)
6
>>> solution(2)
2
>>> solution(1)
1
"""
nfact = factorial(num)
result = split_and_add(nfact)
return result
if __name__ == "__main__":
print(solution(int(input("Enter the Number: ").strip())))
| """
Problem 20: https://projecteuler.net/problem=20
n! means n × (n − 1) × ... × 3 × 2 × 1
For example, 10! = 10 × 9 × ... × 3 × 2 × 1 = 3628800,
and the sum of the digits in the number 10! is 3 + 6 + 2 + 8 + 8 + 0 + 0 = 27.
Find the sum of the digits in the number 100!
"""
def factorial(num: int) -> int:
"""Find the factorial of a given number n"""
fact = 1
for i in range(1, num + 1):
fact *= i
return fact
def split_and_add(number: int) -> int:
"""Split number digits and add them."""
sum_of_digits = 0
while number > 0:
last_digit = number % 10
sum_of_digits += last_digit
number = number // 10 # Removing the last_digit from the given number
return sum_of_digits
def solution(num: int = 100) -> int:
"""Returns the sum of the digits in the factorial of num
>>> solution(100)
648
>>> solution(50)
216
>>> solution(10)
27
>>> solution(5)
3
>>> solution(3)
6
>>> solution(2)
2
>>> solution(1)
1
"""
nfact = factorial(num)
result = split_and_add(nfact)
return result
if __name__ == "__main__":
print(solution(int(input("Enter the Number: ").strip())))
| -1 |
TheAlgorithms/Python | 9,062 | Delete empty junk file | ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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 | "2023-09-16T18:59:42Z" | "2023-09-16T22:12:31Z" | 1488cdea708485eb1d81c73126eab13cb9b04a47 | fbad85d3ecbbb826a5891807c823149d38bbaed3 | Delete empty junk file. ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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 | 9,062 | Delete empty junk file | ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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 | "2023-09-16T18:59:42Z" | "2023-09-16T22:12:31Z" | 1488cdea708485eb1d81c73126eab13cb9b04a47 | fbad85d3ecbbb826a5891807c823149d38bbaed3 | Delete empty junk file. ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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 : lightXu
# @File : convolve.py
# @Time : 2019/7/8 0008 下午 16:13
from cv2 import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import array, dot, pad, ravel, uint8, zeros
def im2col(image, block_size):
rows, cols = image.shape
dst_height = cols - block_size[1] + 1
dst_width = rows - block_size[0] + 1
image_array = zeros((dst_height * dst_width, block_size[1] * block_size[0]))
row = 0
for i in range(dst_height):
for j in range(dst_width):
window = ravel(image[i : i + block_size[0], j : j + block_size[1]])
image_array[row, :] = window
row += 1
return image_array
def img_convolve(image, filter_kernel):
height, width = image.shape[0], image.shape[1]
k_size = filter_kernel.shape[0]
pad_size = k_size // 2
# Pads image with the edge values of array.
image_tmp = pad(image, pad_size, mode="edge")
# im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
image_array = im2col(image_tmp, (k_size, k_size))
# turn the kernel into shape(k*k, 1)
kernel_array = ravel(filter_kernel)
# reshape and get the dst image
dst = dot(image_array, kernel_array).reshape(height, width)
return dst
if __name__ == "__main__":
# read original image
img = imread(r"../image_data/lena.jpg")
# turn image in gray scale value
gray = cvtColor(img, COLOR_BGR2GRAY)
# Laplace operator
Laplace_kernel = array([[0, 1, 0], [1, -4, 1], [0, 1, 0]])
out = img_convolve(gray, Laplace_kernel).astype(uint8)
imshow("Laplacian", out)
waitKey(0)
| # @Author : lightXu
# @File : convolve.py
# @Time : 2019/7/8 0008 下午 16:13
from cv2 import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import array, dot, pad, ravel, uint8, zeros
def im2col(image, block_size):
rows, cols = image.shape
dst_height = cols - block_size[1] + 1
dst_width = rows - block_size[0] + 1
image_array = zeros((dst_height * dst_width, block_size[1] * block_size[0]))
row = 0
for i in range(dst_height):
for j in range(dst_width):
window = ravel(image[i : i + block_size[0], j : j + block_size[1]])
image_array[row, :] = window
row += 1
return image_array
def img_convolve(image, filter_kernel):
height, width = image.shape[0], image.shape[1]
k_size = filter_kernel.shape[0]
pad_size = k_size // 2
# Pads image with the edge values of array.
image_tmp = pad(image, pad_size, mode="edge")
# im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
image_array = im2col(image_tmp, (k_size, k_size))
# turn the kernel into shape(k*k, 1)
kernel_array = ravel(filter_kernel)
# reshape and get the dst image
dst = dot(image_array, kernel_array).reshape(height, width)
return dst
if __name__ == "__main__":
# read original image
img = imread(r"../image_data/lena.jpg")
# turn image in gray scale value
gray = cvtColor(img, COLOR_BGR2GRAY)
# Laplace operator
Laplace_kernel = array([[0, 1, 0], [1, -4, 1], [0, 1, 0]])
out = img_convolve(gray, Laplace_kernel).astype(uint8)
imshow("Laplacian", out)
waitKey(0)
| -1 |
TheAlgorithms/Python | 9,062 | Delete empty junk file | ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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 | "2023-09-16T18:59:42Z" | "2023-09-16T22:12:31Z" | 1488cdea708485eb1d81c73126eab13cb9b04a47 | fbad85d3ecbbb826a5891807c823149d38bbaed3 | Delete empty junk file. ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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 is_palindrome(head):
if not head:
return True
# split the list to two parts
fast, slow = head.next, head
while fast and fast.next:
fast = fast.next.next
slow = slow.next
second = slow.next
slow.next = None # Don't forget here! But forget still works!
# reverse the second part
node = None
while second:
nxt = second.next
second.next = node
node = second
second = nxt
# compare two parts
# second part has the same or one less node
while node:
if node.val != head.val:
return False
node = node.next
head = head.next
return True
def is_palindrome_stack(head):
if not head or not head.next:
return True
# 1. Get the midpoint (slow)
slow = fast = cur = head
while fast and fast.next:
fast, slow = fast.next.next, slow.next
# 2. Push the second half into the stack
stack = [slow.val]
while slow.next:
slow = slow.next
stack.append(slow.val)
# 3. Comparison
while stack:
if stack.pop() != cur.val:
return False
cur = cur.next
return True
def is_palindrome_dict(head):
if not head or not head.next:
return True
d = {}
pos = 0
while head:
if head.val in d:
d[head.val].append(pos)
else:
d[head.val] = [pos]
head = head.next
pos += 1
checksum = pos - 1
middle = 0
for v in d.values():
if len(v) % 2 != 0:
middle += 1
else:
step = 0
for i in range(len(v)):
if v[i] + v[len(v) - 1 - step] != checksum:
return False
step += 1
if middle > 1:
return False
return True
| def is_palindrome(head):
if not head:
return True
# split the list to two parts
fast, slow = head.next, head
while fast and fast.next:
fast = fast.next.next
slow = slow.next
second = slow.next
slow.next = None # Don't forget here! But forget still works!
# reverse the second part
node = None
while second:
nxt = second.next
second.next = node
node = second
second = nxt
# compare two parts
# second part has the same or one less node
while node:
if node.val != head.val:
return False
node = node.next
head = head.next
return True
def is_palindrome_stack(head):
if not head or not head.next:
return True
# 1. Get the midpoint (slow)
slow = fast = cur = head
while fast and fast.next:
fast, slow = fast.next.next, slow.next
# 2. Push the second half into the stack
stack = [slow.val]
while slow.next:
slow = slow.next
stack.append(slow.val)
# 3. Comparison
while stack:
if stack.pop() != cur.val:
return False
cur = cur.next
return True
def is_palindrome_dict(head):
if not head or not head.next:
return True
d = {}
pos = 0
while head:
if head.val in d:
d[head.val].append(pos)
else:
d[head.val] = [pos]
head = head.next
pos += 1
checksum = pos - 1
middle = 0
for v in d.values():
if len(v) % 2 != 0:
middle += 1
else:
step = 0
for i in range(len(v)):
if v[i] + v[len(v) - 1 - step] != checksum:
return False
step += 1
if middle > 1:
return False
return True
| -1 |
TheAlgorithms/Python | 9,062 | Delete empty junk file | ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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 | "2023-09-16T18:59:42Z" | "2023-09-16T22:12:31Z" | 1488cdea708485eb1d81c73126eab13cb9b04a47 | fbad85d3ecbbb826a5891807c823149d38bbaed3 | Delete empty junk file. ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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".
| """
Wikipedia: https://en.wikipedia.org/wiki/Enigma_machine
Video explanation: https://youtu.be/QwQVMqfoB2E
Also check out Numberphile's and Computerphile's videos on this topic
This module contains function 'enigma' which emulates
the famous Enigma machine from WWII.
Module includes:
- enigma function
- showcase of function usage
- 9 randomly generated rotors
- reflector (aka static rotor)
- original alphabet
Created by TrapinchO
"""
from __future__ import annotations
RotorPositionT = tuple[int, int, int]
RotorSelectionT = tuple[str, str, str]
# used alphabet --------------------------
# from string.ascii_uppercase
abc = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
# -------------------------- default selection --------------------------
# rotors --------------------------
rotor1 = "EGZWVONAHDCLFQMSIPJBYUKXTR"
rotor2 = "FOBHMDKEXQNRAULPGSJVTYICZW"
rotor3 = "ZJXESIUQLHAVRMDOYGTNFWPBKC"
# reflector --------------------------
reflector = {
"A": "N",
"N": "A",
"B": "O",
"O": "B",
"C": "P",
"P": "C",
"D": "Q",
"Q": "D",
"E": "R",
"R": "E",
"F": "S",
"S": "F",
"G": "T",
"T": "G",
"H": "U",
"U": "H",
"I": "V",
"V": "I",
"J": "W",
"W": "J",
"K": "X",
"X": "K",
"L": "Y",
"Y": "L",
"M": "Z",
"Z": "M",
}
# -------------------------- extra rotors --------------------------
rotor4 = "RMDJXFUWGISLHVTCQNKYPBEZOA"
rotor5 = "SGLCPQWZHKXAREONTFBVIYJUDM"
rotor6 = "HVSICLTYKQUBXDWAJZOMFGPREN"
rotor7 = "RZWQHFMVDBKICJLNTUXAGYPSOE"
rotor8 = "LFKIJODBEGAMQPXVUHYSTCZRWN"
rotor9 = "KOAEGVDHXPQZMLFTYWJNBRCIUS"
def _validator(
rotpos: RotorPositionT, rotsel: RotorSelectionT, pb: str
) -> tuple[RotorPositionT, RotorSelectionT, dict[str, str]]:
"""
Checks if the values can be used for the 'enigma' function
>>> _validator((1,1,1), (rotor1, rotor2, rotor3), 'POLAND')
((1, 1, 1), ('EGZWVONAHDCLFQMSIPJBYUKXTR', 'FOBHMDKEXQNRAULPGSJVTYICZW', \
'ZJXESIUQLHAVRMDOYGTNFWPBKC'), \
{'P': 'O', 'O': 'P', 'L': 'A', 'A': 'L', 'N': 'D', 'D': 'N'})
:param rotpos: rotor_positon
:param rotsel: rotor_selection
:param pb: plugb -> validated and transformed
:return: (rotpos, rotsel, pb)
"""
# Checks if there are 3 unique rotors
if (unique_rotsel := len(set(rotsel))) < 3:
msg = f"Please use 3 unique rotors (not {unique_rotsel})"
raise Exception(msg)
# Checks if rotor positions are valid
rotorpos1, rotorpos2, rotorpos3 = rotpos
if not 0 < rotorpos1 <= len(abc):
msg = f"First rotor position is not within range of 1..26 ({rotorpos1}"
raise ValueError(msg)
if not 0 < rotorpos2 <= len(abc):
msg = f"Second rotor position is not within range of 1..26 ({rotorpos2})"
raise ValueError(msg)
if not 0 < rotorpos3 <= len(abc):
msg = f"Third rotor position is not within range of 1..26 ({rotorpos3})"
raise ValueError(msg)
# Validates string and returns dict
pbdict = _plugboard(pb)
return rotpos, rotsel, pbdict
def _plugboard(pbstring: str) -> dict[str, str]:
"""
https://en.wikipedia.org/wiki/Enigma_machine#Plugboard
>>> _plugboard('PICTURES')
{'P': 'I', 'I': 'P', 'C': 'T', 'T': 'C', 'U': 'R', 'R': 'U', 'E': 'S', 'S': 'E'}
>>> _plugboard('POLAND')
{'P': 'O', 'O': 'P', 'L': 'A', 'A': 'L', 'N': 'D', 'D': 'N'}
In the code, 'pb' stands for 'plugboard'
Pairs can be separated by spaces
:param pbstring: string containing plugboard setting for the Enigma machine
:return: dictionary containing converted pairs
"""
# tests the input string if it
# a) is type string
# b) has even length (so pairs can be made)
if not isinstance(pbstring, str):
msg = f"Plugboard setting isn't type string ({type(pbstring)})"
raise TypeError(msg)
elif len(pbstring) % 2 != 0:
msg = f"Odd number of symbols ({len(pbstring)})"
raise Exception(msg)
elif pbstring == "":
return {}
pbstring.replace(" ", "")
# Checks if all characters are unique
tmppbl = set()
for i in pbstring:
if i not in abc:
msg = f"'{i}' not in list of symbols"
raise Exception(msg)
elif i in tmppbl:
msg = f"Duplicate symbol ({i})"
raise Exception(msg)
else:
tmppbl.add(i)
del tmppbl
# Created the dictionary
pb = {}
for j in range(0, len(pbstring) - 1, 2):
pb[pbstring[j]] = pbstring[j + 1]
pb[pbstring[j + 1]] = pbstring[j]
return pb
def enigma(
text: str,
rotor_position: RotorPositionT,
rotor_selection: RotorSelectionT = (rotor1, rotor2, rotor3),
plugb: str = "",
) -> str:
"""
The only difference with real-world enigma is that I allowed string input.
All characters are converted to uppercase. (non-letter symbol are ignored)
How it works:
(for every letter in the message)
- Input letter goes into the plugboard.
If it is connected to another one, switch it.
- Letter goes through 3 rotors.
Each rotor can be represented as 2 sets of symbol, where one is shuffled.
Each symbol from the first set has corresponding symbol in
the second set and vice versa.
example:
| ABCDEFGHIJKLMNOPQRSTUVWXYZ | e.g. F=D and D=F
| VKLEPDBGRNWTFCJOHQAMUZYIXS |
- Symbol then goes through reflector (static rotor).
There it is switched with paired symbol
The reflector can be represented as2 sets, each with half of the alphanet.
There are usually 10 pairs of letters.
Example:
| ABCDEFGHIJKLM | e.g. E is paired to X
| ZYXWVUTSRQPON | so when E goes in X goes out and vice versa
- Letter then goes through the rotors again
- If the letter is connected to plugboard, it is switched.
- Return the letter
>>> enigma('Hello World!', (1, 2, 1), plugb='pictures')
'KORYH JUHHI!'
>>> enigma('KORYH, juhhi!', (1, 2, 1), plugb='pictures')
'HELLO, WORLD!'
>>> enigma('hello world!', (1, 1, 1), plugb='pictures')
'FPNCZ QWOBU!'
>>> enigma('FPNCZ QWOBU', (1, 1, 1), plugb='pictures')
'HELLO WORLD'
:param text: input message
:param rotor_position: tuple with 3 values in range 1..26
:param rotor_selection: tuple with 3 rotors ()
:param plugb: string containing plugboard configuration (default '')
:return: en/decrypted string
"""
text = text.upper()
rotor_position, rotor_selection, plugboard = _validator(
rotor_position, rotor_selection, plugb.upper()
)
rotorpos1, rotorpos2, rotorpos3 = rotor_position
rotor1, rotor2, rotor3 = rotor_selection
rotorpos1 -= 1
rotorpos2 -= 1
rotorpos3 -= 1
result = []
# encryption/decryption process --------------------------
for symbol in text:
if symbol in abc:
# 1st plugboard --------------------------
if symbol in plugboard:
symbol = plugboard[symbol]
# rotor ra --------------------------
index = abc.index(symbol) + rotorpos1
symbol = rotor1[index % len(abc)]
# rotor rb --------------------------
index = abc.index(symbol) + rotorpos2
symbol = rotor2[index % len(abc)]
# rotor rc --------------------------
index = abc.index(symbol) + rotorpos3
symbol = rotor3[index % len(abc)]
# reflector --------------------------
# this is the reason you don't need another machine to decipher
symbol = reflector[symbol]
# 2nd rotors
symbol = abc[rotor3.index(symbol) - rotorpos3]
symbol = abc[rotor2.index(symbol) - rotorpos2]
symbol = abc[rotor1.index(symbol) - rotorpos1]
# 2nd plugboard
if symbol in plugboard:
symbol = plugboard[symbol]
# moves/resets rotor positions
rotorpos1 += 1
if rotorpos1 >= len(abc):
rotorpos1 = 0
rotorpos2 += 1
if rotorpos2 >= len(abc):
rotorpos2 = 0
rotorpos3 += 1
if rotorpos3 >= len(abc):
rotorpos3 = 0
# else:
# pass
# Error could be also raised
# raise ValueError(
# 'Invalid symbol('+repr(symbol)+')')
result.append(symbol)
return "".join(result)
if __name__ == "__main__":
message = "This is my Python script that emulates the Enigma machine from WWII."
rotor_pos = (1, 1, 1)
pb = "pictures"
rotor_sel = (rotor2, rotor4, rotor8)
en = enigma(message, rotor_pos, rotor_sel, pb)
print("Encrypted message:", en)
print("Decrypted message:", enigma(en, rotor_pos, rotor_sel, pb))
| """
Wikipedia: https://en.wikipedia.org/wiki/Enigma_machine
Video explanation: https://youtu.be/QwQVMqfoB2E
Also check out Numberphile's and Computerphile's videos on this topic
This module contains function 'enigma' which emulates
the famous Enigma machine from WWII.
Module includes:
- enigma function
- showcase of function usage
- 9 randomly generated rotors
- reflector (aka static rotor)
- original alphabet
Created by TrapinchO
"""
from __future__ import annotations
RotorPositionT = tuple[int, int, int]
RotorSelectionT = tuple[str, str, str]
# used alphabet --------------------------
# from string.ascii_uppercase
abc = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
# -------------------------- default selection --------------------------
# rotors --------------------------
rotor1 = "EGZWVONAHDCLFQMSIPJBYUKXTR"
rotor2 = "FOBHMDKEXQNRAULPGSJVTYICZW"
rotor3 = "ZJXESIUQLHAVRMDOYGTNFWPBKC"
# reflector --------------------------
reflector = {
"A": "N",
"N": "A",
"B": "O",
"O": "B",
"C": "P",
"P": "C",
"D": "Q",
"Q": "D",
"E": "R",
"R": "E",
"F": "S",
"S": "F",
"G": "T",
"T": "G",
"H": "U",
"U": "H",
"I": "V",
"V": "I",
"J": "W",
"W": "J",
"K": "X",
"X": "K",
"L": "Y",
"Y": "L",
"M": "Z",
"Z": "M",
}
# -------------------------- extra rotors --------------------------
rotor4 = "RMDJXFUWGISLHVTCQNKYPBEZOA"
rotor5 = "SGLCPQWZHKXAREONTFBVIYJUDM"
rotor6 = "HVSICLTYKQUBXDWAJZOMFGPREN"
rotor7 = "RZWQHFMVDBKICJLNTUXAGYPSOE"
rotor8 = "LFKIJODBEGAMQPXVUHYSTCZRWN"
rotor9 = "KOAEGVDHXPQZMLFTYWJNBRCIUS"
def _validator(
rotpos: RotorPositionT, rotsel: RotorSelectionT, pb: str
) -> tuple[RotorPositionT, RotorSelectionT, dict[str, str]]:
"""
Checks if the values can be used for the 'enigma' function
>>> _validator((1,1,1), (rotor1, rotor2, rotor3), 'POLAND')
((1, 1, 1), ('EGZWVONAHDCLFQMSIPJBYUKXTR', 'FOBHMDKEXQNRAULPGSJVTYICZW', \
'ZJXESIUQLHAVRMDOYGTNFWPBKC'), \
{'P': 'O', 'O': 'P', 'L': 'A', 'A': 'L', 'N': 'D', 'D': 'N'})
:param rotpos: rotor_positon
:param rotsel: rotor_selection
:param pb: plugb -> validated and transformed
:return: (rotpos, rotsel, pb)
"""
# Checks if there are 3 unique rotors
if (unique_rotsel := len(set(rotsel))) < 3:
msg = f"Please use 3 unique rotors (not {unique_rotsel})"
raise Exception(msg)
# Checks if rotor positions are valid
rotorpos1, rotorpos2, rotorpos3 = rotpos
if not 0 < rotorpos1 <= len(abc):
msg = f"First rotor position is not within range of 1..26 ({rotorpos1}"
raise ValueError(msg)
if not 0 < rotorpos2 <= len(abc):
msg = f"Second rotor position is not within range of 1..26 ({rotorpos2})"
raise ValueError(msg)
if not 0 < rotorpos3 <= len(abc):
msg = f"Third rotor position is not within range of 1..26 ({rotorpos3})"
raise ValueError(msg)
# Validates string and returns dict
pbdict = _plugboard(pb)
return rotpos, rotsel, pbdict
def _plugboard(pbstring: str) -> dict[str, str]:
"""
https://en.wikipedia.org/wiki/Enigma_machine#Plugboard
>>> _plugboard('PICTURES')
{'P': 'I', 'I': 'P', 'C': 'T', 'T': 'C', 'U': 'R', 'R': 'U', 'E': 'S', 'S': 'E'}
>>> _plugboard('POLAND')
{'P': 'O', 'O': 'P', 'L': 'A', 'A': 'L', 'N': 'D', 'D': 'N'}
In the code, 'pb' stands for 'plugboard'
Pairs can be separated by spaces
:param pbstring: string containing plugboard setting for the Enigma machine
:return: dictionary containing converted pairs
"""
# tests the input string if it
# a) is type string
# b) has even length (so pairs can be made)
if not isinstance(pbstring, str):
msg = f"Plugboard setting isn't type string ({type(pbstring)})"
raise TypeError(msg)
elif len(pbstring) % 2 != 0:
msg = f"Odd number of symbols ({len(pbstring)})"
raise Exception(msg)
elif pbstring == "":
return {}
pbstring.replace(" ", "")
# Checks if all characters are unique
tmppbl = set()
for i in pbstring:
if i not in abc:
msg = f"'{i}' not in list of symbols"
raise Exception(msg)
elif i in tmppbl:
msg = f"Duplicate symbol ({i})"
raise Exception(msg)
else:
tmppbl.add(i)
del tmppbl
# Created the dictionary
pb = {}
for j in range(0, len(pbstring) - 1, 2):
pb[pbstring[j]] = pbstring[j + 1]
pb[pbstring[j + 1]] = pbstring[j]
return pb
def enigma(
text: str,
rotor_position: RotorPositionT,
rotor_selection: RotorSelectionT = (rotor1, rotor2, rotor3),
plugb: str = "",
) -> str:
"""
The only difference with real-world enigma is that I allowed string input.
All characters are converted to uppercase. (non-letter symbol are ignored)
How it works:
(for every letter in the message)
- Input letter goes into the plugboard.
If it is connected to another one, switch it.
- Letter goes through 3 rotors.
Each rotor can be represented as 2 sets of symbol, where one is shuffled.
Each symbol from the first set has corresponding symbol in
the second set and vice versa.
example:
| ABCDEFGHIJKLMNOPQRSTUVWXYZ | e.g. F=D and D=F
| VKLEPDBGRNWTFCJOHQAMUZYIXS |
- Symbol then goes through reflector (static rotor).
There it is switched with paired symbol
The reflector can be represented as2 sets, each with half of the alphanet.
There are usually 10 pairs of letters.
Example:
| ABCDEFGHIJKLM | e.g. E is paired to X
| ZYXWVUTSRQPON | so when E goes in X goes out and vice versa
- Letter then goes through the rotors again
- If the letter is connected to plugboard, it is switched.
- Return the letter
>>> enigma('Hello World!', (1, 2, 1), plugb='pictures')
'KORYH JUHHI!'
>>> enigma('KORYH, juhhi!', (1, 2, 1), plugb='pictures')
'HELLO, WORLD!'
>>> enigma('hello world!', (1, 1, 1), plugb='pictures')
'FPNCZ QWOBU!'
>>> enigma('FPNCZ QWOBU', (1, 1, 1), plugb='pictures')
'HELLO WORLD'
:param text: input message
:param rotor_position: tuple with 3 values in range 1..26
:param rotor_selection: tuple with 3 rotors ()
:param plugb: string containing plugboard configuration (default '')
:return: en/decrypted string
"""
text = text.upper()
rotor_position, rotor_selection, plugboard = _validator(
rotor_position, rotor_selection, plugb.upper()
)
rotorpos1, rotorpos2, rotorpos3 = rotor_position
rotor1, rotor2, rotor3 = rotor_selection
rotorpos1 -= 1
rotorpos2 -= 1
rotorpos3 -= 1
result = []
# encryption/decryption process --------------------------
for symbol in text:
if symbol in abc:
# 1st plugboard --------------------------
if symbol in plugboard:
symbol = plugboard[symbol]
# rotor ra --------------------------
index = abc.index(symbol) + rotorpos1
symbol = rotor1[index % len(abc)]
# rotor rb --------------------------
index = abc.index(symbol) + rotorpos2
symbol = rotor2[index % len(abc)]
# rotor rc --------------------------
index = abc.index(symbol) + rotorpos3
symbol = rotor3[index % len(abc)]
# reflector --------------------------
# this is the reason you don't need another machine to decipher
symbol = reflector[symbol]
# 2nd rotors
symbol = abc[rotor3.index(symbol) - rotorpos3]
symbol = abc[rotor2.index(symbol) - rotorpos2]
symbol = abc[rotor1.index(symbol) - rotorpos1]
# 2nd plugboard
if symbol in plugboard:
symbol = plugboard[symbol]
# moves/resets rotor positions
rotorpos1 += 1
if rotorpos1 >= len(abc):
rotorpos1 = 0
rotorpos2 += 1
if rotorpos2 >= len(abc):
rotorpos2 = 0
rotorpos3 += 1
if rotorpos3 >= len(abc):
rotorpos3 = 0
# else:
# pass
# Error could be also raised
# raise ValueError(
# 'Invalid symbol('+repr(symbol)+')')
result.append(symbol)
return "".join(result)
if __name__ == "__main__":
message = "This is my Python script that emulates the Enigma machine from WWII."
rotor_pos = (1, 1, 1)
pb = "pictures"
rotor_sel = (rotor2, rotor4, rotor8)
en = enigma(message, rotor_pos, rotor_sel, pb)
print("Encrypted message:", en)
print("Decrypted message:", enigma(en, rotor_pos, rotor_sel, pb))
| -1 |
TheAlgorithms/Python | 9,062 | Delete empty junk file | ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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 | "2023-09-16T18:59:42Z" | "2023-09-16T22:12:31Z" | 1488cdea708485eb1d81c73126eab13cb9b04a47 | fbad85d3ecbbb826a5891807c823149d38bbaed3 | Delete empty junk file. ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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 double ended queue.
"""
from __future__ import annotations
from collections.abc import Iterable
from dataclasses import dataclass
from typing import Any
class Deque:
"""
Deque data structure.
Operations
----------
append(val: Any) -> None
appendleft(val: Any) -> None
extend(iterable: Iterable) -> None
extendleft(iterable: Iterable) -> None
pop() -> Any
popleft() -> Any
Observers
---------
is_empty() -> bool
Attributes
----------
_front: _Node
front of the deque a.k.a. the first element
_back: _Node
back of the element a.k.a. the last element
_len: int
the number of nodes
"""
__slots__ = ("_front", "_back", "_len")
@dataclass
class _Node:
"""
Representation of a node.
Contains a value and a pointer to the next node as well as to the previous one.
"""
val: Any = None
next_node: Deque._Node | None = None
prev_node: Deque._Node | None = None
class _Iterator:
"""
Helper class for iteration. Will be used to implement iteration.
Attributes
----------
_cur: _Node
the current node of the iteration.
"""
__slots__ = ("_cur",)
def __init__(self, cur: Deque._Node | None) -> None:
self._cur = cur
def __iter__(self) -> Deque._Iterator:
"""
>>> our_deque = Deque([1, 2, 3])
>>> iterator = iter(our_deque)
"""
return self
def __next__(self) -> Any:
"""
>>> our_deque = Deque([1, 2, 3])
>>> iterator = iter(our_deque)
>>> next(iterator)
1
>>> next(iterator)
2
>>> next(iterator)
3
"""
if self._cur is None:
# finished iterating
raise StopIteration
val = self._cur.val
self._cur = self._cur.next_node
return val
def __init__(self, iterable: Iterable[Any] | None = None) -> None:
self._front: Any = None
self._back: Any = None
self._len: int = 0
if iterable is not None:
# append every value to the deque
for val in iterable:
self.append(val)
def append(self, val: Any) -> None:
"""
Adds val to the end of the deque.
Time complexity: O(1)
>>> our_deque_1 = Deque([1, 2, 3])
>>> our_deque_1.append(4)
>>> our_deque_1
[1, 2, 3, 4]
>>> our_deque_2 = Deque('ab')
>>> our_deque_2.append('c')
>>> our_deque_2
['a', 'b', 'c']
>>> from collections import deque
>>> deque_collections_1 = deque([1, 2, 3])
>>> deque_collections_1.append(4)
>>> deque_collections_1
deque([1, 2, 3, 4])
>>> deque_collections_2 = deque('ab')
>>> deque_collections_2.append('c')
>>> deque_collections_2
deque(['a', 'b', 'c'])
>>> list(our_deque_1) == list(deque_collections_1)
True
>>> list(our_deque_2) == list(deque_collections_2)
True
"""
node = self._Node(val, None, None)
if self.is_empty():
# front = back
self._front = self._back = node
self._len = 1
else:
# connect nodes
self._back.next_node = node
node.prev_node = self._back
self._back = node # assign new back to the new node
self._len += 1
# make sure there were no errors
assert not self.is_empty(), "Error on appending value."
def appendleft(self, val: Any) -> None:
"""
Adds val to the beginning of the deque.
Time complexity: O(1)
>>> our_deque_1 = Deque([2, 3])
>>> our_deque_1.appendleft(1)
>>> our_deque_1
[1, 2, 3]
>>> our_deque_2 = Deque('bc')
>>> our_deque_2.appendleft('a')
>>> our_deque_2
['a', 'b', 'c']
>>> from collections import deque
>>> deque_collections_1 = deque([2, 3])
>>> deque_collections_1.appendleft(1)
>>> deque_collections_1
deque([1, 2, 3])
>>> deque_collections_2 = deque('bc')
>>> deque_collections_2.appendleft('a')
>>> deque_collections_2
deque(['a', 'b', 'c'])
>>> list(our_deque_1) == list(deque_collections_1)
True
>>> list(our_deque_2) == list(deque_collections_2)
True
"""
node = self._Node(val, None, None)
if self.is_empty():
# front = back
self._front = self._back = node
self._len = 1
else:
# connect nodes
node.next_node = self._front
self._front.prev_node = node
self._front = node # assign new front to the new node
self._len += 1
# make sure there were no errors
assert not self.is_empty(), "Error on appending value."
def extend(self, iterable: Iterable[Any]) -> None:
"""
Appends every value of iterable to the end of the deque.
Time complexity: O(n)
>>> our_deque_1 = Deque([1, 2, 3])
>>> our_deque_1.extend([4, 5])
>>> our_deque_1
[1, 2, 3, 4, 5]
>>> our_deque_2 = Deque('ab')
>>> our_deque_2.extend('cd')
>>> our_deque_2
['a', 'b', 'c', 'd']
>>> from collections import deque
>>> deque_collections_1 = deque([1, 2, 3])
>>> deque_collections_1.extend([4, 5])
>>> deque_collections_1
deque([1, 2, 3, 4, 5])
>>> deque_collections_2 = deque('ab')
>>> deque_collections_2.extend('cd')
>>> deque_collections_2
deque(['a', 'b', 'c', 'd'])
>>> list(our_deque_1) == list(deque_collections_1)
True
>>> list(our_deque_2) == list(deque_collections_2)
True
"""
for val in iterable:
self.append(val)
def extendleft(self, iterable: Iterable[Any]) -> None:
"""
Appends every value of iterable to the beginning of the deque.
Time complexity: O(n)
>>> our_deque_1 = Deque([1, 2, 3])
>>> our_deque_1.extendleft([0, -1])
>>> our_deque_1
[-1, 0, 1, 2, 3]
>>> our_deque_2 = Deque('cd')
>>> our_deque_2.extendleft('ba')
>>> our_deque_2
['a', 'b', 'c', 'd']
>>> from collections import deque
>>> deque_collections_1 = deque([1, 2, 3])
>>> deque_collections_1.extendleft([0, -1])
>>> deque_collections_1
deque([-1, 0, 1, 2, 3])
>>> deque_collections_2 = deque('cd')
>>> deque_collections_2.extendleft('ba')
>>> deque_collections_2
deque(['a', 'b', 'c', 'd'])
>>> list(our_deque_1) == list(deque_collections_1)
True
>>> list(our_deque_2) == list(deque_collections_2)
True
"""
for val in iterable:
self.appendleft(val)
def pop(self) -> Any:
"""
Removes the last element of the deque and returns it.
Time complexity: O(1)
@returns topop.val: the value of the node to pop.
>>> our_deque = Deque([1, 2, 3, 15182])
>>> our_popped = our_deque.pop()
>>> our_popped
15182
>>> our_deque
[1, 2, 3]
>>> from collections import deque
>>> deque_collections = deque([1, 2, 3, 15182])
>>> collections_popped = deque_collections.pop()
>>> collections_popped
15182
>>> deque_collections
deque([1, 2, 3])
>>> list(our_deque) == list(deque_collections)
True
>>> our_popped == collections_popped
True
"""
# make sure the deque has elements to pop
assert not self.is_empty(), "Deque is empty."
topop = self._back
self._back = self._back.prev_node # set new back
# drop the last node - python will deallocate memory automatically
self._back.next_node = None
self._len -= 1
return topop.val
def popleft(self) -> Any:
"""
Removes the first element of the deque and returns it.
Time complexity: O(1)
@returns topop.val: the value of the node to pop.
>>> our_deque = Deque([15182, 1, 2, 3])
>>> our_popped = our_deque.popleft()
>>> our_popped
15182
>>> our_deque
[1, 2, 3]
>>> from collections import deque
>>> deque_collections = deque([15182, 1, 2, 3])
>>> collections_popped = deque_collections.popleft()
>>> collections_popped
15182
>>> deque_collections
deque([1, 2, 3])
>>> list(our_deque) == list(deque_collections)
True
>>> our_popped == collections_popped
True
"""
# make sure the deque has elements to pop
assert not self.is_empty(), "Deque is empty."
topop = self._front
self._front = self._front.next_node # set new front and drop the first node
self._front.prev_node = None
self._len -= 1
return topop.val
def is_empty(self) -> bool:
"""
Checks if the deque is empty.
Time complexity: O(1)
>>> our_deque = Deque([1, 2, 3])
>>> our_deque.is_empty()
False
>>> our_empty_deque = Deque()
>>> our_empty_deque.is_empty()
True
>>> from collections import deque
>>> empty_deque_collections = deque()
>>> list(our_empty_deque) == list(empty_deque_collections)
True
"""
return self._front is None
def __len__(self) -> int:
"""
Implements len() function. Returns the length of the deque.
Time complexity: O(1)
>>> our_deque = Deque([1, 2, 3])
>>> len(our_deque)
3
>>> our_empty_deque = Deque()
>>> len(our_empty_deque)
0
>>> from collections import deque
>>> deque_collections = deque([1, 2, 3])
>>> len(deque_collections)
3
>>> empty_deque_collections = deque()
>>> len(empty_deque_collections)
0
>>> len(our_empty_deque) == len(empty_deque_collections)
True
"""
return self._len
def __eq__(self, other: object) -> bool:
"""
Implements "==" operator. Returns if *self* is equal to *other*.
Time complexity: O(n)
>>> our_deque_1 = Deque([1, 2, 3])
>>> our_deque_2 = Deque([1, 2, 3])
>>> our_deque_1 == our_deque_2
True
>>> our_deque_3 = Deque([1, 2])
>>> our_deque_1 == our_deque_3
False
>>> from collections import deque
>>> deque_collections_1 = deque([1, 2, 3])
>>> deque_collections_2 = deque([1, 2, 3])
>>> deque_collections_1 == deque_collections_2
True
>>> deque_collections_3 = deque([1, 2])
>>> deque_collections_1 == deque_collections_3
False
>>> (our_deque_1 == our_deque_2) == (deque_collections_1 == deque_collections_2)
True
>>> (our_deque_1 == our_deque_3) == (deque_collections_1 == deque_collections_3)
True
"""
if not isinstance(other, Deque):
return NotImplemented
me = self._front
oth = other._front
# if the length of the dequeues are not the same, they are not equal
if len(self) != len(other):
return False
while me is not None and oth is not None:
# compare every value
if me.val != oth.val:
return False
me = me.next_node
oth = oth.next_node
return True
def __iter__(self) -> Deque._Iterator:
"""
Implements iteration.
Time complexity: O(1)
>>> our_deque = Deque([1, 2, 3])
>>> for v in our_deque:
... print(v)
1
2
3
>>> from collections import deque
>>> deque_collections = deque([1, 2, 3])
>>> for v in deque_collections:
... print(v)
1
2
3
"""
return Deque._Iterator(self._front)
def __repr__(self) -> str:
"""
Implements representation of the deque.
Represents it as a list, with its values between '[' and ']'.
Time complexity: O(n)
>>> our_deque = Deque([1, 2, 3])
>>> our_deque
[1, 2, 3]
"""
values_list = []
aux = self._front
while aux is not None:
# append the values in a list to display
values_list.append(aux.val)
aux = aux.next_node
return f"[{', '.join(repr(val) for val in values_list)}]"
if __name__ == "__main__":
import doctest
doctest.testmod()
| """
Implementation of double ended queue.
"""
from __future__ import annotations
from collections.abc import Iterable
from dataclasses import dataclass
from typing import Any
class Deque:
"""
Deque data structure.
Operations
----------
append(val: Any) -> None
appendleft(val: Any) -> None
extend(iterable: Iterable) -> None
extendleft(iterable: Iterable) -> None
pop() -> Any
popleft() -> Any
Observers
---------
is_empty() -> bool
Attributes
----------
_front: _Node
front of the deque a.k.a. the first element
_back: _Node
back of the element a.k.a. the last element
_len: int
the number of nodes
"""
__slots__ = ("_front", "_back", "_len")
@dataclass
class _Node:
"""
Representation of a node.
Contains a value and a pointer to the next node as well as to the previous one.
"""
val: Any = None
next_node: Deque._Node | None = None
prev_node: Deque._Node | None = None
class _Iterator:
"""
Helper class for iteration. Will be used to implement iteration.
Attributes
----------
_cur: _Node
the current node of the iteration.
"""
__slots__ = ("_cur",)
def __init__(self, cur: Deque._Node | None) -> None:
self._cur = cur
def __iter__(self) -> Deque._Iterator:
"""
>>> our_deque = Deque([1, 2, 3])
>>> iterator = iter(our_deque)
"""
return self
def __next__(self) -> Any:
"""
>>> our_deque = Deque([1, 2, 3])
>>> iterator = iter(our_deque)
>>> next(iterator)
1
>>> next(iterator)
2
>>> next(iterator)
3
"""
if self._cur is None:
# finished iterating
raise StopIteration
val = self._cur.val
self._cur = self._cur.next_node
return val
def __init__(self, iterable: Iterable[Any] | None = None) -> None:
self._front: Any = None
self._back: Any = None
self._len: int = 0
if iterable is not None:
# append every value to the deque
for val in iterable:
self.append(val)
def append(self, val: Any) -> None:
"""
Adds val to the end of the deque.
Time complexity: O(1)
>>> our_deque_1 = Deque([1, 2, 3])
>>> our_deque_1.append(4)
>>> our_deque_1
[1, 2, 3, 4]
>>> our_deque_2 = Deque('ab')
>>> our_deque_2.append('c')
>>> our_deque_2
['a', 'b', 'c']
>>> from collections import deque
>>> deque_collections_1 = deque([1, 2, 3])
>>> deque_collections_1.append(4)
>>> deque_collections_1
deque([1, 2, 3, 4])
>>> deque_collections_2 = deque('ab')
>>> deque_collections_2.append('c')
>>> deque_collections_2
deque(['a', 'b', 'c'])
>>> list(our_deque_1) == list(deque_collections_1)
True
>>> list(our_deque_2) == list(deque_collections_2)
True
"""
node = self._Node(val, None, None)
if self.is_empty():
# front = back
self._front = self._back = node
self._len = 1
else:
# connect nodes
self._back.next_node = node
node.prev_node = self._back
self._back = node # assign new back to the new node
self._len += 1
# make sure there were no errors
assert not self.is_empty(), "Error on appending value."
def appendleft(self, val: Any) -> None:
"""
Adds val to the beginning of the deque.
Time complexity: O(1)
>>> our_deque_1 = Deque([2, 3])
>>> our_deque_1.appendleft(1)
>>> our_deque_1
[1, 2, 3]
>>> our_deque_2 = Deque('bc')
>>> our_deque_2.appendleft('a')
>>> our_deque_2
['a', 'b', 'c']
>>> from collections import deque
>>> deque_collections_1 = deque([2, 3])
>>> deque_collections_1.appendleft(1)
>>> deque_collections_1
deque([1, 2, 3])
>>> deque_collections_2 = deque('bc')
>>> deque_collections_2.appendleft('a')
>>> deque_collections_2
deque(['a', 'b', 'c'])
>>> list(our_deque_1) == list(deque_collections_1)
True
>>> list(our_deque_2) == list(deque_collections_2)
True
"""
node = self._Node(val, None, None)
if self.is_empty():
# front = back
self._front = self._back = node
self._len = 1
else:
# connect nodes
node.next_node = self._front
self._front.prev_node = node
self._front = node # assign new front to the new node
self._len += 1
# make sure there were no errors
assert not self.is_empty(), "Error on appending value."
def extend(self, iterable: Iterable[Any]) -> None:
"""
Appends every value of iterable to the end of the deque.
Time complexity: O(n)
>>> our_deque_1 = Deque([1, 2, 3])
>>> our_deque_1.extend([4, 5])
>>> our_deque_1
[1, 2, 3, 4, 5]
>>> our_deque_2 = Deque('ab')
>>> our_deque_2.extend('cd')
>>> our_deque_2
['a', 'b', 'c', 'd']
>>> from collections import deque
>>> deque_collections_1 = deque([1, 2, 3])
>>> deque_collections_1.extend([4, 5])
>>> deque_collections_1
deque([1, 2, 3, 4, 5])
>>> deque_collections_2 = deque('ab')
>>> deque_collections_2.extend('cd')
>>> deque_collections_2
deque(['a', 'b', 'c', 'd'])
>>> list(our_deque_1) == list(deque_collections_1)
True
>>> list(our_deque_2) == list(deque_collections_2)
True
"""
for val in iterable:
self.append(val)
def extendleft(self, iterable: Iterable[Any]) -> None:
"""
Appends every value of iterable to the beginning of the deque.
Time complexity: O(n)
>>> our_deque_1 = Deque([1, 2, 3])
>>> our_deque_1.extendleft([0, -1])
>>> our_deque_1
[-1, 0, 1, 2, 3]
>>> our_deque_2 = Deque('cd')
>>> our_deque_2.extendleft('ba')
>>> our_deque_2
['a', 'b', 'c', 'd']
>>> from collections import deque
>>> deque_collections_1 = deque([1, 2, 3])
>>> deque_collections_1.extendleft([0, -1])
>>> deque_collections_1
deque([-1, 0, 1, 2, 3])
>>> deque_collections_2 = deque('cd')
>>> deque_collections_2.extendleft('ba')
>>> deque_collections_2
deque(['a', 'b', 'c', 'd'])
>>> list(our_deque_1) == list(deque_collections_1)
True
>>> list(our_deque_2) == list(deque_collections_2)
True
"""
for val in iterable:
self.appendleft(val)
def pop(self) -> Any:
"""
Removes the last element of the deque and returns it.
Time complexity: O(1)
@returns topop.val: the value of the node to pop.
>>> our_deque = Deque([1, 2, 3, 15182])
>>> our_popped = our_deque.pop()
>>> our_popped
15182
>>> our_deque
[1, 2, 3]
>>> from collections import deque
>>> deque_collections = deque([1, 2, 3, 15182])
>>> collections_popped = deque_collections.pop()
>>> collections_popped
15182
>>> deque_collections
deque([1, 2, 3])
>>> list(our_deque) == list(deque_collections)
True
>>> our_popped == collections_popped
True
"""
# make sure the deque has elements to pop
assert not self.is_empty(), "Deque is empty."
topop = self._back
self._back = self._back.prev_node # set new back
# drop the last node - python will deallocate memory automatically
self._back.next_node = None
self._len -= 1
return topop.val
def popleft(self) -> Any:
"""
Removes the first element of the deque and returns it.
Time complexity: O(1)
@returns topop.val: the value of the node to pop.
>>> our_deque = Deque([15182, 1, 2, 3])
>>> our_popped = our_deque.popleft()
>>> our_popped
15182
>>> our_deque
[1, 2, 3]
>>> from collections import deque
>>> deque_collections = deque([15182, 1, 2, 3])
>>> collections_popped = deque_collections.popleft()
>>> collections_popped
15182
>>> deque_collections
deque([1, 2, 3])
>>> list(our_deque) == list(deque_collections)
True
>>> our_popped == collections_popped
True
"""
# make sure the deque has elements to pop
assert not self.is_empty(), "Deque is empty."
topop = self._front
self._front = self._front.next_node # set new front and drop the first node
self._front.prev_node = None
self._len -= 1
return topop.val
def is_empty(self) -> bool:
"""
Checks if the deque is empty.
Time complexity: O(1)
>>> our_deque = Deque([1, 2, 3])
>>> our_deque.is_empty()
False
>>> our_empty_deque = Deque()
>>> our_empty_deque.is_empty()
True
>>> from collections import deque
>>> empty_deque_collections = deque()
>>> list(our_empty_deque) == list(empty_deque_collections)
True
"""
return self._front is None
def __len__(self) -> int:
"""
Implements len() function. Returns the length of the deque.
Time complexity: O(1)
>>> our_deque = Deque([1, 2, 3])
>>> len(our_deque)
3
>>> our_empty_deque = Deque()
>>> len(our_empty_deque)
0
>>> from collections import deque
>>> deque_collections = deque([1, 2, 3])
>>> len(deque_collections)
3
>>> empty_deque_collections = deque()
>>> len(empty_deque_collections)
0
>>> len(our_empty_deque) == len(empty_deque_collections)
True
"""
return self._len
def __eq__(self, other: object) -> bool:
"""
Implements "==" operator. Returns if *self* is equal to *other*.
Time complexity: O(n)
>>> our_deque_1 = Deque([1, 2, 3])
>>> our_deque_2 = Deque([1, 2, 3])
>>> our_deque_1 == our_deque_2
True
>>> our_deque_3 = Deque([1, 2])
>>> our_deque_1 == our_deque_3
False
>>> from collections import deque
>>> deque_collections_1 = deque([1, 2, 3])
>>> deque_collections_2 = deque([1, 2, 3])
>>> deque_collections_1 == deque_collections_2
True
>>> deque_collections_3 = deque([1, 2])
>>> deque_collections_1 == deque_collections_3
False
>>> (our_deque_1 == our_deque_2) == (deque_collections_1 == deque_collections_2)
True
>>> (our_deque_1 == our_deque_3) == (deque_collections_1 == deque_collections_3)
True
"""
if not isinstance(other, Deque):
return NotImplemented
me = self._front
oth = other._front
# if the length of the dequeues are not the same, they are not equal
if len(self) != len(other):
return False
while me is not None and oth is not None:
# compare every value
if me.val != oth.val:
return False
me = me.next_node
oth = oth.next_node
return True
def __iter__(self) -> Deque._Iterator:
"""
Implements iteration.
Time complexity: O(1)
>>> our_deque = Deque([1, 2, 3])
>>> for v in our_deque:
... print(v)
1
2
3
>>> from collections import deque
>>> deque_collections = deque([1, 2, 3])
>>> for v in deque_collections:
... print(v)
1
2
3
"""
return Deque._Iterator(self._front)
def __repr__(self) -> str:
"""
Implements representation of the deque.
Represents it as a list, with its values between '[' and ']'.
Time complexity: O(n)
>>> our_deque = Deque([1, 2, 3])
>>> our_deque
[1, 2, 3]
"""
values_list = []
aux = self._front
while aux is not None:
# append the values in a list to display
values_list.append(aux.val)
aux = aux.next_node
return f"[{', '.join(repr(val) for val in values_list)}]"
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 9,062 | Delete empty junk file | ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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 | "2023-09-16T18:59:42Z" | "2023-09-16T22:12:31Z" | 1488cdea708485eb1d81c73126eab13cb9b04a47 | fbad85d3ecbbb826a5891807c823149d38bbaed3 | Delete empty junk file. ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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 | 9,062 | Delete empty junk file | ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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 | "2023-09-16T18:59:42Z" | "2023-09-16T22:12:31Z" | 1488cdea708485eb1d81c73126eab13cb9b04a47 | fbad85d3ecbbb826a5891807c823149d38bbaed3 | Delete empty junk file. ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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".
| """
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 of the triangle below:
75
95 64
17 47 82
18 35 87 10
20 04 82 47 65
19 01 23 75 03 34
88 02 77 73 07 63 67
99 65 04 28 06 16 70 92
41 41 26 56 83 40 80 70 33
41 48 72 33 47 32 37 16 94 29
53 71 44 65 25 43 91 52 97 51 14
70 11 33 28 77 73 17 78 39 68 17 57
91 71 52 38 17 14 91 43 58 50 27 29 48
63 66 04 68 89 53 67 30 73 16 69 87 40 31
04 62 98 27 23 09 70 98 73 93 38 53 60 04 23
"""
import os
def solution():
"""
Finds the maximum total in a triangle as described by the problem statement
above.
>>> solution()
1074
"""
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 = [[int(y) for y in x.rstrip("\r\n").split(" ")] for x in triangle]
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())
| """
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 of the triangle below:
75
95 64
17 47 82
18 35 87 10
20 04 82 47 65
19 01 23 75 03 34
88 02 77 73 07 63 67
99 65 04 28 06 16 70 92
41 41 26 56 83 40 80 70 33
41 48 72 33 47 32 37 16 94 29
53 71 44 65 25 43 91 52 97 51 14
70 11 33 28 77 73 17 78 39 68 17 57
91 71 52 38 17 14 91 43 58 50 27 29 48
63 66 04 68 89 53 67 30 73 16 69 87 40 31
04 62 98 27 23 09 70 98 73 93 38 53 60 04 23
"""
import os
def solution():
"""
Finds the maximum total in a triangle as described by the problem statement
above.
>>> solution()
1074
"""
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 = [[int(y) for y in x.rstrip("\r\n").split(" ")] for x in triangle]
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 | 9,062 | Delete empty junk file | ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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 | "2023-09-16T18:59:42Z" | "2023-09-16T22:12:31Z" | 1488cdea708485eb1d81c73126eab13cb9b04a47 | fbad85d3ecbbb826a5891807c823149d38bbaed3 | Delete empty junk file. ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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".
| hex_table = {hex(i)[2:]: i for i in range(16)} # Use [:2] to strip off the leading '0x'
def hex_to_decimal(hex_string: str) -> int:
"""
Convert a hexadecimal value to its decimal equivalent
#https://www.programiz.com/python-programming/methods/built-in/hex
>>> hex_to_decimal("a")
10
>>> hex_to_decimal("12f")
303
>>> hex_to_decimal(" 12f ")
303
>>> hex_to_decimal("FfFf")
65535
>>> hex_to_decimal("-Ff")
-255
>>> hex_to_decimal("F-f")
Traceback (most recent call last):
...
ValueError: Non-hexadecimal value was passed to the function
>>> hex_to_decimal("")
Traceback (most recent call last):
...
ValueError: Empty string was passed to the function
>>> hex_to_decimal("12m")
Traceback (most recent call last):
...
ValueError: Non-hexadecimal value was passed to the function
"""
hex_string = hex_string.strip().lower()
if not hex_string:
raise ValueError("Empty string was passed to the function")
is_negative = hex_string[0] == "-"
if is_negative:
hex_string = hex_string[1:]
if not all(char in hex_table for char in hex_string):
raise ValueError("Non-hexadecimal value was passed to the function")
decimal_number = 0
for char in hex_string:
decimal_number = 16 * decimal_number + hex_table[char]
return -decimal_number if is_negative else decimal_number
if __name__ == "__main__":
from doctest import testmod
testmod()
| hex_table = {hex(i)[2:]: i for i in range(16)} # Use [:2] to strip off the leading '0x'
def hex_to_decimal(hex_string: str) -> int:
"""
Convert a hexadecimal value to its decimal equivalent
#https://www.programiz.com/python-programming/methods/built-in/hex
>>> hex_to_decimal("a")
10
>>> hex_to_decimal("12f")
303
>>> hex_to_decimal(" 12f ")
303
>>> hex_to_decimal("FfFf")
65535
>>> hex_to_decimal("-Ff")
-255
>>> hex_to_decimal("F-f")
Traceback (most recent call last):
...
ValueError: Non-hexadecimal value was passed to the function
>>> hex_to_decimal("")
Traceback (most recent call last):
...
ValueError: Empty string was passed to the function
>>> hex_to_decimal("12m")
Traceback (most recent call last):
...
ValueError: Non-hexadecimal value was passed to the function
"""
hex_string = hex_string.strip().lower()
if not hex_string:
raise ValueError("Empty string was passed to the function")
is_negative = hex_string[0] == "-"
if is_negative:
hex_string = hex_string[1:]
if not all(char in hex_table for char in hex_string):
raise ValueError("Non-hexadecimal value was passed to the function")
decimal_number = 0
for char in hex_string:
decimal_number = 16 * decimal_number + hex_table[char]
return -decimal_number if is_negative else decimal_number
if __name__ == "__main__":
from doctest import testmod
testmod()
| -1 |
TheAlgorithms/Python | 9,062 | Delete empty junk file | ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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 | "2023-09-16T18:59:42Z" | "2023-09-16T22:12:31Z" | 1488cdea708485eb1d81c73126eab13cb9b04a47 | fbad85d3ecbbb826a5891807c823149d38bbaed3 | Delete empty junk file. ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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 | 9,062 | Delete empty junk file | ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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 | "2023-09-16T18:59:42Z" | "2023-09-16T22:12:31Z" | 1488cdea708485eb1d81c73126eab13cb9b04a47 | fbad85d3ecbbb826a5891807c823149d38bbaed3 | Delete empty junk file. ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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
"""
Illustrate how to implement bucket sort algorithm.
Author: OMKAR PATHAK
This program will illustrate how to implement bucket sort algorithm
Wikipedia says: Bucket sort, or bin sort, is a sorting algorithm that works
by distributing the elements of an array into a number of buckets.
Each bucket is then sorted individually, either using a different sorting
algorithm, or by recursively applying the bucket sorting algorithm. It is a
distribution sort, and is a cousin of radix sort in the most to least
significant digit flavour.
Bucket sort is a generalization of pigeonhole sort. Bucket sort can be
implemented with comparisons and therefore can also be considered a
comparison sort algorithm. The computational complexity estimates involve the
number of buckets.
Time Complexity of Solution:
Worst case scenario occurs when all the elements are placed in a single bucket.
The overall performance would then be dominated by the algorithm used to sort each
bucket. In this case, O(n log n), because of TimSort
Average Case O(n + (n^2)/k + k), where k is the number of buckets
If k = O(n), time complexity is O(n)
Source: https://en.wikipedia.org/wiki/Bucket_sort
"""
from __future__ import annotations
def bucket_sort(my_list: list, bucket_count: int = 10) -> list:
"""
>>> data = [-1, 2, -5, 0]
>>> bucket_sort(data) == sorted(data)
True
>>> data = [9, 8, 7, 6, -12]
>>> bucket_sort(data) == sorted(data)
True
>>> data = [.4, 1.2, .1, .2, -.9]
>>> bucket_sort(data) == sorted(data)
True
>>> bucket_sort([]) == sorted([])
True
>>> data = [-1e10, 1e10]
>>> bucket_sort(data) == sorted(data)
True
>>> import random
>>> collection = random.sample(range(-50, 50), 50)
>>> bucket_sort(collection) == sorted(collection)
True
"""
if len(my_list) == 0 or bucket_count <= 0:
return []
min_value, max_value = min(my_list), max(my_list)
bucket_size = (max_value - min_value) / bucket_count
buckets: list[list] = [[] for _ in range(bucket_count)]
for val in my_list:
index = min(int((val - min_value) / bucket_size), bucket_count - 1)
buckets[index].append(val)
return [val for bucket in buckets for val in sorted(bucket)]
if __name__ == "__main__":
from doctest import testmod
testmod()
assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]
| #!/usr/bin/env python3
"""
Illustrate how to implement bucket sort algorithm.
Author: OMKAR PATHAK
This program will illustrate how to implement bucket sort algorithm
Wikipedia says: Bucket sort, or bin sort, is a sorting algorithm that works
by distributing the elements of an array into a number of buckets.
Each bucket is then sorted individually, either using a different sorting
algorithm, or by recursively applying the bucket sorting algorithm. It is a
distribution sort, and is a cousin of radix sort in the most to least
significant digit flavour.
Bucket sort is a generalization of pigeonhole sort. Bucket sort can be
implemented with comparisons and therefore can also be considered a
comparison sort algorithm. The computational complexity estimates involve the
number of buckets.
Time Complexity of Solution:
Worst case scenario occurs when all the elements are placed in a single bucket.
The overall performance would then be dominated by the algorithm used to sort each
bucket. In this case, O(n log n), because of TimSort
Average Case O(n + (n^2)/k + k), where k is the number of buckets
If k = O(n), time complexity is O(n)
Source: https://en.wikipedia.org/wiki/Bucket_sort
"""
from __future__ import annotations
def bucket_sort(my_list: list, bucket_count: int = 10) -> list:
"""
>>> data = [-1, 2, -5, 0]
>>> bucket_sort(data) == sorted(data)
True
>>> data = [9, 8, 7, 6, -12]
>>> bucket_sort(data) == sorted(data)
True
>>> data = [.4, 1.2, .1, .2, -.9]
>>> bucket_sort(data) == sorted(data)
True
>>> bucket_sort([]) == sorted([])
True
>>> data = [-1e10, 1e10]
>>> bucket_sort(data) == sorted(data)
True
>>> import random
>>> collection = random.sample(range(-50, 50), 50)
>>> bucket_sort(collection) == sorted(collection)
True
"""
if len(my_list) == 0 or bucket_count <= 0:
return []
min_value, max_value = min(my_list), max(my_list)
bucket_size = (max_value - min_value) / bucket_count
buckets: list[list] = [[] for _ in range(bucket_count)]
for val in my_list:
index = min(int((val - min_value) / bucket_size), bucket_count - 1)
buckets[index].append(val)
return [val for bucket in buckets for val in sorted(bucket)]
if __name__ == "__main__":
from doctest import testmod
testmod()
assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]
| -1 |
TheAlgorithms/Python | 9,062 | Delete empty junk file | ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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 | "2023-09-16T18:59:42Z" | "2023-09-16T22:12:31Z" | 1488cdea708485eb1d81c73126eab13cb9b04a47 | fbad85d3ecbbb826a5891807c823149d38bbaed3 | Delete empty junk file. ### Describe your change:
Delete empty file introduced in #9046
* [ ] 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.
* [x] 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 random
from .binary_exp_mod import bin_exp_mod
# This is a probabilistic check to test primality, useful for big numbers!
# if it's a prime, it will return true
# if it's not a prime, the chance of it returning true is at most 1/4**prec
def is_prime_big(n, prec=1000):
"""
>>> from maths.prime_check import is_prime
>>> # all(is_prime_big(i) == is_prime(i) for i in range(1000)) # 3.45s
>>> all(is_prime_big(i) == is_prime(i) for i in range(256))
True
"""
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
d = n - 1
exp = 0
while d % 2 == 0:
d /= 2
exp += 1
# n - 1=d*(2**exp)
count = 0
while count < prec:
a = random.randint(2, n - 1)
b = bin_exp_mod(a, d, n)
if b != 1:
flag = True
for _ in range(exp):
if b == n - 1:
flag = False
break
b = b * b
b %= n
if flag:
return False
count += 1
return True
if __name__ == "__main__":
n = abs(int(input("Enter bound : ").strip()))
print("Here's the list of primes:")
print(", ".join(str(i) for i in range(n + 1) if is_prime_big(i)))
| import random
from .binary_exp_mod import bin_exp_mod
# This is a probabilistic check to test primality, useful for big numbers!
# if it's a prime, it will return true
# if it's not a prime, the chance of it returning true is at most 1/4**prec
def is_prime_big(n, prec=1000):
"""
>>> from maths.prime_check import is_prime
>>> # all(is_prime_big(i) == is_prime(i) for i in range(1000)) # 3.45s
>>> all(is_prime_big(i) == is_prime(i) for i in range(256))
True
"""
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
d = n - 1
exp = 0
while d % 2 == 0:
d /= 2
exp += 1
# n - 1=d*(2**exp)
count = 0
while count < prec:
a = random.randint(2, n - 1)
b = bin_exp_mod(a, d, n)
if b != 1:
flag = True
for _ in range(exp):
if b == n - 1:
flag = False
break
b = b * b
b %= n
if flag:
return False
count += 1
return True
if __name__ == "__main__":
n = abs(int(input("Enter bound : ").strip()))
print("Here's the list of primes:")
print(", ".join(str(i) for i in range(n + 1) if is_prime_big(i)))
| -1 |
TheAlgorithms/Python | 8,960 | Consolidate find_min and find_min recursive and find_max and find_max_recursive | ### Describe your change:
Merges `find_min` and `find_min_recursive` into just `find_min`.
Merges `find_min` and `find_max_recursive` into just `find_max`.
* [ ] Add an algorithm?
* [ ] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
* [x] Merge two different implementations into one file
### 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.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] 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".
| CaedenPH | "2023-08-14T10:30:16Z" | "2023-08-14T11:17:27Z" | 2ab3bf2689d21e7375539c79ecee358e9d7c3359 | fb1b939a89fb08370297cbb455846f61f66847bc | Consolidate find_min and find_min recursive and find_max and find_max_recursive. ### Describe your change:
Merges `find_min` and `find_min_recursive` into just `find_min`.
Merges `find_min` and `find_max_recursive` into just `find_max`.
* [ ] Add an algorithm?
* [ ] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
* [x] Merge two different implementations into one file
### 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.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] 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".
|
## 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)
* [Wa Tor](cellular_automata/wa_tor.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)
* [Regex Match](dynamic_programming/regex_match.py)
* [Rod Cutting](dynamic_programming/rod_cutting.py)
* [Subset Generation](dynamic_programming/subset_generation.py)
* [Sum Of Subset](dynamic_programming/sum_of_subset.py)
* [Tribonacci](dynamic_programming/tribonacci.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)
* [Interquartile Range](maths/interquartile_range.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)
* [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)
* [Is Valid Email Address](strings/is_valid_email_address.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)
* [Wa Tor](cellular_automata/wa_tor.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)
* [Regex Match](dynamic_programming/regex_match.py)
* [Rod Cutting](dynamic_programming/rod_cutting.py)
* [Subset Generation](dynamic_programming/subset_generation.py)
* [Sum Of Subset](dynamic_programming/sum_of_subset.py)
* [Tribonacci](dynamic_programming/tribonacci.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 Min](maths/find_min.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)
* [Interquartile Range](maths/interquartile_range.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)
* [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)
* [Is Valid Email Address](strings/is_valid_email_address.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 | 8,960 | Consolidate find_min and find_min recursive and find_max and find_max_recursive | ### Describe your change:
Merges `find_min` and `find_min_recursive` into just `find_min`.
Merges `find_min` and `find_max_recursive` into just `find_max`.
* [ ] Add an algorithm?
* [ ] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
* [x] Merge two different implementations into one file
### 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.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] 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".
| CaedenPH | "2023-08-14T10:30:16Z" | "2023-08-14T11:17:27Z" | 2ab3bf2689d21e7375539c79ecee358e9d7c3359 | fb1b939a89fb08370297cbb455846f61f66847bc | Consolidate find_min and find_min recursive and find_max and find_max_recursive. ### Describe your change:
Merges `find_min` and `find_min_recursive` into just `find_min`.
Merges `find_min` and `find_max_recursive` into just `find_max`.
* [ ] Add an algorithm?
* [ ] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
* [x] Merge two different implementations into one file
### 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.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] 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
def find_max(nums: list[int | float]) -> int | float:
"""
>>> for nums in ([3, 2, 1], [-3, -2, -1], [3, -3, 0], [3.0, 3.1, 2.9]):
... find_max(nums) == max(nums)
True
True
True
True
>>> find_max([2, 4, 9, 7, 19, 94, 5])
94
>>> find_max([])
Traceback (most recent call last):
...
ValueError: find_max() arg is an empty sequence
"""
if len(nums) == 0:
raise ValueError("find_max() arg is an empty sequence")
max_num = nums[0]
for x in nums:
if x > max_num:
max_num = x
return max_num
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| from __future__ import annotations
def find_max_iterative(nums: list[int | float]) -> int | float:
"""
>>> for nums in ([3, 2, 1], [-3, -2, -1], [3, -3, 0], [3.0, 3.1, 2.9]):
... find_max_iterative(nums) == max(nums)
True
True
True
True
>>> find_max_iterative([2, 4, 9, 7, 19, 94, 5])
94
>>> find_max_iterative([])
Traceback (most recent call last):
...
ValueError: find_max_iterative() arg is an empty sequence
"""
if len(nums) == 0:
raise ValueError("find_max_iterative() arg is an empty sequence")
max_num = nums[0]
for x in nums:
if x > max_num:
max_num = x
return max_num
# Divide and Conquer algorithm
def find_max_recursive(nums: list[int | float], left: int, right: int) -> int | float:
"""
find max value in list
:param nums: contains elements
:param left: index of first element
:param right: index of last element
:return: max in nums
>>> for nums in ([3, 2, 1], [-3, -2, -1], [3, -3, 0], [3.0, 3.1, 2.9]):
... find_max_recursive(nums, 0, len(nums) - 1) == max(nums)
True
True
True
True
>>> nums = [1, 3, 5, 7, 9, 2, 4, 6, 8, 10]
>>> find_max_recursive(nums, 0, len(nums) - 1) == max(nums)
True
>>> find_max_recursive([], 0, 0)
Traceback (most recent call last):
...
ValueError: find_max_recursive() arg is an empty sequence
>>> find_max_recursive(nums, 0, len(nums)) == max(nums)
Traceback (most recent call last):
...
IndexError: list index out of range
>>> find_max_recursive(nums, -len(nums), -1) == max(nums)
True
>>> find_max_recursive(nums, -len(nums) - 1, -1) == max(nums)
Traceback (most recent call last):
...
IndexError: list index out of range
"""
if len(nums) == 0:
raise ValueError("find_max_recursive() arg is an empty sequence")
if (
left >= len(nums)
or left < -len(nums)
or right >= len(nums)
or right < -len(nums)
):
raise IndexError("list index out of range")
if left == right:
return nums[left]
mid = (left + right) >> 1 # the middle
left_max = find_max_recursive(nums, left, mid) # find max in range[left, mid]
right_max = find_max_recursive(
nums, mid + 1, right
) # find max in range[mid + 1, right]
return left_max if left_max >= right_max else right_max
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| 1 |
TheAlgorithms/Python | 8,960 | Consolidate find_min and find_min recursive and find_max and find_max_recursive | ### Describe your change:
Merges `find_min` and `find_min_recursive` into just `find_min`.
Merges `find_min` and `find_max_recursive` into just `find_max`.
* [ ] Add an algorithm?
* [ ] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
* [x] Merge two different implementations into one file
### 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.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] 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".
| CaedenPH | "2023-08-14T10:30:16Z" | "2023-08-14T11:17:27Z" | 2ab3bf2689d21e7375539c79ecee358e9d7c3359 | fb1b939a89fb08370297cbb455846f61f66847bc | Consolidate find_min and find_min recursive and find_max and find_max_recursive. ### Describe your change:
Merges `find_min` and `find_min_recursive` into just `find_min`.
Merges `find_min` and `find_max_recursive` into just `find_max`.
* [ ] Add an algorithm?
* [ ] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
* [x] Merge two different implementations into one file
### 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.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] 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
def find_min(nums: list[int | float]) -> int | float:
"""
Find Minimum Number in a List
:param nums: contains elements
:return: min number in list
>>> for nums in ([3, 2, 1], [-3, -2, -1], [3, -3, 0], [3.0, 3.1, 2.9]):
... find_min(nums) == min(nums)
True
True
True
True
>>> find_min([0, 1, 2, 3, 4, 5, -3, 24, -56])
-56
>>> find_min([])
Traceback (most recent call last):
...
ValueError: find_min() arg is an empty sequence
"""
if len(nums) == 0:
raise ValueError("find_min() arg is an empty sequence")
min_num = nums[0]
for num in nums:
min_num = min(min_num, num)
return min_num
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| from __future__ import annotations
def find_min_iterative(nums: list[int | float]) -> int | float:
"""
Find Minimum Number in a List
:param nums: contains elements
:return: min number in list
>>> for nums in ([3, 2, 1], [-3, -2, -1], [3, -3, 0], [3.0, 3.1, 2.9]):
... find_min_iterative(nums) == min(nums)
True
True
True
True
>>> find_min_iterative([0, 1, 2, 3, 4, 5, -3, 24, -56])
-56
>>> find_min_iterative([])
Traceback (most recent call last):
...
ValueError: find_min_iterative() arg is an empty sequence
"""
if len(nums) == 0:
raise ValueError("find_min_iterative() arg is an empty sequence")
min_num = nums[0]
for num in nums:
min_num = min(min_num, num)
return min_num
# Divide and Conquer algorithm
def find_min_recursive(nums: list[int | float], left: int, right: int) -> int | float:
"""
find min value in list
:param nums: contains elements
:param left: index of first element
:param right: index of last element
:return: min in nums
>>> for nums in ([3, 2, 1], [-3, -2, -1], [3, -3, 0], [3.0, 3.1, 2.9]):
... find_min_recursive(nums, 0, len(nums) - 1) == min(nums)
True
True
True
True
>>> nums = [1, 3, 5, 7, 9, 2, 4, 6, 8, 10]
>>> find_min_recursive(nums, 0, len(nums) - 1) == min(nums)
True
>>> find_min_recursive([], 0, 0)
Traceback (most recent call last):
...
ValueError: find_min_recursive() arg is an empty sequence
>>> find_min_recursive(nums, 0, len(nums)) == min(nums)
Traceback (most recent call last):
...
IndexError: list index out of range
>>> find_min_recursive(nums, -len(nums), -1) == min(nums)
True
>>> find_min_recursive(nums, -len(nums) - 1, -1) == min(nums)
Traceback (most recent call last):
...
IndexError: list index out of range
"""
if len(nums) == 0:
raise ValueError("find_min_recursive() arg is an empty sequence")
if (
left >= len(nums)
or left < -len(nums)
or right >= len(nums)
or right < -len(nums)
):
raise IndexError("list index out of range")
if left == right:
return nums[left]
mid = (left + right) >> 1 # the middle
left_min = find_min_recursive(nums, left, mid) # find min in range[left, mid]
right_min = find_min_recursive(
nums, mid + 1, right
) # find min in range[mid + 1, right]
return left_min if left_min <= right_min else right_min
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| 1 |
TheAlgorithms/Python | 8,960 | Consolidate find_min and find_min recursive and find_max and find_max_recursive | ### Describe your change:
Merges `find_min` and `find_min_recursive` into just `find_min`.
Merges `find_min` and `find_max_recursive` into just `find_max`.
* [ ] Add an algorithm?
* [ ] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
* [x] Merge two different implementations into one file
### 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.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] 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".
| CaedenPH | "2023-08-14T10:30:16Z" | "2023-08-14T11:17:27Z" | 2ab3bf2689d21e7375539c79ecee358e9d7c3359 | fb1b939a89fb08370297cbb455846f61f66847bc | Consolidate find_min and find_min recursive and find_max and find_max_recursive. ### Describe your change:
Merges `find_min` and `find_min_recursive` into just `find_min`.
Merges `find_min` and `find_max_recursive` into just `find_max`.
* [ ] Add an algorithm?
* [ ] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
* [x] Merge two different implementations into one file
### 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.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] 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 104 : https://projecteuler.net/problem=104
The Fibonacci sequence is defined by the recurrence relation:
Fn = Fn−1 + Fn−2, where F1 = 1 and F2 = 1.
It turns out that F541, which contains 113 digits, is the first Fibonacci number
for which the last nine digits are 1-9 pandigital (contain all the digits 1 to 9,
but not necessarily in order). And F2749, which contains 575 digits, is the first
Fibonacci number for which the first nine digits are 1-9 pandigital.
Given that Fk is the first Fibonacci number for which the first nine digits AND
the last nine digits are 1-9 pandigital, find k.
"""
import sys
sys.set_int_max_str_digits(0) # type: ignore
def check(number: int) -> bool:
"""
Takes a number and checks if it is pandigital both from start and end
>>> check(123456789987654321)
True
>>> check(120000987654321)
False
>>> check(1234567895765677987654321)
True
"""
check_last = [0] * 11
check_front = [0] * 11
# mark last 9 numbers
for _ in range(9):
check_last[int(number % 10)] = 1
number = number // 10
# flag
f = True
# check last 9 numbers for pandigitality
for x in range(9):
if not check_last[x + 1]:
f = False
if not f:
return f
# mark first 9 numbers
number = int(str(number)[:9])
for _ in range(9):
check_front[int(number % 10)] = 1
number = number // 10
# check first 9 numbers for pandigitality
for x in range(9):
if not check_front[x + 1]:
f = False
return f
def check1(number: int) -> bool:
"""
Takes a number and checks if it is pandigital from END
>>> check1(123456789987654321)
True
>>> check1(120000987654321)
True
>>> check1(12345678957656779870004321)
False
"""
check_last = [0] * 11
# mark last 9 numbers
for _ in range(9):
check_last[int(number % 10)] = 1
number = number // 10
# flag
f = True
# check last 9 numbers for pandigitality
for x in range(9):
if not check_last[x + 1]:
f = False
return f
def solution() -> int:
"""
Outputs the answer is the least Fibonacci number pandigital from both sides.
>>> solution()
329468
"""
a = 1
b = 1
c = 2
# temporary Fibonacci numbers
a1 = 1
b1 = 1
c1 = 2
# temporary Fibonacci numbers mod 1e9
# mod m=1e9, done for fast optimisation
tocheck = [0] * 1000000
m = 1000000000
for x in range(1000000):
c1 = (a1 + b1) % m
a1 = b1 % m
b1 = c1 % m
if check1(b1):
tocheck[x + 3] = 1
for x in range(1000000):
c = a + b
a = b
b = c
# perform check only if in tocheck
if tocheck[x + 3] and check(b):
return x + 3 # first 2 already done
return -1
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 104 : https://projecteuler.net/problem=104
The Fibonacci sequence is defined by the recurrence relation:
Fn = Fn−1 + Fn−2, where F1 = 1 and F2 = 1.
It turns out that F541, which contains 113 digits, is the first Fibonacci number
for which the last nine digits are 1-9 pandigital (contain all the digits 1 to 9,
but not necessarily in order). And F2749, which contains 575 digits, is the first
Fibonacci number for which the first nine digits are 1-9 pandigital.
Given that Fk is the first Fibonacci number for which the first nine digits AND
the last nine digits are 1-9 pandigital, find k.
"""
import sys
sys.set_int_max_str_digits(0) # type: ignore
def check(number: int) -> bool:
"""
Takes a number and checks if it is pandigital both from start and end
>>> check(123456789987654321)
True
>>> check(120000987654321)
False
>>> check(1234567895765677987654321)
True
"""
check_last = [0] * 11
check_front = [0] * 11
# mark last 9 numbers
for _ in range(9):
check_last[int(number % 10)] = 1
number = number // 10
# flag
f = True
# check last 9 numbers for pandigitality
for x in range(9):
if not check_last[x + 1]:
f = False
if not f:
return f
# mark first 9 numbers
number = int(str(number)[:9])
for _ in range(9):
check_front[int(number % 10)] = 1
number = number // 10
# check first 9 numbers for pandigitality
for x in range(9):
if not check_front[x + 1]:
f = False
return f
def check1(number: int) -> bool:
"""
Takes a number and checks if it is pandigital from END
>>> check1(123456789987654321)
True
>>> check1(120000987654321)
True
>>> check1(12345678957656779870004321)
False
"""
check_last = [0] * 11
# mark last 9 numbers
for _ in range(9):
check_last[int(number % 10)] = 1
number = number // 10
# flag
f = True
# check last 9 numbers for pandigitality
for x in range(9):
if not check_last[x + 1]:
f = False
return f
def solution() -> int:
"""
Outputs the answer is the least Fibonacci number pandigital from both sides.
>>> solution()
329468
"""
a = 1
b = 1
c = 2
# temporary Fibonacci numbers
a1 = 1
b1 = 1
c1 = 2
# temporary Fibonacci numbers mod 1e9
# mod m=1e9, done for fast optimisation
tocheck = [0] * 1000000
m = 1000000000
for x in range(1000000):
c1 = (a1 + b1) % m
a1 = b1 % m
b1 = c1 % m
if check1(b1):
tocheck[x + 3] = 1
for x in range(1000000):
c = a + b
a = b
b = c
# perform check only if in tocheck
if tocheck[x + 3] and check(b):
return x + 3 # first 2 already done
return -1
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 8,960 | Consolidate find_min and find_min recursive and find_max and find_max_recursive | ### Describe your change:
Merges `find_min` and `find_min_recursive` into just `find_min`.
Merges `find_min` and `find_max_recursive` into just `find_max`.
* [ ] Add an algorithm?
* [ ] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
* [x] Merge two different implementations into one file
### 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.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] 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".
| CaedenPH | "2023-08-14T10:30:16Z" | "2023-08-14T11:17:27Z" | 2ab3bf2689d21e7375539c79ecee358e9d7c3359 | fb1b939a89fb08370297cbb455846f61f66847bc | Consolidate find_min and find_min recursive and find_max and find_max_recursive. ### Describe your change:
Merges `find_min` and `find_min_recursive` into just `find_min`.
Merges `find_min` and `find_max_recursive` into just `find_max`.
* [ ] Add an algorithm?
* [ ] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
* [x] Merge two different implementations into one file
### 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.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] 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 | 8,960 | Consolidate find_min and find_min recursive and find_max and find_max_recursive | ### Describe your change:
Merges `find_min` and `find_min_recursive` into just `find_min`.
Merges `find_min` and `find_max_recursive` into just `find_max`.
* [ ] Add an algorithm?
* [ ] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
* [x] Merge two different implementations into one file
### 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.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] 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".
| CaedenPH | "2023-08-14T10:30:16Z" | "2023-08-14T11:17:27Z" | 2ab3bf2689d21e7375539c79ecee358e9d7c3359 | fb1b939a89fb08370297cbb455846f61f66847bc | Consolidate find_min and find_min recursive and find_max and find_max_recursive. ### Describe your change:
Merges `find_min` and `find_min_recursive` into just `find_min`.
Merges `find_min` and `find_max_recursive` into just `find_max`.
* [ ] Add an algorithm?
* [ ] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
* [x] Merge two different implementations into one file
### 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.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] 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 5: https://projecteuler.net/problem=5
Smallest multiple
2520 is the smallest number that can be divided by each of the numbers
from 1 to 10 without any remainder.
What is the smallest positive number that is _evenly divisible_ by all
of the numbers from 1 to 20?
References:
- https://en.wiktionary.org/wiki/evenly_divisible
"""
def solution(n: int = 20) -> int:
"""
Returns the smallest positive number that is evenly divisible (divisible
with no remainder) by all of the numbers from 1 to n.
>>> solution(10)
2520
>>> solution(15)
360360
>>> solution(22)
232792560
>>> solution(3.4)
6
>>> 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 = 0
while 1:
i += n * (n - 1)
nfound = 0
for j in range(2, n):
if i % j != 0:
nfound = 1
break
if nfound == 0:
if i == 0:
i = 1
return i
return None
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 5: https://projecteuler.net/problem=5
Smallest multiple
2520 is the smallest number that can be divided by each of the numbers
from 1 to 10 without any remainder.
What is the smallest positive number that is _evenly divisible_ by all
of the numbers from 1 to 20?
References:
- https://en.wiktionary.org/wiki/evenly_divisible
"""
def solution(n: int = 20) -> int:
"""
Returns the smallest positive number that is evenly divisible (divisible
with no remainder) by all of the numbers from 1 to n.
>>> solution(10)
2520
>>> solution(15)
360360
>>> solution(22)
232792560
>>> solution(3.4)
6
>>> 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 = 0
while 1:
i += n * (n - 1)
nfound = 0
for j in range(2, n):
if i % j != 0:
nfound = 1
break
if nfound == 0:
if i == 0:
i = 1
return i
return None
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 8,960 | Consolidate find_min and find_min recursive and find_max and find_max_recursive | ### Describe your change:
Merges `find_min` and `find_min_recursive` into just `find_min`.
Merges `find_min` and `find_max_recursive` into just `find_max`.
* [ ] Add an algorithm?
* [ ] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
* [x] Merge two different implementations into one file
### 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.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] 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".
| CaedenPH | "2023-08-14T10:30:16Z" | "2023-08-14T11:17:27Z" | 2ab3bf2689d21e7375539c79ecee358e9d7c3359 | fb1b939a89fb08370297cbb455846f61f66847bc | Consolidate find_min and find_min recursive and find_max and find_max_recursive. ### Describe your change:
Merges `find_min` and `find_min_recursive` into just `find_min`.
Merges `find_min` and `find_max_recursive` into just `find_max`.
* [ ] Add an algorithm?
* [ ] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
* [x] Merge two different implementations into one file
### 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.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] 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".
| # XGBoost Regressor Example
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def data_handling(data: dict) -> tuple:
# Split dataset into features and target. Data is features.
"""
>>> data_handling((
... {'data':'[ 8.3252 41. 6.9841269 1.02380952 322. 2.55555556 37.88 -122.23 ]'
... ,'target':([4.526])}))
('[ 8.3252 41. 6.9841269 1.02380952 322. 2.55555556 37.88 -122.23 ]', [4.526])
"""
return (data["data"], data["target"])
def xgboost(
features: np.ndarray, target: np.ndarray, test_features: np.ndarray
) -> np.ndarray:
"""
>>> xgboost(np.array([[ 2.3571 , 52. , 6.00813008, 1.06775068,
... 907. , 2.45799458, 40.58 , -124.26]]),np.array([1.114]),
... np.array([[1.97840000e+00, 3.70000000e+01, 4.98858447e+00, 1.03881279e+00,
... 1.14300000e+03, 2.60958904e+00, 3.67800000e+01, -1.19780000e+02]]))
array([[1.1139996]], dtype=float32)
"""
xgb = XGBRegressor(verbosity=0, random_state=42)
xgb.fit(features, target)
# Predict target for test data
predictions = xgb.predict(test_features)
predictions = predictions.reshape(len(predictions), 1)
return predictions
def main() -> None:
"""
>>> main()
Mean Absolute Error : 0.30957163379906033
Mean Square Error : 0.22611560196662744
The URL for this algorithm
https://xgboost.readthedocs.io/en/stable/
California house price dataset is used to demonstrate the algorithm.
"""
# Load California house price dataset
california = fetch_california_housing()
data, target = data_handling(california)
x_train, x_test, y_train, y_test = train_test_split(
data, target, test_size=0.25, random_state=1
)
predictions = xgboost(x_train, y_train, x_test)
# Error printing
print(f"Mean Absolute Error : {mean_absolute_error(y_test, predictions)}")
print(f"Mean Square Error : {mean_squared_error(y_test, predictions)}")
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main()
| # XGBoost Regressor Example
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def data_handling(data: dict) -> tuple:
# Split dataset into features and target. Data is features.
"""
>>> data_handling((
... {'data':'[ 8.3252 41. 6.9841269 1.02380952 322. 2.55555556 37.88 -122.23 ]'
... ,'target':([4.526])}))
('[ 8.3252 41. 6.9841269 1.02380952 322. 2.55555556 37.88 -122.23 ]', [4.526])
"""
return (data["data"], data["target"])
def xgboost(
features: np.ndarray, target: np.ndarray, test_features: np.ndarray
) -> np.ndarray:
"""
>>> xgboost(np.array([[ 2.3571 , 52. , 6.00813008, 1.06775068,
... 907. , 2.45799458, 40.58 , -124.26]]),np.array([1.114]),
... np.array([[1.97840000e+00, 3.70000000e+01, 4.98858447e+00, 1.03881279e+00,
... 1.14300000e+03, 2.60958904e+00, 3.67800000e+01, -1.19780000e+02]]))
array([[1.1139996]], dtype=float32)
"""
xgb = XGBRegressor(verbosity=0, random_state=42)
xgb.fit(features, target)
# Predict target for test data
predictions = xgb.predict(test_features)
predictions = predictions.reshape(len(predictions), 1)
return predictions
def main() -> None:
"""
>>> main()
Mean Absolute Error : 0.30957163379906033
Mean Square Error : 0.22611560196662744
The URL for this algorithm
https://xgboost.readthedocs.io/en/stable/
California house price dataset is used to demonstrate the algorithm.
"""
# Load California house price dataset
california = fetch_california_housing()
data, target = data_handling(california)
x_train, x_test, y_train, y_test = train_test_split(
data, target, test_size=0.25, random_state=1
)
predictions = xgboost(x_train, y_train, x_test)
# Error printing
print(f"Mean Absolute Error : {mean_absolute_error(y_test, predictions)}")
print(f"Mean Square Error : {mean_squared_error(y_test, predictions)}")
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main()
| -1 |
Subsets and Splits