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TheAlgorithms/Python | 7,262 | refactor: Move constants outside of variable scope | ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T19:51:55Z" | "2022-10-16T09:33:29Z" | 77764116217708933bdc65b29801092fa291398e | c6582b35bf8b8aba622c63096e3ab2f01aa36854 | refactor: Move constants outside of variable scope. ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Pure Python implementation of a binary search algorithm.
For doctests run following command:
python3 -m doctest -v simple_binary_search.py
For manual testing run:
python3 simple_binary_search.py
"""
from __future__ import annotations
def binary_search(a_list: list[int], item: int) -> bool:
"""
>>> test_list = [0, 1, 2, 8, 13, 17, 19, 32, 42]
>>> print(binary_search(test_list, 3))
False
>>> print(binary_search(test_list, 13))
True
>>> print(binary_search([4, 4, 5, 6, 7], 4))
True
>>> print(binary_search([4, 4, 5, 6, 7], -10))
False
>>> print(binary_search([-18, 2], -18))
True
>>> print(binary_search([5], 5))
True
>>> print(binary_search(['a', 'c', 'd'], 'c'))
True
>>> print(binary_search(['a', 'c', 'd'], 'f'))
False
>>> print(binary_search([], 1))
False
>>> print(binary_search([-.1, .1 , .8], .1))
True
>>> binary_search(range(-5000, 5000, 10), 80)
True
>>> binary_search(range(-5000, 5000, 10), 1255)
False
>>> binary_search(range(0, 10000, 5), 2)
False
"""
if len(a_list) == 0:
return False
midpoint = len(a_list) // 2
if a_list[midpoint] == item:
return True
if item < a_list[midpoint]:
return binary_search(a_list[:midpoint], item)
else:
return binary_search(a_list[midpoint + 1 :], item)
if __name__ == "__main__":
user_input = input("Enter numbers separated by comma:\n").strip()
sequence = [int(item.strip()) for item in user_input.split(",")]
target = int(input("Enter the number to be found in the list:\n").strip())
not_str = "" if binary_search(sequence, target) else "not "
print(f"{target} was {not_str}found in {sequence}")
| """
Pure Python implementation of a binary search algorithm.
For doctests run following command:
python3 -m doctest -v simple_binary_search.py
For manual testing run:
python3 simple_binary_search.py
"""
from __future__ import annotations
def binary_search(a_list: list[int], item: int) -> bool:
"""
>>> test_list = [0, 1, 2, 8, 13, 17, 19, 32, 42]
>>> print(binary_search(test_list, 3))
False
>>> print(binary_search(test_list, 13))
True
>>> print(binary_search([4, 4, 5, 6, 7], 4))
True
>>> print(binary_search([4, 4, 5, 6, 7], -10))
False
>>> print(binary_search([-18, 2], -18))
True
>>> print(binary_search([5], 5))
True
>>> print(binary_search(['a', 'c', 'd'], 'c'))
True
>>> print(binary_search(['a', 'c', 'd'], 'f'))
False
>>> print(binary_search([], 1))
False
>>> print(binary_search([-.1, .1 , .8], .1))
True
>>> binary_search(range(-5000, 5000, 10), 80)
True
>>> binary_search(range(-5000, 5000, 10), 1255)
False
>>> binary_search(range(0, 10000, 5), 2)
False
"""
if len(a_list) == 0:
return False
midpoint = len(a_list) // 2
if a_list[midpoint] == item:
return True
if item < a_list[midpoint]:
return binary_search(a_list[:midpoint], item)
else:
return binary_search(a_list[midpoint + 1 :], item)
if __name__ == "__main__":
user_input = input("Enter numbers separated by comma:\n").strip()
sequence = [int(item.strip()) for item in user_input.split(",")]
target = int(input("Enter the number to be found in the list:\n").strip())
not_str = "" if binary_search(sequence, target) else "not "
print(f"{target} was {not_str}found in {sequence}")
| -1 |
TheAlgorithms/Python | 7,262 | refactor: Move constants outside of variable scope | ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T19:51:55Z" | "2022-10-16T09:33:29Z" | 77764116217708933bdc65b29801092fa291398e | c6582b35bf8b8aba622c63096e3ab2f01aa36854 | refactor: Move constants outside of variable scope. ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| import math
from numpy import inf
from scipy.integrate import quad
def gamma(num: float) -> float:
"""
https://en.wikipedia.org/wiki/Gamma_function
In mathematics, the gamma function is one commonly
used extension of the factorial function to complex numbers.
The gamma function is defined for all complex numbers except the non-positive
integers
>>> gamma(-1)
Traceback (most recent call last):
...
ValueError: math domain error
>>> gamma(0)
Traceback (most recent call last):
...
ValueError: math domain error
>>> gamma(9)
40320.0
>>> from math import gamma as math_gamma
>>> all(.99999999 < gamma(i) / math_gamma(i) <= 1.000000001
... for i in range(1, 50))
True
>>> from math import gamma as math_gamma
>>> gamma(-1)/math_gamma(-1) <= 1.000000001
Traceback (most recent call last):
...
ValueError: math domain error
>>> from math import gamma as math_gamma
>>> gamma(3.3) - math_gamma(3.3) <= 0.00000001
True
"""
if num <= 0:
raise ValueError("math domain error")
return quad(integrand, 0, inf, args=(num))[0]
def integrand(x: float, z: float) -> float:
return math.pow(x, z - 1) * math.exp(-x)
if __name__ == "__main__":
from doctest import testmod
testmod()
| import math
from numpy import inf
from scipy.integrate import quad
def gamma(num: float) -> float:
"""
https://en.wikipedia.org/wiki/Gamma_function
In mathematics, the gamma function is one commonly
used extension of the factorial function to complex numbers.
The gamma function is defined for all complex numbers except the non-positive
integers
>>> gamma(-1)
Traceback (most recent call last):
...
ValueError: math domain error
>>> gamma(0)
Traceback (most recent call last):
...
ValueError: math domain error
>>> gamma(9)
40320.0
>>> from math import gamma as math_gamma
>>> all(.99999999 < gamma(i) / math_gamma(i) <= 1.000000001
... for i in range(1, 50))
True
>>> from math import gamma as math_gamma
>>> gamma(-1)/math_gamma(-1) <= 1.000000001
Traceback (most recent call last):
...
ValueError: math domain error
>>> from math import gamma as math_gamma
>>> gamma(3.3) - math_gamma(3.3) <= 0.00000001
True
"""
if num <= 0:
raise ValueError("math domain error")
return quad(integrand, 0, inf, args=(num))[0]
def integrand(x: float, z: float) -> float:
return math.pow(x, z - 1) * math.exp(-x)
if __name__ == "__main__":
from doctest import testmod
testmod()
| -1 |
TheAlgorithms/Python | 7,262 | refactor: Move constants outside of variable scope | ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T19:51:55Z" | "2022-10-16T09:33:29Z" | 77764116217708933bdc65b29801092fa291398e | c6582b35bf8b8aba622c63096e3ab2f01aa36854 | refactor: Move constants outside of variable scope. ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| class FlowNetwork:
def __init__(self, graph, sources, sinks):
self.source_index = None
self.sink_index = None
self.graph = graph
self._normalize_graph(sources, sinks)
self.vertices_count = len(graph)
self.maximum_flow_algorithm = None
# make only one source and one sink
def _normalize_graph(self, sources, sinks):
if sources is int:
sources = [sources]
if sinks is int:
sinks = [sinks]
if len(sources) == 0 or len(sinks) == 0:
return
self.source_index = sources[0]
self.sink_index = sinks[0]
# make fake vertex if there are more
# than one source or sink
if len(sources) > 1 or len(sinks) > 1:
max_input_flow = 0
for i in sources:
max_input_flow += sum(self.graph[i])
size = len(self.graph) + 1
for room in self.graph:
room.insert(0, 0)
self.graph.insert(0, [0] * size)
for i in sources:
self.graph[0][i + 1] = max_input_flow
self.source_index = 0
size = len(self.graph) + 1
for room in self.graph:
room.append(0)
self.graph.append([0] * size)
for i in sinks:
self.graph[i + 1][size - 1] = max_input_flow
self.sink_index = size - 1
def find_maximum_flow(self):
if self.maximum_flow_algorithm is None:
raise Exception("You need to set maximum flow algorithm before.")
if self.source_index is None or self.sink_index is None:
return 0
self.maximum_flow_algorithm.execute()
return self.maximum_flow_algorithm.getMaximumFlow()
def set_maximum_flow_algorithm(self, algorithm):
self.maximum_flow_algorithm = algorithm(self)
class FlowNetworkAlgorithmExecutor:
def __init__(self, flow_network):
self.flow_network = flow_network
self.verticies_count = flow_network.verticesCount
self.source_index = flow_network.sourceIndex
self.sink_index = flow_network.sinkIndex
# it's just a reference, so you shouldn't change
# it in your algorithms, use deep copy before doing that
self.graph = flow_network.graph
self.executed = False
def execute(self):
if not self.executed:
self._algorithm()
self.executed = True
# You should override it
def _algorithm(self):
pass
class MaximumFlowAlgorithmExecutor(FlowNetworkAlgorithmExecutor):
def __init__(self, flow_network):
super().__init__(flow_network)
# use this to save your result
self.maximum_flow = -1
def get_maximum_flow(self):
if not self.executed:
raise Exception("You should execute algorithm before using its result!")
return self.maximum_flow
class PushRelabelExecutor(MaximumFlowAlgorithmExecutor):
def __init__(self, flow_network):
super().__init__(flow_network)
self.preflow = [[0] * self.verticies_count for i in range(self.verticies_count)]
self.heights = [0] * self.verticies_count
self.excesses = [0] * self.verticies_count
def _algorithm(self):
self.heights[self.source_index] = self.verticies_count
# push some substance to graph
for nextvertex_index, bandwidth in enumerate(self.graph[self.source_index]):
self.preflow[self.source_index][nextvertex_index] += bandwidth
self.preflow[nextvertex_index][self.source_index] -= bandwidth
self.excesses[nextvertex_index] += bandwidth
# Relabel-to-front selection rule
vertices_list = [
i
for i in range(self.verticies_count)
if i != self.source_index and i != self.sink_index
]
# move through list
i = 0
while i < len(vertices_list):
vertex_index = vertices_list[i]
previous_height = self.heights[vertex_index]
self.process_vertex(vertex_index)
if self.heights[vertex_index] > previous_height:
# if it was relabeled, swap elements
# and start from 0 index
vertices_list.insert(0, vertices_list.pop(i))
i = 0
else:
i += 1
self.maximum_flow = sum(self.preflow[self.source_index])
def process_vertex(self, vertex_index):
while self.excesses[vertex_index] > 0:
for neighbour_index in range(self.verticies_count):
# if it's neighbour and current vertex is higher
if (
self.graph[vertex_index][neighbour_index]
- self.preflow[vertex_index][neighbour_index]
> 0
and self.heights[vertex_index] > self.heights[neighbour_index]
):
self.push(vertex_index, neighbour_index)
self.relabel(vertex_index)
def push(self, from_index, to_index):
preflow_delta = min(
self.excesses[from_index],
self.graph[from_index][to_index] - self.preflow[from_index][to_index],
)
self.preflow[from_index][to_index] += preflow_delta
self.preflow[to_index][from_index] -= preflow_delta
self.excesses[from_index] -= preflow_delta
self.excesses[to_index] += preflow_delta
def relabel(self, vertex_index):
min_height = None
for to_index in range(self.verticies_count):
if (
self.graph[vertex_index][to_index]
- self.preflow[vertex_index][to_index]
> 0
):
if min_height is None or self.heights[to_index] < min_height:
min_height = self.heights[to_index]
if min_height is not None:
self.heights[vertex_index] = min_height + 1
if __name__ == "__main__":
entrances = [0]
exits = [3]
# graph = [
# [0, 0, 4, 6, 0, 0],
# [0, 0, 5, 2, 0, 0],
# [0, 0, 0, 0, 4, 4],
# [0, 0, 0, 0, 6, 6],
# [0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0],
# ]
graph = [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]
# prepare our network
flow_network = FlowNetwork(graph, entrances, exits)
# set algorithm
flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
# and calculate
maximum_flow = flow_network.find_maximum_flow()
print(f"maximum flow is {maximum_flow}")
| class FlowNetwork:
def __init__(self, graph, sources, sinks):
self.source_index = None
self.sink_index = None
self.graph = graph
self._normalize_graph(sources, sinks)
self.vertices_count = len(graph)
self.maximum_flow_algorithm = None
# make only one source and one sink
def _normalize_graph(self, sources, sinks):
if sources is int:
sources = [sources]
if sinks is int:
sinks = [sinks]
if len(sources) == 0 or len(sinks) == 0:
return
self.source_index = sources[0]
self.sink_index = sinks[0]
# make fake vertex if there are more
# than one source or sink
if len(sources) > 1 or len(sinks) > 1:
max_input_flow = 0
for i in sources:
max_input_flow += sum(self.graph[i])
size = len(self.graph) + 1
for room in self.graph:
room.insert(0, 0)
self.graph.insert(0, [0] * size)
for i in sources:
self.graph[0][i + 1] = max_input_flow
self.source_index = 0
size = len(self.graph) + 1
for room in self.graph:
room.append(0)
self.graph.append([0] * size)
for i in sinks:
self.graph[i + 1][size - 1] = max_input_flow
self.sink_index = size - 1
def find_maximum_flow(self):
if self.maximum_flow_algorithm is None:
raise Exception("You need to set maximum flow algorithm before.")
if self.source_index is None or self.sink_index is None:
return 0
self.maximum_flow_algorithm.execute()
return self.maximum_flow_algorithm.getMaximumFlow()
def set_maximum_flow_algorithm(self, algorithm):
self.maximum_flow_algorithm = algorithm(self)
class FlowNetworkAlgorithmExecutor:
def __init__(self, flow_network):
self.flow_network = flow_network
self.verticies_count = flow_network.verticesCount
self.source_index = flow_network.sourceIndex
self.sink_index = flow_network.sinkIndex
# it's just a reference, so you shouldn't change
# it in your algorithms, use deep copy before doing that
self.graph = flow_network.graph
self.executed = False
def execute(self):
if not self.executed:
self._algorithm()
self.executed = True
# You should override it
def _algorithm(self):
pass
class MaximumFlowAlgorithmExecutor(FlowNetworkAlgorithmExecutor):
def __init__(self, flow_network):
super().__init__(flow_network)
# use this to save your result
self.maximum_flow = -1
def get_maximum_flow(self):
if not self.executed:
raise Exception("You should execute algorithm before using its result!")
return self.maximum_flow
class PushRelabelExecutor(MaximumFlowAlgorithmExecutor):
def __init__(self, flow_network):
super().__init__(flow_network)
self.preflow = [[0] * self.verticies_count for i in range(self.verticies_count)]
self.heights = [0] * self.verticies_count
self.excesses = [0] * self.verticies_count
def _algorithm(self):
self.heights[self.source_index] = self.verticies_count
# push some substance to graph
for nextvertex_index, bandwidth in enumerate(self.graph[self.source_index]):
self.preflow[self.source_index][nextvertex_index] += bandwidth
self.preflow[nextvertex_index][self.source_index] -= bandwidth
self.excesses[nextvertex_index] += bandwidth
# Relabel-to-front selection rule
vertices_list = [
i
for i in range(self.verticies_count)
if i != self.source_index and i != self.sink_index
]
# move through list
i = 0
while i < len(vertices_list):
vertex_index = vertices_list[i]
previous_height = self.heights[vertex_index]
self.process_vertex(vertex_index)
if self.heights[vertex_index] > previous_height:
# if it was relabeled, swap elements
# and start from 0 index
vertices_list.insert(0, vertices_list.pop(i))
i = 0
else:
i += 1
self.maximum_flow = sum(self.preflow[self.source_index])
def process_vertex(self, vertex_index):
while self.excesses[vertex_index] > 0:
for neighbour_index in range(self.verticies_count):
# if it's neighbour and current vertex is higher
if (
self.graph[vertex_index][neighbour_index]
- self.preflow[vertex_index][neighbour_index]
> 0
and self.heights[vertex_index] > self.heights[neighbour_index]
):
self.push(vertex_index, neighbour_index)
self.relabel(vertex_index)
def push(self, from_index, to_index):
preflow_delta = min(
self.excesses[from_index],
self.graph[from_index][to_index] - self.preflow[from_index][to_index],
)
self.preflow[from_index][to_index] += preflow_delta
self.preflow[to_index][from_index] -= preflow_delta
self.excesses[from_index] -= preflow_delta
self.excesses[to_index] += preflow_delta
def relabel(self, vertex_index):
min_height = None
for to_index in range(self.verticies_count):
if (
self.graph[vertex_index][to_index]
- self.preflow[vertex_index][to_index]
> 0
):
if min_height is None or self.heights[to_index] < min_height:
min_height = self.heights[to_index]
if min_height is not None:
self.heights[vertex_index] = min_height + 1
if __name__ == "__main__":
entrances = [0]
exits = [3]
# graph = [
# [0, 0, 4, 6, 0, 0],
# [0, 0, 5, 2, 0, 0],
# [0, 0, 0, 0, 4, 4],
# [0, 0, 0, 0, 6, 6],
# [0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0],
# ]
graph = [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]
# prepare our network
flow_network = FlowNetwork(graph, entrances, exits)
# set algorithm
flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
# and calculate
maximum_flow = flow_network.find_maximum_flow()
print(f"maximum flow is {maximum_flow}")
| -1 |
TheAlgorithms/Python | 7,262 | refactor: Move constants outside of variable scope | ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T19:51:55Z" | "2022-10-16T09:33:29Z" | 77764116217708933bdc65b29801092fa291398e | c6582b35bf8b8aba622c63096e3ab2f01aa36854 | refactor: Move constants outside of variable scope. ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt
import numpy as np
from sklearn.utils import shuffle
import input_data
random_numer = 42
np.random.seed(random_numer)
def ReLu(x):
mask = (x > 0) * 1.0
return mask * x
def d_ReLu(x):
mask = (x > 0) * 1.0
return mask
def arctan(x):
return np.arctan(x)
def d_arctan(x):
return 1 / (1 + x ** 2)
def log(x):
return 1 / (1 + np.exp(-1 * x))
def d_log(x):
return log(x) * (1 - log(x))
def tanh(x):
return np.tanh(x)
def d_tanh(x):
return 1 - np.tanh(x) ** 2
def plot(samples):
fig = plt.figure(figsize=(4, 4))
gs = gridspec.GridSpec(4, 4)
gs.update(wspace=0.05, hspace=0.05)
for i, sample in enumerate(samples):
ax = plt.subplot(gs[i])
plt.axis("off")
ax.set_xticklabels([])
ax.set_yticklabels([])
ax.set_aspect("equal")
plt.imshow(sample.reshape(28, 28), cmap="Greys_r")
return fig
if __name__ == "__main__":
# 1. Load Data and declare hyper
print("--------- Load Data ----------")
mnist = input_data.read_data_sets("MNIST_data", one_hot=False)
temp = mnist.test
images, labels = temp.images, temp.labels
images, labels = shuffle(np.asarray(images), np.asarray(labels))
num_epoch = 10
learing_rate = 0.00009
G_input = 100
hidden_input, hidden_input2, hidden_input3 = 128, 256, 346
hidden_input4, hidden_input5, hidden_input6 = 480, 560, 686
print("--------- Declare Hyper Parameters ----------")
# 2. Declare Weights
D_W1 = (
np.random.normal(size=(784, hidden_input), scale=(1.0 / np.sqrt(784 / 2.0)))
* 0.002
)
# D_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.))) *0.002
D_b1 = np.zeros(hidden_input)
D_W2 = (
np.random.normal(
size=(hidden_input, 1), scale=(1.0 / np.sqrt(hidden_input / 2.0))
)
* 0.002
)
# D_b2 = np.random.normal(size=(1),scale=(1. / np.sqrt(1 / 2.))) *0.002
D_b2 = np.zeros(1)
G_W1 = (
np.random.normal(
size=(G_input, hidden_input), scale=(1.0 / np.sqrt(G_input / 2.0))
)
* 0.002
)
# G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.))) *0.002
G_b1 = np.zeros(hidden_input)
G_W2 = (
np.random.normal(
size=(hidden_input, hidden_input2),
scale=(1.0 / np.sqrt(hidden_input / 2.0)),
)
* 0.002
)
# G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.))) *0.002
G_b2 = np.zeros(hidden_input2)
G_W3 = (
np.random.normal(
size=(hidden_input2, hidden_input3),
scale=(1.0 / np.sqrt(hidden_input2 / 2.0)),
)
* 0.002
)
# G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.))) *0.002
G_b3 = np.zeros(hidden_input3)
G_W4 = (
np.random.normal(
size=(hidden_input3, hidden_input4),
scale=(1.0 / np.sqrt(hidden_input3 / 2.0)),
)
* 0.002
)
# G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.))) *0.002
G_b4 = np.zeros(hidden_input4)
G_W5 = (
np.random.normal(
size=(hidden_input4, hidden_input5),
scale=(1.0 / np.sqrt(hidden_input4 / 2.0)),
)
* 0.002
)
# G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.))) *0.002
G_b5 = np.zeros(hidden_input5)
G_W6 = (
np.random.normal(
size=(hidden_input5, hidden_input6),
scale=(1.0 / np.sqrt(hidden_input5 / 2.0)),
)
* 0.002
)
# G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.))) *0.002
G_b6 = np.zeros(hidden_input6)
G_W7 = (
np.random.normal(
size=(hidden_input6, 784), scale=(1.0 / np.sqrt(hidden_input6 / 2.0))
)
* 0.002
)
# G_b2 = np.random.normal(size=(784),scale=(1. / np.sqrt(784 / 2.))) *0.002
G_b7 = np.zeros(784)
# 3. For Adam Optimzier
v1, m1 = 0, 0
v2, m2 = 0, 0
v3, m3 = 0, 0
v4, m4 = 0, 0
v5, m5 = 0, 0
v6, m6 = 0, 0
v7, m7 = 0, 0
v8, m8 = 0, 0
v9, m9 = 0, 0
v10, m10 = 0, 0
v11, m11 = 0, 0
v12, m12 = 0, 0
v13, m13 = 0, 0
v14, m14 = 0, 0
v15, m15 = 0, 0
v16, m16 = 0, 0
v17, m17 = 0, 0
v18, m18 = 0, 0
beta_1, beta_2, eps = 0.9, 0.999, 0.00000001
print("--------- Started Training ----------")
for iter in range(num_epoch):
random_int = np.random.randint(len(images) - 5)
current_image = np.expand_dims(images[random_int], axis=0)
# Func: Generate The first Fake Data
Z = np.random.uniform(-1.0, 1.0, size=[1, G_input])
Gl1 = Z.dot(G_W1) + G_b1
Gl1A = arctan(Gl1)
Gl2 = Gl1A.dot(G_W2) + G_b2
Gl2A = ReLu(Gl2)
Gl3 = Gl2A.dot(G_W3) + G_b3
Gl3A = arctan(Gl3)
Gl4 = Gl3A.dot(G_W4) + G_b4
Gl4A = ReLu(Gl4)
Gl5 = Gl4A.dot(G_W5) + G_b5
Gl5A = tanh(Gl5)
Gl6 = Gl5A.dot(G_W6) + G_b6
Gl6A = ReLu(Gl6)
Gl7 = Gl6A.dot(G_W7) + G_b7
current_fake_data = log(Gl7)
# Func: Forward Feed for Real data
Dl1_r = current_image.dot(D_W1) + D_b1
Dl1_rA = ReLu(Dl1_r)
Dl2_r = Dl1_rA.dot(D_W2) + D_b2
Dl2_rA = log(Dl2_r)
# Func: Forward Feed for Fake Data
Dl1_f = current_fake_data.dot(D_W1) + D_b1
Dl1_fA = ReLu(Dl1_f)
Dl2_f = Dl1_fA.dot(D_W2) + D_b2
Dl2_fA = log(Dl2_f)
# Func: Cost D
D_cost = -np.log(Dl2_rA) + np.log(1.0 - Dl2_fA)
# Func: Gradient
grad_f_w2_part_1 = 1 / (1.0 - Dl2_fA)
grad_f_w2_part_2 = d_log(Dl2_f)
grad_f_w2_part_3 = Dl1_fA
grad_f_w2 = grad_f_w2_part_3.T.dot(grad_f_w2_part_1 * grad_f_w2_part_2)
grad_f_b2 = grad_f_w2_part_1 * grad_f_w2_part_2
grad_f_w1_part_1 = (grad_f_w2_part_1 * grad_f_w2_part_2).dot(D_W2.T)
grad_f_w1_part_2 = d_ReLu(Dl1_f)
grad_f_w1_part_3 = current_fake_data
grad_f_w1 = grad_f_w1_part_3.T.dot(grad_f_w1_part_1 * grad_f_w1_part_2)
grad_f_b1 = grad_f_w1_part_1 * grad_f_w1_part_2
grad_r_w2_part_1 = -1 / Dl2_rA
grad_r_w2_part_2 = d_log(Dl2_r)
grad_r_w2_part_3 = Dl1_rA
grad_r_w2 = grad_r_w2_part_3.T.dot(grad_r_w2_part_1 * grad_r_w2_part_2)
grad_r_b2 = grad_r_w2_part_1 * grad_r_w2_part_2
grad_r_w1_part_1 = (grad_r_w2_part_1 * grad_r_w2_part_2).dot(D_W2.T)
grad_r_w1_part_2 = d_ReLu(Dl1_r)
grad_r_w1_part_3 = current_image
grad_r_w1 = grad_r_w1_part_3.T.dot(grad_r_w1_part_1 * grad_r_w1_part_2)
grad_r_b1 = grad_r_w1_part_1 * grad_r_w1_part_2
grad_w1 = grad_f_w1 + grad_r_w1
grad_b1 = grad_f_b1 + grad_r_b1
grad_w2 = grad_f_w2 + grad_r_w2
grad_b2 = grad_f_b2 + grad_r_b2
# ---- Update Gradient ----
m1 = beta_1 * m1 + (1 - beta_1) * grad_w1
v1 = beta_2 * v1 + (1 - beta_2) * grad_w1 ** 2
m2 = beta_1 * m2 + (1 - beta_1) * grad_b1
v2 = beta_2 * v2 + (1 - beta_2) * grad_b1 ** 2
m3 = beta_1 * m3 + (1 - beta_1) * grad_w2
v3 = beta_2 * v3 + (1 - beta_2) * grad_w2 ** 2
m4 = beta_1 * m4 + (1 - beta_1) * grad_b2
v4 = beta_2 * v4 + (1 - beta_2) * grad_b2 ** 2
D_W1 = D_W1 - (learing_rate / (np.sqrt(v1 / (1 - beta_2)) + eps)) * (
m1 / (1 - beta_1)
)
D_b1 = D_b1 - (learing_rate / (np.sqrt(v2 / (1 - beta_2)) + eps)) * (
m2 / (1 - beta_1)
)
D_W2 = D_W2 - (learing_rate / (np.sqrt(v3 / (1 - beta_2)) + eps)) * (
m3 / (1 - beta_1)
)
D_b2 = D_b2 - (learing_rate / (np.sqrt(v4 / (1 - beta_2)) + eps)) * (
m4 / (1 - beta_1)
)
# Func: Forward Feed for G
Z = np.random.uniform(-1.0, 1.0, size=[1, G_input])
Gl1 = Z.dot(G_W1) + G_b1
Gl1A = arctan(Gl1)
Gl2 = Gl1A.dot(G_W2) + G_b2
Gl2A = ReLu(Gl2)
Gl3 = Gl2A.dot(G_W3) + G_b3
Gl3A = arctan(Gl3)
Gl4 = Gl3A.dot(G_W4) + G_b4
Gl4A = ReLu(Gl4)
Gl5 = Gl4A.dot(G_W5) + G_b5
Gl5A = tanh(Gl5)
Gl6 = Gl5A.dot(G_W6) + G_b6
Gl6A = ReLu(Gl6)
Gl7 = Gl6A.dot(G_W7) + G_b7
current_fake_data = log(Gl7)
Dl1 = current_fake_data.dot(D_W1) + D_b1
Dl1_A = ReLu(Dl1)
Dl2 = Dl1_A.dot(D_W2) + D_b2
Dl2_A = log(Dl2)
# Func: Cost G
G_cost = -np.log(Dl2_A)
# Func: Gradient
grad_G_w7_part_1 = ((-1 / Dl2_A) * d_log(Dl2).dot(D_W2.T) * (d_ReLu(Dl1))).dot(
D_W1.T
)
grad_G_w7_part_2 = d_log(Gl7)
grad_G_w7_part_3 = Gl6A
grad_G_w7 = grad_G_w7_part_3.T.dot(grad_G_w7_part_1 * grad_G_w7_part_1)
grad_G_b7 = grad_G_w7_part_1 * grad_G_w7_part_2
grad_G_w6_part_1 = (grad_G_w7_part_1 * grad_G_w7_part_2).dot(G_W7.T)
grad_G_w6_part_2 = d_ReLu(Gl6)
grad_G_w6_part_3 = Gl5A
grad_G_w6 = grad_G_w6_part_3.T.dot(grad_G_w6_part_1 * grad_G_w6_part_2)
grad_G_b6 = grad_G_w6_part_1 * grad_G_w6_part_2
grad_G_w5_part_1 = (grad_G_w6_part_1 * grad_G_w6_part_2).dot(G_W6.T)
grad_G_w5_part_2 = d_tanh(Gl5)
grad_G_w5_part_3 = Gl4A
grad_G_w5 = grad_G_w5_part_3.T.dot(grad_G_w5_part_1 * grad_G_w5_part_2)
grad_G_b5 = grad_G_w5_part_1 * grad_G_w5_part_2
grad_G_w4_part_1 = (grad_G_w5_part_1 * grad_G_w5_part_2).dot(G_W5.T)
grad_G_w4_part_2 = d_ReLu(Gl4)
grad_G_w4_part_3 = Gl3A
grad_G_w4 = grad_G_w4_part_3.T.dot(grad_G_w4_part_1 * grad_G_w4_part_2)
grad_G_b4 = grad_G_w4_part_1 * grad_G_w4_part_2
grad_G_w3_part_1 = (grad_G_w4_part_1 * grad_G_w4_part_2).dot(G_W4.T)
grad_G_w3_part_2 = d_arctan(Gl3)
grad_G_w3_part_3 = Gl2A
grad_G_w3 = grad_G_w3_part_3.T.dot(grad_G_w3_part_1 * grad_G_w3_part_2)
grad_G_b3 = grad_G_w3_part_1 * grad_G_w3_part_2
grad_G_w2_part_1 = (grad_G_w3_part_1 * grad_G_w3_part_2).dot(G_W3.T)
grad_G_w2_part_2 = d_ReLu(Gl2)
grad_G_w2_part_3 = Gl1A
grad_G_w2 = grad_G_w2_part_3.T.dot(grad_G_w2_part_1 * grad_G_w2_part_2)
grad_G_b2 = grad_G_w2_part_1 * grad_G_w2_part_2
grad_G_w1_part_1 = (grad_G_w2_part_1 * grad_G_w2_part_2).dot(G_W2.T)
grad_G_w1_part_2 = d_arctan(Gl1)
grad_G_w1_part_3 = Z
grad_G_w1 = grad_G_w1_part_3.T.dot(grad_G_w1_part_1 * grad_G_w1_part_2)
grad_G_b1 = grad_G_w1_part_1 * grad_G_w1_part_2
# ---- Update Gradient ----
m5 = beta_1 * m5 + (1 - beta_1) * grad_G_w1
v5 = beta_2 * v5 + (1 - beta_2) * grad_G_w1 ** 2
m6 = beta_1 * m6 + (1 - beta_1) * grad_G_b1
v6 = beta_2 * v6 + (1 - beta_2) * grad_G_b1 ** 2
m7 = beta_1 * m7 + (1 - beta_1) * grad_G_w2
v7 = beta_2 * v7 + (1 - beta_2) * grad_G_w2 ** 2
m8 = beta_1 * m8 + (1 - beta_1) * grad_G_b2
v8 = beta_2 * v8 + (1 - beta_2) * grad_G_b2 ** 2
m9 = beta_1 * m9 + (1 - beta_1) * grad_G_w3
v9 = beta_2 * v9 + (1 - beta_2) * grad_G_w3 ** 2
m10 = beta_1 * m10 + (1 - beta_1) * grad_G_b3
v10 = beta_2 * v10 + (1 - beta_2) * grad_G_b3 ** 2
m11 = beta_1 * m11 + (1 - beta_1) * grad_G_w4
v11 = beta_2 * v11 + (1 - beta_2) * grad_G_w4 ** 2
m12 = beta_1 * m12 + (1 - beta_1) * grad_G_b4
v12 = beta_2 * v12 + (1 - beta_2) * grad_G_b4 ** 2
m13 = beta_1 * m13 + (1 - beta_1) * grad_G_w5
v13 = beta_2 * v13 + (1 - beta_2) * grad_G_w5 ** 2
m14 = beta_1 * m14 + (1 - beta_1) * grad_G_b5
v14 = beta_2 * v14 + (1 - beta_2) * grad_G_b5 ** 2
m15 = beta_1 * m15 + (1 - beta_1) * grad_G_w6
v15 = beta_2 * v15 + (1 - beta_2) * grad_G_w6 ** 2
m16 = beta_1 * m16 + (1 - beta_1) * grad_G_b6
v16 = beta_2 * v16 + (1 - beta_2) * grad_G_b6 ** 2
m17 = beta_1 * m17 + (1 - beta_1) * grad_G_w7
v17 = beta_2 * v17 + (1 - beta_2) * grad_G_w7 ** 2
m18 = beta_1 * m18 + (1 - beta_1) * grad_G_b7
v18 = beta_2 * v18 + (1 - beta_2) * grad_G_b7 ** 2
G_W1 = G_W1 - (learing_rate / (np.sqrt(v5 / (1 - beta_2)) + eps)) * (
m5 / (1 - beta_1)
)
G_b1 = G_b1 - (learing_rate / (np.sqrt(v6 / (1 - beta_2)) + eps)) * (
m6 / (1 - beta_1)
)
G_W2 = G_W2 - (learing_rate / (np.sqrt(v7 / (1 - beta_2)) + eps)) * (
m7 / (1 - beta_1)
)
G_b2 = G_b2 - (learing_rate / (np.sqrt(v8 / (1 - beta_2)) + eps)) * (
m8 / (1 - beta_1)
)
G_W3 = G_W3 - (learing_rate / (np.sqrt(v9 / (1 - beta_2)) + eps)) * (
m9 / (1 - beta_1)
)
G_b3 = G_b3 - (learing_rate / (np.sqrt(v10 / (1 - beta_2)) + eps)) * (
m10 / (1 - beta_1)
)
G_W4 = G_W4 - (learing_rate / (np.sqrt(v11 / (1 - beta_2)) + eps)) * (
m11 / (1 - beta_1)
)
G_b4 = G_b4 - (learing_rate / (np.sqrt(v12 / (1 - beta_2)) + eps)) * (
m12 / (1 - beta_1)
)
G_W5 = G_W5 - (learing_rate / (np.sqrt(v13 / (1 - beta_2)) + eps)) * (
m13 / (1 - beta_1)
)
G_b5 = G_b5 - (learing_rate / (np.sqrt(v14 / (1 - beta_2)) + eps)) * (
m14 / (1 - beta_1)
)
G_W6 = G_W6 - (learing_rate / (np.sqrt(v15 / (1 - beta_2)) + eps)) * (
m15 / (1 - beta_1)
)
G_b6 = G_b6 - (learing_rate / (np.sqrt(v16 / (1 - beta_2)) + eps)) * (
m16 / (1 - beta_1)
)
G_W7 = G_W7 - (learing_rate / (np.sqrt(v17 / (1 - beta_2)) + eps)) * (
m17 / (1 - beta_1)
)
G_b7 = G_b7 - (learing_rate / (np.sqrt(v18 / (1 - beta_2)) + eps)) * (
m18 / (1 - beta_1)
)
# --- Print Error ----
# print("Current Iter: ",iter, " Current D cost:",D_cost, " Current G cost: ", G_cost,end='\r')
if iter == 0:
learing_rate = learing_rate * 0.01
if iter == 40:
learing_rate = learing_rate * 0.01
# ---- Print to Out put ----
if iter % 10 == 0:
print(
"Current Iter: ",
iter,
" Current D cost:",
D_cost,
" Current G cost: ",
G_cost,
end="\r",
)
print("--------- Show Example Result See Tab Above ----------")
print("--------- Wait for the image to load ---------")
Z = np.random.uniform(-1.0, 1.0, size=[16, G_input])
Gl1 = Z.dot(G_W1) + G_b1
Gl1A = arctan(Gl1)
Gl2 = Gl1A.dot(G_W2) + G_b2
Gl2A = ReLu(Gl2)
Gl3 = Gl2A.dot(G_W3) + G_b3
Gl3A = arctan(Gl3)
Gl4 = Gl3A.dot(G_W4) + G_b4
Gl4A = ReLu(Gl4)
Gl5 = Gl4A.dot(G_W5) + G_b5
Gl5A = tanh(Gl5)
Gl6 = Gl5A.dot(G_W6) + G_b6
Gl6A = ReLu(Gl6)
Gl7 = Gl6A.dot(G_W7) + G_b7
current_fake_data = log(Gl7)
fig = plot(current_fake_data)
fig.savefig(
"Click_Me_{}.png".format(
str(iter).zfill(3)
+ "_Ginput_"
+ str(G_input)
+ "_hiddenone"
+ str(hidden_input)
+ "_hiddentwo"
+ str(hidden_input2)
+ "_LR_"
+ str(learing_rate)
),
bbox_inches="tight",
)
# for complete explanation visit https://towardsdatascience.com/only-numpy-implementing-gan-general-adversarial-networks-and-adam-optimizer-using-numpy-with-2a7e4e032021
# -- end code --
| import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt
import numpy as np
from sklearn.utils import shuffle
import input_data
random_numer = 42
np.random.seed(random_numer)
def ReLu(x):
mask = (x > 0) * 1.0
return mask * x
def d_ReLu(x):
mask = (x > 0) * 1.0
return mask
def arctan(x):
return np.arctan(x)
def d_arctan(x):
return 1 / (1 + x ** 2)
def log(x):
return 1 / (1 + np.exp(-1 * x))
def d_log(x):
return log(x) * (1 - log(x))
def tanh(x):
return np.tanh(x)
def d_tanh(x):
return 1 - np.tanh(x) ** 2
def plot(samples):
fig = plt.figure(figsize=(4, 4))
gs = gridspec.GridSpec(4, 4)
gs.update(wspace=0.05, hspace=0.05)
for i, sample in enumerate(samples):
ax = plt.subplot(gs[i])
plt.axis("off")
ax.set_xticklabels([])
ax.set_yticklabels([])
ax.set_aspect("equal")
plt.imshow(sample.reshape(28, 28), cmap="Greys_r")
return fig
if __name__ == "__main__":
# 1. Load Data and declare hyper
print("--------- Load Data ----------")
mnist = input_data.read_data_sets("MNIST_data", one_hot=False)
temp = mnist.test
images, labels = temp.images, temp.labels
images, labels = shuffle(np.asarray(images), np.asarray(labels))
num_epoch = 10
learing_rate = 0.00009
G_input = 100
hidden_input, hidden_input2, hidden_input3 = 128, 256, 346
hidden_input4, hidden_input5, hidden_input6 = 480, 560, 686
print("--------- Declare Hyper Parameters ----------")
# 2. Declare Weights
D_W1 = (
np.random.normal(size=(784, hidden_input), scale=(1.0 / np.sqrt(784 / 2.0)))
* 0.002
)
# D_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.))) *0.002
D_b1 = np.zeros(hidden_input)
D_W2 = (
np.random.normal(
size=(hidden_input, 1), scale=(1.0 / np.sqrt(hidden_input / 2.0))
)
* 0.002
)
# D_b2 = np.random.normal(size=(1),scale=(1. / np.sqrt(1 / 2.))) *0.002
D_b2 = np.zeros(1)
G_W1 = (
np.random.normal(
size=(G_input, hidden_input), scale=(1.0 / np.sqrt(G_input / 2.0))
)
* 0.002
)
# G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.))) *0.002
G_b1 = np.zeros(hidden_input)
G_W2 = (
np.random.normal(
size=(hidden_input, hidden_input2),
scale=(1.0 / np.sqrt(hidden_input / 2.0)),
)
* 0.002
)
# G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.))) *0.002
G_b2 = np.zeros(hidden_input2)
G_W3 = (
np.random.normal(
size=(hidden_input2, hidden_input3),
scale=(1.0 / np.sqrt(hidden_input2 / 2.0)),
)
* 0.002
)
# G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.))) *0.002
G_b3 = np.zeros(hidden_input3)
G_W4 = (
np.random.normal(
size=(hidden_input3, hidden_input4),
scale=(1.0 / np.sqrt(hidden_input3 / 2.0)),
)
* 0.002
)
# G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.))) *0.002
G_b4 = np.zeros(hidden_input4)
G_W5 = (
np.random.normal(
size=(hidden_input4, hidden_input5),
scale=(1.0 / np.sqrt(hidden_input4 / 2.0)),
)
* 0.002
)
# G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.))) *0.002
G_b5 = np.zeros(hidden_input5)
G_W6 = (
np.random.normal(
size=(hidden_input5, hidden_input6),
scale=(1.0 / np.sqrt(hidden_input5 / 2.0)),
)
* 0.002
)
# G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.))) *0.002
G_b6 = np.zeros(hidden_input6)
G_W7 = (
np.random.normal(
size=(hidden_input6, 784), scale=(1.0 / np.sqrt(hidden_input6 / 2.0))
)
* 0.002
)
# G_b2 = np.random.normal(size=(784),scale=(1. / np.sqrt(784 / 2.))) *0.002
G_b7 = np.zeros(784)
# 3. For Adam Optimzier
v1, m1 = 0, 0
v2, m2 = 0, 0
v3, m3 = 0, 0
v4, m4 = 0, 0
v5, m5 = 0, 0
v6, m6 = 0, 0
v7, m7 = 0, 0
v8, m8 = 0, 0
v9, m9 = 0, 0
v10, m10 = 0, 0
v11, m11 = 0, 0
v12, m12 = 0, 0
v13, m13 = 0, 0
v14, m14 = 0, 0
v15, m15 = 0, 0
v16, m16 = 0, 0
v17, m17 = 0, 0
v18, m18 = 0, 0
beta_1, beta_2, eps = 0.9, 0.999, 0.00000001
print("--------- Started Training ----------")
for iter in range(num_epoch):
random_int = np.random.randint(len(images) - 5)
current_image = np.expand_dims(images[random_int], axis=0)
# Func: Generate The first Fake Data
Z = np.random.uniform(-1.0, 1.0, size=[1, G_input])
Gl1 = Z.dot(G_W1) + G_b1
Gl1A = arctan(Gl1)
Gl2 = Gl1A.dot(G_W2) + G_b2
Gl2A = ReLu(Gl2)
Gl3 = Gl2A.dot(G_W3) + G_b3
Gl3A = arctan(Gl3)
Gl4 = Gl3A.dot(G_W4) + G_b4
Gl4A = ReLu(Gl4)
Gl5 = Gl4A.dot(G_W5) + G_b5
Gl5A = tanh(Gl5)
Gl6 = Gl5A.dot(G_W6) + G_b6
Gl6A = ReLu(Gl6)
Gl7 = Gl6A.dot(G_W7) + G_b7
current_fake_data = log(Gl7)
# Func: Forward Feed for Real data
Dl1_r = current_image.dot(D_W1) + D_b1
Dl1_rA = ReLu(Dl1_r)
Dl2_r = Dl1_rA.dot(D_W2) + D_b2
Dl2_rA = log(Dl2_r)
# Func: Forward Feed for Fake Data
Dl1_f = current_fake_data.dot(D_W1) + D_b1
Dl1_fA = ReLu(Dl1_f)
Dl2_f = Dl1_fA.dot(D_W2) + D_b2
Dl2_fA = log(Dl2_f)
# Func: Cost D
D_cost = -np.log(Dl2_rA) + np.log(1.0 - Dl2_fA)
# Func: Gradient
grad_f_w2_part_1 = 1 / (1.0 - Dl2_fA)
grad_f_w2_part_2 = d_log(Dl2_f)
grad_f_w2_part_3 = Dl1_fA
grad_f_w2 = grad_f_w2_part_3.T.dot(grad_f_w2_part_1 * grad_f_w2_part_2)
grad_f_b2 = grad_f_w2_part_1 * grad_f_w2_part_2
grad_f_w1_part_1 = (grad_f_w2_part_1 * grad_f_w2_part_2).dot(D_W2.T)
grad_f_w1_part_2 = d_ReLu(Dl1_f)
grad_f_w1_part_3 = current_fake_data
grad_f_w1 = grad_f_w1_part_3.T.dot(grad_f_w1_part_1 * grad_f_w1_part_2)
grad_f_b1 = grad_f_w1_part_1 * grad_f_w1_part_2
grad_r_w2_part_1 = -1 / Dl2_rA
grad_r_w2_part_2 = d_log(Dl2_r)
grad_r_w2_part_3 = Dl1_rA
grad_r_w2 = grad_r_w2_part_3.T.dot(grad_r_w2_part_1 * grad_r_w2_part_2)
grad_r_b2 = grad_r_w2_part_1 * grad_r_w2_part_2
grad_r_w1_part_1 = (grad_r_w2_part_1 * grad_r_w2_part_2).dot(D_W2.T)
grad_r_w1_part_2 = d_ReLu(Dl1_r)
grad_r_w1_part_3 = current_image
grad_r_w1 = grad_r_w1_part_3.T.dot(grad_r_w1_part_1 * grad_r_w1_part_2)
grad_r_b1 = grad_r_w1_part_1 * grad_r_w1_part_2
grad_w1 = grad_f_w1 + grad_r_w1
grad_b1 = grad_f_b1 + grad_r_b1
grad_w2 = grad_f_w2 + grad_r_w2
grad_b2 = grad_f_b2 + grad_r_b2
# ---- Update Gradient ----
m1 = beta_1 * m1 + (1 - beta_1) * grad_w1
v1 = beta_2 * v1 + (1 - beta_2) * grad_w1 ** 2
m2 = beta_1 * m2 + (1 - beta_1) * grad_b1
v2 = beta_2 * v2 + (1 - beta_2) * grad_b1 ** 2
m3 = beta_1 * m3 + (1 - beta_1) * grad_w2
v3 = beta_2 * v3 + (1 - beta_2) * grad_w2 ** 2
m4 = beta_1 * m4 + (1 - beta_1) * grad_b2
v4 = beta_2 * v4 + (1 - beta_2) * grad_b2 ** 2
D_W1 = D_W1 - (learing_rate / (np.sqrt(v1 / (1 - beta_2)) + eps)) * (
m1 / (1 - beta_1)
)
D_b1 = D_b1 - (learing_rate / (np.sqrt(v2 / (1 - beta_2)) + eps)) * (
m2 / (1 - beta_1)
)
D_W2 = D_W2 - (learing_rate / (np.sqrt(v3 / (1 - beta_2)) + eps)) * (
m3 / (1 - beta_1)
)
D_b2 = D_b2 - (learing_rate / (np.sqrt(v4 / (1 - beta_2)) + eps)) * (
m4 / (1 - beta_1)
)
# Func: Forward Feed for G
Z = np.random.uniform(-1.0, 1.0, size=[1, G_input])
Gl1 = Z.dot(G_W1) + G_b1
Gl1A = arctan(Gl1)
Gl2 = Gl1A.dot(G_W2) + G_b2
Gl2A = ReLu(Gl2)
Gl3 = Gl2A.dot(G_W3) + G_b3
Gl3A = arctan(Gl3)
Gl4 = Gl3A.dot(G_W4) + G_b4
Gl4A = ReLu(Gl4)
Gl5 = Gl4A.dot(G_W5) + G_b5
Gl5A = tanh(Gl5)
Gl6 = Gl5A.dot(G_W6) + G_b6
Gl6A = ReLu(Gl6)
Gl7 = Gl6A.dot(G_W7) + G_b7
current_fake_data = log(Gl7)
Dl1 = current_fake_data.dot(D_W1) + D_b1
Dl1_A = ReLu(Dl1)
Dl2 = Dl1_A.dot(D_W2) + D_b2
Dl2_A = log(Dl2)
# Func: Cost G
G_cost = -np.log(Dl2_A)
# Func: Gradient
grad_G_w7_part_1 = ((-1 / Dl2_A) * d_log(Dl2).dot(D_W2.T) * (d_ReLu(Dl1))).dot(
D_W1.T
)
grad_G_w7_part_2 = d_log(Gl7)
grad_G_w7_part_3 = Gl6A
grad_G_w7 = grad_G_w7_part_3.T.dot(grad_G_w7_part_1 * grad_G_w7_part_1)
grad_G_b7 = grad_G_w7_part_1 * grad_G_w7_part_2
grad_G_w6_part_1 = (grad_G_w7_part_1 * grad_G_w7_part_2).dot(G_W7.T)
grad_G_w6_part_2 = d_ReLu(Gl6)
grad_G_w6_part_3 = Gl5A
grad_G_w6 = grad_G_w6_part_3.T.dot(grad_G_w6_part_1 * grad_G_w6_part_2)
grad_G_b6 = grad_G_w6_part_1 * grad_G_w6_part_2
grad_G_w5_part_1 = (grad_G_w6_part_1 * grad_G_w6_part_2).dot(G_W6.T)
grad_G_w5_part_2 = d_tanh(Gl5)
grad_G_w5_part_3 = Gl4A
grad_G_w5 = grad_G_w5_part_3.T.dot(grad_G_w5_part_1 * grad_G_w5_part_2)
grad_G_b5 = grad_G_w5_part_1 * grad_G_w5_part_2
grad_G_w4_part_1 = (grad_G_w5_part_1 * grad_G_w5_part_2).dot(G_W5.T)
grad_G_w4_part_2 = d_ReLu(Gl4)
grad_G_w4_part_3 = Gl3A
grad_G_w4 = grad_G_w4_part_3.T.dot(grad_G_w4_part_1 * grad_G_w4_part_2)
grad_G_b4 = grad_G_w4_part_1 * grad_G_w4_part_2
grad_G_w3_part_1 = (grad_G_w4_part_1 * grad_G_w4_part_2).dot(G_W4.T)
grad_G_w3_part_2 = d_arctan(Gl3)
grad_G_w3_part_3 = Gl2A
grad_G_w3 = grad_G_w3_part_3.T.dot(grad_G_w3_part_1 * grad_G_w3_part_2)
grad_G_b3 = grad_G_w3_part_1 * grad_G_w3_part_2
grad_G_w2_part_1 = (grad_G_w3_part_1 * grad_G_w3_part_2).dot(G_W3.T)
grad_G_w2_part_2 = d_ReLu(Gl2)
grad_G_w2_part_3 = Gl1A
grad_G_w2 = grad_G_w2_part_3.T.dot(grad_G_w2_part_1 * grad_G_w2_part_2)
grad_G_b2 = grad_G_w2_part_1 * grad_G_w2_part_2
grad_G_w1_part_1 = (grad_G_w2_part_1 * grad_G_w2_part_2).dot(G_W2.T)
grad_G_w1_part_2 = d_arctan(Gl1)
grad_G_w1_part_3 = Z
grad_G_w1 = grad_G_w1_part_3.T.dot(grad_G_w1_part_1 * grad_G_w1_part_2)
grad_G_b1 = grad_G_w1_part_1 * grad_G_w1_part_2
# ---- Update Gradient ----
m5 = beta_1 * m5 + (1 - beta_1) * grad_G_w1
v5 = beta_2 * v5 + (1 - beta_2) * grad_G_w1 ** 2
m6 = beta_1 * m6 + (1 - beta_1) * grad_G_b1
v6 = beta_2 * v6 + (1 - beta_2) * grad_G_b1 ** 2
m7 = beta_1 * m7 + (1 - beta_1) * grad_G_w2
v7 = beta_2 * v7 + (1 - beta_2) * grad_G_w2 ** 2
m8 = beta_1 * m8 + (1 - beta_1) * grad_G_b2
v8 = beta_2 * v8 + (1 - beta_2) * grad_G_b2 ** 2
m9 = beta_1 * m9 + (1 - beta_1) * grad_G_w3
v9 = beta_2 * v9 + (1 - beta_2) * grad_G_w3 ** 2
m10 = beta_1 * m10 + (1 - beta_1) * grad_G_b3
v10 = beta_2 * v10 + (1 - beta_2) * grad_G_b3 ** 2
m11 = beta_1 * m11 + (1 - beta_1) * grad_G_w4
v11 = beta_2 * v11 + (1 - beta_2) * grad_G_w4 ** 2
m12 = beta_1 * m12 + (1 - beta_1) * grad_G_b4
v12 = beta_2 * v12 + (1 - beta_2) * grad_G_b4 ** 2
m13 = beta_1 * m13 + (1 - beta_1) * grad_G_w5
v13 = beta_2 * v13 + (1 - beta_2) * grad_G_w5 ** 2
m14 = beta_1 * m14 + (1 - beta_1) * grad_G_b5
v14 = beta_2 * v14 + (1 - beta_2) * grad_G_b5 ** 2
m15 = beta_1 * m15 + (1 - beta_1) * grad_G_w6
v15 = beta_2 * v15 + (1 - beta_2) * grad_G_w6 ** 2
m16 = beta_1 * m16 + (1 - beta_1) * grad_G_b6
v16 = beta_2 * v16 + (1 - beta_2) * grad_G_b6 ** 2
m17 = beta_1 * m17 + (1 - beta_1) * grad_G_w7
v17 = beta_2 * v17 + (1 - beta_2) * grad_G_w7 ** 2
m18 = beta_1 * m18 + (1 - beta_1) * grad_G_b7
v18 = beta_2 * v18 + (1 - beta_2) * grad_G_b7 ** 2
G_W1 = G_W1 - (learing_rate / (np.sqrt(v5 / (1 - beta_2)) + eps)) * (
m5 / (1 - beta_1)
)
G_b1 = G_b1 - (learing_rate / (np.sqrt(v6 / (1 - beta_2)) + eps)) * (
m6 / (1 - beta_1)
)
G_W2 = G_W2 - (learing_rate / (np.sqrt(v7 / (1 - beta_2)) + eps)) * (
m7 / (1 - beta_1)
)
G_b2 = G_b2 - (learing_rate / (np.sqrt(v8 / (1 - beta_2)) + eps)) * (
m8 / (1 - beta_1)
)
G_W3 = G_W3 - (learing_rate / (np.sqrt(v9 / (1 - beta_2)) + eps)) * (
m9 / (1 - beta_1)
)
G_b3 = G_b3 - (learing_rate / (np.sqrt(v10 / (1 - beta_2)) + eps)) * (
m10 / (1 - beta_1)
)
G_W4 = G_W4 - (learing_rate / (np.sqrt(v11 / (1 - beta_2)) + eps)) * (
m11 / (1 - beta_1)
)
G_b4 = G_b4 - (learing_rate / (np.sqrt(v12 / (1 - beta_2)) + eps)) * (
m12 / (1 - beta_1)
)
G_W5 = G_W5 - (learing_rate / (np.sqrt(v13 / (1 - beta_2)) + eps)) * (
m13 / (1 - beta_1)
)
G_b5 = G_b5 - (learing_rate / (np.sqrt(v14 / (1 - beta_2)) + eps)) * (
m14 / (1 - beta_1)
)
G_W6 = G_W6 - (learing_rate / (np.sqrt(v15 / (1 - beta_2)) + eps)) * (
m15 / (1 - beta_1)
)
G_b6 = G_b6 - (learing_rate / (np.sqrt(v16 / (1 - beta_2)) + eps)) * (
m16 / (1 - beta_1)
)
G_W7 = G_W7 - (learing_rate / (np.sqrt(v17 / (1 - beta_2)) + eps)) * (
m17 / (1 - beta_1)
)
G_b7 = G_b7 - (learing_rate / (np.sqrt(v18 / (1 - beta_2)) + eps)) * (
m18 / (1 - beta_1)
)
# --- Print Error ----
# print("Current Iter: ",iter, " Current D cost:",D_cost, " Current G cost: ", G_cost,end='\r')
if iter == 0:
learing_rate = learing_rate * 0.01
if iter == 40:
learing_rate = learing_rate * 0.01
# ---- Print to Out put ----
if iter % 10 == 0:
print(
"Current Iter: ",
iter,
" Current D cost:",
D_cost,
" Current G cost: ",
G_cost,
end="\r",
)
print("--------- Show Example Result See Tab Above ----------")
print("--------- Wait for the image to load ---------")
Z = np.random.uniform(-1.0, 1.0, size=[16, G_input])
Gl1 = Z.dot(G_W1) + G_b1
Gl1A = arctan(Gl1)
Gl2 = Gl1A.dot(G_W2) + G_b2
Gl2A = ReLu(Gl2)
Gl3 = Gl2A.dot(G_W3) + G_b3
Gl3A = arctan(Gl3)
Gl4 = Gl3A.dot(G_W4) + G_b4
Gl4A = ReLu(Gl4)
Gl5 = Gl4A.dot(G_W5) + G_b5
Gl5A = tanh(Gl5)
Gl6 = Gl5A.dot(G_W6) + G_b6
Gl6A = ReLu(Gl6)
Gl7 = Gl6A.dot(G_W7) + G_b7
current_fake_data = log(Gl7)
fig = plot(current_fake_data)
fig.savefig(
"Click_Me_{}.png".format(
str(iter).zfill(3)
+ "_Ginput_"
+ str(G_input)
+ "_hiddenone"
+ str(hidden_input)
+ "_hiddentwo"
+ str(hidden_input2)
+ "_LR_"
+ str(learing_rate)
),
bbox_inches="tight",
)
# for complete explanation visit https://towardsdatascience.com/only-numpy-implementing-gan-general-adversarial-networks-and-adam-optimizer-using-numpy-with-2a7e4e032021
# -- end code --
| -1 |
TheAlgorithms/Python | 7,262 | refactor: Move constants outside of variable scope | ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T19:51:55Z" | "2022-10-16T09:33:29Z" | 77764116217708933bdc65b29801092fa291398e | c6582b35bf8b8aba622c63096e3ab2f01aa36854 | refactor: Move constants outside of variable scope. ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Project Euler Problem 115: https://projecteuler.net/problem=115
NOTE: This is a more difficult version of Problem 114
(https://projecteuler.net/problem=114).
A row measuring n units in length has red blocks
with a minimum length of m units placed on it, such that any two red blocks
(which are allowed to be different lengths) are separated by at least one black square.
Let the fill-count function, F(m, n),
represent the number of ways that a row can be filled.
For example, F(3, 29) = 673135 and F(3, 30) = 1089155.
That is, for m = 3, it can be seen that n = 30 is the smallest value
for which the fill-count function first exceeds one million.
In the same way, for m = 10, it can be verified that
F(10, 56) = 880711 and F(10, 57) = 1148904, so n = 57 is the least value
for which the fill-count function first exceeds one million.
For m = 50, find the least value of n
for which the fill-count function first exceeds one million.
"""
from itertools import count
def solution(min_block_length: int = 50) -> int:
"""
Returns for given minimum block length the least value of n
for which the fill-count function first exceeds one million
>>> solution(3)
30
>>> solution(10)
57
"""
fill_count_functions = [1] * min_block_length
for n in count(min_block_length):
fill_count_functions.append(1)
for block_length in range(min_block_length, n + 1):
for block_start in range(n - block_length):
fill_count_functions[n] += fill_count_functions[
n - block_start - block_length - 1
]
fill_count_functions[n] += 1
if fill_count_functions[n] > 1_000_000:
break
return n
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 115: https://projecteuler.net/problem=115
NOTE: This is a more difficult version of Problem 114
(https://projecteuler.net/problem=114).
A row measuring n units in length has red blocks
with a minimum length of m units placed on it, such that any two red blocks
(which are allowed to be different lengths) are separated by at least one black square.
Let the fill-count function, F(m, n),
represent the number of ways that a row can be filled.
For example, F(3, 29) = 673135 and F(3, 30) = 1089155.
That is, for m = 3, it can be seen that n = 30 is the smallest value
for which the fill-count function first exceeds one million.
In the same way, for m = 10, it can be verified that
F(10, 56) = 880711 and F(10, 57) = 1148904, so n = 57 is the least value
for which the fill-count function first exceeds one million.
For m = 50, find the least value of n
for which the fill-count function first exceeds one million.
"""
from itertools import count
def solution(min_block_length: int = 50) -> int:
"""
Returns for given minimum block length the least value of n
for which the fill-count function first exceeds one million
>>> solution(3)
30
>>> solution(10)
57
"""
fill_count_functions = [1] * min_block_length
for n in count(min_block_length):
fill_count_functions.append(1)
for block_length in range(min_block_length, n + 1):
for block_start in range(n - block_length):
fill_count_functions[n] += fill_count_functions[
n - block_start - block_length - 1
]
fill_count_functions[n] += 1
if fill_count_functions[n] > 1_000_000:
break
return n
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 7,262 | refactor: Move constants outside of variable scope | ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T19:51:55Z" | "2022-10-16T09:33:29Z" | 77764116217708933bdc65b29801092fa291398e | c6582b35bf8b8aba622c63096e3ab2f01aa36854 | refactor: Move constants outside of variable scope. ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Finding the peak of a unimodal list using divide and conquer.
A unimodal array is defined as follows: array is increasing up to index p,
then decreasing afterwards. (for p >= 1)
An obvious solution can be performed in O(n),
to find the maximum of the array.
(From Kleinberg and Tardos. Algorithm Design.
Addison Wesley 2006: Chapter 5 Solved Exercise 1)
"""
from __future__ import annotations
def peak(lst: list[int]) -> int:
"""
Return the peak value of `lst`.
>>> peak([1, 2, 3, 4, 5, 4, 3, 2, 1])
5
>>> peak([1, 10, 9, 8, 7, 6, 5, 4])
10
>>> peak([1, 9, 8, 7])
9
>>> peak([1, 2, 3, 4, 5, 6, 7, 0])
7
>>> peak([1, 2, 3, 4, 3, 2, 1, 0, -1, -2])
4
"""
# middle index
m = len(lst) // 2
# choose the middle 3 elements
three = lst[m - 1 : m + 2]
# if middle element is peak
if three[1] > three[0] and three[1] > three[2]:
return three[1]
# if increasing, recurse on right
elif three[0] < three[2]:
if len(lst[:m]) == 2:
m -= 1
return peak(lst[m:])
# decreasing
else:
if len(lst[:m]) == 2:
m += 1
return peak(lst[:m])
if __name__ == "__main__":
import doctest
doctest.testmod()
| """
Finding the peak of a unimodal list using divide and conquer.
A unimodal array is defined as follows: array is increasing up to index p,
then decreasing afterwards. (for p >= 1)
An obvious solution can be performed in O(n),
to find the maximum of the array.
(From Kleinberg and Tardos. Algorithm Design.
Addison Wesley 2006: Chapter 5 Solved Exercise 1)
"""
from __future__ import annotations
def peak(lst: list[int]) -> int:
"""
Return the peak value of `lst`.
>>> peak([1, 2, 3, 4, 5, 4, 3, 2, 1])
5
>>> peak([1, 10, 9, 8, 7, 6, 5, 4])
10
>>> peak([1, 9, 8, 7])
9
>>> peak([1, 2, 3, 4, 5, 6, 7, 0])
7
>>> peak([1, 2, 3, 4, 3, 2, 1, 0, -1, -2])
4
"""
# middle index
m = len(lst) // 2
# choose the middle 3 elements
three = lst[m - 1 : m + 2]
# if middle element is peak
if three[1] > three[0] and three[1] > three[2]:
return three[1]
# if increasing, recurse on right
elif three[0] < three[2]:
if len(lst[:m]) == 2:
m -= 1
return peak(lst[m:])
# decreasing
else:
if len(lst[:m]) == 2:
m += 1
return peak(lst[:m])
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 7,262 | refactor: Move constants outside of variable scope | ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T19:51:55Z" | "2022-10-16T09:33:29Z" | 77764116217708933bdc65b29801092fa291398e | c6582b35bf8b8aba622c63096e3ab2f01aa36854 | refactor: Move constants outside of variable scope. ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
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KC 4H 3C 9D JS 6H 3S 8S AS 8C
7H KC 7D JD 2H JC QH 5S 3H QS
9H TD 3S 8H 7S AC 5C 6C AH 7C
8D 9H AH JD TD QS 7D 3S 9C 8S
AH QH 3C JD KC 4S 5S 5D TD KS
9H 7H 6S JH TH 4C 7C AD 5C 2D
7C KD 5S TC 9D 6S 6C 5D 2S TH
KC 9H 8D 5H 7H 4H QC 3D 7C AS
6S 8S QC TD 4S 5C TH QS QD 2S
8S 5H TH QC 9H 6S KC 7D 7C 5C
7H KD AH 4D KH 5C 4S 2D KC QH
6S 2C TD JC AS 4D 6C 8C 4H 5S
JC TC JD 5S 6S 8D AS 9D AD 3S
6D 6H 5D 5S TC 3D 7D QS 9D QD
4S 6C 8S 3S 7S AD KS 2D 7D 7C
KC QH JC AC QD 5D 8D QS 7H 7D
JS AH 8S 5H 3D TD 3H 4S 6C JH
4S QS 7D AS 9H JS KS 6D TC 5C
2D 5C 6H TC 4D QH 3D 9H 8S 6C
6D 7H TC TH 5S JD 5C 9C KS KD
8D TD QH 6S 4S 6C 8S KC 5C TC
5S 3D KS AC 4S 7D QD 4C TH 2S
TS 8H 9S 6S 7S QH 3C AH 7H 8C
4C 8C TS JS QC 3D 7D 5D 7S JH
8S 7S 9D QC AC 7C 6D 2H JH KC
JS KD 3C 6S 4S 7C AH QC KS 5H
KS 6S 4H JD QS TC 8H KC 6H AS
KH 7C TC 6S TD JC 5C 7D AH 3S
3H 4C 4H TC TH 6S 7H 6D 9C QH
7D 5H 4S 8C JS 4D 3D 8S QH KC
3H 6S AD 7H 3S QC 8S 4S 7S JS
3S JD KH TH 6H QS 9C 6C 2D QD
4S QH 4D 5H KC 7D 6D 8D TH 5S
TD AD 6S 7H KD KH 9H 5S KC JC
3H QC AS TS 4S QD KS 9C 7S KC
TS 6S QC 6C TH TC 9D 5C 5D KD
JS 3S 4H KD 4C QD 6D 9S JC 9D
8S JS 6D 4H JH 6H 6S 6C KS KH
AC 7D 5D TC 9S KH 6S QD 6H AS
AS 7H 6D QH 8D TH 2S KH 5C 5H
4C 7C 3D QC TC 4S KH 8C 2D JS
6H 5D 7S 5H 9C 9H JH 8S TH 7H
AS JS 2S QD KH 8H 4S AC 8D 8S
3H 4C TD KD 8C JC 5C QS 2D JD
TS 7D 5D 6C 2C QS 2H 3C AH KS
4S 7C 9C 7D JH 6C 5C 8H 9D QD
2S TD 7S 6D 9C 9S QS KH QH 5C
JC 6S 9C QH JH 8D 7S JS KH 2H
8D 5H TH KC 4D 4S 3S 6S 3D QS
2D JD 4C TD 7C 6D TH 7S JC AH
QS 7S 4C TH 9D TS AD 4D 3H 6H
2D 3H 7D JD 3D AS 2S 9C QC 8S
4H 9H 9C 2C 7S JH KD 5C 5D 6H
TC 9H 8H JC 3C 9S 8D KS AD KC
TS 5H JD QS QH QC 8D 5D KH AH
5D AS 8S 6S 4C AH QC QD TH 7H
3H 4H 7D 6S 4S 9H AS 8H JS 9D
JD 8C 2C 9D 7D 5H 5S 9S JC KD
KD 9C 4S QD AH 7C AD 9D AC TD
6S 4H 4S 9C 8D KS TC 9D JH 7C
5S JC 5H 4S QH AC 2C JS 2S 9S
8C 5H AS QD AD 5C 7D 8S QC TD
JC 4C 8D 5C KH QS 4D 6H 2H 2C
TH 4S 2D KC 3H QD AC 7H AD 9D
KH QD AS 8H TH KC 8D 7S QH 8C
JC 6C 7D 8C KH AD QS 2H 6S 2D
JC KH 2D 7D JS QC 5H 4C 5D AD
TS 3S AD 4S TD 2D TH 6S 9H JH
9H 2D QS 2C 4S 3D KH AS AC 9D
KH 6S 8H 4S KD 7D 9D TS QD QC
JH 5H AH KS AS AD JC QC 5S KH
5D 7D 6D KS KD 3D 7C 4D JD 3S
AC JS 8D 5H 9C 3H 4H 4D TS 2C
6H KS KH 9D 7C 2S 6S 8S 2H 3D
6H AC JS 7S 3S TD 8H 3H 4H TH
9H TC QC KC 5C KS 6H 4H AC 8S
TC 7D QH 4S JC TS 6D 6C AC KH
QH 7D 7C JH QS QD TH 3H 5D KS
3D 5S 8D JS 4C 2C KS 7H 9C 4H
5H 8S 4H TD 2C 3S QD QC 3H KC
QC JS KD 9C AD 5S 9D 7D 7H TS
8C JC KH 7C 7S 6C TS 2C QD TH
5S 9D TH 3C 7S QH 8S 9C 2H 5H
5D 9H 6H 2S JS KH 3H 7C 2H 5S
JD 5D 5S 2C TC 2S 6S 6C 3C 8S
4D KH 8H 4H 2D KS 3H 5C 2S 9H
3S 2D TD 7H 8S 6H JD KC 9C 8D
6S QD JH 7C 9H 5H 8S 8H TH TD
QS 7S TD 7D TS JC KD 7C 3C 2C
3C JD 8S 4H 2D 2S TD AS 4D AC
AH KS 6C 4C 4S 7D 8C 9H 6H AS
5S 3C 9S 2C QS KD 4D 4S AC 5D
2D TS 2C JS KH QH 5D 8C AS KC
KD 3H 6C TH 8S 7S KH 6H 9S AC
6H 7S 6C QS AH 2S 2H 4H 5D 5H
5H JC QD 2C 2S JD AS QC 6S 7D
6C TC AS KD 8H 9D 2C 7D JH 9S
2H 4C 6C AH 8S TD 3H TH 7C TS
KD 4S TS 6C QH 8D 9D 9C AH 7D
6D JS 5C QD QC 9C 5D 8C 2H KD
3C QH JH AD 6S AH KC 8S 6D 6H
3D 7C 4C 7S 5S 3S 6S 5H JC 3C
QH 7C 5H 3C 3S 8C TS 4C KD 9C
QD 3S 7S 5H 7H QH JC 7C 8C KD
3C KD KH 2S 4C TS AC 6S 2C 7C
2C KH 3C 4C 6H 4D 5H 5S 7S QD
4D 7C 8S QD TS 9D KS 6H KD 3C
QS 4D TS 7S 4C 3H QD 8D 9S TC
TS QH AC 6S 3C 9H 9D QS 8S 6H
3S 7S 5D 4S JS 2D 6C QH 6S TH
4C 4H AS JS 5D 3D TS 9C AC 8S
6S 9C 7C 3S 5C QS AD AS 6H 3C
9S 8C 7H 3H 6S 7C AS 9H JD KH
3D 3H 7S 4D 6C 7C AC 2H 9C TH
4H 5S 3H AC TC TH 9C 9H 9S 8D
8D 9H 5H 4D 6C 2H QD 6S 5D 3S
4C 5C JD QS 4D 3H TH AC QH 8C
QC 5S 3C 7H AD 4C KS 4H JD 6D
QS AH 3H KS 9H 2S JS JH 5H 2H
2H 5S TH 6S TS 3S KS 3C 5H JS
2D 9S 7H 3D KC JH 6D 7D JS TD
AC JS 8H 2C 8C JH JC 2D TH 7S
5D 9S 8H 2H 3D TC AH JC KD 9C
9D QD JC 2H 6D KH TS 9S QH TH
2C 8D 4S JD 5H 3H TH TC 9C KC
AS 3D 9H 7D 4D TH KH 2H 7S 3H
4H 7S KS 2S JS TS 8S 2H QD 8D
5S 6H JH KS 8H 2S QC AC 6S 3S
JC AS AD QS 8H 6C KH 4C 4D QD
2S 3D TS TD 9S KS 6S QS 5C 8D
3C 6D 4S QC KC JH QD TH KH AD
9H AH 4D KS 2S 8D JH JC 7C QS
2D 6C TH 3C 8H QD QH 2S 3S KS
6H 5D 9S 4C TS TD JS QD 9D JD
5H 8H KH 8S KS 7C TD AD 4S KD
2C 7C JC 5S AS 6C 7D 8S 5H 9C
6S QD 9S TS KH QS 5S QH 3C KC
7D 3H 3C KD 5C AS JH 7H 6H JD
9D 5C 9H KC 8H KS 4S AD 4D 2S
3S JD QD 8D 2S 7C 5S 6S 5H TS
6D 9S KC TD 3S 6H QD JD 5C 8D
5H 9D TS KD 8D 6H TD QC 4C 7D
6D 4S JD 9D AH 9S AS TD 9H QD
2D 5S 2H 9C 6H 9S TD QC 7D TC
3S 2H KS TS 2C 9C 8S JS 9D 7D
3C KC 6D 5D 6C 6H 8S AS 7S QS
JH 9S 2H 8D 4C 8H 9H AD TH KH
QC AS 2S JS 5C 6H KD 3H 7H 2C
QD 8H 2S 8D 3S 6D AH 2C TC 5C
JD JS TS 8S 3H 5D TD KC JC 6H
6S QS TC 3H 5D AH JC 7C 7D 4H
7C 5D 8H 9C 2H 9H JH KH 5S 2C
9C 7H 6S TH 3S QC QD 4C AC JD
2H 5D 9S 7D KC 3S QS 2D AS KH
2S 4S 2H 7D 5C TD TH QH 9S 4D
6D 3S TS 6H 4H KS 9D 8H 5S 2D
9H KS 4H 3S 5C 5D KH 6H 6S JS
KC AS 8C 4C JC KH QC TH QD AH
6S KH 9S 2C 5H TC 3C 7H JC 4D
JD 4S 6S 5S 8D 7H 7S 4D 4C 2H
7H 9H 5D KH 9C 7C TS TC 7S 5H
4C 8D QC TS 4S 9H 3D AD JS 7C
8C QS 5C 5D 3H JS AH KC 4S 9D
TS JD 8S QS TH JH KH 2D QD JS
JD QC 5D 6S 9H 3S 2C 8H 9S TS
2S 4C AD 7H JC 5C 2D 6D 4H 3D
7S JS 2C 4H 8C AD QD 9C 3S TD
JD TS 4C 6H 9H 7D QD 6D 3C AS
AS 7C 4C 6S 5D 5S 5C JS QC 4S
KD 6S 9S 7C 3C 5S 7D JH QD JS
4S 7S JH 2C 8S 5D 7H 3D QH AD
TD 6H 2H 8D 4H 2D 7C AD KH 5D
TS 3S 5H 2C QD AH 2S 5C KH TD
KC 4D 8C 5D AS 6C 2H 2S 9H 7C
KD JS QC TS QS KH JH 2C 5D AD
3S 5H KC 6C 9H 3H 2H AD 7D 7S
7S JS JH KD 8S 7D 2S 9H 7C 2H
9H 2D 8D QC 6S AD AS 8H 5H 6C
2S 7H 6C 6D 7D 8C 5D 9D JC 3C
7C 9C 7H JD 2H KD 3S KH AD 4S
QH AS 9H 4D JD KS KD TS KH 5H
4C 8H 5S 3S 3D 7D TD AD 7S KC
JS 8S 5S JC 8H TH 9C 4D 5D KC
7C 5S 9C QD 2C QH JS 5H 8D KH
TD 2S KS 3D AD KC 7S TC 3C 5D
4C 2S AD QS 6C 9S QD TH QH 5C
8C AD QS 2D 2S KC JD KS 6C JC
8D 4D JS 2H 5D QD 7S 7D QH TS
6S 7H 3S 8C 8S 9D QS 8H 6C 9S
4S TC 2S 5C QD 4D QS 6D TH 6S
3S 5C 9D 6H 8D 4C 7D TC 7C TD
AH 6S AS 7H 5S KD 3H 5H AC 4C
8D 8S AH KS QS 2C AD 6H 7D 5D
6H 9H 9S 2H QS 8S 9C 5D 2D KD
TS QC 5S JH 7D 7S TH 9S 9H AC
7H 3H 6S KC 4D 6D 5C 4S QD TS
TD 2S 7C QD 3H JH 9D 4H 7S 7H
KS 3D 4H 5H TC 2S AS 2D 6D 7D
8H 3C 7H TD 3H AD KC TH 9C KH
TC 4C 2C 9S 9D 9C 5C 2H JD 3C
3H AC TS 5D AD 8D 6H QC 6S 8C
2S TS 3S JD 7H 8S QH 4C 5S 8D
AC 4S 6C 3C KH 3D 7C 2D 8S 2H
4H 6C 8S TH 2H 4S 8H 9S 3H 7S
7C 4C 9C 2C 5C AS 5D KD 4D QH
9H 4H TS AS 7D 8D 5D 9S 8C 2H
QC KD AC AD 2H 7S AS 3S 2D 9S
2H QC 8H TC 6D QD QS 5D KH 3C
TH JD QS 4C 2S 5S AD 7H 3S AS
7H JS 3D 6C 3S 6D AS 9S AC QS
9C TS AS 8C TC 8S 6H 9D 8D 6C
4D JD 9C KC 7C 6D KS 3S 8C AS
3H 6S TC 8D TS 3S KC 9S 7C AS
8C QC 4H 4S 8S 6C 3S TC AH AC
4D 7D 5C AS 2H 6S TS QC AD TC
QD QC 8S 4S TH 3D AH TS JH 4H
5C 2D 9S 2C 3H 3C 9D QD QH 7D
KC 9H 6C KD 7S 3C 4D AS TC 2D
3D JS 4D 9D KS 7D TH QC 3H 3C
8D 5S 2H 9D 3H 8C 4C 4H 3C TH
JC TH 4S 6S JD 2D 4D 6C 3D 4C
TS 3S 2D 4H AC 2C 6S 2H JH 6H
TD 8S AD TC AH AC JH 9S 6S 7S
6C KC 4S JD 8D 9H 5S 7H QH AH
KD 8D TS JH 5C 5H 3H AD AS JS
2D 4H 3D 6C 8C 7S AD 5D 5C 8S
TD 5D 7S 9C 4S 5H 6C 8C 4C 8S
JS QH 9C AS 5C QS JC 3D QC 7C
JC 9C KH JH QS QC 2C TS 3D AD
5D JH AC 5C 9S TS 4C JD 8C KS
KC AS 2D KH 9H 2C 5S 4D 3D 6H
TH AH 2D 8S JC 3D 8C QH 7S 3S
8H QD 4H JC AS KH KS 3C 9S 6D
9S QH 7D 9C 4S AC 7H KH 4D KD
AH AD TH 6D 9C 9S KD KS QH 4H
QD 6H 9C 7C QS 6D 6S 9D 5S JH
AH 8D 5H QD 2H JC KS 4H KH 5S
5C 2S JS 8D 9C 8C 3D AS KC AH
JD 9S 2H QS 8H 5S 8C TH 5C 4C
QC QS 8C 2S 2C 3S 9C 4C KS KH
2D 5D 8S AH AD TD 2C JS KS 8C
TC 5S 5H 8H QC 9H 6H JD 4H 9S
3C JH 4H 9H AH 4S 2H 4C 8D AC
8S TH 4D 7D 6D QD QS 7S TC 7C
KH 6D 2D JD 5H JS QD JH 4H 4S
9C 7S JH 4S 3S TS QC 8C TC 4H
QH 9D 4D JH QS 3S 2C 7C 6C 2D
4H 9S JD 5C 5H AH 9D TS 2D 4C
KS JH TS 5D 2D AH JS 7H AS 8D
JS AH 8C AD KS 5S 8H 2C 6C TH
2H 5D AD AC KS 3D 8H TS 6H QC
6D 4H TS 9C 5H JS JH 6S JD 4C
JH QH 4H 2C 6D 3C 5D 4C QS KC
6H 4H 6C 7H 6S 2S 8S KH QC 8C
3H 3D 5D KS 4H TD AD 3S 4D TS
5S 7C 8S 7D 2C KS 7S 6C 8C JS
5D 2H 3S 7C 5C QD 5H 6D 9C 9H
JS 2S KD 9S 8D TD TS AC 8C 9D
5H QD 2S AC 8C 9H KS 7C 4S 3C
KH AS 3H 8S 9C JS QS 4S AD 4D
AS 2S TD AD 4D 9H JC 4C 5H QS
5D 7C 4H TC 2D 6C JS 4S KC 3S
4C 2C 5D AC 9H 3D JD 8S QS QH
2C 8S 6H 3C QH 6D TC KD AC AH
QC 6C 3S QS 4S AC 8D 5C AD KH
5S 4C AC KH AS QC 2C 5C 8D 9C
8H JD 3C KH 8D 5C 9C QD QH 9D
7H TS 2C 8C 4S TD JC 9C 5H QH
JS 4S 2C 7C TH 6C AS KS 7S JD
JH 7C 9H 7H TC 5H 3D 6D 5D 4D
2C QD JH 2H 9D 5S 3D TD AD KS
JD QH 3S 4D TH 7D 6S QS KS 4H
TC KS 5S 8D 8H AD 2S 2D 4C JH
5S JH TC 3S 2D QS 9D 4C KD 9S
AC KH 3H AS 9D KC 9H QD 6C 6S
9H 7S 3D 5C 7D KC TD 8H 4H 6S
3C 7H 8H TC QD 4D 7S 6S QH 6C
6D AD 4C QD 6C 5D 7D 9D KS TS
JH 2H JD 9S 7S TS KH 8D 5D 8H
2D 9S 4C 7D 9D 5H QD 6D AC 6S
7S 6D JC QD JH 4C 6S QS 2H 7D
8C TD JH KD 2H 5C QS 2C JS 7S
TC 5H 4H JH QD 3S 5S 5D 8S KH
KS KH 7C 2C 5D JH 6S 9C 6D JC
5H AH JD 9C JS KC 2H 6H 4D 5S
AS 3C TH QC 6H 9C 8S 8C TD 7C
KC 2C QD 9C KH 4D 7S 3C TS 9H
9C QC 2S TS 8C TD 9S QD 3S 3C
4D 9D TH JH AH 6S 2S JD QH JS
QD 9H 6C KD 7D 7H 5D 6S 8H AH
8H 3C 4S 2H 5H QS QH 7S 4H AC
QS 3C 7S 9S 4H 3S AH KS 9D 7C
AD 5S 6S 2H 2D 5H TC 4S 3C 8C
QH TS 6S 4D JS KS JH AS 8S 6D
2C 8S 2S TD 5H AS TC TS 6C KC
KC TS 8H 2H 3H 7C 4C 5S TH TD
KD AD KH 7H 7S 5D 5H 5S 2D 9C
AD 9S 3D 7S 8C QC 7C 9C KD KS
3C QC 9S 8C 4D 5C AS QD 6C 2C
2H KC 8S JD 7S AC 8D 5C 2S 4D
9D QH 3D 2S TC 3S KS 3C 9H TD
KD 6S AC 2C 7H 5H 3S 6C 6H 8C
QH TC 8S 6S KH TH 4H 5D TS 4D
8C JS 4H 6H 2C 2H 7D AC QD 3D
QS KC 6S 2D 5S 4H TD 3H JH 4C
7S 5H 7H 8H KH 6H QS TH KD 7D
5H AD KD 7C KH 5S TD 6D 3C 6C
8C 9C 5H JD 7C KC KH 7H 2H 3S
7S 4H AD 4D 8S QS TH 3D 7H 5S
8D TC KS KD 9S 6D AD JD 5C 2S
7H 8H 6C QD 2H 6H 9D TC 9S 7C
8D 6D 4C 7C 6C 3C TH KH JS JH
5S 3S 8S JS 9H AS AD 8H 7S KD
JH 7C 2C KC 5H AS AD 9C 9S JS
AD AC 2C 6S QD 7C 3H TH KS KD
9D JD 4H 8H 4C KH 7S TS 8C KC
3S 5S 2H 7S 6H 7D KS 5C 6D AD
5S 8C 9H QS 7H 7S 2H 6C 7D TD
QS 5S TD AC 9D KC 3D TC 2D 4D
TD 2H 7D JD QD 4C 7H 5D KC 3D
4C 3H 8S KD QH 5S QC 9H TC 5H
9C QD TH 5H TS 5C 9H AH QH 2C
4D 6S 3C AC 6C 3D 2C 2H TD TH
AC 9C 5D QC 4D AD 8D 6D 8C KC
AD 3C 4H AC 8D 8H 7S 9S TD JC
4H 9H QH JS 2D TH TD TC KD KS
5S 6S 9S 8D TH AS KH 5H 5C 8S
JD 2S 9S 6S 5S 8S 5D 7S 7H 9D
5D 8C 4C 9D AD TS 2C 7D KD TC
8S QS 4D KC 5C 8D 4S KH JD KD
AS 5C AD QH 7D 2H 9S 7H 7C TC
2S 8S JD KH 7S 6C 6D AD 5D QC
9H 6H 3S 8C 8H AH TC 4H JS TD
2C TS 4D 7H 2D QC 9C 5D TH 7C
6C 8H QC 5D TS JH 5C 5H 9H 4S
2D QC 7H AS JS 8S 2H 4C 4H 8D
JS 6S AC KD 3D 3C 4S 7H TH KC
QH KH 6S QS 5S 4H 3C QD 3S 3H
7H AS KH 8C 4H 9C 5S 3D 6S TS
9C 7C 3H 5S QD 2C 3D AD AC 5H
JH TD 2D 4C TS 3H KH AD 3S 7S
AS 4C 5H 4D 6S KD JC 3C 6H 2D
3H 6S 8C 2D TH 4S AH QH AD 5H
7C 2S 9H 7H KC 5C 6D 5S 3H JC
3C TC 9C 4H QD TD JH 6D 9H 5S
7C 6S 5C 5D 6C 4S 7H 9H 6H AH
AD 2H 7D KC 2C 4C 2S 9S 7H 3S
TH 4C 8S 6S 3S AD KS AS JH TD
5C TD 4S 4D AD 6S 5D TC 9C 7D
8H 3S 4D 4S 5S 6H 5C AC 3H 3D
9H 3C AC 4S QS 8S 9D QH 5H 4D
JC 6C 5H TS AC 9C JD 8C 7C QD
8S 8H 9C JD 2D QC QH 6H 3C 8D
KS JS 2H 6H 5H QH QS 3H 7C 6D
TC 3H 4S 7H QC 2H 3S 8C JS KH
AH 8H 5S 4C 9H JD 3H 7S JC AC
3C 2D 4C 5S 6C 4S QS 3S JD 3D
5H 2D TC AH KS 6D 7H AD 8C 6H
6C 7S 3C JD 7C 8H KS KH AH 6D
AH 7D 3H 8H 8S 7H QS 5H 9D 2D
JD AC 4H 7S 8S 9S KS AS 9D QH
7S 2C 8S 5S JH QS JC AH KD 4C
AH 2S 9H 4H 8D TS TD 6H QH JD
4H JC 3H QS 6D 7S 9C 8S 9D 8D
5H TD 4S 9S 4C 8C 8D 7H 3H 3D
QS KH 3S 2C 2S 3C 7S TD 4S QD
7C TD 4D 5S KH AC AS 7H 4C 6C
2S 5H 6D JD 9H QS 8S 2C 2H TD
2S TS 6H 9H 7S 4H JC 4C 5D 5S
2C 5H 7D 4H 3S QH JC JS 6D 8H
4C QH 7C QD 3S AD TH 8S 5S TS
9H TC 2S TD JC 7D 3S 3D TH QH
7D 4C 8S 5C JH 8H 6S 3S KC 3H
JC 3H KH TC QH TH 6H 2C AC 5H
QS 2H 9D 2C AS 6S 6C 2S 8C 8S
9H 7D QC TH 4H KD QS AC 7S 3C
4D JH 6S 5S 8H KS 9S QC 3S AS
JD 2D 6S 7S TC 9H KC 3H 7D KD
2H KH 7C 4D 4S 3H JS QD 7D KC
4C JC AS 9D 3C JS 6C 8H QD 4D
AH JS 3S 6C 4C 3D JH 6D 9C 9H
9H 2D 8C 7H 5S KS 6H 9C 2S TC
6C 8C AD 7H 6H 3D KH AS 5D TH
KS 8C 3S TS 8S 4D 5S 9S 6C 4H
9H 4S 4H 5C 7D KC 2D 2H 9D JH
5C JS TC 9D 9H 5H 7S KH JC 6S
7C 9H 8H 4D JC KH JD 2H TD TC
8H 6C 2H 2C KH 6H 9D QS QH 5H
AC 7D 2S 3D QD JC 2D 8D JD JH
2H JC 2D 7H 2C 3C 8D KD TD 4H
3S 4H 6D 8D TS 3H TD 3D 6H TH
JH JC 3S AC QH 9H 7H 8S QC 2C
7H TD QS 4S 8S 9C 2S 5D 4D 2H
3D TS 3H 2S QC 8H 6H KC JC KS
5D JD 7D TC 8C 6C 9S 3D 8D AC
8H 6H JH 6C 5D 8D 8S 4H AD 2C
9D 4H 2D 2C 3S TS AS TC 3C 5D
4D TH 5H KS QS 6C 4S 2H 3D AD
5C KC 6H 2C 5S 3C 4D 2D 9H 9S
JD 4C 3H TH QH 9H 5S AH 8S AC
7D 9S 6S 2H TD 9C 4H 8H QS 4C
3C 6H 5D 4H 8C 9C KC 6S QD QS
3S 9H KD TC 2D JS 8C 6S 4H 4S
2S 4C 8S QS 6H KH 3H TH 8C 5D
2C KH 5S 3S 7S 7H 6C 9D QD 8D
8H KS AC 2D KH TS 6C JS KC 7H
9C KS 5C TD QC AH 6C 5H 9S 7C
5D 4D 3H 4H 6S 7C 7S AH QD TD
2H 7D QC 6S TC TS AH 7S 9D 3H
TH 5H QD 9S KS 7S 7C 6H 8C TD
TH 2D 4D QC 5C 7D JD AH 9C 4H
4H 3H AH 8D 6H QC QH 9H 2H 2C
2D AD 4C TS 6H 7S TH 4H QS TD
3C KD 2H 3H QS JD TC QC 5D 8H
KS JC QD TH 9S KD 8D 8C 2D 9C
3C QD KD 6D 4D 8D AH AD QC 8S
8H 3S 9D 2S 3H KS 6H 4C 7C KC
TH 9S 5C 3D 7D 6H AC 7S 4D 2C
5C 3D JD 4D 2D 6D 5H 9H 4C KH
AS 7H TD 6C 2H 3D QD KS 4C 4S
JC 3C AC 7C JD JS 8H 9S QC 5D
JD 6S 5S 2H AS 8C 7D 5H JH 3D
8D TC 5S 9S 8S 3H JC 5H 7S AS
5C TD 3D 7D 4H 8D 7H 4D 5D JS
QS 9C KS TD 2S 8S 5C 2H 4H AS
TH 7S 4H 7D 3H JD KD 5D 2S KC
JD 7H 4S 8H 4C JS 6H QH 5S 4H
2C QS 8C 5S 3H QC 2S 6C QD AD
8C 3D JD TC 4H 2H AD 5S AC 2S
5D 2C JS 2D AD 9D 3D 4C 4S JH
8D 5H 5D 6H 7S 4D KS 9D TD JD
3D 6D 9C 2S AS 7D 5S 5C 8H JD
7C 8S 3S 6S 5H JD TC AD 7H 7S
2S 9D TS 4D AC 8D 6C QD JD 3H
9S KH 2C 3C AC 3D 5H 6H 8D 5D
KS 3D 2D 6S AS 4C 2S 7C 7H KH
AC 2H 3S JC 5C QH 4D 2D 5H 7S
TS AS JD 8C 6H JC 8S 5S 2C 5D
7S QH 7H 6C QC 8H 2D 7C JD 2S
2C QD 2S 2H JC 9C 5D 2D JD JH
7C 5C 9C 8S 7D 6D 8D 6C 9S JH
2C AD 6S 5H 3S KS 7S 9D KH 4C
7H 6C 2C 5C TH 9D 8D 3S QC AH
5S KC 6H TC 5H 8S TH 6D 3C AH
9C KD 4H AD TD 9S 4S 7D 6H 5D
7H 5C 5H 6D AS 4C KD KH 4H 9D
3C 2S 5C 6C JD QS 2H 9D 7D 3H
AC 2S 6S 7S JS QD 5C QS 6H AD
5H TH QC 7H TC 3S 7C 6D KC 3D
4H 3D QC 9S 8H 2C 3S JC KS 5C
4S 6S 2C 6H 8S 3S 3D 9H 3H JS
4S 8C 4D 2D 8H 9H 7D 9D AH TS
9S 2C 9H 4C 8D AS 7D 3D 6D 5S
6S 4C 7H 8C 3H 5H JC AH 9D 9C
2S 7C 5S JD 8C 3S 3D 4D 7D 6S
3C KC 4S 5D 7D 3D JD 7H 3H 4H
9C 9H 4H 4D TH 6D QD 8S 9S 7S
2H AC 8S 4S AD 8C 2C AH 7D TC
TS 9H 3C AD KS TC 3D 8C 8H JD
QC 8D 2C 3C 7D 7C JD 9H 9C 6C
AH 6S JS JH 5D AS QC 2C JD TD
9H KD 2H 5D 2D 3S 7D TC AH TS
TD 8H AS 5D AH QC AC 6S TC 5H
KS 4S 7H 4D 8D 9C TC 2H 6H 3H
3H KD 4S QD QH 3D 8H 8C TD 7S
8S JD TC AH JS QS 2D KH KS 4D
3C AD JC KD JS KH 4S TH 9H 2C
QC 5S JS 9S KS AS 7C QD 2S JD
KC 5S QS 3S 2D AC 5D 9H 8H KS
6H 9C TC AD 2C 6D 5S JD 6C 7C
QS KH TD QD 2C 3H 8S 2S QC AH
9D 9H JH TC QH 3C 2S JS 5C 7H
6C 3S 3D 2S 4S QD 2D TH 5D 2C
2D 6H 6D 2S JC QH AS 7H 4H KH
5H 6S KS AD TC TS 7C AC 4S 4H
AD 3C 4H QS 8C 9D KS 2H 2D 4D
4S 9D 6C 6D 9C AC 8D 3H 7H KD
JC AH 6C TS JD 6D AD 3S 5D QD
JC JH JD 3S 7S 8S JS QC 3H 4S
JD TH 5C 2C AD JS 7H 9S 2H 7S
8D 3S JH 4D QC AS JD 2C KC 6H
2C AC 5H KD 5S 7H QD JH AH 2D
JC QH 8D 8S TC 5H 5C AH 8C 6C
3H JS 8S QD JH 3C 4H 6D 5C 3S
6D 4S 4C AH 5H 5S 3H JD 7C 8D
8H AH 2H 3H JS 3C 7D QC 4H KD
6S 2H KD 5H 8H 2D 3C 8S 7S QD
2S 7S KC QC AH TC QS 6D 4C 8D
5S 9H 2C 3S QD 7S 6C 2H 7C 9D
3C 6C 5C 5S JD JC KS 3S 5D TS
7C KS 6S 5S 2S 2D TC 2H 5H QS
AS 7H 6S TS 5H 9S 9D 3C KD 2H
4S JS QS 3S 4H 7C 2S AC 6S 9D
8C JH 2H 5H 7C 5D QH QS KH QC
3S TD 3H 7C KC 8D 5H 8S KH 8C
4H KH JD TS 3C 7H AS QC JS 5S
AH 9D 2C 8D 4D 2D 6H 6C KC 6S
2S 6H 9D 3S 7H 4D KH 8H KD 3D
9C TC AC JH KH 4D JD 5H TD 3S
7S 4H 9D AS 4C 7D QS 9S 2S KH
3S 8D 8S KS 8C JC 5C KH 2H 5D
8S QH 2C 4D KC JS QC 9D AC 6H
8S 8C 7C JS JD 6S 4C 9C AC 4S
QH 5D 2C 7D JC 8S 2D JS JH 4C
JS 4C 7S TS JH KC KH 5H QD 4S
QD 8C 8D 2D 6S TD 9D AC QH 5S
QH QC JS 3D 3C 5C 4H KH 8S 7H
7C 2C 5S JC 8S 3H QC 5D 2H KC
5S 8D KD 6H 4H QD QH 6D AH 3D
7S KS 6C 2S 4D AC QS 5H TS JD
7C 2D TC 5D QS AC JS QC 6C KC
2C KS 4D 3H TS 8S AD 4H 7S 9S
QD 9H QH 5H 4H 4D KH 3S JC AD
4D AC KC 8D 6D 4C 2D KH 2C JD
2C 9H 2D AH 3H 6D 9C 7D TC KS
8C 3H KD 7C 5C 2S 4S 5H AS AH
TH JD 4H KD 3H TC 5C 3S AC KH
6D 7H AH 7S QC 6H 2D TD JD AS
JH 5D 7H TC 9S 7D JC AS 5S KH
2H 8C AD TH 6H QD KD 9H 6S 6C
QH KC 9D 4D 3S JS JH 4H 2C 9H
TC 7H KH 4H JC 7D 9S 3H QS 7S
AD 7D JH 6C 7H 4H 3S 3H 4D QH
JD 2H 5C AS 6C QC 4D 3C TC JH
AC JD 3H 6H 4C JC AD 7D 7H 9H
4H TC TS 2C 8C 6S KS 2H JD 9S
4C 3H QS QC 9S 9H 6D KC 9D 9C
5C AD 8C 2C QH TH QD JC 8D 8H
QC 2C 2S QD 9C 4D 3S 8D JH QS
9D 3S 2C 7S 7C JC TD 3C TC 9H
3C TS 8H 5C 4C 2C 6S 8D 7C 4H
KS 7H 2H TC 4H 2C 3S AS AH QS
8C 2D 2H 2C 4S 4C 6S 7D 5S 3S
TH QC 5D TD 3C QS KD KC KS AS
4D AH KD 9H KS 5C 4C 6H JC 7S
KC 4H 5C QS TC 2H JC 9S AH QH
4S 9H 3H 5H 3C QD 2H QC JH 8H
5D AS 7H 2C 3D JH 6H 4C 6S 7D
9C JD 9H AH JS 8S QH 3H KS 8H
3S AC QC TS 4D AD 3D AH 8S 9H
7H 3H QS 9C 9S 5H JH JS AH AC
8D 3C JD 2H AC 9C 7H 5S 4D 8H
7C JH 9H 6C JS 9S 7H 8C 9D 4H
2D AS 9S 6H 4D JS JH 9H AD QD
6H 7S JH KH AH 7H TD 5S 6S 2C
8H JH 6S 5H 5S 9D TC 4C QC 9S
7D 2C KD 3H 5H AS QD 7H JS 4D
TS QH 6C 8H TH 5H 3C 3H 9C 9D
AD KH JS 5D 3H AS AC 9S 5C KC
2C KH 8C JC QS 6D AH 2D KC TC
9D 3H 2S 7C 4D 6D KH KS 8D 7D
9H 2S TC JH AC QC 3H 5S 3S 8H
3S AS KD 8H 4C 3H 7C JH QH TS
7S 6D 7H 9D JH 4C 3D 3S 6C AS
4S 2H 2C 4C 8S 5H KC 8C QC QD
3H 3S 6C QS QC 2D 6S 5D 2C 9D
2H 8D JH 2S 3H 2D 6C 5C 7S AD
9H JS 5D QH 8S TS 2H 7S 6S AD
6D QC 9S 7H 5H 5C 7D KC JD 4H
QC 5S 9H 9C 4D 6S KS 2S 4C 7C
9H 7C 4H 8D 3S 6H 5C 8H JS 7S
2D 6H JS TD 4H 4D JC TH 5H KC
AC 7C 8D TH 3H 9S 2D 4C KC 4D
KD QS 9C 7S 3D KS AD TS 4C 4H
QH 9C 8H 2S 7D KS 7H 5D KD 4C
9C 2S 2H JC 6S 6C TC QC JH 5C
7S AC 8H KC 8S 6H QS JC 3D 6S
JS 2D JH 8C 4S 6H 8H 6D 5D AD
6H 7D 2S 4H 9H 7C AS AC 8H 5S
3C JS 4S 6D 5H 2S QH 6S 9C 2C
3D 5S 6S 9S 4C QS 8D QD 8S TC
9C 3D AH 9H 5S 2C 7D AD JC 3S
7H TC AS 3C 6S 6D 7S KH KC 9H
3S TC 8H 6S 5H JH 8C 7D AC 2S
QD 9D 9C 3S JC 8C KS 8H 5D 4D
JS AH JD 6D 9D 8C 9H 9S 8H 3H
2D 6S 4C 4D 8S AD 4S TC AH 9H
TS AC QC TH KC 6D 4H 7S 8C 2H
3C QD JS 9D 5S JC AH 2H TS 9H
3H 4D QH 5D 9C 5H 7D 4S JC 3S
8S TH 3H 7C 2H JD JS TS AC 8D
9C 2H TD KC JD 2S 8C 5S AD 2C
3D KD 7C 5H 4D QH QD TC 6H 7D
7H 2C KC 5S KD 6H AH QC 7S QH
6H 5C AC 5H 2C 9C 2D 7C TD 2S
4D 9D AH 3D 7C JD 4H 8C 4C KS
TH 3C JS QH 8H 4C AS 3D QS QC
4D 7S 5H JH 6D 7D 6H JS KH 3C
QD 8S 7D 2H 2C 7C JC 2S 5H 8C
QH 8S 9D TC 2H AD 7C 8D QD 6S
3S 7C AD 9H 2H 9S JD TS 4C 2D
3S AS 4H QC 2C 8H 8S 7S TD TC
JH TH TD 3S 4D 4H 5S 5D QS 2C
8C QD QH TC 6D 4S 9S 9D 4H QC
8C JS 9D 6H JD 3H AD 6S TD QC
KC 8S 3D 7C TD 7D 8D 9H 4S 3S
6C 4S 3D 9D KD TC KC KS AC 5S
7C 6S QH 3D JS KD 6H 6D 2D 8C
JD 2S 5S 4H 8S AC 2D 6S TS 5C
5H 8C 5S 3C 4S 3D 7C 8D AS 3H
AS TS 7C 3H AD 7D JC QS 6C 6H
3S 9S 4C AC QH 5H 5D 9H TS 4H
6C 5C 7H 7S TD AD JD 5S 2H 2S
7D 6C KC 3S JD 8D 8S TS QS KH
8S QS 8D 6C TH AC AH 2C 8H 9S
7H TD KH QH 8S 3D 4D AH JD AS
TS 3D 2H JC 2S JH KH 6C QC JS
KC TH 2D 6H 7S 2S TC 8C 9D QS
3C 9D 6S KH 8H 6D 5D TH 2C 2H
6H TC 7D AD 4D 8S TS 9H TD 7S
JS 6D JD JC 2H AC 6C 3D KH 8D
KH JD 9S 5D 4H 4C 3H 7S QS 5C
4H JD 5D 3S 3C 4D KH QH QS 7S
JD TS 8S QD AH 4C 6H 3S 5S 2C
QS 3D JD AS 8D TH 7C 6S QC KS
7S 2H 8C QC 7H AC 6D 2D TH KH
5S 6C 7H KH 7D AH 8C 5C 7S 3D
3C KD AD 7D 6C 4D KS 2D 8C 4S
7C 8D 5S 2D 2S AH AD 2C 9D TD
3C AD 4S KS JH 7C 5C 8C 9C TH
AS TD 4D 7C JD 8C QH 3C 5H 9S
3H 9C 8S 9S 6S QD KS AH 5H JH
QC 9C 5S 4H 2H TD 7D AS 8C 9D
8C 2C 9D KD TC 7S 3D KH QC 3C
4D AS 4C QS 5S 9D 6S JD QH KS
6D AH 6C 4C 5H TS 9H 7D 3D 5S
QS JD 7C 8D 9C AC 3S 6S 6C KH
8H JH 5D 9S 6D AS 6S 3S QC 7H
QD AD 5C JH 2H AH 4H AS KC 2C
JH 9C 2C 6H 2D JS 5D 9H KC 6D
7D 9D KD TH 3H AS 6S QC 6H AD
JD 4H 7D KC 3H JS 3C TH 3D QS
4C 3H 8C QD 5H 6H AS 8H AD JD
TH 8S KD 5D QC 7D JS 5S 5H TS
7D KC 9D QS 3H 3C 6D TS 7S AH
7C 4H 7H AH QC AC 4D 5D 6D TH
3C 4H 2S KD 8H 5H JH TC 6C JD
4S 8C 3D 4H JS TD 7S JH QS KD
7C QC KD 4D 7H 6S AD TD TC KH
5H 9H KC 3H 4D 3D AD 6S QD 6H
TH 7C 6H TS QH 5S 2C KC TD 6S
7C 4D 5S JD JH 7D AC KD KH 4H
7D 6C 8D 8H 5C JH 8S QD TH JD
8D 7D 6C 7C 9D KD AS 5C QH JH
9S 2C 8C 3C 4C KS JH 2D 8D 4H
7S 6C JH KH 8H 3H 9D 2D AH 6D
4D TC 9C 8D 7H TD KS TH KD 3C
JD 9H 8D QD AS KD 9D 2C 2S 9C
8D 3H 5C 7H KS 5H QH 2D 8C 9H
2D TH 6D QD 6C KC 3H 3S AD 4C
4H 3H JS 9D 3C TC 5H QH QC JC
3D 5C 6H 3S 3C JC 5S 7S 2S QH
AC 5C 8C 4D 5D 4H 2S QD 3C 3H
2C TD AH 9C KD JS 6S QD 4C QC
QS 8C 3S 4H TC JS 3H 7C JC AD
5H 4D 9C KS JC TD 9S TS 8S 9H
QD TS 7D AS AC 2C TD 6H 8H AH
6S AD 8C 4S 9H 8D 9D KH 8S 3C
QS 4D 2D 7S KH JS JC AD 4C 3C
QS 9S 7H KC TD TH 5H JS AC JH
6D AC 2S QS 7C AS KS 6S KH 5S
6D 8H KH 3C QS 2H 5C 9C 9D 6C
JS 2C 4C 6H 7D JC AC QD TD 3H
4H QC 8H JD 4C KD KS 5C KC 7S
6D 2D 3H 2S QD 5S 7H AS TH 6S
AS 6D 8D 2C 8S TD 8H QD JC AH
9C 9H 2D TD QH 2H 5C TC 3D 8H
KC 8S 3D KH 2S TS TC 6S 4D JH
9H 9D QS AC KC 6H 5D 4D 8D AH
9S 5C QS 4H 7C 7D 2H 8S AD JS
3D AC 9S AS 2C 2D 2H 3H JC KH
7H QH KH JD TC KS 5S 8H 4C 8D
2H 7H 3S 2S 5H QS 3C AS 9H KD
AD 3D JD 6H 5S 9C 6D AC 9S 3S
3D 5D 9C 2D AC 4S 2S AD 6C 6S
QC 4C 2D 3H 6S KC QH QD 2H JH
QC 3C 8S 4D 9S 2H 5C 8H QS QD
6D KD 6S 7H 3S KH 2H 5C JC 6C
3S 9S TC 6S 8H 2D AD 7S 8S TS
3C 6H 9C 3H 5C JC 8H QH TD QD
3C JS QD 5D TD 2C KH 9H TH AS
9S TC JD 3D 5C 5H AD QH 9H KC
TC 7H 4H 8H 3H TD 6S AC 7C 2S
QS 9D 5D 3C JC KS 4D 6C JH 2S
9S 6S 3C 7H TS 4C KD 6D 3D 9C
2D 9H AH AC 7H 2S JH 3S 7C QC
QD 9H 3C 2H AC AS 8S KD 8C KH
2D 7S TD TH 6D JD 8D 4D 2H 5S
8S QH KD JD QS JH 4D KC 5H 3S
3C KH QC 6D 8H 3S AH 7D TD 2D
5S 9H QH 4S 6S 6C 6D TS TH 7S
6C 4C 6D QS JS 9C TS 3H 8D 8S
JS 5C 7S AS 2C AH 2H AD 5S TC
KD 6C 9C 9D TS 2S JC 4H 2C QD
QS 9H TC 3H KC KS 4H 3C AD TH
KH 9C 2H KD 9D TC 7S KC JH 2D
7C 3S KC AS 8C 5D 9C 9S QH 3H
2D 8C TD 4C 2H QC 5D TC 2C 7D
KS 4D 6C QH TD KH 5D 7C AD 8D
2S 9S 8S 4C 8C 3D 6H QD 7C 7H
6C 8S QH 5H TS 5C 3C 4S 2S 2H
8S 6S 2H JC 3S 3H 9D 8C 2S 7H
QC 2C 8H 9C AC JD 4C 4H 6S 3S
3H 3S 7D 4C 9S 5H 8H JC 3D TC
QH 2S 2D 9S KD QD 9H AD 6D 9C
8D 2D KS 9S JC 4C JD KC 4S TH
KH TS 6D 4D 5C KD 5H AS 9H AD
QD JS 7C 6D 5D 5C TH 5H QH QS
9D QH KH 5H JH 4C 4D TC TH 6C
KH AS TS 9D KD 9C 7S 4D 8H 5S
KH AS 2S 7D 9D 4C TS TH AH 7C
KS 4D AC 8S 9S 8D TH QH 9D 5C
5D 5C 8C QS TC 4C 3D 3S 2C 8D
9D KS 2D 3C KC 4S 8C KH 6C JC
8H AH 6H 7D 7S QD 3C 4C 6C KC
3H 2C QH 8H AS 7D 4C 8C 4H KC
QD 5S 4H 2C TD AH JH QH 4C 8S
3H QS 5S JS 8H 2S 9H 9C 3S 2C
6H TS 7S JC QD AC TD KC 5S 3H
QH AS QS 7D JC KC 2C 4C 5C 5S
QH 3D AS JS 4H 8D 7H JC 2S 9C
5D 4D 2S 4S 9D 9C 2D QS 8H 7H
6D 7H 3H JS TS AC 2D JH 7C 8S
JH 5H KC 3C TC 5S 9H 4C 8H 9D
8S KC 5H 9H AD KS 9D KH 8D AH
JC 2H 9H KS 6S 3H QC 5H AH 9C
5C KH 5S AD 6C JC 9H QC 9C TD
5S 5D JC QH 2D KS 8H QS 2H TS
JH 5H 5S AH 7H 3C 8S AS TD KH
6H 3D JD 2C 4C KC 7S AH 6C JH
4C KS 9D AD 7S KC 7D 8H 3S 9C
7H 5C 5H 3C 8H QC 3D KH 6D JC
2D 4H 5D 7D QC AD AH 9H QH 8H
KD 8C JS 9D 3S 3C 2H 5D 6D 2S
8S 6S TS 3C 6H 8D 5S 3H TD 6C
KS 3D JH 9C 7C 9S QS 5S 4H 6H
7S 6S TH 4S KC KD 3S JC JH KS
7C 3C 2S 6D QH 2C 7S 5H 8H AH
KC 8D QD 6D KH 5C 7H 9D 3D 9C
6H 2D 8S JS 9S 2S 6D KC 7C TC
KD 9C JH 7H KC 8S 2S 7S 3D 6H
4H 9H 2D 4C 8H 7H 5S 8S 2H 8D
AD 7C 3C 7S 5S 4D 9H 3D JC KH
5D AS 7D 6D 9C JC 4C QH QS KH
KD JD 7D 3D QS QC 8S 6D JS QD
6S 8C 5S QH TH 9H AS AC 2C JD
QC KS QH 7S 3C 4C 5C KC 5D AH
6C 4H 9D AH 2C 3H KD 3D TS 5C
TD 8S QS AS JS 3H KD AC 4H KS
7D 5D TS 9H 4H 4C 9C 2H 8C QC
2C 7D 9H 4D KS 4C QH AD KD JS
QD AD AH KH 9D JS 9H JC KD JD
8S 3C 4S TS 7S 4D 5C 2S 6H 7C
JS 7S 5C KD 6D QH 8S TD 2H 6S
QH 6C TC 6H TD 4C 9D 2H QC 8H
3D TS 4D 2H 6H 6S 2C 7H 8S 6C
9H 9D JD JH 3S AH 2C 6S 3H 8S
2C QS 8C 5S 3H 2S 7D 3C AD 4S
5C QC QH AS TS 4S 6S 4C 5H JS
JH 5C TD 4C 6H JS KD KH QS 4H
TC KH JC 4D 9H 9D 8D KC 3C 8H
2H TC 8S AD 9S 4H TS 7H 2C 5C
4H 2S 6C 5S KS AH 9C 7C 8H KD
TS QH TD QS 3C JH AH 2C 8D 7D
5D KC 3H 5S AC 4S 7H QS 4C 2H
3D 7D QC KH JH 6D 6C TD TH KD
5S 8D TH 6C 9D 7D KH 8C 9S 6D
JD QS 7S QC 2S QH JC 4S KS 8D
7S 5S 9S JD KD 9C JC AD 2D 7C
4S 5H AH JH 9C 5D TD 7C 2D 6S
KC 6C 7H 6S 9C QD 5S 4H KS TD
6S 8D KS 2D TH TD 9H JD TS 3S
KH JS 4H 5D 9D TC TD QC JD TS
QS QD AC AD 4C 6S 2D AS 3H KC
4C 7C 3C TD QS 9C KC AS 8D AD
KC 7H QC 6D 8H 6S 5S AH 7S 8C
3S AD 9H JC 6D JD AS KH 6S JH
AD 3D TS KS 7H JH 2D JS QD AC
9C JD 7C 6D TC 6H 6C JC 3D 3S
QC KC 3S JC KD 2C 8D AH QS TS
AS KD 3D JD 8H 7C 8C 5C QD 6C
| 8C TS KC 9H 4S 7D 2S 5D 3S AC
5C AD 5D AC 9C 7C 5H 8D TD KS
3H 7H 6S KC JS QH TD JC 2D 8S
TH 8H 5C QS TC 9H 4D JC KS JS
7C 5H KC QH JD AS KH 4C AD 4S
5H KS 9C 7D 9H 8D 3S 5D 5C AH
6H 4H 5C 3H 2H 3S QH 5S 6S AS
TD 8C 4H 7C TC KC 4C 3H 7S KS
7C 9C 6D KD 3H 4C QS QC AC KH
JC 6S 5H 2H 2D KD 9D 7C AS JS
AD QH TH 9D 8H TS 6D 3S AS AC
2H 4S 5C 5S TC KC JD 6C TS 3C
QD AS 6H JS 2C 3D 9H KC 4H 8S
KD 8S 9S 7C 2S 3S 6D 6S 4H KC
3C 8C 2D 7D 4D 9S 4S QH 4H JD
8C KC 7S TC 2D TS 8H QD AC 5C
3D KH QD 6C 6S AD AS 8H 2H QS
6S 8D 4C 8S 6C QH TC 6D 7D 9D
2S 8D 8C 4C TS 9S 9D 9C AC 3D
3C QS 2S 4H JH 3D 2D TD 8S 9H
5H QS 8S 6D 3C 8C JD AS 7H 7D
6H TD 9D AS JH 6C QC 9S KD JC
AH 8S QS 4D TH AC TS 3C 3D 5C
5S 4D JS 3D 8H 6C TS 3S AD 8C
6D 7C 5D 5H 3S 5C JC 2H 5S 3D
5H 6H 2S KS 3D 5D JD 7H JS 8H
KH 4H AS JS QS QC TC 6D 7C KS
3D QS TS 2H JS 4D AS 9S JC KD
QD 5H 4D 5D KH 7H 3D JS KD 4H
2C 9H 6H 5C 9D 6C JC 2D TH 9S
7D 6D AS QD JH 4D JS 7C QS 5C
3H KH QD AD 8C 8H 3S TH 9D 5S
AH 9S 4D 9D 8S 4H JS 3C TC 8D
2C KS 5H QD 3S TS 9H AH AD 8S
5C 7H 5D KD 9H 4D 3D 2D KS AD
KS KC 9S 6D 2C QH 9D 9H TS TC
9C 6H 5D QH 4D AD 6D QC JS KH
9S 3H 9D JD 5C 4D 9H AS TC QH
2C 6D JC 9C 3C AD 9S KH 9D 7D
KC 9C 7C JC JS KD 3H AS 3C 7D
QD KH QS 2C 3S 8S 8H 9H 9C JC
QH 8D 3C KC 4C 4H 6D AD 9H 9D
3S KS QS 7H KH 7D 5H 5D JD AD
2H 2C 6H TH TC 7D 8D 4H 8C AS
4S 2H AC QC 3S 6D TH 4D 4C KH
4D TC KS AS 7C 3C 6D 2D 9H 6C
8C TD 5D QS 2C 7H 4C 9C 3H 9H
5H JH TS 7S TD 6H AD QD 8H 8S
5S AD 9C 8C 7C 8D 5H 9D 8S 2S
4H KH KS 9S 2S KC 5S AD 4S 7D
QS 9C QD 6H JS 5D AC 8D 2S AS
KH AC JC 3S 9D 9S 3C 9C 5S JS
AD 3C 3D KS 3S 5C 9C 8C TS 4S
JH 8D 5D 6H KD QS QD 3D 6C KC
8S JD 6C 3S 8C TC QC 3C QH JS
KC JC 8H 2S 9H 9C JH 8S 8C 9S
8S 2H QH 4D QC 9D KC AS TH 3C
8S 6H TH 7C 2H 6S 3C 3H AS 7S
QH 5S JS 4H 5H TS 8H AH AC JC
9D 8H 2S 4S TC JC 3C 7H 3H 5C
3D AD 3C 3S 4C QC AS 5D TH 8C
6S 9D 4C JS KH AH TS JD 8H AD
4C 6S 9D 7S AC 4D 3D 3S TC JD
AD 7H 6H 4H JH KC TD TS 7D 6S
8H JH TC 3S 8D 8C 9S 2C 5C 4D
2C 9D KC QH TH QS JC 9C 4H TS
QS 3C QD 8H KH 4H 8D TD 8S AC
7C 3C TH 5S 8H 8C 9C JD TC KD
QC TC JD TS 8C 3H 6H KD 7C TD
JH QS KS 9C 6D 6S AS 9H KH 6H
2H 4D AH 2D JH 6H TD 5D 4H JD
KD 8C 9S JH QD JS 2C QS 5C 7C
4S TC 7H 8D 2S 6H 7S 9C 7C KC
8C 5D 7H 4S TD QC 8S JS 4H KS
AD 8S JH 6D TD KD 7C 6C 2D 7D
JC 6H 6S JS 4H QH 9H AH 4C 3C
6H 5H AS 7C 7S 3D KH KC 5D 5C
JC 3D TD AS 4D 6D 6S QH JD KS
8C 7S 8S QH 2S JD 5C 7H AH QD
8S 3C 6H 6C 2C 8D TD 7D 4C 4D
5D QH KH 7C 2S 7H JS 6D QC QD
AD 6C 6S 7D TH 6H 2H 8H KH 4H
KS JS KD 5D 2D KH 7D 9C 8C 3D
9C 6D QD 3C KS 3S 7S AH JD 2D
AH QH AS JC 8S 8H 4C KC TH 7D
JC 5H TD 7C 5D KD 4C AD 8H JS
KC 2H AC AH 7D JH KH 5D 7S 6D
9S 5S 9C 6H 8S TD JD 9H 6C AC
7D 8S 6D TS KD 7H AC 5S 7C 5D
AH QC JC 4C TC 8C 2H TS 2C 7D
KD KC 6S 3D 7D 2S 8S 3H 5S 5C
8S 5D 8H 4C 6H KC 3H 7C 5S KD
JH 8C 3D 3C 6C KC TD 7H 7C 4C
JC KC 6H TS QS TD KS 8H 8C 9S
6C 5S 9C QH 7D AH KS KC 9S 2C
4D 4S 8H TD 9C 3S 7D 9D AS TH
6S 7D 3C 6H 5D KD 2C 5C 9D 9C
2H KC 3D AD 3H QD QS 8D JC 4S
8C 3H 9C 7C AD 5D JC 9D JS AS
5D 9H 5C 7H 6S 6C QC JC QD 9S
JC QS JH 2C 6S 9C QC 3D 4S TC
4H 5S 8D 3D 4D 2S KC 2H JS 2C
TD 3S TH KD 4D 7H JH JS KS AC
7S 8C 9S 2D 8S 7D 5C AD 9D AS
8C 7H 2S 6C TH 3H 4C 3S 8H AC
KD 5H JC 8H JD 2D 4H TD JH 5C
3D AS QH KS 7H JD 8S 5S 6D 5H
9S 6S TC QS JC 5C 5D 9C TH 8C
5H 3S JH 9H 2S 2C 6S 7S AS KS
8C QD JC QS TC QC 4H AC KH 6C
TC 5H 7D JH 4H 2H 8D JC KS 4D
5S 9C KH KD 9H 5C TS 3D 7D 2D
5H AS TC 4D 8C 2C TS 9D 3H 8D
6H 8D 2D 9H JD 6C 4S 5H 5S 6D
AD 9C JC 7D 6H 9S 6D JS 9H 3C
AD JH TC QS 4C 5D 9S 7C 9C AH
KD 6H 2H TH 8S QD KS 9D 9H AS
4H 8H 8D 5H 6C AH 5S AS AD 8S
QS 5D 4S 2H TD KS 5H AC 3H JC
9C 7D QD KD AC 6D 5H QH 6H 5S
KC AH QH 2H 7D QS 3H KS 7S JD
6C 8S 3H 6D KS QD 5D 5C 8H TC
9H 4D 4S 6S 9D KH QC 4H 6C JD
TD 2D QH 4S 6H JH KD 3C QD 8C
4S 6H 7C QD 9D AS AH 6S AD 3C
2C KC TH 6H 8D AH 5C 6D 8S 5D
TD TS 7C AD JC QD 9H 3C KC 7H
5D 4D 5S 8H 4H 7D 3H JD KD 2D
JH TD 6H QS 4S KD 5C 8S 7D 8H
AC 3D AS 8C TD 7H KH 5D 6C JD
9D KS 7C 6D QH TC JD KD AS KC
JH 8S 5S 7S 7D AS 2D 3D AD 2H
2H 5D AS 3C QD KC 6H 9H 9S 2C
9D 5D TH 4C JH 3H 8D TC 8H 9H
6H KD 2C TD 2H 6C 9D 2D JS 8C
KD 7S 3C 7C AS QH TS AD 8C 2S
QS 8H 6C JS 4C 9S QC AD TD TS
2H 7C TS TC 8C 3C 9H 2D 6D JC
TC 2H 8D JH KS 6D 3H TD TH 8H
9D TD 9H QC 5D 6C 8H 8C KC TS
2H 8C 3D AH 4D TH TC 7D 8H KC
TS 5C 2D 8C 6S KH AH 5H 6H KC
5S 5D AH TC 4C JD 8D 6H 8C 6C
KC QD 3D 8H 2D JC 9H 4H AD 2S
TD 6S 7D JS KD 4H QS 2S 3S 8C
4C 9H JH TS 3S 4H QC 5S 9S 9C
2C KD 9H JS 9S 3H JC TS 5D AC
AS 2H 5D AD 5H JC 7S TD JS 4C
2D 4S 8H 3D 7D 2C AD KD 9C TS
7H QD JH 5H JS AC 3D TH 4C 8H
6D KH KC QD 5C AD 7C 2D 4H AC
3D 9D TC 8S QD 2C JC 4H JD AH
6C TD 5S TC 8S AH 2C 5D AS AC
TH 7S 3D AS 6C 4C 7H 7D 4H AH
5C 2H KS 6H 7S 4H 5H 3D 3C 7H
3C 9S AC 7S QH 2H 3D 6S 3S 3H
2D 3H AS 2C 6H TC JS 6S 9C 6C
QH KD QD 6D AC 6H KH 2C TS 8C
8H 7D 3S 9H 5D 3H 4S QC 9S 5H
2D 9D 7H 6H 3C 8S 5H 4D 3S 4S
KD 9S 4S TC 7S QC 3S 8S 2H 7H
TC 3D 8C 3H 6C 2H 6H KS KD 4D
KC 3D 9S 3H JS 4S 8H 2D 6C 8S
6H QS 6C TC QD 9H 7D 7C 5H 4D
TD 9D 8D 6S 6C TC 5D TS JS 8H
4H KC JD 9H TC 2C 6S 5H 8H AS
JS 9C 5C 6S 9D JD 8H KC 4C 6D
4D 8D 8S 6C 7C 6H 7H 8H 5C KC
TC 3D JC 6D KS 9S 6H 7S 9C 2C
6C 3S KD 5H TS 7D 9H 9S 6H KH
3D QD 4C 6H TS AC 3S 5C 2H KD
4C AS JS 9S 7C TS 7H 9H JC KS
4H 8C JD 3H 6H AD 9S 4S 5S KS
4C 2C 7D 3D AS 9C 2S QS KC 6C
8S 5H 3D 2S AC 9D 6S 3S 4D TD
QD TH 7S TS 3D AC 7H 6C 5D QC
TC QD AD 9C QS 5C 8D KD 3D 3C
9D 8H AS 3S 7C 8S JD 2D 8D KC
4C TH AC QH JS 8D 7D 7S 9C KH
9D 8D 4C JH 2C 2S QD KD TS 4H
4D 6D 5D 2D JH 3S 8S 3H TC KH
AD 4D 2C QS 8C KD JH JD AH 5C
5C 6C 5H 2H JH 4H KS 7C TC 3H
3C 4C QC 5D JH 9C QD KH 8D TC
3H 9C JS 7H QH AS 7C 9H 5H JC
2D 5S QD 4S 3C KC 6S 6C 5C 4C
5D KH 2D TS 8S 9C AS 9S 7C 4C
7C AH 8C 8D 5S KD QH QS JH 2C
8C 9D AH 2H AC QC 5S 8H 7H 2C
QD 9H 5S QS QC 9C 5H JC TH 4H
6C 6S 3H 5H 3S 6H KS 8D AC 7S
AC QH 7H 8C 4S KC 6C 3D 3S TC
9D 3D JS TH AC 5H 3H 8S 3S TC
QD KH JS KS 9S QC 8D AH 3C AC
5H 6C KH 3S 9S JH 2D QD AS 8C
6C 4D 7S 7H 5S JC 6S 9H 4H JH
AH 5S 6H 9S AD 3S TH 2H 9D 8C
4C 8D 9H 7C QC AD 4S 9C KC 5S
9D 6H 4D TC 4C JH 2S 5D 3S AS
2H 6C 7C KH 5C AD QS TH JD 8S
3S 4S 7S AH AS KC JS 2S AD TH
JS KC 2S 7D 8C 5C 9C TS 5H 9D
7S 9S 4D TD JH JS KH 6H 5D 2C
JD JS JC TH 2D 3D QD 8C AC 5H
7S KH 5S 9D 5D TD 4S 6H 3C 2D
4S 5D AC 8D 4D 7C AD AS AH 9C
6S TH TS KS 2C QC AH AS 3C 4S
2H 8C 3S JC 5C 7C 3H 3C KH JH
7S 3H JC 5S 6H 4C 2S 4D KC 7H
4D 7C 4H 9S 8S 6S AD TC 6C JC
KH QS 3S TC 4C 8H 8S AC 3C TS
QD QS TH 3C TS 7H 7D AH TD JC
TD JD QC 4D 9S 7S TS AD 7D AC
AH 7H 4S 6D 7C 2H 9D KS JC TD
7C AH JD 4H 6D QS TS 2H 2C 5C
TC KC 8C 9S 4C JS 3C JC 6S AH
AS 7D QC 3D 5S JC JD 9D TD KH
TH 3C 2S 6H AH AC 5H 5C 7S 8H
QC 2D AC QD 2S 3S JD QS 6S 8H
KC 4H 3C 9D JS 6H 3S 8S AS 8C
7H KC 7D JD 2H JC QH 5S 3H QS
9H TD 3S 8H 7S AC 5C 6C AH 7C
8D 9H AH JD TD QS 7D 3S 9C 8S
AH QH 3C JD KC 4S 5S 5D TD KS
9H 7H 6S JH TH 4C 7C AD 5C 2D
7C KD 5S TC 9D 6S 6C 5D 2S TH
KC 9H 8D 5H 7H 4H QC 3D 7C AS
6S 8S QC TD 4S 5C TH QS QD 2S
8S 5H TH QC 9H 6S KC 7D 7C 5C
7H KD AH 4D KH 5C 4S 2D KC QH
6S 2C TD JC AS 4D 6C 8C 4H 5S
JC TC JD 5S 6S 8D AS 9D AD 3S
6D 6H 5D 5S TC 3D 7D QS 9D QD
4S 6C 8S 3S 7S AD KS 2D 7D 7C
KC QH JC AC QD 5D 8D QS 7H 7D
JS AH 8S 5H 3D TD 3H 4S 6C JH
4S QS 7D AS 9H JS KS 6D TC 5C
2D 5C 6H TC 4D QH 3D 9H 8S 6C
6D 7H TC TH 5S JD 5C 9C KS KD
8D TD QH 6S 4S 6C 8S KC 5C TC
5S 3D KS AC 4S 7D QD 4C TH 2S
TS 8H 9S 6S 7S QH 3C AH 7H 8C
4C 8C TS JS QC 3D 7D 5D 7S JH
8S 7S 9D QC AC 7C 6D 2H JH KC
JS KD 3C 6S 4S 7C AH QC KS 5H
KS 6S 4H JD QS TC 8H KC 6H AS
KH 7C TC 6S TD JC 5C 7D AH 3S
3H 4C 4H TC TH 6S 7H 6D 9C QH
7D 5H 4S 8C JS 4D 3D 8S QH KC
3H 6S AD 7H 3S QC 8S 4S 7S JS
3S JD KH TH 6H QS 9C 6C 2D QD
4S QH 4D 5H KC 7D 6D 8D TH 5S
TD AD 6S 7H KD KH 9H 5S KC JC
3H QC AS TS 4S QD KS 9C 7S KC
TS 6S QC 6C TH TC 9D 5C 5D KD
JS 3S 4H KD 4C QD 6D 9S JC 9D
8S JS 6D 4H JH 6H 6S 6C KS KH
AC 7D 5D TC 9S KH 6S QD 6H AS
AS 7H 6D QH 8D TH 2S KH 5C 5H
4C 7C 3D QC TC 4S KH 8C 2D JS
6H 5D 7S 5H 9C 9H JH 8S TH 7H
AS JS 2S QD KH 8H 4S AC 8D 8S
3H 4C TD KD 8C JC 5C QS 2D JD
TS 7D 5D 6C 2C QS 2H 3C AH KS
4S 7C 9C 7D JH 6C 5C 8H 9D QD
2S TD 7S 6D 9C 9S QS KH QH 5C
JC 6S 9C QH JH 8D 7S JS KH 2H
8D 5H TH KC 4D 4S 3S 6S 3D QS
2D JD 4C TD 7C 6D TH 7S JC AH
QS 7S 4C TH 9D TS AD 4D 3H 6H
2D 3H 7D JD 3D AS 2S 9C QC 8S
4H 9H 9C 2C 7S JH KD 5C 5D 6H
TC 9H 8H JC 3C 9S 8D KS AD KC
TS 5H JD QS QH QC 8D 5D KH AH
5D AS 8S 6S 4C AH QC QD TH 7H
3H 4H 7D 6S 4S 9H AS 8H JS 9D
JD 8C 2C 9D 7D 5H 5S 9S JC KD
KD 9C 4S QD AH 7C AD 9D AC TD
6S 4H 4S 9C 8D KS TC 9D JH 7C
5S JC 5H 4S QH AC 2C JS 2S 9S
8C 5H AS QD AD 5C 7D 8S QC TD
JC 4C 8D 5C KH QS 4D 6H 2H 2C
TH 4S 2D KC 3H QD AC 7H AD 9D
KH QD AS 8H TH KC 8D 7S QH 8C
JC 6C 7D 8C KH AD QS 2H 6S 2D
JC KH 2D 7D JS QC 5H 4C 5D AD
TS 3S AD 4S TD 2D TH 6S 9H JH
9H 2D QS 2C 4S 3D KH AS AC 9D
KH 6S 8H 4S KD 7D 9D TS QD QC
JH 5H AH KS AS AD JC QC 5S KH
5D 7D 6D KS KD 3D 7C 4D JD 3S
AC JS 8D 5H 9C 3H 4H 4D TS 2C
6H KS KH 9D 7C 2S 6S 8S 2H 3D
6H AC JS 7S 3S TD 8H 3H 4H TH
9H TC QC KC 5C KS 6H 4H AC 8S
TC 7D QH 4S JC TS 6D 6C AC KH
QH 7D 7C JH QS QD TH 3H 5D KS
3D 5S 8D JS 4C 2C KS 7H 9C 4H
5H 8S 4H TD 2C 3S QD QC 3H KC
QC JS KD 9C AD 5S 9D 7D 7H TS
8C JC KH 7C 7S 6C TS 2C QD TH
5S 9D TH 3C 7S QH 8S 9C 2H 5H
5D 9H 6H 2S JS KH 3H 7C 2H 5S
JD 5D 5S 2C TC 2S 6S 6C 3C 8S
4D KH 8H 4H 2D KS 3H 5C 2S 9H
3S 2D TD 7H 8S 6H JD KC 9C 8D
6S QD JH 7C 9H 5H 8S 8H TH TD
QS 7S TD 7D TS JC KD 7C 3C 2C
3C JD 8S 4H 2D 2S TD AS 4D AC
AH KS 6C 4C 4S 7D 8C 9H 6H AS
5S 3C 9S 2C QS KD 4D 4S AC 5D
2D TS 2C JS KH QH 5D 8C AS KC
KD 3H 6C TH 8S 7S KH 6H 9S AC
6H 7S 6C QS AH 2S 2H 4H 5D 5H
5H JC QD 2C 2S JD AS QC 6S 7D
6C TC AS KD 8H 9D 2C 7D JH 9S
2H 4C 6C AH 8S TD 3H TH 7C TS
KD 4S TS 6C QH 8D 9D 9C AH 7D
6D JS 5C QD QC 9C 5D 8C 2H KD
3C QH JH AD 6S AH KC 8S 6D 6H
3D 7C 4C 7S 5S 3S 6S 5H JC 3C
QH 7C 5H 3C 3S 8C TS 4C KD 9C
QD 3S 7S 5H 7H QH JC 7C 8C KD
3C KD KH 2S 4C TS AC 6S 2C 7C
2C KH 3C 4C 6H 4D 5H 5S 7S QD
4D 7C 8S QD TS 9D KS 6H KD 3C
QS 4D TS 7S 4C 3H QD 8D 9S TC
TS QH AC 6S 3C 9H 9D QS 8S 6H
3S 7S 5D 4S JS 2D 6C QH 6S TH
4C 4H AS JS 5D 3D TS 9C AC 8S
6S 9C 7C 3S 5C QS AD AS 6H 3C
9S 8C 7H 3H 6S 7C AS 9H JD KH
3D 3H 7S 4D 6C 7C AC 2H 9C TH
4H 5S 3H AC TC TH 9C 9H 9S 8D
8D 9H 5H 4D 6C 2H QD 6S 5D 3S
4C 5C JD QS 4D 3H TH AC QH 8C
QC 5S 3C 7H AD 4C KS 4H JD 6D
QS AH 3H KS 9H 2S JS JH 5H 2H
2H 5S TH 6S TS 3S KS 3C 5H JS
2D 9S 7H 3D KC JH 6D 7D JS TD
AC JS 8H 2C 8C JH JC 2D TH 7S
5D 9S 8H 2H 3D TC AH JC KD 9C
9D QD JC 2H 6D KH TS 9S QH TH
2C 8D 4S JD 5H 3H TH TC 9C KC
AS 3D 9H 7D 4D TH KH 2H 7S 3H
4H 7S KS 2S JS TS 8S 2H QD 8D
5S 6H JH KS 8H 2S QC AC 6S 3S
JC AS AD QS 8H 6C KH 4C 4D QD
2S 3D TS TD 9S KS 6S QS 5C 8D
3C 6D 4S QC KC JH QD TH KH AD
9H AH 4D KS 2S 8D JH JC 7C QS
2D 6C TH 3C 8H QD QH 2S 3S KS
6H 5D 9S 4C TS TD JS QD 9D JD
5H 8H KH 8S KS 7C TD AD 4S KD
2C 7C JC 5S AS 6C 7D 8S 5H 9C
6S QD 9S TS KH QS 5S QH 3C KC
7D 3H 3C KD 5C AS JH 7H 6H JD
9D 5C 9H KC 8H KS 4S AD 4D 2S
3S JD QD 8D 2S 7C 5S 6S 5H TS
6D 9S KC TD 3S 6H QD JD 5C 8D
5H 9D TS KD 8D 6H TD QC 4C 7D
6D 4S JD 9D AH 9S AS TD 9H QD
2D 5S 2H 9C 6H 9S TD QC 7D TC
3S 2H KS TS 2C 9C 8S JS 9D 7D
3C KC 6D 5D 6C 6H 8S AS 7S QS
JH 9S 2H 8D 4C 8H 9H AD TH KH
QC AS 2S JS 5C 6H KD 3H 7H 2C
QD 8H 2S 8D 3S 6D AH 2C TC 5C
JD JS TS 8S 3H 5D TD KC JC 6H
6S QS TC 3H 5D AH JC 7C 7D 4H
7C 5D 8H 9C 2H 9H JH KH 5S 2C
9C 7H 6S TH 3S QC QD 4C AC JD
2H 5D 9S 7D KC 3S QS 2D AS KH
2S 4S 2H 7D 5C TD TH QH 9S 4D
6D 3S TS 6H 4H KS 9D 8H 5S 2D
9H KS 4H 3S 5C 5D KH 6H 6S JS
KC AS 8C 4C JC KH QC TH QD AH
6S KH 9S 2C 5H TC 3C 7H JC 4D
JD 4S 6S 5S 8D 7H 7S 4D 4C 2H
7H 9H 5D KH 9C 7C TS TC 7S 5H
4C 8D QC TS 4S 9H 3D AD JS 7C
8C QS 5C 5D 3H JS AH KC 4S 9D
TS JD 8S QS TH JH KH 2D QD JS
JD QC 5D 6S 9H 3S 2C 8H 9S TS
2S 4C AD 7H JC 5C 2D 6D 4H 3D
7S JS 2C 4H 8C AD QD 9C 3S TD
JD TS 4C 6H 9H 7D QD 6D 3C AS
AS 7C 4C 6S 5D 5S 5C JS QC 4S
KD 6S 9S 7C 3C 5S 7D JH QD JS
4S 7S JH 2C 8S 5D 7H 3D QH AD
TD 6H 2H 8D 4H 2D 7C AD KH 5D
TS 3S 5H 2C QD AH 2S 5C KH TD
KC 4D 8C 5D AS 6C 2H 2S 9H 7C
KD JS QC TS QS KH JH 2C 5D AD
3S 5H KC 6C 9H 3H 2H AD 7D 7S
7S JS JH KD 8S 7D 2S 9H 7C 2H
9H 2D 8D QC 6S AD AS 8H 5H 6C
2S 7H 6C 6D 7D 8C 5D 9D JC 3C
7C 9C 7H JD 2H KD 3S KH AD 4S
QH AS 9H 4D JD KS KD TS KH 5H
4C 8H 5S 3S 3D 7D TD AD 7S KC
JS 8S 5S JC 8H TH 9C 4D 5D KC
7C 5S 9C QD 2C QH JS 5H 8D KH
TD 2S KS 3D AD KC 7S TC 3C 5D
4C 2S AD QS 6C 9S QD TH QH 5C
8C AD QS 2D 2S KC JD KS 6C JC
8D 4D JS 2H 5D QD 7S 7D QH TS
6S 7H 3S 8C 8S 9D QS 8H 6C 9S
4S TC 2S 5C QD 4D QS 6D TH 6S
3S 5C 9D 6H 8D 4C 7D TC 7C TD
AH 6S AS 7H 5S KD 3H 5H AC 4C
8D 8S AH KS QS 2C AD 6H 7D 5D
6H 9H 9S 2H QS 8S 9C 5D 2D KD
TS QC 5S JH 7D 7S TH 9S 9H AC
7H 3H 6S KC 4D 6D 5C 4S QD TS
TD 2S 7C QD 3H JH 9D 4H 7S 7H
KS 3D 4H 5H TC 2S AS 2D 6D 7D
8H 3C 7H TD 3H AD KC TH 9C KH
TC 4C 2C 9S 9D 9C 5C 2H JD 3C
3H AC TS 5D AD 8D 6H QC 6S 8C
2S TS 3S JD 7H 8S QH 4C 5S 8D
AC 4S 6C 3C KH 3D 7C 2D 8S 2H
4H 6C 8S TH 2H 4S 8H 9S 3H 7S
7C 4C 9C 2C 5C AS 5D KD 4D QH
9H 4H TS AS 7D 8D 5D 9S 8C 2H
QC KD AC AD 2H 7S AS 3S 2D 9S
2H QC 8H TC 6D QD QS 5D KH 3C
TH JD QS 4C 2S 5S AD 7H 3S AS
7H JS 3D 6C 3S 6D AS 9S AC QS
9C TS AS 8C TC 8S 6H 9D 8D 6C
4D JD 9C KC 7C 6D KS 3S 8C AS
3H 6S TC 8D TS 3S KC 9S 7C AS
8C QC 4H 4S 8S 6C 3S TC AH AC
4D 7D 5C AS 2H 6S TS QC AD TC
QD QC 8S 4S TH 3D AH TS JH 4H
5C 2D 9S 2C 3H 3C 9D QD QH 7D
KC 9H 6C KD 7S 3C 4D AS TC 2D
3D JS 4D 9D KS 7D TH QC 3H 3C
8D 5S 2H 9D 3H 8C 4C 4H 3C TH
JC TH 4S 6S JD 2D 4D 6C 3D 4C
TS 3S 2D 4H AC 2C 6S 2H JH 6H
TD 8S AD TC AH AC JH 9S 6S 7S
6C KC 4S JD 8D 9H 5S 7H QH AH
KD 8D TS JH 5C 5H 3H AD AS JS
2D 4H 3D 6C 8C 7S AD 5D 5C 8S
TD 5D 7S 9C 4S 5H 6C 8C 4C 8S
JS QH 9C AS 5C QS JC 3D QC 7C
JC 9C KH JH QS QC 2C TS 3D AD
5D JH AC 5C 9S TS 4C JD 8C KS
KC AS 2D KH 9H 2C 5S 4D 3D 6H
TH AH 2D 8S JC 3D 8C QH 7S 3S
8H QD 4H JC AS KH KS 3C 9S 6D
9S QH 7D 9C 4S AC 7H KH 4D KD
AH AD TH 6D 9C 9S KD KS QH 4H
QD 6H 9C 7C QS 6D 6S 9D 5S JH
AH 8D 5H QD 2H JC KS 4H KH 5S
5C 2S JS 8D 9C 8C 3D AS KC AH
JD 9S 2H QS 8H 5S 8C TH 5C 4C
QC QS 8C 2S 2C 3S 9C 4C KS KH
2D 5D 8S AH AD TD 2C JS KS 8C
TC 5S 5H 8H QC 9H 6H JD 4H 9S
3C JH 4H 9H AH 4S 2H 4C 8D AC
8S TH 4D 7D 6D QD QS 7S TC 7C
KH 6D 2D JD 5H JS QD JH 4H 4S
9C 7S JH 4S 3S TS QC 8C TC 4H
QH 9D 4D JH QS 3S 2C 7C 6C 2D
4H 9S JD 5C 5H AH 9D TS 2D 4C
KS JH TS 5D 2D AH JS 7H AS 8D
JS AH 8C AD KS 5S 8H 2C 6C TH
2H 5D AD AC KS 3D 8H TS 6H QC
6D 4H TS 9C 5H JS JH 6S JD 4C
JH QH 4H 2C 6D 3C 5D 4C QS KC
6H 4H 6C 7H 6S 2S 8S KH QC 8C
3H 3D 5D KS 4H TD AD 3S 4D TS
5S 7C 8S 7D 2C KS 7S 6C 8C JS
5D 2H 3S 7C 5C QD 5H 6D 9C 9H
JS 2S KD 9S 8D TD TS AC 8C 9D
5H QD 2S AC 8C 9H KS 7C 4S 3C
KH AS 3H 8S 9C JS QS 4S AD 4D
AS 2S TD AD 4D 9H JC 4C 5H QS
5D 7C 4H TC 2D 6C JS 4S KC 3S
4C 2C 5D AC 9H 3D JD 8S QS QH
2C 8S 6H 3C QH 6D TC KD AC AH
QC 6C 3S QS 4S AC 8D 5C AD KH
5S 4C AC KH AS QC 2C 5C 8D 9C
8H JD 3C KH 8D 5C 9C QD QH 9D
7H TS 2C 8C 4S TD JC 9C 5H QH
JS 4S 2C 7C TH 6C AS KS 7S JD
JH 7C 9H 7H TC 5H 3D 6D 5D 4D
2C QD JH 2H 9D 5S 3D TD AD KS
JD QH 3S 4D TH 7D 6S QS KS 4H
TC KS 5S 8D 8H AD 2S 2D 4C JH
5S JH TC 3S 2D QS 9D 4C KD 9S
AC KH 3H AS 9D KC 9H QD 6C 6S
9H 7S 3D 5C 7D KC TD 8H 4H 6S
3C 7H 8H TC QD 4D 7S 6S QH 6C
6D AD 4C QD 6C 5D 7D 9D KS TS
JH 2H JD 9S 7S TS KH 8D 5D 8H
2D 9S 4C 7D 9D 5H QD 6D AC 6S
7S 6D JC QD JH 4C 6S QS 2H 7D
8C TD JH KD 2H 5C QS 2C JS 7S
TC 5H 4H JH QD 3S 5S 5D 8S KH
KS KH 7C 2C 5D JH 6S 9C 6D JC
5H AH JD 9C JS KC 2H 6H 4D 5S
AS 3C TH QC 6H 9C 8S 8C TD 7C
KC 2C QD 9C KH 4D 7S 3C TS 9H
9C QC 2S TS 8C TD 9S QD 3S 3C
4D 9D TH JH AH 6S 2S JD QH JS
QD 9H 6C KD 7D 7H 5D 6S 8H AH
8H 3C 4S 2H 5H QS QH 7S 4H AC
QS 3C 7S 9S 4H 3S AH KS 9D 7C
AD 5S 6S 2H 2D 5H TC 4S 3C 8C
QH TS 6S 4D JS KS JH AS 8S 6D
2C 8S 2S TD 5H AS TC TS 6C KC
KC TS 8H 2H 3H 7C 4C 5S TH TD
KD AD KH 7H 7S 5D 5H 5S 2D 9C
AD 9S 3D 7S 8C QC 7C 9C KD KS
3C QC 9S 8C 4D 5C AS QD 6C 2C
2H KC 8S JD 7S AC 8D 5C 2S 4D
9D QH 3D 2S TC 3S KS 3C 9H TD
KD 6S AC 2C 7H 5H 3S 6C 6H 8C
QH TC 8S 6S KH TH 4H 5D TS 4D
8C JS 4H 6H 2C 2H 7D AC QD 3D
QS KC 6S 2D 5S 4H TD 3H JH 4C
7S 5H 7H 8H KH 6H QS TH KD 7D
5H AD KD 7C KH 5S TD 6D 3C 6C
8C 9C 5H JD 7C KC KH 7H 2H 3S
7S 4H AD 4D 8S QS TH 3D 7H 5S
8D TC KS KD 9S 6D AD JD 5C 2S
7H 8H 6C QD 2H 6H 9D TC 9S 7C
8D 6D 4C 7C 6C 3C TH KH JS JH
5S 3S 8S JS 9H AS AD 8H 7S KD
JH 7C 2C KC 5H AS AD 9C 9S JS
AD AC 2C 6S QD 7C 3H TH KS KD
9D JD 4H 8H 4C KH 7S TS 8C KC
3S 5S 2H 7S 6H 7D KS 5C 6D AD
5S 8C 9H QS 7H 7S 2H 6C 7D TD
QS 5S TD AC 9D KC 3D TC 2D 4D
TD 2H 7D JD QD 4C 7H 5D KC 3D
4C 3H 8S KD QH 5S QC 9H TC 5H
9C QD TH 5H TS 5C 9H AH QH 2C
4D 6S 3C AC 6C 3D 2C 2H TD TH
AC 9C 5D QC 4D AD 8D 6D 8C KC
AD 3C 4H AC 8D 8H 7S 9S TD JC
4H 9H QH JS 2D TH TD TC KD KS
5S 6S 9S 8D TH AS KH 5H 5C 8S
JD 2S 9S 6S 5S 8S 5D 7S 7H 9D
5D 8C 4C 9D AD TS 2C 7D KD TC
8S QS 4D KC 5C 8D 4S KH JD KD
AS 5C AD QH 7D 2H 9S 7H 7C TC
2S 8S JD KH 7S 6C 6D AD 5D QC
9H 6H 3S 8C 8H AH TC 4H JS TD
2C TS 4D 7H 2D QC 9C 5D TH 7C
6C 8H QC 5D TS JH 5C 5H 9H 4S
2D QC 7H AS JS 8S 2H 4C 4H 8D
JS 6S AC KD 3D 3C 4S 7H TH KC
QH KH 6S QS 5S 4H 3C QD 3S 3H
7H AS KH 8C 4H 9C 5S 3D 6S TS
9C 7C 3H 5S QD 2C 3D AD AC 5H
JH TD 2D 4C TS 3H KH AD 3S 7S
AS 4C 5H 4D 6S KD JC 3C 6H 2D
3H 6S 8C 2D TH 4S AH QH AD 5H
7C 2S 9H 7H KC 5C 6D 5S 3H JC
3C TC 9C 4H QD TD JH 6D 9H 5S
7C 6S 5C 5D 6C 4S 7H 9H 6H AH
AD 2H 7D KC 2C 4C 2S 9S 7H 3S
TH 4C 8S 6S 3S AD KS AS JH TD
5C TD 4S 4D AD 6S 5D TC 9C 7D
8H 3S 4D 4S 5S 6H 5C AC 3H 3D
9H 3C AC 4S QS 8S 9D QH 5H 4D
JC 6C 5H TS AC 9C JD 8C 7C QD
8S 8H 9C JD 2D QC QH 6H 3C 8D
KS JS 2H 6H 5H QH QS 3H 7C 6D
TC 3H 4S 7H QC 2H 3S 8C JS KH
AH 8H 5S 4C 9H JD 3H 7S JC AC
3C 2D 4C 5S 6C 4S QS 3S JD 3D
5H 2D TC AH KS 6D 7H AD 8C 6H
6C 7S 3C JD 7C 8H KS KH AH 6D
AH 7D 3H 8H 8S 7H QS 5H 9D 2D
JD AC 4H 7S 8S 9S KS AS 9D QH
7S 2C 8S 5S JH QS JC AH KD 4C
AH 2S 9H 4H 8D TS TD 6H QH JD
4H JC 3H QS 6D 7S 9C 8S 9D 8D
5H TD 4S 9S 4C 8C 8D 7H 3H 3D
QS KH 3S 2C 2S 3C 7S TD 4S QD
7C TD 4D 5S KH AC AS 7H 4C 6C
2S 5H 6D JD 9H QS 8S 2C 2H TD
2S TS 6H 9H 7S 4H JC 4C 5D 5S
2C 5H 7D 4H 3S QH JC JS 6D 8H
4C QH 7C QD 3S AD TH 8S 5S TS
9H TC 2S TD JC 7D 3S 3D TH QH
7D 4C 8S 5C JH 8H 6S 3S KC 3H
JC 3H KH TC QH TH 6H 2C AC 5H
QS 2H 9D 2C AS 6S 6C 2S 8C 8S
9H 7D QC TH 4H KD QS AC 7S 3C
4D JH 6S 5S 8H KS 9S QC 3S AS
JD 2D 6S 7S TC 9H KC 3H 7D KD
2H KH 7C 4D 4S 3H JS QD 7D KC
4C JC AS 9D 3C JS 6C 8H QD 4D
AH JS 3S 6C 4C 3D JH 6D 9C 9H
9H 2D 8C 7H 5S KS 6H 9C 2S TC
6C 8C AD 7H 6H 3D KH AS 5D TH
KS 8C 3S TS 8S 4D 5S 9S 6C 4H
9H 4S 4H 5C 7D KC 2D 2H 9D JH
5C JS TC 9D 9H 5H 7S KH JC 6S
7C 9H 8H 4D JC KH JD 2H TD TC
8H 6C 2H 2C KH 6H 9D QS QH 5H
AC 7D 2S 3D QD JC 2D 8D JD JH
2H JC 2D 7H 2C 3C 8D KD TD 4H
3S 4H 6D 8D TS 3H TD 3D 6H TH
JH JC 3S AC QH 9H 7H 8S QC 2C
7H TD QS 4S 8S 9C 2S 5D 4D 2H
3D TS 3H 2S QC 8H 6H KC JC KS
5D JD 7D TC 8C 6C 9S 3D 8D AC
8H 6H JH 6C 5D 8D 8S 4H AD 2C
9D 4H 2D 2C 3S TS AS TC 3C 5D
4D TH 5H KS QS 6C 4S 2H 3D AD
5C KC 6H 2C 5S 3C 4D 2D 9H 9S
JD 4C 3H TH QH 9H 5S AH 8S AC
7D 9S 6S 2H TD 9C 4H 8H QS 4C
3C 6H 5D 4H 8C 9C KC 6S QD QS
3S 9H KD TC 2D JS 8C 6S 4H 4S
2S 4C 8S QS 6H KH 3H TH 8C 5D
2C KH 5S 3S 7S 7H 6C 9D QD 8D
8H KS AC 2D KH TS 6C JS KC 7H
9C KS 5C TD QC AH 6C 5H 9S 7C
5D 4D 3H 4H 6S 7C 7S AH QD TD
2H 7D QC 6S TC TS AH 7S 9D 3H
TH 5H QD 9S KS 7S 7C 6H 8C TD
TH 2D 4D QC 5C 7D JD AH 9C 4H
4H 3H AH 8D 6H QC QH 9H 2H 2C
2D AD 4C TS 6H 7S TH 4H QS TD
3C KD 2H 3H QS JD TC QC 5D 8H
KS JC QD TH 9S KD 8D 8C 2D 9C
3C QD KD 6D 4D 8D AH AD QC 8S
8H 3S 9D 2S 3H KS 6H 4C 7C KC
TH 9S 5C 3D 7D 6H AC 7S 4D 2C
5C 3D JD 4D 2D 6D 5H 9H 4C KH
AS 7H TD 6C 2H 3D QD KS 4C 4S
JC 3C AC 7C JD JS 8H 9S QC 5D
JD 6S 5S 2H AS 8C 7D 5H JH 3D
8D TC 5S 9S 8S 3H JC 5H 7S AS
5C TD 3D 7D 4H 8D 7H 4D 5D JS
QS 9C KS TD 2S 8S 5C 2H 4H AS
TH 7S 4H 7D 3H JD KD 5D 2S KC
JD 7H 4S 8H 4C JS 6H QH 5S 4H
2C QS 8C 5S 3H QC 2S 6C QD AD
8C 3D JD TC 4H 2H AD 5S AC 2S
5D 2C JS 2D AD 9D 3D 4C 4S JH
8D 5H 5D 6H 7S 4D KS 9D TD JD
3D 6D 9C 2S AS 7D 5S 5C 8H JD
7C 8S 3S 6S 5H JD TC AD 7H 7S
2S 9D TS 4D AC 8D 6C QD JD 3H
9S KH 2C 3C AC 3D 5H 6H 8D 5D
KS 3D 2D 6S AS 4C 2S 7C 7H KH
AC 2H 3S JC 5C QH 4D 2D 5H 7S
TS AS JD 8C 6H JC 8S 5S 2C 5D
7S QH 7H 6C QC 8H 2D 7C JD 2S
2C QD 2S 2H JC 9C 5D 2D JD JH
7C 5C 9C 8S 7D 6D 8D 6C 9S JH
2C AD 6S 5H 3S KS 7S 9D KH 4C
7H 6C 2C 5C TH 9D 8D 3S QC AH
5S KC 6H TC 5H 8S TH 6D 3C AH
9C KD 4H AD TD 9S 4S 7D 6H 5D
7H 5C 5H 6D AS 4C KD KH 4H 9D
3C 2S 5C 6C JD QS 2H 9D 7D 3H
AC 2S 6S 7S JS QD 5C QS 6H AD
5H TH QC 7H TC 3S 7C 6D KC 3D
4H 3D QC 9S 8H 2C 3S JC KS 5C
4S 6S 2C 6H 8S 3S 3D 9H 3H JS
4S 8C 4D 2D 8H 9H 7D 9D AH TS
9S 2C 9H 4C 8D AS 7D 3D 6D 5S
6S 4C 7H 8C 3H 5H JC AH 9D 9C
2S 7C 5S JD 8C 3S 3D 4D 7D 6S
3C KC 4S 5D 7D 3D JD 7H 3H 4H
9C 9H 4H 4D TH 6D QD 8S 9S 7S
2H AC 8S 4S AD 8C 2C AH 7D TC
TS 9H 3C AD KS TC 3D 8C 8H JD
QC 8D 2C 3C 7D 7C JD 9H 9C 6C
AH 6S JS JH 5D AS QC 2C JD TD
9H KD 2H 5D 2D 3S 7D TC AH TS
TD 8H AS 5D AH QC AC 6S TC 5H
KS 4S 7H 4D 8D 9C TC 2H 6H 3H
3H KD 4S QD QH 3D 8H 8C TD 7S
8S JD TC AH JS QS 2D KH KS 4D
3C AD JC KD JS KH 4S TH 9H 2C
QC 5S JS 9S KS AS 7C QD 2S JD
KC 5S QS 3S 2D AC 5D 9H 8H KS
6H 9C TC AD 2C 6D 5S JD 6C 7C
QS KH TD QD 2C 3H 8S 2S QC AH
9D 9H JH TC QH 3C 2S JS 5C 7H
6C 3S 3D 2S 4S QD 2D TH 5D 2C
2D 6H 6D 2S JC QH AS 7H 4H KH
5H 6S KS AD TC TS 7C AC 4S 4H
AD 3C 4H QS 8C 9D KS 2H 2D 4D
4S 9D 6C 6D 9C AC 8D 3H 7H KD
JC AH 6C TS JD 6D AD 3S 5D QD
JC JH JD 3S 7S 8S JS QC 3H 4S
JD TH 5C 2C AD JS 7H 9S 2H 7S
8D 3S JH 4D QC AS JD 2C KC 6H
2C AC 5H KD 5S 7H QD JH AH 2D
JC QH 8D 8S TC 5H 5C AH 8C 6C
3H JS 8S QD JH 3C 4H 6D 5C 3S
6D 4S 4C AH 5H 5S 3H JD 7C 8D
8H AH 2H 3H JS 3C 7D QC 4H KD
6S 2H KD 5H 8H 2D 3C 8S 7S QD
2S 7S KC QC AH TC QS 6D 4C 8D
5S 9H 2C 3S QD 7S 6C 2H 7C 9D
3C 6C 5C 5S JD JC KS 3S 5D TS
7C KS 6S 5S 2S 2D TC 2H 5H QS
AS 7H 6S TS 5H 9S 9D 3C KD 2H
4S JS QS 3S 4H 7C 2S AC 6S 9D
8C JH 2H 5H 7C 5D QH QS KH QC
3S TD 3H 7C KC 8D 5H 8S KH 8C
4H KH JD TS 3C 7H AS QC JS 5S
AH 9D 2C 8D 4D 2D 6H 6C KC 6S
2S 6H 9D 3S 7H 4D KH 8H KD 3D
9C TC AC JH KH 4D JD 5H TD 3S
7S 4H 9D AS 4C 7D QS 9S 2S KH
3S 8D 8S KS 8C JC 5C KH 2H 5D
8S QH 2C 4D KC JS QC 9D AC 6H
8S 8C 7C JS JD 6S 4C 9C AC 4S
QH 5D 2C 7D JC 8S 2D JS JH 4C
JS 4C 7S TS JH KC KH 5H QD 4S
QD 8C 8D 2D 6S TD 9D AC QH 5S
QH QC JS 3D 3C 5C 4H KH 8S 7H
7C 2C 5S JC 8S 3H QC 5D 2H KC
5S 8D KD 6H 4H QD QH 6D AH 3D
7S KS 6C 2S 4D AC QS 5H TS JD
7C 2D TC 5D QS AC JS QC 6C KC
2C KS 4D 3H TS 8S AD 4H 7S 9S
QD 9H QH 5H 4H 4D KH 3S JC AD
4D AC KC 8D 6D 4C 2D KH 2C JD
2C 9H 2D AH 3H 6D 9C 7D TC KS
8C 3H KD 7C 5C 2S 4S 5H AS AH
TH JD 4H KD 3H TC 5C 3S AC KH
6D 7H AH 7S QC 6H 2D TD JD AS
JH 5D 7H TC 9S 7D JC AS 5S KH
2H 8C AD TH 6H QD KD 9H 6S 6C
QH KC 9D 4D 3S JS JH 4H 2C 9H
TC 7H KH 4H JC 7D 9S 3H QS 7S
AD 7D JH 6C 7H 4H 3S 3H 4D QH
JD 2H 5C AS 6C QC 4D 3C TC JH
AC JD 3H 6H 4C JC AD 7D 7H 9H
4H TC TS 2C 8C 6S KS 2H JD 9S
4C 3H QS QC 9S 9H 6D KC 9D 9C
5C AD 8C 2C QH TH QD JC 8D 8H
QC 2C 2S QD 9C 4D 3S 8D JH QS
9D 3S 2C 7S 7C JC TD 3C TC 9H
3C TS 8H 5C 4C 2C 6S 8D 7C 4H
KS 7H 2H TC 4H 2C 3S AS AH QS
8C 2D 2H 2C 4S 4C 6S 7D 5S 3S
TH QC 5D TD 3C QS KD KC KS AS
4D AH KD 9H KS 5C 4C 6H JC 7S
KC 4H 5C QS TC 2H JC 9S AH QH
4S 9H 3H 5H 3C QD 2H QC JH 8H
5D AS 7H 2C 3D JH 6H 4C 6S 7D
9C JD 9H AH JS 8S QH 3H KS 8H
3S AC QC TS 4D AD 3D AH 8S 9H
7H 3H QS 9C 9S 5H JH JS AH AC
8D 3C JD 2H AC 9C 7H 5S 4D 8H
7C JH 9H 6C JS 9S 7H 8C 9D 4H
2D AS 9S 6H 4D JS JH 9H AD QD
6H 7S JH KH AH 7H TD 5S 6S 2C
8H JH 6S 5H 5S 9D TC 4C QC 9S
7D 2C KD 3H 5H AS QD 7H JS 4D
TS QH 6C 8H TH 5H 3C 3H 9C 9D
AD KH JS 5D 3H AS AC 9S 5C KC
2C KH 8C JC QS 6D AH 2D KC TC
9D 3H 2S 7C 4D 6D KH KS 8D 7D
9H 2S TC JH AC QC 3H 5S 3S 8H
3S AS KD 8H 4C 3H 7C JH QH TS
7S 6D 7H 9D JH 4C 3D 3S 6C AS
4S 2H 2C 4C 8S 5H KC 8C QC QD
3H 3S 6C QS QC 2D 6S 5D 2C 9D
2H 8D JH 2S 3H 2D 6C 5C 7S AD
9H JS 5D QH 8S TS 2H 7S 6S AD
6D QC 9S 7H 5H 5C 7D KC JD 4H
QC 5S 9H 9C 4D 6S KS 2S 4C 7C
9H 7C 4H 8D 3S 6H 5C 8H JS 7S
2D 6H JS TD 4H 4D JC TH 5H KC
AC 7C 8D TH 3H 9S 2D 4C KC 4D
KD QS 9C 7S 3D KS AD TS 4C 4H
QH 9C 8H 2S 7D KS 7H 5D KD 4C
9C 2S 2H JC 6S 6C TC QC JH 5C
7S AC 8H KC 8S 6H QS JC 3D 6S
JS 2D JH 8C 4S 6H 8H 6D 5D AD
6H 7D 2S 4H 9H 7C AS AC 8H 5S
3C JS 4S 6D 5H 2S QH 6S 9C 2C
3D 5S 6S 9S 4C QS 8D QD 8S TC
9C 3D AH 9H 5S 2C 7D AD JC 3S
7H TC AS 3C 6S 6D 7S KH KC 9H
3S TC 8H 6S 5H JH 8C 7D AC 2S
QD 9D 9C 3S JC 8C KS 8H 5D 4D
JS AH JD 6D 9D 8C 9H 9S 8H 3H
2D 6S 4C 4D 8S AD 4S TC AH 9H
TS AC QC TH KC 6D 4H 7S 8C 2H
3C QD JS 9D 5S JC AH 2H TS 9H
3H 4D QH 5D 9C 5H 7D 4S JC 3S
8S TH 3H 7C 2H JD JS TS AC 8D
9C 2H TD KC JD 2S 8C 5S AD 2C
3D KD 7C 5H 4D QH QD TC 6H 7D
7H 2C KC 5S KD 6H AH QC 7S QH
6H 5C AC 5H 2C 9C 2D 7C TD 2S
4D 9D AH 3D 7C JD 4H 8C 4C KS
TH 3C JS QH 8H 4C AS 3D QS QC
4D 7S 5H JH 6D 7D 6H JS KH 3C
QD 8S 7D 2H 2C 7C JC 2S 5H 8C
QH 8S 9D TC 2H AD 7C 8D QD 6S
3S 7C AD 9H 2H 9S JD TS 4C 2D
3S AS 4H QC 2C 8H 8S 7S TD TC
JH TH TD 3S 4D 4H 5S 5D QS 2C
8C QD QH TC 6D 4S 9S 9D 4H QC
8C JS 9D 6H JD 3H AD 6S TD QC
KC 8S 3D 7C TD 7D 8D 9H 4S 3S
6C 4S 3D 9D KD TC KC KS AC 5S
7C 6S QH 3D JS KD 6H 6D 2D 8C
JD 2S 5S 4H 8S AC 2D 6S TS 5C
5H 8C 5S 3C 4S 3D 7C 8D AS 3H
AS TS 7C 3H AD 7D JC QS 6C 6H
3S 9S 4C AC QH 5H 5D 9H TS 4H
6C 5C 7H 7S TD AD JD 5S 2H 2S
7D 6C KC 3S JD 8D 8S TS QS KH
8S QS 8D 6C TH AC AH 2C 8H 9S
7H TD KH QH 8S 3D 4D AH JD AS
TS 3D 2H JC 2S JH KH 6C QC JS
KC TH 2D 6H 7S 2S TC 8C 9D QS
3C 9D 6S KH 8H 6D 5D TH 2C 2H
6H TC 7D AD 4D 8S TS 9H TD 7S
JS 6D JD JC 2H AC 6C 3D KH 8D
KH JD 9S 5D 4H 4C 3H 7S QS 5C
4H JD 5D 3S 3C 4D KH QH QS 7S
JD TS 8S QD AH 4C 6H 3S 5S 2C
QS 3D JD AS 8D TH 7C 6S QC KS
7S 2H 8C QC 7H AC 6D 2D TH KH
5S 6C 7H KH 7D AH 8C 5C 7S 3D
3C KD AD 7D 6C 4D KS 2D 8C 4S
7C 8D 5S 2D 2S AH AD 2C 9D TD
3C AD 4S KS JH 7C 5C 8C 9C TH
AS TD 4D 7C JD 8C QH 3C 5H 9S
3H 9C 8S 9S 6S QD KS AH 5H JH
QC 9C 5S 4H 2H TD 7D AS 8C 9D
8C 2C 9D KD TC 7S 3D KH QC 3C
4D AS 4C QS 5S 9D 6S JD QH KS
6D AH 6C 4C 5H TS 9H 7D 3D 5S
QS JD 7C 8D 9C AC 3S 6S 6C KH
8H JH 5D 9S 6D AS 6S 3S QC 7H
QD AD 5C JH 2H AH 4H AS KC 2C
JH 9C 2C 6H 2D JS 5D 9H KC 6D
7D 9D KD TH 3H AS 6S QC 6H AD
JD 4H 7D KC 3H JS 3C TH 3D QS
4C 3H 8C QD 5H 6H AS 8H AD JD
TH 8S KD 5D QC 7D JS 5S 5H TS
7D KC 9D QS 3H 3C 6D TS 7S AH
7C 4H 7H AH QC AC 4D 5D 6D TH
3C 4H 2S KD 8H 5H JH TC 6C JD
4S 8C 3D 4H JS TD 7S JH QS KD
7C QC KD 4D 7H 6S AD TD TC KH
5H 9H KC 3H 4D 3D AD 6S QD 6H
TH 7C 6H TS QH 5S 2C KC TD 6S
7C 4D 5S JD JH 7D AC KD KH 4H
7D 6C 8D 8H 5C JH 8S QD TH JD
8D 7D 6C 7C 9D KD AS 5C QH JH
9S 2C 8C 3C 4C KS JH 2D 8D 4H
7S 6C JH KH 8H 3H 9D 2D AH 6D
4D TC 9C 8D 7H TD KS TH KD 3C
JD 9H 8D QD AS KD 9D 2C 2S 9C
8D 3H 5C 7H KS 5H QH 2D 8C 9H
2D TH 6D QD 6C KC 3H 3S AD 4C
4H 3H JS 9D 3C TC 5H QH QC JC
3D 5C 6H 3S 3C JC 5S 7S 2S QH
AC 5C 8C 4D 5D 4H 2S QD 3C 3H
2C TD AH 9C KD JS 6S QD 4C QC
QS 8C 3S 4H TC JS 3H 7C JC AD
5H 4D 9C KS JC TD 9S TS 8S 9H
QD TS 7D AS AC 2C TD 6H 8H AH
6S AD 8C 4S 9H 8D 9D KH 8S 3C
QS 4D 2D 7S KH JS JC AD 4C 3C
QS 9S 7H KC TD TH 5H JS AC JH
6D AC 2S QS 7C AS KS 6S KH 5S
6D 8H KH 3C QS 2H 5C 9C 9D 6C
JS 2C 4C 6H 7D JC AC QD TD 3H
4H QC 8H JD 4C KD KS 5C KC 7S
6D 2D 3H 2S QD 5S 7H AS TH 6S
AS 6D 8D 2C 8S TD 8H QD JC AH
9C 9H 2D TD QH 2H 5C TC 3D 8H
KC 8S 3D KH 2S TS TC 6S 4D JH
9H 9D QS AC KC 6H 5D 4D 8D AH
9S 5C QS 4H 7C 7D 2H 8S AD JS
3D AC 9S AS 2C 2D 2H 3H JC KH
7H QH KH JD TC KS 5S 8H 4C 8D
2H 7H 3S 2S 5H QS 3C AS 9H KD
AD 3D JD 6H 5S 9C 6D AC 9S 3S
3D 5D 9C 2D AC 4S 2S AD 6C 6S
QC 4C 2D 3H 6S KC QH QD 2H JH
QC 3C 8S 4D 9S 2H 5C 8H QS QD
6D KD 6S 7H 3S KH 2H 5C JC 6C
3S 9S TC 6S 8H 2D AD 7S 8S TS
3C 6H 9C 3H 5C JC 8H QH TD QD
3C JS QD 5D TD 2C KH 9H TH AS
9S TC JD 3D 5C 5H AD QH 9H KC
TC 7H 4H 8H 3H TD 6S AC 7C 2S
QS 9D 5D 3C JC KS 4D 6C JH 2S
9S 6S 3C 7H TS 4C KD 6D 3D 9C
2D 9H AH AC 7H 2S JH 3S 7C QC
QD 9H 3C 2H AC AS 8S KD 8C KH
2D 7S TD TH 6D JD 8D 4D 2H 5S
8S QH KD JD QS JH 4D KC 5H 3S
3C KH QC 6D 8H 3S AH 7D TD 2D
5S 9H QH 4S 6S 6C 6D TS TH 7S
6C 4C 6D QS JS 9C TS 3H 8D 8S
JS 5C 7S AS 2C AH 2H AD 5S TC
KD 6C 9C 9D TS 2S JC 4H 2C QD
QS 9H TC 3H KC KS 4H 3C AD TH
KH 9C 2H KD 9D TC 7S KC JH 2D
7C 3S KC AS 8C 5D 9C 9S QH 3H
2D 8C TD 4C 2H QC 5D TC 2C 7D
KS 4D 6C QH TD KH 5D 7C AD 8D
2S 9S 8S 4C 8C 3D 6H QD 7C 7H
6C 8S QH 5H TS 5C 3C 4S 2S 2H
8S 6S 2H JC 3S 3H 9D 8C 2S 7H
QC 2C 8H 9C AC JD 4C 4H 6S 3S
3H 3S 7D 4C 9S 5H 8H JC 3D TC
QH 2S 2D 9S KD QD 9H AD 6D 9C
8D 2D KS 9S JC 4C JD KC 4S TH
KH TS 6D 4D 5C KD 5H AS 9H AD
QD JS 7C 6D 5D 5C TH 5H QH QS
9D QH KH 5H JH 4C 4D TC TH 6C
KH AS TS 9D KD 9C 7S 4D 8H 5S
KH AS 2S 7D 9D 4C TS TH AH 7C
KS 4D AC 8S 9S 8D TH QH 9D 5C
5D 5C 8C QS TC 4C 3D 3S 2C 8D
9D KS 2D 3C KC 4S 8C KH 6C JC
8H AH 6H 7D 7S QD 3C 4C 6C KC
3H 2C QH 8H AS 7D 4C 8C 4H KC
QD 5S 4H 2C TD AH JH QH 4C 8S
3H QS 5S JS 8H 2S 9H 9C 3S 2C
6H TS 7S JC QD AC TD KC 5S 3H
QH AS QS 7D JC KC 2C 4C 5C 5S
QH 3D AS JS 4H 8D 7H JC 2S 9C
5D 4D 2S 4S 9D 9C 2D QS 8H 7H
6D 7H 3H JS TS AC 2D JH 7C 8S
JH 5H KC 3C TC 5S 9H 4C 8H 9D
8S KC 5H 9H AD KS 9D KH 8D AH
JC 2H 9H KS 6S 3H QC 5H AH 9C
5C KH 5S AD 6C JC 9H QC 9C TD
5S 5D JC QH 2D KS 8H QS 2H TS
JH 5H 5S AH 7H 3C 8S AS TD KH
6H 3D JD 2C 4C KC 7S AH 6C JH
4C KS 9D AD 7S KC 7D 8H 3S 9C
7H 5C 5H 3C 8H QC 3D KH 6D JC
2D 4H 5D 7D QC AD AH 9H QH 8H
KD 8C JS 9D 3S 3C 2H 5D 6D 2S
8S 6S TS 3C 6H 8D 5S 3H TD 6C
KS 3D JH 9C 7C 9S QS 5S 4H 6H
7S 6S TH 4S KC KD 3S JC JH KS
7C 3C 2S 6D QH 2C 7S 5H 8H AH
KC 8D QD 6D KH 5C 7H 9D 3D 9C
6H 2D 8S JS 9S 2S 6D KC 7C TC
KD 9C JH 7H KC 8S 2S 7S 3D 6H
4H 9H 2D 4C 8H 7H 5S 8S 2H 8D
AD 7C 3C 7S 5S 4D 9H 3D JC KH
5D AS 7D 6D 9C JC 4C QH QS KH
KD JD 7D 3D QS QC 8S 6D JS QD
6S 8C 5S QH TH 9H AS AC 2C JD
QC KS QH 7S 3C 4C 5C KC 5D AH
6C 4H 9D AH 2C 3H KD 3D TS 5C
TD 8S QS AS JS 3H KD AC 4H KS
7D 5D TS 9H 4H 4C 9C 2H 8C QC
2C 7D 9H 4D KS 4C QH AD KD JS
QD AD AH KH 9D JS 9H JC KD JD
8S 3C 4S TS 7S 4D 5C 2S 6H 7C
JS 7S 5C KD 6D QH 8S TD 2H 6S
QH 6C TC 6H TD 4C 9D 2H QC 8H
3D TS 4D 2H 6H 6S 2C 7H 8S 6C
9H 9D JD JH 3S AH 2C 6S 3H 8S
2C QS 8C 5S 3H 2S 7D 3C AD 4S
5C QC QH AS TS 4S 6S 4C 5H JS
JH 5C TD 4C 6H JS KD KH QS 4H
TC KH JC 4D 9H 9D 8D KC 3C 8H
2H TC 8S AD 9S 4H TS 7H 2C 5C
4H 2S 6C 5S KS AH 9C 7C 8H KD
TS QH TD QS 3C JH AH 2C 8D 7D
5D KC 3H 5S AC 4S 7H QS 4C 2H
3D 7D QC KH JH 6D 6C TD TH KD
5S 8D TH 6C 9D 7D KH 8C 9S 6D
JD QS 7S QC 2S QH JC 4S KS 8D
7S 5S 9S JD KD 9C JC AD 2D 7C
4S 5H AH JH 9C 5D TD 7C 2D 6S
KC 6C 7H 6S 9C QD 5S 4H KS TD
6S 8D KS 2D TH TD 9H JD TS 3S
KH JS 4H 5D 9D TC TD QC JD TS
QS QD AC AD 4C 6S 2D AS 3H KC
4C 7C 3C TD QS 9C KC AS 8D AD
KC 7H QC 6D 8H 6S 5S AH 7S 8C
3S AD 9H JC 6D JD AS KH 6S JH
AD 3D TS KS 7H JH 2D JS QD AC
9C JD 7C 6D TC 6H 6C JC 3D 3S
QC KC 3S JC KD 2C 8D AH QS TS
AS KD 3D JD 8H 7C 8C 5C QD 6C
| -1 |
TheAlgorithms/Python | 7,262 | refactor: Move constants outside of variable scope | ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T19:51:55Z" | "2022-10-16T09:33:29Z" | 77764116217708933bdc65b29801092fa291398e | c6582b35bf8b8aba622c63096e3ab2f01aa36854 | refactor: Move constants outside of variable scope. ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Project Euler Problem 2: https://projecteuler.net/problem=2
Even Fibonacci Numbers
Each new term in the Fibonacci sequence is generated by adding the previous
two terms. By starting with 1 and 2, the first 10 terms will be:
1, 2, 3, 5, 8, 13, 21, 34, 55, 89, ...
By considering the terms in the Fibonacci sequence whose values do not exceed
four million, find the sum of the even-valued terms.
References:
- https://en.wikipedia.org/wiki/Fibonacci_number
"""
import math
from decimal import Decimal, getcontext
def solution(n: int = 4000000) -> int:
"""
Returns the sum of all even fibonacci sequence elements that are lower
or equal to n.
>>> solution(10)
10
>>> solution(15)
10
>>> solution(2)
2
>>> solution(1)
0
>>> solution(34)
44
>>> solution(3.4)
2
>>> 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.")
getcontext().prec = 100
phi = (Decimal(5) ** Decimal(0.5) + 1) / Decimal(2)
index = (math.floor(math.log(n * (phi + 2), phi) - 1) // 3) * 3 + 2
num = Decimal(round(phi ** Decimal(index + 1))) / (phi + 2)
total = num // 2
return int(total)
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 2: https://projecteuler.net/problem=2
Even Fibonacci Numbers
Each new term in the Fibonacci sequence is generated by adding the previous
two terms. By starting with 1 and 2, the first 10 terms will be:
1, 2, 3, 5, 8, 13, 21, 34, 55, 89, ...
By considering the terms in the Fibonacci sequence whose values do not exceed
four million, find the sum of the even-valued terms.
References:
- https://en.wikipedia.org/wiki/Fibonacci_number
"""
import math
from decimal import Decimal, getcontext
def solution(n: int = 4000000) -> int:
"""
Returns the sum of all even fibonacci sequence elements that are lower
or equal to n.
>>> solution(10)
10
>>> solution(15)
10
>>> solution(2)
2
>>> solution(1)
0
>>> solution(34)
44
>>> solution(3.4)
2
>>> 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.")
getcontext().prec = 100
phi = (Decimal(5) ** Decimal(0.5) + 1) / Decimal(2)
index = (math.floor(math.log(n * (phi + 2), phi) - 1) // 3) * 3 + 2
num = Decimal(round(phi ** Decimal(index + 1))) / (phi + 2)
total = num // 2
return int(total)
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 7,262 | refactor: Move constants outside of variable scope | ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T19:51:55Z" | "2022-10-16T09:33:29Z" | 77764116217708933bdc65b29801092fa291398e | c6582b35bf8b8aba622c63096e3ab2f01aa36854 | refactor: Move constants outside of variable scope. ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # Primality Testing with the Rabin-Miller Algorithm
import random
def rabin_miller(num: int) -> bool:
s = num - 1
t = 0
while s % 2 == 0:
s = s // 2
t += 1
for _ in range(5):
a = random.randrange(2, num - 1)
v = pow(a, s, num)
if v != 1:
i = 0
while v != (num - 1):
if i == t - 1:
return False
else:
i = i + 1
v = (v**2) % num
return True
def is_prime_low_num(num: int) -> bool:
if num < 2:
return False
low_primes = [
2,
3,
5,
7,
11,
13,
17,
19,
23,
29,
31,
37,
41,
43,
47,
53,
59,
61,
67,
71,
73,
79,
83,
89,
97,
101,
103,
107,
109,
113,
127,
131,
137,
139,
149,
151,
157,
163,
167,
173,
179,
181,
191,
193,
197,
199,
211,
223,
227,
229,
233,
239,
241,
251,
257,
263,
269,
271,
277,
281,
283,
293,
307,
311,
313,
317,
331,
337,
347,
349,
353,
359,
367,
373,
379,
383,
389,
397,
401,
409,
419,
421,
431,
433,
439,
443,
449,
457,
461,
463,
467,
479,
487,
491,
499,
503,
509,
521,
523,
541,
547,
557,
563,
569,
571,
577,
587,
593,
599,
601,
607,
613,
617,
619,
631,
641,
643,
647,
653,
659,
661,
673,
677,
683,
691,
701,
709,
719,
727,
733,
739,
743,
751,
757,
761,
769,
773,
787,
797,
809,
811,
821,
823,
827,
829,
839,
853,
857,
859,
863,
877,
881,
883,
887,
907,
911,
919,
929,
937,
941,
947,
953,
967,
971,
977,
983,
991,
997,
]
if num in low_primes:
return True
for prime in low_primes:
if (num % prime) == 0:
return False
return rabin_miller(num)
def generate_large_prime(keysize: int = 1024) -> int:
while True:
num = random.randrange(2 ** (keysize - 1), 2 ** (keysize))
if is_prime_low_num(num):
return num
if __name__ == "__main__":
num = generate_large_prime()
print(("Prime number:", num))
print(("is_prime_low_num:", is_prime_low_num(num)))
| # Primality Testing with the Rabin-Miller Algorithm
import random
def rabin_miller(num: int) -> bool:
s = num - 1
t = 0
while s % 2 == 0:
s = s // 2
t += 1
for _ in range(5):
a = random.randrange(2, num - 1)
v = pow(a, s, num)
if v != 1:
i = 0
while v != (num - 1):
if i == t - 1:
return False
else:
i = i + 1
v = (v**2) % num
return True
def is_prime_low_num(num: int) -> bool:
if num < 2:
return False
low_primes = [
2,
3,
5,
7,
11,
13,
17,
19,
23,
29,
31,
37,
41,
43,
47,
53,
59,
61,
67,
71,
73,
79,
83,
89,
97,
101,
103,
107,
109,
113,
127,
131,
137,
139,
149,
151,
157,
163,
167,
173,
179,
181,
191,
193,
197,
199,
211,
223,
227,
229,
233,
239,
241,
251,
257,
263,
269,
271,
277,
281,
283,
293,
307,
311,
313,
317,
331,
337,
347,
349,
353,
359,
367,
373,
379,
383,
389,
397,
401,
409,
419,
421,
431,
433,
439,
443,
449,
457,
461,
463,
467,
479,
487,
491,
499,
503,
509,
521,
523,
541,
547,
557,
563,
569,
571,
577,
587,
593,
599,
601,
607,
613,
617,
619,
631,
641,
643,
647,
653,
659,
661,
673,
677,
683,
691,
701,
709,
719,
727,
733,
739,
743,
751,
757,
761,
769,
773,
787,
797,
809,
811,
821,
823,
827,
829,
839,
853,
857,
859,
863,
877,
881,
883,
887,
907,
911,
919,
929,
937,
941,
947,
953,
967,
971,
977,
983,
991,
997,
]
if num in low_primes:
return True
for prime in low_primes:
if (num % prime) == 0:
return False
return rabin_miller(num)
def generate_large_prime(keysize: int = 1024) -> int:
while True:
num = random.randrange(2 ** (keysize - 1), 2 ** (keysize))
if is_prime_low_num(num):
return num
if __name__ == "__main__":
num = generate_large_prime()
print(("Prime number:", num))
print(("is_prime_low_num:", is_prime_low_num(num)))
| -1 |
TheAlgorithms/Python | 7,262 | refactor: Move constants outside of variable scope | ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T19:51:55Z" | "2022-10-16T09:33:29Z" | 77764116217708933bdc65b29801092fa291398e | c6582b35bf8b8aba622c63096e3ab2f01aa36854 | refactor: Move constants outside of variable scope. ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Gamma function is a very useful tool in math and physics.
It helps calculating complex integral in a convenient way.
for more info: https://en.wikipedia.org/wiki/Gamma_function
Python's Standard Library math.gamma() function overflows around gamma(171.624).
"""
from math import pi, sqrt
def gamma(num: float) -> float:
"""
Calculates the value of Gamma function of num
where num is either an integer (1, 2, 3..) or a half-integer (0.5, 1.5, 2.5 ...).
Implemented using recursion
Examples:
>>> from math import isclose, gamma as math_gamma
>>> gamma(0.5)
1.7724538509055159
>>> gamma(2)
1.0
>>> gamma(3.5)
3.3233509704478426
>>> gamma(171.5)
9.483367566824795e+307
>>> all(isclose(gamma(num), math_gamma(num)) for num in (0.5, 2, 3.5, 171.5))
True
>>> gamma(0)
Traceback (most recent call last):
...
ValueError: math domain error
>>> gamma(-1.1)
Traceback (most recent call last):
...
ValueError: math domain error
>>> gamma(-4)
Traceback (most recent call last):
...
ValueError: math domain error
>>> gamma(172)
Traceback (most recent call last):
...
OverflowError: math range error
>>> gamma(1.1)
Traceback (most recent call last):
...
NotImplementedError: num must be an integer or a half-integer
"""
if num <= 0:
raise ValueError("math domain error")
if num > 171.5:
raise OverflowError("math range error")
elif num - int(num) not in (0, 0.5):
raise NotImplementedError("num must be an integer or a half-integer")
elif num == 0.5:
return sqrt(pi)
else:
return 1.0 if num == 1 else (num - 1) * gamma(num - 1)
def test_gamma() -> None:
"""
>>> test_gamma()
"""
assert gamma(0.5) == sqrt(pi)
assert gamma(1) == 1.0
assert gamma(2) == 1.0
if __name__ == "__main__":
from doctest import testmod
testmod()
num = 1.0
while num:
num = float(input("Gamma of: "))
print(f"gamma({num}) = {gamma(num)}")
print("\nEnter 0 to exit...")
| """
Gamma function is a very useful tool in math and physics.
It helps calculating complex integral in a convenient way.
for more info: https://en.wikipedia.org/wiki/Gamma_function
Python's Standard Library math.gamma() function overflows around gamma(171.624).
"""
from math import pi, sqrt
def gamma(num: float) -> float:
"""
Calculates the value of Gamma function of num
where num is either an integer (1, 2, 3..) or a half-integer (0.5, 1.5, 2.5 ...).
Implemented using recursion
Examples:
>>> from math import isclose, gamma as math_gamma
>>> gamma(0.5)
1.7724538509055159
>>> gamma(2)
1.0
>>> gamma(3.5)
3.3233509704478426
>>> gamma(171.5)
9.483367566824795e+307
>>> all(isclose(gamma(num), math_gamma(num)) for num in (0.5, 2, 3.5, 171.5))
True
>>> gamma(0)
Traceback (most recent call last):
...
ValueError: math domain error
>>> gamma(-1.1)
Traceback (most recent call last):
...
ValueError: math domain error
>>> gamma(-4)
Traceback (most recent call last):
...
ValueError: math domain error
>>> gamma(172)
Traceback (most recent call last):
...
OverflowError: math range error
>>> gamma(1.1)
Traceback (most recent call last):
...
NotImplementedError: num must be an integer or a half-integer
"""
if num <= 0:
raise ValueError("math domain error")
if num > 171.5:
raise OverflowError("math range error")
elif num - int(num) not in (0, 0.5):
raise NotImplementedError("num must be an integer or a half-integer")
elif num == 0.5:
return sqrt(pi)
else:
return 1.0 if num == 1 else (num - 1) * gamma(num - 1)
def test_gamma() -> None:
"""
>>> test_gamma()
"""
assert gamma(0.5) == sqrt(pi)
assert gamma(1) == 1.0
assert gamma(2) == 1.0
if __name__ == "__main__":
from doctest import testmod
testmod()
num = 1.0
while num:
num = float(input("Gamma of: "))
print(f"gamma({num}) = {gamma(num)}")
print("\nEnter 0 to exit...")
| -1 |
TheAlgorithms/Python | 7,262 | refactor: Move constants outside of variable scope | ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T19:51:55Z" | "2022-10-16T09:33:29Z" | 77764116217708933bdc65b29801092fa291398e | c6582b35bf8b8aba622c63096e3ab2f01aa36854 | refactor: Move constants outside of variable scope. ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$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 | 7,262 | refactor: Move constants outside of variable scope | ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T19:51:55Z" | "2022-10-16T09:33:29Z" | 77764116217708933bdc65b29801092fa291398e | c6582b35bf8b8aba622c63096e3ab2f01aa36854 | refactor: Move constants outside of variable scope. ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| 519432,525806
632382,518061
78864,613712
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780495,510032
525895,525320
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479030,529064
341758,543090
620223,518824
251661,556451
561790,522696
497733,527521
724201,512863
489217,528217
415623,534867
624610,518548
847541,506953
432295,533249
400391,536421
961158,502319
139173,584284
421225,534315
579083,521501
74274,617000
701142,514087
374465,539219
217814,562985
358972,540995
88629,607424
288597,550389
285819,550812
538400,524385
809930,508645
738326,512126
955461,502535
163829,576343
826475,507891
376488,538987
102234,599905
114650,594002
52815,636341
434037,533082
804744,508880
98385,601905
856620,506559
220057,562517
844734,507078
150677,580387
558697,522917
621751,518719
207067,565321
135297,585677
932968,503404
604456,519822
579728,521462
244138,557813
706487,513800
711627,513523
853833,506674
497220,527562
59428,629511
564845,522486
623621,518603
242689,558077
125091,589591
363819,540432
686453,514901
656813,516594
489901,528155
386380,537905
542819,524052
243987,557841
693412,514514
488484,528271
896331,504881
336730,543721
728298,512647
604215,519840
153729,579413
595687,520398
540360,524240
245779,557511
924873,503730
509628,526577
528523,525122
3509,847707
522756,525555
895447,504922
44840,646067
45860,644715
463487,530404
398164,536654
894483,504959
619415,518874
966306,502129
990922,501212
835756,507474
548881,523618
453578,531282
474993,529410
80085,612879
737091,512193
50789,638638
979768,501620
792018,509483
665001,516122
86552,608694
462772,530469
589233,520821
891694,505072
592605,520594
209645,564741
42531,649269
554376,523226
803814,508929
334157,544042
175836,572970
868379,506051
658166,516520
278203,551995
966198,502126
627162,518387
296774,549165
311803,547027
843797,507118
702304,514032
563875,522553
33103,664910
191932,568841
543514,524006
506835,526794
868368,506052
847025,506971
678623,515342
876139,505726
571997,521984
598632,520198
213590,563892
625404,518497
726508,512738
689426,514738
332495,544264
411366,535302
242546,558110
315209,546555
797544,509219
93889,604371
858879,506454
124906,589666
449072,531693
235960,559345
642403,517454
720567,513047
705534,513858
603692,519870
488137,528302
157370,578285
63515,625730
666326,516041
619226,518883
443613,532186
597717,520257
96225,603069
86940,608450
40725,651929
460976,530625
268875,553508
270671,553214
363254,540500
384248,538137
762889,510892
377941,538833
278878,551890
176615,572755
860008,506412
944392,502967
608395,519571
225283,561450
45095,645728
333798,544090
625733,518476
995584,501037
506135,526853
238050,558952
557943,522972
530978,524938
634244,517949
177168,572616
85200,609541
953043,502630
523661,525484
999295,500902
840803,507246
961490,502312
471747,529685
380705,538523
911180,504275
334149,544046
478992,529065
325789,545133
335884,543826
426976,533760
749007,511582
667067,516000
607586,519623
674054,515599
188534,569675
565185,522464
172090,573988
87592,608052
907432,504424
8912,760841
928318,503590
757917,511138
718693,513153
315141,546566
728326,512645
353492,541647
638429,517695
628892,518280
877286,505672
620895,518778
385878,537959
423311,534113
633501,517997
884833,505360
883402,505416
999665,500894
708395,513697
548142,523667
756491,511205
987352,501340
766520,510705
591775,520647
833758,507563
843890,507108
925551,503698
74816,616598
646942,517187
354923,541481
256291,555638
634470,517942
930904,503494
134221,586071
282663,551304
986070,501394
123636,590176
123678,590164
481717,528841
423076,534137
866246,506145
93313,604697
783632,509880
317066,546304
502977,527103
141272,583545
71708,618938
617748,518975
581190,521362
193824,568382
682368,515131
352956,541712
351375,541905
505362,526909
905165,504518
128645,588188
267143,553787
158409,577965
482776,528754
628896,518282
485233,528547
563606,522574
111001,595655
115920,593445
365510,540237
959724,502374
938763,503184
930044,503520
970959,501956
913658,504176
68117,621790
989729,501253
567697,522288
820427,508163
54236,634794
291557,549938
124961,589646
403177,536130
405421,535899
410233,535417
815111,508403
213176,563974
83099,610879
998588,500934
513640,526263
129817,587733
1820,921851
287584,550539
299160,548820
860621,506386
529258,525059
586297,521017
953406,502616
441234,532410
986217,501386
781938,509957
461247,530595
735424,512277
146623,581722
839838,507288
510667,526494
935085,503327
737523,512167
303455,548204
992779,501145
60240,628739
939095,503174
794368,509370
501825,527189
459028,530798
884641,505363
512287,526364
835165,507499
307723,547590
160587,577304
735043,512300
493289,527887
110717,595785
306480,547772
318593,546089
179810,571911
200531,566799
314999,546580
197020,567622
301465,548487
237808,559000
131944,586923
882527,505449
468117,530003
711319,513541
156240,578628
965452,502162
992756,501148
437959,532715
739938,512046
614249,519196
391496,537356
62746,626418
688215,514806
75501,616091
883573,505412
558824,522910
759371,511061
173913,573489
891351,505089
727464,512693
164833,576051
812317,508529
540320,524243
698061,514257
69149,620952
471673,529694
159092,577753
428134,533653
89997,606608
711061,513557
779403,510081
203327,566155
798176,509187
667688,515963
636120,517833
137410,584913
217615,563034
556887,523038
667229,515991
672276,515708
325361,545187
172115,573985
13846,725685
| -1 |
TheAlgorithms/Python | 7,262 | refactor: Move constants outside of variable scope | ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T19:51:55Z" | "2022-10-16T09:33:29Z" | 77764116217708933bdc65b29801092fa291398e | c6582b35bf8b8aba622c63096e3ab2f01aa36854 | refactor: Move constants outside of variable scope. ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| #!/usr/bin/env python3
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
filepaths = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
upper_files = [file for file in filepaths if file != file.lower()]
if upper_files:
print(f"{len(upper_files)} files contain uppercase characters:")
print("\n".join(upper_files) + "\n")
space_files = [file for file in filepaths if " " in file]
if space_files:
print(f"{len(space_files)} files contain space characters:")
print("\n".join(space_files) + "\n")
hyphen_files = [file for file in filepaths if "-" in file]
if hyphen_files:
print(f"{len(hyphen_files)} files contain hyphen characters:")
print("\n".join(hyphen_files) + "\n")
nodir_files = [file for file in filepaths if os.sep not in file]
if nodir_files:
print(f"{len(nodir_files)} files are not in a directory:")
print("\n".join(nodir_files) + "\n")
bad_files = len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
import sys
sys.exit(bad_files)
| #!/usr/bin/env python3
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
filepaths = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
upper_files = [file for file in filepaths if file != file.lower()]
if upper_files:
print(f"{len(upper_files)} files contain uppercase characters:")
print("\n".join(upper_files) + "\n")
space_files = [file for file in filepaths if " " in file]
if space_files:
print(f"{len(space_files)} files contain space characters:")
print("\n".join(space_files) + "\n")
hyphen_files = [file for file in filepaths if "-" in file]
if hyphen_files:
print(f"{len(hyphen_files)} files contain hyphen characters:")
print("\n".join(hyphen_files) + "\n")
nodir_files = [file for file in filepaths if os.sep not in file]
if nodir_files:
print(f"{len(nodir_files)} files are not in a directory:")
print("\n".join(nodir_files) + "\n")
bad_files = len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
import sys
sys.exit(bad_files)
| -1 |
TheAlgorithms/Python | 7,262 | refactor: Move constants outside of variable scope | ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T19:51:55Z" | "2022-10-16T09:33:29Z" | 77764116217708933bdc65b29801092fa291398e | c6582b35bf8b8aba622c63096e3ab2f01aa36854 | refactor: Move constants outside of variable scope. ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from datetime import datetime
import requests
def download_video(url: str) -> bytes:
base_url = "https://downloadgram.net/wp-json/wppress/video-downloader/video?url="
video_url = requests.get(base_url + url).json()[0]["urls"][0]["src"]
return requests.get(video_url).content
if __name__ == "__main__":
url = input("Enter Video/IGTV url: ").strip()
file_name = f"{datetime.now():%Y-%m-%d_%H:%M:%S}.mp4"
with open(file_name, "wb") as fp:
fp.write(download_video(url))
print(f"Done. Video saved to disk as {file_name}.")
| from datetime import datetime
import requests
def download_video(url: str) -> bytes:
base_url = "https://downloadgram.net/wp-json/wppress/video-downloader/video?url="
video_url = requests.get(base_url + url).json()[0]["urls"][0]["src"]
return requests.get(video_url).content
if __name__ == "__main__":
url = input("Enter Video/IGTV url: ").strip()
file_name = f"{datetime.now():%Y-%m-%d_%H:%M:%S}.mp4"
with open(file_name, "wb") as fp:
fp.write(download_video(url))
print(f"Done. Video saved to disk as {file_name}.")
| -1 |
TheAlgorithms/Python | 7,262 | refactor: Move constants outside of variable scope | ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T19:51:55Z" | "2022-10-16T09:33:29Z" | 77764116217708933bdc65b29801092fa291398e | c6582b35bf8b8aba622c63096e3ab2f01aa36854 | refactor: Move constants outside of variable scope. ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from typing import Any
class Node:
def __init__(self, data: Any):
self.data = data
self.next = None
class LinkedList:
def __init__(self):
self.head = None
def print_list(self):
temp = self.head
while temp is not None:
print(temp.data, end=" ")
temp = temp.next
print()
# adding nodes
def push(self, new_data: Any):
new_node = Node(new_data)
new_node.next = self.head
self.head = new_node
# swapping nodes
def swap_nodes(self, node_data_1, node_data_2):
if node_data_1 == node_data_2:
return
else:
node_1 = self.head
while node_1 is not None and node_1.data != node_data_1:
node_1 = node_1.next
node_2 = self.head
while node_2 is not None and node_2.data != node_data_2:
node_2 = node_2.next
if node_1 is None or node_2 is None:
return
node_1.data, node_2.data = node_2.data, node_1.data
if __name__ == "__main__":
ll = LinkedList()
for i in range(5, 0, -1):
ll.push(i)
ll.print_list()
ll.swap_nodes(1, 4)
print("After swapping")
ll.print_list()
| from typing import Any
class Node:
def __init__(self, data: Any):
self.data = data
self.next = None
class LinkedList:
def __init__(self):
self.head = None
def print_list(self):
temp = self.head
while temp is not None:
print(temp.data, end=" ")
temp = temp.next
print()
# adding nodes
def push(self, new_data: Any):
new_node = Node(new_data)
new_node.next = self.head
self.head = new_node
# swapping nodes
def swap_nodes(self, node_data_1, node_data_2):
if node_data_1 == node_data_2:
return
else:
node_1 = self.head
while node_1 is not None and node_1.data != node_data_1:
node_1 = node_1.next
node_2 = self.head
while node_2 is not None and node_2.data != node_data_2:
node_2 = node_2.next
if node_1 is None or node_2 is None:
return
node_1.data, node_2.data = node_2.data, node_1.data
if __name__ == "__main__":
ll = LinkedList()
for i in range(5, 0, -1):
ll.push(i)
ll.print_list()
ll.swap_nodes(1, 4)
print("After swapping")
ll.print_list()
| -1 |
TheAlgorithms/Python | 7,262 | refactor: Move constants outside of variable scope | ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T19:51:55Z" | "2022-10-16T09:33:29Z" | 77764116217708933bdc65b29801092fa291398e | c6582b35bf8b8aba622c63096e3ab2f01aa36854 | refactor: Move constants outside of variable scope. ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| def get_set_bits_count(number: int) -> int:
"""
Count the number of set bits in a 32 bit integer
>>> get_set_bits_count(25)
3
>>> get_set_bits_count(37)
3
>>> get_set_bits_count(21)
3
>>> get_set_bits_count(58)
4
>>> get_set_bits_count(0)
0
>>> get_set_bits_count(256)
1
>>> get_set_bits_count(-1)
Traceback (most recent call last):
...
ValueError: the value of input must be positive
"""
if number < 0:
raise ValueError("the value of input must be positive")
result = 0
while number:
if number % 2 == 1:
result += 1
number = number >> 1
return result
if __name__ == "__main__":
import doctest
doctest.testmod()
| def get_set_bits_count(number: int) -> int:
"""
Count the number of set bits in a 32 bit integer
>>> get_set_bits_count(25)
3
>>> get_set_bits_count(37)
3
>>> get_set_bits_count(21)
3
>>> get_set_bits_count(58)
4
>>> get_set_bits_count(0)
0
>>> get_set_bits_count(256)
1
>>> get_set_bits_count(-1)
Traceback (most recent call last):
...
ValueError: the value of input must be positive
"""
if number < 0:
raise ValueError("the value of input must be positive")
result = 0
while number:
if number % 2 == 1:
result += 1
number = number >> 1
return result
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 7,262 | refactor: Move constants outside of variable scope | ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T19:51:55Z" | "2022-10-16T09:33:29Z" | 77764116217708933bdc65b29801092fa291398e | c6582b35bf8b8aba622c63096e3ab2f01aa36854 | refactor: Move constants outside of variable scope. ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| import re
def dna(dna: str) -> str:
"""
https://en.wikipedia.org/wiki/DNA
Returns the second side of a DNA strand
>>> dna("GCTA")
'CGAT'
>>> dna("ATGC")
'TACG'
>>> dna("CTGA")
'GACT'
>>> dna("GFGG")
'Invalid Strand'
"""
r = len(re.findall("[ATCG]", dna)) != len(dna)
val = dna.translate(dna.maketrans("ATCG", "TAGC"))
return "Invalid Strand" if r else val
if __name__ == "__main__":
__import__("doctest").testmod()
| import re
def dna(dna: str) -> str:
"""
https://en.wikipedia.org/wiki/DNA
Returns the second side of a DNA strand
>>> dna("GCTA")
'CGAT'
>>> dna("ATGC")
'TACG'
>>> dna("CTGA")
'GACT'
>>> dna("GFGG")
'Invalid Strand'
"""
r = len(re.findall("[ATCG]", dna)) != len(dna)
val = dna.translate(dna.maketrans("ATCG", "TAGC"))
return "Invalid Strand" if r else val
if __name__ == "__main__":
__import__("doctest").testmod()
| -1 |
TheAlgorithms/Python | 7,262 | refactor: Move constants outside of variable scope | ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T19:51:55Z" | "2022-10-16T09:33:29Z" | 77764116217708933bdc65b29801092fa291398e | c6582b35bf8b8aba622c63096e3ab2f01aa36854 | refactor: Move constants outside of variable scope. ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """ https://en.wikipedia.org/wiki/Cocktail_shaker_sort """
def cocktail_shaker_sort(unsorted: list) -> list:
"""
Pure implementation of the cocktail shaker sort algorithm in Python.
>>> cocktail_shaker_sort([4, 5, 2, 1, 2])
[1, 2, 2, 4, 5]
>>> cocktail_shaker_sort([-4, 5, 0, 1, 2, 11])
[-4, 0, 1, 2, 5, 11]
>>> cocktail_shaker_sort([0.1, -2.4, 4.4, 2.2])
[-2.4, 0.1, 2.2, 4.4]
>>> cocktail_shaker_sort([1, 2, 3, 4, 5])
[1, 2, 3, 4, 5]
>>> cocktail_shaker_sort([-4, -5, -24, -7, -11])
[-24, -11, -7, -5, -4]
"""
for i in range(len(unsorted) - 1, 0, -1):
swapped = False
for j in range(i, 0, -1):
if unsorted[j] < unsorted[j - 1]:
unsorted[j], unsorted[j - 1] = unsorted[j - 1], unsorted[j]
swapped = True
for j in range(i):
if unsorted[j] > unsorted[j + 1]:
unsorted[j], unsorted[j + 1] = unsorted[j + 1], unsorted[j]
swapped = True
if not swapped:
break
return unsorted
if __name__ == "__main__":
import doctest
doctest.testmod()
user_input = input("Enter numbers separated by a comma:\n").strip()
unsorted = [int(item) for item in user_input.split(",")]
print(f"{cocktail_shaker_sort(unsorted) = }")
| """ https://en.wikipedia.org/wiki/Cocktail_shaker_sort """
def cocktail_shaker_sort(unsorted: list) -> list:
"""
Pure implementation of the cocktail shaker sort algorithm in Python.
>>> cocktail_shaker_sort([4, 5, 2, 1, 2])
[1, 2, 2, 4, 5]
>>> cocktail_shaker_sort([-4, 5, 0, 1, 2, 11])
[-4, 0, 1, 2, 5, 11]
>>> cocktail_shaker_sort([0.1, -2.4, 4.4, 2.2])
[-2.4, 0.1, 2.2, 4.4]
>>> cocktail_shaker_sort([1, 2, 3, 4, 5])
[1, 2, 3, 4, 5]
>>> cocktail_shaker_sort([-4, -5, -24, -7, -11])
[-24, -11, -7, -5, -4]
"""
for i in range(len(unsorted) - 1, 0, -1):
swapped = False
for j in range(i, 0, -1):
if unsorted[j] < unsorted[j - 1]:
unsorted[j], unsorted[j - 1] = unsorted[j - 1], unsorted[j]
swapped = True
for j in range(i):
if unsorted[j] > unsorted[j + 1]:
unsorted[j], unsorted[j + 1] = unsorted[j + 1], unsorted[j]
swapped = True
if not swapped:
break
return unsorted
if __name__ == "__main__":
import doctest
doctest.testmod()
user_input = input("Enter numbers separated by a comma:\n").strip()
unsorted = [int(item) for item in user_input.split(",")]
print(f"{cocktail_shaker_sort(unsorted) = }")
| -1 |
TheAlgorithms/Python | 7,262 | refactor: Move constants outside of variable scope | ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T19:51:55Z" | "2022-10-16T09:33:29Z" | 77764116217708933bdc65b29801092fa291398e | c6582b35bf8b8aba622c63096e3ab2f01aa36854 | refactor: Move constants outside of variable scope. ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # https://www.tutorialspoint.com/python3/bitwise_operators_example.htm
def binary_xor(a: int, b: int) -> str:
"""
Take in 2 integers, convert them to binary,
return a binary number that is the
result of a binary xor operation on the integers provided.
>>> binary_xor(25, 32)
'0b111001'
>>> binary_xor(37, 50)
'0b010111'
>>> binary_xor(21, 30)
'0b01011'
>>> binary_xor(58, 73)
'0b1110011'
>>> binary_xor(0, 255)
'0b11111111'
>>> binary_xor(256, 256)
'0b000000000'
>>> binary_xor(0, -1)
Traceback (most recent call last):
...
ValueError: the value of both inputs must be positive
>>> binary_xor(0, 1.1)
Traceback (most recent call last):
...
TypeError: 'float' object cannot be interpreted as an integer
>>> binary_xor("0", "1")
Traceback (most recent call last):
...
TypeError: '<' not supported between instances of 'str' and 'int'
"""
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive")
a_binary = str(bin(a))[2:] # remove the leading "0b"
b_binary = str(bin(b))[2:] # remove the leading "0b"
max_len = max(len(a_binary), len(b_binary))
return "0b" + "".join(
str(int(char_a != char_b))
for char_a, char_b in zip(a_binary.zfill(max_len), b_binary.zfill(max_len))
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| # https://www.tutorialspoint.com/python3/bitwise_operators_example.htm
def binary_xor(a: int, b: int) -> str:
"""
Take in 2 integers, convert them to binary,
return a binary number that is the
result of a binary xor operation on the integers provided.
>>> binary_xor(25, 32)
'0b111001'
>>> binary_xor(37, 50)
'0b010111'
>>> binary_xor(21, 30)
'0b01011'
>>> binary_xor(58, 73)
'0b1110011'
>>> binary_xor(0, 255)
'0b11111111'
>>> binary_xor(256, 256)
'0b000000000'
>>> binary_xor(0, -1)
Traceback (most recent call last):
...
ValueError: the value of both inputs must be positive
>>> binary_xor(0, 1.1)
Traceback (most recent call last):
...
TypeError: 'float' object cannot be interpreted as an integer
>>> binary_xor("0", "1")
Traceback (most recent call last):
...
TypeError: '<' not supported between instances of 'str' and 'int'
"""
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive")
a_binary = str(bin(a))[2:] # remove the leading "0b"
b_binary = str(bin(b))[2:] # remove the leading "0b"
max_len = max(len(a_binary), len(b_binary))
return "0b" + "".join(
str(int(char_a != char_b))
for char_a, char_b in zip(a_binary.zfill(max_len), b_binary.zfill(max_len))
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 7,262 | refactor: Move constants outside of variable scope | ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T19:51:55Z" | "2022-10-16T09:33:29Z" | 77764116217708933bdc65b29801092fa291398e | c6582b35bf8b8aba622c63096e3ab2f01aa36854 | refactor: Move constants outside of variable scope. ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| ### Interest
* Compound Interest: "Compound interest is calculated by multiplying the initial principal amount by one plus the annual interest rate raised to the number of compound periods minus one." [Compound Interest](https://www.investopedia.com/)
* Simple Interest: "Simple interest paid or received over a certain period is a fixed percentage of the principal amount that was borrowed or lent. " [Simple Interest](https://www.investopedia.com/)
| ### Interest
* Compound Interest: "Compound interest is calculated by multiplying the initial principal amount by one plus the annual interest rate raised to the number of compound periods minus one." [Compound Interest](https://www.investopedia.com/)
* Simple Interest: "Simple interest paid or received over a certain period is a fixed percentage of the principal amount that was borrowed or lent. " [Simple Interest](https://www.investopedia.com/)
| -1 |
TheAlgorithms/Python | 7,262 | refactor: Move constants outside of variable scope | ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T19:51:55Z" | "2022-10-16T09:33:29Z" | 77764116217708933bdc65b29801092fa291398e | c6582b35bf8b8aba622c63096e3ab2f01aa36854 | refactor: Move constants outside of variable scope. ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from timeit import timeit
def sum_of_digits(n: int) -> int:
"""
Find the sum of digits of a number.
>>> sum_of_digits(12345)
15
>>> sum_of_digits(123)
6
>>> sum_of_digits(-123)
6
>>> sum_of_digits(0)
0
"""
n = -n if n < 0 else n
res = 0
while n > 0:
res += n % 10
n = n // 10
return res
def sum_of_digits_recursion(n: int) -> int:
"""
Find the sum of digits of a number using recursion
>>> sum_of_digits_recursion(12345)
15
>>> sum_of_digits_recursion(123)
6
>>> sum_of_digits_recursion(-123)
6
>>> sum_of_digits_recursion(0)
0
"""
n = -n if n < 0 else n
return n if n < 10 else n % 10 + sum_of_digits(n // 10)
def sum_of_digits_compact(n: int) -> int:
"""
Find the sum of digits of a number
>>> sum_of_digits_compact(12345)
15
>>> sum_of_digits_compact(123)
6
>>> sum_of_digits_compact(-123)
6
>>> sum_of_digits_compact(0)
0
"""
return sum(int(c) for c in str(abs(n)))
def benchmark() -> None:
"""
Benchmark code for comparing 3 functions,
with 3 different length int values.
"""
print("\nFor small_num = ", small_num, ":")
print(
"> sum_of_digits()",
"\t\tans =",
sum_of_digits(small_num),
"\ttime =",
timeit("z.sum_of_digits(z.small_num)", setup="import __main__ as z"),
"seconds",
)
print(
"> sum_of_digits_recursion()",
"\tans =",
sum_of_digits_recursion(small_num),
"\ttime =",
timeit("z.sum_of_digits_recursion(z.small_num)", setup="import __main__ as z"),
"seconds",
)
print(
"> sum_of_digits_compact()",
"\tans =",
sum_of_digits_compact(small_num),
"\ttime =",
timeit("z.sum_of_digits_compact(z.small_num)", setup="import __main__ as z"),
"seconds",
)
print("\nFor medium_num = ", medium_num, ":")
print(
"> sum_of_digits()",
"\t\tans =",
sum_of_digits(medium_num),
"\ttime =",
timeit("z.sum_of_digits(z.medium_num)", setup="import __main__ as z"),
"seconds",
)
print(
"> sum_of_digits_recursion()",
"\tans =",
sum_of_digits_recursion(medium_num),
"\ttime =",
timeit("z.sum_of_digits_recursion(z.medium_num)", setup="import __main__ as z"),
"seconds",
)
print(
"> sum_of_digits_compact()",
"\tans =",
sum_of_digits_compact(medium_num),
"\ttime =",
timeit("z.sum_of_digits_compact(z.medium_num)", setup="import __main__ as z"),
"seconds",
)
print("\nFor large_num = ", large_num, ":")
print(
"> sum_of_digits()",
"\t\tans =",
sum_of_digits(large_num),
"\ttime =",
timeit("z.sum_of_digits(z.large_num)", setup="import __main__ as z"),
"seconds",
)
print(
"> sum_of_digits_recursion()",
"\tans =",
sum_of_digits_recursion(large_num),
"\ttime =",
timeit("z.sum_of_digits_recursion(z.large_num)", setup="import __main__ as z"),
"seconds",
)
print(
"> sum_of_digits_compact()",
"\tans =",
sum_of_digits_compact(large_num),
"\ttime =",
timeit("z.sum_of_digits_compact(z.large_num)", setup="import __main__ as z"),
"seconds",
)
if __name__ == "__main__":
small_num = 262144
medium_num = 1125899906842624
large_num = 1267650600228229401496703205376
benchmark()
import doctest
doctest.testmod()
| from timeit import timeit
def sum_of_digits(n: int) -> int:
"""
Find the sum of digits of a number.
>>> sum_of_digits(12345)
15
>>> sum_of_digits(123)
6
>>> sum_of_digits(-123)
6
>>> sum_of_digits(0)
0
"""
n = -n if n < 0 else n
res = 0
while n > 0:
res += n % 10
n = n // 10
return res
def sum_of_digits_recursion(n: int) -> int:
"""
Find the sum of digits of a number using recursion
>>> sum_of_digits_recursion(12345)
15
>>> sum_of_digits_recursion(123)
6
>>> sum_of_digits_recursion(-123)
6
>>> sum_of_digits_recursion(0)
0
"""
n = -n if n < 0 else n
return n if n < 10 else n % 10 + sum_of_digits(n // 10)
def sum_of_digits_compact(n: int) -> int:
"""
Find the sum of digits of a number
>>> sum_of_digits_compact(12345)
15
>>> sum_of_digits_compact(123)
6
>>> sum_of_digits_compact(-123)
6
>>> sum_of_digits_compact(0)
0
"""
return sum(int(c) for c in str(abs(n)))
def benchmark() -> None:
"""
Benchmark code for comparing 3 functions,
with 3 different length int values.
"""
print("\nFor small_num = ", small_num, ":")
print(
"> sum_of_digits()",
"\t\tans =",
sum_of_digits(small_num),
"\ttime =",
timeit("z.sum_of_digits(z.small_num)", setup="import __main__ as z"),
"seconds",
)
print(
"> sum_of_digits_recursion()",
"\tans =",
sum_of_digits_recursion(small_num),
"\ttime =",
timeit("z.sum_of_digits_recursion(z.small_num)", setup="import __main__ as z"),
"seconds",
)
print(
"> sum_of_digits_compact()",
"\tans =",
sum_of_digits_compact(small_num),
"\ttime =",
timeit("z.sum_of_digits_compact(z.small_num)", setup="import __main__ as z"),
"seconds",
)
print("\nFor medium_num = ", medium_num, ":")
print(
"> sum_of_digits()",
"\t\tans =",
sum_of_digits(medium_num),
"\ttime =",
timeit("z.sum_of_digits(z.medium_num)", setup="import __main__ as z"),
"seconds",
)
print(
"> sum_of_digits_recursion()",
"\tans =",
sum_of_digits_recursion(medium_num),
"\ttime =",
timeit("z.sum_of_digits_recursion(z.medium_num)", setup="import __main__ as z"),
"seconds",
)
print(
"> sum_of_digits_compact()",
"\tans =",
sum_of_digits_compact(medium_num),
"\ttime =",
timeit("z.sum_of_digits_compact(z.medium_num)", setup="import __main__ as z"),
"seconds",
)
print("\nFor large_num = ", large_num, ":")
print(
"> sum_of_digits()",
"\t\tans =",
sum_of_digits(large_num),
"\ttime =",
timeit("z.sum_of_digits(z.large_num)", setup="import __main__ as z"),
"seconds",
)
print(
"> sum_of_digits_recursion()",
"\tans =",
sum_of_digits_recursion(large_num),
"\ttime =",
timeit("z.sum_of_digits_recursion(z.large_num)", setup="import __main__ as z"),
"seconds",
)
print(
"> sum_of_digits_compact()",
"\tans =",
sum_of_digits_compact(large_num),
"\ttime =",
timeit("z.sum_of_digits_compact(z.large_num)", setup="import __main__ as z"),
"seconds",
)
if __name__ == "__main__":
small_num = 262144
medium_num = 1125899906842624
large_num = 1267650600228229401496703205376
benchmark()
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 7,262 | refactor: Move constants outside of variable scope | ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T19:51:55Z" | "2022-10-16T09:33:29Z" | 77764116217708933bdc65b29801092fa291398e | c6582b35bf8b8aba622c63096e3ab2f01aa36854 | refactor: Move constants outside of variable scope. ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from __future__ import annotations
def merge(left_half: list, right_half: list) -> list:
"""Helper function for mergesort.
>>> left_half = [-2]
>>> right_half = [-1]
>>> merge(left_half, right_half)
[-2, -1]
>>> left_half = [1,2,3]
>>> right_half = [4,5,6]
>>> merge(left_half, right_half)
[1, 2, 3, 4, 5, 6]
>>> left_half = [-2]
>>> right_half = [-1]
>>> merge(left_half, right_half)
[-2, -1]
>>> left_half = [12, 15]
>>> right_half = [13, 14]
>>> merge(left_half, right_half)
[12, 13, 14, 15]
>>> left_half = []
>>> right_half = []
>>> merge(left_half, right_half)
[]
"""
sorted_array = [None] * (len(right_half) + len(left_half))
pointer1 = 0 # pointer to current index for left Half
pointer2 = 0 # pointer to current index for the right Half
index = 0 # pointer to current index for the sorted array Half
while pointer1 < len(left_half) and pointer2 < len(right_half):
if left_half[pointer1] < right_half[pointer2]:
sorted_array[index] = left_half[pointer1]
pointer1 += 1
index += 1
else:
sorted_array[index] = right_half[pointer2]
pointer2 += 1
index += 1
while pointer1 < len(left_half):
sorted_array[index] = left_half[pointer1]
pointer1 += 1
index += 1
while pointer2 < len(right_half):
sorted_array[index] = right_half[pointer2]
pointer2 += 1
index += 1
return sorted_array
def merge_sort(array: list) -> list:
"""Returns a list of sorted array elements using merge sort.
>>> from random import shuffle
>>> array = [-2, 3, -10, 11, 99, 100000, 100, -200]
>>> shuffle(array)
>>> merge_sort(array)
[-200, -10, -2, 3, 11, 99, 100, 100000]
>>> shuffle(array)
>>> merge_sort(array)
[-200, -10, -2, 3, 11, 99, 100, 100000]
>>> array = [-200]
>>> merge_sort(array)
[-200]
>>> array = [-2, 3, -10, 11, 99, 100000, 100, -200]
>>> shuffle(array)
>>> sorted(array) == merge_sort(array)
True
>>> array = [-2]
>>> merge_sort(array)
[-2]
>>> array = []
>>> merge_sort(array)
[]
>>> array = [10000000, 1, -1111111111, 101111111112, 9000002]
>>> sorted(array) == merge_sort(array)
True
"""
if len(array) <= 1:
return array
# the actual formula to calculate the middle element = left + (right - left) // 2
# this avoids integer overflow in case of large N
middle = 0 + (len(array) - 0) // 2
# Split the array into halves till the array length becomes equal to One
# merge the arrays of single length returned by mergeSort function and
# pass them into the merge arrays function which merges the array
left_half = array[:middle]
right_half = array[middle:]
return merge(merge_sort(left_half), merge_sort(right_half))
if __name__ == "__main__":
import doctest
doctest.testmod()
| from __future__ import annotations
def merge(left_half: list, right_half: list) -> list:
"""Helper function for mergesort.
>>> left_half = [-2]
>>> right_half = [-1]
>>> merge(left_half, right_half)
[-2, -1]
>>> left_half = [1,2,3]
>>> right_half = [4,5,6]
>>> merge(left_half, right_half)
[1, 2, 3, 4, 5, 6]
>>> left_half = [-2]
>>> right_half = [-1]
>>> merge(left_half, right_half)
[-2, -1]
>>> left_half = [12, 15]
>>> right_half = [13, 14]
>>> merge(left_half, right_half)
[12, 13, 14, 15]
>>> left_half = []
>>> right_half = []
>>> merge(left_half, right_half)
[]
"""
sorted_array = [None] * (len(right_half) + len(left_half))
pointer1 = 0 # pointer to current index for left Half
pointer2 = 0 # pointer to current index for the right Half
index = 0 # pointer to current index for the sorted array Half
while pointer1 < len(left_half) and pointer2 < len(right_half):
if left_half[pointer1] < right_half[pointer2]:
sorted_array[index] = left_half[pointer1]
pointer1 += 1
index += 1
else:
sorted_array[index] = right_half[pointer2]
pointer2 += 1
index += 1
while pointer1 < len(left_half):
sorted_array[index] = left_half[pointer1]
pointer1 += 1
index += 1
while pointer2 < len(right_half):
sorted_array[index] = right_half[pointer2]
pointer2 += 1
index += 1
return sorted_array
def merge_sort(array: list) -> list:
"""Returns a list of sorted array elements using merge sort.
>>> from random import shuffle
>>> array = [-2, 3, -10, 11, 99, 100000, 100, -200]
>>> shuffle(array)
>>> merge_sort(array)
[-200, -10, -2, 3, 11, 99, 100, 100000]
>>> shuffle(array)
>>> merge_sort(array)
[-200, -10, -2, 3, 11, 99, 100, 100000]
>>> array = [-200]
>>> merge_sort(array)
[-200]
>>> array = [-2, 3, -10, 11, 99, 100000, 100, -200]
>>> shuffle(array)
>>> sorted(array) == merge_sort(array)
True
>>> array = [-2]
>>> merge_sort(array)
[-2]
>>> array = []
>>> merge_sort(array)
[]
>>> array = [10000000, 1, -1111111111, 101111111112, 9000002]
>>> sorted(array) == merge_sort(array)
True
"""
if len(array) <= 1:
return array
# the actual formula to calculate the middle element = left + (right - left) // 2
# this avoids integer overflow in case of large N
middle = 0 + (len(array) - 0) // 2
# Split the array into halves till the array length becomes equal to One
# merge the arrays of single length returned by mergeSort function and
# pass them into the merge arrays function which merges the array
left_half = array[:middle]
right_half = array[middle:]
return merge(merge_sort(left_half), merge_sort(right_half))
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 7,262 | refactor: Move constants outside of variable scope | ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T19:51:55Z" | "2022-10-16T09:33:29Z" | 77764116217708933bdc65b29801092fa291398e | c6582b35bf8b8aba622c63096e3ab2f01aa36854 | refactor: Move constants outside of variable scope. ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 7,262 | refactor: Move constants outside of variable scope | ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T19:51:55Z" | "2022-10-16T09:33:29Z" | 77764116217708933bdc65b29801092fa291398e | c6582b35bf8b8aba622c63096e3ab2f01aa36854 | refactor: Move constants outside of variable scope. ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # Welcome to Quantum Algorithms
Started at https://github.com/TheAlgorithms/Python/issues/1831
* D-Wave: https://www.dwavesys.com and https://github.com/dwavesystems
* Google: https://research.google/teams/applied-science/quantum
* IBM: https://qiskit.org and https://github.com/Qiskit
* Rigetti: https://rigetti.com and https://github.com/rigetti
* Zapata: https://www.zapatacomputing.com and https://github.com/zapatacomputing
## IBM Qiskit
- Start using by installing `pip install qiskit`, refer the [docs](https://qiskit.org/documentation/install.html) for more info.
- Tutorials & References
- https://github.com/Qiskit/qiskit-tutorials
- https://quantum-computing.ibm.com/docs/iql/first-circuit
- https://medium.com/qiskit/how-to-program-a-quantum-computer-982a9329ed02
## Google Cirq
- Start using by installing `python -m pip install cirq`, refer the [docs](https://quantumai.google/cirq/start/install) for more info.
- Tutorials & references
- https://github.com/quantumlib/cirq
- https://quantumai.google/cirq/experiments
- https://tanishabassan.medium.com/quantum-programming-with-google-cirq-3209805279bc
| # Welcome to Quantum Algorithms
Started at https://github.com/TheAlgorithms/Python/issues/1831
* D-Wave: https://www.dwavesys.com and https://github.com/dwavesystems
* Google: https://research.google/teams/applied-science/quantum
* IBM: https://qiskit.org and https://github.com/Qiskit
* Rigetti: https://rigetti.com and https://github.com/rigetti
* Zapata: https://www.zapatacomputing.com and https://github.com/zapatacomputing
## IBM Qiskit
- Start using by installing `pip install qiskit`, refer the [docs](https://qiskit.org/documentation/install.html) for more info.
- Tutorials & References
- https://github.com/Qiskit/qiskit-tutorials
- https://quantum-computing.ibm.com/docs/iql/first-circuit
- https://medium.com/qiskit/how-to-program-a-quantum-computer-982a9329ed02
## Google Cirq
- Start using by installing `python -m pip install cirq`, refer the [docs](https://quantumai.google/cirq/start/install) for more info.
- Tutorials & references
- https://github.com/quantumlib/cirq
- https://quantumai.google/cirq/experiments
- https://tanishabassan.medium.com/quantum-programming-with-google-cirq-3209805279bc
| -1 |
TheAlgorithms/Python | 7,262 | refactor: Move constants outside of variable scope | ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T19:51:55Z" | "2022-10-16T09:33:29Z" | 77764116217708933bdc65b29801092fa291398e | c6582b35bf8b8aba622c63096e3ab2f01aa36854 | refactor: Move constants outside of variable scope. ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
PyTest's for Digital Image Processing
"""
import numpy as np
from cv2 import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uint8
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny as canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs
img = imread(r"digital_image_processing/image_data/lena_small.jpg")
gray = cvtColor(img, COLOR_BGR2GRAY)
# Test: convert_to_negative()
def test_convert_to_negative():
negative_img = cn.convert_to_negative(img)
# assert negative_img array for at least one True
assert negative_img.any()
# Test: change_contrast()
def test_change_contrast():
with Image.open("digital_image_processing/image_data/lena_small.jpg") as img:
# Work around assertion for response
assert str(cc.change_contrast(img, 110)).startswith(
"<PIL.Image.Image image mode=RGB size=100x100 at"
)
# canny.gen_gaussian_kernel()
def test_gen_gaussian_kernel():
resp = canny.gen_gaussian_kernel(9, sigma=1.4)
# Assert ambiguous array
assert resp.all()
# canny.py
def test_canny():
canny_img = imread("digital_image_processing/image_data/lena_small.jpg", 0)
# assert ambiguous array for all == True
assert canny_img.all()
canny_array = canny.canny(canny_img)
# assert canny array for at least one True
assert canny_array.any()
# filters/gaussian_filter.py
def test_gen_gaussian_kernel_filter():
assert gg.gaussian_filter(gray, 5, sigma=0.9).all()
def test_convolve_filter():
# laplace diagonals
laplace = array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]])
res = conv.img_convolve(gray, laplace).astype(uint8)
assert res.any()
def test_median_filter():
assert med.median_filter(gray, 3).any()
def test_sobel_filter():
grad, theta = sob.sobel_filter(gray)
assert grad.any() and theta.any()
def test_sepia():
sepia = sp.make_sepia(img, 20)
assert sepia.all()
def test_burkes(file_path: str = "digital_image_processing/image_data/lena_small.jpg"):
burkes = bs.Burkes(imread(file_path, 1), 120)
burkes.process()
assert burkes.output_img.any()
def test_nearest_neighbour(
file_path: str = "digital_image_processing/image_data/lena_small.jpg",
):
nn = rs.NearestNeighbour(imread(file_path, 1), 400, 200)
nn.process()
assert nn.output.any()
def test_local_binary_pattern():
file_path: str = "digital_image_processing/image_data/lena.jpg"
# Reading the image and converting it to grayscale.
image = imread(file_path, 0)
# Test for get_neighbors_pixel function() return not None
x_coordinate = 0
y_coordinate = 0
center = image[x_coordinate][y_coordinate]
neighbors_pixels = lbp.get_neighbors_pixel(
image, x_coordinate, y_coordinate, center
)
assert neighbors_pixels is not None
# Test for local_binary_pattern function()
# Create a numpy array as the same height and width of read image
lbp_image = np.zeros((image.shape[0], image.shape[1]))
# Iterating through the image and calculating the local binary pattern value
# for each pixel.
for i in range(0, image.shape[0]):
for j in range(0, image.shape[1]):
lbp_image[i][j] = lbp.local_binary_value(image, i, j)
assert lbp_image.any()
| """
PyTest's for Digital Image Processing
"""
import numpy as np
from cv2 import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uint8
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny as canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs
img = imread(r"digital_image_processing/image_data/lena_small.jpg")
gray = cvtColor(img, COLOR_BGR2GRAY)
# Test: convert_to_negative()
def test_convert_to_negative():
negative_img = cn.convert_to_negative(img)
# assert negative_img array for at least one True
assert negative_img.any()
# Test: change_contrast()
def test_change_contrast():
with Image.open("digital_image_processing/image_data/lena_small.jpg") as img:
# Work around assertion for response
assert str(cc.change_contrast(img, 110)).startswith(
"<PIL.Image.Image image mode=RGB size=100x100 at"
)
# canny.gen_gaussian_kernel()
def test_gen_gaussian_kernel():
resp = canny.gen_gaussian_kernel(9, sigma=1.4)
# Assert ambiguous array
assert resp.all()
# canny.py
def test_canny():
canny_img = imread("digital_image_processing/image_data/lena_small.jpg", 0)
# assert ambiguous array for all == True
assert canny_img.all()
canny_array = canny.canny(canny_img)
# assert canny array for at least one True
assert canny_array.any()
# filters/gaussian_filter.py
def test_gen_gaussian_kernel_filter():
assert gg.gaussian_filter(gray, 5, sigma=0.9).all()
def test_convolve_filter():
# laplace diagonals
laplace = array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]])
res = conv.img_convolve(gray, laplace).astype(uint8)
assert res.any()
def test_median_filter():
assert med.median_filter(gray, 3).any()
def test_sobel_filter():
grad, theta = sob.sobel_filter(gray)
assert grad.any() and theta.any()
def test_sepia():
sepia = sp.make_sepia(img, 20)
assert sepia.all()
def test_burkes(file_path: str = "digital_image_processing/image_data/lena_small.jpg"):
burkes = bs.Burkes(imread(file_path, 1), 120)
burkes.process()
assert burkes.output_img.any()
def test_nearest_neighbour(
file_path: str = "digital_image_processing/image_data/lena_small.jpg",
):
nn = rs.NearestNeighbour(imread(file_path, 1), 400, 200)
nn.process()
assert nn.output.any()
def test_local_binary_pattern():
file_path: str = "digital_image_processing/image_data/lena.jpg"
# Reading the image and converting it to grayscale.
image = imread(file_path, 0)
# Test for get_neighbors_pixel function() return not None
x_coordinate = 0
y_coordinate = 0
center = image[x_coordinate][y_coordinate]
neighbors_pixels = lbp.get_neighbors_pixel(
image, x_coordinate, y_coordinate, center
)
assert neighbors_pixels is not None
# Test for local_binary_pattern function()
# Create a numpy array as the same height and width of read image
lbp_image = np.zeros((image.shape[0], image.shape[1]))
# Iterating through the image and calculating the local binary pattern value
# for each pixel.
for i in range(0, image.shape[0]):
for j in range(0, image.shape[1]):
lbp_image[i][j] = lbp.local_binary_value(image, i, j)
assert lbp_image.any()
| -1 |
TheAlgorithms/Python | 7,262 | refactor: Move constants outside of variable scope | ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T19:51:55Z" | "2022-10-16T09:33:29Z" | 77764116217708933bdc65b29801092fa291398e | c6582b35bf8b8aba622c63096e3ab2f01aa36854 | refactor: Move constants outside of variable scope. ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 7,262 | refactor: Move constants outside of variable scope | ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T19:51:55Z" | "2022-10-16T09:33:29Z" | 77764116217708933bdc65b29801092fa291398e | c6582b35bf8b8aba622c63096e3ab2f01aa36854 | refactor: Move constants outside of variable scope. ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # Author: João Gustavo A. Amorim
# Author email: [email protected]
# Coding date: jan 2019
# python/black: True
# Imports
import numpy as np
# Class implemented to calculus the index
class IndexCalculation:
"""
# Class Summary
This algorithm consists in calculating vegetation indices, these
indices can be used for precision agriculture for example (or remote
sensing). There are functions to define the data and to calculate the
implemented indices.
# Vegetation index
https://en.wikipedia.org/wiki/Vegetation_Index
A Vegetation Index (VI) is a spectral transformation of two or more bands
designed to enhance the contribution of vegetation properties and allow
reliable spatial and temporal inter-comparisons of terrestrial
photosynthetic activity and canopy structural variations
# Information about channels (Wavelength range for each)
* nir - near-infrared
https://www.malvernpanalytical.com/br/products/technology/near-infrared-spectroscopy
Wavelength Range 700 nm to 2500 nm
* Red Edge
https://en.wikipedia.org/wiki/Red_edge
Wavelength Range 680 nm to 730 nm
* red
https://en.wikipedia.org/wiki/Color
Wavelength Range 635 nm to 700 nm
* blue
https://en.wikipedia.org/wiki/Color
Wavelength Range 450 nm to 490 nm
* green
https://en.wikipedia.org/wiki/Color
Wavelength Range 520 nm to 560 nm
# Implemented index list
#"abbreviationOfIndexName" -- list of channels used
#"ARVI2" -- red, nir
#"CCCI" -- red, redEdge, nir
#"CVI" -- red, green, nir
#"GLI" -- red, green, blue
#"NDVI" -- red, nir
#"BNDVI" -- blue, nir
#"redEdgeNDVI" -- red, redEdge
#"GNDVI" -- green, nir
#"GBNDVI" -- green, blue, nir
#"GRNDVI" -- red, green, nir
#"RBNDVI" -- red, blue, nir
#"PNDVI" -- red, green, blue, nir
#"ATSAVI" -- red, nir
#"BWDRVI" -- blue, nir
#"CIgreen" -- green, nir
#"CIrededge" -- redEdge, nir
#"CI" -- red, blue
#"CTVI" -- red, nir
#"GDVI" -- green, nir
#"EVI" -- red, blue, nir
#"GEMI" -- red, nir
#"GOSAVI" -- green, nir
#"GSAVI" -- green, nir
#"Hue" -- red, green, blue
#"IVI" -- red, nir
#"IPVI" -- red, nir
#"I" -- red, green, blue
#"RVI" -- red, nir
#"MRVI" -- red, nir
#"MSAVI" -- red, nir
#"NormG" -- red, green, nir
#"NormNIR" -- red, green, nir
#"NormR" -- red, green, nir
#"NGRDI" -- red, green
#"RI" -- red, green
#"S" -- red, green, blue
#"IF" -- red, green, blue
#"DVI" -- red, nir
#"TVI" -- red, nir
#"NDRE" -- redEdge, nir
#list of all index implemented
#allIndex = ["ARVI2", "CCCI", "CVI", "GLI", "NDVI", "BNDVI", "redEdgeNDVI",
"GNDVI", "GBNDVI", "GRNDVI", "RBNDVI", "PNDVI", "ATSAVI",
"BWDRVI", "CIgreen", "CIrededge", "CI", "CTVI", "GDVI", "EVI",
"GEMI", "GOSAVI", "GSAVI", "Hue", "IVI", "IPVI", "I", "RVI",
"MRVI", "MSAVI", "NormG", "NormNIR", "NormR", "NGRDI", "RI",
"S", "IF", "DVI", "TVI", "NDRE"]
#list of index with not blue channel
#notBlueIndex = ["ARVI2", "CCCI", "CVI", "NDVI", "redEdgeNDVI", "GNDVI",
"GRNDVI", "ATSAVI", "CIgreen", "CIrededge", "CTVI", "GDVI",
"GEMI", "GOSAVI", "GSAVI", "IVI", "IPVI", "RVI", "MRVI",
"MSAVI", "NormG", "NormNIR", "NormR", "NGRDI", "RI", "DVI",
"TVI", "NDRE"]
#list of index just with RGB channels
#RGBIndex = ["GLI", "CI", "Hue", "I", "NGRDI", "RI", "S", "IF"]
"""
def __init__(self, red=None, green=None, blue=None, red_edge=None, nir=None):
# print("Numpy version: " + np.__version__)
self.set_matricies(red=red, green=green, blue=blue, red_edge=red_edge, nir=nir)
def set_matricies(self, red=None, green=None, blue=None, red_edge=None, nir=None):
if red is not None:
self.red = red
if green is not None:
self.green = green
if blue is not None:
self.blue = blue
if red_edge is not None:
self.redEdge = red_edge
if nir is not None:
self.nir = nir
return True
def calculation(
self, index="", red=None, green=None, blue=None, red_edge=None, nir=None
):
"""
performs the calculation of the index with the values instantiated in the class
:str index: abbreviation of index name to perform
"""
self.set_matricies(red=red, green=green, blue=blue, red_edge=red_edge, nir=nir)
funcs = {
"ARVI2": self.arv12,
"CCCI": self.ccci,
"CVI": self.cvi,
"GLI": self.gli,
"NDVI": self.ndvi,
"BNDVI": self.bndvi,
"redEdgeNDVI": self.red_edge_ndvi,
"GNDVI": self.gndvi,
"GBNDVI": self.gbndvi,
"GRNDVI": self.grndvi,
"RBNDVI": self.rbndvi,
"PNDVI": self.pndvi,
"ATSAVI": self.atsavi,
"BWDRVI": self.bwdrvi,
"CIgreen": self.ci_green,
"CIrededge": self.ci_rededge,
"CI": self.ci,
"CTVI": self.ctvi,
"GDVI": self.gdvi,
"EVI": self.evi,
"GEMI": self.gemi,
"GOSAVI": self.gosavi,
"GSAVI": self.gsavi,
"Hue": self.hue,
"IVI": self.ivi,
"IPVI": self.ipvi,
"I": self.i,
"RVI": self.rvi,
"MRVI": self.mrvi,
"MSAVI": self.m_savi,
"NormG": self.norm_g,
"NormNIR": self.norm_nir,
"NormR": self.norm_r,
"NGRDI": self.ngrdi,
"RI": self.ri,
"S": self.s,
"IF": self._if,
"DVI": self.dvi,
"TVI": self.tvi,
"NDRE": self.ndre,
}
try:
return funcs[index]()
except KeyError:
print("Index not in the list!")
return False
def arv12(self):
"""
Atmospherically Resistant Vegetation Index 2
https://www.indexdatabase.de/db/i-single.php?id=396
:return: index
−0.18+1.17*(self.nir−self.red)/(self.nir+self.red)
"""
return -0.18 + (1.17 * ((self.nir - self.red) / (self.nir + self.red)))
def ccci(self):
"""
Canopy Chlorophyll Content Index
https://www.indexdatabase.de/db/i-single.php?id=224
:return: index
"""
return ((self.nir - self.redEdge) / (self.nir + self.redEdge)) / (
(self.nir - self.red) / (self.nir + self.red)
)
def cvi(self):
"""
Chlorophyll vegetation index
https://www.indexdatabase.de/db/i-single.php?id=391
:return: index
"""
return self.nir * (self.red / (self.green**2))
def gli(self):
"""
self.green leaf index
https://www.indexdatabase.de/db/i-single.php?id=375
:return: index
"""
return (2 * self.green - self.red - self.blue) / (
2 * self.green + self.red + self.blue
)
def ndvi(self):
"""
Normalized Difference self.nir/self.red Normalized Difference Vegetation
Index, Calibrated NDVI - CDVI
https://www.indexdatabase.de/db/i-single.php?id=58
:return: index
"""
return (self.nir - self.red) / (self.nir + self.red)
def bndvi(self):
"""
Normalized Difference self.nir/self.blue self.blue-normalized difference
vegetation index
https://www.indexdatabase.de/db/i-single.php?id=135
:return: index
"""
return (self.nir - self.blue) / (self.nir + self.blue)
def red_edge_ndvi(self):
"""
Normalized Difference self.rededge/self.red
https://www.indexdatabase.de/db/i-single.php?id=235
:return: index
"""
return (self.redEdge - self.red) / (self.redEdge + self.red)
def gndvi(self):
"""
Normalized Difference self.nir/self.green self.green NDVI
https://www.indexdatabase.de/db/i-single.php?id=401
:return: index
"""
return (self.nir - self.green) / (self.nir + self.green)
def gbndvi(self):
"""
self.green-self.blue NDVI
https://www.indexdatabase.de/db/i-single.php?id=186
:return: index
"""
return (self.nir - (self.green + self.blue)) / (
self.nir + (self.green + self.blue)
)
def grndvi(self):
"""
self.green-self.red NDVI
https://www.indexdatabase.de/db/i-single.php?id=185
:return: index
"""
return (self.nir - (self.green + self.red)) / (
self.nir + (self.green + self.red)
)
def rbndvi(self):
"""
self.red-self.blue NDVI
https://www.indexdatabase.de/db/i-single.php?id=187
:return: index
"""
return (self.nir - (self.blue + self.red)) / (self.nir + (self.blue + self.red))
def pndvi(self):
"""
Pan NDVI
https://www.indexdatabase.de/db/i-single.php?id=188
:return: index
"""
return (self.nir - (self.green + self.red + self.blue)) / (
self.nir + (self.green + self.red + self.blue)
)
def atsavi(self, x=0.08, a=1.22, b=0.03):
"""
Adjusted transformed soil-adjusted VI
https://www.indexdatabase.de/db/i-single.php?id=209
:return: index
"""
return a * (
(self.nir - a * self.red - b)
/ (a * self.nir + self.red - a * b + x * (1 + a**2))
)
def bwdrvi(self):
"""
self.blue-wide dynamic range vegetation index
https://www.indexdatabase.de/db/i-single.php?id=136
:return: index
"""
return (0.1 * self.nir - self.blue) / (0.1 * self.nir + self.blue)
def ci_green(self):
"""
Chlorophyll Index self.green
https://www.indexdatabase.de/db/i-single.php?id=128
:return: index
"""
return (self.nir / self.green) - 1
def ci_rededge(self):
"""
Chlorophyll Index self.redEdge
https://www.indexdatabase.de/db/i-single.php?id=131
:return: index
"""
return (self.nir / self.redEdge) - 1
def ci(self):
"""
Coloration Index
https://www.indexdatabase.de/db/i-single.php?id=11
:return: index
"""
return (self.red - self.blue) / self.red
def ctvi(self):
"""
Corrected Transformed Vegetation Index
https://www.indexdatabase.de/db/i-single.php?id=244
:return: index
"""
ndvi = self.ndvi()
return ((ndvi + 0.5) / (abs(ndvi + 0.5))) * (abs(ndvi + 0.5) ** (1 / 2))
def gdvi(self):
"""
Difference self.nir/self.green self.green Difference Vegetation Index
https://www.indexdatabase.de/db/i-single.php?id=27
:return: index
"""
return self.nir - self.green
def evi(self):
"""
Enhanced Vegetation Index
https://www.indexdatabase.de/db/i-single.php?id=16
:return: index
"""
return 2.5 * (
(self.nir - self.red) / (self.nir + 6 * self.red - 7.5 * self.blue + 1)
)
def gemi(self):
"""
Global Environment Monitoring Index
https://www.indexdatabase.de/db/i-single.php?id=25
:return: index
"""
n = (2 * (self.nir**2 - self.red**2) + 1.5 * self.nir + 0.5 * self.red) / (
self.nir + self.red + 0.5
)
return n * (1 - 0.25 * n) - (self.red - 0.125) / (1 - self.red)
def gosavi(self, y=0.16):
"""
self.green Optimized Soil Adjusted Vegetation Index
https://www.indexdatabase.de/db/i-single.php?id=29
mit Y = 0,16
:return: index
"""
return (self.nir - self.green) / (self.nir + self.green + y)
def gsavi(self, n=0.5):
"""
self.green Soil Adjusted Vegetation Index
https://www.indexdatabase.de/db/i-single.php?id=31
mit N = 0,5
:return: index
"""
return ((self.nir - self.green) / (self.nir + self.green + n)) * (1 + n)
def hue(self):
"""
Hue
https://www.indexdatabase.de/db/i-single.php?id=34
:return: index
"""
return np.arctan(
((2 * self.red - self.green - self.blue) / 30.5) * (self.green - self.blue)
)
def ivi(self, a=None, b=None):
"""
Ideal vegetation index
https://www.indexdatabase.de/db/i-single.php?id=276
b=intercept of vegetation line
a=soil line slope
:return: index
"""
return (self.nir - b) / (a * self.red)
def ipvi(self):
"""
Infraself.red percentage vegetation index
https://www.indexdatabase.de/db/i-single.php?id=35
:return: index
"""
return (self.nir / ((self.nir + self.red) / 2)) * (self.ndvi() + 1)
def i(self): # noqa: E741,E743
"""
Intensity
https://www.indexdatabase.de/db/i-single.php?id=36
:return: index
"""
return (self.red + self.green + self.blue) / 30.5
def rvi(self):
"""
Ratio-Vegetation-Index
http://www.seos-project.eu/modules/remotesensing/remotesensing-c03-s01-p01.html
:return: index
"""
return self.nir / self.red
def mrvi(self):
"""
Modified Normalized Difference Vegetation Index RVI
https://www.indexdatabase.de/db/i-single.php?id=275
:return: index
"""
return (self.rvi() - 1) / (self.rvi() + 1)
def m_savi(self):
"""
Modified Soil Adjusted Vegetation Index
https://www.indexdatabase.de/db/i-single.php?id=44
:return: index
"""
return (
(2 * self.nir + 1)
- ((2 * self.nir + 1) ** 2 - 8 * (self.nir - self.red)) ** (1 / 2)
) / 2
def norm_g(self):
"""
Norm G
https://www.indexdatabase.de/db/i-single.php?id=50
:return: index
"""
return self.green / (self.nir + self.red + self.green)
def norm_nir(self):
"""
Norm self.nir
https://www.indexdatabase.de/db/i-single.php?id=51
:return: index
"""
return self.nir / (self.nir + self.red + self.green)
def norm_r(self):
"""
Norm R
https://www.indexdatabase.de/db/i-single.php?id=52
:return: index
"""
return self.red / (self.nir + self.red + self.green)
def ngrdi(self):
"""
Normalized Difference self.green/self.red Normalized self.green self.red
difference index, Visible Atmospherically Resistant Indices self.green
(VIself.green)
https://www.indexdatabase.de/db/i-single.php?id=390
:return: index
"""
return (self.green - self.red) / (self.green + self.red)
def ri(self):
"""
Normalized Difference self.red/self.green self.redness Index
https://www.indexdatabase.de/db/i-single.php?id=74
:return: index
"""
return (self.red - self.green) / (self.red + self.green)
def s(self):
"""
Saturation
https://www.indexdatabase.de/db/i-single.php?id=77
:return: index
"""
max_value = np.max([np.max(self.red), np.max(self.green), np.max(self.blue)])
min_value = np.min([np.min(self.red), np.min(self.green), np.min(self.blue)])
return (max_value - min_value) / max_value
def _if(self):
"""
Shape Index
https://www.indexdatabase.de/db/i-single.php?id=79
:return: index
"""
return (2 * self.red - self.green - self.blue) / (self.green - self.blue)
def dvi(self):
"""
Simple Ratio self.nir/self.red Difference Vegetation Index, Vegetation Index
Number (VIN)
https://www.indexdatabase.de/db/i-single.php?id=12
:return: index
"""
return self.nir / self.red
def tvi(self):
"""
Transformed Vegetation Index
https://www.indexdatabase.de/db/i-single.php?id=98
:return: index
"""
return (self.ndvi() + 0.5) ** (1 / 2)
def ndre(self):
return (self.nir - self.redEdge) / (self.nir + self.redEdge)
"""
# genering a random matrices to test this class
red = np.ones((1000,1000, 1),dtype="float64") * 46787
green = np.ones((1000,1000, 1),dtype="float64") * 23487
blue = np.ones((1000,1000, 1),dtype="float64") * 14578
redEdge = np.ones((1000,1000, 1),dtype="float64") * 51045
nir = np.ones((1000,1000, 1),dtype="float64") * 52200
# Examples of how to use the class
# instantiating the class
cl = IndexCalculation()
# instantiating the class with the values
#cl = indexCalculation(red=red, green=green, blue=blue, redEdge=redEdge, nir=nir)
# how set the values after instantiate the class cl, (for update the data or when don't
# instantiating the class with the values)
cl.setMatrices(red=red, green=green, blue=blue, redEdge=redEdge, nir=nir)
# calculating the indices for the instantiated values in the class
# Note: the CCCI index can be changed to any index implemented in the class.
indexValue_form1 = cl.calculation("CCCI", red=red, green=green, blue=blue,
redEdge=redEdge, nir=nir).astype(np.float64)
indexValue_form2 = cl.CCCI()
# calculating the index with the values directly -- you can set just the values
# preferred note: the *calculation* function performs the function *setMatrices*
indexValue_form3 = cl.calculation("CCCI", red=red, green=green, blue=blue,
redEdge=redEdge, nir=nir).astype(np.float64)
print("Form 1: "+np.array2string(indexValue_form1, precision=20, separator=', ',
floatmode='maxprec_equal'))
print("Form 2: "+np.array2string(indexValue_form2, precision=20, separator=', ',
floatmode='maxprec_equal'))
print("Form 3: "+np.array2string(indexValue_form3, precision=20, separator=', ',
floatmode='maxprec_equal'))
# A list of examples results for different type of data at NDVI
# float16 -> 0.31567383 #NDVI (red = 50, nir = 100)
# float32 -> 0.31578946 #NDVI (red = 50, nir = 100)
# float64 -> 0.3157894736842105 #NDVI (red = 50, nir = 100)
# longdouble -> 0.3157894736842105 #NDVI (red = 50, nir = 100)
"""
| # Author: João Gustavo A. Amorim
# Author email: [email protected]
# Coding date: jan 2019
# python/black: True
# Imports
import numpy as np
# Class implemented to calculus the index
class IndexCalculation:
"""
# Class Summary
This algorithm consists in calculating vegetation indices, these
indices can be used for precision agriculture for example (or remote
sensing). There are functions to define the data and to calculate the
implemented indices.
# Vegetation index
https://en.wikipedia.org/wiki/Vegetation_Index
A Vegetation Index (VI) is a spectral transformation of two or more bands
designed to enhance the contribution of vegetation properties and allow
reliable spatial and temporal inter-comparisons of terrestrial
photosynthetic activity and canopy structural variations
# Information about channels (Wavelength range for each)
* nir - near-infrared
https://www.malvernpanalytical.com/br/products/technology/near-infrared-spectroscopy
Wavelength Range 700 nm to 2500 nm
* Red Edge
https://en.wikipedia.org/wiki/Red_edge
Wavelength Range 680 nm to 730 nm
* red
https://en.wikipedia.org/wiki/Color
Wavelength Range 635 nm to 700 nm
* blue
https://en.wikipedia.org/wiki/Color
Wavelength Range 450 nm to 490 nm
* green
https://en.wikipedia.org/wiki/Color
Wavelength Range 520 nm to 560 nm
# Implemented index list
#"abbreviationOfIndexName" -- list of channels used
#"ARVI2" -- red, nir
#"CCCI" -- red, redEdge, nir
#"CVI" -- red, green, nir
#"GLI" -- red, green, blue
#"NDVI" -- red, nir
#"BNDVI" -- blue, nir
#"redEdgeNDVI" -- red, redEdge
#"GNDVI" -- green, nir
#"GBNDVI" -- green, blue, nir
#"GRNDVI" -- red, green, nir
#"RBNDVI" -- red, blue, nir
#"PNDVI" -- red, green, blue, nir
#"ATSAVI" -- red, nir
#"BWDRVI" -- blue, nir
#"CIgreen" -- green, nir
#"CIrededge" -- redEdge, nir
#"CI" -- red, blue
#"CTVI" -- red, nir
#"GDVI" -- green, nir
#"EVI" -- red, blue, nir
#"GEMI" -- red, nir
#"GOSAVI" -- green, nir
#"GSAVI" -- green, nir
#"Hue" -- red, green, blue
#"IVI" -- red, nir
#"IPVI" -- red, nir
#"I" -- red, green, blue
#"RVI" -- red, nir
#"MRVI" -- red, nir
#"MSAVI" -- red, nir
#"NormG" -- red, green, nir
#"NormNIR" -- red, green, nir
#"NormR" -- red, green, nir
#"NGRDI" -- red, green
#"RI" -- red, green
#"S" -- red, green, blue
#"IF" -- red, green, blue
#"DVI" -- red, nir
#"TVI" -- red, nir
#"NDRE" -- redEdge, nir
#list of all index implemented
#allIndex = ["ARVI2", "CCCI", "CVI", "GLI", "NDVI", "BNDVI", "redEdgeNDVI",
"GNDVI", "GBNDVI", "GRNDVI", "RBNDVI", "PNDVI", "ATSAVI",
"BWDRVI", "CIgreen", "CIrededge", "CI", "CTVI", "GDVI", "EVI",
"GEMI", "GOSAVI", "GSAVI", "Hue", "IVI", "IPVI", "I", "RVI",
"MRVI", "MSAVI", "NormG", "NormNIR", "NormR", "NGRDI", "RI",
"S", "IF", "DVI", "TVI", "NDRE"]
#list of index with not blue channel
#notBlueIndex = ["ARVI2", "CCCI", "CVI", "NDVI", "redEdgeNDVI", "GNDVI",
"GRNDVI", "ATSAVI", "CIgreen", "CIrededge", "CTVI", "GDVI",
"GEMI", "GOSAVI", "GSAVI", "IVI", "IPVI", "RVI", "MRVI",
"MSAVI", "NormG", "NormNIR", "NormR", "NGRDI", "RI", "DVI",
"TVI", "NDRE"]
#list of index just with RGB channels
#RGBIndex = ["GLI", "CI", "Hue", "I", "NGRDI", "RI", "S", "IF"]
"""
def __init__(self, red=None, green=None, blue=None, red_edge=None, nir=None):
# print("Numpy version: " + np.__version__)
self.set_matricies(red=red, green=green, blue=blue, red_edge=red_edge, nir=nir)
def set_matricies(self, red=None, green=None, blue=None, red_edge=None, nir=None):
if red is not None:
self.red = red
if green is not None:
self.green = green
if blue is not None:
self.blue = blue
if red_edge is not None:
self.redEdge = red_edge
if nir is not None:
self.nir = nir
return True
def calculation(
self, index="", red=None, green=None, blue=None, red_edge=None, nir=None
):
"""
performs the calculation of the index with the values instantiated in the class
:str index: abbreviation of index name to perform
"""
self.set_matricies(red=red, green=green, blue=blue, red_edge=red_edge, nir=nir)
funcs = {
"ARVI2": self.arv12,
"CCCI": self.ccci,
"CVI": self.cvi,
"GLI": self.gli,
"NDVI": self.ndvi,
"BNDVI": self.bndvi,
"redEdgeNDVI": self.red_edge_ndvi,
"GNDVI": self.gndvi,
"GBNDVI": self.gbndvi,
"GRNDVI": self.grndvi,
"RBNDVI": self.rbndvi,
"PNDVI": self.pndvi,
"ATSAVI": self.atsavi,
"BWDRVI": self.bwdrvi,
"CIgreen": self.ci_green,
"CIrededge": self.ci_rededge,
"CI": self.ci,
"CTVI": self.ctvi,
"GDVI": self.gdvi,
"EVI": self.evi,
"GEMI": self.gemi,
"GOSAVI": self.gosavi,
"GSAVI": self.gsavi,
"Hue": self.hue,
"IVI": self.ivi,
"IPVI": self.ipvi,
"I": self.i,
"RVI": self.rvi,
"MRVI": self.mrvi,
"MSAVI": self.m_savi,
"NormG": self.norm_g,
"NormNIR": self.norm_nir,
"NormR": self.norm_r,
"NGRDI": self.ngrdi,
"RI": self.ri,
"S": self.s,
"IF": self._if,
"DVI": self.dvi,
"TVI": self.tvi,
"NDRE": self.ndre,
}
try:
return funcs[index]()
except KeyError:
print("Index not in the list!")
return False
def arv12(self):
"""
Atmospherically Resistant Vegetation Index 2
https://www.indexdatabase.de/db/i-single.php?id=396
:return: index
−0.18+1.17*(self.nir−self.red)/(self.nir+self.red)
"""
return -0.18 + (1.17 * ((self.nir - self.red) / (self.nir + self.red)))
def ccci(self):
"""
Canopy Chlorophyll Content Index
https://www.indexdatabase.de/db/i-single.php?id=224
:return: index
"""
return ((self.nir - self.redEdge) / (self.nir + self.redEdge)) / (
(self.nir - self.red) / (self.nir + self.red)
)
def cvi(self):
"""
Chlorophyll vegetation index
https://www.indexdatabase.de/db/i-single.php?id=391
:return: index
"""
return self.nir * (self.red / (self.green**2))
def gli(self):
"""
self.green leaf index
https://www.indexdatabase.de/db/i-single.php?id=375
:return: index
"""
return (2 * self.green - self.red - self.blue) / (
2 * self.green + self.red + self.blue
)
def ndvi(self):
"""
Normalized Difference self.nir/self.red Normalized Difference Vegetation
Index, Calibrated NDVI - CDVI
https://www.indexdatabase.de/db/i-single.php?id=58
:return: index
"""
return (self.nir - self.red) / (self.nir + self.red)
def bndvi(self):
"""
Normalized Difference self.nir/self.blue self.blue-normalized difference
vegetation index
https://www.indexdatabase.de/db/i-single.php?id=135
:return: index
"""
return (self.nir - self.blue) / (self.nir + self.blue)
def red_edge_ndvi(self):
"""
Normalized Difference self.rededge/self.red
https://www.indexdatabase.de/db/i-single.php?id=235
:return: index
"""
return (self.redEdge - self.red) / (self.redEdge + self.red)
def gndvi(self):
"""
Normalized Difference self.nir/self.green self.green NDVI
https://www.indexdatabase.de/db/i-single.php?id=401
:return: index
"""
return (self.nir - self.green) / (self.nir + self.green)
def gbndvi(self):
"""
self.green-self.blue NDVI
https://www.indexdatabase.de/db/i-single.php?id=186
:return: index
"""
return (self.nir - (self.green + self.blue)) / (
self.nir + (self.green + self.blue)
)
def grndvi(self):
"""
self.green-self.red NDVI
https://www.indexdatabase.de/db/i-single.php?id=185
:return: index
"""
return (self.nir - (self.green + self.red)) / (
self.nir + (self.green + self.red)
)
def rbndvi(self):
"""
self.red-self.blue NDVI
https://www.indexdatabase.de/db/i-single.php?id=187
:return: index
"""
return (self.nir - (self.blue + self.red)) / (self.nir + (self.blue + self.red))
def pndvi(self):
"""
Pan NDVI
https://www.indexdatabase.de/db/i-single.php?id=188
:return: index
"""
return (self.nir - (self.green + self.red + self.blue)) / (
self.nir + (self.green + self.red + self.blue)
)
def atsavi(self, x=0.08, a=1.22, b=0.03):
"""
Adjusted transformed soil-adjusted VI
https://www.indexdatabase.de/db/i-single.php?id=209
:return: index
"""
return a * (
(self.nir - a * self.red - b)
/ (a * self.nir + self.red - a * b + x * (1 + a**2))
)
def bwdrvi(self):
"""
self.blue-wide dynamic range vegetation index
https://www.indexdatabase.de/db/i-single.php?id=136
:return: index
"""
return (0.1 * self.nir - self.blue) / (0.1 * self.nir + self.blue)
def ci_green(self):
"""
Chlorophyll Index self.green
https://www.indexdatabase.de/db/i-single.php?id=128
:return: index
"""
return (self.nir / self.green) - 1
def ci_rededge(self):
"""
Chlorophyll Index self.redEdge
https://www.indexdatabase.de/db/i-single.php?id=131
:return: index
"""
return (self.nir / self.redEdge) - 1
def ci(self):
"""
Coloration Index
https://www.indexdatabase.de/db/i-single.php?id=11
:return: index
"""
return (self.red - self.blue) / self.red
def ctvi(self):
"""
Corrected Transformed Vegetation Index
https://www.indexdatabase.de/db/i-single.php?id=244
:return: index
"""
ndvi = self.ndvi()
return ((ndvi + 0.5) / (abs(ndvi + 0.5))) * (abs(ndvi + 0.5) ** (1 / 2))
def gdvi(self):
"""
Difference self.nir/self.green self.green Difference Vegetation Index
https://www.indexdatabase.de/db/i-single.php?id=27
:return: index
"""
return self.nir - self.green
def evi(self):
"""
Enhanced Vegetation Index
https://www.indexdatabase.de/db/i-single.php?id=16
:return: index
"""
return 2.5 * (
(self.nir - self.red) / (self.nir + 6 * self.red - 7.5 * self.blue + 1)
)
def gemi(self):
"""
Global Environment Monitoring Index
https://www.indexdatabase.de/db/i-single.php?id=25
:return: index
"""
n = (2 * (self.nir**2 - self.red**2) + 1.5 * self.nir + 0.5 * self.red) / (
self.nir + self.red + 0.5
)
return n * (1 - 0.25 * n) - (self.red - 0.125) / (1 - self.red)
def gosavi(self, y=0.16):
"""
self.green Optimized Soil Adjusted Vegetation Index
https://www.indexdatabase.de/db/i-single.php?id=29
mit Y = 0,16
:return: index
"""
return (self.nir - self.green) / (self.nir + self.green + y)
def gsavi(self, n=0.5):
"""
self.green Soil Adjusted Vegetation Index
https://www.indexdatabase.de/db/i-single.php?id=31
mit N = 0,5
:return: index
"""
return ((self.nir - self.green) / (self.nir + self.green + n)) * (1 + n)
def hue(self):
"""
Hue
https://www.indexdatabase.de/db/i-single.php?id=34
:return: index
"""
return np.arctan(
((2 * self.red - self.green - self.blue) / 30.5) * (self.green - self.blue)
)
def ivi(self, a=None, b=None):
"""
Ideal vegetation index
https://www.indexdatabase.de/db/i-single.php?id=276
b=intercept of vegetation line
a=soil line slope
:return: index
"""
return (self.nir - b) / (a * self.red)
def ipvi(self):
"""
Infraself.red percentage vegetation index
https://www.indexdatabase.de/db/i-single.php?id=35
:return: index
"""
return (self.nir / ((self.nir + self.red) / 2)) * (self.ndvi() + 1)
def i(self): # noqa: E741,E743
"""
Intensity
https://www.indexdatabase.de/db/i-single.php?id=36
:return: index
"""
return (self.red + self.green + self.blue) / 30.5
def rvi(self):
"""
Ratio-Vegetation-Index
http://www.seos-project.eu/modules/remotesensing/remotesensing-c03-s01-p01.html
:return: index
"""
return self.nir / self.red
def mrvi(self):
"""
Modified Normalized Difference Vegetation Index RVI
https://www.indexdatabase.de/db/i-single.php?id=275
:return: index
"""
return (self.rvi() - 1) / (self.rvi() + 1)
def m_savi(self):
"""
Modified Soil Adjusted Vegetation Index
https://www.indexdatabase.de/db/i-single.php?id=44
:return: index
"""
return (
(2 * self.nir + 1)
- ((2 * self.nir + 1) ** 2 - 8 * (self.nir - self.red)) ** (1 / 2)
) / 2
def norm_g(self):
"""
Norm G
https://www.indexdatabase.de/db/i-single.php?id=50
:return: index
"""
return self.green / (self.nir + self.red + self.green)
def norm_nir(self):
"""
Norm self.nir
https://www.indexdatabase.de/db/i-single.php?id=51
:return: index
"""
return self.nir / (self.nir + self.red + self.green)
def norm_r(self):
"""
Norm R
https://www.indexdatabase.de/db/i-single.php?id=52
:return: index
"""
return self.red / (self.nir + self.red + self.green)
def ngrdi(self):
"""
Normalized Difference self.green/self.red Normalized self.green self.red
difference index, Visible Atmospherically Resistant Indices self.green
(VIself.green)
https://www.indexdatabase.de/db/i-single.php?id=390
:return: index
"""
return (self.green - self.red) / (self.green + self.red)
def ri(self):
"""
Normalized Difference self.red/self.green self.redness Index
https://www.indexdatabase.de/db/i-single.php?id=74
:return: index
"""
return (self.red - self.green) / (self.red + self.green)
def s(self):
"""
Saturation
https://www.indexdatabase.de/db/i-single.php?id=77
:return: index
"""
max_value = np.max([np.max(self.red), np.max(self.green), np.max(self.blue)])
min_value = np.min([np.min(self.red), np.min(self.green), np.min(self.blue)])
return (max_value - min_value) / max_value
def _if(self):
"""
Shape Index
https://www.indexdatabase.de/db/i-single.php?id=79
:return: index
"""
return (2 * self.red - self.green - self.blue) / (self.green - self.blue)
def dvi(self):
"""
Simple Ratio self.nir/self.red Difference Vegetation Index, Vegetation Index
Number (VIN)
https://www.indexdatabase.de/db/i-single.php?id=12
:return: index
"""
return self.nir / self.red
def tvi(self):
"""
Transformed Vegetation Index
https://www.indexdatabase.de/db/i-single.php?id=98
:return: index
"""
return (self.ndvi() + 0.5) ** (1 / 2)
def ndre(self):
return (self.nir - self.redEdge) / (self.nir + self.redEdge)
"""
# genering a random matrices to test this class
red = np.ones((1000,1000, 1),dtype="float64") * 46787
green = np.ones((1000,1000, 1),dtype="float64") * 23487
blue = np.ones((1000,1000, 1),dtype="float64") * 14578
redEdge = np.ones((1000,1000, 1),dtype="float64") * 51045
nir = np.ones((1000,1000, 1),dtype="float64") * 52200
# Examples of how to use the class
# instantiating the class
cl = IndexCalculation()
# instantiating the class with the values
#cl = indexCalculation(red=red, green=green, blue=blue, redEdge=redEdge, nir=nir)
# how set the values after instantiate the class cl, (for update the data or when don't
# instantiating the class with the values)
cl.setMatrices(red=red, green=green, blue=blue, redEdge=redEdge, nir=nir)
# calculating the indices for the instantiated values in the class
# Note: the CCCI index can be changed to any index implemented in the class.
indexValue_form1 = cl.calculation("CCCI", red=red, green=green, blue=blue,
redEdge=redEdge, nir=nir).astype(np.float64)
indexValue_form2 = cl.CCCI()
# calculating the index with the values directly -- you can set just the values
# preferred note: the *calculation* function performs the function *setMatrices*
indexValue_form3 = cl.calculation("CCCI", red=red, green=green, blue=blue,
redEdge=redEdge, nir=nir).astype(np.float64)
print("Form 1: "+np.array2string(indexValue_form1, precision=20, separator=', ',
floatmode='maxprec_equal'))
print("Form 2: "+np.array2string(indexValue_form2, precision=20, separator=', ',
floatmode='maxprec_equal'))
print("Form 3: "+np.array2string(indexValue_form3, precision=20, separator=', ',
floatmode='maxprec_equal'))
# A list of examples results for different type of data at NDVI
# float16 -> 0.31567383 #NDVI (red = 50, nir = 100)
# float32 -> 0.31578946 #NDVI (red = 50, nir = 100)
# float64 -> 0.3157894736842105 #NDVI (red = 50, nir = 100)
# longdouble -> 0.3157894736842105 #NDVI (red = 50, nir = 100)
"""
| -1 |
TheAlgorithms/Python | 7,262 | refactor: Move constants outside of variable scope | ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T19:51:55Z" | "2022-10-16T09:33:29Z" | 77764116217708933bdc65b29801092fa291398e | c6582b35bf8b8aba622c63096e3ab2f01aa36854 | refactor: Move constants outside of variable scope. ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
https://en.wikipedia.org/wiki/Shellsort#Pseudocode
"""
def shell_sort(collection):
"""Pure implementation of shell sort algorithm in Python
:param collection: Some mutable ordered collection with heterogeneous
comparable items inside
:return: the same collection ordered by ascending
>>> shell_sort([0, 5, 3, 2, 2])
[0, 2, 2, 3, 5]
>>> shell_sort([])
[]
>>> shell_sort([-2, -5, -45])
[-45, -5, -2]
"""
# Marcin Ciura's gap sequence
gaps = [701, 301, 132, 57, 23, 10, 4, 1]
for gap in gaps:
for i in range(gap, len(collection)):
insert_value = collection[i]
j = i
while j >= gap and collection[j - gap] > insert_value:
collection[j] = collection[j - gap]
j -= gap
if j != i:
collection[j] = insert_value
return collection
if __name__ == "__main__":
from doctest import testmod
testmod()
user_input = input("Enter numbers separated by a comma:\n").strip()
unsorted = [int(item) for item in user_input.split(",")]
print(shell_sort(unsorted))
| """
https://en.wikipedia.org/wiki/Shellsort#Pseudocode
"""
def shell_sort(collection):
"""Pure implementation of shell sort algorithm in Python
:param collection: Some mutable ordered collection with heterogeneous
comparable items inside
:return: the same collection ordered by ascending
>>> shell_sort([0, 5, 3, 2, 2])
[0, 2, 2, 3, 5]
>>> shell_sort([])
[]
>>> shell_sort([-2, -5, -45])
[-45, -5, -2]
"""
# Marcin Ciura's gap sequence
gaps = [701, 301, 132, 57, 23, 10, 4, 1]
for gap in gaps:
for i in range(gap, len(collection)):
insert_value = collection[i]
j = i
while j >= gap and collection[j - gap] > insert_value:
collection[j] = collection[j - gap]
j -= gap
if j != i:
collection[j] = insert_value
return collection
if __name__ == "__main__":
from doctest import testmod
testmod()
user_input = input("Enter numbers separated by a comma:\n").strip()
unsorted = [int(item) for item in user_input.split(",")]
print(shell_sort(unsorted))
| -1 |
TheAlgorithms/Python | 7,262 | refactor: Move constants outside of variable scope | ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T19:51:55Z" | "2022-10-16T09:33:29Z" | 77764116217708933bdc65b29801092fa291398e | c6582b35bf8b8aba622c63096e3ab2f01aa36854 | refactor: Move constants outside of variable scope. ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from __future__ import annotations
import random
import string
class ShuffledShiftCipher:
"""
This algorithm uses the Caesar Cipher algorithm but removes the option to
use brute force to decrypt the message.
The passcode is a random password from the selection buffer of
1. uppercase letters of the English alphabet
2. lowercase letters of the English alphabet
3. digits from 0 to 9
Using unique characters from the passcode, the normal list of characters,
that can be allowed in the plaintext, is pivoted and shuffled. Refer to docstring
of __make_key_list() to learn more about the shuffling.
Then, using the passcode, a number is calculated which is used to encrypt the
plaintext message with the normal shift cipher method, only in this case, the
reference, to look back at while decrypting, is shuffled.
Each cipher object can possess an optional argument as passcode, without which a
new passcode is generated for that object automatically.
cip1 = ShuffledShiftCipher('d4usr9TWxw9wMD')
cip2 = ShuffledShiftCipher()
"""
def __init__(self, passcode: str | None = None) -> None:
"""
Initializes a cipher object with a passcode as it's entity
Note: No new passcode is generated if user provides a passcode
while creating the object
"""
self.__passcode = passcode or self.__passcode_creator()
self.__key_list = self.__make_key_list()
self.__shift_key = self.__make_shift_key()
def __str__(self) -> str:
"""
:return: passcode of the cipher object
"""
return "Passcode is: " + "".join(self.__passcode)
def __neg_pos(self, iterlist: list[int]) -> list[int]:
"""
Mutates the list by changing the sign of each alternate element
:param iterlist: takes a list iterable
:return: the mutated list
"""
for i in range(1, len(iterlist), 2):
iterlist[i] *= -1
return iterlist
def __passcode_creator(self) -> list[str]:
"""
Creates a random password from the selection buffer of
1. uppercase letters of the English alphabet
2. lowercase letters of the English alphabet
3. digits from 0 to 9
:rtype: list
:return: a password of a random length between 10 to 20
"""
choices = string.ascii_letters + string.digits
password = [random.choice(choices) for _ in range(random.randint(10, 20))]
return password
def __make_key_list(self) -> list[str]:
"""
Shuffles the ordered character choices by pivoting at breakpoints
Breakpoints are the set of characters in the passcode
eg:
if, ABCDEFGHIJKLMNOPQRSTUVWXYZ are the possible characters
and CAMERA is the passcode
then, breakpoints = [A,C,E,M,R] # sorted set of characters from passcode
shuffled parts: [A,CB,ED,MLKJIHGF,RQPON,ZYXWVUTS]
shuffled __key_list : ACBEDMLKJIHGFRQPONZYXWVUTS
Shuffling only 26 letters of the english alphabet can generate 26!
combinations for the shuffled list. In the program we consider, a set of
97 characters (including letters, digits, punctuation and whitespaces),
thereby creating a possibility of 97! combinations (which is a 152 digit number
in itself), thus diminishing the possibility of a brute force approach.
Moreover, shift keys even introduce a multiple of 26 for a brute force approach
for each of the already 97! combinations.
"""
# key_list_options contain nearly all printable except few elements from
# string.whitespace
key_list_options = (
string.ascii_letters + string.digits + string.punctuation + " \t\n"
)
keys_l = []
# creates points known as breakpoints to break the key_list_options at those
# points and pivot each substring
breakpoints = sorted(set(self.__passcode))
temp_list: list[str] = []
# algorithm for creating a new shuffled list, keys_l, out of key_list_options
for i in key_list_options:
temp_list.extend(i)
# checking breakpoints at which to pivot temporary sublist and add it into
# keys_l
if i in breakpoints or i == key_list_options[-1]:
keys_l.extend(temp_list[::-1])
temp_list.clear()
# returning a shuffled keys_l to prevent brute force guessing of shift key
return keys_l
def __make_shift_key(self) -> int:
"""
sum() of the mutated list of ascii values of all characters where the
mutated list is the one returned by __neg_pos()
"""
num = sum(self.__neg_pos([ord(x) for x in self.__passcode]))
return num if num > 0 else len(self.__passcode)
def decrypt(self, encoded_message: str) -> str:
"""
Performs shifting of the encoded_message w.r.t. the shuffled __key_list
to create the decoded_message
>>> ssc = ShuffledShiftCipher('4PYIXyqeQZr44')
>>> ssc.decrypt("d>**-1z6&'5z'5z:z+-='$'>=zp:>5:#z<'.&>#")
'Hello, this is a modified Caesar cipher'
"""
decoded_message = ""
# decoding shift like Caesar cipher algorithm implementing negative shift or
# reverse shift or left shift
for i in encoded_message:
position = self.__key_list.index(i)
decoded_message += self.__key_list[
(position - self.__shift_key) % -len(self.__key_list)
]
return decoded_message
def encrypt(self, plaintext: str) -> str:
"""
Performs shifting of the plaintext w.r.t. the shuffled __key_list
to create the encoded_message
>>> ssc = ShuffledShiftCipher('4PYIXyqeQZr44')
>>> ssc.encrypt('Hello, this is a modified Caesar cipher')
"d>**-1z6&'5z'5z:z+-='$'>=zp:>5:#z<'.&>#"
"""
encoded_message = ""
# encoding shift like Caesar cipher algorithm implementing positive shift or
# forward shift or right shift
for i in plaintext:
position = self.__key_list.index(i)
encoded_message += self.__key_list[
(position + self.__shift_key) % len(self.__key_list)
]
return encoded_message
def test_end_to_end(msg: str = "Hello, this is a modified Caesar cipher") -> str:
"""
>>> test_end_to_end()
'Hello, this is a modified Caesar cipher'
"""
cip1 = ShuffledShiftCipher()
return cip1.decrypt(cip1.encrypt(msg))
if __name__ == "__main__":
import doctest
doctest.testmod()
| from __future__ import annotations
import random
import string
class ShuffledShiftCipher:
"""
This algorithm uses the Caesar Cipher algorithm but removes the option to
use brute force to decrypt the message.
The passcode is a random password from the selection buffer of
1. uppercase letters of the English alphabet
2. lowercase letters of the English alphabet
3. digits from 0 to 9
Using unique characters from the passcode, the normal list of characters,
that can be allowed in the plaintext, is pivoted and shuffled. Refer to docstring
of __make_key_list() to learn more about the shuffling.
Then, using the passcode, a number is calculated which is used to encrypt the
plaintext message with the normal shift cipher method, only in this case, the
reference, to look back at while decrypting, is shuffled.
Each cipher object can possess an optional argument as passcode, without which a
new passcode is generated for that object automatically.
cip1 = ShuffledShiftCipher('d4usr9TWxw9wMD')
cip2 = ShuffledShiftCipher()
"""
def __init__(self, passcode: str | None = None) -> None:
"""
Initializes a cipher object with a passcode as it's entity
Note: No new passcode is generated if user provides a passcode
while creating the object
"""
self.__passcode = passcode or self.__passcode_creator()
self.__key_list = self.__make_key_list()
self.__shift_key = self.__make_shift_key()
def __str__(self) -> str:
"""
:return: passcode of the cipher object
"""
return "Passcode is: " + "".join(self.__passcode)
def __neg_pos(self, iterlist: list[int]) -> list[int]:
"""
Mutates the list by changing the sign of each alternate element
:param iterlist: takes a list iterable
:return: the mutated list
"""
for i in range(1, len(iterlist), 2):
iterlist[i] *= -1
return iterlist
def __passcode_creator(self) -> list[str]:
"""
Creates a random password from the selection buffer of
1. uppercase letters of the English alphabet
2. lowercase letters of the English alphabet
3. digits from 0 to 9
:rtype: list
:return: a password of a random length between 10 to 20
"""
choices = string.ascii_letters + string.digits
password = [random.choice(choices) for _ in range(random.randint(10, 20))]
return password
def __make_key_list(self) -> list[str]:
"""
Shuffles the ordered character choices by pivoting at breakpoints
Breakpoints are the set of characters in the passcode
eg:
if, ABCDEFGHIJKLMNOPQRSTUVWXYZ are the possible characters
and CAMERA is the passcode
then, breakpoints = [A,C,E,M,R] # sorted set of characters from passcode
shuffled parts: [A,CB,ED,MLKJIHGF,RQPON,ZYXWVUTS]
shuffled __key_list : ACBEDMLKJIHGFRQPONZYXWVUTS
Shuffling only 26 letters of the english alphabet can generate 26!
combinations for the shuffled list. In the program we consider, a set of
97 characters (including letters, digits, punctuation and whitespaces),
thereby creating a possibility of 97! combinations (which is a 152 digit number
in itself), thus diminishing the possibility of a brute force approach.
Moreover, shift keys even introduce a multiple of 26 for a brute force approach
for each of the already 97! combinations.
"""
# key_list_options contain nearly all printable except few elements from
# string.whitespace
key_list_options = (
string.ascii_letters + string.digits + string.punctuation + " \t\n"
)
keys_l = []
# creates points known as breakpoints to break the key_list_options at those
# points and pivot each substring
breakpoints = sorted(set(self.__passcode))
temp_list: list[str] = []
# algorithm for creating a new shuffled list, keys_l, out of key_list_options
for i in key_list_options:
temp_list.extend(i)
# checking breakpoints at which to pivot temporary sublist and add it into
# keys_l
if i in breakpoints or i == key_list_options[-1]:
keys_l.extend(temp_list[::-1])
temp_list.clear()
# returning a shuffled keys_l to prevent brute force guessing of shift key
return keys_l
def __make_shift_key(self) -> int:
"""
sum() of the mutated list of ascii values of all characters where the
mutated list is the one returned by __neg_pos()
"""
num = sum(self.__neg_pos([ord(x) for x in self.__passcode]))
return num if num > 0 else len(self.__passcode)
def decrypt(self, encoded_message: str) -> str:
"""
Performs shifting of the encoded_message w.r.t. the shuffled __key_list
to create the decoded_message
>>> ssc = ShuffledShiftCipher('4PYIXyqeQZr44')
>>> ssc.decrypt("d>**-1z6&'5z'5z:z+-='$'>=zp:>5:#z<'.&>#")
'Hello, this is a modified Caesar cipher'
"""
decoded_message = ""
# decoding shift like Caesar cipher algorithm implementing negative shift or
# reverse shift or left shift
for i in encoded_message:
position = self.__key_list.index(i)
decoded_message += self.__key_list[
(position - self.__shift_key) % -len(self.__key_list)
]
return decoded_message
def encrypt(self, plaintext: str) -> str:
"""
Performs shifting of the plaintext w.r.t. the shuffled __key_list
to create the encoded_message
>>> ssc = ShuffledShiftCipher('4PYIXyqeQZr44')
>>> ssc.encrypt('Hello, this is a modified Caesar cipher')
"d>**-1z6&'5z'5z:z+-='$'>=zp:>5:#z<'.&>#"
"""
encoded_message = ""
# encoding shift like Caesar cipher algorithm implementing positive shift or
# forward shift or right shift
for i in plaintext:
position = self.__key_list.index(i)
encoded_message += self.__key_list[
(position + self.__shift_key) % len(self.__key_list)
]
return encoded_message
def test_end_to_end(msg: str = "Hello, this is a modified Caesar cipher") -> str:
"""
>>> test_end_to_end()
'Hello, this is a modified Caesar cipher'
"""
cip1 = ShuffledShiftCipher()
return cip1.decrypt(cip1.encrypt(msg))
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 7,262 | refactor: Move constants outside of variable scope | ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T19:51:55Z" | "2022-10-16T09:33:29Z" | 77764116217708933bdc65b29801092fa291398e | c6582b35bf8b8aba622c63096e3ab2f01aa36854 | refactor: Move constants outside of variable scope. ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Project Euler Problem 6: https://projecteuler.net/problem=6
Sum square difference
The sum of the squares of the first ten natural numbers is,
1^2 + 2^2 + ... + 10^2 = 385
The square of the sum of the first ten natural numbers is,
(1 + 2 + ... + 10)^2 = 55^2 = 3025
Hence the difference between the sum of the squares of the first ten
natural numbers and the square of the sum is 3025 - 385 = 2640.
Find the difference between the sum of the squares of the first one
hundred natural numbers and the square of the sum.
"""
def solution(n: int = 100) -> int:
"""
Returns the difference between the sum of the squares of the first n
natural numbers and the square of the sum.
>>> solution(10)
2640
>>> solution(15)
13160
>>> solution(20)
41230
>>> solution(50)
1582700
"""
sum_cubes = (n * (n + 1) // 2) ** 2
sum_squares = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 6: https://projecteuler.net/problem=6
Sum square difference
The sum of the squares of the first ten natural numbers is,
1^2 + 2^2 + ... + 10^2 = 385
The square of the sum of the first ten natural numbers is,
(1 + 2 + ... + 10)^2 = 55^2 = 3025
Hence the difference between the sum of the squares of the first ten
natural numbers and the square of the sum is 3025 - 385 = 2640.
Find the difference between the sum of the squares of the first one
hundred natural numbers and the square of the sum.
"""
def solution(n: int = 100) -> int:
"""
Returns the difference between the sum of the squares of the first n
natural numbers and the square of the sum.
>>> solution(10)
2640
>>> solution(15)
13160
>>> solution(20)
41230
>>> solution(50)
1582700
"""
sum_cubes = (n * (n + 1) // 2) ** 2
sum_squares = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 7,262 | refactor: Move constants outside of variable scope | ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T19:51:55Z" | "2022-10-16T09:33:29Z" | 77764116217708933bdc65b29801092fa291398e | c6582b35bf8b8aba622c63096e3ab2f01aa36854 | refactor: Move constants outside of variable scope. ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from json import loads
from pathlib import Path
import numpy as np
from yulewalker import yulewalk
from audio_filters.butterworth_filter import make_highpass
from audio_filters.iir_filter import IIRFilter
data = loads((Path(__file__).resolve().parent / "loudness_curve.json").read_text())
class EqualLoudnessFilter:
r"""
An equal-loudness filter which compensates for the human ear's non-linear response
to sound.
This filter corrects this by cascading a yulewalk filter and a butterworth filter.
Designed for use with samplerate of 44.1kHz and above. If you're using a lower
samplerate, use with caution.
Code based on matlab implementation at https://bit.ly/3eqh2HU
(url shortened for flake8)
Target curve: https://i.imgur.com/3g2VfaM.png
Yulewalk response: https://i.imgur.com/J9LnJ4C.png
Butterworth and overall response: https://i.imgur.com/3g2VfaM.png
Images and original matlab implementation by David Robinson, 2001
"""
def __init__(self, samplerate: int = 44100) -> None:
self.yulewalk_filter = IIRFilter(10)
self.butterworth_filter = make_highpass(150, samplerate)
# pad the data to nyquist
curve_freqs = np.array(data["frequencies"] + [max(20000.0, samplerate / 2)])
curve_gains = np.array(data["gains"] + [140])
# Convert to angular frequency
freqs_normalized = curve_freqs / samplerate * 2
# Invert the curve and normalize to 0dB
gains_normalized = np.power(10, (np.min(curve_gains) - curve_gains) / 20)
# Scipy's `yulewalk` function is a stub, so we're using the
# `yulewalker` library instead.
# This function computes the coefficients using a least-squares
# fit to the specified curve.
ya, yb = yulewalk(10, freqs_normalized, gains_normalized)
self.yulewalk_filter.set_coefficients(ya, yb)
def process(self, sample: float) -> float:
"""
Process a single sample through both filters
>>> filt = EqualLoudnessFilter()
>>> filt.process(0.0)
0.0
"""
tmp = self.yulewalk_filter.process(sample)
return self.butterworth_filter.process(tmp)
| from json import loads
from pathlib import Path
import numpy as np
from yulewalker import yulewalk
from audio_filters.butterworth_filter import make_highpass
from audio_filters.iir_filter import IIRFilter
data = loads((Path(__file__).resolve().parent / "loudness_curve.json").read_text())
class EqualLoudnessFilter:
r"""
An equal-loudness filter which compensates for the human ear's non-linear response
to sound.
This filter corrects this by cascading a yulewalk filter and a butterworth filter.
Designed for use with samplerate of 44.1kHz and above. If you're using a lower
samplerate, use with caution.
Code based on matlab implementation at https://bit.ly/3eqh2HU
(url shortened for flake8)
Target curve: https://i.imgur.com/3g2VfaM.png
Yulewalk response: https://i.imgur.com/J9LnJ4C.png
Butterworth and overall response: https://i.imgur.com/3g2VfaM.png
Images and original matlab implementation by David Robinson, 2001
"""
def __init__(self, samplerate: int = 44100) -> None:
self.yulewalk_filter = IIRFilter(10)
self.butterworth_filter = make_highpass(150, samplerate)
# pad the data to nyquist
curve_freqs = np.array(data["frequencies"] + [max(20000.0, samplerate / 2)])
curve_gains = np.array(data["gains"] + [140])
# Convert to angular frequency
freqs_normalized = curve_freqs / samplerate * 2
# Invert the curve and normalize to 0dB
gains_normalized = np.power(10, (np.min(curve_gains) - curve_gains) / 20)
# Scipy's `yulewalk` function is a stub, so we're using the
# `yulewalker` library instead.
# This function computes the coefficients using a least-squares
# fit to the specified curve.
ya, yb = yulewalk(10, freqs_normalized, gains_normalized)
self.yulewalk_filter.set_coefficients(ya, yb)
def process(self, sample: float) -> float:
"""
Process a single sample through both filters
>>> filt = EqualLoudnessFilter()
>>> filt.process(0.0)
0.0
"""
tmp = self.yulewalk_filter.process(sample)
return self.butterworth_filter.process(tmp)
| -1 |
TheAlgorithms/Python | 7,262 | refactor: Move constants outside of variable scope | ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T19:51:55Z" | "2022-10-16T09:33:29Z" | 77764116217708933bdc65b29801092fa291398e | c6582b35bf8b8aba622c63096e3ab2f01aa36854 | refactor: Move constants outside of variable scope. ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
This is a pure Python implementation of the merge sort algorithm
For doctests run following command:
python -m doctest -v merge_sort.py
or
python3 -m doctest -v merge_sort.py
For manual testing run:
python merge_sort.py
"""
def merge_sort(collection: list) -> list:
"""Pure implementation of the merge sort algorithm in Python
:param collection: some mutable ordered collection with heterogeneous
comparable items inside
:return: the same collection ordered by ascending
Examples:
>>> merge_sort([0, 5, 3, 2, 2])
[0, 2, 2, 3, 5]
>>> merge_sort([])
[]
>>> merge_sort([-2, -5, -45])
[-45, -5, -2]
"""
def merge(left: list, right: list) -> list:
"""merge left and right
:param left: left collection
:param right: right collection
:return: merge result
"""
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0)
yield from left
yield from right
return list(_merge())
if len(collection) <= 1:
return collection
mid = len(collection) // 2
return merge(merge_sort(collection[:mid]), merge_sort(collection[mid:]))
if __name__ == "__main__":
import doctest
doctest.testmod()
user_input = input("Enter numbers separated by a comma:\n").strip()
unsorted = [int(item) for item in user_input.split(",")]
print(*merge_sort(unsorted), sep=",")
| """
This is a pure Python implementation of the merge sort algorithm
For doctests run following command:
python -m doctest -v merge_sort.py
or
python3 -m doctest -v merge_sort.py
For manual testing run:
python merge_sort.py
"""
def merge_sort(collection: list) -> list:
"""Pure implementation of the merge sort algorithm in Python
:param collection: some mutable ordered collection with heterogeneous
comparable items inside
:return: the same collection ordered by ascending
Examples:
>>> merge_sort([0, 5, 3, 2, 2])
[0, 2, 2, 3, 5]
>>> merge_sort([])
[]
>>> merge_sort([-2, -5, -45])
[-45, -5, -2]
"""
def merge(left: list, right: list) -> list:
"""merge left and right
:param left: left collection
:param right: right collection
:return: merge result
"""
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0)
yield from left
yield from right
return list(_merge())
if len(collection) <= 1:
return collection
mid = len(collection) // 2
return merge(merge_sort(collection[:mid]), merge_sort(collection[mid:]))
if __name__ == "__main__":
import doctest
doctest.testmod()
user_input = input("Enter numbers separated by a comma:\n").strip()
unsorted = [int(item) for item in user_input.split(",")]
print(*merge_sort(unsorted), sep=",")
| -1 |
TheAlgorithms/Python | 7,262 | refactor: Move constants outside of variable scope | ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T19:51:55Z" | "2022-10-16T09:33:29Z" | 77764116217708933bdc65b29801092fa291398e | c6582b35bf8b8aba622c63096e3ab2f01aa36854 | refactor: Move constants outside of variable scope. ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
A recursive implementation of the insertion sort algorithm
"""
from __future__ import annotations
def rec_insertion_sort(collection: list, n: int):
"""
Given a collection of numbers and its length, sorts the collections
in ascending order
:param collection: A mutable collection of comparable elements
:param n: The length of collections
>>> col = [1, 2, 1]
>>> rec_insertion_sort(col, len(col))
>>> print(col)
[1, 1, 2]
>>> col = [2, 1, 0, -1, -2]
>>> rec_insertion_sort(col, len(col))
>>> print(col)
[-2, -1, 0, 1, 2]
>>> col = [1]
>>> rec_insertion_sort(col, len(col))
>>> print(col)
[1]
"""
# Checks if the entire collection has been sorted
if len(collection) <= 1 or n <= 1:
return
insert_next(collection, n - 1)
rec_insertion_sort(collection, n - 1)
def insert_next(collection: list, index: int):
"""
Inserts the '(index-1)th' element into place
>>> col = [3, 2, 4, 2]
>>> insert_next(col, 1)
>>> print(col)
[2, 3, 4, 2]
>>> col = [3, 2, 3]
>>> insert_next(col, 2)
>>> print(col)
[3, 2, 3]
>>> col = []
>>> insert_next(col, 1)
>>> print(col)
[]
"""
# Checks order between adjacent elements
if index >= len(collection) or collection[index - 1] <= collection[index]:
return
# Swaps adjacent elements since they are not in ascending order
collection[index - 1], collection[index] = (
collection[index],
collection[index - 1],
)
insert_next(collection, index + 1)
if __name__ == "__main__":
numbers = input("Enter integers separated by spaces: ")
number_list: list[int] = [int(num) for num in numbers.split()]
rec_insertion_sort(number_list, len(number_list))
print(number_list)
| """
A recursive implementation of the insertion sort algorithm
"""
from __future__ import annotations
def rec_insertion_sort(collection: list, n: int):
"""
Given a collection of numbers and its length, sorts the collections
in ascending order
:param collection: A mutable collection of comparable elements
:param n: The length of collections
>>> col = [1, 2, 1]
>>> rec_insertion_sort(col, len(col))
>>> print(col)
[1, 1, 2]
>>> col = [2, 1, 0, -1, -2]
>>> rec_insertion_sort(col, len(col))
>>> print(col)
[-2, -1, 0, 1, 2]
>>> col = [1]
>>> rec_insertion_sort(col, len(col))
>>> print(col)
[1]
"""
# Checks if the entire collection has been sorted
if len(collection) <= 1 or n <= 1:
return
insert_next(collection, n - 1)
rec_insertion_sort(collection, n - 1)
def insert_next(collection: list, index: int):
"""
Inserts the '(index-1)th' element into place
>>> col = [3, 2, 4, 2]
>>> insert_next(col, 1)
>>> print(col)
[2, 3, 4, 2]
>>> col = [3, 2, 3]
>>> insert_next(col, 2)
>>> print(col)
[3, 2, 3]
>>> col = []
>>> insert_next(col, 1)
>>> print(col)
[]
"""
# Checks order between adjacent elements
if index >= len(collection) or collection[index - 1] <= collection[index]:
return
# Swaps adjacent elements since they are not in ascending order
collection[index - 1], collection[index] = (
collection[index],
collection[index - 1],
)
insert_next(collection, index + 1)
if __name__ == "__main__":
numbers = input("Enter integers separated by spaces: ")
number_list: list[int] = [int(num) for num in numbers.split()]
rec_insertion_sort(number_list, len(number_list))
print(number_list)
| -1 |
TheAlgorithms/Python | 7,262 | refactor: Move constants outside of variable scope | ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T19:51:55Z" | "2022-10-16T09:33:29Z" | 77764116217708933bdc65b29801092fa291398e | c6582b35bf8b8aba622c63096e3ab2f01aa36854 | refactor: Move constants outside of variable scope. ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$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 | 7,262 | refactor: Move constants outside of variable scope | ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T19:51:55Z" | "2022-10-16T09:33:29Z" | 77764116217708933bdc65b29801092fa291398e | c6582b35bf8b8aba622c63096e3ab2f01aa36854 | refactor: Move constants outside of variable scope. ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Project Euler Problem 234: https://projecteuler.net/problem=234
For any integer n, consider the three functions
f1,n(x,y,z) = x^(n+1) + y^(n+1) - z^(n+1)
f2,n(x,y,z) = (xy + yz + zx)*(x^(n-1) + y^(n-1) - z^(n-1))
f3,n(x,y,z) = xyz*(xn-2 + yn-2 - zn-2)
and their combination
fn(x,y,z) = f1,n(x,y,z) + f2,n(x,y,z) - f3,n(x,y,z)
We call (x,y,z) a golden triple of order k if x, y, and z are all rational numbers
of the form a / b with 0 < a < b ≤ k and there is (at least) one integer n,
so that fn(x,y,z) = 0.
Let s(x,y,z) = x + y + z.
Let t = u / v be the sum of all distinct s(x,y,z) for all golden triples
(x,y,z) of order 35.
All the s(x,y,z) and t must be in reduced form.
Find u + v.
Solution:
By expanding the brackets it is easy to show that
fn(x, y, z) = (x + y + z) * (x^n + y^n - z^n).
Since x,y,z are positive, the requirement fn(x, y, z) = 0 is fulfilled if and
only if x^n + y^n = z^n.
By Fermat's Last Theorem, this means that the absolute value of n can not
exceed 2, i.e. n is in {-2, -1, 0, 1, 2}. We can eliminate n = 0 since then the
equation would reduce to 1 + 1 = 1, for which there are no solutions.
So all we have to do is iterate through the possible numerators and denominators
of x and y, calculate the corresponding z, and check if the corresponding numerator and
denominator are integer and satisfy 0 < z_num < z_den <= 0. We use a set "uniquq_s"
to make sure there are no duplicates, and the fractions.Fraction class to make sure
we get the right numerator and denominator.
Reference:
https://en.wikipedia.org/wiki/Fermat%27s_Last_Theorem
"""
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def is_sq(number: int) -> bool:
"""
Check if number is a perfect square.
>>> is_sq(1)
True
>>> is_sq(1000001)
False
>>> is_sq(1000000)
True
"""
sq: int = int(number**0.5)
return number == sq * sq
def add_three(
x_num: int, x_den: int, y_num: int, y_den: int, z_num: int, z_den: int
) -> tuple[int, int]:
"""
Given the numerators and denominators of three fractions, return the
numerator and denominator of their sum in lowest form.
>>> add_three(1, 3, 1, 3, 1, 3)
(1, 1)
>>> add_three(2, 5, 4, 11, 12, 3)
(262, 55)
"""
top: int = x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
bottom: int = x_den * y_den * z_den
hcf: int = gcd(top, bottom)
top //= hcf
bottom //= hcf
return top, bottom
def solution(order: int = 35) -> int:
"""
Find the sum of the numerator and denominator of the sum of all s(x,y,z) for
golden triples (x,y,z) of the given order.
>>> solution(5)
296
>>> solution(10)
12519
>>> solution(20)
19408891927
"""
unique_s: set = set()
hcf: int
total: Fraction = Fraction(0)
fraction_sum: tuple[int, int]
for x_num in range(1, order + 1):
for x_den in range(x_num + 1, order + 1):
for y_num in range(1, order + 1):
for y_den in range(y_num + 1, order + 1):
# n=1
z_num = x_num * y_den + x_den * y_num
z_den = x_den * y_den
hcf = gcd(z_num, z_den)
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
fraction_sum = add_three(
x_num, x_den, y_num, y_den, z_num, z_den
)
unique_s.add(fraction_sum)
# n=2
z_num = (
x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
)
z_den = x_den * x_den * y_den * y_den
if is_sq(z_num) and is_sq(z_den):
z_num = int(sqrt(z_num))
z_den = int(sqrt(z_den))
hcf = gcd(z_num, z_den)
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
fraction_sum = add_three(
x_num, x_den, y_num, y_den, z_num, z_den
)
unique_s.add(fraction_sum)
# n=-1
z_num = x_num * y_num
z_den = x_den * y_num + x_num * y_den
hcf = gcd(z_num, z_den)
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
fraction_sum = add_three(
x_num, x_den, y_num, y_den, z_num, z_den
)
unique_s.add(fraction_sum)
# n=2
z_num = x_num * x_num * y_num * y_num
z_den = (
x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
)
if is_sq(z_num) and is_sq(z_den):
z_num = int(sqrt(z_num))
z_den = int(sqrt(z_den))
hcf = gcd(z_num, z_den)
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
fraction_sum = add_three(
x_num, x_den, y_num, y_den, z_num, z_den
)
unique_s.add(fraction_sum)
for num, den in unique_s:
total += Fraction(num, den)
return total.denominator + total.numerator
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 234: https://projecteuler.net/problem=234
For any integer n, consider the three functions
f1,n(x,y,z) = x^(n+1) + y^(n+1) - z^(n+1)
f2,n(x,y,z) = (xy + yz + zx)*(x^(n-1) + y^(n-1) - z^(n-1))
f3,n(x,y,z) = xyz*(xn-2 + yn-2 - zn-2)
and their combination
fn(x,y,z) = f1,n(x,y,z) + f2,n(x,y,z) - f3,n(x,y,z)
We call (x,y,z) a golden triple of order k if x, y, and z are all rational numbers
of the form a / b with 0 < a < b ≤ k and there is (at least) one integer n,
so that fn(x,y,z) = 0.
Let s(x,y,z) = x + y + z.
Let t = u / v be the sum of all distinct s(x,y,z) for all golden triples
(x,y,z) of order 35.
All the s(x,y,z) and t must be in reduced form.
Find u + v.
Solution:
By expanding the brackets it is easy to show that
fn(x, y, z) = (x + y + z) * (x^n + y^n - z^n).
Since x,y,z are positive, the requirement fn(x, y, z) = 0 is fulfilled if and
only if x^n + y^n = z^n.
By Fermat's Last Theorem, this means that the absolute value of n can not
exceed 2, i.e. n is in {-2, -1, 0, 1, 2}. We can eliminate n = 0 since then the
equation would reduce to 1 + 1 = 1, for which there are no solutions.
So all we have to do is iterate through the possible numerators and denominators
of x and y, calculate the corresponding z, and check if the corresponding numerator and
denominator are integer and satisfy 0 < z_num < z_den <= 0. We use a set "uniquq_s"
to make sure there are no duplicates, and the fractions.Fraction class to make sure
we get the right numerator and denominator.
Reference:
https://en.wikipedia.org/wiki/Fermat%27s_Last_Theorem
"""
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def is_sq(number: int) -> bool:
"""
Check if number is a perfect square.
>>> is_sq(1)
True
>>> is_sq(1000001)
False
>>> is_sq(1000000)
True
"""
sq: int = int(number**0.5)
return number == sq * sq
def add_three(
x_num: int, x_den: int, y_num: int, y_den: int, z_num: int, z_den: int
) -> tuple[int, int]:
"""
Given the numerators and denominators of three fractions, return the
numerator and denominator of their sum in lowest form.
>>> add_three(1, 3, 1, 3, 1, 3)
(1, 1)
>>> add_three(2, 5, 4, 11, 12, 3)
(262, 55)
"""
top: int = x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
bottom: int = x_den * y_den * z_den
hcf: int = gcd(top, bottom)
top //= hcf
bottom //= hcf
return top, bottom
def solution(order: int = 35) -> int:
"""
Find the sum of the numerator and denominator of the sum of all s(x,y,z) for
golden triples (x,y,z) of the given order.
>>> solution(5)
296
>>> solution(10)
12519
>>> solution(20)
19408891927
"""
unique_s: set = set()
hcf: int
total: Fraction = Fraction(0)
fraction_sum: tuple[int, int]
for x_num in range(1, order + 1):
for x_den in range(x_num + 1, order + 1):
for y_num in range(1, order + 1):
for y_den in range(y_num + 1, order + 1):
# n=1
z_num = x_num * y_den + x_den * y_num
z_den = x_den * y_den
hcf = gcd(z_num, z_den)
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
fraction_sum = add_three(
x_num, x_den, y_num, y_den, z_num, z_den
)
unique_s.add(fraction_sum)
# n=2
z_num = (
x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
)
z_den = x_den * x_den * y_den * y_den
if is_sq(z_num) and is_sq(z_den):
z_num = int(sqrt(z_num))
z_den = int(sqrt(z_den))
hcf = gcd(z_num, z_den)
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
fraction_sum = add_three(
x_num, x_den, y_num, y_den, z_num, z_den
)
unique_s.add(fraction_sum)
# n=-1
z_num = x_num * y_num
z_den = x_den * y_num + x_num * y_den
hcf = gcd(z_num, z_den)
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
fraction_sum = add_three(
x_num, x_den, y_num, y_den, z_num, z_den
)
unique_s.add(fraction_sum)
# n=2
z_num = x_num * x_num * y_num * y_num
z_den = (
x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
)
if is_sq(z_num) and is_sq(z_den):
z_num = int(sqrt(z_num))
z_den = int(sqrt(z_den))
hcf = gcd(z_num, z_den)
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
fraction_sum = add_three(
x_num, x_den, y_num, y_den, z_num, z_den
)
unique_s.add(fraction_sum)
for num, den in unique_s:
total += Fraction(num, den)
return total.denominator + total.numerator
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 7,262 | refactor: Move constants outside of variable scope | ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T19:51:55Z" | "2022-10-16T09:33:29Z" | 77764116217708933bdc65b29801092fa291398e | c6582b35bf8b8aba622c63096e3ab2f01aa36854 | refactor: Move constants outside of variable scope. ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| #!/usr/bin/python
# Logistic Regression from scratch
# In[62]:
# In[63]:
# importing all the required libraries
"""
Implementing logistic regression for classification problem
Helpful resources:
Coursera ML course
https://medium.com/@martinpella/logistic-regression-from-scratch-in-python-124c5636b8ac
"""
import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
# get_ipython().run_line_magic('matplotlib', 'inline')
# In[67]:
# sigmoid function or logistic function is used as a hypothesis function in
# classification problems
def sigmoid_function(z):
return 1 / (1 + np.exp(-z))
def cost_function(h, y):
return (-y * np.log(h) - (1 - y) * np.log(1 - h)).mean()
def log_likelihood(x, y, weights):
scores = np.dot(x, weights)
return np.sum(y * scores - np.log(1 + np.exp(scores)))
# here alpha is the learning rate, X is the feature matrix,y is the target matrix
def logistic_reg(alpha, x, y, max_iterations=70000):
theta = np.zeros(x.shape[1])
for iterations in range(max_iterations):
z = np.dot(x, theta)
h = sigmoid_function(z)
gradient = np.dot(x.T, h - y) / y.size
theta = theta - alpha * gradient # updating the weights
z = np.dot(x, theta)
h = sigmoid_function(z)
j = cost_function(h, y)
if iterations % 100 == 0:
print(f"loss: {j} \t") # printing the loss after every 100 iterations
return theta
# In[68]:
if __name__ == "__main__":
iris = datasets.load_iris()
x = iris.data[:, :2]
y = (iris.target != 0) * 1
alpha = 0.1
theta = logistic_reg(alpha, x, y, max_iterations=70000)
print("theta: ", theta) # printing the theta i.e our weights vector
def predict_prob(x):
return sigmoid_function(
np.dot(x, theta)
) # predicting the value of probability from the logistic regression algorithm
plt.figure(figsize=(10, 6))
plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color="b", label="0")
plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color="r", label="1")
(x1_min, x1_max) = (x[:, 0].min(), x[:, 0].max())
(x2_min, x2_max) = (x[:, 1].min(), x[:, 1].max())
(xx1, xx2) = np.meshgrid(np.linspace(x1_min, x1_max), np.linspace(x2_min, x2_max))
grid = np.c_[xx1.ravel(), xx2.ravel()]
probs = predict_prob(grid).reshape(xx1.shape)
plt.contour(xx1, xx2, probs, [0.5], linewidths=1, colors="black")
plt.legend()
plt.show()
| #!/usr/bin/python
# Logistic Regression from scratch
# In[62]:
# In[63]:
# importing all the required libraries
"""
Implementing logistic regression for classification problem
Helpful resources:
Coursera ML course
https://medium.com/@martinpella/logistic-regression-from-scratch-in-python-124c5636b8ac
"""
import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
# get_ipython().run_line_magic('matplotlib', 'inline')
# In[67]:
# sigmoid function or logistic function is used as a hypothesis function in
# classification problems
def sigmoid_function(z):
return 1 / (1 + np.exp(-z))
def cost_function(h, y):
return (-y * np.log(h) - (1 - y) * np.log(1 - h)).mean()
def log_likelihood(x, y, weights):
scores = np.dot(x, weights)
return np.sum(y * scores - np.log(1 + np.exp(scores)))
# here alpha is the learning rate, X is the feature matrix,y is the target matrix
def logistic_reg(alpha, x, y, max_iterations=70000):
theta = np.zeros(x.shape[1])
for iterations in range(max_iterations):
z = np.dot(x, theta)
h = sigmoid_function(z)
gradient = np.dot(x.T, h - y) / y.size
theta = theta - alpha * gradient # updating the weights
z = np.dot(x, theta)
h = sigmoid_function(z)
j = cost_function(h, y)
if iterations % 100 == 0:
print(f"loss: {j} \t") # printing the loss after every 100 iterations
return theta
# In[68]:
if __name__ == "__main__":
iris = datasets.load_iris()
x = iris.data[:, :2]
y = (iris.target != 0) * 1
alpha = 0.1
theta = logistic_reg(alpha, x, y, max_iterations=70000)
print("theta: ", theta) # printing the theta i.e our weights vector
def predict_prob(x):
return sigmoid_function(
np.dot(x, theta)
) # predicting the value of probability from the logistic regression algorithm
plt.figure(figsize=(10, 6))
plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color="b", label="0")
plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color="r", label="1")
(x1_min, x1_max) = (x[:, 0].min(), x[:, 0].max())
(x2_min, x2_max) = (x[:, 1].min(), x[:, 1].max())
(xx1, xx2) = np.meshgrid(np.linspace(x1_min, x1_max), np.linspace(x2_min, x2_max))
grid = np.c_[xx1.ravel(), xx2.ravel()]
probs = predict_prob(grid).reshape(xx1.shape)
plt.contour(xx1, xx2, probs, [0.5], linewidths=1, colors="black")
plt.legend()
plt.show()
| -1 |
TheAlgorithms/Python | 7,262 | refactor: Move constants outside of variable scope | ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T19:51:55Z" | "2022-10-16T09:33:29Z" | 77764116217708933bdc65b29801092fa291398e | c6582b35bf8b8aba622c63096e3ab2f01aa36854 | refactor: Move constants outside of variable scope. ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 7,262 | refactor: Move constants outside of variable scope | ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T19:51:55Z" | "2022-10-16T09:33:29Z" | 77764116217708933bdc65b29801092fa291398e | c6582b35bf8b8aba622c63096e3ab2f01aa36854 | refactor: Move constants outside of variable scope. ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """example of simple chaos machine"""
# Chaos Machine (K, t, m)
K = [0.33, 0.44, 0.55, 0.44, 0.33]
t = 3
m = 5
# Buffer Space (with Parameters Space)
buffer_space: list[float] = []
params_space: list[float] = []
# Machine Time
machine_time = 0
def push(seed):
global buffer_space, params_space, machine_time, K, m, t
# Choosing Dynamical Systems (All)
for key, value in enumerate(buffer_space):
# Evolution Parameter
e = float(seed / value)
# Control Theory: Orbit Change
value = (buffer_space[(key + 1) % m] + e) % 1
# Control Theory: Trajectory Change
r = (params_space[key] + e) % 1 + 3
# Modification (Transition Function) - Jumps
buffer_space[key] = round(float(r * value * (1 - value)), 10)
params_space[key] = r # Saving to Parameters Space
# Logistic Map
assert max(buffer_space) < 1
assert max(params_space) < 4
# Machine Time
machine_time += 1
def pull():
global buffer_space, params_space, machine_time, K, m, t
# PRNG (Xorshift by George Marsaglia)
def xorshift(x, y):
x ^= y >> 13
y ^= x << 17
x ^= y >> 5
return x
# Choosing Dynamical Systems (Increment)
key = machine_time % m
# Evolution (Time Length)
for _ in range(0, t):
# Variables (Position + Parameters)
r = params_space[key]
value = buffer_space[key]
# Modification (Transition Function) - Flow
buffer_space[key] = round(float(r * value * (1 - value)), 10)
params_space[key] = (machine_time * 0.01 + r * 1.01) % 1 + 3
# Choosing Chaotic Data
x = int(buffer_space[(key + 2) % m] * (10**10))
y = int(buffer_space[(key - 2) % m] * (10**10))
# Machine Time
machine_time += 1
return xorshift(x, y) % 0xFFFFFFFF
def reset():
global buffer_space, params_space, machine_time, K, m, t
buffer_space = K
params_space = [0] * m
machine_time = 0
if __name__ == "__main__":
# Initialization
reset()
# Pushing Data (Input)
import random
message = random.sample(range(0xFFFFFFFF), 100)
for chunk in message:
push(chunk)
# for controlling
inp = ""
# Pulling Data (Output)
while inp in ("e", "E"):
print(f"{format(pull(), '#04x')}")
print(buffer_space)
print(params_space)
inp = input("(e)exit? ").strip()
| """example of simple chaos machine"""
# Chaos Machine (K, t, m)
K = [0.33, 0.44, 0.55, 0.44, 0.33]
t = 3
m = 5
# Buffer Space (with Parameters Space)
buffer_space: list[float] = []
params_space: list[float] = []
# Machine Time
machine_time = 0
def push(seed):
global buffer_space, params_space, machine_time, K, m, t
# Choosing Dynamical Systems (All)
for key, value in enumerate(buffer_space):
# Evolution Parameter
e = float(seed / value)
# Control Theory: Orbit Change
value = (buffer_space[(key + 1) % m] + e) % 1
# Control Theory: Trajectory Change
r = (params_space[key] + e) % 1 + 3
# Modification (Transition Function) - Jumps
buffer_space[key] = round(float(r * value * (1 - value)), 10)
params_space[key] = r # Saving to Parameters Space
# Logistic Map
assert max(buffer_space) < 1
assert max(params_space) < 4
# Machine Time
machine_time += 1
def pull():
global buffer_space, params_space, machine_time, K, m, t
# PRNG (Xorshift by George Marsaglia)
def xorshift(x, y):
x ^= y >> 13
y ^= x << 17
x ^= y >> 5
return x
# Choosing Dynamical Systems (Increment)
key = machine_time % m
# Evolution (Time Length)
for _ in range(0, t):
# Variables (Position + Parameters)
r = params_space[key]
value = buffer_space[key]
# Modification (Transition Function) - Flow
buffer_space[key] = round(float(r * value * (1 - value)), 10)
params_space[key] = (machine_time * 0.01 + r * 1.01) % 1 + 3
# Choosing Chaotic Data
x = int(buffer_space[(key + 2) % m] * (10**10))
y = int(buffer_space[(key - 2) % m] * (10**10))
# Machine Time
machine_time += 1
return xorshift(x, y) % 0xFFFFFFFF
def reset():
global buffer_space, params_space, machine_time, K, m, t
buffer_space = K
params_space = [0] * m
machine_time = 0
if __name__ == "__main__":
# Initialization
reset()
# Pushing Data (Input)
import random
message = random.sample(range(0xFFFFFFFF), 100)
for chunk in message:
push(chunk)
# for controlling
inp = ""
# Pulling Data (Output)
while inp in ("e", "E"):
print(f"{format(pull(), '#04x')}")
print(buffer_space)
print(params_space)
inp = input("(e)exit? ").strip()
| -1 |
TheAlgorithms/Python | 7,262 | refactor: Move constants outside of variable scope | ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T19:51:55Z" | "2022-10-16T09:33:29Z" | 77764116217708933bdc65b29801092fa291398e | c6582b35bf8b8aba622c63096e3ab2f01aa36854 | refactor: Move constants outside of variable scope. ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| import tensorflow as tf
from random import shuffle
from numpy import array
def TFKMeansCluster(vectors, noofclusters):
"""
K-Means Clustering using TensorFlow.
'vectors' should be a n*k 2-D NumPy array, where n is the number
of vectors of dimensionality k.
'noofclusters' should be an integer.
"""
noofclusters = int(noofclusters)
assert noofclusters < len(vectors)
# Find out the dimensionality
dim = len(vectors[0])
# Will help select random centroids from among the available vectors
vector_indices = list(range(len(vectors)))
shuffle(vector_indices)
# GRAPH OF COMPUTATION
# We initialize a new graph and set it as the default during each run
# of this algorithm. This ensures that as this function is called
# multiple times, the default graph doesn't keep getting crowded with
# unused ops and Variables from previous function calls.
graph = tf.Graph()
with graph.as_default():
# SESSION OF COMPUTATION
sess = tf.Session()
##CONSTRUCTING THE ELEMENTS OF COMPUTATION
##First lets ensure we have a Variable vector for each centroid,
##initialized to one of the vectors from the available data points
centroids = [
tf.Variable(vectors[vector_indices[i]]) for i in range(noofclusters)
]
##These nodes will assign the centroid Variables the appropriate
##values
centroid_value = tf.placeholder("float64", [dim])
cent_assigns = []
for centroid in centroids:
cent_assigns.append(tf.assign(centroid, centroid_value))
##Variables for cluster assignments of individual vectors(initialized
##to 0 at first)
assignments = [tf.Variable(0) for i in range(len(vectors))]
##These nodes will assign an assignment Variable the appropriate
##value
assignment_value = tf.placeholder("int32")
cluster_assigns = []
for assignment in assignments:
cluster_assigns.append(tf.assign(assignment, assignment_value))
##Now lets construct the node that will compute the mean
# The placeholder for the input
mean_input = tf.placeholder("float", [None, dim])
# The Node/op takes the input and computes a mean along the 0th
# dimension, i.e. the list of input vectors
mean_op = tf.reduce_mean(mean_input, 0)
##Node for computing Euclidean distances
# Placeholders for input
v1 = tf.placeholder("float", [dim])
v2 = tf.placeholder("float", [dim])
euclid_dist = tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(v1, v2), 2)))
##This node will figure out which cluster to assign a vector to,
##based on Euclidean distances of the vector from the centroids.
# Placeholder for input
centroid_distances = tf.placeholder("float", [noofclusters])
cluster_assignment = tf.argmin(centroid_distances, 0)
##INITIALIZING STATE VARIABLES
##This will help initialization of all Variables defined with respect
##to the graph. The Variable-initializer should be defined after
##all the Variables have been constructed, so that each of them
##will be included in the initialization.
init_op = tf.initialize_all_variables()
# Initialize all variables
sess.run(init_op)
##CLUSTERING ITERATIONS
# Now perform the Expectation-Maximization steps of K-Means clustering
# iterations. To keep things simple, we will only do a set number of
# iterations, instead of using a Stopping Criterion.
noofiterations = 100
for iteration_n in range(noofiterations):
##EXPECTATION STEP
##Based on the centroid locations till last iteration, compute
##the _expected_ centroid assignments.
# Iterate over each vector
for vector_n in range(len(vectors)):
vect = vectors[vector_n]
# Compute Euclidean distance between this vector and each
# centroid. Remember that this list cannot be named
#'centroid_distances', since that is the input to the
# cluster assignment node.
distances = [
sess.run(euclid_dist, feed_dict={v1: vect, v2: sess.run(centroid)})
for centroid in centroids
]
# Now use the cluster assignment node, with the distances
# as the input
assignment = sess.run(
cluster_assignment, feed_dict={centroid_distances: distances}
)
# Now assign the value to the appropriate state variable
sess.run(
cluster_assigns[vector_n], feed_dict={assignment_value: assignment}
)
##MAXIMIZATION STEP
# Based on the expected state computed from the Expectation Step,
# compute the locations of the centroids so as to maximize the
# overall objective of minimizing within-cluster Sum-of-Squares
for cluster_n in range(noofclusters):
# Collect all the vectors assigned to this cluster
assigned_vects = [
vectors[i]
for i in range(len(vectors))
if sess.run(assignments[i]) == cluster_n
]
# Compute new centroid location
new_location = sess.run(
mean_op, feed_dict={mean_input: array(assigned_vects)}
)
# Assign value to appropriate variable
sess.run(
cent_assigns[cluster_n], feed_dict={centroid_value: new_location}
)
# Return centroids and assignments
centroids = sess.run(centroids)
assignments = sess.run(assignments)
return centroids, assignments
| import tensorflow as tf
from random import shuffle
from numpy import array
def TFKMeansCluster(vectors, noofclusters):
"""
K-Means Clustering using TensorFlow.
'vectors' should be a n*k 2-D NumPy array, where n is the number
of vectors of dimensionality k.
'noofclusters' should be an integer.
"""
noofclusters = int(noofclusters)
assert noofclusters < len(vectors)
# Find out the dimensionality
dim = len(vectors[0])
# Will help select random centroids from among the available vectors
vector_indices = list(range(len(vectors)))
shuffle(vector_indices)
# GRAPH OF COMPUTATION
# We initialize a new graph and set it as the default during each run
# of this algorithm. This ensures that as this function is called
# multiple times, the default graph doesn't keep getting crowded with
# unused ops and Variables from previous function calls.
graph = tf.Graph()
with graph.as_default():
# SESSION OF COMPUTATION
sess = tf.Session()
##CONSTRUCTING THE ELEMENTS OF COMPUTATION
##First lets ensure we have a Variable vector for each centroid,
##initialized to one of the vectors from the available data points
centroids = [
tf.Variable(vectors[vector_indices[i]]) for i in range(noofclusters)
]
##These nodes will assign the centroid Variables the appropriate
##values
centroid_value = tf.placeholder("float64", [dim])
cent_assigns = []
for centroid in centroids:
cent_assigns.append(tf.assign(centroid, centroid_value))
##Variables for cluster assignments of individual vectors(initialized
##to 0 at first)
assignments = [tf.Variable(0) for i in range(len(vectors))]
##These nodes will assign an assignment Variable the appropriate
##value
assignment_value = tf.placeholder("int32")
cluster_assigns = []
for assignment in assignments:
cluster_assigns.append(tf.assign(assignment, assignment_value))
##Now lets construct the node that will compute the mean
# The placeholder for the input
mean_input = tf.placeholder("float", [None, dim])
# The Node/op takes the input and computes a mean along the 0th
# dimension, i.e. the list of input vectors
mean_op = tf.reduce_mean(mean_input, 0)
##Node for computing Euclidean distances
# Placeholders for input
v1 = tf.placeholder("float", [dim])
v2 = tf.placeholder("float", [dim])
euclid_dist = tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(v1, v2), 2)))
##This node will figure out which cluster to assign a vector to,
##based on Euclidean distances of the vector from the centroids.
# Placeholder for input
centroid_distances = tf.placeholder("float", [noofclusters])
cluster_assignment = tf.argmin(centroid_distances, 0)
##INITIALIZING STATE VARIABLES
##This will help initialization of all Variables defined with respect
##to the graph. The Variable-initializer should be defined after
##all the Variables have been constructed, so that each of them
##will be included in the initialization.
init_op = tf.initialize_all_variables()
# Initialize all variables
sess.run(init_op)
##CLUSTERING ITERATIONS
# Now perform the Expectation-Maximization steps of K-Means clustering
# iterations. To keep things simple, we will only do a set number of
# iterations, instead of using a Stopping Criterion.
noofiterations = 100
for iteration_n in range(noofiterations):
##EXPECTATION STEP
##Based on the centroid locations till last iteration, compute
##the _expected_ centroid assignments.
# Iterate over each vector
for vector_n in range(len(vectors)):
vect = vectors[vector_n]
# Compute Euclidean distance between this vector and each
# centroid. Remember that this list cannot be named
#'centroid_distances', since that is the input to the
# cluster assignment node.
distances = [
sess.run(euclid_dist, feed_dict={v1: vect, v2: sess.run(centroid)})
for centroid in centroids
]
# Now use the cluster assignment node, with the distances
# as the input
assignment = sess.run(
cluster_assignment, feed_dict={centroid_distances: distances}
)
# Now assign the value to the appropriate state variable
sess.run(
cluster_assigns[vector_n], feed_dict={assignment_value: assignment}
)
##MAXIMIZATION STEP
# Based on the expected state computed from the Expectation Step,
# compute the locations of the centroids so as to maximize the
# overall objective of minimizing within-cluster Sum-of-Squares
for cluster_n in range(noofclusters):
# Collect all the vectors assigned to this cluster
assigned_vects = [
vectors[i]
for i in range(len(vectors))
if sess.run(assignments[i]) == cluster_n
]
# Compute new centroid location
new_location = sess.run(
mean_op, feed_dict={mean_input: array(assigned_vects)}
)
# Assign value to appropriate variable
sess.run(
cent_assigns[cluster_n], feed_dict={centroid_value: new_location}
)
# Return centroids and assignments
centroids = sess.run(centroids)
assignments = sess.run(assignments)
return centroids, assignments
| -1 |
TheAlgorithms/Python | 7,262 | refactor: Move constants outside of variable scope | ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T19:51:55Z" | "2022-10-16T09:33:29Z" | 77764116217708933bdc65b29801092fa291398e | c6582b35bf8b8aba622c63096e3ab2f01aa36854 | refactor: Move constants outside of variable scope. ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Problem 43: https://projecteuler.net/problem=43
The number, 1406357289, is a 0 to 9 pandigital number because it is made up of
each of the digits 0 to 9 in some order, but it also has a rather interesting
sub-string divisibility property.
Let d1 be the 1st digit, d2 be the 2nd digit, and so on. In this way, we note
the following:
d2d3d4=406 is divisible by 2
d3d4d5=063 is divisible by 3
d4d5d6=635 is divisible by 5
d5d6d7=357 is divisible by 7
d6d7d8=572 is divisible by 11
d7d8d9=728 is divisible by 13
d8d9d10=289 is divisible by 17
Find the sum of all 0 to 9 pandigital numbers with this property.
"""
from itertools import permutations
def is_substring_divisible(num: tuple) -> bool:
"""
Returns True if the pandigital number passes
all the divisibility tests.
>>> is_substring_divisible((0, 1, 2, 4, 6, 5, 7, 3, 8, 9))
False
>>> is_substring_divisible((5, 1, 2, 4, 6, 0, 7, 8, 3, 9))
False
>>> is_substring_divisible((1, 4, 0, 6, 3, 5, 7, 2, 8, 9))
True
"""
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
tests = [7, 11, 13, 17]
for i, test in enumerate(tests):
if (num[i + 4] * 100 + num[i + 5] * 10 + num[i + 6]) % test != 0:
return False
return True
def solution(n: int = 10) -> int:
"""
Returns the sum of all pandigital numbers which pass the
divisibility tests.
>>> solution(10)
16695334890
"""
return sum(
int("".join(map(str, num)))
for num in permutations(range(n))
if is_substring_divisible(num)
)
if __name__ == "__main__":
print(f"{solution() = }")
| """
Problem 43: https://projecteuler.net/problem=43
The number, 1406357289, is a 0 to 9 pandigital number because it is made up of
each of the digits 0 to 9 in some order, but it also has a rather interesting
sub-string divisibility property.
Let d1 be the 1st digit, d2 be the 2nd digit, and so on. In this way, we note
the following:
d2d3d4=406 is divisible by 2
d3d4d5=063 is divisible by 3
d4d5d6=635 is divisible by 5
d5d6d7=357 is divisible by 7
d6d7d8=572 is divisible by 11
d7d8d9=728 is divisible by 13
d8d9d10=289 is divisible by 17
Find the sum of all 0 to 9 pandigital numbers with this property.
"""
from itertools import permutations
def is_substring_divisible(num: tuple) -> bool:
"""
Returns True if the pandigital number passes
all the divisibility tests.
>>> is_substring_divisible((0, 1, 2, 4, 6, 5, 7, 3, 8, 9))
False
>>> is_substring_divisible((5, 1, 2, 4, 6, 0, 7, 8, 3, 9))
False
>>> is_substring_divisible((1, 4, 0, 6, 3, 5, 7, 2, 8, 9))
True
"""
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
tests = [7, 11, 13, 17]
for i, test in enumerate(tests):
if (num[i + 4] * 100 + num[i + 5] * 10 + num[i + 6]) % test != 0:
return False
return True
def solution(n: int = 10) -> int:
"""
Returns the sum of all pandigital numbers which pass the
divisibility tests.
>>> solution(10)
16695334890
"""
return sum(
int("".join(map(str, num)))
for num in permutations(range(n))
if is_substring_divisible(num)
)
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 7,262 | refactor: Move constants outside of variable scope | ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T19:51:55Z" | "2022-10-16T09:33:29Z" | 77764116217708933bdc65b29801092fa291398e | c6582b35bf8b8aba622c63096e3ab2f01aa36854 | refactor: Move constants outside of variable scope. ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Project Euler Problem 6: https://projecteuler.net/problem=6
Sum square difference
The sum of the squares of the first ten natural numbers is,
1^2 + 2^2 + ... + 10^2 = 385
The square of the sum of the first ten natural numbers is,
(1 + 2 + ... + 10)^2 = 55^2 = 3025
Hence the difference between the sum of the squares of the first ten
natural numbers and the square of the sum is 3025 - 385 = 2640.
Find the difference between the sum of the squares of the first one
hundred natural numbers and the square of the sum.
"""
import math
def solution(n: int = 100) -> int:
"""
Returns the difference between the sum of the squares of the first n
natural numbers and the square of the sum.
>>> solution(10)
2640
>>> solution(15)
13160
>>> solution(20)
41230
>>> solution(50)
1582700
"""
sum_of_squares = sum(i * i for i in range(1, n + 1))
square_of_sum = int(math.pow(sum(range(1, n + 1)), 2))
return square_of_sum - sum_of_squares
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 6: https://projecteuler.net/problem=6
Sum square difference
The sum of the squares of the first ten natural numbers is,
1^2 + 2^2 + ... + 10^2 = 385
The square of the sum of the first ten natural numbers is,
(1 + 2 + ... + 10)^2 = 55^2 = 3025
Hence the difference between the sum of the squares of the first ten
natural numbers and the square of the sum is 3025 - 385 = 2640.
Find the difference between the sum of the squares of the first one
hundred natural numbers and the square of the sum.
"""
import math
def solution(n: int = 100) -> int:
"""
Returns the difference between the sum of the squares of the first n
natural numbers and the square of the sum.
>>> solution(10)
2640
>>> solution(15)
13160
>>> solution(20)
41230
>>> solution(50)
1582700
"""
sum_of_squares = sum(i * i for i in range(1, n + 1))
square_of_sum = int(math.pow(sum(range(1, n + 1)), 2))
return square_of_sum - sum_of_squares
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 7,262 | refactor: Move constants outside of variable scope | ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T19:51:55Z" | "2022-10-16T09:33:29Z" | 77764116217708933bdc65b29801092fa291398e | c6582b35bf8b8aba622c63096e3ab2f01aa36854 | refactor: Move constants outside of variable scope. ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Truncatable primes
Problem 37: https://projecteuler.net/problem=37
The number 3797 has an interesting property. Being prime itself, it is possible
to continuously remove digits from left to right, and remain prime at each stage:
3797, 797, 97, and 7. Similarly we can work from right to left: 3797, 379, 37, and 3.
Find the sum of the only eleven primes that are both truncatable from left to right
and right to left.
NOTE: 2, 3, 5, and 7 are not considered to be truncatable primes.
"""
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
def list_truncated_nums(n: int) -> list[int]:
"""
Returns a list of all left and right truncated numbers of n
>>> list_truncated_nums(927628)
[927628, 27628, 92762, 7628, 9276, 628, 927, 28, 92, 8, 9]
>>> list_truncated_nums(467)
[467, 67, 46, 7, 4]
>>> list_truncated_nums(58)
[58, 8, 5]
"""
str_num = str(n)
list_nums = [n]
for i in range(1, len(str_num)):
list_nums.append(int(str_num[i:]))
list_nums.append(int(str_num[:-i]))
return list_nums
def validate(n: int) -> bool:
"""
To optimize the approach, we will rule out the numbers above 1000,
whose first or last three digits are not prime
>>> validate(74679)
False
>>> validate(235693)
False
>>> validate(3797)
True
"""
if len(str(n)) > 3:
if not is_prime(int(str(n)[-3:])) or not is_prime(int(str(n)[:3])):
return False
return True
def compute_truncated_primes(count: int = 11) -> list[int]:
"""
Returns the list of truncated primes
>>> compute_truncated_primes(11)
[23, 37, 53, 73, 313, 317, 373, 797, 3137, 3797, 739397]
"""
list_truncated_primes: list[int] = []
num = 13
while len(list_truncated_primes) != count:
if validate(num):
list_nums = list_truncated_nums(num)
if all(is_prime(i) for i in list_nums):
list_truncated_primes.append(num)
num += 2
return list_truncated_primes
def solution() -> int:
"""
Returns the sum of truncated primes
"""
return sum(compute_truncated_primes(11))
if __name__ == "__main__":
print(f"{sum(compute_truncated_primes(11)) = }")
| """
Truncatable primes
Problem 37: https://projecteuler.net/problem=37
The number 3797 has an interesting property. Being prime itself, it is possible
to continuously remove digits from left to right, and remain prime at each stage:
3797, 797, 97, and 7. Similarly we can work from right to left: 3797, 379, 37, and 3.
Find the sum of the only eleven primes that are both truncatable from left to right
and right to left.
NOTE: 2, 3, 5, and 7 are not considered to be truncatable primes.
"""
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
def list_truncated_nums(n: int) -> list[int]:
"""
Returns a list of all left and right truncated numbers of n
>>> list_truncated_nums(927628)
[927628, 27628, 92762, 7628, 9276, 628, 927, 28, 92, 8, 9]
>>> list_truncated_nums(467)
[467, 67, 46, 7, 4]
>>> list_truncated_nums(58)
[58, 8, 5]
"""
str_num = str(n)
list_nums = [n]
for i in range(1, len(str_num)):
list_nums.append(int(str_num[i:]))
list_nums.append(int(str_num[:-i]))
return list_nums
def validate(n: int) -> bool:
"""
To optimize the approach, we will rule out the numbers above 1000,
whose first or last three digits are not prime
>>> validate(74679)
False
>>> validate(235693)
False
>>> validate(3797)
True
"""
if len(str(n)) > 3:
if not is_prime(int(str(n)[-3:])) or not is_prime(int(str(n)[:3])):
return False
return True
def compute_truncated_primes(count: int = 11) -> list[int]:
"""
Returns the list of truncated primes
>>> compute_truncated_primes(11)
[23, 37, 53, 73, 313, 317, 373, 797, 3137, 3797, 739397]
"""
list_truncated_primes: list[int] = []
num = 13
while len(list_truncated_primes) != count:
if validate(num):
list_nums = list_truncated_nums(num)
if all(is_prime(i) for i in list_nums):
list_truncated_primes.append(num)
num += 2
return list_truncated_primes
def solution() -> int:
"""
Returns the sum of truncated primes
"""
return sum(compute_truncated_primes(11))
if __name__ == "__main__":
print(f"{sum(compute_truncated_primes(11)) = }")
| -1 |
TheAlgorithms/Python | 7,262 | refactor: Move constants outside of variable scope | ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T19:51:55Z" | "2022-10-16T09:33:29Z" | 77764116217708933bdc65b29801092fa291398e | c6582b35bf8b8aba622c63096e3ab2f01aa36854 | refactor: Move constants outside of variable scope. ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Modular Exponential.
Modular exponentiation is a type of exponentiation performed over a modulus.
For more explanation, please check
https://en.wikipedia.org/wiki/Modular_exponentiation
"""
"""Calculate Modular Exponential."""
def modular_exponential(base: int, power: int, mod: int):
"""
>>> modular_exponential(5, 0, 10)
1
>>> modular_exponential(2, 8, 7)
4
>>> modular_exponential(3, -2, 9)
-1
"""
if power < 0:
return -1
base %= mod
result = 1
while power > 0:
if power & 1:
result = (result * base) % mod
power = power >> 1
base = (base * base) % mod
return result
def main():
"""Call Modular Exponential Function."""
print(modular_exponential(3, 200, 13))
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| """
Modular Exponential.
Modular exponentiation is a type of exponentiation performed over a modulus.
For more explanation, please check
https://en.wikipedia.org/wiki/Modular_exponentiation
"""
"""Calculate Modular Exponential."""
def modular_exponential(base: int, power: int, mod: int):
"""
>>> modular_exponential(5, 0, 10)
1
>>> modular_exponential(2, 8, 7)
4
>>> modular_exponential(3, -2, 9)
-1
"""
if power < 0:
return -1
base %= mod
result = 1
while power > 0:
if power & 1:
result = (result * base) % mod
power = power >> 1
base = (base * base) % mod
return result
def main():
"""Call Modular Exponential Function."""
print(modular_exponential(3, 200, 13))
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| -1 |
TheAlgorithms/Python | 7,262 | refactor: Move constants outside of variable scope | ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T19:51:55Z" | "2022-10-16T09:33:29Z" | 77764116217708933bdc65b29801092fa291398e | c6582b35bf8b8aba622c63096e3ab2f01aa36854 | refactor: Move constants outside of variable scope. ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from __future__ import annotations
def find_min(nums: list[int | float]) -> int | float:
"""
Find Minimum Number in a List
:param nums: contains elements
:return: min number in list
>>> for nums in ([3, 2, 1], [-3, -2, -1], [3, -3, 0], [3.0, 3.1, 2.9]):
... find_min(nums) == min(nums)
True
True
True
True
>>> find_min([0, 1, 2, 3, 4, 5, -3, 24, -56])
-56
>>> find_min([])
Traceback (most recent call last):
...
ValueError: find_min() arg is an empty sequence
"""
if len(nums) == 0:
raise ValueError("find_min() arg is an empty sequence")
min_num = nums[0]
for num in nums:
if min_num > num:
min_num = num
return min_num
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| from __future__ import annotations
def find_min(nums: list[int | float]) -> int | float:
"""
Find Minimum Number in a List
:param nums: contains elements
:return: min number in list
>>> for nums in ([3, 2, 1], [-3, -2, -1], [3, -3, 0], [3.0, 3.1, 2.9]):
... find_min(nums) == min(nums)
True
True
True
True
>>> find_min([0, 1, 2, 3, 4, 5, -3, 24, -56])
-56
>>> find_min([])
Traceback (most recent call last):
...
ValueError: find_min() arg is an empty sequence
"""
if len(nums) == 0:
raise ValueError("find_min() arg is an empty sequence")
min_num = nums[0]
for num in nums:
if min_num > num:
min_num = num
return min_num
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| -1 |
TheAlgorithms/Python | 7,262 | refactor: Move constants outside of variable scope | ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T19:51:55Z" | "2022-10-16T09:33:29Z" | 77764116217708933bdc65b29801092fa291398e | c6582b35bf8b8aba622c63096e3ab2f01aa36854 | refactor: Move constants outside of variable scope. ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
This script demonstrates the implementation of the Softmax function.
Its a function that takes as input a vector of K real numbers, and normalizes
it into a probability distribution consisting of K probabilities proportional
to the exponentials of the input numbers. After softmax, the elements of the
vector always sum up to 1.
Script inspired from its corresponding Wikipedia article
https://en.wikipedia.org/wiki/Softmax_function
"""
import numpy as np
def softmax(vector):
"""
Implements the softmax function
Parameters:
vector (np.array,list,tuple): A numpy array of shape (1,n)
consisting of real values or a similar list,tuple
Returns:
softmax_vec (np.array): The input numpy array after applying
softmax.
The softmax vector adds up to one. We need to ceil to mitigate for
precision
>>> np.ceil(np.sum(softmax([1,2,3,4])))
1.0
>>> vec = np.array([5,5])
>>> softmax(vec)
array([0.5, 0.5])
>>> softmax([0])
array([1.])
"""
# Calculate e^x for each x in your vector where e is Euler's
# number (approximately 2.718)
exponent_vector = np.exp(vector)
# Add up the all the exponentials
sum_of_exponents = np.sum(exponent_vector)
# Divide every exponent by the sum of all exponents
softmax_vector = exponent_vector / sum_of_exponents
return softmax_vector
if __name__ == "__main__":
print(softmax((0,)))
| """
This script demonstrates the implementation of the Softmax function.
Its a function that takes as input a vector of K real numbers, and normalizes
it into a probability distribution consisting of K probabilities proportional
to the exponentials of the input numbers. After softmax, the elements of the
vector always sum up to 1.
Script inspired from its corresponding Wikipedia article
https://en.wikipedia.org/wiki/Softmax_function
"""
import numpy as np
def softmax(vector):
"""
Implements the softmax function
Parameters:
vector (np.array,list,tuple): A numpy array of shape (1,n)
consisting of real values or a similar list,tuple
Returns:
softmax_vec (np.array): The input numpy array after applying
softmax.
The softmax vector adds up to one. We need to ceil to mitigate for
precision
>>> np.ceil(np.sum(softmax([1,2,3,4])))
1.0
>>> vec = np.array([5,5])
>>> softmax(vec)
array([0.5, 0.5])
>>> softmax([0])
array([1.])
"""
# Calculate e^x for each x in your vector where e is Euler's
# number (approximately 2.718)
exponent_vector = np.exp(vector)
# Add up the all the exponentials
sum_of_exponents = np.sum(exponent_vector)
# Divide every exponent by the sum of all exponents
softmax_vector = exponent_vector / sum_of_exponents
return softmax_vector
if __name__ == "__main__":
print(softmax((0,)))
| -1 |
TheAlgorithms/Python | 7,262 | refactor: Move constants outside of variable scope | ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T19:51:55Z" | "2022-10-16T09:33:29Z" | 77764116217708933bdc65b29801092fa291398e | c6582b35bf8b8aba622c63096e3ab2f01aa36854 | refactor: Move constants outside of variable scope. ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Horizontal Projectile Motion problem in physics.
This algorithm solves a specific problem in which
the motion starts from the ground as can be seen below:
(v = 0)
**
* *
* *
* *
* *
* *
GROUND GROUND
For more info: https://en.wikipedia.org/wiki/Projectile_motion
"""
# Importing packages
from math import radians as angle_to_radians
from math import sin
# Acceleration Constant on Earth (unit m/s^2)
g = 9.80665
def check_args(init_velocity: float, angle: float) -> None:
"""
Check that the arguments are valid
"""
# Ensure valid instance
if not isinstance(init_velocity, (int, float)):
raise TypeError("Invalid velocity. Should be a positive number.")
if not isinstance(angle, (int, float)):
raise TypeError("Invalid angle. Range is 1-90 degrees.")
# Ensure valid angle
if angle > 90 or angle < 1:
raise ValueError("Invalid angle. Range is 1-90 degrees.")
# Ensure valid velocity
if init_velocity < 0:
raise ValueError("Invalid velocity. Should be a positive number.")
def horizontal_distance(init_velocity: float, angle: float) -> float:
"""
Returns the horizontal distance that the object cover
Formula:
v_0^2 * sin(2 * alpha)
---------------------
g
v_0 - initial velocity
alpha - angle
>>> horizontal_distance(30, 45)
91.77
>>> horizontal_distance(100, 78)
414.76
>>> horizontal_distance(-1, 20)
Traceback (most recent call last):
...
ValueError: Invalid velocity. Should be a positive number.
>>> horizontal_distance(30, -20)
Traceback (most recent call last):
...
ValueError: Invalid angle. Range is 1-90 degrees.
"""
check_args(init_velocity, angle)
radians = angle_to_radians(2 * angle)
return round(init_velocity**2 * sin(radians) / g, 2)
def max_height(init_velocity: float, angle: float) -> float:
"""
Returns the maximum height that the object reach
Formula:
v_0^2 * sin^2(alpha)
--------------------
2g
v_0 - initial velocity
alpha - angle
>>> max_height(30, 45)
22.94
>>> max_height(100, 78)
487.82
>>> max_height("a", 20)
Traceback (most recent call last):
...
TypeError: Invalid velocity. Should be a positive number.
>>> horizontal_distance(30, "b")
Traceback (most recent call last):
...
TypeError: Invalid angle. Range is 1-90 degrees.
"""
check_args(init_velocity, angle)
radians = angle_to_radians(angle)
return round(init_velocity**2 * sin(radians) ** 2 / (2 * g), 2)
def total_time(init_velocity: float, angle: float) -> float:
"""
Returns total time of the motion
Formula:
2 * v_0 * sin(alpha)
--------------------
g
v_0 - initial velocity
alpha - angle
>>> total_time(30, 45)
4.33
>>> total_time(100, 78)
19.95
>>> total_time(-10, 40)
Traceback (most recent call last):
...
ValueError: Invalid velocity. Should be a positive number.
>>> total_time(30, "b")
Traceback (most recent call last):
...
TypeError: Invalid angle. Range is 1-90 degrees.
"""
check_args(init_velocity, angle)
radians = angle_to_radians(angle)
return round(2 * init_velocity * sin(radians) / g, 2)
def test_motion() -> None:
"""
>>> test_motion()
"""
v0, angle = 25, 20
assert horizontal_distance(v0, angle) == 40.97
assert max_height(v0, angle) == 3.73
assert total_time(v0, angle) == 1.74
if __name__ == "__main__":
from doctest import testmod
testmod()
# Get input from user
init_vel = float(input("Initial Velocity: ").strip())
# Get input from user
angle = float(input("angle: ").strip())
# Print results
print()
print("Results: ")
print(f"Horizontal Distance: {str(horizontal_distance(init_vel, angle))} [m]")
print(f"Maximum Height: {str(max_height(init_vel, angle))} [m]")
print(f"Total Time: {str(total_time(init_vel, angle))} [s]")
| """
Horizontal Projectile Motion problem in physics.
This algorithm solves a specific problem in which
the motion starts from the ground as can be seen below:
(v = 0)
**
* *
* *
* *
* *
* *
GROUND GROUND
For more info: https://en.wikipedia.org/wiki/Projectile_motion
"""
# Importing packages
from math import radians as angle_to_radians
from math import sin
# Acceleration Constant on Earth (unit m/s^2)
g = 9.80665
def check_args(init_velocity: float, angle: float) -> None:
"""
Check that the arguments are valid
"""
# Ensure valid instance
if not isinstance(init_velocity, (int, float)):
raise TypeError("Invalid velocity. Should be a positive number.")
if not isinstance(angle, (int, float)):
raise TypeError("Invalid angle. Range is 1-90 degrees.")
# Ensure valid angle
if angle > 90 or angle < 1:
raise ValueError("Invalid angle. Range is 1-90 degrees.")
# Ensure valid velocity
if init_velocity < 0:
raise ValueError("Invalid velocity. Should be a positive number.")
def horizontal_distance(init_velocity: float, angle: float) -> float:
"""
Returns the horizontal distance that the object cover
Formula:
v_0^2 * sin(2 * alpha)
---------------------
g
v_0 - initial velocity
alpha - angle
>>> horizontal_distance(30, 45)
91.77
>>> horizontal_distance(100, 78)
414.76
>>> horizontal_distance(-1, 20)
Traceback (most recent call last):
...
ValueError: Invalid velocity. Should be a positive number.
>>> horizontal_distance(30, -20)
Traceback (most recent call last):
...
ValueError: Invalid angle. Range is 1-90 degrees.
"""
check_args(init_velocity, angle)
radians = angle_to_radians(2 * angle)
return round(init_velocity**2 * sin(radians) / g, 2)
def max_height(init_velocity: float, angle: float) -> float:
"""
Returns the maximum height that the object reach
Formula:
v_0^2 * sin^2(alpha)
--------------------
2g
v_0 - initial velocity
alpha - angle
>>> max_height(30, 45)
22.94
>>> max_height(100, 78)
487.82
>>> max_height("a", 20)
Traceback (most recent call last):
...
TypeError: Invalid velocity. Should be a positive number.
>>> horizontal_distance(30, "b")
Traceback (most recent call last):
...
TypeError: Invalid angle. Range is 1-90 degrees.
"""
check_args(init_velocity, angle)
radians = angle_to_radians(angle)
return round(init_velocity**2 * sin(radians) ** 2 / (2 * g), 2)
def total_time(init_velocity: float, angle: float) -> float:
"""
Returns total time of the motion
Formula:
2 * v_0 * sin(alpha)
--------------------
g
v_0 - initial velocity
alpha - angle
>>> total_time(30, 45)
4.33
>>> total_time(100, 78)
19.95
>>> total_time(-10, 40)
Traceback (most recent call last):
...
ValueError: Invalid velocity. Should be a positive number.
>>> total_time(30, "b")
Traceback (most recent call last):
...
TypeError: Invalid angle. Range is 1-90 degrees.
"""
check_args(init_velocity, angle)
radians = angle_to_radians(angle)
return round(2 * init_velocity * sin(radians) / g, 2)
def test_motion() -> None:
"""
>>> test_motion()
"""
v0, angle = 25, 20
assert horizontal_distance(v0, angle) == 40.97
assert max_height(v0, angle) == 3.73
assert total_time(v0, angle) == 1.74
if __name__ == "__main__":
from doctest import testmod
testmod()
# Get input from user
init_vel = float(input("Initial Velocity: ").strip())
# Get input from user
angle = float(input("angle: ").strip())
# Print results
print()
print("Results: ")
print(f"Horizontal Distance: {str(horizontal_distance(init_vel, angle))} [m]")
print(f"Maximum Height: {str(max_height(init_vel, angle))} [m]")
print(f"Total Time: {str(total_time(init_vel, angle))} [s]")
| -1 |
TheAlgorithms/Python | 7,262 | refactor: Move constants outside of variable scope | ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T19:51:55Z" | "2022-10-16T09:33:29Z" | 77764116217708933bdc65b29801092fa291398e | c6582b35bf8b8aba622c63096e3ab2f01aa36854 | refactor: Move constants outside of variable scope. ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$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 | 7,262 | refactor: Move constants outside of variable scope | ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T19:51:55Z" | "2022-10-16T09:33:29Z" | 77764116217708933bdc65b29801092fa291398e | c6582b35bf8b8aba622c63096e3ab2f01aa36854 | refactor: Move constants outside of variable scope. ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 7,262 | refactor: Move constants outside of variable scope | ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T19:51:55Z" | "2022-10-16T09:33:29Z" | 77764116217708933bdc65b29801092fa291398e | c6582b35bf8b8aba622c63096e3ab2f01aa36854 | refactor: Move constants outside of variable scope. ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 7,262 | refactor: Move constants outside of variable scope | ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T19:51:55Z" | "2022-10-16T09:33:29Z" | 77764116217708933bdc65b29801092fa291398e | c6582b35bf8b8aba622c63096e3ab2f01aa36854 | refactor: Move constants outside of variable scope. ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """Factorial of a positive integer -- https://en.wikipedia.org/wiki/Factorial
"""
def factorial(number: int) -> int:
"""
Calculate the factorial of specified number (n!).
>>> 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
>>> factorial(1)
1
>>> factorial(6)
720
>>> factorial(0)
1
"""
if number != int(number):
raise ValueError("factorial() only accepts integral values")
if number < 0:
raise ValueError("factorial() not defined for negative values")
value = 1
for i in range(1, number + 1):
value *= i
return value
if __name__ == "__main__":
import doctest
doctest.testmod()
n = int(input("Enter a positive integer: ").strip() or 0)
print(f"factorial{n} is {factorial(n)}")
| """Factorial of a positive integer -- https://en.wikipedia.org/wiki/Factorial
"""
def factorial(number: int) -> int:
"""
Calculate the factorial of specified number (n!).
>>> 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
>>> factorial(1)
1
>>> factorial(6)
720
>>> factorial(0)
1
"""
if number != int(number):
raise ValueError("factorial() only accepts integral values")
if number < 0:
raise ValueError("factorial() not defined for negative values")
value = 1
for i in range(1, number + 1):
value *= i
return value
if __name__ == "__main__":
import doctest
doctest.testmod()
n = int(input("Enter a positive integer: ").strip() or 0)
print(f"factorial{n} is {factorial(n)}")
| -1 |
TheAlgorithms/Python | 7,262 | refactor: Move constants outside of variable scope | ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T19:51:55Z" | "2022-10-16T09:33:29Z" | 77764116217708933bdc65b29801092fa291398e | c6582b35bf8b8aba622c63096e3ab2f01aa36854 | refactor: Move constants outside of variable scope. ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
In the game of darts a player throws three darts at a target board which is
split into twenty equal sized sections numbered one to twenty.

The score of a dart is determined by the number of the region that the dart
lands in. A dart landing outside the red/green outer ring scores zero. The black
and cream regions inside this ring represent single scores. However, the red/green
outer ring and middle ring score double and treble scores respectively.
At the centre of the board are two concentric circles called the bull region, or
bulls-eye. The outer bull is worth 25 points and the inner bull is a double,
worth 50 points.
There are many variations of rules but in the most popular game the players will
begin with a score 301 or 501 and the first player to reduce their running total
to zero is a winner. However, it is normal to play a "doubles out" system, which
means that the player must land a double (including the double bulls-eye at the
centre of the board) on their final dart to win; any other dart that would reduce
their running total to one or lower means the score for that set of three darts
is "bust".
When a player is able to finish on their current score it is called a "checkout"
and the highest checkout is 170: T20 T20 D25 (two treble 20s and double bull).
There are exactly eleven distinct ways to checkout on a score of 6:
D3
D1 D2
S2 D2
D2 D1
S4 D1
S1 S1 D2
S1 T1 D1
S1 S3 D1
D1 D1 D1
D1 S2 D1
S2 S2 D1
Note that D1 D2 is considered different to D2 D1 as they finish on different
doubles. However, the combination S1 T1 D1 is considered the same as T1 S1 D1.
In addition we shall not include misses in considering combinations; for example,
D3 is the same as 0 D3 and 0 0 D3.
Incredibly there are 42336 distinct ways of checking out in total.
How many distinct ways can a player checkout with a score less than 100?
Solution:
We first construct a list of the possible dart values, separated by type.
We then iterate through the doubles, followed by the possible 2 following throws.
If the total of these three darts is less than the given limit, we increment
the counter.
"""
from itertools import combinations_with_replacement
def solution(limit: int = 100) -> int:
"""
Count the number of distinct ways a player can checkout with a score
less than limit.
>>> solution(171)
42336
>>> solution(50)
12577
"""
singles: list[int] = list(range(1, 21)) + [25]
doubles: list[int] = [2 * x for x in range(1, 21)] + [50]
triples: list[int] = [3 * x for x in range(1, 21)]
all_values: list[int] = singles + doubles + triples + [0]
num_checkouts: int = 0
double: int
throw1: int
throw2: int
checkout_total: int
for double in doubles:
for throw1, throw2 in combinations_with_replacement(all_values, 2):
checkout_total = double + throw1 + throw2
if checkout_total < limit:
num_checkouts += 1
return num_checkouts
if __name__ == "__main__":
print(f"{solution() = }")
| """
In the game of darts a player throws three darts at a target board which is
split into twenty equal sized sections numbered one to twenty.

The score of a dart is determined by the number of the region that the dart
lands in. A dart landing outside the red/green outer ring scores zero. The black
and cream regions inside this ring represent single scores. However, the red/green
outer ring and middle ring score double and treble scores respectively.
At the centre of the board are two concentric circles called the bull region, or
bulls-eye. The outer bull is worth 25 points and the inner bull is a double,
worth 50 points.
There are many variations of rules but in the most popular game the players will
begin with a score 301 or 501 and the first player to reduce their running total
to zero is a winner. However, it is normal to play a "doubles out" system, which
means that the player must land a double (including the double bulls-eye at the
centre of the board) on their final dart to win; any other dart that would reduce
their running total to one or lower means the score for that set of three darts
is "bust".
When a player is able to finish on their current score it is called a "checkout"
and the highest checkout is 170: T20 T20 D25 (two treble 20s and double bull).
There are exactly eleven distinct ways to checkout on a score of 6:
D3
D1 D2
S2 D2
D2 D1
S4 D1
S1 S1 D2
S1 T1 D1
S1 S3 D1
D1 D1 D1
D1 S2 D1
S2 S2 D1
Note that D1 D2 is considered different to D2 D1 as they finish on different
doubles. However, the combination S1 T1 D1 is considered the same as T1 S1 D1.
In addition we shall not include misses in considering combinations; for example,
D3 is the same as 0 D3 and 0 0 D3.
Incredibly there are 42336 distinct ways of checking out in total.
How many distinct ways can a player checkout with a score less than 100?
Solution:
We first construct a list of the possible dart values, separated by type.
We then iterate through the doubles, followed by the possible 2 following throws.
If the total of these three darts is less than the given limit, we increment
the counter.
"""
from itertools import combinations_with_replacement
def solution(limit: int = 100) -> int:
"""
Count the number of distinct ways a player can checkout with a score
less than limit.
>>> solution(171)
42336
>>> solution(50)
12577
"""
singles: list[int] = list(range(1, 21)) + [25]
doubles: list[int] = [2 * x for x in range(1, 21)] + [50]
triples: list[int] = [3 * x for x in range(1, 21)]
all_values: list[int] = singles + doubles + triples + [0]
num_checkouts: int = 0
double: int
throw1: int
throw2: int
checkout_total: int
for double in doubles:
for throw1, throw2 in combinations_with_replacement(all_values, 2):
checkout_total = double + throw1 + throw2
if checkout_total < limit:
num_checkouts += 1
return num_checkouts
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 7,262 | refactor: Move constants outside of variable scope | ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is 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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T19:51:55Z" | "2022-10-16T09:33:29Z" | 77764116217708933bdc65b29801092fa291398e | c6582b35bf8b8aba622c63096e3ab2f01aa36854 | refactor: Move constants outside of variable scope. ### Describe your change:
Moves constants outside of variable scope, removing the need for `noqa: N806`
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 7,235 | Add Flake8 comprehensions to pre-commit | ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [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] I know that pull requests will not be merged if they fail the automated tests.
* [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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T14:17:49Z" | "2022-10-15T17:29:42Z" | 98a4c2487814cdfe0822526e05c4e63ff6aef7d0 | a652905b605ddcc43626072366d1130315801dc9 | Add Flake8 comprehensions to pre-commit. ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| repos:
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: v4.3.0
hooks:
- id: check-executables-have-shebangs
- id: check-yaml
- id: end-of-file-fixer
types: [python]
- id: trailing-whitespace
exclude: |
(?x)^(
data_structures/heap/binomial_heap.py
)$
- id: requirements-txt-fixer
- repo: https://github.com/psf/black
rev: 22.10.0
hooks:
- id: black
- repo: https://github.com/PyCQA/isort
rev: 5.10.1
hooks:
- id: isort
args:
- --profile=black
- repo: https://github.com/asottile/pyupgrade
rev: v3.0.0
hooks:
- id: pyupgrade
args:
- --py310-plus
- repo: https://github.com/PyCQA/flake8
rev: 5.0.4
hooks:
- id: flake8 # See .flake8 for args
additional_dependencies:
- flake8-bugbear
- flake8-builtins
- pep8-naming
- 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]
- repo: https://github.com/codespell-project/codespell
rev: v2.2.1
hooks:
- id: codespell
args:
- --ignore-words-list=ans,crate,damon,fo,followings,hist,iff,mater,secant,som,sur,tim,zar
- --skip="./.*,./strings/dictionary.txt,./strings/words.txt,./project_euler/problem_022/p022_names.txt"
exclude: |
(?x)^(
strings/dictionary.txt |
strings/words.txt |
project_euler/problem_022/p022_names.txt
)$
- repo: local
hooks:
- id: validate-filenames
name: Validate filenames
entry: ./scripts/validate_filenames.py
language: script
pass_filenames: false
| repos:
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: v4.3.0
hooks:
- id: check-executables-have-shebangs
- id: check-yaml
- id: end-of-file-fixer
types: [python]
- id: trailing-whitespace
exclude: |
(?x)^(
data_structures/heap/binomial_heap.py
)$
- id: requirements-txt-fixer
- repo: https://github.com/psf/black
rev: 22.10.0
hooks:
- id: black
- repo: https://github.com/PyCQA/isort
rev: 5.10.1
hooks:
- id: isort
args:
- --profile=black
- repo: https://github.com/asottile/pyupgrade
rev: v3.0.0
hooks:
- id: pyupgrade
args:
- --py310-plus
- repo: https://github.com/PyCQA/flake8
rev: 5.0.4
hooks:
- id: flake8 # See .flake8 for args
additional_dependencies:
- flake8-bugbear
- flake8-builtins
- flake8-comprehensions
- pep8-naming
- 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]
- repo: https://github.com/codespell-project/codespell
rev: v2.2.1
hooks:
- id: codespell
args:
- --ignore-words-list=ans,crate,damon,fo,followings,hist,iff,mater,secant,som,sur,tim,zar
- --skip="./.*,./strings/dictionary.txt,./strings/words.txt,./project_euler/problem_022/p022_names.txt"
exclude: |
(?x)^(
strings/dictionary.txt |
strings/words.txt |
project_euler/problem_022/p022_names.txt
)$
- repo: local
hooks:
- id: validate-filenames
name: Validate filenames
entry: ./scripts/validate_filenames.py
language: script
pass_filenames: false
| 1 |
TheAlgorithms/Python | 7,235 | Add Flake8 comprehensions to pre-commit | ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [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] I know that pull requests will not be merged if they fail the automated tests.
* [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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T14:17:49Z" | "2022-10-15T17:29:42Z" | 98a4c2487814cdfe0822526e05c4e63ff6aef7d0 | a652905b605ddcc43626072366d1130315801dc9 | Add Flake8 comprehensions to pre-commit. ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| import random
class Onepad:
@staticmethod
def encrypt(text: str) -> tuple[list[int], list[int]]:
"""Function to encrypt text using pseudo-random numbers"""
plain = [ord(i) for i in text]
key = []
cipher = []
for i in plain:
k = random.randint(1, 300)
c = (i + k) * k
cipher.append(c)
key.append(k)
return cipher, key
@staticmethod
def decrypt(cipher: list[int], key: list[int]) -> str:
"""Function to decrypt text using pseudo-random numbers."""
plain = []
for i in range(len(key)):
p = int((cipher[i] - (key[i]) ** 2) / key[i])
plain.append(chr(p))
return "".join([i for i in plain])
if __name__ == "__main__":
c, k = Onepad().encrypt("Hello")
print(c, k)
print(Onepad().decrypt(c, k))
| import random
class Onepad:
@staticmethod
def encrypt(text: str) -> tuple[list[int], list[int]]:
"""Function to encrypt text using pseudo-random numbers"""
plain = [ord(i) for i in text]
key = []
cipher = []
for i in plain:
k = random.randint(1, 300)
c = (i + k) * k
cipher.append(c)
key.append(k)
return cipher, key
@staticmethod
def decrypt(cipher: list[int], key: list[int]) -> str:
"""Function to decrypt text using pseudo-random numbers."""
plain = []
for i in range(len(key)):
p = int((cipher[i] - (key[i]) ** 2) / key[i])
plain.append(chr(p))
return "".join(plain)
if __name__ == "__main__":
c, k = Onepad().encrypt("Hello")
print(c, k)
print(Onepad().decrypt(c, k))
| 1 |
TheAlgorithms/Python | 7,235 | Add Flake8 comprehensions to pre-commit | ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [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] I know that pull requests will not be merged if they fail the automated tests.
* [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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T14:17:49Z" | "2022-10-15T17:29:42Z" | 98a4c2487814cdfe0822526e05c4e63ff6aef7d0 | a652905b605ddcc43626072366d1130315801dc9 | Add Flake8 comprehensions to pre-commit. ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """ https://en.wikipedia.org/wiki/Rail_fence_cipher """
def encrypt(input_string: str, key: int) -> str:
"""
Shuffles the character of a string by placing each of them
in a grid (the height is dependent on the key) in a zigzag
formation and reading it left to right.
>>> encrypt("Hello World", 4)
'HWe olordll'
>>> encrypt("This is a message", 0)
Traceback (most recent call last):
...
ValueError: Height of grid can't be 0 or negative
>>> encrypt(b"This is a byte string", 5)
Traceback (most recent call last):
...
TypeError: sequence item 0: expected str instance, int found
"""
temp_grid: list[list[str]] = [[] for _ in range(key)]
lowest = key - 1
if key <= 0:
raise ValueError("Height of grid can't be 0 or negative")
if key == 1 or len(input_string) <= key:
return input_string
for position, character in enumerate(input_string):
num = position % (lowest * 2) # puts it in bounds
num = min(num, lowest * 2 - num) # creates zigzag pattern
temp_grid[num].append(character)
grid = ["".join(row) for row in temp_grid]
output_string = "".join(grid)
return output_string
def decrypt(input_string: str, key: int) -> str:
"""
Generates a template based on the key and fills it in with
the characters of the input string and then reading it in
a zigzag formation.
>>> decrypt("HWe olordll", 4)
'Hello World'
>>> decrypt("This is a message", -10)
Traceback (most recent call last):
...
ValueError: Height of grid can't be 0 or negative
>>> decrypt("My key is very big", 100)
'My key is very big'
"""
grid = []
lowest = key - 1
if key <= 0:
raise ValueError("Height of grid can't be 0 or negative")
if key == 1:
return input_string
temp_grid: list[list[str]] = [[] for _ in range(key)] # generates template
for position in range(len(input_string)):
num = position % (lowest * 2) # puts it in bounds
num = min(num, lowest * 2 - num) # creates zigzag pattern
temp_grid[num].append("*")
counter = 0
for row in temp_grid: # fills in the characters
splice = input_string[counter : counter + len(row)]
grid.append([character for character in splice])
counter += len(row)
output_string = "" # reads as zigzag
for position in range(len(input_string)):
num = position % (lowest * 2) # puts it in bounds
num = min(num, lowest * 2 - num) # creates zigzag pattern
output_string += grid[num][0]
grid[num].pop(0)
return output_string
def bruteforce(input_string: str) -> dict[int, str]:
"""Uses decrypt function by guessing every key
>>> bruteforce("HWe olordll")[4]
'Hello World'
"""
results = {}
for key_guess in range(1, len(input_string)): # tries every key
results[key_guess] = decrypt(input_string, key_guess)
return results
if __name__ == "__main__":
import doctest
doctest.testmod()
| """ https://en.wikipedia.org/wiki/Rail_fence_cipher """
def encrypt(input_string: str, key: int) -> str:
"""
Shuffles the character of a string by placing each of them
in a grid (the height is dependent on the key) in a zigzag
formation and reading it left to right.
>>> encrypt("Hello World", 4)
'HWe olordll'
>>> encrypt("This is a message", 0)
Traceback (most recent call last):
...
ValueError: Height of grid can't be 0 or negative
>>> encrypt(b"This is a byte string", 5)
Traceback (most recent call last):
...
TypeError: sequence item 0: expected str instance, int found
"""
temp_grid: list[list[str]] = [[] for _ in range(key)]
lowest = key - 1
if key <= 0:
raise ValueError("Height of grid can't be 0 or negative")
if key == 1 or len(input_string) <= key:
return input_string
for position, character in enumerate(input_string):
num = position % (lowest * 2) # puts it in bounds
num = min(num, lowest * 2 - num) # creates zigzag pattern
temp_grid[num].append(character)
grid = ["".join(row) for row in temp_grid]
output_string = "".join(grid)
return output_string
def decrypt(input_string: str, key: int) -> str:
"""
Generates a template based on the key and fills it in with
the characters of the input string and then reading it in
a zigzag formation.
>>> decrypt("HWe olordll", 4)
'Hello World'
>>> decrypt("This is a message", -10)
Traceback (most recent call last):
...
ValueError: Height of grid can't be 0 or negative
>>> decrypt("My key is very big", 100)
'My key is very big'
"""
grid = []
lowest = key - 1
if key <= 0:
raise ValueError("Height of grid can't be 0 or negative")
if key == 1:
return input_string
temp_grid: list[list[str]] = [[] for _ in range(key)] # generates template
for position in range(len(input_string)):
num = position % (lowest * 2) # puts it in bounds
num = min(num, lowest * 2 - num) # creates zigzag pattern
temp_grid[num].append("*")
counter = 0
for row in temp_grid: # fills in the characters
splice = input_string[counter : counter + len(row)]
grid.append(list(splice))
counter += len(row)
output_string = "" # reads as zigzag
for position in range(len(input_string)):
num = position % (lowest * 2) # puts it in bounds
num = min(num, lowest * 2 - num) # creates zigzag pattern
output_string += grid[num][0]
grid[num].pop(0)
return output_string
def bruteforce(input_string: str) -> dict[int, str]:
"""Uses decrypt function by guessing every key
>>> bruteforce("HWe olordll")[4]
'Hello World'
"""
results = {}
for key_guess in range(1, len(input_string)): # tries every key
results[key_guess] = decrypt(input_string, key_guess)
return results
if __name__ == "__main__":
import doctest
doctest.testmod()
| 1 |
TheAlgorithms/Python | 7,235 | Add Flake8 comprehensions to pre-commit | ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [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] I know that pull requests will not be merged if they fail the automated tests.
* [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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T14:17:49Z" | "2022-10-15T17:29:42Z" | 98a4c2487814cdfe0822526e05c4e63ff6aef7d0 | a652905b605ddcc43626072366d1130315801dc9 | Add Flake8 comprehensions to pre-commit. ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| #!/usr/bin/env python3
from .number_theory.prime_numbers import next_prime
class HashTable:
"""
Basic Hash Table example with open addressing and linear probing
"""
def __init__(
self,
size_table: int,
charge_factor: int | None = None,
lim_charge: float | None = None,
) -> None:
self.size_table = size_table
self.values = [None] * self.size_table
self.lim_charge = 0.75 if lim_charge is None else lim_charge
self.charge_factor = 1 if charge_factor is None else charge_factor
self.__aux_list: list = []
self._keys: dict = {}
def keys(self):
return self._keys
def balanced_factor(self):
return sum(1 for slot in self.values if slot is not None) / (
self.size_table * self.charge_factor
)
def hash_function(self, key):
return key % self.size_table
def _step_by_step(self, step_ord):
print(f"step {step_ord}")
print([i for i in range(len(self.values))])
print(self.values)
def bulk_insert(self, values):
i = 1
self.__aux_list = values
for value in values:
self.insert_data(value)
self._step_by_step(i)
i += 1
def _set_value(self, key, data):
self.values[key] = data
self._keys[key] = data
def _collision_resolution(self, key, data=None):
new_key = self.hash_function(key + 1)
while self.values[new_key] is not None and self.values[new_key] != key:
if self.values.count(None) > 0:
new_key = self.hash_function(new_key + 1)
else:
new_key = None
break
return new_key
def rehashing(self):
survivor_values = [value for value in self.values if value is not None]
self.size_table = next_prime(self.size_table, factor=2)
self._keys.clear()
self.values = [None] * self.size_table # hell's pointers D: don't DRY ;/
for value in survivor_values:
self.insert_data(value)
def insert_data(self, data):
key = self.hash_function(data)
if self.values[key] is None:
self._set_value(key, data)
elif self.values[key] == data:
pass
else:
collision_resolution = self._collision_resolution(key, data)
if collision_resolution is not None:
self._set_value(collision_resolution, data)
else:
self.rehashing()
self.insert_data(data)
| #!/usr/bin/env python3
from .number_theory.prime_numbers import next_prime
class HashTable:
"""
Basic Hash Table example with open addressing and linear probing
"""
def __init__(
self,
size_table: int,
charge_factor: int | None = None,
lim_charge: float | None = None,
) -> None:
self.size_table = size_table
self.values = [None] * self.size_table
self.lim_charge = 0.75 if lim_charge is None else lim_charge
self.charge_factor = 1 if charge_factor is None else charge_factor
self.__aux_list: list = []
self._keys: dict = {}
def keys(self):
return self._keys
def balanced_factor(self):
return sum(1 for slot in self.values if slot is not None) / (
self.size_table * self.charge_factor
)
def hash_function(self, key):
return key % self.size_table
def _step_by_step(self, step_ord):
print(f"step {step_ord}")
print(list(range(len(self.values))))
print(self.values)
def bulk_insert(self, values):
i = 1
self.__aux_list = values
for value in values:
self.insert_data(value)
self._step_by_step(i)
i += 1
def _set_value(self, key, data):
self.values[key] = data
self._keys[key] = data
def _collision_resolution(self, key, data=None):
new_key = self.hash_function(key + 1)
while self.values[new_key] is not None and self.values[new_key] != key:
if self.values.count(None) > 0:
new_key = self.hash_function(new_key + 1)
else:
new_key = None
break
return new_key
def rehashing(self):
survivor_values = [value for value in self.values if value is not None]
self.size_table = next_prime(self.size_table, factor=2)
self._keys.clear()
self.values = [None] * self.size_table # hell's pointers D: don't DRY ;/
for value in survivor_values:
self.insert_data(value)
def insert_data(self, data):
key = self.hash_function(data)
if self.values[key] is None:
self._set_value(key, data)
elif self.values[key] == data:
pass
else:
collision_resolution = self._collision_resolution(key, data)
if collision_resolution is not None:
self._set_value(collision_resolution, data)
else:
self.rehashing()
self.insert_data(data)
| 1 |
TheAlgorithms/Python | 7,235 | Add Flake8 comprehensions to pre-commit | ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [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] I know that pull requests will not be merged if they fail the automated tests.
* [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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T14:17:49Z" | "2022-10-15T17:29:42Z" | 98a4c2487814cdfe0822526e05c4e63ff6aef7d0 | a652905b605ddcc43626072366d1130315801dc9 | Add Flake8 comprehensions to pre-commit. ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$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 reversed(sorted(ints)):
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 |
TheAlgorithms/Python | 7,235 | Add Flake8 comprehensions to pre-commit | ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [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] I know that pull requests will not be merged if they fail the automated tests.
* [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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T14:17:49Z" | "2022-10-15T17:29:42Z" | 98a4c2487814cdfe0822526e05c4e63ff6aef7d0 | a652905b605ddcc43626072366d1130315801dc9 | Add Flake8 comprehensions to pre-commit. ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from 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 = list(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 | 7,235 | Add Flake8 comprehensions to pre-commit | ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [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] I know that pull requests will not be merged if they fail the automated tests.
* [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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T14:17:49Z" | "2022-10-15T17:29:42Z" | 98a4c2487814cdfe0822526e05c4e63ff6aef7d0 | a652905b605ddcc43626072366d1130315801dc9 | Add Flake8 comprehensions to pre-commit. ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from __future__ import annotations
def print_distance(distance: list[float], src):
print(f"Vertex\tShortest Distance from vertex {src}")
for i, d in enumerate(distance):
print(f"{i}\t\t{d}")
def check_negative_cycle(
graph: list[dict[str, int]], distance: list[float], edge_count: int
):
for j in range(edge_count):
u, v, w = (graph[j][k] for k in ["src", "dst", "weight"])
if distance[u] != float("inf") and distance[u] + w < distance[v]:
return True
return False
def bellman_ford(
graph: list[dict[str, int]], vertex_count: int, edge_count: int, src: int
) -> list[float]:
"""
Returns shortest paths from a vertex src to all
other vertices.
>>> edges = [(2, 1, -10), (3, 2, 3), (0, 3, 5), (0, 1, 4)]
>>> g = [{"src": s, "dst": d, "weight": w} for s, d, w in edges]
>>> bellman_ford(g, 4, 4, 0)
[0.0, -2.0, 8.0, 5.0]
>>> g = [{"src": s, "dst": d, "weight": w} for s, d, w in edges + [(1, 3, 5)]]
>>> bellman_ford(g, 4, 5, 0)
Traceback (most recent call last):
...
Exception: Negative cycle found
"""
distance = [float("inf")] * vertex_count
distance[src] = 0.0
for _ in range(vertex_count - 1):
for j in range(edge_count):
u, v, w = (graph[j][k] for k in ["src", "dst", "weight"])
if distance[u] != float("inf") and distance[u] + w < distance[v]:
distance[v] = distance[u] + w
negative_cycle_exists = check_negative_cycle(graph, distance, edge_count)
if negative_cycle_exists:
raise Exception("Negative cycle found")
return distance
if __name__ == "__main__":
import doctest
doctest.testmod()
V = int(input("Enter number of vertices: ").strip())
E = int(input("Enter number of edges: ").strip())
graph: list[dict[str, int]] = [dict() for j in range(E)]
for i in range(E):
print("Edge ", i + 1)
src, dest, weight = (
int(x)
for x in input("Enter source, destination, weight: ").strip().split(" ")
)
graph[i] = {"src": src, "dst": dest, "weight": weight}
source = int(input("\nEnter shortest path source:").strip())
shortest_distance = bellman_ford(graph, V, E, source)
print_distance(shortest_distance, 0)
| from __future__ import annotations
def print_distance(distance: list[float], src):
print(f"Vertex\tShortest Distance from vertex {src}")
for i, d in enumerate(distance):
print(f"{i}\t\t{d}")
def check_negative_cycle(
graph: list[dict[str, int]], distance: list[float], edge_count: int
):
for j in range(edge_count):
u, v, w = (graph[j][k] for k in ["src", "dst", "weight"])
if distance[u] != float("inf") and distance[u] + w < distance[v]:
return True
return False
def bellman_ford(
graph: list[dict[str, int]], vertex_count: int, edge_count: int, src: int
) -> list[float]:
"""
Returns shortest paths from a vertex src to all
other vertices.
>>> edges = [(2, 1, -10), (3, 2, 3), (0, 3, 5), (0, 1, 4)]
>>> g = [{"src": s, "dst": d, "weight": w} for s, d, w in edges]
>>> bellman_ford(g, 4, 4, 0)
[0.0, -2.0, 8.0, 5.0]
>>> g = [{"src": s, "dst": d, "weight": w} for s, d, w in edges + [(1, 3, 5)]]
>>> bellman_ford(g, 4, 5, 0)
Traceback (most recent call last):
...
Exception: Negative cycle found
"""
distance = [float("inf")] * vertex_count
distance[src] = 0.0
for _ in range(vertex_count - 1):
for j in range(edge_count):
u, v, w = (graph[j][k] for k in ["src", "dst", "weight"])
if distance[u] != float("inf") and distance[u] + w < distance[v]:
distance[v] = distance[u] + w
negative_cycle_exists = check_negative_cycle(graph, distance, edge_count)
if negative_cycle_exists:
raise Exception("Negative cycle found")
return distance
if __name__ == "__main__":
import doctest
doctest.testmod()
V = int(input("Enter number of vertices: ").strip())
E = int(input("Enter number of edges: ").strip())
graph: list[dict[str, int]] = [{} for _ in range(E)]
for i in range(E):
print("Edge ", i + 1)
src, dest, weight = (
int(x)
for x in input("Enter source, destination, weight: ").strip().split(" ")
)
graph[i] = {"src": src, "dst": dest, "weight": weight}
source = int(input("\nEnter shortest path source:").strip())
shortest_distance = bellman_ford(graph, V, E, source)
print_distance(shortest_distance, 0)
| 1 |
TheAlgorithms/Python | 7,235 | Add Flake8 comprehensions to pre-commit | ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [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] I know that pull requests will not be merged if they fail the automated tests.
* [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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T14:17:49Z" | "2022-10-15T17:29:42Z" | 98a4c2487814cdfe0822526e05c4e63ff6aef7d0 | a652905b605ddcc43626072366d1130315801dc9 | Add Flake8 comprehensions to pre-commit. ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
FP-GraphMiner - A Fast Frequent Pattern Mining Algorithm for Network Graphs
A novel Frequent Pattern Graph Mining algorithm, FP-GraphMiner, that compactly
represents a set of network graphs as a Frequent Pattern Graph (or FP-Graph).
This graph can be used to efficiently mine frequent subgraphs including maximal
frequent subgraphs and maximum common subgraphs.
URL: https://www.researchgate.net/publication/235255851
"""
# fmt: off
edge_array = [
['ab-e1', 'ac-e3', 'ad-e5', 'bc-e4', 'bd-e2', 'be-e6', 'bh-e12', 'cd-e2', 'ce-e4',
'de-e1', 'df-e8', 'dg-e5', 'dh-e10', 'ef-e3', 'eg-e2', 'fg-e6', 'gh-e6', 'hi-e3'],
['ab-e1', 'ac-e3', 'ad-e5', 'bc-e4', 'bd-e2', 'be-e6', 'cd-e2', 'de-e1', 'df-e8',
'ef-e3', 'eg-e2', 'fg-e6'],
['ab-e1', 'ac-e3', 'bc-e4', 'bd-e2', 'de-e1', 'df-e8', 'dg-e5', 'ef-e3', 'eg-e2',
'eh-e12', 'fg-e6', 'fh-e10', 'gh-e6'],
['ab-e1', 'ac-e3', 'bc-e4', 'bd-e2', 'bh-e12', 'cd-e2', 'df-e8', 'dh-e10'],
['ab-e1', 'ac-e3', 'ad-e5', 'bc-e4', 'bd-e2', 'cd-e2', 'ce-e4', 'de-e1', 'df-e8',
'dg-e5', 'ef-e3', 'eg-e2', 'fg-e6']
]
# fmt: on
def get_distinct_edge(edge_array):
"""
Return Distinct edges from edge array of multiple graphs
>>> sorted(get_distinct_edge(edge_array))
['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h']
"""
distinct_edge = set()
for row in edge_array:
for item in row:
distinct_edge.add(item[0])
return list(distinct_edge)
def get_bitcode(edge_array, distinct_edge):
"""
Return bitcode of distinct_edge
"""
bitcode = ["0"] * len(edge_array)
for i, row in enumerate(edge_array):
for item in row:
if distinct_edge in item[0]:
bitcode[i] = "1"
break
return "".join(bitcode)
def get_frequency_table(edge_array):
"""
Returns Frequency Table
"""
distinct_edge = get_distinct_edge(edge_array)
frequency_table = {}
for item in distinct_edge:
bit = get_bitcode(edge_array, item)
# print('bit',bit)
# bt=''.join(bit)
s = bit.count("1")
frequency_table[item] = [s, bit]
# Store [Distinct edge, WT(Bitcode), Bitcode] in descending order
sorted_frequency_table = [
[k, v[0], v[1]]
for k, v in sorted(frequency_table.items(), key=lambda v: v[1][0], reverse=True)
]
return sorted_frequency_table
def get_nodes(frequency_table):
"""
Returns nodes
format nodes={bitcode:edges that represent the bitcode}
>>> get_nodes([['ab', 5, '11111'], ['ac', 5, '11111'], ['df', 5, '11111'],
... ['bd', 5, '11111'], ['bc', 5, '11111']])
{'11111': ['ab', 'ac', 'df', 'bd', 'bc']}
"""
nodes = {}
for _, item in enumerate(frequency_table):
nodes.setdefault(item[2], []).append(item[0])
return nodes
def get_cluster(nodes):
"""
Returns cluster
format cluster:{WT(bitcode):nodes with same WT}
"""
cluster = {}
for key, value in nodes.items():
cluster.setdefault(key.count("1"), {})[key] = value
return cluster
def get_support(cluster):
"""
Returns support
>>> get_support({5: {'11111': ['ab', 'ac', 'df', 'bd', 'bc']},
... 4: {'11101': ['ef', 'eg', 'de', 'fg'], '11011': ['cd']},
... 3: {'11001': ['ad'], '10101': ['dg']},
... 2: {'10010': ['dh', 'bh'], '11000': ['be'], '10100': ['gh'],
... '10001': ['ce']},
... 1: {'00100': ['fh', 'eh'], '10000': ['hi']}})
[100.0, 80.0, 60.0, 40.0, 20.0]
"""
return [i * 100 / len(cluster) for i in cluster]
def print_all() -> None:
print("\nNodes\n")
for key, value in nodes.items():
print(key, value)
print("\nSupport\n")
print(support)
print("\n Cluster \n")
for key, value in sorted(cluster.items(), reverse=True):
print(key, value)
print("\n Graph\n")
for key, value in graph.items():
print(key, value)
print("\n Edge List of Frequent subgraphs \n")
for edge_list in freq_subgraph_edge_list:
print(edge_list)
def create_edge(nodes, graph, cluster, c1):
"""
create edge between the nodes
"""
for i in cluster[c1].keys():
count = 0
c2 = c1 + 1
while c2 < max(cluster.keys()):
for j in cluster[c2].keys():
"""
creates edge only if the condition satisfies
"""
if int(i, 2) & int(j, 2) == int(i, 2):
if tuple(nodes[i]) in graph:
graph[tuple(nodes[i])].append(nodes[j])
else:
graph[tuple(nodes[i])] = [nodes[j]]
count += 1
if count == 0:
c2 = c2 + 1
else:
break
def construct_graph(cluster, nodes):
x = cluster[max(cluster.keys())]
cluster[max(cluster.keys()) + 1] = "Header"
graph = {}
for i in x:
if tuple(["Header"]) in graph:
graph[tuple(["Header"])].append(x[i])
else:
graph[tuple(["Header"])] = [x[i]]
for i in x:
graph[tuple(x[i])] = [["Header"]]
i = 1
while i < max(cluster) - 1:
create_edge(nodes, graph, cluster, i)
i = i + 1
return graph
def my_dfs(graph, start, end, path=None):
"""
find different DFS walk from given node to Header node
"""
path = (path or []) + [start]
if start == end:
paths.append(path)
for node in graph[start]:
if tuple(node) not in path:
my_dfs(graph, tuple(node), end, path)
def find_freq_subgraph_given_support(s, cluster, graph):
"""
find edges of multiple frequent subgraphs
"""
k = int(s / 100 * (len(cluster) - 1))
for i in cluster[k].keys():
my_dfs(graph, tuple(cluster[k][i]), tuple(["Header"]))
def freq_subgraphs_edge_list(paths):
"""
returns Edge list for frequent subgraphs
"""
freq_sub_el = []
for edges in paths:
el = []
for j in range(len(edges) - 1):
temp = list(edges[j])
for e in temp:
edge = (e[0], e[1])
el.append(edge)
freq_sub_el.append(el)
return freq_sub_el
def preprocess(edge_array):
"""
Preprocess the edge array
>>> preprocess([['ab-e1', 'ac-e3', 'ad-e5', 'bc-e4', 'bd-e2', 'be-e6', 'bh-e12',
... 'cd-e2', 'ce-e4', 'de-e1', 'df-e8', 'dg-e5', 'dh-e10', 'ef-e3',
... 'eg-e2', 'fg-e6', 'gh-e6', 'hi-e3']])
"""
for i in range(len(edge_array)):
for j in range(len(edge_array[i])):
t = edge_array[i][j].split("-")
edge_array[i][j] = t
if __name__ == "__main__":
preprocess(edge_array)
frequency_table = get_frequency_table(edge_array)
nodes = get_nodes(frequency_table)
cluster = get_cluster(nodes)
support = get_support(cluster)
graph = construct_graph(cluster, nodes)
find_freq_subgraph_given_support(60, cluster, graph)
paths: list = []
freq_subgraph_edge_list = freq_subgraphs_edge_list(paths)
print_all()
| """
FP-GraphMiner - A Fast Frequent Pattern Mining Algorithm for Network Graphs
A novel Frequent Pattern Graph Mining algorithm, FP-GraphMiner, that compactly
represents a set of network graphs as a Frequent Pattern Graph (or FP-Graph).
This graph can be used to efficiently mine frequent subgraphs including maximal
frequent subgraphs and maximum common subgraphs.
URL: https://www.researchgate.net/publication/235255851
"""
# fmt: off
edge_array = [
['ab-e1', 'ac-e3', 'ad-e5', 'bc-e4', 'bd-e2', 'be-e6', 'bh-e12', 'cd-e2', 'ce-e4',
'de-e1', 'df-e8', 'dg-e5', 'dh-e10', 'ef-e3', 'eg-e2', 'fg-e6', 'gh-e6', 'hi-e3'],
['ab-e1', 'ac-e3', 'ad-e5', 'bc-e4', 'bd-e2', 'be-e6', 'cd-e2', 'de-e1', 'df-e8',
'ef-e3', 'eg-e2', 'fg-e6'],
['ab-e1', 'ac-e3', 'bc-e4', 'bd-e2', 'de-e1', 'df-e8', 'dg-e5', 'ef-e3', 'eg-e2',
'eh-e12', 'fg-e6', 'fh-e10', 'gh-e6'],
['ab-e1', 'ac-e3', 'bc-e4', 'bd-e2', 'bh-e12', 'cd-e2', 'df-e8', 'dh-e10'],
['ab-e1', 'ac-e3', 'ad-e5', 'bc-e4', 'bd-e2', 'cd-e2', 'ce-e4', 'de-e1', 'df-e8',
'dg-e5', 'ef-e3', 'eg-e2', 'fg-e6']
]
# fmt: on
def get_distinct_edge(edge_array):
"""
Return Distinct edges from edge array of multiple graphs
>>> sorted(get_distinct_edge(edge_array))
['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h']
"""
distinct_edge = set()
for row in edge_array:
for item in row:
distinct_edge.add(item[0])
return list(distinct_edge)
def get_bitcode(edge_array, distinct_edge):
"""
Return bitcode of distinct_edge
"""
bitcode = ["0"] * len(edge_array)
for i, row in enumerate(edge_array):
for item in row:
if distinct_edge in item[0]:
bitcode[i] = "1"
break
return "".join(bitcode)
def get_frequency_table(edge_array):
"""
Returns Frequency Table
"""
distinct_edge = get_distinct_edge(edge_array)
frequency_table = {}
for item in distinct_edge:
bit = get_bitcode(edge_array, item)
# print('bit',bit)
# bt=''.join(bit)
s = bit.count("1")
frequency_table[item] = [s, bit]
# Store [Distinct edge, WT(Bitcode), Bitcode] in descending order
sorted_frequency_table = [
[k, v[0], v[1]]
for k, v in sorted(frequency_table.items(), key=lambda v: v[1][0], reverse=True)
]
return sorted_frequency_table
def get_nodes(frequency_table):
"""
Returns nodes
format nodes={bitcode:edges that represent the bitcode}
>>> get_nodes([['ab', 5, '11111'], ['ac', 5, '11111'], ['df', 5, '11111'],
... ['bd', 5, '11111'], ['bc', 5, '11111']])
{'11111': ['ab', 'ac', 'df', 'bd', 'bc']}
"""
nodes = {}
for _, item in enumerate(frequency_table):
nodes.setdefault(item[2], []).append(item[0])
return nodes
def get_cluster(nodes):
"""
Returns cluster
format cluster:{WT(bitcode):nodes with same WT}
"""
cluster = {}
for key, value in nodes.items():
cluster.setdefault(key.count("1"), {})[key] = value
return cluster
def get_support(cluster):
"""
Returns support
>>> get_support({5: {'11111': ['ab', 'ac', 'df', 'bd', 'bc']},
... 4: {'11101': ['ef', 'eg', 'de', 'fg'], '11011': ['cd']},
... 3: {'11001': ['ad'], '10101': ['dg']},
... 2: {'10010': ['dh', 'bh'], '11000': ['be'], '10100': ['gh'],
... '10001': ['ce']},
... 1: {'00100': ['fh', 'eh'], '10000': ['hi']}})
[100.0, 80.0, 60.0, 40.0, 20.0]
"""
return [i * 100 / len(cluster) for i in cluster]
def print_all() -> None:
print("\nNodes\n")
for key, value in nodes.items():
print(key, value)
print("\nSupport\n")
print(support)
print("\n Cluster \n")
for key, value in sorted(cluster.items(), reverse=True):
print(key, value)
print("\n Graph\n")
for key, value in graph.items():
print(key, value)
print("\n Edge List of Frequent subgraphs \n")
for edge_list in freq_subgraph_edge_list:
print(edge_list)
def create_edge(nodes, graph, cluster, c1):
"""
create edge between the nodes
"""
for i in cluster[c1].keys():
count = 0
c2 = c1 + 1
while c2 < max(cluster.keys()):
for j in cluster[c2].keys():
"""
creates edge only if the condition satisfies
"""
if int(i, 2) & int(j, 2) == int(i, 2):
if tuple(nodes[i]) in graph:
graph[tuple(nodes[i])].append(nodes[j])
else:
graph[tuple(nodes[i])] = [nodes[j]]
count += 1
if count == 0:
c2 = c2 + 1
else:
break
def construct_graph(cluster, nodes):
x = cluster[max(cluster.keys())]
cluster[max(cluster.keys()) + 1] = "Header"
graph = {}
for i in x:
if (["Header"],) in graph:
graph[(["Header"],)].append(x[i])
else:
graph[(["Header"],)] = [x[i]]
for i in x:
graph[(x[i],)] = [["Header"]]
i = 1
while i < max(cluster) - 1:
create_edge(nodes, graph, cluster, i)
i = i + 1
return graph
def my_dfs(graph, start, end, path=None):
"""
find different DFS walk from given node to Header node
"""
path = (path or []) + [start]
if start == end:
paths.append(path)
for node in graph[start]:
if tuple(node) not in path:
my_dfs(graph, tuple(node), end, path)
def find_freq_subgraph_given_support(s, cluster, graph):
"""
find edges of multiple frequent subgraphs
"""
k = int(s / 100 * (len(cluster) - 1))
for i in cluster[k].keys():
my_dfs(graph, tuple(cluster[k][i]), (["Header"],))
def freq_subgraphs_edge_list(paths):
"""
returns Edge list for frequent subgraphs
"""
freq_sub_el = []
for edges in paths:
el = []
for j in range(len(edges) - 1):
temp = list(edges[j])
for e in temp:
edge = (e[0], e[1])
el.append(edge)
freq_sub_el.append(el)
return freq_sub_el
def preprocess(edge_array):
"""
Preprocess the edge array
>>> preprocess([['ab-e1', 'ac-e3', 'ad-e5', 'bc-e4', 'bd-e2', 'be-e6', 'bh-e12',
... 'cd-e2', 'ce-e4', 'de-e1', 'df-e8', 'dg-e5', 'dh-e10', 'ef-e3',
... 'eg-e2', 'fg-e6', 'gh-e6', 'hi-e3']])
"""
for i in range(len(edge_array)):
for j in range(len(edge_array[i])):
t = edge_array[i][j].split("-")
edge_array[i][j] = t
if __name__ == "__main__":
preprocess(edge_array)
frequency_table = get_frequency_table(edge_array)
nodes = get_nodes(frequency_table)
cluster = get_cluster(nodes)
support = get_support(cluster)
graph = construct_graph(cluster, nodes)
find_freq_subgraph_given_support(60, cluster, graph)
paths: list = []
freq_subgraph_edge_list = freq_subgraphs_edge_list(paths)
print_all()
| 1 |
TheAlgorithms/Python | 7,235 | Add Flake8 comprehensions to pre-commit | ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [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] I know that pull requests will not be merged if they fail the automated tests.
* [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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T14:17:49Z" | "2022-10-15T17:29:42Z" | 98a4c2487814cdfe0822526e05c4e63ff6aef7d0 | a652905b605ddcc43626072366d1130315801dc9 | Add Flake8 comprehensions to pre-commit. ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| alphabets = [chr(i) for i in range(32, 126)]
gear_one = [i for i in range(len(alphabets))]
gear_two = [i for i in range(len(alphabets))]
gear_three = [i for i in range(len(alphabets))]
reflector = [i for i in reversed(range(len(alphabets)))]
code = []
gear_one_pos = gear_two_pos = gear_three_pos = 0
def rotator():
global gear_one_pos
global gear_two_pos
global gear_three_pos
i = gear_one[0]
gear_one.append(i)
del gear_one[0]
gear_one_pos += 1
if gear_one_pos % int(len(alphabets)) == 0:
i = gear_two[0]
gear_two.append(i)
del gear_two[0]
gear_two_pos += 1
if gear_two_pos % int(len(alphabets)) == 0:
i = gear_three[0]
gear_three.append(i)
del gear_three[0]
gear_three_pos += 1
def engine(input_character):
target = alphabets.index(input_character)
target = gear_one[target]
target = gear_two[target]
target = gear_three[target]
target = reflector[target]
target = gear_three.index(target)
target = gear_two.index(target)
target = gear_one.index(target)
code.append(alphabets[target])
rotator()
if __name__ == "__main__":
decode = list(input("Type your message:\n"))
while True:
try:
token = int(input("Please set token:(must be only digits)\n"))
break
except Exception as error:
print(error)
for _ in range(token):
rotator()
for j in decode:
engine(j)
print("\n" + "".join(code))
print(
f"\nYour Token is {token} please write it down.\nIf you want to decode "
"this message again you should input same digits as token!"
)
| alphabets = [chr(i) for i in range(32, 126)]
gear_one = list(range(len(alphabets)))
gear_two = list(range(len(alphabets)))
gear_three = list(range(len(alphabets)))
reflector = list(reversed(range(len(alphabets))))
code = []
gear_one_pos = gear_two_pos = gear_three_pos = 0
def rotator():
global gear_one_pos
global gear_two_pos
global gear_three_pos
i = gear_one[0]
gear_one.append(i)
del gear_one[0]
gear_one_pos += 1
if gear_one_pos % int(len(alphabets)) == 0:
i = gear_two[0]
gear_two.append(i)
del gear_two[0]
gear_two_pos += 1
if gear_two_pos % int(len(alphabets)) == 0:
i = gear_three[0]
gear_three.append(i)
del gear_three[0]
gear_three_pos += 1
def engine(input_character):
target = alphabets.index(input_character)
target = gear_one[target]
target = gear_two[target]
target = gear_three[target]
target = reflector[target]
target = gear_three.index(target)
target = gear_two.index(target)
target = gear_one.index(target)
code.append(alphabets[target])
rotator()
if __name__ == "__main__":
decode = list(input("Type your message:\n"))
while True:
try:
token = int(input("Please set token:(must be only digits)\n"))
break
except Exception as error:
print(error)
for _ in range(token):
rotator()
for j in decode:
engine(j)
print("\n" + "".join(code))
print(
f"\nYour Token is {token} please write it down.\nIf you want to decode "
"this message again you should input same digits as token!"
)
| 1 |
TheAlgorithms/Python | 7,235 | Add Flake8 comprehensions to pre-commit | ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [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] I know that pull requests will not be merged if they fail the automated tests.
* [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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T14:17:49Z" | "2022-10-15T17:29:42Z" | 98a4c2487814cdfe0822526e05c4e63ff6aef7d0 | a652905b605ddcc43626072366d1130315801dc9 | Add Flake8 comprehensions to pre-commit. ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Created on Thu Oct 5 16:44:23 2017
@author: Christian Bender
This Python library contains some useful functions to deal with
prime numbers and whole numbers.
Overview:
is_prime(number)
sieve_er(N)
get_prime_numbers(N)
prime_factorization(number)
greatest_prime_factor(number)
smallest_prime_factor(number)
get_prime(n)
get_primes_between(pNumber1, pNumber2)
----
is_even(number)
is_odd(number)
gcd(number1, number2) // greatest common divisor
kg_v(number1, number2) // least common multiple
get_divisors(number) // all divisors of 'number' inclusive 1, number
is_perfect_number(number)
NEW-FUNCTIONS
simplify_fraction(numerator, denominator)
factorial (n) // n!
fib (n) // calculate the n-th fibonacci term.
-----
goldbach(number) // Goldbach's assumption
"""
from math import sqrt
def is_prime(number: int) -> bool:
"""
input: positive integer 'number'
returns true if 'number' is prime otherwise false.
"""
# precondition
assert isinstance(number, int) and (
number >= 0
), "'number' must been an int and positive"
status = True
# 0 and 1 are none primes.
if number <= 1:
status = False
for divisor in range(2, int(round(sqrt(number))) + 1):
# if 'number' divisible by 'divisor' then sets 'status'
# of false and break up the loop.
if number % divisor == 0:
status = False
break
# precondition
assert isinstance(status, bool), "'status' must been from type bool"
return status
# ------------------------------------------
def sieve_er(n):
"""
input: positive integer 'N' > 2
returns a list of prime numbers from 2 up to N.
This function implements the algorithm called
sieve of erathostenes.
"""
# precondition
assert isinstance(n, int) and (n > 2), "'N' must been an int and > 2"
# beginList: contains all natural numbers from 2 up to N
begin_list = [x for x in range(2, n + 1)]
ans = [] # this list will be returns.
# actual sieve of erathostenes
for i in range(len(begin_list)):
for j in range(i + 1, len(begin_list)):
if (begin_list[i] != 0) and (begin_list[j] % begin_list[i] == 0):
begin_list[j] = 0
# filters actual prime numbers.
ans = [x for x in begin_list if x != 0]
# precondition
assert isinstance(ans, list), "'ans' must been from type list"
return ans
# --------------------------------
def get_prime_numbers(n):
"""
input: positive integer 'N' > 2
returns a list of prime numbers from 2 up to N (inclusive)
This function is more efficient as function 'sieveEr(...)'
"""
# precondition
assert isinstance(n, int) and (n > 2), "'N' must been an int and > 2"
ans = []
# iterates over all numbers between 2 up to N+1
# if a number is prime then appends to list 'ans'
for number in range(2, n + 1):
if is_prime(number):
ans.append(number)
# precondition
assert isinstance(ans, list), "'ans' must been from type list"
return ans
# -----------------------------------------
def prime_factorization(number):
"""
input: positive integer 'number'
returns a list of the prime number factors of 'number'
"""
# precondition
assert isinstance(number, int) and number >= 0, "'number' must been an int and >= 0"
ans = [] # this list will be returns of the function.
# potential prime number factors.
factor = 2
quotient = number
if number == 0 or number == 1:
ans.append(number)
# if 'number' not prime then builds the prime factorization of 'number'
elif not is_prime(number):
while quotient != 1:
if is_prime(factor) and (quotient % factor == 0):
ans.append(factor)
quotient /= factor
else:
factor += 1
else:
ans.append(number)
# precondition
assert isinstance(ans, list), "'ans' must been from type list"
return ans
# -----------------------------------------
def greatest_prime_factor(number):
"""
input: positive integer 'number' >= 0
returns the greatest prime number factor of 'number'
"""
# precondition
assert isinstance(number, int) and (
number >= 0
), "'number' bust been an int and >= 0"
ans = 0
# prime factorization of 'number'
prime_factors = prime_factorization(number)
ans = max(prime_factors)
# precondition
assert isinstance(ans, int), "'ans' must been from type int"
return ans
# ----------------------------------------------
def smallest_prime_factor(number):
"""
input: integer 'number' >= 0
returns the smallest prime number factor of 'number'
"""
# precondition
assert isinstance(number, int) and (
number >= 0
), "'number' bust been an int and >= 0"
ans = 0
# prime factorization of 'number'
prime_factors = prime_factorization(number)
ans = min(prime_factors)
# precondition
assert isinstance(ans, int), "'ans' must been from type int"
return ans
# ----------------------
def is_even(number):
"""
input: integer 'number'
returns true if 'number' is even, otherwise false.
"""
# precondition
assert isinstance(number, int), "'number' must been an int"
assert isinstance(number % 2 == 0, bool), "compare bust been from type bool"
return number % 2 == 0
# ------------------------
def is_odd(number):
"""
input: integer 'number'
returns true if 'number' is odd, otherwise false.
"""
# precondition
assert isinstance(number, int), "'number' must been an int"
assert isinstance(number % 2 != 0, bool), "compare bust been from type bool"
return number % 2 != 0
# ------------------------
def goldbach(number):
"""
Goldbach's assumption
input: a even positive integer 'number' > 2
returns a list of two prime numbers whose sum is equal to 'number'
"""
# precondition
assert (
isinstance(number, int) and (number > 2) and is_even(number)
), "'number' must been an int, even and > 2"
ans = [] # this list will returned
# creates a list of prime numbers between 2 up to 'number'
prime_numbers = get_prime_numbers(number)
len_pn = len(prime_numbers)
# run variable for while-loops.
i = 0
j = None
# exit variable. for break up the loops
loop = True
while i < len_pn and loop:
j = i + 1
while j < len_pn and loop:
if prime_numbers[i] + prime_numbers[j] == number:
loop = False
ans.append(prime_numbers[i])
ans.append(prime_numbers[j])
j += 1
i += 1
# precondition
assert (
isinstance(ans, list)
and (len(ans) == 2)
and (ans[0] + ans[1] == number)
and is_prime(ans[0])
and is_prime(ans[1])
), "'ans' must contains two primes. And sum of elements must been eq 'number'"
return ans
# ----------------------------------------------
def gcd(number1, number2):
"""
Greatest common divisor
input: two positive integer 'number1' and 'number2'
returns the greatest common divisor of 'number1' and 'number2'
"""
# precondition
assert (
isinstance(number1, int)
and isinstance(number2, int)
and (number1 >= 0)
and (number2 >= 0)
), "'number1' and 'number2' must been positive integer."
rest = 0
while number2 != 0:
rest = number1 % number2
number1 = number2
number2 = rest
# precondition
assert isinstance(number1, int) and (
number1 >= 0
), "'number' must been from type int and positive"
return number1
# ----------------------------------------------------
def kg_v(number1, number2):
"""
Least common multiple
input: two positive integer 'number1' and 'number2'
returns the least common multiple of 'number1' and 'number2'
"""
# precondition
assert (
isinstance(number1, int)
and isinstance(number2, int)
and (number1 >= 1)
and (number2 >= 1)
), "'number1' and 'number2' must been positive integer."
ans = 1 # actual answer that will be return.
# for kgV (x,1)
if number1 > 1 and number2 > 1:
# builds the prime factorization of 'number1' and 'number2'
prime_fac_1 = prime_factorization(number1)
prime_fac_2 = prime_factorization(number2)
elif number1 == 1 or number2 == 1:
prime_fac_1 = []
prime_fac_2 = []
ans = max(number1, number2)
count1 = 0
count2 = 0
done = [] # captured numbers int both 'primeFac1' and 'primeFac2'
# iterates through primeFac1
for n in prime_fac_1:
if n not in done:
if n in prime_fac_2:
count1 = prime_fac_1.count(n)
count2 = prime_fac_2.count(n)
for _ in range(max(count1, count2)):
ans *= n
else:
count1 = prime_fac_1.count(n)
for _ in range(count1):
ans *= n
done.append(n)
# iterates through primeFac2
for n in prime_fac_2:
if n not in done:
count2 = prime_fac_2.count(n)
for _ in range(count2):
ans *= n
done.append(n)
# precondition
assert isinstance(ans, int) and (
ans >= 0
), "'ans' must been from type int and positive"
return ans
# ----------------------------------
def get_prime(n):
"""
Gets the n-th prime number.
input: positive integer 'n' >= 0
returns the n-th prime number, beginning at index 0
"""
# precondition
assert isinstance(n, int) and (n >= 0), "'number' must been a positive int"
index = 0
ans = 2 # this variable holds the answer
while index < n:
index += 1
ans += 1 # counts to the next number
# if ans not prime then
# runs to the next prime number.
while not is_prime(ans):
ans += 1
# precondition
assert isinstance(ans, int) and is_prime(
ans
), "'ans' must been a prime number and from type int"
return ans
# ---------------------------------------------------
def get_primes_between(p_number_1, p_number_2):
"""
input: prime numbers 'pNumber1' and 'pNumber2'
pNumber1 < pNumber2
returns a list of all prime numbers between 'pNumber1' (exclusive)
and 'pNumber2' (exclusive)
"""
# precondition
assert (
is_prime(p_number_1) and is_prime(p_number_2) and (p_number_1 < p_number_2)
), "The arguments must been prime numbers and 'pNumber1' < 'pNumber2'"
number = p_number_1 + 1 # jump to the next number
ans = [] # this list will be returns.
# if number is not prime then
# fetch the next prime number.
while not is_prime(number):
number += 1
while number < p_number_2:
ans.append(number)
number += 1
# fetch the next prime number.
while not is_prime(number):
number += 1
# precondition
assert (
isinstance(ans, list)
and ans[0] != p_number_1
and ans[len(ans) - 1] != p_number_2
), "'ans' must been a list without the arguments"
# 'ans' contains not 'pNumber1' and 'pNumber2' !
return ans
# ----------------------------------------------------
def get_divisors(n):
"""
input: positive integer 'n' >= 1
returns all divisors of n (inclusive 1 and 'n')
"""
# precondition
assert isinstance(n, int) and (n >= 1), "'n' must been int and >= 1"
ans = [] # will be returned.
for divisor in range(1, n + 1):
if n % divisor == 0:
ans.append(divisor)
# precondition
assert ans[0] == 1 and ans[len(ans) - 1] == n, "Error in function getDivisiors(...)"
return ans
# ----------------------------------------------------
def is_perfect_number(number):
"""
input: positive integer 'number' > 1
returns true if 'number' is a perfect number otherwise false.
"""
# precondition
assert isinstance(number, int) and (
number > 1
), "'number' must been an int and >= 1"
divisors = get_divisors(number)
# precondition
assert (
isinstance(divisors, list)
and (divisors[0] == 1)
and (divisors[len(divisors) - 1] == number)
), "Error in help-function getDivisiors(...)"
# summed all divisors up to 'number' (exclusive), hence [:-1]
return sum(divisors[:-1]) == number
# ------------------------------------------------------------
def simplify_fraction(numerator, denominator):
"""
input: two integer 'numerator' and 'denominator'
assumes: 'denominator' != 0
returns: a tuple with simplify numerator and denominator.
"""
# precondition
assert (
isinstance(numerator, int)
and isinstance(denominator, int)
and (denominator != 0)
), "The arguments must been from type int and 'denominator' != 0"
# build the greatest common divisor of numerator and denominator.
gcd_of_fraction = gcd(abs(numerator), abs(denominator))
# precondition
assert (
isinstance(gcd_of_fraction, int)
and (numerator % gcd_of_fraction == 0)
and (denominator % gcd_of_fraction == 0)
), "Error in function gcd(...,...)"
return (numerator // gcd_of_fraction, denominator // gcd_of_fraction)
# -----------------------------------------------------------------
def factorial(n):
"""
input: positive integer 'n'
returns the factorial of 'n' (n!)
"""
# precondition
assert isinstance(n, int) and (n >= 0), "'n' must been a int and >= 0"
ans = 1 # this will be return.
for factor in range(1, n + 1):
ans *= factor
return ans
# -------------------------------------------------------------------
def fib(n):
"""
input: positive integer 'n'
returns the n-th fibonacci term , indexing by 0
"""
# precondition
assert isinstance(n, int) and (n >= 0), "'n' must been an int and >= 0"
tmp = 0
fib1 = 1
ans = 1 # this will be return
for _ in range(n - 1):
tmp = ans
ans += fib1
fib1 = tmp
return ans
| """
Created on Thu Oct 5 16:44:23 2017
@author: Christian Bender
This Python library contains some useful functions to deal with
prime numbers and whole numbers.
Overview:
is_prime(number)
sieve_er(N)
get_prime_numbers(N)
prime_factorization(number)
greatest_prime_factor(number)
smallest_prime_factor(number)
get_prime(n)
get_primes_between(pNumber1, pNumber2)
----
is_even(number)
is_odd(number)
gcd(number1, number2) // greatest common divisor
kg_v(number1, number2) // least common multiple
get_divisors(number) // all divisors of 'number' inclusive 1, number
is_perfect_number(number)
NEW-FUNCTIONS
simplify_fraction(numerator, denominator)
factorial (n) // n!
fib (n) // calculate the n-th fibonacci term.
-----
goldbach(number) // Goldbach's assumption
"""
from math import sqrt
def is_prime(number: int) -> bool:
"""
input: positive integer 'number'
returns true if 'number' is prime otherwise false.
"""
# precondition
assert isinstance(number, int) and (
number >= 0
), "'number' must been an int and positive"
status = True
# 0 and 1 are none primes.
if number <= 1:
status = False
for divisor in range(2, int(round(sqrt(number))) + 1):
# if 'number' divisible by 'divisor' then sets 'status'
# of false and break up the loop.
if number % divisor == 0:
status = False
break
# precondition
assert isinstance(status, bool), "'status' must been from type bool"
return status
# ------------------------------------------
def sieve_er(n):
"""
input: positive integer 'N' > 2
returns a list of prime numbers from 2 up to N.
This function implements the algorithm called
sieve of erathostenes.
"""
# precondition
assert isinstance(n, int) and (n > 2), "'N' must been an int and > 2"
# beginList: contains all natural numbers from 2 up to N
begin_list = list(range(2, n + 1))
ans = [] # this list will be returns.
# actual sieve of erathostenes
for i in range(len(begin_list)):
for j in range(i + 1, len(begin_list)):
if (begin_list[i] != 0) and (begin_list[j] % begin_list[i] == 0):
begin_list[j] = 0
# filters actual prime numbers.
ans = [x for x in begin_list if x != 0]
# precondition
assert isinstance(ans, list), "'ans' must been from type list"
return ans
# --------------------------------
def get_prime_numbers(n):
"""
input: positive integer 'N' > 2
returns a list of prime numbers from 2 up to N (inclusive)
This function is more efficient as function 'sieveEr(...)'
"""
# precondition
assert isinstance(n, int) and (n > 2), "'N' must been an int and > 2"
ans = []
# iterates over all numbers between 2 up to N+1
# if a number is prime then appends to list 'ans'
for number in range(2, n + 1):
if is_prime(number):
ans.append(number)
# precondition
assert isinstance(ans, list), "'ans' must been from type list"
return ans
# -----------------------------------------
def prime_factorization(number):
"""
input: positive integer 'number'
returns a list of the prime number factors of 'number'
"""
# precondition
assert isinstance(number, int) and number >= 0, "'number' must been an int and >= 0"
ans = [] # this list will be returns of the function.
# potential prime number factors.
factor = 2
quotient = number
if number == 0 or number == 1:
ans.append(number)
# if 'number' not prime then builds the prime factorization of 'number'
elif not is_prime(number):
while quotient != 1:
if is_prime(factor) and (quotient % factor == 0):
ans.append(factor)
quotient /= factor
else:
factor += 1
else:
ans.append(number)
# precondition
assert isinstance(ans, list), "'ans' must been from type list"
return ans
# -----------------------------------------
def greatest_prime_factor(number):
"""
input: positive integer 'number' >= 0
returns the greatest prime number factor of 'number'
"""
# precondition
assert isinstance(number, int) and (
number >= 0
), "'number' bust been an int and >= 0"
ans = 0
# prime factorization of 'number'
prime_factors = prime_factorization(number)
ans = max(prime_factors)
# precondition
assert isinstance(ans, int), "'ans' must been from type int"
return ans
# ----------------------------------------------
def smallest_prime_factor(number):
"""
input: integer 'number' >= 0
returns the smallest prime number factor of 'number'
"""
# precondition
assert isinstance(number, int) and (
number >= 0
), "'number' bust been an int and >= 0"
ans = 0
# prime factorization of 'number'
prime_factors = prime_factorization(number)
ans = min(prime_factors)
# precondition
assert isinstance(ans, int), "'ans' must been from type int"
return ans
# ----------------------
def is_even(number):
"""
input: integer 'number'
returns true if 'number' is even, otherwise false.
"""
# precondition
assert isinstance(number, int), "'number' must been an int"
assert isinstance(number % 2 == 0, bool), "compare bust been from type bool"
return number % 2 == 0
# ------------------------
def is_odd(number):
"""
input: integer 'number'
returns true if 'number' is odd, otherwise false.
"""
# precondition
assert isinstance(number, int), "'number' must been an int"
assert isinstance(number % 2 != 0, bool), "compare bust been from type bool"
return number % 2 != 0
# ------------------------
def goldbach(number):
"""
Goldbach's assumption
input: a even positive integer 'number' > 2
returns a list of two prime numbers whose sum is equal to 'number'
"""
# precondition
assert (
isinstance(number, int) and (number > 2) and is_even(number)
), "'number' must been an int, even and > 2"
ans = [] # this list will returned
# creates a list of prime numbers between 2 up to 'number'
prime_numbers = get_prime_numbers(number)
len_pn = len(prime_numbers)
# run variable for while-loops.
i = 0
j = None
# exit variable. for break up the loops
loop = True
while i < len_pn and loop:
j = i + 1
while j < len_pn and loop:
if prime_numbers[i] + prime_numbers[j] == number:
loop = False
ans.append(prime_numbers[i])
ans.append(prime_numbers[j])
j += 1
i += 1
# precondition
assert (
isinstance(ans, list)
and (len(ans) == 2)
and (ans[0] + ans[1] == number)
and is_prime(ans[0])
and is_prime(ans[1])
), "'ans' must contains two primes. And sum of elements must been eq 'number'"
return ans
# ----------------------------------------------
def gcd(number1, number2):
"""
Greatest common divisor
input: two positive integer 'number1' and 'number2'
returns the greatest common divisor of 'number1' and 'number2'
"""
# precondition
assert (
isinstance(number1, int)
and isinstance(number2, int)
and (number1 >= 0)
and (number2 >= 0)
), "'number1' and 'number2' must been positive integer."
rest = 0
while number2 != 0:
rest = number1 % number2
number1 = number2
number2 = rest
# precondition
assert isinstance(number1, int) and (
number1 >= 0
), "'number' must been from type int and positive"
return number1
# ----------------------------------------------------
def kg_v(number1, number2):
"""
Least common multiple
input: two positive integer 'number1' and 'number2'
returns the least common multiple of 'number1' and 'number2'
"""
# precondition
assert (
isinstance(number1, int)
and isinstance(number2, int)
and (number1 >= 1)
and (number2 >= 1)
), "'number1' and 'number2' must been positive integer."
ans = 1 # actual answer that will be return.
# for kgV (x,1)
if number1 > 1 and number2 > 1:
# builds the prime factorization of 'number1' and 'number2'
prime_fac_1 = prime_factorization(number1)
prime_fac_2 = prime_factorization(number2)
elif number1 == 1 or number2 == 1:
prime_fac_1 = []
prime_fac_2 = []
ans = max(number1, number2)
count1 = 0
count2 = 0
done = [] # captured numbers int both 'primeFac1' and 'primeFac2'
# iterates through primeFac1
for n in prime_fac_1:
if n not in done:
if n in prime_fac_2:
count1 = prime_fac_1.count(n)
count2 = prime_fac_2.count(n)
for _ in range(max(count1, count2)):
ans *= n
else:
count1 = prime_fac_1.count(n)
for _ in range(count1):
ans *= n
done.append(n)
# iterates through primeFac2
for n in prime_fac_2:
if n not in done:
count2 = prime_fac_2.count(n)
for _ in range(count2):
ans *= n
done.append(n)
# precondition
assert isinstance(ans, int) and (
ans >= 0
), "'ans' must been from type int and positive"
return ans
# ----------------------------------
def get_prime(n):
"""
Gets the n-th prime number.
input: positive integer 'n' >= 0
returns the n-th prime number, beginning at index 0
"""
# precondition
assert isinstance(n, int) and (n >= 0), "'number' must been a positive int"
index = 0
ans = 2 # this variable holds the answer
while index < n:
index += 1
ans += 1 # counts to the next number
# if ans not prime then
# runs to the next prime number.
while not is_prime(ans):
ans += 1
# precondition
assert isinstance(ans, int) and is_prime(
ans
), "'ans' must been a prime number and from type int"
return ans
# ---------------------------------------------------
def get_primes_between(p_number_1, p_number_2):
"""
input: prime numbers 'pNumber1' and 'pNumber2'
pNumber1 < pNumber2
returns a list of all prime numbers between 'pNumber1' (exclusive)
and 'pNumber2' (exclusive)
"""
# precondition
assert (
is_prime(p_number_1) and is_prime(p_number_2) and (p_number_1 < p_number_2)
), "The arguments must been prime numbers and 'pNumber1' < 'pNumber2'"
number = p_number_1 + 1 # jump to the next number
ans = [] # this list will be returns.
# if number is not prime then
# fetch the next prime number.
while not is_prime(number):
number += 1
while number < p_number_2:
ans.append(number)
number += 1
# fetch the next prime number.
while not is_prime(number):
number += 1
# precondition
assert (
isinstance(ans, list)
and ans[0] != p_number_1
and ans[len(ans) - 1] != p_number_2
), "'ans' must been a list without the arguments"
# 'ans' contains not 'pNumber1' and 'pNumber2' !
return ans
# ----------------------------------------------------
def get_divisors(n):
"""
input: positive integer 'n' >= 1
returns all divisors of n (inclusive 1 and 'n')
"""
# precondition
assert isinstance(n, int) and (n >= 1), "'n' must been int and >= 1"
ans = [] # will be returned.
for divisor in range(1, n + 1):
if n % divisor == 0:
ans.append(divisor)
# precondition
assert ans[0] == 1 and ans[len(ans) - 1] == n, "Error in function getDivisiors(...)"
return ans
# ----------------------------------------------------
def is_perfect_number(number):
"""
input: positive integer 'number' > 1
returns true if 'number' is a perfect number otherwise false.
"""
# precondition
assert isinstance(number, int) and (
number > 1
), "'number' must been an int and >= 1"
divisors = get_divisors(number)
# precondition
assert (
isinstance(divisors, list)
and (divisors[0] == 1)
and (divisors[len(divisors) - 1] == number)
), "Error in help-function getDivisiors(...)"
# summed all divisors up to 'number' (exclusive), hence [:-1]
return sum(divisors[:-1]) == number
# ------------------------------------------------------------
def simplify_fraction(numerator, denominator):
"""
input: two integer 'numerator' and 'denominator'
assumes: 'denominator' != 0
returns: a tuple with simplify numerator and denominator.
"""
# precondition
assert (
isinstance(numerator, int)
and isinstance(denominator, int)
and (denominator != 0)
), "The arguments must been from type int and 'denominator' != 0"
# build the greatest common divisor of numerator and denominator.
gcd_of_fraction = gcd(abs(numerator), abs(denominator))
# precondition
assert (
isinstance(gcd_of_fraction, int)
and (numerator % gcd_of_fraction == 0)
and (denominator % gcd_of_fraction == 0)
), "Error in function gcd(...,...)"
return (numerator // gcd_of_fraction, denominator // gcd_of_fraction)
# -----------------------------------------------------------------
def factorial(n):
"""
input: positive integer 'n'
returns the factorial of 'n' (n!)
"""
# precondition
assert isinstance(n, int) and (n >= 0), "'n' must been a int and >= 0"
ans = 1 # this will be return.
for factor in range(1, n + 1):
ans *= factor
return ans
# -------------------------------------------------------------------
def fib(n):
"""
input: positive integer 'n'
returns the n-th fibonacci term , indexing by 0
"""
# precondition
assert isinstance(n, int) and (n >= 0), "'n' must been an int and >= 0"
tmp = 0
fib1 = 1
ans = 1 # this will be return
for _ in range(n - 1):
tmp = ans
ans += fib1
fib1 = tmp
return ans
| 1 |
TheAlgorithms/Python | 7,235 | Add Flake8 comprehensions to pre-commit | ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [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] I know that pull requests will not be merged if they fail the automated tests.
* [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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T14:17:49Z" | "2022-10-15T17:29:42Z" | 98a4c2487814cdfe0822526e05c4e63ff6aef7d0 | a652905b605ddcc43626072366d1130315801dc9 | Add Flake8 comprehensions to pre-commit. ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
This program print the matrix in spiral form.
This problem has been solved through recursive way.
Matrix must satisfy below conditions
i) matrix should be only one or two dimensional
ii) number of column of all rows should be equal
"""
def check_matrix(matrix: list[list[int]]) -> bool:
# must be
matrix = list(list(row) for row in matrix)
if matrix and isinstance(matrix, list):
if isinstance(matrix[0], list):
prev_len = 0
for row in matrix:
if prev_len == 0:
prev_len = len(row)
result = True
else:
result = prev_len == len(row)
else:
result = True
else:
result = False
return result
def spiral_print_clockwise(a: list[list[int]]) -> None:
"""
>>> spiral_print_clockwise([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
1
2
3
4
8
12
11
10
9
5
6
7
"""
if check_matrix(a) and len(a) > 0:
a = list(list(row) for row in a)
mat_row = len(a)
if isinstance(a[0], list):
mat_col = len(a[0])
else:
for dat in a:
print(dat)
return
# horizotal printing increasing
for i in range(0, mat_col):
print(a[0][i])
# vertical printing down
for i in range(1, mat_row):
print(a[i][mat_col - 1])
# horizotal printing decreasing
if mat_row > 1:
for i in range(mat_col - 2, -1, -1):
print(a[mat_row - 1][i])
# vertical printing up
for i in range(mat_row - 2, 0, -1):
print(a[i][0])
remain_mat = [row[1 : mat_col - 1] for row in a[1 : mat_row - 1]]
if len(remain_mat) > 0:
spiral_print_clockwise(remain_mat)
else:
return
else:
print("Not a valid matrix")
return
# driver code
if __name__ == "__main__":
a = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]
spiral_print_clockwise(a)
| """
This program print the matrix in spiral form.
This problem has been solved through recursive way.
Matrix must satisfy below conditions
i) matrix should be only one or two dimensional
ii) number of column of all rows should be equal
"""
def check_matrix(matrix: list[list[int]]) -> bool:
# must be
matrix = [list(row) for row in matrix]
if matrix and isinstance(matrix, list):
if isinstance(matrix[0], list):
prev_len = 0
for row in matrix:
if prev_len == 0:
prev_len = len(row)
result = True
else:
result = prev_len == len(row)
else:
result = True
else:
result = False
return result
def spiral_print_clockwise(a: list[list[int]]) -> None:
"""
>>> spiral_print_clockwise([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
1
2
3
4
8
12
11
10
9
5
6
7
"""
if check_matrix(a) and len(a) > 0:
a = [list(row) for row in a]
mat_row = len(a)
if isinstance(a[0], list):
mat_col = len(a[0])
else:
for dat in a:
print(dat)
return
# horizotal printing increasing
for i in range(0, mat_col):
print(a[0][i])
# vertical printing down
for i in range(1, mat_row):
print(a[i][mat_col - 1])
# horizotal printing decreasing
if mat_row > 1:
for i in range(mat_col - 2, -1, -1):
print(a[mat_row - 1][i])
# vertical printing up
for i in range(mat_row - 2, 0, -1):
print(a[i][0])
remain_mat = [row[1 : mat_col - 1] for row in a[1 : mat_row - 1]]
if len(remain_mat) > 0:
spiral_print_clockwise(remain_mat)
else:
return
else:
print("Not a valid matrix")
return
# driver code
if __name__ == "__main__":
a = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]
spiral_print_clockwise(a)
| 1 |
TheAlgorithms/Python | 7,235 | Add Flake8 comprehensions to pre-commit | ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [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] I know that pull requests will not be merged if they fail the automated tests.
* [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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T14:17:49Z" | "2022-10-15T17:29:42Z" | 98a4c2487814cdfe0822526e05c4e63ff6aef7d0 | a652905b605ddcc43626072366d1130315801dc9 | Add Flake8 comprehensions to pre-commit. ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| #!/usr/bin/env python3
"""
Davis–Putnam–Logemann–Loveland (DPLL) algorithm is a complete, backtracking-based
search algorithm for deciding the satisfiability of propositional logic formulae in
conjunctive normal form, i.e, for solving the Conjunctive Normal Form SATisfiability
(CNF-SAT) problem.
For more information about the algorithm: https://en.wikipedia.org/wiki/DPLL_algorithm
"""
from __future__ import annotations
import random
from collections.abc import Iterable
class Clause:
"""
A clause represented in Conjunctive Normal Form.
A clause is a set of literals, either complemented or otherwise.
For example:
{A1, A2, A3'} is the clause (A1 v A2 v A3')
{A5', A2', A1} is the clause (A5' v A2' v A1)
Create model
>>> clause = Clause(["A1", "A2'", "A3"])
>>> clause.evaluate({"A1": True})
True
"""
def __init__(self, literals: list[str]) -> None:
"""
Represent the literals and an assignment in a clause."
"""
# Assign all literals to None initially
self.literals: dict[str, bool | None] = {literal: None for literal in literals}
def __str__(self) -> str:
"""
To print a clause as in Conjunctive Normal Form.
>>> str(Clause(["A1", "A2'", "A3"]))
"{A1 , A2' , A3}"
"""
return "{" + " , ".join(self.literals) + "}"
def __len__(self) -> int:
"""
To print a clause as in Conjunctive Normal Form.
>>> len(Clause([]))
0
>>> len(Clause(["A1", "A2'", "A3"]))
3
"""
return len(self.literals)
def assign(self, model: dict[str, bool | None]) -> None:
"""
Assign values to literals of the clause as given by model.
"""
for literal in self.literals:
symbol = literal[:2]
if symbol in model:
value = model[symbol]
else:
continue
if value is not None:
# Complement assignment if literal is in complemented form
if literal.endswith("'"):
value = not value
self.literals[literal] = value
def evaluate(self, model: dict[str, bool | None]) -> bool | None:
"""
Evaluates the clause with the assignments in model.
This has the following steps:
1. Return True if both a literal and its complement exist in the clause.
2. Return True if a single literal has the assignment True.
3. Return None(unable to complete evaluation) if a literal has no assignment.
4. Compute disjunction of all values assigned in clause.
"""
for literal in self.literals:
symbol = literal.rstrip("'") if literal.endswith("'") else literal + "'"
if symbol in self.literals:
return True
self.assign(model)
for value in self.literals.values():
if value in (True, None):
return value
return any(self.literals.values())
class Formula:
"""
A formula represented in Conjunctive Normal Form.
A formula is a set of clauses.
For example,
{{A1, A2, A3'}, {A5', A2', A1}} is ((A1 v A2 v A3') and (A5' v A2' v A1))
"""
def __init__(self, clauses: Iterable[Clause]) -> None:
"""
Represent the number of clauses and the clauses themselves.
"""
self.clauses = list(clauses)
def __str__(self) -> str:
"""
To print a formula as in Conjunctive Normal Form.
str(Formula([Clause(["A1", "A2'", "A3"]), Clause(["A5'", "A2'", "A1"])]))
"{{A1 , A2' , A3} , {A5' , A2' , A1}}"
"""
return "{" + " , ".join(str(clause) for clause in self.clauses) + "}"
def generate_clause() -> Clause:
"""
Randomly generate a clause.
All literals have the name Ax, where x is an integer from 1 to 5.
"""
literals = []
no_of_literals = random.randint(1, 5)
base_var = "A"
i = 0
while i < no_of_literals:
var_no = random.randint(1, 5)
var_name = base_var + str(var_no)
var_complement = random.randint(0, 1)
if var_complement == 1:
var_name += "'"
if var_name in literals:
i -= 1
else:
literals.append(var_name)
i += 1
return Clause(literals)
def generate_formula() -> Formula:
"""
Randomly generate a formula.
"""
clauses: set[Clause] = set()
no_of_clauses = random.randint(1, 10)
while len(clauses) < no_of_clauses:
clauses.add(generate_clause())
return Formula(clauses)
def generate_parameters(formula: Formula) -> tuple[list[Clause], list[str]]:
"""
Return the clauses and symbols from a formula.
A symbol is the uncomplemented form of a literal.
For example,
Symbol of A3 is A3.
Symbol of A5' is A5.
>>> formula = Formula([Clause(["A1", "A2'", "A3"]), Clause(["A5'", "A2'", "A1"])])
>>> clauses, symbols = generate_parameters(formula)
>>> clauses_list = [str(i) for i in clauses]
>>> clauses_list
["{A1 , A2' , A3}", "{A5' , A2' , A1}"]
>>> symbols
['A1', 'A2', 'A3', 'A5']
"""
clauses = formula.clauses
symbols_set = []
for clause in formula.clauses:
for literal in clause.literals:
symbol = literal[:2]
if symbol not in symbols_set:
symbols_set.append(symbol)
return clauses, symbols_set
def find_pure_symbols(
clauses: list[Clause], symbols: list[str], model: dict[str, bool | None]
) -> tuple[list[str], dict[str, bool | None]]:
"""
Return pure symbols and their values to satisfy clause.
Pure symbols are symbols in a formula that exist only
in one form, either complemented or otherwise.
For example,
{ { A4 , A3 , A5' , A1 , A3' } , { A4 } , { A3 } } has
pure symbols A4, A5' and A1.
This has the following steps:
1. Ignore clauses that have already evaluated to be True.
2. Find symbols that occur only in one form in the rest of the clauses.
3. Assign value True or False depending on whether the symbols occurs
in normal or complemented form respectively.
>>> formula = Formula([Clause(["A1", "A2'", "A3"]), Clause(["A5'", "A2'", "A1"])])
>>> clauses, symbols = generate_parameters(formula)
>>> pure_symbols, values = find_pure_symbols(clauses, symbols, {})
>>> pure_symbols
['A1', 'A2', 'A3', 'A5']
>>> values
{'A1': True, 'A2': False, 'A3': True, 'A5': False}
"""
pure_symbols = []
assignment: dict[str, bool | None] = {}
literals = []
for clause in clauses:
if clause.evaluate(model):
continue
for literal in clause.literals:
literals.append(literal)
for s in symbols:
sym = s + "'"
if (s in literals and sym not in literals) or (
s not in literals and sym in literals
):
pure_symbols.append(s)
for p in pure_symbols:
assignment[p] = None
for s in pure_symbols:
sym = s + "'"
if s in literals:
assignment[s] = True
elif sym in literals:
assignment[s] = False
return pure_symbols, assignment
def find_unit_clauses(
clauses: list[Clause], model: dict[str, bool | None]
) -> tuple[list[str], dict[str, bool | None]]:
"""
Returns the unit symbols and their values to satisfy clause.
Unit symbols are symbols in a formula that are:
- Either the only symbol in a clause
- Or all other literals in that clause have been assigned False
This has the following steps:
1. Find symbols that are the only occurrences in a clause.
2. Find symbols in a clause where all other literals are assigned False.
3. Assign True or False depending on whether the symbols occurs in
normal or complemented form respectively.
>>> clause1 = Clause(["A4", "A3", "A5'", "A1", "A3'"])
>>> clause2 = Clause(["A4"])
>>> clause3 = Clause(["A3"])
>>> clauses, symbols = generate_parameters(Formula([clause1, clause2, clause3]))
>>> unit_clauses, values = find_unit_clauses(clauses, {})
>>> unit_clauses
['A4', 'A3']
>>> values
{'A4': True, 'A3': True}
"""
unit_symbols = []
for clause in clauses:
if len(clause) == 1:
unit_symbols.append(list(clause.literals.keys())[0])
else:
f_count, n_count = 0, 0
for literal, value in clause.literals.items():
if value is False:
f_count += 1
elif value is None:
sym = literal
n_count += 1
if f_count == len(clause) - 1 and n_count == 1:
unit_symbols.append(sym)
assignment: dict[str, bool | None] = {}
for i in unit_symbols:
symbol = i[:2]
assignment[symbol] = len(i) == 2
unit_symbols = [i[:2] for i in unit_symbols]
return unit_symbols, assignment
def dpll_algorithm(
clauses: list[Clause], symbols: list[str], model: dict[str, bool | None]
) -> tuple[bool | None, dict[str, bool | None] | None]:
"""
Returns the model if the formula is satisfiable, else None
This has the following steps:
1. If every clause in clauses is True, return True.
2. If some clause in clauses is False, return False.
3. Find pure symbols.
4. Find unit symbols.
>>> formula = Formula([Clause(["A4", "A3", "A5'", "A1", "A3'"]), Clause(["A4"])])
>>> clauses, symbols = generate_parameters(formula)
>>> soln, model = dpll_algorithm(clauses, symbols, {})
>>> soln
True
>>> model
{'A4': True}
"""
check_clause_all_true = True
for clause in clauses:
clause_check = clause.evaluate(model)
if clause_check is False:
return False, None
elif clause_check is None:
check_clause_all_true = False
continue
if check_clause_all_true:
return True, model
try:
pure_symbols, assignment = find_pure_symbols(clauses, symbols, model)
except RecursionError:
print("raises a RecursionError and is")
return None, {}
p = None
if len(pure_symbols) > 0:
p, value = pure_symbols[0], assignment[pure_symbols[0]]
if p:
tmp_model = model
tmp_model[p] = value
tmp_symbols = [i for i in symbols]
if p in tmp_symbols:
tmp_symbols.remove(p)
return dpll_algorithm(clauses, tmp_symbols, tmp_model)
unit_symbols, assignment = find_unit_clauses(clauses, model)
p = None
if len(unit_symbols) > 0:
p, value = unit_symbols[0], assignment[unit_symbols[0]]
if p:
tmp_model = model
tmp_model[p] = value
tmp_symbols = [i for i in symbols]
if p in tmp_symbols:
tmp_symbols.remove(p)
return dpll_algorithm(clauses, tmp_symbols, tmp_model)
p = symbols[0]
rest = symbols[1:]
tmp1, tmp2 = model, model
tmp1[p], tmp2[p] = True, False
return dpll_algorithm(clauses, rest, tmp1) or dpll_algorithm(clauses, rest, tmp2)
if __name__ == "__main__":
import doctest
doctest.testmod()
formula = generate_formula()
print(f"The formula {formula} is", end=" ")
clauses, symbols = generate_parameters(formula)
solution, model = dpll_algorithm(clauses, symbols, {})
if solution:
print(f"satisfiable with the assignment {model}.")
else:
print("not satisfiable.")
| #!/usr/bin/env python3
"""
Davis–Putnam–Logemann–Loveland (DPLL) algorithm is a complete, backtracking-based
search algorithm for deciding the satisfiability of propositional logic formulae in
conjunctive normal form, i.e, for solving the Conjunctive Normal Form SATisfiability
(CNF-SAT) problem.
For more information about the algorithm: https://en.wikipedia.org/wiki/DPLL_algorithm
"""
from __future__ import annotations
import random
from collections.abc import Iterable
class Clause:
"""
A clause represented in Conjunctive Normal Form.
A clause is a set of literals, either complemented or otherwise.
For example:
{A1, A2, A3'} is the clause (A1 v A2 v A3')
{A5', A2', A1} is the clause (A5' v A2' v A1)
Create model
>>> clause = Clause(["A1", "A2'", "A3"])
>>> clause.evaluate({"A1": True})
True
"""
def __init__(self, literals: list[str]) -> None:
"""
Represent the literals and an assignment in a clause."
"""
# Assign all literals to None initially
self.literals: dict[str, bool | None] = {literal: None for literal in literals}
def __str__(self) -> str:
"""
To print a clause as in Conjunctive Normal Form.
>>> str(Clause(["A1", "A2'", "A3"]))
"{A1 , A2' , A3}"
"""
return "{" + " , ".join(self.literals) + "}"
def __len__(self) -> int:
"""
To print a clause as in Conjunctive Normal Form.
>>> len(Clause([]))
0
>>> len(Clause(["A1", "A2'", "A3"]))
3
"""
return len(self.literals)
def assign(self, model: dict[str, bool | None]) -> None:
"""
Assign values to literals of the clause as given by model.
"""
for literal in self.literals:
symbol = literal[:2]
if symbol in model:
value = model[symbol]
else:
continue
if value is not None:
# Complement assignment if literal is in complemented form
if literal.endswith("'"):
value = not value
self.literals[literal] = value
def evaluate(self, model: dict[str, bool | None]) -> bool | None:
"""
Evaluates the clause with the assignments in model.
This has the following steps:
1. Return True if both a literal and its complement exist in the clause.
2. Return True if a single literal has the assignment True.
3. Return None(unable to complete evaluation) if a literal has no assignment.
4. Compute disjunction of all values assigned in clause.
"""
for literal in self.literals:
symbol = literal.rstrip("'") if literal.endswith("'") else literal + "'"
if symbol in self.literals:
return True
self.assign(model)
for value in self.literals.values():
if value in (True, None):
return value
return any(self.literals.values())
class Formula:
"""
A formula represented in Conjunctive Normal Form.
A formula is a set of clauses.
For example,
{{A1, A2, A3'}, {A5', A2', A1}} is ((A1 v A2 v A3') and (A5' v A2' v A1))
"""
def __init__(self, clauses: Iterable[Clause]) -> None:
"""
Represent the number of clauses and the clauses themselves.
"""
self.clauses = list(clauses)
def __str__(self) -> str:
"""
To print a formula as in Conjunctive Normal Form.
str(Formula([Clause(["A1", "A2'", "A3"]), Clause(["A5'", "A2'", "A1"])]))
"{{A1 , A2' , A3} , {A5' , A2' , A1}}"
"""
return "{" + " , ".join(str(clause) for clause in self.clauses) + "}"
def generate_clause() -> Clause:
"""
Randomly generate a clause.
All literals have the name Ax, where x is an integer from 1 to 5.
"""
literals = []
no_of_literals = random.randint(1, 5)
base_var = "A"
i = 0
while i < no_of_literals:
var_no = random.randint(1, 5)
var_name = base_var + str(var_no)
var_complement = random.randint(0, 1)
if var_complement == 1:
var_name += "'"
if var_name in literals:
i -= 1
else:
literals.append(var_name)
i += 1
return Clause(literals)
def generate_formula() -> Formula:
"""
Randomly generate a formula.
"""
clauses: set[Clause] = set()
no_of_clauses = random.randint(1, 10)
while len(clauses) < no_of_clauses:
clauses.add(generate_clause())
return Formula(clauses)
def generate_parameters(formula: Formula) -> tuple[list[Clause], list[str]]:
"""
Return the clauses and symbols from a formula.
A symbol is the uncomplemented form of a literal.
For example,
Symbol of A3 is A3.
Symbol of A5' is A5.
>>> formula = Formula([Clause(["A1", "A2'", "A3"]), Clause(["A5'", "A2'", "A1"])])
>>> clauses, symbols = generate_parameters(formula)
>>> clauses_list = [str(i) for i in clauses]
>>> clauses_list
["{A1 , A2' , A3}", "{A5' , A2' , A1}"]
>>> symbols
['A1', 'A2', 'A3', 'A5']
"""
clauses = formula.clauses
symbols_set = []
for clause in formula.clauses:
for literal in clause.literals:
symbol = literal[:2]
if symbol not in symbols_set:
symbols_set.append(symbol)
return clauses, symbols_set
def find_pure_symbols(
clauses: list[Clause], symbols: list[str], model: dict[str, bool | None]
) -> tuple[list[str], dict[str, bool | None]]:
"""
Return pure symbols and their values to satisfy clause.
Pure symbols are symbols in a formula that exist only
in one form, either complemented or otherwise.
For example,
{ { A4 , A3 , A5' , A1 , A3' } , { A4 } , { A3 } } has
pure symbols A4, A5' and A1.
This has the following steps:
1. Ignore clauses that have already evaluated to be True.
2. Find symbols that occur only in one form in the rest of the clauses.
3. Assign value True or False depending on whether the symbols occurs
in normal or complemented form respectively.
>>> formula = Formula([Clause(["A1", "A2'", "A3"]), Clause(["A5'", "A2'", "A1"])])
>>> clauses, symbols = generate_parameters(formula)
>>> pure_symbols, values = find_pure_symbols(clauses, symbols, {})
>>> pure_symbols
['A1', 'A2', 'A3', 'A5']
>>> values
{'A1': True, 'A2': False, 'A3': True, 'A5': False}
"""
pure_symbols = []
assignment: dict[str, bool | None] = {}
literals = []
for clause in clauses:
if clause.evaluate(model):
continue
for literal in clause.literals:
literals.append(literal)
for s in symbols:
sym = s + "'"
if (s in literals and sym not in literals) or (
s not in literals and sym in literals
):
pure_symbols.append(s)
for p in pure_symbols:
assignment[p] = None
for s in pure_symbols:
sym = s + "'"
if s in literals:
assignment[s] = True
elif sym in literals:
assignment[s] = False
return pure_symbols, assignment
def find_unit_clauses(
clauses: list[Clause], model: dict[str, bool | None]
) -> tuple[list[str], dict[str, bool | None]]:
"""
Returns the unit symbols and their values to satisfy clause.
Unit symbols are symbols in a formula that are:
- Either the only symbol in a clause
- Or all other literals in that clause have been assigned False
This has the following steps:
1. Find symbols that are the only occurrences in a clause.
2. Find symbols in a clause where all other literals are assigned False.
3. Assign True or False depending on whether the symbols occurs in
normal or complemented form respectively.
>>> clause1 = Clause(["A4", "A3", "A5'", "A1", "A3'"])
>>> clause2 = Clause(["A4"])
>>> clause3 = Clause(["A3"])
>>> clauses, symbols = generate_parameters(Formula([clause1, clause2, clause3]))
>>> unit_clauses, values = find_unit_clauses(clauses, {})
>>> unit_clauses
['A4', 'A3']
>>> values
{'A4': True, 'A3': True}
"""
unit_symbols = []
for clause in clauses:
if len(clause) == 1:
unit_symbols.append(list(clause.literals.keys())[0])
else:
f_count, n_count = 0, 0
for literal, value in clause.literals.items():
if value is False:
f_count += 1
elif value is None:
sym = literal
n_count += 1
if f_count == len(clause) - 1 and n_count == 1:
unit_symbols.append(sym)
assignment: dict[str, bool | None] = {}
for i in unit_symbols:
symbol = i[:2]
assignment[symbol] = len(i) == 2
unit_symbols = [i[:2] for i in unit_symbols]
return unit_symbols, assignment
def dpll_algorithm(
clauses: list[Clause], symbols: list[str], model: dict[str, bool | None]
) -> tuple[bool | None, dict[str, bool | None] | None]:
"""
Returns the model if the formula is satisfiable, else None
This has the following steps:
1. If every clause in clauses is True, return True.
2. If some clause in clauses is False, return False.
3. Find pure symbols.
4. Find unit symbols.
>>> formula = Formula([Clause(["A4", "A3", "A5'", "A1", "A3'"]), Clause(["A4"])])
>>> clauses, symbols = generate_parameters(formula)
>>> soln, model = dpll_algorithm(clauses, symbols, {})
>>> soln
True
>>> model
{'A4': True}
"""
check_clause_all_true = True
for clause in clauses:
clause_check = clause.evaluate(model)
if clause_check is False:
return False, None
elif clause_check is None:
check_clause_all_true = False
continue
if check_clause_all_true:
return True, model
try:
pure_symbols, assignment = find_pure_symbols(clauses, symbols, model)
except RecursionError:
print("raises a RecursionError and is")
return None, {}
p = None
if len(pure_symbols) > 0:
p, value = pure_symbols[0], assignment[pure_symbols[0]]
if p:
tmp_model = model
tmp_model[p] = value
tmp_symbols = list(symbols)
if p in tmp_symbols:
tmp_symbols.remove(p)
return dpll_algorithm(clauses, tmp_symbols, tmp_model)
unit_symbols, assignment = find_unit_clauses(clauses, model)
p = None
if len(unit_symbols) > 0:
p, value = unit_symbols[0], assignment[unit_symbols[0]]
if p:
tmp_model = model
tmp_model[p] = value
tmp_symbols = list(symbols)
if p in tmp_symbols:
tmp_symbols.remove(p)
return dpll_algorithm(clauses, tmp_symbols, tmp_model)
p = symbols[0]
rest = symbols[1:]
tmp1, tmp2 = model, model
tmp1[p], tmp2[p] = True, False
return dpll_algorithm(clauses, rest, tmp1) or dpll_algorithm(clauses, rest, tmp2)
if __name__ == "__main__":
import doctest
doctest.testmod()
formula = generate_formula()
print(f"The formula {formula} is", end=" ")
clauses, symbols = generate_parameters(formula)
solution, model = dpll_algorithm(clauses, symbols, {})
if solution:
print(f"satisfiable with the assignment {model}.")
else:
print("not satisfiable.")
| 1 |
TheAlgorithms/Python | 7,235 | Add Flake8 comprehensions to pre-commit | ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [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] I know that pull requests will not be merged if they fail the automated tests.
* [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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T14:17:49Z" | "2022-10-15T17:29:42Z" | 98a4c2487814cdfe0822526e05c4e63ff6aef7d0 | a652905b605ddcc43626072366d1130315801dc9 | Add Flake8 comprehensions to pre-commit. ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
The nth term of the sequence of triangle numbers is given by, tn = ½n(n+1); so
the first ten triangle numbers are:
1, 3, 6, 10, 15, 21, 28, 36, 45, 55, ...
By converting each letter in a word to a number corresponding to its
alphabetical position and adding these values we form a word value. For example,
the word value for SKY is 19 + 11 + 25 = 55 = t10. If the word value is a
triangle number then we shall call the word a triangle word.
Using words.txt (right click and 'Save Link/Target As...'), a 16K text file
containing nearly two-thousand common English words, how many are triangle
words?
"""
import os
# Precomputes a list of the 100 first triangular numbers
TRIANGULAR_NUMBERS = [int(0.5 * n * (n + 1)) for n in range(1, 101)]
def solution():
"""
Finds the amount of triangular words in the words file.
>>> solution()
162
"""
script_dir = os.path.dirname(os.path.realpath(__file__))
words_file_path = os.path.join(script_dir, "words.txt")
words = ""
with open(words_file_path) as f:
words = f.readline()
words = list(map(lambda word: word.strip('"'), words.strip("\r\n").split(",")))
words = list(
filter(
lambda word: word in TRIANGULAR_NUMBERS,
map(lambda word: sum(map(lambda x: ord(x) - 64, word)), words),
)
)
return len(words)
if __name__ == "__main__":
print(solution())
| """
The nth term of the sequence of triangle numbers is given by, tn = ½n(n+1); so
the first ten triangle numbers are:
1, 3, 6, 10, 15, 21, 28, 36, 45, 55, ...
By converting each letter in a word to a number corresponding to its
alphabetical position and adding these values we form a word value. For example,
the word value for SKY is 19 + 11 + 25 = 55 = t10. If the word value is a
triangle number then we shall call the word a triangle word.
Using words.txt (right click and 'Save Link/Target As...'), a 16K text file
containing nearly two-thousand common English words, how many are triangle
words?
"""
import os
# Precomputes a list of the 100 first triangular numbers
TRIANGULAR_NUMBERS = [int(0.5 * n * (n + 1)) for n in range(1, 101)]
def solution():
"""
Finds the amount of triangular words in the words file.
>>> solution()
162
"""
script_dir = os.path.dirname(os.path.realpath(__file__))
words_file_path = os.path.join(script_dir, "words.txt")
words = ""
with open(words_file_path) as f:
words = f.readline()
words = [word.strip('"') for word in words.strip("\r\n").split(",")]
words = list(
filter(
lambda word: word in TRIANGULAR_NUMBERS,
(sum(ord(x) - 64 for x in word) for word in words),
)
)
return len(words)
if __name__ == "__main__":
print(solution())
| 1 |
TheAlgorithms/Python | 7,235 | Add Flake8 comprehensions to pre-commit | ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [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] I know that pull requests will not be merged if they fail the automated tests.
* [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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T14:17:49Z" | "2022-10-15T17:29:42Z" | 98a4c2487814cdfe0822526e05c4e63ff6aef7d0 | a652905b605ddcc43626072366d1130315801dc9 | Add Flake8 comprehensions to pre-commit. ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Permuted multiples
Problem 52
It can be seen that the number, 125874, and its double, 251748, contain exactly
the same digits, but in a different order.
Find the smallest positive integer, x, such that 2x, 3x, 4x, 5x, and 6x,
contain the same digits.
"""
def solution():
"""Returns the smallest positive integer, x, such that 2x, 3x, 4x, 5x, and
6x, contain the same digits.
>>> solution()
142857
"""
i = 1
while True:
if (
sorted(list(str(i)))
== sorted(list(str(2 * i)))
== sorted(list(str(3 * i)))
== sorted(list(str(4 * i)))
== sorted(list(str(5 * i)))
== sorted(list(str(6 * i)))
):
return i
i += 1
if __name__ == "__main__":
print(solution())
| """
Permuted multiples
Problem 52
It can be seen that the number, 125874, and its double, 251748, contain exactly
the same digits, but in a different order.
Find the smallest positive integer, x, such that 2x, 3x, 4x, 5x, and 6x,
contain the same digits.
"""
def solution():
"""Returns the smallest positive integer, x, such that 2x, 3x, 4x, 5x, and
6x, contain the same digits.
>>> solution()
142857
"""
i = 1
while True:
if (
sorted(str(i))
== sorted(str(2 * i))
== sorted(str(3 * i))
== sorted(str(4 * i))
== sorted(str(5 * i))
== sorted(str(6 * i))
):
return i
i += 1
if __name__ == "__main__":
print(solution())
| 1 |
TheAlgorithms/Python | 7,235 | Add Flake8 comprehensions to pre-commit | ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [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] I know that pull requests will not be merged if they fail the automated tests.
* [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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T14:17:49Z" | "2022-10-15T17:29:42Z" | 98a4c2487814cdfe0822526e05c4e63ff6aef7d0 | a652905b605ddcc43626072366d1130315801dc9 | Add Flake8 comprehensions to pre-commit. ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Project Euler 62
https://projecteuler.net/problem=62
The cube, 41063625 (345^3), can be permuted to produce two other cubes:
56623104 (384^3) and 66430125 (405^3). In fact, 41063625 is the smallest cube
which has exactly three permutations of its digits which are also cube.
Find the smallest cube for which exactly five permutations of its digits are
cube.
"""
from collections import defaultdict
def solution(max_base: int = 5) -> int:
"""
Iterate through every possible cube and sort the cube's digits in
ascending order. Sorting maintains an ordering of the digits that allows
you to compare permutations. Store each sorted sequence of digits in a
dictionary, whose key is the sequence of digits and value is a list of
numbers that are the base of the cube.
Once you find 5 numbers that produce the same sequence of digits, return
the smallest one, which is at index 0 since we insert each base number in
ascending order.
>>> solution(2)
125
>>> solution(3)
41063625
"""
freqs = defaultdict(list)
num = 0
while True:
digits = get_digits(num)
freqs[digits].append(num)
if len(freqs[digits]) == max_base:
base = freqs[digits][0] ** 3
return base
num += 1
def get_digits(num: int) -> str:
"""
Computes the sorted sequence of digits of the cube of num.
>>> get_digits(3)
'27'
>>> get_digits(99)
'027999'
>>> get_digits(123)
'0166788'
"""
return "".join(sorted(list(str(num**3))))
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler 62
https://projecteuler.net/problem=62
The cube, 41063625 (345^3), can be permuted to produce two other cubes:
56623104 (384^3) and 66430125 (405^3). In fact, 41063625 is the smallest cube
which has exactly three permutations of its digits which are also cube.
Find the smallest cube for which exactly five permutations of its digits are
cube.
"""
from collections import defaultdict
def solution(max_base: int = 5) -> int:
"""
Iterate through every possible cube and sort the cube's digits in
ascending order. Sorting maintains an ordering of the digits that allows
you to compare permutations. Store each sorted sequence of digits in a
dictionary, whose key is the sequence of digits and value is a list of
numbers that are the base of the cube.
Once you find 5 numbers that produce the same sequence of digits, return
the smallest one, which is at index 0 since we insert each base number in
ascending order.
>>> solution(2)
125
>>> solution(3)
41063625
"""
freqs = defaultdict(list)
num = 0
while True:
digits = get_digits(num)
freqs[digits].append(num)
if len(freqs[digits]) == max_base:
base = freqs[digits][0] ** 3
return base
num += 1
def get_digits(num: int) -> str:
"""
Computes the sorted sequence of digits of the cube of num.
>>> get_digits(3)
'27'
>>> get_digits(99)
'027999'
>>> get_digits(123)
'0166788'
"""
return "".join(sorted(str(num**3)))
if __name__ == "__main__":
print(f"{solution() = }")
| 1 |
TheAlgorithms/Python | 7,235 | Add Flake8 comprehensions to pre-commit | ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [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] I know that pull requests will not be merged if they fail the automated tests.
* [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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T14:17:49Z" | "2022-10-15T17:29:42Z" | 98a4c2487814cdfe0822526e05c4e63ff6aef7d0 | a652905b605ddcc43626072366d1130315801dc9 | Add Flake8 comprehensions to pre-commit. ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Problem Statement:
By starting at the top of the triangle below and moving to adjacent numbers on
the row below, the maximum total from top to bottom is 23.
3
7 4
2 4 6
8 5 9 3
That is, 3 + 7 + 4 + 9 = 23.
Find the maximum total from top to bottom in triangle.txt (right click and
'Save Link/Target As...'), a 15K text file containing a triangle with
one-hundred rows.
"""
import os
def solution():
"""
Finds the maximum total in a triangle as described by the problem statement
above.
>>> solution()
7273
"""
script_dir = os.path.dirname(os.path.realpath(__file__))
triangle = os.path.join(script_dir, "triangle.txt")
with open(triangle) as f:
triangle = f.readlines()
a = map(lambda x: x.rstrip("\r\n").split(" "), triangle)
a = list(map(lambda x: list(map(int, x)), a))
for i in range(1, len(a)):
for j in range(len(a[i])):
if j != len(a[i - 1]):
number1 = a[i - 1][j]
else:
number1 = 0
if j > 0:
number2 = a[i - 1][j - 1]
else:
number2 = 0
a[i][j] += max(number1, number2)
return max(a[-1])
if __name__ == "__main__":
print(solution())
| """
Problem Statement:
By starting at the top of the triangle below and moving to adjacent numbers on
the row below, the maximum total from top to bottom is 23.
3
7 4
2 4 6
8 5 9 3
That is, 3 + 7 + 4 + 9 = 23.
Find the maximum total from top to bottom in triangle.txt (right click and
'Save Link/Target As...'), a 15K text file containing a triangle with
one-hundred rows.
"""
import os
def solution():
"""
Finds the maximum total in a triangle as described by the problem statement
above.
>>> solution()
7273
"""
script_dir = os.path.dirname(os.path.realpath(__file__))
triangle = os.path.join(script_dir, "triangle.txt")
with open(triangle) as f:
triangle = f.readlines()
a = (x.rstrip("\r\n").split(" ") for x in triangle)
a = [list(map(int, x)) for x in a]
for i in range(1, len(a)):
for j in range(len(a[i])):
if j != len(a[i - 1]):
number1 = a[i - 1][j]
else:
number1 = 0
if j > 0:
number2 = a[i - 1][j - 1]
else:
number2 = 0
a[i][j] += max(number1, number2)
return max(a[-1])
if __name__ == "__main__":
print(solution())
| 1 |
TheAlgorithms/Python | 7,235 | Add Flake8 comprehensions to pre-commit | ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [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] I know that pull requests will not be merged if they fail the automated tests.
* [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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T14:17:49Z" | "2022-10-15T17:29:42Z" | 98a4c2487814cdfe0822526e05c4e63ff6aef7d0 | a652905b605ddcc43626072366d1130315801dc9 | Add Flake8 comprehensions to pre-commit. ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
In the game of darts a player throws three darts at a target board which is
split into twenty equal sized sections numbered one to twenty.

The score of a dart is determined by the number of the region that the dart
lands in. A dart landing outside the red/green outer ring scores zero. The black
and cream regions inside this ring represent single scores. However, the red/green
outer ring and middle ring score double and treble scores respectively.
At the centre of the board are two concentric circles called the bull region, or
bulls-eye. The outer bull is worth 25 points and the inner bull is a double,
worth 50 points.
There are many variations of rules but in the most popular game the players will
begin with a score 301 or 501 and the first player to reduce their running total
to zero is a winner. However, it is normal to play a "doubles out" system, which
means that the player must land a double (including the double bulls-eye at the
centre of the board) on their final dart to win; any other dart that would reduce
their running total to one or lower means the score for that set of three darts
is "bust".
When a player is able to finish on their current score it is called a "checkout"
and the highest checkout is 170: T20 T20 D25 (two treble 20s and double bull).
There are exactly eleven distinct ways to checkout on a score of 6:
D3
D1 D2
S2 D2
D2 D1
S4 D1
S1 S1 D2
S1 T1 D1
S1 S3 D1
D1 D1 D1
D1 S2 D1
S2 S2 D1
Note that D1 D2 is considered different to D2 D1 as they finish on different
doubles. However, the combination S1 T1 D1 is considered the same as T1 S1 D1.
In addition we shall not include misses in considering combinations; for example,
D3 is the same as 0 D3 and 0 0 D3.
Incredibly there are 42336 distinct ways of checking out in total.
How many distinct ways can a player checkout with a score less than 100?
Solution:
We first construct a list of the possible dart values, separated by type.
We then iterate through the doubles, followed by the possible 2 following throws.
If the total of these three darts is less than the given limit, we increment
the counter.
"""
from itertools import combinations_with_replacement
def solution(limit: int = 100) -> int:
"""
Count the number of distinct ways a player can checkout with a score
less than limit.
>>> solution(171)
42336
>>> solution(50)
12577
"""
singles: list[int] = [x for x in range(1, 21)] + [25]
doubles: list[int] = [2 * x for x in range(1, 21)] + [50]
triples: list[int] = [3 * x for x in range(1, 21)]
all_values: list[int] = singles + doubles + triples + [0]
num_checkouts: int = 0
double: int
throw1: int
throw2: int
checkout_total: int
for double in doubles:
for throw1, throw2 in combinations_with_replacement(all_values, 2):
checkout_total = double + throw1 + throw2
if checkout_total < limit:
num_checkouts += 1
return num_checkouts
if __name__ == "__main__":
print(f"{solution() = }")
| """
In the game of darts a player throws three darts at a target board which is
split into twenty equal sized sections numbered one to twenty.

The score of a dart is determined by the number of the region that the dart
lands in. A dart landing outside the red/green outer ring scores zero. The black
and cream regions inside this ring represent single scores. However, the red/green
outer ring and middle ring score double and treble scores respectively.
At the centre of the board are two concentric circles called the bull region, or
bulls-eye. The outer bull is worth 25 points and the inner bull is a double,
worth 50 points.
There are many variations of rules but in the most popular game the players will
begin with a score 301 or 501 and the first player to reduce their running total
to zero is a winner. However, it is normal to play a "doubles out" system, which
means that the player must land a double (including the double bulls-eye at the
centre of the board) on their final dart to win; any other dart that would reduce
their running total to one or lower means the score for that set of three darts
is "bust".
When a player is able to finish on their current score it is called a "checkout"
and the highest checkout is 170: T20 T20 D25 (two treble 20s and double bull).
There are exactly eleven distinct ways to checkout on a score of 6:
D3
D1 D2
S2 D2
D2 D1
S4 D1
S1 S1 D2
S1 T1 D1
S1 S3 D1
D1 D1 D1
D1 S2 D1
S2 S2 D1
Note that D1 D2 is considered different to D2 D1 as they finish on different
doubles. However, the combination S1 T1 D1 is considered the same as T1 S1 D1.
In addition we shall not include misses in considering combinations; for example,
D3 is the same as 0 D3 and 0 0 D3.
Incredibly there are 42336 distinct ways of checking out in total.
How many distinct ways can a player checkout with a score less than 100?
Solution:
We first construct a list of the possible dart values, separated by type.
We then iterate through the doubles, followed by the possible 2 following throws.
If the total of these three darts is less than the given limit, we increment
the counter.
"""
from itertools import combinations_with_replacement
def solution(limit: int = 100) -> int:
"""
Count the number of distinct ways a player can checkout with a score
less than limit.
>>> solution(171)
42336
>>> solution(50)
12577
"""
singles: list[int] = list(range(1, 21)) + [25]
doubles: list[int] = [2 * x for x in range(1, 21)] + [50]
triples: list[int] = [3 * x for x in range(1, 21)]
all_values: list[int] = singles + doubles + triples + [0]
num_checkouts: int = 0
double: int
throw1: int
throw2: int
checkout_total: int
for double in doubles:
for throw1, throw2 in combinations_with_replacement(all_values, 2):
checkout_total = double + throw1 + throw2
if checkout_total < limit:
num_checkouts += 1
return num_checkouts
if __name__ == "__main__":
print(f"{solution() = }")
| 1 |
TheAlgorithms/Python | 7,235 | Add Flake8 comprehensions to pre-commit | ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [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] I know that pull requests will not be merged if they fail the automated tests.
* [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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T14:17:49Z" | "2022-10-15T17:29:42Z" | 98a4c2487814cdfe0822526e05c4e63ff6aef7d0 | a652905b605ddcc43626072366d1130315801dc9 | Add Flake8 comprehensions to pre-commit. ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Sum of digits sequence
Problem 551
Let a(0), a(1),... be an integer sequence defined by:
a(0) = 1
for n >= 1, a(n) is the sum of the digits of all preceding terms
The sequence starts with 1, 1, 2, 4, 8, ...
You are given a(10^6) = 31054319.
Find a(10^15)
"""
ks = [k for k in range(2, 20 + 1)]
base = [10**k for k in range(ks[-1] + 1)]
memo: dict[int, dict[int, list[list[int]]]] = {}
def next_term(a_i, k, i, n):
"""
Calculates and updates a_i in-place to either the n-th term or the
smallest term for which c > 10^k when the terms are written in the form:
a(i) = b * 10^k + c
For any a(i), if digitsum(b) and c have the same value, the difference
between subsequent terms will be the same until c >= 10^k. This difference
is cached to greatly speed up the computation.
Arguments:
a_i -- array of digits starting from the one's place that represent
the i-th term in the sequence
k -- k when terms are written in the from a(i) = b*10^k + c.
Term are calulcated until c > 10^k or the n-th term is reached.
i -- position along the sequence
n -- term to calculate up to if k is large enough
Return: a tuple of difference between ending term and starting term, and
the number of terms calculated. ex. if starting term is a_0=1, and
ending term is a_10=62, then (61, 9) is returned.
"""
# ds_b - digitsum(b)
ds_b = sum(a_i[j] for j in range(k, len(a_i)))
c = sum(a_i[j] * base[j] for j in range(min(len(a_i), k)))
diff, dn = 0, 0
max_dn = n - i
sub_memo = memo.get(ds_b)
if sub_memo is not None:
jumps = sub_memo.get(c)
if jumps is not None and len(jumps) > 0:
# find and make the largest jump without going over
max_jump = -1
for _k in range(len(jumps) - 1, -1, -1):
if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
max_jump = _k
break
if max_jump >= 0:
diff, dn, _kk = jumps[max_jump]
# since the difference between jumps is cached, add c
new_c = diff + c
for j in range(min(k, len(a_i))):
new_c, a_i[j] = divmod(new_c, 10)
if new_c > 0:
add(a_i, k, new_c)
else:
sub_memo[c] = []
else:
sub_memo = {c: []}
memo[ds_b] = sub_memo
if dn >= max_dn or c + diff >= base[k]:
return diff, dn
if k > ks[0]:
while True:
# keep doing smaller jumps
_diff, terms_jumped = next_term(a_i, k - 1, i + dn, n)
diff += _diff
dn += terms_jumped
if dn >= max_dn or c + diff >= base[k]:
break
else:
# would be too small a jump, just compute sequential terms instead
_diff, terms_jumped = compute(a_i, k, i + dn, n)
diff += _diff
dn += terms_jumped
jumps = sub_memo[c]
# keep jumps sorted by # of terms skipped
j = 0
while j < len(jumps):
if jumps[j][1] > dn:
break
j += 1
# cache the jump for this value digitsum(b) and c
sub_memo[c].insert(j, (diff, dn, k))
return (diff, dn)
def compute(a_i, k, i, n):
"""
same as next_term(a_i, k, i, n) but computes terms without memoizing results.
"""
if i >= n:
return 0, i
if k > len(a_i):
a_i.extend([0 for _ in range(k - len(a_i))])
# note: a_i -> b * 10^k + c
# ds_b -> digitsum(b)
# ds_c -> digitsum(c)
start_i = i
ds_b, ds_c, diff = 0, 0, 0
for j in range(len(a_i)):
if j >= k:
ds_b += a_i[j]
else:
ds_c += a_i[j]
while i < n:
i += 1
addend = ds_c + ds_b
diff += addend
ds_c = 0
for j in range(k):
s = a_i[j] + addend
addend, a_i[j] = divmod(s, 10)
ds_c += a_i[j]
if addend > 0:
break
if addend > 0:
add(a_i, k, addend)
return diff, i - start_i
def add(digits, k, addend):
"""
adds addend to digit array given in digits
starting at index k
"""
for j in range(k, len(digits)):
s = digits[j] + addend
if s >= 10:
quotient, digits[j] = divmod(s, 10)
addend = addend // 10 + quotient
else:
digits[j] = s
addend = addend // 10
if addend == 0:
break
while addend > 0:
addend, digit = divmod(addend, 10)
digits.append(digit)
def solution(n: int = 10**15) -> int:
"""
returns n-th term of sequence
>>> solution(10)
62
>>> solution(10**6)
31054319
>>> solution(10**15)
73597483551591773
"""
digits = [1]
i = 1
dn = 0
while True:
diff, terms_jumped = next_term(digits, 20, i + dn, n)
dn += terms_jumped
if dn == n - i:
break
a_n = 0
for j in range(len(digits)):
a_n += digits[j] * 10**j
return a_n
if __name__ == "__main__":
print(f"{solution() = }")
| """
Sum of digits sequence
Problem 551
Let a(0), a(1),... be an integer sequence defined by:
a(0) = 1
for n >= 1, a(n) is the sum of the digits of all preceding terms
The sequence starts with 1, 1, 2, 4, 8, ...
You are given a(10^6) = 31054319.
Find a(10^15)
"""
ks = range(2, 20 + 1)
base = [10**k for k in range(ks[-1] + 1)]
memo: dict[int, dict[int, list[list[int]]]] = {}
def next_term(a_i, k, i, n):
"""
Calculates and updates a_i in-place to either the n-th term or the
smallest term for which c > 10^k when the terms are written in the form:
a(i) = b * 10^k + c
For any a(i), if digitsum(b) and c have the same value, the difference
between subsequent terms will be the same until c >= 10^k. This difference
is cached to greatly speed up the computation.
Arguments:
a_i -- array of digits starting from the one's place that represent
the i-th term in the sequence
k -- k when terms are written in the from a(i) = b*10^k + c.
Term are calulcated until c > 10^k or the n-th term is reached.
i -- position along the sequence
n -- term to calculate up to if k is large enough
Return: a tuple of difference between ending term and starting term, and
the number of terms calculated. ex. if starting term is a_0=1, and
ending term is a_10=62, then (61, 9) is returned.
"""
# ds_b - digitsum(b)
ds_b = sum(a_i[j] for j in range(k, len(a_i)))
c = sum(a_i[j] * base[j] for j in range(min(len(a_i), k)))
diff, dn = 0, 0
max_dn = n - i
sub_memo = memo.get(ds_b)
if sub_memo is not None:
jumps = sub_memo.get(c)
if jumps is not None and len(jumps) > 0:
# find and make the largest jump without going over
max_jump = -1
for _k in range(len(jumps) - 1, -1, -1):
if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
max_jump = _k
break
if max_jump >= 0:
diff, dn, _kk = jumps[max_jump]
# since the difference between jumps is cached, add c
new_c = diff + c
for j in range(min(k, len(a_i))):
new_c, a_i[j] = divmod(new_c, 10)
if new_c > 0:
add(a_i, k, new_c)
else:
sub_memo[c] = []
else:
sub_memo = {c: []}
memo[ds_b] = sub_memo
if dn >= max_dn or c + diff >= base[k]:
return diff, dn
if k > ks[0]:
while True:
# keep doing smaller jumps
_diff, terms_jumped = next_term(a_i, k - 1, i + dn, n)
diff += _diff
dn += terms_jumped
if dn >= max_dn or c + diff >= base[k]:
break
else:
# would be too small a jump, just compute sequential terms instead
_diff, terms_jumped = compute(a_i, k, i + dn, n)
diff += _diff
dn += terms_jumped
jumps = sub_memo[c]
# keep jumps sorted by # of terms skipped
j = 0
while j < len(jumps):
if jumps[j][1] > dn:
break
j += 1
# cache the jump for this value digitsum(b) and c
sub_memo[c].insert(j, (diff, dn, k))
return (diff, dn)
def compute(a_i, k, i, n):
"""
same as next_term(a_i, k, i, n) but computes terms without memoizing results.
"""
if i >= n:
return 0, i
if k > len(a_i):
a_i.extend([0 for _ in range(k - len(a_i))])
# note: a_i -> b * 10^k + c
# ds_b -> digitsum(b)
# ds_c -> digitsum(c)
start_i = i
ds_b, ds_c, diff = 0, 0, 0
for j in range(len(a_i)):
if j >= k:
ds_b += a_i[j]
else:
ds_c += a_i[j]
while i < n:
i += 1
addend = ds_c + ds_b
diff += addend
ds_c = 0
for j in range(k):
s = a_i[j] + addend
addend, a_i[j] = divmod(s, 10)
ds_c += a_i[j]
if addend > 0:
break
if addend > 0:
add(a_i, k, addend)
return diff, i - start_i
def add(digits, k, addend):
"""
adds addend to digit array given in digits
starting at index k
"""
for j in range(k, len(digits)):
s = digits[j] + addend
if s >= 10:
quotient, digits[j] = divmod(s, 10)
addend = addend // 10 + quotient
else:
digits[j] = s
addend = addend // 10
if addend == 0:
break
while addend > 0:
addend, digit = divmod(addend, 10)
digits.append(digit)
def solution(n: int = 10**15) -> int:
"""
returns n-th term of sequence
>>> solution(10)
62
>>> solution(10**6)
31054319
>>> solution(10**15)
73597483551591773
"""
digits = [1]
i = 1
dn = 0
while True:
diff, terms_jumped = next_term(digits, 20, i + dn, n)
dn += terms_jumped
if dn == n - i:
break
a_n = 0
for j in range(len(digits)):
a_n += digits[j] * 10**j
return a_n
if __name__ == "__main__":
print(f"{solution() = }")
| 1 |
TheAlgorithms/Python | 7,235 | Add Flake8 comprehensions to pre-commit | ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [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] I know that pull requests will not be merged if they fail the automated tests.
* [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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T14:17:49Z" | "2022-10-15T17:29:42Z" | 98a4c2487814cdfe0822526e05c4e63ff6aef7d0 | a652905b605ddcc43626072366d1130315801dc9 | Add Flake8 comprehensions to pre-commit. ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
This is a pure Python implementation of the radix sort algorithm
Source: https://en.wikipedia.org/wiki/Radix_sort
"""
from __future__ import annotations
def radix_sort(list_of_ints: list[int]) -> list[int]:
"""
Examples:
>>> radix_sort([0, 5, 3, 2, 2])
[0, 2, 2, 3, 5]
>>> radix_sort(list(range(15))) == sorted(range(15))
True
>>> radix_sort(list(range(14,-1,-1))) == sorted(range(15))
True
>>> radix_sort([1,100,10,1000]) == sorted([1,100,10,1000])
True
"""
RADIX = 10 # noqa: N806
placement = 1
max_digit = max(list_of_ints)
while placement <= max_digit:
# declare and initialize empty buckets
buckets: list[list] = [list() for _ in range(RADIX)]
# split list_of_ints between the buckets
for i in list_of_ints:
tmp = int((i / placement) % RADIX)
buckets[tmp].append(i)
# put each buckets' contents into list_of_ints
a = 0
for b in range(RADIX):
for i in buckets[b]:
list_of_ints[a] = i
a += 1
# move to next
placement *= RADIX
return list_of_ints
if __name__ == "__main__":
import doctest
doctest.testmod()
| """
This is a pure Python implementation of the radix sort algorithm
Source: https://en.wikipedia.org/wiki/Radix_sort
"""
from __future__ import annotations
def radix_sort(list_of_ints: list[int]) -> list[int]:
"""
Examples:
>>> radix_sort([0, 5, 3, 2, 2])
[0, 2, 2, 3, 5]
>>> radix_sort(list(range(15))) == sorted(range(15))
True
>>> radix_sort(list(range(14,-1,-1))) == sorted(range(15))
True
>>> radix_sort([1,100,10,1000]) == sorted([1,100,10,1000])
True
"""
RADIX = 10 # noqa: N806
placement = 1
max_digit = max(list_of_ints)
while placement <= max_digit:
# declare and initialize empty buckets
buckets: list[list] = [[] for _ in range(RADIX)]
# split list_of_ints between the buckets
for i in list_of_ints:
tmp = int((i / placement) % RADIX)
buckets[tmp].append(i)
# put each buckets' contents into list_of_ints
a = 0
for b in range(RADIX):
for i in buckets[b]:
list_of_ints[a] = i
a += 1
# move to next
placement *= RADIX
return list_of_ints
if __name__ == "__main__":
import doctest
doctest.testmod()
| 1 |
TheAlgorithms/Python | 7,235 | Add Flake8 comprehensions to pre-commit | ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [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] I know that pull requests will not be merged if they fail the automated tests.
* [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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T14:17:49Z" | "2022-10-15T17:29:42Z" | 98a4c2487814cdfe0822526e05c4e63ff6aef7d0 | a652905b605ddcc43626072366d1130315801dc9 | Add Flake8 comprehensions to pre-commit. ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from __future__ import annotations
from collections import deque
class Automaton:
def __init__(self, keywords: list[str]):
self.adlist: list[dict] = []
self.adlist.append(
{"value": "", "next_states": [], "fail_state": 0, "output": []}
)
for keyword in keywords:
self.add_keyword(keyword)
self.set_fail_transitions()
def find_next_state(self, current_state: int, char: str) -> int | None:
for state in self.adlist[current_state]["next_states"]:
if char == self.adlist[state]["value"]:
return state
return None
def add_keyword(self, keyword: str) -> None:
current_state = 0
for character in keyword:
next_state = self.find_next_state(current_state, character)
if next_state is None:
self.adlist.append(
{
"value": character,
"next_states": [],
"fail_state": 0,
"output": [],
}
)
self.adlist[current_state]["next_states"].append(len(self.adlist) - 1)
current_state = len(self.adlist) - 1
else:
current_state = next_state
self.adlist[current_state]["output"].append(keyword)
def set_fail_transitions(self) -> None:
q: deque = deque()
for node in self.adlist[0]["next_states"]:
q.append(node)
self.adlist[node]["fail_state"] = 0
while q:
r = q.popleft()
for child in self.adlist[r]["next_states"]:
q.append(child)
state = self.adlist[r]["fail_state"]
while (
self.find_next_state(state, self.adlist[child]["value"]) is None
and state != 0
):
state = self.adlist[state]["fail_state"]
self.adlist[child]["fail_state"] = self.find_next_state(
state, self.adlist[child]["value"]
)
if self.adlist[child]["fail_state"] is None:
self.adlist[child]["fail_state"] = 0
self.adlist[child]["output"] = (
self.adlist[child]["output"]
+ self.adlist[self.adlist[child]["fail_state"]]["output"]
)
def search_in(self, string: str) -> dict[str, list[int]]:
"""
>>> A = Automaton(["what", "hat", "ver", "er"])
>>> A.search_in("whatever, err ... , wherever")
{'what': [0], 'hat': [1], 'ver': [5, 25], 'er': [6, 10, 22, 26]}
"""
result: dict = (
dict()
) # returns a dict with keywords and list of its occurrences
current_state = 0
for i in range(len(string)):
while (
self.find_next_state(current_state, string[i]) is None
and current_state != 0
):
current_state = self.adlist[current_state]["fail_state"]
next_state = self.find_next_state(current_state, string[i])
if next_state is None:
current_state = 0
else:
current_state = next_state
for key in self.adlist[current_state]["output"]:
if not (key in result):
result[key] = []
result[key].append(i - len(key) + 1)
return result
if __name__ == "__main__":
import doctest
doctest.testmod()
| from __future__ import annotations
from collections import deque
class Automaton:
def __init__(self, keywords: list[str]):
self.adlist: list[dict] = []
self.adlist.append(
{"value": "", "next_states": [], "fail_state": 0, "output": []}
)
for keyword in keywords:
self.add_keyword(keyword)
self.set_fail_transitions()
def find_next_state(self, current_state: int, char: str) -> int | None:
for state in self.adlist[current_state]["next_states"]:
if char == self.adlist[state]["value"]:
return state
return None
def add_keyword(self, keyword: str) -> None:
current_state = 0
for character in keyword:
next_state = self.find_next_state(current_state, character)
if next_state is None:
self.adlist.append(
{
"value": character,
"next_states": [],
"fail_state": 0,
"output": [],
}
)
self.adlist[current_state]["next_states"].append(len(self.adlist) - 1)
current_state = len(self.adlist) - 1
else:
current_state = next_state
self.adlist[current_state]["output"].append(keyword)
def set_fail_transitions(self) -> None:
q: deque = deque()
for node in self.adlist[0]["next_states"]:
q.append(node)
self.adlist[node]["fail_state"] = 0
while q:
r = q.popleft()
for child in self.adlist[r]["next_states"]:
q.append(child)
state = self.adlist[r]["fail_state"]
while (
self.find_next_state(state, self.adlist[child]["value"]) is None
and state != 0
):
state = self.adlist[state]["fail_state"]
self.adlist[child]["fail_state"] = self.find_next_state(
state, self.adlist[child]["value"]
)
if self.adlist[child]["fail_state"] is None:
self.adlist[child]["fail_state"] = 0
self.adlist[child]["output"] = (
self.adlist[child]["output"]
+ self.adlist[self.adlist[child]["fail_state"]]["output"]
)
def search_in(self, string: str) -> dict[str, list[int]]:
"""
>>> A = Automaton(["what", "hat", "ver", "er"])
>>> A.search_in("whatever, err ... , wherever")
{'what': [0], 'hat': [1], 'ver': [5, 25], 'er': [6, 10, 22, 26]}
"""
result: dict = {} # returns a dict with keywords and list of its occurrences
current_state = 0
for i in range(len(string)):
while (
self.find_next_state(current_state, string[i]) is None
and current_state != 0
):
current_state = self.adlist[current_state]["fail_state"]
next_state = self.find_next_state(current_state, string[i])
if next_state is None:
current_state = 0
else:
current_state = next_state
for key in self.adlist[current_state]["output"]:
if not (key in result):
result[key] = []
result[key].append(i - len(key) + 1)
return result
if __name__ == "__main__":
import doctest
doctest.testmod()
| 1 |
TheAlgorithms/Python | 7,235 | Add Flake8 comprehensions to pre-commit | ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [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] I know that pull requests will not be merged if they fail the automated tests.
* [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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T14:17:49Z" | "2022-10-15T17:29:42Z" | 98a4c2487814cdfe0822526e05c4e63ff6aef7d0 | a652905b605ddcc43626072366d1130315801dc9 | Add Flake8 comprehensions to pre-commit. ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from __future__ import annotations
def double_linear_search(array: list[int], search_item: int) -> int:
"""
Iterate through the array from both sides to find the index of search_item.
:param array: the array to be searched
:param search_item: the item to be searched
:return the index of search_item, if search_item is in array, else -1
Examples:
>>> double_linear_search([1, 5, 5, 10], 1)
0
>>> double_linear_search([1, 5, 5, 10], 5)
1
>>> double_linear_search([1, 5, 5, 10], 100)
-1
>>> double_linear_search([1, 5, 5, 10], 10)
3
"""
# define the start and end index of the given array
start_ind, end_ind = 0, len(array) - 1
while start_ind <= end_ind:
if array[start_ind] == search_item:
return start_ind
elif array[end_ind] == search_item:
return end_ind
else:
start_ind += 1
end_ind -= 1
# returns -1 if search_item is not found in array
return -1
if __name__ == "__main__":
print(double_linear_search(list(range(100)), 40))
| from __future__ import annotations
def double_linear_search(array: list[int], search_item: int) -> int:
"""
Iterate through the array from both sides to find the index of search_item.
:param array: the array to be searched
:param search_item: the item to be searched
:return the index of search_item, if search_item is in array, else -1
Examples:
>>> double_linear_search([1, 5, 5, 10], 1)
0
>>> double_linear_search([1, 5, 5, 10], 5)
1
>>> double_linear_search([1, 5, 5, 10], 100)
-1
>>> double_linear_search([1, 5, 5, 10], 10)
3
"""
# define the start and end index of the given array
start_ind, end_ind = 0, len(array) - 1
while start_ind <= end_ind:
if array[start_ind] == search_item:
return start_ind
elif array[end_ind] == search_item:
return end_ind
else:
start_ind += 1
end_ind -= 1
# returns -1 if search_item is not found in array
return -1
if __name__ == "__main__":
print(double_linear_search(list(range(100)), 40))
| -1 |
TheAlgorithms/Python | 7,235 | Add Flake8 comprehensions to pre-commit | ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [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] I know that pull requests will not be merged if they fail the automated tests.
* [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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T14:17:49Z" | "2022-10-15T17:29:42Z" | 98a4c2487814cdfe0822526e05c4e63ff6aef7d0 | a652905b605ddcc43626072366d1130315801dc9 | Add Flake8 comprehensions to pre-commit. ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| def max_subarray(nums: list[int]) -> int:
"""
Returns the subarray with maximum sum
>>> max_subarray([1,2,3,4,-2])
10
>>> max_subarray([-2,1,-3,4,-1,2,1,-5,4])
6
"""
curr_max = ans = nums[0]
for i in range(1, len(nums)):
if curr_max >= 0:
curr_max = curr_max + nums[i]
else:
curr_max = nums[i]
ans = max(curr_max, ans)
return ans
if __name__ == "__main__":
n = int(input("Enter number of elements : ").strip())
array = list(map(int, input("\nEnter the numbers : ").strip().split()))[:n]
print(max_subarray(array))
| def max_subarray(nums: list[int]) -> int:
"""
Returns the subarray with maximum sum
>>> max_subarray([1,2,3,4,-2])
10
>>> max_subarray([-2,1,-3,4,-1,2,1,-5,4])
6
"""
curr_max = ans = nums[0]
for i in range(1, len(nums)):
if curr_max >= 0:
curr_max = curr_max + nums[i]
else:
curr_max = nums[i]
ans = max(curr_max, ans)
return ans
if __name__ == "__main__":
n = int(input("Enter number of elements : ").strip())
array = list(map(int, input("\nEnter the numbers : ").strip().split()))[:n]
print(max_subarray(array))
| -1 |
TheAlgorithms/Python | 7,235 | Add Flake8 comprehensions to pre-commit | ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [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] I know that pull requests will not be merged if they fail the automated tests.
* [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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T14:17:49Z" | "2022-10-15T17:29:42Z" | 98a4c2487814cdfe0822526e05c4e63ff6aef7d0 | a652905b605ddcc43626072366d1130315801dc9 | Add Flake8 comprehensions to pre-commit. ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Project Euler Problem 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() -> int:
"""
Returns 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 = 1000
>>> solution()
31875000
"""
for a in range(300):
for b in range(a + 1, 400):
for c in range(b + 1, 500):
if (a + b + c) == 1000:
if (a**2) + (b**2) == (c**2):
return a * b * c
return -1
def solution_fast() -> int:
"""
Returns 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 = 1000
>>> solution_fast()
31875000
"""
for a in range(300):
for b in range(400):
c = 1000 - a - b
if a < b < c and (a**2) + (b**2) == (c**2):
return a * b * c
return -1
def benchmark() -> None:
"""
Benchmark code comparing two different version function.
"""
import timeit
print(
timeit.timeit("solution()", setup="from __main__ import solution", number=1000)
)
print(
timeit.timeit(
"solution_fast()", setup="from __main__ import solution_fast", number=1000
)
)
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() -> int:
"""
Returns 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 = 1000
>>> solution()
31875000
"""
for a in range(300):
for b in range(a + 1, 400):
for c in range(b + 1, 500):
if (a + b + c) == 1000:
if (a**2) + (b**2) == (c**2):
return a * b * c
return -1
def solution_fast() -> int:
"""
Returns 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 = 1000
>>> solution_fast()
31875000
"""
for a in range(300):
for b in range(400):
c = 1000 - a - b
if a < b < c and (a**2) + (b**2) == (c**2):
return a * b * c
return -1
def benchmark() -> None:
"""
Benchmark code comparing two different version function.
"""
import timeit
print(
timeit.timeit("solution()", setup="from __main__ import solution", number=1000)
)
print(
timeit.timeit(
"solution_fast()", setup="from __main__ import solution_fast", number=1000
)
)
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 7,235 | Add Flake8 comprehensions to pre-commit | ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [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] I know that pull requests will not be merged if they fail the automated tests.
* [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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T14:17:49Z" | "2022-10-15T17:29:42Z" | 98a4c2487814cdfe0822526e05c4e63ff6aef7d0 | a652905b605ddcc43626072366d1130315801dc9 | Add Flake8 comprehensions to pre-commit. ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """ Problem Statement (Digit Fifth Powers): https://projecteuler.net/problem=30
Surprisingly there are only three numbers that can be written as the sum of fourth
powers of their digits:
1634 = 1^4 + 6^4 + 3^4 + 4^4
8208 = 8^4 + 2^4 + 0^4 + 8^4
9474 = 9^4 + 4^4 + 7^4 + 4^4
As 1 = 1^4 is not a sum it is not included.
The sum of these numbers is 1634 + 8208 + 9474 = 19316.
Find the sum of all the numbers that can be written as the sum of fifth powers of their
digits.
9^5 = 59049
59049 * 7 = 413343 (which is only 6 digit number)
So, numbers greater than 999999 are rejected
and also 59049 * 3 = 177147 (which exceeds the criteria of number being 3 digit)
So, number > 999
and hence a number between 1000 and 1000000
"""
DIGITS_FIFTH_POWER = {str(digit): digit**5 for digit in range(10)}
def digits_fifth_powers_sum(number: int) -> int:
"""
>>> digits_fifth_powers_sum(1234)
1300
"""
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(number))
def solution() -> int:
return sum(
number
for number in range(1000, 1000000)
if number == digits_fifth_powers_sum(number)
)
if __name__ == "__main__":
print(solution())
| """ Problem Statement (Digit Fifth Powers): https://projecteuler.net/problem=30
Surprisingly there are only three numbers that can be written as the sum of fourth
powers of their digits:
1634 = 1^4 + 6^4 + 3^4 + 4^4
8208 = 8^4 + 2^4 + 0^4 + 8^4
9474 = 9^4 + 4^4 + 7^4 + 4^4
As 1 = 1^4 is not a sum it is not included.
The sum of these numbers is 1634 + 8208 + 9474 = 19316.
Find the sum of all the numbers that can be written as the sum of fifth powers of their
digits.
9^5 = 59049
59049 * 7 = 413343 (which is only 6 digit number)
So, numbers greater than 999999 are rejected
and also 59049 * 3 = 177147 (which exceeds the criteria of number being 3 digit)
So, number > 999
and hence a number between 1000 and 1000000
"""
DIGITS_FIFTH_POWER = {str(digit): digit**5 for digit in range(10)}
def digits_fifth_powers_sum(number: int) -> int:
"""
>>> digits_fifth_powers_sum(1234)
1300
"""
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(number))
def solution() -> int:
return sum(
number
for number in range(1000, 1000000)
if number == digits_fifth_powers_sum(number)
)
if __name__ == "__main__":
print(solution())
| -1 |
TheAlgorithms/Python | 7,235 | Add Flake8 comprehensions to pre-commit | ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [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] I know that pull requests will not be merged if they fail the automated tests.
* [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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T14:17:49Z" | "2022-10-15T17:29:42Z" | 98a4c2487814cdfe0822526e05c4e63ff6aef7d0 | a652905b605ddcc43626072366d1130315801dc9 | Add Flake8 comprehensions to pre-commit. ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$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.
>>> solution(3)
0
>>> solution(4)
3
>>> solution(10)
23
>>> solution(600)
83700
"""
a = 3
result = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 15 == 0:
result -= a
a += 1
return result
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.
>>> solution(3)
0
>>> solution(4)
3
>>> solution(10)
23
>>> solution(600)
83700
"""
a = 3
result = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 15 == 0:
result -= a
a += 1
return result
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 7,235 | Add Flake8 comprehensions to pre-commit | ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [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] I know that pull requests will not be merged if they fail the automated tests.
* [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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T14:17:49Z" | "2022-10-15T17:29:42Z" | 98a4c2487814cdfe0822526e05c4e63ff6aef7d0 | a652905b605ddcc43626072366d1130315801dc9 | Add Flake8 comprehensions to pre-commit. ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from __future__ import annotations
from collections.abc import Sequence
def compare_string(string1: str, string2: str) -> str:
"""
>>> compare_string('0010','0110')
'0_10'
>>> compare_string('0110','1101')
'X'
"""
list1 = list(string1)
list2 = list(string2)
count = 0
for i in range(len(list1)):
if list1[i] != list2[i]:
count += 1
list1[i] = "_"
if count > 1:
return "X"
else:
return "".join(list1)
def check(binary: list[str]) -> list[str]:
"""
>>> check(['0.00.01.5'])
['0.00.01.5']
"""
pi = []
while True:
check1 = ["$"] * len(binary)
temp = []
for i in range(len(binary)):
for j in range(i + 1, len(binary)):
k = compare_string(binary[i], binary[j])
if k != "X":
check1[i] = "*"
check1[j] = "*"
temp.append(k)
for i in range(len(binary)):
if check1[i] == "$":
pi.append(binary[i])
if len(temp) == 0:
return pi
binary = list(set(temp))
def decimal_to_binary(no_of_variable: int, minterms: Sequence[float]) -> list[str]:
"""
>>> decimal_to_binary(3,[1.5])
['0.00.01.5']
"""
temp = []
for minterm in minterms:
string = ""
for _ in range(no_of_variable):
string = str(minterm % 2) + string
minterm //= 2
temp.append(string)
return temp
def is_for_table(string1: str, string2: str, count: int) -> bool:
"""
>>> is_for_table('__1','011',2)
True
>>> is_for_table('01_','001',1)
False
"""
list1 = list(string1)
list2 = list(string2)
count_n = 0
for i in range(len(list1)):
if list1[i] != list2[i]:
count_n += 1
return count_n == count
def selection(chart: list[list[int]], prime_implicants: list[str]) -> list[str]:
"""
>>> selection([[1]],['0.00.01.5'])
['0.00.01.5']
>>> selection([[1]],['0.00.01.5'])
['0.00.01.5']
"""
temp = []
select = [0] * len(chart)
for i in range(len(chart[0])):
count = 0
rem = -1
for j in range(len(chart)):
if chart[j][i] == 1:
count += 1
rem = j
if count == 1:
select[rem] = 1
for i in range(len(select)):
if select[i] == 1:
for j in range(len(chart[0])):
if chart[i][j] == 1:
for k in range(len(chart)):
chart[k][j] = 0
temp.append(prime_implicants[i])
while True:
max_n = 0
rem = -1
count_n = 0
for i in range(len(chart)):
count_n = chart[i].count(1)
if count_n > max_n:
max_n = count_n
rem = i
if max_n == 0:
return temp
temp.append(prime_implicants[rem])
for i in range(len(chart[0])):
if chart[rem][i] == 1:
for j in range(len(chart)):
chart[j][i] = 0
def prime_implicant_chart(
prime_implicants: list[str], binary: list[str]
) -> list[list[int]]:
"""
>>> prime_implicant_chart(['0.00.01.5'],['0.00.01.5'])
[[1]]
"""
chart = [[0 for x in range(len(binary))] for x in range(len(prime_implicants))]
for i in range(len(prime_implicants)):
count = prime_implicants[i].count("_")
for j in range(len(binary)):
if is_for_table(prime_implicants[i], binary[j], count):
chart[i][j] = 1
return chart
def main() -> None:
no_of_variable = int(input("Enter the no. of variables\n"))
minterms = [
float(x)
for x in input(
"Enter the decimal representation of Minterms 'Spaces Separated'\n"
).split()
]
binary = decimal_to_binary(no_of_variable, minterms)
prime_implicants = check(binary)
print("Prime Implicants are:")
print(prime_implicants)
chart = prime_implicant_chart(prime_implicants, binary)
essential_prime_implicants = selection(chart, prime_implicants)
print("Essential Prime Implicants are:")
print(essential_prime_implicants)
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| from __future__ import annotations
from collections.abc import Sequence
def compare_string(string1: str, string2: str) -> str:
"""
>>> compare_string('0010','0110')
'0_10'
>>> compare_string('0110','1101')
'X'
"""
list1 = list(string1)
list2 = list(string2)
count = 0
for i in range(len(list1)):
if list1[i] != list2[i]:
count += 1
list1[i] = "_"
if count > 1:
return "X"
else:
return "".join(list1)
def check(binary: list[str]) -> list[str]:
"""
>>> check(['0.00.01.5'])
['0.00.01.5']
"""
pi = []
while True:
check1 = ["$"] * len(binary)
temp = []
for i in range(len(binary)):
for j in range(i + 1, len(binary)):
k = compare_string(binary[i], binary[j])
if k != "X":
check1[i] = "*"
check1[j] = "*"
temp.append(k)
for i in range(len(binary)):
if check1[i] == "$":
pi.append(binary[i])
if len(temp) == 0:
return pi
binary = list(set(temp))
def decimal_to_binary(no_of_variable: int, minterms: Sequence[float]) -> list[str]:
"""
>>> decimal_to_binary(3,[1.5])
['0.00.01.5']
"""
temp = []
for minterm in minterms:
string = ""
for _ in range(no_of_variable):
string = str(minterm % 2) + string
minterm //= 2
temp.append(string)
return temp
def is_for_table(string1: str, string2: str, count: int) -> bool:
"""
>>> is_for_table('__1','011',2)
True
>>> is_for_table('01_','001',1)
False
"""
list1 = list(string1)
list2 = list(string2)
count_n = 0
for i in range(len(list1)):
if list1[i] != list2[i]:
count_n += 1
return count_n == count
def selection(chart: list[list[int]], prime_implicants: list[str]) -> list[str]:
"""
>>> selection([[1]],['0.00.01.5'])
['0.00.01.5']
>>> selection([[1]],['0.00.01.5'])
['0.00.01.5']
"""
temp = []
select = [0] * len(chart)
for i in range(len(chart[0])):
count = 0
rem = -1
for j in range(len(chart)):
if chart[j][i] == 1:
count += 1
rem = j
if count == 1:
select[rem] = 1
for i in range(len(select)):
if select[i] == 1:
for j in range(len(chart[0])):
if chart[i][j] == 1:
for k in range(len(chart)):
chart[k][j] = 0
temp.append(prime_implicants[i])
while True:
max_n = 0
rem = -1
count_n = 0
for i in range(len(chart)):
count_n = chart[i].count(1)
if count_n > max_n:
max_n = count_n
rem = i
if max_n == 0:
return temp
temp.append(prime_implicants[rem])
for i in range(len(chart[0])):
if chart[rem][i] == 1:
for j in range(len(chart)):
chart[j][i] = 0
def prime_implicant_chart(
prime_implicants: list[str], binary: list[str]
) -> list[list[int]]:
"""
>>> prime_implicant_chart(['0.00.01.5'],['0.00.01.5'])
[[1]]
"""
chart = [[0 for x in range(len(binary))] for x in range(len(prime_implicants))]
for i in range(len(prime_implicants)):
count = prime_implicants[i].count("_")
for j in range(len(binary)):
if is_for_table(prime_implicants[i], binary[j], count):
chart[i][j] = 1
return chart
def main() -> None:
no_of_variable = int(input("Enter the no. of variables\n"))
minterms = [
float(x)
for x in input(
"Enter the decimal representation of Minterms 'Spaces Separated'\n"
).split()
]
binary = decimal_to_binary(no_of_variable, minterms)
prime_implicants = check(binary)
print("Prime Implicants are:")
print(prime_implicants)
chart = prime_implicant_chart(prime_implicants, binary)
essential_prime_implicants = selection(chart, prime_implicants)
print("Essential Prime Implicants are:")
print(essential_prime_implicants)
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| -1 |
TheAlgorithms/Python | 7,235 | Add Flake8 comprehensions to pre-commit | ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [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] I know that pull requests will not be merged if they fail the automated tests.
* [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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T14:17:49Z" | "2022-10-15T17:29:42Z" | 98a4c2487814cdfe0822526e05c4e63ff6aef7d0 | a652905b605ddcc43626072366d1130315801dc9 | Add Flake8 comprehensions to pre-commit. ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 7,235 | Add Flake8 comprehensions to pre-commit | ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [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] I know that pull requests will not be merged if they fail the automated tests.
* [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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T14:17:49Z" | "2022-10-15T17:29:42Z" | 98a4c2487814cdfe0822526e05c4e63ff6aef7d0 | a652905b605ddcc43626072366d1130315801dc9 | Add Flake8 comprehensions to pre-commit. ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| #!/usr/bin/env python3
# This Python program implements an optimal binary search tree (abbreviated BST)
# building dynamic programming algorithm that delivers O(n^2) performance.
#
# The goal of the optimal BST problem is to build a low-cost BST for a
# given set of nodes, each with its own key and frequency. The frequency
# of the node is defined as how many time the node is being searched.
# The search cost of binary search tree is given by this formula:
#
# cost(1, n) = sum{i = 1 to n}((depth(node_i) + 1) * node_i_freq)
#
# where n is number of nodes in the BST. The characteristic of low-cost
# BSTs is having a faster overall search time than other implementations.
# The reason for their fast search time is that the nodes with high
# frequencies will be placed near the root of the tree while the nodes
# with low frequencies will be placed near the leaves of the tree thus
# reducing search time in the most frequent instances.
import sys
from random import randint
class Node:
"""Binary Search Tree Node"""
def __init__(self, key, freq):
self.key = key
self.freq = freq
def __str__(self):
"""
>>> str(Node(1, 2))
'Node(key=1, freq=2)'
"""
return f"Node(key={self.key}, freq={self.freq})"
def print_binary_search_tree(root, key, i, j, parent, is_left):
"""
Recursive function to print a BST from a root table.
>>> key = [3, 8, 9, 10, 17, 21]
>>> root = [[0, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 3], [0, 0, 2, 3, 3, 3], \
[0, 0, 0, 3, 3, 3], [0, 0, 0, 0, 4, 5], [0, 0, 0, 0, 0, 5]]
>>> print_binary_search_tree(root, key, 0, 5, -1, False)
8 is the root of the binary search tree.
3 is the left child of key 8.
10 is the right child of key 8.
9 is the left child of key 10.
21 is the right child of key 10.
17 is the left child of key 21.
"""
if i > j or i < 0 or j > len(root) - 1:
return
node = root[i][j]
if parent == -1: # root does not have a parent
print(f"{key[node]} is the root of the binary search tree.")
elif is_left:
print(f"{key[node]} is the left child of key {parent}.")
else:
print(f"{key[node]} is the right child of key {parent}.")
print_binary_search_tree(root, key, i, node - 1, key[node], True)
print_binary_search_tree(root, key, node + 1, j, key[node], False)
def find_optimal_binary_search_tree(nodes):
"""
This function calculates and prints the optimal binary search tree.
The dynamic programming algorithm below runs in O(n^2) time.
Implemented from CLRS (Introduction to Algorithms) book.
https://en.wikipedia.org/wiki/Introduction_to_Algorithms
>>> find_optimal_binary_search_tree([Node(12, 8), Node(10, 34), Node(20, 50), \
Node(42, 3), Node(25, 40), Node(37, 30)])
Binary search tree nodes:
Node(key=10, freq=34)
Node(key=12, freq=8)
Node(key=20, freq=50)
Node(key=25, freq=40)
Node(key=37, freq=30)
Node(key=42, freq=3)
<BLANKLINE>
The cost of optimal BST for given tree nodes is 324.
20 is the root of the binary search tree.
10 is the left child of key 20.
12 is the right child of key 10.
25 is the right child of key 20.
37 is the right child of key 25.
42 is the right child of key 37.
"""
# Tree nodes must be sorted first, the code below sorts the keys in
# increasing order and rearrange its frequencies accordingly.
nodes.sort(key=lambda node: node.key)
n = len(nodes)
keys = [nodes[i].key for i in range(n)]
freqs = [nodes[i].freq for i in range(n)]
# This 2D array stores the overall tree cost (which's as minimized as possible);
# for a single key, cost is equal to frequency of the key.
dp = [[freqs[i] if i == j else 0 for j in range(n)] for i in range(n)]
# sum[i][j] stores the sum of key frequencies between i and j inclusive in nodes
# array
total = [[freqs[i] if i == j else 0 for j in range(n)] for i in range(n)]
# stores tree roots that will be used later for constructing binary search tree
root = [[i if i == j else 0 for j in range(n)] for i in range(n)]
for interval_length in range(2, n + 1):
for i in range(n - interval_length + 1):
j = i + interval_length - 1
dp[i][j] = sys.maxsize # set the value to "infinity"
total[i][j] = total[i][j - 1] + freqs[j]
# Apply Knuth's optimization
# Loop without optimization: for r in range(i, j + 1):
for r in range(root[i][j - 1], root[i + 1][j] + 1): # r is a temporal root
left = dp[i][r - 1] if r != i else 0 # optimal cost for left subtree
right = dp[r + 1][j] if r != j else 0 # optimal cost for right subtree
cost = left + total[i][j] + right
if dp[i][j] > cost:
dp[i][j] = cost
root[i][j] = r
print("Binary search tree nodes:")
for node in nodes:
print(node)
print(f"\nThe cost of optimal BST for given tree nodes is {dp[0][n - 1]}.")
print_binary_search_tree(root, keys, 0, n - 1, -1, False)
def main():
# A sample binary search tree
nodes = [Node(i, randint(1, 50)) for i in range(10, 0, -1)]
find_optimal_binary_search_tree(nodes)
if __name__ == "__main__":
main()
| #!/usr/bin/env python3
# This Python program implements an optimal binary search tree (abbreviated BST)
# building dynamic programming algorithm that delivers O(n^2) performance.
#
# The goal of the optimal BST problem is to build a low-cost BST for a
# given set of nodes, each with its own key and frequency. The frequency
# of the node is defined as how many time the node is being searched.
# The search cost of binary search tree is given by this formula:
#
# cost(1, n) = sum{i = 1 to n}((depth(node_i) + 1) * node_i_freq)
#
# where n is number of nodes in the BST. The characteristic of low-cost
# BSTs is having a faster overall search time than other implementations.
# The reason for their fast search time is that the nodes with high
# frequencies will be placed near the root of the tree while the nodes
# with low frequencies will be placed near the leaves of the tree thus
# reducing search time in the most frequent instances.
import sys
from random import randint
class Node:
"""Binary Search Tree Node"""
def __init__(self, key, freq):
self.key = key
self.freq = freq
def __str__(self):
"""
>>> str(Node(1, 2))
'Node(key=1, freq=2)'
"""
return f"Node(key={self.key}, freq={self.freq})"
def print_binary_search_tree(root, key, i, j, parent, is_left):
"""
Recursive function to print a BST from a root table.
>>> key = [3, 8, 9, 10, 17, 21]
>>> root = [[0, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 3], [0, 0, 2, 3, 3, 3], \
[0, 0, 0, 3, 3, 3], [0, 0, 0, 0, 4, 5], [0, 0, 0, 0, 0, 5]]
>>> print_binary_search_tree(root, key, 0, 5, -1, False)
8 is the root of the binary search tree.
3 is the left child of key 8.
10 is the right child of key 8.
9 is the left child of key 10.
21 is the right child of key 10.
17 is the left child of key 21.
"""
if i > j or i < 0 or j > len(root) - 1:
return
node = root[i][j]
if parent == -1: # root does not have a parent
print(f"{key[node]} is the root of the binary search tree.")
elif is_left:
print(f"{key[node]} is the left child of key {parent}.")
else:
print(f"{key[node]} is the right child of key {parent}.")
print_binary_search_tree(root, key, i, node - 1, key[node], True)
print_binary_search_tree(root, key, node + 1, j, key[node], False)
def find_optimal_binary_search_tree(nodes):
"""
This function calculates and prints the optimal binary search tree.
The dynamic programming algorithm below runs in O(n^2) time.
Implemented from CLRS (Introduction to Algorithms) book.
https://en.wikipedia.org/wiki/Introduction_to_Algorithms
>>> find_optimal_binary_search_tree([Node(12, 8), Node(10, 34), Node(20, 50), \
Node(42, 3), Node(25, 40), Node(37, 30)])
Binary search tree nodes:
Node(key=10, freq=34)
Node(key=12, freq=8)
Node(key=20, freq=50)
Node(key=25, freq=40)
Node(key=37, freq=30)
Node(key=42, freq=3)
<BLANKLINE>
The cost of optimal BST for given tree nodes is 324.
20 is the root of the binary search tree.
10 is the left child of key 20.
12 is the right child of key 10.
25 is the right child of key 20.
37 is the right child of key 25.
42 is the right child of key 37.
"""
# Tree nodes must be sorted first, the code below sorts the keys in
# increasing order and rearrange its frequencies accordingly.
nodes.sort(key=lambda node: node.key)
n = len(nodes)
keys = [nodes[i].key for i in range(n)]
freqs = [nodes[i].freq for i in range(n)]
# This 2D array stores the overall tree cost (which's as minimized as possible);
# for a single key, cost is equal to frequency of the key.
dp = [[freqs[i] if i == j else 0 for j in range(n)] for i in range(n)]
# sum[i][j] stores the sum of key frequencies between i and j inclusive in nodes
# array
total = [[freqs[i] if i == j else 0 for j in range(n)] for i in range(n)]
# stores tree roots that will be used later for constructing binary search tree
root = [[i if i == j else 0 for j in range(n)] for i in range(n)]
for interval_length in range(2, n + 1):
for i in range(n - interval_length + 1):
j = i + interval_length - 1
dp[i][j] = sys.maxsize # set the value to "infinity"
total[i][j] = total[i][j - 1] + freqs[j]
# Apply Knuth's optimization
# Loop without optimization: for r in range(i, j + 1):
for r in range(root[i][j - 1], root[i + 1][j] + 1): # r is a temporal root
left = dp[i][r - 1] if r != i else 0 # optimal cost for left subtree
right = dp[r + 1][j] if r != j else 0 # optimal cost for right subtree
cost = left + total[i][j] + right
if dp[i][j] > cost:
dp[i][j] = cost
root[i][j] = r
print("Binary search tree nodes:")
for node in nodes:
print(node)
print(f"\nThe cost of optimal BST for given tree nodes is {dp[0][n - 1]}.")
print_binary_search_tree(root, keys, 0, n - 1, -1, False)
def main():
# A sample binary search tree
nodes = [Node(i, randint(1, 50)) for i in range(10, 0, -1)]
find_optimal_binary_search_tree(nodes)
if __name__ == "__main__":
main()
| -1 |
TheAlgorithms/Python | 7,235 | Add Flake8 comprehensions to pre-commit | ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [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] I know that pull requests will not be merged if they fail the automated tests.
* [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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T14:17:49Z" | "2022-10-15T17:29:42Z" | 98a4c2487814cdfe0822526e05c4e63ff6aef7d0 | a652905b605ddcc43626072366d1130315801dc9 | Add Flake8 comprehensions to pre-commit. ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 7,235 | Add Flake8 comprehensions to pre-commit | ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [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] I know that pull requests will not be merged if they fail the automated tests.
* [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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T14:17:49Z" | "2022-10-15T17:29:42Z" | 98a4c2487814cdfe0822526e05c4e63ff6aef7d0 | a652905b605ddcc43626072366d1130315801dc9 | Add Flake8 comprehensions to pre-commit. ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
This is a pure Python implementation of the heap sort algorithm.
For doctests run following command:
python -m doctest -v heap_sort.py
or
python3 -m doctest -v heap_sort.py
For manual testing run:
python heap_sort.py
"""
def heapify(unsorted, index, heap_size):
largest = index
left_index = 2 * index + 1
right_index = 2 * index + 2
if left_index < heap_size and unsorted[left_index] > unsorted[largest]:
largest = left_index
if right_index < heap_size and unsorted[right_index] > unsorted[largest]:
largest = right_index
if largest != index:
unsorted[largest], unsorted[index] = unsorted[index], unsorted[largest]
heapify(unsorted, largest, heap_size)
def heap_sort(unsorted):
"""
Pure implementation of the heap sort algorithm in Python
:param collection: some mutable ordered collection with heterogeneous
comparable items inside
:return: the same collection ordered by ascending
Examples:
>>> heap_sort([0, 5, 3, 2, 2])
[0, 2, 2, 3, 5]
>>> heap_sort([])
[]
>>> heap_sort([-2, -5, -45])
[-45, -5, -2]
"""
n = len(unsorted)
for i in range(n // 2 - 1, -1, -1):
heapify(unsorted, i, n)
for i in range(n - 1, 0, -1):
unsorted[0], unsorted[i] = unsorted[i], unsorted[0]
heapify(unsorted, 0, i)
return unsorted
if __name__ == "__main__":
user_input = input("Enter numbers separated by a comma:\n").strip()
unsorted = [int(item) for item in user_input.split(",")]
print(heap_sort(unsorted))
| """
This is a pure Python implementation of the heap sort algorithm.
For doctests run following command:
python -m doctest -v heap_sort.py
or
python3 -m doctest -v heap_sort.py
For manual testing run:
python heap_sort.py
"""
def heapify(unsorted, index, heap_size):
largest = index
left_index = 2 * index + 1
right_index = 2 * index + 2
if left_index < heap_size and unsorted[left_index] > unsorted[largest]:
largest = left_index
if right_index < heap_size and unsorted[right_index] > unsorted[largest]:
largest = right_index
if largest != index:
unsorted[largest], unsorted[index] = unsorted[index], unsorted[largest]
heapify(unsorted, largest, heap_size)
def heap_sort(unsorted):
"""
Pure implementation of the heap sort algorithm in Python
:param collection: some mutable ordered collection with heterogeneous
comparable items inside
:return: the same collection ordered by ascending
Examples:
>>> heap_sort([0, 5, 3, 2, 2])
[0, 2, 2, 3, 5]
>>> heap_sort([])
[]
>>> heap_sort([-2, -5, -45])
[-45, -5, -2]
"""
n = len(unsorted)
for i in range(n // 2 - 1, -1, -1):
heapify(unsorted, i, n)
for i in range(n - 1, 0, -1):
unsorted[0], unsorted[i] = unsorted[i], unsorted[0]
heapify(unsorted, 0, i)
return unsorted
if __name__ == "__main__":
user_input = input("Enter numbers separated by a comma:\n").strip()
unsorted = [int(item) for item in user_input.split(",")]
print(heap_sort(unsorted))
| -1 |
TheAlgorithms/Python | 7,235 | Add Flake8 comprehensions to pre-commit | ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [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] I know that pull requests will not be merged if they fail the automated tests.
* [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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T14:17:49Z" | "2022-10-15T17:29:42Z" | 98a4c2487814cdfe0822526e05c4e63ff6aef7d0 | a652905b605ddcc43626072366d1130315801dc9 | Add Flake8 comprehensions to pre-commit. ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Pure Python implementation of a binary search algorithm.
For doctests run following command:
python3 -m doctest -v simple_binary_search.py
For manual testing run:
python3 simple_binary_search.py
"""
from __future__ import annotations
def binary_search(a_list: list[int], item: int) -> bool:
"""
>>> test_list = [0, 1, 2, 8, 13, 17, 19, 32, 42]
>>> print(binary_search(test_list, 3))
False
>>> print(binary_search(test_list, 13))
True
>>> print(binary_search([4, 4, 5, 6, 7], 4))
True
>>> print(binary_search([4, 4, 5, 6, 7], -10))
False
>>> print(binary_search([-18, 2], -18))
True
>>> print(binary_search([5], 5))
True
>>> print(binary_search(['a', 'c', 'd'], 'c'))
True
>>> print(binary_search(['a', 'c', 'd'], 'f'))
False
>>> print(binary_search([], 1))
False
>>> print(binary_search([-.1, .1 , .8], .1))
True
>>> binary_search(range(-5000, 5000, 10), 80)
True
>>> binary_search(range(-5000, 5000, 10), 1255)
False
>>> binary_search(range(0, 10000, 5), 2)
False
"""
if len(a_list) == 0:
return False
midpoint = len(a_list) // 2
if a_list[midpoint] == item:
return True
if item < a_list[midpoint]:
return binary_search(a_list[:midpoint], item)
else:
return binary_search(a_list[midpoint + 1 :], item)
if __name__ == "__main__":
user_input = input("Enter numbers separated by comma:\n").strip()
sequence = [int(item.strip()) for item in user_input.split(",")]
target = int(input("Enter the number to be found in the list:\n").strip())
not_str = "" if binary_search(sequence, target) else "not "
print(f"{target} was {not_str}found in {sequence}")
| """
Pure Python implementation of a binary search algorithm.
For doctests run following command:
python3 -m doctest -v simple_binary_search.py
For manual testing run:
python3 simple_binary_search.py
"""
from __future__ import annotations
def binary_search(a_list: list[int], item: int) -> bool:
"""
>>> test_list = [0, 1, 2, 8, 13, 17, 19, 32, 42]
>>> print(binary_search(test_list, 3))
False
>>> print(binary_search(test_list, 13))
True
>>> print(binary_search([4, 4, 5, 6, 7], 4))
True
>>> print(binary_search([4, 4, 5, 6, 7], -10))
False
>>> print(binary_search([-18, 2], -18))
True
>>> print(binary_search([5], 5))
True
>>> print(binary_search(['a', 'c', 'd'], 'c'))
True
>>> print(binary_search(['a', 'c', 'd'], 'f'))
False
>>> print(binary_search([], 1))
False
>>> print(binary_search([-.1, .1 , .8], .1))
True
>>> binary_search(range(-5000, 5000, 10), 80)
True
>>> binary_search(range(-5000, 5000, 10), 1255)
False
>>> binary_search(range(0, 10000, 5), 2)
False
"""
if len(a_list) == 0:
return False
midpoint = len(a_list) // 2
if a_list[midpoint] == item:
return True
if item < a_list[midpoint]:
return binary_search(a_list[:midpoint], item)
else:
return binary_search(a_list[midpoint + 1 :], item)
if __name__ == "__main__":
user_input = input("Enter numbers separated by comma:\n").strip()
sequence = [int(item.strip()) for item in user_input.split(",")]
target = int(input("Enter the number to be found in the list:\n").strip())
not_str = "" if binary_search(sequence, target) else "not "
print(f"{target} was {not_str}found in {sequence}")
| -1 |
TheAlgorithms/Python | 7,235 | Add Flake8 comprehensions to pre-commit | ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [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] I know that pull requests will not be merged if they fail the automated tests.
* [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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T14:17:49Z" | "2022-10-15T17:29:42Z" | 98a4c2487814cdfe0822526e05c4e63ff6aef7d0 | a652905b605ddcc43626072366d1130315801dc9 | Add Flake8 comprehensions to pre-commit. ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| def reverse_letters(input_str: str) -> str:
"""
Reverses letters in a given string without adjusting the position of the words
>>> reverse_letters('The cat in the hat')
'ehT tac ni eht tah'
>>> reverse_letters('The quick brown fox jumped over the lazy dog.')
'ehT kciuq nworb xof depmuj revo eht yzal .god'
>>> reverse_letters('Is this true?')
'sI siht ?eurt'
>>> reverse_letters("I love Python")
'I evol nohtyP'
"""
return " ".join([word[::-1] for word in input_str.split()])
if __name__ == "__main__":
import doctest
doctest.testmod()
| def reverse_letters(input_str: str) -> str:
"""
Reverses letters in a given string without adjusting the position of the words
>>> reverse_letters('The cat in the hat')
'ehT tac ni eht tah'
>>> reverse_letters('The quick brown fox jumped over the lazy dog.')
'ehT kciuq nworb xof depmuj revo eht yzal .god'
>>> reverse_letters('Is this true?')
'sI siht ?eurt'
>>> reverse_letters("I love Python")
'I evol nohtyP'
"""
return " ".join([word[::-1] for word in input_str.split()])
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 7,235 | Add Flake8 comprehensions to pre-commit | ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [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] I know that pull requests will not be merged if they fail the automated tests.
* [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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T14:17:49Z" | "2022-10-15T17:29:42Z" | 98a4c2487814cdfe0822526e05c4e63ff6aef7d0 | a652905b605ddcc43626072366d1130315801dc9 | Add Flake8 comprehensions to pre-commit. ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| def stooge_sort(arr):
"""
Examples:
>>> stooge_sort([18.1, 0, -7.1, -1, 2, 2])
[-7.1, -1, 0, 2, 2, 18.1]
>>> stooge_sort([])
[]
"""
stooge(arr, 0, len(arr) - 1)
return arr
def stooge(arr, i, h):
if i >= h:
return
# If first element is smaller than the last then swap them
if arr[i] > arr[h]:
arr[i], arr[h] = arr[h], arr[i]
# If there are more than 2 elements in the array
if h - i + 1 > 2:
t = (int)((h - i + 1) / 3)
# Recursively sort first 2/3 elements
stooge(arr, i, (h - t))
# Recursively sort last 2/3 elements
stooge(arr, i + t, (h))
# Recursively sort first 2/3 elements
stooge(arr, i, (h - t))
if __name__ == "__main__":
user_input = input("Enter numbers separated by a comma:\n").strip()
unsorted = [int(item) for item in user_input.split(",")]
print(stooge_sort(unsorted))
| def stooge_sort(arr):
"""
Examples:
>>> stooge_sort([18.1, 0, -7.1, -1, 2, 2])
[-7.1, -1, 0, 2, 2, 18.1]
>>> stooge_sort([])
[]
"""
stooge(arr, 0, len(arr) - 1)
return arr
def stooge(arr, i, h):
if i >= h:
return
# If first element is smaller than the last then swap them
if arr[i] > arr[h]:
arr[i], arr[h] = arr[h], arr[i]
# If there are more than 2 elements in the array
if h - i + 1 > 2:
t = (int)((h - i + 1) / 3)
# Recursively sort first 2/3 elements
stooge(arr, i, (h - t))
# Recursively sort last 2/3 elements
stooge(arr, i + t, (h))
# Recursively sort first 2/3 elements
stooge(arr, i, (h - t))
if __name__ == "__main__":
user_input = input("Enter numbers separated by a comma:\n").strip()
unsorted = [int(item) for item in user_input.split(",")]
print(stooge_sort(unsorted))
| -1 |
TheAlgorithms/Python | 7,235 | Add Flake8 comprehensions to pre-commit | ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [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] I know that pull requests will not be merged if they fail the automated tests.
* [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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T14:17:49Z" | "2022-10-15T17:29:42Z" | 98a4c2487814cdfe0822526e05c4e63ff6aef7d0 | a652905b605ddcc43626072366d1130315801dc9 | Add Flake8 comprehensions to pre-commit. ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 7,235 | Add Flake8 comprehensions to pre-commit | ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [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] I know that pull requests will not be merged if they fail the automated tests.
* [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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T14:17:49Z" | "2022-10-15T17:29:42Z" | 98a4c2487814cdfe0822526e05c4e63ff6aef7d0 | a652905b605ddcc43626072366d1130315801dc9 | Add Flake8 comprehensions to pre-commit. ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Project Euler Problem 206: https://projecteuler.net/problem=206
Find the unique positive integer whose square has the form 1_2_3_4_5_6_7_8_9_0,
where each “_” is a single digit.
-----
Instead of computing every single permutation of that number and going
through a 10^9 search space, we can narrow it down considerably.
If the square ends in a 0, then the square root must also end in a 0. Thus,
the last missing digit must be 0 and the square root is a multiple of 10.
We can narrow the search space down to the first 8 digits and multiply the
result of that by 10 at the end.
Now the last digit is a 9, which can only happen if the square root ends
in a 3 or 7. From this point, we can try one of two different methods to find
the answer:
1. Start at the lowest possible base number whose square would be in the
format, and count up. The base we would start at is 101010103, whose square is
the closest number to 10203040506070809. Alternate counting up by 4 and 6 so
the last digit of the base is always a 3 or 7.
2. Start at the highest possible base number whose square would be in the
format, and count down. That base would be 138902663, whose square is the
closest number to 1929394959697989. Alternate counting down by 6 and 4 so the
last digit of the base is always a 3 or 7.
The solution does option 2 because the answer happens to be much closer to the
starting point.
"""
def is_square_form(num: int) -> bool:
"""
Determines if num is in the form 1_2_3_4_5_6_7_8_9
>>> is_square_form(1)
False
>>> is_square_form(112233445566778899)
True
>>> is_square_form(123456789012345678)
False
"""
digit = 9
while num > 0:
if num % 10 != digit:
return False
num //= 100
digit -= 1
return True
def solution() -> int:
"""
Returns the first integer whose square is of the form 1_2_3_4_5_6_7_8_9_0
"""
num = 138902663
while not is_square_form(num * num):
if num % 10 == 3:
num -= 6 # (3 - 6) % 10 = 7
else:
num -= 4 # (7 - 4) % 10 = 3
return num * 10
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 206: https://projecteuler.net/problem=206
Find the unique positive integer whose square has the form 1_2_3_4_5_6_7_8_9_0,
where each “_” is a single digit.
-----
Instead of computing every single permutation of that number and going
through a 10^9 search space, we can narrow it down considerably.
If the square ends in a 0, then the square root must also end in a 0. Thus,
the last missing digit must be 0 and the square root is a multiple of 10.
We can narrow the search space down to the first 8 digits and multiply the
result of that by 10 at the end.
Now the last digit is a 9, which can only happen if the square root ends
in a 3 or 7. From this point, we can try one of two different methods to find
the answer:
1. Start at the lowest possible base number whose square would be in the
format, and count up. The base we would start at is 101010103, whose square is
the closest number to 10203040506070809. Alternate counting up by 4 and 6 so
the last digit of the base is always a 3 or 7.
2. Start at the highest possible base number whose square would be in the
format, and count down. That base would be 138902663, whose square is the
closest number to 1929394959697989. Alternate counting down by 6 and 4 so the
last digit of the base is always a 3 or 7.
The solution does option 2 because the answer happens to be much closer to the
starting point.
"""
def is_square_form(num: int) -> bool:
"""
Determines if num is in the form 1_2_3_4_5_6_7_8_9
>>> is_square_form(1)
False
>>> is_square_form(112233445566778899)
True
>>> is_square_form(123456789012345678)
False
"""
digit = 9
while num > 0:
if num % 10 != digit:
return False
num //= 100
digit -= 1
return True
def solution() -> int:
"""
Returns the first integer whose square is of the form 1_2_3_4_5_6_7_8_9_0
"""
num = 138902663
while not is_square_form(num * num):
if num % 10 == 3:
num -= 6 # (3 - 6) % 10 = 7
else:
num -= 4 # (7 - 4) % 10 = 3
return num * 10
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 7,235 | Add Flake8 comprehensions to pre-commit | ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [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] I know that pull requests will not be merged if they fail the automated tests.
* [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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T14:17:49Z" | "2022-10-15T17:29:42Z" | 98a4c2487814cdfe0822526e05c4e63ff6aef7d0 | a652905b605ddcc43626072366d1130315801dc9 | Add Flake8 comprehensions to pre-commit. ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Problem 13: https://projecteuler.net/problem=13
Problem Statement:
Work out the first ten digits of the sum of the following one-hundred 50-digit
numbers.
"""
import os
def solution():
"""
Returns the first ten digits of the sum of the array elements
from the file num.txt
>>> solution()
'5537376230'
"""
file_path = os.path.join(os.path.dirname(__file__), "num.txt")
with open(file_path) as file_hand:
return str(sum(int(line) for line in file_hand))[:10]
if __name__ == "__main__":
print(solution())
| """
Problem 13: https://projecteuler.net/problem=13
Problem Statement:
Work out the first ten digits of the sum of the following one-hundred 50-digit
numbers.
"""
import os
def solution():
"""
Returns the first ten digits of the sum of the array elements
from the file num.txt
>>> solution()
'5537376230'
"""
file_path = os.path.join(os.path.dirname(__file__), "num.txt")
with open(file_path) as file_hand:
return str(sum(int(line) for line in file_hand))[:10]
if __name__ == "__main__":
print(solution())
| -1 |
TheAlgorithms/Python | 7,235 | Add Flake8 comprehensions to pre-commit | ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [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] I know that pull requests will not be merged if they fail the automated tests.
* [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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T14:17:49Z" | "2022-10-15T17:29:42Z" | 98a4c2487814cdfe0822526e05c4e63ff6aef7d0 | a652905b605ddcc43626072366d1130315801dc9 | Add Flake8 comprehensions to pre-commit. ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| def decrypt(message: str) -> None:
"""
>>> decrypt('TMDETUX PMDVU')
Decryption using Key #0: TMDETUX PMDVU
Decryption using Key #1: SLCDSTW OLCUT
Decryption using Key #2: RKBCRSV NKBTS
Decryption using Key #3: QJABQRU MJASR
Decryption using Key #4: PIZAPQT LIZRQ
Decryption using Key #5: OHYZOPS KHYQP
Decryption using Key #6: NGXYNOR JGXPO
Decryption using Key #7: MFWXMNQ IFWON
Decryption using Key #8: LEVWLMP HEVNM
Decryption using Key #9: KDUVKLO GDUML
Decryption using Key #10: JCTUJKN FCTLK
Decryption using Key #11: IBSTIJM EBSKJ
Decryption using Key #12: HARSHIL DARJI
Decryption using Key #13: GZQRGHK CZQIH
Decryption using Key #14: FYPQFGJ BYPHG
Decryption using Key #15: EXOPEFI AXOGF
Decryption using Key #16: DWNODEH ZWNFE
Decryption using Key #17: CVMNCDG YVMED
Decryption using Key #18: BULMBCF XULDC
Decryption using Key #19: ATKLABE WTKCB
Decryption using Key #20: ZSJKZAD VSJBA
Decryption using Key #21: YRIJYZC URIAZ
Decryption using Key #22: XQHIXYB TQHZY
Decryption using Key #23: WPGHWXA SPGYX
Decryption using Key #24: VOFGVWZ ROFXW
Decryption using Key #25: UNEFUVY QNEWV
"""
LETTERS = "ABCDEFGHIJKLMNOPQRSTUVWXYZ" # noqa: N806
for key in range(len(LETTERS)):
translated = ""
for symbol in message:
if symbol in LETTERS:
num = LETTERS.find(symbol)
num = num - key
if num < 0:
num = num + len(LETTERS)
translated = translated + LETTERS[num]
else:
translated = translated + symbol
print(f"Decryption using Key #{key}: {translated}")
def main() -> None:
message = input("Encrypted message: ")
message = message.upper()
decrypt(message)
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| def decrypt(message: str) -> None:
"""
>>> decrypt('TMDETUX PMDVU')
Decryption using Key #0: TMDETUX PMDVU
Decryption using Key #1: SLCDSTW OLCUT
Decryption using Key #2: RKBCRSV NKBTS
Decryption using Key #3: QJABQRU MJASR
Decryption using Key #4: PIZAPQT LIZRQ
Decryption using Key #5: OHYZOPS KHYQP
Decryption using Key #6: NGXYNOR JGXPO
Decryption using Key #7: MFWXMNQ IFWON
Decryption using Key #8: LEVWLMP HEVNM
Decryption using Key #9: KDUVKLO GDUML
Decryption using Key #10: JCTUJKN FCTLK
Decryption using Key #11: IBSTIJM EBSKJ
Decryption using Key #12: HARSHIL DARJI
Decryption using Key #13: GZQRGHK CZQIH
Decryption using Key #14: FYPQFGJ BYPHG
Decryption using Key #15: EXOPEFI AXOGF
Decryption using Key #16: DWNODEH ZWNFE
Decryption using Key #17: CVMNCDG YVMED
Decryption using Key #18: BULMBCF XULDC
Decryption using Key #19: ATKLABE WTKCB
Decryption using Key #20: ZSJKZAD VSJBA
Decryption using Key #21: YRIJYZC URIAZ
Decryption using Key #22: XQHIXYB TQHZY
Decryption using Key #23: WPGHWXA SPGYX
Decryption using Key #24: VOFGVWZ ROFXW
Decryption using Key #25: UNEFUVY QNEWV
"""
LETTERS = "ABCDEFGHIJKLMNOPQRSTUVWXYZ" # noqa: N806
for key in range(len(LETTERS)):
translated = ""
for symbol in message:
if symbol in LETTERS:
num = LETTERS.find(symbol)
num = num - key
if num < 0:
num = num + len(LETTERS)
translated = translated + LETTERS[num]
else:
translated = translated + symbol
print(f"Decryption using Key #{key}: {translated}")
def main() -> None:
message = input("Encrypted message: ")
message = message.upper()
decrypt(message)
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| -1 |
TheAlgorithms/Python | 7,235 | Add Flake8 comprehensions to pre-commit | ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [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] I know that pull requests will not be merged if they fail the automated tests.
* [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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T14:17:49Z" | "2022-10-15T17:29:42Z" | 98a4c2487814cdfe0822526e05c4e63ff6aef7d0 | a652905b605ddcc43626072366d1130315801dc9 | Add Flake8 comprehensions to pre-commit. ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Implementation of Bilateral filter
Inputs:
img: A 2d image with values in between 0 and 1
varS: variance in space dimension.
varI: variance in Intensity.
N: Kernel size(Must be an odd number)
Output:
img:A 2d zero padded image with values in between 0 and 1
"""
import math
import sys
import cv2
import numpy as np
def vec_gaussian(img: np.ndarray, variance: float) -> np.ndarray:
# For applying gaussian function for each element in matrix.
sigma = math.sqrt(variance)
cons = 1 / (sigma * math.sqrt(2 * math.pi))
return cons * np.exp(-((img / sigma) ** 2) * 0.5)
def get_slice(img: np.ndarray, x: int, y: int, kernel_size: int) -> np.ndarray:
half = kernel_size // 2
return img[x - half : x + half + 1, y - half : y + half + 1]
def get_gauss_kernel(kernel_size: int, spatial_variance: float) -> np.ndarray:
# Creates a gaussian kernel of given dimension.
arr = np.zeros((kernel_size, kernel_size))
for i in range(0, kernel_size):
for j in range(0, kernel_size):
arr[i, j] = math.sqrt(
abs(i - kernel_size // 2) ** 2 + abs(j - kernel_size // 2) ** 2
)
return vec_gaussian(arr, spatial_variance)
def bilateral_filter(
img: np.ndarray,
spatial_variance: float,
intensity_variance: float,
kernel_size: int,
) -> np.ndarray:
img2 = np.zeros(img.shape)
gauss_ker = get_gauss_kernel(kernel_size, spatial_variance)
size_x, size_y = img.shape
for i in range(kernel_size // 2, size_x - kernel_size // 2):
for j in range(kernel_size // 2, size_y - kernel_size // 2):
img_s = get_slice(img, i, j, kernel_size)
img_i = img_s - img_s[kernel_size // 2, kernel_size // 2]
img_ig = vec_gaussian(img_i, intensity_variance)
weights = np.multiply(gauss_ker, img_ig)
vals = np.multiply(img_s, weights)
val = np.sum(vals) / np.sum(weights)
img2[i, j] = val
return img2
def parse_args(args: list) -> tuple:
filename = args[1] if args[1:] else "../image_data/lena.jpg"
spatial_variance = float(args[2]) if args[2:] else 1.0
intensity_variance = float(args[3]) if args[3:] else 1.0
if args[4:]:
kernel_size = int(args[4])
kernel_size = kernel_size + abs(kernel_size % 2 - 1)
else:
kernel_size = 5
return filename, spatial_variance, intensity_variance, kernel_size
if __name__ == "__main__":
filename, spatial_variance, intensity_variance, kernel_size = parse_args(sys.argv)
img = cv2.imread(filename, 0)
cv2.imshow("input image", img)
out = img / 255
out = out.astype("float32")
out = bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
out = out * 255
out = np.uint8(out)
cv2.imshow("output image", out)
cv2.waitKey(0)
cv2.destroyAllWindows()
| """
Implementation of Bilateral filter
Inputs:
img: A 2d image with values in between 0 and 1
varS: variance in space dimension.
varI: variance in Intensity.
N: Kernel size(Must be an odd number)
Output:
img:A 2d zero padded image with values in between 0 and 1
"""
import math
import sys
import cv2
import numpy as np
def vec_gaussian(img: np.ndarray, variance: float) -> np.ndarray:
# For applying gaussian function for each element in matrix.
sigma = math.sqrt(variance)
cons = 1 / (sigma * math.sqrt(2 * math.pi))
return cons * np.exp(-((img / sigma) ** 2) * 0.5)
def get_slice(img: np.ndarray, x: int, y: int, kernel_size: int) -> np.ndarray:
half = kernel_size // 2
return img[x - half : x + half + 1, y - half : y + half + 1]
def get_gauss_kernel(kernel_size: int, spatial_variance: float) -> np.ndarray:
# Creates a gaussian kernel of given dimension.
arr = np.zeros((kernel_size, kernel_size))
for i in range(0, kernel_size):
for j in range(0, kernel_size):
arr[i, j] = math.sqrt(
abs(i - kernel_size // 2) ** 2 + abs(j - kernel_size // 2) ** 2
)
return vec_gaussian(arr, spatial_variance)
def bilateral_filter(
img: np.ndarray,
spatial_variance: float,
intensity_variance: float,
kernel_size: int,
) -> np.ndarray:
img2 = np.zeros(img.shape)
gauss_ker = get_gauss_kernel(kernel_size, spatial_variance)
size_x, size_y = img.shape
for i in range(kernel_size // 2, size_x - kernel_size // 2):
for j in range(kernel_size // 2, size_y - kernel_size // 2):
img_s = get_slice(img, i, j, kernel_size)
img_i = img_s - img_s[kernel_size // 2, kernel_size // 2]
img_ig = vec_gaussian(img_i, intensity_variance)
weights = np.multiply(gauss_ker, img_ig)
vals = np.multiply(img_s, weights)
val = np.sum(vals) / np.sum(weights)
img2[i, j] = val
return img2
def parse_args(args: list) -> tuple:
filename = args[1] if args[1:] else "../image_data/lena.jpg"
spatial_variance = float(args[2]) if args[2:] else 1.0
intensity_variance = float(args[3]) if args[3:] else 1.0
if args[4:]:
kernel_size = int(args[4])
kernel_size = kernel_size + abs(kernel_size % 2 - 1)
else:
kernel_size = 5
return filename, spatial_variance, intensity_variance, kernel_size
if __name__ == "__main__":
filename, spatial_variance, intensity_variance, kernel_size = parse_args(sys.argv)
img = cv2.imread(filename, 0)
cv2.imshow("input image", img)
out = img / 255
out = out.astype("float32")
out = bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
out = out * 255
out = np.uint8(out)
cv2.imshow("output image", out)
cv2.waitKey(0)
cv2.destroyAllWindows()
| -1 |
TheAlgorithms/Python | 7,235 | Add Flake8 comprehensions to pre-commit | ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [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] I know that pull requests will not be merged if they fail the automated tests.
* [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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T14:17:49Z" | "2022-10-15T17:29:42Z" | 98a4c2487814cdfe0822526e05c4e63ff6aef7d0 | a652905b605ddcc43626072366d1130315801dc9 | Add Flake8 comprehensions to pre-commit. ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 7,235 | Add Flake8 comprehensions to pre-commit | ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [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] I know that pull requests will not be merged if they fail the automated tests.
* [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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T14:17:49Z" | "2022-10-15T17:29:42Z" | 98a4c2487814cdfe0822526e05c4e63ff6aef7d0 | a652905b605ddcc43626072366d1130315801dc9 | Add Flake8 comprehensions to pre-commit. ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Project Euler Problem 36
https://projecteuler.net/problem=36
Problem Statement:
Double-base palindromes
Problem 36
The decimal number, 585 = 10010010012 (binary), is palindromic in both bases.
Find the sum of all numbers, less than one million, which are palindromic in
base 10 and base 2.
(Please note that the palindromic number, in either base, may not include
leading zeros.)
"""
from __future__ import annotations
def is_palindrome(n: int | str) -> bool:
"""
Return true if the input n is a palindrome.
Otherwise return false. n can be an integer or a string.
>>> is_palindrome(909)
True
>>> is_palindrome(908)
False
>>> is_palindrome('10101')
True
>>> is_palindrome('10111')
False
"""
n = str(n)
return True if n == n[::-1] else False
def solution(n: int = 1000000):
"""Return the sum of all numbers, less than n , which are palindromic in
base 10 and base 2.
>>> solution(1000000)
872187
>>> solution(500000)
286602
>>> solution(100000)
286602
>>> solution(1000)
1772
>>> solution(100)
157
>>> solution(10)
25
>>> solution(2)
1
>>> solution(1)
0
"""
total = 0
for i in range(1, n):
if is_palindrome(i) and is_palindrome(bin(i).split("b")[1]):
total += i
return total
if __name__ == "__main__":
print(solution(int(str(input().strip()))))
| """
Project Euler Problem 36
https://projecteuler.net/problem=36
Problem Statement:
Double-base palindromes
Problem 36
The decimal number, 585 = 10010010012 (binary), is palindromic in both bases.
Find the sum of all numbers, less than one million, which are palindromic in
base 10 and base 2.
(Please note that the palindromic number, in either base, may not include
leading zeros.)
"""
from __future__ import annotations
def is_palindrome(n: int | str) -> bool:
"""
Return true if the input n is a palindrome.
Otherwise return false. n can be an integer or a string.
>>> is_palindrome(909)
True
>>> is_palindrome(908)
False
>>> is_palindrome('10101')
True
>>> is_palindrome('10111')
False
"""
n = str(n)
return True if n == n[::-1] else False
def solution(n: int = 1000000):
"""Return the sum of all numbers, less than n , which are palindromic in
base 10 and base 2.
>>> solution(1000000)
872187
>>> solution(500000)
286602
>>> solution(100000)
286602
>>> solution(1000)
1772
>>> solution(100)
157
>>> solution(10)
25
>>> solution(2)
1
>>> solution(1)
0
"""
total = 0
for i in range(1, n):
if is_palindrome(i) and is_palindrome(bin(i).split("b")[1]):
total += i
return total
if __name__ == "__main__":
print(solution(int(str(input().strip()))))
| -1 |
TheAlgorithms/Python | 7,235 | Add Flake8 comprehensions to pre-commit | ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [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] I know that pull requests will not be merged if they fail the automated tests.
* [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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T14:17:49Z" | "2022-10-15T17:29:42Z" | 98a4c2487814cdfe0822526e05c4e63ff6aef7d0 | a652905b605ddcc43626072366d1130315801dc9 | Add Flake8 comprehensions to pre-commit. ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
This is a pure Python implementation of the Harmonic Series algorithm
https://en.wikipedia.org/wiki/Harmonic_series_(mathematics)
For doctests run following command:
python -m doctest -v harmonic_series.py
or
python3 -m doctest -v harmonic_series.py
For manual testing run:
python3 harmonic_series.py
"""
def harmonic_series(n_term: str) -> list:
"""Pure Python implementation of Harmonic Series algorithm
:param n_term: The last (nth) term of Harmonic Series
:return: The Harmonic Series starting from 1 to last (nth) term
Examples:
>>> harmonic_series(5)
['1', '1/2', '1/3', '1/4', '1/5']
>>> harmonic_series(5.0)
['1', '1/2', '1/3', '1/4', '1/5']
>>> harmonic_series(5.1)
['1', '1/2', '1/3', '1/4', '1/5']
>>> harmonic_series(-5)
[]
>>> harmonic_series(0)
[]
>>> harmonic_series(1)
['1']
"""
if n_term == "":
return []
series: list = []
for temp in range(int(n_term)):
series.append(f"1/{temp + 1}" if series else "1")
return series
if __name__ == "__main__":
nth_term = input("Enter the last number (nth term) of the Harmonic Series")
print("Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n")
print(harmonic_series(nth_term))
| """
This is a pure Python implementation of the Harmonic Series algorithm
https://en.wikipedia.org/wiki/Harmonic_series_(mathematics)
For doctests run following command:
python -m doctest -v harmonic_series.py
or
python3 -m doctest -v harmonic_series.py
For manual testing run:
python3 harmonic_series.py
"""
def harmonic_series(n_term: str) -> list:
"""Pure Python implementation of Harmonic Series algorithm
:param n_term: The last (nth) term of Harmonic Series
:return: The Harmonic Series starting from 1 to last (nth) term
Examples:
>>> harmonic_series(5)
['1', '1/2', '1/3', '1/4', '1/5']
>>> harmonic_series(5.0)
['1', '1/2', '1/3', '1/4', '1/5']
>>> harmonic_series(5.1)
['1', '1/2', '1/3', '1/4', '1/5']
>>> harmonic_series(-5)
[]
>>> harmonic_series(0)
[]
>>> harmonic_series(1)
['1']
"""
if n_term == "":
return []
series: list = []
for temp in range(int(n_term)):
series.append(f"1/{temp + 1}" if series else "1")
return series
if __name__ == "__main__":
nth_term = input("Enter the last number (nth term) of the Harmonic Series")
print("Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n")
print(harmonic_series(nth_term))
| -1 |
TheAlgorithms/Python | 7,235 | Add Flake8 comprehensions to pre-commit | ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [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] I know that pull requests will not be merged if they fail the automated tests.
* [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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T14:17:49Z" | "2022-10-15T17:29:42Z" | 98a4c2487814cdfe0822526e05c4e63ff6aef7d0 | a652905b605ddcc43626072366d1130315801dc9 | Add Flake8 comprehensions to pre-commit. ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 7,235 | Add Flake8 comprehensions to pre-commit | ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [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] I know that pull requests will not be merged if they fail the automated tests.
* [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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T14:17:49Z" | "2022-10-15T17:29:42Z" | 98a4c2487814cdfe0822526e05c4e63ff6aef7d0 | a652905b605ddcc43626072366d1130315801dc9 | Add Flake8 comprehensions to pre-commit. ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| import requests
from bs4 import BeautifulSoup
def stock_price(symbol: str = "AAPL") -> str:
url = f"https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"
soup = BeautifulSoup(requests.get(url).text, "html.parser")
class_ = "My(6px) Pos(r) smartphone_Mt(6px)"
return soup.find("div", class_=class_).find("span").text
if __name__ == "__main__":
for symbol in "AAPL AMZN IBM GOOG MSFT ORCL".split():
print(f"Current {symbol:<4} stock price is {stock_price(symbol):>8}")
| import requests
from bs4 import BeautifulSoup
def stock_price(symbol: str = "AAPL") -> str:
url = f"https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"
soup = BeautifulSoup(requests.get(url).text, "html.parser")
class_ = "My(6px) Pos(r) smartphone_Mt(6px)"
return soup.find("div", class_=class_).find("span").text
if __name__ == "__main__":
for symbol in "AAPL AMZN IBM GOOG MSFT ORCL".split():
print(f"Current {symbol:<4} stock price is {stock_price(symbol):>8}")
| -1 |
TheAlgorithms/Python | 7,235 | Add Flake8 comprehensions to pre-commit | ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [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] I know that pull requests will not be merged if they fail the automated tests.
* [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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T14:17:49Z" | "2022-10-15T17:29:42Z" | 98a4c2487814cdfe0822526e05c4e63ff6aef7d0 | a652905b605ddcc43626072366d1130315801dc9 | Add Flake8 comprehensions to pre-commit. ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Horizontal Projectile Motion problem in physics.
This algorithm solves a specific problem in which
the motion starts from the ground as can be seen below:
(v = 0)
**
* *
* *
* *
* *
* *
GROUND GROUND
For more info: https://en.wikipedia.org/wiki/Projectile_motion
"""
# Importing packages
from math import radians as angle_to_radians
from math import sin
# Acceleration Constant on Earth (unit m/s^2)
g = 9.80665
def check_args(init_velocity: float, angle: float) -> None:
"""
Check that the arguments are valid
"""
# Ensure valid instance
if not isinstance(init_velocity, (int, float)):
raise TypeError("Invalid velocity. Should be a positive number.")
if not isinstance(angle, (int, float)):
raise TypeError("Invalid angle. Range is 1-90 degrees.")
# Ensure valid angle
if angle > 90 or angle < 1:
raise ValueError("Invalid angle. Range is 1-90 degrees.")
# Ensure valid velocity
if init_velocity < 0:
raise ValueError("Invalid velocity. Should be a positive number.")
def horizontal_distance(init_velocity: float, angle: float) -> float:
"""
Returns the horizontal distance that the object cover
Formula:
v_0^2 * sin(2 * alpha)
---------------------
g
v_0 - initial velocity
alpha - angle
>>> horizontal_distance(30, 45)
91.77
>>> horizontal_distance(100, 78)
414.76
>>> horizontal_distance(-1, 20)
Traceback (most recent call last):
...
ValueError: Invalid velocity. Should be a positive number.
>>> horizontal_distance(30, -20)
Traceback (most recent call last):
...
ValueError: Invalid angle. Range is 1-90 degrees.
"""
check_args(init_velocity, angle)
radians = angle_to_radians(2 * angle)
return round(init_velocity**2 * sin(radians) / g, 2)
def max_height(init_velocity: float, angle: float) -> float:
"""
Returns the maximum height that the object reach
Formula:
v_0^2 * sin^2(alpha)
--------------------
2g
v_0 - initial velocity
alpha - angle
>>> max_height(30, 45)
22.94
>>> max_height(100, 78)
487.82
>>> max_height("a", 20)
Traceback (most recent call last):
...
TypeError: Invalid velocity. Should be a positive number.
>>> horizontal_distance(30, "b")
Traceback (most recent call last):
...
TypeError: Invalid angle. Range is 1-90 degrees.
"""
check_args(init_velocity, angle)
radians = angle_to_radians(angle)
return round(init_velocity**2 * sin(radians) ** 2 / (2 * g), 2)
def total_time(init_velocity: float, angle: float) -> float:
"""
Returns total time of the motion
Formula:
2 * v_0 * sin(alpha)
--------------------
g
v_0 - initial velocity
alpha - angle
>>> total_time(30, 45)
4.33
>>> total_time(100, 78)
19.95
>>> total_time(-10, 40)
Traceback (most recent call last):
...
ValueError: Invalid velocity. Should be a positive number.
>>> total_time(30, "b")
Traceback (most recent call last):
...
TypeError: Invalid angle. Range is 1-90 degrees.
"""
check_args(init_velocity, angle)
radians = angle_to_radians(angle)
return round(2 * init_velocity * sin(radians) / g, 2)
def test_motion() -> None:
"""
>>> test_motion()
"""
v0, angle = 25, 20
assert horizontal_distance(v0, angle) == 40.97
assert max_height(v0, angle) == 3.73
assert total_time(v0, angle) == 1.74
if __name__ == "__main__":
from doctest import testmod
testmod()
# Get input from user
init_vel = float(input("Initial Velocity: ").strip())
# Get input from user
angle = float(input("angle: ").strip())
# Print results
print()
print("Results: ")
print(f"Horizontal Distance: {str(horizontal_distance(init_vel, angle))} [m]")
print(f"Maximum Height: {str(max_height(init_vel, angle))} [m]")
print(f"Total Time: {str(total_time(init_vel, angle))} [s]")
| """
Horizontal Projectile Motion problem in physics.
This algorithm solves a specific problem in which
the motion starts from the ground as can be seen below:
(v = 0)
**
* *
* *
* *
* *
* *
GROUND GROUND
For more info: https://en.wikipedia.org/wiki/Projectile_motion
"""
# Importing packages
from math import radians as angle_to_radians
from math import sin
# Acceleration Constant on Earth (unit m/s^2)
g = 9.80665
def check_args(init_velocity: float, angle: float) -> None:
"""
Check that the arguments are valid
"""
# Ensure valid instance
if not isinstance(init_velocity, (int, float)):
raise TypeError("Invalid velocity. Should be a positive number.")
if not isinstance(angle, (int, float)):
raise TypeError("Invalid angle. Range is 1-90 degrees.")
# Ensure valid angle
if angle > 90 or angle < 1:
raise ValueError("Invalid angle. Range is 1-90 degrees.")
# Ensure valid velocity
if init_velocity < 0:
raise ValueError("Invalid velocity. Should be a positive number.")
def horizontal_distance(init_velocity: float, angle: float) -> float:
"""
Returns the horizontal distance that the object cover
Formula:
v_0^2 * sin(2 * alpha)
---------------------
g
v_0 - initial velocity
alpha - angle
>>> horizontal_distance(30, 45)
91.77
>>> horizontal_distance(100, 78)
414.76
>>> horizontal_distance(-1, 20)
Traceback (most recent call last):
...
ValueError: Invalid velocity. Should be a positive number.
>>> horizontal_distance(30, -20)
Traceback (most recent call last):
...
ValueError: Invalid angle. Range is 1-90 degrees.
"""
check_args(init_velocity, angle)
radians = angle_to_radians(2 * angle)
return round(init_velocity**2 * sin(radians) / g, 2)
def max_height(init_velocity: float, angle: float) -> float:
"""
Returns the maximum height that the object reach
Formula:
v_0^2 * sin^2(alpha)
--------------------
2g
v_0 - initial velocity
alpha - angle
>>> max_height(30, 45)
22.94
>>> max_height(100, 78)
487.82
>>> max_height("a", 20)
Traceback (most recent call last):
...
TypeError: Invalid velocity. Should be a positive number.
>>> horizontal_distance(30, "b")
Traceback (most recent call last):
...
TypeError: Invalid angle. Range is 1-90 degrees.
"""
check_args(init_velocity, angle)
radians = angle_to_radians(angle)
return round(init_velocity**2 * sin(radians) ** 2 / (2 * g), 2)
def total_time(init_velocity: float, angle: float) -> float:
"""
Returns total time of the motion
Formula:
2 * v_0 * sin(alpha)
--------------------
g
v_0 - initial velocity
alpha - angle
>>> total_time(30, 45)
4.33
>>> total_time(100, 78)
19.95
>>> total_time(-10, 40)
Traceback (most recent call last):
...
ValueError: Invalid velocity. Should be a positive number.
>>> total_time(30, "b")
Traceback (most recent call last):
...
TypeError: Invalid angle. Range is 1-90 degrees.
"""
check_args(init_velocity, angle)
radians = angle_to_radians(angle)
return round(2 * init_velocity * sin(radians) / g, 2)
def test_motion() -> None:
"""
>>> test_motion()
"""
v0, angle = 25, 20
assert horizontal_distance(v0, angle) == 40.97
assert max_height(v0, angle) == 3.73
assert total_time(v0, angle) == 1.74
if __name__ == "__main__":
from doctest import testmod
testmod()
# Get input from user
init_vel = float(input("Initial Velocity: ").strip())
# Get input from user
angle = float(input("angle: ").strip())
# Print results
print()
print("Results: ")
print(f"Horizontal Distance: {str(horizontal_distance(init_vel, angle))} [m]")
print(f"Maximum Height: {str(max_height(init_vel, angle))} [m]")
print(f"Total Time: {str(total_time(init_vel, angle))} [s]")
| -1 |
TheAlgorithms/Python | 7,235 | Add Flake8 comprehensions to pre-commit | ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [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] I know that pull requests will not be merged if they fail the automated tests.
* [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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T14:17:49Z" | "2022-10-15T17:29:42Z" | 98a4c2487814cdfe0822526e05c4e63ff6aef7d0 | a652905b605ddcc43626072366d1130315801dc9 | Add Flake8 comprehensions to pre-commit. ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| import base64
def base16_encode(inp: str) -> bytes:
"""
Encodes a given utf-8 string into base-16.
>>> base16_encode('Hello World!')
b'48656C6C6F20576F726C6421'
>>> base16_encode('HELLO WORLD!')
b'48454C4C4F20574F524C4421'
>>> base16_encode('')
b''
"""
# encode the input into a bytes-like object and then encode b16encode that
return base64.b16encode(inp.encode("utf-8"))
def base16_decode(b16encoded: bytes) -> str:
"""
Decodes from base-16 to a utf-8 string.
>>> base16_decode(b'48656C6C6F20576F726C6421')
'Hello World!'
>>> base16_decode(b'48454C4C4F20574F524C4421')
'HELLO WORLD!'
>>> base16_decode(b'')
''
"""
# b16decode the input into bytes and decode that into a human readable string
return base64.b16decode(b16encoded).decode("utf-8")
if __name__ == "__main__":
import doctest
doctest.testmod()
| import base64
def base16_encode(inp: str) -> bytes:
"""
Encodes a given utf-8 string into base-16.
>>> base16_encode('Hello World!')
b'48656C6C6F20576F726C6421'
>>> base16_encode('HELLO WORLD!')
b'48454C4C4F20574F524C4421'
>>> base16_encode('')
b''
"""
# encode the input into a bytes-like object and then encode b16encode that
return base64.b16encode(inp.encode("utf-8"))
def base16_decode(b16encoded: bytes) -> str:
"""
Decodes from base-16 to a utf-8 string.
>>> base16_decode(b'48656C6C6F20576F726C6421')
'Hello World!'
>>> base16_decode(b'48454C4C4F20574F524C4421')
'HELLO WORLD!'
>>> base16_decode(b'')
''
"""
# b16decode the input into bytes and decode that into a human readable string
return base64.b16decode(b16encoded).decode("utf-8")
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 7,235 | Add Flake8 comprehensions to pre-commit | ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [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] I know that pull requests will not be merged if they fail the automated tests.
* [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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T14:17:49Z" | "2022-10-15T17:29:42Z" | 98a4c2487814cdfe0822526e05c4e63ff6aef7d0 | a652905b605ddcc43626072366d1130315801dc9 | Add Flake8 comprehensions to pre-commit. ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Project Euler Problem 4: https://projecteuler.net/problem=4
Largest palindrome product
A palindromic number reads the same both ways. The largest palindrome made
from the product of two 2-digit numbers is 9009 = 91 × 99.
Find the largest palindrome made from the product of two 3-digit numbers.
References:
- https://en.wikipedia.org/wiki/Palindromic_number
"""
def solution(n: int = 998001) -> int:
"""
Returns the largest palindrome made from the product of two 3-digit
numbers which is less than n.
>>> solution(20000)
19591
>>> solution(30000)
29992
>>> solution(40000)
39893
>>> solution(10000)
Traceback (most recent call last):
...
ValueError: That number is larger than our acceptable range.
"""
# fetches the next number
for number in range(n - 1, 9999, -1):
str_number = str(number)
# checks whether 'str_number' is a palindrome.
if str_number == str_number[::-1]:
divisor = 999
# if 'number' is a product of two 3-digit numbers
# then number is the answer otherwise fetch next number.
while divisor != 99:
if (number % divisor == 0) and (len(str(number // divisor)) == 3.0):
return number
divisor -= 1
raise ValueError("That number is larger than our acceptable range.")
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 4: https://projecteuler.net/problem=4
Largest palindrome product
A palindromic number reads the same both ways. The largest palindrome made
from the product of two 2-digit numbers is 9009 = 91 × 99.
Find the largest palindrome made from the product of two 3-digit numbers.
References:
- https://en.wikipedia.org/wiki/Palindromic_number
"""
def solution(n: int = 998001) -> int:
"""
Returns the largest palindrome made from the product of two 3-digit
numbers which is less than n.
>>> solution(20000)
19591
>>> solution(30000)
29992
>>> solution(40000)
39893
>>> solution(10000)
Traceback (most recent call last):
...
ValueError: That number is larger than our acceptable range.
"""
# fetches the next number
for number in range(n - 1, 9999, -1):
str_number = str(number)
# checks whether 'str_number' is a palindrome.
if str_number == str_number[::-1]:
divisor = 999
# if 'number' is a product of two 3-digit numbers
# then number is the answer otherwise fetch next number.
while divisor != 99:
if (number % divisor == 0) and (len(str(number // divisor)) == 3.0):
return number
divisor -= 1
raise ValueError("That number is larger than our acceptable range.")
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 7,235 | Add Flake8 comprehensions to pre-commit | ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [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] I know that pull requests will not be merged if they fail the automated tests.
* [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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T14:17:49Z" | "2022-10-15T17:29:42Z" | 98a4c2487814cdfe0822526e05c4e63ff6aef7d0 | a652905b605ddcc43626072366d1130315801dc9 | Add Flake8 comprehensions to pre-commit. ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Counting Summations
Problem 76: https://projecteuler.net/problem=76
It is possible to write five as a sum in exactly six different ways:
4 + 1
3 + 2
3 + 1 + 1
2 + 2 + 1
2 + 1 + 1 + 1
1 + 1 + 1 + 1 + 1
How many different ways can one hundred be written as a sum of at least two
positive integers?
"""
def solution(m: int = 100) -> int:
"""
Returns the number of different ways the number m can be written as a
sum of at least two positive integers.
>>> solution(100)
190569291
>>> solution(50)
204225
>>> solution(30)
5603
>>> solution(10)
41
>>> solution(5)
6
>>> solution(3)
2
>>> solution(2)
1
>>> solution(1)
0
"""
memo = [[0 for _ in range(m)] for _ in range(m + 1)]
for i in range(m + 1):
memo[i][0] = 1
for n in range(m + 1):
for k in range(1, m):
memo[n][k] += memo[n][k - 1]
if n > k:
memo[n][k] += memo[n - k - 1][k]
return memo[m][m - 1] - 1
if __name__ == "__main__":
print(solution(int(input("Enter a number: ").strip())))
| """
Counting Summations
Problem 76: https://projecteuler.net/problem=76
It is possible to write five as a sum in exactly six different ways:
4 + 1
3 + 2
3 + 1 + 1
2 + 2 + 1
2 + 1 + 1 + 1
1 + 1 + 1 + 1 + 1
How many different ways can one hundred be written as a sum of at least two
positive integers?
"""
def solution(m: int = 100) -> int:
"""
Returns the number of different ways the number m can be written as a
sum of at least two positive integers.
>>> solution(100)
190569291
>>> solution(50)
204225
>>> solution(30)
5603
>>> solution(10)
41
>>> solution(5)
6
>>> solution(3)
2
>>> solution(2)
1
>>> solution(1)
0
"""
memo = [[0 for _ in range(m)] for _ in range(m + 1)]
for i in range(m + 1):
memo[i][0] = 1
for n in range(m + 1):
for k in range(1, m):
memo[n][k] += memo[n][k - 1]
if n > k:
memo[n][k] += memo[n - k - 1][k]
return memo[m][m - 1] - 1
if __name__ == "__main__":
print(solution(int(input("Enter a number: ").strip())))
| -1 |
TheAlgorithms/Python | 7,235 | Add Flake8 comprehensions to pre-commit | ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [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] I know that pull requests will not be merged if they fail the automated tests.
* [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: #{$ISSUE_NO}`.
| CaedenPH | "2022-10-15T14:17:49Z" | "2022-10-15T17:29:42Z" | 98a4c2487814cdfe0822526e05c4e63ff6aef7d0 | a652905b605ddcc43626072366d1130315801dc9 | Add Flake8 comprehensions to pre-commit. ### Describe your change:
Implements (#7233)
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [x] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [x] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from .stack import Stack
def balanced_parentheses(parentheses: str) -> bool:
"""Use a stack to check if a string of parentheses is balanced.
>>> balanced_parentheses("([]{})")
True
>>> balanced_parentheses("[()]{}{[()()]()}")
True
>>> balanced_parentheses("[(])")
False
>>> balanced_parentheses("1+2*3-4")
True
>>> balanced_parentheses("")
True
"""
stack: Stack[str] = Stack()
bracket_pairs = {"(": ")", "[": "]", "{": "}"}
for bracket in parentheses:
if bracket in bracket_pairs:
stack.push(bracket)
elif bracket in (")", "]", "}"):
if stack.is_empty() or bracket_pairs[stack.pop()] != bracket:
return False
return stack.is_empty()
if __name__ == "__main__":
from doctest import testmod
testmod()
examples = ["((()))", "((())", "(()))"]
print("Balanced parentheses demonstration:\n")
for example in examples:
not_str = "" if balanced_parentheses(example) else "not "
print(f"{example} is {not_str}balanced")
| from .stack import Stack
def balanced_parentheses(parentheses: str) -> bool:
"""Use a stack to check if a string of parentheses is balanced.
>>> balanced_parentheses("([]{})")
True
>>> balanced_parentheses("[()]{}{[()()]()}")
True
>>> balanced_parentheses("[(])")
False
>>> balanced_parentheses("1+2*3-4")
True
>>> balanced_parentheses("")
True
"""
stack: Stack[str] = Stack()
bracket_pairs = {"(": ")", "[": "]", "{": "}"}
for bracket in parentheses:
if bracket in bracket_pairs:
stack.push(bracket)
elif bracket in (")", "]", "}"):
if stack.is_empty() or bracket_pairs[stack.pop()] != bracket:
return False
return stack.is_empty()
if __name__ == "__main__":
from doctest import testmod
testmod()
examples = ["((()))", "((())", "(()))"]
print("Balanced parentheses demonstration:\n")
for example in examples:
not_str = "" if balanced_parentheses(example) else "not "
print(f"{example} is {not_str}balanced")
| -1 |
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