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TheAlgorithms/Python | 7,417 | Remove references to depreciated QasmSimulator | ### Describe your change:
Replaced instances of `qiskit.Aer.get_backend("qasm_simulator")` in `quantum/` with `q.Aer.get_backend("aer_simulator")`, as the former is depreciated and raises warnings (Qiskit's [documentation](https://qiskit.org/documentation/apidoc/aer_provider.html) says that `QasmSimulator` is legacy).
This PR edits multiple code files because they all raise the same warning and are mentioned in the same GitHub issue.
Fixes #7308
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [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 have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| tianyizheng02 | 2022-10-19T03:31:43Z | 2022-10-19T20:12:44Z | 50da472ddcdc2d79d1ad325ec05cda3558802fda | 2859d4bf3aa96737a4715c65d4a9051d9c62d24d | Remove references to depreciated QasmSimulator. ### Describe your change:
Replaced instances of `qiskit.Aer.get_backend("qasm_simulator")` in `quantum/` with `q.Aer.get_backend("aer_simulator")`, as the former is depreciated and raises warnings (Qiskit's [documentation](https://qiskit.org/documentation/apidoc/aer_provider.html) says that `QasmSimulator` is legacy).
This PR edits multiple code files because they all raise the same warning and are mentioned in the same GitHub issue.
Fixes #7308
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """ Luhn Algorithm """
from __future__ import annotations
def is_luhn(string: str) -> bool:
"""
Perform Luhn validation on an input string
Algorithm:
* Double every other digit starting from 2nd last digit.
* Subtract 9 if number is greater than 9.
* Sum the numbers
*
>>> test_cases = (79927398710, 79927398711, 79927398712, 79927398713,
... 79927398714, 79927398715, 79927398716, 79927398717, 79927398718,
... 79927398719)
>>> [is_luhn(str(test_case)) for test_case in test_cases]
[False, False, False, True, False, False, False, False, False, False]
"""
check_digit: int
_vector: list[str] = list(string)
__vector, check_digit = _vector[:-1], int(_vector[-1])
vector: list[int] = [int(digit) for digit in __vector]
vector.reverse()
for i, digit in enumerate(vector):
if i & 1 == 0:
doubled: int = digit * 2
if doubled > 9:
doubled -= 9
check_digit += doubled
else:
check_digit += digit
return check_digit % 10 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
assert is_luhn("79927398713")
assert not is_luhn("79927398714")
| """ Luhn Algorithm """
from __future__ import annotations
def is_luhn(string: str) -> bool:
"""
Perform Luhn validation on an input string
Algorithm:
* Double every other digit starting from 2nd last digit.
* Subtract 9 if number is greater than 9.
* Sum the numbers
*
>>> test_cases = (79927398710, 79927398711, 79927398712, 79927398713,
... 79927398714, 79927398715, 79927398716, 79927398717, 79927398718,
... 79927398719)
>>> [is_luhn(str(test_case)) for test_case in test_cases]
[False, False, False, True, False, False, False, False, False, False]
"""
check_digit: int
_vector: list[str] = list(string)
__vector, check_digit = _vector[:-1], int(_vector[-1])
vector: list[int] = [int(digit) for digit in __vector]
vector.reverse()
for i, digit in enumerate(vector):
if i & 1 == 0:
doubled: int = digit * 2
if doubled > 9:
doubled -= 9
check_digit += doubled
else:
check_digit += digit
return check_digit % 10 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
assert is_luhn("79927398713")
assert not is_luhn("79927398714")
| -1 |
TheAlgorithms/Python | 7,417 | Remove references to depreciated QasmSimulator | ### Describe your change:
Replaced instances of `qiskit.Aer.get_backend("qasm_simulator")` in `quantum/` with `q.Aer.get_backend("aer_simulator")`, as the former is depreciated and raises warnings (Qiskit's [documentation](https://qiskit.org/documentation/apidoc/aer_provider.html) says that `QasmSimulator` is legacy).
This PR edits multiple code files because they all raise the same warning and are mentioned in the same GitHub issue.
Fixes #7308
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [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 have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| tianyizheng02 | 2022-10-19T03:31:43Z | 2022-10-19T20:12:44Z | 50da472ddcdc2d79d1ad325ec05cda3558802fda | 2859d4bf3aa96737a4715c65d4a9051d9c62d24d | Remove references to depreciated QasmSimulator. ### Describe your change:
Replaced instances of `qiskit.Aer.get_backend("qasm_simulator")` in `quantum/` with `q.Aer.get_backend("aer_simulator")`, as the former is depreciated and raises warnings (Qiskit's [documentation](https://qiskit.org/documentation/apidoc/aer_provider.html) says that `QasmSimulator` is legacy).
This PR edits multiple code files because they all raise the same warning and are mentioned in the same GitHub issue.
Fixes #7308
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # A naive recursive implementation of 0-1 Knapsack Problem
This overview is taken from:
https://en.wikipedia.org/wiki/Knapsack_problem
---
## Overview
The knapsack problem is a problem in combinatorial optimization: Given a set of items, each with a weight and a value, determine the number of each item to include in a collection so that the total weight is less than or equal to a given limit and the total value is as large as possible. It derives its name from the problem faced by someone who is constrained by a fixed-size knapsack and must fill it with the most valuable items. The problem often arises in resource allocation where the decision makers have to choose from a set of non-divisible projects or tasks under a fixed budget or time constraint, respectively.
The knapsack problem has been studied for more than a century, with early works dating as far back as 1897 The name "knapsack problem" dates back to the early works of mathematician Tobias Dantzig (1884–1956), and refers to the commonplace problem of packing the most valuable or useful items without overloading the luggage.
---
## Documentation
This module uses docstrings to enable the use of Python's in-built `help(...)` function.
For instance, try `help(Vector)`, `help(unit_basis_vector)`, and `help(CLASSNAME.METHODNAME)`.
---
## Usage
Import the module `knapsack.py` from the **.** directory into your project.
---
## Tests
`.` contains Python unit tests which can be run with `python3 -m unittest -v`.
| # A naive recursive implementation of 0-1 Knapsack Problem
This overview is taken from:
https://en.wikipedia.org/wiki/Knapsack_problem
---
## Overview
The knapsack problem is a problem in combinatorial optimization: Given a set of items, each with a weight and a value, determine the number of each item to include in a collection so that the total weight is less than or equal to a given limit and the total value is as large as possible. It derives its name from the problem faced by someone who is constrained by a fixed-size knapsack and must fill it with the most valuable items. The problem often arises in resource allocation where the decision makers have to choose from a set of non-divisible projects or tasks under a fixed budget or time constraint, respectively.
The knapsack problem has been studied for more than a century, with early works dating as far back as 1897 The name "knapsack problem" dates back to the early works of mathematician Tobias Dantzig (1884–1956), and refers to the commonplace problem of packing the most valuable or useful items without overloading the luggage.
---
## Documentation
This module uses docstrings to enable the use of Python's in-built `help(...)` function.
For instance, try `help(Vector)`, `help(unit_basis_vector)`, and `help(CLASSNAME.METHODNAME)`.
---
## Usage
Import the module `knapsack.py` from the **.** directory into your project.
---
## Tests
`.` contains Python unit tests which can be run with `python3 -m unittest -v`.
| -1 |
TheAlgorithms/Python | 7,417 | Remove references to depreciated QasmSimulator | ### Describe your change:
Replaced instances of `qiskit.Aer.get_backend("qasm_simulator")` in `quantum/` with `q.Aer.get_backend("aer_simulator")`, as the former is depreciated and raises warnings (Qiskit's [documentation](https://qiskit.org/documentation/apidoc/aer_provider.html) says that `QasmSimulator` is legacy).
This PR edits multiple code files because they all raise the same warning and are mentioned in the same GitHub issue.
Fixes #7308
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [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 have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| tianyizheng02 | 2022-10-19T03:31:43Z | 2022-10-19T20:12:44Z | 50da472ddcdc2d79d1ad325ec05cda3558802fda | 2859d4bf3aa96737a4715c65d4a9051d9c62d24d | Remove references to depreciated QasmSimulator. ### Describe your change:
Replaced instances of `qiskit.Aer.get_backend("qasm_simulator")` in `quantum/` with `q.Aer.get_backend("aer_simulator")`, as the former is depreciated and raises warnings (Qiskit's [documentation](https://qiskit.org/documentation/apidoc/aer_provider.html) says that `QasmSimulator` is legacy).
This PR edits multiple code files because they all raise the same warning and are mentioned in the same GitHub issue.
Fixes #7308
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Project Euler Problem 686: https://projecteuler.net/problem=686
2^7 = 128 is the first power of two whose leading digits are "12".
The next power of two whose leading digits are "12" is 2^80.
Define p(L,n) to be the nth-smallest value of j such that
the base 10 representation of 2^j begins with the digits of L.
So p(12, 1) = 7 and p(12, 2) = 80.
You are given that p(123, 45) = 12710.
Find p(123, 678910).
"""
import math
def log_difference(number: int) -> float:
"""
This function returns the decimal value of a number multiplied with log(2)
Since the problem is on powers of two, finding the powers of two with
large exponents is time consuming. Hence we use log to reduce compute time.
We can find out that the first power of 2 with starting digits 123 is 90.
Computing 2^90 is time consuming.
Hence we find log(2^90) = 90*log(2) = 27.092699609758302
But we require only the decimal part to determine whether the power starts with 123.
So we just return the decimal part of the log product.
Therefore we return 0.092699609758302
>>> log_difference(90)
0.092699609758302
>>> log_difference(379)
0.090368356648852
"""
log_number = math.log(2, 10) * number
difference = round((log_number - int(log_number)), 15)
return difference
def solution(number: int = 678910) -> int:
"""
This function calculates the power of two which is nth (n = number)
smallest value of power of 2
such that the starting digits of the 2^power is 123.
For example the powers of 2 for which starting digits is 123 are:
90, 379, 575, 864, 1060, 1545, 1741, 2030, 2226, 2515 and so on.
90 is the first power of 2 whose starting digits are 123,
379 is second power of 2 whose starting digits are 123,
and so on.
So if number = 10, then solution returns 2515 as we observe from above series.
We will define a lowerbound and upperbound.
lowerbound = log(1.23), upperbound = log(1.24)
because we need to find the powers that yield 123 as starting digits.
log(1.23) = 0.08990511143939792, log(1,24) = 0.09342168516223506.
We use 1.23 and not 12.3 or 123, because log(1.23) yields only decimal value
which is less than 1.
log(12.3) will be same decimal value but 1 added to it
which is log(12.3) = 1.093421685162235.
We observe that decimal value remains same no matter 1.23 or 12.3
Since we use the function log_difference(),
which returns the value that is only decimal part, using 1.23 is logical.
If we see, 90*log(2) = 27.092699609758302,
decimal part = 0.092699609758302, which is inside the range of lowerbound
and upperbound.
If we compute the difference between all the powers which lead to 123
starting digits is as follows:
379 - 90 = 289
575 - 379 = 196
864 - 575 = 289
1060 - 864 = 196
We see a pattern here. The difference is either 196 or 289 = 196 + 93.
Hence to optimize the algorithm we will increment by 196 or 93 depending upon the
log_difference() value.
Let's take for example 90.
Since 90 is the first power leading to staring digits as 123,
we will increment iterator by 196.
Because the difference between any two powers leading to 123
as staring digits is greater than or equal to 196.
After incrementing by 196 we get 286.
log_difference(286) = 0.09457875989861 which is greater than upperbound.
The next power is 379, and we need to add 93 to get there.
The iterator will now become 379,
which is the next power leading to 123 as starting digits.
Let's take 1060. We increment by 196, we get 1256.
log_difference(1256) = 0.09367455396034,
Which is greater than upperbound hence we increment by 93. Now iterator is 1349.
log_difference(1349) = 0.08946415071057 which is less than lowerbound.
The next power is 1545 and we need to add 196 to get 1545.
Conditions are as follows:
1) If we find a power whose log_difference() is in the range of
lower and upperbound, we will increment by 196.
which implies that the power is a number which will lead to 123 as starting digits.
2) If we find a power, whose log_difference() is greater than or equal upperbound,
we will increment by 93.
3) if log_difference() < lowerbound, we increment by 196.
Reference to the above logic:
https://math.stackexchange.com/questions/4093970/powers-of-2-starting-with-123-does-a-pattern-exist
>>> solution(1000)
284168
>>> solution(56000)
15924915
>>> solution(678910)
193060223
"""
power_iterator = 90
position = 0
lower_limit = math.log(1.23, 10)
upper_limit = math.log(1.24, 10)
previous_power = 0
while position < number:
difference = log_difference(power_iterator)
if difference >= upper_limit:
power_iterator += 93
elif difference < lower_limit:
power_iterator += 196
else:
previous_power = power_iterator
power_iterator += 196
position += 1
return previous_power
if __name__ == "__main__":
import doctest
doctest.testmod()
print(f"{solution() = }")
| """
Project Euler Problem 686: https://projecteuler.net/problem=686
2^7 = 128 is the first power of two whose leading digits are "12".
The next power of two whose leading digits are "12" is 2^80.
Define p(L,n) to be the nth-smallest value of j such that
the base 10 representation of 2^j begins with the digits of L.
So p(12, 1) = 7 and p(12, 2) = 80.
You are given that p(123, 45) = 12710.
Find p(123, 678910).
"""
import math
def log_difference(number: int) -> float:
"""
This function returns the decimal value of a number multiplied with log(2)
Since the problem is on powers of two, finding the powers of two with
large exponents is time consuming. Hence we use log to reduce compute time.
We can find out that the first power of 2 with starting digits 123 is 90.
Computing 2^90 is time consuming.
Hence we find log(2^90) = 90*log(2) = 27.092699609758302
But we require only the decimal part to determine whether the power starts with 123.
So we just return the decimal part of the log product.
Therefore we return 0.092699609758302
>>> log_difference(90)
0.092699609758302
>>> log_difference(379)
0.090368356648852
"""
log_number = math.log(2, 10) * number
difference = round((log_number - int(log_number)), 15)
return difference
def solution(number: int = 678910) -> int:
"""
This function calculates the power of two which is nth (n = number)
smallest value of power of 2
such that the starting digits of the 2^power is 123.
For example the powers of 2 for which starting digits is 123 are:
90, 379, 575, 864, 1060, 1545, 1741, 2030, 2226, 2515 and so on.
90 is the first power of 2 whose starting digits are 123,
379 is second power of 2 whose starting digits are 123,
and so on.
So if number = 10, then solution returns 2515 as we observe from above series.
We will define a lowerbound and upperbound.
lowerbound = log(1.23), upperbound = log(1.24)
because we need to find the powers that yield 123 as starting digits.
log(1.23) = 0.08990511143939792, log(1,24) = 0.09342168516223506.
We use 1.23 and not 12.3 or 123, because log(1.23) yields only decimal value
which is less than 1.
log(12.3) will be same decimal value but 1 added to it
which is log(12.3) = 1.093421685162235.
We observe that decimal value remains same no matter 1.23 or 12.3
Since we use the function log_difference(),
which returns the value that is only decimal part, using 1.23 is logical.
If we see, 90*log(2) = 27.092699609758302,
decimal part = 0.092699609758302, which is inside the range of lowerbound
and upperbound.
If we compute the difference between all the powers which lead to 123
starting digits is as follows:
379 - 90 = 289
575 - 379 = 196
864 - 575 = 289
1060 - 864 = 196
We see a pattern here. The difference is either 196 or 289 = 196 + 93.
Hence to optimize the algorithm we will increment by 196 or 93 depending upon the
log_difference() value.
Let's take for example 90.
Since 90 is the first power leading to staring digits as 123,
we will increment iterator by 196.
Because the difference between any two powers leading to 123
as staring digits is greater than or equal to 196.
After incrementing by 196 we get 286.
log_difference(286) = 0.09457875989861 which is greater than upperbound.
The next power is 379, and we need to add 93 to get there.
The iterator will now become 379,
which is the next power leading to 123 as starting digits.
Let's take 1060. We increment by 196, we get 1256.
log_difference(1256) = 0.09367455396034,
Which is greater than upperbound hence we increment by 93. Now iterator is 1349.
log_difference(1349) = 0.08946415071057 which is less than lowerbound.
The next power is 1545 and we need to add 196 to get 1545.
Conditions are as follows:
1) If we find a power whose log_difference() is in the range of
lower and upperbound, we will increment by 196.
which implies that the power is a number which will lead to 123 as starting digits.
2) If we find a power, whose log_difference() is greater than or equal upperbound,
we will increment by 93.
3) if log_difference() < lowerbound, we increment by 196.
Reference to the above logic:
https://math.stackexchange.com/questions/4093970/powers-of-2-starting-with-123-does-a-pattern-exist
>>> solution(1000)
284168
>>> solution(56000)
15924915
>>> solution(678910)
193060223
"""
power_iterator = 90
position = 0
lower_limit = math.log(1.23, 10)
upper_limit = math.log(1.24, 10)
previous_power = 0
while position < number:
difference = log_difference(power_iterator)
if difference >= upper_limit:
power_iterator += 93
elif difference < lower_limit:
power_iterator += 196
else:
previous_power = power_iterator
power_iterator += 196
position += 1
return previous_power
if __name__ == "__main__":
import doctest
doctest.testmod()
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 7,417 | Remove references to depreciated QasmSimulator | ### Describe your change:
Replaced instances of `qiskit.Aer.get_backend("qasm_simulator")` in `quantum/` with `q.Aer.get_backend("aer_simulator")`, as the former is depreciated and raises warnings (Qiskit's [documentation](https://qiskit.org/documentation/apidoc/aer_provider.html) says that `QasmSimulator` is legacy).
This PR edits multiple code files because they all raise the same warning and are mentioned in the same GitHub issue.
Fixes #7308
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [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 have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| tianyizheng02 | 2022-10-19T03:31:43Z | 2022-10-19T20:12:44Z | 50da472ddcdc2d79d1ad325ec05cda3558802fda | 2859d4bf3aa96737a4715c65d4a9051d9c62d24d | Remove references to depreciated QasmSimulator. ### Describe your change:
Replaced instances of `qiskit.Aer.get_backend("qasm_simulator")` in `quantum/` with `q.Aer.get_backend("aer_simulator")`, as the former is depreciated and raises warnings (Qiskit's [documentation](https://qiskit.org/documentation/apidoc/aer_provider.html) says that `QasmSimulator` is legacy).
This PR edits multiple code files because they all raise the same warning and are mentioned in the same GitHub issue.
Fixes #7308
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| def hamming_distance(string1: str, string2: str) -> int:
"""Calculate the Hamming distance between two equal length strings
In information theory, the Hamming distance between two strings of equal
length is the number of positions at which the corresponding symbols are
different. https://en.wikipedia.org/wiki/Hamming_distance
Args:
string1 (str): Sequence 1
string2 (str): Sequence 2
Returns:
int: Hamming distance
>>> hamming_distance("python", "python")
0
>>> hamming_distance("karolin", "kathrin")
3
>>> hamming_distance("00000", "11111")
5
>>> hamming_distance("karolin", "kath")
Traceback (most recent call last):
...
ValueError: String lengths must match!
"""
if len(string1) != len(string2):
raise ValueError("String lengths must match!")
count = 0
for char1, char2 in zip(string1, string2):
if char1 != char2:
count += 1
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| def hamming_distance(string1: str, string2: str) -> int:
"""Calculate the Hamming distance between two equal length strings
In information theory, the Hamming distance between two strings of equal
length is the number of positions at which the corresponding symbols are
different. https://en.wikipedia.org/wiki/Hamming_distance
Args:
string1 (str): Sequence 1
string2 (str): Sequence 2
Returns:
int: Hamming distance
>>> hamming_distance("python", "python")
0
>>> hamming_distance("karolin", "kathrin")
3
>>> hamming_distance("00000", "11111")
5
>>> hamming_distance("karolin", "kath")
Traceback (most recent call last):
...
ValueError: String lengths must match!
"""
if len(string1) != len(string2):
raise ValueError("String lengths must match!")
count = 0
for char1, char2 in zip(string1, string2):
if char1 != char2:
count += 1
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 7,417 | Remove references to depreciated QasmSimulator | ### Describe your change:
Replaced instances of `qiskit.Aer.get_backend("qasm_simulator")` in `quantum/` with `q.Aer.get_backend("aer_simulator")`, as the former is depreciated and raises warnings (Qiskit's [documentation](https://qiskit.org/documentation/apidoc/aer_provider.html) says that `QasmSimulator` is legacy).
This PR edits multiple code files because they all raise the same warning and are mentioned in the same GitHub issue.
Fixes #7308
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [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 have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| tianyizheng02 | 2022-10-19T03:31:43Z | 2022-10-19T20:12:44Z | 50da472ddcdc2d79d1ad325ec05cda3558802fda | 2859d4bf3aa96737a4715c65d4a9051d9c62d24d | Remove references to depreciated QasmSimulator. ### Describe your change:
Replaced instances of `qiskit.Aer.get_backend("qasm_simulator")` in `quantum/` with `q.Aer.get_backend("aer_simulator")`, as the former is depreciated and raises warnings (Qiskit's [documentation](https://qiskit.org/documentation/apidoc/aer_provider.html) says that `QasmSimulator` is legacy).
This PR edits multiple code files because they all raise the same warning and are mentioned in the same GitHub issue.
Fixes #7308
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Implementation of median filter algorithm
"""
from cv2 import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import divide, int8, multiply, ravel, sort, zeros_like
def median_filter(gray_img, mask=3):
"""
:param gray_img: gray image
:param mask: mask size
:return: image with median filter
"""
# set image borders
bd = int(mask / 2)
# copy image size
median_img = zeros_like(gray_img)
for i in range(bd, gray_img.shape[0] - bd):
for j in range(bd, gray_img.shape[1] - bd):
# get mask according with mask
kernel = ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1])
# calculate mask median
median = sort(kernel)[int8(divide((multiply(mask, mask)), 2) + 1)]
median_img[i, j] = median
return median_img
if __name__ == "__main__":
# read original image
img = imread("../image_data/lena.jpg")
# turn image in gray scale value
gray = cvtColor(img, COLOR_BGR2GRAY)
# get values with two different mask size
median3x3 = median_filter(gray, 3)
median5x5 = median_filter(gray, 5)
# show result images
imshow("median filter with 3x3 mask", median3x3)
imshow("median filter with 5x5 mask", median5x5)
waitKey(0)
| """
Implementation of median filter algorithm
"""
from cv2 import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import divide, int8, multiply, ravel, sort, zeros_like
def median_filter(gray_img, mask=3):
"""
:param gray_img: gray image
:param mask: mask size
:return: image with median filter
"""
# set image borders
bd = int(mask / 2)
# copy image size
median_img = zeros_like(gray_img)
for i in range(bd, gray_img.shape[0] - bd):
for j in range(bd, gray_img.shape[1] - bd):
# get mask according with mask
kernel = ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1])
# calculate mask median
median = sort(kernel)[int8(divide((multiply(mask, mask)), 2) + 1)]
median_img[i, j] = median
return median_img
if __name__ == "__main__":
# read original image
img = imread("../image_data/lena.jpg")
# turn image in gray scale value
gray = cvtColor(img, COLOR_BGR2GRAY)
# get values with two different mask size
median3x3 = median_filter(gray, 3)
median5x5 = median_filter(gray, 5)
# show result images
imshow("median filter with 3x3 mask", median3x3)
imshow("median filter with 5x5 mask", median5x5)
waitKey(0)
| -1 |
TheAlgorithms/Python | 7,417 | Remove references to depreciated QasmSimulator | ### Describe your change:
Replaced instances of `qiskit.Aer.get_backend("qasm_simulator")` in `quantum/` with `q.Aer.get_backend("aer_simulator")`, as the former is depreciated and raises warnings (Qiskit's [documentation](https://qiskit.org/documentation/apidoc/aer_provider.html) says that `QasmSimulator` is legacy).
This PR edits multiple code files because they all raise the same warning and are mentioned in the same GitHub issue.
Fixes #7308
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [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 have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| tianyizheng02 | 2022-10-19T03:31:43Z | 2022-10-19T20:12:44Z | 50da472ddcdc2d79d1ad325ec05cda3558802fda | 2859d4bf3aa96737a4715c65d4a9051d9c62d24d | Remove references to depreciated QasmSimulator. ### Describe your change:
Replaced instances of `qiskit.Aer.get_backend("qasm_simulator")` in `quantum/` with `q.Aer.get_backend("aer_simulator")`, as the former is depreciated and raises warnings (Qiskit's [documentation](https://qiskit.org/documentation/apidoc/aer_provider.html) says that `QasmSimulator` is legacy).
This PR edits multiple code files because they all raise the same warning and are mentioned in the same GitHub issue.
Fixes #7308
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 7,417 | Remove references to depreciated QasmSimulator | ### Describe your change:
Replaced instances of `qiskit.Aer.get_backend("qasm_simulator")` in `quantum/` with `q.Aer.get_backend("aer_simulator")`, as the former is depreciated and raises warnings (Qiskit's [documentation](https://qiskit.org/documentation/apidoc/aer_provider.html) says that `QasmSimulator` is legacy).
This PR edits multiple code files because they all raise the same warning and are mentioned in the same GitHub issue.
Fixes #7308
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [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 have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| tianyizheng02 | 2022-10-19T03:31:43Z | 2022-10-19T20:12:44Z | 50da472ddcdc2d79d1ad325ec05cda3558802fda | 2859d4bf3aa96737a4715c65d4a9051d9c62d24d | Remove references to depreciated QasmSimulator. ### Describe your change:
Replaced instances of `qiskit.Aer.get_backend("qasm_simulator")` in `quantum/` with `q.Aer.get_backend("aer_simulator")`, as the former is depreciated and raises warnings (Qiskit's [documentation](https://qiskit.org/documentation/apidoc/aer_provider.html) says that `QasmSimulator` is legacy).
This PR edits multiple code files because they all raise the same warning and are mentioned in the same GitHub issue.
Fixes #7308
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
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iQL(( ()h
J((( ( ( ()h((((((( ( ( ( ( ( ( ( ( ( ( ()h'z(})Q@}C 4Q@QLBRE QE QH)QE QE RQE QE QE QE QE QE QE QE QE QE QE QE QE QE QE QE Q@ EPEPEPEPQE QE QE QE QE QE QE QE QE RE QE %Q@-PIE QE RE %Q@Q@ IE Q@Q@Q@-PQE QE QE QE QE QE QE QE QE QE %Q@ | -1 |
TheAlgorithms/Python | 7,417 | Remove references to depreciated QasmSimulator | ### Describe your change:
Replaced instances of `qiskit.Aer.get_backend("qasm_simulator")` in `quantum/` with `q.Aer.get_backend("aer_simulator")`, as the former is depreciated and raises warnings (Qiskit's [documentation](https://qiskit.org/documentation/apidoc/aer_provider.html) says that `QasmSimulator` is legacy).
This PR edits multiple code files because they all raise the same warning and are mentioned in the same GitHub issue.
Fixes #7308
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [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 have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| tianyizheng02 | 2022-10-19T03:31:43Z | 2022-10-19T20:12:44Z | 50da472ddcdc2d79d1ad325ec05cda3558802fda | 2859d4bf3aa96737a4715c65d4a9051d9c62d24d | Remove references to depreciated QasmSimulator. ### Describe your change:
Replaced instances of `qiskit.Aer.get_backend("qasm_simulator")` in `quantum/` with `q.Aer.get_backend("aer_simulator")`, as the former is depreciated and raises warnings (Qiskit's [documentation](https://qiskit.org/documentation/apidoc/aer_provider.html) says that `QasmSimulator` is legacy).
This PR edits multiple code files because they all raise the same warning and are mentioned in the same GitHub issue.
Fixes #7308
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 7,417 | Remove references to depreciated QasmSimulator | ### Describe your change:
Replaced instances of `qiskit.Aer.get_backend("qasm_simulator")` in `quantum/` with `q.Aer.get_backend("aer_simulator")`, as the former is depreciated and raises warnings (Qiskit's [documentation](https://qiskit.org/documentation/apidoc/aer_provider.html) says that `QasmSimulator` is legacy).
This PR edits multiple code files because they all raise the same warning and are mentioned in the same GitHub issue.
Fixes #7308
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [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 have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| tianyizheng02 | 2022-10-19T03:31:43Z | 2022-10-19T20:12:44Z | 50da472ddcdc2d79d1ad325ec05cda3558802fda | 2859d4bf3aa96737a4715c65d4a9051d9c62d24d | Remove references to depreciated QasmSimulator. ### Describe your change:
Replaced instances of `qiskit.Aer.get_backend("qasm_simulator")` in `quantum/` with `q.Aer.get_backend("aer_simulator")`, as the former is depreciated and raises warnings (Qiskit's [documentation](https://qiskit.org/documentation/apidoc/aer_provider.html) says that `QasmSimulator` is legacy).
This PR edits multiple code files because they all raise the same warning and are mentioned in the same GitHub issue.
Fixes #7308
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 7,417 | Remove references to depreciated QasmSimulator | ### Describe your change:
Replaced instances of `qiskit.Aer.get_backend("qasm_simulator")` in `quantum/` with `q.Aer.get_backend("aer_simulator")`, as the former is depreciated and raises warnings (Qiskit's [documentation](https://qiskit.org/documentation/apidoc/aer_provider.html) says that `QasmSimulator` is legacy).
This PR edits multiple code files because they all raise the same warning and are mentioned in the same GitHub issue.
Fixes #7308
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [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 have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| tianyizheng02 | 2022-10-19T03:31:43Z | 2022-10-19T20:12:44Z | 50da472ddcdc2d79d1ad325ec05cda3558802fda | 2859d4bf3aa96737a4715c65d4a9051d9c62d24d | Remove references to depreciated QasmSimulator. ### Describe your change:
Replaced instances of `qiskit.Aer.get_backend("qasm_simulator")` in `quantum/` with `q.Aer.get_backend("aer_simulator")`, as the former is depreciated and raises warnings (Qiskit's [documentation](https://qiskit.org/documentation/apidoc/aer_provider.html) says that `QasmSimulator` is legacy).
This PR edits multiple code files because they all raise the same warning and are mentioned in the same GitHub issue.
Fixes #7308
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # 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 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>
| -1 |
TheAlgorithms/Python | 7,417 | Remove references to depreciated QasmSimulator | ### Describe your change:
Replaced instances of `qiskit.Aer.get_backend("qasm_simulator")` in `quantum/` with `q.Aer.get_backend("aer_simulator")`, as the former is depreciated and raises warnings (Qiskit's [documentation](https://qiskit.org/documentation/apidoc/aer_provider.html) says that `QasmSimulator` is legacy).
This PR edits multiple code files because they all raise the same warning and are mentioned in the same GitHub issue.
Fixes #7308
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [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 have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| tianyizheng02 | 2022-10-19T03:31:43Z | 2022-10-19T20:12:44Z | 50da472ddcdc2d79d1ad325ec05cda3558802fda | 2859d4bf3aa96737a4715c65d4a9051d9c62d24d | Remove references to depreciated QasmSimulator. ### Describe your change:
Replaced instances of `qiskit.Aer.get_backend("qasm_simulator")` in `quantum/` with `q.Aer.get_backend("aer_simulator")`, as the former is depreciated and raises warnings (Qiskit's [documentation](https://qiskit.org/documentation/apidoc/aer_provider.html) says that `QasmSimulator` is legacy).
This PR edits multiple code files because they all raise the same warning and are mentioned in the same GitHub issue.
Fixes #7308
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Greatest Common Divisor.
Wikipedia reference: https://en.wikipedia.org/wiki/Greatest_common_divisor
gcd(a, b) = gcd(a, -b) = gcd(-a, b) = gcd(-a, -b) by definition of divisibility
"""
def greatest_common_divisor(a: int, b: int) -> int:
"""
Calculate Greatest Common Divisor (GCD).
>>> greatest_common_divisor(24, 40)
8
>>> greatest_common_divisor(1, 1)
1
>>> greatest_common_divisor(1, 800)
1
>>> greatest_common_divisor(11, 37)
1
>>> greatest_common_divisor(3, 5)
1
>>> greatest_common_divisor(16, 4)
4
>>> greatest_common_divisor(-3, 9)
3
>>> greatest_common_divisor(9, -3)
3
>>> greatest_common_divisor(3, -9)
3
>>> greatest_common_divisor(-3, -9)
3
"""
return abs(b) if a == 0 else greatest_common_divisor(b % a, a)
def gcd_by_iterative(x: int, y: int) -> int:
"""
Below method is more memory efficient because it does not create additional
stack frames for recursive functions calls (as done in the above method).
>>> gcd_by_iterative(24, 40)
8
>>> greatest_common_divisor(24, 40) == gcd_by_iterative(24, 40)
True
>>> gcd_by_iterative(-3, -9)
3
>>> gcd_by_iterative(3, -9)
3
>>> gcd_by_iterative(1, -800)
1
>>> gcd_by_iterative(11, 37)
1
"""
while y: # --> when y=0 then loop will terminate and return x as final GCD.
x, y = y, x % y
return abs(x)
def main():
"""
Call Greatest Common Divisor function.
"""
try:
nums = input("Enter two integers separated by comma (,): ").split(",")
num_1 = int(nums[0])
num_2 = int(nums[1])
print(
f"greatest_common_divisor({num_1}, {num_2}) = "
f"{greatest_common_divisor(num_1, num_2)}"
)
print(f"By iterative gcd({num_1}, {num_2}) = {gcd_by_iterative(num_1, num_2)}")
except (IndexError, UnboundLocalError, ValueError):
print("Wrong input")
if __name__ == "__main__":
main()
| """
Greatest Common Divisor.
Wikipedia reference: https://en.wikipedia.org/wiki/Greatest_common_divisor
gcd(a, b) = gcd(a, -b) = gcd(-a, b) = gcd(-a, -b) by definition of divisibility
"""
def greatest_common_divisor(a: int, b: int) -> int:
"""
Calculate Greatest Common Divisor (GCD).
>>> greatest_common_divisor(24, 40)
8
>>> greatest_common_divisor(1, 1)
1
>>> greatest_common_divisor(1, 800)
1
>>> greatest_common_divisor(11, 37)
1
>>> greatest_common_divisor(3, 5)
1
>>> greatest_common_divisor(16, 4)
4
>>> greatest_common_divisor(-3, 9)
3
>>> greatest_common_divisor(9, -3)
3
>>> greatest_common_divisor(3, -9)
3
>>> greatest_common_divisor(-3, -9)
3
"""
return abs(b) if a == 0 else greatest_common_divisor(b % a, a)
def gcd_by_iterative(x: int, y: int) -> int:
"""
Below method is more memory efficient because it does not create additional
stack frames for recursive functions calls (as done in the above method).
>>> gcd_by_iterative(24, 40)
8
>>> greatest_common_divisor(24, 40) == gcd_by_iterative(24, 40)
True
>>> gcd_by_iterative(-3, -9)
3
>>> gcd_by_iterative(3, -9)
3
>>> gcd_by_iterative(1, -800)
1
>>> gcd_by_iterative(11, 37)
1
"""
while y: # --> when y=0 then loop will terminate and return x as final GCD.
x, y = y, x % y
return abs(x)
def main():
"""
Call Greatest Common Divisor function.
"""
try:
nums = input("Enter two integers separated by comma (,): ").split(",")
num_1 = int(nums[0])
num_2 = int(nums[1])
print(
f"greatest_common_divisor({num_1}, {num_2}) = "
f"{greatest_common_divisor(num_1, num_2)}"
)
print(f"By iterative gcd({num_1}, {num_2}) = {gcd_by_iterative(num_1, num_2)}")
except (IndexError, UnboundLocalError, ValueError):
print("Wrong input")
if __name__ == "__main__":
main()
| -1 |
TheAlgorithms/Python | 7,417 | Remove references to depreciated QasmSimulator | ### Describe your change:
Replaced instances of `qiskit.Aer.get_backend("qasm_simulator")` in `quantum/` with `q.Aer.get_backend("aer_simulator")`, as the former is depreciated and raises warnings (Qiskit's [documentation](https://qiskit.org/documentation/apidoc/aer_provider.html) says that `QasmSimulator` is legacy).
This PR edits multiple code files because they all raise the same warning and are mentioned in the same GitHub issue.
Fixes #7308
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [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 have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| tianyizheng02 | 2022-10-19T03:31:43Z | 2022-10-19T20:12:44Z | 50da472ddcdc2d79d1ad325ec05cda3558802fda | 2859d4bf3aa96737a4715c65d4a9051d9c62d24d | Remove references to depreciated QasmSimulator. ### Describe your change:
Replaced instances of `qiskit.Aer.get_backend("qasm_simulator")` in `quantum/` with `q.Aer.get_backend("aer_simulator")`, as the former is depreciated and raises warnings (Qiskit's [documentation](https://qiskit.org/documentation/apidoc/aer_provider.html) says that `QasmSimulator` is legacy).
This PR edits multiple code files because they all raise the same warning and are mentioned in the same GitHub issue.
Fixes #7308
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 7,417 | Remove references to depreciated QasmSimulator | ### Describe your change:
Replaced instances of `qiskit.Aer.get_backend("qasm_simulator")` in `quantum/` with `q.Aer.get_backend("aer_simulator")`, as the former is depreciated and raises warnings (Qiskit's [documentation](https://qiskit.org/documentation/apidoc/aer_provider.html) says that `QasmSimulator` is legacy).
This PR edits multiple code files because they all raise the same warning and are mentioned in the same GitHub issue.
Fixes #7308
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [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 have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| tianyizheng02 | 2022-10-19T03:31:43Z | 2022-10-19T20:12:44Z | 50da472ddcdc2d79d1ad325ec05cda3558802fda | 2859d4bf3aa96737a4715c65d4a9051d9c62d24d | Remove references to depreciated QasmSimulator. ### Describe your change:
Replaced instances of `qiskit.Aer.get_backend("qasm_simulator")` in `quantum/` with `q.Aer.get_backend("aer_simulator")`, as the former is depreciated and raises warnings (Qiskit's [documentation](https://qiskit.org/documentation/apidoc/aer_provider.html) says that `QasmSimulator` is legacy).
This PR edits multiple code files because they all raise the same warning and are mentioned in the same GitHub issue.
Fixes #7308
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| import numpy as np
""" Here I implemented the scoring functions.
MAE, MSE, RMSE, RMSLE are included.
Those are used for calculating differences between
predicted values and actual values.
Metrics are slightly differentiated. Sometimes squared, rooted,
even log is used.
Using log and roots can be perceived as tools for penalizing big
errors. However, using appropriate metrics depends on the situations,
and types of data
"""
# Mean Absolute Error
def mae(predict, actual):
"""
Examples(rounded for precision):
>>> actual = [1,2,3];predict = [1,4,3]
>>> np.around(mae(predict,actual),decimals = 2)
0.67
>>> actual = [1,1,1];predict = [1,1,1]
>>> mae(predict,actual)
0.0
"""
predict = np.array(predict)
actual = np.array(actual)
difference = abs(predict - actual)
score = difference.mean()
return score
# Mean Squared Error
def mse(predict, actual):
"""
Examples(rounded for precision):
>>> actual = [1,2,3];predict = [1,4,3]
>>> np.around(mse(predict,actual),decimals = 2)
1.33
>>> actual = [1,1,1];predict = [1,1,1]
>>> mse(predict,actual)
0.0
"""
predict = np.array(predict)
actual = np.array(actual)
difference = predict - actual
square_diff = np.square(difference)
score = square_diff.mean()
return score
# Root Mean Squared Error
def rmse(predict, actual):
"""
Examples(rounded for precision):
>>> actual = [1,2,3];predict = [1,4,3]
>>> np.around(rmse(predict,actual),decimals = 2)
1.15
>>> actual = [1,1,1];predict = [1,1,1]
>>> rmse(predict,actual)
0.0
"""
predict = np.array(predict)
actual = np.array(actual)
difference = predict - actual
square_diff = np.square(difference)
mean_square_diff = square_diff.mean()
score = np.sqrt(mean_square_diff)
return score
# Root Mean Square Logarithmic Error
def rmsle(predict, actual):
"""
Examples(rounded for precision):
>>> actual = [10,10,30];predict = [10,2,30]
>>> np.around(rmsle(predict,actual),decimals = 2)
0.75
>>> actual = [1,1,1];predict = [1,1,1]
>>> rmsle(predict,actual)
0.0
"""
predict = np.array(predict)
actual = np.array(actual)
log_predict = np.log(predict + 1)
log_actual = np.log(actual + 1)
difference = log_predict - log_actual
square_diff = np.square(difference)
mean_square_diff = square_diff.mean()
score = np.sqrt(mean_square_diff)
return score
# Mean Bias Deviation
def mbd(predict, actual):
"""
This value is Negative, if the model underpredicts,
positive, if it overpredicts.
Example(rounded for precision):
Here the model overpredicts
>>> actual = [1,2,3];predict = [2,3,4]
>>> np.around(mbd(predict,actual),decimals = 2)
50.0
Here the model underpredicts
>>> actual = [1,2,3];predict = [0,1,1]
>>> np.around(mbd(predict,actual),decimals = 2)
-66.67
"""
predict = np.array(predict)
actual = np.array(actual)
difference = predict - actual
numerator = np.sum(difference) / len(predict)
denumerator = np.sum(actual) / len(predict)
# print(numerator, denumerator)
score = float(numerator) / denumerator * 100
return score
def manual_accuracy(predict, actual):
return np.mean(np.array(actual) == np.array(predict))
| import numpy as np
""" Here I implemented the scoring functions.
MAE, MSE, RMSE, RMSLE are included.
Those are used for calculating differences between
predicted values and actual values.
Metrics are slightly differentiated. Sometimes squared, rooted,
even log is used.
Using log and roots can be perceived as tools for penalizing big
errors. However, using appropriate metrics depends on the situations,
and types of data
"""
# Mean Absolute Error
def mae(predict, actual):
"""
Examples(rounded for precision):
>>> actual = [1,2,3];predict = [1,4,3]
>>> np.around(mae(predict,actual),decimals = 2)
0.67
>>> actual = [1,1,1];predict = [1,1,1]
>>> mae(predict,actual)
0.0
"""
predict = np.array(predict)
actual = np.array(actual)
difference = abs(predict - actual)
score = difference.mean()
return score
# Mean Squared Error
def mse(predict, actual):
"""
Examples(rounded for precision):
>>> actual = [1,2,3];predict = [1,4,3]
>>> np.around(mse(predict,actual),decimals = 2)
1.33
>>> actual = [1,1,1];predict = [1,1,1]
>>> mse(predict,actual)
0.0
"""
predict = np.array(predict)
actual = np.array(actual)
difference = predict - actual
square_diff = np.square(difference)
score = square_diff.mean()
return score
# Root Mean Squared Error
def rmse(predict, actual):
"""
Examples(rounded for precision):
>>> actual = [1,2,3];predict = [1,4,3]
>>> np.around(rmse(predict,actual),decimals = 2)
1.15
>>> actual = [1,1,1];predict = [1,1,1]
>>> rmse(predict,actual)
0.0
"""
predict = np.array(predict)
actual = np.array(actual)
difference = predict - actual
square_diff = np.square(difference)
mean_square_diff = square_diff.mean()
score = np.sqrt(mean_square_diff)
return score
# Root Mean Square Logarithmic Error
def rmsle(predict, actual):
"""
Examples(rounded for precision):
>>> actual = [10,10,30];predict = [10,2,30]
>>> np.around(rmsle(predict,actual),decimals = 2)
0.75
>>> actual = [1,1,1];predict = [1,1,1]
>>> rmsle(predict,actual)
0.0
"""
predict = np.array(predict)
actual = np.array(actual)
log_predict = np.log(predict + 1)
log_actual = np.log(actual + 1)
difference = log_predict - log_actual
square_diff = np.square(difference)
mean_square_diff = square_diff.mean()
score = np.sqrt(mean_square_diff)
return score
# Mean Bias Deviation
def mbd(predict, actual):
"""
This value is Negative, if the model underpredicts,
positive, if it overpredicts.
Example(rounded for precision):
Here the model overpredicts
>>> actual = [1,2,3];predict = [2,3,4]
>>> np.around(mbd(predict,actual),decimals = 2)
50.0
Here the model underpredicts
>>> actual = [1,2,3];predict = [0,1,1]
>>> np.around(mbd(predict,actual),decimals = 2)
-66.67
"""
predict = np.array(predict)
actual = np.array(actual)
difference = predict - actual
numerator = np.sum(difference) / len(predict)
denumerator = np.sum(actual) / len(predict)
# print(numerator, denumerator)
score = float(numerator) / denumerator * 100
return score
def manual_accuracy(predict, actual):
return np.mean(np.array(actual) == np.array(predict))
| -1 |
TheAlgorithms/Python | 7,417 | Remove references to depreciated QasmSimulator | ### Describe your change:
Replaced instances of `qiskit.Aer.get_backend("qasm_simulator")` in `quantum/` with `q.Aer.get_backend("aer_simulator")`, as the former is depreciated and raises warnings (Qiskit's [documentation](https://qiskit.org/documentation/apidoc/aer_provider.html) says that `QasmSimulator` is legacy).
This PR edits multiple code files because they all raise the same warning and are mentioned in the same GitHub issue.
Fixes #7308
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [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 have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| tianyizheng02 | 2022-10-19T03:31:43Z | 2022-10-19T20:12:44Z | 50da472ddcdc2d79d1ad325ec05cda3558802fda | 2859d4bf3aa96737a4715c65d4a9051d9c62d24d | Remove references to depreciated QasmSimulator. ### Describe your change:
Replaced instances of `qiskit.Aer.get_backend("qasm_simulator")` in `quantum/` with `q.Aer.get_backend("aer_simulator")`, as the former is depreciated and raises warnings (Qiskit's [documentation](https://qiskit.org/documentation/apidoc/aer_provider.html) says that `QasmSimulator` is legacy).
This PR edits multiple code files because they all raise the same warning and are mentioned in the same GitHub issue.
Fixes #7308
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 7,417 | Remove references to depreciated QasmSimulator | ### Describe your change:
Replaced instances of `qiskit.Aer.get_backend("qasm_simulator")` in `quantum/` with `q.Aer.get_backend("aer_simulator")`, as the former is depreciated and raises warnings (Qiskit's [documentation](https://qiskit.org/documentation/apidoc/aer_provider.html) says that `QasmSimulator` is legacy).
This PR edits multiple code files because they all raise the same warning and are mentioned in the same GitHub issue.
Fixes #7308
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [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 have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| tianyizheng02 | 2022-10-19T03:31:43Z | 2022-10-19T20:12:44Z | 50da472ddcdc2d79d1ad325ec05cda3558802fda | 2859d4bf3aa96737a4715c65d4a9051d9c62d24d | Remove references to depreciated QasmSimulator. ### Describe your change:
Replaced instances of `qiskit.Aer.get_backend("qasm_simulator")` in `quantum/` with `q.Aer.get_backend("aer_simulator")`, as the former is depreciated and raises warnings (Qiskit's [documentation](https://qiskit.org/documentation/apidoc/aer_provider.html) says that `QasmSimulator` is legacy).
This PR edits multiple code files because they all raise the same warning and are mentioned in the same GitHub issue.
Fixes #7308
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """Breath First Search (BFS) can be used when finding the shortest path
from a given source node to a target node in an unweighted graph.
"""
from __future__ import annotations
graph = {
"A": ["B", "C", "E"],
"B": ["A", "D", "E"],
"C": ["A", "F", "G"],
"D": ["B"],
"E": ["A", "B", "D"],
"F": ["C"],
"G": ["C"],
}
class Graph:
def __init__(self, graph: dict[str, list[str]], source_vertex: str) -> None:
"""
Graph is implemented as dictionary of adjacency lists. Also,
Source vertex have to be defined upon initialization.
"""
self.graph = graph
# mapping node to its parent in resulting breadth first tree
self.parent: dict[str, str | None] = {}
self.source_vertex = source_vertex
def breath_first_search(self) -> None:
"""
This function is a helper for running breath first search on this graph.
>>> g = Graph(graph, "G")
>>> g.breath_first_search()
>>> g.parent
{'G': None, 'C': 'G', 'A': 'C', 'F': 'C', 'B': 'A', 'E': 'A', 'D': 'B'}
"""
visited = {self.source_vertex}
self.parent[self.source_vertex] = None
queue = [self.source_vertex] # first in first out queue
while queue:
vertex = queue.pop(0)
for adjacent_vertex in self.graph[vertex]:
if adjacent_vertex not in visited:
visited.add(adjacent_vertex)
self.parent[adjacent_vertex] = vertex
queue.append(adjacent_vertex)
def shortest_path(self, target_vertex: str) -> str:
"""
This shortest path function returns a string, describing the result:
1.) No path is found. The string is a human readable message to indicate this.
2.) The shortest path is found. The string is in the form
`v1(->v2->v3->...->vn)`, where v1 is the source vertex and vn is the target
vertex, if it exists separately.
>>> g = Graph(graph, "G")
>>> g.breath_first_search()
Case 1 - No path is found.
>>> g.shortest_path("Foo")
'No path from vertex:G to vertex:Foo'
Case 2 - The path is found.
>>> g.shortest_path("D")
'G->C->A->B->D'
>>> g.shortest_path("G")
'G'
"""
if target_vertex == self.source_vertex:
return self.source_vertex
target_vertex_parent = self.parent.get(target_vertex)
if target_vertex_parent is None:
return f"No path from vertex:{self.source_vertex} to vertex:{target_vertex}"
return self.shortest_path(target_vertex_parent) + f"->{target_vertex}"
if __name__ == "__main__":
g = Graph(graph, "G")
g.breath_first_search()
print(g.shortest_path("D"))
print(g.shortest_path("G"))
print(g.shortest_path("Foo"))
| """Breath First Search (BFS) can be used when finding the shortest path
from a given source node to a target node in an unweighted graph.
"""
from __future__ import annotations
graph = {
"A": ["B", "C", "E"],
"B": ["A", "D", "E"],
"C": ["A", "F", "G"],
"D": ["B"],
"E": ["A", "B", "D"],
"F": ["C"],
"G": ["C"],
}
class Graph:
def __init__(self, graph: dict[str, list[str]], source_vertex: str) -> None:
"""
Graph is implemented as dictionary of adjacency lists. Also,
Source vertex have to be defined upon initialization.
"""
self.graph = graph
# mapping node to its parent in resulting breadth first tree
self.parent: dict[str, str | None] = {}
self.source_vertex = source_vertex
def breath_first_search(self) -> None:
"""
This function is a helper for running breath first search on this graph.
>>> g = Graph(graph, "G")
>>> g.breath_first_search()
>>> g.parent
{'G': None, 'C': 'G', 'A': 'C', 'F': 'C', 'B': 'A', 'E': 'A', 'D': 'B'}
"""
visited = {self.source_vertex}
self.parent[self.source_vertex] = None
queue = [self.source_vertex] # first in first out queue
while queue:
vertex = queue.pop(0)
for adjacent_vertex in self.graph[vertex]:
if adjacent_vertex not in visited:
visited.add(adjacent_vertex)
self.parent[adjacent_vertex] = vertex
queue.append(adjacent_vertex)
def shortest_path(self, target_vertex: str) -> str:
"""
This shortest path function returns a string, describing the result:
1.) No path is found. The string is a human readable message to indicate this.
2.) The shortest path is found. The string is in the form
`v1(->v2->v3->...->vn)`, where v1 is the source vertex and vn is the target
vertex, if it exists separately.
>>> g = Graph(graph, "G")
>>> g.breath_first_search()
Case 1 - No path is found.
>>> g.shortest_path("Foo")
'No path from vertex:G to vertex:Foo'
Case 2 - The path is found.
>>> g.shortest_path("D")
'G->C->A->B->D'
>>> g.shortest_path("G")
'G'
"""
if target_vertex == self.source_vertex:
return self.source_vertex
target_vertex_parent = self.parent.get(target_vertex)
if target_vertex_parent is None:
return f"No path from vertex:{self.source_vertex} to vertex:{target_vertex}"
return self.shortest_path(target_vertex_parent) + f"->{target_vertex}"
if __name__ == "__main__":
g = Graph(graph, "G")
g.breath_first_search()
print(g.shortest_path("D"))
print(g.shortest_path("G"))
print(g.shortest_path("Foo"))
| -1 |
TheAlgorithms/Python | 7,417 | Remove references to depreciated QasmSimulator | ### Describe your change:
Replaced instances of `qiskit.Aer.get_backend("qasm_simulator")` in `quantum/` with `q.Aer.get_backend("aer_simulator")`, as the former is depreciated and raises warnings (Qiskit's [documentation](https://qiskit.org/documentation/apidoc/aer_provider.html) says that `QasmSimulator` is legacy).
This PR edits multiple code files because they all raise the same warning and are mentioned in the same GitHub issue.
Fixes #7308
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [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 have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| tianyizheng02 | 2022-10-19T03:31:43Z | 2022-10-19T20:12:44Z | 50da472ddcdc2d79d1ad325ec05cda3558802fda | 2859d4bf3aa96737a4715c65d4a9051d9c62d24d | Remove references to depreciated QasmSimulator. ### Describe your change:
Replaced instances of `qiskit.Aer.get_backend("qasm_simulator")` in `quantum/` with `q.Aer.get_backend("aer_simulator")`, as the former is depreciated and raises warnings (Qiskit's [documentation](https://qiskit.org/documentation/apidoc/aer_provider.html) says that `QasmSimulator` is legacy).
This PR edits multiple code files because they all raise the same warning and are mentioned in the same GitHub issue.
Fixes #7308
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # Source : https://computersciencewiki.org/index.php/Max-pooling_/_Pooling
# Importing the libraries
import numpy as np
from PIL import Image
# Maxpooling Function
def maxpooling(arr: np.ndarray, size: int, stride: int) -> np.ndarray:
"""
This function is used to perform maxpooling on the input array of 2D matrix(image)
Args:
arr: numpy array
size: size of pooling matrix
stride: the number of pixels shifts over the input matrix
Returns:
numpy array of maxpooled matrix
Sample Input Output:
>>> maxpooling([[1,2,3,4],[5,6,7,8],[9,10,11,12],[13,14,15,16]], 2, 2)
array([[ 6., 8.],
[14., 16.]])
>>> maxpooling([[147, 180, 122],[241, 76, 32],[126, 13, 157]], 2, 1)
array([[241., 180.],
[241., 157.]])
"""
arr = np.array(arr)
if arr.shape[0] != arr.shape[1]:
raise ValueError("The input array is not a square matrix")
i = 0
j = 0
mat_i = 0
mat_j = 0
# compute the shape of the output matrix
maxpool_shape = (arr.shape[0] - size) // stride + 1
# initialize the output matrix with zeros of shape maxpool_shape
updated_arr = np.zeros((maxpool_shape, maxpool_shape))
while i < arr.shape[0]:
if i + size > arr.shape[0]:
# if the end of the matrix is reached, break
break
while j < arr.shape[1]:
# if the end of the matrix is reached, break
if j + size > arr.shape[1]:
break
# compute the maximum of the pooling matrix
updated_arr[mat_i][mat_j] = np.max(arr[i : i + size, j : j + size])
# shift the pooling matrix by stride of column pixels
j += stride
mat_j += 1
# shift the pooling matrix by stride of row pixels
i += stride
mat_i += 1
# reset the column index to 0
j = 0
mat_j = 0
return updated_arr
# Averagepooling Function
def avgpooling(arr: np.ndarray, size: int, stride: int) -> np.ndarray:
"""
This function is used to perform avgpooling on the input array of 2D matrix(image)
Args:
arr: numpy array
size: size of pooling matrix
stride: the number of pixels shifts over the input matrix
Returns:
numpy array of avgpooled matrix
Sample Input Output:
>>> avgpooling([[1,2,3,4],[5,6,7,8],[9,10,11,12],[13,14,15,16]], 2, 2)
array([[ 3., 5.],
[11., 13.]])
>>> avgpooling([[147, 180, 122],[241, 76, 32],[126, 13, 157]], 2, 1)
array([[161., 102.],
[114., 69.]])
"""
arr = np.array(arr)
if arr.shape[0] != arr.shape[1]:
raise ValueError("The input array is not a square matrix")
i = 0
j = 0
mat_i = 0
mat_j = 0
# compute the shape of the output matrix
avgpool_shape = (arr.shape[0] - size) // stride + 1
# initialize the output matrix with zeros of shape avgpool_shape
updated_arr = np.zeros((avgpool_shape, avgpool_shape))
while i < arr.shape[0]:
# if the end of the matrix is reached, break
if i + size > arr.shape[0]:
break
while j < arr.shape[1]:
# if the end of the matrix is reached, break
if j + size > arr.shape[1]:
break
# compute the average of the pooling matrix
updated_arr[mat_i][mat_j] = int(np.average(arr[i : i + size, j : j + size]))
# shift the pooling matrix by stride of column pixels
j += stride
mat_j += 1
# shift the pooling matrix by stride of row pixels
i += stride
mat_i += 1
# reset the column index to 0
j = 0
mat_j = 0
return updated_arr
# Main Function
if __name__ == "__main__":
from doctest import testmod
testmod(name="avgpooling", verbose=True)
# Loading the image
image = Image.open("path_to_image")
# Converting the image to numpy array and maxpooling, displaying the result
# Ensure that the image is a square matrix
Image.fromarray(maxpooling(np.array(image), size=3, stride=2)).show()
# Converting the image to numpy array and averagepooling, displaying the result
# Ensure that the image is a square matrix
Image.fromarray(avgpooling(np.array(image), size=3, stride=2)).show()
| # Source : https://computersciencewiki.org/index.php/Max-pooling_/_Pooling
# Importing the libraries
import numpy as np
from PIL import Image
# Maxpooling Function
def maxpooling(arr: np.ndarray, size: int, stride: int) -> np.ndarray:
"""
This function is used to perform maxpooling on the input array of 2D matrix(image)
Args:
arr: numpy array
size: size of pooling matrix
stride: the number of pixels shifts over the input matrix
Returns:
numpy array of maxpooled matrix
Sample Input Output:
>>> maxpooling([[1,2,3,4],[5,6,7,8],[9,10,11,12],[13,14,15,16]], 2, 2)
array([[ 6., 8.],
[14., 16.]])
>>> maxpooling([[147, 180, 122],[241, 76, 32],[126, 13, 157]], 2, 1)
array([[241., 180.],
[241., 157.]])
"""
arr = np.array(arr)
if arr.shape[0] != arr.shape[1]:
raise ValueError("The input array is not a square matrix")
i = 0
j = 0
mat_i = 0
mat_j = 0
# compute the shape of the output matrix
maxpool_shape = (arr.shape[0] - size) // stride + 1
# initialize the output matrix with zeros of shape maxpool_shape
updated_arr = np.zeros((maxpool_shape, maxpool_shape))
while i < arr.shape[0]:
if i + size > arr.shape[0]:
# if the end of the matrix is reached, break
break
while j < arr.shape[1]:
# if the end of the matrix is reached, break
if j + size > arr.shape[1]:
break
# compute the maximum of the pooling matrix
updated_arr[mat_i][mat_j] = np.max(arr[i : i + size, j : j + size])
# shift the pooling matrix by stride of column pixels
j += stride
mat_j += 1
# shift the pooling matrix by stride of row pixels
i += stride
mat_i += 1
# reset the column index to 0
j = 0
mat_j = 0
return updated_arr
# Averagepooling Function
def avgpooling(arr: np.ndarray, size: int, stride: int) -> np.ndarray:
"""
This function is used to perform avgpooling on the input array of 2D matrix(image)
Args:
arr: numpy array
size: size of pooling matrix
stride: the number of pixels shifts over the input matrix
Returns:
numpy array of avgpooled matrix
Sample Input Output:
>>> avgpooling([[1,2,3,4],[5,6,7,8],[9,10,11,12],[13,14,15,16]], 2, 2)
array([[ 3., 5.],
[11., 13.]])
>>> avgpooling([[147, 180, 122],[241, 76, 32],[126, 13, 157]], 2, 1)
array([[161., 102.],
[114., 69.]])
"""
arr = np.array(arr)
if arr.shape[0] != arr.shape[1]:
raise ValueError("The input array is not a square matrix")
i = 0
j = 0
mat_i = 0
mat_j = 0
# compute the shape of the output matrix
avgpool_shape = (arr.shape[0] - size) // stride + 1
# initialize the output matrix with zeros of shape avgpool_shape
updated_arr = np.zeros((avgpool_shape, avgpool_shape))
while i < arr.shape[0]:
# if the end of the matrix is reached, break
if i + size > arr.shape[0]:
break
while j < arr.shape[1]:
# if the end of the matrix is reached, break
if j + size > arr.shape[1]:
break
# compute the average of the pooling matrix
updated_arr[mat_i][mat_j] = int(np.average(arr[i : i + size, j : j + size]))
# shift the pooling matrix by stride of column pixels
j += stride
mat_j += 1
# shift the pooling matrix by stride of row pixels
i += stride
mat_i += 1
# reset the column index to 0
j = 0
mat_j = 0
return updated_arr
# Main Function
if __name__ == "__main__":
from doctest import testmod
testmod(name="avgpooling", verbose=True)
# Loading the image
image = Image.open("path_to_image")
# Converting the image to numpy array and maxpooling, displaying the result
# Ensure that the image is a square matrix
Image.fromarray(maxpooling(np.array(image), size=3, stride=2)).show()
# Converting the image to numpy array and averagepooling, displaying the result
# Ensure that the image is a square matrix
Image.fromarray(avgpooling(np.array(image), size=3, stride=2)).show()
| -1 |
TheAlgorithms/Python | 7,417 | Remove references to depreciated QasmSimulator | ### Describe your change:
Replaced instances of `qiskit.Aer.get_backend("qasm_simulator")` in `quantum/` with `q.Aer.get_backend("aer_simulator")`, as the former is depreciated and raises warnings (Qiskit's [documentation](https://qiskit.org/documentation/apidoc/aer_provider.html) says that `QasmSimulator` is legacy).
This PR edits multiple code files because they all raise the same warning and are mentioned in the same GitHub issue.
Fixes #7308
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [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 have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| tianyizheng02 | 2022-10-19T03:31:43Z | 2022-10-19T20:12:44Z | 50da472ddcdc2d79d1ad325ec05cda3558802fda | 2859d4bf3aa96737a4715c65d4a9051d9c62d24d | Remove references to depreciated QasmSimulator. ### Describe your change:
Replaced instances of `qiskit.Aer.get_backend("qasm_simulator")` in `quantum/` with `q.Aer.get_backend("aer_simulator")`, as the former is depreciated and raises warnings (Qiskit's [documentation](https://qiskit.org/documentation/apidoc/aer_provider.html) says that `QasmSimulator` is legacy).
This PR edits multiple code files because they all raise the same warning and are mentioned in the same GitHub issue.
Fixes #7308
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 7,417 | Remove references to depreciated QasmSimulator | ### Describe your change:
Replaced instances of `qiskit.Aer.get_backend("qasm_simulator")` in `quantum/` with `q.Aer.get_backend("aer_simulator")`, as the former is depreciated and raises warnings (Qiskit's [documentation](https://qiskit.org/documentation/apidoc/aer_provider.html) says that `QasmSimulator` is legacy).
This PR edits multiple code files because they all raise the same warning and are mentioned in the same GitHub issue.
Fixes #7308
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [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 have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| tianyizheng02 | 2022-10-19T03:31:43Z | 2022-10-19T20:12:44Z | 50da472ddcdc2d79d1ad325ec05cda3558802fda | 2859d4bf3aa96737a4715c65d4a9051d9c62d24d | Remove references to depreciated QasmSimulator. ### Describe your change:
Replaced instances of `qiskit.Aer.get_backend("qasm_simulator")` in `quantum/` with `q.Aer.get_backend("aer_simulator")`, as the former is depreciated and raises warnings (Qiskit's [documentation](https://qiskit.org/documentation/apidoc/aer_provider.html) says that `QasmSimulator` is legacy).
This PR edits multiple code files because they all raise the same warning and are mentioned in the same GitHub issue.
Fixes #7308
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # floyd_warshall.py
"""
The problem is to find the shortest distance between all pairs of vertices in a
weighted directed graph that can have negative edge weights.
"""
def _print_dist(dist, v):
print("\nThe shortest path matrix using Floyd Warshall algorithm\n")
for i in range(v):
for j in range(v):
if dist[i][j] != float("inf"):
print(int(dist[i][j]), end="\t")
else:
print("INF", end="\t")
print()
def floyd_warshall(graph, v):
"""
:param graph: 2D array calculated from weight[edge[i, j]]
:type graph: List[List[float]]
:param v: number of vertices
:type v: int
:return: shortest distance between all vertex pairs
distance[u][v] will contain the shortest distance from vertex u to v.
1. For all edges from v to n, distance[i][j] = weight(edge(i, j)).
3. The algorithm then performs distance[i][j] = min(distance[i][j], distance[i][k] +
distance[k][j]) for each possible pair i, j of vertices.
4. The above is repeated for each vertex k in the graph.
5. Whenever distance[i][j] is given a new minimum value, next vertex[i][j] is
updated to the next vertex[i][k].
"""
dist = [[float("inf") for _ in range(v)] for _ in range(v)]
for i in range(v):
for j in range(v):
dist[i][j] = graph[i][j]
# check vertex k against all other vertices (i, j)
for k in range(v):
# looping through rows of graph array
for i in range(v):
# looping through columns of graph array
for j in range(v):
if (
dist[i][k] != float("inf")
and dist[k][j] != float("inf")
and dist[i][k] + dist[k][j] < dist[i][j]
):
dist[i][j] = dist[i][k] + dist[k][j]
_print_dist(dist, v)
return dist, v
if __name__ == "__main__":
v = int(input("Enter number of vertices: "))
e = int(input("Enter number of edges: "))
graph = [[float("inf") for i in range(v)] for j in range(v)]
for i in range(v):
graph[i][i] = 0.0
# src and dst are indices that must be within the array size graph[e][v]
# failure to follow this will result in an error
for i in range(e):
print("\nEdge ", i + 1)
src = int(input("Enter source:"))
dst = int(input("Enter destination:"))
weight = float(input("Enter weight:"))
graph[src][dst] = weight
floyd_warshall(graph, v)
# Example Input
# Enter number of vertices: 3
# Enter number of edges: 2
# # generated graph from vertex and edge inputs
# [[inf, inf, inf], [inf, inf, inf], [inf, inf, inf]]
# [[0.0, inf, inf], [inf, 0.0, inf], [inf, inf, 0.0]]
# specify source, destination and weight for edge #1
# Edge 1
# Enter source:1
# Enter destination:2
# Enter weight:2
# specify source, destination and weight for edge #2
# Edge 2
# Enter source:2
# Enter destination:1
# Enter weight:1
# # Expected Output from the vertice, edge and src, dst, weight inputs!!
# 0 INF INF
# INF 0 2
# INF 1 0
| # floyd_warshall.py
"""
The problem is to find the shortest distance between all pairs of vertices in a
weighted directed graph that can have negative edge weights.
"""
def _print_dist(dist, v):
print("\nThe shortest path matrix using Floyd Warshall algorithm\n")
for i in range(v):
for j in range(v):
if dist[i][j] != float("inf"):
print(int(dist[i][j]), end="\t")
else:
print("INF", end="\t")
print()
def floyd_warshall(graph, v):
"""
:param graph: 2D array calculated from weight[edge[i, j]]
:type graph: List[List[float]]
:param v: number of vertices
:type v: int
:return: shortest distance between all vertex pairs
distance[u][v] will contain the shortest distance from vertex u to v.
1. For all edges from v to n, distance[i][j] = weight(edge(i, j)).
3. The algorithm then performs distance[i][j] = min(distance[i][j], distance[i][k] +
distance[k][j]) for each possible pair i, j of vertices.
4. The above is repeated for each vertex k in the graph.
5. Whenever distance[i][j] is given a new minimum value, next vertex[i][j] is
updated to the next vertex[i][k].
"""
dist = [[float("inf") for _ in range(v)] for _ in range(v)]
for i in range(v):
for j in range(v):
dist[i][j] = graph[i][j]
# check vertex k against all other vertices (i, j)
for k in range(v):
# looping through rows of graph array
for i in range(v):
# looping through columns of graph array
for j in range(v):
if (
dist[i][k] != float("inf")
and dist[k][j] != float("inf")
and dist[i][k] + dist[k][j] < dist[i][j]
):
dist[i][j] = dist[i][k] + dist[k][j]
_print_dist(dist, v)
return dist, v
if __name__ == "__main__":
v = int(input("Enter number of vertices: "))
e = int(input("Enter number of edges: "))
graph = [[float("inf") for i in range(v)] for j in range(v)]
for i in range(v):
graph[i][i] = 0.0
# src and dst are indices that must be within the array size graph[e][v]
# failure to follow this will result in an error
for i in range(e):
print("\nEdge ", i + 1)
src = int(input("Enter source:"))
dst = int(input("Enter destination:"))
weight = float(input("Enter weight:"))
graph[src][dst] = weight
floyd_warshall(graph, v)
# Example Input
# Enter number of vertices: 3
# Enter number of edges: 2
# # generated graph from vertex and edge inputs
# [[inf, inf, inf], [inf, inf, inf], [inf, inf, inf]]
# [[0.0, inf, inf], [inf, 0.0, inf], [inf, inf, 0.0]]
# specify source, destination and weight for edge #1
# Edge 1
# Enter source:1
# Enter destination:2
# Enter weight:2
# specify source, destination and weight for edge #2
# Edge 2
# Enter source:2
# Enter destination:1
# Enter weight:1
# # Expected Output from the vertice, edge and src, dst, weight inputs!!
# 0 INF INF
# INF 0 2
# INF 1 0
| -1 |
TheAlgorithms/Python | 7,417 | Remove references to depreciated QasmSimulator | ### Describe your change:
Replaced instances of `qiskit.Aer.get_backend("qasm_simulator")` in `quantum/` with `q.Aer.get_backend("aer_simulator")`, as the former is depreciated and raises warnings (Qiskit's [documentation](https://qiskit.org/documentation/apidoc/aer_provider.html) says that `QasmSimulator` is legacy).
This PR edits multiple code files because they all raise the same warning and are mentioned in the same GitHub issue.
Fixes #7308
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [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 have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| tianyizheng02 | 2022-10-19T03:31:43Z | 2022-10-19T20:12:44Z | 50da472ddcdc2d79d1ad325ec05cda3558802fda | 2859d4bf3aa96737a4715c65d4a9051d9c62d24d | Remove references to depreciated QasmSimulator. ### Describe your change:
Replaced instances of `qiskit.Aer.get_backend("qasm_simulator")` in `quantum/` with `q.Aer.get_backend("aer_simulator")`, as the former is depreciated and raises warnings (Qiskit's [documentation](https://qiskit.org/documentation/apidoc/aer_provider.html) says that `QasmSimulator` is legacy).
This PR edits multiple code files because they all raise the same warning and are mentioned in the same GitHub issue.
Fixes #7308
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 7,417 | Remove references to depreciated QasmSimulator | ### Describe your change:
Replaced instances of `qiskit.Aer.get_backend("qasm_simulator")` in `quantum/` with `q.Aer.get_backend("aer_simulator")`, as the former is depreciated and raises warnings (Qiskit's [documentation](https://qiskit.org/documentation/apidoc/aer_provider.html) says that `QasmSimulator` is legacy).
This PR edits multiple code files because they all raise the same warning and are mentioned in the same GitHub issue.
Fixes #7308
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [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 have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| tianyizheng02 | 2022-10-19T03:31:43Z | 2022-10-19T20:12:44Z | 50da472ddcdc2d79d1ad325ec05cda3558802fda | 2859d4bf3aa96737a4715c65d4a9051d9c62d24d | Remove references to depreciated QasmSimulator. ### Describe your change:
Replaced instances of `qiskit.Aer.get_backend("qasm_simulator")` in `quantum/` with `q.Aer.get_backend("aer_simulator")`, as the former is depreciated and raises warnings (Qiskit's [documentation](https://qiskit.org/documentation/apidoc/aer_provider.html) says that `QasmSimulator` is legacy).
This PR edits multiple code files because they all raise the same warning and are mentioned in the same GitHub issue.
Fixes #7308
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
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
"""
import math
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 (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 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.")
max_number = 0
if is_prime(n):
return n
while n % 2 == 0:
n //= 2
if is_prime(n):
return n
for i in range(3, int(math.sqrt(n)) + 1, 2):
if n % i == 0:
if is_prime(n // i):
max_number = n // i
break
elif is_prime(i):
max_number = i
return max_number
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
"""
import math
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 (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 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.")
max_number = 0
if is_prime(n):
return n
while n % 2 == 0:
n //= 2
if is_prime(n):
return n
for i in range(3, int(math.sqrt(n)) + 1, 2):
if n % i == 0:
if is_prime(n // i):
max_number = n // i
break
elif is_prime(i):
max_number = i
return max_number
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 7,417 | Remove references to depreciated QasmSimulator | ### Describe your change:
Replaced instances of `qiskit.Aer.get_backend("qasm_simulator")` in `quantum/` with `q.Aer.get_backend("aer_simulator")`, as the former is depreciated and raises warnings (Qiskit's [documentation](https://qiskit.org/documentation/apidoc/aer_provider.html) says that `QasmSimulator` is legacy).
This PR edits multiple code files because they all raise the same warning and are mentioned in the same GitHub issue.
Fixes #7308
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [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 have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| tianyizheng02 | 2022-10-19T03:31:43Z | 2022-10-19T20:12:44Z | 50da472ddcdc2d79d1ad325ec05cda3558802fda | 2859d4bf3aa96737a4715c65d4a9051d9c62d24d | Remove references to depreciated QasmSimulator. ### Describe your change:
Replaced instances of `qiskit.Aer.get_backend("qasm_simulator")` in `quantum/` with `q.Aer.get_backend("aer_simulator")`, as the former is depreciated and raises warnings (Qiskit's [documentation](https://qiskit.org/documentation/apidoc/aer_provider.html) says that `QasmSimulator` is legacy).
This PR edits multiple code files because they all raise the same warning and are mentioned in the same GitHub issue.
Fixes #7308
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 7,417 | Remove references to depreciated QasmSimulator | ### Describe your change:
Replaced instances of `qiskit.Aer.get_backend("qasm_simulator")` in `quantum/` with `q.Aer.get_backend("aer_simulator")`, as the former is depreciated and raises warnings (Qiskit's [documentation](https://qiskit.org/documentation/apidoc/aer_provider.html) says that `QasmSimulator` is legacy).
This PR edits multiple code files because they all raise the same warning and are mentioned in the same GitHub issue.
Fixes #7308
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [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 have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| tianyizheng02 | 2022-10-19T03:31:43Z | 2022-10-19T20:12:44Z | 50da472ddcdc2d79d1ad325ec05cda3558802fda | 2859d4bf3aa96737a4715c65d4a9051d9c62d24d | Remove references to depreciated QasmSimulator. ### Describe your change:
Replaced instances of `qiskit.Aer.get_backend("qasm_simulator")` in `quantum/` with `q.Aer.get_backend("aer_simulator")`, as the former is depreciated and raises warnings (Qiskit's [documentation](https://qiskit.org/documentation/apidoc/aer_provider.html) says that `QasmSimulator` is legacy).
This PR edits multiple code files because they all raise the same warning and are mentioned in the same GitHub issue.
Fixes #7308
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 7,417 | Remove references to depreciated QasmSimulator | ### Describe your change:
Replaced instances of `qiskit.Aer.get_backend("qasm_simulator")` in `quantum/` with `q.Aer.get_backend("aer_simulator")`, as the former is depreciated and raises warnings (Qiskit's [documentation](https://qiskit.org/documentation/apidoc/aer_provider.html) says that `QasmSimulator` is legacy).
This PR edits multiple code files because they all raise the same warning and are mentioned in the same GitHub issue.
Fixes #7308
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [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 have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| tianyizheng02 | 2022-10-19T03:31:43Z | 2022-10-19T20:12:44Z | 50da472ddcdc2d79d1ad325ec05cda3558802fda | 2859d4bf3aa96737a4715c65d4a9051d9c62d24d | Remove references to depreciated QasmSimulator. ### Describe your change:
Replaced instances of `qiskit.Aer.get_backend("qasm_simulator")` in `quantum/` with `q.Aer.get_backend("aer_simulator")`, as the former is depreciated and raises warnings (Qiskit's [documentation](https://qiskit.org/documentation/apidoc/aer_provider.html) says that `QasmSimulator` is legacy).
This PR edits multiple code files because they all raise the same warning and are mentioned in the same GitHub issue.
Fixes #7308
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Problem 46: https://projecteuler.net/problem=46
It was proposed by Christian Goldbach that every odd composite number can be
written as the sum of a prime and twice a square.
9 = 7 + 2 × 12
15 = 7 + 2 × 22
21 = 3 + 2 × 32
25 = 7 + 2 × 32
27 = 19 + 2 × 22
33 = 31 + 2 × 12
It turns out that the conjecture was false.
What is the smallest odd composite that cannot be written as the sum of a
prime and twice a square?
"""
from __future__ import annotations
import math
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
"""
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
odd_composites = [num for num in range(3, 100001, 2) if not is_prime(num)]
def compute_nums(n: int) -> list[int]:
"""
Returns a list of first n odd composite numbers which do
not follow the conjecture.
>>> compute_nums(1)
[5777]
>>> compute_nums(2)
[5777, 5993]
>>> compute_nums(0)
Traceback (most recent call last):
...
ValueError: n must be >= 0
>>> compute_nums("a")
Traceback (most recent call last):
...
ValueError: n must be an integer
>>> compute_nums(1.1)
Traceback (most recent call last):
...
ValueError: n must be an integer
"""
if not isinstance(n, int):
raise ValueError("n must be an integer")
if n <= 0:
raise ValueError("n must be >= 0")
list_nums = []
for num in range(len(odd_composites)):
i = 0
while 2 * i * i <= odd_composites[num]:
rem = odd_composites[num] - 2 * i * i
if is_prime(rem):
break
i += 1
else:
list_nums.append(odd_composites[num])
if len(list_nums) == n:
return list_nums
return []
def solution() -> int:
"""Return the solution to the problem"""
return compute_nums(1)[0]
if __name__ == "__main__":
print(f"{solution() = }")
| """
Problem 46: https://projecteuler.net/problem=46
It was proposed by Christian Goldbach that every odd composite number can be
written as the sum of a prime and twice a square.
9 = 7 + 2 × 12
15 = 7 + 2 × 22
21 = 3 + 2 × 32
25 = 7 + 2 × 32
27 = 19 + 2 × 22
33 = 31 + 2 × 12
It turns out that the conjecture was false.
What is the smallest odd composite that cannot be written as the sum of a
prime and twice a square?
"""
from __future__ import annotations
import math
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
"""
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
odd_composites = [num for num in range(3, 100001, 2) if not is_prime(num)]
def compute_nums(n: int) -> list[int]:
"""
Returns a list of first n odd composite numbers which do
not follow the conjecture.
>>> compute_nums(1)
[5777]
>>> compute_nums(2)
[5777, 5993]
>>> compute_nums(0)
Traceback (most recent call last):
...
ValueError: n must be >= 0
>>> compute_nums("a")
Traceback (most recent call last):
...
ValueError: n must be an integer
>>> compute_nums(1.1)
Traceback (most recent call last):
...
ValueError: n must be an integer
"""
if not isinstance(n, int):
raise ValueError("n must be an integer")
if n <= 0:
raise ValueError("n must be >= 0")
list_nums = []
for num in range(len(odd_composites)):
i = 0
while 2 * i * i <= odd_composites[num]:
rem = odd_composites[num] - 2 * i * i
if is_prime(rem):
break
i += 1
else:
list_nums.append(odd_composites[num])
if len(list_nums) == n:
return list_nums
return []
def solution() -> int:
"""Return the solution to the problem"""
return compute_nums(1)[0]
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 7,417 | Remove references to depreciated QasmSimulator | ### Describe your change:
Replaced instances of `qiskit.Aer.get_backend("qasm_simulator")` in `quantum/` with `q.Aer.get_backend("aer_simulator")`, as the former is depreciated and raises warnings (Qiskit's [documentation](https://qiskit.org/documentation/apidoc/aer_provider.html) says that `QasmSimulator` is legacy).
This PR edits multiple code files because they all raise the same warning and are mentioned in the same GitHub issue.
Fixes #7308
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [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 have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| tianyizheng02 | 2022-10-19T03:31:43Z | 2022-10-19T20:12:44Z | 50da472ddcdc2d79d1ad325ec05cda3558802fda | 2859d4bf3aa96737a4715c65d4a9051d9c62d24d | Remove references to depreciated QasmSimulator. ### Describe your change:
Replaced instances of `qiskit.Aer.get_backend("qasm_simulator")` in `quantum/` with `q.Aer.get_backend("aer_simulator")`, as the former is depreciated and raises warnings (Qiskit's [documentation](https://qiskit.org/documentation/apidoc/aer_provider.html) says that `QasmSimulator` is legacy).
This PR edits multiple code files because they all raise the same warning and are mentioned in the same GitHub issue.
Fixes #7308
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Algorithm that merges two sorted linked lists into one sorted linked list.
"""
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
test_data_odd = (3, 9, -11, 0, 7, 5, 1, -1)
test_data_even = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class Node:
data: int
next: Node | None
class SortedLinkedList:
def __init__(self, ints: Iterable[int]) -> None:
self.head: Node | None = None
for i in sorted(ints, reverse=True):
self.head = Node(i, self.head)
def __iter__(self) -> Iterator[int]:
"""
>>> tuple(SortedLinkedList(test_data_odd)) == tuple(sorted(test_data_odd))
True
>>> tuple(SortedLinkedList(test_data_even)) == tuple(sorted(test_data_even))
True
"""
node = self.head
while node:
yield node.data
node = node.next
def __len__(self) -> int:
"""
>>> for i in range(3):
... len(SortedLinkedList(range(i))) == i
True
True
True
>>> len(SortedLinkedList(test_data_odd))
8
"""
return len(tuple(iter(self)))
def __str__(self) -> str:
"""
>>> str(SortedLinkedList([]))
''
>>> str(SortedLinkedList(test_data_odd))
'-11 -> -1 -> 0 -> 1 -> 3 -> 5 -> 7 -> 9'
>>> str(SortedLinkedList(test_data_even))
'-2 -> 0 -> 2 -> 3 -> 4 -> 6 -> 8 -> 10'
"""
return " -> ".join([str(node) for node in self])
def merge_lists(
sll_one: SortedLinkedList, sll_two: SortedLinkedList
) -> SortedLinkedList:
"""
>>> SSL = SortedLinkedList
>>> merged = merge_lists(SSL(test_data_odd), SSL(test_data_even))
>>> len(merged)
16
>>> str(merged)
'-11 -> -2 -> -1 -> 0 -> 0 -> 1 -> 2 -> 3 -> 3 -> 4 -> 5 -> 6 -> 7 -> 8 -> 9 -> 10'
>>> list(merged) == list(sorted(test_data_odd + test_data_even))
True
"""
return SortedLinkedList(list(sll_one) + list(sll_two))
if __name__ == "__main__":
import doctest
doctest.testmod()
SSL = SortedLinkedList
print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))
| """
Algorithm that merges two sorted linked lists into one sorted linked list.
"""
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
test_data_odd = (3, 9, -11, 0, 7, 5, 1, -1)
test_data_even = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class Node:
data: int
next: Node | None
class SortedLinkedList:
def __init__(self, ints: Iterable[int]) -> None:
self.head: Node | None = None
for i in sorted(ints, reverse=True):
self.head = Node(i, self.head)
def __iter__(self) -> Iterator[int]:
"""
>>> tuple(SortedLinkedList(test_data_odd)) == tuple(sorted(test_data_odd))
True
>>> tuple(SortedLinkedList(test_data_even)) == tuple(sorted(test_data_even))
True
"""
node = self.head
while node:
yield node.data
node = node.next
def __len__(self) -> int:
"""
>>> for i in range(3):
... len(SortedLinkedList(range(i))) == i
True
True
True
>>> len(SortedLinkedList(test_data_odd))
8
"""
return len(tuple(iter(self)))
def __str__(self) -> str:
"""
>>> str(SortedLinkedList([]))
''
>>> str(SortedLinkedList(test_data_odd))
'-11 -> -1 -> 0 -> 1 -> 3 -> 5 -> 7 -> 9'
>>> str(SortedLinkedList(test_data_even))
'-2 -> 0 -> 2 -> 3 -> 4 -> 6 -> 8 -> 10'
"""
return " -> ".join([str(node) for node in self])
def merge_lists(
sll_one: SortedLinkedList, sll_two: SortedLinkedList
) -> SortedLinkedList:
"""
>>> SSL = SortedLinkedList
>>> merged = merge_lists(SSL(test_data_odd), SSL(test_data_even))
>>> len(merged)
16
>>> str(merged)
'-11 -> -2 -> -1 -> 0 -> 0 -> 1 -> 2 -> 3 -> 3 -> 4 -> 5 -> 6 -> 7 -> 8 -> 9 -> 10'
>>> list(merged) == list(sorted(test_data_odd + test_data_even))
True
"""
return SortedLinkedList(list(sll_one) + list(sll_two))
if __name__ == "__main__":
import doctest
doctest.testmod()
SSL = SortedLinkedList
print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))
| -1 |
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(?x)^(
data_structures/heap/binomial_heap.py
)$
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rev: 22.10.0
hooks:
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hooks:
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- flake8-builtins
- flake8-broken-line
- flake8-comprehensions
- pep8-naming
- yesqa
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rev: v0.982
hooks:
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args:
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- --non-interactive
additional_dependencies: [types-requests]
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rev: v2.2.1
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exclude: |
(?x)^(
strings/dictionary.txt |
strings/words.txt |
project_euler/problem_022/p022_names.txt
)$
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hooks:
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name: Validate filenames
entry: ./scripts/validate_filenames.py
language: script
pass_filenames: false
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rev: v4.3.0
hooks:
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types: [python]
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exclude: |
(?x)^(
data_structures/heap/binomial_heap.py
)$
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rev: 22.10.0
hooks:
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rev: v3.1.0
hooks:
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args:
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rev: 5.0.4
hooks:
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additional_dependencies:
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- flake8-builtins
- flake8-broken-line
- flake8-comprehensions
- pep8-naming
- yesqa
- repo: https://github.com/pre-commit/mirrors-mypy
rev: v0.982
hooks:
- id: mypy
args:
- --ignore-missing-imports
- --install-types # See mirrors-mypy README.md
- --non-interactive
additional_dependencies: [types-requests]
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rev: v2.2.2
hooks:
- id: codespell
args:
- --ignore-words-list=ans,crate,damon,fo,followings,hist,iff,mater,secant,som,sur,tim,zar
exclude: |
(?x)^(
ciphers/prehistoric_men.txt |
strings/dictionary.txt |
strings/words.txt |
project_euler/problem_022/p022_names.txt
)$
- repo: local
hooks:
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name: Validate filenames
entry: ./scripts/validate_filenames.py
language: script
pass_filenames: false
| 1 |
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<!--pre-commit.ci end--> |
## 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)
* [Equal Loudness Filter](audio_filters/equal_loudness_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)
* [Hamiltonian Cycle](backtracking/hamiltonian_cycle.py)
* [Knight Tour](backtracking/knight_tour.py)
* [Minimax](backtracking/minimax.py)
* [N Queens](backtracking/n_queens.py)
* [N Queens Math](backtracking/n_queens_math.py)
* [Rat In Maze](backtracking/rat_in_maze.py)
* [Sudoku](backtracking/sudoku.py)
* [Sum Of Subsets](backtracking/sum_of_subsets.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)
* [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
* [Norgate](boolean_algebra/norgate.py)
* [Quine Mc Cluskey](boolean_algebra/quine_mc_cluskey.py)
## Cellular Automata
* [Conways Game Of Life](cellular_automata/conways_game_of_life.py)
* [Game Of Life](cellular_automata/game_of_life.py)
* [Nagel Schrekenberg](cellular_automata/nagel_schrekenberg.py)
* [One Dimensional](cellular_automata/one_dimensional.py)
## Ciphers
* [A1Z26](ciphers/a1z26.py)
* [Affine Cipher](ciphers/affine_cipher.py)
* [Atbash](ciphers/atbash.py)
* [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)
* [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)
* [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
* 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 Traversals](data_structures/binary_tree/binary_tree_traversals.py)
* [Fenwick Tree](data_structures/binary_tree/fenwick_tree.py)
* [Inorder Tree Traversal 2022](data_structures/binary_tree/inorder_tree_traversal_2022.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
* [Double Hash](data_structures/hashing/double_hash.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)
* 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 On List](data_structures/queue/queue_on_list.py)
* [Queue On Pseudo Stack](data_structures/queue/queue_on_pseudo_stack.py)
* Stacks
* [Balanced Parentheses](data_structures/stacks/balanced_parentheses.py)
* [Dijkstras Two Stack Algorithm](data_structures/stacks/dijkstras_two_stack_algorithm.py)
* [Evaluate Postfix Notations](data_structures/stacks/evaluate_postfix_notations.py)
* [Infix To Postfix Conversion](data_structures/stacks/infix_to_postfix_conversion.py)
* [Infix To Prefix Conversion](data_structures/stacks/infix_to_prefix_conversion.py)
* [Next Greater Element](data_structures/stacks/next_greater_element.py)
* [Postfix Evaluation](data_structures/stacks/postfix_evaluation.py)
* [Prefix Evaluation](data_structures/stacks/prefix_evaluation.py)
* [Stack](data_structures/stacks/stack.py)
* [Stack With Doubly Linked List](data_structures/stacks/stack_with_doubly_linked_list.py)
* [Stack With Singly Linked List](data_structures/stacks/stack_with_singly_linked_list.py)
* [Stock Span Problem](data_structures/stacks/stock_span_problem.py)
* Trie
* [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 Sum](divide_and_conquer/max_subarray_sum.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)
* [Edit Distance](dynamic_programming/edit_distance.py)
* [Factorial](dynamic_programming/factorial.py)
* [Fast Fibonacci](dynamic_programming/fast_fibonacci.py)
* [Fibonacci](dynamic_programming/fibonacci.py)
* [Floyd Warshall](dynamic_programming/floyd_warshall.py)
* [Integer Partition](dynamic_programming/integer_partition.py)
* [Iterating Through Submasks](dynamic_programming/iterating_through_submasks.py)
* [Knapsack](dynamic_programming/knapsack.py)
* [Longest Common Subsequence](dynamic_programming/longest_common_subsequence.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 Sub Array](dynamic_programming/max_sub_array.py)
* [Max Sum Contiguous Subsequence](dynamic_programming/max_sum_contiguous_subsequence.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 Steps To One](dynamic_programming/minimum_steps_to_one.py)
* [Optimal Binary Search Tree](dynamic_programming/optimal_binary_search_tree.py)
* [Rod Cutting](dynamic_programming/rod_cutting.py)
* [Subset Generation](dynamic_programming/subset_generation.py)
* [Sum Of Subset](dynamic_programming/sum_of_subset.py)
## Electronics
* [Carrier Concentration](electronics/carrier_concentration.py)
* [Coulombs Law](electronics/coulombs_law.py)
* [Electric Power](electronics/electric_power.py)
* [Ohms Law](electronics/ohms_law.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)
## 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)
* [Bfs Shortest Path](graphs/bfs_shortest_path.py)
* [Bfs Zero One Shortest Path](graphs/bfs_zero_one_shortest_path.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)
* [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)
* [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 List](graphs/graph_list.py)
* [Graph Matrix](graphs/graph_matrix.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)
* [Optimal Merge Pattern](greedy_methods/optimal_merge_pattern.py)
## Hashes
* [Adler32](hashes/adler32.py)
* [Chaos Machine](hashes/chaos_machine.py)
* [Djb2](hashes/djb2.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)
* 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)
* [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)
## Machine Learning
* [Astar](machine_learning/astar.py)
* [Data Transformations](machine_learning/data_transformations.py)
* [Decision Tree](machine_learning/decision_tree.py)
* Forecasting
* [Run](machine_learning/forecasting/run.py)
* [Gaussian Naive Bayes](machine_learning/gaussian_naive_bayes.py)
* [Gradient Boosting Regressor](machine_learning/gradient_boosting_regressor.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)
* [Polymonial Regression](machine_learning/polymonial_regression.py)
* [Random Forest Classifier](machine_learning/random_forest_classifier.py)
* [Random Forest Regressor](machine_learning/random_forest_regressor.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)
## Maths
* [3N Plus 1](maths/3n_plus_1.py)
* [Abs](maths/abs.py)
* [Abs Max](maths/abs_max.py)
* [Abs Min](maths/abs_min.py)
* [Add](maths/add.py)
* [Aliquot Sum](maths/aliquot_sum.py)
* [Allocation Number](maths/allocation_number.py)
* [Area](maths/area.py)
* [Area Under Curve](maths/area_under_curve.py)
* [Armstrong Numbers](maths/armstrong_numbers.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)
* [Double Factorial Iterative](maths/double_factorial_iterative.py)
* [Double Factorial Recursive](maths/double_factorial_recursive.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 Iterative](maths/factorial_iterative.py)
* [Factorial Recursive](maths/factorial_recursive.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)
* [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)
* [Integration By Simpson Approx](maths/integration_by_simpson_approx.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)
* [Kadanes](maths/kadanes.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)
* [Largest Subarray Sum](maths/largest_subarray_sum.py)
* [Least Common Multiple](maths/least_common_multiple.py)
* [Line Length](maths/line_length.py)
* [Lucas Lehmer Primality Test](maths/lucas_lehmer_primality_test.py)
* [Lucas Series](maths/lucas_series.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)
* [Perfect Cube](maths/perfect_cube.py)
* [Perfect Number](maths/perfect_number.py)
* [Perfect Square](maths/perfect_square.py)
* [Persistence](maths/persistence.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)
* [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)
* [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)
* [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)
* [Simpson Rule](maths/simpson_rule.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)
* [Sylvester Sequence](maths/sylvester_sequence.py)
* [Test Prime Check](maths/test_prime_check.py)
* [Trapezoidal Rule](maths/trapezoidal_rule.py)
* [Triplet Sum](maths/triplet_sum.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)
* [Inverse Of Matrix](matrix/inverse_of_matrix.py)
* [Matrix Class](matrix/matrix_class.py)
* [Matrix Operation](matrix/matrix_operation.py)
* [Nth Fibonacci Using Matrix Exponentiation](matrix/nth_fibonacci_using_matrix_exponentiation.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)
* [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)
## Other
* [Activity Selection](other/activity_selection.py)
* [Alternative List Arrange](other/alternative_list_arrange.py)
* [Check Strong Password](other/check_strong_password.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)
* [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 Subarray](other/maximum_subarray.py)
* [Nested Brackets](other/nested_brackets.py)
* [Password Generator](other/password_generator.py)
* [Scoring Algorithm](other/scoring_algorithm.py)
* [Sdes](other/sdes.py)
* [Tower Of Hanoi](other/tower_of_hanoi.py)
## Physics
* [Horizontal Projectile Motion](physics/horizontal_projectile_motion.py)
* [Lorentz Transformation Four Vector](physics/lorentz_transformation_four_vector.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)
## 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 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 080
* [Sol1](project_euler/problem_080/sol1.py)
* Problem 081
* [Sol1](project_euler/problem_081/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 097
* [Sol1](project_euler/problem_097/sol1.py)
* Problem 099
* [Sol1](project_euler/problem_099/sol1.py)
* Problem 101
* [Sol1](project_euler/problem_101/sol1.py)
* Problem 102
* [Sol1](project_euler/problem_102/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 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 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 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)
## Quantum
* [Deutsch Jozsa](quantum/deutsch_jozsa.py)
* [Half Adder](quantum/half_adder.py)
* [Not Gate](quantum/not_gate.py)
* [Quantum Entanglement](quantum/quantum_entanglement.py)
* [Ripple Adder Classic](quantum/ripple_adder_classic.py)
* [Single Qubit Measure](quantum/single_qubit_measure.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)
* [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)
* [Check Pangram](strings/check_pangram.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 Palindrome](strings/is_palindrome.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)
* [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 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)
* [Equal Loudness Filter](audio_filters/equal_loudness_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)
* [Hamiltonian Cycle](backtracking/hamiltonian_cycle.py)
* [Knight Tour](backtracking/knight_tour.py)
* [Minimax](backtracking/minimax.py)
* [N Queens](backtracking/n_queens.py)
* [N Queens Math](backtracking/n_queens_math.py)
* [Rat In Maze](backtracking/rat_in_maze.py)
* [Sudoku](backtracking/sudoku.py)
* [Sum Of Subsets](backtracking/sum_of_subsets.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)
* [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
* [Norgate](boolean_algebra/norgate.py)
* [Quine Mc Cluskey](boolean_algebra/quine_mc_cluskey.py)
## Cellular Automata
* [Conways Game Of Life](cellular_automata/conways_game_of_life.py)
* [Game Of Life](cellular_automata/game_of_life.py)
* [Nagel Schrekenberg](cellular_automata/nagel_schrekenberg.py)
* [One Dimensional](cellular_automata/one_dimensional.py)
## Ciphers
* [A1Z26](ciphers/a1z26.py)
* [Affine Cipher](ciphers/affine_cipher.py)
* [Atbash](ciphers/atbash.py)
* [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)
* [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)
* [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
* 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 Traversals](data_structures/binary_tree/binary_tree_traversals.py)
* [Fenwick Tree](data_structures/binary_tree/fenwick_tree.py)
* [Inorder Tree Traversal 2022](data_structures/binary_tree/inorder_tree_traversal_2022.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
* [Double Hash](data_structures/hashing/double_hash.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)
* 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 On List](data_structures/queue/queue_on_list.py)
* [Queue On Pseudo Stack](data_structures/queue/queue_on_pseudo_stack.py)
* Stacks
* [Balanced Parentheses](data_structures/stacks/balanced_parentheses.py)
* [Dijkstras Two Stack Algorithm](data_structures/stacks/dijkstras_two_stack_algorithm.py)
* [Evaluate Postfix Notations](data_structures/stacks/evaluate_postfix_notations.py)
* [Infix To Postfix Conversion](data_structures/stacks/infix_to_postfix_conversion.py)
* [Infix To Prefix Conversion](data_structures/stacks/infix_to_prefix_conversion.py)
* [Next Greater Element](data_structures/stacks/next_greater_element.py)
* [Postfix Evaluation](data_structures/stacks/postfix_evaluation.py)
* [Prefix Evaluation](data_structures/stacks/prefix_evaluation.py)
* [Stack](data_structures/stacks/stack.py)
* [Stack With Doubly Linked List](data_structures/stacks/stack_with_doubly_linked_list.py)
* [Stack With Singly Linked List](data_structures/stacks/stack_with_singly_linked_list.py)
* [Stock Span Problem](data_structures/stacks/stock_span_problem.py)
* Trie
* [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 Sum](divide_and_conquer/max_subarray_sum.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)
* [Edit Distance](dynamic_programming/edit_distance.py)
* [Factorial](dynamic_programming/factorial.py)
* [Fast Fibonacci](dynamic_programming/fast_fibonacci.py)
* [Fibonacci](dynamic_programming/fibonacci.py)
* [Floyd Warshall](dynamic_programming/floyd_warshall.py)
* [Integer Partition](dynamic_programming/integer_partition.py)
* [Iterating Through Submasks](dynamic_programming/iterating_through_submasks.py)
* [Knapsack](dynamic_programming/knapsack.py)
* [Longest Common Subsequence](dynamic_programming/longest_common_subsequence.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 Sub Array](dynamic_programming/max_sub_array.py)
* [Max Sum Contiguous Subsequence](dynamic_programming/max_sum_contiguous_subsequence.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 Steps To One](dynamic_programming/minimum_steps_to_one.py)
* [Optimal Binary Search Tree](dynamic_programming/optimal_binary_search_tree.py)
* [Rod Cutting](dynamic_programming/rod_cutting.py)
* [Subset Generation](dynamic_programming/subset_generation.py)
* [Sum Of Subset](dynamic_programming/sum_of_subset.py)
## Electronics
* [Carrier Concentration](electronics/carrier_concentration.py)
* [Coulombs Law](electronics/coulombs_law.py)
* [Electric Power](electronics/electric_power.py)
* [Ohms Law](electronics/ohms_law.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)
## 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)
* [Bfs Shortest Path](graphs/bfs_shortest_path.py)
* [Bfs Zero One Shortest Path](graphs/bfs_zero_one_shortest_path.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)
* [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)
* [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 List](graphs/graph_list.py)
* [Graph Matrix](graphs/graph_matrix.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)
* [Optimal Merge Pattern](greedy_methods/optimal_merge_pattern.py)
## Hashes
* [Adler32](hashes/adler32.py)
* [Chaos Machine](hashes/chaos_machine.py)
* [Djb2](hashes/djb2.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)
* 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)
* [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)
## Machine Learning
* [Astar](machine_learning/astar.py)
* [Data Transformations](machine_learning/data_transformations.py)
* [Decision Tree](machine_learning/decision_tree.py)
* Forecasting
* [Run](machine_learning/forecasting/run.py)
* [Gaussian Naive Bayes](machine_learning/gaussian_naive_bayes.py)
* [Gradient Boosting Regressor](machine_learning/gradient_boosting_regressor.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)
* [Polymonial Regression](machine_learning/polymonial_regression.py)
* [Random Forest Classifier](machine_learning/random_forest_classifier.py)
* [Random Forest Regressor](machine_learning/random_forest_regressor.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)
## Maths
* [3N Plus 1](maths/3n_plus_1.py)
* [Abs](maths/abs.py)
* [Abs Max](maths/abs_max.py)
* [Abs Min](maths/abs_min.py)
* [Add](maths/add.py)
* [Aliquot Sum](maths/aliquot_sum.py)
* [Allocation Number](maths/allocation_number.py)
* [Area](maths/area.py)
* [Area Under Curve](maths/area_under_curve.py)
* [Armstrong Numbers](maths/armstrong_numbers.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)
* [Double Factorial Iterative](maths/double_factorial_iterative.py)
* [Double Factorial Recursive](maths/double_factorial_recursive.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 Iterative](maths/factorial_iterative.py)
* [Factorial Recursive](maths/factorial_recursive.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)
* [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)
* [Integration By Simpson Approx](maths/integration_by_simpson_approx.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)
* [Kadanes](maths/kadanes.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)
* [Largest Subarray Sum](maths/largest_subarray_sum.py)
* [Least Common Multiple](maths/least_common_multiple.py)
* [Line Length](maths/line_length.py)
* [Lucas Lehmer Primality Test](maths/lucas_lehmer_primality_test.py)
* [Lucas Series](maths/lucas_series.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)
* [Perfect Cube](maths/perfect_cube.py)
* [Perfect Number](maths/perfect_number.py)
* [Perfect Square](maths/perfect_square.py)
* [Persistence](maths/persistence.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)
* [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)
* [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)
* [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)
* [Simpson Rule](maths/simpson_rule.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)
* [Sylvester Sequence](maths/sylvester_sequence.py)
* [Test Prime Check](maths/test_prime_check.py)
* [Trapezoidal Rule](maths/trapezoidal_rule.py)
* [Triplet Sum](maths/triplet_sum.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)
* [Inverse Of Matrix](matrix/inverse_of_matrix.py)
* [Matrix Class](matrix/matrix_class.py)
* [Matrix Operation](matrix/matrix_operation.py)
* [Nth Fibonacci Using Matrix Exponentiation](matrix/nth_fibonacci_using_matrix_exponentiation.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)
* [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)
## Other
* [Activity Selection](other/activity_selection.py)
* [Alternative List Arrange](other/alternative_list_arrange.py)
* [Check Strong Password](other/check_strong_password.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)
* [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 Subarray](other/maximum_subarray.py)
* [Nested Brackets](other/nested_brackets.py)
* [Password Generator](other/password_generator.py)
* [Scoring Algorithm](other/scoring_algorithm.py)
* [Sdes](other/sdes.py)
* [Tower Of Hanoi](other/tower_of_hanoi.py)
## Physics
* [Casimir Effect](physics/casimir_effect.py)
* [Horizontal Projectile Motion](physics/horizontal_projectile_motion.py)
* [Lorentz Transformation Four Vector](physics/lorentz_transformation_four_vector.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)
## 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 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 080
* [Sol1](project_euler/problem_080/sol1.py)
* Problem 081
* [Sol1](project_euler/problem_081/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 097
* [Sol1](project_euler/problem_097/sol1.py)
* Problem 099
* [Sol1](project_euler/problem_099/sol1.py)
* Problem 101
* [Sol1](project_euler/problem_101/sol1.py)
* Problem 102
* [Sol1](project_euler/problem_102/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 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 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 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)
## Quantum
* [Deutsch Jozsa](quantum/deutsch_jozsa.py)
* [Half Adder](quantum/half_adder.py)
* [Not Gate](quantum/not_gate.py)
* [Q Full Adder](quantum/q_full_adder.py)
* [Quantum Entanglement](quantum/quantum_entanglement.py)
* [Ripple Adder Classic](quantum/ripple_adder_classic.py)
* [Single Qubit Measure](quantum/single_qubit_measure.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)
* [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)
* [Check Pangram](strings/check_pangram.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 Palindrome](strings/is_palindrome.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)
* [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 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 | 7,387 | [pre-commit.ci] pre-commit autoupdate | <!--pre-commit.ci start-->
updates:
- [github.com/asottile/pyupgrade: v3.0.0 → v3.1.0](https://github.com/asottile/pyupgrade/compare/v3.0.0...v3.1.0)
- [github.com/codespell-project/codespell: v2.2.1 → v2.2.2](https://github.com/codespell-project/codespell/compare/v2.2.1...v2.2.2)
<!--pre-commit.ci end--> | pre-commit-ci[bot] | 2022-10-17T19:35:07Z | 2022-10-17T19:59:26Z | a34b756fd40e5cdfb69abc06dcd42f5f1b5fa21e | 0c7c5fa7b0161a7433467240155356c93ae106b8 | [pre-commit.ci] pre-commit autoupdate. <!--pre-commit.ci start-->
updates:
- [github.com/asottile/pyupgrade: v3.0.0 → v3.1.0](https://github.com/asottile/pyupgrade/compare/v3.0.0...v3.1.0)
- [github.com/codespell-project/codespell: v2.2.1 → v2.2.2](https://github.com/codespell-project/codespell/compare/v2.2.1...v2.2.2)
<!--pre-commit.ci end--> | # Locally Weighted Linear Regression
It is a non-parametric ML algorithm that does not learn on a fixed set of parameters such as **linear regression**. \
So, here comes a question of what is *linear regression*? \
**Linear regression** is a supervised learning algorithm used for computing linear relationships between input (X) and output (Y). \
### Terminology Involved
number_of_features(i) = Number of features involved. \
number_of_training_examples(m) = Number of training examples. \
output_sequence(y) = Output Sequence. \
$\theta$ $^T$ x = predicted point. \
J($\theta$) = COst function of point.
The steps involved in ordinary linear regression are:
Training phase: Compute \theta to minimize the cost. \
J($\theta$) = $\sum_{i=1}^m$ (($\theta$)$^T$ $x^i$ - $y^i$)$^2$
Predict output: for given query point x, \
return: ($\theta$)$^T$ x
<img src="https://miro.medium.com/max/700/1*FZsLp8yTULf77qrp0Qd91g.png" alt="Linear Regression">
This training phase is possible when data points are linear, but there again comes a question can we predict non-linear relationship between x and y ? as shown below
<img src="https://miro.medium.com/max/700/1*DHYvJg55uN-Kj8jHaxDKvQ.png" alt="Non-linear Data">
<br />
<br />
So, here comes the role of non-parametric algorithm which doesn't compute predictions based on fixed set of params. Rather parameters $\theta$ are computed individually for each query point/data point x.
<br />
<br />
While Computing $\theta$ , a higher "preferance" is given to points in the vicinity of x than points farther from x.
Cost Function J($\theta$) = $\sum_{i=1}^m$ $w^i$ (($\theta$)$^T$ $x^i$ - $y^i$)$^2$
$w^i$ is non-negative weight associated to training point $x^i$. \
$w^i$ is large fr $x^i$'s lying closer to query point $x_i$. \
$w^i$ is small for $x^i$'s lying farther to query point $x_i$.
A Typical weight can be computed using \
$w^i$ = $\exp$(-$\frac{(x^i-x)(x^i-x)^T}{2\tau^2}$)
Where $\tau$ is the bandwidth parameter that controls $w^i$ distance from x.
Let's look at a example :
Suppose, we had a query point x=5.0 and training points $x^1$=4.9 and $x^2$=5.0 than we can calculate weights as :
$w^i$ = $\exp$(-$\frac{(x^i-x)(x^i-x)^T}{2\tau^2}$) with $\tau$=0.5
$w^1$ = $\exp$(-$\frac{(4.9-5)^2}{2(0.5)^2}$) = 0.9802
$w^2$ = $\exp$(-$\frac{(3-5)^2}{2(0.5)^2}$) = 0.000335
So, J($\theta$) = 0.9802*($\theta$ $^T$ $x^1$ - $y^1$) + 0.000335*($\theta$ $^T$ $x^2$ - $y^2$)
So, here by we can conclude that the weight fall exponentially as the distance between x & $x^i$ increases and So, does the contribution of error in prediction for $x^i$ to the cost.
Steps involved in LWL are : \
Compute \theta to minimize the cost.
J($\theta$) = $\sum_{i=1}^m$ $w^i$ (($\theta$)$^T$ $x^i$ - $y^i$)$^2$ \
Predict Output: for given query point x, \
return : $\theta$ $^T$ x
<img src="https://miro.medium.com/max/700/1*H3QS05Q1GJtY-tiBL00iug.png" alt="LWL">
| # Locally Weighted Linear Regression
It is a non-parametric ML algorithm that does not learn on a fixed set of parameters such as **linear regression**. \
So, here comes a question of what is *linear regression*? \
**Linear regression** is a supervised learning algorithm used for computing linear relationships between input (X) and output (Y). \
### Terminology Involved
number_of_features(i) = Number of features involved. \
number_of_training_examples(m) = Number of training examples. \
output_sequence(y) = Output Sequence. \
$\theta$ $^T$ x = predicted point. \
J($\theta$) = COst function of point.
The steps involved in ordinary linear regression are:
Training phase: Compute \theta to minimize the cost. \
J($\theta$) = $\sum_{i=1}^m$ (($\theta$)$^T$ $x^i$ - $y^i$)$^2$
Predict output: for given query point x, \
return: ($\theta$)$^T$ x
<img src="https://miro.medium.com/max/700/1*FZsLp8yTULf77qrp0Qd91g.png" alt="Linear Regression">
This training phase is possible when data points are linear, but there again comes a question can we predict non-linear relationship between x and y ? as shown below
<img src="https://miro.medium.com/max/700/1*DHYvJg55uN-Kj8jHaxDKvQ.png" alt="Non-linear Data">
<br />
<br />
So, here comes the role of non-parametric algorithm which doesn't compute predictions based on fixed set of params. Rather parameters $\theta$ are computed individually for each query point/data point x.
<br />
<br />
While Computing $\theta$ , a higher preference is given to points in the vicinity of x than points farther from x.
Cost Function J($\theta$) = $\sum_{i=1}^m$ $w^i$ (($\theta$)$^T$ $x^i$ - $y^i$)$^2$
$w^i$ is non-negative weight associated to training point $x^i$. \
$w^i$ is large fr $x^i$'s lying closer to query point $x_i$. \
$w^i$ is small for $x^i$'s lying farther to query point $x_i$.
A Typical weight can be computed using \
$w^i$ = $\exp$(-$\frac{(x^i-x)(x^i-x)^T}{2\tau^2}$)
Where $\tau$ is the bandwidth parameter that controls $w^i$ distance from x.
Let's look at a example :
Suppose, we had a query point x=5.0 and training points $x^1$=4.9 and $x^2$=5.0 than we can calculate weights as :
$w^i$ = $\exp$(-$\frac{(x^i-x)(x^i-x)^T}{2\tau^2}$) with $\tau$=0.5
$w^1$ = $\exp$(-$\frac{(4.9-5)^2}{2(0.5)^2}$) = 0.9802
$w^2$ = $\exp$(-$\frac{(3-5)^2}{2(0.5)^2}$) = 0.000335
So, J($\theta$) = 0.9802*($\theta$ $^T$ $x^1$ - $y^1$) + 0.000335*($\theta$ $^T$ $x^2$ - $y^2$)
So, here by we can conclude that the weight fall exponentially as the distance between x & $x^i$ increases and So, does the contribution of error in prediction for $x^i$ to the cost.
Steps involved in LWL are : \
Compute \theta to minimize the cost.
J($\theta$) = $\sum_{i=1}^m$ $w^i$ (($\theta$)$^T$ $x^i$ - $y^i$)$^2$ \
Predict Output: for given query point x, \
return : $\theta$ $^T$ x
<img src="https://miro.medium.com/max/700/1*H3QS05Q1GJtY-tiBL00iug.png" alt="LWL">
| 1 |
TheAlgorithms/Python | 7,387 | [pre-commit.ci] pre-commit autoupdate | <!--pre-commit.ci start-->
updates:
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<!--pre-commit.ci end--> | """
References: wikipedia:square free number
python/black : True
flake8 : True
"""
from __future__ import annotations
def is_square_free(factors: list[int]) -> bool:
"""
# doctest: +NORMALIZE_WHITESPACE
This functions takes a list of prime factors as input.
returns True if the factors are square free.
>>> is_square_free([1, 1, 2, 3, 4])
False
These are wrong but should return some value
it simply checks for repition in the numbers.
>>> is_square_free([1, 3, 4, 'sd', 0.0])
True
>>> is_square_free([1, 0.5, 2, 0.0])
True
>>> is_square_free([1, 2, 2, 5])
False
>>> is_square_free('asd')
True
>>> is_square_free(24)
Traceback (most recent call last):
...
TypeError: 'int' object is not iterable
"""
return len(set(factors)) == len(factors)
if __name__ == "__main__":
import doctest
doctest.testmod()
| """
References: wikipedia:square free number
python/black : True
flake8 : True
"""
from __future__ import annotations
def is_square_free(factors: list[int]) -> bool:
"""
# doctest: +NORMALIZE_WHITESPACE
This functions takes a list of prime factors as input.
returns True if the factors are square free.
>>> is_square_free([1, 1, 2, 3, 4])
False
These are wrong but should return some value
it simply checks for repetition in the numbers.
>>> is_square_free([1, 3, 4, 'sd', 0.0])
True
>>> is_square_free([1, 0.5, 2, 0.0])
True
>>> is_square_free([1, 2, 2, 5])
False
>>> is_square_free('asd')
True
>>> is_square_free(24)
Traceback (most recent call last):
...
TypeError: 'int' object is not iterable
"""
return len(set(factors)) == len(factors)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 1 |
TheAlgorithms/Python | 7,387 | [pre-commit.ci] pre-commit autoupdate | <!--pre-commit.ci start-->
updates:
- [github.com/asottile/pyupgrade: v3.0.0 → v3.1.0](https://github.com/asottile/pyupgrade/compare/v3.0.0...v3.1.0)
- [github.com/codespell-project/codespell: v2.2.1 → v2.2.2](https://github.com/codespell-project/codespell/compare/v2.2.1...v2.2.2)
<!--pre-commit.ci end--> | pre-commit-ci[bot] | 2022-10-17T19:35:07Z | 2022-10-17T19:59:26Z | a34b756fd40e5cdfb69abc06dcd42f5f1b5fa21e | 0c7c5fa7b0161a7433467240155356c93ae106b8 | [pre-commit.ci] pre-commit autoupdate. <!--pre-commit.ci start-->
updates:
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- [github.com/codespell-project/codespell: v2.2.1 → v2.2.2](https://github.com/codespell-project/codespell/compare/v2.2.1...v2.2.2)
<!--pre-commit.ci end--> | """
Author: Alexander Joslin
GitHub: github.com/echoaj
Explanation: https://medium.com/@haleesammar/implemented-in-js-dijkstras-2-stack-
algorithm-for-evaluating-mathematical-expressions-fc0837dae1ea
We can use Dijkstra's two stack algorithm to solve an equation
such as: (5 + ((4 * 2) * (2 + 3)))
THESE ARE THE ALGORITHM'S RULES:
RULE 1: Scan the expression from left to right. When an operand is encountered,
push it onto the operand stack.
RULE 2: When an operator is encountered in the expression,
push it onto the operator stack.
RULE 3: When a left parenthesis is encountered in the expression, ignore it.
RULE 4: When a right parenthesis is encountered in the expression,
pop an operator off the operator stack. The two operands it must
operate on must be the last two operands pushed onto the operand stack.
We therefore pop the operand stack twice, perform the operation,
and push the result back onto the operand stack so it will be available
for use as an operand of the next operator popped off the operator stack.
RULE 5: When the entire infix expression has been scanned, the value left on
the operand stack represents the value of the expression.
NOTE: It only works with whole numbers.
"""
__author__ = "Alexander Joslin"
import operator as op
from .stack import Stack
def dijkstras_two_stack_algorithm(equation: str) -> int:
"""
DocTests
>>> dijkstras_two_stack_algorithm("(5 + 3)")
8
>>> dijkstras_two_stack_algorithm("((9 - (2 + 9)) + (8 - 1))")
5
>>> dijkstras_two_stack_algorithm("((((3 - 2) - (2 + 3)) + (2 - 4)) + 3)")
-3
:param equation: a string
:return: result: an integer
"""
operators = {"*": op.mul, "/": op.truediv, "+": op.add, "-": op.sub}
operand_stack: Stack[int] = Stack()
operator_stack: Stack[str] = Stack()
for i in equation:
if i.isdigit():
# RULE 1
operand_stack.push(int(i))
elif i in operators:
# RULE 2
operator_stack.push(i)
elif i == ")":
# RULE 4
opr = operator_stack.peek()
operator_stack.pop()
num1 = operand_stack.peek()
operand_stack.pop()
num2 = operand_stack.peek()
operand_stack.pop()
total = operators[opr](num2, num1)
operand_stack.push(total)
# RULE 5
return operand_stack.peek()
if __name__ == "__main__":
equation = "(5 + ((4 * 2) * (2 + 3)))"
# answer = 45
print(f"{equation} = {dijkstras_two_stack_algorithm(equation)}")
| """
Author: Alexander Joslin
GitHub: github.com/echoaj
Explanation: https://medium.com/@haleesammar/implemented-in-js-dijkstras-2-stack-
algorithm-for-evaluating-mathematical-expressions-fc0837dae1ea
We can use Dijkstra's two stack algorithm to solve an equation
such as: (5 + ((4 * 2) * (2 + 3)))
THESE ARE THE ALGORITHM'S RULES:
RULE 1: Scan the expression from left to right. When an operand is encountered,
push it onto the operand stack.
RULE 2: When an operator is encountered in the expression,
push it onto the operator stack.
RULE 3: When a left parenthesis is encountered in the expression, ignore it.
RULE 4: When a right parenthesis is encountered in the expression,
pop an operator off the operator stack. The two operands it must
operate on must be the last two operands pushed onto the operand stack.
We therefore pop the operand stack twice, perform the operation,
and push the result back onto the operand stack so it will be available
for use as an operand of the next operator popped off the operator stack.
RULE 5: When the entire infix expression has been scanned, the value left on
the operand stack represents the value of the expression.
NOTE: It only works with whole numbers.
"""
__author__ = "Alexander Joslin"
import operator as op
from .stack import Stack
def dijkstras_two_stack_algorithm(equation: str) -> int:
"""
DocTests
>>> dijkstras_two_stack_algorithm("(5 + 3)")
8
>>> dijkstras_two_stack_algorithm("((9 - (2 + 9)) + (8 - 1))")
5
>>> dijkstras_two_stack_algorithm("((((3 - 2) - (2 + 3)) + (2 - 4)) + 3)")
-3
:param equation: a string
:return: result: an integer
"""
operators = {"*": op.mul, "/": op.truediv, "+": op.add, "-": op.sub}
operand_stack: Stack[int] = Stack()
operator_stack: Stack[str] = Stack()
for i in equation:
if i.isdigit():
# RULE 1
operand_stack.push(int(i))
elif i in operators:
# RULE 2
operator_stack.push(i)
elif i == ")":
# RULE 4
opr = operator_stack.peek()
operator_stack.pop()
num1 = operand_stack.peek()
operand_stack.pop()
num2 = operand_stack.peek()
operand_stack.pop()
total = operators[opr](num2, num1)
operand_stack.push(total)
# RULE 5
return operand_stack.peek()
if __name__ == "__main__":
equation = "(5 + ((4 * 2) * (2 + 3)))"
# answer = 45
print(f"{equation} = {dijkstras_two_stack_algorithm(equation)}")
| -1 |
TheAlgorithms/Python | 7,387 | [pre-commit.ci] pre-commit autoupdate | <!--pre-commit.ci start-->
updates:
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<!--pre-commit.ci end--> | pre-commit-ci[bot] | 2022-10-17T19:35:07Z | 2022-10-17T19:59:26Z | a34b756fd40e5cdfb69abc06dcd42f5f1b5fa21e | 0c7c5fa7b0161a7433467240155356c93ae106b8 | [pre-commit.ci] pre-commit autoupdate. <!--pre-commit.ci start-->
updates:
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<!--pre-commit.ci end--> | """
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
- https://en.wikipedia.org/wiki/Euclidean_algorithm
- https://en.wikipedia.org/wiki/Least_common_multiple
"""
def greatest_common_divisor(x: int, y: int) -> int:
"""
Euclidean Greatest Common Divisor algorithm
>>> greatest_common_divisor(0, 0)
0
>>> greatest_common_divisor(23, 42)
1
>>> greatest_common_divisor(15, 33)
3
>>> greatest_common_divisor(12345, 67890)
15
"""
return x if y == 0 else greatest_common_divisor(y, x % y)
def lcm(x: int, y: int) -> int:
"""
Least Common Multiple.
Using the property that lcm(a, b) * greatest_common_divisor(a, b) = a*b
>>> lcm(3, 15)
15
>>> lcm(1, 27)
27
>>> lcm(13, 27)
351
>>> lcm(64, 48)
192
"""
return (x * y) // greatest_common_divisor(x, y)
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
"""
g = 1
for i in range(1, n + 1):
g = lcm(g, i)
return g
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
- https://en.wikipedia.org/wiki/Euclidean_algorithm
- https://en.wikipedia.org/wiki/Least_common_multiple
"""
def greatest_common_divisor(x: int, y: int) -> int:
"""
Euclidean Greatest Common Divisor algorithm
>>> greatest_common_divisor(0, 0)
0
>>> greatest_common_divisor(23, 42)
1
>>> greatest_common_divisor(15, 33)
3
>>> greatest_common_divisor(12345, 67890)
15
"""
return x if y == 0 else greatest_common_divisor(y, x % y)
def lcm(x: int, y: int) -> int:
"""
Least Common Multiple.
Using the property that lcm(a, b) * greatest_common_divisor(a, b) = a*b
>>> lcm(3, 15)
15
>>> lcm(1, 27)
27
>>> lcm(13, 27)
351
>>> lcm(64, 48)
192
"""
return (x * y) // greatest_common_divisor(x, y)
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
"""
g = 1
for i in range(1, n + 1):
g = lcm(g, i)
return g
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 7,387 | [pre-commit.ci] pre-commit autoupdate | <!--pre-commit.ci start-->
updates:
- [github.com/asottile/pyupgrade: v3.0.0 → v3.1.0](https://github.com/asottile/pyupgrade/compare/v3.0.0...v3.1.0)
- [github.com/codespell-project/codespell: v2.2.1 → v2.2.2](https://github.com/codespell-project/codespell/compare/v2.2.1...v2.2.2)
<!--pre-commit.ci end--> | pre-commit-ci[bot] | 2022-10-17T19:35:07Z | 2022-10-17T19:59:26Z | a34b756fd40e5cdfb69abc06dcd42f5f1b5fa21e | 0c7c5fa7b0161a7433467240155356c93ae106b8 | [pre-commit.ci] pre-commit autoupdate. <!--pre-commit.ci start-->
updates:
- [github.com/asottile/pyupgrade: v3.0.0 → v3.1.0](https://github.com/asottile/pyupgrade/compare/v3.0.0...v3.1.0)
- [github.com/codespell-project/codespell: v2.2.1 → v2.2.2](https://github.com/codespell-project/codespell/compare/v2.2.1...v2.2.2)
<!--pre-commit.ci end--> | """
Champernowne's constant
Problem 40
An irrational decimal fraction is created by concatenating the positive
integers:
0.123456789101112131415161718192021...
It can be seen that the 12th digit of the fractional part is 1.
If dn represents the nth digit of the fractional part, find the value of the
following expression.
d1 × d10 × d100 × d1000 × d10000 × d100000 × d1000000
"""
def solution():
"""Returns
>>> solution()
210
"""
constant = []
i = 1
while len(constant) < 1e6:
constant.append(str(i))
i += 1
constant = "".join(constant)
return (
int(constant[0])
* int(constant[9])
* int(constant[99])
* int(constant[999])
* int(constant[9999])
* int(constant[99999])
* int(constant[999999])
)
if __name__ == "__main__":
print(solution())
| """
Champernowne's constant
Problem 40
An irrational decimal fraction is created by concatenating the positive
integers:
0.123456789101112131415161718192021...
It can be seen that the 12th digit of the fractional part is 1.
If dn represents the nth digit of the fractional part, find the value of the
following expression.
d1 × d10 × d100 × d1000 × d10000 × d100000 × d1000000
"""
def solution():
"""Returns
>>> solution()
210
"""
constant = []
i = 1
while len(constant) < 1e6:
constant.append(str(i))
i += 1
constant = "".join(constant)
return (
int(constant[0])
* int(constant[9])
* int(constant[99])
* int(constant[999])
* int(constant[9999])
* int(constant[99999])
* int(constant[999999])
)
if __name__ == "__main__":
print(solution())
| -1 |
TheAlgorithms/Python | 7,387 | [pre-commit.ci] pre-commit autoupdate | <!--pre-commit.ci start-->
updates:
- [github.com/asottile/pyupgrade: v3.0.0 → v3.1.0](https://github.com/asottile/pyupgrade/compare/v3.0.0...v3.1.0)
- [github.com/codespell-project/codespell: v2.2.1 → v2.2.2](https://github.com/codespell-project/codespell/compare/v2.2.1...v2.2.2)
<!--pre-commit.ci end--> | pre-commit-ci[bot] | 2022-10-17T19:35:07Z | 2022-10-17T19:59:26Z | a34b756fd40e5cdfb69abc06dcd42f5f1b5fa21e | 0c7c5fa7b0161a7433467240155356c93ae106b8 | [pre-commit.ci] pre-commit autoupdate. <!--pre-commit.ci start-->
updates:
- [github.com/asottile/pyupgrade: v3.0.0 → v3.1.0](https://github.com/asottile/pyupgrade/compare/v3.0.0...v3.1.0)
- [github.com/codespell-project/codespell: v2.2.1 → v2.2.2](https://github.com/codespell-project/codespell/compare/v2.2.1...v2.2.2)
<!--pre-commit.ci end--> | from __future__ import annotations
from collections import Counter
from random import random
class MarkovChainGraphUndirectedUnweighted:
"""
Undirected Unweighted Graph for running Markov Chain Algorithm
"""
def __init__(self):
self.connections = {}
def add_node(self, node: str) -> None:
self.connections[node] = {}
def add_transition_probability(
self, node1: str, node2: str, probability: float
) -> None:
if node1 not in self.connections:
self.add_node(node1)
if node2 not in self.connections:
self.add_node(node2)
self.connections[node1][node2] = probability
def get_nodes(self) -> list[str]:
return list(self.connections)
def transition(self, node: str) -> str:
current_probability = 0
random_value = random()
for dest in self.connections[node]:
current_probability += self.connections[node][dest]
if current_probability > random_value:
return dest
return ""
def get_transitions(
start: str, transitions: list[tuple[str, str, float]], steps: int
) -> dict[str, int]:
"""
Running Markov Chain algorithm and calculating the number of times each node is
visited
>>> transitions = [
... ('a', 'a', 0.9),
... ('a', 'b', 0.075),
... ('a', 'c', 0.025),
... ('b', 'a', 0.15),
... ('b', 'b', 0.8),
... ('b', 'c', 0.05),
... ('c', 'a', 0.25),
... ('c', 'b', 0.25),
... ('c', 'c', 0.5)
... ]
>>> result = get_transitions('a', transitions, 5000)
>>> result['a'] > result['b'] > result['c']
True
"""
graph = MarkovChainGraphUndirectedUnweighted()
for node1, node2, probability in transitions:
graph.add_transition_probability(node1, node2, probability)
visited = Counter(graph.get_nodes())
node = start
for _ in range(steps):
node = graph.transition(node)
visited[node] += 1
return visited
if __name__ == "__main__":
import doctest
doctest.testmod()
| from __future__ import annotations
from collections import Counter
from random import random
class MarkovChainGraphUndirectedUnweighted:
"""
Undirected Unweighted Graph for running Markov Chain Algorithm
"""
def __init__(self):
self.connections = {}
def add_node(self, node: str) -> None:
self.connections[node] = {}
def add_transition_probability(
self, node1: str, node2: str, probability: float
) -> None:
if node1 not in self.connections:
self.add_node(node1)
if node2 not in self.connections:
self.add_node(node2)
self.connections[node1][node2] = probability
def get_nodes(self) -> list[str]:
return list(self.connections)
def transition(self, node: str) -> str:
current_probability = 0
random_value = random()
for dest in self.connections[node]:
current_probability += self.connections[node][dest]
if current_probability > random_value:
return dest
return ""
def get_transitions(
start: str, transitions: list[tuple[str, str, float]], steps: int
) -> dict[str, int]:
"""
Running Markov Chain algorithm and calculating the number of times each node is
visited
>>> transitions = [
... ('a', 'a', 0.9),
... ('a', 'b', 0.075),
... ('a', 'c', 0.025),
... ('b', 'a', 0.15),
... ('b', 'b', 0.8),
... ('b', 'c', 0.05),
... ('c', 'a', 0.25),
... ('c', 'b', 0.25),
... ('c', 'c', 0.5)
... ]
>>> result = get_transitions('a', transitions, 5000)
>>> result['a'] > result['b'] > result['c']
True
"""
graph = MarkovChainGraphUndirectedUnweighted()
for node1, node2, probability in transitions:
graph.add_transition_probability(node1, node2, probability)
visited = Counter(graph.get_nodes())
node = start
for _ in range(steps):
node = graph.transition(node)
visited[node] += 1
return visited
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 7,387 | [pre-commit.ci] pre-commit autoupdate | <!--pre-commit.ci start-->
updates:
- [github.com/asottile/pyupgrade: v3.0.0 → v3.1.0](https://github.com/asottile/pyupgrade/compare/v3.0.0...v3.1.0)
- [github.com/codespell-project/codespell: v2.2.1 → v2.2.2](https://github.com/codespell-project/codespell/compare/v2.2.1...v2.2.2)
<!--pre-commit.ci end--> | pre-commit-ci[bot] | 2022-10-17T19:35:07Z | 2022-10-17T19:59:26Z | a34b756fd40e5cdfb69abc06dcd42f5f1b5fa21e | 0c7c5fa7b0161a7433467240155356c93ae106b8 | [pre-commit.ci] pre-commit autoupdate. <!--pre-commit.ci start-->
updates:
- [github.com/asottile/pyupgrade: v3.0.0 → v3.1.0](https://github.com/asottile/pyupgrade/compare/v3.0.0...v3.1.0)
- [github.com/codespell-project/codespell: v2.2.1 → v2.2.2](https://github.com/codespell-project/codespell/compare/v2.2.1...v2.2.2)
<!--pre-commit.ci end--> | """
This is a pure Python implementation of the merge-insertion sort algorithm
Source: https://en.wikipedia.org/wiki/Graham_scan
For doctests run following command:
python3 -m doctest -v graham_scan.py
"""
from __future__ import annotations
from collections import deque
from enum import Enum
from math import atan2, degrees
from sys import maxsize
def graham_scan(points: list[tuple[int, int]]) -> list[tuple[int, int]]:
"""Pure implementation of graham scan algorithm in Python
:param points: The unique points on coordinates.
:return: The points on convex hell.
Examples:
>>> graham_scan([(9, 6), (3, 1), (0, 0), (5, 5), (5, 2), (7, 0), (3, 3), (1, 4)])
[(0, 0), (7, 0), (9, 6), (5, 5), (1, 4)]
>>> graham_scan([(0, 0), (1, 0), (1, 1), (0, 1)])
[(0, 0), (1, 0), (1, 1), (0, 1)]
>>> graham_scan([(0, 0), (1, 1), (2, 2), (3, 3), (-1, 2)])
[(0, 0), (1, 1), (2, 2), (3, 3), (-1, 2)]
>>> graham_scan([(-100, 20), (99, 3), (1, 10000001), (5133186, -25), (-66, -4)])
[(5133186, -25), (1, 10000001), (-100, 20), (-66, -4)]
"""
if len(points) <= 2:
# There is no convex hull
raise ValueError("graham_scan: argument must contain more than 3 points.")
if len(points) == 3:
return points
# find the lowest and the most left point
minidx = 0
miny, minx = maxsize, maxsize
for i, point in enumerate(points):
x = point[0]
y = point[1]
if y < miny:
miny = y
minx = x
minidx = i
if y == miny:
if x < minx:
minx = x
minidx = i
# remove the lowest and the most left point from points for preparing for sort
points.pop(minidx)
def angle_comparer(point: tuple[int, int], minx: int, miny: int) -> float:
"""Return the angle toward to point from (minx, miny)
:param point: The target point
minx: The starting point's x
miny: The starting point's y
:return: the angle
Examples:
>>> angle_comparer((1,1), 0, 0)
45.0
>>> angle_comparer((100,1), 10, 10)
-5.710593137499642
>>> angle_comparer((5,5), 2, 3)
33.690067525979785
"""
# sort the points accorgind to the angle from the lowest and the most left point
x = point[0]
y = point[1]
angle = degrees(atan2(y - miny, x - minx))
return angle
sorted_points = sorted(points, key=lambda point: angle_comparer(point, minx, miny))
# This insert actually costs complexity,
# and you should instead add (minx, miny) into stack later.
# I'm using insert just for easy understanding.
sorted_points.insert(0, (minx, miny))
# traversal from the lowest and the most left point in anti-clockwise direction
# if direction gets right, the previous point is not the convex hull.
class Direction(Enum):
left = 1
straight = 2
right = 3
def check_direction(
starting: tuple[int, int], via: tuple[int, int], target: tuple[int, int]
) -> Direction:
"""Return the direction toward to the line from via to target from starting
:param starting: The starting point
via: The via point
target: The target point
:return: the Direction
Examples:
>>> check_direction((1,1), (2,2), (3,3))
Direction.straight
>>> check_direction((60,1), (-50,199), (30,2))
Direction.left
>>> check_direction((0,0), (5,5), (10,0))
Direction.right
"""
x0, y0 = starting
x1, y1 = via
x2, y2 = target
via_angle = degrees(atan2(y1 - y0, x1 - x0))
if via_angle < 0:
via_angle += 360
target_angle = degrees(atan2(y2 - y0, x2 - x0))
if target_angle < 0:
target_angle += 360
# t-
# \ \
# \ v
# \|
# s
# via_angle is always lower than target_angle, if direction is left.
# If they are same, it means they are on a same line of convex hull.
if target_angle > via_angle:
return Direction.left
elif target_angle == via_angle:
return Direction.straight
else:
return Direction.right
stack: deque[tuple[int, int]] = deque()
stack.append(sorted_points[0])
stack.append(sorted_points[1])
stack.append(sorted_points[2])
# In any ways, the first 3 points line are towards left.
# Because we sort them the angle from minx, miny.
current_direction = Direction.left
for i in range(3, len(sorted_points)):
while True:
starting = stack[-2]
via = stack[-1]
target = sorted_points[i]
next_direction = check_direction(starting, via, target)
if next_direction == Direction.left:
current_direction = Direction.left
break
if next_direction == Direction.straight:
if current_direction == Direction.left:
# We keep current_direction as left.
# Because if the straight line keeps as straight,
# we want to know if this straight line is towards left.
break
elif current_direction == Direction.right:
# If the straight line is towards right,
# every previous points on those straigh line is not convex hull.
stack.pop()
if next_direction == Direction.right:
stack.pop()
stack.append(sorted_points[i])
return list(stack)
| """
This is a pure Python implementation of the merge-insertion sort algorithm
Source: https://en.wikipedia.org/wiki/Graham_scan
For doctests run following command:
python3 -m doctest -v graham_scan.py
"""
from __future__ import annotations
from collections import deque
from enum import Enum
from math import atan2, degrees
from sys import maxsize
def graham_scan(points: list[tuple[int, int]]) -> list[tuple[int, int]]:
"""Pure implementation of graham scan algorithm in Python
:param points: The unique points on coordinates.
:return: The points on convex hell.
Examples:
>>> graham_scan([(9, 6), (3, 1), (0, 0), (5, 5), (5, 2), (7, 0), (3, 3), (1, 4)])
[(0, 0), (7, 0), (9, 6), (5, 5), (1, 4)]
>>> graham_scan([(0, 0), (1, 0), (1, 1), (0, 1)])
[(0, 0), (1, 0), (1, 1), (0, 1)]
>>> graham_scan([(0, 0), (1, 1), (2, 2), (3, 3), (-1, 2)])
[(0, 0), (1, 1), (2, 2), (3, 3), (-1, 2)]
>>> graham_scan([(-100, 20), (99, 3), (1, 10000001), (5133186, -25), (-66, -4)])
[(5133186, -25), (1, 10000001), (-100, 20), (-66, -4)]
"""
if len(points) <= 2:
# There is no convex hull
raise ValueError("graham_scan: argument must contain more than 3 points.")
if len(points) == 3:
return points
# find the lowest and the most left point
minidx = 0
miny, minx = maxsize, maxsize
for i, point in enumerate(points):
x = point[0]
y = point[1]
if y < miny:
miny = y
minx = x
minidx = i
if y == miny:
if x < minx:
minx = x
minidx = i
# remove the lowest and the most left point from points for preparing for sort
points.pop(minidx)
def angle_comparer(point: tuple[int, int], minx: int, miny: int) -> float:
"""Return the angle toward to point from (minx, miny)
:param point: The target point
minx: The starting point's x
miny: The starting point's y
:return: the angle
Examples:
>>> angle_comparer((1,1), 0, 0)
45.0
>>> angle_comparer((100,1), 10, 10)
-5.710593137499642
>>> angle_comparer((5,5), 2, 3)
33.690067525979785
"""
# sort the points accorgind to the angle from the lowest and the most left point
x = point[0]
y = point[1]
angle = degrees(atan2(y - miny, x - minx))
return angle
sorted_points = sorted(points, key=lambda point: angle_comparer(point, minx, miny))
# This insert actually costs complexity,
# and you should instead add (minx, miny) into stack later.
# I'm using insert just for easy understanding.
sorted_points.insert(0, (minx, miny))
# traversal from the lowest and the most left point in anti-clockwise direction
# if direction gets right, the previous point is not the convex hull.
class Direction(Enum):
left = 1
straight = 2
right = 3
def check_direction(
starting: tuple[int, int], via: tuple[int, int], target: tuple[int, int]
) -> Direction:
"""Return the direction toward to the line from via to target from starting
:param starting: The starting point
via: The via point
target: The target point
:return: the Direction
Examples:
>>> check_direction((1,1), (2,2), (3,3))
Direction.straight
>>> check_direction((60,1), (-50,199), (30,2))
Direction.left
>>> check_direction((0,0), (5,5), (10,0))
Direction.right
"""
x0, y0 = starting
x1, y1 = via
x2, y2 = target
via_angle = degrees(atan2(y1 - y0, x1 - x0))
if via_angle < 0:
via_angle += 360
target_angle = degrees(atan2(y2 - y0, x2 - x0))
if target_angle < 0:
target_angle += 360
# t-
# \ \
# \ v
# \|
# s
# via_angle is always lower than target_angle, if direction is left.
# If they are same, it means they are on a same line of convex hull.
if target_angle > via_angle:
return Direction.left
elif target_angle == via_angle:
return Direction.straight
else:
return Direction.right
stack: deque[tuple[int, int]] = deque()
stack.append(sorted_points[0])
stack.append(sorted_points[1])
stack.append(sorted_points[2])
# In any ways, the first 3 points line are towards left.
# Because we sort them the angle from minx, miny.
current_direction = Direction.left
for i in range(3, len(sorted_points)):
while True:
starting = stack[-2]
via = stack[-1]
target = sorted_points[i]
next_direction = check_direction(starting, via, target)
if next_direction == Direction.left:
current_direction = Direction.left
break
if next_direction == Direction.straight:
if current_direction == Direction.left:
# We keep current_direction as left.
# Because if the straight line keeps as straight,
# we want to know if this straight line is towards left.
break
elif current_direction == Direction.right:
# If the straight line is towards right,
# every previous points on those straigh line is not convex hull.
stack.pop()
if next_direction == Direction.right:
stack.pop()
stack.append(sorted_points[i])
return list(stack)
| -1 |
TheAlgorithms/Python | 7,387 | [pre-commit.ci] pre-commit autoupdate | <!--pre-commit.ci start-->
updates:
- [github.com/asottile/pyupgrade: v3.0.0 → v3.1.0](https://github.com/asottile/pyupgrade/compare/v3.0.0...v3.1.0)
- [github.com/codespell-project/codespell: v2.2.1 → v2.2.2](https://github.com/codespell-project/codespell/compare/v2.2.1...v2.2.2)
<!--pre-commit.ci end--> | pre-commit-ci[bot] | 2022-10-17T19:35:07Z | 2022-10-17T19:59:26Z | a34b756fd40e5cdfb69abc06dcd42f5f1b5fa21e | 0c7c5fa7b0161a7433467240155356c93ae106b8 | [pre-commit.ci] pre-commit autoupdate. <!--pre-commit.ci start-->
updates:
- [github.com/asottile/pyupgrade: v3.0.0 → v3.1.0](https://github.com/asottile/pyupgrade/compare/v3.0.0...v3.1.0)
- [github.com/codespell-project/codespell: v2.2.1 → v2.2.2](https://github.com/codespell-project/codespell/compare/v2.2.1...v2.2.2)
<!--pre-commit.ci end--> | -1 |
||
TheAlgorithms/Python | 7,387 | [pre-commit.ci] pre-commit autoupdate | <!--pre-commit.ci start-->
updates:
- [github.com/asottile/pyupgrade: v3.0.0 → v3.1.0](https://github.com/asottile/pyupgrade/compare/v3.0.0...v3.1.0)
- [github.com/codespell-project/codespell: v2.2.1 → v2.2.2](https://github.com/codespell-project/codespell/compare/v2.2.1...v2.2.2)
<!--pre-commit.ci end--> | pre-commit-ci[bot] | 2022-10-17T19:35:07Z | 2022-10-17T19:59:26Z | a34b756fd40e5cdfb69abc06dcd42f5f1b5fa21e | 0c7c5fa7b0161a7433467240155356c93ae106b8 | [pre-commit.ci] pre-commit autoupdate. <!--pre-commit.ci start-->
updates:
- [github.com/asottile/pyupgrade: v3.0.0 → v3.1.0](https://github.com/asottile/pyupgrade/compare/v3.0.0...v3.1.0)
- [github.com/codespell-project/codespell: v2.2.1 → v2.2.2](https://github.com/codespell-project/codespell/compare/v2.2.1...v2.2.2)
<!--pre-commit.ci end--> | #!/usr/bin/env python3
import os
from collections.abc import Iterator
def good_file_paths(top_dir: str = ".") -> Iterator[str]:
for dir_path, dir_names, filenames in os.walk(top_dir):
dir_names[:] = [d for d in dir_names if d != "scripts" and d[0] not in "._"]
for filename in filenames:
if filename == "__init__.py":
continue
if os.path.splitext(filename)[1] in (".py", ".ipynb"):
yield os.path.join(dir_path, filename).lstrip("./")
def md_prefix(i):
return f"{i * ' '}*" if i else "\n##"
def print_path(old_path: str, new_path: str) -> str:
old_parts = old_path.split(os.sep)
for i, new_part in enumerate(new_path.split(os.sep)):
if i + 1 > len(old_parts) or old_parts[i] != new_part:
if new_part:
print(f"{md_prefix(i)} {new_part.replace('_', ' ').title()}")
return new_path
def print_directory_md(top_dir: str = ".") -> None:
old_path = ""
for filepath in sorted(good_file_paths(top_dir)):
filepath, filename = os.path.split(filepath)
if filepath != old_path:
old_path = print_path(old_path, filepath)
indent = (filepath.count(os.sep) + 1) if filepath else 0
url = "/".join((filepath, filename)).replace(" ", "%20")
filename = os.path.splitext(filename.replace("_", " ").title())[0]
print(f"{md_prefix(indent)} [{filename}]({url})")
if __name__ == "__main__":
print_directory_md(".")
| #!/usr/bin/env python3
import os
from collections.abc import Iterator
def good_file_paths(top_dir: str = ".") -> Iterator[str]:
for dir_path, dir_names, filenames in os.walk(top_dir):
dir_names[:] = [d for d in dir_names if d != "scripts" and d[0] not in "._"]
for filename in filenames:
if filename == "__init__.py":
continue
if os.path.splitext(filename)[1] in (".py", ".ipynb"):
yield os.path.join(dir_path, filename).lstrip("./")
def md_prefix(i):
return f"{i * ' '}*" if i else "\n##"
def print_path(old_path: str, new_path: str) -> str:
old_parts = old_path.split(os.sep)
for i, new_part in enumerate(new_path.split(os.sep)):
if i + 1 > len(old_parts) or old_parts[i] != new_part:
if new_part:
print(f"{md_prefix(i)} {new_part.replace('_', ' ').title()}")
return new_path
def print_directory_md(top_dir: str = ".") -> None:
old_path = ""
for filepath in sorted(good_file_paths(top_dir)):
filepath, filename = os.path.split(filepath)
if filepath != old_path:
old_path = print_path(old_path, filepath)
indent = (filepath.count(os.sep) + 1) if filepath else 0
url = "/".join((filepath, filename)).replace(" ", "%20")
filename = os.path.splitext(filename.replace("_", " ").title())[0]
print(f"{md_prefix(indent)} [{filename}]({url})")
if __name__ == "__main__":
print_directory_md(".")
| -1 |
TheAlgorithms/Python | 7,387 | [pre-commit.ci] pre-commit autoupdate | <!--pre-commit.ci start-->
updates:
- [github.com/asottile/pyupgrade: v3.0.0 → v3.1.0](https://github.com/asottile/pyupgrade/compare/v3.0.0...v3.1.0)
- [github.com/codespell-project/codespell: v2.2.1 → v2.2.2](https://github.com/codespell-project/codespell/compare/v2.2.1...v2.2.2)
<!--pre-commit.ci end--> | pre-commit-ci[bot] | 2022-10-17T19:35:07Z | 2022-10-17T19:59:26Z | a34b756fd40e5cdfb69abc06dcd42f5f1b5fa21e | 0c7c5fa7b0161a7433467240155356c93ae106b8 | [pre-commit.ci] pre-commit autoupdate. <!--pre-commit.ci start-->
updates:
- [github.com/asottile/pyupgrade: v3.0.0 → v3.1.0](https://github.com/asottile/pyupgrade/compare/v3.0.0...v3.1.0)
- [github.com/codespell-project/codespell: v2.2.1 → v2.2.2](https://github.com/codespell-project/codespell/compare/v2.2.1...v2.2.2)
<!--pre-commit.ci end--> | # floyd_warshall.py
"""
The problem is to find the shortest distance between all pairs of vertices in a
weighted directed graph that can have negative edge weights.
"""
def _print_dist(dist, v):
print("\nThe shortest path matrix using Floyd Warshall algorithm\n")
for i in range(v):
for j in range(v):
if dist[i][j] != float("inf"):
print(int(dist[i][j]), end="\t")
else:
print("INF", end="\t")
print()
def floyd_warshall(graph, v):
"""
:param graph: 2D array calculated from weight[edge[i, j]]
:type graph: List[List[float]]
:param v: number of vertices
:type v: int
:return: shortest distance between all vertex pairs
distance[u][v] will contain the shortest distance from vertex u to v.
1. For all edges from v to n, distance[i][j] = weight(edge(i, j)).
3. The algorithm then performs distance[i][j] = min(distance[i][j], distance[i][k] +
distance[k][j]) for each possible pair i, j of vertices.
4. The above is repeated for each vertex k in the graph.
5. Whenever distance[i][j] is given a new minimum value, next vertex[i][j] is
updated to the next vertex[i][k].
"""
dist = [[float("inf") for _ in range(v)] for _ in range(v)]
for i in range(v):
for j in range(v):
dist[i][j] = graph[i][j]
# check vertex k against all other vertices (i, j)
for k in range(v):
# looping through rows of graph array
for i in range(v):
# looping through columns of graph array
for j in range(v):
if (
dist[i][k] != float("inf")
and dist[k][j] != float("inf")
and dist[i][k] + dist[k][j] < dist[i][j]
):
dist[i][j] = dist[i][k] + dist[k][j]
_print_dist(dist, v)
return dist, v
if __name__ == "__main__":
v = int(input("Enter number of vertices: "))
e = int(input("Enter number of edges: "))
graph = [[float("inf") for i in range(v)] for j in range(v)]
for i in range(v):
graph[i][i] = 0.0
# src and dst are indices that must be within the array size graph[e][v]
# failure to follow this will result in an error
for i in range(e):
print("\nEdge ", i + 1)
src = int(input("Enter source:"))
dst = int(input("Enter destination:"))
weight = float(input("Enter weight:"))
graph[src][dst] = weight
floyd_warshall(graph, v)
# Example Input
# Enter number of vertices: 3
# Enter number of edges: 2
# # generated graph from vertex and edge inputs
# [[inf, inf, inf], [inf, inf, inf], [inf, inf, inf]]
# [[0.0, inf, inf], [inf, 0.0, inf], [inf, inf, 0.0]]
# specify source, destination and weight for edge #1
# Edge 1
# Enter source:1
# Enter destination:2
# Enter weight:2
# specify source, destination and weight for edge #2
# Edge 2
# Enter source:2
# Enter destination:1
# Enter weight:1
# # Expected Output from the vertice, edge and src, dst, weight inputs!!
# 0 INF INF
# INF 0 2
# INF 1 0
| # floyd_warshall.py
"""
The problem is to find the shortest distance between all pairs of vertices in a
weighted directed graph that can have negative edge weights.
"""
def _print_dist(dist, v):
print("\nThe shortest path matrix using Floyd Warshall algorithm\n")
for i in range(v):
for j in range(v):
if dist[i][j] != float("inf"):
print(int(dist[i][j]), end="\t")
else:
print("INF", end="\t")
print()
def floyd_warshall(graph, v):
"""
:param graph: 2D array calculated from weight[edge[i, j]]
:type graph: List[List[float]]
:param v: number of vertices
:type v: int
:return: shortest distance between all vertex pairs
distance[u][v] will contain the shortest distance from vertex u to v.
1. For all edges from v to n, distance[i][j] = weight(edge(i, j)).
3. The algorithm then performs distance[i][j] = min(distance[i][j], distance[i][k] +
distance[k][j]) for each possible pair i, j of vertices.
4. The above is repeated for each vertex k in the graph.
5. Whenever distance[i][j] is given a new minimum value, next vertex[i][j] is
updated to the next vertex[i][k].
"""
dist = [[float("inf") for _ in range(v)] for _ in range(v)]
for i in range(v):
for j in range(v):
dist[i][j] = graph[i][j]
# check vertex k against all other vertices (i, j)
for k in range(v):
# looping through rows of graph array
for i in range(v):
# looping through columns of graph array
for j in range(v):
if (
dist[i][k] != float("inf")
and dist[k][j] != float("inf")
and dist[i][k] + dist[k][j] < dist[i][j]
):
dist[i][j] = dist[i][k] + dist[k][j]
_print_dist(dist, v)
return dist, v
if __name__ == "__main__":
v = int(input("Enter number of vertices: "))
e = int(input("Enter number of edges: "))
graph = [[float("inf") for i in range(v)] for j in range(v)]
for i in range(v):
graph[i][i] = 0.0
# src and dst are indices that must be within the array size graph[e][v]
# failure to follow this will result in an error
for i in range(e):
print("\nEdge ", i + 1)
src = int(input("Enter source:"))
dst = int(input("Enter destination:"))
weight = float(input("Enter weight:"))
graph[src][dst] = weight
floyd_warshall(graph, v)
# Example Input
# Enter number of vertices: 3
# Enter number of edges: 2
# # generated graph from vertex and edge inputs
# [[inf, inf, inf], [inf, inf, inf], [inf, inf, inf]]
# [[0.0, inf, inf], [inf, 0.0, inf], [inf, inf, 0.0]]
# specify source, destination and weight for edge #1
# Edge 1
# Enter source:1
# Enter destination:2
# Enter weight:2
# specify source, destination and weight for edge #2
# Edge 2
# Enter source:2
# Enter destination:1
# Enter weight:1
# # Expected Output from the vertice, edge and src, dst, weight inputs!!
# 0 INF INF
# INF 0 2
# INF 1 0
| -1 |
TheAlgorithms/Python | 7,387 | [pre-commit.ci] pre-commit autoupdate | <!--pre-commit.ci start-->
updates:
- [github.com/asottile/pyupgrade: v3.0.0 → v3.1.0](https://github.com/asottile/pyupgrade/compare/v3.0.0...v3.1.0)
- [github.com/codespell-project/codespell: v2.2.1 → v2.2.2](https://github.com/codespell-project/codespell/compare/v2.2.1...v2.2.2)
<!--pre-commit.ci end--> | pre-commit-ci[bot] | 2022-10-17T19:35:07Z | 2022-10-17T19:59:26Z | a34b756fd40e5cdfb69abc06dcd42f5f1b5fa21e | 0c7c5fa7b0161a7433467240155356c93ae106b8 | [pre-commit.ci] pre-commit autoupdate. <!--pre-commit.ci start-->
updates:
- [github.com/asottile/pyupgrade: v3.0.0 → v3.1.0](https://github.com/asottile/pyupgrade/compare/v3.0.0...v3.1.0)
- [github.com/codespell-project/codespell: v2.2.1 → v2.2.2](https://github.com/codespell-project/codespell/compare/v2.2.1...v2.2.2)
<!--pre-commit.ci end--> | """
This is a python3 implementation of binary search tree using recursion
To run tests:
python -m unittest binary_search_tree_recursive.py
To run an example:
python binary_search_tree_recursive.py
"""
from __future__ import annotations
import unittest
from collections.abc import Iterator
class Node:
def __init__(self, label: int, parent: Node | None) -> None:
self.label = label
self.parent = parent
self.left: Node | None = None
self.right: Node | None = None
class BinarySearchTree:
def __init__(self) -> None:
self.root: Node | None = None
def empty(self) -> None:
"""
Empties the tree
>>> t = BinarySearchTree()
>>> assert t.root is None
>>> t.put(8)
>>> assert t.root is not None
"""
self.root = None
def is_empty(self) -> bool:
"""
Checks if the tree is empty
>>> t = BinarySearchTree()
>>> t.is_empty()
True
>>> t.put(8)
>>> t.is_empty()
False
"""
return self.root is None
def put(self, label: int) -> None:
"""
Put a new node in the tree
>>> t = BinarySearchTree()
>>> t.put(8)
>>> assert t.root.parent is None
>>> assert t.root.label == 8
>>> t.put(10)
>>> assert t.root.right.parent == t.root
>>> assert t.root.right.label == 10
>>> t.put(3)
>>> assert t.root.left.parent == t.root
>>> assert t.root.left.label == 3
"""
self.root = self._put(self.root, label)
def _put(self, node: Node | None, label: int, parent: Node | None = None) -> Node:
if node is None:
node = Node(label, parent)
else:
if label < node.label:
node.left = self._put(node.left, label, node)
elif label > node.label:
node.right = self._put(node.right, label, node)
else:
raise Exception(f"Node with label {label} already exists")
return node
def search(self, label: int) -> Node:
"""
Searches a node in the tree
>>> t = BinarySearchTree()
>>> t.put(8)
>>> t.put(10)
>>> node = t.search(8)
>>> assert node.label == 8
>>> node = t.search(3)
Traceback (most recent call last):
...
Exception: Node with label 3 does not exist
"""
return self._search(self.root, label)
def _search(self, node: Node | None, label: int) -> Node:
if node is None:
raise Exception(f"Node with label {label} does not exist")
else:
if label < node.label:
node = self._search(node.left, label)
elif label > node.label:
node = self._search(node.right, label)
return node
def remove(self, label: int) -> None:
"""
Removes a node in the tree
>>> t = BinarySearchTree()
>>> t.put(8)
>>> t.put(10)
>>> t.remove(8)
>>> assert t.root.label == 10
>>> t.remove(3)
Traceback (most recent call last):
...
Exception: Node with label 3 does not exist
"""
node = self.search(label)
if node.right and node.left:
lowest_node = self._get_lowest_node(node.right)
lowest_node.left = node.left
lowest_node.right = node.right
node.left.parent = lowest_node
if node.right:
node.right.parent = lowest_node
self._reassign_nodes(node, lowest_node)
elif not node.right and node.left:
self._reassign_nodes(node, node.left)
elif node.right and not node.left:
self._reassign_nodes(node, node.right)
else:
self._reassign_nodes(node, None)
def _reassign_nodes(self, node: Node, new_children: Node | None) -> None:
if new_children:
new_children.parent = node.parent
if node.parent:
if node.parent.right == node:
node.parent.right = new_children
else:
node.parent.left = new_children
else:
self.root = new_children
def _get_lowest_node(self, node: Node) -> Node:
if node.left:
lowest_node = self._get_lowest_node(node.left)
else:
lowest_node = node
self._reassign_nodes(node, node.right)
return lowest_node
def exists(self, label: int) -> bool:
"""
Checks if a node exists in the tree
>>> t = BinarySearchTree()
>>> t.put(8)
>>> t.put(10)
>>> t.exists(8)
True
>>> t.exists(3)
False
"""
try:
self.search(label)
return True
except Exception:
return False
def get_max_label(self) -> int:
"""
Gets the max label inserted in the tree
>>> t = BinarySearchTree()
>>> t.get_max_label()
Traceback (most recent call last):
...
Exception: Binary search tree is empty
>>> t.put(8)
>>> t.put(10)
>>> t.get_max_label()
10
"""
if self.root is None:
raise Exception("Binary search tree is empty")
node = self.root
while node.right is not None:
node = node.right
return node.label
def get_min_label(self) -> int:
"""
Gets the min label inserted in the tree
>>> t = BinarySearchTree()
>>> t.get_min_label()
Traceback (most recent call last):
...
Exception: Binary search tree is empty
>>> t.put(8)
>>> t.put(10)
>>> t.get_min_label()
8
"""
if self.root is None:
raise Exception("Binary search tree is empty")
node = self.root
while node.left is not None:
node = node.left
return node.label
def inorder_traversal(self) -> Iterator[Node]:
"""
Return the inorder traversal of the tree
>>> t = BinarySearchTree()
>>> [i.label for i in t.inorder_traversal()]
[]
>>> t.put(8)
>>> t.put(10)
>>> t.put(9)
>>> [i.label for i in t.inorder_traversal()]
[8, 9, 10]
"""
return self._inorder_traversal(self.root)
def _inorder_traversal(self, node: Node | None) -> Iterator[Node]:
if node is not None:
yield from self._inorder_traversal(node.left)
yield node
yield from self._inorder_traversal(node.right)
def preorder_traversal(self) -> Iterator[Node]:
"""
Return the preorder traversal of the tree
>>> t = BinarySearchTree()
>>> [i.label for i in t.preorder_traversal()]
[]
>>> t.put(8)
>>> t.put(10)
>>> t.put(9)
>>> [i.label for i in t.preorder_traversal()]
[8, 10, 9]
"""
return self._preorder_traversal(self.root)
def _preorder_traversal(self, node: Node | None) -> Iterator[Node]:
if node is not None:
yield node
yield from self._preorder_traversal(node.left)
yield from self._preorder_traversal(node.right)
class BinarySearchTreeTest(unittest.TestCase):
@staticmethod
def _get_binary_search_tree() -> BinarySearchTree:
r"""
8
/ \
3 10
/ \ \
1 6 14
/ \ /
4 7 13
\
5
"""
t = BinarySearchTree()
t.put(8)
t.put(3)
t.put(6)
t.put(1)
t.put(10)
t.put(14)
t.put(13)
t.put(4)
t.put(7)
t.put(5)
return t
def test_put(self) -> None:
t = BinarySearchTree()
assert t.is_empty()
t.put(8)
r"""
8
"""
assert t.root is not None
assert t.root.parent is None
assert t.root.label == 8
t.put(10)
r"""
8
\
10
"""
assert t.root.right is not None
assert t.root.right.parent == t.root
assert t.root.right.label == 10
t.put(3)
r"""
8
/ \
3 10
"""
assert t.root.left is not None
assert t.root.left.parent == t.root
assert t.root.left.label == 3
t.put(6)
r"""
8
/ \
3 10
\
6
"""
assert t.root.left.right is not None
assert t.root.left.right.parent == t.root.left
assert t.root.left.right.label == 6
t.put(1)
r"""
8
/ \
3 10
/ \
1 6
"""
assert t.root.left.left is not None
assert t.root.left.left.parent == t.root.left
assert t.root.left.left.label == 1
with self.assertRaises(Exception): # noqa: B017
t.put(1)
def test_search(self) -> None:
t = self._get_binary_search_tree()
node = t.search(6)
assert node.label == 6
node = t.search(13)
assert node.label == 13
with self.assertRaises(Exception): # noqa: B017
t.search(2)
def test_remove(self) -> None:
t = self._get_binary_search_tree()
t.remove(13)
r"""
8
/ \
3 10
/ \ \
1 6 14
/ \
4 7
\
5
"""
assert t.root is not None
assert t.root.right is not None
assert t.root.right.right is not None
assert t.root.right.right.right is None
assert t.root.right.right.left is None
t.remove(7)
r"""
8
/ \
3 10
/ \ \
1 6 14
/
4
\
5
"""
assert t.root.left is not None
assert t.root.left.right is not None
assert t.root.left.right.left is not None
assert t.root.left.right.right is None
assert t.root.left.right.left.label == 4
t.remove(6)
r"""
8
/ \
3 10
/ \ \
1 4 14
\
5
"""
assert t.root.left.left is not None
assert t.root.left.right.right is not None
assert t.root.left.left.label == 1
assert t.root.left.right.label == 4
assert t.root.left.right.right.label == 5
assert t.root.left.right.left is None
assert t.root.left.left.parent == t.root.left
assert t.root.left.right.parent == t.root.left
t.remove(3)
r"""
8
/ \
4 10
/ \ \
1 5 14
"""
assert t.root is not None
assert t.root.left.label == 4
assert t.root.left.right.label == 5
assert t.root.left.left.label == 1
assert t.root.left.parent == t.root
assert t.root.left.left.parent == t.root.left
assert t.root.left.right.parent == t.root.left
t.remove(4)
r"""
8
/ \
5 10
/ \
1 14
"""
assert t.root.left is not None
assert t.root.left.left is not None
assert t.root.left.label == 5
assert t.root.left.right is None
assert t.root.left.left.label == 1
assert t.root.left.parent == t.root
assert t.root.left.left.parent == t.root.left
def test_remove_2(self) -> None:
t = self._get_binary_search_tree()
t.remove(3)
r"""
8
/ \
4 10
/ \ \
1 6 14
/ \ /
5 7 13
"""
assert t.root is not None
assert t.root.left is not None
assert t.root.left.left is not None
assert t.root.left.right is not None
assert t.root.left.right.left is not None
assert t.root.left.right.right is not None
assert t.root.left.label == 4
assert t.root.left.right.label == 6
assert t.root.left.left.label == 1
assert t.root.left.right.right.label == 7
assert t.root.left.right.left.label == 5
assert t.root.left.parent == t.root
assert t.root.left.right.parent == t.root.left
assert t.root.left.left.parent == t.root.left
assert t.root.left.right.left.parent == t.root.left.right
def test_empty(self) -> None:
t = self._get_binary_search_tree()
t.empty()
assert t.root is None
def test_is_empty(self) -> None:
t = self._get_binary_search_tree()
assert not t.is_empty()
t.empty()
assert t.is_empty()
def test_exists(self) -> None:
t = self._get_binary_search_tree()
assert t.exists(6)
assert not t.exists(-1)
def test_get_max_label(self) -> None:
t = self._get_binary_search_tree()
assert t.get_max_label() == 14
t.empty()
with self.assertRaises(Exception): # noqa: B017
t.get_max_label()
def test_get_min_label(self) -> None:
t = self._get_binary_search_tree()
assert t.get_min_label() == 1
t.empty()
with self.assertRaises(Exception): # noqa: B017
t.get_min_label()
def test_inorder_traversal(self) -> None:
t = self._get_binary_search_tree()
inorder_traversal_nodes = [i.label for i in t.inorder_traversal()]
assert inorder_traversal_nodes == [1, 3, 4, 5, 6, 7, 8, 10, 13, 14]
def test_preorder_traversal(self) -> None:
t = self._get_binary_search_tree()
preorder_traversal_nodes = [i.label for i in t.preorder_traversal()]
assert preorder_traversal_nodes == [8, 3, 1, 6, 4, 5, 7, 10, 14, 13]
def binary_search_tree_example() -> None:
r"""
Example
8
/ \
3 10
/ \ \
1 6 14
/ \ /
4 7 13
\
5
Example After Deletion
4
/ \
1 7
\
5
"""
t = BinarySearchTree()
t.put(8)
t.put(3)
t.put(6)
t.put(1)
t.put(10)
t.put(14)
t.put(13)
t.put(4)
t.put(7)
t.put(5)
print(
"""
8
/ \\
3 10
/ \\ \\
1 6 14
/ \\ /
4 7 13
\\
5
"""
)
print("Label 6 exists:", t.exists(6))
print("Label 13 exists:", t.exists(13))
print("Label -1 exists:", t.exists(-1))
print("Label 12 exists:", t.exists(12))
# Prints all the elements of the list in inorder traversal
inorder_traversal_nodes = [i.label for i in t.inorder_traversal()]
print("Inorder traversal:", inorder_traversal_nodes)
# Prints all the elements of the list in preorder traversal
preorder_traversal_nodes = [i.label for i in t.preorder_traversal()]
print("Preorder traversal:", preorder_traversal_nodes)
print("Max. label:", t.get_max_label())
print("Min. label:", t.get_min_label())
# Delete elements
print("\nDeleting elements 13, 10, 8, 3, 6, 14")
print(
"""
4
/ \\
1 7
\\
5
"""
)
t.remove(13)
t.remove(10)
t.remove(8)
t.remove(3)
t.remove(6)
t.remove(14)
# Prints all the elements of the list in inorder traversal after delete
inorder_traversal_nodes = [i.label for i in t.inorder_traversal()]
print("Inorder traversal after delete:", inorder_traversal_nodes)
# Prints all the elements of the list in preorder traversal after delete
preorder_traversal_nodes = [i.label for i in t.preorder_traversal()]
print("Preorder traversal after delete:", preorder_traversal_nodes)
print("Max. label:", t.get_max_label())
print("Min. label:", t.get_min_label())
if __name__ == "__main__":
binary_search_tree_example()
| """
This is a python3 implementation of binary search tree using recursion
To run tests:
python -m unittest binary_search_tree_recursive.py
To run an example:
python binary_search_tree_recursive.py
"""
from __future__ import annotations
import unittest
from collections.abc import Iterator
class Node:
def __init__(self, label: int, parent: Node | None) -> None:
self.label = label
self.parent = parent
self.left: Node | None = None
self.right: Node | None = None
class BinarySearchTree:
def __init__(self) -> None:
self.root: Node | None = None
def empty(self) -> None:
"""
Empties the tree
>>> t = BinarySearchTree()
>>> assert t.root is None
>>> t.put(8)
>>> assert t.root is not None
"""
self.root = None
def is_empty(self) -> bool:
"""
Checks if the tree is empty
>>> t = BinarySearchTree()
>>> t.is_empty()
True
>>> t.put(8)
>>> t.is_empty()
False
"""
return self.root is None
def put(self, label: int) -> None:
"""
Put a new node in the tree
>>> t = BinarySearchTree()
>>> t.put(8)
>>> assert t.root.parent is None
>>> assert t.root.label == 8
>>> t.put(10)
>>> assert t.root.right.parent == t.root
>>> assert t.root.right.label == 10
>>> t.put(3)
>>> assert t.root.left.parent == t.root
>>> assert t.root.left.label == 3
"""
self.root = self._put(self.root, label)
def _put(self, node: Node | None, label: int, parent: Node | None = None) -> Node:
if node is None:
node = Node(label, parent)
else:
if label < node.label:
node.left = self._put(node.left, label, node)
elif label > node.label:
node.right = self._put(node.right, label, node)
else:
raise Exception(f"Node with label {label} already exists")
return node
def search(self, label: int) -> Node:
"""
Searches a node in the tree
>>> t = BinarySearchTree()
>>> t.put(8)
>>> t.put(10)
>>> node = t.search(8)
>>> assert node.label == 8
>>> node = t.search(3)
Traceback (most recent call last):
...
Exception: Node with label 3 does not exist
"""
return self._search(self.root, label)
def _search(self, node: Node | None, label: int) -> Node:
if node is None:
raise Exception(f"Node with label {label} does not exist")
else:
if label < node.label:
node = self._search(node.left, label)
elif label > node.label:
node = self._search(node.right, label)
return node
def remove(self, label: int) -> None:
"""
Removes a node in the tree
>>> t = BinarySearchTree()
>>> t.put(8)
>>> t.put(10)
>>> t.remove(8)
>>> assert t.root.label == 10
>>> t.remove(3)
Traceback (most recent call last):
...
Exception: Node with label 3 does not exist
"""
node = self.search(label)
if node.right and node.left:
lowest_node = self._get_lowest_node(node.right)
lowest_node.left = node.left
lowest_node.right = node.right
node.left.parent = lowest_node
if node.right:
node.right.parent = lowest_node
self._reassign_nodes(node, lowest_node)
elif not node.right and node.left:
self._reassign_nodes(node, node.left)
elif node.right and not node.left:
self._reassign_nodes(node, node.right)
else:
self._reassign_nodes(node, None)
def _reassign_nodes(self, node: Node, new_children: Node | None) -> None:
if new_children:
new_children.parent = node.parent
if node.parent:
if node.parent.right == node:
node.parent.right = new_children
else:
node.parent.left = new_children
else:
self.root = new_children
def _get_lowest_node(self, node: Node) -> Node:
if node.left:
lowest_node = self._get_lowest_node(node.left)
else:
lowest_node = node
self._reassign_nodes(node, node.right)
return lowest_node
def exists(self, label: int) -> bool:
"""
Checks if a node exists in the tree
>>> t = BinarySearchTree()
>>> t.put(8)
>>> t.put(10)
>>> t.exists(8)
True
>>> t.exists(3)
False
"""
try:
self.search(label)
return True
except Exception:
return False
def get_max_label(self) -> int:
"""
Gets the max label inserted in the tree
>>> t = BinarySearchTree()
>>> t.get_max_label()
Traceback (most recent call last):
...
Exception: Binary search tree is empty
>>> t.put(8)
>>> t.put(10)
>>> t.get_max_label()
10
"""
if self.root is None:
raise Exception("Binary search tree is empty")
node = self.root
while node.right is not None:
node = node.right
return node.label
def get_min_label(self) -> int:
"""
Gets the min label inserted in the tree
>>> t = BinarySearchTree()
>>> t.get_min_label()
Traceback (most recent call last):
...
Exception: Binary search tree is empty
>>> t.put(8)
>>> t.put(10)
>>> t.get_min_label()
8
"""
if self.root is None:
raise Exception("Binary search tree is empty")
node = self.root
while node.left is not None:
node = node.left
return node.label
def inorder_traversal(self) -> Iterator[Node]:
"""
Return the inorder traversal of the tree
>>> t = BinarySearchTree()
>>> [i.label for i in t.inorder_traversal()]
[]
>>> t.put(8)
>>> t.put(10)
>>> t.put(9)
>>> [i.label for i in t.inorder_traversal()]
[8, 9, 10]
"""
return self._inorder_traversal(self.root)
def _inorder_traversal(self, node: Node | None) -> Iterator[Node]:
if node is not None:
yield from self._inorder_traversal(node.left)
yield node
yield from self._inorder_traversal(node.right)
def preorder_traversal(self) -> Iterator[Node]:
"""
Return the preorder traversal of the tree
>>> t = BinarySearchTree()
>>> [i.label for i in t.preorder_traversal()]
[]
>>> t.put(8)
>>> t.put(10)
>>> t.put(9)
>>> [i.label for i in t.preorder_traversal()]
[8, 10, 9]
"""
return self._preorder_traversal(self.root)
def _preorder_traversal(self, node: Node | None) -> Iterator[Node]:
if node is not None:
yield node
yield from self._preorder_traversal(node.left)
yield from self._preorder_traversal(node.right)
class BinarySearchTreeTest(unittest.TestCase):
@staticmethod
def _get_binary_search_tree() -> BinarySearchTree:
r"""
8
/ \
3 10
/ \ \
1 6 14
/ \ /
4 7 13
\
5
"""
t = BinarySearchTree()
t.put(8)
t.put(3)
t.put(6)
t.put(1)
t.put(10)
t.put(14)
t.put(13)
t.put(4)
t.put(7)
t.put(5)
return t
def test_put(self) -> None:
t = BinarySearchTree()
assert t.is_empty()
t.put(8)
r"""
8
"""
assert t.root is not None
assert t.root.parent is None
assert t.root.label == 8
t.put(10)
r"""
8
\
10
"""
assert t.root.right is not None
assert t.root.right.parent == t.root
assert t.root.right.label == 10
t.put(3)
r"""
8
/ \
3 10
"""
assert t.root.left is not None
assert t.root.left.parent == t.root
assert t.root.left.label == 3
t.put(6)
r"""
8
/ \
3 10
\
6
"""
assert t.root.left.right is not None
assert t.root.left.right.parent == t.root.left
assert t.root.left.right.label == 6
t.put(1)
r"""
8
/ \
3 10
/ \
1 6
"""
assert t.root.left.left is not None
assert t.root.left.left.parent == t.root.left
assert t.root.left.left.label == 1
with self.assertRaises(Exception): # noqa: B017
t.put(1)
def test_search(self) -> None:
t = self._get_binary_search_tree()
node = t.search(6)
assert node.label == 6
node = t.search(13)
assert node.label == 13
with self.assertRaises(Exception): # noqa: B017
t.search(2)
def test_remove(self) -> None:
t = self._get_binary_search_tree()
t.remove(13)
r"""
8
/ \
3 10
/ \ \
1 6 14
/ \
4 7
\
5
"""
assert t.root is not None
assert t.root.right is not None
assert t.root.right.right is not None
assert t.root.right.right.right is None
assert t.root.right.right.left is None
t.remove(7)
r"""
8
/ \
3 10
/ \ \
1 6 14
/
4
\
5
"""
assert t.root.left is not None
assert t.root.left.right is not None
assert t.root.left.right.left is not None
assert t.root.left.right.right is None
assert t.root.left.right.left.label == 4
t.remove(6)
r"""
8
/ \
3 10
/ \ \
1 4 14
\
5
"""
assert t.root.left.left is not None
assert t.root.left.right.right is not None
assert t.root.left.left.label == 1
assert t.root.left.right.label == 4
assert t.root.left.right.right.label == 5
assert t.root.left.right.left is None
assert t.root.left.left.parent == t.root.left
assert t.root.left.right.parent == t.root.left
t.remove(3)
r"""
8
/ \
4 10
/ \ \
1 5 14
"""
assert t.root is not None
assert t.root.left.label == 4
assert t.root.left.right.label == 5
assert t.root.left.left.label == 1
assert t.root.left.parent == t.root
assert t.root.left.left.parent == t.root.left
assert t.root.left.right.parent == t.root.left
t.remove(4)
r"""
8
/ \
5 10
/ \
1 14
"""
assert t.root.left is not None
assert t.root.left.left is not None
assert t.root.left.label == 5
assert t.root.left.right is None
assert t.root.left.left.label == 1
assert t.root.left.parent == t.root
assert t.root.left.left.parent == t.root.left
def test_remove_2(self) -> None:
t = self._get_binary_search_tree()
t.remove(3)
r"""
8
/ \
4 10
/ \ \
1 6 14
/ \ /
5 7 13
"""
assert t.root is not None
assert t.root.left is not None
assert t.root.left.left is not None
assert t.root.left.right is not None
assert t.root.left.right.left is not None
assert t.root.left.right.right is not None
assert t.root.left.label == 4
assert t.root.left.right.label == 6
assert t.root.left.left.label == 1
assert t.root.left.right.right.label == 7
assert t.root.left.right.left.label == 5
assert t.root.left.parent == t.root
assert t.root.left.right.parent == t.root.left
assert t.root.left.left.parent == t.root.left
assert t.root.left.right.left.parent == t.root.left.right
def test_empty(self) -> None:
t = self._get_binary_search_tree()
t.empty()
assert t.root is None
def test_is_empty(self) -> None:
t = self._get_binary_search_tree()
assert not t.is_empty()
t.empty()
assert t.is_empty()
def test_exists(self) -> None:
t = self._get_binary_search_tree()
assert t.exists(6)
assert not t.exists(-1)
def test_get_max_label(self) -> None:
t = self._get_binary_search_tree()
assert t.get_max_label() == 14
t.empty()
with self.assertRaises(Exception): # noqa: B017
t.get_max_label()
def test_get_min_label(self) -> None:
t = self._get_binary_search_tree()
assert t.get_min_label() == 1
t.empty()
with self.assertRaises(Exception): # noqa: B017
t.get_min_label()
def test_inorder_traversal(self) -> None:
t = self._get_binary_search_tree()
inorder_traversal_nodes = [i.label for i in t.inorder_traversal()]
assert inorder_traversal_nodes == [1, 3, 4, 5, 6, 7, 8, 10, 13, 14]
def test_preorder_traversal(self) -> None:
t = self._get_binary_search_tree()
preorder_traversal_nodes = [i.label for i in t.preorder_traversal()]
assert preorder_traversal_nodes == [8, 3, 1, 6, 4, 5, 7, 10, 14, 13]
def binary_search_tree_example() -> None:
r"""
Example
8
/ \
3 10
/ \ \
1 6 14
/ \ /
4 7 13
\
5
Example After Deletion
4
/ \
1 7
\
5
"""
t = BinarySearchTree()
t.put(8)
t.put(3)
t.put(6)
t.put(1)
t.put(10)
t.put(14)
t.put(13)
t.put(4)
t.put(7)
t.put(5)
print(
"""
8
/ \\
3 10
/ \\ \\
1 6 14
/ \\ /
4 7 13
\\
5
"""
)
print("Label 6 exists:", t.exists(6))
print("Label 13 exists:", t.exists(13))
print("Label -1 exists:", t.exists(-1))
print("Label 12 exists:", t.exists(12))
# Prints all the elements of the list in inorder traversal
inorder_traversal_nodes = [i.label for i in t.inorder_traversal()]
print("Inorder traversal:", inorder_traversal_nodes)
# Prints all the elements of the list in preorder traversal
preorder_traversal_nodes = [i.label for i in t.preorder_traversal()]
print("Preorder traversal:", preorder_traversal_nodes)
print("Max. label:", t.get_max_label())
print("Min. label:", t.get_min_label())
# Delete elements
print("\nDeleting elements 13, 10, 8, 3, 6, 14")
print(
"""
4
/ \\
1 7
\\
5
"""
)
t.remove(13)
t.remove(10)
t.remove(8)
t.remove(3)
t.remove(6)
t.remove(14)
# Prints all the elements of the list in inorder traversal after delete
inorder_traversal_nodes = [i.label for i in t.inorder_traversal()]
print("Inorder traversal after delete:", inorder_traversal_nodes)
# Prints all the elements of the list in preorder traversal after delete
preorder_traversal_nodes = [i.label for i in t.preorder_traversal()]
print("Preorder traversal after delete:", preorder_traversal_nodes)
print("Max. label:", t.get_max_label())
print("Min. label:", t.get_min_label())
if __name__ == "__main__":
binary_search_tree_example()
| -1 |
TheAlgorithms/Python | 7,387 | [pre-commit.ci] pre-commit autoupdate | <!--pre-commit.ci start-->
updates:
- [github.com/asottile/pyupgrade: v3.0.0 → v3.1.0](https://github.com/asottile/pyupgrade/compare/v3.0.0...v3.1.0)
- [github.com/codespell-project/codespell: v2.2.1 → v2.2.2](https://github.com/codespell-project/codespell/compare/v2.2.1...v2.2.2)
<!--pre-commit.ci end--> | pre-commit-ci[bot] | 2022-10-17T19:35:07Z | 2022-10-17T19:59:26Z | a34b756fd40e5cdfb69abc06dcd42f5f1b5fa21e | 0c7c5fa7b0161a7433467240155356c93ae106b8 | [pre-commit.ci] pre-commit autoupdate. <!--pre-commit.ci start-->
updates:
- [github.com/asottile/pyupgrade: v3.0.0 → v3.1.0](https://github.com/asottile/pyupgrade/compare/v3.0.0...v3.1.0)
- [github.com/codespell-project/codespell: v2.2.1 → v2.2.2](https://github.com/codespell-project/codespell/compare/v2.2.1...v2.2.2)
<!--pre-commit.ci end--> | -1 |
||
TheAlgorithms/Python | 7,387 | [pre-commit.ci] pre-commit autoupdate | <!--pre-commit.ci start-->
updates:
- [github.com/asottile/pyupgrade: v3.0.0 → v3.1.0](https://github.com/asottile/pyupgrade/compare/v3.0.0...v3.1.0)
- [github.com/codespell-project/codespell: v2.2.1 → v2.2.2](https://github.com/codespell-project/codespell/compare/v2.2.1...v2.2.2)
<!--pre-commit.ci end--> | pre-commit-ci[bot] | 2022-10-17T19:35:07Z | 2022-10-17T19:59:26Z | a34b756fd40e5cdfb69abc06dcd42f5f1b5fa21e | 0c7c5fa7b0161a7433467240155356c93ae106b8 | [pre-commit.ci] pre-commit autoupdate. <!--pre-commit.ci start-->
updates:
- [github.com/asottile/pyupgrade: v3.0.0 → v3.1.0](https://github.com/asottile/pyupgrade/compare/v3.0.0...v3.1.0)
- [github.com/codespell-project/codespell: v2.2.1 → v2.2.2](https://github.com/codespell-project/codespell/compare/v2.2.1...v2.2.2)
<!--pre-commit.ci end--> | 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 | 7,387 | [pre-commit.ci] pre-commit autoupdate | <!--pre-commit.ci start-->
updates:
- [github.com/asottile/pyupgrade: v3.0.0 → v3.1.0](https://github.com/asottile/pyupgrade/compare/v3.0.0...v3.1.0)
- [github.com/codespell-project/codespell: v2.2.1 → v2.2.2](https://github.com/codespell-project/codespell/compare/v2.2.1...v2.2.2)
<!--pre-commit.ci end--> | pre-commit-ci[bot] | 2022-10-17T19:35:07Z | 2022-10-17T19:59:26Z | a34b756fd40e5cdfb69abc06dcd42f5f1b5fa21e | 0c7c5fa7b0161a7433467240155356c93ae106b8 | [pre-commit.ci] pre-commit autoupdate. <!--pre-commit.ci start-->
updates:
- [github.com/asottile/pyupgrade: v3.0.0 → v3.1.0](https://github.com/asottile/pyupgrade/compare/v3.0.0...v3.1.0)
- [github.com/codespell-project/codespell: v2.2.1 → v2.2.2](https://github.com/codespell-project/codespell/compare/v2.2.1...v2.2.2)
<!--pre-commit.ci end--> | from __future__ import annotations
from cmath import sqrt
def quadratic_roots(a: int, b: int, c: int) -> tuple[complex, complex]:
"""
Given the numerical coefficients a, b and c,
calculates the roots for any quadratic equation of the form ax^2 + bx + c
>>> quadratic_roots(a=1, b=3, c=-4)
(1.0, -4.0)
>>> quadratic_roots(5, 6, 1)
(-0.2, -1.0)
>>> quadratic_roots(1, -6, 25)
((3+4j), (3-4j))
"""
if a == 0:
raise ValueError("Coefficient 'a' must not be zero.")
delta = b * b - 4 * a * c
root_1 = (-b + sqrt(delta)) / (2 * a)
root_2 = (-b - sqrt(delta)) / (2 * a)
return (
root_1.real if not root_1.imag else root_1,
root_2.real if not root_2.imag else root_2,
)
def main():
solution1, solution2 = quadratic_roots(a=5, b=6, c=1)
print(f"The solutions are: {solution1} and {solution2}")
if __name__ == "__main__":
main()
| from __future__ import annotations
from cmath import sqrt
def quadratic_roots(a: int, b: int, c: int) -> tuple[complex, complex]:
"""
Given the numerical coefficients a, b and c,
calculates the roots for any quadratic equation of the form ax^2 + bx + c
>>> quadratic_roots(a=1, b=3, c=-4)
(1.0, -4.0)
>>> quadratic_roots(5, 6, 1)
(-0.2, -1.0)
>>> quadratic_roots(1, -6, 25)
((3+4j), (3-4j))
"""
if a == 0:
raise ValueError("Coefficient 'a' must not be zero.")
delta = b * b - 4 * a * c
root_1 = (-b + sqrt(delta)) / (2 * a)
root_2 = (-b - sqrt(delta)) / (2 * a)
return (
root_1.real if not root_1.imag else root_1,
root_2.real if not root_2.imag else root_2,
)
def main():
solution1, solution2 = quadratic_roots(a=5, b=6, c=1)
print(f"The solutions are: {solution1} and {solution2}")
if __name__ == "__main__":
main()
| -1 |
TheAlgorithms/Python | 7,387 | [pre-commit.ci] pre-commit autoupdate | <!--pre-commit.ci start-->
updates:
- [github.com/asottile/pyupgrade: v3.0.0 → v3.1.0](https://github.com/asottile/pyupgrade/compare/v3.0.0...v3.1.0)
- [github.com/codespell-project/codespell: v2.2.1 → v2.2.2](https://github.com/codespell-project/codespell/compare/v2.2.1...v2.2.2)
<!--pre-commit.ci end--> | pre-commit-ci[bot] | 2022-10-17T19:35:07Z | 2022-10-17T19:59:26Z | a34b756fd40e5cdfb69abc06dcd42f5f1b5fa21e | 0c7c5fa7b0161a7433467240155356c93ae106b8 | [pre-commit.ci] pre-commit autoupdate. <!--pre-commit.ci start-->
updates:
- [github.com/asottile/pyupgrade: v3.0.0 → v3.1.0](https://github.com/asottile/pyupgrade/compare/v3.0.0...v3.1.0)
- [github.com/codespell-project/codespell: v2.2.1 → v2.2.2](https://github.com/codespell-project/codespell/compare/v2.2.1...v2.2.2)
<!--pre-commit.ci end--> | -1 |
||
TheAlgorithms/Python | 7,387 | [pre-commit.ci] pre-commit autoupdate | <!--pre-commit.ci start-->
updates:
- [github.com/asottile/pyupgrade: v3.0.0 → v3.1.0](https://github.com/asottile/pyupgrade/compare/v3.0.0...v3.1.0)
- [github.com/codespell-project/codespell: v2.2.1 → v2.2.2](https://github.com/codespell-project/codespell/compare/v2.2.1...v2.2.2)
<!--pre-commit.ci end--> | pre-commit-ci[bot] | 2022-10-17T19:35:07Z | 2022-10-17T19:59:26Z | a34b756fd40e5cdfb69abc06dcd42f5f1b5fa21e | 0c7c5fa7b0161a7433467240155356c93ae106b8 | [pre-commit.ci] pre-commit autoupdate. <!--pre-commit.ci start-->
updates:
- [github.com/asottile/pyupgrade: v3.0.0 → v3.1.0](https://github.com/asottile/pyupgrade/compare/v3.0.0...v3.1.0)
- [github.com/codespell-project/codespell: v2.2.1 → v2.2.2](https://github.com/codespell-project/codespell/compare/v2.2.1...v2.2.2)
<!--pre-commit.ci end--> | import numpy as np
def qr_householder(a):
"""Return a QR-decomposition of the matrix A using Householder reflection.
The QR-decomposition decomposes the matrix A of shape (m, n) into an
orthogonal matrix Q of shape (m, m) and an upper triangular matrix R of
shape (m, n). Note that the matrix A does not have to be square. This
method of decomposing A uses the Householder reflection, which is
numerically stable and of complexity O(n^3).
https://en.wikipedia.org/wiki/QR_decomposition#Using_Householder_reflections
Arguments:
A -- a numpy.ndarray of shape (m, n)
Note: several optimizations can be made for numeric efficiency, but this is
intended to demonstrate how it would be represented in a mathematics
textbook. In cases where efficiency is particularly important, an optimized
version from BLAS should be used.
>>> A = np.array([[12, -51, 4], [6, 167, -68], [-4, 24, -41]], dtype=float)
>>> Q, R = qr_householder(A)
>>> # check that the decomposition is correct
>>> np.allclose(Q@R, A)
True
>>> # check that Q is orthogonal
>>> np.allclose([email protected], np.eye(A.shape[0]))
True
>>> np.allclose(Q.T@Q, np.eye(A.shape[0]))
True
>>> # check that R is upper triangular
>>> np.allclose(np.triu(R), R)
True
"""
m, n = a.shape
t = min(m, n)
q = np.eye(m)
r = a.copy()
for k in range(t - 1):
# select a column of modified matrix A':
x = r[k:, [k]]
# construct first basis vector
e1 = np.zeros_like(x)
e1[0] = 1.0
# determine scaling factor
alpha = np.linalg.norm(x)
# construct vector v for Householder reflection
v = x + np.sign(x[0]) * alpha * e1
v /= np.linalg.norm(v)
# construct the Householder matrix
q_k = np.eye(m - k) - 2.0 * v @ v.T
# pad with ones and zeros as necessary
q_k = np.block([[np.eye(k), np.zeros((k, m - k))], [np.zeros((m - k, k)), q_k]])
q = q @ q_k.T
r = q_k @ r
return q, r
if __name__ == "__main__":
import doctest
doctest.testmod()
| import numpy as np
def qr_householder(a):
"""Return a QR-decomposition of the matrix A using Householder reflection.
The QR-decomposition decomposes the matrix A of shape (m, n) into an
orthogonal matrix Q of shape (m, m) and an upper triangular matrix R of
shape (m, n). Note that the matrix A does not have to be square. This
method of decomposing A uses the Householder reflection, which is
numerically stable and of complexity O(n^3).
https://en.wikipedia.org/wiki/QR_decomposition#Using_Householder_reflections
Arguments:
A -- a numpy.ndarray of shape (m, n)
Note: several optimizations can be made for numeric efficiency, but this is
intended to demonstrate how it would be represented in a mathematics
textbook. In cases where efficiency is particularly important, an optimized
version from BLAS should be used.
>>> A = np.array([[12, -51, 4], [6, 167, -68], [-4, 24, -41]], dtype=float)
>>> Q, R = qr_householder(A)
>>> # check that the decomposition is correct
>>> np.allclose(Q@R, A)
True
>>> # check that Q is orthogonal
>>> np.allclose([email protected], np.eye(A.shape[0]))
True
>>> np.allclose(Q.T@Q, np.eye(A.shape[0]))
True
>>> # check that R is upper triangular
>>> np.allclose(np.triu(R), R)
True
"""
m, n = a.shape
t = min(m, n)
q = np.eye(m)
r = a.copy()
for k in range(t - 1):
# select a column of modified matrix A':
x = r[k:, [k]]
# construct first basis vector
e1 = np.zeros_like(x)
e1[0] = 1.0
# determine scaling factor
alpha = np.linalg.norm(x)
# construct vector v for Householder reflection
v = x + np.sign(x[0]) * alpha * e1
v /= np.linalg.norm(v)
# construct the Householder matrix
q_k = np.eye(m - k) - 2.0 * v @ v.T
# pad with ones and zeros as necessary
q_k = np.block([[np.eye(k), np.zeros((k, m - k))], [np.zeros((m - k, k)), q_k]])
q = q @ q_k.T
r = q_k @ r
return q, r
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 7,387 | [pre-commit.ci] pre-commit autoupdate | <!--pre-commit.ci start-->
updates:
- [github.com/asottile/pyupgrade: v3.0.0 → v3.1.0](https://github.com/asottile/pyupgrade/compare/v3.0.0...v3.1.0)
- [github.com/codespell-project/codespell: v2.2.1 → v2.2.2](https://github.com/codespell-project/codespell/compare/v2.2.1...v2.2.2)
<!--pre-commit.ci end--> | pre-commit-ci[bot] | 2022-10-17T19:35:07Z | 2022-10-17T19:59:26Z | a34b756fd40e5cdfb69abc06dcd42f5f1b5fa21e | 0c7c5fa7b0161a7433467240155356c93ae106b8 | [pre-commit.ci] pre-commit autoupdate. <!--pre-commit.ci start-->
updates:
- [github.com/asottile/pyupgrade: v3.0.0 → v3.1.0](https://github.com/asottile/pyupgrade/compare/v3.0.0...v3.1.0)
- [github.com/codespell-project/codespell: v2.2.1 → v2.2.2](https://github.com/codespell-project/codespell/compare/v2.2.1...v2.2.2)
<!--pre-commit.ci end--> | """
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
unique_rotsel = len(set(rotsel))
if unique_rotsel < 3:
raise Exception(f"Please use 3 unique rotors (not {unique_rotsel})")
# Checks if rotor positions are valid
rotorpos1, rotorpos2, rotorpos3 = rotpos
if not 0 < rotorpos1 <= len(abc):
raise ValueError(
"First rotor position is not within range of 1..26 (" f"{rotorpos1}"
)
if not 0 < rotorpos2 <= len(abc):
raise ValueError(
"Second rotor position is not within range of 1..26 (" f"{rotorpos2})"
)
if not 0 < rotorpos3 <= len(abc):
raise ValueError(
"Third rotor position is not within range of 1..26 (" f"{rotorpos3})"
)
# 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):
raise TypeError(f"Plugboard setting isn't type string ({type(pbstring)})")
elif len(pbstring) % 2 != 0:
raise Exception(f"Odd number of symbols ({len(pbstring)})")
elif pbstring == "":
return {}
pbstring.replace(" ", "")
# Checks if all characters are unique
tmppbl = set()
for i in pbstring:
if i not in abc:
raise Exception(f"'{i}' not in list of symbols")
elif i in tmppbl:
raise Exception(f"Duplicate symbol ({i})")
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
unique_rotsel = len(set(rotsel))
if unique_rotsel < 3:
raise Exception(f"Please use 3 unique rotors (not {unique_rotsel})")
# Checks if rotor positions are valid
rotorpos1, rotorpos2, rotorpos3 = rotpos
if not 0 < rotorpos1 <= len(abc):
raise ValueError(
"First rotor position is not within range of 1..26 (" f"{rotorpos1}"
)
if not 0 < rotorpos2 <= len(abc):
raise ValueError(
"Second rotor position is not within range of 1..26 (" f"{rotorpos2})"
)
if not 0 < rotorpos3 <= len(abc):
raise ValueError(
"Third rotor position is not within range of 1..26 (" f"{rotorpos3})"
)
# 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):
raise TypeError(f"Plugboard setting isn't type string ({type(pbstring)})")
elif len(pbstring) % 2 != 0:
raise Exception(f"Odd number of symbols ({len(pbstring)})")
elif pbstring == "":
return {}
pbstring.replace(" ", "")
# Checks if all characters are unique
tmppbl = set()
for i in pbstring:
if i not in abc:
raise Exception(f"'{i}' not in list of symbols")
elif i in tmppbl:
raise Exception(f"Duplicate symbol ({i})")
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 | 7,387 | [pre-commit.ci] pre-commit autoupdate | <!--pre-commit.ci start-->
updates:
- [github.com/asottile/pyupgrade: v3.0.0 → v3.1.0](https://github.com/asottile/pyupgrade/compare/v3.0.0...v3.1.0)
- [github.com/codespell-project/codespell: v2.2.1 → v2.2.2](https://github.com/codespell-project/codespell/compare/v2.2.1...v2.2.2)
<!--pre-commit.ci end--> | pre-commit-ci[bot] | 2022-10-17T19:35:07Z | 2022-10-17T19:59:26Z | a34b756fd40e5cdfb69abc06dcd42f5f1b5fa21e | 0c7c5fa7b0161a7433467240155356c93ae106b8 | [pre-commit.ci] pre-commit autoupdate. <!--pre-commit.ci start-->
updates:
- [github.com/asottile/pyupgrade: v3.0.0 → v3.1.0](https://github.com/asottile/pyupgrade/compare/v3.0.0...v3.1.0)
- [github.com/codespell-project/codespell: v2.2.1 → v2.2.2](https://github.com/codespell-project/codespell/compare/v2.2.1...v2.2.2)
<!--pre-commit.ci end--> | # Arithmetic analysis
Arithmetic analysis is a branch of mathematics that deals with solving linear equations.
* <https://en.wikipedia.org/wiki/System_of_linear_equations>
* <https://en.wikipedia.org/wiki/Gaussian_elimination>
* <https://en.wikipedia.org/wiki/Root-finding_algorithms>
| # Arithmetic analysis
Arithmetic analysis is a branch of mathematics that deals with solving linear equations.
* <https://en.wikipedia.org/wiki/System_of_linear_equations>
* <https://en.wikipedia.org/wiki/Gaussian_elimination>
* <https://en.wikipedia.org/wiki/Root-finding_algorithms>
| -1 |
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<!--pre-commit.ci end--> | from math import asin, atan, cos, radians, sin, sqrt, tan
AXIS_A = 6378137.0
AXIS_B = 6356752.314245
RADIUS = 6378137
def haversine_distance(lat1: float, lon1: float, lat2: float, lon2: float) -> float:
"""
Calculate great circle distance between two points in a sphere,
given longitudes and latitudes https://en.wikipedia.org/wiki/Haversine_formula
We know that the globe is "sort of" spherical, so a path between two points
isn't exactly a straight line. We need to account for the Earth's curvature
when calculating distance from point A to B. This effect is negligible for
small distances but adds up as distance increases. The Haversine method treats
the earth as a sphere which allows us to "project" the two points A and B
onto the surface of that sphere and approximate the spherical distance between
them. Since the Earth is not a perfect sphere, other methods which model the
Earth's ellipsoidal nature are more accurate but a quick and modifiable
computation like Haversine can be handy for shorter range distances.
Args:
lat1, lon1: latitude and longitude of coordinate 1
lat2, lon2: latitude and longitude of coordinate 2
Returns:
geographical distance between two points in metres
>>> from collections import namedtuple
>>> point_2d = namedtuple("point_2d", "lat lon")
>>> SAN_FRANCISCO = point_2d(37.774856, -122.424227)
>>> YOSEMITE = point_2d(37.864742, -119.537521)
>>> f"{haversine_distance(*SAN_FRANCISCO, *YOSEMITE):0,.0f} meters"
'254,352 meters'
"""
# CONSTANTS per WGS84 https://en.wikipedia.org/wiki/World_Geodetic_System
# Distance in metres(m)
# Equation parameters
# Equation https://en.wikipedia.org/wiki/Haversine_formula#Formulation
flattening = (AXIS_A - AXIS_B) / AXIS_A
phi_1 = atan((1 - flattening) * tan(radians(lat1)))
phi_2 = atan((1 - flattening) * tan(radians(lat2)))
lambda_1 = radians(lon1)
lambda_2 = radians(lon2)
# Equation
sin_sq_phi = sin((phi_2 - phi_1) / 2)
sin_sq_lambda = sin((lambda_2 - lambda_1) / 2)
# Square both values
sin_sq_phi *= sin_sq_phi
sin_sq_lambda *= sin_sq_lambda
h_value = sqrt(sin_sq_phi + (cos(phi_1) * cos(phi_2) * sin_sq_lambda))
return 2 * RADIUS * asin(h_value)
if __name__ == "__main__":
import doctest
doctest.testmod()
| from math import asin, atan, cos, radians, sin, sqrt, tan
AXIS_A = 6378137.0
AXIS_B = 6356752.314245
RADIUS = 6378137
def haversine_distance(lat1: float, lon1: float, lat2: float, lon2: float) -> float:
"""
Calculate great circle distance between two points in a sphere,
given longitudes and latitudes https://en.wikipedia.org/wiki/Haversine_formula
We know that the globe is "sort of" spherical, so a path between two points
isn't exactly a straight line. We need to account for the Earth's curvature
when calculating distance from point A to B. This effect is negligible for
small distances but adds up as distance increases. The Haversine method treats
the earth as a sphere which allows us to "project" the two points A and B
onto the surface of that sphere and approximate the spherical distance between
them. Since the Earth is not a perfect sphere, other methods which model the
Earth's ellipsoidal nature are more accurate but a quick and modifiable
computation like Haversine can be handy for shorter range distances.
Args:
lat1, lon1: latitude and longitude of coordinate 1
lat2, lon2: latitude and longitude of coordinate 2
Returns:
geographical distance between two points in metres
>>> from collections import namedtuple
>>> point_2d = namedtuple("point_2d", "lat lon")
>>> SAN_FRANCISCO = point_2d(37.774856, -122.424227)
>>> YOSEMITE = point_2d(37.864742, -119.537521)
>>> f"{haversine_distance(*SAN_FRANCISCO, *YOSEMITE):0,.0f} meters"
'254,352 meters'
"""
# CONSTANTS per WGS84 https://en.wikipedia.org/wiki/World_Geodetic_System
# Distance in metres(m)
# Equation parameters
# Equation https://en.wikipedia.org/wiki/Haversine_formula#Formulation
flattening = (AXIS_A - AXIS_B) / AXIS_A
phi_1 = atan((1 - flattening) * tan(radians(lat1)))
phi_2 = atan((1 - flattening) * tan(radians(lat2)))
lambda_1 = radians(lon1)
lambda_2 = radians(lon2)
# Equation
sin_sq_phi = sin((phi_2 - phi_1) / 2)
sin_sq_lambda = sin((lambda_2 - lambda_1) / 2)
# Square both values
sin_sq_phi *= sin_sq_phi
sin_sq_lambda *= sin_sq_lambda
h_value = sqrt(sin_sq_phi + (cos(phi_1) * cos(phi_2) * sin_sq_lambda))
return 2 * RADIUS * asin(h_value)
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
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<!--pre-commit.ci end--> | # Print all subset combinations of n element in given set of r element.
def combination_util(arr, n, r, index, data, i):
"""
Current combination is ready to be printed, print it
arr[] ---> Input Array
data[] ---> Temporary array to store current combination
start & end ---> Staring and Ending indexes in arr[]
index ---> Current index in data[]
r ---> Size of a combination to be printed
"""
if index == r:
for j in range(r):
print(data[j], end=" ")
print(" ")
return
# When no more elements are there to put in data[]
if i >= n:
return
# current is included, put next at next location
data[index] = arr[i]
combination_util(arr, n, r, index + 1, data, i + 1)
# current is excluded, replace it with
# next (Note that i+1 is passed, but
# index is not changed)
combination_util(arr, n, r, index, data, i + 1)
# The main function that prints all combinations
# of size r in arr[] of size n. This function
# mainly uses combinationUtil()
def print_combination(arr, n, r):
# A temporary array to store all combination one by one
data = [0] * r
# Print all combination using temporary array 'data[]'
combination_util(arr, n, r, 0, data, 0)
# Driver function to check for above function
arr = [10, 20, 30, 40, 50]
print_combination(arr, len(arr), 3)
# This code is contributed by Ambuj sahu
| # Print all subset combinations of n element in given set of r element.
def combination_util(arr, n, r, index, data, i):
"""
Current combination is ready to be printed, print it
arr[] ---> Input Array
data[] ---> Temporary array to store current combination
start & end ---> Staring and Ending indexes in arr[]
index ---> Current index in data[]
r ---> Size of a combination to be printed
"""
if index == r:
for j in range(r):
print(data[j], end=" ")
print(" ")
return
# When no more elements are there to put in data[]
if i >= n:
return
# current is included, put next at next location
data[index] = arr[i]
combination_util(arr, n, r, index + 1, data, i + 1)
# current is excluded, replace it with
# next (Note that i+1 is passed, but
# index is not changed)
combination_util(arr, n, r, index, data, i + 1)
# The main function that prints all combinations
# of size r in arr[] of size n. This function
# mainly uses combinationUtil()
def print_combination(arr, n, r):
# A temporary array to store all combination one by one
data = [0] * r
# Print all combination using temporary array 'data[]'
combination_util(arr, n, r, 0, data, 0)
# Driver function to check for above function
arr = [10, 20, 30, 40, 50]
print_combination(arr, len(arr), 3)
# This code is contributed by Ambuj sahu
| -1 |
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<!--pre-commit.ci end--> | #!/usr/bin/env python3
from __future__ import annotations
import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar
T = TypeVar("T", bound=bool)
class RandomizedHeapNode(Generic[T]):
"""
One node of the randomized heap. Contains the value and references to
two children.
"""
def __init__(self, value: T) -> None:
self._value: T = value
self.left: RandomizedHeapNode[T] | None = None
self.right: RandomizedHeapNode[T] | None = None
@property
def value(self) -> T:
"""Return the value of the node."""
return self._value
@staticmethod
def merge(
root1: RandomizedHeapNode[T] | None, root2: RandomizedHeapNode[T] | None
) -> RandomizedHeapNode[T] | None:
"""Merge 2 nodes together."""
if not root1:
return root2
if not root2:
return root1
if root1.value > root2.value:
root1, root2 = root2, root1
if random.choice([True, False]):
root1.left, root1.right = root1.right, root1.left
root1.left = RandomizedHeapNode.merge(root1.left, root2)
return root1
class RandomizedHeap(Generic[T]):
"""
A data structure that allows inserting a new value and to pop the smallest
values. Both operations take O(logN) time where N is the size of the
structure.
Wiki: https://en.wikipedia.org/wiki/Randomized_meldable_heap
>>> RandomizedHeap([2, 3, 1, 5, 1, 7]).to_sorted_list()
[1, 1, 2, 3, 5, 7]
>>> rh = RandomizedHeap()
>>> rh.pop()
Traceback (most recent call last):
...
IndexError: Can't get top element for the empty heap.
>>> rh.insert(1)
>>> rh.insert(-1)
>>> rh.insert(0)
>>> rh.to_sorted_list()
[-1, 0, 1]
"""
def __init__(self, data: Iterable[T] | None = ()) -> None:
"""
>>> rh = RandomizedHeap([3, 1, 3, 7])
>>> rh.to_sorted_list()
[1, 3, 3, 7]
"""
self._root: RandomizedHeapNode[T] | None = None
if data:
for item in data:
self.insert(item)
def insert(self, value: T) -> None:
"""
Insert the value into the heap.
>>> rh = RandomizedHeap()
>>> rh.insert(3)
>>> rh.insert(1)
>>> rh.insert(3)
>>> rh.insert(7)
>>> rh.to_sorted_list()
[1, 3, 3, 7]
"""
self._root = RandomizedHeapNode.merge(self._root, RandomizedHeapNode(value))
def pop(self) -> T | None:
"""
Pop the smallest value from the heap and return it.
>>> rh = RandomizedHeap([3, 1, 3, 7])
>>> rh.pop()
1
>>> rh.pop()
3
>>> rh.pop()
3
>>> rh.pop()
7
>>> rh.pop()
Traceback (most recent call last):
...
IndexError: Can't get top element for the empty heap.
"""
result = self.top()
if self._root is None:
return None
self._root = RandomizedHeapNode.merge(self._root.left, self._root.right)
return result
def top(self) -> T:
"""
Return the smallest value from the heap.
>>> rh = RandomizedHeap()
>>> rh.insert(3)
>>> rh.top()
3
>>> rh.insert(1)
>>> rh.top()
1
>>> rh.insert(3)
>>> rh.top()
1
>>> rh.insert(7)
>>> rh.top()
1
"""
if not self._root:
raise IndexError("Can't get top element for the empty heap.")
return self._root.value
def clear(self) -> None:
"""
Clear the heap.
>>> rh = RandomizedHeap([3, 1, 3, 7])
>>> rh.clear()
>>> rh.pop()
Traceback (most recent call last):
...
IndexError: Can't get top element for the empty heap.
"""
self._root = None
def to_sorted_list(self) -> list[Any]:
"""
Returns sorted list containing all the values in the heap.
>>> rh = RandomizedHeap([3, 1, 3, 7])
>>> rh.to_sorted_list()
[1, 3, 3, 7]
"""
result = []
while self:
result.append(self.pop())
return result
def __bool__(self) -> bool:
"""
Check if the heap is not empty.
>>> rh = RandomizedHeap()
>>> bool(rh)
False
>>> rh.insert(1)
>>> bool(rh)
True
>>> rh.clear()
>>> bool(rh)
False
"""
return self._root is not None
if __name__ == "__main__":
import doctest
doctest.testmod()
| #!/usr/bin/env python3
from __future__ import annotations
import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar
T = TypeVar("T", bound=bool)
class RandomizedHeapNode(Generic[T]):
"""
One node of the randomized heap. Contains the value and references to
two children.
"""
def __init__(self, value: T) -> None:
self._value: T = value
self.left: RandomizedHeapNode[T] | None = None
self.right: RandomizedHeapNode[T] | None = None
@property
def value(self) -> T:
"""Return the value of the node."""
return self._value
@staticmethod
def merge(
root1: RandomizedHeapNode[T] | None, root2: RandomizedHeapNode[T] | None
) -> RandomizedHeapNode[T] | None:
"""Merge 2 nodes together."""
if not root1:
return root2
if not root2:
return root1
if root1.value > root2.value:
root1, root2 = root2, root1
if random.choice([True, False]):
root1.left, root1.right = root1.right, root1.left
root1.left = RandomizedHeapNode.merge(root1.left, root2)
return root1
class RandomizedHeap(Generic[T]):
"""
A data structure that allows inserting a new value and to pop the smallest
values. Both operations take O(logN) time where N is the size of the
structure.
Wiki: https://en.wikipedia.org/wiki/Randomized_meldable_heap
>>> RandomizedHeap([2, 3, 1, 5, 1, 7]).to_sorted_list()
[1, 1, 2, 3, 5, 7]
>>> rh = RandomizedHeap()
>>> rh.pop()
Traceback (most recent call last):
...
IndexError: Can't get top element for the empty heap.
>>> rh.insert(1)
>>> rh.insert(-1)
>>> rh.insert(0)
>>> rh.to_sorted_list()
[-1, 0, 1]
"""
def __init__(self, data: Iterable[T] | None = ()) -> None:
"""
>>> rh = RandomizedHeap([3, 1, 3, 7])
>>> rh.to_sorted_list()
[1, 3, 3, 7]
"""
self._root: RandomizedHeapNode[T] | None = None
if data:
for item in data:
self.insert(item)
def insert(self, value: T) -> None:
"""
Insert the value into the heap.
>>> rh = RandomizedHeap()
>>> rh.insert(3)
>>> rh.insert(1)
>>> rh.insert(3)
>>> rh.insert(7)
>>> rh.to_sorted_list()
[1, 3, 3, 7]
"""
self._root = RandomizedHeapNode.merge(self._root, RandomizedHeapNode(value))
def pop(self) -> T | None:
"""
Pop the smallest value from the heap and return it.
>>> rh = RandomizedHeap([3, 1, 3, 7])
>>> rh.pop()
1
>>> rh.pop()
3
>>> rh.pop()
3
>>> rh.pop()
7
>>> rh.pop()
Traceback (most recent call last):
...
IndexError: Can't get top element for the empty heap.
"""
result = self.top()
if self._root is None:
return None
self._root = RandomizedHeapNode.merge(self._root.left, self._root.right)
return result
def top(self) -> T:
"""
Return the smallest value from the heap.
>>> rh = RandomizedHeap()
>>> rh.insert(3)
>>> rh.top()
3
>>> rh.insert(1)
>>> rh.top()
1
>>> rh.insert(3)
>>> rh.top()
1
>>> rh.insert(7)
>>> rh.top()
1
"""
if not self._root:
raise IndexError("Can't get top element for the empty heap.")
return self._root.value
def clear(self) -> None:
"""
Clear the heap.
>>> rh = RandomizedHeap([3, 1, 3, 7])
>>> rh.clear()
>>> rh.pop()
Traceback (most recent call last):
...
IndexError: Can't get top element for the empty heap.
"""
self._root = None
def to_sorted_list(self) -> list[Any]:
"""
Returns sorted list containing all the values in the heap.
>>> rh = RandomizedHeap([3, 1, 3, 7])
>>> rh.to_sorted_list()
[1, 3, 3, 7]
"""
result = []
while self:
result.append(self.pop())
return result
def __bool__(self) -> bool:
"""
Check if the heap is not empty.
>>> rh = RandomizedHeap()
>>> bool(rh)
False
>>> rh.insert(1)
>>> bool(rh)
True
>>> rh.clear()
>>> bool(rh)
False
"""
return self._root is not None
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
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<!--pre-commit.ci end--> | """
Project Euler Problem 85: https://projecteuler.net/problem=85
By counting carefully it can be seen that a rectangular grid measuring 3 by 2
contains eighteen rectangles.

Although there exists no rectangular grid that contains exactly two million
rectangles, find the area of the grid with the nearest solution.
Solution:
For a grid with side-lengths a and b, the number of rectangles contained in the grid
is [a*(a+1)/2] * [b*(b+1)/2)], which happens to be the product of the a-th and b-th
triangle numbers. So to find the solution grid (a,b), we need to find the two
triangle numbers whose product is closest to two million.
Denote these two triangle numbers Ta and Tb. We want their product Ta*Tb to be
as close as possible to 2m. Assuming that the best solution is fairly close to 2m,
We can assume that both Ta and Tb are roughly bounded by 2m. Since Ta = a(a+1)/2,
we can assume that a (and similarly b) are roughly bounded by sqrt(2 * 2m) = 2000.
Since this is a rough bound, to be on the safe side we add 10%. Therefore we start
by generating all the triangle numbers Ta for 1 <= a <= 2200. This can be done
iteratively since the ith triangle number is the sum of 1,2, ... ,i, and so
T(i) = T(i-1) + i.
We then search this list of triangle numbers for the two that give a product
closest to our target of two million. Rather than testing every combination of 2
elements of the list, which would find the result in quadratic time, we can find
the best pair in linear time.
We iterate through the list of triangle numbers using enumerate() so we have a
and Ta. Since we want Ta * Tb to be as close as possible to 2m, we know that Tb
needs to be roughly 2m / Ta. Using the formula Tb = b*(b+1)/2 as well as the
quadratic formula, we can solve for b:
b is roughly (-1 + sqrt(1 + 8 * 2m / Ta)) / 2.
Since the closest integers to this estimate will give product closest to 2m,
we only need to consider the integers above and below. It's then a simple matter
to get the triangle numbers corresponding to those integers, calculate the product
Ta * Tb, compare that product to our target 2m, and keep track of the (a,b) pair
that comes the closest.
Reference: https://en.wikipedia.org/wiki/Triangular_number
https://en.wikipedia.org/wiki/Quadratic_formula
"""
from __future__ import annotations
from math import ceil, floor, sqrt
def solution(target: int = 2000000) -> int:
"""
Find the area of the grid which contains as close to two million rectangles
as possible.
>>> solution(20)
6
>>> solution(2000)
72
>>> solution(2000000000)
86595
"""
triangle_numbers: list[int] = [0]
idx: int
for idx in range(1, ceil(sqrt(target * 2) * 1.1)):
triangle_numbers.append(triangle_numbers[-1] + idx)
# we want this to be as close as possible to target
best_product: int = 0
# the area corresponding to the grid that gives the product closest to target
area: int = 0
# an estimate of b, using the quadratic formula
b_estimate: float
# the largest integer less than b_estimate
b_floor: int
# the largest integer less than b_estimate
b_ceil: int
# the triangle number corresponding to b_floor
triangle_b_first_guess: int
# the triangle number corresponding to b_ceil
triangle_b_second_guess: int
for idx_a, triangle_a in enumerate(triangle_numbers[1:], 1):
b_estimate = (-1 + sqrt(1 + 8 * target / triangle_a)) / 2
b_floor = floor(b_estimate)
b_ceil = ceil(b_estimate)
triangle_b_first_guess = triangle_numbers[b_floor]
triangle_b_second_guess = triangle_numbers[b_ceil]
if abs(target - triangle_b_first_guess * triangle_a) < abs(
target - best_product
):
best_product = triangle_b_first_guess * triangle_a
area = idx_a * b_floor
if abs(target - triangle_b_second_guess * triangle_a) < abs(
target - best_product
):
best_product = triangle_b_second_guess * triangle_a
area = idx_a * b_ceil
return area
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 85: https://projecteuler.net/problem=85
By counting carefully it can be seen that a rectangular grid measuring 3 by 2
contains eighteen rectangles.

Although there exists no rectangular grid that contains exactly two million
rectangles, find the area of the grid with the nearest solution.
Solution:
For a grid with side-lengths a and b, the number of rectangles contained in the grid
is [a*(a+1)/2] * [b*(b+1)/2)], which happens to be the product of the a-th and b-th
triangle numbers. So to find the solution grid (a,b), we need to find the two
triangle numbers whose product is closest to two million.
Denote these two triangle numbers Ta and Tb. We want their product Ta*Tb to be
as close as possible to 2m. Assuming that the best solution is fairly close to 2m,
We can assume that both Ta and Tb are roughly bounded by 2m. Since Ta = a(a+1)/2,
we can assume that a (and similarly b) are roughly bounded by sqrt(2 * 2m) = 2000.
Since this is a rough bound, to be on the safe side we add 10%. Therefore we start
by generating all the triangle numbers Ta for 1 <= a <= 2200. This can be done
iteratively since the ith triangle number is the sum of 1,2, ... ,i, and so
T(i) = T(i-1) + i.
We then search this list of triangle numbers for the two that give a product
closest to our target of two million. Rather than testing every combination of 2
elements of the list, which would find the result in quadratic time, we can find
the best pair in linear time.
We iterate through the list of triangle numbers using enumerate() so we have a
and Ta. Since we want Ta * Tb to be as close as possible to 2m, we know that Tb
needs to be roughly 2m / Ta. Using the formula Tb = b*(b+1)/2 as well as the
quadratic formula, we can solve for b:
b is roughly (-1 + sqrt(1 + 8 * 2m / Ta)) / 2.
Since the closest integers to this estimate will give product closest to 2m,
we only need to consider the integers above and below. It's then a simple matter
to get the triangle numbers corresponding to those integers, calculate the product
Ta * Tb, compare that product to our target 2m, and keep track of the (a,b) pair
that comes the closest.
Reference: https://en.wikipedia.org/wiki/Triangular_number
https://en.wikipedia.org/wiki/Quadratic_formula
"""
from __future__ import annotations
from math import ceil, floor, sqrt
def solution(target: int = 2000000) -> int:
"""
Find the area of the grid which contains as close to two million rectangles
as possible.
>>> solution(20)
6
>>> solution(2000)
72
>>> solution(2000000000)
86595
"""
triangle_numbers: list[int] = [0]
idx: int
for idx in range(1, ceil(sqrt(target * 2) * 1.1)):
triangle_numbers.append(triangle_numbers[-1] + idx)
# we want this to be as close as possible to target
best_product: int = 0
# the area corresponding to the grid that gives the product closest to target
area: int = 0
# an estimate of b, using the quadratic formula
b_estimate: float
# the largest integer less than b_estimate
b_floor: int
# the largest integer less than b_estimate
b_ceil: int
# the triangle number corresponding to b_floor
triangle_b_first_guess: int
# the triangle number corresponding to b_ceil
triangle_b_second_guess: int
for idx_a, triangle_a in enumerate(triangle_numbers[1:], 1):
b_estimate = (-1 + sqrt(1 + 8 * target / triangle_a)) / 2
b_floor = floor(b_estimate)
b_ceil = ceil(b_estimate)
triangle_b_first_guess = triangle_numbers[b_floor]
triangle_b_second_guess = triangle_numbers[b_ceil]
if abs(target - triangle_b_first_guess * triangle_a) < abs(
target - best_product
):
best_product = triangle_b_first_guess * triangle_a
area = idx_a * b_floor
if abs(target - triangle_b_second_guess * triangle_a) < abs(
target - best_product
):
best_product = triangle_b_second_guess * triangle_a
area = idx_a * b_ceil
return area
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
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updates:
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- [github.com/codespell-project/codespell: v2.2.1 → v2.2.2](https://github.com/codespell-project/codespell/compare/v2.2.1...v2.2.2)
<!--pre-commit.ci end--> | pre-commit-ci[bot] | 2022-10-17T19:35:07Z | 2022-10-17T19:59:26Z | a34b756fd40e5cdfb69abc06dcd42f5f1b5fa21e | 0c7c5fa7b0161a7433467240155356c93ae106b8 | [pre-commit.ci] pre-commit autoupdate. <!--pre-commit.ci start-->
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<!--pre-commit.ci end--> | -1 |
||
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updates:
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<!--pre-commit.ci end--> | pre-commit-ci[bot] | 2022-10-17T19:35:07Z | 2022-10-17T19:59:26Z | a34b756fd40e5cdfb69abc06dcd42f5f1b5fa21e | 0c7c5fa7b0161a7433467240155356c93ae106b8 | [pre-commit.ci] pre-commit autoupdate. <!--pre-commit.ci start-->
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<!--pre-commit.ci end--> | def factorial(n: int) -> int:
"""
Calculate the factorial of a positive integer
https://en.wikipedia.org/wiki/Factorial
>>> import math
>>> all(factorial(i) == math.factorial(i) for i in range(20))
True
>>> factorial(0.1)
Traceback (most recent call last):
...
ValueError: factorial() only accepts integral values
>>> factorial(-1)
Traceback (most recent call last):
...
ValueError: factorial() not defined for negative values
"""
if not isinstance(n, int):
raise ValueError("factorial() only accepts integral values")
if n < 0:
raise ValueError("factorial() not defined for negative values")
return 1 if n == 0 or n == 1 else n * factorial(n - 1)
if __name__ == "__main__":
import doctest
doctest.testmod()
| def factorial(n: int) -> int:
"""
Calculate the factorial of a positive integer
https://en.wikipedia.org/wiki/Factorial
>>> import math
>>> all(factorial(i) == math.factorial(i) for i in range(20))
True
>>> factorial(0.1)
Traceback (most recent call last):
...
ValueError: factorial() only accepts integral values
>>> factorial(-1)
Traceback (most recent call last):
...
ValueError: factorial() not defined for negative values
"""
if not isinstance(n, int):
raise ValueError("factorial() only accepts integral values")
if n < 0:
raise ValueError("factorial() not defined for negative values")
return 1 if n == 0 or n == 1 else n * factorial(n - 1)
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 7,387 | [pre-commit.ci] pre-commit autoupdate | <!--pre-commit.ci start-->
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<!--pre-commit.ci end--> | pre-commit-ci[bot] | 2022-10-17T19:35:07Z | 2022-10-17T19:59:26Z | a34b756fd40e5cdfb69abc06dcd42f5f1b5fa21e | 0c7c5fa7b0161a7433467240155356c93ae106b8 | [pre-commit.ci] pre-commit autoupdate. <!--pre-commit.ci start-->
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- [github.com/codespell-project/codespell: v2.2.1 → v2.2.2](https://github.com/codespell-project/codespell/compare/v2.2.1...v2.2.2)
<!--pre-commit.ci end--> | # https://en.wikipedia.org/wiki/Hill_climbing
import math
class SearchProblem:
"""
An interface to define search problems.
The interface will be illustrated using the example of mathematical function.
"""
def __init__(self, x: int, y: int, step_size: int, function_to_optimize):
"""
The constructor of the search problem.
x: the x coordinate of the current search state.
y: the y coordinate of the current search state.
step_size: size of the step to take when looking for neighbors.
function_to_optimize: a function to optimize having the signature f(x, y).
"""
self.x = x
self.y = y
self.step_size = step_size
self.function = function_to_optimize
def score(self) -> int:
"""
Returns the output of the function called with current x and y coordinates.
>>> def test_function(x, y):
... return x + y
>>> SearchProblem(0, 0, 1, test_function).score() # 0 + 0 = 0
0
>>> SearchProblem(5, 7, 1, test_function).score() # 5 + 7 = 12
12
"""
return self.function(self.x, self.y)
def get_neighbors(self):
"""
Returns a list of coordinates of neighbors adjacent to the current coordinates.
Neighbors:
| 0 | 1 | 2 |
| 3 | _ | 4 |
| 5 | 6 | 7 |
"""
step_size = self.step_size
return [
SearchProblem(x, y, step_size, self.function)
for x, y in (
(self.x - step_size, self.y - step_size),
(self.x - step_size, self.y),
(self.x - step_size, self.y + step_size),
(self.x, self.y - step_size),
(self.x, self.y + step_size),
(self.x + step_size, self.y - step_size),
(self.x + step_size, self.y),
(self.x + step_size, self.y + step_size),
)
]
def __hash__(self):
"""
hash the string representation of the current search state.
"""
return hash(str(self))
def __eq__(self, obj):
"""
Check if the 2 objects are equal.
"""
if isinstance(obj, SearchProblem):
return hash(str(self)) == hash(str(obj))
return False
def __str__(self):
"""
string representation of the current search state.
>>> str(SearchProblem(0, 0, 1, None))
'x: 0 y: 0'
>>> str(SearchProblem(2, 5, 1, None))
'x: 2 y: 5'
"""
return f"x: {self.x} y: {self.y}"
def hill_climbing(
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,
max_iter: int = 10000,
) -> SearchProblem:
"""
Implementation of the hill climbling algorithm.
We start with a given state, find all its neighbors,
move towards the neighbor which provides the maximum (or minimum) change.
We keep doing this until we are at a state where we do not have any
neighbors which can improve the solution.
Args:
search_prob: The search state at the start.
find_max: If True, the algorithm should find the maximum 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.
max_iter: number of times to run the iteration.
Returns a search state having the maximum (or minimum) score.
"""
current_state = search_prob
scores = [] # list to store the current score at each iteration
iterations = 0
solution_found = False
visited = set()
while not solution_found and iterations < max_iter:
visited.add(current_state)
iterations += 1
current_score = current_state.score()
scores.append(current_score)
neighbors = current_state.get_neighbors()
max_change = -math.inf
min_change = math.inf
next_state = None # to hold the next best neighbor
for neighbor in neighbors:
if neighbor in visited:
continue # do not want to visit the same state again
if (
neighbor.x > max_x
or neighbor.x < min_x
or neighbor.y > max_y
or neighbor.y < min_y
):
continue # neighbor outside our bounds
change = neighbor.score() - current_score
if find_max: # finding max
# going to direction with greatest ascent
if change > max_change and change > 0:
max_change = change
next_state = neighbor
else: # finding min
# to direction with greatest descent
if change < min_change and change < 0:
min_change = change
next_state = neighbor
if next_state is not None:
# we found at least one neighbor which improved the current state
current_state = next_state
else:
# since we have no neighbor that improves the solution we stop the search
solution_found = True
if visualization:
from matplotlib import pyplot as plt
plt.plot(range(iterations), scores)
plt.xlabel("Iterations")
plt.ylabel("Function values")
plt.show()
return current_state
if __name__ == "__main__":
import doctest
doctest.testmod()
def test_f1(x, y):
return (x**2) + (y**2)
# starting the problem with initial coordinates (3, 4)
prob = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_f1)
local_min = hill_climbing(prob, find_max=False)
print(
"The minimum score for f(x, y) = x^2 + y^2 found via hill climbing: "
f"{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 = hill_climbing(
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()}"
)
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 = hill_climbing(prob, find_max=True)
print(
"The maximum score for f(x, y) = x^2 + y^2 found via hill climbing: "
f"{local_min.score()}"
)
| # https://en.wikipedia.org/wiki/Hill_climbing
import math
class SearchProblem:
"""
An interface to define search problems.
The interface will be illustrated using the example of mathematical function.
"""
def __init__(self, x: int, y: int, step_size: int, function_to_optimize):
"""
The constructor of the search problem.
x: the x coordinate of the current search state.
y: the y coordinate of the current search state.
step_size: size of the step to take when looking for neighbors.
function_to_optimize: a function to optimize having the signature f(x, y).
"""
self.x = x
self.y = y
self.step_size = step_size
self.function = function_to_optimize
def score(self) -> int:
"""
Returns the output of the function called with current x and y coordinates.
>>> def test_function(x, y):
... return x + y
>>> SearchProblem(0, 0, 1, test_function).score() # 0 + 0 = 0
0
>>> SearchProblem(5, 7, 1, test_function).score() # 5 + 7 = 12
12
"""
return self.function(self.x, self.y)
def get_neighbors(self):
"""
Returns a list of coordinates of neighbors adjacent to the current coordinates.
Neighbors:
| 0 | 1 | 2 |
| 3 | _ | 4 |
| 5 | 6 | 7 |
"""
step_size = self.step_size
return [
SearchProblem(x, y, step_size, self.function)
for x, y in (
(self.x - step_size, self.y - step_size),
(self.x - step_size, self.y),
(self.x - step_size, self.y + step_size),
(self.x, self.y - step_size),
(self.x, self.y + step_size),
(self.x + step_size, self.y - step_size),
(self.x + step_size, self.y),
(self.x + step_size, self.y + step_size),
)
]
def __hash__(self):
"""
hash the string representation of the current search state.
"""
return hash(str(self))
def __eq__(self, obj):
"""
Check if the 2 objects are equal.
"""
if isinstance(obj, SearchProblem):
return hash(str(self)) == hash(str(obj))
return False
def __str__(self):
"""
string representation of the current search state.
>>> str(SearchProblem(0, 0, 1, None))
'x: 0 y: 0'
>>> str(SearchProblem(2, 5, 1, None))
'x: 2 y: 5'
"""
return f"x: {self.x} y: {self.y}"
def hill_climbing(
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,
max_iter: int = 10000,
) -> SearchProblem:
"""
Implementation of the hill climbling algorithm.
We start with a given state, find all its neighbors,
move towards the neighbor which provides the maximum (or minimum) change.
We keep doing this until we are at a state where we do not have any
neighbors which can improve the solution.
Args:
search_prob: The search state at the start.
find_max: If True, the algorithm should find the maximum 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.
max_iter: number of times to run the iteration.
Returns a search state having the maximum (or minimum) score.
"""
current_state = search_prob
scores = [] # list to store the current score at each iteration
iterations = 0
solution_found = False
visited = set()
while not solution_found and iterations < max_iter:
visited.add(current_state)
iterations += 1
current_score = current_state.score()
scores.append(current_score)
neighbors = current_state.get_neighbors()
max_change = -math.inf
min_change = math.inf
next_state = None # to hold the next best neighbor
for neighbor in neighbors:
if neighbor in visited:
continue # do not want to visit the same state again
if (
neighbor.x > max_x
or neighbor.x < min_x
or neighbor.y > max_y
or neighbor.y < min_y
):
continue # neighbor outside our bounds
change = neighbor.score() - current_score
if find_max: # finding max
# going to direction with greatest ascent
if change > max_change and change > 0:
max_change = change
next_state = neighbor
else: # finding min
# to direction with greatest descent
if change < min_change and change < 0:
min_change = change
next_state = neighbor
if next_state is not None:
# we found at least one neighbor which improved the current state
current_state = next_state
else:
# since we have no neighbor that improves the solution we stop the search
solution_found = True
if visualization:
from matplotlib import pyplot as plt
plt.plot(range(iterations), scores)
plt.xlabel("Iterations")
plt.ylabel("Function values")
plt.show()
return current_state
if __name__ == "__main__":
import doctest
doctest.testmod()
def test_f1(x, y):
return (x**2) + (y**2)
# starting the problem with initial coordinates (3, 4)
prob = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_f1)
local_min = hill_climbing(prob, find_max=False)
print(
"The minimum score for f(x, y) = x^2 + y^2 found via hill climbing: "
f"{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 = hill_climbing(
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()}"
)
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 = hill_climbing(prob, find_max=True)
print(
"The maximum score for f(x, y) = x^2 + y^2 found via hill climbing: "
f"{local_min.score()}"
)
| -1 |
TheAlgorithms/Python | 7,387 | [pre-commit.ci] pre-commit autoupdate | <!--pre-commit.ci start-->
updates:
- [github.com/asottile/pyupgrade: v3.0.0 → v3.1.0](https://github.com/asottile/pyupgrade/compare/v3.0.0...v3.1.0)
- [github.com/codespell-project/codespell: v2.2.1 → v2.2.2](https://github.com/codespell-project/codespell/compare/v2.2.1...v2.2.2)
<!--pre-commit.ci end--> | pre-commit-ci[bot] | 2022-10-17T19:35:07Z | 2022-10-17T19:59:26Z | a34b756fd40e5cdfb69abc06dcd42f5f1b5fa21e | 0c7c5fa7b0161a7433467240155356c93ae106b8 | [pre-commit.ci] pre-commit autoupdate. <!--pre-commit.ci start-->
updates:
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- [github.com/codespell-project/codespell: v2.2.1 → v2.2.2](https://github.com/codespell-project/codespell/compare/v2.2.1...v2.2.2)
<!--pre-commit.ci end--> | """
Pure Python implementations of a Fixed Priority Queue and an Element Priority Queue
using Python lists.
"""
class OverFlowError(Exception):
pass
class UnderFlowError(Exception):
pass
class FixedPriorityQueue:
"""
Tasks can be added to a Priority Queue at any time and in any order but when Tasks
are removed then the Task with the highest priority is removed in FIFO order. In
code we will use three levels of priority with priority zero Tasks being the most
urgent (high priority) and priority 2 tasks being the least urgent.
Examples
>>> fpq = FixedPriorityQueue()
>>> fpq.enqueue(0, 10)
>>> fpq.enqueue(1, 70)
>>> fpq.enqueue(0, 100)
>>> fpq.enqueue(2, 1)
>>> fpq.enqueue(2, 5)
>>> fpq.enqueue(1, 7)
>>> fpq.enqueue(2, 4)
>>> fpq.enqueue(1, 64)
>>> fpq.enqueue(0, 128)
>>> print(fpq)
Priority 0: [10, 100, 128]
Priority 1: [70, 7, 64]
Priority 2: [1, 5, 4]
>>> fpq.dequeue()
10
>>> fpq.dequeue()
100
>>> fpq.dequeue()
128
>>> fpq.dequeue()
70
>>> fpq.dequeue()
7
>>> print(fpq)
Priority 0: []
Priority 1: [64]
Priority 2: [1, 5, 4]
>>> fpq.dequeue()
64
>>> fpq.dequeue()
1
>>> fpq.dequeue()
5
>>> fpq.dequeue()
4
>>> fpq.dequeue()
Traceback (most recent call last):
...
data_structures.queue.priority_queue_using_list.UnderFlowError: All queues are empty
>>> print(fpq)
Priority 0: []
Priority 1: []
Priority 2: []
"""
def __init__(self):
self.queues = [
[],
[],
[],
]
def enqueue(self, priority: int, data: int) -> None:
"""
Add an element to a queue based on its priority.
If the priority is invalid ValueError is raised.
If the queue is full an OverFlowError is raised.
"""
try:
if len(self.queues[priority]) >= 100:
raise OverflowError("Maximum queue size is 100")
self.queues[priority].append(data)
except IndexError:
raise ValueError("Valid priorities are 0, 1, and 2")
def dequeue(self) -> int:
"""
Return the highest priority element in FIFO order.
If the queue is empty then an under flow exception is raised.
"""
for queue in self.queues:
if queue:
return queue.pop(0)
raise UnderFlowError("All queues are empty")
def __str__(self) -> str:
return "\n".join(f"Priority {i}: {q}" for i, q in enumerate(self.queues))
class ElementPriorityQueue:
"""
Element Priority Queue is the same as Fixed Priority Queue except that the value of
the element itself is the priority. The rules for priorities are the same the as
Fixed Priority Queue.
>>> epq = ElementPriorityQueue()
>>> epq.enqueue(10)
>>> epq.enqueue(70)
>>> epq.enqueue(4)
>>> epq.enqueue(1)
>>> epq.enqueue(5)
>>> epq.enqueue(7)
>>> epq.enqueue(4)
>>> epq.enqueue(64)
>>> epq.enqueue(128)
>>> print(epq)
[10, 70, 4, 1, 5, 7, 4, 64, 128]
>>> epq.dequeue()
1
>>> epq.dequeue()
4
>>> epq.dequeue()
4
>>> epq.dequeue()
5
>>> epq.dequeue()
7
>>> epq.dequeue()
10
>>> print(epq)
[70, 64, 128]
>>> epq.dequeue()
64
>>> epq.dequeue()
70
>>> epq.dequeue()
128
>>> epq.dequeue()
Traceback (most recent call last):
...
data_structures.queue.priority_queue_using_list.UnderFlowError: The queue is empty
>>> print(epq)
[]
"""
def __init__(self):
self.queue = []
def enqueue(self, data: int) -> None:
"""
This function enters the element into the queue
If the queue is full an Exception is raised saying Over Flow!
"""
if len(self.queue) == 100:
raise OverFlowError("Maximum queue size is 100")
self.queue.append(data)
def dequeue(self) -> int:
"""
Return the highest priority element in FIFO order.
If the queue is empty then an under flow exception is raised.
"""
if not self.queue:
raise UnderFlowError("The queue is empty")
else:
data = min(self.queue)
self.queue.remove(data)
return data
def __str__(self) -> str:
"""
Prints all the elements within the Element Priority Queue
"""
return str(self.queue)
def fixed_priority_queue():
fpq = FixedPriorityQueue()
fpq.enqueue(0, 10)
fpq.enqueue(1, 70)
fpq.enqueue(0, 100)
fpq.enqueue(2, 1)
fpq.enqueue(2, 5)
fpq.enqueue(1, 7)
fpq.enqueue(2, 4)
fpq.enqueue(1, 64)
fpq.enqueue(0, 128)
print(fpq)
print(fpq.dequeue())
print(fpq.dequeue())
print(fpq.dequeue())
print(fpq.dequeue())
print(fpq.dequeue())
print(fpq)
print(fpq.dequeue())
print(fpq.dequeue())
print(fpq.dequeue())
print(fpq.dequeue())
print(fpq.dequeue())
def element_priority_queue():
epq = ElementPriorityQueue()
epq.enqueue(10)
epq.enqueue(70)
epq.enqueue(100)
epq.enqueue(1)
epq.enqueue(5)
epq.enqueue(7)
epq.enqueue(4)
epq.enqueue(64)
epq.enqueue(128)
print(epq)
print(epq.dequeue())
print(epq.dequeue())
print(epq.dequeue())
print(epq.dequeue())
print(epq.dequeue())
print(epq)
print(epq.dequeue())
print(epq.dequeue())
print(epq.dequeue())
print(epq.dequeue())
print(epq.dequeue())
if __name__ == "__main__":
fixed_priority_queue()
element_priority_queue()
| """
Pure Python implementations of a Fixed Priority Queue and an Element Priority Queue
using Python lists.
"""
class OverFlowError(Exception):
pass
class UnderFlowError(Exception):
pass
class FixedPriorityQueue:
"""
Tasks can be added to a Priority Queue at any time and in any order but when Tasks
are removed then the Task with the highest priority is removed in FIFO order. In
code we will use three levels of priority with priority zero Tasks being the most
urgent (high priority) and priority 2 tasks being the least urgent.
Examples
>>> fpq = FixedPriorityQueue()
>>> fpq.enqueue(0, 10)
>>> fpq.enqueue(1, 70)
>>> fpq.enqueue(0, 100)
>>> fpq.enqueue(2, 1)
>>> fpq.enqueue(2, 5)
>>> fpq.enqueue(1, 7)
>>> fpq.enqueue(2, 4)
>>> fpq.enqueue(1, 64)
>>> fpq.enqueue(0, 128)
>>> print(fpq)
Priority 0: [10, 100, 128]
Priority 1: [70, 7, 64]
Priority 2: [1, 5, 4]
>>> fpq.dequeue()
10
>>> fpq.dequeue()
100
>>> fpq.dequeue()
128
>>> fpq.dequeue()
70
>>> fpq.dequeue()
7
>>> print(fpq)
Priority 0: []
Priority 1: [64]
Priority 2: [1, 5, 4]
>>> fpq.dequeue()
64
>>> fpq.dequeue()
1
>>> fpq.dequeue()
5
>>> fpq.dequeue()
4
>>> fpq.dequeue()
Traceback (most recent call last):
...
data_structures.queue.priority_queue_using_list.UnderFlowError: All queues are empty
>>> print(fpq)
Priority 0: []
Priority 1: []
Priority 2: []
"""
def __init__(self):
self.queues = [
[],
[],
[],
]
def enqueue(self, priority: int, data: int) -> None:
"""
Add an element to a queue based on its priority.
If the priority is invalid ValueError is raised.
If the queue is full an OverFlowError is raised.
"""
try:
if len(self.queues[priority]) >= 100:
raise OverflowError("Maximum queue size is 100")
self.queues[priority].append(data)
except IndexError:
raise ValueError("Valid priorities are 0, 1, and 2")
def dequeue(self) -> int:
"""
Return the highest priority element in FIFO order.
If the queue is empty then an under flow exception is raised.
"""
for queue in self.queues:
if queue:
return queue.pop(0)
raise UnderFlowError("All queues are empty")
def __str__(self) -> str:
return "\n".join(f"Priority {i}: {q}" for i, q in enumerate(self.queues))
class ElementPriorityQueue:
"""
Element Priority Queue is the same as Fixed Priority Queue except that the value of
the element itself is the priority. The rules for priorities are the same the as
Fixed Priority Queue.
>>> epq = ElementPriorityQueue()
>>> epq.enqueue(10)
>>> epq.enqueue(70)
>>> epq.enqueue(4)
>>> epq.enqueue(1)
>>> epq.enqueue(5)
>>> epq.enqueue(7)
>>> epq.enqueue(4)
>>> epq.enqueue(64)
>>> epq.enqueue(128)
>>> print(epq)
[10, 70, 4, 1, 5, 7, 4, 64, 128]
>>> epq.dequeue()
1
>>> epq.dequeue()
4
>>> epq.dequeue()
4
>>> epq.dequeue()
5
>>> epq.dequeue()
7
>>> epq.dequeue()
10
>>> print(epq)
[70, 64, 128]
>>> epq.dequeue()
64
>>> epq.dequeue()
70
>>> epq.dequeue()
128
>>> epq.dequeue()
Traceback (most recent call last):
...
data_structures.queue.priority_queue_using_list.UnderFlowError: The queue is empty
>>> print(epq)
[]
"""
def __init__(self):
self.queue = []
def enqueue(self, data: int) -> None:
"""
This function enters the element into the queue
If the queue is full an Exception is raised saying Over Flow!
"""
if len(self.queue) == 100:
raise OverFlowError("Maximum queue size is 100")
self.queue.append(data)
def dequeue(self) -> int:
"""
Return the highest priority element in FIFO order.
If the queue is empty then an under flow exception is raised.
"""
if not self.queue:
raise UnderFlowError("The queue is empty")
else:
data = min(self.queue)
self.queue.remove(data)
return data
def __str__(self) -> str:
"""
Prints all the elements within the Element Priority Queue
"""
return str(self.queue)
def fixed_priority_queue():
fpq = FixedPriorityQueue()
fpq.enqueue(0, 10)
fpq.enqueue(1, 70)
fpq.enqueue(0, 100)
fpq.enqueue(2, 1)
fpq.enqueue(2, 5)
fpq.enqueue(1, 7)
fpq.enqueue(2, 4)
fpq.enqueue(1, 64)
fpq.enqueue(0, 128)
print(fpq)
print(fpq.dequeue())
print(fpq.dequeue())
print(fpq.dequeue())
print(fpq.dequeue())
print(fpq.dequeue())
print(fpq)
print(fpq.dequeue())
print(fpq.dequeue())
print(fpq.dequeue())
print(fpq.dequeue())
print(fpq.dequeue())
def element_priority_queue():
epq = ElementPriorityQueue()
epq.enqueue(10)
epq.enqueue(70)
epq.enqueue(100)
epq.enqueue(1)
epq.enqueue(5)
epq.enqueue(7)
epq.enqueue(4)
epq.enqueue(64)
epq.enqueue(128)
print(epq)
print(epq.dequeue())
print(epq.dequeue())
print(epq.dequeue())
print(epq.dequeue())
print(epq.dequeue())
print(epq)
print(epq.dequeue())
print(epq.dequeue())
print(epq.dequeue())
print(epq.dequeue())
print(epq.dequeue())
if __name__ == "__main__":
fixed_priority_queue()
element_priority_queue()
| -1 |
TheAlgorithms/Python | 7,387 | [pre-commit.ci] pre-commit autoupdate | <!--pre-commit.ci start-->
updates:
- [github.com/asottile/pyupgrade: v3.0.0 → v3.1.0](https://github.com/asottile/pyupgrade/compare/v3.0.0...v3.1.0)
- [github.com/codespell-project/codespell: v2.2.1 → v2.2.2](https://github.com/codespell-project/codespell/compare/v2.2.1...v2.2.2)
<!--pre-commit.ci end--> | pre-commit-ci[bot] | 2022-10-17T19:35:07Z | 2022-10-17T19:59:26Z | a34b756fd40e5cdfb69abc06dcd42f5f1b5fa21e | 0c7c5fa7b0161a7433467240155356c93ae106b8 | [pre-commit.ci] pre-commit autoupdate. <!--pre-commit.ci start-->
updates:
- [github.com/asottile/pyupgrade: v3.0.0 → v3.1.0](https://github.com/asottile/pyupgrade/compare/v3.0.0...v3.1.0)
- [github.com/codespell-project/codespell: v2.2.1 → v2.2.2](https://github.com/codespell-project/codespell/compare/v2.2.1...v2.2.2)
<!--pre-commit.ci end--> | from __future__ import annotations
def modular_division(a: int, b: int, n: int) -> int:
"""
Modular Division :
An efficient algorithm for dividing b by a modulo n.
GCD ( Greatest Common Divisor ) or HCF ( Highest Common Factor )
Given three integers a, b, and n, such that gcd(a,n)=1 and n>1, the algorithm should
return an integer x such that 0≤x≤n−1, and b/a=x(modn) (that is, b=ax(modn)).
Theorem:
a has a multiplicative inverse modulo n iff gcd(a,n) = 1
This find x = b*a^(-1) mod n
Uses ExtendedEuclid to find the inverse of a
>>> modular_division(4,8,5)
2
>>> modular_division(3,8,5)
1
>>> modular_division(4, 11, 5)
4
"""
assert n > 1 and a > 0 and greatest_common_divisor(a, n) == 1
(d, t, s) = extended_gcd(n, a) # Implemented below
x = (b * s) % n
return x
def invert_modulo(a: int, n: int) -> int:
"""
This function find the inverses of a i.e., a^(-1)
>>> invert_modulo(2, 5)
3
>>> invert_modulo(8,7)
1
"""
(b, x) = extended_euclid(a, n) # Implemented below
if b < 0:
b = (b % n + n) % n
return b
# ------------------ Finding Modular division using invert_modulo -------------------
def modular_division2(a: int, b: int, n: int) -> int:
"""
This function used the above inversion of a to find x = (b*a^(-1))mod n
>>> modular_division2(4,8,5)
2
>>> modular_division2(3,8,5)
1
>>> modular_division2(4, 11, 5)
4
"""
s = invert_modulo(a, n)
x = (b * s) % n
return x
def extended_gcd(a: int, b: int) -> tuple[int, int, int]:
"""
Extended Euclid's Algorithm : If d divides a and b and d = a*x + b*y for integers x
and y, then d = gcd(a,b)
>>> extended_gcd(10, 6)
(2, -1, 2)
>>> extended_gcd(7, 5)
(1, -2, 3)
** extended_gcd function is used when d = gcd(a,b) is required in output
"""
assert a >= 0 and b >= 0
if b == 0:
d, x, y = a, 1, 0
else:
(d, p, q) = extended_gcd(b, a % b)
x = q
y = p - q * (a // b)
assert a % d == 0 and b % d == 0
assert d == a * x + b * y
return (d, x, y)
def extended_euclid(a: int, b: int) -> tuple[int, int]:
"""
Extended Euclid
>>> extended_euclid(10, 6)
(-1, 2)
>>> extended_euclid(7, 5)
(-2, 3)
"""
if b == 0:
return (1, 0)
(x, y) = extended_euclid(b, a % b)
k = a // b
return (y, x - k * y)
def greatest_common_divisor(a: int, b: int) -> int:
"""
Euclid's Lemma : d divides a and b, if and only if d divides a-b and b
Euclid's Algorithm
>>> greatest_common_divisor(7,5)
1
Note : In number theory, two integers a and b are said to be relatively prime,
mutually prime, or co-prime if the only positive integer (factor) that divides
both of them is 1 i.e., gcd(a,b) = 1.
>>> greatest_common_divisor(121, 11)
11
"""
if a < b:
a, b = b, a
while a % b != 0:
a, b = b, a % b
return b
if __name__ == "__main__":
from doctest import testmod
testmod(name="modular_division", verbose=True)
testmod(name="modular_division2", verbose=True)
testmod(name="invert_modulo", verbose=True)
testmod(name="extended_gcd", verbose=True)
testmod(name="extended_euclid", verbose=True)
testmod(name="greatest_common_divisor", verbose=True)
| from __future__ import annotations
def modular_division(a: int, b: int, n: int) -> int:
"""
Modular Division :
An efficient algorithm for dividing b by a modulo n.
GCD ( Greatest Common Divisor ) or HCF ( Highest Common Factor )
Given three integers a, b, and n, such that gcd(a,n)=1 and n>1, the algorithm should
return an integer x such that 0≤x≤n−1, and b/a=x(modn) (that is, b=ax(modn)).
Theorem:
a has a multiplicative inverse modulo n iff gcd(a,n) = 1
This find x = b*a^(-1) mod n
Uses ExtendedEuclid to find the inverse of a
>>> modular_division(4,8,5)
2
>>> modular_division(3,8,5)
1
>>> modular_division(4, 11, 5)
4
"""
assert n > 1 and a > 0 and greatest_common_divisor(a, n) == 1
(d, t, s) = extended_gcd(n, a) # Implemented below
x = (b * s) % n
return x
def invert_modulo(a: int, n: int) -> int:
"""
This function find the inverses of a i.e., a^(-1)
>>> invert_modulo(2, 5)
3
>>> invert_modulo(8,7)
1
"""
(b, x) = extended_euclid(a, n) # Implemented below
if b < 0:
b = (b % n + n) % n
return b
# ------------------ Finding Modular division using invert_modulo -------------------
def modular_division2(a: int, b: int, n: int) -> int:
"""
This function used the above inversion of a to find x = (b*a^(-1))mod n
>>> modular_division2(4,8,5)
2
>>> modular_division2(3,8,5)
1
>>> modular_division2(4, 11, 5)
4
"""
s = invert_modulo(a, n)
x = (b * s) % n
return x
def extended_gcd(a: int, b: int) -> tuple[int, int, int]:
"""
Extended Euclid's Algorithm : If d divides a and b and d = a*x + b*y for integers x
and y, then d = gcd(a,b)
>>> extended_gcd(10, 6)
(2, -1, 2)
>>> extended_gcd(7, 5)
(1, -2, 3)
** extended_gcd function is used when d = gcd(a,b) is required in output
"""
assert a >= 0 and b >= 0
if b == 0:
d, x, y = a, 1, 0
else:
(d, p, q) = extended_gcd(b, a % b)
x = q
y = p - q * (a // b)
assert a % d == 0 and b % d == 0
assert d == a * x + b * y
return (d, x, y)
def extended_euclid(a: int, b: int) -> tuple[int, int]:
"""
Extended Euclid
>>> extended_euclid(10, 6)
(-1, 2)
>>> extended_euclid(7, 5)
(-2, 3)
"""
if b == 0:
return (1, 0)
(x, y) = extended_euclid(b, a % b)
k = a // b
return (y, x - k * y)
def greatest_common_divisor(a: int, b: int) -> int:
"""
Euclid's Lemma : d divides a and b, if and only if d divides a-b and b
Euclid's Algorithm
>>> greatest_common_divisor(7,5)
1
Note : In number theory, two integers a and b are said to be relatively prime,
mutually prime, or co-prime if the only positive integer (factor) that divides
both of them is 1 i.e., gcd(a,b) = 1.
>>> greatest_common_divisor(121, 11)
11
"""
if a < b:
a, b = b, a
while a % b != 0:
a, b = b, a % b
return b
if __name__ == "__main__":
from doctest import testmod
testmod(name="modular_division", verbose=True)
testmod(name="modular_division2", verbose=True)
testmod(name="invert_modulo", verbose=True)
testmod(name="extended_gcd", verbose=True)
testmod(name="extended_euclid", verbose=True)
testmod(name="greatest_common_divisor", verbose=True)
| -1 |
TheAlgorithms/Python | 7,387 | [pre-commit.ci] pre-commit autoupdate | <!--pre-commit.ci start-->
updates:
- [github.com/asottile/pyupgrade: v3.0.0 → v3.1.0](https://github.com/asottile/pyupgrade/compare/v3.0.0...v3.1.0)
- [github.com/codespell-project/codespell: v2.2.1 → v2.2.2](https://github.com/codespell-project/codespell/compare/v2.2.1...v2.2.2)
<!--pre-commit.ci end--> | pre-commit-ci[bot] | 2022-10-17T19:35:07Z | 2022-10-17T19:59:26Z | a34b756fd40e5cdfb69abc06dcd42f5f1b5fa21e | 0c7c5fa7b0161a7433467240155356c93ae106b8 | [pre-commit.ci] pre-commit autoupdate. <!--pre-commit.ci start-->
updates:
- [github.com/asottile/pyupgrade: v3.0.0 → v3.1.0](https://github.com/asottile/pyupgrade/compare/v3.0.0...v3.1.0)
- [github.com/codespell-project/codespell: v2.2.1 → v2.2.2](https://github.com/codespell-project/codespell/compare/v2.2.1...v2.2.2)
<!--pre-commit.ci end--> | #!/bin/sh
#
# An example hook script to prepare the commit log message.
# Called by "git commit" with the name of the file that has the
# commit message, followed by the description of the commit
# message's source. The hook's purpose is to edit the commit
# message file. If the hook fails with a non-zero status,
# the commit is aborted.
#
# To enable this hook, rename this file to "prepare-commit-msg".
# This hook includes three examples. The first one removes the
# "# Please enter the commit message..." help message.
#
# The second includes the output of "git diff --name-status -r"
# into the message, just before the "git status" output. It is
# commented because it doesn't cope with --amend or with squashed
# commits.
#
# The third example adds a Signed-off-by line to the message, that can
# still be edited. This is rarely a good idea.
COMMIT_MSG_FILE=$1
COMMIT_SOURCE=$2
SHA1=$3
/usr/bin/perl -i.bak -ne 'print unless(m/^. Please enter the commit message/..m/^#$/)' "$COMMIT_MSG_FILE"
# case "$COMMIT_SOURCE,$SHA1" in
# ,|template,)
# /usr/bin/perl -i.bak -pe '
# print "\n" . `git diff --cached --name-status -r`
# if /^#/ && $first++ == 0' "$COMMIT_MSG_FILE" ;;
# *) ;;
# esac
# SOB=$(git var GIT_COMMITTER_IDENT | sed -n 's/^\(.*>\).*$/Signed-off-by: \1/p')
# git interpret-trailers --in-place --trailer "$SOB" "$COMMIT_MSG_FILE"
# if test -z "$COMMIT_SOURCE"
# then
# /usr/bin/perl -i.bak -pe 'print "\n" if !$first_line++' "$COMMIT_MSG_FILE"
# fi
| #!/bin/sh
#
# An example hook script to prepare the commit log message.
# Called by "git commit" with the name of the file that has the
# commit message, followed by the description of the commit
# message's source. The hook's purpose is to edit the commit
# message file. If the hook fails with a non-zero status,
# the commit is aborted.
#
# To enable this hook, rename this file to "prepare-commit-msg".
# This hook includes three examples. The first one removes the
# "# Please enter the commit message..." help message.
#
# The second includes the output of "git diff --name-status -r"
# into the message, just before the "git status" output. It is
# commented because it doesn't cope with --amend or with squashed
# commits.
#
# The third example adds a Signed-off-by line to the message, that can
# still be edited. This is rarely a good idea.
COMMIT_MSG_FILE=$1
COMMIT_SOURCE=$2
SHA1=$3
/usr/bin/perl -i.bak -ne 'print unless(m/^. Please enter the commit message/..m/^#$/)' "$COMMIT_MSG_FILE"
# case "$COMMIT_SOURCE,$SHA1" in
# ,|template,)
# /usr/bin/perl -i.bak -pe '
# print "\n" . `git diff --cached --name-status -r`
# if /^#/ && $first++ == 0' "$COMMIT_MSG_FILE" ;;
# *) ;;
# esac
# SOB=$(git var GIT_COMMITTER_IDENT | sed -n 's/^\(.*>\).*$/Signed-off-by: \1/p')
# git interpret-trailers --in-place --trailer "$SOB" "$COMMIT_MSG_FILE"
# if test -z "$COMMIT_SOURCE"
# then
# /usr/bin/perl -i.bak -pe 'print "\n" if !$first_line++' "$COMMIT_MSG_FILE"
# fi
| -1 |
TheAlgorithms/Python | 7,387 | [pre-commit.ci] pre-commit autoupdate | <!--pre-commit.ci start-->
updates:
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<!--pre-commit.ci end--> | pre-commit-ci[bot] | 2022-10-17T19:35:07Z | 2022-10-17T19:59:26Z | a34b756fd40e5cdfb69abc06dcd42f5f1b5fa21e | 0c7c5fa7b0161a7433467240155356c93ae106b8 | [pre-commit.ci] pre-commit autoupdate. <!--pre-commit.ci start-->
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- [github.com/codespell-project/codespell: v2.2.1 → v2.2.2](https://github.com/codespell-project/codespell/compare/v2.2.1...v2.2.2)
<!--pre-commit.ci end--> | 9caf4784aada17dc75348f77cc8c356df503c0f3 https://github.com/TheAlgorithms/Python
| 9caf4784aada17dc75348f77cc8c356df503c0f3 https://github.com/TheAlgorithms/Python
| -1 |
TheAlgorithms/Python | 7,387 | [pre-commit.ci] pre-commit autoupdate | <!--pre-commit.ci start-->
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<!--pre-commit.ci end--> | pre-commit-ci[bot] | 2022-10-17T19:35:07Z | 2022-10-17T19:59:26Z | a34b756fd40e5cdfb69abc06dcd42f5f1b5fa21e | 0c7c5fa7b0161a7433467240155356c93ae106b8 | [pre-commit.ci] pre-commit autoupdate. <!--pre-commit.ci start-->
updates:
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<!--pre-commit.ci end--> | """
Project Euler Problem 9: https://projecteuler.net/problem=9
Special Pythagorean triplet
A Pythagorean triplet is a set of three natural numbers, a < b < c, for which,
a^2 + b^2 = c^2
For example, 3^2 + 4^2 = 9 + 16 = 25 = 5^2.
There exists exactly one Pythagorean triplet for which a + b + c = 1000.
Find the product a*b*c.
References:
- https://en.wikipedia.org/wiki/Pythagorean_triple
"""
def solution(n: int = 1000) -> int:
"""
Return the product of a,b,c which are Pythagorean Triplet that satisfies
the following:
1. a < b < c
2. a**2 + b**2 = c**2
3. a + b + c = n
>>> solution(36)
1620
>>> solution(126)
66780
"""
product = -1
candidate = 0
for a in range(1, n // 3):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
b = (n * n - 2 * a * n) // (2 * n - 2 * a)
c = n - a - b
if c * c == (a * a + b * b):
candidate = a * b * c
if candidate >= product:
product = candidate
return product
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 9: https://projecteuler.net/problem=9
Special Pythagorean triplet
A Pythagorean triplet is a set of three natural numbers, a < b < c, for which,
a^2 + b^2 = c^2
For example, 3^2 + 4^2 = 9 + 16 = 25 = 5^2.
There exists exactly one Pythagorean triplet for which a + b + c = 1000.
Find the product a*b*c.
References:
- https://en.wikipedia.org/wiki/Pythagorean_triple
"""
def solution(n: int = 1000) -> int:
"""
Return the product of a,b,c which are Pythagorean Triplet that satisfies
the following:
1. a < b < c
2. a**2 + b**2 = c**2
3. a + b + c = n
>>> solution(36)
1620
>>> solution(126)
66780
"""
product = -1
candidate = 0
for a in range(1, n // 3):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
b = (n * n - 2 * a * n) // (2 * n - 2 * a)
c = n - a - b
if c * c == (a * a + b * b):
candidate = a * b * c
if candidate >= product:
product = candidate
return product
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 7,387 | [pre-commit.ci] pre-commit autoupdate | <!--pre-commit.ci start-->
updates:
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<!--pre-commit.ci end--> | pre-commit-ci[bot] | 2022-10-17T19:35:07Z | 2022-10-17T19:59:26Z | a34b756fd40e5cdfb69abc06dcd42f5f1b5fa21e | 0c7c5fa7b0161a7433467240155356c93ae106b8 | [pre-commit.ci] pre-commit autoupdate. <!--pre-commit.ci start-->
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<!--pre-commit.ci end--> | """ 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 | 7,387 | [pre-commit.ci] pre-commit autoupdate | <!--pre-commit.ci start-->
updates:
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<!--pre-commit.ci end--> | pre-commit-ci[bot] | 2022-10-17T19:35:07Z | 2022-10-17T19:59:26Z | a34b756fd40e5cdfb69abc06dcd42f5f1b5fa21e | 0c7c5fa7b0161a7433467240155356c93ae106b8 | [pre-commit.ci] pre-commit autoupdate. <!--pre-commit.ci start-->
updates:
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- [github.com/codespell-project/codespell: v2.2.1 → v2.2.2](https://github.com/codespell-project/codespell/compare/v2.2.1...v2.2.2)
<!--pre-commit.ci end--> | """
Lychrel numbers
Problem 55: https://projecteuler.net/problem=55
If we take 47, reverse and add, 47 + 74 = 121, which is palindromic.
Not all numbers produce palindromes so quickly. For example,
349 + 943 = 1292,
1292 + 2921 = 4213
4213 + 3124 = 7337
That is, 349 took three iterations to arrive at a palindrome.
Although no one has proved it yet, it is thought that some numbers, like 196,
never produce a palindrome. A number that never forms a palindrome through the
reverse and add process is called a Lychrel number. Due to the theoretical nature
of these numbers, and for the purpose of this problem, we shall assume that a number
is Lychrel until proven otherwise. In addition you are given that for every number
below ten-thousand, it will either (i) become a palindrome in less than fifty
iterations, or, (ii) no one, with all the computing power that exists, has managed
so far to map it to a palindrome. In fact, 10677 is the first number to be shown
to require over fifty iterations before producing a palindrome:
4668731596684224866951378664 (53 iterations, 28-digits).
Surprisingly, there are palindromic numbers that are themselves Lychrel numbers;
the first example is 4994.
How many Lychrel numbers are there below ten-thousand?
"""
def is_palindrome(n: int) -> bool:
"""
Returns True if a number is palindrome.
>>> is_palindrome(12567321)
False
>>> is_palindrome(1221)
True
>>> is_palindrome(9876789)
True
"""
return str(n) == str(n)[::-1]
def sum_reverse(n: int) -> int:
"""
Returns the sum of n and reverse of n.
>>> sum_reverse(123)
444
>>> sum_reverse(3478)
12221
>>> sum_reverse(12)
33
"""
return int(n) + int(str(n)[::-1])
def solution(limit: int = 10000) -> int:
"""
Returns the count of all lychrel numbers below limit.
>>> solution(10000)
249
>>> solution(5000)
76
>>> solution(1000)
13
"""
lychrel_nums = []
for num in range(1, limit):
iterations = 0
a = num
while iterations < 50:
num = sum_reverse(num)
iterations += 1
if is_palindrome(num):
break
else:
lychrel_nums.append(a)
return len(lychrel_nums)
if __name__ == "__main__":
print(f"{solution() = }")
| """
Lychrel numbers
Problem 55: https://projecteuler.net/problem=55
If we take 47, reverse and add, 47 + 74 = 121, which is palindromic.
Not all numbers produce palindromes so quickly. For example,
349 + 943 = 1292,
1292 + 2921 = 4213
4213 + 3124 = 7337
That is, 349 took three iterations to arrive at a palindrome.
Although no one has proved it yet, it is thought that some numbers, like 196,
never produce a palindrome. A number that never forms a palindrome through the
reverse and add process is called a Lychrel number. Due to the theoretical nature
of these numbers, and for the purpose of this problem, we shall assume that a number
is Lychrel until proven otherwise. In addition you are given that for every number
below ten-thousand, it will either (i) become a palindrome in less than fifty
iterations, or, (ii) no one, with all the computing power that exists, has managed
so far to map it to a palindrome. In fact, 10677 is the first number to be shown
to require over fifty iterations before producing a palindrome:
4668731596684224866951378664 (53 iterations, 28-digits).
Surprisingly, there are palindromic numbers that are themselves Lychrel numbers;
the first example is 4994.
How many Lychrel numbers are there below ten-thousand?
"""
def is_palindrome(n: int) -> bool:
"""
Returns True if a number is palindrome.
>>> is_palindrome(12567321)
False
>>> is_palindrome(1221)
True
>>> is_palindrome(9876789)
True
"""
return str(n) == str(n)[::-1]
def sum_reverse(n: int) -> int:
"""
Returns the sum of n and reverse of n.
>>> sum_reverse(123)
444
>>> sum_reverse(3478)
12221
>>> sum_reverse(12)
33
"""
return int(n) + int(str(n)[::-1])
def solution(limit: int = 10000) -> int:
"""
Returns the count of all lychrel numbers below limit.
>>> solution(10000)
249
>>> solution(5000)
76
>>> solution(1000)
13
"""
lychrel_nums = []
for num in range(1, limit):
iterations = 0
a = num
while iterations < 50:
num = sum_reverse(num)
iterations += 1
if is_palindrome(num):
break
else:
lychrel_nums.append(a)
return len(lychrel_nums)
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 7,387 | [pre-commit.ci] pre-commit autoupdate | <!--pre-commit.ci start-->
updates:
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<!--pre-commit.ci end--> | pre-commit-ci[bot] | 2022-10-17T19:35:07Z | 2022-10-17T19:59:26Z | a34b756fd40e5cdfb69abc06dcd42f5f1b5fa21e | 0c7c5fa7b0161a7433467240155356c93ae106b8 | [pre-commit.ci] pre-commit autoupdate. <!--pre-commit.ci start-->
updates:
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- [github.com/codespell-project/codespell: v2.2.1 → v2.2.2](https://github.com/codespell-project/codespell/compare/v2.2.1...v2.2.2)
<!--pre-commit.ci end--> | """
Project Euler Problem 80: https://projecteuler.net/problem=80
Author: Sandeep Gupta
Problem statement: For the first one hundred natural numbers, find the total of
the digital sums of the first one hundred decimal digits for all the irrational
square roots.
Time: 5 October 2020, 18:30
"""
import decimal
def solution() -> int:
"""
To evaluate the sum, Used decimal python module to calculate the decimal
places up to 100, the most important thing would be take calculate
a few extra places for decimal otherwise there will be rounding
error.
>>> solution()
40886
"""
answer = 0
decimal_context = decimal.Context(prec=105)
for i in range(2, 100):
number = decimal.Decimal(i)
sqrt_number = number.sqrt(decimal_context)
if len(str(sqrt_number)) > 1:
answer += int(str(sqrt_number)[0])
sqrt_number_str = str(sqrt_number)[2:101]
answer += sum(int(x) for x in sqrt_number_str)
return answer
if __name__ == "__main__":
import doctest
doctest.testmod()
print(f"{solution() = }")
| """
Project Euler Problem 80: https://projecteuler.net/problem=80
Author: Sandeep Gupta
Problem statement: For the first one hundred natural numbers, find the total of
the digital sums of the first one hundred decimal digits for all the irrational
square roots.
Time: 5 October 2020, 18:30
"""
import decimal
def solution() -> int:
"""
To evaluate the sum, Used decimal python module to calculate the decimal
places up to 100, the most important thing would be take calculate
a few extra places for decimal otherwise there will be rounding
error.
>>> solution()
40886
"""
answer = 0
decimal_context = decimal.Context(prec=105)
for i in range(2, 100):
number = decimal.Decimal(i)
sqrt_number = number.sqrt(decimal_context)
if len(str(sqrt_number)) > 1:
answer += int(str(sqrt_number)[0])
sqrt_number_str = str(sqrt_number)[2:101]
answer += sum(int(x) for x in sqrt_number_str)
return answer
if __name__ == "__main__":
import doctest
doctest.testmod()
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 7,387 | [pre-commit.ci] pre-commit autoupdate | <!--pre-commit.ci start-->
updates:
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<!--pre-commit.ci end--> | ref: refs/remotes/origin/master
| ref: refs/remotes/origin/master
| -1 |
TheAlgorithms/Python | 7,387 | [pre-commit.ci] pre-commit autoupdate | <!--pre-commit.ci start-->
updates:
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<!--pre-commit.ci end--> | pre-commit-ci[bot] | 2022-10-17T19:35:07Z | 2022-10-17T19:59:26Z | a34b756fd40e5cdfb69abc06dcd42f5f1b5fa21e | 0c7c5fa7b0161a7433467240155356c93ae106b8 | [pre-commit.ci] pre-commit autoupdate. <!--pre-commit.ci start-->
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- [github.com/codespell-project/codespell: v2.2.1 → v2.2.2](https://github.com/codespell-project/codespell/compare/v2.2.1...v2.2.2)
<!--pre-commit.ci end--> | def is_palindrome(s: str) -> bool:
"""
Determine whether the string is palindrome
:param s:
:return: Boolean
>>> is_palindrome("a man a plan a canal panama".replace(" ", ""))
True
>>> is_palindrome("Hello")
False
>>> is_palindrome("Able was I ere I saw Elba")
True
>>> is_palindrome("racecar")
True
>>> is_palindrome("Mr. Owl ate my metal worm?")
True
"""
# Since Punctuation, capitalization, and spaces are usually ignored while checking
# Palindrome, we first remove them from our string.
s = "".join([character for character in s.lower() if character.isalnum()])
return s == s[::-1]
if __name__ == "__main__":
s = input("Enter string to determine whether its palindrome or not: ").strip()
if is_palindrome(s):
print("Given string is palindrome")
else:
print("Given string is not palindrome")
| def is_palindrome(s: str) -> bool:
"""
Determine whether the string is palindrome
:param s:
:return: Boolean
>>> is_palindrome("a man a plan a canal panama".replace(" ", ""))
True
>>> is_palindrome("Hello")
False
>>> is_palindrome("Able was I ere I saw Elba")
True
>>> is_palindrome("racecar")
True
>>> is_palindrome("Mr. Owl ate my metal worm?")
True
"""
# Since Punctuation, capitalization, and spaces are usually ignored while checking
# Palindrome, we first remove them from our string.
s = "".join([character for character in s.lower() if character.isalnum()])
return s == s[::-1]
if __name__ == "__main__":
s = input("Enter string to determine whether its palindrome or not: ").strip()
if is_palindrome(s):
print("Given string is palindrome")
else:
print("Given string is not palindrome")
| -1 |
TheAlgorithms/Python | 7,387 | [pre-commit.ci] pre-commit autoupdate | <!--pre-commit.ci start-->
updates:
- [github.com/asottile/pyupgrade: v3.0.0 → v3.1.0](https://github.com/asottile/pyupgrade/compare/v3.0.0...v3.1.0)
- [github.com/codespell-project/codespell: v2.2.1 → v2.2.2](https://github.com/codespell-project/codespell/compare/v2.2.1...v2.2.2)
<!--pre-commit.ci end--> | pre-commit-ci[bot] | 2022-10-17T19:35:07Z | 2022-10-17T19:59:26Z | a34b756fd40e5cdfb69abc06dcd42f5f1b5fa21e | 0c7c5fa7b0161a7433467240155356c93ae106b8 | [pre-commit.ci] pre-commit autoupdate. <!--pre-commit.ci start-->
updates:
- [github.com/asottile/pyupgrade: v3.0.0 → v3.1.0](https://github.com/asottile/pyupgrade/compare/v3.0.0...v3.1.0)
- [github.com/codespell-project/codespell: v2.2.1 → v2.2.2](https://github.com/codespell-project/codespell/compare/v2.2.1...v2.2.2)
<!--pre-commit.ci end--> | """For reference
https://en.wikipedia.org/wiki/Odd%E2%80%93even_sort
"""
def odd_even_sort(input_list: list) -> list:
"""this algorithm uses the same idea of bubblesort,
but by first dividing in two phase (odd and even).
Originally developed for use on parallel processors
with local interconnections.
:param collection: mutable ordered sequence of elements
:return: same collection in ascending order
Examples:
>>> odd_even_sort([5 , 4 ,3 ,2 ,1])
[1, 2, 3, 4, 5]
>>> odd_even_sort([])
[]
>>> odd_even_sort([-10 ,-1 ,10 ,2])
[-10, -1, 2, 10]
>>> odd_even_sort([1 ,2 ,3 ,4])
[1, 2, 3, 4]
"""
is_sorted = False
while is_sorted is False: # Until all the indices are traversed keep looping
is_sorted = True
for i in range(0, len(input_list) - 1, 2): # iterating over all even indices
if input_list[i] > input_list[i + 1]:
input_list[i], input_list[i + 1] = input_list[i + 1], input_list[i]
# swapping if elements not in order
is_sorted = False
for i in range(1, len(input_list) - 1, 2): # iterating over all odd indices
if input_list[i] > input_list[i + 1]:
input_list[i], input_list[i + 1] = input_list[i + 1], input_list[i]
# swapping if elements not in order
is_sorted = False
return input_list
if __name__ == "__main__":
print("Enter list to be sorted")
input_list = [int(x) for x in input().split()]
# inputing elements of the list in one line
sorted_list = odd_even_sort(input_list)
print("The sorted list is")
print(sorted_list)
| """For reference
https://en.wikipedia.org/wiki/Odd%E2%80%93even_sort
"""
def odd_even_sort(input_list: list) -> list:
"""this algorithm uses the same idea of bubblesort,
but by first dividing in two phase (odd and even).
Originally developed for use on parallel processors
with local interconnections.
:param collection: mutable ordered sequence of elements
:return: same collection in ascending order
Examples:
>>> odd_even_sort([5 , 4 ,3 ,2 ,1])
[1, 2, 3, 4, 5]
>>> odd_even_sort([])
[]
>>> odd_even_sort([-10 ,-1 ,10 ,2])
[-10, -1, 2, 10]
>>> odd_even_sort([1 ,2 ,3 ,4])
[1, 2, 3, 4]
"""
is_sorted = False
while is_sorted is False: # Until all the indices are traversed keep looping
is_sorted = True
for i in range(0, len(input_list) - 1, 2): # iterating over all even indices
if input_list[i] > input_list[i + 1]:
input_list[i], input_list[i + 1] = input_list[i + 1], input_list[i]
# swapping if elements not in order
is_sorted = False
for i in range(1, len(input_list) - 1, 2): # iterating over all odd indices
if input_list[i] > input_list[i + 1]:
input_list[i], input_list[i + 1] = input_list[i + 1], input_list[i]
# swapping if elements not in order
is_sorted = False
return input_list
if __name__ == "__main__":
print("Enter list to be sorted")
input_list = [int(x) for x in input().split()]
# inputing elements of the list in one line
sorted_list = odd_even_sort(input_list)
print("The sorted list is")
print(sorted_list)
| -1 |
TheAlgorithms/Python | 7,387 | [pre-commit.ci] pre-commit autoupdate | <!--pre-commit.ci start-->
updates:
- [github.com/asottile/pyupgrade: v3.0.0 → v3.1.0](https://github.com/asottile/pyupgrade/compare/v3.0.0...v3.1.0)
- [github.com/codespell-project/codespell: v2.2.1 → v2.2.2](https://github.com/codespell-project/codespell/compare/v2.2.1...v2.2.2)
<!--pre-commit.ci end--> | pre-commit-ci[bot] | 2022-10-17T19:35:07Z | 2022-10-17T19:59:26Z | a34b756fd40e5cdfb69abc06dcd42f5f1b5fa21e | 0c7c5fa7b0161a7433467240155356c93ae106b8 | [pre-commit.ci] pre-commit autoupdate. <!--pre-commit.ci start-->
updates:
- [github.com/asottile/pyupgrade: v3.0.0 → v3.1.0](https://github.com/asottile/pyupgrade/compare/v3.0.0...v3.1.0)
- [github.com/codespell-project/codespell: v2.2.1 → v2.2.2](https://github.com/codespell-project/codespell/compare/v2.2.1...v2.2.2)
<!--pre-commit.ci end--> | # 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 | 7,387 | [pre-commit.ci] pre-commit autoupdate | <!--pre-commit.ci start-->
updates:
- [github.com/asottile/pyupgrade: v3.0.0 → v3.1.0](https://github.com/asottile/pyupgrade/compare/v3.0.0...v3.1.0)
- [github.com/codespell-project/codespell: v2.2.1 → v2.2.2](https://github.com/codespell-project/codespell/compare/v2.2.1...v2.2.2)
<!--pre-commit.ci end--> | pre-commit-ci[bot] | 2022-10-17T19:35:07Z | 2022-10-17T19:59:26Z | a34b756fd40e5cdfb69abc06dcd42f5f1b5fa21e | 0c7c5fa7b0161a7433467240155356c93ae106b8 | [pre-commit.ci] pre-commit autoupdate. <!--pre-commit.ci start-->
updates:
- [github.com/asottile/pyupgrade: v3.0.0 → v3.1.0](https://github.com/asottile/pyupgrade/compare/v3.0.0...v3.1.0)
- [github.com/codespell-project/codespell: v2.2.1 → v2.2.2](https://github.com/codespell-project/codespell/compare/v2.2.1...v2.2.2)
<!--pre-commit.ci end--> | -1 |
||
TheAlgorithms/Python | 7,387 | [pre-commit.ci] pre-commit autoupdate | <!--pre-commit.ci start-->
updates:
- [github.com/asottile/pyupgrade: v3.0.0 → v3.1.0](https://github.com/asottile/pyupgrade/compare/v3.0.0...v3.1.0)
- [github.com/codespell-project/codespell: v2.2.1 → v2.2.2](https://github.com/codespell-project/codespell/compare/v2.2.1...v2.2.2)
<!--pre-commit.ci end--> | pre-commit-ci[bot] | 2022-10-17T19:35:07Z | 2022-10-17T19:59:26Z | a34b756fd40e5cdfb69abc06dcd42f5f1b5fa21e | 0c7c5fa7b0161a7433467240155356c93ae106b8 | [pre-commit.ci] pre-commit autoupdate. <!--pre-commit.ci start-->
updates:
- [github.com/asottile/pyupgrade: v3.0.0 → v3.1.0](https://github.com/asottile/pyupgrade/compare/v3.0.0...v3.1.0)
- [github.com/codespell-project/codespell: v2.2.1 → v2.2.2](https://github.com/codespell-project/codespell/compare/v2.2.1...v2.2.2)
<!--pre-commit.ci end--> | # 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):
File "<stdin>", line 37, in <module>
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):
File "<stdin>", line 40, in <module>
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):
File "<stdin>", line 44, in <module>
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):
File "<stdin>", line 48, in <module>
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):
File "<stdin>", line 37, in <module>
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):
File "<stdin>", line 40, in <module>
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):
File "<stdin>", line 44, in <module>
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):
File "<stdin>", line 48, in <module>
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 | 7,387 | [pre-commit.ci] pre-commit autoupdate | <!--pre-commit.ci start-->
updates:
- [github.com/asottile/pyupgrade: v3.0.0 → v3.1.0](https://github.com/asottile/pyupgrade/compare/v3.0.0...v3.1.0)
- [github.com/codespell-project/codespell: v2.2.1 → v2.2.2](https://github.com/codespell-project/codespell/compare/v2.2.1...v2.2.2)
<!--pre-commit.ci end--> | pre-commit-ci[bot] | 2022-10-17T19:35:07Z | 2022-10-17T19:59:26Z | a34b756fd40e5cdfb69abc06dcd42f5f1b5fa21e | 0c7c5fa7b0161a7433467240155356c93ae106b8 | [pre-commit.ci] pre-commit autoupdate. <!--pre-commit.ci start-->
updates:
- [github.com/asottile/pyupgrade: v3.0.0 → v3.1.0](https://github.com/asottile/pyupgrade/compare/v3.0.0...v3.1.0)
- [github.com/codespell-project/codespell: v2.2.1 → v2.2.2](https://github.com/codespell-project/codespell/compare/v2.2.1...v2.2.2)
<!--pre-commit.ci end--> | -1 |
||
TheAlgorithms/Python | 7,339 | Follow Flake8 pep3101 and remove modulo formatting | ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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 have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | 2022-10-16T20:16:16Z | 2022-10-16T20:50:11Z | 7f6e0b656f6362e452b11d06acde50b8b81cb31a | f15cc2f01c2a4124ff6dc0843c728a546f9d9f79 | Follow Flake8 pep3101 and remove modulo formatting. ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
min_primitive_root = 3
# I have written my code naively same as definition of primitive root
# however every time I run this program, memory exceeded...
# so I used 4.80 Algorithm in
# Handbook of Applied Cryptography(CRC Press, ISBN : 0-8493-8523-7, October 1996)
# and it seems to run nicely!
def primitive_root(p_val: int) -> int:
print("Generating primitive root of p")
while True:
g = random.randrange(3, p_val)
if pow(g, 2, p_val) == 1:
continue
if pow(g, p_val, p_val) == 1:
continue
return g
def generate_key(key_size: int) -> tuple[tuple[int, int, int, int], tuple[int, int]]:
print("Generating prime p...")
p = rabin_miller.generate_large_prime(key_size) # select large prime number.
e_1 = primitive_root(p) # one primitive root on modulo p.
d = random.randrange(3, p) # private_key -> have to be greater than 2 for safety.
e_2 = cryptomath.find_mod_inverse(pow(e_1, d, p), p)
public_key = (key_size, e_1, e_2, p)
private_key = (key_size, d)
return public_key, private_key
def make_key_files(name: str, key_size: int) -> None:
if os.path.exists(f"{name}_pubkey.txt") or os.path.exists(f"{name}_privkey.txt"):
print("\nWARNING:")
print(
'"%s_pubkey.txt" or "%s_privkey.txt" already exists. \n'
"Use a different name or delete these files and re-run this program."
% (name, name)
)
sys.exit()
public_key, private_key = generate_key(key_size)
print(f"\nWriting public key to file {name}_pubkey.txt...")
with open(f"{name}_pubkey.txt", "w") as fo:
fo.write(
"%d,%d,%d,%d" % (public_key[0], public_key[1], public_key[2], public_key[3])
)
print(f"Writing private key to file {name}_privkey.txt...")
with open(f"{name}_privkey.txt", "w") as fo:
fo.write("%d,%d" % (private_key[0], private_key[1]))
def main() -> None:
print("Making key files...")
make_key_files("elgamal", 2048)
print("Key files generation successful")
if __name__ == "__main__":
main()
| import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
min_primitive_root = 3
# I have written my code naively same as definition of primitive root
# however every time I run this program, memory exceeded...
# so I used 4.80 Algorithm in
# Handbook of Applied Cryptography(CRC Press, ISBN : 0-8493-8523-7, October 1996)
# and it seems to run nicely!
def primitive_root(p_val: int) -> int:
print("Generating primitive root of p")
while True:
g = random.randrange(3, p_val)
if pow(g, 2, p_val) == 1:
continue
if pow(g, p_val, p_val) == 1:
continue
return g
def generate_key(key_size: int) -> tuple[tuple[int, int, int, int], tuple[int, int]]:
print("Generating prime p...")
p = rabin_miller.generate_large_prime(key_size) # select large prime number.
e_1 = primitive_root(p) # one primitive root on modulo p.
d = random.randrange(3, p) # private_key -> have to be greater than 2 for safety.
e_2 = cryptomath.find_mod_inverse(pow(e_1, d, p), p)
public_key = (key_size, e_1, e_2, p)
private_key = (key_size, d)
return public_key, private_key
def make_key_files(name: str, key_size: int) -> None:
if os.path.exists(f"{name}_pubkey.txt") or os.path.exists(f"{name}_privkey.txt"):
print("\nWARNING:")
print(
f'"{name}_pubkey.txt" or "{name}_privkey.txt" already exists. \n'
"Use a different name or delete these files and re-run this program."
)
sys.exit()
public_key, private_key = generate_key(key_size)
print(f"\nWriting public key to file {name}_pubkey.txt...")
with open(f"{name}_pubkey.txt", "w") as fo:
fo.write(f"{public_key[0]},{public_key[1]},{public_key[2]},{public_key[3]}")
print(f"Writing private key to file {name}_privkey.txt...")
with open(f"{name}_privkey.txt", "w") as fo:
fo.write(f"{private_key[0]},{private_key[1]}")
def main() -> None:
print("Making key files...")
make_key_files("elgamal", 2048)
print("Key files generation successful")
if __name__ == "__main__":
main()
| 1 |
TheAlgorithms/Python | 7,339 | Follow Flake8 pep3101 and remove modulo formatting | ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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 have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | 2022-10-16T20:16:16Z | 2022-10-16T20:50:11Z | 7f6e0b656f6362e452b11d06acde50b8b81cb31a | f15cc2f01c2a4124ff6dc0843c728a546f9d9f79 | Follow Flake8 pep3101 and remove modulo formatting. ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| import os
import random
import sys
from . import cryptomath_module as cryptoMath # noqa: N812
from . import rabin_miller as rabinMiller # noqa: N812
def main() -> None:
print("Making key files...")
make_key_files("rsa", 1024)
print("Key files generation successful.")
def generate_key(key_size: int) -> tuple[tuple[int, int], tuple[int, int]]:
print("Generating prime p...")
p = rabinMiller.generate_large_prime(key_size)
print("Generating prime q...")
q = rabinMiller.generate_large_prime(key_size)
n = p * q
print("Generating e that is relatively prime to (p - 1) * (q - 1)...")
while True:
e = random.randrange(2 ** (key_size - 1), 2 ** (key_size))
if cryptoMath.gcd(e, (p - 1) * (q - 1)) == 1:
break
print("Calculating d that is mod inverse of e...")
d = cryptoMath.find_mod_inverse(e, (p - 1) * (q - 1))
public_key = (n, e)
private_key = (n, d)
return (public_key, private_key)
def make_key_files(name: str, key_size: int) -> None:
if os.path.exists(f"{name}_pubkey.txt") or os.path.exists(f"{name}_privkey.txt"):
print("\nWARNING:")
print(
'"%s_pubkey.txt" or "%s_privkey.txt" already exists. \n'
"Use a different name or delete these files and re-run this program."
% (name, name)
)
sys.exit()
public_key, private_key = generate_key(key_size)
print(f"\nWriting public key to file {name}_pubkey.txt...")
with open(f"{name}_pubkey.txt", "w") as out_file:
out_file.write(f"{key_size},{public_key[0]},{public_key[1]}")
print(f"Writing private key to file {name}_privkey.txt...")
with open(f"{name}_privkey.txt", "w") as out_file:
out_file.write(f"{key_size},{private_key[0]},{private_key[1]}")
if __name__ == "__main__":
main()
| import os
import random
import sys
from . import cryptomath_module as cryptoMath # noqa: N812
from . import rabin_miller as rabinMiller # noqa: N812
def main() -> None:
print("Making key files...")
make_key_files("rsa", 1024)
print("Key files generation successful.")
def generate_key(key_size: int) -> tuple[tuple[int, int], tuple[int, int]]:
print("Generating prime p...")
p = rabinMiller.generate_large_prime(key_size)
print("Generating prime q...")
q = rabinMiller.generate_large_prime(key_size)
n = p * q
print("Generating e that is relatively prime to (p - 1) * (q - 1)...")
while True:
e = random.randrange(2 ** (key_size - 1), 2 ** (key_size))
if cryptoMath.gcd(e, (p - 1) * (q - 1)) == 1:
break
print("Calculating d that is mod inverse of e...")
d = cryptoMath.find_mod_inverse(e, (p - 1) * (q - 1))
public_key = (n, e)
private_key = (n, d)
return (public_key, private_key)
def make_key_files(name: str, key_size: int) -> None:
if os.path.exists(f"{name}_pubkey.txt") or os.path.exists(f"{name}_privkey.txt"):
print("\nWARNING:")
print(
f'"{name}_pubkey.txt" or "{name}_privkey.txt" already exists. \n'
"Use a different name or delete these files and re-run this program."
)
sys.exit()
public_key, private_key = generate_key(key_size)
print(f"\nWriting public key to file {name}_pubkey.txt...")
with open(f"{name}_pubkey.txt", "w") as out_file:
out_file.write(f"{key_size},{public_key[0]},{public_key[1]}")
print(f"Writing private key to file {name}_privkey.txt...")
with open(f"{name}_privkey.txt", "w") as out_file:
out_file.write(f"{key_size},{private_key[0]},{private_key[1]}")
if __name__ == "__main__":
main()
| 1 |
TheAlgorithms/Python | 7,339 | Follow Flake8 pep3101 and remove modulo formatting | ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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 have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | 2022-10-16T20:16:16Z | 2022-10-16T20:50:11Z | 7f6e0b656f6362e452b11d06acde50b8b81cb31a | f15cc2f01c2a4124ff6dc0843c728a546f9d9f79 | Follow Flake8 pep3101 and remove modulo formatting. ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Author : Turfa Auliarachman
Date : October 12, 2016
This is a pure Python implementation of Dynamic Programming solution to the edit
distance problem.
The problem is :
Given two strings A and B. Find the minimum number of operations to string B such that
A = B. The permitted operations are removal, insertion, and substitution.
"""
class EditDistance:
"""
Use :
solver = EditDistance()
editDistanceResult = solver.solve(firstString, secondString)
"""
def __init__(self):
self.__prepare__()
def __prepare__(self, n=0, m=0):
self.dp = [[-1 for y in range(0, m)] for x in range(0, n)]
def __solve_dp(self, x, y):
if x == -1:
return y + 1
elif y == -1:
return x + 1
elif self.dp[x][y] > -1:
return self.dp[x][y]
else:
if self.a[x] == self.b[y]:
self.dp[x][y] = self.__solve_dp(x - 1, y - 1)
else:
self.dp[x][y] = 1 + min(
self.__solve_dp(x, y - 1),
self.__solve_dp(x - 1, y),
self.__solve_dp(x - 1, y - 1),
)
return self.dp[x][y]
def solve(self, a, b):
if isinstance(a, bytes):
a = a.decode("ascii")
if isinstance(b, bytes):
b = b.decode("ascii")
self.a = str(a)
self.b = str(b)
self.__prepare__(len(a), len(b))
return self.__solve_dp(len(a) - 1, len(b) - 1)
def min_distance_bottom_up(word1: str, word2: str) -> int:
"""
>>> min_distance_bottom_up("intention", "execution")
5
>>> min_distance_bottom_up("intention", "")
9
>>> min_distance_bottom_up("", "")
0
"""
m = len(word1)
n = len(word2)
dp = [[0 for _ in range(n + 1)] for _ in range(m + 1)]
for i in range(m + 1):
for j in range(n + 1):
if i == 0: # first string is empty
dp[i][j] = j
elif j == 0: # second string is empty
dp[i][j] = i
elif (
word1[i - 1] == word2[j - 1]
): # last character of both substing is equal
dp[i][j] = dp[i - 1][j - 1]
else:
insert = dp[i][j - 1]
delete = dp[i - 1][j]
replace = dp[i - 1][j - 1]
dp[i][j] = 1 + min(insert, delete, replace)
return dp[m][n]
if __name__ == "__main__":
solver = EditDistance()
print("****************** Testing Edit Distance DP Algorithm ******************")
print()
S1 = input("Enter the first string: ").strip()
S2 = input("Enter the second string: ").strip()
print()
print("The minimum Edit Distance is: %d" % (solver.solve(S1, S2)))
print("The minimum Edit Distance is: %d" % (min_distance_bottom_up(S1, S2)))
print()
print("*************** End of Testing Edit Distance DP Algorithm ***************")
| """
Author : Turfa Auliarachman
Date : October 12, 2016
This is a pure Python implementation of Dynamic Programming solution to the edit
distance problem.
The problem is :
Given two strings A and B. Find the minimum number of operations to string B such that
A = B. The permitted operations are removal, insertion, and substitution.
"""
class EditDistance:
"""
Use :
solver = EditDistance()
editDistanceResult = solver.solve(firstString, secondString)
"""
def __init__(self):
self.__prepare__()
def __prepare__(self, n=0, m=0):
self.dp = [[-1 for y in range(0, m)] for x in range(0, n)]
def __solve_dp(self, x, y):
if x == -1:
return y + 1
elif y == -1:
return x + 1
elif self.dp[x][y] > -1:
return self.dp[x][y]
else:
if self.a[x] == self.b[y]:
self.dp[x][y] = self.__solve_dp(x - 1, y - 1)
else:
self.dp[x][y] = 1 + min(
self.__solve_dp(x, y - 1),
self.__solve_dp(x - 1, y),
self.__solve_dp(x - 1, y - 1),
)
return self.dp[x][y]
def solve(self, a, b):
if isinstance(a, bytes):
a = a.decode("ascii")
if isinstance(b, bytes):
b = b.decode("ascii")
self.a = str(a)
self.b = str(b)
self.__prepare__(len(a), len(b))
return self.__solve_dp(len(a) - 1, len(b) - 1)
def min_distance_bottom_up(word1: str, word2: str) -> int:
"""
>>> min_distance_bottom_up("intention", "execution")
5
>>> min_distance_bottom_up("intention", "")
9
>>> min_distance_bottom_up("", "")
0
"""
m = len(word1)
n = len(word2)
dp = [[0 for _ in range(n + 1)] for _ in range(m + 1)]
for i in range(m + 1):
for j in range(n + 1):
if i == 0: # first string is empty
dp[i][j] = j
elif j == 0: # second string is empty
dp[i][j] = i
elif (
word1[i - 1] == word2[j - 1]
): # last character of both substing is equal
dp[i][j] = dp[i - 1][j - 1]
else:
insert = dp[i][j - 1]
delete = dp[i - 1][j]
replace = dp[i - 1][j - 1]
dp[i][j] = 1 + min(insert, delete, replace)
return dp[m][n]
if __name__ == "__main__":
solver = EditDistance()
print("****************** Testing Edit Distance DP Algorithm ******************")
print()
S1 = input("Enter the first string: ").strip()
S2 = input("Enter the second string: ").strip()
print()
print(f"The minimum Edit Distance is: {solver.solve(S1, S2)}")
print(f"The minimum Edit Distance is: {min_distance_bottom_up(S1, S2)}")
print()
print("*************** End of Testing Edit Distance DP Algorithm ***************")
| 1 |
TheAlgorithms/Python | 7,339 | Follow Flake8 pep3101 and remove modulo formatting | ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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 have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | 2022-10-16T20:16:16Z | 2022-10-16T20:50:11Z | 7f6e0b656f6362e452b11d06acde50b8b81cb31a | f15cc2f01c2a4124ff6dc0843c728a546f9d9f79 | Follow Flake8 pep3101 and remove modulo formatting. ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Simple multithreaded algorithm to show how the 4 phases of a genetic algorithm works
(Evaluation, Selection, Crossover and Mutation)
https://en.wikipedia.org/wiki/Genetic_algorithm
Author: D4rkia
"""
from __future__ import annotations
import random
# Maximum size of the population. bigger could be faster but is more memory expensive
N_POPULATION = 200
# Number of elements selected in every generation for evolution the selection takes
# place from the best to the worst of that generation must be smaller than N_POPULATION
N_SELECTED = 50
# Probability that an element of a generation can mutate changing one of its genes this
# guarantees that all genes will be used during evolution
MUTATION_PROBABILITY = 0.4
# just a seed to improve randomness required by the algorithm
random.seed(random.randint(0, 1000))
def basic(target: str, genes: list[str], debug: bool = True) -> tuple[int, int, str]:
"""
Verify that the target contains no genes besides the ones inside genes variable.
>>> from string import ascii_lowercase
>>> basic("doctest", ascii_lowercase, debug=False)[2]
'doctest'
>>> genes = list(ascii_lowercase)
>>> genes.remove("e")
>>> basic("test", genes)
Traceback (most recent call last):
...
ValueError: ['e'] is not in genes list, evolution cannot converge
>>> genes.remove("s")
>>> basic("test", genes)
Traceback (most recent call last):
...
ValueError: ['e', 's'] is not in genes list, evolution cannot converge
>>> genes.remove("t")
>>> basic("test", genes)
Traceback (most recent call last):
...
ValueError: ['e', 's', 't'] is not in genes list, evolution cannot converge
"""
# Verify if N_POPULATION is bigger than N_SELECTED
if N_POPULATION < N_SELECTED:
raise ValueError(f"{N_POPULATION} must be bigger than {N_SELECTED}")
# Verify that the target contains no genes besides the ones inside genes variable.
not_in_genes_list = sorted({c for c in target if c not in genes})
if not_in_genes_list:
raise ValueError(
f"{not_in_genes_list} is not in genes list, evolution cannot converge"
)
# Generate random starting population
population = []
for _ in range(N_POPULATION):
population.append("".join([random.choice(genes) for i in range(len(target))]))
# Just some logs to know what the algorithms is doing
generation, total_population = 0, 0
# This loop will end when we will find a perfect match for our target
while True:
generation += 1
total_population += len(population)
# Random population created now it's time to evaluate
def evaluate(item: str, main_target: str = target) -> tuple[str, float]:
"""
Evaluate how similar the item is with the target by just
counting each char in the right position
>>> evaluate("Helxo Worlx", Hello World)
["Helxo Worlx", 9]
"""
score = len(
[g for position, g in enumerate(item) if g == main_target[position]]
)
return (item, float(score)) # noqa: B023
# Adding a bit of concurrency can make everything faster,
#
# import concurrent.futures
# population_score: list[tuple[str, float]] = []
# with concurrent.futures.ThreadPoolExecutor(
# max_workers=NUM_WORKERS) as executor:
# futures = {executor.submit(evaluate, item) for item in population}
# concurrent.futures.wait(futures)
# population_score = [item.result() for item in futures]
#
# but with a simple algorithm like this will probably be slower
# we just need to call evaluate for every item inside population
population_score = [evaluate(item) for item in population]
# Check if there is a matching evolution
population_score = sorted(population_score, key=lambda x: x[1], reverse=True)
if population_score[0][0] == target:
return (generation, total_population, population_score[0][0])
# Print the Best result every 10 generation
# just to know that the algorithm is working
if debug and generation % 10 == 0:
print(
f"\nGeneration: {generation}"
f"\nTotal Population:{total_population}"
f"\nBest score: {population_score[0][1]}"
f"\nBest string: {population_score[0][0]}"
)
# Flush the old population keeping some of the best evolutions
# Keeping this avoid regression of evolution
population_best = population[: int(N_POPULATION / 3)]
population.clear()
population.extend(population_best)
# Normalize population score from 0 to 1
population_score = [
(item, score / len(target)) for item, score in population_score
]
# Select, Crossover and Mutate a new population
def select(parent_1: tuple[str, float]) -> list[str]:
"""Select the second parent and generate new population"""
pop = []
# Generate more child proportionally to the fitness score
child_n = int(parent_1[1] * 100) + 1
child_n = 10 if child_n >= 10 else child_n
for _ in range(child_n):
parent_2 = population_score[ # noqa: B023
random.randint(0, N_SELECTED)
][0]
child_1, child_2 = crossover(parent_1[0], parent_2)
# Append new string to the population list
pop.append(mutate(child_1))
pop.append(mutate(child_2))
return pop
def crossover(parent_1: str, parent_2: str) -> tuple[str, str]:
"""Slice and combine two string in a random point"""
random_slice = random.randint(0, len(parent_1) - 1)
child_1 = parent_1[:random_slice] + parent_2[random_slice:]
child_2 = parent_2[:random_slice] + parent_1[random_slice:]
return (child_1, child_2)
def mutate(child: str) -> str:
"""Mutate a random gene of a child with another one from the list"""
child_list = list(child)
if random.uniform(0, 1) < MUTATION_PROBABILITY:
child_list[random.randint(0, len(child)) - 1] = random.choice(genes)
return "".join(child_list)
# This is Selection
for i in range(N_SELECTED):
population.extend(select(population_score[int(i)]))
# Check if the population has already reached the maximum value and if so,
# break the cycle. if this check is disabled the algorithm will take
# forever to compute large strings but will also calculate small string in
# a lot fewer generations
if len(population) > N_POPULATION:
break
if __name__ == "__main__":
target_str = (
"This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"
)
genes_list = list(
" ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"
"nopqrstuvwxyz.,;!?+-*#@^'èéòà€ù=)(&%$£/\\"
)
print(
"\nGeneration: %s\nTotal Population: %s\nTarget: %s"
% basic(target_str, genes_list)
)
| """
Simple multithreaded algorithm to show how the 4 phases of a genetic algorithm works
(Evaluation, Selection, Crossover and Mutation)
https://en.wikipedia.org/wiki/Genetic_algorithm
Author: D4rkia
"""
from __future__ import annotations
import random
# Maximum size of the population. bigger could be faster but is more memory expensive
N_POPULATION = 200
# Number of elements selected in every generation for evolution the selection takes
# place from the best to the worst of that generation must be smaller than N_POPULATION
N_SELECTED = 50
# Probability that an element of a generation can mutate changing one of its genes this
# guarantees that all genes will be used during evolution
MUTATION_PROBABILITY = 0.4
# just a seed to improve randomness required by the algorithm
random.seed(random.randint(0, 1000))
def basic(target: str, genes: list[str], debug: bool = True) -> tuple[int, int, str]:
"""
Verify that the target contains no genes besides the ones inside genes variable.
>>> from string import ascii_lowercase
>>> basic("doctest", ascii_lowercase, debug=False)[2]
'doctest'
>>> genes = list(ascii_lowercase)
>>> genes.remove("e")
>>> basic("test", genes)
Traceback (most recent call last):
...
ValueError: ['e'] is not in genes list, evolution cannot converge
>>> genes.remove("s")
>>> basic("test", genes)
Traceback (most recent call last):
...
ValueError: ['e', 's'] is not in genes list, evolution cannot converge
>>> genes.remove("t")
>>> basic("test", genes)
Traceback (most recent call last):
...
ValueError: ['e', 's', 't'] is not in genes list, evolution cannot converge
"""
# Verify if N_POPULATION is bigger than N_SELECTED
if N_POPULATION < N_SELECTED:
raise ValueError(f"{N_POPULATION} must be bigger than {N_SELECTED}")
# Verify that the target contains no genes besides the ones inside genes variable.
not_in_genes_list = sorted({c for c in target if c not in genes})
if not_in_genes_list:
raise ValueError(
f"{not_in_genes_list} is not in genes list, evolution cannot converge"
)
# Generate random starting population
population = []
for _ in range(N_POPULATION):
population.append("".join([random.choice(genes) for i in range(len(target))]))
# Just some logs to know what the algorithms is doing
generation, total_population = 0, 0
# This loop will end when we will find a perfect match for our target
while True:
generation += 1
total_population += len(population)
# Random population created now it's time to evaluate
def evaluate(item: str, main_target: str = target) -> tuple[str, float]:
"""
Evaluate how similar the item is with the target by just
counting each char in the right position
>>> evaluate("Helxo Worlx", Hello World)
["Helxo Worlx", 9]
"""
score = len(
[g for position, g in enumerate(item) if g == main_target[position]]
)
return (item, float(score)) # noqa: B023
# Adding a bit of concurrency can make everything faster,
#
# import concurrent.futures
# population_score: list[tuple[str, float]] = []
# with concurrent.futures.ThreadPoolExecutor(
# max_workers=NUM_WORKERS) as executor:
# futures = {executor.submit(evaluate, item) for item in population}
# concurrent.futures.wait(futures)
# population_score = [item.result() for item in futures]
#
# but with a simple algorithm like this will probably be slower
# we just need to call evaluate for every item inside population
population_score = [evaluate(item) for item in population]
# Check if there is a matching evolution
population_score = sorted(population_score, key=lambda x: x[1], reverse=True)
if population_score[0][0] == target:
return (generation, total_population, population_score[0][0])
# Print the Best result every 10 generation
# just to know that the algorithm is working
if debug and generation % 10 == 0:
print(
f"\nGeneration: {generation}"
f"\nTotal Population:{total_population}"
f"\nBest score: {population_score[0][1]}"
f"\nBest string: {population_score[0][0]}"
)
# Flush the old population keeping some of the best evolutions
# Keeping this avoid regression of evolution
population_best = population[: int(N_POPULATION / 3)]
population.clear()
population.extend(population_best)
# Normalize population score from 0 to 1
population_score = [
(item, score / len(target)) for item, score in population_score
]
# Select, Crossover and Mutate a new population
def select(parent_1: tuple[str, float]) -> list[str]:
"""Select the second parent and generate new population"""
pop = []
# Generate more child proportionally to the fitness score
child_n = int(parent_1[1] * 100) + 1
child_n = 10 if child_n >= 10 else child_n
for _ in range(child_n):
parent_2 = population_score[ # noqa: B023
random.randint(0, N_SELECTED)
][0]
child_1, child_2 = crossover(parent_1[0], parent_2)
# Append new string to the population list
pop.append(mutate(child_1))
pop.append(mutate(child_2))
return pop
def crossover(parent_1: str, parent_2: str) -> tuple[str, str]:
"""Slice and combine two string in a random point"""
random_slice = random.randint(0, len(parent_1) - 1)
child_1 = parent_1[:random_slice] + parent_2[random_slice:]
child_2 = parent_2[:random_slice] + parent_1[random_slice:]
return (child_1, child_2)
def mutate(child: str) -> str:
"""Mutate a random gene of a child with another one from the list"""
child_list = list(child)
if random.uniform(0, 1) < MUTATION_PROBABILITY:
child_list[random.randint(0, len(child)) - 1] = random.choice(genes)
return "".join(child_list)
# This is Selection
for i in range(N_SELECTED):
population.extend(select(population_score[int(i)]))
# Check if the population has already reached the maximum value and if so,
# break the cycle. if this check is disabled the algorithm will take
# forever to compute large strings but will also calculate small string in
# a lot fewer generations
if len(population) > N_POPULATION:
break
if __name__ == "__main__":
target_str = (
"This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"
)
genes_list = list(
" ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"
"nopqrstuvwxyz.,;!?+-*#@^'èéòà€ù=)(&%$£/\\"
)
generation, population, target = basic(target_str, genes_list)
print(
f"\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}"
)
| 1 |
TheAlgorithms/Python | 7,339 | Follow Flake8 pep3101 and remove modulo formatting | ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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 have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | 2022-10-16T20:16:16Z | 2022-10-16T20:50:11Z | 7f6e0b656f6362e452b11d06acde50b8b81cb31a | f15cc2f01c2a4124ff6dc0843c728a546f9d9f79 | Follow Flake8 pep3101 and remove modulo formatting. ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| class Graph:
"""
Data structure to store graphs (based on adjacency lists)
"""
def __init__(self):
self.num_vertices = 0
self.num_edges = 0
self.adjacency = {}
def add_vertex(self, vertex):
"""
Adds a vertex to the graph
"""
if vertex not in self.adjacency:
self.adjacency[vertex] = {}
self.num_vertices += 1
def add_edge(self, head, tail, weight):
"""
Adds an edge to the graph
"""
self.add_vertex(head)
self.add_vertex(tail)
if head == tail:
return
self.adjacency[head][tail] = weight
self.adjacency[tail][head] = weight
def distinct_weight(self):
"""
For Boruvks's algorithm the weights should be distinct
Converts the weights to be distinct
"""
edges = self.get_edges()
for edge in edges:
head, tail, weight = edge
edges.remove((tail, head, weight))
for i in range(len(edges)):
edges[i] = list(edges[i])
edges.sort(key=lambda e: e[2])
for i in range(len(edges) - 1):
if edges[i][2] >= edges[i + 1][2]:
edges[i + 1][2] = edges[i][2] + 1
for edge in edges:
head, tail, weight = edge
self.adjacency[head][tail] = weight
self.adjacency[tail][head] = weight
def __str__(self):
"""
Returns string representation of the graph
"""
string = ""
for tail in self.adjacency:
for head in self.adjacency[tail]:
weight = self.adjacency[head][tail]
string += "%d -> %d == %d\n" % (head, tail, weight)
return string.rstrip("\n")
def get_edges(self):
"""
Returna all edges in the graph
"""
output = []
for tail in self.adjacency:
for head in self.adjacency[tail]:
output.append((tail, head, self.adjacency[head][tail]))
return output
def get_vertices(self):
"""
Returns all vertices in the graph
"""
return self.adjacency.keys()
@staticmethod
def build(vertices=None, edges=None):
"""
Builds a graph from the given set of vertices and edges
"""
g = Graph()
if vertices is None:
vertices = []
if edges is None:
edge = []
for vertex in vertices:
g.add_vertex(vertex)
for edge in edges:
g.add_edge(*edge)
return g
class UnionFind:
"""
Disjoint set Union and Find for Boruvka's algorithm
"""
def __init__(self):
self.parent = {}
self.rank = {}
def __len__(self):
return len(self.parent)
def make_set(self, item):
if item in self.parent:
return self.find(item)
self.parent[item] = item
self.rank[item] = 0
return item
def find(self, item):
if item not in self.parent:
return self.make_set(item)
if item != self.parent[item]:
self.parent[item] = self.find(self.parent[item])
return self.parent[item]
def union(self, item1, item2):
root1 = self.find(item1)
root2 = self.find(item2)
if root1 == root2:
return root1
if self.rank[root1] > self.rank[root2]:
self.parent[root2] = root1
return root1
if self.rank[root1] < self.rank[root2]:
self.parent[root1] = root2
return root2
if self.rank[root1] == self.rank[root2]:
self.rank[root1] += 1
self.parent[root2] = root1
return root1
@staticmethod
def boruvka_mst(graph):
"""
Implementation of Boruvka's algorithm
>>> g = Graph()
>>> g = Graph.build([0, 1, 2, 3], [[0, 1, 1], [0, 2, 1],[2, 3, 1]])
>>> g.distinct_weight()
>>> bg = Graph.boruvka_mst(g)
>>> print(bg)
1 -> 0 == 1
2 -> 0 == 2
0 -> 1 == 1
0 -> 2 == 2
3 -> 2 == 3
2 -> 3 == 3
"""
num_components = graph.num_vertices
union_find = Graph.UnionFind()
mst_edges = []
while num_components > 1:
cheap_edge = {}
for vertex in graph.get_vertices():
cheap_edge[vertex] = -1
edges = graph.get_edges()
for edge in edges:
head, tail, weight = edge
edges.remove((tail, head, weight))
for edge in edges:
head, tail, weight = edge
set1 = union_find.find(head)
set2 = union_find.find(tail)
if set1 != set2:
if cheap_edge[set1] == -1 or cheap_edge[set1][2] > weight:
cheap_edge[set1] = [head, tail, weight]
if cheap_edge[set2] == -1 or cheap_edge[set2][2] > weight:
cheap_edge[set2] = [head, tail, weight]
for vertex in cheap_edge:
if cheap_edge[vertex] != -1:
head, tail, weight = cheap_edge[vertex]
if union_find.find(head) != union_find.find(tail):
union_find.union(head, tail)
mst_edges.append(cheap_edge[vertex])
num_components = num_components - 1
mst = Graph.build(edges=mst_edges)
return mst
| class Graph:
"""
Data structure to store graphs (based on adjacency lists)
"""
def __init__(self):
self.num_vertices = 0
self.num_edges = 0
self.adjacency = {}
def add_vertex(self, vertex):
"""
Adds a vertex to the graph
"""
if vertex not in self.adjacency:
self.adjacency[vertex] = {}
self.num_vertices += 1
def add_edge(self, head, tail, weight):
"""
Adds an edge to the graph
"""
self.add_vertex(head)
self.add_vertex(tail)
if head == tail:
return
self.adjacency[head][tail] = weight
self.adjacency[tail][head] = weight
def distinct_weight(self):
"""
For Boruvks's algorithm the weights should be distinct
Converts the weights to be distinct
"""
edges = self.get_edges()
for edge in edges:
head, tail, weight = edge
edges.remove((tail, head, weight))
for i in range(len(edges)):
edges[i] = list(edges[i])
edges.sort(key=lambda e: e[2])
for i in range(len(edges) - 1):
if edges[i][2] >= edges[i + 1][2]:
edges[i + 1][2] = edges[i][2] + 1
for edge in edges:
head, tail, weight = edge
self.adjacency[head][tail] = weight
self.adjacency[tail][head] = weight
def __str__(self):
"""
Returns string representation of the graph
"""
string = ""
for tail in self.adjacency:
for head in self.adjacency[tail]:
weight = self.adjacency[head][tail]
string += f"{head} -> {tail} == {weight}\n"
return string.rstrip("\n")
def get_edges(self):
"""
Returna all edges in the graph
"""
output = []
for tail in self.adjacency:
for head in self.adjacency[tail]:
output.append((tail, head, self.adjacency[head][tail]))
return output
def get_vertices(self):
"""
Returns all vertices in the graph
"""
return self.adjacency.keys()
@staticmethod
def build(vertices=None, edges=None):
"""
Builds a graph from the given set of vertices and edges
"""
g = Graph()
if vertices is None:
vertices = []
if edges is None:
edge = []
for vertex in vertices:
g.add_vertex(vertex)
for edge in edges:
g.add_edge(*edge)
return g
class UnionFind:
"""
Disjoint set Union and Find for Boruvka's algorithm
"""
def __init__(self):
self.parent = {}
self.rank = {}
def __len__(self):
return len(self.parent)
def make_set(self, item):
if item in self.parent:
return self.find(item)
self.parent[item] = item
self.rank[item] = 0
return item
def find(self, item):
if item not in self.parent:
return self.make_set(item)
if item != self.parent[item]:
self.parent[item] = self.find(self.parent[item])
return self.parent[item]
def union(self, item1, item2):
root1 = self.find(item1)
root2 = self.find(item2)
if root1 == root2:
return root1
if self.rank[root1] > self.rank[root2]:
self.parent[root2] = root1
return root1
if self.rank[root1] < self.rank[root2]:
self.parent[root1] = root2
return root2
if self.rank[root1] == self.rank[root2]:
self.rank[root1] += 1
self.parent[root2] = root1
return root1
@staticmethod
def boruvka_mst(graph):
"""
Implementation of Boruvka's algorithm
>>> g = Graph()
>>> g = Graph.build([0, 1, 2, 3], [[0, 1, 1], [0, 2, 1],[2, 3, 1]])
>>> g.distinct_weight()
>>> bg = Graph.boruvka_mst(g)
>>> print(bg)
1 -> 0 == 1
2 -> 0 == 2
0 -> 1 == 1
0 -> 2 == 2
3 -> 2 == 3
2 -> 3 == 3
"""
num_components = graph.num_vertices
union_find = Graph.UnionFind()
mst_edges = []
while num_components > 1:
cheap_edge = {}
for vertex in graph.get_vertices():
cheap_edge[vertex] = -1
edges = graph.get_edges()
for edge in edges:
head, tail, weight = edge
edges.remove((tail, head, weight))
for edge in edges:
head, tail, weight = edge
set1 = union_find.find(head)
set2 = union_find.find(tail)
if set1 != set2:
if cheap_edge[set1] == -1 or cheap_edge[set1][2] > weight:
cheap_edge[set1] = [head, tail, weight]
if cheap_edge[set2] == -1 or cheap_edge[set2][2] > weight:
cheap_edge[set2] = [head, tail, weight]
for vertex in cheap_edge:
if cheap_edge[vertex] != -1:
head, tail, weight = cheap_edge[vertex]
if union_find.find(head) != union_find.find(tail):
union_find.union(head, tail)
mst_edges.append(cheap_edge[vertex])
num_components = num_components - 1
mst = Graph.build(edges=mst_edges)
return mst
| 1 |
TheAlgorithms/Python | 7,339 | Follow Flake8 pep3101 and remove modulo formatting | ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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 have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | 2022-10-16T20:16:16Z | 2022-10-16T20:50:11Z | 7f6e0b656f6362e452b11d06acde50b8b81cb31a | f15cc2f01c2a4124ff6dc0843c728a546f9d9f79 | Follow Flake8 pep3101 and remove modulo formatting. ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Linear regression is the most basic type of regression commonly used for
predictive analysis. The idea is pretty simple: we have a dataset and we have
features associated with it. Features should be chosen very cautiously
as they determine how much our model will be able to make future predictions.
We try to set the weight of these features, over many iterations, so that they best
fit our dataset. In this particular code, I had used a CSGO dataset (ADR vs
Rating). We try to best fit a line through dataset and estimate the parameters.
"""
import numpy as np
import requests
def collect_dataset():
"""Collect dataset of CSGO
The dataset contains ADR vs Rating of a Player
:return : dataset obtained from the link, as matrix
"""
response = requests.get(
"https://raw.githubusercontent.com/yashLadha/"
+ "The_Math_of_Intelligence/master/Week1/ADRvs"
+ "Rating.csv"
)
lines = response.text.splitlines()
data = []
for item in lines:
item = item.split(",")
data.append(item)
data.pop(0) # This is for removing the labels from the list
dataset = np.matrix(data)
return dataset
def run_steep_gradient_descent(data_x, data_y, len_data, alpha, theta):
"""Run steep gradient descent and updates the Feature vector accordingly_
:param data_x : contains the dataset
:param data_y : contains the output associated with each data-entry
:param len_data : length of the data_
:param alpha : Learning rate of the model
:param theta : Feature vector (weight's for our model)
;param return : Updated Feature's, using
curr_features - alpha_ * gradient(w.r.t. feature)
"""
n = len_data
prod = np.dot(theta, data_x.transpose())
prod -= data_y.transpose()
sum_grad = np.dot(prod, data_x)
theta = theta - (alpha / n) * sum_grad
return theta
def sum_of_square_error(data_x, data_y, len_data, theta):
"""Return sum of square error for error calculation
:param data_x : contains our dataset
:param data_y : contains the output (result vector)
:param len_data : len of the dataset
:param theta : contains the feature vector
:return : sum of square error computed from given feature's
"""
prod = np.dot(theta, data_x.transpose())
prod -= data_y.transpose()
sum_elem = np.sum(np.square(prod))
error = sum_elem / (2 * len_data)
return error
def run_linear_regression(data_x, data_y):
"""Implement Linear regression over the dataset
:param data_x : contains our dataset
:param data_y : contains the output (result vector)
:return : feature for line of best fit (Feature vector)
"""
iterations = 100000
alpha = 0.0001550
no_features = data_x.shape[1]
len_data = data_x.shape[0] - 1
theta = np.zeros((1, no_features))
for i in range(0, iterations):
theta = run_steep_gradient_descent(data_x, data_y, len_data, alpha, theta)
error = sum_of_square_error(data_x, data_y, len_data, theta)
print("At Iteration %d - Error is %.5f " % (i + 1, error))
return theta
def main():
"""Driver function"""
data = collect_dataset()
len_data = data.shape[0]
data_x = np.c_[np.ones(len_data), data[:, :-1]].astype(float)
data_y = data[:, -1].astype(float)
theta = run_linear_regression(data_x, data_y)
len_result = theta.shape[1]
print("Resultant Feature vector : ")
for i in range(0, len_result):
print(f"{theta[0, i]:.5f}")
if __name__ == "__main__":
main()
| """
Linear regression is the most basic type of regression commonly used for
predictive analysis. The idea is pretty simple: we have a dataset and we have
features associated with it. Features should be chosen very cautiously
as they determine how much our model will be able to make future predictions.
We try to set the weight of these features, over many iterations, so that they best
fit our dataset. In this particular code, I had used a CSGO dataset (ADR vs
Rating). We try to best fit a line through dataset and estimate the parameters.
"""
import numpy as np
import requests
def collect_dataset():
"""Collect dataset of CSGO
The dataset contains ADR vs Rating of a Player
:return : dataset obtained from the link, as matrix
"""
response = requests.get(
"https://raw.githubusercontent.com/yashLadha/"
+ "The_Math_of_Intelligence/master/Week1/ADRvs"
+ "Rating.csv"
)
lines = response.text.splitlines()
data = []
for item in lines:
item = item.split(",")
data.append(item)
data.pop(0) # This is for removing the labels from the list
dataset = np.matrix(data)
return dataset
def run_steep_gradient_descent(data_x, data_y, len_data, alpha, theta):
"""Run steep gradient descent and updates the Feature vector accordingly_
:param data_x : contains the dataset
:param data_y : contains the output associated with each data-entry
:param len_data : length of the data_
:param alpha : Learning rate of the model
:param theta : Feature vector (weight's for our model)
;param return : Updated Feature's, using
curr_features - alpha_ * gradient(w.r.t. feature)
"""
n = len_data
prod = np.dot(theta, data_x.transpose())
prod -= data_y.transpose()
sum_grad = np.dot(prod, data_x)
theta = theta - (alpha / n) * sum_grad
return theta
def sum_of_square_error(data_x, data_y, len_data, theta):
"""Return sum of square error for error calculation
:param data_x : contains our dataset
:param data_y : contains the output (result vector)
:param len_data : len of the dataset
:param theta : contains the feature vector
:return : sum of square error computed from given feature's
"""
prod = np.dot(theta, data_x.transpose())
prod -= data_y.transpose()
sum_elem = np.sum(np.square(prod))
error = sum_elem / (2 * len_data)
return error
def run_linear_regression(data_x, data_y):
"""Implement Linear regression over the dataset
:param data_x : contains our dataset
:param data_y : contains the output (result vector)
:return : feature for line of best fit (Feature vector)
"""
iterations = 100000
alpha = 0.0001550
no_features = data_x.shape[1]
len_data = data_x.shape[0] - 1
theta = np.zeros((1, no_features))
for i in range(0, iterations):
theta = run_steep_gradient_descent(data_x, data_y, len_data, alpha, theta)
error = sum_of_square_error(data_x, data_y, len_data, theta)
print(f"At Iteration {i + 1} - Error is {error:.5f}")
return theta
def main():
"""Driver function"""
data = collect_dataset()
len_data = data.shape[0]
data_x = np.c_[np.ones(len_data), data[:, :-1]].astype(float)
data_y = data[:, -1].astype(float)
theta = run_linear_regression(data_x, data_y)
len_result = theta.shape[1]
print("Resultant Feature vector : ")
for i in range(0, len_result):
print(f"{theta[0, i]:.5f}")
if __name__ == "__main__":
main()
| 1 |
TheAlgorithms/Python | 7,339 | Follow Flake8 pep3101 and remove modulo formatting | ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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 have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | 2022-10-16T20:16:16Z | 2022-10-16T20:50:11Z | 7f6e0b656f6362e452b11d06acde50b8b81cb31a | f15cc2f01c2a4124ff6dc0843c728a546f9d9f79 | Follow Flake8 pep3101 and remove modulo formatting. ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from __future__ import annotations
from typing import Any
class Matrix:
"""
<class Matrix>
Matrix structure.
"""
def __init__(self, row: int, column: int, default_value: float = 0) -> None:
"""
<method Matrix.__init__>
Initialize matrix with given size and default value.
Example:
>>> a = Matrix(2, 3, 1)
>>> a
Matrix consist of 2 rows and 3 columns
[1, 1, 1]
[1, 1, 1]
"""
self.row, self.column = row, column
self.array = [[default_value for c in range(column)] for r in range(row)]
def __str__(self) -> str:
"""
<method Matrix.__str__>
Return string representation of this matrix.
"""
# Prefix
s = "Matrix consist of %d rows and %d columns\n" % (self.row, self.column)
# Make string identifier
max_element_length = 0
for row_vector in self.array:
for obj in row_vector:
max_element_length = max(max_element_length, len(str(obj)))
string_format_identifier = "%%%ds" % (max_element_length,)
# Make string and return
def single_line(row_vector: list[float]) -> str:
nonlocal string_format_identifier
line = "["
line += ", ".join(string_format_identifier % (obj,) for obj in row_vector)
line += "]"
return line
s += "\n".join(single_line(row_vector) for row_vector in self.array)
return s
def __repr__(self) -> str:
return str(self)
def validate_indicies(self, loc: tuple[int, int]) -> bool:
"""
<method Matrix.validate_indicies>
Check if given indices are valid to pick element from matrix.
Example:
>>> a = Matrix(2, 6, 0)
>>> a.validate_indicies((2, 7))
False
>>> a.validate_indicies((0, 0))
True
"""
if not (isinstance(loc, (list, tuple)) and len(loc) == 2):
return False
elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
return False
else:
return True
def __getitem__(self, loc: tuple[int, int]) -> Any:
"""
<method Matrix.__getitem__>
Return array[row][column] where loc = (row, column).
Example:
>>> a = Matrix(3, 2, 7)
>>> a[1, 0]
7
"""
assert self.validate_indicies(loc)
return self.array[loc[0]][loc[1]]
def __setitem__(self, loc: tuple[int, int], value: float) -> None:
"""
<method Matrix.__setitem__>
Set array[row][column] = value where loc = (row, column).
Example:
>>> a = Matrix(2, 3, 1)
>>> a[1, 2] = 51
>>> a
Matrix consist of 2 rows and 3 columns
[ 1, 1, 1]
[ 1, 1, 51]
"""
assert self.validate_indicies(loc)
self.array[loc[0]][loc[1]] = value
def __add__(self, another: Matrix) -> Matrix:
"""
<method Matrix.__add__>
Return self + another.
Example:
>>> a = Matrix(2, 1, -4)
>>> b = Matrix(2, 1, 3)
>>> a+b
Matrix consist of 2 rows and 1 columns
[-1]
[-1]
"""
# Validation
assert isinstance(another, Matrix)
assert self.row == another.row and self.column == another.column
# Add
result = Matrix(self.row, self.column)
for r in range(self.row):
for c in range(self.column):
result[r, c] = self[r, c] + another[r, c]
return result
def __neg__(self) -> Matrix:
"""
<method Matrix.__neg__>
Return -self.
Example:
>>> a = Matrix(2, 2, 3)
>>> a[0, 1] = a[1, 0] = -2
>>> -a
Matrix consist of 2 rows and 2 columns
[-3, 2]
[ 2, -3]
"""
result = Matrix(self.row, self.column)
for r in range(self.row):
for c in range(self.column):
result[r, c] = -self[r, c]
return result
def __sub__(self, another: Matrix) -> Matrix:
return self + (-another)
def __mul__(self, another: int | float | Matrix) -> Matrix:
"""
<method Matrix.__mul__>
Return self * another.
Example:
>>> a = Matrix(2, 3, 1)
>>> a[0,2] = a[1,2] = 3
>>> a * -2
Matrix consist of 2 rows and 3 columns
[-2, -2, -6]
[-2, -2, -6]
"""
if isinstance(another, (int, float)): # Scalar multiplication
result = Matrix(self.row, self.column)
for r in range(self.row):
for c in range(self.column):
result[r, c] = self[r, c] * another
return result
elif isinstance(another, Matrix): # Matrix multiplication
assert self.column == another.row
result = Matrix(self.row, another.column)
for r in range(self.row):
for c in range(another.column):
for i in range(self.column):
result[r, c] += self[r, i] * another[i, c]
return result
else:
raise TypeError(f"Unsupported type given for another ({type(another)})")
def transpose(self) -> Matrix:
"""
<method Matrix.transpose>
Return self^T.
Example:
>>> a = Matrix(2, 3)
>>> for r in range(2):
... for c in range(3):
... a[r,c] = r*c
...
>>> a.transpose()
Matrix consist of 3 rows and 2 columns
[0, 0]
[0, 1]
[0, 2]
"""
result = Matrix(self.column, self.row)
for r in range(self.row):
for c in range(self.column):
result[c, r] = self[r, c]
return result
def sherman_morrison(self, u: Matrix, v: Matrix) -> Any:
"""
<method Matrix.sherman_morrison>
Apply Sherman-Morrison formula in O(n^2).
To learn this formula, please look this:
https://en.wikipedia.org/wiki/Sherman%E2%80%93Morrison_formula
This method returns (A + uv^T)^(-1) where A^(-1) is self. Returns None if it's
impossible to calculate.
Warning: This method doesn't check if self is invertible.
Make sure self is invertible before execute this method.
Example:
>>> ainv = Matrix(3, 3, 0)
>>> for i in range(3): ainv[i,i] = 1
...
>>> u = Matrix(3, 1, 0)
>>> u[0,0], u[1,0], u[2,0] = 1, 2, -3
>>> v = Matrix(3, 1, 0)
>>> v[0,0], v[1,0], v[2,0] = 4, -2, 5
>>> ainv.sherman_morrison(u, v)
Matrix consist of 3 rows and 3 columns
[ 1.2857142857142856, -0.14285714285714285, 0.3571428571428571]
[ 0.5714285714285714, 0.7142857142857143, 0.7142857142857142]
[ -0.8571428571428571, 0.42857142857142855, -0.0714285714285714]
"""
# Size validation
assert isinstance(u, Matrix) and isinstance(v, Matrix)
assert self.row == self.column == u.row == v.row # u, v should be column vector
assert u.column == v.column == 1 # u, v should be column vector
# Calculate
v_t = v.transpose()
numerator_factor = (v_t * self * u)[0, 0] + 1
if numerator_factor == 0:
return None # It's not invertable
return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))
# Testing
if __name__ == "__main__":
def test1() -> None:
# a^(-1)
ainv = Matrix(3, 3, 0)
for i in range(3):
ainv[i, i] = 1
print(f"a^(-1) is {ainv}")
# u, v
u = Matrix(3, 1, 0)
u[0, 0], u[1, 0], u[2, 0] = 1, 2, -3
v = Matrix(3, 1, 0)
v[0, 0], v[1, 0], v[2, 0] = 4, -2, 5
print(f"u is {u}")
print(f"v is {v}")
print("uv^T is %s" % (u * v.transpose()))
# Sherman Morrison
print(f"(a + uv^T)^(-1) is {ainv.sherman_morrison(u, v)}")
def test2() -> None:
import doctest
doctest.testmod()
test2()
| from __future__ import annotations
from typing import Any
class Matrix:
"""
<class Matrix>
Matrix structure.
"""
def __init__(self, row: int, column: int, default_value: float = 0) -> None:
"""
<method Matrix.__init__>
Initialize matrix with given size and default value.
Example:
>>> a = Matrix(2, 3, 1)
>>> a
Matrix consist of 2 rows and 3 columns
[1, 1, 1]
[1, 1, 1]
"""
self.row, self.column = row, column
self.array = [[default_value for c in range(column)] for r in range(row)]
def __str__(self) -> str:
"""
<method Matrix.__str__>
Return string representation of this matrix.
"""
# Prefix
s = f"Matrix consist of {self.row} rows and {self.column} columns\n"
# Make string identifier
max_element_length = 0
for row_vector in self.array:
for obj in row_vector:
max_element_length = max(max_element_length, len(str(obj)))
string_format_identifier = f"%{max_element_length}s"
# Make string and return
def single_line(row_vector: list[float]) -> str:
nonlocal string_format_identifier
line = "["
line += ", ".join(string_format_identifier % (obj,) for obj in row_vector)
line += "]"
return line
s += "\n".join(single_line(row_vector) for row_vector in self.array)
return s
def __repr__(self) -> str:
return str(self)
def validate_indicies(self, loc: tuple[int, int]) -> bool:
"""
<method Matrix.validate_indicies>
Check if given indices are valid to pick element from matrix.
Example:
>>> a = Matrix(2, 6, 0)
>>> a.validate_indicies((2, 7))
False
>>> a.validate_indicies((0, 0))
True
"""
if not (isinstance(loc, (list, tuple)) and len(loc) == 2):
return False
elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
return False
else:
return True
def __getitem__(self, loc: tuple[int, int]) -> Any:
"""
<method Matrix.__getitem__>
Return array[row][column] where loc = (row, column).
Example:
>>> a = Matrix(3, 2, 7)
>>> a[1, 0]
7
"""
assert self.validate_indicies(loc)
return self.array[loc[0]][loc[1]]
def __setitem__(self, loc: tuple[int, int], value: float) -> None:
"""
<method Matrix.__setitem__>
Set array[row][column] = value where loc = (row, column).
Example:
>>> a = Matrix(2, 3, 1)
>>> a[1, 2] = 51
>>> a
Matrix consist of 2 rows and 3 columns
[ 1, 1, 1]
[ 1, 1, 51]
"""
assert self.validate_indicies(loc)
self.array[loc[0]][loc[1]] = value
def __add__(self, another: Matrix) -> Matrix:
"""
<method Matrix.__add__>
Return self + another.
Example:
>>> a = Matrix(2, 1, -4)
>>> b = Matrix(2, 1, 3)
>>> a+b
Matrix consist of 2 rows and 1 columns
[-1]
[-1]
"""
# Validation
assert isinstance(another, Matrix)
assert self.row == another.row and self.column == another.column
# Add
result = Matrix(self.row, self.column)
for r in range(self.row):
for c in range(self.column):
result[r, c] = self[r, c] + another[r, c]
return result
def __neg__(self) -> Matrix:
"""
<method Matrix.__neg__>
Return -self.
Example:
>>> a = Matrix(2, 2, 3)
>>> a[0, 1] = a[1, 0] = -2
>>> -a
Matrix consist of 2 rows and 2 columns
[-3, 2]
[ 2, -3]
"""
result = Matrix(self.row, self.column)
for r in range(self.row):
for c in range(self.column):
result[r, c] = -self[r, c]
return result
def __sub__(self, another: Matrix) -> Matrix:
return self + (-another)
def __mul__(self, another: int | float | Matrix) -> Matrix:
"""
<method Matrix.__mul__>
Return self * another.
Example:
>>> a = Matrix(2, 3, 1)
>>> a[0,2] = a[1,2] = 3
>>> a * -2
Matrix consist of 2 rows and 3 columns
[-2, -2, -6]
[-2, -2, -6]
"""
if isinstance(another, (int, float)): # Scalar multiplication
result = Matrix(self.row, self.column)
for r in range(self.row):
for c in range(self.column):
result[r, c] = self[r, c] * another
return result
elif isinstance(another, Matrix): # Matrix multiplication
assert self.column == another.row
result = Matrix(self.row, another.column)
for r in range(self.row):
for c in range(another.column):
for i in range(self.column):
result[r, c] += self[r, i] * another[i, c]
return result
else:
raise TypeError(f"Unsupported type given for another ({type(another)})")
def transpose(self) -> Matrix:
"""
<method Matrix.transpose>
Return self^T.
Example:
>>> a = Matrix(2, 3)
>>> for r in range(2):
... for c in range(3):
... a[r,c] = r*c
...
>>> a.transpose()
Matrix consist of 3 rows and 2 columns
[0, 0]
[0, 1]
[0, 2]
"""
result = Matrix(self.column, self.row)
for r in range(self.row):
for c in range(self.column):
result[c, r] = self[r, c]
return result
def sherman_morrison(self, u: Matrix, v: Matrix) -> Any:
"""
<method Matrix.sherman_morrison>
Apply Sherman-Morrison formula in O(n^2).
To learn this formula, please look this:
https://en.wikipedia.org/wiki/Sherman%E2%80%93Morrison_formula
This method returns (A + uv^T)^(-1) where A^(-1) is self. Returns None if it's
impossible to calculate.
Warning: This method doesn't check if self is invertible.
Make sure self is invertible before execute this method.
Example:
>>> ainv = Matrix(3, 3, 0)
>>> for i in range(3): ainv[i,i] = 1
...
>>> u = Matrix(3, 1, 0)
>>> u[0,0], u[1,0], u[2,0] = 1, 2, -3
>>> v = Matrix(3, 1, 0)
>>> v[0,0], v[1,0], v[2,0] = 4, -2, 5
>>> ainv.sherman_morrison(u, v)
Matrix consist of 3 rows and 3 columns
[ 1.2857142857142856, -0.14285714285714285, 0.3571428571428571]
[ 0.5714285714285714, 0.7142857142857143, 0.7142857142857142]
[ -0.8571428571428571, 0.42857142857142855, -0.0714285714285714]
"""
# Size validation
assert isinstance(u, Matrix) and isinstance(v, Matrix)
assert self.row == self.column == u.row == v.row # u, v should be column vector
assert u.column == v.column == 1 # u, v should be column vector
# Calculate
v_t = v.transpose()
numerator_factor = (v_t * self * u)[0, 0] + 1
if numerator_factor == 0:
return None # It's not invertable
return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))
# Testing
if __name__ == "__main__":
def test1() -> None:
# a^(-1)
ainv = Matrix(3, 3, 0)
for i in range(3):
ainv[i, i] = 1
print(f"a^(-1) is {ainv}")
# u, v
u = Matrix(3, 1, 0)
u[0, 0], u[1, 0], u[2, 0] = 1, 2, -3
v = Matrix(3, 1, 0)
v[0, 0], v[1, 0], v[2, 0] = 4, -2, 5
print(f"u is {u}")
print(f"v is {v}")
print(f"uv^T is {u * v.transpose()}")
# Sherman Morrison
print(f"(a + uv^T)^(-1) is {ainv.sherman_morrison(u, v)}")
def test2() -> None:
import doctest
doctest.testmod()
test2()
| 1 |
TheAlgorithms/Python | 7,339 | Follow Flake8 pep3101 and remove modulo formatting | ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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 have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | 2022-10-16T20:16:16Z | 2022-10-16T20:50:11Z | 7f6e0b656f6362e452b11d06acde50b8b81cb31a | f15cc2f01c2a4124ff6dc0843c728a546f9d9f79 | Follow Flake8 pep3101 and remove modulo formatting. ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| #!/usr/bin/python
"""
A Framework of Back Propagation Neural Network（BP） model
Easy to use:
* add many layers as you want ！！！
* clearly see how the loss decreasing
Easy to expand:
* more activation functions
* more loss functions
* more optimization method
Author: Stephen Lee
Github : https://github.com/RiptideBo
Date: 2017.11.23
"""
import numpy as np
from matplotlib import pyplot as plt
def sigmoid(x):
return 1 / (1 + np.exp(-1 * x))
class DenseLayer:
"""
Layers of BP neural network
"""
def __init__(
self, units, activation=None, learning_rate=None, is_input_layer=False
):
"""
common connected layer of bp network
:param units: numbers of neural units
:param activation: activation function
:param learning_rate: learning rate for paras
:param is_input_layer: whether it is input layer or not
"""
self.units = units
self.weight = None
self.bias = None
self.activation = activation
if learning_rate is None:
learning_rate = 0.3
self.learn_rate = learning_rate
self.is_input_layer = is_input_layer
def initializer(self, back_units):
self.weight = np.asmatrix(np.random.normal(0, 0.5, (self.units, back_units)))
self.bias = np.asmatrix(np.random.normal(0, 0.5, self.units)).T
if self.activation is None:
self.activation = sigmoid
def cal_gradient(self):
# activation function may be sigmoid or linear
if self.activation == sigmoid:
gradient_mat = np.dot(self.output, (1 - self.output).T)
gradient_activation = np.diag(np.diag(gradient_mat))
else:
gradient_activation = 1
return gradient_activation
def forward_propagation(self, xdata):
self.xdata = xdata
if self.is_input_layer:
# input layer
self.wx_plus_b = xdata
self.output = xdata
return xdata
else:
self.wx_plus_b = np.dot(self.weight, self.xdata) - self.bias
self.output = self.activation(self.wx_plus_b)
return self.output
def back_propagation(self, gradient):
gradient_activation = self.cal_gradient() # i * i 维
gradient = np.asmatrix(np.dot(gradient.T, gradient_activation))
self._gradient_weight = np.asmatrix(self.xdata)
self._gradient_bias = -1
self._gradient_x = self.weight
self.gradient_weight = np.dot(gradient.T, self._gradient_weight.T)
self.gradient_bias = gradient * self._gradient_bias
self.gradient = np.dot(gradient, self._gradient_x).T
# upgrade: the Negative gradient direction
self.weight = self.weight - self.learn_rate * self.gradient_weight
self.bias = self.bias - self.learn_rate * self.gradient_bias.T
# updates the weights and bias according to learning rate (0.3 if undefined)
return self.gradient
class BPNN:
"""
Back Propagation Neural Network model
"""
def __init__(self):
self.layers = []
self.train_mse = []
self.fig_loss = plt.figure()
self.ax_loss = self.fig_loss.add_subplot(1, 1, 1)
def add_layer(self, layer):
self.layers.append(layer)
def build(self):
for i, layer in enumerate(self.layers[:]):
if i < 1:
layer.is_input_layer = True
else:
layer.initializer(self.layers[i - 1].units)
def summary(self):
for i, layer in enumerate(self.layers[:]):
print("------- layer %d -------" % i)
print("weight.shape ", np.shape(layer.weight))
print("bias.shape ", np.shape(layer.bias))
def train(self, xdata, ydata, train_round, accuracy):
self.train_round = train_round
self.accuracy = accuracy
self.ax_loss.hlines(self.accuracy, 0, self.train_round * 1.1)
x_shape = np.shape(xdata)
for _ in range(train_round):
all_loss = 0
for row in range(x_shape[0]):
_xdata = np.asmatrix(xdata[row, :]).T
_ydata = np.asmatrix(ydata[row, :]).T
# forward propagation
for layer in self.layers:
_xdata = layer.forward_propagation(_xdata)
loss, gradient = self.cal_loss(_ydata, _xdata)
all_loss = all_loss + loss
# back propagation: the input_layer does not upgrade
for layer in self.layers[:0:-1]:
gradient = layer.back_propagation(gradient)
mse = all_loss / x_shape[0]
self.train_mse.append(mse)
self.plot_loss()
if mse < self.accuracy:
print("----达到精度----")
return mse
def cal_loss(self, ydata, ydata_):
self.loss = np.sum(np.power((ydata - ydata_), 2))
self.loss_gradient = 2 * (ydata_ - ydata)
# vector (shape is the same as _ydata.shape)
return self.loss, self.loss_gradient
def plot_loss(self):
if self.ax_loss.lines:
self.ax_loss.lines.remove(self.ax_loss.lines[0])
self.ax_loss.plot(self.train_mse, "r-")
plt.ion()
plt.xlabel("step")
plt.ylabel("loss")
plt.show()
plt.pause(0.1)
def example():
x = np.random.randn(10, 10)
y = np.asarray(
[
[0.8, 0.4],
[0.4, 0.3],
[0.34, 0.45],
[0.67, 0.32],
[0.88, 0.67],
[0.78, 0.77],
[0.55, 0.66],
[0.55, 0.43],
[0.54, 0.1],
[0.1, 0.5],
]
)
model = BPNN()
for i in (10, 20, 30, 2):
model.add_layer(DenseLayer(i))
model.build()
model.summary()
model.train(xdata=x, ydata=y, train_round=100, accuracy=0.01)
if __name__ == "__main__":
example()
| #!/usr/bin/python
"""
A Framework of Back Propagation Neural Network（BP） model
Easy to use:
* add many layers as you want ！！！
* clearly see how the loss decreasing
Easy to expand:
* more activation functions
* more loss functions
* more optimization method
Author: Stephen Lee
Github : https://github.com/RiptideBo
Date: 2017.11.23
"""
import numpy as np
from matplotlib import pyplot as plt
def sigmoid(x):
return 1 / (1 + np.exp(-1 * x))
class DenseLayer:
"""
Layers of BP neural network
"""
def __init__(
self, units, activation=None, learning_rate=None, is_input_layer=False
):
"""
common connected layer of bp network
:param units: numbers of neural units
:param activation: activation function
:param learning_rate: learning rate for paras
:param is_input_layer: whether it is input layer or not
"""
self.units = units
self.weight = None
self.bias = None
self.activation = activation
if learning_rate is None:
learning_rate = 0.3
self.learn_rate = learning_rate
self.is_input_layer = is_input_layer
def initializer(self, back_units):
self.weight = np.asmatrix(np.random.normal(0, 0.5, (self.units, back_units)))
self.bias = np.asmatrix(np.random.normal(0, 0.5, self.units)).T
if self.activation is None:
self.activation = sigmoid
def cal_gradient(self):
# activation function may be sigmoid or linear
if self.activation == sigmoid:
gradient_mat = np.dot(self.output, (1 - self.output).T)
gradient_activation = np.diag(np.diag(gradient_mat))
else:
gradient_activation = 1
return gradient_activation
def forward_propagation(self, xdata):
self.xdata = xdata
if self.is_input_layer:
# input layer
self.wx_plus_b = xdata
self.output = xdata
return xdata
else:
self.wx_plus_b = np.dot(self.weight, self.xdata) - self.bias
self.output = self.activation(self.wx_plus_b)
return self.output
def back_propagation(self, gradient):
gradient_activation = self.cal_gradient() # i * i 维
gradient = np.asmatrix(np.dot(gradient.T, gradient_activation))
self._gradient_weight = np.asmatrix(self.xdata)
self._gradient_bias = -1
self._gradient_x = self.weight
self.gradient_weight = np.dot(gradient.T, self._gradient_weight.T)
self.gradient_bias = gradient * self._gradient_bias
self.gradient = np.dot(gradient, self._gradient_x).T
# upgrade: the Negative gradient direction
self.weight = self.weight - self.learn_rate * self.gradient_weight
self.bias = self.bias - self.learn_rate * self.gradient_bias.T
# updates the weights and bias according to learning rate (0.3 if undefined)
return self.gradient
class BPNN:
"""
Back Propagation Neural Network model
"""
def __init__(self):
self.layers = []
self.train_mse = []
self.fig_loss = plt.figure()
self.ax_loss = self.fig_loss.add_subplot(1, 1, 1)
def add_layer(self, layer):
self.layers.append(layer)
def build(self):
for i, layer in enumerate(self.layers[:]):
if i < 1:
layer.is_input_layer = True
else:
layer.initializer(self.layers[i - 1].units)
def summary(self):
for i, layer in enumerate(self.layers[:]):
print(f"------- layer {i} -------")
print("weight.shape ", np.shape(layer.weight))
print("bias.shape ", np.shape(layer.bias))
def train(self, xdata, ydata, train_round, accuracy):
self.train_round = train_round
self.accuracy = accuracy
self.ax_loss.hlines(self.accuracy, 0, self.train_round * 1.1)
x_shape = np.shape(xdata)
for _ in range(train_round):
all_loss = 0
for row in range(x_shape[0]):
_xdata = np.asmatrix(xdata[row, :]).T
_ydata = np.asmatrix(ydata[row, :]).T
# forward propagation
for layer in self.layers:
_xdata = layer.forward_propagation(_xdata)
loss, gradient = self.cal_loss(_ydata, _xdata)
all_loss = all_loss + loss
# back propagation: the input_layer does not upgrade
for layer in self.layers[:0:-1]:
gradient = layer.back_propagation(gradient)
mse = all_loss / x_shape[0]
self.train_mse.append(mse)
self.plot_loss()
if mse < self.accuracy:
print("----达到精度----")
return mse
def cal_loss(self, ydata, ydata_):
self.loss = np.sum(np.power((ydata - ydata_), 2))
self.loss_gradient = 2 * (ydata_ - ydata)
# vector (shape is the same as _ydata.shape)
return self.loss, self.loss_gradient
def plot_loss(self):
if self.ax_loss.lines:
self.ax_loss.lines.remove(self.ax_loss.lines[0])
self.ax_loss.plot(self.train_mse, "r-")
plt.ion()
plt.xlabel("step")
plt.ylabel("loss")
plt.show()
plt.pause(0.1)
def example():
x = np.random.randn(10, 10)
y = np.asarray(
[
[0.8, 0.4],
[0.4, 0.3],
[0.34, 0.45],
[0.67, 0.32],
[0.88, 0.67],
[0.78, 0.77],
[0.55, 0.66],
[0.55, 0.43],
[0.54, 0.1],
[0.1, 0.5],
]
)
model = BPNN()
for i in (10, 20, 30, 2):
model.add_layer(DenseLayer(i))
model.build()
model.summary()
model.train(xdata=x, ydata=y, train_round=100, accuracy=0.01)
if __name__ == "__main__":
example()
| 1 |
TheAlgorithms/Python | 7,339 | Follow Flake8 pep3101 and remove modulo formatting | ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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 have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | 2022-10-16T20:16:16Z | 2022-10-16T20:50:11Z | 7f6e0b656f6362e452b11d06acde50b8b81cb31a | f15cc2f01c2a4124ff6dc0843c728a546f9d9f79 | Follow Flake8 pep3101 and remove modulo formatting. ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
- - - - - -- - - - - - - - - - - - - - - - - - - - - - -
Name - - CNN - Convolution Neural Network For Photo Recognizing
Goal - - Recognize Handing Writing Word Photo
Detail：Total 5 layers neural network
* Convolution layer
* Pooling layer
* Input layer layer of BP
* Hidden layer of BP
* Output layer of BP
Author: Stephen Lee
Github: [email protected]
Date: 2017.9.20
- - - - - -- - - - - - - - - - - - - - - - - - - - - - -
"""
import pickle
import numpy as np
from matplotlib import pyplot as plt
class CNN:
def __init__(
self, conv1_get, size_p1, bp_num1, bp_num2, bp_num3, rate_w=0.2, rate_t=0.2
):
"""
:param conv1_get: [a,c,d]，size, number, step of convolution kernel
:param size_p1: pooling size
:param bp_num1: units number of flatten layer
:param bp_num2: units number of hidden layer
:param bp_num3: units number of output layer
:param rate_w: rate of weight learning
:param rate_t: rate of threshold learning
"""
self.num_bp1 = bp_num1
self.num_bp2 = bp_num2
self.num_bp3 = bp_num3
self.conv1 = conv1_get[:2]
self.step_conv1 = conv1_get[2]
self.size_pooling1 = size_p1
self.rate_weight = rate_w
self.rate_thre = rate_t
self.w_conv1 = [
np.mat(-1 * np.random.rand(self.conv1[0], self.conv1[0]) + 0.5)
for i in range(self.conv1[1])
]
self.wkj = np.mat(-1 * np.random.rand(self.num_bp3, self.num_bp2) + 0.5)
self.vji = np.mat(-1 * np.random.rand(self.num_bp2, self.num_bp1) + 0.5)
self.thre_conv1 = -2 * np.random.rand(self.conv1[1]) + 1
self.thre_bp2 = -2 * np.random.rand(self.num_bp2) + 1
self.thre_bp3 = -2 * np.random.rand(self.num_bp3) + 1
def save_model(self, save_path):
# save model dict with pickle
model_dic = {
"num_bp1": self.num_bp1,
"num_bp2": self.num_bp2,
"num_bp3": self.num_bp3,
"conv1": self.conv1,
"step_conv1": self.step_conv1,
"size_pooling1": self.size_pooling1,
"rate_weight": self.rate_weight,
"rate_thre": self.rate_thre,
"w_conv1": self.w_conv1,
"wkj": self.wkj,
"vji": self.vji,
"thre_conv1": self.thre_conv1,
"thre_bp2": self.thre_bp2,
"thre_bp3": self.thre_bp3,
}
with open(save_path, "wb") as f:
pickle.dump(model_dic, f)
print(f"Model saved： {save_path}")
@classmethod
def read_model(cls, model_path):
# read saved model
with open(model_path, "rb") as f:
model_dic = pickle.load(f)
conv_get = model_dic.get("conv1")
conv_get.append(model_dic.get("step_conv1"))
size_p1 = model_dic.get("size_pooling1")
bp1 = model_dic.get("num_bp1")
bp2 = model_dic.get("num_bp2")
bp3 = model_dic.get("num_bp3")
r_w = model_dic.get("rate_weight")
r_t = model_dic.get("rate_thre")
# create model instance
conv_ins = CNN(conv_get, size_p1, bp1, bp2, bp3, r_w, r_t)
# modify model parameter
conv_ins.w_conv1 = model_dic.get("w_conv1")
conv_ins.wkj = model_dic.get("wkj")
conv_ins.vji = model_dic.get("vji")
conv_ins.thre_conv1 = model_dic.get("thre_conv1")
conv_ins.thre_bp2 = model_dic.get("thre_bp2")
conv_ins.thre_bp3 = model_dic.get("thre_bp3")
return conv_ins
def sig(self, x):
return 1 / (1 + np.exp(-1 * x))
def do_round(self, x):
return round(x, 3)
def convolute(self, data, convs, w_convs, thre_convs, conv_step):
# convolution process
size_conv = convs[0]
num_conv = convs[1]
size_data = np.shape(data)[0]
# get the data slice of original image data, data_focus
data_focus = []
for i_focus in range(0, size_data - size_conv + 1, conv_step):
for j_focus in range(0, size_data - size_conv + 1, conv_step):
focus = data[
i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
]
data_focus.append(focus)
# calculate the feature map of every single kernel, and saved as list of matrix
data_featuremap = []
size_feature_map = int((size_data - size_conv) / conv_step + 1)
for i_map in range(num_conv):
featuremap = []
for i_focus in range(len(data_focus)):
net_focus = (
np.sum(np.multiply(data_focus[i_focus], w_convs[i_map]))
- thre_convs[i_map]
)
featuremap.append(self.sig(net_focus))
featuremap = np.asmatrix(featuremap).reshape(
size_feature_map, size_feature_map
)
data_featuremap.append(featuremap)
# expanding the data slice to One dimenssion
focus1_list = []
for each_focus in data_focus:
focus1_list.extend(self.Expand_Mat(each_focus))
focus_list = np.asarray(focus1_list)
return focus_list, data_featuremap
def pooling(self, featuremaps, size_pooling, pooling_type="average_pool"):
# pooling process
size_map = len(featuremaps[0])
size_pooled = int(size_map / size_pooling)
featuremap_pooled = []
for i_map in range(len(featuremaps)):
feature_map = featuremaps[i_map]
map_pooled = []
for i_focus in range(0, size_map, size_pooling):
for j_focus in range(0, size_map, size_pooling):
focus = feature_map[
i_focus : i_focus + size_pooling,
j_focus : j_focus + size_pooling,
]
if pooling_type == "average_pool":
# average pooling
map_pooled.append(np.average(focus))
elif pooling_type == "max_pooling":
# max pooling
map_pooled.append(np.max(focus))
map_pooled = np.asmatrix(map_pooled).reshape(size_pooled, size_pooled)
featuremap_pooled.append(map_pooled)
return featuremap_pooled
def _expand(self, data):
# expanding three dimension data to one dimension list
data_expanded = []
for i in range(len(data)):
shapes = np.shape(data[i])
data_listed = data[i].reshape(1, shapes[0] * shapes[1])
data_listed = data_listed.getA().tolist()[0]
data_expanded.extend(data_listed)
data_expanded = np.asarray(data_expanded)
return data_expanded
def _expand_mat(self, data_mat):
# expanding matrix to one dimension list
data_mat = np.asarray(data_mat)
shapes = np.shape(data_mat)
data_expanded = data_mat.reshape(1, shapes[0] * shapes[1])
return data_expanded
def _calculate_gradient_from_pool(
self, out_map, pd_pool, num_map, size_map, size_pooling
):
"""
calculate the gradient from the data slice of pool layer
pd_pool: list of matrix
out_map: the shape of data slice(size_map*size_map)
return: pd_all: list of matrix, [num, size_map, size_map]
"""
pd_all = []
i_pool = 0
for i_map in range(num_map):
pd_conv1 = np.ones((size_map, size_map))
for i in range(0, size_map, size_pooling):
for j in range(0, size_map, size_pooling):
pd_conv1[i : i + size_pooling, j : j + size_pooling] = pd_pool[
i_pool
]
i_pool = i_pool + 1
pd_conv2 = np.multiply(
pd_conv1, np.multiply(out_map[i_map], (1 - out_map[i_map]))
)
pd_all.append(pd_conv2)
return pd_all
def train(
self, patterns, datas_train, datas_teach, n_repeat, error_accuracy, draw_e=bool
):
# model traning
print("----------------------Start Training-------------------------")
print((" - - Shape: Train_Data ", np.shape(datas_train)))
print((" - - Shape: Teach_Data ", np.shape(datas_teach)))
rp = 0
all_mse = []
mse = 10000
while rp < n_repeat and mse >= error_accuracy:
error_count = 0
print("-------------Learning Time %d--------------" % rp)
for p in range(len(datas_train)):
# print('------------Learning Image: %d--------------'%p)
data_train = np.asmatrix(datas_train[p])
data_teach = np.asarray(datas_teach[p])
data_focus1, data_conved1 = self.convolute(
data_train,
self.conv1,
self.w_conv1,
self.thre_conv1,
conv_step=self.step_conv1,
)
data_pooled1 = self.pooling(data_conved1, self.size_pooling1)
shape_featuremap1 = np.shape(data_conved1)
"""
print(' -----original shape ', np.shape(data_train))
print(' ---- after convolution ',np.shape(data_conv1))
print(' -----after pooling ',np.shape(data_pooled1))
"""
data_bp_input = self._expand(data_pooled1)
bp_out1 = data_bp_input
bp_net_j = np.dot(bp_out1, self.vji.T) - self.thre_bp2
bp_out2 = self.sig(bp_net_j)
bp_net_k = np.dot(bp_out2, self.wkj.T) - self.thre_bp3
bp_out3 = self.sig(bp_net_k)
# --------------Model Leaning ------------------------
# calculate error and gradient---------------
pd_k_all = np.multiply(
(data_teach - bp_out3), np.multiply(bp_out3, (1 - bp_out3))
)
pd_j_all = np.multiply(
np.dot(pd_k_all, self.wkj), np.multiply(bp_out2, (1 - bp_out2))
)
pd_i_all = np.dot(pd_j_all, self.vji)
pd_conv1_pooled = pd_i_all / (self.size_pooling1 * self.size_pooling1)
pd_conv1_pooled = pd_conv1_pooled.T.getA().tolist()
pd_conv1_all = self._calculate_gradient_from_pool(
data_conved1,
pd_conv1_pooled,
shape_featuremap1[0],
shape_featuremap1[1],
self.size_pooling1,
)
# weight and threshold learning process---------
# convolution layer
for k_conv in range(self.conv1[1]):
pd_conv_list = self._expand_mat(pd_conv1_all[k_conv])
delta_w = self.rate_weight * np.dot(pd_conv_list, data_focus1)
self.w_conv1[k_conv] = self.w_conv1[k_conv] + delta_w.reshape(
(self.conv1[0], self.conv1[0])
)
self.thre_conv1[k_conv] = (
self.thre_conv1[k_conv]
- np.sum(pd_conv1_all[k_conv]) * self.rate_thre
)
# all connected layer
self.wkj = self.wkj + pd_k_all.T * bp_out2 * self.rate_weight
self.vji = self.vji + pd_j_all.T * bp_out1 * self.rate_weight
self.thre_bp3 = self.thre_bp3 - pd_k_all * self.rate_thre
self.thre_bp2 = self.thre_bp2 - pd_j_all * self.rate_thre
# calculate the sum error of all single image
errors = np.sum(abs(data_teach - bp_out3))
error_count += errors
# print(' ----Teach ',data_teach)
# print(' ----BP_output ',bp_out3)
rp = rp + 1
mse = error_count / patterns
all_mse.append(mse)
def draw_error():
yplot = [error_accuracy for i in range(int(n_repeat * 1.2))]
plt.plot(all_mse, "+-")
plt.plot(yplot, "r--")
plt.xlabel("Learning Times")
plt.ylabel("All_mse")
plt.grid(True, alpha=0.5)
plt.show()
print("------------------Training Complished---------------------")
print((" - - Training epoch: ", rp, f" - - Mse: {mse:.6f}"))
if draw_e:
draw_error()
return mse
def predict(self, datas_test):
# model predict
produce_out = []
print("-------------------Start Testing-------------------------")
print((" - - Shape: Test_Data ", np.shape(datas_test)))
for p in range(len(datas_test)):
data_test = np.asmatrix(datas_test[p])
data_focus1, data_conved1 = self.convolute(
data_test,
self.conv1,
self.w_conv1,
self.thre_conv1,
conv_step=self.step_conv1,
)
data_pooled1 = self.pooling(data_conved1, self.size_pooling1)
data_bp_input = self._expand(data_pooled1)
bp_out1 = data_bp_input
bp_net_j = bp_out1 * self.vji.T - self.thre_bp2
bp_out2 = self.sig(bp_net_j)
bp_net_k = bp_out2 * self.wkj.T - self.thre_bp3
bp_out3 = self.sig(bp_net_k)
produce_out.extend(bp_out3.getA().tolist())
res = [list(map(self.do_round, each)) for each in produce_out]
return np.asarray(res)
def convolution(self, data):
# return the data of image after convoluting process so we can check it out
data_test = np.asmatrix(data)
data_focus1, data_conved1 = self.convolute(
data_test,
self.conv1,
self.w_conv1,
self.thre_conv1,
conv_step=self.step_conv1,
)
data_pooled1 = self.pooling(data_conved1, self.size_pooling1)
return data_conved1, data_pooled1
if __name__ == "__main__":
"""
I will put the example on other file
"""
| """
- - - - - -- - - - - - - - - - - - - - - - - - - - - - -
Name - - CNN - Convolution Neural Network For Photo Recognizing
Goal - - Recognize Handing Writing Word Photo
Detail：Total 5 layers neural network
* Convolution layer
* Pooling layer
* Input layer layer of BP
* Hidden layer of BP
* Output layer of BP
Author: Stephen Lee
Github: [email protected]
Date: 2017.9.20
- - - - - -- - - - - - - - - - - - - - - - - - - - - - -
"""
import pickle
import numpy as np
from matplotlib import pyplot as plt
class CNN:
def __init__(
self, conv1_get, size_p1, bp_num1, bp_num2, bp_num3, rate_w=0.2, rate_t=0.2
):
"""
:param conv1_get: [a,c,d]，size, number, step of convolution kernel
:param size_p1: pooling size
:param bp_num1: units number of flatten layer
:param bp_num2: units number of hidden layer
:param bp_num3: units number of output layer
:param rate_w: rate of weight learning
:param rate_t: rate of threshold learning
"""
self.num_bp1 = bp_num1
self.num_bp2 = bp_num2
self.num_bp3 = bp_num3
self.conv1 = conv1_get[:2]
self.step_conv1 = conv1_get[2]
self.size_pooling1 = size_p1
self.rate_weight = rate_w
self.rate_thre = rate_t
self.w_conv1 = [
np.mat(-1 * np.random.rand(self.conv1[0], self.conv1[0]) + 0.5)
for i in range(self.conv1[1])
]
self.wkj = np.mat(-1 * np.random.rand(self.num_bp3, self.num_bp2) + 0.5)
self.vji = np.mat(-1 * np.random.rand(self.num_bp2, self.num_bp1) + 0.5)
self.thre_conv1 = -2 * np.random.rand(self.conv1[1]) + 1
self.thre_bp2 = -2 * np.random.rand(self.num_bp2) + 1
self.thre_bp3 = -2 * np.random.rand(self.num_bp3) + 1
def save_model(self, save_path):
# save model dict with pickle
model_dic = {
"num_bp1": self.num_bp1,
"num_bp2": self.num_bp2,
"num_bp3": self.num_bp3,
"conv1": self.conv1,
"step_conv1": self.step_conv1,
"size_pooling1": self.size_pooling1,
"rate_weight": self.rate_weight,
"rate_thre": self.rate_thre,
"w_conv1": self.w_conv1,
"wkj": self.wkj,
"vji": self.vji,
"thre_conv1": self.thre_conv1,
"thre_bp2": self.thre_bp2,
"thre_bp3": self.thre_bp3,
}
with open(save_path, "wb") as f:
pickle.dump(model_dic, f)
print(f"Model saved： {save_path}")
@classmethod
def read_model(cls, model_path):
# read saved model
with open(model_path, "rb") as f:
model_dic = pickle.load(f)
conv_get = model_dic.get("conv1")
conv_get.append(model_dic.get("step_conv1"))
size_p1 = model_dic.get("size_pooling1")
bp1 = model_dic.get("num_bp1")
bp2 = model_dic.get("num_bp2")
bp3 = model_dic.get("num_bp3")
r_w = model_dic.get("rate_weight")
r_t = model_dic.get("rate_thre")
# create model instance
conv_ins = CNN(conv_get, size_p1, bp1, bp2, bp3, r_w, r_t)
# modify model parameter
conv_ins.w_conv1 = model_dic.get("w_conv1")
conv_ins.wkj = model_dic.get("wkj")
conv_ins.vji = model_dic.get("vji")
conv_ins.thre_conv1 = model_dic.get("thre_conv1")
conv_ins.thre_bp2 = model_dic.get("thre_bp2")
conv_ins.thre_bp3 = model_dic.get("thre_bp3")
return conv_ins
def sig(self, x):
return 1 / (1 + np.exp(-1 * x))
def do_round(self, x):
return round(x, 3)
def convolute(self, data, convs, w_convs, thre_convs, conv_step):
# convolution process
size_conv = convs[0]
num_conv = convs[1]
size_data = np.shape(data)[0]
# get the data slice of original image data, data_focus
data_focus = []
for i_focus in range(0, size_data - size_conv + 1, conv_step):
for j_focus in range(0, size_data - size_conv + 1, conv_step):
focus = data[
i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
]
data_focus.append(focus)
# calculate the feature map of every single kernel, and saved as list of matrix
data_featuremap = []
size_feature_map = int((size_data - size_conv) / conv_step + 1)
for i_map in range(num_conv):
featuremap = []
for i_focus in range(len(data_focus)):
net_focus = (
np.sum(np.multiply(data_focus[i_focus], w_convs[i_map]))
- thre_convs[i_map]
)
featuremap.append(self.sig(net_focus))
featuremap = np.asmatrix(featuremap).reshape(
size_feature_map, size_feature_map
)
data_featuremap.append(featuremap)
# expanding the data slice to One dimenssion
focus1_list = []
for each_focus in data_focus:
focus1_list.extend(self.Expand_Mat(each_focus))
focus_list = np.asarray(focus1_list)
return focus_list, data_featuremap
def pooling(self, featuremaps, size_pooling, pooling_type="average_pool"):
# pooling process
size_map = len(featuremaps[0])
size_pooled = int(size_map / size_pooling)
featuremap_pooled = []
for i_map in range(len(featuremaps)):
feature_map = featuremaps[i_map]
map_pooled = []
for i_focus in range(0, size_map, size_pooling):
for j_focus in range(0, size_map, size_pooling):
focus = feature_map[
i_focus : i_focus + size_pooling,
j_focus : j_focus + size_pooling,
]
if pooling_type == "average_pool":
# average pooling
map_pooled.append(np.average(focus))
elif pooling_type == "max_pooling":
# max pooling
map_pooled.append(np.max(focus))
map_pooled = np.asmatrix(map_pooled).reshape(size_pooled, size_pooled)
featuremap_pooled.append(map_pooled)
return featuremap_pooled
def _expand(self, data):
# expanding three dimension data to one dimension list
data_expanded = []
for i in range(len(data)):
shapes = np.shape(data[i])
data_listed = data[i].reshape(1, shapes[0] * shapes[1])
data_listed = data_listed.getA().tolist()[0]
data_expanded.extend(data_listed)
data_expanded = np.asarray(data_expanded)
return data_expanded
def _expand_mat(self, data_mat):
# expanding matrix to one dimension list
data_mat = np.asarray(data_mat)
shapes = np.shape(data_mat)
data_expanded = data_mat.reshape(1, shapes[0] * shapes[1])
return data_expanded
def _calculate_gradient_from_pool(
self, out_map, pd_pool, num_map, size_map, size_pooling
):
"""
calculate the gradient from the data slice of pool layer
pd_pool: list of matrix
out_map: the shape of data slice(size_map*size_map)
return: pd_all: list of matrix, [num, size_map, size_map]
"""
pd_all = []
i_pool = 0
for i_map in range(num_map):
pd_conv1 = np.ones((size_map, size_map))
for i in range(0, size_map, size_pooling):
for j in range(0, size_map, size_pooling):
pd_conv1[i : i + size_pooling, j : j + size_pooling] = pd_pool[
i_pool
]
i_pool = i_pool + 1
pd_conv2 = np.multiply(
pd_conv1, np.multiply(out_map[i_map], (1 - out_map[i_map]))
)
pd_all.append(pd_conv2)
return pd_all
def train(
self, patterns, datas_train, datas_teach, n_repeat, error_accuracy, draw_e=bool
):
# model traning
print("----------------------Start Training-------------------------")
print((" - - Shape: Train_Data ", np.shape(datas_train)))
print((" - - Shape: Teach_Data ", np.shape(datas_teach)))
rp = 0
all_mse = []
mse = 10000
while rp < n_repeat and mse >= error_accuracy:
error_count = 0
print(f"-------------Learning Time {rp}--------------")
for p in range(len(datas_train)):
# print('------------Learning Image: %d--------------'%p)
data_train = np.asmatrix(datas_train[p])
data_teach = np.asarray(datas_teach[p])
data_focus1, data_conved1 = self.convolute(
data_train,
self.conv1,
self.w_conv1,
self.thre_conv1,
conv_step=self.step_conv1,
)
data_pooled1 = self.pooling(data_conved1, self.size_pooling1)
shape_featuremap1 = np.shape(data_conved1)
"""
print(' -----original shape ', np.shape(data_train))
print(' ---- after convolution ',np.shape(data_conv1))
print(' -----after pooling ',np.shape(data_pooled1))
"""
data_bp_input = self._expand(data_pooled1)
bp_out1 = data_bp_input
bp_net_j = np.dot(bp_out1, self.vji.T) - self.thre_bp2
bp_out2 = self.sig(bp_net_j)
bp_net_k = np.dot(bp_out2, self.wkj.T) - self.thre_bp3
bp_out3 = self.sig(bp_net_k)
# --------------Model Leaning ------------------------
# calculate error and gradient---------------
pd_k_all = np.multiply(
(data_teach - bp_out3), np.multiply(bp_out3, (1 - bp_out3))
)
pd_j_all = np.multiply(
np.dot(pd_k_all, self.wkj), np.multiply(bp_out2, (1 - bp_out2))
)
pd_i_all = np.dot(pd_j_all, self.vji)
pd_conv1_pooled = pd_i_all / (self.size_pooling1 * self.size_pooling1)
pd_conv1_pooled = pd_conv1_pooled.T.getA().tolist()
pd_conv1_all = self._calculate_gradient_from_pool(
data_conved1,
pd_conv1_pooled,
shape_featuremap1[0],
shape_featuremap1[1],
self.size_pooling1,
)
# weight and threshold learning process---------
# convolution layer
for k_conv in range(self.conv1[1]):
pd_conv_list = self._expand_mat(pd_conv1_all[k_conv])
delta_w = self.rate_weight * np.dot(pd_conv_list, data_focus1)
self.w_conv1[k_conv] = self.w_conv1[k_conv] + delta_w.reshape(
(self.conv1[0], self.conv1[0])
)
self.thre_conv1[k_conv] = (
self.thre_conv1[k_conv]
- np.sum(pd_conv1_all[k_conv]) * self.rate_thre
)
# all connected layer
self.wkj = self.wkj + pd_k_all.T * bp_out2 * self.rate_weight
self.vji = self.vji + pd_j_all.T * bp_out1 * self.rate_weight
self.thre_bp3 = self.thre_bp3 - pd_k_all * self.rate_thre
self.thre_bp2 = self.thre_bp2 - pd_j_all * self.rate_thre
# calculate the sum error of all single image
errors = np.sum(abs(data_teach - bp_out3))
error_count += errors
# print(' ----Teach ',data_teach)
# print(' ----BP_output ',bp_out3)
rp = rp + 1
mse = error_count / patterns
all_mse.append(mse)
def draw_error():
yplot = [error_accuracy for i in range(int(n_repeat * 1.2))]
plt.plot(all_mse, "+-")
plt.plot(yplot, "r--")
plt.xlabel("Learning Times")
plt.ylabel("All_mse")
plt.grid(True, alpha=0.5)
plt.show()
print("------------------Training Complished---------------------")
print((" - - Training epoch: ", rp, f" - - Mse: {mse:.6f}"))
if draw_e:
draw_error()
return mse
def predict(self, datas_test):
# model predict
produce_out = []
print("-------------------Start Testing-------------------------")
print((" - - Shape: Test_Data ", np.shape(datas_test)))
for p in range(len(datas_test)):
data_test = np.asmatrix(datas_test[p])
data_focus1, data_conved1 = self.convolute(
data_test,
self.conv1,
self.w_conv1,
self.thre_conv1,
conv_step=self.step_conv1,
)
data_pooled1 = self.pooling(data_conved1, self.size_pooling1)
data_bp_input = self._expand(data_pooled1)
bp_out1 = data_bp_input
bp_net_j = bp_out1 * self.vji.T - self.thre_bp2
bp_out2 = self.sig(bp_net_j)
bp_net_k = bp_out2 * self.wkj.T - self.thre_bp3
bp_out3 = self.sig(bp_net_k)
produce_out.extend(bp_out3.getA().tolist())
res = [list(map(self.do_round, each)) for each in produce_out]
return np.asarray(res)
def convolution(self, data):
# return the data of image after convoluting process so we can check it out
data_test = np.asmatrix(data)
data_focus1, data_conved1 = self.convolute(
data_test,
self.conv1,
self.w_conv1,
self.thre_conv1,
conv_step=self.step_conv1,
)
data_pooled1 = self.pooling(data_conved1, self.size_pooling1)
return data_conved1, data_pooled1
if __name__ == "__main__":
"""
I will put the example on other file
"""
| 1 |
TheAlgorithms/Python | 7,339 | Follow Flake8 pep3101 and remove modulo formatting | ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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 have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | 2022-10-16T20:16:16Z | 2022-10-16T20:50:11Z | 7f6e0b656f6362e452b11d06acde50b8b81cb31a | f15cc2f01c2a4124ff6dc0843c728a546f9d9f79 | Follow Flake8 pep3101 and remove modulo formatting. ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 7,339 | Follow Flake8 pep3101 and remove modulo formatting | ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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 have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | 2022-10-16T20:16:16Z | 2022-10-16T20:50:11Z | 7f6e0b656f6362e452b11d06acde50b8b81cb31a | f15cc2f01c2a4124ff6dc0843c728a546f9d9f79 | Follow Flake8 pep3101 and remove modulo formatting. ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 7,339 | Follow Flake8 pep3101 and remove modulo formatting | ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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 have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | 2022-10-16T20:16:16Z | 2022-10-16T20:50:11Z | 7f6e0b656f6362e452b11d06acde50b8b81cb31a | f15cc2f01c2a4124ff6dc0843c728a546f9d9f79 | Follow Flake8 pep3101 and remove modulo formatting. ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| def is_palindrome(s: str) -> bool:
"""
Determine whether the string is palindrome
:param s:
:return: Boolean
>>> is_palindrome("a man a plan a canal panama".replace(" ", ""))
True
>>> is_palindrome("Hello")
False
>>> is_palindrome("Able was I ere I saw Elba")
True
>>> is_palindrome("racecar")
True
>>> is_palindrome("Mr. Owl ate my metal worm?")
True
"""
# Since Punctuation, capitalization, and spaces are usually ignored while checking
# Palindrome, we first remove them from our string.
s = "".join([character for character in s.lower() if character.isalnum()])
return s == s[::-1]
if __name__ == "__main__":
s = input("Enter string to determine whether its palindrome or not: ").strip()
if is_palindrome(s):
print("Given string is palindrome")
else:
print("Given string is not palindrome")
| def is_palindrome(s: str) -> bool:
"""
Determine whether the string is palindrome
:param s:
:return: Boolean
>>> is_palindrome("a man a plan a canal panama".replace(" ", ""))
True
>>> is_palindrome("Hello")
False
>>> is_palindrome("Able was I ere I saw Elba")
True
>>> is_palindrome("racecar")
True
>>> is_palindrome("Mr. Owl ate my metal worm?")
True
"""
# Since Punctuation, capitalization, and spaces are usually ignored while checking
# Palindrome, we first remove them from our string.
s = "".join([character for character in s.lower() if character.isalnum()])
return s == s[::-1]
if __name__ == "__main__":
s = input("Enter string to determine whether its palindrome or not: ").strip()
if is_palindrome(s):
print("Given string is palindrome")
else:
print("Given string is not palindrome")
| -1 |
TheAlgorithms/Python | 7,339 | Follow Flake8 pep3101 and remove modulo formatting | ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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 have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | 2022-10-16T20:16:16Z | 2022-10-16T20:50:11Z | 7f6e0b656f6362e452b11d06acde50b8b81cb31a | f15cc2f01c2a4124ff6dc0843c728a546f9d9f79 | Follow Flake8 pep3101 and remove modulo formatting. ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 7,339 | Follow Flake8 pep3101 and remove modulo formatting | ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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 have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | 2022-10-16T20:16:16Z | 2022-10-16T20:50:11Z | 7f6e0b656f6362e452b11d06acde50b8b81cb31a | f15cc2f01c2a4124ff6dc0843c728a546f9d9f79 | Follow Flake8 pep3101 and remove modulo formatting. ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| def snake_to_camel_case(input_str: str, use_pascal: bool = False) -> str:
"""
Transforms a snake_case given string to camelCase (or PascalCase if indicated)
(defaults to not use Pascal)
>>> snake_to_camel_case("some_random_string")
'someRandomString'
>>> snake_to_camel_case("some_random_string", use_pascal=True)
'SomeRandomString'
>>> snake_to_camel_case("some_random_string_with_numbers_123")
'someRandomStringWithNumbers123'
>>> snake_to_camel_case("some_random_string_with_numbers_123", use_pascal=True)
'SomeRandomStringWithNumbers123'
>>> snake_to_camel_case(123)
Traceback (most recent call last):
...
ValueError: Expected string as input, found <class 'int'>
>>> snake_to_camel_case("some_string", use_pascal="True")
Traceback (most recent call last):
...
ValueError: Expected boolean as use_pascal parameter, found <class 'str'>
"""
if not isinstance(input_str, str):
raise ValueError(f"Expected string as input, found {type(input_str)}")
if not isinstance(use_pascal, bool):
raise ValueError(
f"Expected boolean as use_pascal parameter, found {type(use_pascal)}"
)
words = input_str.split("_")
start_index = 0 if use_pascal else 1
words_to_capitalize = words[start_index:]
capitalized_words = [word[0].upper() + word[1:] for word in words_to_capitalize]
initial_word = "" if use_pascal else words[0]
return "".join([initial_word] + capitalized_words)
if __name__ == "__main__":
from doctest import testmod
testmod()
| def snake_to_camel_case(input_str: str, use_pascal: bool = False) -> str:
"""
Transforms a snake_case given string to camelCase (or PascalCase if indicated)
(defaults to not use Pascal)
>>> snake_to_camel_case("some_random_string")
'someRandomString'
>>> snake_to_camel_case("some_random_string", use_pascal=True)
'SomeRandomString'
>>> snake_to_camel_case("some_random_string_with_numbers_123")
'someRandomStringWithNumbers123'
>>> snake_to_camel_case("some_random_string_with_numbers_123", use_pascal=True)
'SomeRandomStringWithNumbers123'
>>> snake_to_camel_case(123)
Traceback (most recent call last):
...
ValueError: Expected string as input, found <class 'int'>
>>> snake_to_camel_case("some_string", use_pascal="True")
Traceback (most recent call last):
...
ValueError: Expected boolean as use_pascal parameter, found <class 'str'>
"""
if not isinstance(input_str, str):
raise ValueError(f"Expected string as input, found {type(input_str)}")
if not isinstance(use_pascal, bool):
raise ValueError(
f"Expected boolean as use_pascal parameter, found {type(use_pascal)}"
)
words = input_str.split("_")
start_index = 0 if use_pascal else 1
words_to_capitalize = words[start_index:]
capitalized_words = [word[0].upper() + word[1:] for word in words_to_capitalize]
initial_word = "" if use_pascal else words[0]
return "".join([initial_word] + capitalized_words)
if __name__ == "__main__":
from doctest import testmod
testmod()
| -1 |
TheAlgorithms/Python | 7,339 | Follow Flake8 pep3101 and remove modulo formatting | ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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 have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | 2022-10-16T20:16:16Z | 2022-10-16T20:50:11Z | 7f6e0b656f6362e452b11d06acde50b8b81cb31a | f15cc2f01c2a4124ff6dc0843c728a546f9d9f79 | Follow Flake8 pep3101 and remove modulo formatting. ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Created on Mon Feb 26 14:29:11 2018
@author: Christian Bender
@license: MIT-license
This module contains some useful classes and functions for dealing
with linear algebra in python.
Overview:
- class Vector
- function zero_vector(dimension)
- function unit_basis_vector(dimension, pos)
- function axpy(scalar, vector1, vector2)
- function random_vector(N, a, b)
- class Matrix
- function square_zero_matrix(N)
- function random_matrix(W, H, a, b)
"""
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class Vector:
"""
This class represents a vector of arbitrary size.
You need to give the vector components.
Overview of the methods:
__init__(components: Collection[float] | None): init the vector
__len__(): gets the size of the vector (number of components)
__str__(): returns a string representation
__add__(other: Vector): vector addition
__sub__(other: Vector): vector subtraction
__mul__(other: float): scalar multiplication
__mul__(other: Vector): dot product
set(components: Collection[float]): changes the vector components
copy(): copies this vector and returns it
component(i): gets the i-th component (0-indexed)
change_component(pos: int, value: float): changes specified component
euclidean_length(): returns the euclidean length of the vector
angle(other: Vector, deg: bool): returns the angle between two vectors
TODO: compare-operator
"""
def __init__(self, components: Collection[float] | None = None) -> None:
"""
input: components or nothing
simple constructor for init the vector
"""
if components is None:
components = []
self.__components = list(components)
def __len__(self) -> int:
"""
returns the size of the vector
"""
return len(self.__components)
def __str__(self) -> str:
"""
returns a string representation of the vector
"""
return "(" + ",".join(map(str, self.__components)) + ")"
def __add__(self, other: Vector) -> Vector:
"""
input: other vector
assumes: other vector has the same size
returns a new vector that represents the sum.
"""
size = len(self)
if size == len(other):
result = [self.__components[i] + other.component(i) for i in range(size)]
return Vector(result)
else:
raise Exception("must have the same size")
def __sub__(self, other: Vector) -> Vector:
"""
input: other vector
assumes: other vector has the same size
returns a new vector that represents the difference.
"""
size = len(self)
if size == len(other):
result = [self.__components[i] - other.component(i) for i in range(size)]
return Vector(result)
else: # error case
raise Exception("must have the same size")
@overload
def __mul__(self, other: float) -> Vector:
...
@overload
def __mul__(self, other: Vector) -> float:
...
def __mul__(self, other: float | Vector) -> float | Vector:
"""
mul implements the scalar multiplication
and the dot-product
"""
if isinstance(other, float) or isinstance(other, int):
ans = [c * other for c in self.__components]
return Vector(ans)
elif isinstance(other, Vector) and len(self) == len(other):
size = len(self)
prods = [self.__components[i] * other.component(i) for i in range(size)]
return sum(prods)
else: # error case
raise Exception("invalid operand!")
def set(self, components: Collection[float]) -> None:
"""
input: new components
changes the components of the vector.
replaces the components with newer one.
"""
if len(components) > 0:
self.__components = list(components)
else:
raise Exception("please give any vector")
def copy(self) -> Vector:
"""
copies this vector and returns it.
"""
return Vector(self.__components)
def component(self, i: int) -> float:
"""
input: index (0-indexed)
output: the i-th component of the vector.
"""
if type(i) is int and -len(self.__components) <= i < len(self.__components):
return self.__components[i]
else:
raise Exception("index out of range")
def change_component(self, pos: int, value: float) -> None:
"""
input: an index (pos) and a value
changes the specified component (pos) with the
'value'
"""
# precondition
assert -len(self.__components) <= pos < len(self.__components)
self.__components[pos] = value
def euclidean_length(self) -> float:
"""
returns the euclidean length of the vector
>>> Vector([2, 3, 4]).euclidean_length()
5.385164807134504
>>> Vector([1]).euclidean_length()
1.0
>>> Vector([0, -1, -2, -3, 4, 5, 6]).euclidean_length()
9.539392014169456
>>> Vector([]).euclidean_length()
Traceback (most recent call last):
...
Exception: Vector is empty
"""
if len(self.__components) == 0:
raise Exception("Vector is empty")
squares = [c**2 for c in self.__components]
return math.sqrt(sum(squares))
def angle(self, other: Vector, deg: bool = False) -> float:
"""
find angle between two Vector (self, Vector)
>>> Vector([3, 4, -1]).angle(Vector([2, -1, 1]))
1.4906464636572374
>>> Vector([3, 4, -1]).angle(Vector([2, -1, 1]), deg = True)
85.40775111366095
>>> Vector([3, 4, -1]).angle(Vector([2, -1]))
Traceback (most recent call last):
...
Exception: invalid operand!
"""
num = self * other
den = self.euclidean_length() * other.euclidean_length()
if deg:
return math.degrees(math.acos(num / den))
else:
return math.acos(num / den)
def zero_vector(dimension: int) -> Vector:
"""
returns a zero-vector of size 'dimension'
"""
# precondition
assert isinstance(dimension, int)
return Vector([0] * dimension)
def unit_basis_vector(dimension: int, pos: int) -> Vector:
"""
returns a unit basis vector with a One
at index 'pos' (indexing at 0)
"""
# precondition
assert isinstance(dimension, int) and (isinstance(pos, int))
ans = [0] * dimension
ans[pos] = 1
return Vector(ans)
def axpy(scalar: float, x: Vector, y: Vector) -> Vector:
"""
input: a 'scalar' and two vectors 'x' and 'y'
output: a vector
computes the axpy operation
"""
# precondition
assert (
isinstance(x, Vector)
and isinstance(y, Vector)
and (isinstance(scalar, int) or isinstance(scalar, float))
)
return x * scalar + y
def random_vector(n: int, a: int, b: int) -> Vector:
"""
input: size (N) of the vector.
random range (a,b)
output: returns a random vector of size N, with
random integer components between 'a' and 'b'.
"""
random.seed(None)
ans = [random.randint(a, b) for _ in range(n)]
return Vector(ans)
class Matrix:
"""
class: Matrix
This class represents an arbitrary matrix.
Overview of the methods:
__init__():
__str__(): returns a string representation
__add__(other: Matrix): matrix addition
__sub__(other: Matrix): matrix subtraction
__mul__(other: float): scalar multiplication
__mul__(other: Vector): vector multiplication
height() : returns height
width() : returns width
component(x: int, y: int): returns specified component
change_component(x: int, y: int, value: float): changes specified component
minor(x: int, y: int): returns minor along (x, y)
cofactor(x: int, y: int): returns cofactor along (x, y)
determinant() : returns determinant
"""
def __init__(self, matrix: list[list[float]], w: int, h: int) -> None:
"""
simple constructor for initializing the matrix with components.
"""
self.__matrix = matrix
self.__width = w
self.__height = h
def __str__(self) -> str:
"""
returns a string representation of this matrix.
"""
ans = ""
for i in range(self.__height):
ans += "|"
for j in range(self.__width):
if j < self.__width - 1:
ans += str(self.__matrix[i][j]) + ","
else:
ans += str(self.__matrix[i][j]) + "|\n"
return ans
def __add__(self, other: Matrix) -> Matrix:
"""
implements matrix addition.
"""
if self.__width == other.width() and self.__height == other.height():
matrix = []
for i in range(self.__height):
row = [
self.__matrix[i][j] + other.component(i, j)
for j in range(self.__width)
]
matrix.append(row)
return Matrix(matrix, self.__width, self.__height)
else:
raise Exception("matrix must have the same dimension!")
def __sub__(self, other: Matrix) -> Matrix:
"""
implements matrix subtraction.
"""
if self.__width == other.width() and self.__height == other.height():
matrix = []
for i in range(self.__height):
row = [
self.__matrix[i][j] - other.component(i, j)
for j in range(self.__width)
]
matrix.append(row)
return Matrix(matrix, self.__width, self.__height)
else:
raise Exception("matrices must have the same dimension!")
@overload
def __mul__(self, other: float) -> Matrix:
...
@overload
def __mul__(self, other: Vector) -> Vector:
...
def __mul__(self, other: float | Vector) -> Vector | Matrix:
"""
implements the matrix-vector multiplication.
implements the matrix-scalar multiplication
"""
if isinstance(other, Vector): # matrix-vector
if len(other) == self.__width:
ans = zero_vector(self.__height)
for i in range(self.__height):
prods = [
self.__matrix[i][j] * other.component(j)
for j in range(self.__width)
]
ans.change_component(i, sum(prods))
return ans
else:
raise Exception(
"vector must have the same size as the "
"number of columns of the matrix!"
)
elif isinstance(other, int) or isinstance(other, float): # matrix-scalar
matrix = [
[self.__matrix[i][j] * other for j in range(self.__width)]
for i in range(self.__height)
]
return Matrix(matrix, self.__width, self.__height)
def height(self) -> int:
"""
getter for the height
"""
return self.__height
def width(self) -> int:
"""
getter for the width
"""
return self.__width
def component(self, x: int, y: int) -> float:
"""
returns the specified (x,y) component
"""
if 0 <= x < self.__height and 0 <= y < self.__width:
return self.__matrix[x][y]
else:
raise Exception("change_component: indices out of bounds")
def change_component(self, x: int, y: int, value: float) -> None:
"""
changes the x-y component of this matrix
"""
if 0 <= x < self.__height and 0 <= y < self.__width:
self.__matrix[x][y] = value
else:
raise Exception("change_component: indices out of bounds")
def minor(self, x: int, y: int) -> float:
"""
returns the minor along (x, y)
"""
if self.__height != self.__width:
raise Exception("Matrix is not square")
minor = self.__matrix[:x] + self.__matrix[x + 1 :]
for i in range(len(minor)):
minor[i] = minor[i][:y] + minor[i][y + 1 :]
return Matrix(minor, self.__width - 1, self.__height - 1).determinant()
def cofactor(self, x: int, y: int) -> float:
"""
returns the cofactor (signed minor) along (x, y)
"""
if self.__height != self.__width:
raise Exception("Matrix is not square")
if 0 <= x < self.__height and 0 <= y < self.__width:
return (-1) ** (x + y) * self.minor(x, y)
else:
raise Exception("Indices out of bounds")
def determinant(self) -> float:
"""
returns the determinant of an nxn matrix using Laplace expansion
"""
if self.__height != self.__width:
raise Exception("Matrix is not square")
if self.__height < 1:
raise Exception("Matrix has no element")
elif self.__height == 1:
return self.__matrix[0][0]
elif self.__height == 2:
return (
self.__matrix[0][0] * self.__matrix[1][1]
- self.__matrix[0][1] * self.__matrix[1][0]
)
else:
cofactor_prods = [
self.__matrix[0][y] * self.cofactor(0, y) for y in range(self.__width)
]
return sum(cofactor_prods)
def square_zero_matrix(n: int) -> Matrix:
"""
returns a square zero-matrix of dimension NxN
"""
ans: list[list[float]] = [[0] * n for _ in range(n)]
return Matrix(ans, n, n)
def random_matrix(width: int, height: int, a: int, b: int) -> Matrix:
"""
returns a random matrix WxH with integer components
between 'a' and 'b'
"""
random.seed(None)
matrix: list[list[float]] = [
[random.randint(a, b) for _ in range(width)] for _ in range(height)
]
return Matrix(matrix, width, height)
| """
Created on Mon Feb 26 14:29:11 2018
@author: Christian Bender
@license: MIT-license
This module contains some useful classes and functions for dealing
with linear algebra in python.
Overview:
- class Vector
- function zero_vector(dimension)
- function unit_basis_vector(dimension, pos)
- function axpy(scalar, vector1, vector2)
- function random_vector(N, a, b)
- class Matrix
- function square_zero_matrix(N)
- function random_matrix(W, H, a, b)
"""
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class Vector:
"""
This class represents a vector of arbitrary size.
You need to give the vector components.
Overview of the methods:
__init__(components: Collection[float] | None): init the vector
__len__(): gets the size of the vector (number of components)
__str__(): returns a string representation
__add__(other: Vector): vector addition
__sub__(other: Vector): vector subtraction
__mul__(other: float): scalar multiplication
__mul__(other: Vector): dot product
set(components: Collection[float]): changes the vector components
copy(): copies this vector and returns it
component(i): gets the i-th component (0-indexed)
change_component(pos: int, value: float): changes specified component
euclidean_length(): returns the euclidean length of the vector
angle(other: Vector, deg: bool): returns the angle between two vectors
TODO: compare-operator
"""
def __init__(self, components: Collection[float] | None = None) -> None:
"""
input: components or nothing
simple constructor for init the vector
"""
if components is None:
components = []
self.__components = list(components)
def __len__(self) -> int:
"""
returns the size of the vector
"""
return len(self.__components)
def __str__(self) -> str:
"""
returns a string representation of the vector
"""
return "(" + ",".join(map(str, self.__components)) + ")"
def __add__(self, other: Vector) -> Vector:
"""
input: other vector
assumes: other vector has the same size
returns a new vector that represents the sum.
"""
size = len(self)
if size == len(other):
result = [self.__components[i] + other.component(i) for i in range(size)]
return Vector(result)
else:
raise Exception("must have the same size")
def __sub__(self, other: Vector) -> Vector:
"""
input: other vector
assumes: other vector has the same size
returns a new vector that represents the difference.
"""
size = len(self)
if size == len(other):
result = [self.__components[i] - other.component(i) for i in range(size)]
return Vector(result)
else: # error case
raise Exception("must have the same size")
@overload
def __mul__(self, other: float) -> Vector:
...
@overload
def __mul__(self, other: Vector) -> float:
...
def __mul__(self, other: float | Vector) -> float | Vector:
"""
mul implements the scalar multiplication
and the dot-product
"""
if isinstance(other, float) or isinstance(other, int):
ans = [c * other for c in self.__components]
return Vector(ans)
elif isinstance(other, Vector) and len(self) == len(other):
size = len(self)
prods = [self.__components[i] * other.component(i) for i in range(size)]
return sum(prods)
else: # error case
raise Exception("invalid operand!")
def set(self, components: Collection[float]) -> None:
"""
input: new components
changes the components of the vector.
replaces the components with newer one.
"""
if len(components) > 0:
self.__components = list(components)
else:
raise Exception("please give any vector")
def copy(self) -> Vector:
"""
copies this vector and returns it.
"""
return Vector(self.__components)
def component(self, i: int) -> float:
"""
input: index (0-indexed)
output: the i-th component of the vector.
"""
if type(i) is int and -len(self.__components) <= i < len(self.__components):
return self.__components[i]
else:
raise Exception("index out of range")
def change_component(self, pos: int, value: float) -> None:
"""
input: an index (pos) and a value
changes the specified component (pos) with the
'value'
"""
# precondition
assert -len(self.__components) <= pos < len(self.__components)
self.__components[pos] = value
def euclidean_length(self) -> float:
"""
returns the euclidean length of the vector
>>> Vector([2, 3, 4]).euclidean_length()
5.385164807134504
>>> Vector([1]).euclidean_length()
1.0
>>> Vector([0, -1, -2, -3, 4, 5, 6]).euclidean_length()
9.539392014169456
>>> Vector([]).euclidean_length()
Traceback (most recent call last):
...
Exception: Vector is empty
"""
if len(self.__components) == 0:
raise Exception("Vector is empty")
squares = [c**2 for c in self.__components]
return math.sqrt(sum(squares))
def angle(self, other: Vector, deg: bool = False) -> float:
"""
find angle between two Vector (self, Vector)
>>> Vector([3, 4, -1]).angle(Vector([2, -1, 1]))
1.4906464636572374
>>> Vector([3, 4, -1]).angle(Vector([2, -1, 1]), deg = True)
85.40775111366095
>>> Vector([3, 4, -1]).angle(Vector([2, -1]))
Traceback (most recent call last):
...
Exception: invalid operand!
"""
num = self * other
den = self.euclidean_length() * other.euclidean_length()
if deg:
return math.degrees(math.acos(num / den))
else:
return math.acos(num / den)
def zero_vector(dimension: int) -> Vector:
"""
returns a zero-vector of size 'dimension'
"""
# precondition
assert isinstance(dimension, int)
return Vector([0] * dimension)
def unit_basis_vector(dimension: int, pos: int) -> Vector:
"""
returns a unit basis vector with a One
at index 'pos' (indexing at 0)
"""
# precondition
assert isinstance(dimension, int) and (isinstance(pos, int))
ans = [0] * dimension
ans[pos] = 1
return Vector(ans)
def axpy(scalar: float, x: Vector, y: Vector) -> Vector:
"""
input: a 'scalar' and two vectors 'x' and 'y'
output: a vector
computes the axpy operation
"""
# precondition
assert (
isinstance(x, Vector)
and isinstance(y, Vector)
and (isinstance(scalar, int) or isinstance(scalar, float))
)
return x * scalar + y
def random_vector(n: int, a: int, b: int) -> Vector:
"""
input: size (N) of the vector.
random range (a,b)
output: returns a random vector of size N, with
random integer components between 'a' and 'b'.
"""
random.seed(None)
ans = [random.randint(a, b) for _ in range(n)]
return Vector(ans)
class Matrix:
"""
class: Matrix
This class represents an arbitrary matrix.
Overview of the methods:
__init__():
__str__(): returns a string representation
__add__(other: Matrix): matrix addition
__sub__(other: Matrix): matrix subtraction
__mul__(other: float): scalar multiplication
__mul__(other: Vector): vector multiplication
height() : returns height
width() : returns width
component(x: int, y: int): returns specified component
change_component(x: int, y: int, value: float): changes specified component
minor(x: int, y: int): returns minor along (x, y)
cofactor(x: int, y: int): returns cofactor along (x, y)
determinant() : returns determinant
"""
def __init__(self, matrix: list[list[float]], w: int, h: int) -> None:
"""
simple constructor for initializing the matrix with components.
"""
self.__matrix = matrix
self.__width = w
self.__height = h
def __str__(self) -> str:
"""
returns a string representation of this matrix.
"""
ans = ""
for i in range(self.__height):
ans += "|"
for j in range(self.__width):
if j < self.__width - 1:
ans += str(self.__matrix[i][j]) + ","
else:
ans += str(self.__matrix[i][j]) + "|\n"
return ans
def __add__(self, other: Matrix) -> Matrix:
"""
implements matrix addition.
"""
if self.__width == other.width() and self.__height == other.height():
matrix = []
for i in range(self.__height):
row = [
self.__matrix[i][j] + other.component(i, j)
for j in range(self.__width)
]
matrix.append(row)
return Matrix(matrix, self.__width, self.__height)
else:
raise Exception("matrix must have the same dimension!")
def __sub__(self, other: Matrix) -> Matrix:
"""
implements matrix subtraction.
"""
if self.__width == other.width() and self.__height == other.height():
matrix = []
for i in range(self.__height):
row = [
self.__matrix[i][j] - other.component(i, j)
for j in range(self.__width)
]
matrix.append(row)
return Matrix(matrix, self.__width, self.__height)
else:
raise Exception("matrices must have the same dimension!")
@overload
def __mul__(self, other: float) -> Matrix:
...
@overload
def __mul__(self, other: Vector) -> Vector:
...
def __mul__(self, other: float | Vector) -> Vector | Matrix:
"""
implements the matrix-vector multiplication.
implements the matrix-scalar multiplication
"""
if isinstance(other, Vector): # matrix-vector
if len(other) == self.__width:
ans = zero_vector(self.__height)
for i in range(self.__height):
prods = [
self.__matrix[i][j] * other.component(j)
for j in range(self.__width)
]
ans.change_component(i, sum(prods))
return ans
else:
raise Exception(
"vector must have the same size as the "
"number of columns of the matrix!"
)
elif isinstance(other, int) or isinstance(other, float): # matrix-scalar
matrix = [
[self.__matrix[i][j] * other for j in range(self.__width)]
for i in range(self.__height)
]
return Matrix(matrix, self.__width, self.__height)
def height(self) -> int:
"""
getter for the height
"""
return self.__height
def width(self) -> int:
"""
getter for the width
"""
return self.__width
def component(self, x: int, y: int) -> float:
"""
returns the specified (x,y) component
"""
if 0 <= x < self.__height and 0 <= y < self.__width:
return self.__matrix[x][y]
else:
raise Exception("change_component: indices out of bounds")
def change_component(self, x: int, y: int, value: float) -> None:
"""
changes the x-y component of this matrix
"""
if 0 <= x < self.__height and 0 <= y < self.__width:
self.__matrix[x][y] = value
else:
raise Exception("change_component: indices out of bounds")
def minor(self, x: int, y: int) -> float:
"""
returns the minor along (x, y)
"""
if self.__height != self.__width:
raise Exception("Matrix is not square")
minor = self.__matrix[:x] + self.__matrix[x + 1 :]
for i in range(len(minor)):
minor[i] = minor[i][:y] + minor[i][y + 1 :]
return Matrix(minor, self.__width - 1, self.__height - 1).determinant()
def cofactor(self, x: int, y: int) -> float:
"""
returns the cofactor (signed minor) along (x, y)
"""
if self.__height != self.__width:
raise Exception("Matrix is not square")
if 0 <= x < self.__height and 0 <= y < self.__width:
return (-1) ** (x + y) * self.minor(x, y)
else:
raise Exception("Indices out of bounds")
def determinant(self) -> float:
"""
returns the determinant of an nxn matrix using Laplace expansion
"""
if self.__height != self.__width:
raise Exception("Matrix is not square")
if self.__height < 1:
raise Exception("Matrix has no element")
elif self.__height == 1:
return self.__matrix[0][0]
elif self.__height == 2:
return (
self.__matrix[0][0] * self.__matrix[1][1]
- self.__matrix[0][1] * self.__matrix[1][0]
)
else:
cofactor_prods = [
self.__matrix[0][y] * self.cofactor(0, y) for y in range(self.__width)
]
return sum(cofactor_prods)
def square_zero_matrix(n: int) -> Matrix:
"""
returns a square zero-matrix of dimension NxN
"""
ans: list[list[float]] = [[0] * n for _ in range(n)]
return Matrix(ans, n, n)
def random_matrix(width: int, height: int, a: int, b: int) -> Matrix:
"""
returns a random matrix WxH with integer components
between 'a' and 'b'
"""
random.seed(None)
matrix: list[list[float]] = [
[random.randint(a, b) for _ in range(width)] for _ in range(height)
]
return Matrix(matrix, width, height)
| -1 |
TheAlgorithms/Python | 7,339 | Follow Flake8 pep3101 and remove modulo formatting | ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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 have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | 2022-10-16T20:16:16Z | 2022-10-16T20:50:11Z | 7f6e0b656f6362e452b11d06acde50b8b81cb31a | f15cc2f01c2a4124ff6dc0843c728a546f9d9f79 | Follow Flake8 pep3101 and remove modulo formatting. ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from __future__ import annotations
from .abs import abs_val
def abs_min(x: list[int]) -> int:
"""
>>> abs_min([0,5,1,11])
0
>>> abs_min([3,-10,-2])
-2
>>> abs_min([])
Traceback (most recent call last):
...
ValueError: abs_min() arg is an empty sequence
"""
if len(x) == 0:
raise ValueError("abs_min() arg is an empty sequence")
j = x[0]
for i in x:
if abs_val(i) < abs_val(j):
j = i
return j
def main():
a = [-3, -1, 2, -11]
print(abs_min(a)) # = -1
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main()
| from __future__ import annotations
from .abs import abs_val
def abs_min(x: list[int]) -> int:
"""
>>> abs_min([0,5,1,11])
0
>>> abs_min([3,-10,-2])
-2
>>> abs_min([])
Traceback (most recent call last):
...
ValueError: abs_min() arg is an empty sequence
"""
if len(x) == 0:
raise ValueError("abs_min() arg is an empty sequence")
j = x[0]
for i in x:
if abs_val(i) < abs_val(j):
j = i
return j
def main():
a = [-3, -1, 2, -11]
print(abs_min(a)) # = -1
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main()
| -1 |
TheAlgorithms/Python | 7,339 | Follow Flake8 pep3101 and remove modulo formatting | ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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 have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | 2022-10-16T20:16:16Z | 2022-10-16T20:50:11Z | 7f6e0b656f6362e452b11d06acde50b8b81cb31a | f15cc2f01c2a4124ff6dc0843c728a546f9d9f79 | Follow Flake8 pep3101 and remove modulo formatting. ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Project Euler Problem 1: https://projecteuler.net/problem=1
Multiples of 3 and 5
If we list all the natural numbers below 10 that are multiples of 3 or 5,
we get 3, 5, 6 and 9. The sum of these multiples is 23.
Find the sum of all the multiples of 3 or 5 below 1000.
"""
def solution(n: int = 1000) -> int:
"""
Returns the sum of all the multiples of 3 or 5 below n.
A straightforward pythonic solution using list comprehension.
>>> solution(3)
0
>>> solution(4)
3
>>> solution(10)
23
>>> solution(600)
83700
"""
return sum(i for i in range(n) if i % 3 == 0 or i % 5 == 0)
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 1: https://projecteuler.net/problem=1
Multiples of 3 and 5
If we list all the natural numbers below 10 that are multiples of 3 or 5,
we get 3, 5, 6 and 9. The sum of these multiples is 23.
Find the sum of all the multiples of 3 or 5 below 1000.
"""
def solution(n: int = 1000) -> int:
"""
Returns the sum of all the multiples of 3 or 5 below n.
A straightforward pythonic solution using list comprehension.
>>> solution(3)
0
>>> solution(4)
3
>>> solution(10)
23
>>> solution(600)
83700
"""
return sum(i for i in range(n) if i % 3 == 0 or i % 5 == 0)
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 7,339 | Follow Flake8 pep3101 and remove modulo formatting | ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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 have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | 2022-10-16T20:16:16Z | 2022-10-16T20:50:11Z | 7f6e0b656f6362e452b11d06acde50b8b81cb31a | f15cc2f01c2a4124ff6dc0843c728a546f9d9f79 | Follow Flake8 pep3101 and remove modulo formatting. ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # An island in matrix is a group of linked areas, all having the same value.
# This code counts number of islands in a given matrix, with including diagonal
# connections.
class Matrix: # Public class to implement a graph
def __init__(self, row: int, col: int, graph: list[list[bool]]) -> None:
self.ROW = row
self.COL = col
self.graph = graph
def is_safe(self, i: int, j: int, visited: list[list[bool]]) -> bool:
return (
0 <= i < self.ROW
and 0 <= j < self.COL
and not visited[i][j]
and self.graph[i][j]
)
def diffs(self, i: int, j: int, visited: list[list[bool]]) -> None:
# Checking all 8 elements surrounding nth element
row_nbr = [-1, -1, -1, 0, 0, 1, 1, 1] # Coordinate order
col_nbr = [-1, 0, 1, -1, 1, -1, 0, 1]
visited[i][j] = True # Make those cells visited
for k in range(8):
if self.is_safe(i + row_nbr[k], j + col_nbr[k], visited):
self.diffs(i + row_nbr[k], j + col_nbr[k], visited)
def count_islands(self) -> int: # And finally, count all islands.
visited = [[False for j in range(self.COL)] for i in range(self.ROW)]
count = 0
for i in range(self.ROW):
for j in range(self.COL):
if visited[i][j] is False and self.graph[i][j] == 1:
self.diffs(i, j, visited)
count += 1
return count
| # An island in matrix is a group of linked areas, all having the same value.
# This code counts number of islands in a given matrix, with including diagonal
# connections.
class Matrix: # Public class to implement a graph
def __init__(self, row: int, col: int, graph: list[list[bool]]) -> None:
self.ROW = row
self.COL = col
self.graph = graph
def is_safe(self, i: int, j: int, visited: list[list[bool]]) -> bool:
return (
0 <= i < self.ROW
and 0 <= j < self.COL
and not visited[i][j]
and self.graph[i][j]
)
def diffs(self, i: int, j: int, visited: list[list[bool]]) -> None:
# Checking all 8 elements surrounding nth element
row_nbr = [-1, -1, -1, 0, 0, 1, 1, 1] # Coordinate order
col_nbr = [-1, 0, 1, -1, 1, -1, 0, 1]
visited[i][j] = True # Make those cells visited
for k in range(8):
if self.is_safe(i + row_nbr[k], j + col_nbr[k], visited):
self.diffs(i + row_nbr[k], j + col_nbr[k], visited)
def count_islands(self) -> int: # And finally, count all islands.
visited = [[False for j in range(self.COL)] for i in range(self.ROW)]
count = 0
for i in range(self.ROW):
for j in range(self.COL):
if visited[i][j] is False and self.graph[i][j] == 1:
self.diffs(i, j, visited)
count += 1
return count
| -1 |
TheAlgorithms/Python | 7,339 | Follow Flake8 pep3101 and remove modulo formatting | ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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 have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | 2022-10-16T20:16:16Z | 2022-10-16T20:50:11Z | 7f6e0b656f6362e452b11d06acde50b8b81cb31a | f15cc2f01c2a4124ff6dc0843c728a546f9d9f79 | Follow Flake8 pep3101 and remove modulo formatting. ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Problem 72 Counting fractions: https://projecteuler.net/problem=72
Description:
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 21 elements in this set.
How many elements would be contained in the set of reduced proper fractions for
d ≤ 1,000,000?
Solution:
Number of numbers between 1 and n that are coprime to n is given by the Euler's Totient
function, phi(n). So, the answer is simply the sum of phi(n) for 2 <= n <= 1,000,000
Sum of phi(d), for all d|n = n. This result can be used to find phi(n) using a sieve.
Time: 1 sec
"""
def solution(limit: int = 1_000_000) -> int:
"""
Returns an integer, the solution to the problem
>>> solution(10)
31
>>> solution(100)
3043
>>> solution(1_000)
304191
"""
phi = [i - 1 for i in range(limit + 1)]
for i in range(2, limit + 1):
if phi[i] == i - 1:
for j in range(2 * i, limit + 1, i):
phi[j] -= phi[j] // i
return sum(phi[2 : limit + 1])
if __name__ == "__main__":
print(solution())
| """
Problem 72 Counting fractions: https://projecteuler.net/problem=72
Description:
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 21 elements in this set.
How many elements would be contained in the set of reduced proper fractions for
d ≤ 1,000,000?
Solution:
Number of numbers between 1 and n that are coprime to n is given by the Euler's Totient
function, phi(n). So, the answer is simply the sum of phi(n) for 2 <= n <= 1,000,000
Sum of phi(d), for all d|n = n. This result can be used to find phi(n) using a sieve.
Time: 1 sec
"""
def solution(limit: int = 1_000_000) -> int:
"""
Returns an integer, the solution to the problem
>>> solution(10)
31
>>> solution(100)
3043
>>> solution(1_000)
304191
"""
phi = [i - 1 for i in range(limit + 1)]
for i in range(2, limit + 1):
if phi[i] == i - 1:
for j in range(2 * i, limit + 1, i):
phi[j] -= phi[j] // i
return sum(phi[2 : limit + 1])
if __name__ == "__main__":
print(solution())
| -1 |
TheAlgorithms/Python | 7,339 | Follow Flake8 pep3101 and remove modulo formatting | ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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 have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | 2022-10-16T20:16:16Z | 2022-10-16T20:50:11Z | 7f6e0b656f6362e452b11d06acde50b8b81cb31a | f15cc2f01c2a4124ff6dc0843c728a546f9d9f79 | Follow Flake8 pep3101 and remove modulo formatting. ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
The stock span problem is a financial problem where we have a series of n daily
price quotes for a stock and we need to calculate span of stock's price for all n days.
The span Si of the stock's price on a given day i is defined as the maximum
number of consecutive days just before the given day, for which the price of the stock
on the current day is less than or equal to its price on the given day.
"""
def calculation_span(price, s):
n = len(price)
# Create a stack and push index of fist element to it
st = []
st.append(0)
# Span value of first element is always 1
s[0] = 1
# Calculate span values for rest of the elements
for i in range(1, n):
# Pop elements from stack while stack is not
# empty and top of stack is smaller than price[i]
while len(st) > 0 and price[st[0]] <= price[i]:
st.pop()
# If stack becomes empty, then price[i] is greater
# than all elements on left of it, i.e. price[0],
# price[1], ..price[i-1]. Else the price[i] is
# greater than elements after top of stack
s[i] = i + 1 if len(st) <= 0 else (i - st[0])
# Push this element to stack
st.append(i)
# A utility function to print elements of array
def print_array(arr, n):
for i in range(0, n):
print(arr[i], end=" ")
# Driver program to test above function
price = [10, 4, 5, 90, 120, 80]
S = [0 for i in range(len(price) + 1)]
# Fill the span values in array S[]
calculation_span(price, S)
# Print the calculated span values
print_array(S, len(price))
| """
The stock span problem is a financial problem where we have a series of n daily
price quotes for a stock and we need to calculate span of stock's price for all n days.
The span Si of the stock's price on a given day i is defined as the maximum
number of consecutive days just before the given day, for which the price of the stock
on the current day is less than or equal to its price on the given day.
"""
def calculation_span(price, s):
n = len(price)
# Create a stack and push index of fist element to it
st = []
st.append(0)
# Span value of first element is always 1
s[0] = 1
# Calculate span values for rest of the elements
for i in range(1, n):
# Pop elements from stack while stack is not
# empty and top of stack is smaller than price[i]
while len(st) > 0 and price[st[0]] <= price[i]:
st.pop()
# If stack becomes empty, then price[i] is greater
# than all elements on left of it, i.e. price[0],
# price[1], ..price[i-1]. Else the price[i] is
# greater than elements after top of stack
s[i] = i + 1 if len(st) <= 0 else (i - st[0])
# Push this element to stack
st.append(i)
# A utility function to print elements of array
def print_array(arr, n):
for i in range(0, n):
print(arr[i], end=" ")
# Driver program to test above function
price = [10, 4, 5, 90, 120, 80]
S = [0 for i in range(len(price) + 1)]
# Fill the span values in array S[]
calculation_span(price, S)
# Print the calculated span values
print_array(S, len(price))
| -1 |
TheAlgorithms/Python | 7,339 | Follow Flake8 pep3101 and remove modulo formatting | ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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 have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | 2022-10-16T20:16:16Z | 2022-10-16T20:50:11Z | 7f6e0b656f6362e452b11d06acde50b8b81cb31a | f15cc2f01c2a4124ff6dc0843c728a546f9d9f79 | Follow Flake8 pep3101 and remove modulo formatting. ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Conway's Game of Life implemented in Python.
https://en.wikipedia.org/wiki/Conway%27s_Game_of_Life
"""
from __future__ import annotations
from PIL import Image
# Define glider example
GLIDER = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
]
# Define blinker example
BLINKER = [[0, 1, 0], [0, 1, 0], [0, 1, 0]]
def new_generation(cells: list[list[int]]) -> list[list[int]]:
"""
Generates the next generation for a given state of Conway's Game of Life.
>>> new_generation(BLINKER)
[[0, 0, 0], [1, 1, 1], [0, 0, 0]]
"""
next_generation = []
for i in range(len(cells)):
next_generation_row = []
for j in range(len(cells[i])):
# Get the number of live neighbours
neighbour_count = 0
if i > 0 and j > 0:
neighbour_count += cells[i - 1][j - 1]
if i > 0:
neighbour_count += cells[i - 1][j]
if i > 0 and j < len(cells[i]) - 1:
neighbour_count += cells[i - 1][j + 1]
if j > 0:
neighbour_count += cells[i][j - 1]
if j < len(cells[i]) - 1:
neighbour_count += cells[i][j + 1]
if i < len(cells) - 1 and j > 0:
neighbour_count += cells[i + 1][j - 1]
if i < len(cells) - 1:
neighbour_count += cells[i + 1][j]
if i < len(cells) - 1 and j < len(cells[i]) - 1:
neighbour_count += cells[i + 1][j + 1]
# Rules of the game of life (excerpt from Wikipedia):
# 1. Any live cell with two or three live neighbours survives.
# 2. Any dead cell with three live neighbours becomes a live cell.
# 3. All other live cells die in the next generation.
# Similarly, all other dead cells stay dead.
alive = cells[i][j] == 1
if (
(alive and 2 <= neighbour_count <= 3)
or not alive
and neighbour_count == 3
):
next_generation_row.append(1)
else:
next_generation_row.append(0)
next_generation.append(next_generation_row)
return next_generation
def generate_images(cells: list[list[int]], frames: int) -> list[Image.Image]:
"""
Generates a list of images of subsequent Game of Life states.
"""
images = []
for _ in range(frames):
# Create output image
img = Image.new("RGB", (len(cells[0]), len(cells)))
pixels = img.load()
# Save cells to image
for x in range(len(cells)):
for y in range(len(cells[0])):
colour = 255 - cells[y][x] * 255
pixels[x, y] = (colour, colour, colour)
# Save image
images.append(img)
cells = new_generation(cells)
return images
if __name__ == "__main__":
images = generate_images(GLIDER, 16)
images[0].save("out.gif", save_all=True, append_images=images[1:])
| """
Conway's Game of Life implemented in Python.
https://en.wikipedia.org/wiki/Conway%27s_Game_of_Life
"""
from __future__ import annotations
from PIL import Image
# Define glider example
GLIDER = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
]
# Define blinker example
BLINKER = [[0, 1, 0], [0, 1, 0], [0, 1, 0]]
def new_generation(cells: list[list[int]]) -> list[list[int]]:
"""
Generates the next generation for a given state of Conway's Game of Life.
>>> new_generation(BLINKER)
[[0, 0, 0], [1, 1, 1], [0, 0, 0]]
"""
next_generation = []
for i in range(len(cells)):
next_generation_row = []
for j in range(len(cells[i])):
# Get the number of live neighbours
neighbour_count = 0
if i > 0 and j > 0:
neighbour_count += cells[i - 1][j - 1]
if i > 0:
neighbour_count += cells[i - 1][j]
if i > 0 and j < len(cells[i]) - 1:
neighbour_count += cells[i - 1][j + 1]
if j > 0:
neighbour_count += cells[i][j - 1]
if j < len(cells[i]) - 1:
neighbour_count += cells[i][j + 1]
if i < len(cells) - 1 and j > 0:
neighbour_count += cells[i + 1][j - 1]
if i < len(cells) - 1:
neighbour_count += cells[i + 1][j]
if i < len(cells) - 1 and j < len(cells[i]) - 1:
neighbour_count += cells[i + 1][j + 1]
# Rules of the game of life (excerpt from Wikipedia):
# 1. Any live cell with two or three live neighbours survives.
# 2. Any dead cell with three live neighbours becomes a live cell.
# 3. All other live cells die in the next generation.
# Similarly, all other dead cells stay dead.
alive = cells[i][j] == 1
if (
(alive and 2 <= neighbour_count <= 3)
or not alive
and neighbour_count == 3
):
next_generation_row.append(1)
else:
next_generation_row.append(0)
next_generation.append(next_generation_row)
return next_generation
def generate_images(cells: list[list[int]], frames: int) -> list[Image.Image]:
"""
Generates a list of images of subsequent Game of Life states.
"""
images = []
for _ in range(frames):
# Create output image
img = Image.new("RGB", (len(cells[0]), len(cells)))
pixels = img.load()
# Save cells to image
for x in range(len(cells)):
for y in range(len(cells[0])):
colour = 255 - cells[y][x] * 255
pixels[x, y] = (colour, colour, colour)
# Save image
images.append(img)
cells = new_generation(cells)
return images
if __name__ == "__main__":
images = generate_images(GLIDER, 16)
images[0].save("out.gif", save_all=True, append_images=images[1:])
| -1 |
TheAlgorithms/Python | 7,339 | Follow Flake8 pep3101 and remove modulo formatting | ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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 have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | 2022-10-16T20:16:16Z | 2022-10-16T20:50:11Z | 7f6e0b656f6362e452b11d06acde50b8b81cb31a | f15cc2f01c2a4124ff6dc0843c728a546f9d9f79 | Follow Flake8 pep3101 and remove modulo formatting. ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 7,339 | Follow Flake8 pep3101 and remove modulo formatting | ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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 have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | 2022-10-16T20:16:16Z | 2022-10-16T20:50:11Z | 7f6e0b656f6362e452b11d06acde50b8b81cb31a | f15cc2f01c2a4124ff6dc0843c728a546f9d9f79 | Follow Flake8 pep3101 and remove modulo formatting. ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Project Euler Problem 38: https://projecteuler.net/problem=38
Take the number 192 and multiply it by each of 1, 2, and 3:
192 × 1 = 192
192 × 2 = 384
192 × 3 = 576
By concatenating each product we get the 1 to 9 pandigital, 192384576. We will call
192384576 the concatenated product of 192 and (1,2,3)
The same can be achieved by starting with 9 and multiplying by 1, 2, 3, 4, and 5,
giving the pandigital, 918273645, which is the concatenated product of 9 and
(1,2,3,4,5).
What is the largest 1 to 9 pandigital 9-digit number that can be formed as the
concatenated product of an integer with (1,2, ... , n) where n > 1?
Solution:
Since n>1, the largest candidate for the solution will be a concactenation of
a 4-digit number and its double, a 5-digit number.
Let a be the 4-digit number.
a has 4 digits => 1000 <= a < 10000
2a has 5 digits => 10000 <= 2a < 100000
=> 5000 <= a < 10000
The concatenation of a with 2a = a * 10^5 + 2a
so our candidate for a given a is 100002 * a.
We iterate through the search space 5000 <= a < 10000 in reverse order,
calculating the candidates for each a and checking if they are 1-9 pandigital.
In case there are no 4-digit numbers that satisfy this property, we check
the 3-digit numbers with a similar formula (the example a=192 gives a lower
bound on the length of a):
a has 3 digits, etc...
=> 100 <= a < 334, candidate = a * 10^6 + 2a * 10^3 + 3a
= 1002003 * a
"""
from __future__ import annotations
def is_9_pandigital(n: int) -> bool:
"""
Checks whether n is a 9-digit 1 to 9 pandigital number.
>>> is_9_pandigital(12345)
False
>>> is_9_pandigital(156284973)
True
>>> is_9_pandigital(1562849733)
False
"""
s = str(n)
return len(s) == 9 and set(s) == set("123456789")
def solution() -> int | None:
"""
Return the largest 1 to 9 pandigital 9-digital number that can be formed as the
concatenated product of an integer with (1,2,...,n) where n > 1.
"""
for base_num in range(9999, 4999, -1):
candidate = 100002 * base_num
if is_9_pandigital(candidate):
return candidate
for base_num in range(333, 99, -1):
candidate = 1002003 * base_num
if is_9_pandigital(candidate):
return candidate
return None
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 38: https://projecteuler.net/problem=38
Take the number 192 and multiply it by each of 1, 2, and 3:
192 × 1 = 192
192 × 2 = 384
192 × 3 = 576
By concatenating each product we get the 1 to 9 pandigital, 192384576. We will call
192384576 the concatenated product of 192 and (1,2,3)
The same can be achieved by starting with 9 and multiplying by 1, 2, 3, 4, and 5,
giving the pandigital, 918273645, which is the concatenated product of 9 and
(1,2,3,4,5).
What is the largest 1 to 9 pandigital 9-digit number that can be formed as the
concatenated product of an integer with (1,2, ... , n) where n > 1?
Solution:
Since n>1, the largest candidate for the solution will be a concactenation of
a 4-digit number and its double, a 5-digit number.
Let a be the 4-digit number.
a has 4 digits => 1000 <= a < 10000
2a has 5 digits => 10000 <= 2a < 100000
=> 5000 <= a < 10000
The concatenation of a with 2a = a * 10^5 + 2a
so our candidate for a given a is 100002 * a.
We iterate through the search space 5000 <= a < 10000 in reverse order,
calculating the candidates for each a and checking if they are 1-9 pandigital.
In case there are no 4-digit numbers that satisfy this property, we check
the 3-digit numbers with a similar formula (the example a=192 gives a lower
bound on the length of a):
a has 3 digits, etc...
=> 100 <= a < 334, candidate = a * 10^6 + 2a * 10^3 + 3a
= 1002003 * a
"""
from __future__ import annotations
def is_9_pandigital(n: int) -> bool:
"""
Checks whether n is a 9-digit 1 to 9 pandigital number.
>>> is_9_pandigital(12345)
False
>>> is_9_pandigital(156284973)
True
>>> is_9_pandigital(1562849733)
False
"""
s = str(n)
return len(s) == 9 and set(s) == set("123456789")
def solution() -> int | None:
"""
Return the largest 1 to 9 pandigital 9-digital number that can be formed as the
concatenated product of an integer with (1,2,...,n) where n > 1.
"""
for base_num in range(9999, 4999, -1):
candidate = 100002 * base_num
if is_9_pandigital(candidate):
return candidate
for base_num in range(333, 99, -1):
candidate = 1002003 * base_num
if is_9_pandigital(candidate):
return candidate
return None
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 7,339 | Follow Flake8 pep3101 and remove modulo formatting | ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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 have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | 2022-10-16T20:16:16Z | 2022-10-16T20:50:11Z | 7f6e0b656f6362e452b11d06acde50b8b81cb31a | f15cc2f01c2a4124ff6dc0843c728a546f9d9f79 | Follow Flake8 pep3101 and remove modulo formatting. ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 7,339 | Follow Flake8 pep3101 and remove modulo formatting | ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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 have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | 2022-10-16T20:16:16Z | 2022-10-16T20:50:11Z | 7f6e0b656f6362e452b11d06acde50b8b81cb31a | f15cc2f01c2a4124ff6dc0843c728a546f9d9f79 | Follow Flake8 pep3101 and remove modulo formatting. ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from __future__ import annotations
from typing import Generic, TypeVar
T = TypeVar("T")
class StackOverflowError(BaseException):
pass
class StackUnderflowError(BaseException):
pass
class Stack(Generic[T]):
"""A stack is an abstract data type that serves as a collection of
elements with two principal operations: push() and pop(). push() adds an
element to the top of the stack, and pop() removes an element from the top
of a stack. The order in which elements come off of a stack are
Last In, First Out (LIFO).
https://en.wikipedia.org/wiki/Stack_(abstract_data_type)
"""
def __init__(self, limit: int = 10):
self.stack: list[T] = []
self.limit = limit
def __bool__(self) -> bool:
return bool(self.stack)
def __str__(self) -> str:
return str(self.stack)
def push(self, data: T) -> None:
"""Push an element to the top of the stack."""
if len(self.stack) >= self.limit:
raise StackOverflowError
self.stack.append(data)
def pop(self) -> T:
"""
Pop an element off of the top of the stack.
>>> Stack().pop()
Traceback (most recent call last):
...
data_structures.stacks.stack.StackUnderflowError
"""
if not self.stack:
raise StackUnderflowError
return self.stack.pop()
def peek(self) -> T:
"""
Peek at the top-most element of the stack.
>>> Stack().pop()
Traceback (most recent call last):
...
data_structures.stacks.stack.StackUnderflowError
"""
if not self.stack:
raise StackUnderflowError
return self.stack[-1]
def is_empty(self) -> bool:
"""Check if a stack is empty."""
return not bool(self.stack)
def is_full(self) -> bool:
return self.size() == self.limit
def size(self) -> int:
"""Return the size of the stack."""
return len(self.stack)
def __contains__(self, item: T) -> bool:
"""Check if item is in stack"""
return item in self.stack
def test_stack() -> None:
"""
>>> test_stack()
"""
stack: Stack[int] = Stack(10)
assert bool(stack) is False
assert stack.is_empty() is True
assert stack.is_full() is False
assert str(stack) == "[]"
try:
_ = stack.pop()
raise AssertionError() # This should not happen
except StackUnderflowError:
assert True # This should happen
try:
_ = stack.peek()
raise AssertionError() # This should not happen
except StackUnderflowError:
assert True # This should happen
for i in range(10):
assert stack.size() == i
stack.push(i)
assert bool(stack)
assert not stack.is_empty()
assert stack.is_full()
assert str(stack) == str(list(range(10)))
assert stack.pop() == 9
assert stack.peek() == 8
stack.push(100)
assert str(stack) == str([0, 1, 2, 3, 4, 5, 6, 7, 8, 100])
try:
stack.push(200)
raise AssertionError() # This should not happen
except StackOverflowError:
assert True # This should happen
assert not stack.is_empty()
assert stack.size() == 10
assert 5 in stack
assert 55 not in stack
if __name__ == "__main__":
test_stack()
| from __future__ import annotations
from typing import Generic, TypeVar
T = TypeVar("T")
class StackOverflowError(BaseException):
pass
class StackUnderflowError(BaseException):
pass
class Stack(Generic[T]):
"""A stack is an abstract data type that serves as a collection of
elements with two principal operations: push() and pop(). push() adds an
element to the top of the stack, and pop() removes an element from the top
of a stack. The order in which elements come off of a stack are
Last In, First Out (LIFO).
https://en.wikipedia.org/wiki/Stack_(abstract_data_type)
"""
def __init__(self, limit: int = 10):
self.stack: list[T] = []
self.limit = limit
def __bool__(self) -> bool:
return bool(self.stack)
def __str__(self) -> str:
return str(self.stack)
def push(self, data: T) -> None:
"""Push an element to the top of the stack."""
if len(self.stack) >= self.limit:
raise StackOverflowError
self.stack.append(data)
def pop(self) -> T:
"""
Pop an element off of the top of the stack.
>>> Stack().pop()
Traceback (most recent call last):
...
data_structures.stacks.stack.StackUnderflowError
"""
if not self.stack:
raise StackUnderflowError
return self.stack.pop()
def peek(self) -> T:
"""
Peek at the top-most element of the stack.
>>> Stack().pop()
Traceback (most recent call last):
...
data_structures.stacks.stack.StackUnderflowError
"""
if not self.stack:
raise StackUnderflowError
return self.stack[-1]
def is_empty(self) -> bool:
"""Check if a stack is empty."""
return not bool(self.stack)
def is_full(self) -> bool:
return self.size() == self.limit
def size(self) -> int:
"""Return the size of the stack."""
return len(self.stack)
def __contains__(self, item: T) -> bool:
"""Check if item is in stack"""
return item in self.stack
def test_stack() -> None:
"""
>>> test_stack()
"""
stack: Stack[int] = Stack(10)
assert bool(stack) is False
assert stack.is_empty() is True
assert stack.is_full() is False
assert str(stack) == "[]"
try:
_ = stack.pop()
raise AssertionError() # This should not happen
except StackUnderflowError:
assert True # This should happen
try:
_ = stack.peek()
raise AssertionError() # This should not happen
except StackUnderflowError:
assert True # This should happen
for i in range(10):
assert stack.size() == i
stack.push(i)
assert bool(stack)
assert not stack.is_empty()
assert stack.is_full()
assert str(stack) == str(list(range(10)))
assert stack.pop() == 9
assert stack.peek() == 8
stack.push(100)
assert str(stack) == str([0, 1, 2, 3, 4, 5, 6, 7, 8, 100])
try:
stack.push(200)
raise AssertionError() # This should not happen
except StackOverflowError:
assert True # This should happen
assert not stack.is_empty()
assert stack.size() == 10
assert 5 in stack
assert 55 not in stack
if __name__ == "__main__":
test_stack()
| -1 |
TheAlgorithms/Python | 7,339 | Follow Flake8 pep3101 and remove modulo formatting | ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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 have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | 2022-10-16T20:16:16Z | 2022-10-16T20:50:11Z | 7f6e0b656f6362e452b11d06acde50b8b81cb31a | f15cc2f01c2a4124ff6dc0843c728a546f9d9f79 | Follow Flake8 pep3101 and remove modulo formatting. ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 7,339 | Follow Flake8 pep3101 and remove modulo formatting | ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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 have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | 2022-10-16T20:16:16Z | 2022-10-16T20:50:11Z | 7f6e0b656f6362e452b11d06acde50b8b81cb31a | f15cc2f01c2a4124ff6dc0843c728a546f9d9f79 | Follow Flake8 pep3101 and remove modulo formatting. ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 7,339 | Follow Flake8 pep3101 and remove modulo formatting | ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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 have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | 2022-10-16T20:16:16Z | 2022-10-16T20:50:11Z | 7f6e0b656f6362e452b11d06acde50b8b81cb31a | f15cc2f01c2a4124ff6dc0843c728a546f9d9f79 | Follow Flake8 pep3101 and remove modulo formatting. ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # flake8: noqa
"""
Binomial Heap
Reference: Advanced Data Structures, Peter Brass
"""
class Node:
"""
Node in a doubly-linked binomial tree, containing:
- value
- size of left subtree
- link to left, right and parent nodes
"""
def __init__(self, val):
self.val = val
# Number of nodes in left subtree
self.left_tree_size = 0
self.left = None
self.right = None
self.parent = None
def mergeTrees(self, other):
"""
In-place merge of two binomial trees of equal size.
Returns the root of the resulting tree
"""
assert self.left_tree_size == other.left_tree_size, "Unequal Sizes of Blocks"
if self.val < other.val:
other.left = self.right
other.parent = None
if self.right:
self.right.parent = other
self.right = other
self.left_tree_size = self.left_tree_size * 2 + 1
return self
else:
self.left = other.right
self.parent = None
if other.right:
other.right.parent = self
other.right = self
other.left_tree_size = other.left_tree_size * 2 + 1
return other
class BinomialHeap:
r"""
Min-oriented priority queue implemented with the Binomial Heap data
structure implemented with the BinomialHeap class. It supports:
- Insert element in a heap with n elements: Guaranteed logn, amoratized 1
- Merge (meld) heaps of size m and n: O(logn + logm)
- Delete Min: O(logn)
- Peek (return min without deleting it): O(1)
Example:
Create a random permutation of 30 integers to be inserted and 19 of them deleted
>>> import numpy as np
>>> permutation = np.random.permutation(list(range(30)))
Create a Heap and insert the 30 integers
__init__() test
>>> first_heap = BinomialHeap()
30 inserts - insert() test
>>> for number in permutation:
... first_heap.insert(number)
Size test
>>> print(first_heap.size)
30
Deleting - delete() test
>>> for i in range(25):
... print(first_heap.deleteMin(), end=" ")
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Create a new Heap
>>> second_heap = BinomialHeap()
>>> vals = [17, 20, 31, 34]
>>> for value in vals:
... second_heap.insert(value)
The heap should have the following structure:
17
/ \
# 31
/ \
20 34
/ \ / \
# # # #
preOrder() test
>>> print(second_heap.preOrder())
[(17, 0), ('#', 1), (31, 1), (20, 2), ('#', 3), ('#', 3), (34, 2), ('#', 3), ('#', 3)]
printing Heap - __str__() test
>>> print(second_heap)
17
-#
-31
--20
---#
---#
--34
---#
---#
mergeHeaps() test
>>> merged = second_heap.mergeHeaps(first_heap)
>>> merged.peek()
17
values in merged heap; (merge is inplace)
>>> while not first_heap.isEmpty():
... print(first_heap.deleteMin(), end=" ")
17 20 25 26 27 28 29 31 34
"""
def __init__(self, bottom_root=None, min_node=None, heap_size=0):
self.size = heap_size
self.bottom_root = bottom_root
self.min_node = min_node
def mergeHeaps(self, other):
"""
In-place merge of two binomial heaps.
Both of them become the resulting merged heap
"""
# Empty heaps corner cases
if other.size == 0:
return
if self.size == 0:
self.size = other.size
self.bottom_root = other.bottom_root
self.min_node = other.min_node
return
# Update size
self.size = self.size + other.size
# Update min.node
if self.min_node.val > other.min_node.val:
self.min_node = other.min_node
# Merge
# Order roots by left_subtree_size
combined_roots_list = []
i, j = self.bottom_root, other.bottom_root
while i or j:
if i and ((not j) or i.left_tree_size < j.left_tree_size):
combined_roots_list.append((i, True))
i = i.parent
else:
combined_roots_list.append((j, False))
j = j.parent
# Insert links between them
for i in range(len(combined_roots_list) - 1):
if combined_roots_list[i][1] != combined_roots_list[i + 1][1]:
combined_roots_list[i][0].parent = combined_roots_list[i + 1][0]
combined_roots_list[i + 1][0].left = combined_roots_list[i][0]
# Consecutively merge roots with same left_tree_size
i = combined_roots_list[0][0]
while i.parent:
if (
(i.left_tree_size == i.parent.left_tree_size) and (not i.parent.parent)
) or (
i.left_tree_size == i.parent.left_tree_size
and i.left_tree_size != i.parent.parent.left_tree_size
):
# Neighbouring Nodes
previous_node = i.left
next_node = i.parent.parent
# Merging trees
i = i.mergeTrees(i.parent)
# Updating links
i.left = previous_node
i.parent = next_node
if previous_node:
previous_node.parent = i
if next_node:
next_node.left = i
else:
i = i.parent
# Updating self.bottom_root
while i.left:
i = i.left
self.bottom_root = i
# Update other
other.size = self.size
other.bottom_root = self.bottom_root
other.min_node = self.min_node
# Return the merged heap
return self
def insert(self, val):
"""
insert a value in the heap
"""
if self.size == 0:
self.bottom_root = Node(val)
self.size = 1
self.min_node = self.bottom_root
else:
# Create new node
new_node = Node(val)
# Update size
self.size += 1
# update min_node
if val < self.min_node.val:
self.min_node = new_node
# Put new_node as a bottom_root in heap
self.bottom_root.left = new_node
new_node.parent = self.bottom_root
self.bottom_root = new_node
# Consecutively merge roots with same left_tree_size
while (
self.bottom_root.parent
and self.bottom_root.left_tree_size
== self.bottom_root.parent.left_tree_size
):
# Next node
next_node = self.bottom_root.parent.parent
# Merge
self.bottom_root = self.bottom_root.mergeTrees(self.bottom_root.parent)
# Update Links
self.bottom_root.parent = next_node
self.bottom_root.left = None
if next_node:
next_node.left = self.bottom_root
def peek(self):
"""
return min element without deleting it
"""
return self.min_node.val
def isEmpty(self):
return self.size == 0
def deleteMin(self):
"""
delete min element and return it
"""
# assert not self.isEmpty(), "Empty Heap"
# Save minimal value
min_value = self.min_node.val
# Last element in heap corner case
if self.size == 1:
# Update size
self.size = 0
# Update bottom root
self.bottom_root = None
# Update min_node
self.min_node = None
return min_value
# No right subtree corner case
# The structure of the tree implies that this should be the bottom root
# and there is at least one other root
if self.min_node.right is None:
# Update size
self.size -= 1
# Update bottom root
self.bottom_root = self.bottom_root.parent
self.bottom_root.left = None
# Update min_node
self.min_node = self.bottom_root
i = self.bottom_root.parent
while i:
if i.val < self.min_node.val:
self.min_node = i
i = i.parent
return min_value
# General case
# Find the BinomialHeap of the right subtree of min_node
bottom_of_new = self.min_node.right
bottom_of_new.parent = None
min_of_new = bottom_of_new
size_of_new = 1
# Size, min_node and bottom_root
while bottom_of_new.left:
size_of_new = size_of_new * 2 + 1
bottom_of_new = bottom_of_new.left
if bottom_of_new.val < min_of_new.val:
min_of_new = bottom_of_new
# Corner case of single root on top left path
if (not self.min_node.left) and (not self.min_node.parent):
self.size = size_of_new
self.bottom_root = bottom_of_new
self.min_node = min_of_new
# print("Single root, multiple nodes case")
return min_value
# Remaining cases
# Construct heap of right subtree
newHeap = BinomialHeap(
bottom_root=bottom_of_new, min_node=min_of_new, heap_size=size_of_new
)
# Update size
self.size = self.size - 1 - size_of_new
# Neighbour nodes
previous_node = self.min_node.left
next_node = self.min_node.parent
# Initialize new bottom_root and min_node
self.min_node = previous_node or next_node
self.bottom_root = next_node
# Update links of previous_node and search below for new min_node and
# bottom_root
if previous_node:
previous_node.parent = next_node
# Update bottom_root and search for min_node below
self.bottom_root = previous_node
self.min_node = previous_node
while self.bottom_root.left:
self.bottom_root = self.bottom_root.left
if self.bottom_root.val < self.min_node.val:
self.min_node = self.bottom_root
if next_node:
next_node.left = previous_node
# Search for new min_node above min_node
i = next_node
while i:
if i.val < self.min_node.val:
self.min_node = i
i = i.parent
# Merge heaps
self.mergeHeaps(newHeap)
return min_value
def preOrder(self):
"""
Returns the Pre-order representation of the heap including
values of nodes plus their level distance from the root;
Empty nodes appear as #
"""
# Find top root
top_root = self.bottom_root
while top_root.parent:
top_root = top_root.parent
# preorder
heap_preOrder = []
self.__traversal(top_root, heap_preOrder)
return heap_preOrder
def __traversal(self, curr_node, preorder, level=0):
"""
Pre-order traversal of nodes
"""
if curr_node:
preorder.append((curr_node.val, level))
self.__traversal(curr_node.left, preorder, level + 1)
self.__traversal(curr_node.right, preorder, level + 1)
else:
preorder.append(("#", level))
def __str__(self):
"""
Overwriting str for a pre-order print of nodes in heap;
Performance is poor, so use only for small examples
"""
if self.isEmpty():
return ""
preorder_heap = self.preOrder()
return "\n".join(("-" * level + str(value)) for value, level in preorder_heap)
# Unit Tests
if __name__ == "__main__":
import doctest
doctest.testmod()
| # flake8: noqa
"""
Binomial Heap
Reference: Advanced Data Structures, Peter Brass
"""
class Node:
"""
Node in a doubly-linked binomial tree, containing:
- value
- size of left subtree
- link to left, right and parent nodes
"""
def __init__(self, val):
self.val = val
# Number of nodes in left subtree
self.left_tree_size = 0
self.left = None
self.right = None
self.parent = None
def mergeTrees(self, other):
"""
In-place merge of two binomial trees of equal size.
Returns the root of the resulting tree
"""
assert self.left_tree_size == other.left_tree_size, "Unequal Sizes of Blocks"
if self.val < other.val:
other.left = self.right
other.parent = None
if self.right:
self.right.parent = other
self.right = other
self.left_tree_size = self.left_tree_size * 2 + 1
return self
else:
self.left = other.right
self.parent = None
if other.right:
other.right.parent = self
other.right = self
other.left_tree_size = other.left_tree_size * 2 + 1
return other
class BinomialHeap:
r"""
Min-oriented priority queue implemented with the Binomial Heap data
structure implemented with the BinomialHeap class. It supports:
- Insert element in a heap with n elements: Guaranteed logn, amoratized 1
- Merge (meld) heaps of size m and n: O(logn + logm)
- Delete Min: O(logn)
- Peek (return min without deleting it): O(1)
Example:
Create a random permutation of 30 integers to be inserted and 19 of them deleted
>>> import numpy as np
>>> permutation = np.random.permutation(list(range(30)))
Create a Heap and insert the 30 integers
__init__() test
>>> first_heap = BinomialHeap()
30 inserts - insert() test
>>> for number in permutation:
... first_heap.insert(number)
Size test
>>> print(first_heap.size)
30
Deleting - delete() test
>>> for i in range(25):
... print(first_heap.deleteMin(), end=" ")
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Create a new Heap
>>> second_heap = BinomialHeap()
>>> vals = [17, 20, 31, 34]
>>> for value in vals:
... second_heap.insert(value)
The heap should have the following structure:
17
/ \
# 31
/ \
20 34
/ \ / \
# # # #
preOrder() test
>>> print(second_heap.preOrder())
[(17, 0), ('#', 1), (31, 1), (20, 2), ('#', 3), ('#', 3), (34, 2), ('#', 3), ('#', 3)]
printing Heap - __str__() test
>>> print(second_heap)
17
-#
-31
--20
---#
---#
--34
---#
---#
mergeHeaps() test
>>> merged = second_heap.mergeHeaps(first_heap)
>>> merged.peek()
17
values in merged heap; (merge is inplace)
>>> while not first_heap.isEmpty():
... print(first_heap.deleteMin(), end=" ")
17 20 25 26 27 28 29 31 34
"""
def __init__(self, bottom_root=None, min_node=None, heap_size=0):
self.size = heap_size
self.bottom_root = bottom_root
self.min_node = min_node
def mergeHeaps(self, other):
"""
In-place merge of two binomial heaps.
Both of them become the resulting merged heap
"""
# Empty heaps corner cases
if other.size == 0:
return
if self.size == 0:
self.size = other.size
self.bottom_root = other.bottom_root
self.min_node = other.min_node
return
# Update size
self.size = self.size + other.size
# Update min.node
if self.min_node.val > other.min_node.val:
self.min_node = other.min_node
# Merge
# Order roots by left_subtree_size
combined_roots_list = []
i, j = self.bottom_root, other.bottom_root
while i or j:
if i and ((not j) or i.left_tree_size < j.left_tree_size):
combined_roots_list.append((i, True))
i = i.parent
else:
combined_roots_list.append((j, False))
j = j.parent
# Insert links between them
for i in range(len(combined_roots_list) - 1):
if combined_roots_list[i][1] != combined_roots_list[i + 1][1]:
combined_roots_list[i][0].parent = combined_roots_list[i + 1][0]
combined_roots_list[i + 1][0].left = combined_roots_list[i][0]
# Consecutively merge roots with same left_tree_size
i = combined_roots_list[0][0]
while i.parent:
if (
(i.left_tree_size == i.parent.left_tree_size) and (not i.parent.parent)
) or (
i.left_tree_size == i.parent.left_tree_size
and i.left_tree_size != i.parent.parent.left_tree_size
):
# Neighbouring Nodes
previous_node = i.left
next_node = i.parent.parent
# Merging trees
i = i.mergeTrees(i.parent)
# Updating links
i.left = previous_node
i.parent = next_node
if previous_node:
previous_node.parent = i
if next_node:
next_node.left = i
else:
i = i.parent
# Updating self.bottom_root
while i.left:
i = i.left
self.bottom_root = i
# Update other
other.size = self.size
other.bottom_root = self.bottom_root
other.min_node = self.min_node
# Return the merged heap
return self
def insert(self, val):
"""
insert a value in the heap
"""
if self.size == 0:
self.bottom_root = Node(val)
self.size = 1
self.min_node = self.bottom_root
else:
# Create new node
new_node = Node(val)
# Update size
self.size += 1
# update min_node
if val < self.min_node.val:
self.min_node = new_node
# Put new_node as a bottom_root in heap
self.bottom_root.left = new_node
new_node.parent = self.bottom_root
self.bottom_root = new_node
# Consecutively merge roots with same left_tree_size
while (
self.bottom_root.parent
and self.bottom_root.left_tree_size
== self.bottom_root.parent.left_tree_size
):
# Next node
next_node = self.bottom_root.parent.parent
# Merge
self.bottom_root = self.bottom_root.mergeTrees(self.bottom_root.parent)
# Update Links
self.bottom_root.parent = next_node
self.bottom_root.left = None
if next_node:
next_node.left = self.bottom_root
def peek(self):
"""
return min element without deleting it
"""
return self.min_node.val
def isEmpty(self):
return self.size == 0
def deleteMin(self):
"""
delete min element and return it
"""
# assert not self.isEmpty(), "Empty Heap"
# Save minimal value
min_value = self.min_node.val
# Last element in heap corner case
if self.size == 1:
# Update size
self.size = 0
# Update bottom root
self.bottom_root = None
# Update min_node
self.min_node = None
return min_value
# No right subtree corner case
# The structure of the tree implies that this should be the bottom root
# and there is at least one other root
if self.min_node.right is None:
# Update size
self.size -= 1
# Update bottom root
self.bottom_root = self.bottom_root.parent
self.bottom_root.left = None
# Update min_node
self.min_node = self.bottom_root
i = self.bottom_root.parent
while i:
if i.val < self.min_node.val:
self.min_node = i
i = i.parent
return min_value
# General case
# Find the BinomialHeap of the right subtree of min_node
bottom_of_new = self.min_node.right
bottom_of_new.parent = None
min_of_new = bottom_of_new
size_of_new = 1
# Size, min_node and bottom_root
while bottom_of_new.left:
size_of_new = size_of_new * 2 + 1
bottom_of_new = bottom_of_new.left
if bottom_of_new.val < min_of_new.val:
min_of_new = bottom_of_new
# Corner case of single root on top left path
if (not self.min_node.left) and (not self.min_node.parent):
self.size = size_of_new
self.bottom_root = bottom_of_new
self.min_node = min_of_new
# print("Single root, multiple nodes case")
return min_value
# Remaining cases
# Construct heap of right subtree
newHeap = BinomialHeap(
bottom_root=bottom_of_new, min_node=min_of_new, heap_size=size_of_new
)
# Update size
self.size = self.size - 1 - size_of_new
# Neighbour nodes
previous_node = self.min_node.left
next_node = self.min_node.parent
# Initialize new bottom_root and min_node
self.min_node = previous_node or next_node
self.bottom_root = next_node
# Update links of previous_node and search below for new min_node and
# bottom_root
if previous_node:
previous_node.parent = next_node
# Update bottom_root and search for min_node below
self.bottom_root = previous_node
self.min_node = previous_node
while self.bottom_root.left:
self.bottom_root = self.bottom_root.left
if self.bottom_root.val < self.min_node.val:
self.min_node = self.bottom_root
if next_node:
next_node.left = previous_node
# Search for new min_node above min_node
i = next_node
while i:
if i.val < self.min_node.val:
self.min_node = i
i = i.parent
# Merge heaps
self.mergeHeaps(newHeap)
return min_value
def preOrder(self):
"""
Returns the Pre-order representation of the heap including
values of nodes plus their level distance from the root;
Empty nodes appear as #
"""
# Find top root
top_root = self.bottom_root
while top_root.parent:
top_root = top_root.parent
# preorder
heap_preOrder = []
self.__traversal(top_root, heap_preOrder)
return heap_preOrder
def __traversal(self, curr_node, preorder, level=0):
"""
Pre-order traversal of nodes
"""
if curr_node:
preorder.append((curr_node.val, level))
self.__traversal(curr_node.left, preorder, level + 1)
self.__traversal(curr_node.right, preorder, level + 1)
else:
preorder.append(("#", level))
def __str__(self):
"""
Overwriting str for a pre-order print of nodes in heap;
Performance is poor, so use only for small examples
"""
if self.isEmpty():
return ""
preorder_heap = self.preOrder()
return "\n".join(("-" * level + str(value)) for value, level in preorder_heap)
# Unit Tests
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 7,339 | Follow Flake8 pep3101 and remove modulo formatting | ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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 have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | 2022-10-16T20:16:16Z | 2022-10-16T20:50:11Z | 7f6e0b656f6362e452b11d06acde50b8b81cb31a | f15cc2f01c2a4124ff6dc0843c728a546f9d9f79 | Follow Flake8 pep3101 and remove modulo formatting. ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """ https://en.wikipedia.org/wiki/Atbash """
import string
def atbash_slow(sequence: str) -> str:
"""
>>> atbash_slow("ABCDEFG")
'ZYXWVUT'
>>> atbash_slow("aW;;123BX")
'zD;;123YC'
"""
output = ""
for i in sequence:
extract = ord(i)
if 65 <= extract <= 90:
output += chr(155 - extract)
elif 97 <= extract <= 122:
output += chr(219 - extract)
else:
output += i
return output
def atbash(sequence: str) -> str:
"""
>>> atbash("ABCDEFG")
'ZYXWVUT'
>>> atbash("aW;;123BX")
'zD;;123YC'
"""
letters = string.ascii_letters
letters_reversed = string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
return "".join(
letters_reversed[letters.index(c)] if c in letters else c for c in sequence
)
def benchmark() -> None:
"""Let's benchmark them side-by-side..."""
from timeit import timeit
print("Running performance benchmarks...")
print(
"> atbash_slow()",
timeit(
"atbash_slow(printable)",
setup="from string import printable ; from __main__ import atbash_slow",
),
"seconds",
)
print(
"> atbash()",
timeit(
"atbash(printable)",
setup="from string import printable ; from __main__ import atbash",
),
"seconds",
)
if __name__ == "__main__":
for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
print(f"{example} encrypted in atbash: {atbash(example)}")
benchmark()
| """ https://en.wikipedia.org/wiki/Atbash """
import string
def atbash_slow(sequence: str) -> str:
"""
>>> atbash_slow("ABCDEFG")
'ZYXWVUT'
>>> atbash_slow("aW;;123BX")
'zD;;123YC'
"""
output = ""
for i in sequence:
extract = ord(i)
if 65 <= extract <= 90:
output += chr(155 - extract)
elif 97 <= extract <= 122:
output += chr(219 - extract)
else:
output += i
return output
def atbash(sequence: str) -> str:
"""
>>> atbash("ABCDEFG")
'ZYXWVUT'
>>> atbash("aW;;123BX")
'zD;;123YC'
"""
letters = string.ascii_letters
letters_reversed = string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
return "".join(
letters_reversed[letters.index(c)] if c in letters else c for c in sequence
)
def benchmark() -> None:
"""Let's benchmark them side-by-side..."""
from timeit import timeit
print("Running performance benchmarks...")
print(
"> atbash_slow()",
timeit(
"atbash_slow(printable)",
setup="from string import printable ; from __main__ import atbash_slow",
),
"seconds",
)
print(
"> atbash()",
timeit(
"atbash(printable)",
setup="from string import printable ; from __main__ import atbash",
),
"seconds",
)
if __name__ == "__main__":
for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
print(f"{example} encrypted in atbash: {atbash(example)}")
benchmark()
| -1 |
TheAlgorithms/Python | 7,339 | Follow Flake8 pep3101 and remove modulo formatting | ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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 have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | 2022-10-16T20:16:16Z | 2022-10-16T20:50:11Z | 7f6e0b656f6362e452b11d06acde50b8b81cb31a | f15cc2f01c2a4124ff6dc0843c728a546f9d9f79 | Follow Flake8 pep3101 and remove modulo formatting. ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 7,339 | Follow Flake8 pep3101 and remove modulo formatting | ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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 have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | 2022-10-16T20:16:16Z | 2022-10-16T20:50:11Z | 7f6e0b656f6362e452b11d06acde50b8b81cb31a | f15cc2f01c2a4124ff6dc0843c728a546f9d9f79 | Follow Flake8 pep3101 and remove modulo formatting. ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| import math
def rearrange(bit_string_32):
"""[summary]
Regroups the given binary string.
Arguments:
bitString32 {[string]} -- [32 bit binary]
Raises:
ValueError -- [if the given string not are 32 bit binary string]
Returns:
[string] -- [32 bit binary string]
>>> rearrange('1234567890abcdfghijklmnopqrstuvw')
'pqrstuvwhijklmno90abcdfg12345678'
"""
if len(bit_string_32) != 32:
raise ValueError("Need length 32")
new_string = ""
for i in [3, 2, 1, 0]:
new_string += bit_string_32[8 * i : 8 * i + 8]
return new_string
def reformat_hex(i):
"""[summary]
Converts the given integer into 8-digit hex number.
Arguments:
i {[int]} -- [integer]
>>> reformat_hex(666)
'9a020000'
"""
hexrep = format(i, "08x")
thing = ""
for i in [3, 2, 1, 0]:
thing += hexrep[2 * i : 2 * i + 2]
return thing
def pad(bit_string):
"""[summary]
Fills up the binary string to a 512 bit binary string
Arguments:
bitString {[string]} -- [binary string]
Returns:
[string] -- [binary string]
"""
start_length = len(bit_string)
bit_string += "1"
while len(bit_string) % 512 != 448:
bit_string += "0"
last_part = format(start_length, "064b")
bit_string += rearrange(last_part[32:]) + rearrange(last_part[:32])
return bit_string
def get_block(bit_string):
"""[summary]
Iterator:
Returns by each call a list of length 16 with the 32 bit
integer blocks.
Arguments:
bit_string {[string]} -- [binary string >= 512]
"""
curr_pos = 0
while curr_pos < len(bit_string):
curr_part = bit_string[curr_pos : curr_pos + 512]
my_splits = []
for i in range(16):
my_splits.append(int(rearrange(curr_part[32 * i : 32 * i + 32]), 2))
yield my_splits
curr_pos += 512
def not32(i):
"""
>>> not32(34)
4294967261
"""
i_str = format(i, "032b")
new_str = ""
for c in i_str:
new_str += "1" if c == "0" else "0"
return int(new_str, 2)
def sum32(a, b):
return (a + b) % 2**32
def leftrot32(i, s):
return (i << s) ^ (i >> (32 - s))
def md5me(test_string):
"""[summary]
Returns a 32-bit hash code of the string 'testString'
Arguments:
testString {[string]} -- [message]
"""
bs = ""
for i in test_string:
bs += format(ord(i), "08b")
bs = pad(bs)
tvals = [int(2**32 * abs(math.sin(i + 1))) for i in range(64)]
a0 = 0x67452301
b0 = 0xEFCDAB89
c0 = 0x98BADCFE
d0 = 0x10325476
s = [
7,
12,
17,
22,
7,
12,
17,
22,
7,
12,
17,
22,
7,
12,
17,
22,
5,
9,
14,
20,
5,
9,
14,
20,
5,
9,
14,
20,
5,
9,
14,
20,
4,
11,
16,
23,
4,
11,
16,
23,
4,
11,
16,
23,
4,
11,
16,
23,
6,
10,
15,
21,
6,
10,
15,
21,
6,
10,
15,
21,
6,
10,
15,
21,
]
for m in get_block(bs):
a = a0
b = b0
c = c0
d = d0
for i in range(64):
if i <= 15:
# f = (B & C) | (not32(B) & D)
f = d ^ (b & (c ^ d))
g = i
elif i <= 31:
# f = (D & B) | (not32(D) & C)
f = c ^ (d & (b ^ c))
g = (5 * i + 1) % 16
elif i <= 47:
f = b ^ c ^ d
g = (3 * i + 5) % 16
else:
f = c ^ (b | not32(d))
g = (7 * i) % 16
dtemp = d
d = c
c = b
b = sum32(b, leftrot32((a + f + tvals[i] + m[g]) % 2**32, s[i]))
a = dtemp
a0 = sum32(a0, a)
b0 = sum32(b0, b)
c0 = sum32(c0, c)
d0 = sum32(d0, d)
digest = reformat_hex(a0) + reformat_hex(b0) + reformat_hex(c0) + reformat_hex(d0)
return digest
def test():
assert md5me("") == "d41d8cd98f00b204e9800998ecf8427e"
assert (
md5me("The quick brown fox jumps over the lazy dog")
== "9e107d9d372bb6826bd81d3542a419d6"
)
print("Success.")
if __name__ == "__main__":
test()
import doctest
doctest.testmod()
| import math
def rearrange(bit_string_32):
"""[summary]
Regroups the given binary string.
Arguments:
bitString32 {[string]} -- [32 bit binary]
Raises:
ValueError -- [if the given string not are 32 bit binary string]
Returns:
[string] -- [32 bit binary string]
>>> rearrange('1234567890abcdfghijklmnopqrstuvw')
'pqrstuvwhijklmno90abcdfg12345678'
"""
if len(bit_string_32) != 32:
raise ValueError("Need length 32")
new_string = ""
for i in [3, 2, 1, 0]:
new_string += bit_string_32[8 * i : 8 * i + 8]
return new_string
def reformat_hex(i):
"""[summary]
Converts the given integer into 8-digit hex number.
Arguments:
i {[int]} -- [integer]
>>> reformat_hex(666)
'9a020000'
"""
hexrep = format(i, "08x")
thing = ""
for i in [3, 2, 1, 0]:
thing += hexrep[2 * i : 2 * i + 2]
return thing
def pad(bit_string):
"""[summary]
Fills up the binary string to a 512 bit binary string
Arguments:
bitString {[string]} -- [binary string]
Returns:
[string] -- [binary string]
"""
start_length = len(bit_string)
bit_string += "1"
while len(bit_string) % 512 != 448:
bit_string += "0"
last_part = format(start_length, "064b")
bit_string += rearrange(last_part[32:]) + rearrange(last_part[:32])
return bit_string
def get_block(bit_string):
"""[summary]
Iterator:
Returns by each call a list of length 16 with the 32 bit
integer blocks.
Arguments:
bit_string {[string]} -- [binary string >= 512]
"""
curr_pos = 0
while curr_pos < len(bit_string):
curr_part = bit_string[curr_pos : curr_pos + 512]
my_splits = []
for i in range(16):
my_splits.append(int(rearrange(curr_part[32 * i : 32 * i + 32]), 2))
yield my_splits
curr_pos += 512
def not32(i):
"""
>>> not32(34)
4294967261
"""
i_str = format(i, "032b")
new_str = ""
for c in i_str:
new_str += "1" if c == "0" else "0"
return int(new_str, 2)
def sum32(a, b):
return (a + b) % 2**32
def leftrot32(i, s):
return (i << s) ^ (i >> (32 - s))
def md5me(test_string):
"""[summary]
Returns a 32-bit hash code of the string 'testString'
Arguments:
testString {[string]} -- [message]
"""
bs = ""
for i in test_string:
bs += format(ord(i), "08b")
bs = pad(bs)
tvals = [int(2**32 * abs(math.sin(i + 1))) for i in range(64)]
a0 = 0x67452301
b0 = 0xEFCDAB89
c0 = 0x98BADCFE
d0 = 0x10325476
s = [
7,
12,
17,
22,
7,
12,
17,
22,
7,
12,
17,
22,
7,
12,
17,
22,
5,
9,
14,
20,
5,
9,
14,
20,
5,
9,
14,
20,
5,
9,
14,
20,
4,
11,
16,
23,
4,
11,
16,
23,
4,
11,
16,
23,
4,
11,
16,
23,
6,
10,
15,
21,
6,
10,
15,
21,
6,
10,
15,
21,
6,
10,
15,
21,
]
for m in get_block(bs):
a = a0
b = b0
c = c0
d = d0
for i in range(64):
if i <= 15:
# f = (B & C) | (not32(B) & D)
f = d ^ (b & (c ^ d))
g = i
elif i <= 31:
# f = (D & B) | (not32(D) & C)
f = c ^ (d & (b ^ c))
g = (5 * i + 1) % 16
elif i <= 47:
f = b ^ c ^ d
g = (3 * i + 5) % 16
else:
f = c ^ (b | not32(d))
g = (7 * i) % 16
dtemp = d
d = c
c = b
b = sum32(b, leftrot32((a + f + tvals[i] + m[g]) % 2**32, s[i]))
a = dtemp
a0 = sum32(a0, a)
b0 = sum32(b0, b)
c0 = sum32(c0, c)
d0 = sum32(d0, d)
digest = reformat_hex(a0) + reformat_hex(b0) + reformat_hex(c0) + reformat_hex(d0)
return digest
def test():
assert md5me("") == "d41d8cd98f00b204e9800998ecf8427e"
assert (
md5me("The quick brown fox jumps over the lazy dog")
== "9e107d9d372bb6826bd81d3542a419d6"
)
print("Success.")
if __name__ == "__main__":
test()
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 7,339 | Follow Flake8 pep3101 and remove modulo formatting | ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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 have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| CaedenPH | 2022-10-16T20:16:16Z | 2022-10-16T20:50:11Z | 7f6e0b656f6362e452b11d06acde50b8b81cb31a | f15cc2f01c2a4124ff6dc0843c728a546f9d9f79 | Follow Flake8 pep3101 and remove modulo formatting. ### Describe your change:
Follow Flake8 pep3101 and remove modulo formatting (replace with f-strings)
For example
```py
"Hello %s" % name
```
goes to
```py
f"Hello {name}"
```
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| 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 |
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