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TheAlgorithms/Python | 4,927 | [mypy] Fix type annotations for data_structures->linked_list directory files | ```Shell
$ python -m mypy -V
mypy 0.910
$ python -m mypy data_structures/linked_list/
Success: no issues found in 14 source files
```
Related Issue: #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| ParthSatodiya | "2021-10-02T18:42:18Z" | "2021-10-07T15:18:23Z" | d654806eae5dc6027911424ea828e566a64641fd | d324f91fe75cc859335ee1f7c9c6307d958d0558 | [mypy] Fix type annotations for data_structures->linked_list directory files. ```Shell
$ python -m mypy -V
mypy 0.910
$ python -m mypy data_structures/linked_list/
Success: no issues found in 14 source files
```
Related Issue: #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| 63,13,28,75,0,23,14,8,0,76,22,89,12,4,13,14,69,16,24,69,29,4,18,23,69,69,59,14,69,11,14,4,29,18
| 63,13,28,75,0,23,14,8,0,76,22,89,12,4,13,14,69,16,24,69,29,4,18,23,69,69,59,14,69,11,14,4,29,18
| -1 |
TheAlgorithms/Python | 4,927 | [mypy] Fix type annotations for data_structures->linked_list directory files | ```Shell
$ python -m mypy -V
mypy 0.910
$ python -m mypy data_structures/linked_list/
Success: no issues found in 14 source files
```
Related Issue: #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| ParthSatodiya | "2021-10-02T18:42:18Z" | "2021-10-07T15:18:23Z" | d654806eae5dc6027911424ea828e566a64641fd | d324f91fe75cc859335ee1f7c9c6307d958d0558 | [mypy] Fix type annotations for data_structures->linked_list directory files. ```Shell
$ python -m mypy -V
mypy 0.910
$ python -m mypy data_structures/linked_list/
Success: no issues found in 14 source files
```
Related Issue: #4052
### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [ ] All new Python files are placed inside an existing directory.
* [ ] All filenames are in all lowercase characters with no spaces or dashes.
* [ ] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from __future__ import annotations
from typing import Callable
class DoubleLinkedListNode:
"""
Double Linked List Node built specifically for LRU Cache
"""
def __init__(self, key: int, val: int):
self.key = key
self.val = val
self.next = None
self.prev = None
class DoubleLinkedList:
"""
Double Linked List built specifically for LRU Cache
"""
def __init__(self):
self.head = DoubleLinkedListNode(None, None)
self.rear = DoubleLinkedListNode(None, None)
self.head.next, self.rear.prev = self.rear, self.head
def add(self, node: DoubleLinkedListNode) -> None:
"""
Adds the given node to the end of the list (before rear)
"""
temp = self.rear.prev
temp.next, node.prev = node, temp
self.rear.prev, node.next = node, self.rear
def remove(self, node: DoubleLinkedListNode) -> DoubleLinkedListNode:
"""
Removes and returns the given node from the list
"""
temp_last, temp_next = node.prev, node.next
node.prev, node.next = None, None
temp_last.next, temp_next.prev = temp_next, temp_last
return node
class LRUCache:
"""
LRU Cache to store a given capacity of data. Can be used as a stand-alone object
or as a function decorator.
>>> cache = LRUCache(2)
>>> cache.set(1, 1)
>>> cache.set(2, 2)
>>> cache.get(1)
1
>>> cache.set(3, 3)
>>> cache.get(2) # None returned
>>> cache.set(4, 4)
>>> cache.get(1) # None returned
>>> cache.get(3)
3
>>> cache.get(4)
4
>>> cache
CacheInfo(hits=3, misses=2, capacity=2, current size=2)
>>> @LRUCache.decorator(100)
... def fib(num):
... if num in (1, 2):
... return 1
... return fib(num - 1) + fib(num - 2)
>>> for i in range(1, 100):
... res = fib(i)
>>> fib.cache_info()
CacheInfo(hits=194, misses=99, capacity=100, current size=99)
"""
# class variable to map the decorator functions to their respective instance
decorator_function_to_instance_map = {}
def __init__(self, capacity: int):
self.list = DoubleLinkedList()
self.capacity = capacity
self.num_keys = 0
self.hits = 0
self.miss = 0
self.cache = {}
def __repr__(self) -> str:
"""
Return the details for the cache instance
[hits, misses, capacity, current_size]
"""
return (
f"CacheInfo(hits={self.hits}, misses={self.miss}, "
f"capacity={self.capacity}, current size={self.num_keys})"
)
def __contains__(self, key: int) -> bool:
"""
>>> cache = LRUCache(1)
>>> 1 in cache
False
>>> cache.set(1, 1)
>>> 1 in cache
True
"""
return key in self.cache
def get(self, key: int) -> int | None:
"""
Returns the value for the input key and updates the Double Linked List. Returns
None if key is not present in cache
"""
if key in self.cache:
self.hits += 1
self.list.add(self.list.remove(self.cache[key]))
return self.cache[key].val
self.miss += 1
return None
def set(self, key: int, value: int) -> None:
"""
Sets the value for the input key and updates the Double Linked List
"""
if key not in self.cache:
if self.num_keys >= self.capacity:
key_to_delete = self.list.head.next.key
self.list.remove(self.cache[key_to_delete])
del self.cache[key_to_delete]
self.num_keys -= 1
self.cache[key] = DoubleLinkedListNode(key, value)
self.list.add(self.cache[key])
self.num_keys += 1
else:
node = self.list.remove(self.cache[key])
node.val = value
self.list.add(node)
@staticmethod
def decorator(size: int = 128):
"""
Decorator version of LRU Cache
"""
def cache_decorator_inner(func: Callable):
def cache_decorator_wrapper(*args, **kwargs):
if func not in LRUCache.decorator_function_to_instance_map:
LRUCache.decorator_function_to_instance_map[func] = LRUCache(size)
result = LRUCache.decorator_function_to_instance_map[func].get(args[0])
if result is None:
result = func(*args, **kwargs)
LRUCache.decorator_function_to_instance_map[func].set(
args[0], result
)
return result
def cache_info():
return LRUCache.decorator_function_to_instance_map[func]
cache_decorator_wrapper.cache_info = cache_info
return cache_decorator_wrapper
return cache_decorator_inner
if __name__ == "__main__":
import doctest
doctest.testmod()
| from __future__ import annotations
from typing import Callable
class DoubleLinkedListNode:
"""
Double Linked List Node built specifically for LRU Cache
"""
def __init__(self, key: int, val: int):
self.key = key
self.val = val
self.next = None
self.prev = None
class DoubleLinkedList:
"""
Double Linked List built specifically for LRU Cache
"""
def __init__(self):
self.head = DoubleLinkedListNode(None, None)
self.rear = DoubleLinkedListNode(None, None)
self.head.next, self.rear.prev = self.rear, self.head
def add(self, node: DoubleLinkedListNode) -> None:
"""
Adds the given node to the end of the list (before rear)
"""
temp = self.rear.prev
temp.next, node.prev = node, temp
self.rear.prev, node.next = node, self.rear
def remove(self, node: DoubleLinkedListNode) -> DoubleLinkedListNode:
"""
Removes and returns the given node from the list
"""
temp_last, temp_next = node.prev, node.next
node.prev, node.next = None, None
temp_last.next, temp_next.prev = temp_next, temp_last
return node
class LRUCache:
"""
LRU Cache to store a given capacity of data. Can be used as a stand-alone object
or as a function decorator.
>>> cache = LRUCache(2)
>>> cache.set(1, 1)
>>> cache.set(2, 2)
>>> cache.get(1)
1
>>> cache.set(3, 3)
>>> cache.get(2) # None returned
>>> cache.set(4, 4)
>>> cache.get(1) # None returned
>>> cache.get(3)
3
>>> cache.get(4)
4
>>> cache
CacheInfo(hits=3, misses=2, capacity=2, current size=2)
>>> @LRUCache.decorator(100)
... def fib(num):
... if num in (1, 2):
... return 1
... return fib(num - 1) + fib(num - 2)
>>> for i in range(1, 100):
... res = fib(i)
>>> fib.cache_info()
CacheInfo(hits=194, misses=99, capacity=100, current size=99)
"""
# class variable to map the decorator functions to their respective instance
decorator_function_to_instance_map = {}
def __init__(self, capacity: int):
self.list = DoubleLinkedList()
self.capacity = capacity
self.num_keys = 0
self.hits = 0
self.miss = 0
self.cache = {}
def __repr__(self) -> str:
"""
Return the details for the cache instance
[hits, misses, capacity, current_size]
"""
return (
f"CacheInfo(hits={self.hits}, misses={self.miss}, "
f"capacity={self.capacity}, current size={self.num_keys})"
)
def __contains__(self, key: int) -> bool:
"""
>>> cache = LRUCache(1)
>>> 1 in cache
False
>>> cache.set(1, 1)
>>> 1 in cache
True
"""
return key in self.cache
def get(self, key: int) -> int | None:
"""
Returns the value for the input key and updates the Double Linked List. Returns
None if key is not present in cache
"""
if key in self.cache:
self.hits += 1
self.list.add(self.list.remove(self.cache[key]))
return self.cache[key].val
self.miss += 1
return None
def set(self, key: int, value: int) -> None:
"""
Sets the value for the input key and updates the Double Linked List
"""
if key not in self.cache:
if self.num_keys >= self.capacity:
key_to_delete = self.list.head.next.key
self.list.remove(self.cache[key_to_delete])
del self.cache[key_to_delete]
self.num_keys -= 1
self.cache[key] = DoubleLinkedListNode(key, value)
self.list.add(self.cache[key])
self.num_keys += 1
else:
node = self.list.remove(self.cache[key])
node.val = value
self.list.add(node)
@staticmethod
def decorator(size: int = 128):
"""
Decorator version of LRU Cache
"""
def cache_decorator_inner(func: Callable):
def cache_decorator_wrapper(*args, **kwargs):
if func not in LRUCache.decorator_function_to_instance_map:
LRUCache.decorator_function_to_instance_map[func] = LRUCache(size)
result = LRUCache.decorator_function_to_instance_map[func].get(args[0])
if result is None:
result = func(*args, **kwargs)
LRUCache.decorator_function_to_instance_map[func].set(
args[0], result
)
return result
def cache_info():
return LRUCache.decorator_function_to_instance_map[func]
cache_decorator_wrapper.cache_info = cache_info
return cache_decorator_wrapper
return cache_decorator_inner
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 4,881 | bugfix: Add empty list detection for find_max/min | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [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.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| taseikyo | "2021-10-02T04:03:28Z" | "2021-10-07T15:20:32Z" | d324f91fe75cc859335ee1f7c9c6307d958d0558 | 77b243e62b09cdf6201916af6762b03c54d8f77a | bugfix: Add empty list detection for find_max/min. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [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.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # NguyenU
def find_max(nums):
"""
>>> for nums in ([3, 2, 1], [-3, -2, -1], [3, -3, 0], [3.0, 3.1, 2.9]):
... find_max(nums) == max(nums)
True
True
True
True
"""
max_num = nums[0]
for x in nums:
if x > max_num:
max_num = x
return max_num
def main():
print(find_max([2, 4, 9, 7, 19, 94, 5])) # 94
if __name__ == "__main__":
main()
| from __future__ import annotations
def find_max(nums: list[int | float]) -> int | float:
"""
>>> for nums in ([3, 2, 1], [-3, -2, -1], [3, -3, 0], [3.0, 3.1, 2.9]):
... find_max(nums) == max(nums)
True
True
True
True
>>> find_max([2, 4, 9, 7, 19, 94, 5])
94
>>> find_max([])
Traceback (most recent call last):
...
ValueError: find_max() arg is an empty sequence
"""
if len(nums) == 0:
raise ValueError("find_max() arg is an empty sequence")
max_num = nums[0]
for x in nums:
if x > max_num:
max_num = x
return max_num
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| 1 |
TheAlgorithms/Python | 4,881 | bugfix: Add empty list detection for find_max/min | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [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.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| taseikyo | "2021-10-02T04:03:28Z" | "2021-10-07T15:20:32Z" | d324f91fe75cc859335ee1f7c9c6307d958d0558 | 77b243e62b09cdf6201916af6762b03c54d8f77a | bugfix: Add empty list detection for find_max/min. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [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.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # Divide and Conquer algorithm
def find_max(nums, left, right):
"""
find max value in list
:param nums: contains elements
:param left: index of first element
:param right: index of last element
:return: max in nums
>>> nums = [1, 3, 5, 7, 9, 2, 4, 6, 8, 10]
>>> find_max(nums, 0, len(nums) - 1) == max(nums)
True
"""
if left == right:
return nums[left]
mid = (left + right) >> 1 # the middle
left_max = find_max(nums, left, mid) # find max in range[left, mid]
right_max = find_max(nums, mid + 1, right) # find max in range[mid + 1, right]
return left_max if left_max >= right_max else right_max
if __name__ == "__main__":
nums = [1, 3, 5, 7, 9, 2, 4, 6, 8, 10]
assert find_max(nums, 0, len(nums) - 1) == 10
| from __future__ import annotations
# Divide and Conquer algorithm
def find_max(nums: list[int | float], left: int, right: int) -> int | float:
"""
find max value in list
:param nums: contains elements
:param left: index of first element
:param right: index of last element
:return: max in nums
>>> for nums in ([3, 2, 1], [-3, -2, -1], [3, -3, 0], [3.0, 3.1, 2.9]):
... find_max(nums, 0, len(nums) - 1) == max(nums)
True
True
True
True
>>> nums = [1, 3, 5, 7, 9, 2, 4, 6, 8, 10]
>>> find_max(nums, 0, len(nums) - 1) == max(nums)
True
>>> find_max([], 0, 0)
Traceback (most recent call last):
...
ValueError: find_max() arg is an empty sequence
>>> find_max(nums, 0, len(nums)) == max(nums)
Traceback (most recent call last):
...
IndexError: list index out of range
>>> find_max(nums, -len(nums), -1) == max(nums)
True
>>> find_max(nums, -len(nums) - 1, -1) == max(nums)
Traceback (most recent call last):
...
IndexError: list index out of range
"""
if len(nums) == 0:
raise ValueError("find_max() arg is an empty sequence")
if (
left >= len(nums)
or left < -len(nums)
or right >= len(nums)
or right < -len(nums)
):
raise IndexError("list index out of range")
if left == right:
return nums[left]
mid = (left + right) >> 1 # the middle
left_max = find_max(nums, left, mid) # find max in range[left, mid]
right_max = find_max(nums, mid + 1, right) # find max in range[mid + 1, right]
return left_max if left_max >= right_max else right_max
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| 1 |
TheAlgorithms/Python | 4,881 | bugfix: Add empty list detection for find_max/min | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [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.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| taseikyo | "2021-10-02T04:03:28Z" | "2021-10-07T15:20:32Z" | d324f91fe75cc859335ee1f7c9c6307d958d0558 | 77b243e62b09cdf6201916af6762b03c54d8f77a | bugfix: Add empty list detection for find_max/min. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [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.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| def find_min(nums):
"""
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
"""
min_num = nums[0]
for num in nums:
if min_num > num:
min_num = num
return min_num
def main():
assert find_min([0, 1, 2, 3, 4, 5, -3, 24, -56]) == -56
if __name__ == "__main__":
main()
| 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 | 4,881 | bugfix: Add empty list detection for find_max/min | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [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.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| taseikyo | "2021-10-02T04:03:28Z" | "2021-10-07T15:20:32Z" | d324f91fe75cc859335ee1f7c9c6307d958d0558 | 77b243e62b09cdf6201916af6762b03c54d8f77a | bugfix: Add empty list detection for find_max/min. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [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.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # Divide and Conquer algorithm
def find_min(nums, left, right):
"""
find min value in list
:param nums: contains elements
:param left: index of first element
:param right: index of last element
:return: min in nums
>>> nums = [1, 3, 5, 7, 9, 2, 4, 6, 8, 10]
>>> find_min(nums, 0, len(nums) - 1) == min(nums)
True
"""
if left == right:
return nums[left]
mid = (left + right) >> 1 # the middle
left_min = find_min(nums, left, mid) # find min in range[left, mid]
right_min = find_min(nums, mid + 1, right) # find min in range[mid + 1, right]
return left_min if left_min <= right_min else right_min
if __name__ == "__main__":
nums = [1, 3, 5, 7, 9, 2, 4, 6, 8, 10]
assert find_min(nums, 0, len(nums) - 1) == 1
| from __future__ import annotations
# Divide and Conquer algorithm
def find_min(nums: list[int | float], left: int, right: int) -> int | float:
"""
find min value in list
:param nums: contains elements
:param left: index of first element
:param right: index of last element
:return: min in nums
>>> for nums in ([3, 2, 1], [-3, -2, -1], [3, -3, 0], [3.0, 3.1, 2.9]):
... find_min(nums, 0, len(nums) - 1) == min(nums)
True
True
True
True
>>> nums = [1, 3, 5, 7, 9, 2, 4, 6, 8, 10]
>>> find_min(nums, 0, len(nums) - 1) == min(nums)
True
>>> find_min([], 0, 0)
Traceback (most recent call last):
...
ValueError: find_min() arg is an empty sequence
>>> find_min(nums, 0, len(nums)) == min(nums)
Traceback (most recent call last):
...
IndexError: list index out of range
>>> find_min(nums, -len(nums), -1) == min(nums)
True
>>> find_min(nums, -len(nums) - 1, -1) == min(nums)
Traceback (most recent call last):
...
IndexError: list index out of range
"""
if len(nums) == 0:
raise ValueError("find_min() arg is an empty sequence")
if (
left >= len(nums)
or left < -len(nums)
or right >= len(nums)
or right < -len(nums)
):
raise IndexError("list index out of range")
if left == right:
return nums[left]
mid = (left + right) >> 1 # the middle
left_min = find_min(nums, left, mid) # find min in range[left, mid]
right_min = find_min(nums, mid + 1, right) # find min in range[mid + 1, right]
return left_min if left_min <= right_min else right_min
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| 1 |
TheAlgorithms/Python | 4,881 | bugfix: Add empty list detection for find_max/min | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [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.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| taseikyo | "2021-10-02T04:03:28Z" | "2021-10-07T15:20:32Z" | d324f91fe75cc859335ee1f7c9c6307d958d0558 | 77b243e62b09cdf6201916af6762b03c54d8f77a | bugfix: Add empty list detection for find_max/min. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [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.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,881 | bugfix: Add empty list detection for find_max/min | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| taseikyo | "2021-10-02T04:03:28Z" | "2021-10-07T15:20:32Z" | d324f91fe75cc859335ee1f7c9c6307d958d0558 | 77b243e62b09cdf6201916af6762b03c54d8f77a | bugfix: Add empty list detection for find_max/min. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [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.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| import math
def fx(x: float, a: float) -> float:
return math.pow(x, 2) - a
def fx_derivative(x: float) -> float:
return 2 * x
def get_initial_point(a: float) -> float:
start = 2.0
while start <= a:
start = math.pow(start, 2)
return start
def square_root_iterative(
a: float, max_iter: int = 9999, tolerance: float = 0.00000000000001
) -> float:
"""
Square root is aproximated using Newtons method.
https://en.wikipedia.org/wiki/Newton%27s_method
>>> all(abs(square_root_iterative(i)-math.sqrt(i)) <= .00000000000001
... for i in range(500))
True
>>> square_root_iterative(-1)
Traceback (most recent call last):
...
ValueError: math domain error
>>> square_root_iterative(4)
2.0
>>> square_root_iterative(3.2)
1.788854381999832
>>> square_root_iterative(140)
11.832159566199232
"""
if a < 0:
raise ValueError("math domain error")
value = get_initial_point(a)
for i in range(max_iter):
prev_value = value
value = value - fx(value, a) / fx_derivative(value)
if abs(prev_value - value) < tolerance:
return value
return value
if __name__ == "__main__":
from doctest import testmod
testmod()
| import math
def fx(x: float, a: float) -> float:
return math.pow(x, 2) - a
def fx_derivative(x: float) -> float:
return 2 * x
def get_initial_point(a: float) -> float:
start = 2.0
while start <= a:
start = math.pow(start, 2)
return start
def square_root_iterative(
a: float, max_iter: int = 9999, tolerance: float = 0.00000000000001
) -> float:
"""
Square root is aproximated using Newtons method.
https://en.wikipedia.org/wiki/Newton%27s_method
>>> all(abs(square_root_iterative(i)-math.sqrt(i)) <= .00000000000001
... for i in range(500))
True
>>> square_root_iterative(-1)
Traceback (most recent call last):
...
ValueError: math domain error
>>> square_root_iterative(4)
2.0
>>> square_root_iterative(3.2)
1.788854381999832
>>> square_root_iterative(140)
11.832159566199232
"""
if a < 0:
raise ValueError("math domain error")
value = get_initial_point(a)
for i in range(max_iter):
prev_value = value
value = value - fx(value, a) / fx_derivative(value)
if abs(prev_value - value) < tolerance:
return value
return value
if __name__ == "__main__":
from doctest import testmod
testmod()
| -1 |
TheAlgorithms/Python | 4,881 | bugfix: Add empty list detection for find_max/min | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [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.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| taseikyo | "2021-10-02T04:03:28Z" | "2021-10-07T15:20:32Z" | d324f91fe75cc859335ee1f7c9c6307d958d0558 | 77b243e62b09cdf6201916af6762b03c54d8f77a | bugfix: Add empty list detection for find_max/min. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [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.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,881 | bugfix: Add empty list detection for find_max/min | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| taseikyo | "2021-10-02T04:03:28Z" | "2021-10-07T15:20:32Z" | d324f91fe75cc859335ee1f7c9c6307d958d0558 | 77b243e62b09cdf6201916af6762b03c54d8f77a | bugfix: Add empty list detection for find_max/min. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [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.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,881 | bugfix: Add empty list detection for find_max/min | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| taseikyo | "2021-10-02T04:03:28Z" | "2021-10-07T15:20:32Z" | d324f91fe75cc859335ee1f7c9c6307d958d0558 | 77b243e62b09cdf6201916af6762b03c54d8f77a | bugfix: Add empty list detection for find_max/min. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [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.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Problem 20: https://projecteuler.net/problem=20
n! means n × (n − 1) × ... × 3 × 2 × 1
For example, 10! = 10 × 9 × ... × 3 × 2 × 1 = 3628800,
and the sum of the digits in the number 10! is 3 + 6 + 2 + 8 + 8 + 0 + 0 = 27.
Find the sum of the digits in the number 100!
"""
def factorial(num: int) -> int:
"""Find the factorial of a given number n"""
fact = 1
for i in range(1, num + 1):
fact *= i
return fact
def split_and_add(number: int) -> int:
"""Split number digits and add them."""
sum_of_digits = 0
while number > 0:
last_digit = number % 10
sum_of_digits += last_digit
number = number // 10 # Removing the last_digit from the given number
return sum_of_digits
def solution(num: int = 100) -> int:
"""Returns the sum of the digits in the factorial of num
>>> solution(100)
648
>>> solution(50)
216
>>> solution(10)
27
>>> solution(5)
3
>>> solution(3)
6
>>> solution(2)
2
>>> solution(1)
1
"""
nfact = factorial(num)
result = split_and_add(nfact)
return result
if __name__ == "__main__":
print(solution(int(input("Enter the Number: ").strip())))
| """
Problem 20: https://projecteuler.net/problem=20
n! means n × (n − 1) × ... × 3 × 2 × 1
For example, 10! = 10 × 9 × ... × 3 × 2 × 1 = 3628800,
and the sum of the digits in the number 10! is 3 + 6 + 2 + 8 + 8 + 0 + 0 = 27.
Find the sum of the digits in the number 100!
"""
def factorial(num: int) -> int:
"""Find the factorial of a given number n"""
fact = 1
for i in range(1, num + 1):
fact *= i
return fact
def split_and_add(number: int) -> int:
"""Split number digits and add them."""
sum_of_digits = 0
while number > 0:
last_digit = number % 10
sum_of_digits += last_digit
number = number // 10 # Removing the last_digit from the given number
return sum_of_digits
def solution(num: int = 100) -> int:
"""Returns the sum of the digits in the factorial of num
>>> solution(100)
648
>>> solution(50)
216
>>> solution(10)
27
>>> solution(5)
3
>>> solution(3)
6
>>> solution(2)
2
>>> solution(1)
1
"""
nfact = factorial(num)
result = split_and_add(nfact)
return result
if __name__ == "__main__":
print(solution(int(input("Enter the Number: ").strip())))
| -1 |
TheAlgorithms/Python | 4,881 | bugfix: Add empty list detection for find_max/min | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [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.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| taseikyo | "2021-10-02T04:03:28Z" | "2021-10-07T15:20:32Z" | d324f91fe75cc859335ee1f7c9c6307d958d0558 | 77b243e62b09cdf6201916af6762b03c54d8f77a | bugfix: Add empty list detection for find_max/min. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [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.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Project Euler Problem 7: https://projecteuler.net/problem=7
10001st prime
By listing the first six prime numbers: 2, 3, 5, 7, 11, and 13, we
can see that the 6th prime is 13.
What is the 10001st prime number?
References:
- https://en.wikipedia.org/wiki/Prime_number
"""
def isprime(number: int) -> bool:
"""
Determines whether the given number is prime or not
>>> isprime(2)
True
>>> isprime(15)
False
>>> isprime(29)
True
"""
for i in range(2, int(number ** 0.5) + 1):
if number % i == 0:
return False
return True
def solution(nth: int = 10001) -> int:
"""
Returns the n-th prime number.
>>> solution(6)
13
>>> solution(1)
2
>>> solution(3)
5
>>> solution(20)
71
>>> solution(50)
229
>>> solution(100)
541
>>> solution(3.4)
5
>>> solution(0)
Traceback (most recent call last):
...
ValueError: Parameter nth must be greater than or equal to one.
>>> solution(-17)
Traceback (most recent call last):
...
ValueError: Parameter nth must be greater than or equal to one.
>>> solution([])
Traceback (most recent call last):
...
TypeError: Parameter nth must be int or castable to int.
>>> solution("asd")
Traceback (most recent call last):
...
TypeError: Parameter nth must be int or castable to int.
"""
try:
nth = int(nth)
except (TypeError, ValueError):
raise TypeError("Parameter nth must be int or castable to int.") from None
if nth <= 0:
raise ValueError("Parameter nth must be greater than or equal to one.")
primes = []
num = 2
while len(primes) < nth:
if isprime(num):
primes.append(num)
num += 1
else:
num += 1
return primes[len(primes) - 1]
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 7: https://projecteuler.net/problem=7
10001st prime
By listing the first six prime numbers: 2, 3, 5, 7, 11, and 13, we
can see that the 6th prime is 13.
What is the 10001st prime number?
References:
- https://en.wikipedia.org/wiki/Prime_number
"""
def isprime(number: int) -> bool:
"""
Determines whether the given number is prime or not
>>> isprime(2)
True
>>> isprime(15)
False
>>> isprime(29)
True
"""
for i in range(2, int(number ** 0.5) + 1):
if number % i == 0:
return False
return True
def solution(nth: int = 10001) -> int:
"""
Returns the n-th prime number.
>>> solution(6)
13
>>> solution(1)
2
>>> solution(3)
5
>>> solution(20)
71
>>> solution(50)
229
>>> solution(100)
541
>>> solution(3.4)
5
>>> solution(0)
Traceback (most recent call last):
...
ValueError: Parameter nth must be greater than or equal to one.
>>> solution(-17)
Traceback (most recent call last):
...
ValueError: Parameter nth must be greater than or equal to one.
>>> solution([])
Traceback (most recent call last):
...
TypeError: Parameter nth must be int or castable to int.
>>> solution("asd")
Traceback (most recent call last):
...
TypeError: Parameter nth must be int or castable to int.
"""
try:
nth = int(nth)
except (TypeError, ValueError):
raise TypeError("Parameter nth must be int or castable to int.") from None
if nth <= 0:
raise ValueError("Parameter nth must be greater than or equal to one.")
primes = []
num = 2
while len(primes) < nth:
if isprime(num):
primes.append(num)
num += 1
else:
num += 1
return primes[len(primes) - 1]
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 4,881 | bugfix: Add empty list detection for find_max/min | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [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.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| taseikyo | "2021-10-02T04:03:28Z" | "2021-10-07T15:20:32Z" | d324f91fe75cc859335ee1f7c9c6307d958d0558 | 77b243e62b09cdf6201916af6762b03c54d8f77a | bugfix: Add empty list detection for find_max/min. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [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.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Given an array-like data structure A[1..n], how many pairs
(i, j) for all 1 <= i < j <= n such that A[i] > A[j]? These pairs are
called inversions. Counting the number of such inversions in an array-like
object is the important. Among other things, counting inversions can help
us determine how close a given array is to being sorted
In this implementation, I provide two algorithms, a divide-and-conquer
algorithm which runs in nlogn and the brute-force n^2 algorithm.
"""
def count_inversions_bf(arr):
"""
Counts the number of inversions using a a naive brute-force algorithm
Parameters
----------
arr: arr: array-like, the list containing the items for which the number
of inversions is desired. The elements of `arr` must be comparable.
Returns
-------
num_inversions: The total number of inversions in `arr`
Examples
---------
>>> count_inversions_bf([1, 4, 2, 4, 1])
4
>>> count_inversions_bf([1, 1, 2, 4, 4])
0
>>> count_inversions_bf([])
0
"""
num_inversions = 0
n = len(arr)
for i in range(n - 1):
for j in range(i + 1, n):
if arr[i] > arr[j]:
num_inversions += 1
return num_inversions
def count_inversions_recursive(arr):
"""
Counts the number of inversions using a divide-and-conquer algorithm
Parameters
-----------
arr: array-like, the list containing the items for which the number
of inversions is desired. The elements of `arr` must be comparable.
Returns
-------
C: a sorted copy of `arr`.
num_inversions: int, the total number of inversions in 'arr'
Examples
--------
>>> count_inversions_recursive([1, 4, 2, 4, 1])
([1, 1, 2, 4, 4], 4)
>>> count_inversions_recursive([1, 1, 2, 4, 4])
([1, 1, 2, 4, 4], 0)
>>> count_inversions_recursive([])
([], 0)
"""
if len(arr) <= 1:
return arr, 0
else:
mid = len(arr) // 2
P = arr[0:mid]
Q = arr[mid:]
A, inversion_p = count_inversions_recursive(P)
B, inversions_q = count_inversions_recursive(Q)
C, cross_inversions = _count_cross_inversions(A, B)
num_inversions = inversion_p + inversions_q + cross_inversions
return C, num_inversions
def _count_cross_inversions(P, Q):
"""
Counts the inversions across two sorted arrays.
And combine the two arrays into one sorted array
For all 1<= i<=len(P) and for all 1 <= j <= len(Q),
if P[i] > Q[j], then (i, j) is a cross inversion
Parameters
----------
P: array-like, sorted in non-decreasing order
Q: array-like, sorted in non-decreasing order
Returns
------
R: array-like, a sorted array of the elements of `P` and `Q`
num_inversion: int, the number of inversions across `P` and `Q`
Examples
--------
>>> _count_cross_inversions([1, 2, 3], [0, 2, 5])
([0, 1, 2, 2, 3, 5], 4)
>>> _count_cross_inversions([1, 2, 3], [3, 4, 5])
([1, 2, 3, 3, 4, 5], 0)
"""
R = []
i = j = num_inversion = 0
while i < len(P) and j < len(Q):
if P[i] > Q[j]:
# if P[1] > Q[j], then P[k] > Q[k] for all i < k <= len(P)
# These are all inversions. The claim emerges from the
# property that P is sorted.
num_inversion += len(P) - i
R.append(Q[j])
j += 1
else:
R.append(P[i])
i += 1
if i < len(P):
R.extend(P[i:])
else:
R.extend(Q[j:])
return R, num_inversion
def main():
arr_1 = [10, 2, 1, 5, 5, 2, 11]
# this arr has 8 inversions:
# (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)
num_inversions_bf = count_inversions_bf(arr_1)
_, num_inversions_recursive = count_inversions_recursive(arr_1)
assert num_inversions_bf == num_inversions_recursive == 8
print("number of inversions = ", num_inversions_bf)
# testing an array with zero inversion (a sorted arr_1)
arr_1.sort()
num_inversions_bf = count_inversions_bf(arr_1)
_, num_inversions_recursive = count_inversions_recursive(arr_1)
assert num_inversions_bf == num_inversions_recursive == 0
print("number of inversions = ", num_inversions_bf)
# an empty list should also have zero inversions
arr_1 = []
num_inversions_bf = count_inversions_bf(arr_1)
_, num_inversions_recursive = count_inversions_recursive(arr_1)
assert num_inversions_bf == num_inversions_recursive == 0
print("number of inversions = ", num_inversions_bf)
if __name__ == "__main__":
main()
| """
Given an array-like data structure A[1..n], how many pairs
(i, j) for all 1 <= i < j <= n such that A[i] > A[j]? These pairs are
called inversions. Counting the number of such inversions in an array-like
object is the important. Among other things, counting inversions can help
us determine how close a given array is to being sorted
In this implementation, I provide two algorithms, a divide-and-conquer
algorithm which runs in nlogn and the brute-force n^2 algorithm.
"""
def count_inversions_bf(arr):
"""
Counts the number of inversions using a a naive brute-force algorithm
Parameters
----------
arr: arr: array-like, the list containing the items for which the number
of inversions is desired. The elements of `arr` must be comparable.
Returns
-------
num_inversions: The total number of inversions in `arr`
Examples
---------
>>> count_inversions_bf([1, 4, 2, 4, 1])
4
>>> count_inversions_bf([1, 1, 2, 4, 4])
0
>>> count_inversions_bf([])
0
"""
num_inversions = 0
n = len(arr)
for i in range(n - 1):
for j in range(i + 1, n):
if arr[i] > arr[j]:
num_inversions += 1
return num_inversions
def count_inversions_recursive(arr):
"""
Counts the number of inversions using a divide-and-conquer algorithm
Parameters
-----------
arr: array-like, the list containing the items for which the number
of inversions is desired. The elements of `arr` must be comparable.
Returns
-------
C: a sorted copy of `arr`.
num_inversions: int, the total number of inversions in 'arr'
Examples
--------
>>> count_inversions_recursive([1, 4, 2, 4, 1])
([1, 1, 2, 4, 4], 4)
>>> count_inversions_recursive([1, 1, 2, 4, 4])
([1, 1, 2, 4, 4], 0)
>>> count_inversions_recursive([])
([], 0)
"""
if len(arr) <= 1:
return arr, 0
else:
mid = len(arr) // 2
P = arr[0:mid]
Q = arr[mid:]
A, inversion_p = count_inversions_recursive(P)
B, inversions_q = count_inversions_recursive(Q)
C, cross_inversions = _count_cross_inversions(A, B)
num_inversions = inversion_p + inversions_q + cross_inversions
return C, num_inversions
def _count_cross_inversions(P, Q):
"""
Counts the inversions across two sorted arrays.
And combine the two arrays into one sorted array
For all 1<= i<=len(P) and for all 1 <= j <= len(Q),
if P[i] > Q[j], then (i, j) is a cross inversion
Parameters
----------
P: array-like, sorted in non-decreasing order
Q: array-like, sorted in non-decreasing order
Returns
------
R: array-like, a sorted array of the elements of `P` and `Q`
num_inversion: int, the number of inversions across `P` and `Q`
Examples
--------
>>> _count_cross_inversions([1, 2, 3], [0, 2, 5])
([0, 1, 2, 2, 3, 5], 4)
>>> _count_cross_inversions([1, 2, 3], [3, 4, 5])
([1, 2, 3, 3, 4, 5], 0)
"""
R = []
i = j = num_inversion = 0
while i < len(P) and j < len(Q):
if P[i] > Q[j]:
# if P[1] > Q[j], then P[k] > Q[k] for all i < k <= len(P)
# These are all inversions. The claim emerges from the
# property that P is sorted.
num_inversion += len(P) - i
R.append(Q[j])
j += 1
else:
R.append(P[i])
i += 1
if i < len(P):
R.extend(P[i:])
else:
R.extend(Q[j:])
return R, num_inversion
def main():
arr_1 = [10, 2, 1, 5, 5, 2, 11]
# this arr has 8 inversions:
# (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)
num_inversions_bf = count_inversions_bf(arr_1)
_, num_inversions_recursive = count_inversions_recursive(arr_1)
assert num_inversions_bf == num_inversions_recursive == 8
print("number of inversions = ", num_inversions_bf)
# testing an array with zero inversion (a sorted arr_1)
arr_1.sort()
num_inversions_bf = count_inversions_bf(arr_1)
_, num_inversions_recursive = count_inversions_recursive(arr_1)
assert num_inversions_bf == num_inversions_recursive == 0
print("number of inversions = ", num_inversions_bf)
# an empty list should also have zero inversions
arr_1 = []
num_inversions_bf = count_inversions_bf(arr_1)
_, num_inversions_recursive = count_inversions_recursive(arr_1)
assert num_inversions_bf == num_inversions_recursive == 0
print("number of inversions = ", num_inversions_bf)
if __name__ == "__main__":
main()
| -1 |
TheAlgorithms/Python | 4,881 | bugfix: Add empty list detection for find_max/min | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [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.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| taseikyo | "2021-10-02T04:03:28Z" | "2021-10-07T15:20:32Z" | d324f91fe75cc859335ee1f7c9c6307d958d0558 | 77b243e62b09cdf6201916af6762b03c54d8f77a | bugfix: Add empty list detection for find_max/min. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [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.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Lychrel numbers
Problem 55: https://projecteuler.net/problem=55
If we take 47, reverse and add, 47 + 74 = 121, which is palindromic.
Not all numbers produce palindromes so quickly. For example,
349 + 943 = 1292,
1292 + 2921 = 4213
4213 + 3124 = 7337
That is, 349 took three iterations to arrive at a palindrome.
Although no one has proved it yet, it is thought that some numbers, like 196,
never produce a palindrome. A number that never forms a palindrome through the
reverse and add process is called a Lychrel number. Due to the theoretical nature
of these numbers, and for the purpose of this problem, we shall assume that a number
is Lychrel until proven otherwise. In addition you are given that for every number
below ten-thousand, it will either (i) become a palindrome in less than fifty
iterations, or, (ii) no one, with all the computing power that exists, has managed
so far to map it to a palindrome. In fact, 10677 is the first number to be shown
to require over fifty iterations before producing a palindrome:
4668731596684224866951378664 (53 iterations, 28-digits).
Surprisingly, there are palindromic numbers that are themselves Lychrel numbers;
the first example is 4994.
How many Lychrel numbers are there below ten-thousand?
"""
def is_palindrome(n: int) -> bool:
"""
Returns True if a number is palindrome.
>>> is_palindrome(12567321)
False
>>> is_palindrome(1221)
True
>>> is_palindrome(9876789)
True
"""
return str(n) == str(n)[::-1]
def sum_reverse(n: int) -> int:
"""
Returns the sum of n and reverse of n.
>>> sum_reverse(123)
444
>>> sum_reverse(3478)
12221
>>> sum_reverse(12)
33
"""
return int(n) + int(str(n)[::-1])
def solution(limit: int = 10000) -> int:
"""
Returns the count of all lychrel numbers below limit.
>>> solution(10000)
249
>>> solution(5000)
76
>>> solution(1000)
13
"""
lychrel_nums = []
for num in range(1, limit):
iterations = 0
a = num
while iterations < 50:
num = sum_reverse(num)
iterations += 1
if is_palindrome(num):
break
else:
lychrel_nums.append(a)
return len(lychrel_nums)
if __name__ == "__main__":
print(f"{solution() = }")
| """
Lychrel numbers
Problem 55: https://projecteuler.net/problem=55
If we take 47, reverse and add, 47 + 74 = 121, which is palindromic.
Not all numbers produce palindromes so quickly. For example,
349 + 943 = 1292,
1292 + 2921 = 4213
4213 + 3124 = 7337
That is, 349 took three iterations to arrive at a palindrome.
Although no one has proved it yet, it is thought that some numbers, like 196,
never produce a palindrome. A number that never forms a palindrome through the
reverse and add process is called a Lychrel number. Due to the theoretical nature
of these numbers, and for the purpose of this problem, we shall assume that a number
is Lychrel until proven otherwise. In addition you are given that for every number
below ten-thousand, it will either (i) become a palindrome in less than fifty
iterations, or, (ii) no one, with all the computing power that exists, has managed
so far to map it to a palindrome. In fact, 10677 is the first number to be shown
to require over fifty iterations before producing a palindrome:
4668731596684224866951378664 (53 iterations, 28-digits).
Surprisingly, there are palindromic numbers that are themselves Lychrel numbers;
the first example is 4994.
How many Lychrel numbers are there below ten-thousand?
"""
def is_palindrome(n: int) -> bool:
"""
Returns True if a number is palindrome.
>>> is_palindrome(12567321)
False
>>> is_palindrome(1221)
True
>>> is_palindrome(9876789)
True
"""
return str(n) == str(n)[::-1]
def sum_reverse(n: int) -> int:
"""
Returns the sum of n and reverse of n.
>>> sum_reverse(123)
444
>>> sum_reverse(3478)
12221
>>> sum_reverse(12)
33
"""
return int(n) + int(str(n)[::-1])
def solution(limit: int = 10000) -> int:
"""
Returns the count of all lychrel numbers below limit.
>>> solution(10000)
249
>>> solution(5000)
76
>>> solution(1000)
13
"""
lychrel_nums = []
for num in range(1, limit):
iterations = 0
a = num
while iterations < 50:
num = sum_reverse(num)
iterations += 1
if is_palindrome(num):
break
else:
lychrel_nums.append(a)
return len(lychrel_nums)
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 4,881 | bugfix: Add empty list detection for find_max/min | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [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.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| taseikyo | "2021-10-02T04:03:28Z" | "2021-10-07T15:20:32Z" | d324f91fe75cc859335ee1f7c9c6307d958d0558 | 77b243e62b09cdf6201916af6762b03c54d8f77a | bugfix: Add empty list detection for find_max/min. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [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.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """https://en.wikipedia.org/wiki/Jaro%E2%80%93Winkler_distance"""
def jaro_winkler(str1: str, str2: str) -> float:
"""
Jaro–Winkler distance is a string metric measuring an edit distance between two
sequences.
Output value is between 0.0 and 1.0.
>>> jaro_winkler("martha", "marhta")
0.9611111111111111
>>> jaro_winkler("CRATE", "TRACE")
0.7333333333333334
>>> jaro_winkler("test", "dbdbdbdb")
0.0
>>> jaro_winkler("test", "test")
1.0
>>> jaro_winkler("hello world", "HeLLo W0rlD")
0.6363636363636364
>>> jaro_winkler("test", "")
0.0
>>> jaro_winkler("hello", "world")
0.4666666666666666
>>> jaro_winkler("hell**o", "*world")
0.4365079365079365
"""
def get_matched_characters(_str1: str, _str2: str) -> str:
matched = []
limit = min(len(_str1), len(_str2)) // 2
for i, l in enumerate(_str1):
left = int(max(0, i - limit))
right = int(min(i + limit + 1, len(_str2)))
if l in _str2[left:right]:
matched.append(l)
_str2 = f"{_str2[0:_str2.index(l)]} {_str2[_str2.index(l) + 1:]}"
return "".join(matched)
# matching characters
matching_1 = get_matched_characters(str1, str2)
matching_2 = get_matched_characters(str2, str1)
match_count = len(matching_1)
# transposition
transpositions = (
len([(c1, c2) for c1, c2 in zip(matching_1, matching_2) if c1 != c2]) // 2
)
if not match_count:
jaro = 0.0
else:
jaro = (
1
/ 3
* (
match_count / len(str1)
+ match_count / len(str2)
+ (match_count - transpositions) / match_count
)
)
# common prefix up to 4 characters
prefix_len = 0
for c1, c2 in zip(str1[:4], str2[:4]):
if c1 == c2:
prefix_len += 1
else:
break
return jaro + 0.1 * prefix_len * (1 - jaro)
if __name__ == "__main__":
import doctest
doctest.testmod()
print(jaro_winkler("hello", "world"))
| """https://en.wikipedia.org/wiki/Jaro%E2%80%93Winkler_distance"""
def jaro_winkler(str1: str, str2: str) -> float:
"""
Jaro–Winkler distance is a string metric measuring an edit distance between two
sequences.
Output value is between 0.0 and 1.0.
>>> jaro_winkler("martha", "marhta")
0.9611111111111111
>>> jaro_winkler("CRATE", "TRACE")
0.7333333333333334
>>> jaro_winkler("test", "dbdbdbdb")
0.0
>>> jaro_winkler("test", "test")
1.0
>>> jaro_winkler("hello world", "HeLLo W0rlD")
0.6363636363636364
>>> jaro_winkler("test", "")
0.0
>>> jaro_winkler("hello", "world")
0.4666666666666666
>>> jaro_winkler("hell**o", "*world")
0.4365079365079365
"""
def get_matched_characters(_str1: str, _str2: str) -> str:
matched = []
limit = min(len(_str1), len(_str2)) // 2
for i, l in enumerate(_str1):
left = int(max(0, i - limit))
right = int(min(i + limit + 1, len(_str2)))
if l in _str2[left:right]:
matched.append(l)
_str2 = f"{_str2[0:_str2.index(l)]} {_str2[_str2.index(l) + 1:]}"
return "".join(matched)
# matching characters
matching_1 = get_matched_characters(str1, str2)
matching_2 = get_matched_characters(str2, str1)
match_count = len(matching_1)
# transposition
transpositions = (
len([(c1, c2) for c1, c2 in zip(matching_1, matching_2) if c1 != c2]) // 2
)
if not match_count:
jaro = 0.0
else:
jaro = (
1
/ 3
* (
match_count / len(str1)
+ match_count / len(str2)
+ (match_count - transpositions) / match_count
)
)
# common prefix up to 4 characters
prefix_len = 0
for c1, c2 in zip(str1[:4], str2[:4]):
if c1 == c2:
prefix_len += 1
else:
break
return jaro + 0.1 * prefix_len * (1 - jaro)
if __name__ == "__main__":
import doctest
doctest.testmod()
print(jaro_winkler("hello", "world"))
| -1 |
TheAlgorithms/Python | 4,881 | bugfix: Add empty list detection for find_max/min | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [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.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| taseikyo | "2021-10-02T04:03:28Z" | "2021-10-07T15:20:32Z" | d324f91fe75cc859335ee1f7c9c6307d958d0558 | 77b243e62b09cdf6201916af6762b03c54d8f77a | bugfix: Add empty list detection for find_max/min. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [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.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
In a multi-threaded download, this algorithm could be used to provide
each worker thread with a block of non-overlapping bytes to download.
For example:
for i in allocation_list:
requests.get(url,headers={'Range':f'bytes={i}'})
"""
from __future__ import annotations
def allocation_num(number_of_bytes: int, partitions: int) -> list[str]:
"""
Divide a number of bytes into x partitions.
:param number_of_bytes: the total of bytes.
:param partitions: the number of partition need to be allocated.
:return: list of bytes to be assigned to each worker thread
>>> allocation_num(16647, 4)
['1-4161', '4162-8322', '8323-12483', '12484-16647']
>>> allocation_num(50000, 5)
['1-10000', '10001-20000', '20001-30000', '30001-40000', '40001-50000']
>>> allocation_num(888, 999)
Traceback (most recent call last):
...
ValueError: partitions can not > number_of_bytes!
>>> allocation_num(888, -4)
Traceback (most recent call last):
...
ValueError: partitions must be a positive number!
"""
if partitions <= 0:
raise ValueError("partitions must be a positive number!")
if partitions > number_of_bytes:
raise ValueError("partitions can not > number_of_bytes!")
bytes_per_partition = number_of_bytes // partitions
allocation_list = []
for i in range(partitions):
start_bytes = i * bytes_per_partition + 1
end_bytes = (
number_of_bytes if i == partitions - 1 else (i + 1) * bytes_per_partition
)
allocation_list.append(f"{start_bytes}-{end_bytes}")
return allocation_list
if __name__ == "__main__":
import doctest
doctest.testmod()
| """
In a multi-threaded download, this algorithm could be used to provide
each worker thread with a block of non-overlapping bytes to download.
For example:
for i in allocation_list:
requests.get(url,headers={'Range':f'bytes={i}'})
"""
from __future__ import annotations
def allocation_num(number_of_bytes: int, partitions: int) -> list[str]:
"""
Divide a number of bytes into x partitions.
:param number_of_bytes: the total of bytes.
:param partitions: the number of partition need to be allocated.
:return: list of bytes to be assigned to each worker thread
>>> allocation_num(16647, 4)
['1-4161', '4162-8322', '8323-12483', '12484-16647']
>>> allocation_num(50000, 5)
['1-10000', '10001-20000', '20001-30000', '30001-40000', '40001-50000']
>>> allocation_num(888, 999)
Traceback (most recent call last):
...
ValueError: partitions can not > number_of_bytes!
>>> allocation_num(888, -4)
Traceback (most recent call last):
...
ValueError: partitions must be a positive number!
"""
if partitions <= 0:
raise ValueError("partitions must be a positive number!")
if partitions > number_of_bytes:
raise ValueError("partitions can not > number_of_bytes!")
bytes_per_partition = number_of_bytes // partitions
allocation_list = []
for i in range(partitions):
start_bytes = i * bytes_per_partition + 1
end_bytes = (
number_of_bytes if i == partitions - 1 else (i + 1) * bytes_per_partition
)
allocation_list.append(f"{start_bytes}-{end_bytes}")
return allocation_list
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 4,881 | bugfix: Add empty list detection for find_max/min | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [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.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| taseikyo | "2021-10-02T04:03:28Z" | "2021-10-07T15:20:32Z" | d324f91fe75cc859335ee1f7c9c6307d958d0558 | 77b243e62b09cdf6201916af6762b03c54d8f77a | bugfix: Add empty list detection for find_max/min. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [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.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # Python program to implement Morse Code Translator
# Dictionary representing the morse code chart
MORSE_CODE_DICT = {
"A": ".-",
"B": "-...",
"C": "-.-.",
"D": "-..",
"E": ".",
"F": "..-.",
"G": "--.",
"H": "....",
"I": "..",
"J": ".---",
"K": "-.-",
"L": ".-..",
"M": "--",
"N": "-.",
"O": "---",
"P": ".--.",
"Q": "--.-",
"R": ".-.",
"S": "...",
"T": "-",
"U": "..-",
"V": "...-",
"W": ".--",
"X": "-..-",
"Y": "-.--",
"Z": "--..",
"1": ".----",
"2": "..---",
"3": "...--",
"4": "....-",
"5": ".....",
"6": "-....",
"7": "--...",
"8": "---..",
"9": "----.",
"0": "-----",
"&": ".-...",
"@": ".--.-.",
":": "---...",
",": "--..--",
".": ".-.-.-",
"'": ".----.",
'"': ".-..-.",
"?": "..--..",
"/": "-..-.",
"=": "-...-",
"+": ".-.-.",
"-": "-....-",
"(": "-.--.",
")": "-.--.-",
# Exclamation mark is not in ITU-R recommendation
"!": "-.-.--",
}
def encrypt(message: str) -> str:
cipher = ""
for letter in message:
if letter != " ":
cipher += MORSE_CODE_DICT[letter] + " "
else:
cipher += "/ "
# Remove trailing space added on line 64
return cipher[:-1]
def decrypt(message: str) -> str:
decipher = ""
letters = message.split(" ")
for letter in letters:
if letter != "/":
decipher += list(MORSE_CODE_DICT.keys())[
list(MORSE_CODE_DICT.values()).index(letter)
]
else:
decipher += " "
return decipher
def main() -> None:
message = "Morse code here"
result = encrypt(message.upper())
print(result)
message = result
result = decrypt(message)
print(result)
if __name__ == "__main__":
main()
| # Python program to implement Morse Code Translator
# Dictionary representing the morse code chart
MORSE_CODE_DICT = {
"A": ".-",
"B": "-...",
"C": "-.-.",
"D": "-..",
"E": ".",
"F": "..-.",
"G": "--.",
"H": "....",
"I": "..",
"J": ".---",
"K": "-.-",
"L": ".-..",
"M": "--",
"N": "-.",
"O": "---",
"P": ".--.",
"Q": "--.-",
"R": ".-.",
"S": "...",
"T": "-",
"U": "..-",
"V": "...-",
"W": ".--",
"X": "-..-",
"Y": "-.--",
"Z": "--..",
"1": ".----",
"2": "..---",
"3": "...--",
"4": "....-",
"5": ".....",
"6": "-....",
"7": "--...",
"8": "---..",
"9": "----.",
"0": "-----",
"&": ".-...",
"@": ".--.-.",
":": "---...",
",": "--..--",
".": ".-.-.-",
"'": ".----.",
'"': ".-..-.",
"?": "..--..",
"/": "-..-.",
"=": "-...-",
"+": ".-.-.",
"-": "-....-",
"(": "-.--.",
")": "-.--.-",
# Exclamation mark is not in ITU-R recommendation
"!": "-.-.--",
}
def encrypt(message: str) -> str:
cipher = ""
for letter in message:
if letter != " ":
cipher += MORSE_CODE_DICT[letter] + " "
else:
cipher += "/ "
# Remove trailing space added on line 64
return cipher[:-1]
def decrypt(message: str) -> str:
decipher = ""
letters = message.split(" ")
for letter in letters:
if letter != "/":
decipher += list(MORSE_CODE_DICT.keys())[
list(MORSE_CODE_DICT.values()).index(letter)
]
else:
decipher += " "
return decipher
def main() -> None:
message = "Morse code here"
result = encrypt(message.upper())
print(result)
message = result
result = decrypt(message)
print(result)
if __name__ == "__main__":
main()
| -1 |
TheAlgorithms/Python | 4,881 | bugfix: Add empty list detection for find_max/min | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [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.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| taseikyo | "2021-10-02T04:03:28Z" | "2021-10-07T15:20:32Z" | d324f91fe75cc859335ee1f7c9c6307d958d0558 | 77b243e62b09cdf6201916af6762b03c54d8f77a | bugfix: Add empty list detection for find_max/min. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [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.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # https://en.wikipedia.org/wiki/Simulated_annealing
import math
import random
from .hill_climbing import SearchProblem
def simulated_annealing(
search_prob,
find_max: bool = True,
max_x: float = math.inf,
min_x: float = -math.inf,
max_y: float = math.inf,
min_y: float = -math.inf,
visualization: bool = False,
start_temperate: float = 100,
rate_of_decrease: float = 0.01,
threshold_temp: float = 1,
) -> SearchProblem:
"""
Implementation of the simulated annealing algorithm. We start with a given state,
find all its neighbors. Pick a random neighbor, if that neighbor improves the
solution, we move in that direction, if that neighbor does not improve the solution,
we generate a random real number between 0 and 1, if the number is within a certain
range (calculated using temperature) we move in that direction, else we pick
another neighbor randomly and repeat the process.
Args:
search_prob: The search state at the start.
find_max: If True, the algorithm should find the minimum else the minimum.
max_x, min_x, max_y, min_y: the maximum and minimum bounds of x and y.
visualization: If True, a matplotlib graph is displayed.
start_temperate: the initial temperate of the system when the program starts.
rate_of_decrease: the rate at which the temperate decreases in each iteration.
threshold_temp: the threshold temperature below which we end the search
Returns a search state having the maximum (or minimum) score.
"""
search_end = False
current_state = search_prob
current_temp = start_temperate
scores = []
iterations = 0
best_state = None
while not search_end:
current_score = current_state.score()
if best_state is None or current_score > best_state.score():
best_state = current_state
scores.append(current_score)
iterations += 1
next_state = None
neighbors = current_state.get_neighbors()
while (
next_state is None and neighbors
): # till we do not find a neighbor that we can move to
index = random.randint(0, len(neighbors) - 1) # picking a random neighbor
picked_neighbor = neighbors.pop(index)
change = picked_neighbor.score() - current_score
if (
picked_neighbor.x > max_x
or picked_neighbor.x < min_x
or picked_neighbor.y > max_y
or picked_neighbor.y < min_y
):
continue # neighbor outside our bounds
if not find_max:
change = change * -1 # in case we are finding minimum
if change > 0: # improves the solution
next_state = picked_neighbor
else:
probability = (math.e) ** (
change / current_temp
) # probability generation function
if random.random() < probability: # random number within probability
next_state = picked_neighbor
current_temp = current_temp - (current_temp * rate_of_decrease)
if current_temp < threshold_temp or next_state is None:
# temperature below threshold, or could not find a suitable neighbor
search_end = True
else:
current_state = next_state
if visualization:
from matplotlib import pyplot as plt
plt.plot(range(iterations), scores)
plt.xlabel("Iterations")
plt.ylabel("Function values")
plt.show()
return best_state
if __name__ == "__main__":
def test_f1(x, y):
return (x ** 2) + (y ** 2)
# starting the problem with initial coordinates (12, 47)
prob = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_f1)
local_min = simulated_annealing(
prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
)
print(
"The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 "
f"and 50 > y > - 5 found via hill climbing: {local_min.score()}"
)
# starting the problem with initial coordinates (12, 47)
prob = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_f1)
local_min = simulated_annealing(
prob, find_max=True, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
)
print(
"The maximum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 "
f"and 50 > y > - 5 found via hill climbing: {local_min.score()}"
)
def test_f2(x, y):
return (3 * x ** 2) - (6 * y)
prob = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_f1)
local_min = simulated_annealing(prob, find_max=False, visualization=True)
print(
"The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: "
f"{local_min.score()}"
)
prob = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_f1)
local_min = simulated_annealing(prob, find_max=True, visualization=True)
print(
"The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: "
f"{local_min.score()}"
)
| # https://en.wikipedia.org/wiki/Simulated_annealing
import math
import random
from .hill_climbing import SearchProblem
def simulated_annealing(
search_prob,
find_max: bool = True,
max_x: float = math.inf,
min_x: float = -math.inf,
max_y: float = math.inf,
min_y: float = -math.inf,
visualization: bool = False,
start_temperate: float = 100,
rate_of_decrease: float = 0.01,
threshold_temp: float = 1,
) -> SearchProblem:
"""
Implementation of the simulated annealing algorithm. We start with a given state,
find all its neighbors. Pick a random neighbor, if that neighbor improves the
solution, we move in that direction, if that neighbor does not improve the solution,
we generate a random real number between 0 and 1, if the number is within a certain
range (calculated using temperature) we move in that direction, else we pick
another neighbor randomly and repeat the process.
Args:
search_prob: The search state at the start.
find_max: If True, the algorithm should find the minimum else the minimum.
max_x, min_x, max_y, min_y: the maximum and minimum bounds of x and y.
visualization: If True, a matplotlib graph is displayed.
start_temperate: the initial temperate of the system when the program starts.
rate_of_decrease: the rate at which the temperate decreases in each iteration.
threshold_temp: the threshold temperature below which we end the search
Returns a search state having the maximum (or minimum) score.
"""
search_end = False
current_state = search_prob
current_temp = start_temperate
scores = []
iterations = 0
best_state = None
while not search_end:
current_score = current_state.score()
if best_state is None or current_score > best_state.score():
best_state = current_state
scores.append(current_score)
iterations += 1
next_state = None
neighbors = current_state.get_neighbors()
while (
next_state is None and neighbors
): # till we do not find a neighbor that we can move to
index = random.randint(0, len(neighbors) - 1) # picking a random neighbor
picked_neighbor = neighbors.pop(index)
change = picked_neighbor.score() - current_score
if (
picked_neighbor.x > max_x
or picked_neighbor.x < min_x
or picked_neighbor.y > max_y
or picked_neighbor.y < min_y
):
continue # neighbor outside our bounds
if not find_max:
change = change * -1 # in case we are finding minimum
if change > 0: # improves the solution
next_state = picked_neighbor
else:
probability = (math.e) ** (
change / current_temp
) # probability generation function
if random.random() < probability: # random number within probability
next_state = picked_neighbor
current_temp = current_temp - (current_temp * rate_of_decrease)
if current_temp < threshold_temp or next_state is None:
# temperature below threshold, or could not find a suitable neighbor
search_end = True
else:
current_state = next_state
if visualization:
from matplotlib import pyplot as plt
plt.plot(range(iterations), scores)
plt.xlabel("Iterations")
plt.ylabel("Function values")
plt.show()
return best_state
if __name__ == "__main__":
def test_f1(x, y):
return (x ** 2) + (y ** 2)
# starting the problem with initial coordinates (12, 47)
prob = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_f1)
local_min = simulated_annealing(
prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
)
print(
"The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 "
f"and 50 > y > - 5 found via hill climbing: {local_min.score()}"
)
# starting the problem with initial coordinates (12, 47)
prob = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_f1)
local_min = simulated_annealing(
prob, find_max=True, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
)
print(
"The maximum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 "
f"and 50 > y > - 5 found via hill climbing: {local_min.score()}"
)
def test_f2(x, y):
return (3 * x ** 2) - (6 * y)
prob = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_f1)
local_min = simulated_annealing(prob, find_max=False, visualization=True)
print(
"The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: "
f"{local_min.score()}"
)
prob = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_f1)
local_min = simulated_annealing(prob, find_max=True, visualization=True)
print(
"The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: "
f"{local_min.score()}"
)
| -1 |
TheAlgorithms/Python | 4,881 | bugfix: Add empty list detection for find_max/min | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [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.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| taseikyo | "2021-10-02T04:03:28Z" | "2021-10-07T15:20:32Z" | d324f91fe75cc859335ee1f7c9c6307d958d0558 | 77b243e62b09cdf6201916af6762b03c54d8f77a | bugfix: Add empty list detection for find_max/min. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [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.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,881 | bugfix: Add empty list detection for find_max/min | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| taseikyo | "2021-10-02T04:03:28Z" | "2021-10-07T15:20:32Z" | d324f91fe75cc859335ee1f7c9c6307d958d0558 | 77b243e62b09cdf6201916af6762b03c54d8f77a | bugfix: Add empty list detection for find_max/min. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [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.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from collections import defaultdict
from graphs.minimum_spanning_tree_prims import PrimsAlgorithm as mst
def test_prim_successful_result():
num_nodes, num_edges = 9, 14 # noqa: F841
edges = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
[8, 6, 6],
[2, 3, 7],
[2, 5, 4],
[6, 5, 2],
[3, 5, 14],
[3, 4, 9],
[5, 4, 10],
[1, 7, 11],
]
adjancency = defaultdict(list)
for node1, node2, cost in edges:
adjancency[node1].append([node2, cost])
adjancency[node2].append([node1, cost])
result = mst(adjancency)
expected = [
[7, 6, 1],
[2, 8, 2],
[6, 5, 2],
[0, 1, 4],
[2, 5, 4],
[2, 3, 7],
[0, 7, 8],
[3, 4, 9],
]
for answer in expected:
edge = tuple(answer[:2])
reverse = tuple(edge[::-1])
assert edge in result or reverse in result
| from collections import defaultdict
from graphs.minimum_spanning_tree_prims import PrimsAlgorithm as mst
def test_prim_successful_result():
num_nodes, num_edges = 9, 14 # noqa: F841
edges = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
[8, 6, 6],
[2, 3, 7],
[2, 5, 4],
[6, 5, 2],
[3, 5, 14],
[3, 4, 9],
[5, 4, 10],
[1, 7, 11],
]
adjancency = defaultdict(list)
for node1, node2, cost in edges:
adjancency[node1].append([node2, cost])
adjancency[node2].append([node1, cost])
result = mst(adjancency)
expected = [
[7, 6, 1],
[2, 8, 2],
[6, 5, 2],
[0, 1, 4],
[2, 5, 4],
[2, 3, 7],
[0, 7, 8],
[3, 4, 9],
]
for answer in expected:
edge = tuple(answer[:2])
reverse = tuple(edge[::-1])
assert edge in result or reverse in result
| -1 |
TheAlgorithms/Python | 4,881 | bugfix: Add empty list detection for find_max/min | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [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.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| taseikyo | "2021-10-02T04:03:28Z" | "2021-10-07T15:20:32Z" | d324f91fe75cc859335ee1f7c9c6307d958d0558 | 77b243e62b09cdf6201916af6762b03c54d8f77a | bugfix: Add empty list detection for find_max/min. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [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.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Champernowne's constant
Problem 40
An irrational decimal fraction is created by concatenating the positive
integers:
0.123456789101112131415161718192021...
It can be seen that the 12th digit of the fractional part is 1.
If dn represents the nth digit of the fractional part, find the value of the
following expression.
d1 × d10 × d100 × d1000 × d10000 × d100000 × d1000000
"""
def solution():
"""Returns
>>> solution()
210
"""
constant = []
i = 1
while len(constant) < 1e6:
constant.append(str(i))
i += 1
constant = "".join(constant)
return (
int(constant[0])
* int(constant[9])
* int(constant[99])
* int(constant[999])
* int(constant[9999])
* int(constant[99999])
* int(constant[999999])
)
if __name__ == "__main__":
print(solution())
| """
Champernowne's constant
Problem 40
An irrational decimal fraction is created by concatenating the positive
integers:
0.123456789101112131415161718192021...
It can be seen that the 12th digit of the fractional part is 1.
If dn represents the nth digit of the fractional part, find the value of the
following expression.
d1 × d10 × d100 × d1000 × d10000 × d100000 × d1000000
"""
def solution():
"""Returns
>>> solution()
210
"""
constant = []
i = 1
while len(constant) < 1e6:
constant.append(str(i))
i += 1
constant = "".join(constant)
return (
int(constant[0])
* int(constant[9])
* int(constant[99])
* int(constant[999])
* int(constant[9999])
* int(constant[99999])
* int(constant[999999])
)
if __name__ == "__main__":
print(solution())
| -1 |
TheAlgorithms/Python | 4,881 | bugfix: Add empty list detection for find_max/min | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [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.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| taseikyo | "2021-10-02T04:03:28Z" | "2021-10-07T15:20:32Z" | d324f91fe75cc859335ee1f7c9c6307d958d0558 | 77b243e62b09cdf6201916af6762b03c54d8f77a | bugfix: Add empty list detection for find_max/min. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [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.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Project Euler Problem 8: https://projecteuler.net/problem=8
Largest product in a series
The four adjacent digits in the 1000-digit number that have the greatest
product are 9 × 9 × 8 × 9 = 5832.
73167176531330624919225119674426574742355349194934
96983520312774506326239578318016984801869478851843
85861560789112949495459501737958331952853208805511
12540698747158523863050715693290963295227443043557
66896648950445244523161731856403098711121722383113
62229893423380308135336276614282806444486645238749
30358907296290491560440772390713810515859307960866
70172427121883998797908792274921901699720888093776
65727333001053367881220235421809751254540594752243
52584907711670556013604839586446706324415722155397
53697817977846174064955149290862569321978468622482
83972241375657056057490261407972968652414535100474
82166370484403199890008895243450658541227588666881
16427171479924442928230863465674813919123162824586
17866458359124566529476545682848912883142607690042
24219022671055626321111109370544217506941658960408
07198403850962455444362981230987879927244284909188
84580156166097919133875499200524063689912560717606
05886116467109405077541002256983155200055935729725
71636269561882670428252483600823257530420752963450
Find the thirteen adjacent digits in the 1000-digit number that have the
greatest product. What is the value of this product?
"""
from functools import reduce
N = (
"73167176531330624919225119674426574742355349194934"
"96983520312774506326239578318016984801869478851843"
"85861560789112949495459501737958331952853208805511"
"12540698747158523863050715693290963295227443043557"
"66896648950445244523161731856403098711121722383113"
"62229893423380308135336276614282806444486645238749"
"30358907296290491560440772390713810515859307960866"
"70172427121883998797908792274921901699720888093776"
"65727333001053367881220235421809751254540594752243"
"52584907711670556013604839586446706324415722155397"
"53697817977846174064955149290862569321978468622482"
"83972241375657056057490261407972968652414535100474"
"82166370484403199890008895243450658541227588666881"
"16427171479924442928230863465674813919123162824586"
"17866458359124566529476545682848912883142607690042"
"24219022671055626321111109370544217506941658960408"
"07198403850962455444362981230987879927244284909188"
"84580156166097919133875499200524063689912560717606"
"05886116467109405077541002256983155200055935729725"
"71636269561882670428252483600823257530420752963450"
)
def solution(n: str = N) -> int:
"""
Find the thirteen adjacent digits in the 1000-digit number n that have
the greatest product and returns it.
>>> solution("13978431290823798458352374")
609638400
>>> solution("13978431295823798458352374")
2612736000
>>> solution("1397843129582379841238352374")
209018880
"""
return max(
reduce(lambda x, y: int(x) * int(y), n[i : i + 13]) for i in range(len(n) - 12)
)
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 8: https://projecteuler.net/problem=8
Largest product in a series
The four adjacent digits in the 1000-digit number that have the greatest
product are 9 × 9 × 8 × 9 = 5832.
73167176531330624919225119674426574742355349194934
96983520312774506326239578318016984801869478851843
85861560789112949495459501737958331952853208805511
12540698747158523863050715693290963295227443043557
66896648950445244523161731856403098711121722383113
62229893423380308135336276614282806444486645238749
30358907296290491560440772390713810515859307960866
70172427121883998797908792274921901699720888093776
65727333001053367881220235421809751254540594752243
52584907711670556013604839586446706324415722155397
53697817977846174064955149290862569321978468622482
83972241375657056057490261407972968652414535100474
82166370484403199890008895243450658541227588666881
16427171479924442928230863465674813919123162824586
17866458359124566529476545682848912883142607690042
24219022671055626321111109370544217506941658960408
07198403850962455444362981230987879927244284909188
84580156166097919133875499200524063689912560717606
05886116467109405077541002256983155200055935729725
71636269561882670428252483600823257530420752963450
Find the thirteen adjacent digits in the 1000-digit number that have the
greatest product. What is the value of this product?
"""
from functools import reduce
N = (
"73167176531330624919225119674426574742355349194934"
"96983520312774506326239578318016984801869478851843"
"85861560789112949495459501737958331952853208805511"
"12540698747158523863050715693290963295227443043557"
"66896648950445244523161731856403098711121722383113"
"62229893423380308135336276614282806444486645238749"
"30358907296290491560440772390713810515859307960866"
"70172427121883998797908792274921901699720888093776"
"65727333001053367881220235421809751254540594752243"
"52584907711670556013604839586446706324415722155397"
"53697817977846174064955149290862569321978468622482"
"83972241375657056057490261407972968652414535100474"
"82166370484403199890008895243450658541227588666881"
"16427171479924442928230863465674813919123162824586"
"17866458359124566529476545682848912883142607690042"
"24219022671055626321111109370544217506941658960408"
"07198403850962455444362981230987879927244284909188"
"84580156166097919133875499200524063689912560717606"
"05886116467109405077541002256983155200055935729725"
"71636269561882670428252483600823257530420752963450"
)
def solution(n: str = N) -> int:
"""
Find the thirteen adjacent digits in the 1000-digit number n that have
the greatest product and returns it.
>>> solution("13978431290823798458352374")
609638400
>>> solution("13978431295823798458352374")
2612736000
>>> solution("1397843129582379841238352374")
209018880
"""
return max(
reduce(lambda x, y: int(x) * int(y), n[i : i + 13]) for i in range(len(n) - 12)
)
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 4,881 | bugfix: Add empty list detection for find_max/min | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [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.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| taseikyo | "2021-10-02T04:03:28Z" | "2021-10-07T15:20:32Z" | d324f91fe75cc859335ee1f7c9c6307d958d0558 | 77b243e62b09cdf6201916af6762b03c54d8f77a | bugfix: Add empty list detection for find_max/min. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [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.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,881 | bugfix: Add empty list detection for find_max/min | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| taseikyo | "2021-10-02T04:03:28Z" | "2021-10-07T15:20:32Z" | d324f91fe75cc859335ee1f7c9c6307d958d0558 | 77b243e62b09cdf6201916af6762b03c54d8f77a | bugfix: Add empty list detection for find_max/min. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [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.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Project Euler Problem 85: https://projecteuler.net/problem=85
By counting carefully it can be seen that a rectangular grid measuring 3 by 2
contains eighteen rectangles.

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

Although there exists no rectangular grid that contains exactly two million
rectangles, find the area of the grid with the nearest solution.
Solution:
For a grid with side-lengths a and b, the number of rectangles contained in the grid
is [a*(a+1)/2] * [b*(b+1)/2)], which happens to be the product of the a-th and b-th
triangle numbers. So to find the solution grid (a,b), we need to find the two
triangle numbers whose product is closest to two million.
Denote these two triangle numbers Ta and Tb. We want their product Ta*Tb to be
as close as possible to 2m. Assuming that the best solution is fairly close to 2m,
We can assume that both Ta and Tb are roughly bounded by 2m. Since Ta = a(a+1)/2,
we can assume that a (and similarly b) are roughly bounded by sqrt(2 * 2m) = 2000.
Since this is a rough bound, to be on the safe side we add 10%. Therefore we start
by generating all the triangle numbers Ta for 1 <= a <= 2200. This can be done
iteratively since the ith triangle number is the sum of 1,2, ... ,i, and so
T(i) = T(i-1) + i.
We then search this list of triangle numbers for the two that give a product
closest to our target of two million. Rather than testing every combination of 2
elements of the list, which would find the result in quadratic time, we can find
the best pair in linear time.
We iterate through the list of triangle numbers using enumerate() so we have a
and Ta. Since we want Ta * Tb to be as close as possible to 2m, we know that Tb
needs to be roughly 2m / Ta. Using the formula Tb = b*(b+1)/2 as well as the
quadratic formula, we can solve for b:
b is roughly (-1 + sqrt(1 + 8 * 2m / Ta)) / 2.
Since the closest integers to this estimate will give product closest to 2m,
we only need to consider the integers above and below. It's then a simple matter
to get the triangle numbers corresponding to those integers, calculate the product
Ta * Tb, compare that product to our target 2m, and keep track of the (a,b) pair
that comes the closest.
Reference: https://en.wikipedia.org/wiki/Triangular_number
https://en.wikipedia.org/wiki/Quadratic_formula
"""
from __future__ import annotations
from math import ceil, floor, sqrt
def solution(target: int = 2000000) -> int:
"""
Find the area of the grid which contains as close to two million rectangles
as possible.
>>> solution(20)
6
>>> solution(2000)
72
>>> solution(2000000000)
86595
"""
triangle_numbers: list[int] = [0]
idx: int
for idx in range(1, ceil(sqrt(target * 2) * 1.1)):
triangle_numbers.append(triangle_numbers[-1] + idx)
# we want this to be as close as possible to target
best_product: int = 0
# the area corresponding to the grid that gives the product closest to target
area: int = 0
# an estimate of b, using the quadratic formula
b_estimate: float
# the largest integer less than b_estimate
b_floor: int
# the largest integer less than b_estimate
b_ceil: int
# the triangle number corresponding to b_floor
triangle_b_first_guess: int
# the triangle number corresponding to b_ceil
triangle_b_second_guess: int
for idx_a, triangle_a in enumerate(triangle_numbers[1:], 1):
b_estimate = (-1 + sqrt(1 + 8 * target / triangle_a)) / 2
b_floor = floor(b_estimate)
b_ceil = ceil(b_estimate)
triangle_b_first_guess = triangle_numbers[b_floor]
triangle_b_second_guess = triangle_numbers[b_ceil]
if abs(target - triangle_b_first_guess * triangle_a) < abs(
target - best_product
):
best_product = triangle_b_first_guess * triangle_a
area = idx_a * b_floor
if abs(target - triangle_b_second_guess * triangle_a) < abs(
target - best_product
):
best_product = triangle_b_second_guess * triangle_a
area = idx_a * b_ceil
return area
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 4,881 | bugfix: Add empty list detection for find_max/min | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [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.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| taseikyo | "2021-10-02T04:03:28Z" | "2021-10-07T15:20:32Z" | d324f91fe75cc859335ee1f7c9c6307d958d0558 | 77b243e62b09cdf6201916af6762b03c54d8f77a | bugfix: Add empty list detection for find_max/min. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Project Euler Problem 8: https://projecteuler.net/problem=8
Largest product in a series
The four adjacent digits in the 1000-digit number that have the greatest
product are 9 × 9 × 8 × 9 = 5832.
73167176531330624919225119674426574742355349194934
96983520312774506326239578318016984801869478851843
85861560789112949495459501737958331952853208805511
12540698747158523863050715693290963295227443043557
66896648950445244523161731856403098711121722383113
62229893423380308135336276614282806444486645238749
30358907296290491560440772390713810515859307960866
70172427121883998797908792274921901699720888093776
65727333001053367881220235421809751254540594752243
52584907711670556013604839586446706324415722155397
53697817977846174064955149290862569321978468622482
83972241375657056057490261407972968652414535100474
82166370484403199890008895243450658541227588666881
16427171479924442928230863465674813919123162824586
17866458359124566529476545682848912883142607690042
24219022671055626321111109370544217506941658960408
07198403850962455444362981230987879927244284909188
84580156166097919133875499200524063689912560717606
05886116467109405077541002256983155200055935729725
71636269561882670428252483600823257530420752963450
Find the thirteen adjacent digits in the 1000-digit number that have the
greatest product. What is the value of this product?
"""
import sys
N = """73167176531330624919225119674426574742355349194934\
96983520312774506326239578318016984801869478851843\
85861560789112949495459501737958331952853208805511\
12540698747158523863050715693290963295227443043557\
66896648950445244523161731856403098711121722383113\
62229893423380308135336276614282806444486645238749\
30358907296290491560440772390713810515859307960866\
70172427121883998797908792274921901699720888093776\
65727333001053367881220235421809751254540594752243\
52584907711670556013604839586446706324415722155397\
53697817977846174064955149290862569321978468622482\
83972241375657056057490261407972968652414535100474\
82166370484403199890008895243450658541227588666881\
16427171479924442928230863465674813919123162824586\
17866458359124566529476545682848912883142607690042\
24219022671055626321111109370544217506941658960408\
07198403850962455444362981230987879927244284909188\
84580156166097919133875499200524063689912560717606\
05886116467109405077541002256983155200055935729725\
71636269561882670428252483600823257530420752963450"""
def str_eval(s: str) -> int:
"""
Returns product of digits in given string n
>>> str_eval("987654321")
362880
>>> str_eval("22222222")
256
"""
product = 1
for digit in s:
product *= int(digit)
return product
def solution(n: str = N) -> int:
"""
Find the thirteen adjacent digits in the 1000-digit number n that have
the greatest product and returns it.
"""
largest_product = -sys.maxsize - 1
substr = n[:13]
cur_index = 13
while cur_index < len(n) - 13:
if int(n[cur_index]) >= int(substr[0]):
substr = substr[1:] + n[cur_index]
cur_index += 1
else:
largest_product = max(largest_product, str_eval(substr))
substr = n[cur_index : cur_index + 13]
cur_index += 13
return largest_product
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 8: https://projecteuler.net/problem=8
Largest product in a series
The four adjacent digits in the 1000-digit number that have the greatest
product are 9 × 9 × 8 × 9 = 5832.
73167176531330624919225119674426574742355349194934
96983520312774506326239578318016984801869478851843
85861560789112949495459501737958331952853208805511
12540698747158523863050715693290963295227443043557
66896648950445244523161731856403098711121722383113
62229893423380308135336276614282806444486645238749
30358907296290491560440772390713810515859307960866
70172427121883998797908792274921901699720888093776
65727333001053367881220235421809751254540594752243
52584907711670556013604839586446706324415722155397
53697817977846174064955149290862569321978468622482
83972241375657056057490261407972968652414535100474
82166370484403199890008895243450658541227588666881
16427171479924442928230863465674813919123162824586
17866458359124566529476545682848912883142607690042
24219022671055626321111109370544217506941658960408
07198403850962455444362981230987879927244284909188
84580156166097919133875499200524063689912560717606
05886116467109405077541002256983155200055935729725
71636269561882670428252483600823257530420752963450
Find the thirteen adjacent digits in the 1000-digit number that have the
greatest product. What is the value of this product?
"""
import sys
N = """73167176531330624919225119674426574742355349194934\
96983520312774506326239578318016984801869478851843\
85861560789112949495459501737958331952853208805511\
12540698747158523863050715693290963295227443043557\
66896648950445244523161731856403098711121722383113\
62229893423380308135336276614282806444486645238749\
30358907296290491560440772390713810515859307960866\
70172427121883998797908792274921901699720888093776\
65727333001053367881220235421809751254540594752243\
52584907711670556013604839586446706324415722155397\
53697817977846174064955149290862569321978468622482\
83972241375657056057490261407972968652414535100474\
82166370484403199890008895243450658541227588666881\
16427171479924442928230863465674813919123162824586\
17866458359124566529476545682848912883142607690042\
24219022671055626321111109370544217506941658960408\
07198403850962455444362981230987879927244284909188\
84580156166097919133875499200524063689912560717606\
05886116467109405077541002256983155200055935729725\
71636269561882670428252483600823257530420752963450"""
def str_eval(s: str) -> int:
"""
Returns product of digits in given string n
>>> str_eval("987654321")
362880
>>> str_eval("22222222")
256
"""
product = 1
for digit in s:
product *= int(digit)
return product
def solution(n: str = N) -> int:
"""
Find the thirteen adjacent digits in the 1000-digit number n that have
the greatest product and returns it.
"""
largest_product = -sys.maxsize - 1
substr = n[:13]
cur_index = 13
while cur_index < len(n) - 13:
if int(n[cur_index]) >= int(substr[0]):
substr = substr[1:] + n[cur_index]
cur_index += 1
else:
largest_product = max(largest_product, str_eval(substr))
substr = n[cur_index : cur_index + 13]
cur_index += 13
return largest_product
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 4,881 | bugfix: Add empty list detection for find_max/min | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [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.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| taseikyo | "2021-10-02T04:03:28Z" | "2021-10-07T15:20:32Z" | d324f91fe75cc859335ee1f7c9c6307d958d0558 | 77b243e62b09cdf6201916af6762b03c54d8f77a | bugfix: Add empty list detection for find_max/min. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Project Euler Problem 7: https://projecteuler.net/problem=7
10001st prime
By listing the first six prime numbers: 2, 3, 5, 7, 11, and 13, we
can see that the 6th prime is 13.
What is the 10001st prime number?
References:
- https://en.wikipedia.org/wiki/Prime_number
"""
import itertools
import math
def prime_check(number: int) -> bool:
"""
Determines whether a given number is prime or not
>>> prime_check(2)
True
>>> prime_check(15)
False
>>> prime_check(29)
True
"""
if number % 2 == 0 and number > 2:
return False
return all(number % i for i in range(3, int(math.sqrt(number)) + 1, 2))
def prime_generator():
"""
Generate a sequence of prime numbers
"""
num = 2
while True:
if prime_check(num):
yield num
num += 1
def solution(nth: int = 10001) -> int:
"""
Returns the n-th prime number.
>>> solution(6)
13
>>> solution(1)
2
>>> solution(3)
5
>>> solution(20)
71
>>> solution(50)
229
>>> solution(100)
541
"""
return next(itertools.islice(prime_generator(), nth - 1, nth))
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 7: https://projecteuler.net/problem=7
10001st prime
By listing the first six prime numbers: 2, 3, 5, 7, 11, and 13, we
can see that the 6th prime is 13.
What is the 10001st prime number?
References:
- https://en.wikipedia.org/wiki/Prime_number
"""
import itertools
import math
def prime_check(number: int) -> bool:
"""
Determines whether a given number is prime or not
>>> prime_check(2)
True
>>> prime_check(15)
False
>>> prime_check(29)
True
"""
if number % 2 == 0 and number > 2:
return False
return all(number % i for i in range(3, int(math.sqrt(number)) + 1, 2))
def prime_generator():
"""
Generate a sequence of prime numbers
"""
num = 2
while True:
if prime_check(num):
yield num
num += 1
def solution(nth: int = 10001) -> int:
"""
Returns the n-th prime number.
>>> solution(6)
13
>>> solution(1)
2
>>> solution(3)
5
>>> solution(20)
71
>>> solution(50)
229
>>> solution(100)
541
"""
return next(itertools.islice(prime_generator(), nth - 1, nth))
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 4,881 | bugfix: Add empty list detection for find_max/min | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [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.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| taseikyo | "2021-10-02T04:03:28Z" | "2021-10-07T15:20:32Z" | d324f91fe75cc859335ee1f7c9c6307d958d0558 | 77b243e62b09cdf6201916af6762b03c54d8f77a | bugfix: Add empty list detection for find_max/min. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,881 | bugfix: Add empty list detection for find_max/min | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [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.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| taseikyo | "2021-10-02T04:03:28Z" | "2021-10-07T15:20:32Z" | d324f91fe75cc859335ee1f7c9c6307d958d0558 | 77b243e62b09cdf6201916af6762b03c54d8f77a | bugfix: Add empty list detection for find_max/min. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """Breadth-first search shortest path implementations.
doctest:
python -m doctest -v bfs_shortest_path.py
Manual test:
python bfs_shortest_path.py
"""
demo_graph = {
"A": ["B", "C", "E"],
"B": ["A", "D", "E"],
"C": ["A", "F", "G"],
"D": ["B"],
"E": ["A", "B", "D"],
"F": ["C"],
"G": ["C"],
}
def bfs_shortest_path(graph: dict, start, goal) -> list[str]:
"""Find shortest path between `start` and `goal` nodes.
Args:
graph (dict): node/list of neighboring nodes key/value pairs.
start: start node.
goal: target node.
Returns:
Shortest path between `start` and `goal` nodes as a string of nodes.
'Not found' string if no path found.
Example:
>>> bfs_shortest_path(demo_graph, "G", "D")
['G', 'C', 'A', 'B', 'D']
>>> bfs_shortest_path(demo_graph, "G", "G")
['G']
>>> bfs_shortest_path(demo_graph, "G", "Unknown")
[]
"""
# keep track of explored nodes
explored = set()
# keep track of all the paths to be checked
queue = [[start]]
# return path if start is goal
if start == goal:
return [start]
# keeps looping until all possible paths have been checked
while queue:
# pop the first path from the queue
path = queue.pop(0)
# get the last node from the path
node = path[-1]
if node not in explored:
neighbours = graph[node]
# go through all neighbour nodes, construct a new path and
# push it into the queue
for neighbour in neighbours:
new_path = list(path)
new_path.append(neighbour)
queue.append(new_path)
# return path if neighbour is goal
if neighbour == goal:
return new_path
# mark node as explored
explored.add(node)
# in case there's no path between the 2 nodes
return []
def bfs_shortest_path_distance(graph: dict, start, target) -> int:
"""Find shortest path distance between `start` and `target` nodes.
Args:
graph: node/list of neighboring nodes key/value pairs.
start: node to start search from.
target: node to search for.
Returns:
Number of edges in shortest path between `start` and `target` nodes.
-1 if no path exists.
Example:
>>> bfs_shortest_path_distance(demo_graph, "G", "D")
4
>>> bfs_shortest_path_distance(demo_graph, "A", "A")
0
>>> bfs_shortest_path_distance(demo_graph, "A", "Unknown")
-1
"""
if not graph or start not in graph or target not in graph:
return -1
if start == target:
return 0
queue = [start]
visited = set(start)
# Keep tab on distances from `start` node.
dist = {start: 0, target: -1}
while queue:
node = queue.pop(0)
if node == target:
dist[target] = (
dist[node] if dist[target] == -1 else min(dist[target], dist[node])
)
for adjacent in graph[node]:
if adjacent not in visited:
visited.add(adjacent)
queue.append(adjacent)
dist[adjacent] = dist[node] + 1
return dist[target]
if __name__ == "__main__":
print(bfs_shortest_path(demo_graph, "G", "D")) # returns ['G', 'C', 'A', 'B', 'D']
print(bfs_shortest_path_distance(demo_graph, "G", "D")) # returns 4
| """Breadth-first search shortest path implementations.
doctest:
python -m doctest -v bfs_shortest_path.py
Manual test:
python bfs_shortest_path.py
"""
demo_graph = {
"A": ["B", "C", "E"],
"B": ["A", "D", "E"],
"C": ["A", "F", "G"],
"D": ["B"],
"E": ["A", "B", "D"],
"F": ["C"],
"G": ["C"],
}
def bfs_shortest_path(graph: dict, start, goal) -> list[str]:
"""Find shortest path between `start` and `goal` nodes.
Args:
graph (dict): node/list of neighboring nodes key/value pairs.
start: start node.
goal: target node.
Returns:
Shortest path between `start` and `goal` nodes as a string of nodes.
'Not found' string if no path found.
Example:
>>> bfs_shortest_path(demo_graph, "G", "D")
['G', 'C', 'A', 'B', 'D']
>>> bfs_shortest_path(demo_graph, "G", "G")
['G']
>>> bfs_shortest_path(demo_graph, "G", "Unknown")
[]
"""
# keep track of explored nodes
explored = set()
# keep track of all the paths to be checked
queue = [[start]]
# return path if start is goal
if start == goal:
return [start]
# keeps looping until all possible paths have been checked
while queue:
# pop the first path from the queue
path = queue.pop(0)
# get the last node from the path
node = path[-1]
if node not in explored:
neighbours = graph[node]
# go through all neighbour nodes, construct a new path and
# push it into the queue
for neighbour in neighbours:
new_path = list(path)
new_path.append(neighbour)
queue.append(new_path)
# return path if neighbour is goal
if neighbour == goal:
return new_path
# mark node as explored
explored.add(node)
# in case there's no path between the 2 nodes
return []
def bfs_shortest_path_distance(graph: dict, start, target) -> int:
"""Find shortest path distance between `start` and `target` nodes.
Args:
graph: node/list of neighboring nodes key/value pairs.
start: node to start search from.
target: node to search for.
Returns:
Number of edges in shortest path between `start` and `target` nodes.
-1 if no path exists.
Example:
>>> bfs_shortest_path_distance(demo_graph, "G", "D")
4
>>> bfs_shortest_path_distance(demo_graph, "A", "A")
0
>>> bfs_shortest_path_distance(demo_graph, "A", "Unknown")
-1
"""
if not graph or start not in graph or target not in graph:
return -1
if start == target:
return 0
queue = [start]
visited = set(start)
# Keep tab on distances from `start` node.
dist = {start: 0, target: -1}
while queue:
node = queue.pop(0)
if node == target:
dist[target] = (
dist[node] if dist[target] == -1 else min(dist[target], dist[node])
)
for adjacent in graph[node]:
if adjacent not in visited:
visited.add(adjacent)
queue.append(adjacent)
dist[adjacent] = dist[node] + 1
return dist[target]
if __name__ == "__main__":
print(bfs_shortest_path(demo_graph, "G", "D")) # returns ['G', 'C', 'A', 'B', 'D']
print(bfs_shortest_path_distance(demo_graph, "G", "D")) # returns 4
| -1 |
TheAlgorithms/Python | 4,881 | bugfix: Add empty list detection for find_max/min | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [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.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| taseikyo | "2021-10-02T04:03:28Z" | "2021-10-07T15:20:32Z" | d324f91fe75cc859335ee1f7c9c6307d958d0558 | 77b243e62b09cdf6201916af6762b03c54d8f77a | bugfix: Add empty list detection for find_max/min. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| #!/usr/bin/env python3
import os
from typing import Iterator
URL_BASE = "https://github.com/TheAlgorithms/Python/blob/master"
def good_file_paths(top_dir: str = ".") -> Iterator[str]:
for dir_path, dir_names, filenames in os.walk(top_dir):
dir_names[:] = [d for d in dir_names if d != "scripts" and d[0] not in "._"]
for filename in filenames:
if filename == "__init__.py":
continue
if os.path.splitext(filename)[1] in (".py", ".ipynb"):
yield os.path.join(dir_path, filename).lstrip("./")
def md_prefix(i):
return f"{i * ' '}*" if i else "\n##"
def print_path(old_path: str, new_path: str) -> str:
old_parts = old_path.split(os.sep)
for i, new_part in enumerate(new_path.split(os.sep)):
if i + 1 > len(old_parts) or old_parts[i] != new_part:
if new_part:
print(f"{md_prefix(i)} {new_part.replace('_', ' ').title()}")
return new_path
def print_directory_md(top_dir: str = ".") -> None:
old_path = ""
for filepath in sorted(good_file_paths(top_dir)):
filepath, filename = os.path.split(filepath)
if filepath != old_path:
old_path = print_path(old_path, filepath)
indent = (filepath.count(os.sep) + 1) if filepath else 0
url = "/".join((URL_BASE, filepath, filename)).replace(" ", "%20")
filename = os.path.splitext(filename.replace("_", " ").title())[0]
print(f"{md_prefix(indent)} [{filename}]({url})")
if __name__ == "__main__":
print_directory_md(".")
| #!/usr/bin/env python3
import os
from typing import Iterator
URL_BASE = "https://github.com/TheAlgorithms/Python/blob/master"
def good_file_paths(top_dir: str = ".") -> Iterator[str]:
for dir_path, dir_names, filenames in os.walk(top_dir):
dir_names[:] = [d for d in dir_names if d != "scripts" and d[0] not in "._"]
for filename in filenames:
if filename == "__init__.py":
continue
if os.path.splitext(filename)[1] in (".py", ".ipynb"):
yield os.path.join(dir_path, filename).lstrip("./")
def md_prefix(i):
return f"{i * ' '}*" if i else "\n##"
def print_path(old_path: str, new_path: str) -> str:
old_parts = old_path.split(os.sep)
for i, new_part in enumerate(new_path.split(os.sep)):
if i + 1 > len(old_parts) or old_parts[i] != new_part:
if new_part:
print(f"{md_prefix(i)} {new_part.replace('_', ' ').title()}")
return new_path
def print_directory_md(top_dir: str = ".") -> None:
old_path = ""
for filepath in sorted(good_file_paths(top_dir)):
filepath, filename = os.path.split(filepath)
if filepath != old_path:
old_path = print_path(old_path, filepath)
indent = (filepath.count(os.sep) + 1) if filepath else 0
url = "/".join((URL_BASE, filepath, filename)).replace(" ", "%20")
filename = os.path.splitext(filename.replace("_", " ").title())[0]
print(f"{md_prefix(indent)} [{filename}]({url})")
if __name__ == "__main__":
print_directory_md(".")
| -1 |
TheAlgorithms/Python | 4,881 | bugfix: Add empty list detection for find_max/min | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [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.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| taseikyo | "2021-10-02T04:03:28Z" | "2021-10-07T15:20:32Z" | d324f91fe75cc859335ee1f7c9c6307d958d0558 | 77b243e62b09cdf6201916af6762b03c54d8f77a | bugfix: Add empty list detection for find_max/min. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$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 = {}
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 = [k for k in range(2, 20 + 1)]
base = [10 ** k for k in range(ks[-1] + 1)]
memo = {}
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 | 4,881 | bugfix: Add empty list detection for find_max/min | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [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.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| taseikyo | "2021-10-02T04:03:28Z" | "2021-10-07T15:20:32Z" | d324f91fe75cc859335ee1f7c9c6307d958d0558 | 77b243e62b09cdf6201916af6762b03c54d8f77a | bugfix: Add empty list detection for find_max/min. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,881 | bugfix: Add empty list detection for find_max/min | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [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.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| taseikyo | "2021-10-02T04:03:28Z" | "2021-10-07T15:20:32Z" | d324f91fe75cc859335ee1f7c9c6307d958d0558 | 77b243e62b09cdf6201916af6762b03c54d8f77a | bugfix: Add empty list detection for find_max/min. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| 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| -1 |
TheAlgorithms/Python | 4,881 | bugfix: Add empty list detection for find_max/min | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [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.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| taseikyo | "2021-10-02T04:03:28Z" | "2021-10-07T15:20:32Z" | d324f91fe75cc859335ee1f7c9c6307d958d0558 | 77b243e62b09cdf6201916af6762b03c54d8f77a | bugfix: Add empty list detection for find_max/min. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$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_of_squares = 0
sum_of_ints = 0
for i in range(1, n + 1):
sum_of_squares += i ** 2
sum_of_ints += i
return sum_of_ints ** 2 - sum_of_squares
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 6: https://projecteuler.net/problem=6
Sum square difference
The sum of the squares of the first ten natural numbers is,
1^2 + 2^2 + ... + 10^2 = 385
The square of the sum of the first ten natural numbers is,
(1 + 2 + ... + 10)^2 = 55^2 = 3025
Hence the difference between the sum of the squares of the first ten
natural numbers and the square of the sum is 3025 - 385 = 2640.
Find the difference between the sum of the squares of the first one
hundred natural numbers and the square of the sum.
"""
def solution(n: int = 100) -> int:
"""
Returns the difference between the sum of the squares of the first n
natural numbers and the square of the sum.
>>> solution(10)
2640
>>> solution(15)
13160
>>> solution(20)
41230
>>> solution(50)
1582700
"""
sum_of_squares = 0
sum_of_ints = 0
for i in range(1, n + 1):
sum_of_squares += i ** 2
sum_of_ints += i
return sum_of_ints ** 2 - sum_of_squares
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 4,881 | bugfix: Add empty list detection for find_max/min | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [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.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| taseikyo | "2021-10-02T04:03:28Z" | "2021-10-07T15:20:32Z" | d324f91fe75cc859335ee1f7c9c6307d958d0558 | 77b243e62b09cdf6201916af6762b03c54d8f77a | bugfix: Add empty list detection for find_max/min. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,881 | bugfix: Add empty list detection for find_max/min | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [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.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| taseikyo | "2021-10-02T04:03:28Z" | "2021-10-07T15:20:32Z" | d324f91fe75cc859335ee1f7c9c6307d958d0558 | 77b243e62b09cdf6201916af6762b03c54d8f77a | bugfix: Add empty list detection for find_max/min. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
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(? | -1 |
TheAlgorithms/Python | 4,881 | bugfix: Add empty list detection for find_max/min | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [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.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| taseikyo | "2021-10-02T04:03:28Z" | "2021-10-07T15:20:32Z" | d324f91fe75cc859335ee1f7c9c6307d958d0558 | 77b243e62b09cdf6201916af6762b03c54d8f77a | bugfix: Add empty list detection for find_max/min. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """For more information about the Binomial Distribution -
https://en.wikipedia.org/wiki/Binomial_distribution"""
from math import factorial
def binomial_distribution(successes: int, trials: int, prob: float) -> float:
"""
Return probability of k successes out of n tries, with p probability for one
success
The function uses the factorial function in order to calculate the binomial
coefficient
>>> binomial_distribution(3, 5, 0.7)
0.30870000000000003
>>> binomial_distribution (2, 4, 0.5)
0.375
"""
if successes > trials:
raise ValueError("""successes must be lower or equal to trials""")
if trials < 0 or successes < 0:
raise ValueError("the function is defined for non-negative integers")
if not isinstance(successes, int) or not isinstance(trials, int):
raise ValueError("the function is defined for non-negative integers")
if not 0 < prob < 1:
raise ValueError("prob has to be in range of 1 - 0")
probability = (prob ** successes) * ((1 - prob) ** (trials - successes))
# Calculate the binomial coefficient: n! / k!(n-k)!
coefficient = float(factorial(trials))
coefficient /= factorial(successes) * factorial(trials - successes)
return probability * coefficient
if __name__ == "__main__":
from doctest import testmod
testmod()
print("Probability of 2 successes out of 4 trails")
print("with probability of 0.75 is:", end=" ")
print(binomial_distribution(2, 4, 0.75))
| """For more information about the Binomial Distribution -
https://en.wikipedia.org/wiki/Binomial_distribution"""
from math import factorial
def binomial_distribution(successes: int, trials: int, prob: float) -> float:
"""
Return probability of k successes out of n tries, with p probability for one
success
The function uses the factorial function in order to calculate the binomial
coefficient
>>> binomial_distribution(3, 5, 0.7)
0.30870000000000003
>>> binomial_distribution (2, 4, 0.5)
0.375
"""
if successes > trials:
raise ValueError("""successes must be lower or equal to trials""")
if trials < 0 or successes < 0:
raise ValueError("the function is defined for non-negative integers")
if not isinstance(successes, int) or not isinstance(trials, int):
raise ValueError("the function is defined for non-negative integers")
if not 0 < prob < 1:
raise ValueError("prob has to be in range of 1 - 0")
probability = (prob ** successes) * ((1 - prob) ** (trials - successes))
# Calculate the binomial coefficient: n! / k!(n-k)!
coefficient = float(factorial(trials))
coefficient /= factorial(successes) * factorial(trials - successes)
return probability * coefficient
if __name__ == "__main__":
from doctest import testmod
testmod()
print("Probability of 2 successes out of 4 trails")
print("with probability of 0.75 is:", end=" ")
print(binomial_distribution(2, 4, 0.75))
| -1 |
TheAlgorithms/Python | 4,881 | bugfix: Add empty list detection for find_max/min | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [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.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| taseikyo | "2021-10-02T04:03:28Z" | "2021-10-07T15:20:32Z" | d324f91fe75cc859335ee1f7c9c6307d958d0558 | 77b243e62b09cdf6201916af6762b03c54d8f77a | bugfix: Add empty list detection for find_max/min. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,881 | bugfix: Add empty list detection for find_max/min | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [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.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| taseikyo | "2021-10-02T04:03:28Z" | "2021-10-07T15:20:32Z" | d324f91fe75cc859335ee1f7c9c6307d958d0558 | 77b243e62b09cdf6201916af6762b03c54d8f77a | bugfix: Add empty list detection for find_max/min. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,881 | bugfix: Add empty list detection for find_max/min | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [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.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| taseikyo | "2021-10-02T04:03:28Z" | "2021-10-07T15:20:32Z" | d324f91fe75cc859335ee1f7c9c6307d958d0558 | 77b243e62b09cdf6201916af6762b03c54d8f77a | bugfix: Add empty list detection for find_max/min. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
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TheAlgorithms/Python | 4,881 | bugfix: Add empty list detection for find_max/min | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [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.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| taseikyo | "2021-10-02T04:03:28Z" | "2021-10-07T15:20:32Z" | d324f91fe75cc859335ee1f7c9c6307d958d0558 | 77b243e62b09cdf6201916af6762b03c54d8f77a | bugfix: Add empty list detection for find_max/min. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,881 | bugfix: Add empty list detection for find_max/min | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [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.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| taseikyo | "2021-10-02T04:03:28Z" | "2021-10-07T15:20:32Z" | d324f91fe75cc859335ee1f7c9c6307d958d0558 | 77b243e62b09cdf6201916af6762b03c54d8f77a | bugfix: Add empty list detection for find_max/min. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # Setup for pytest
[pytest]
markers =
mat_ops: mark a test as utilizing matrix operations.
addopts = --durations=10
| # Setup for pytest
[pytest]
markers =
mat_ops: mark a test as utilizing matrix operations.
addopts = --durations=10
| -1 |
TheAlgorithms/Python | 4,881 | bugfix: Add empty list detection for find_max/min | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [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.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| taseikyo | "2021-10-02T04:03:28Z" | "2021-10-07T15:20:32Z" | d324f91fe75cc859335ee1f7c9c6307d958d0558 | 77b243e62b09cdf6201916af6762b03c54d8f77a | bugfix: Add empty list detection for find_max/min. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,881 | bugfix: Add empty list detection for find_max/min | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [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.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| taseikyo | "2021-10-02T04:03:28Z" | "2021-10-07T15:20:32Z" | d324f91fe75cc859335ee1f7c9c6307d958d0558 | 77b243e62b09cdf6201916af6762b03c54d8f77a | bugfix: Add empty list detection for find_max/min. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| class BinaryHeap:
"""
A max-heap implementation in Python
>>> binary_heap = BinaryHeap()
>>> binary_heap.insert(6)
>>> binary_heap.insert(10)
>>> binary_heap.insert(15)
>>> binary_heap.insert(12)
>>> binary_heap.pop()
15
>>> binary_heap.pop()
12
>>> binary_heap.get_list
[10, 6]
>>> len(binary_heap)
2
"""
def __init__(self):
self.__heap = [0]
self.__size = 0
def __swap_up(self, i: int) -> None:
"""Swap the element up"""
temporary = self.__heap[i]
while i // 2 > 0:
if self.__heap[i] > self.__heap[i // 2]:
self.__heap[i] = self.__heap[i // 2]
self.__heap[i // 2] = temporary
i //= 2
def insert(self, value: int) -> None:
"""Insert new element"""
self.__heap.append(value)
self.__size += 1
self.__swap_up(self.__size)
def __swap_down(self, i: int) -> None:
"""Swap the element down"""
while self.__size >= 2 * i:
if 2 * i + 1 > self.__size:
bigger_child = 2 * i
else:
if self.__heap[2 * i] > self.__heap[2 * i + 1]:
bigger_child = 2 * i
else:
bigger_child = 2 * i + 1
temporary = self.__heap[i]
if self.__heap[i] < self.__heap[bigger_child]:
self.__heap[i] = self.__heap[bigger_child]
self.__heap[bigger_child] = temporary
i = bigger_child
def pop(self) -> int:
"""Pop the root element"""
max_value = self.__heap[1]
self.__heap[1] = self.__heap[self.__size]
self.__size -= 1
self.__heap.pop()
self.__swap_down(1)
return max_value
@property
def get_list(self):
return self.__heap[1:]
def __len__(self):
"""Length of the array"""
return self.__size
if __name__ == "__main__":
import doctest
doctest.testmod()
# create an instance of BinaryHeap
binary_heap = BinaryHeap()
binary_heap.insert(6)
binary_heap.insert(10)
binary_heap.insert(15)
binary_heap.insert(12)
# pop root(max-values because it is max heap)
print(binary_heap.pop()) # 15
print(binary_heap.pop()) # 12
# get the list and size after operations
print(binary_heap.get_list)
print(len(binary_heap))
| class BinaryHeap:
"""
A max-heap implementation in Python
>>> binary_heap = BinaryHeap()
>>> binary_heap.insert(6)
>>> binary_heap.insert(10)
>>> binary_heap.insert(15)
>>> binary_heap.insert(12)
>>> binary_heap.pop()
15
>>> binary_heap.pop()
12
>>> binary_heap.get_list
[10, 6]
>>> len(binary_heap)
2
"""
def __init__(self):
self.__heap = [0]
self.__size = 0
def __swap_up(self, i: int) -> None:
"""Swap the element up"""
temporary = self.__heap[i]
while i // 2 > 0:
if self.__heap[i] > self.__heap[i // 2]:
self.__heap[i] = self.__heap[i // 2]
self.__heap[i // 2] = temporary
i //= 2
def insert(self, value: int) -> None:
"""Insert new element"""
self.__heap.append(value)
self.__size += 1
self.__swap_up(self.__size)
def __swap_down(self, i: int) -> None:
"""Swap the element down"""
while self.__size >= 2 * i:
if 2 * i + 1 > self.__size:
bigger_child = 2 * i
else:
if self.__heap[2 * i] > self.__heap[2 * i + 1]:
bigger_child = 2 * i
else:
bigger_child = 2 * i + 1
temporary = self.__heap[i]
if self.__heap[i] < self.__heap[bigger_child]:
self.__heap[i] = self.__heap[bigger_child]
self.__heap[bigger_child] = temporary
i = bigger_child
def pop(self) -> int:
"""Pop the root element"""
max_value = self.__heap[1]
self.__heap[1] = self.__heap[self.__size]
self.__size -= 1
self.__heap.pop()
self.__swap_down(1)
return max_value
@property
def get_list(self):
return self.__heap[1:]
def __len__(self):
"""Length of the array"""
return self.__size
if __name__ == "__main__":
import doctest
doctest.testmod()
# create an instance of BinaryHeap
binary_heap = BinaryHeap()
binary_heap.insert(6)
binary_heap.insert(10)
binary_heap.insert(15)
binary_heap.insert(12)
# pop root(max-values because it is max heap)
print(binary_heap.pop()) # 15
print(binary_heap.pop()) # 12
# get the list and size after operations
print(binary_heap.get_list)
print(len(binary_heap))
| -1 |
TheAlgorithms/Python | 4,881 | bugfix: Add empty list detection for find_max/min | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [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.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| taseikyo | "2021-10-02T04:03:28Z" | "2021-10-07T15:20:32Z" | d324f91fe75cc859335ee1f7c9c6307d958d0558 | 77b243e62b09cdf6201916af6762b03c54d8f77a | bugfix: Add empty list detection for find_max/min. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,881 | bugfix: Add empty list detection for find_max/min | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [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.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| taseikyo | "2021-10-02T04:03:28Z" | "2021-10-07T15:20:32Z" | d324f91fe75cc859335ee1f7c9c6307d958d0558 | 77b243e62b09cdf6201916af6762b03c54d8f77a | bugfix: Add empty list detection for find_max/min. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from datetime import datetime
import requests
from bs4 import BeautifulSoup
if __name__ == "__main__":
url = input("Enter image url: ").strip()
print(f"Downloading image from {url} ...")
soup = BeautifulSoup(requests.get(url).content, "html.parser")
# The image URL is in the content field of the first meta tag with property og:image
image_url = soup.find("meta", {"property": "og:image"})["content"]
image_data = requests.get(image_url).content
file_name = f"{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg"
with open(file_name, "wb") as fp:
fp.write(image_data)
print(f"Done. Image saved to disk as {file_name}.")
| from datetime import datetime
import requests
from bs4 import BeautifulSoup
if __name__ == "__main__":
url = input("Enter image url: ").strip()
print(f"Downloading image from {url} ...")
soup = BeautifulSoup(requests.get(url).content, "html.parser")
# The image URL is in the content field of the first meta tag with property og:image
image_url = soup.find("meta", {"property": "og:image"})["content"]
image_data = requests.get(image_url).content
file_name = f"{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg"
with open(file_name, "wb") as fp:
fp.write(image_data)
print(f"Done. Image saved to disk as {file_name}.")
| -1 |
TheAlgorithms/Python | 4,881 | bugfix: Add empty list detection for find_max/min | ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [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.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| taseikyo | "2021-10-02T04:03:28Z" | "2021-10-07T15:20:32Z" | d324f91fe75cc859335ee1f7c9c6307d958d0558 | 77b243e62b09cdf6201916af6762b03c54d8f77a | bugfix: Add empty list detection for find_max/min. ### **Describe your change:**
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [x] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [x] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
An Armstrong number is equal to the sum of its own digits each raised to the
power of the number of digits.
For example, 370 is an Armstrong number because 3*3*3 + 7*7*7 + 0*0*0 = 370.
Armstrong numbers are also called Narcissistic numbers and Pluperfect numbers.
On-Line Encyclopedia of Integer Sequences entry: https://oeis.org/A005188
"""
PASSING = (1, 153, 370, 371, 1634, 24678051, 115132219018763992565095597973971522401)
FAILING: tuple = (-153, -1, 0, 1.2, 200, "A", [], {}, None)
def armstrong_number(n: int) -> bool:
"""
Return True if n is an Armstrong number or False if it is not.
>>> all(armstrong_number(n) for n in PASSING)
True
>>> any(armstrong_number(n) for n in FAILING)
False
"""
if not isinstance(n, int) or n < 1:
return False
# Initialization of sum and number of digits.
sum = 0
number_of_digits = 0
temp = n
# Calculation of digits of the number
while temp > 0:
number_of_digits += 1
temp //= 10
# Dividing number into separate digits and find Armstrong number
temp = n
while temp > 0:
rem = temp % 10
sum += rem ** number_of_digits
temp //= 10
return n == sum
def pluperfect_number(n: int) -> bool:
"""Return True if n is a pluperfect number or False if it is not
>>> all(armstrong_number(n) for n in PASSING)
True
>>> any(armstrong_number(n) for n in FAILING)
False
"""
if not isinstance(n, int) or n < 1:
return False
# Init a "histogram" of the digits
digit_histogram = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
digit_total = 0
sum = 0
temp = n
while temp > 0:
temp, rem = divmod(temp, 10)
digit_histogram[rem] += 1
digit_total += 1
for (cnt, i) in zip(digit_histogram, range(len(digit_histogram))):
sum += cnt * i ** digit_total
return n == sum
def narcissistic_number(n: int) -> bool:
"""Return True if n is a narcissistic number or False if it is not.
>>> all(armstrong_number(n) for n in PASSING)
True
>>> any(armstrong_number(n) for n in FAILING)
False
"""
if not isinstance(n, int) or n < 1:
return False
expo = len(str(n)) # the power that all digits will be raised to
# check if sum of each digit multiplied expo times is equal to number
return n == sum(int(i) ** expo for i in str(n))
def main():
"""
Request that user input an integer and tell them if it is Armstrong number.
"""
num = int(input("Enter an integer to see if it is an Armstrong number: ").strip())
print(f"{num} is {'' if armstrong_number(num) else 'not '}an Armstrong number.")
print(f"{num} is {'' if narcissistic_number(num) else 'not '}an Armstrong number.")
print(f"{num} is {'' if pluperfect_number(num) else 'not '}an Armstrong number.")
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| """
An Armstrong number is equal to the sum of its own digits each raised to the
power of the number of digits.
For example, 370 is an Armstrong number because 3*3*3 + 7*7*7 + 0*0*0 = 370.
Armstrong numbers are also called Narcissistic numbers and Pluperfect numbers.
On-Line Encyclopedia of Integer Sequences entry: https://oeis.org/A005188
"""
PASSING = (1, 153, 370, 371, 1634, 24678051, 115132219018763992565095597973971522401)
FAILING: tuple = (-153, -1, 0, 1.2, 200, "A", [], {}, None)
def armstrong_number(n: int) -> bool:
"""
Return True if n is an Armstrong number or False if it is not.
>>> all(armstrong_number(n) for n in PASSING)
True
>>> any(armstrong_number(n) for n in FAILING)
False
"""
if not isinstance(n, int) or n < 1:
return False
# Initialization of sum and number of digits.
sum = 0
number_of_digits = 0
temp = n
# Calculation of digits of the number
while temp > 0:
number_of_digits += 1
temp //= 10
# Dividing number into separate digits and find Armstrong number
temp = n
while temp > 0:
rem = temp % 10
sum += rem ** number_of_digits
temp //= 10
return n == sum
def pluperfect_number(n: int) -> bool:
"""Return True if n is a pluperfect number or False if it is not
>>> all(armstrong_number(n) for n in PASSING)
True
>>> any(armstrong_number(n) for n in FAILING)
False
"""
if not isinstance(n, int) or n < 1:
return False
# Init a "histogram" of the digits
digit_histogram = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
digit_total = 0
sum = 0
temp = n
while temp > 0:
temp, rem = divmod(temp, 10)
digit_histogram[rem] += 1
digit_total += 1
for (cnt, i) in zip(digit_histogram, range(len(digit_histogram))):
sum += cnt * i ** digit_total
return n == sum
def narcissistic_number(n: int) -> bool:
"""Return True if n is a narcissistic number or False if it is not.
>>> all(armstrong_number(n) for n in PASSING)
True
>>> any(armstrong_number(n) for n in FAILING)
False
"""
if not isinstance(n, int) or n < 1:
return False
expo = len(str(n)) # the power that all digits will be raised to
# check if sum of each digit multiplied expo times is equal to number
return n == sum(int(i) ** expo for i in str(n))
def main():
"""
Request that user input an integer and tell them if it is Armstrong number.
"""
num = int(input("Enter an integer to see if it is an Armstrong number: ").strip())
print(f"{num} is {'' if armstrong_number(num) else 'not '}an Armstrong number.")
print(f"{num} is {'' if narcissistic_number(num) else 'not '}an Armstrong number.")
print(f"{num} is {'' if pluperfect_number(num) else 'not '}an Armstrong number.")
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| -1 |
TheAlgorithms/Python | 4,763 | from __future__ import annotations | ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2021-09-14T22:54:26Z" | "2021-09-22T21:11:51Z" | dc07a850763d8154e012c9d1be7f8fe78326e8fb | 15d1cfabb15903c5e9d4d103e2390876efb3f85f | from __future__ import annotations. ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
|
## Arithmetic Analysis
* [Bisection](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/bisection.py)
* [Gaussian Elimination](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/gaussian_elimination.py)
* [In Static Equilibrium](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/in_static_equilibrium.py)
* [Intersection](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/intersection.py)
* [Lu Decomposition](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/lu_decomposition.py)
* [Newton Forward Interpolation](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/newton_forward_interpolation.py)
* [Newton Method](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/newton_method.py)
* [Newton Raphson](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/newton_raphson.py)
* [Secant Method](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/secant_method.py)
## Backtracking
* [All Combinations](https://github.com/TheAlgorithms/Python/blob/master/backtracking/all_combinations.py)
* [All Permutations](https://github.com/TheAlgorithms/Python/blob/master/backtracking/all_permutations.py)
* [All Subsequences](https://github.com/TheAlgorithms/Python/blob/master/backtracking/all_subsequences.py)
* [Coloring](https://github.com/TheAlgorithms/Python/blob/master/backtracking/coloring.py)
* [Hamiltonian Cycle](https://github.com/TheAlgorithms/Python/blob/master/backtracking/hamiltonian_cycle.py)
* [Knight Tour](https://github.com/TheAlgorithms/Python/blob/master/backtracking/knight_tour.py)
* [Minimax](https://github.com/TheAlgorithms/Python/blob/master/backtracking/minimax.py)
* [N Queens](https://github.com/TheAlgorithms/Python/blob/master/backtracking/n_queens.py)
* [N Queens Math](https://github.com/TheAlgorithms/Python/blob/master/backtracking/n_queens_math.py)
* [Rat In Maze](https://github.com/TheAlgorithms/Python/blob/master/backtracking/rat_in_maze.py)
* [Sudoku](https://github.com/TheAlgorithms/Python/blob/master/backtracking/sudoku.py)
* [Sum Of Subsets](https://github.com/TheAlgorithms/Python/blob/master/backtracking/sum_of_subsets.py)
## Bit Manipulation
* [Binary And Operator](https://github.com/TheAlgorithms/Python/blob/master/bit_manipulation/binary_and_operator.py)
* [Binary Count Setbits](https://github.com/TheAlgorithms/Python/blob/master/bit_manipulation/binary_count_setbits.py)
* [Binary Count Trailing Zeros](https://github.com/TheAlgorithms/Python/blob/master/bit_manipulation/binary_count_trailing_zeros.py)
* [Binary Or Operator](https://github.com/TheAlgorithms/Python/blob/master/bit_manipulation/binary_or_operator.py)
* [Binary Shifts](https://github.com/TheAlgorithms/Python/blob/master/bit_manipulation/binary_shifts.py)
* [Binary Twos Complement](https://github.com/TheAlgorithms/Python/blob/master/bit_manipulation/binary_twos_complement.py)
* [Binary Xor Operator](https://github.com/TheAlgorithms/Python/blob/master/bit_manipulation/binary_xor_operator.py)
* [Count Number Of One Bits](https://github.com/TheAlgorithms/Python/blob/master/bit_manipulation/count_number_of_one_bits.py)
* [Reverse Bits](https://github.com/TheAlgorithms/Python/blob/master/bit_manipulation/reverse_bits.py)
* [Single Bit Manipulation Operations](https://github.com/TheAlgorithms/Python/blob/master/bit_manipulation/single_bit_manipulation_operations.py)
## Blockchain
* [Chinese Remainder Theorem](https://github.com/TheAlgorithms/Python/blob/master/blockchain/chinese_remainder_theorem.py)
* [Diophantine Equation](https://github.com/TheAlgorithms/Python/blob/master/blockchain/diophantine_equation.py)
* [Modular Division](https://github.com/TheAlgorithms/Python/blob/master/blockchain/modular_division.py)
## Boolean Algebra
* [Quine Mc Cluskey](https://github.com/TheAlgorithms/Python/blob/master/boolean_algebra/quine_mc_cluskey.py)
## Cellular Automata
* [Conways Game Of Life](https://github.com/TheAlgorithms/Python/blob/master/cellular_automata/conways_game_of_life.py)
* [Game Of Life](https://github.com/TheAlgorithms/Python/blob/master/cellular_automata/game_of_life.py)
* [One Dimensional](https://github.com/TheAlgorithms/Python/blob/master/cellular_automata/one_dimensional.py)
## Ciphers
* [A1Z26](https://github.com/TheAlgorithms/Python/blob/master/ciphers/a1z26.py)
* [Affine Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/affine_cipher.py)
* [Atbash](https://github.com/TheAlgorithms/Python/blob/master/ciphers/atbash.py)
* [Base16](https://github.com/TheAlgorithms/Python/blob/master/ciphers/base16.py)
* [Base32](https://github.com/TheAlgorithms/Python/blob/master/ciphers/base32.py)
* [Base64 Encoding](https://github.com/TheAlgorithms/Python/blob/master/ciphers/base64_encoding.py)
* [Base85](https://github.com/TheAlgorithms/Python/blob/master/ciphers/base85.py)
* [Beaufort Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/beaufort_cipher.py)
* [Brute Force Caesar Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/brute_force_caesar_cipher.py)
* [Caesar Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/caesar_cipher.py)
* [Cryptomath Module](https://github.com/TheAlgorithms/Python/blob/master/ciphers/cryptomath_module.py)
* [Decrypt Caesar With Chi Squared](https://github.com/TheAlgorithms/Python/blob/master/ciphers/decrypt_caesar_with_chi_squared.py)
* [Deterministic Miller Rabin](https://github.com/TheAlgorithms/Python/blob/master/ciphers/deterministic_miller_rabin.py)
* [Diffie](https://github.com/TheAlgorithms/Python/blob/master/ciphers/diffie.py)
* [Diffie Hellman](https://github.com/TheAlgorithms/Python/blob/master/ciphers/diffie_hellman.py)
* [Elgamal Key Generator](https://github.com/TheAlgorithms/Python/blob/master/ciphers/elgamal_key_generator.py)
* [Enigma Machine2](https://github.com/TheAlgorithms/Python/blob/master/ciphers/enigma_machine2.py)
* [Hill Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/hill_cipher.py)
* [Mixed Keyword Cypher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/mixed_keyword_cypher.py)
* [Mono Alphabetic Ciphers](https://github.com/TheAlgorithms/Python/blob/master/ciphers/mono_alphabetic_ciphers.py)
* [Morse Code Implementation](https://github.com/TheAlgorithms/Python/blob/master/ciphers/morse_code_implementation.py)
* [Onepad Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/onepad_cipher.py)
* [Playfair Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/playfair_cipher.py)
* [Porta Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/porta_cipher.py)
* [Rabin Miller](https://github.com/TheAlgorithms/Python/blob/master/ciphers/rabin_miller.py)
* [Rail Fence Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/rail_fence_cipher.py)
* [Rot13](https://github.com/TheAlgorithms/Python/blob/master/ciphers/rot13.py)
* [Rsa Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/rsa_cipher.py)
* [Rsa Factorization](https://github.com/TheAlgorithms/Python/blob/master/ciphers/rsa_factorization.py)
* [Rsa Key Generator](https://github.com/TheAlgorithms/Python/blob/master/ciphers/rsa_key_generator.py)
* [Shuffled Shift Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/shuffled_shift_cipher.py)
* [Simple Keyword Cypher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/simple_keyword_cypher.py)
* [Simple Substitution Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/simple_substitution_cipher.py)
* [Trafid Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/trafid_cipher.py)
* [Transposition Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/transposition_cipher.py)
* [Transposition Cipher Encrypt Decrypt File](https://github.com/TheAlgorithms/Python/blob/master/ciphers/transposition_cipher_encrypt_decrypt_file.py)
* [Vigenere Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/vigenere_cipher.py)
* [Xor Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/xor_cipher.py)
## Compression
* [Burrows Wheeler](https://github.com/TheAlgorithms/Python/blob/master/compression/burrows_wheeler.py)
* [Huffman](https://github.com/TheAlgorithms/Python/blob/master/compression/huffman.py)
* [Lempel Ziv](https://github.com/TheAlgorithms/Python/blob/master/compression/lempel_ziv.py)
* [Lempel Ziv Decompress](https://github.com/TheAlgorithms/Python/blob/master/compression/lempel_ziv_decompress.py)
* [Peak Signal To Noise Ratio](https://github.com/TheAlgorithms/Python/blob/master/compression/peak_signal_to_noise_ratio.py)
## Computer Vision
* [Cnn Classification](https://github.com/TheAlgorithms/Python/blob/master/computer_vision/cnn_classification.py)
* [Harris Corner](https://github.com/TheAlgorithms/Python/blob/master/computer_vision/harris_corner.py)
* [Mean Threshold](https://github.com/TheAlgorithms/Python/blob/master/computer_vision/mean_threshold.py)
## Conversions
* [Binary To Decimal](https://github.com/TheAlgorithms/Python/blob/master/conversions/binary_to_decimal.py)
* [Binary To Octal](https://github.com/TheAlgorithms/Python/blob/master/conversions/binary_to_octal.py)
* [Decimal To Any](https://github.com/TheAlgorithms/Python/blob/master/conversions/decimal_to_any.py)
* [Decimal To Binary](https://github.com/TheAlgorithms/Python/blob/master/conversions/decimal_to_binary.py)
* [Decimal To Binary Recursion](https://github.com/TheAlgorithms/Python/blob/master/conversions/decimal_to_binary_recursion.py)
* [Decimal To Hexadecimal](https://github.com/TheAlgorithms/Python/blob/master/conversions/decimal_to_hexadecimal.py)
* [Decimal To Octal](https://github.com/TheAlgorithms/Python/blob/master/conversions/decimal_to_octal.py)
* [Hex To Bin](https://github.com/TheAlgorithms/Python/blob/master/conversions/hex_to_bin.py)
* [Hexadecimal To Decimal](https://github.com/TheAlgorithms/Python/blob/master/conversions/hexadecimal_to_decimal.py)
* [Molecular Chemistry](https://github.com/TheAlgorithms/Python/blob/master/conversions/molecular_chemistry.py)
* [Octal To Decimal](https://github.com/TheAlgorithms/Python/blob/master/conversions/octal_to_decimal.py)
* [Prefix Conversions](https://github.com/TheAlgorithms/Python/blob/master/conversions/prefix_conversions.py)
* [Rgb Hsv Conversion](https://github.com/TheAlgorithms/Python/blob/master/conversions/rgb_hsv_conversion.py)
* [Roman Numerals](https://github.com/TheAlgorithms/Python/blob/master/conversions/roman_numerals.py)
* [Temperature Conversions](https://github.com/TheAlgorithms/Python/blob/master/conversions/temperature_conversions.py)
* [Weight Conversion](https://github.com/TheAlgorithms/Python/blob/master/conversions/weight_conversion.py)
## Data Structures
* Binary Tree
* [Avl Tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/avl_tree.py)
* [Basic Binary Tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/basic_binary_tree.py)
* [Binary Search Tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/binary_search_tree.py)
* [Binary Search Tree Recursive](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/binary_search_tree_recursive.py)
* [Binary Tree Mirror](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/binary_tree_mirror.py)
* [Binary Tree Traversals](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/binary_tree_traversals.py)
* [Fenwick Tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/fenwick_tree.py)
* [Lazy Segment Tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/lazy_segment_tree.py)
* [Lowest Common Ancestor](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/lowest_common_ancestor.py)
* [Merge Two Binary Trees](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/merge_two_binary_trees.py)
* [Non Recursive Segment Tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/non_recursive_segment_tree.py)
* [Number Of Possible Binary Trees](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/number_of_possible_binary_trees.py)
* [Red Black Tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/red_black_tree.py)
* [Segment Tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/segment_tree.py)
* [Segment Tree Other](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/segment_tree_other.py)
* [Treap](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/treap.py)
* [Wavelet Tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/wavelet_tree.py)
* Disjoint Set
* [Alternate Disjoint Set](https://github.com/TheAlgorithms/Python/blob/master/data_structures/disjoint_set/alternate_disjoint_set.py)
* [Disjoint Set](https://github.com/TheAlgorithms/Python/blob/master/data_structures/disjoint_set/disjoint_set.py)
* Hashing
* [Double Hash](https://github.com/TheAlgorithms/Python/blob/master/data_structures/hashing/double_hash.py)
* [Hash Table](https://github.com/TheAlgorithms/Python/blob/master/data_structures/hashing/hash_table.py)
* [Hash Table With Linked List](https://github.com/TheAlgorithms/Python/blob/master/data_structures/hashing/hash_table_with_linked_list.py)
* Number Theory
* [Prime Numbers](https://github.com/TheAlgorithms/Python/blob/master/data_structures/hashing/number_theory/prime_numbers.py)
* [Quadratic Probing](https://github.com/TheAlgorithms/Python/blob/master/data_structures/hashing/quadratic_probing.py)
* Heap
* [Binomial Heap](https://github.com/TheAlgorithms/Python/blob/master/data_structures/heap/binomial_heap.py)
* [Heap](https://github.com/TheAlgorithms/Python/blob/master/data_structures/heap/heap.py)
* [Heap Generic](https://github.com/TheAlgorithms/Python/blob/master/data_structures/heap/heap_generic.py)
* [Max Heap](https://github.com/TheAlgorithms/Python/blob/master/data_structures/heap/max_heap.py)
* [Min Heap](https://github.com/TheAlgorithms/Python/blob/master/data_structures/heap/min_heap.py)
* [Randomized Heap](https://github.com/TheAlgorithms/Python/blob/master/data_structures/heap/randomized_heap.py)
* [Skew Heap](https://github.com/TheAlgorithms/Python/blob/master/data_structures/heap/skew_heap.py)
* Linked List
* [Circular Linked List](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/circular_linked_list.py)
* [Deque Doubly](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/deque_doubly.py)
* [Doubly Linked List](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/doubly_linked_list.py)
* [Doubly Linked List Two](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/doubly_linked_list_two.py)
* [From Sequence](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/from_sequence.py)
* [Has Loop](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/has_loop.py)
* [Is Palindrome](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/is_palindrome.py)
* [Merge Two Lists](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/merge_two_lists.py)
* [Middle Element Of Linked List](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/middle_element_of_linked_list.py)
* [Print Reverse](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/print_reverse.py)
* [Singly Linked List](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/singly_linked_list.py)
* [Skip List](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/skip_list.py)
* [Swap Nodes](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/swap_nodes.py)
* Queue
* [Circular Queue](https://github.com/TheAlgorithms/Python/blob/master/data_structures/queue/circular_queue.py)
* [Double Ended Queue](https://github.com/TheAlgorithms/Python/blob/master/data_structures/queue/double_ended_queue.py)
* [Linked Queue](https://github.com/TheAlgorithms/Python/blob/master/data_structures/queue/linked_queue.py)
* [Priority Queue Using List](https://github.com/TheAlgorithms/Python/blob/master/data_structures/queue/priority_queue_using_list.py)
* [Queue On List](https://github.com/TheAlgorithms/Python/blob/master/data_structures/queue/queue_on_list.py)
* [Queue On Pseudo Stack](https://github.com/TheAlgorithms/Python/blob/master/data_structures/queue/queue_on_pseudo_stack.py)
* Stacks
* [Balanced Parentheses](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/balanced_parentheses.py)
* [Dijkstras Two Stack Algorithm](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/dijkstras_two_stack_algorithm.py)
* [Evaluate Postfix Notations](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/evaluate_postfix_notations.py)
* [Infix To Postfix Conversion](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/infix_to_postfix_conversion.py)
* [Infix To Prefix Conversion](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/infix_to_prefix_conversion.py)
* [Linked Stack](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/linked_stack.py)
* [Next Greater Element](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/next_greater_element.py)
* [Postfix Evaluation](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/postfix_evaluation.py)
* [Prefix Evaluation](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/prefix_evaluation.py)
* [Stack](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/stack.py)
* [Stack Using Dll](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/stack_using_dll.py)
* [Stock Span Problem](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/stock_span_problem.py)
* Trie
* [Trie](https://github.com/TheAlgorithms/Python/blob/master/data_structures/trie/trie.py)
## Digital Image Processing
* [Change Brightness](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/change_brightness.py)
* [Change Contrast](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/change_contrast.py)
* [Convert To Negative](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/convert_to_negative.py)
* Dithering
* [Burkes](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/dithering/burkes.py)
* Edge Detection
* [Canny](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/edge_detection/canny.py)
* Filters
* [Bilateral Filter](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/filters/bilateral_filter.py)
* [Convolve](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/filters/convolve.py)
* [Gaussian Filter](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/filters/gaussian_filter.py)
* [Median Filter](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/filters/median_filter.py)
* [Sobel Filter](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/filters/sobel_filter.py)
* Histogram Equalization
* [Histogram Stretch](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/histogram_equalization/histogram_stretch.py)
* [Index Calculation](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/index_calculation.py)
* Resize
* [Resize](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/resize/resize.py)
* Rotation
* [Rotation](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/rotation/rotation.py)
* [Sepia](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/sepia.py)
* [Test Digital Image Processing](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/test_digital_image_processing.py)
## Divide And Conquer
* [Closest Pair Of Points](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/closest_pair_of_points.py)
* [Convex Hull](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/convex_hull.py)
* [Heaps Algorithm](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/heaps_algorithm.py)
* [Heaps Algorithm Iterative](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/heaps_algorithm_iterative.py)
* [Inversions](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/inversions.py)
* [Kth Order Statistic](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/kth_order_statistic.py)
* [Max Difference Pair](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/max_difference_pair.py)
* [Max Subarray Sum](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/max_subarray_sum.py)
* [Mergesort](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/mergesort.py)
* [Peak](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/peak.py)
* [Power](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/power.py)
* [Strassen Matrix Multiplication](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/strassen_matrix_multiplication.py)
## Dynamic Programming
* [Abbreviation](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/abbreviation.py)
* [Bitmask](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/bitmask.py)
* [Catalan Numbers](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/catalan_numbers.py)
* [Climbing Stairs](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/climbing_stairs.py)
* [Edit Distance](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/edit_distance.py)
* [Factorial](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/factorial.py)
* [Fast Fibonacci](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/fast_fibonacci.py)
* [Fibonacci](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/fibonacci.py)
* [Floyd Warshall](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/floyd_warshall.py)
* [Fractional Knapsack](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/fractional_knapsack.py)
* [Fractional Knapsack 2](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/fractional_knapsack_2.py)
* [Integer Partition](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/integer_partition.py)
* [Iterating Through Submasks](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/iterating_through_submasks.py)
* [Knapsack](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/knapsack.py)
* [Longest Common Subsequence](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/longest_common_subsequence.py)
* [Longest Increasing Subsequence](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/longest_increasing_subsequence.py)
* [Longest Increasing Subsequence O(Nlogn)](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/longest_increasing_subsequence_o(nlogn).py)
* [Longest Sub Array](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/longest_sub_array.py)
* [Matrix Chain Order](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/matrix_chain_order.py)
* [Max Non Adjacent Sum](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/max_non_adjacent_sum.py)
* [Max Sub Array](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/max_sub_array.py)
* [Max Sum Contiguous Subsequence](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/max_sum_contiguous_subsequence.py)
* [Minimum Coin Change](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/minimum_coin_change.py)
* [Minimum Cost Path](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/minimum_cost_path.py)
* [Minimum Partition](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/minimum_partition.py)
* [Minimum Steps To One](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/minimum_steps_to_one.py)
* [Optimal Binary Search Tree](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/optimal_binary_search_tree.py)
* [Rod Cutting](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/rod_cutting.py)
* [Subset Generation](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/subset_generation.py)
* [Sum Of Subset](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/sum_of_subset.py)
## Electronics
* [Electric Power](https://github.com/TheAlgorithms/Python/blob/master/electronics/electric_power.py)
* [Ohms Law](https://github.com/TheAlgorithms/Python/blob/master/electronics/ohms_law.py)
## File Transfer
* [Receive File](https://github.com/TheAlgorithms/Python/blob/master/file_transfer/receive_file.py)
* [Send File](https://github.com/TheAlgorithms/Python/blob/master/file_transfer/send_file.py)
* Tests
* [Test Send File](https://github.com/TheAlgorithms/Python/blob/master/file_transfer/tests/test_send_file.py)
## Fractals
* [Koch Snowflake](https://github.com/TheAlgorithms/Python/blob/master/fractals/koch_snowflake.py)
* [Mandelbrot](https://github.com/TheAlgorithms/Python/blob/master/fractals/mandelbrot.py)
* [Sierpinski Triangle](https://github.com/TheAlgorithms/Python/blob/master/fractals/sierpinski_triangle.py)
## Fuzzy Logic
* [Fuzzy Operations](https://github.com/TheAlgorithms/Python/blob/master/fuzzy_logic/fuzzy_operations.py)
## Genetic Algorithm
* [Basic String](https://github.com/TheAlgorithms/Python/blob/master/genetic_algorithm/basic_string.py)
## Geodesy
* [Haversine Distance](https://github.com/TheAlgorithms/Python/blob/master/geodesy/haversine_distance.py)
* [Lamberts Ellipsoidal Distance](https://github.com/TheAlgorithms/Python/blob/master/geodesy/lamberts_ellipsoidal_distance.py)
## Graphics
* [Bezier Curve](https://github.com/TheAlgorithms/Python/blob/master/graphics/bezier_curve.py)
* [Vector3 For 2D Rendering](https://github.com/TheAlgorithms/Python/blob/master/graphics/vector3_for_2d_rendering.py)
## Graphs
* [A Star](https://github.com/TheAlgorithms/Python/blob/master/graphs/a_star.py)
* [Articulation Points](https://github.com/TheAlgorithms/Python/blob/master/graphs/articulation_points.py)
* [Basic Graphs](https://github.com/TheAlgorithms/Python/blob/master/graphs/basic_graphs.py)
* [Bellman Ford](https://github.com/TheAlgorithms/Python/blob/master/graphs/bellman_ford.py)
* [Bfs Shortest Path](https://github.com/TheAlgorithms/Python/blob/master/graphs/bfs_shortest_path.py)
* [Bfs Zero One Shortest Path](https://github.com/TheAlgorithms/Python/blob/master/graphs/bfs_zero_one_shortest_path.py)
* [Bidirectional A Star](https://github.com/TheAlgorithms/Python/blob/master/graphs/bidirectional_a_star.py)
* [Bidirectional Breadth First Search](https://github.com/TheAlgorithms/Python/blob/master/graphs/bidirectional_breadth_first_search.py)
* [Boruvka](https://github.com/TheAlgorithms/Python/blob/master/graphs/boruvka.py)
* [Breadth First Search](https://github.com/TheAlgorithms/Python/blob/master/graphs/breadth_first_search.py)
* [Breadth First Search 2](https://github.com/TheAlgorithms/Python/blob/master/graphs/breadth_first_search_2.py)
* [Breadth First Search Shortest Path](https://github.com/TheAlgorithms/Python/blob/master/graphs/breadth_first_search_shortest_path.py)
* [Check Bipartite Graph Bfs](https://github.com/TheAlgorithms/Python/blob/master/graphs/check_bipartite_graph_bfs.py)
* [Check Bipartite Graph Dfs](https://github.com/TheAlgorithms/Python/blob/master/graphs/check_bipartite_graph_dfs.py)
* [Connected Components](https://github.com/TheAlgorithms/Python/blob/master/graphs/connected_components.py)
* [Depth First Search](https://github.com/TheAlgorithms/Python/blob/master/graphs/depth_first_search.py)
* [Depth First Search 2](https://github.com/TheAlgorithms/Python/blob/master/graphs/depth_first_search_2.py)
* [Dijkstra](https://github.com/TheAlgorithms/Python/blob/master/graphs/dijkstra.py)
* [Dijkstra 2](https://github.com/TheAlgorithms/Python/blob/master/graphs/dijkstra_2.py)
* [Dijkstra Algorithm](https://github.com/TheAlgorithms/Python/blob/master/graphs/dijkstra_algorithm.py)
* [Dinic](https://github.com/TheAlgorithms/Python/blob/master/graphs/dinic.py)
* [Directed And Undirected (Weighted) Graph](https://github.com/TheAlgorithms/Python/blob/master/graphs/directed_and_undirected_(weighted)_graph.py)
* [Edmonds Karp Multiple Source And Sink](https://github.com/TheAlgorithms/Python/blob/master/graphs/edmonds_karp_multiple_source_and_sink.py)
* [Eulerian Path And Circuit For Undirected Graph](https://github.com/TheAlgorithms/Python/blob/master/graphs/eulerian_path_and_circuit_for_undirected_graph.py)
* [Even Tree](https://github.com/TheAlgorithms/Python/blob/master/graphs/even_tree.py)
* [Finding Bridges](https://github.com/TheAlgorithms/Python/blob/master/graphs/finding_bridges.py)
* [Frequent Pattern Graph Miner](https://github.com/TheAlgorithms/Python/blob/master/graphs/frequent_pattern_graph_miner.py)
* [G Topological Sort](https://github.com/TheAlgorithms/Python/blob/master/graphs/g_topological_sort.py)
* [Gale Shapley Bigraph](https://github.com/TheAlgorithms/Python/blob/master/graphs/gale_shapley_bigraph.py)
* [Graph List](https://github.com/TheAlgorithms/Python/blob/master/graphs/graph_list.py)
* [Graph Matrix](https://github.com/TheAlgorithms/Python/blob/master/graphs/graph_matrix.py)
* [Graphs Floyd Warshall](https://github.com/TheAlgorithms/Python/blob/master/graphs/graphs_floyd_warshall.py)
* [Greedy Best First](https://github.com/TheAlgorithms/Python/blob/master/graphs/greedy_best_first.py)
* [Kahns Algorithm Long](https://github.com/TheAlgorithms/Python/blob/master/graphs/kahns_algorithm_long.py)
* [Kahns Algorithm Topo](https://github.com/TheAlgorithms/Python/blob/master/graphs/kahns_algorithm_topo.py)
* [Karger](https://github.com/TheAlgorithms/Python/blob/master/graphs/karger.py)
* [Markov Chain](https://github.com/TheAlgorithms/Python/blob/master/graphs/markov_chain.py)
* [Minimum Spanning Tree Boruvka](https://github.com/TheAlgorithms/Python/blob/master/graphs/minimum_spanning_tree_boruvka.py)
* [Minimum Spanning Tree Kruskal](https://github.com/TheAlgorithms/Python/blob/master/graphs/minimum_spanning_tree_kruskal.py)
* [Minimum Spanning Tree Kruskal2](https://github.com/TheAlgorithms/Python/blob/master/graphs/minimum_spanning_tree_kruskal2.py)
* [Minimum Spanning Tree Prims](https://github.com/TheAlgorithms/Python/blob/master/graphs/minimum_spanning_tree_prims.py)
* [Minimum Spanning Tree Prims2](https://github.com/TheAlgorithms/Python/blob/master/graphs/minimum_spanning_tree_prims2.py)
* [Multi Heuristic Astar](https://github.com/TheAlgorithms/Python/blob/master/graphs/multi_heuristic_astar.py)
* [Page Rank](https://github.com/TheAlgorithms/Python/blob/master/graphs/page_rank.py)
* [Prim](https://github.com/TheAlgorithms/Python/blob/master/graphs/prim.py)
* [Scc Kosaraju](https://github.com/TheAlgorithms/Python/blob/master/graphs/scc_kosaraju.py)
* [Strongly Connected Components](https://github.com/TheAlgorithms/Python/blob/master/graphs/strongly_connected_components.py)
* [Tarjans Scc](https://github.com/TheAlgorithms/Python/blob/master/graphs/tarjans_scc.py)
* Tests
* [Test Min Spanning Tree Kruskal](https://github.com/TheAlgorithms/Python/blob/master/graphs/tests/test_min_spanning_tree_kruskal.py)
* [Test Min Spanning Tree Prim](https://github.com/TheAlgorithms/Python/blob/master/graphs/tests/test_min_spanning_tree_prim.py)
## Hashes
* [Adler32](https://github.com/TheAlgorithms/Python/blob/master/hashes/adler32.py)
* [Chaos Machine](https://github.com/TheAlgorithms/Python/blob/master/hashes/chaos_machine.py)
* [Djb2](https://github.com/TheAlgorithms/Python/blob/master/hashes/djb2.py)
* [Enigma Machine](https://github.com/TheAlgorithms/Python/blob/master/hashes/enigma_machine.py)
* [Hamming Code](https://github.com/TheAlgorithms/Python/blob/master/hashes/hamming_code.py)
* [Luhn](https://github.com/TheAlgorithms/Python/blob/master/hashes/luhn.py)
* [Md5](https://github.com/TheAlgorithms/Python/blob/master/hashes/md5.py)
* [Sdbm](https://github.com/TheAlgorithms/Python/blob/master/hashes/sdbm.py)
* [Sha1](https://github.com/TheAlgorithms/Python/blob/master/hashes/sha1.py)
## Knapsack
* [Greedy Knapsack](https://github.com/TheAlgorithms/Python/blob/master/knapsack/greedy_knapsack.py)
* [Knapsack](https://github.com/TheAlgorithms/Python/blob/master/knapsack/knapsack.py)
* Tests
* [Test Greedy Knapsack](https://github.com/TheAlgorithms/Python/blob/master/knapsack/tests/test_greedy_knapsack.py)
* [Test Knapsack](https://github.com/TheAlgorithms/Python/blob/master/knapsack/tests/test_knapsack.py)
## Linear Algebra
* Src
* [Conjugate Gradient](https://github.com/TheAlgorithms/Python/blob/master/linear_algebra/src/conjugate_gradient.py)
* [Lib](https://github.com/TheAlgorithms/Python/blob/master/linear_algebra/src/lib.py)
* [Polynom For Points](https://github.com/TheAlgorithms/Python/blob/master/linear_algebra/src/polynom_for_points.py)
* [Power Iteration](https://github.com/TheAlgorithms/Python/blob/master/linear_algebra/src/power_iteration.py)
* [Rayleigh Quotient](https://github.com/TheAlgorithms/Python/blob/master/linear_algebra/src/rayleigh_quotient.py)
* [Test Linear Algebra](https://github.com/TheAlgorithms/Python/blob/master/linear_algebra/src/test_linear_algebra.py)
* [Transformations 2D](https://github.com/TheAlgorithms/Python/blob/master/linear_algebra/src/transformations_2d.py)
## Machine Learning
* [Astar](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/astar.py)
* [Data Transformations](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/data_transformations.py)
* [Decision Tree](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/decision_tree.py)
* Forecasting
* [Run](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/forecasting/run.py)
* [Gaussian Naive Bayes](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/gaussian_naive_bayes.py)
* [Gradient Boosting Regressor](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/gradient_boosting_regressor.py)
* [Gradient Descent](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/gradient_descent.py)
* [K Means Clust](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/k_means_clust.py)
* [K Nearest Neighbours](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/k_nearest_neighbours.py)
* [Knn Sklearn](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/knn_sklearn.py)
* [Linear Discriminant Analysis](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/linear_discriminant_analysis.py)
* [Linear Regression](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/linear_regression.py)
* [Logistic Regression](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/logistic_regression.py)
* Lstm
* [Lstm Prediction](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/lstm/lstm_prediction.py)
* [Multilayer Perceptron Classifier](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/multilayer_perceptron_classifier.py)
* [Polymonial Regression](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/polymonial_regression.py)
* [Random Forest Classifier](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/random_forest_classifier.py)
* [Random Forest Regressor](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/random_forest_regressor.py)
* [Scoring Functions](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/scoring_functions.py)
* [Sequential Minimum Optimization](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/sequential_minimum_optimization.py)
* [Similarity Search](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/similarity_search.py)
* [Support Vector Machines](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/support_vector_machines.py)
* [Word Frequency Functions](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/word_frequency_functions.py)
## Maths
* [3N Plus 1](https://github.com/TheAlgorithms/Python/blob/master/maths/3n_plus_1.py)
* [Abs](https://github.com/TheAlgorithms/Python/blob/master/maths/abs.py)
* [Abs Max](https://github.com/TheAlgorithms/Python/blob/master/maths/abs_max.py)
* [Abs Min](https://github.com/TheAlgorithms/Python/blob/master/maths/abs_min.py)
* [Add](https://github.com/TheAlgorithms/Python/blob/master/maths/add.py)
* [Aliquot Sum](https://github.com/TheAlgorithms/Python/blob/master/maths/aliquot_sum.py)
* [Allocation Number](https://github.com/TheAlgorithms/Python/blob/master/maths/allocation_number.py)
* [Area](https://github.com/TheAlgorithms/Python/blob/master/maths/area.py)
* [Area Under Curve](https://github.com/TheAlgorithms/Python/blob/master/maths/area_under_curve.py)
* [Armstrong Numbers](https://github.com/TheAlgorithms/Python/blob/master/maths/armstrong_numbers.py)
* [Average Mean](https://github.com/TheAlgorithms/Python/blob/master/maths/average_mean.py)
* [Average Median](https://github.com/TheAlgorithms/Python/blob/master/maths/average_median.py)
* [Average Mode](https://github.com/TheAlgorithms/Python/blob/master/maths/average_mode.py)
* [Bailey Borwein Plouffe](https://github.com/TheAlgorithms/Python/blob/master/maths/bailey_borwein_plouffe.py)
* [Basic Maths](https://github.com/TheAlgorithms/Python/blob/master/maths/basic_maths.py)
* [Binary Exp Mod](https://github.com/TheAlgorithms/Python/blob/master/maths/binary_exp_mod.py)
* [Binary Exponentiation](https://github.com/TheAlgorithms/Python/blob/master/maths/binary_exponentiation.py)
* [Binary Exponentiation 2](https://github.com/TheAlgorithms/Python/blob/master/maths/binary_exponentiation_2.py)
* [Binary Exponentiation 3](https://github.com/TheAlgorithms/Python/blob/master/maths/binary_exponentiation_3.py)
* [Binomial Coefficient](https://github.com/TheAlgorithms/Python/blob/master/maths/binomial_coefficient.py)
* [Binomial Distribution](https://github.com/TheAlgorithms/Python/blob/master/maths/binomial_distribution.py)
* [Bisection](https://github.com/TheAlgorithms/Python/blob/master/maths/bisection.py)
* [Ceil](https://github.com/TheAlgorithms/Python/blob/master/maths/ceil.py)
* [Chudnovsky Algorithm](https://github.com/TheAlgorithms/Python/blob/master/maths/chudnovsky_algorithm.py)
* [Collatz Sequence](https://github.com/TheAlgorithms/Python/blob/master/maths/collatz_sequence.py)
* [Combinations](https://github.com/TheAlgorithms/Python/blob/master/maths/combinations.py)
* [Decimal Isolate](https://github.com/TheAlgorithms/Python/blob/master/maths/decimal_isolate.py)
* [Double Factorial Recursive](https://github.com/TheAlgorithms/Python/blob/master/maths/double_factorial_recursive.py)
* [Entropy](https://github.com/TheAlgorithms/Python/blob/master/maths/entropy.py)
* [Euclidean Distance](https://github.com/TheAlgorithms/Python/blob/master/maths/euclidean_distance.py)
* [Euclidean Gcd](https://github.com/TheAlgorithms/Python/blob/master/maths/euclidean_gcd.py)
* [Euler Method](https://github.com/TheAlgorithms/Python/blob/master/maths/euler_method.py)
* [Eulers Totient](https://github.com/TheAlgorithms/Python/blob/master/maths/eulers_totient.py)
* [Extended Euclidean Algorithm](https://github.com/TheAlgorithms/Python/blob/master/maths/extended_euclidean_algorithm.py)
* [Factorial Iterative](https://github.com/TheAlgorithms/Python/blob/master/maths/factorial_iterative.py)
* [Factorial Python](https://github.com/TheAlgorithms/Python/blob/master/maths/factorial_python.py)
* [Factorial Recursive](https://github.com/TheAlgorithms/Python/blob/master/maths/factorial_recursive.py)
* [Factors](https://github.com/TheAlgorithms/Python/blob/master/maths/factors.py)
* [Fermat Little Theorem](https://github.com/TheAlgorithms/Python/blob/master/maths/fermat_little_theorem.py)
* [Fibonacci](https://github.com/TheAlgorithms/Python/blob/master/maths/fibonacci.py)
* [Fibonacci Sequence Recursion](https://github.com/TheAlgorithms/Python/blob/master/maths/fibonacci_sequence_recursion.py)
* [Find Max](https://github.com/TheAlgorithms/Python/blob/master/maths/find_max.py)
* [Find Max Recursion](https://github.com/TheAlgorithms/Python/blob/master/maths/find_max_recursion.py)
* [Find Min](https://github.com/TheAlgorithms/Python/blob/master/maths/find_min.py)
* [Find Min Recursion](https://github.com/TheAlgorithms/Python/blob/master/maths/find_min_recursion.py)
* [Floor](https://github.com/TheAlgorithms/Python/blob/master/maths/floor.py)
* [Gamma](https://github.com/TheAlgorithms/Python/blob/master/maths/gamma.py)
* [Gaussian](https://github.com/TheAlgorithms/Python/blob/master/maths/gaussian.py)
* [Greatest Common Divisor](https://github.com/TheAlgorithms/Python/blob/master/maths/greatest_common_divisor.py)
* [Greedy Coin Change](https://github.com/TheAlgorithms/Python/blob/master/maths/greedy_coin_change.py)
* [Hardy Ramanujanalgo](https://github.com/TheAlgorithms/Python/blob/master/maths/hardy_ramanujanalgo.py)
* [Integration By Simpson Approx](https://github.com/TheAlgorithms/Python/blob/master/maths/integration_by_simpson_approx.py)
* [Is Ip V4 Address Valid](https://github.com/TheAlgorithms/Python/blob/master/maths/is_ip_v4_address_valid.py)
* [Is Square Free](https://github.com/TheAlgorithms/Python/blob/master/maths/is_square_free.py)
* [Jaccard Similarity](https://github.com/TheAlgorithms/Python/blob/master/maths/jaccard_similarity.py)
* [Kadanes](https://github.com/TheAlgorithms/Python/blob/master/maths/kadanes.py)
* [Karatsuba](https://github.com/TheAlgorithms/Python/blob/master/maths/karatsuba.py)
* [Krishnamurthy Number](https://github.com/TheAlgorithms/Python/blob/master/maths/krishnamurthy_number.py)
* [Kth Lexicographic Permutation](https://github.com/TheAlgorithms/Python/blob/master/maths/kth_lexicographic_permutation.py)
* [Largest Of Very Large Numbers](https://github.com/TheAlgorithms/Python/blob/master/maths/largest_of_very_large_numbers.py)
* [Largest Subarray Sum](https://github.com/TheAlgorithms/Python/blob/master/maths/largest_subarray_sum.py)
* [Least Common Multiple](https://github.com/TheAlgorithms/Python/blob/master/maths/least_common_multiple.py)
* [Line Length](https://github.com/TheAlgorithms/Python/blob/master/maths/line_length.py)
* [Lucas Lehmer Primality Test](https://github.com/TheAlgorithms/Python/blob/master/maths/lucas_lehmer_primality_test.py)
* [Lucas Series](https://github.com/TheAlgorithms/Python/blob/master/maths/lucas_series.py)
* [Matrix Exponentiation](https://github.com/TheAlgorithms/Python/blob/master/maths/matrix_exponentiation.py)
* [Max Sum Sliding Window](https://github.com/TheAlgorithms/Python/blob/master/maths/max_sum_sliding_window.py)
* [Median Of Two Arrays](https://github.com/TheAlgorithms/Python/blob/master/maths/median_of_two_arrays.py)
* [Miller Rabin](https://github.com/TheAlgorithms/Python/blob/master/maths/miller_rabin.py)
* [Mobius Function](https://github.com/TheAlgorithms/Python/blob/master/maths/mobius_function.py)
* [Modular Exponential](https://github.com/TheAlgorithms/Python/blob/master/maths/modular_exponential.py)
* [Monte Carlo](https://github.com/TheAlgorithms/Python/blob/master/maths/monte_carlo.py)
* [Monte Carlo Dice](https://github.com/TheAlgorithms/Python/blob/master/maths/monte_carlo_dice.py)
* [Newton Raphson](https://github.com/TheAlgorithms/Python/blob/master/maths/newton_raphson.py)
* [Number Of Digits](https://github.com/TheAlgorithms/Python/blob/master/maths/number_of_digits.py)
* [Numerical Integration](https://github.com/TheAlgorithms/Python/blob/master/maths/numerical_integration.py)
* [Perfect Cube](https://github.com/TheAlgorithms/Python/blob/master/maths/perfect_cube.py)
* [Perfect Number](https://github.com/TheAlgorithms/Python/blob/master/maths/perfect_number.py)
* [Perfect Square](https://github.com/TheAlgorithms/Python/blob/master/maths/perfect_square.py)
* [Pi Monte Carlo Estimation](https://github.com/TheAlgorithms/Python/blob/master/maths/pi_monte_carlo_estimation.py)
* [Polynomial Evaluation](https://github.com/TheAlgorithms/Python/blob/master/maths/polynomial_evaluation.py)
* [Power Using Recursion](https://github.com/TheAlgorithms/Python/blob/master/maths/power_using_recursion.py)
* [Prime Check](https://github.com/TheAlgorithms/Python/blob/master/maths/prime_check.py)
* [Prime Factors](https://github.com/TheAlgorithms/Python/blob/master/maths/prime_factors.py)
* [Prime Numbers](https://github.com/TheAlgorithms/Python/blob/master/maths/prime_numbers.py)
* [Prime Sieve Eratosthenes](https://github.com/TheAlgorithms/Python/blob/master/maths/prime_sieve_eratosthenes.py)
* [Primelib](https://github.com/TheAlgorithms/Python/blob/master/maths/primelib.py)
* [Pythagoras](https://github.com/TheAlgorithms/Python/blob/master/maths/pythagoras.py)
* [Qr Decomposition](https://github.com/TheAlgorithms/Python/blob/master/maths/qr_decomposition.py)
* [Quadratic Equations Complex Numbers](https://github.com/TheAlgorithms/Python/blob/master/maths/quadratic_equations_complex_numbers.py)
* [Radians](https://github.com/TheAlgorithms/Python/blob/master/maths/radians.py)
* [Radix2 Fft](https://github.com/TheAlgorithms/Python/blob/master/maths/radix2_fft.py)
* [Relu](https://github.com/TheAlgorithms/Python/blob/master/maths/relu.py)
* [Runge Kutta](https://github.com/TheAlgorithms/Python/blob/master/maths/runge_kutta.py)
* [Segmented Sieve](https://github.com/TheAlgorithms/Python/blob/master/maths/segmented_sieve.py)
* Series
* [Arithmetic Mean](https://github.com/TheAlgorithms/Python/blob/master/maths/series/arithmetic_mean.py)
* [Geometric Mean](https://github.com/TheAlgorithms/Python/blob/master/maths/series/geometric_mean.py)
* [Geometric Series](https://github.com/TheAlgorithms/Python/blob/master/maths/series/geometric_series.py)
* [Harmonic Series](https://github.com/TheAlgorithms/Python/blob/master/maths/series/harmonic_series.py)
* [P Series](https://github.com/TheAlgorithms/Python/blob/master/maths/series/p_series.py)
* [Sieve Of Eratosthenes](https://github.com/TheAlgorithms/Python/blob/master/maths/sieve_of_eratosthenes.py)
* [Sigmoid](https://github.com/TheAlgorithms/Python/blob/master/maths/sigmoid.py)
* [Simpson Rule](https://github.com/TheAlgorithms/Python/blob/master/maths/simpson_rule.py)
* [Softmax](https://github.com/TheAlgorithms/Python/blob/master/maths/softmax.py)
* [Square Root](https://github.com/TheAlgorithms/Python/blob/master/maths/square_root.py)
* [Sum Of Arithmetic Series](https://github.com/TheAlgorithms/Python/blob/master/maths/sum_of_arithmetic_series.py)
* [Sum Of Digits](https://github.com/TheAlgorithms/Python/blob/master/maths/sum_of_digits.py)
* [Sum Of Geometric Progression](https://github.com/TheAlgorithms/Python/blob/master/maths/sum_of_geometric_progression.py)
* [Test Prime Check](https://github.com/TheAlgorithms/Python/blob/master/maths/test_prime_check.py)
* [Trapezoidal Rule](https://github.com/TheAlgorithms/Python/blob/master/maths/trapezoidal_rule.py)
* [Triplet Sum](https://github.com/TheAlgorithms/Python/blob/master/maths/triplet_sum.py)
* [Two Pointer](https://github.com/TheAlgorithms/Python/blob/master/maths/two_pointer.py)
* [Two Sum](https://github.com/TheAlgorithms/Python/blob/master/maths/two_sum.py)
* [Ugly Numbers](https://github.com/TheAlgorithms/Python/blob/master/maths/ugly_numbers.py)
* [Volume](https://github.com/TheAlgorithms/Python/blob/master/maths/volume.py)
* [Zellers Congruence](https://github.com/TheAlgorithms/Python/blob/master/maths/zellers_congruence.py)
## Matrix
* [Count Islands In Matrix](https://github.com/TheAlgorithms/Python/blob/master/matrix/count_islands_in_matrix.py)
* [Inverse Of Matrix](https://github.com/TheAlgorithms/Python/blob/master/matrix/inverse_of_matrix.py)
* [Matrix Class](https://github.com/TheAlgorithms/Python/blob/master/matrix/matrix_class.py)
* [Matrix Operation](https://github.com/TheAlgorithms/Python/blob/master/matrix/matrix_operation.py)
* [Nth Fibonacci Using Matrix Exponentiation](https://github.com/TheAlgorithms/Python/blob/master/matrix/nth_fibonacci_using_matrix_exponentiation.py)
* [Rotate Matrix](https://github.com/TheAlgorithms/Python/blob/master/matrix/rotate_matrix.py)
* [Searching In Sorted Matrix](https://github.com/TheAlgorithms/Python/blob/master/matrix/searching_in_sorted_matrix.py)
* [Sherman Morrison](https://github.com/TheAlgorithms/Python/blob/master/matrix/sherman_morrison.py)
* [Spiral Print](https://github.com/TheAlgorithms/Python/blob/master/matrix/spiral_print.py)
* Tests
* [Test Matrix Operation](https://github.com/TheAlgorithms/Python/blob/master/matrix/tests/test_matrix_operation.py)
## Networking Flow
* [Ford Fulkerson](https://github.com/TheAlgorithms/Python/blob/master/networking_flow/ford_fulkerson.py)
* [Minimum Cut](https://github.com/TheAlgorithms/Python/blob/master/networking_flow/minimum_cut.py)
## Neural Network
* [2 Hidden Layers Neural Network](https://github.com/TheAlgorithms/Python/blob/master/neural_network/2_hidden_layers_neural_network.py)
* [Back Propagation Neural Network](https://github.com/TheAlgorithms/Python/blob/master/neural_network/back_propagation_neural_network.py)
* [Convolution Neural Network](https://github.com/TheAlgorithms/Python/blob/master/neural_network/convolution_neural_network.py)
* [Perceptron](https://github.com/TheAlgorithms/Python/blob/master/neural_network/perceptron.py)
## Other
* [Activity Selection](https://github.com/TheAlgorithms/Python/blob/master/other/activity_selection.py)
* [Date To Weekday](https://github.com/TheAlgorithms/Python/blob/master/other/date_to_weekday.py)
* [Davisb Putnamb Logemannb Loveland](https://github.com/TheAlgorithms/Python/blob/master/other/davisb_putnamb_logemannb_loveland.py)
* [Dijkstra Bankers Algorithm](https://github.com/TheAlgorithms/Python/blob/master/other/dijkstra_bankers_algorithm.py)
* [Doomsday](https://github.com/TheAlgorithms/Python/blob/master/other/doomsday.py)
* [Fischer Yates Shuffle](https://github.com/TheAlgorithms/Python/blob/master/other/fischer_yates_shuffle.py)
* [Gauss Easter](https://github.com/TheAlgorithms/Python/blob/master/other/gauss_easter.py)
* [Graham Scan](https://github.com/TheAlgorithms/Python/blob/master/other/graham_scan.py)
* [Greedy](https://github.com/TheAlgorithms/Python/blob/master/other/greedy.py)
* [Least Recently Used](https://github.com/TheAlgorithms/Python/blob/master/other/least_recently_used.py)
* [Lfu Cache](https://github.com/TheAlgorithms/Python/blob/master/other/lfu_cache.py)
* [Linear Congruential Generator](https://github.com/TheAlgorithms/Python/blob/master/other/linear_congruential_generator.py)
* [Lru Cache](https://github.com/TheAlgorithms/Python/blob/master/other/lru_cache.py)
* [Magicdiamondpattern](https://github.com/TheAlgorithms/Python/blob/master/other/magicdiamondpattern.py)
* [Nested Brackets](https://github.com/TheAlgorithms/Python/blob/master/other/nested_brackets.py)
* [Password Generator](https://github.com/TheAlgorithms/Python/blob/master/other/password_generator.py)
* [Scoring Algorithm](https://github.com/TheAlgorithms/Python/blob/master/other/scoring_algorithm.py)
* [Sdes](https://github.com/TheAlgorithms/Python/blob/master/other/sdes.py)
* [Tower Of Hanoi](https://github.com/TheAlgorithms/Python/blob/master/other/tower_of_hanoi.py)
## Physics
* [N Body Simulation](https://github.com/TheAlgorithms/Python/blob/master/physics/n_body_simulation.py)
## Project Euler
* Problem 001
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_001/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_001/sol2.py)
* [Sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_001/sol3.py)
* [Sol4](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_001/sol4.py)
* [Sol5](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_001/sol5.py)
* [Sol6](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_001/sol6.py)
* [Sol7](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_001/sol7.py)
* Problem 002
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_002/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_002/sol2.py)
* [Sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_002/sol3.py)
* [Sol4](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_002/sol4.py)
* [Sol5](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_002/sol5.py)
* Problem 003
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_003/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_003/sol2.py)
* [Sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_003/sol3.py)
* Problem 004
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_004/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_004/sol2.py)
* Problem 005
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_005/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_005/sol2.py)
* Problem 006
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_006/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_006/sol2.py)
* [Sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_006/sol3.py)
* [Sol4](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_006/sol4.py)
* Problem 007
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_007/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_007/sol2.py)
* [Sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_007/sol3.py)
* Problem 008
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_008/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_008/sol2.py)
* [Sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_008/sol3.py)
* Problem 009
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_009/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_009/sol2.py)
* [Sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_009/sol3.py)
* Problem 010
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_010/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_010/sol2.py)
* [Sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_010/sol3.py)
* Problem 011
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_011/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_011/sol2.py)
* Problem 012
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_012/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_012/sol2.py)
* Problem 013
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_013/sol1.py)
* Problem 014
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_014/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_014/sol2.py)
* Problem 015
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_015/sol1.py)
* Problem 016
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_016/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_016/sol2.py)
* Problem 017
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_017/sol1.py)
* Problem 018
* [Solution](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_018/solution.py)
* Problem 019
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_019/sol1.py)
* Problem 020
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_020/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_020/sol2.py)
* [Sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_020/sol3.py)
* [Sol4](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_020/sol4.py)
* Problem 021
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_021/sol1.py)
* Problem 022
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_022/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_022/sol2.py)
* Problem 023
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_023/sol1.py)
* Problem 024
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_024/sol1.py)
* Problem 025
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_025/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_025/sol2.py)
* [Sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_025/sol3.py)
* Problem 026
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_026/sol1.py)
* Problem 027
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_027/sol1.py)
* Problem 028
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_028/sol1.py)
* Problem 029
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_029/sol1.py)
* Problem 030
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_030/sol1.py)
* Problem 031
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_031/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_031/sol2.py)
* Problem 032
* [Sol32](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_032/sol32.py)
* Problem 033
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_033/sol1.py)
* Problem 034
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_034/sol1.py)
* Problem 035
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_035/sol1.py)
* Problem 036
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_036/sol1.py)
* Problem 037
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_037/sol1.py)
* Problem 038
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_038/sol1.py)
* Problem 039
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_039/sol1.py)
* Problem 040
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_040/sol1.py)
* Problem 041
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_041/sol1.py)
* Problem 042
* [Solution42](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_042/solution42.py)
* Problem 043
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_043/sol1.py)
* Problem 044
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_044/sol1.py)
* Problem 045
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_045/sol1.py)
* Problem 046
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_046/sol1.py)
* Problem 047
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_047/sol1.py)
* Problem 048
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_048/sol1.py)
* Problem 049
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_049/sol1.py)
* Problem 050
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_050/sol1.py)
* Problem 051
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_051/sol1.py)
* Problem 052
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_052/sol1.py)
* Problem 053
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_053/sol1.py)
* Problem 054
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_054/sol1.py)
* [Test Poker Hand](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_054/test_poker_hand.py)
* Problem 055
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_055/sol1.py)
* Problem 056
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_056/sol1.py)
* Problem 057
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_057/sol1.py)
* Problem 058
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_058/sol1.py)
* Problem 059
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_059/sol1.py)
* Problem 062
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_062/sol1.py)
* Problem 063
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_063/sol1.py)
* Problem 064
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_064/sol1.py)
* Problem 065
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_065/sol1.py)
* Problem 067
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_067/sol1.py)
* Problem 069
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_069/sol1.py)
* Problem 070
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_070/sol1.py)
* Problem 071
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_071/sol1.py)
* Problem 072
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_072/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_072/sol2.py)
* Problem 074
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_074/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_074/sol2.py)
* Problem 075
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_075/sol1.py)
* Problem 076
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_076/sol1.py)
* Problem 077
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_077/sol1.py)
* Problem 080
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_080/sol1.py)
* Problem 081
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_081/sol1.py)
* Problem 085
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_085/sol1.py)
* Problem 086
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_086/sol1.py)
* Problem 087
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_087/sol1.py)
* Problem 089
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_089/sol1.py)
* Problem 091
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_091/sol1.py)
* Problem 097
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_097/sol1.py)
* Problem 099
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_099/sol1.py)
* Problem 101
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_101/sol1.py)
* Problem 102
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_102/sol1.py)
* Problem 107
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_107/sol1.py)
* Problem 109
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_109/sol1.py)
* Problem 112
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_112/sol1.py)
* Problem 113
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_113/sol1.py)
* Problem 119
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_119/sol1.py)
* Problem 120
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_120/sol1.py)
* Problem 121
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_121/sol1.py)
* Problem 123
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_123/sol1.py)
* Problem 125
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_125/sol1.py)
* Problem 129
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_129/sol1.py)
* Problem 135
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_135/sol1.py)
* Problem 144
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_144/sol1.py)
* Problem 173
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_173/sol1.py)
* Problem 174
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_174/sol1.py)
* Problem 180
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_180/sol1.py)
* Problem 188
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_188/sol1.py)
* Problem 191
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_191/sol1.py)
* Problem 203
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_203/sol1.py)
* Problem 206
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_206/sol1.py)
* Problem 207
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_207/sol1.py)
* Problem 234
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_234/sol1.py)
* Problem 301
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_301/sol1.py)
* Problem 551
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_551/sol1.py)
## Quantum
* [Deutsch Jozsa](https://github.com/TheAlgorithms/Python/blob/master/quantum/deutsch_jozsa.py)
* [Half Adder](https://github.com/TheAlgorithms/Python/blob/master/quantum/half_adder.py)
* [Not Gate](https://github.com/TheAlgorithms/Python/blob/master/quantum/not_gate.py)
* [Quantum Entanglement](https://github.com/TheAlgorithms/Python/blob/master/quantum/quantum_entanglement.py)
* [Ripple Adder Classic](https://github.com/TheAlgorithms/Python/blob/master/quantum/ripple_adder_classic.py)
* [Single Qubit Measure](https://github.com/TheAlgorithms/Python/blob/master/quantum/single_qubit_measure.py)
## Scheduling
* [First Come First Served](https://github.com/TheAlgorithms/Python/blob/master/scheduling/first_come_first_served.py)
* [Round Robin](https://github.com/TheAlgorithms/Python/blob/master/scheduling/round_robin.py)
* [Shortest Job First](https://github.com/TheAlgorithms/Python/blob/master/scheduling/shortest_job_first.py)
## Searches
* [Binary Search](https://github.com/TheAlgorithms/Python/blob/master/searches/binary_search.py)
* [Binary Tree Traversal](https://github.com/TheAlgorithms/Python/blob/master/searches/binary_tree_traversal.py)
* [Double Linear Search](https://github.com/TheAlgorithms/Python/blob/master/searches/double_linear_search.py)
* [Double Linear Search Recursion](https://github.com/TheAlgorithms/Python/blob/master/searches/double_linear_search_recursion.py)
* [Fibonacci Search](https://github.com/TheAlgorithms/Python/blob/master/searches/fibonacci_search.py)
* [Hill Climbing](https://github.com/TheAlgorithms/Python/blob/master/searches/hill_climbing.py)
* [Interpolation Search](https://github.com/TheAlgorithms/Python/blob/master/searches/interpolation_search.py)
* [Jump Search](https://github.com/TheAlgorithms/Python/blob/master/searches/jump_search.py)
* [Linear Search](https://github.com/TheAlgorithms/Python/blob/master/searches/linear_search.py)
* [Quick Select](https://github.com/TheAlgorithms/Python/blob/master/searches/quick_select.py)
* [Sentinel Linear Search](https://github.com/TheAlgorithms/Python/blob/master/searches/sentinel_linear_search.py)
* [Simple Binary Search](https://github.com/TheAlgorithms/Python/blob/master/searches/simple_binary_search.py)
* [Simulated Annealing](https://github.com/TheAlgorithms/Python/blob/master/searches/simulated_annealing.py)
* [Tabu Search](https://github.com/TheAlgorithms/Python/blob/master/searches/tabu_search.py)
* [Ternary Search](https://github.com/TheAlgorithms/Python/blob/master/searches/ternary_search.py)
## Sorts
* [Bead Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/bead_sort.py)
* [Bitonic Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/bitonic_sort.py)
* [Bogo Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/bogo_sort.py)
* [Bubble Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/bubble_sort.py)
* [Bucket Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/bucket_sort.py)
* [Cocktail Shaker Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/cocktail_shaker_sort.py)
* [Comb Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/comb_sort.py)
* [Counting Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/counting_sort.py)
* [Cycle Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/cycle_sort.py)
* [Double Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/double_sort.py)
* [Dutch National Flag Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/dutch_national_flag_sort.py)
* [Exchange Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/exchange_sort.py)
* [External Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/external_sort.py)
* [Gnome Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/gnome_sort.py)
* [Heap Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/heap_sort.py)
* [Insertion Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/insertion_sort.py)
* [Intro Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/intro_sort.py)
* [Iterative Merge Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/iterative_merge_sort.py)
* [Merge Insertion Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/merge_insertion_sort.py)
* [Merge Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/merge_sort.py)
* [Msd Radix Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/msd_radix_sort.py)
* [Natural Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/natural_sort.py)
* [Odd Even Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/odd_even_sort.py)
* [Odd Even Transposition Parallel](https://github.com/TheAlgorithms/Python/blob/master/sorts/odd_even_transposition_parallel.py)
* [Odd Even Transposition Single Threaded](https://github.com/TheAlgorithms/Python/blob/master/sorts/odd_even_transposition_single_threaded.py)
* [Pancake Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/pancake_sort.py)
* [Patience Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/patience_sort.py)
* [Pigeon Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/pigeon_sort.py)
* [Pigeonhole Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/pigeonhole_sort.py)
* [Quick Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/quick_sort.py)
* [Quick Sort 3 Partition](https://github.com/TheAlgorithms/Python/blob/master/sorts/quick_sort_3_partition.py)
* [Radix Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/radix_sort.py)
* [Random Normal Distribution Quicksort](https://github.com/TheAlgorithms/Python/blob/master/sorts/random_normal_distribution_quicksort.py)
* [Random Pivot Quick Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/random_pivot_quick_sort.py)
* [Recursive Bubble Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/recursive_bubble_sort.py)
* [Recursive Insertion Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/recursive_insertion_sort.py)
* [Recursive Mergesort Array](https://github.com/TheAlgorithms/Python/blob/master/sorts/recursive_mergesort_array.py)
* [Recursive Quick Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/recursive_quick_sort.py)
* [Selection Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/selection_sort.py)
* [Shell Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/shell_sort.py)
* [Slowsort](https://github.com/TheAlgorithms/Python/blob/master/sorts/slowsort.py)
* [Stooge Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/stooge_sort.py)
* [Strand Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/strand_sort.py)
* [Tim Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/tim_sort.py)
* [Topological Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/topological_sort.py)
* [Tree Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/tree_sort.py)
* [Unknown Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/unknown_sort.py)
* [Wiggle Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/wiggle_sort.py)
## Strings
* [Aho Corasick](https://github.com/TheAlgorithms/Python/blob/master/strings/aho_corasick.py)
* [Alternative String Arrange](https://github.com/TheAlgorithms/Python/blob/master/strings/alternative_string_arrange.py)
* [Anagrams](https://github.com/TheAlgorithms/Python/blob/master/strings/anagrams.py)
* [Autocomplete Using Trie](https://github.com/TheAlgorithms/Python/blob/master/strings/autocomplete_using_trie.py)
* [Boyer Moore Search](https://github.com/TheAlgorithms/Python/blob/master/strings/boyer_moore_search.py)
* [Can String Be Rearranged As Palindrome](https://github.com/TheAlgorithms/Python/blob/master/strings/can_string_be_rearranged_as_palindrome.py)
* [Capitalize](https://github.com/TheAlgorithms/Python/blob/master/strings/capitalize.py)
* [Check Anagrams](https://github.com/TheAlgorithms/Python/blob/master/strings/check_anagrams.py)
* [Check Pangram](https://github.com/TheAlgorithms/Python/blob/master/strings/check_pangram.py)
* [Detecting English Programmatically](https://github.com/TheAlgorithms/Python/blob/master/strings/detecting_english_programmatically.py)
* [Frequency Finder](https://github.com/TheAlgorithms/Python/blob/master/strings/frequency_finder.py)
* [Indian Phone Validator](https://github.com/TheAlgorithms/Python/blob/master/strings/indian_phone_validator.py)
* [Is Palindrome](https://github.com/TheAlgorithms/Python/blob/master/strings/is_palindrome.py)
* [Jaro Winkler](https://github.com/TheAlgorithms/Python/blob/master/strings/jaro_winkler.py)
* [Knuth Morris Pratt](https://github.com/TheAlgorithms/Python/blob/master/strings/knuth_morris_pratt.py)
* [Levenshtein Distance](https://github.com/TheAlgorithms/Python/blob/master/strings/levenshtein_distance.py)
* [Lower](https://github.com/TheAlgorithms/Python/blob/master/strings/lower.py)
* [Manacher](https://github.com/TheAlgorithms/Python/blob/master/strings/manacher.py)
* [Min Cost String Conversion](https://github.com/TheAlgorithms/Python/blob/master/strings/min_cost_string_conversion.py)
* [Naive String Search](https://github.com/TheAlgorithms/Python/blob/master/strings/naive_string_search.py)
* [Palindrome](https://github.com/TheAlgorithms/Python/blob/master/strings/palindrome.py)
* [Prefix Function](https://github.com/TheAlgorithms/Python/blob/master/strings/prefix_function.py)
* [Rabin Karp](https://github.com/TheAlgorithms/Python/blob/master/strings/rabin_karp.py)
* [Remove Duplicate](https://github.com/TheAlgorithms/Python/blob/master/strings/remove_duplicate.py)
* [Reverse Letters](https://github.com/TheAlgorithms/Python/blob/master/strings/reverse_letters.py)
* [Reverse Words](https://github.com/TheAlgorithms/Python/blob/master/strings/reverse_words.py)
* [Split](https://github.com/TheAlgorithms/Python/blob/master/strings/split.py)
* [Swap Case](https://github.com/TheAlgorithms/Python/blob/master/strings/swap_case.py)
* [Upper](https://github.com/TheAlgorithms/Python/blob/master/strings/upper.py)
* [Word Occurrence](https://github.com/TheAlgorithms/Python/blob/master/strings/word_occurrence.py)
* [Word Patterns](https://github.com/TheAlgorithms/Python/blob/master/strings/word_patterns.py)
* [Z Function](https://github.com/TheAlgorithms/Python/blob/master/strings/z_function.py)
## Web Programming
* [Co2 Emission](https://github.com/TheAlgorithms/Python/blob/master/web_programming/co2_emission.py)
* [Covid Stats Via Xpath](https://github.com/TheAlgorithms/Python/blob/master/web_programming/covid_stats_via_xpath.py)
* [Crawl Google Results](https://github.com/TheAlgorithms/Python/blob/master/web_programming/crawl_google_results.py)
* [Crawl Google Scholar Citation](https://github.com/TheAlgorithms/Python/blob/master/web_programming/crawl_google_scholar_citation.py)
* [Currency Converter](https://github.com/TheAlgorithms/Python/blob/master/web_programming/currency_converter.py)
* [Current Stock Price](https://github.com/TheAlgorithms/Python/blob/master/web_programming/current_stock_price.py)
* [Current Weather](https://github.com/TheAlgorithms/Python/blob/master/web_programming/current_weather.py)
* [Daily Horoscope](https://github.com/TheAlgorithms/Python/blob/master/web_programming/daily_horoscope.py)
* [Emails From Url](https://github.com/TheAlgorithms/Python/blob/master/web_programming/emails_from_url.py)
* [Fetch Bbc News](https://github.com/TheAlgorithms/Python/blob/master/web_programming/fetch_bbc_news.py)
* [Fetch Github Info](https://github.com/TheAlgorithms/Python/blob/master/web_programming/fetch_github_info.py)
* [Fetch Jobs](https://github.com/TheAlgorithms/Python/blob/master/web_programming/fetch_jobs.py)
* [Get Imdb Top 250 Movies Csv](https://github.com/TheAlgorithms/Python/blob/master/web_programming/get_imdb_top_250_movies_csv.py)
* [Get Imdbtop](https://github.com/TheAlgorithms/Python/blob/master/web_programming/get_imdbtop.py)
* [Instagram Crawler](https://github.com/TheAlgorithms/Python/blob/master/web_programming/instagram_crawler.py)
* [Instagram Pic](https://github.com/TheAlgorithms/Python/blob/master/web_programming/instagram_pic.py)
* [Instagram Video](https://github.com/TheAlgorithms/Python/blob/master/web_programming/instagram_video.py)
* [Random Anime Character](https://github.com/TheAlgorithms/Python/blob/master/web_programming/random_anime_character.py)
* [Recaptcha Verification](https://github.com/TheAlgorithms/Python/blob/master/web_programming/recaptcha_verification.py)
* [Slack Message](https://github.com/TheAlgorithms/Python/blob/master/web_programming/slack_message.py)
* [Test Fetch Github Info](https://github.com/TheAlgorithms/Python/blob/master/web_programming/test_fetch_github_info.py)
* [World Covid19 Stats](https://github.com/TheAlgorithms/Python/blob/master/web_programming/world_covid19_stats.py)
|
## Arithmetic Analysis
* [Bisection](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/bisection.py)
* [Gaussian Elimination](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/gaussian_elimination.py)
* [In Static Equilibrium](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/in_static_equilibrium.py)
* [Intersection](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/intersection.py)
* [Lu Decomposition](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/lu_decomposition.py)
* [Newton Forward Interpolation](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/newton_forward_interpolation.py)
* [Newton Method](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/newton_method.py)
* [Newton Raphson](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/newton_raphson.py)
* [Secant Method](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/secant_method.py)
## Backtracking
* [All Combinations](https://github.com/TheAlgorithms/Python/blob/master/backtracking/all_combinations.py)
* [All Permutations](https://github.com/TheAlgorithms/Python/blob/master/backtracking/all_permutations.py)
* [All Subsequences](https://github.com/TheAlgorithms/Python/blob/master/backtracking/all_subsequences.py)
* [Coloring](https://github.com/TheAlgorithms/Python/blob/master/backtracking/coloring.py)
* [Hamiltonian Cycle](https://github.com/TheAlgorithms/Python/blob/master/backtracking/hamiltonian_cycle.py)
* [Knight Tour](https://github.com/TheAlgorithms/Python/blob/master/backtracking/knight_tour.py)
* [Minimax](https://github.com/TheAlgorithms/Python/blob/master/backtracking/minimax.py)
* [N Queens](https://github.com/TheAlgorithms/Python/blob/master/backtracking/n_queens.py)
* [N Queens Math](https://github.com/TheAlgorithms/Python/blob/master/backtracking/n_queens_math.py)
* [Rat In Maze](https://github.com/TheAlgorithms/Python/blob/master/backtracking/rat_in_maze.py)
* [Sudoku](https://github.com/TheAlgorithms/Python/blob/master/backtracking/sudoku.py)
* [Sum Of Subsets](https://github.com/TheAlgorithms/Python/blob/master/backtracking/sum_of_subsets.py)
## Bit Manipulation
* [Binary And Operator](https://github.com/TheAlgorithms/Python/blob/master/bit_manipulation/binary_and_operator.py)
* [Binary Count Setbits](https://github.com/TheAlgorithms/Python/blob/master/bit_manipulation/binary_count_setbits.py)
* [Binary Count Trailing Zeros](https://github.com/TheAlgorithms/Python/blob/master/bit_manipulation/binary_count_trailing_zeros.py)
* [Binary Or Operator](https://github.com/TheAlgorithms/Python/blob/master/bit_manipulation/binary_or_operator.py)
* [Binary Shifts](https://github.com/TheAlgorithms/Python/blob/master/bit_manipulation/binary_shifts.py)
* [Binary Twos Complement](https://github.com/TheAlgorithms/Python/blob/master/bit_manipulation/binary_twos_complement.py)
* [Binary Xor Operator](https://github.com/TheAlgorithms/Python/blob/master/bit_manipulation/binary_xor_operator.py)
* [Count Number Of One Bits](https://github.com/TheAlgorithms/Python/blob/master/bit_manipulation/count_number_of_one_bits.py)
* [Reverse Bits](https://github.com/TheAlgorithms/Python/blob/master/bit_manipulation/reverse_bits.py)
* [Single Bit Manipulation Operations](https://github.com/TheAlgorithms/Python/blob/master/bit_manipulation/single_bit_manipulation_operations.py)
## Blockchain
* [Chinese Remainder Theorem](https://github.com/TheAlgorithms/Python/blob/master/blockchain/chinese_remainder_theorem.py)
* [Diophantine Equation](https://github.com/TheAlgorithms/Python/blob/master/blockchain/diophantine_equation.py)
* [Modular Division](https://github.com/TheAlgorithms/Python/blob/master/blockchain/modular_division.py)
## Boolean Algebra
* [Quine Mc Cluskey](https://github.com/TheAlgorithms/Python/blob/master/boolean_algebra/quine_mc_cluskey.py)
## Cellular Automata
* [Conways Game Of Life](https://github.com/TheAlgorithms/Python/blob/master/cellular_automata/conways_game_of_life.py)
* [Game Of Life](https://github.com/TheAlgorithms/Python/blob/master/cellular_automata/game_of_life.py)
* [One Dimensional](https://github.com/TheAlgorithms/Python/blob/master/cellular_automata/one_dimensional.py)
## Ciphers
* [A1Z26](https://github.com/TheAlgorithms/Python/blob/master/ciphers/a1z26.py)
* [Affine Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/affine_cipher.py)
* [Atbash](https://github.com/TheAlgorithms/Python/blob/master/ciphers/atbash.py)
* [Base16](https://github.com/TheAlgorithms/Python/blob/master/ciphers/base16.py)
* [Base32](https://github.com/TheAlgorithms/Python/blob/master/ciphers/base32.py)
* [Base64 Encoding](https://github.com/TheAlgorithms/Python/blob/master/ciphers/base64_encoding.py)
* [Base85](https://github.com/TheAlgorithms/Python/blob/master/ciphers/base85.py)
* [Beaufort Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/beaufort_cipher.py)
* [Brute Force Caesar Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/brute_force_caesar_cipher.py)
* [Caesar Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/caesar_cipher.py)
* [Cryptomath Module](https://github.com/TheAlgorithms/Python/blob/master/ciphers/cryptomath_module.py)
* [Decrypt Caesar With Chi Squared](https://github.com/TheAlgorithms/Python/blob/master/ciphers/decrypt_caesar_with_chi_squared.py)
* [Deterministic Miller Rabin](https://github.com/TheAlgorithms/Python/blob/master/ciphers/deterministic_miller_rabin.py)
* [Diffie](https://github.com/TheAlgorithms/Python/blob/master/ciphers/diffie.py)
* [Diffie Hellman](https://github.com/TheAlgorithms/Python/blob/master/ciphers/diffie_hellman.py)
* [Elgamal Key Generator](https://github.com/TheAlgorithms/Python/blob/master/ciphers/elgamal_key_generator.py)
* [Enigma Machine2](https://github.com/TheAlgorithms/Python/blob/master/ciphers/enigma_machine2.py)
* [Hill Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/hill_cipher.py)
* [Mixed Keyword Cypher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/mixed_keyword_cypher.py)
* [Mono Alphabetic Ciphers](https://github.com/TheAlgorithms/Python/blob/master/ciphers/mono_alphabetic_ciphers.py)
* [Morse Code Implementation](https://github.com/TheAlgorithms/Python/blob/master/ciphers/morse_code_implementation.py)
* [Onepad Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/onepad_cipher.py)
* [Playfair Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/playfair_cipher.py)
* [Porta Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/porta_cipher.py)
* [Rabin Miller](https://github.com/TheAlgorithms/Python/blob/master/ciphers/rabin_miller.py)
* [Rail Fence Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/rail_fence_cipher.py)
* [Rot13](https://github.com/TheAlgorithms/Python/blob/master/ciphers/rot13.py)
* [Rsa Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/rsa_cipher.py)
* [Rsa Factorization](https://github.com/TheAlgorithms/Python/blob/master/ciphers/rsa_factorization.py)
* [Rsa Key Generator](https://github.com/TheAlgorithms/Python/blob/master/ciphers/rsa_key_generator.py)
* [Shuffled Shift Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/shuffled_shift_cipher.py)
* [Simple Keyword Cypher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/simple_keyword_cypher.py)
* [Simple Substitution Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/simple_substitution_cipher.py)
* [Trafid Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/trafid_cipher.py)
* [Transposition Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/transposition_cipher.py)
* [Transposition Cipher Encrypt Decrypt File](https://github.com/TheAlgorithms/Python/blob/master/ciphers/transposition_cipher_encrypt_decrypt_file.py)
* [Vigenere Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/vigenere_cipher.py)
* [Xor Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/xor_cipher.py)
## Compression
* [Burrows Wheeler](https://github.com/TheAlgorithms/Python/blob/master/compression/burrows_wheeler.py)
* [Huffman](https://github.com/TheAlgorithms/Python/blob/master/compression/huffman.py)
* [Lempel Ziv](https://github.com/TheAlgorithms/Python/blob/master/compression/lempel_ziv.py)
* [Lempel Ziv Decompress](https://github.com/TheAlgorithms/Python/blob/master/compression/lempel_ziv_decompress.py)
* [Peak Signal To Noise Ratio](https://github.com/TheAlgorithms/Python/blob/master/compression/peak_signal_to_noise_ratio.py)
## Computer Vision
* [Cnn Classification](https://github.com/TheAlgorithms/Python/blob/master/computer_vision/cnn_classification.py)
* [Harris Corner](https://github.com/TheAlgorithms/Python/blob/master/computer_vision/harris_corner.py)
* [Mean Threshold](https://github.com/TheAlgorithms/Python/blob/master/computer_vision/mean_threshold.py)
## Conversions
* [Binary To Decimal](https://github.com/TheAlgorithms/Python/blob/master/conversions/binary_to_decimal.py)
* [Binary To Octal](https://github.com/TheAlgorithms/Python/blob/master/conversions/binary_to_octal.py)
* [Decimal To Any](https://github.com/TheAlgorithms/Python/blob/master/conversions/decimal_to_any.py)
* [Decimal To Binary](https://github.com/TheAlgorithms/Python/blob/master/conversions/decimal_to_binary.py)
* [Decimal To Binary Recursion](https://github.com/TheAlgorithms/Python/blob/master/conversions/decimal_to_binary_recursion.py)
* [Decimal To Hexadecimal](https://github.com/TheAlgorithms/Python/blob/master/conversions/decimal_to_hexadecimal.py)
* [Decimal To Octal](https://github.com/TheAlgorithms/Python/blob/master/conversions/decimal_to_octal.py)
* [Hex To Bin](https://github.com/TheAlgorithms/Python/blob/master/conversions/hex_to_bin.py)
* [Hexadecimal To Decimal](https://github.com/TheAlgorithms/Python/blob/master/conversions/hexadecimal_to_decimal.py)
* [Molecular Chemistry](https://github.com/TheAlgorithms/Python/blob/master/conversions/molecular_chemistry.py)
* [Octal To Decimal](https://github.com/TheAlgorithms/Python/blob/master/conversions/octal_to_decimal.py)
* [Prefix Conversions](https://github.com/TheAlgorithms/Python/blob/master/conversions/prefix_conversions.py)
* [Rgb Hsv Conversion](https://github.com/TheAlgorithms/Python/blob/master/conversions/rgb_hsv_conversion.py)
* [Roman Numerals](https://github.com/TheAlgorithms/Python/blob/master/conversions/roman_numerals.py)
* [Temperature Conversions](https://github.com/TheAlgorithms/Python/blob/master/conversions/temperature_conversions.py)
* [Weight Conversion](https://github.com/TheAlgorithms/Python/blob/master/conversions/weight_conversion.py)
## Data Structures
* Binary Tree
* [Avl Tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/avl_tree.py)
* [Basic Binary Tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/basic_binary_tree.py)
* [Binary Search Tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/binary_search_tree.py)
* [Binary Search Tree Recursive](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/binary_search_tree_recursive.py)
* [Binary Tree Mirror](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/binary_tree_mirror.py)
* [Binary Tree Traversals](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/binary_tree_traversals.py)
* [Fenwick Tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/fenwick_tree.py)
* [Lazy Segment Tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/lazy_segment_tree.py)
* [Lowest Common Ancestor](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/lowest_common_ancestor.py)
* [Merge Two Binary Trees](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/merge_two_binary_trees.py)
* [Non Recursive Segment Tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/non_recursive_segment_tree.py)
* [Number Of Possible Binary Trees](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/number_of_possible_binary_trees.py)
* [Red Black Tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/red_black_tree.py)
* [Segment Tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/segment_tree.py)
* [Segment Tree Other](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/segment_tree_other.py)
* [Treap](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/treap.py)
* [Wavelet Tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/wavelet_tree.py)
* Disjoint Set
* [Alternate Disjoint Set](https://github.com/TheAlgorithms/Python/blob/master/data_structures/disjoint_set/alternate_disjoint_set.py)
* [Disjoint Set](https://github.com/TheAlgorithms/Python/blob/master/data_structures/disjoint_set/disjoint_set.py)
* Hashing
* [Double Hash](https://github.com/TheAlgorithms/Python/blob/master/data_structures/hashing/double_hash.py)
* [Hash Table](https://github.com/TheAlgorithms/Python/blob/master/data_structures/hashing/hash_table.py)
* [Hash Table With Linked List](https://github.com/TheAlgorithms/Python/blob/master/data_structures/hashing/hash_table_with_linked_list.py)
* Number Theory
* [Prime Numbers](https://github.com/TheAlgorithms/Python/blob/master/data_structures/hashing/number_theory/prime_numbers.py)
* [Quadratic Probing](https://github.com/TheAlgorithms/Python/blob/master/data_structures/hashing/quadratic_probing.py)
* Heap
* [Binomial Heap](https://github.com/TheAlgorithms/Python/blob/master/data_structures/heap/binomial_heap.py)
* [Heap](https://github.com/TheAlgorithms/Python/blob/master/data_structures/heap/heap.py)
* [Heap Generic](https://github.com/TheAlgorithms/Python/blob/master/data_structures/heap/heap_generic.py)
* [Max Heap](https://github.com/TheAlgorithms/Python/blob/master/data_structures/heap/max_heap.py)
* [Min Heap](https://github.com/TheAlgorithms/Python/blob/master/data_structures/heap/min_heap.py)
* [Randomized Heap](https://github.com/TheAlgorithms/Python/blob/master/data_structures/heap/randomized_heap.py)
* [Skew Heap](https://github.com/TheAlgorithms/Python/blob/master/data_structures/heap/skew_heap.py)
* Linked List
* [Circular Linked List](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/circular_linked_list.py)
* [Deque Doubly](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/deque_doubly.py)
* [Doubly Linked List](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/doubly_linked_list.py)
* [Doubly Linked List Two](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/doubly_linked_list_two.py)
* [From Sequence](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/from_sequence.py)
* [Has Loop](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/has_loop.py)
* [Is Palindrome](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/is_palindrome.py)
* [Merge Two Lists](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/merge_two_lists.py)
* [Middle Element Of Linked List](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/middle_element_of_linked_list.py)
* [Print Reverse](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/print_reverse.py)
* [Singly Linked List](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/singly_linked_list.py)
* [Skip List](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/skip_list.py)
* [Swap Nodes](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/swap_nodes.py)
* Queue
* [Circular Queue](https://github.com/TheAlgorithms/Python/blob/master/data_structures/queue/circular_queue.py)
* [Double Ended Queue](https://github.com/TheAlgorithms/Python/blob/master/data_structures/queue/double_ended_queue.py)
* [Linked Queue](https://github.com/TheAlgorithms/Python/blob/master/data_structures/queue/linked_queue.py)
* [Priority Queue Using List](https://github.com/TheAlgorithms/Python/blob/master/data_structures/queue/priority_queue_using_list.py)
* [Queue On List](https://github.com/TheAlgorithms/Python/blob/master/data_structures/queue/queue_on_list.py)
* [Queue On Pseudo Stack](https://github.com/TheAlgorithms/Python/blob/master/data_structures/queue/queue_on_pseudo_stack.py)
* Stacks
* [Balanced Parentheses](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/balanced_parentheses.py)
* [Dijkstras Two Stack Algorithm](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/dijkstras_two_stack_algorithm.py)
* [Evaluate Postfix Notations](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/evaluate_postfix_notations.py)
* [Infix To Postfix Conversion](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/infix_to_postfix_conversion.py)
* [Infix To Prefix Conversion](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/infix_to_prefix_conversion.py)
* [Linked Stack](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/linked_stack.py)
* [Next Greater Element](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/next_greater_element.py)
* [Postfix Evaluation](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/postfix_evaluation.py)
* [Prefix Evaluation](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/prefix_evaluation.py)
* [Stack](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/stack.py)
* [Stack Using Dll](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/stack_using_dll.py)
* [Stock Span Problem](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/stock_span_problem.py)
* Trie
* [Trie](https://github.com/TheAlgorithms/Python/blob/master/data_structures/trie/trie.py)
## Digital Image Processing
* [Change Brightness](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/change_brightness.py)
* [Change Contrast](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/change_contrast.py)
* [Convert To Negative](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/convert_to_negative.py)
* Dithering
* [Burkes](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/dithering/burkes.py)
* Edge Detection
* [Canny](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/edge_detection/canny.py)
* Filters
* [Bilateral Filter](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/filters/bilateral_filter.py)
* [Convolve](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/filters/convolve.py)
* [Gaussian Filter](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/filters/gaussian_filter.py)
* [Median Filter](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/filters/median_filter.py)
* [Sobel Filter](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/filters/sobel_filter.py)
* Histogram Equalization
* [Histogram Stretch](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/histogram_equalization/histogram_stretch.py)
* [Index Calculation](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/index_calculation.py)
* Resize
* [Resize](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/resize/resize.py)
* Rotation
* [Rotation](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/rotation/rotation.py)
* [Sepia](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/sepia.py)
* [Test Digital Image Processing](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/test_digital_image_processing.py)
## Divide And Conquer
* [Closest Pair Of Points](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/closest_pair_of_points.py)
* [Convex Hull](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/convex_hull.py)
* [Heaps Algorithm](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/heaps_algorithm.py)
* [Heaps Algorithm Iterative](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/heaps_algorithm_iterative.py)
* [Inversions](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/inversions.py)
* [Kth Order Statistic](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/kth_order_statistic.py)
* [Max Difference Pair](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/max_difference_pair.py)
* [Max Subarray Sum](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/max_subarray_sum.py)
* [Mergesort](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/mergesort.py)
* [Peak](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/peak.py)
* [Power](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/power.py)
* [Strassen Matrix Multiplication](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/strassen_matrix_multiplication.py)
## Dynamic Programming
* [Abbreviation](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/abbreviation.py)
* [Bitmask](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/bitmask.py)
* [Catalan Numbers](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/catalan_numbers.py)
* [Climbing Stairs](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/climbing_stairs.py)
* [Edit Distance](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/edit_distance.py)
* [Factorial](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/factorial.py)
* [Fast Fibonacci](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/fast_fibonacci.py)
* [Fibonacci](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/fibonacci.py)
* [Floyd Warshall](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/floyd_warshall.py)
* [Fractional Knapsack](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/fractional_knapsack.py)
* [Fractional Knapsack 2](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/fractional_knapsack_2.py)
* [Integer Partition](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/integer_partition.py)
* [Iterating Through Submasks](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/iterating_through_submasks.py)
* [Knapsack](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/knapsack.py)
* [Longest Common Subsequence](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/longest_common_subsequence.py)
* [Longest Increasing Subsequence](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/longest_increasing_subsequence.py)
* [Longest Increasing Subsequence O(Nlogn)](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/longest_increasing_subsequence_o(nlogn).py)
* [Longest Sub Array](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/longest_sub_array.py)
* [Matrix Chain Order](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/matrix_chain_order.py)
* [Max Non Adjacent Sum](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/max_non_adjacent_sum.py)
* [Max Sub Array](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/max_sub_array.py)
* [Max Sum Contiguous Subsequence](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/max_sum_contiguous_subsequence.py)
* [Minimum Coin Change](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/minimum_coin_change.py)
* [Minimum Cost Path](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/minimum_cost_path.py)
* [Minimum Partition](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/minimum_partition.py)
* [Minimum Steps To One](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/minimum_steps_to_one.py)
* [Optimal Binary Search Tree](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/optimal_binary_search_tree.py)
* [Rod Cutting](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/rod_cutting.py)
* [Subset Generation](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/subset_generation.py)
* [Sum Of Subset](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/sum_of_subset.py)
## Electronics
* [Electric Power](https://github.com/TheAlgorithms/Python/blob/master/electronics/electric_power.py)
* [Ohms Law](https://github.com/TheAlgorithms/Python/blob/master/electronics/ohms_law.py)
## File Transfer
* [Receive File](https://github.com/TheAlgorithms/Python/blob/master/file_transfer/receive_file.py)
* [Send File](https://github.com/TheAlgorithms/Python/blob/master/file_transfer/send_file.py)
* Tests
* [Test Send File](https://github.com/TheAlgorithms/Python/blob/master/file_transfer/tests/test_send_file.py)
## Fractals
* [Koch Snowflake](https://github.com/TheAlgorithms/Python/blob/master/fractals/koch_snowflake.py)
* [Mandelbrot](https://github.com/TheAlgorithms/Python/blob/master/fractals/mandelbrot.py)
* [Sierpinski Triangle](https://github.com/TheAlgorithms/Python/blob/master/fractals/sierpinski_triangle.py)
## Fuzzy Logic
* [Fuzzy Operations](https://github.com/TheAlgorithms/Python/blob/master/fuzzy_logic/fuzzy_operations.py)
## Genetic Algorithm
* [Basic String](https://github.com/TheAlgorithms/Python/blob/master/genetic_algorithm/basic_string.py)
## Geodesy
* [Haversine Distance](https://github.com/TheAlgorithms/Python/blob/master/geodesy/haversine_distance.py)
* [Lamberts Ellipsoidal Distance](https://github.com/TheAlgorithms/Python/blob/master/geodesy/lamberts_ellipsoidal_distance.py)
## Graphics
* [Bezier Curve](https://github.com/TheAlgorithms/Python/blob/master/graphics/bezier_curve.py)
* [Vector3 For 2D Rendering](https://github.com/TheAlgorithms/Python/blob/master/graphics/vector3_for_2d_rendering.py)
## Graphs
* [A Star](https://github.com/TheAlgorithms/Python/blob/master/graphs/a_star.py)
* [Articulation Points](https://github.com/TheAlgorithms/Python/blob/master/graphs/articulation_points.py)
* [Basic Graphs](https://github.com/TheAlgorithms/Python/blob/master/graphs/basic_graphs.py)
* [Bellman Ford](https://github.com/TheAlgorithms/Python/blob/master/graphs/bellman_ford.py)
* [Bfs Shortest Path](https://github.com/TheAlgorithms/Python/blob/master/graphs/bfs_shortest_path.py)
* [Bfs Zero One Shortest Path](https://github.com/TheAlgorithms/Python/blob/master/graphs/bfs_zero_one_shortest_path.py)
* [Bidirectional A Star](https://github.com/TheAlgorithms/Python/blob/master/graphs/bidirectional_a_star.py)
* [Bidirectional Breadth First Search](https://github.com/TheAlgorithms/Python/blob/master/graphs/bidirectional_breadth_first_search.py)
* [Boruvka](https://github.com/TheAlgorithms/Python/blob/master/graphs/boruvka.py)
* [Breadth First Search](https://github.com/TheAlgorithms/Python/blob/master/graphs/breadth_first_search.py)
* [Breadth First Search 2](https://github.com/TheAlgorithms/Python/blob/master/graphs/breadth_first_search_2.py)
* [Breadth First Search Shortest Path](https://github.com/TheAlgorithms/Python/blob/master/graphs/breadth_first_search_shortest_path.py)
* [Check Bipartite Graph Bfs](https://github.com/TheAlgorithms/Python/blob/master/graphs/check_bipartite_graph_bfs.py)
* [Check Bipartite Graph Dfs](https://github.com/TheAlgorithms/Python/blob/master/graphs/check_bipartite_graph_dfs.py)
* [Connected Components](https://github.com/TheAlgorithms/Python/blob/master/graphs/connected_components.py)
* [Depth First Search](https://github.com/TheAlgorithms/Python/blob/master/graphs/depth_first_search.py)
* [Depth First Search 2](https://github.com/TheAlgorithms/Python/blob/master/graphs/depth_first_search_2.py)
* [Dijkstra](https://github.com/TheAlgorithms/Python/blob/master/graphs/dijkstra.py)
* [Dijkstra 2](https://github.com/TheAlgorithms/Python/blob/master/graphs/dijkstra_2.py)
* [Dijkstra Algorithm](https://github.com/TheAlgorithms/Python/blob/master/graphs/dijkstra_algorithm.py)
* [Dinic](https://github.com/TheAlgorithms/Python/blob/master/graphs/dinic.py)
* [Directed And Undirected (Weighted) Graph](https://github.com/TheAlgorithms/Python/blob/master/graphs/directed_and_undirected_(weighted)_graph.py)
* [Edmonds Karp Multiple Source And Sink](https://github.com/TheAlgorithms/Python/blob/master/graphs/edmonds_karp_multiple_source_and_sink.py)
* [Eulerian Path And Circuit For Undirected Graph](https://github.com/TheAlgorithms/Python/blob/master/graphs/eulerian_path_and_circuit_for_undirected_graph.py)
* [Even Tree](https://github.com/TheAlgorithms/Python/blob/master/graphs/even_tree.py)
* [Finding Bridges](https://github.com/TheAlgorithms/Python/blob/master/graphs/finding_bridges.py)
* [Frequent Pattern Graph Miner](https://github.com/TheAlgorithms/Python/blob/master/graphs/frequent_pattern_graph_miner.py)
* [G Topological Sort](https://github.com/TheAlgorithms/Python/blob/master/graphs/g_topological_sort.py)
* [Gale Shapley Bigraph](https://github.com/TheAlgorithms/Python/blob/master/graphs/gale_shapley_bigraph.py)
* [Graph List](https://github.com/TheAlgorithms/Python/blob/master/graphs/graph_list.py)
* [Graph Matrix](https://github.com/TheAlgorithms/Python/blob/master/graphs/graph_matrix.py)
* [Graphs Floyd Warshall](https://github.com/TheAlgorithms/Python/blob/master/graphs/graphs_floyd_warshall.py)
* [Greedy Best First](https://github.com/TheAlgorithms/Python/blob/master/graphs/greedy_best_first.py)
* [Kahns Algorithm Long](https://github.com/TheAlgorithms/Python/blob/master/graphs/kahns_algorithm_long.py)
* [Kahns Algorithm Topo](https://github.com/TheAlgorithms/Python/blob/master/graphs/kahns_algorithm_topo.py)
* [Karger](https://github.com/TheAlgorithms/Python/blob/master/graphs/karger.py)
* [Markov Chain](https://github.com/TheAlgorithms/Python/blob/master/graphs/markov_chain.py)
* [Minimum Spanning Tree Boruvka](https://github.com/TheAlgorithms/Python/blob/master/graphs/minimum_spanning_tree_boruvka.py)
* [Minimum Spanning Tree Kruskal](https://github.com/TheAlgorithms/Python/blob/master/graphs/minimum_spanning_tree_kruskal.py)
* [Minimum Spanning Tree Kruskal2](https://github.com/TheAlgorithms/Python/blob/master/graphs/minimum_spanning_tree_kruskal2.py)
* [Minimum Spanning Tree Prims](https://github.com/TheAlgorithms/Python/blob/master/graphs/minimum_spanning_tree_prims.py)
* [Minimum Spanning Tree Prims2](https://github.com/TheAlgorithms/Python/blob/master/graphs/minimum_spanning_tree_prims2.py)
* [Multi Heuristic Astar](https://github.com/TheAlgorithms/Python/blob/master/graphs/multi_heuristic_astar.py)
* [Page Rank](https://github.com/TheAlgorithms/Python/blob/master/graphs/page_rank.py)
* [Prim](https://github.com/TheAlgorithms/Python/blob/master/graphs/prim.py)
* [Scc Kosaraju](https://github.com/TheAlgorithms/Python/blob/master/graphs/scc_kosaraju.py)
* [Strongly Connected Components](https://github.com/TheAlgorithms/Python/blob/master/graphs/strongly_connected_components.py)
* [Tarjans Scc](https://github.com/TheAlgorithms/Python/blob/master/graphs/tarjans_scc.py)
* Tests
* [Test Min Spanning Tree Kruskal](https://github.com/TheAlgorithms/Python/blob/master/graphs/tests/test_min_spanning_tree_kruskal.py)
* [Test Min Spanning Tree Prim](https://github.com/TheAlgorithms/Python/blob/master/graphs/tests/test_min_spanning_tree_prim.py)
## Hashes
* [Adler32](https://github.com/TheAlgorithms/Python/blob/master/hashes/adler32.py)
* [Chaos Machine](https://github.com/TheAlgorithms/Python/blob/master/hashes/chaos_machine.py)
* [Djb2](https://github.com/TheAlgorithms/Python/blob/master/hashes/djb2.py)
* [Enigma Machine](https://github.com/TheAlgorithms/Python/blob/master/hashes/enigma_machine.py)
* [Hamming Code](https://github.com/TheAlgorithms/Python/blob/master/hashes/hamming_code.py)
* [Luhn](https://github.com/TheAlgorithms/Python/blob/master/hashes/luhn.py)
* [Md5](https://github.com/TheAlgorithms/Python/blob/master/hashes/md5.py)
* [Sdbm](https://github.com/TheAlgorithms/Python/blob/master/hashes/sdbm.py)
* [Sha1](https://github.com/TheAlgorithms/Python/blob/master/hashes/sha1.py)
## Knapsack
* [Greedy Knapsack](https://github.com/TheAlgorithms/Python/blob/master/knapsack/greedy_knapsack.py)
* [Knapsack](https://github.com/TheAlgorithms/Python/blob/master/knapsack/knapsack.py)
* Tests
* [Test Greedy Knapsack](https://github.com/TheAlgorithms/Python/blob/master/knapsack/tests/test_greedy_knapsack.py)
* [Test Knapsack](https://github.com/TheAlgorithms/Python/blob/master/knapsack/tests/test_knapsack.py)
## Linear Algebra
* Src
* [Conjugate Gradient](https://github.com/TheAlgorithms/Python/blob/master/linear_algebra/src/conjugate_gradient.py)
* [Lib](https://github.com/TheAlgorithms/Python/blob/master/linear_algebra/src/lib.py)
* [Polynom For Points](https://github.com/TheAlgorithms/Python/blob/master/linear_algebra/src/polynom_for_points.py)
* [Power Iteration](https://github.com/TheAlgorithms/Python/blob/master/linear_algebra/src/power_iteration.py)
* [Rayleigh Quotient](https://github.com/TheAlgorithms/Python/blob/master/linear_algebra/src/rayleigh_quotient.py)
* [Test Linear Algebra](https://github.com/TheAlgorithms/Python/blob/master/linear_algebra/src/test_linear_algebra.py)
* [Transformations 2D](https://github.com/TheAlgorithms/Python/blob/master/linear_algebra/src/transformations_2d.py)
## Machine Learning
* [Astar](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/astar.py)
* [Data Transformations](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/data_transformations.py)
* [Decision Tree](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/decision_tree.py)
* Forecasting
* [Run](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/forecasting/run.py)
* [Gaussian Naive Bayes](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/gaussian_naive_bayes.py)
* [Gradient Boosting Regressor](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/gradient_boosting_regressor.py)
* [Gradient Descent](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/gradient_descent.py)
* [K Means Clust](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/k_means_clust.py)
* [K Nearest Neighbours](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/k_nearest_neighbours.py)
* [Knn Sklearn](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/knn_sklearn.py)
* [Linear Discriminant Analysis](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/linear_discriminant_analysis.py)
* [Linear Regression](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/linear_regression.py)
* [Logistic Regression](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/logistic_regression.py)
* Lstm
* [Lstm Prediction](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/lstm/lstm_prediction.py)
* [Multilayer Perceptron Classifier](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/multilayer_perceptron_classifier.py)
* [Polymonial Regression](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/polymonial_regression.py)
* [Random Forest Classifier](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/random_forest_classifier.py)
* [Random Forest Regressor](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/random_forest_regressor.py)
* [Scoring Functions](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/scoring_functions.py)
* [Sequential Minimum Optimization](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/sequential_minimum_optimization.py)
* [Similarity Search](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/similarity_search.py)
* [Support Vector Machines](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/support_vector_machines.py)
* [Word Frequency Functions](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/word_frequency_functions.py)
## Maths
* [3N Plus 1](https://github.com/TheAlgorithms/Python/blob/master/maths/3n_plus_1.py)
* [Abs](https://github.com/TheAlgorithms/Python/blob/master/maths/abs.py)
* [Abs Max](https://github.com/TheAlgorithms/Python/blob/master/maths/abs_max.py)
* [Abs Min](https://github.com/TheAlgorithms/Python/blob/master/maths/abs_min.py)
* [Add](https://github.com/TheAlgorithms/Python/blob/master/maths/add.py)
* [Aliquot Sum](https://github.com/TheAlgorithms/Python/blob/master/maths/aliquot_sum.py)
* [Allocation Number](https://github.com/TheAlgorithms/Python/blob/master/maths/allocation_number.py)
* [Area](https://github.com/TheAlgorithms/Python/blob/master/maths/area.py)
* [Area Under Curve](https://github.com/TheAlgorithms/Python/blob/master/maths/area_under_curve.py)
* [Armstrong Numbers](https://github.com/TheAlgorithms/Python/blob/master/maths/armstrong_numbers.py)
* [Average Mean](https://github.com/TheAlgorithms/Python/blob/master/maths/average_mean.py)
* [Average Median](https://github.com/TheAlgorithms/Python/blob/master/maths/average_median.py)
* [Average Mode](https://github.com/TheAlgorithms/Python/blob/master/maths/average_mode.py)
* [Bailey Borwein Plouffe](https://github.com/TheAlgorithms/Python/blob/master/maths/bailey_borwein_plouffe.py)
* [Basic Maths](https://github.com/TheAlgorithms/Python/blob/master/maths/basic_maths.py)
* [Binary Exp Mod](https://github.com/TheAlgorithms/Python/blob/master/maths/binary_exp_mod.py)
* [Binary Exponentiation](https://github.com/TheAlgorithms/Python/blob/master/maths/binary_exponentiation.py)
* [Binary Exponentiation 2](https://github.com/TheAlgorithms/Python/blob/master/maths/binary_exponentiation_2.py)
* [Binary Exponentiation 3](https://github.com/TheAlgorithms/Python/blob/master/maths/binary_exponentiation_3.py)
* [Binomial Coefficient](https://github.com/TheAlgorithms/Python/blob/master/maths/binomial_coefficient.py)
* [Binomial Distribution](https://github.com/TheAlgorithms/Python/blob/master/maths/binomial_distribution.py)
* [Bisection](https://github.com/TheAlgorithms/Python/blob/master/maths/bisection.py)
* [Ceil](https://github.com/TheAlgorithms/Python/blob/master/maths/ceil.py)
* [Chudnovsky Algorithm](https://github.com/TheAlgorithms/Python/blob/master/maths/chudnovsky_algorithm.py)
* [Collatz Sequence](https://github.com/TheAlgorithms/Python/blob/master/maths/collatz_sequence.py)
* [Combinations](https://github.com/TheAlgorithms/Python/blob/master/maths/combinations.py)
* [Decimal Isolate](https://github.com/TheAlgorithms/Python/blob/master/maths/decimal_isolate.py)
* [Double Factorial Recursive](https://github.com/TheAlgorithms/Python/blob/master/maths/double_factorial_recursive.py)
* [Entropy](https://github.com/TheAlgorithms/Python/blob/master/maths/entropy.py)
* [Euclidean Distance](https://github.com/TheAlgorithms/Python/blob/master/maths/euclidean_distance.py)
* [Euclidean Gcd](https://github.com/TheAlgorithms/Python/blob/master/maths/euclidean_gcd.py)
* [Euler Method](https://github.com/TheAlgorithms/Python/blob/master/maths/euler_method.py)
* [Eulers Totient](https://github.com/TheAlgorithms/Python/blob/master/maths/eulers_totient.py)
* [Extended Euclidean Algorithm](https://github.com/TheAlgorithms/Python/blob/master/maths/extended_euclidean_algorithm.py)
* [Factorial Iterative](https://github.com/TheAlgorithms/Python/blob/master/maths/factorial_iterative.py)
* [Factorial Python](https://github.com/TheAlgorithms/Python/blob/master/maths/factorial_python.py)
* [Factorial Recursive](https://github.com/TheAlgorithms/Python/blob/master/maths/factorial_recursive.py)
* [Factors](https://github.com/TheAlgorithms/Python/blob/master/maths/factors.py)
* [Fermat Little Theorem](https://github.com/TheAlgorithms/Python/blob/master/maths/fermat_little_theorem.py)
* [Fibonacci](https://github.com/TheAlgorithms/Python/blob/master/maths/fibonacci.py)
* [Fibonacci Sequence Recursion](https://github.com/TheAlgorithms/Python/blob/master/maths/fibonacci_sequence_recursion.py)
* [Find Max](https://github.com/TheAlgorithms/Python/blob/master/maths/find_max.py)
* [Find Max Recursion](https://github.com/TheAlgorithms/Python/blob/master/maths/find_max_recursion.py)
* [Find Min](https://github.com/TheAlgorithms/Python/blob/master/maths/find_min.py)
* [Find Min Recursion](https://github.com/TheAlgorithms/Python/blob/master/maths/find_min_recursion.py)
* [Floor](https://github.com/TheAlgorithms/Python/blob/master/maths/floor.py)
* [Gamma](https://github.com/TheAlgorithms/Python/blob/master/maths/gamma.py)
* [Gamma Recursive](https://github.com/TheAlgorithms/Python/blob/master/maths/gamma_recursive.py)
* [Gaussian](https://github.com/TheAlgorithms/Python/blob/master/maths/gaussian.py)
* [Greatest Common Divisor](https://github.com/TheAlgorithms/Python/blob/master/maths/greatest_common_divisor.py)
* [Greedy Coin Change](https://github.com/TheAlgorithms/Python/blob/master/maths/greedy_coin_change.py)
* [Hardy Ramanujanalgo](https://github.com/TheAlgorithms/Python/blob/master/maths/hardy_ramanujanalgo.py)
* [Integration By Simpson Approx](https://github.com/TheAlgorithms/Python/blob/master/maths/integration_by_simpson_approx.py)
* [Is Ip V4 Address Valid](https://github.com/TheAlgorithms/Python/blob/master/maths/is_ip_v4_address_valid.py)
* [Is Square Free](https://github.com/TheAlgorithms/Python/blob/master/maths/is_square_free.py)
* [Jaccard Similarity](https://github.com/TheAlgorithms/Python/blob/master/maths/jaccard_similarity.py)
* [Kadanes](https://github.com/TheAlgorithms/Python/blob/master/maths/kadanes.py)
* [Karatsuba](https://github.com/TheAlgorithms/Python/blob/master/maths/karatsuba.py)
* [Krishnamurthy Number](https://github.com/TheAlgorithms/Python/blob/master/maths/krishnamurthy_number.py)
* [Kth Lexicographic Permutation](https://github.com/TheAlgorithms/Python/blob/master/maths/kth_lexicographic_permutation.py)
* [Largest Of Very Large Numbers](https://github.com/TheAlgorithms/Python/blob/master/maths/largest_of_very_large_numbers.py)
* [Largest Subarray Sum](https://github.com/TheAlgorithms/Python/blob/master/maths/largest_subarray_sum.py)
* [Least Common Multiple](https://github.com/TheAlgorithms/Python/blob/master/maths/least_common_multiple.py)
* [Line Length](https://github.com/TheAlgorithms/Python/blob/master/maths/line_length.py)
* [Lucas Lehmer Primality Test](https://github.com/TheAlgorithms/Python/blob/master/maths/lucas_lehmer_primality_test.py)
* [Lucas Series](https://github.com/TheAlgorithms/Python/blob/master/maths/lucas_series.py)
* [Matrix Exponentiation](https://github.com/TheAlgorithms/Python/blob/master/maths/matrix_exponentiation.py)
* [Max Sum Sliding Window](https://github.com/TheAlgorithms/Python/blob/master/maths/max_sum_sliding_window.py)
* [Median Of Two Arrays](https://github.com/TheAlgorithms/Python/blob/master/maths/median_of_two_arrays.py)
* [Miller Rabin](https://github.com/TheAlgorithms/Python/blob/master/maths/miller_rabin.py)
* [Mobius Function](https://github.com/TheAlgorithms/Python/blob/master/maths/mobius_function.py)
* [Modular Exponential](https://github.com/TheAlgorithms/Python/blob/master/maths/modular_exponential.py)
* [Monte Carlo](https://github.com/TheAlgorithms/Python/blob/master/maths/monte_carlo.py)
* [Monte Carlo Dice](https://github.com/TheAlgorithms/Python/blob/master/maths/monte_carlo_dice.py)
* [Newton Raphson](https://github.com/TheAlgorithms/Python/blob/master/maths/newton_raphson.py)
* [Number Of Digits](https://github.com/TheAlgorithms/Python/blob/master/maths/number_of_digits.py)
* [Numerical Integration](https://github.com/TheAlgorithms/Python/blob/master/maths/numerical_integration.py)
* [Perfect Cube](https://github.com/TheAlgorithms/Python/blob/master/maths/perfect_cube.py)
* [Perfect Number](https://github.com/TheAlgorithms/Python/blob/master/maths/perfect_number.py)
* [Perfect Square](https://github.com/TheAlgorithms/Python/blob/master/maths/perfect_square.py)
* [Pi Monte Carlo Estimation](https://github.com/TheAlgorithms/Python/blob/master/maths/pi_monte_carlo_estimation.py)
* [Polynomial Evaluation](https://github.com/TheAlgorithms/Python/blob/master/maths/polynomial_evaluation.py)
* [Power Using Recursion](https://github.com/TheAlgorithms/Python/blob/master/maths/power_using_recursion.py)
* [Prime Check](https://github.com/TheAlgorithms/Python/blob/master/maths/prime_check.py)
* [Prime Factors](https://github.com/TheAlgorithms/Python/blob/master/maths/prime_factors.py)
* [Prime Numbers](https://github.com/TheAlgorithms/Python/blob/master/maths/prime_numbers.py)
* [Prime Sieve Eratosthenes](https://github.com/TheAlgorithms/Python/blob/master/maths/prime_sieve_eratosthenes.py)
* [Primelib](https://github.com/TheAlgorithms/Python/blob/master/maths/primelib.py)
* [Pythagoras](https://github.com/TheAlgorithms/Python/blob/master/maths/pythagoras.py)
* [Qr Decomposition](https://github.com/TheAlgorithms/Python/blob/master/maths/qr_decomposition.py)
* [Quadratic Equations Complex Numbers](https://github.com/TheAlgorithms/Python/blob/master/maths/quadratic_equations_complex_numbers.py)
* [Radians](https://github.com/TheAlgorithms/Python/blob/master/maths/radians.py)
* [Radix2 Fft](https://github.com/TheAlgorithms/Python/blob/master/maths/radix2_fft.py)
* [Relu](https://github.com/TheAlgorithms/Python/blob/master/maths/relu.py)
* [Runge Kutta](https://github.com/TheAlgorithms/Python/blob/master/maths/runge_kutta.py)
* [Segmented Sieve](https://github.com/TheAlgorithms/Python/blob/master/maths/segmented_sieve.py)
* Series
* [Arithmetic Mean](https://github.com/TheAlgorithms/Python/blob/master/maths/series/arithmetic_mean.py)
* [Geometric Mean](https://github.com/TheAlgorithms/Python/blob/master/maths/series/geometric_mean.py)
* [Geometric Series](https://github.com/TheAlgorithms/Python/blob/master/maths/series/geometric_series.py)
* [Harmonic Series](https://github.com/TheAlgorithms/Python/blob/master/maths/series/harmonic_series.py)
* [P Series](https://github.com/TheAlgorithms/Python/blob/master/maths/series/p_series.py)
* [Sieve Of Eratosthenes](https://github.com/TheAlgorithms/Python/blob/master/maths/sieve_of_eratosthenes.py)
* [Sigmoid](https://github.com/TheAlgorithms/Python/blob/master/maths/sigmoid.py)
* [Simpson Rule](https://github.com/TheAlgorithms/Python/blob/master/maths/simpson_rule.py)
* [Softmax](https://github.com/TheAlgorithms/Python/blob/master/maths/softmax.py)
* [Square Root](https://github.com/TheAlgorithms/Python/blob/master/maths/square_root.py)
* [Sum Of Arithmetic Series](https://github.com/TheAlgorithms/Python/blob/master/maths/sum_of_arithmetic_series.py)
* [Sum Of Digits](https://github.com/TheAlgorithms/Python/blob/master/maths/sum_of_digits.py)
* [Sum Of Geometric Progression](https://github.com/TheAlgorithms/Python/blob/master/maths/sum_of_geometric_progression.py)
* [Test Prime Check](https://github.com/TheAlgorithms/Python/blob/master/maths/test_prime_check.py)
* [Trapezoidal Rule](https://github.com/TheAlgorithms/Python/blob/master/maths/trapezoidal_rule.py)
* [Triplet Sum](https://github.com/TheAlgorithms/Python/blob/master/maths/triplet_sum.py)
* [Two Pointer](https://github.com/TheAlgorithms/Python/blob/master/maths/two_pointer.py)
* [Two Sum](https://github.com/TheAlgorithms/Python/blob/master/maths/two_sum.py)
* [Ugly Numbers](https://github.com/TheAlgorithms/Python/blob/master/maths/ugly_numbers.py)
* [Volume](https://github.com/TheAlgorithms/Python/blob/master/maths/volume.py)
* [Zellers Congruence](https://github.com/TheAlgorithms/Python/blob/master/maths/zellers_congruence.py)
## Matrix
* [Count Islands In Matrix](https://github.com/TheAlgorithms/Python/blob/master/matrix/count_islands_in_matrix.py)
* [Inverse Of Matrix](https://github.com/TheAlgorithms/Python/blob/master/matrix/inverse_of_matrix.py)
* [Matrix Class](https://github.com/TheAlgorithms/Python/blob/master/matrix/matrix_class.py)
* [Matrix Operation](https://github.com/TheAlgorithms/Python/blob/master/matrix/matrix_operation.py)
* [Nth Fibonacci Using Matrix Exponentiation](https://github.com/TheAlgorithms/Python/blob/master/matrix/nth_fibonacci_using_matrix_exponentiation.py)
* [Rotate Matrix](https://github.com/TheAlgorithms/Python/blob/master/matrix/rotate_matrix.py)
* [Searching In Sorted Matrix](https://github.com/TheAlgorithms/Python/blob/master/matrix/searching_in_sorted_matrix.py)
* [Sherman Morrison](https://github.com/TheAlgorithms/Python/blob/master/matrix/sherman_morrison.py)
* [Spiral Print](https://github.com/TheAlgorithms/Python/blob/master/matrix/spiral_print.py)
* Tests
* [Test Matrix Operation](https://github.com/TheAlgorithms/Python/blob/master/matrix/tests/test_matrix_operation.py)
## Networking Flow
* [Ford Fulkerson](https://github.com/TheAlgorithms/Python/blob/master/networking_flow/ford_fulkerson.py)
* [Minimum Cut](https://github.com/TheAlgorithms/Python/blob/master/networking_flow/minimum_cut.py)
## Neural Network
* [2 Hidden Layers Neural Network](https://github.com/TheAlgorithms/Python/blob/master/neural_network/2_hidden_layers_neural_network.py)
* [Back Propagation Neural Network](https://github.com/TheAlgorithms/Python/blob/master/neural_network/back_propagation_neural_network.py)
* [Convolution Neural Network](https://github.com/TheAlgorithms/Python/blob/master/neural_network/convolution_neural_network.py)
* [Perceptron](https://github.com/TheAlgorithms/Python/blob/master/neural_network/perceptron.py)
## Other
* [Activity Selection](https://github.com/TheAlgorithms/Python/blob/master/other/activity_selection.py)
* [Date To Weekday](https://github.com/TheAlgorithms/Python/blob/master/other/date_to_weekday.py)
* [Davisb Putnamb Logemannb Loveland](https://github.com/TheAlgorithms/Python/blob/master/other/davisb_putnamb_logemannb_loveland.py)
* [Dijkstra Bankers Algorithm](https://github.com/TheAlgorithms/Python/blob/master/other/dijkstra_bankers_algorithm.py)
* [Doomsday](https://github.com/TheAlgorithms/Python/blob/master/other/doomsday.py)
* [Fischer Yates Shuffle](https://github.com/TheAlgorithms/Python/blob/master/other/fischer_yates_shuffle.py)
* [Gauss Easter](https://github.com/TheAlgorithms/Python/blob/master/other/gauss_easter.py)
* [Graham Scan](https://github.com/TheAlgorithms/Python/blob/master/other/graham_scan.py)
* [Greedy](https://github.com/TheAlgorithms/Python/blob/master/other/greedy.py)
* [Least Recently Used](https://github.com/TheAlgorithms/Python/blob/master/other/least_recently_used.py)
* [Lfu Cache](https://github.com/TheAlgorithms/Python/blob/master/other/lfu_cache.py)
* [Linear Congruential Generator](https://github.com/TheAlgorithms/Python/blob/master/other/linear_congruential_generator.py)
* [Lru Cache](https://github.com/TheAlgorithms/Python/blob/master/other/lru_cache.py)
* [Magicdiamondpattern](https://github.com/TheAlgorithms/Python/blob/master/other/magicdiamondpattern.py)
* [Nested Brackets](https://github.com/TheAlgorithms/Python/blob/master/other/nested_brackets.py)
* [Password Generator](https://github.com/TheAlgorithms/Python/blob/master/other/password_generator.py)
* [Scoring Algorithm](https://github.com/TheAlgorithms/Python/blob/master/other/scoring_algorithm.py)
* [Sdes](https://github.com/TheAlgorithms/Python/blob/master/other/sdes.py)
* [Tower Of Hanoi](https://github.com/TheAlgorithms/Python/blob/master/other/tower_of_hanoi.py)
## Physics
* [N Body Simulation](https://github.com/TheAlgorithms/Python/blob/master/physics/n_body_simulation.py)
## Project Euler
* Problem 001
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_001/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_001/sol2.py)
* [Sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_001/sol3.py)
* [Sol4](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_001/sol4.py)
* [Sol5](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_001/sol5.py)
* [Sol6](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_001/sol6.py)
* [Sol7](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_001/sol7.py)
* Problem 002
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_002/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_002/sol2.py)
* [Sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_002/sol3.py)
* [Sol4](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_002/sol4.py)
* [Sol5](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_002/sol5.py)
* Problem 003
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_003/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_003/sol2.py)
* [Sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_003/sol3.py)
* Problem 004
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_004/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_004/sol2.py)
* Problem 005
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_005/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_005/sol2.py)
* Problem 006
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_006/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_006/sol2.py)
* [Sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_006/sol3.py)
* [Sol4](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_006/sol4.py)
* Problem 007
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_007/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_007/sol2.py)
* [Sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_007/sol3.py)
* Problem 008
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_008/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_008/sol2.py)
* [Sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_008/sol3.py)
* Problem 009
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_009/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_009/sol2.py)
* [Sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_009/sol3.py)
* Problem 010
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_010/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_010/sol2.py)
* [Sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_010/sol3.py)
* Problem 011
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_011/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_011/sol2.py)
* Problem 012
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_012/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_012/sol2.py)
* Problem 013
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_013/sol1.py)
* Problem 014
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_014/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_014/sol2.py)
* Problem 015
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_015/sol1.py)
* Problem 016
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_016/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_016/sol2.py)
* Problem 017
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_017/sol1.py)
* Problem 018
* [Solution](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_018/solution.py)
* Problem 019
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_019/sol1.py)
* Problem 020
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_020/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_020/sol2.py)
* [Sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_020/sol3.py)
* [Sol4](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_020/sol4.py)
* Problem 021
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_021/sol1.py)
* Problem 022
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_022/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_022/sol2.py)
* Problem 023
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_023/sol1.py)
* Problem 024
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_024/sol1.py)
* Problem 025
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_025/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_025/sol2.py)
* [Sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_025/sol3.py)
* Problem 026
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_026/sol1.py)
* Problem 027
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_027/sol1.py)
* Problem 028
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_028/sol1.py)
* Problem 029
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_029/sol1.py)
* Problem 030
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_030/sol1.py)
* Problem 031
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_031/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_031/sol2.py)
* Problem 032
* [Sol32](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_032/sol32.py)
* Problem 033
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_033/sol1.py)
* Problem 034
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_034/sol1.py)
* Problem 035
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_035/sol1.py)
* Problem 036
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_036/sol1.py)
* Problem 037
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_037/sol1.py)
* Problem 038
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_038/sol1.py)
* Problem 039
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_039/sol1.py)
* Problem 040
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_040/sol1.py)
* Problem 041
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_041/sol1.py)
* Problem 042
* [Solution42](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_042/solution42.py)
* Problem 043
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_043/sol1.py)
* Problem 044
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_044/sol1.py)
* Problem 045
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_045/sol1.py)
* Problem 046
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_046/sol1.py)
* Problem 047
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_047/sol1.py)
* Problem 048
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_048/sol1.py)
* Problem 049
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_049/sol1.py)
* Problem 050
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_050/sol1.py)
* Problem 051
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_051/sol1.py)
* Problem 052
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_052/sol1.py)
* Problem 053
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_053/sol1.py)
* Problem 054
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_054/sol1.py)
* [Test Poker Hand](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_054/test_poker_hand.py)
* Problem 055
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_055/sol1.py)
* Problem 056
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_056/sol1.py)
* Problem 057
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_057/sol1.py)
* Problem 058
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_058/sol1.py)
* Problem 059
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_059/sol1.py)
* Problem 062
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_062/sol1.py)
* Problem 063
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_063/sol1.py)
* Problem 064
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_064/sol1.py)
* Problem 065
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_065/sol1.py)
* Problem 067
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_067/sol1.py)
* Problem 069
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_069/sol1.py)
* Problem 070
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_070/sol1.py)
* Problem 071
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_071/sol1.py)
* Problem 072
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_072/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_072/sol2.py)
* Problem 074
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_074/sol1.py)
* [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_074/sol2.py)
* Problem 075
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_075/sol1.py)
* Problem 076
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_076/sol1.py)
* Problem 077
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_077/sol1.py)
* Problem 080
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_080/sol1.py)
* Problem 081
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_081/sol1.py)
* Problem 085
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_085/sol1.py)
* Problem 086
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_086/sol1.py)
* Problem 087
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_087/sol1.py)
* Problem 089
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_089/sol1.py)
* Problem 091
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_091/sol1.py)
* Problem 097
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_097/sol1.py)
* Problem 099
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_099/sol1.py)
* Problem 101
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_101/sol1.py)
* Problem 102
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_102/sol1.py)
* Problem 107
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_107/sol1.py)
* Problem 109
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_109/sol1.py)
* Problem 112
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_112/sol1.py)
* Problem 113
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_113/sol1.py)
* Problem 119
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_119/sol1.py)
* Problem 120
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_120/sol1.py)
* Problem 121
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_121/sol1.py)
* Problem 123
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_123/sol1.py)
* Problem 125
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_125/sol1.py)
* Problem 129
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_129/sol1.py)
* Problem 135
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_135/sol1.py)
* Problem 144
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_144/sol1.py)
* Problem 173
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_173/sol1.py)
* Problem 174
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_174/sol1.py)
* Problem 180
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_180/sol1.py)
* Problem 188
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_188/sol1.py)
* Problem 191
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_191/sol1.py)
* Problem 203
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_203/sol1.py)
* Problem 206
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_206/sol1.py)
* Problem 207
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_207/sol1.py)
* Problem 234
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_234/sol1.py)
* Problem 301
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_301/sol1.py)
* Problem 551
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_551/sol1.py)
## Quantum
* [Deutsch Jozsa](https://github.com/TheAlgorithms/Python/blob/master/quantum/deutsch_jozsa.py)
* [Half Adder](https://github.com/TheAlgorithms/Python/blob/master/quantum/half_adder.py)
* [Not Gate](https://github.com/TheAlgorithms/Python/blob/master/quantum/not_gate.py)
* [Quantum Entanglement](https://github.com/TheAlgorithms/Python/blob/master/quantum/quantum_entanglement.py)
* [Ripple Adder Classic](https://github.com/TheAlgorithms/Python/blob/master/quantum/ripple_adder_classic.py)
* [Single Qubit Measure](https://github.com/TheAlgorithms/Python/blob/master/quantum/single_qubit_measure.py)
## Scheduling
* [First Come First Served](https://github.com/TheAlgorithms/Python/blob/master/scheduling/first_come_first_served.py)
* [Round Robin](https://github.com/TheAlgorithms/Python/blob/master/scheduling/round_robin.py)
* [Shortest Job First](https://github.com/TheAlgorithms/Python/blob/master/scheduling/shortest_job_first.py)
## Searches
* [Binary Search](https://github.com/TheAlgorithms/Python/blob/master/searches/binary_search.py)
* [Binary Tree Traversal](https://github.com/TheAlgorithms/Python/blob/master/searches/binary_tree_traversal.py)
* [Double Linear Search](https://github.com/TheAlgorithms/Python/blob/master/searches/double_linear_search.py)
* [Double Linear Search Recursion](https://github.com/TheAlgorithms/Python/blob/master/searches/double_linear_search_recursion.py)
* [Fibonacci Search](https://github.com/TheAlgorithms/Python/blob/master/searches/fibonacci_search.py)
* [Hill Climbing](https://github.com/TheAlgorithms/Python/blob/master/searches/hill_climbing.py)
* [Interpolation Search](https://github.com/TheAlgorithms/Python/blob/master/searches/interpolation_search.py)
* [Jump Search](https://github.com/TheAlgorithms/Python/blob/master/searches/jump_search.py)
* [Linear Search](https://github.com/TheAlgorithms/Python/blob/master/searches/linear_search.py)
* [Quick Select](https://github.com/TheAlgorithms/Python/blob/master/searches/quick_select.py)
* [Sentinel Linear Search](https://github.com/TheAlgorithms/Python/blob/master/searches/sentinel_linear_search.py)
* [Simple Binary Search](https://github.com/TheAlgorithms/Python/blob/master/searches/simple_binary_search.py)
* [Simulated Annealing](https://github.com/TheAlgorithms/Python/blob/master/searches/simulated_annealing.py)
* [Tabu Search](https://github.com/TheAlgorithms/Python/blob/master/searches/tabu_search.py)
* [Ternary Search](https://github.com/TheAlgorithms/Python/blob/master/searches/ternary_search.py)
## Sorts
* [Bead Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/bead_sort.py)
* [Bitonic Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/bitonic_sort.py)
* [Bogo Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/bogo_sort.py)
* [Bubble Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/bubble_sort.py)
* [Bucket Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/bucket_sort.py)
* [Cocktail Shaker Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/cocktail_shaker_sort.py)
* [Comb Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/comb_sort.py)
* [Counting Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/counting_sort.py)
* [Cycle Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/cycle_sort.py)
* [Double Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/double_sort.py)
* [Dutch National Flag Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/dutch_national_flag_sort.py)
* [Exchange Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/exchange_sort.py)
* [External Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/external_sort.py)
* [Gnome Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/gnome_sort.py)
* [Heap Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/heap_sort.py)
* [Insertion Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/insertion_sort.py)
* [Intro Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/intro_sort.py)
* [Iterative Merge Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/iterative_merge_sort.py)
* [Merge Insertion Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/merge_insertion_sort.py)
* [Merge Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/merge_sort.py)
* [Msd Radix Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/msd_radix_sort.py)
* [Natural Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/natural_sort.py)
* [Odd Even Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/odd_even_sort.py)
* [Odd Even Transposition Parallel](https://github.com/TheAlgorithms/Python/blob/master/sorts/odd_even_transposition_parallel.py)
* [Odd Even Transposition Single Threaded](https://github.com/TheAlgorithms/Python/blob/master/sorts/odd_even_transposition_single_threaded.py)
* [Pancake Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/pancake_sort.py)
* [Patience Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/patience_sort.py)
* [Pigeon Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/pigeon_sort.py)
* [Pigeonhole Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/pigeonhole_sort.py)
* [Quick Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/quick_sort.py)
* [Quick Sort 3 Partition](https://github.com/TheAlgorithms/Python/blob/master/sorts/quick_sort_3_partition.py)
* [Radix Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/radix_sort.py)
* [Random Normal Distribution Quicksort](https://github.com/TheAlgorithms/Python/blob/master/sorts/random_normal_distribution_quicksort.py)
* [Random Pivot Quick Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/random_pivot_quick_sort.py)
* [Recursive Bubble Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/recursive_bubble_sort.py)
* [Recursive Insertion Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/recursive_insertion_sort.py)
* [Recursive Mergesort Array](https://github.com/TheAlgorithms/Python/blob/master/sorts/recursive_mergesort_array.py)
* [Recursive Quick Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/recursive_quick_sort.py)
* [Selection Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/selection_sort.py)
* [Shell Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/shell_sort.py)
* [Slowsort](https://github.com/TheAlgorithms/Python/blob/master/sorts/slowsort.py)
* [Stooge Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/stooge_sort.py)
* [Strand Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/strand_sort.py)
* [Tim Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/tim_sort.py)
* [Topological Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/topological_sort.py)
* [Tree Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/tree_sort.py)
* [Unknown Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/unknown_sort.py)
* [Wiggle Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/wiggle_sort.py)
## Strings
* [Aho Corasick](https://github.com/TheAlgorithms/Python/blob/master/strings/aho_corasick.py)
* [Alternative String Arrange](https://github.com/TheAlgorithms/Python/blob/master/strings/alternative_string_arrange.py)
* [Anagrams](https://github.com/TheAlgorithms/Python/blob/master/strings/anagrams.py)
* [Autocomplete Using Trie](https://github.com/TheAlgorithms/Python/blob/master/strings/autocomplete_using_trie.py)
* [Boyer Moore Search](https://github.com/TheAlgorithms/Python/blob/master/strings/boyer_moore_search.py)
* [Can String Be Rearranged As Palindrome](https://github.com/TheAlgorithms/Python/blob/master/strings/can_string_be_rearranged_as_palindrome.py)
* [Capitalize](https://github.com/TheAlgorithms/Python/blob/master/strings/capitalize.py)
* [Check Anagrams](https://github.com/TheAlgorithms/Python/blob/master/strings/check_anagrams.py)
* [Check Pangram](https://github.com/TheAlgorithms/Python/blob/master/strings/check_pangram.py)
* [Detecting English Programmatically](https://github.com/TheAlgorithms/Python/blob/master/strings/detecting_english_programmatically.py)
* [Frequency Finder](https://github.com/TheAlgorithms/Python/blob/master/strings/frequency_finder.py)
* [Indian Phone Validator](https://github.com/TheAlgorithms/Python/blob/master/strings/indian_phone_validator.py)
* [Is Palindrome](https://github.com/TheAlgorithms/Python/blob/master/strings/is_palindrome.py)
* [Jaro Winkler](https://github.com/TheAlgorithms/Python/blob/master/strings/jaro_winkler.py)
* [Knuth Morris Pratt](https://github.com/TheAlgorithms/Python/blob/master/strings/knuth_morris_pratt.py)
* [Levenshtein Distance](https://github.com/TheAlgorithms/Python/blob/master/strings/levenshtein_distance.py)
* [Lower](https://github.com/TheAlgorithms/Python/blob/master/strings/lower.py)
* [Manacher](https://github.com/TheAlgorithms/Python/blob/master/strings/manacher.py)
* [Min Cost String Conversion](https://github.com/TheAlgorithms/Python/blob/master/strings/min_cost_string_conversion.py)
* [Naive String Search](https://github.com/TheAlgorithms/Python/blob/master/strings/naive_string_search.py)
* [Palindrome](https://github.com/TheAlgorithms/Python/blob/master/strings/palindrome.py)
* [Prefix Function](https://github.com/TheAlgorithms/Python/blob/master/strings/prefix_function.py)
* [Rabin Karp](https://github.com/TheAlgorithms/Python/blob/master/strings/rabin_karp.py)
* [Remove Duplicate](https://github.com/TheAlgorithms/Python/blob/master/strings/remove_duplicate.py)
* [Reverse Letters](https://github.com/TheAlgorithms/Python/blob/master/strings/reverse_letters.py)
* [Reverse Words](https://github.com/TheAlgorithms/Python/blob/master/strings/reverse_words.py)
* [Split](https://github.com/TheAlgorithms/Python/blob/master/strings/split.py)
* [Swap Case](https://github.com/TheAlgorithms/Python/blob/master/strings/swap_case.py)
* [Upper](https://github.com/TheAlgorithms/Python/blob/master/strings/upper.py)
* [Word Occurrence](https://github.com/TheAlgorithms/Python/blob/master/strings/word_occurrence.py)
* [Word Patterns](https://github.com/TheAlgorithms/Python/blob/master/strings/word_patterns.py)
* [Z Function](https://github.com/TheAlgorithms/Python/blob/master/strings/z_function.py)
## Web Programming
* [Co2 Emission](https://github.com/TheAlgorithms/Python/blob/master/web_programming/co2_emission.py)
* [Covid Stats Via Xpath](https://github.com/TheAlgorithms/Python/blob/master/web_programming/covid_stats_via_xpath.py)
* [Crawl Google Results](https://github.com/TheAlgorithms/Python/blob/master/web_programming/crawl_google_results.py)
* [Crawl Google Scholar Citation](https://github.com/TheAlgorithms/Python/blob/master/web_programming/crawl_google_scholar_citation.py)
* [Currency Converter](https://github.com/TheAlgorithms/Python/blob/master/web_programming/currency_converter.py)
* [Current Stock Price](https://github.com/TheAlgorithms/Python/blob/master/web_programming/current_stock_price.py)
* [Current Weather](https://github.com/TheAlgorithms/Python/blob/master/web_programming/current_weather.py)
* [Daily Horoscope](https://github.com/TheAlgorithms/Python/blob/master/web_programming/daily_horoscope.py)
* [Emails From Url](https://github.com/TheAlgorithms/Python/blob/master/web_programming/emails_from_url.py)
* [Fetch Bbc News](https://github.com/TheAlgorithms/Python/blob/master/web_programming/fetch_bbc_news.py)
* [Fetch Github Info](https://github.com/TheAlgorithms/Python/blob/master/web_programming/fetch_github_info.py)
* [Fetch Jobs](https://github.com/TheAlgorithms/Python/blob/master/web_programming/fetch_jobs.py)
* [Get Imdb Top 250 Movies Csv](https://github.com/TheAlgorithms/Python/blob/master/web_programming/get_imdb_top_250_movies_csv.py)
* [Get Imdbtop](https://github.com/TheAlgorithms/Python/blob/master/web_programming/get_imdbtop.py)
* [Instagram Crawler](https://github.com/TheAlgorithms/Python/blob/master/web_programming/instagram_crawler.py)
* [Instagram Pic](https://github.com/TheAlgorithms/Python/blob/master/web_programming/instagram_pic.py)
* [Instagram Video](https://github.com/TheAlgorithms/Python/blob/master/web_programming/instagram_video.py)
* [Random Anime Character](https://github.com/TheAlgorithms/Python/blob/master/web_programming/random_anime_character.py)
* [Recaptcha Verification](https://github.com/TheAlgorithms/Python/blob/master/web_programming/recaptcha_verification.py)
* [Slack Message](https://github.com/TheAlgorithms/Python/blob/master/web_programming/slack_message.py)
* [Test Fetch Github Info](https://github.com/TheAlgorithms/Python/blob/master/web_programming/test_fetch_github_info.py)
* [World Covid19 Stats](https://github.com/TheAlgorithms/Python/blob/master/web_programming/world_covid19_stats.py)
| 1 |
TheAlgorithms/Python | 4,763 | from __future__ import annotations | ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2021-09-14T22:54:26Z" | "2021-09-22T21:11:51Z" | dc07a850763d8154e012c9d1be7f8fe78326e8fb | 15d1cfabb15903c5e9d4d103e2390876efb3f85f | from __future__ import annotations. ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Convert a string of characters to a sequence of numbers
corresponding to the character's position in the alphabet.
https://www.dcode.fr/letter-number-cipher
http://bestcodes.weebly.com/a1z26.html
"""
def encode(plain: str) -> list[int]:
"""
>>> encode("myname")
[13, 25, 14, 1, 13, 5]
"""
return [ord(elem) - 96 for elem in plain]
def decode(encoded: list[int]) -> str:
"""
>>> decode([13, 25, 14, 1, 13, 5])
'myname'
"""
return "".join(chr(elem + 96) for elem in encoded)
def main() -> None:
encoded = encode(input("-> ").strip().lower())
print("Encoded: ", encoded)
print("Decoded:", decode(encoded))
if __name__ == "__main__":
main()
| """
Convert a string of characters to a sequence of numbers
corresponding to the character's position in the alphabet.
https://www.dcode.fr/letter-number-cipher
http://bestcodes.weebly.com/a1z26.html
"""
from __future__ import annotations
def encode(plain: str) -> list[int]:
"""
>>> encode("myname")
[13, 25, 14, 1, 13, 5]
"""
return [ord(elem) - 96 for elem in plain]
def decode(encoded: list[int]) -> str:
"""
>>> decode([13, 25, 14, 1, 13, 5])
'myname'
"""
return "".join(chr(elem + 96) for elem in encoded)
def main() -> None:
encoded = encode(input("-> ").strip().lower())
print("Encoded: ", encoded)
print("Decoded:", decode(encoded))
if __name__ == "__main__":
main()
| 1 |
TheAlgorithms/Python | 4,763 | from __future__ import annotations | ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2021-09-14T22:54:26Z" | "2021-09-22T21:11:51Z" | dc07a850763d8154e012c9d1be7f8fe78326e8fb | 15d1cfabb15903c5e9d4d103e2390876efb3f85f | from __future__ import annotations. ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Wikipedia: https://en.wikipedia.org/wiki/Enigma_machine
Video explanation: https://youtu.be/QwQVMqfoB2E
Also check out Numberphile's and Computerphile's videos on this topic
This module contains function 'enigma' which emulates
the famous Enigma machine from WWII.
Module includes:
- enigma function
- showcase of function usage
- 9 randnomly generated rotors
- reflector (aka static rotor)
- original alphabet
Created by TrapinchO
"""
RotorPositionT = tuple[int, int, int]
RotorSelectionT = tuple[str, str, str]
# used alphabet --------------------------
# from string.ascii_uppercase
abc = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
# -------------------------- default selection --------------------------
# rotors --------------------------
rotor1 = "EGZWVONAHDCLFQMSIPJBYUKXTR"
rotor2 = "FOBHMDKEXQNRAULPGSJVTYICZW"
rotor3 = "ZJXESIUQLHAVRMDOYGTNFWPBKC"
# reflector --------------------------
reflector = {
"A": "N",
"N": "A",
"B": "O",
"O": "B",
"C": "P",
"P": "C",
"D": "Q",
"Q": "D",
"E": "R",
"R": "E",
"F": "S",
"S": "F",
"G": "T",
"T": "G",
"H": "U",
"U": "H",
"I": "V",
"V": "I",
"J": "W",
"W": "J",
"K": "X",
"X": "K",
"L": "Y",
"Y": "L",
"M": "Z",
"Z": "M",
}
# -------------------------- extra rotors --------------------------
rotor4 = "RMDJXFUWGISLHVTCQNKYPBEZOA"
rotor5 = "SGLCPQWZHKXAREONTFBVIYJUDM"
rotor6 = "HVSICLTYKQUBXDWAJZOMFGPREN"
rotor7 = "RZWQHFMVDBKICJLNTUXAGYPSOE"
rotor8 = "LFKIJODBEGAMQPXVUHYSTCZRWN"
rotor9 = "KOAEGVDHXPQZMLFTYWJNBRCIUS"
def _validator(
rotpos: RotorPositionT, rotsel: RotorSelectionT, pb: str
) -> tuple[RotorPositionT, RotorSelectionT, dict[str, str]]:
"""
Checks if the values can be used for the 'enigma' function
>>> _validator((1,1,1), (rotor1, rotor2, rotor3), 'POLAND')
((1, 1, 1), ('EGZWVONAHDCLFQMSIPJBYUKXTR', 'FOBHMDKEXQNRAULPGSJVTYICZW', \
'ZJXESIUQLHAVRMDOYGTNFWPBKC'), \
{'P': 'O', 'O': 'P', 'L': 'A', 'A': 'L', 'N': 'D', 'D': 'N'})
:param rotpos: rotor_positon
:param rotsel: rotor_selection
:param pb: plugb -> validated and transformed
:return: (rotpos, rotsel, pb)
"""
# Checks if there are 3 unique rotors
unique_rotsel = len(set(rotsel))
if unique_rotsel < 3:
raise Exception(f"Please use 3 unique rotors (not {unique_rotsel})")
# Checks if rotor positions are valid
rotorpos1, rotorpos2, rotorpos3 = rotpos
if not 0 < rotorpos1 <= len(abc):
raise ValueError(
f"First rotor position is not within range of 1..26 (" f"{rotorpos1}"
)
if not 0 < rotorpos2 <= len(abc):
raise ValueError(
f"Second rotor position is not within range of 1..26 (" f"{rotorpos2})"
)
if not 0 < rotorpos3 <= len(abc):
raise ValueError(
f"Third rotor position is not within range of 1..26 (" f"{rotorpos3})"
)
# Validates string and returns dict
pbdict = _plugboard(pb)
return rotpos, rotsel, pbdict
def _plugboard(pbstring: str) -> dict[str, str]:
"""
https://en.wikipedia.org/wiki/Enigma_machine#Plugboard
>>> _plugboard('PICTURES')
{'P': 'I', 'I': 'P', 'C': 'T', 'T': 'C', 'U': 'R', 'R': 'U', 'E': 'S', 'S': 'E'}
>>> _plugboard('POLAND')
{'P': 'O', 'O': 'P', 'L': 'A', 'A': 'L', 'N': 'D', 'D': 'N'}
In the code, 'pb' stands for 'plugboard'
Pairs can be separated by spaces
:param pbstring: string containing plugboard setting for the Enigma machine
:return: dictionary containing converted pairs
"""
# tests the input string if it
# a) is type string
# b) has even length (so pairs can be made)
if not isinstance(pbstring, str):
raise TypeError(f"Plugboard setting isn't type string ({type(pbstring)})")
elif len(pbstring) % 2 != 0:
raise Exception(f"Odd number of symbols ({len(pbstring)})")
elif pbstring == "":
return {}
pbstring.replace(" ", "")
# Checks if all characters are unique
tmppbl = set()
for i in pbstring:
if i not in abc:
raise Exception(f"'{i}' not in list of symbols")
elif i in tmppbl:
raise Exception(f"Duplicate symbol ({i})")
else:
tmppbl.add(i)
del tmppbl
# Created the dictionary
pb = {}
for j in range(0, len(pbstring) - 1, 2):
pb[pbstring[j]] = pbstring[j + 1]
pb[pbstring[j + 1]] = pbstring[j]
return pb
def enigma(
text: str,
rotor_position: RotorPositionT,
rotor_selection: RotorSelectionT = (rotor1, rotor2, rotor3),
plugb: str = "",
) -> str:
"""
The only difference with real-world enigma is that I allowed string input.
All characters are converted to uppercase. (non-letter symbol are ignored)
How it works:
(for every letter in the message)
- Input letter goes into the plugboard.
If it is connected to another one, switch it.
- Letter goes through 3 rotors.
Each rotor can be represented as 2 sets of symbol, where one is shuffled.
Each symbol from the first set has corresponding symbol in
the second set and vice versa.
example:
| ABCDEFGHIJKLMNOPQRSTUVWXYZ | e.g. F=D and D=F
| VKLEPDBGRNWTFCJOHQAMUZYIXS |
- Symbol then goes through reflector (static rotor).
There it is switched with paired symbol
The reflector can be represented as2 sets, each with half of the alphanet.
There are usually 10 pairs of letters.
Example:
| ABCDEFGHIJKLM | e.g. E is paired to X
| ZYXWVUTSRQPON | so when E goes in X goes out and vice versa
- Letter then goes through the rotors again
- If the letter is connected to plugboard, it is switched.
- Return the letter
>>> enigma('Hello World!', (1, 2, 1), plugb='pictures')
'KORYH JUHHI!'
>>> enigma('KORYH, juhhi!', (1, 2, 1), plugb='pictures')
'HELLO, WORLD!'
>>> enigma('hello world!', (1, 1, 1), plugb='pictures')
'FPNCZ QWOBU!'
>>> enigma('FPNCZ QWOBU', (1, 1, 1), plugb='pictures')
'HELLO WORLD'
:param text: input message
:param rotor_position: tuple with 3 values in range 1..26
:param rotor_selection: tuple with 3 rotors ()
:param plugb: string containing plugboard configuration (default '')
:return: en/decrypted string
"""
text = text.upper()
rotor_position, rotor_selection, plugboard = _validator(
rotor_position, rotor_selection, plugb.upper()
)
rotorpos1, rotorpos2, rotorpos3 = rotor_position
rotor1, rotor2, rotor3 = rotor_selection
rotorpos1 -= 1
rotorpos2 -= 1
rotorpos3 -= 1
result = []
# encryption/decryption process --------------------------
for symbol in text:
if symbol in abc:
# 1st plugboard --------------------------
if symbol in plugboard:
symbol = plugboard[symbol]
# rotor ra --------------------------
index = abc.index(symbol) + rotorpos1
symbol = rotor1[index % len(abc)]
# rotor rb --------------------------
index = abc.index(symbol) + rotorpos2
symbol = rotor2[index % len(abc)]
# rotor rc --------------------------
index = abc.index(symbol) + rotorpos3
symbol = rotor3[index % len(abc)]
# reflector --------------------------
# this is the reason you don't need another machine to decipher
symbol = reflector[symbol]
# 2nd rotors
symbol = abc[rotor3.index(symbol) - rotorpos3]
symbol = abc[rotor2.index(symbol) - rotorpos2]
symbol = abc[rotor1.index(symbol) - rotorpos1]
# 2nd plugboard
if symbol in plugboard:
symbol = plugboard[symbol]
# moves/resets rotor positions
rotorpos1 += 1
if rotorpos1 >= len(abc):
rotorpos1 = 0
rotorpos2 += 1
if rotorpos2 >= len(abc):
rotorpos2 = 0
rotorpos3 += 1
if rotorpos3 >= len(abc):
rotorpos3 = 0
# else:
# pass
# Error could be also raised
# raise ValueError(
# 'Invalid symbol('+repr(symbol)+')')
result.append(symbol)
return "".join(result)
if __name__ == "__main__":
message = "This is my Python script that emulates the Enigma machine from WWII."
rotor_pos = (1, 1, 1)
pb = "pictures"
rotor_sel = (rotor2, rotor4, rotor8)
en = enigma(message, rotor_pos, rotor_sel, pb)
print("Encrypted message:", en)
print("Decrypted message:", enigma(en, rotor_pos, rotor_sel, pb))
| """
Wikipedia: https://en.wikipedia.org/wiki/Enigma_machine
Video explanation: https://youtu.be/QwQVMqfoB2E
Also check out Numberphile's and Computerphile's videos on this topic
This module contains function 'enigma' which emulates
the famous Enigma machine from WWII.
Module includes:
- enigma function
- showcase of function usage
- 9 randnomly generated rotors
- reflector (aka static rotor)
- original alphabet
Created by TrapinchO
"""
from __future__ import annotations
RotorPositionT = tuple[int, int, int]
RotorSelectionT = tuple[str, str, str]
# used alphabet --------------------------
# from string.ascii_uppercase
abc = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
# -------------------------- default selection --------------------------
# rotors --------------------------
rotor1 = "EGZWVONAHDCLFQMSIPJBYUKXTR"
rotor2 = "FOBHMDKEXQNRAULPGSJVTYICZW"
rotor3 = "ZJXESIUQLHAVRMDOYGTNFWPBKC"
# reflector --------------------------
reflector = {
"A": "N",
"N": "A",
"B": "O",
"O": "B",
"C": "P",
"P": "C",
"D": "Q",
"Q": "D",
"E": "R",
"R": "E",
"F": "S",
"S": "F",
"G": "T",
"T": "G",
"H": "U",
"U": "H",
"I": "V",
"V": "I",
"J": "W",
"W": "J",
"K": "X",
"X": "K",
"L": "Y",
"Y": "L",
"M": "Z",
"Z": "M",
}
# -------------------------- extra rotors --------------------------
rotor4 = "RMDJXFUWGISLHVTCQNKYPBEZOA"
rotor5 = "SGLCPQWZHKXAREONTFBVIYJUDM"
rotor6 = "HVSICLTYKQUBXDWAJZOMFGPREN"
rotor7 = "RZWQHFMVDBKICJLNTUXAGYPSOE"
rotor8 = "LFKIJODBEGAMQPXVUHYSTCZRWN"
rotor9 = "KOAEGVDHXPQZMLFTYWJNBRCIUS"
def _validator(
rotpos: RotorPositionT, rotsel: RotorSelectionT, pb: str
) -> tuple[RotorPositionT, RotorSelectionT, dict[str, str]]:
"""
Checks if the values can be used for the 'enigma' function
>>> _validator((1,1,1), (rotor1, rotor2, rotor3), 'POLAND')
((1, 1, 1), ('EGZWVONAHDCLFQMSIPJBYUKXTR', 'FOBHMDKEXQNRAULPGSJVTYICZW', \
'ZJXESIUQLHAVRMDOYGTNFWPBKC'), \
{'P': 'O', 'O': 'P', 'L': 'A', 'A': 'L', 'N': 'D', 'D': 'N'})
:param rotpos: rotor_positon
:param rotsel: rotor_selection
:param pb: plugb -> validated and transformed
:return: (rotpos, rotsel, pb)
"""
# Checks if there are 3 unique rotors
unique_rotsel = len(set(rotsel))
if unique_rotsel < 3:
raise Exception(f"Please use 3 unique rotors (not {unique_rotsel})")
# Checks if rotor positions are valid
rotorpos1, rotorpos2, rotorpos3 = rotpos
if not 0 < rotorpos1 <= len(abc):
raise ValueError(
f"First rotor position is not within range of 1..26 (" f"{rotorpos1}"
)
if not 0 < rotorpos2 <= len(abc):
raise ValueError(
f"Second rotor position is not within range of 1..26 (" f"{rotorpos2})"
)
if not 0 < rotorpos3 <= len(abc):
raise ValueError(
f"Third rotor position is not within range of 1..26 (" f"{rotorpos3})"
)
# Validates string and returns dict
pbdict = _plugboard(pb)
return rotpos, rotsel, pbdict
def _plugboard(pbstring: str) -> dict[str, str]:
"""
https://en.wikipedia.org/wiki/Enigma_machine#Plugboard
>>> _plugboard('PICTURES')
{'P': 'I', 'I': 'P', 'C': 'T', 'T': 'C', 'U': 'R', 'R': 'U', 'E': 'S', 'S': 'E'}
>>> _plugboard('POLAND')
{'P': 'O', 'O': 'P', 'L': 'A', 'A': 'L', 'N': 'D', 'D': 'N'}
In the code, 'pb' stands for 'plugboard'
Pairs can be separated by spaces
:param pbstring: string containing plugboard setting for the Enigma machine
:return: dictionary containing converted pairs
"""
# tests the input string if it
# a) is type string
# b) has even length (so pairs can be made)
if not isinstance(pbstring, str):
raise TypeError(f"Plugboard setting isn't type string ({type(pbstring)})")
elif len(pbstring) % 2 != 0:
raise Exception(f"Odd number of symbols ({len(pbstring)})")
elif pbstring == "":
return {}
pbstring.replace(" ", "")
# Checks if all characters are unique
tmppbl = set()
for i in pbstring:
if i not in abc:
raise Exception(f"'{i}' not in list of symbols")
elif i in tmppbl:
raise Exception(f"Duplicate symbol ({i})")
else:
tmppbl.add(i)
del tmppbl
# Created the dictionary
pb = {}
for j in range(0, len(pbstring) - 1, 2):
pb[pbstring[j]] = pbstring[j + 1]
pb[pbstring[j + 1]] = pbstring[j]
return pb
def enigma(
text: str,
rotor_position: RotorPositionT,
rotor_selection: RotorSelectionT = (rotor1, rotor2, rotor3),
plugb: str = "",
) -> str:
"""
The only difference with real-world enigma is that I allowed string input.
All characters are converted to uppercase. (non-letter symbol are ignored)
How it works:
(for every letter in the message)
- Input letter goes into the plugboard.
If it is connected to another one, switch it.
- Letter goes through 3 rotors.
Each rotor can be represented as 2 sets of symbol, where one is shuffled.
Each symbol from the first set has corresponding symbol in
the second set and vice versa.
example:
| ABCDEFGHIJKLMNOPQRSTUVWXYZ | e.g. F=D and D=F
| VKLEPDBGRNWTFCJOHQAMUZYIXS |
- Symbol then goes through reflector (static rotor).
There it is switched with paired symbol
The reflector can be represented as2 sets, each with half of the alphanet.
There are usually 10 pairs of letters.
Example:
| ABCDEFGHIJKLM | e.g. E is paired to X
| ZYXWVUTSRQPON | so when E goes in X goes out and vice versa
- Letter then goes through the rotors again
- If the letter is connected to plugboard, it is switched.
- Return the letter
>>> enigma('Hello World!', (1, 2, 1), plugb='pictures')
'KORYH JUHHI!'
>>> enigma('KORYH, juhhi!', (1, 2, 1), plugb='pictures')
'HELLO, WORLD!'
>>> enigma('hello world!', (1, 1, 1), plugb='pictures')
'FPNCZ QWOBU!'
>>> enigma('FPNCZ QWOBU', (1, 1, 1), plugb='pictures')
'HELLO WORLD'
:param text: input message
:param rotor_position: tuple with 3 values in range 1..26
:param rotor_selection: tuple with 3 rotors ()
:param plugb: string containing plugboard configuration (default '')
:return: en/decrypted string
"""
text = text.upper()
rotor_position, rotor_selection, plugboard = _validator(
rotor_position, rotor_selection, plugb.upper()
)
rotorpos1, rotorpos2, rotorpos3 = rotor_position
rotor1, rotor2, rotor3 = rotor_selection
rotorpos1 -= 1
rotorpos2 -= 1
rotorpos3 -= 1
result = []
# encryption/decryption process --------------------------
for symbol in text:
if symbol in abc:
# 1st plugboard --------------------------
if symbol in plugboard:
symbol = plugboard[symbol]
# rotor ra --------------------------
index = abc.index(symbol) + rotorpos1
symbol = rotor1[index % len(abc)]
# rotor rb --------------------------
index = abc.index(symbol) + rotorpos2
symbol = rotor2[index % len(abc)]
# rotor rc --------------------------
index = abc.index(symbol) + rotorpos3
symbol = rotor3[index % len(abc)]
# reflector --------------------------
# this is the reason you don't need another machine to decipher
symbol = reflector[symbol]
# 2nd rotors
symbol = abc[rotor3.index(symbol) - rotorpos3]
symbol = abc[rotor2.index(symbol) - rotorpos2]
symbol = abc[rotor1.index(symbol) - rotorpos1]
# 2nd plugboard
if symbol in plugboard:
symbol = plugboard[symbol]
# moves/resets rotor positions
rotorpos1 += 1
if rotorpos1 >= len(abc):
rotorpos1 = 0
rotorpos2 += 1
if rotorpos2 >= len(abc):
rotorpos2 = 0
rotorpos3 += 1
if rotorpos3 >= len(abc):
rotorpos3 = 0
# else:
# pass
# Error could be also raised
# raise ValueError(
# 'Invalid symbol('+repr(symbol)+')')
result.append(symbol)
return "".join(result)
if __name__ == "__main__":
message = "This is my Python script that emulates the Enigma machine from WWII."
rotor_pos = (1, 1, 1)
pb = "pictures"
rotor_sel = (rotor2, rotor4, rotor8)
en = enigma(message, rotor_pos, rotor_sel, pb)
print("Encrypted message:", en)
print("Decrypted message:", enigma(en, rotor_pos, rotor_sel, pb))
| 1 |
TheAlgorithms/Python | 4,763 | from __future__ import annotations | ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2021-09-14T22:54:26Z" | "2021-09-22T21:11:51Z" | dc07a850763d8154e012c9d1be7f8fe78326e8fb | 15d1cfabb15903c5e9d4d103e2390876efb3f85f | from __future__ import annotations. ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # https://en.wikipedia.org/wiki/Trifid_cipher
def __encryptPart(messagePart: str, character2Number: dict[str, str]) -> str:
one, two, three = "", "", ""
tmp = []
for character in messagePart:
tmp.append(character2Number[character])
for each in tmp:
one += each[0]
two += each[1]
three += each[2]
return one + two + three
def __decryptPart(
messagePart: str, character2Number: dict[str, str]
) -> tuple[str, str, str]:
tmp, thisPart = "", ""
result = []
for character in messagePart:
thisPart += character2Number[character]
for digit in thisPart:
tmp += digit
if len(tmp) == len(messagePart):
result.append(tmp)
tmp = ""
return result[0], result[1], result[2]
def __prepare(
message: str, alphabet: str
) -> tuple[str, str, dict[str, str], dict[str, str]]:
# Validate message and alphabet, set to upper and remove spaces
alphabet = alphabet.replace(" ", "").upper()
message = message.replace(" ", "").upper()
# Check length and characters
if len(alphabet) != 27:
raise KeyError("Length of alphabet has to be 27.")
for each in message:
if each not in alphabet:
raise ValueError("Each message character has to be included in alphabet!")
# Generate dictionares
numbers = (
"111",
"112",
"113",
"121",
"122",
"123",
"131",
"132",
"133",
"211",
"212",
"213",
"221",
"222",
"223",
"231",
"232",
"233",
"311",
"312",
"313",
"321",
"322",
"323",
"331",
"332",
"333",
)
character2Number = {}
number2Character = {}
for letter, number in zip(alphabet, numbers):
character2Number[letter] = number
number2Character[number] = letter
return message, alphabet, character2Number, number2Character
def encryptMessage(
message: str, alphabet: str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ.", period: int = 5
) -> str:
message, alphabet, character2Number, number2Character = __prepare(message, alphabet)
encrypted, encrypted_numeric = "", ""
for i in range(0, len(message) + 1, period):
encrypted_numeric += __encryptPart(message[i : i + period], character2Number)
for i in range(0, len(encrypted_numeric), 3):
encrypted += number2Character[encrypted_numeric[i : i + 3]]
return encrypted
def decryptMessage(
message: str, alphabet: str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ.", period: int = 5
) -> str:
message, alphabet, character2Number, number2Character = __prepare(message, alphabet)
decrypted_numeric = []
decrypted = ""
for i in range(0, len(message) + 1, period):
a, b, c = __decryptPart(message[i : i + period], character2Number)
for j in range(0, len(a)):
decrypted_numeric.append(a[j] + b[j] + c[j])
for each in decrypted_numeric:
decrypted += number2Character[each]
return decrypted
if __name__ == "__main__":
msg = "DEFEND THE EAST WALL OF THE CASTLE."
encrypted = encryptMessage(msg, "EPSDUCVWYM.ZLKXNBTFGORIJHAQ")
decrypted = decryptMessage(encrypted, "EPSDUCVWYM.ZLKXNBTFGORIJHAQ")
print(f"Encrypted: {encrypted}\nDecrypted: {decrypted}")
| # https://en.wikipedia.org/wiki/Trifid_cipher
from __future__ import annotations
def __encryptPart(messagePart: str, character2Number: dict[str, str]) -> str:
one, two, three = "", "", ""
tmp = []
for character in messagePart:
tmp.append(character2Number[character])
for each in tmp:
one += each[0]
two += each[1]
three += each[2]
return one + two + three
def __decryptPart(
messagePart: str, character2Number: dict[str, str]
) -> tuple[str, str, str]:
tmp, thisPart = "", ""
result = []
for character in messagePart:
thisPart += character2Number[character]
for digit in thisPart:
tmp += digit
if len(tmp) == len(messagePart):
result.append(tmp)
tmp = ""
return result[0], result[1], result[2]
def __prepare(
message: str, alphabet: str
) -> tuple[str, str, dict[str, str], dict[str, str]]:
# Validate message and alphabet, set to upper and remove spaces
alphabet = alphabet.replace(" ", "").upper()
message = message.replace(" ", "").upper()
# Check length and characters
if len(alphabet) != 27:
raise KeyError("Length of alphabet has to be 27.")
for each in message:
if each not in alphabet:
raise ValueError("Each message character has to be included in alphabet!")
# Generate dictionares
numbers = (
"111",
"112",
"113",
"121",
"122",
"123",
"131",
"132",
"133",
"211",
"212",
"213",
"221",
"222",
"223",
"231",
"232",
"233",
"311",
"312",
"313",
"321",
"322",
"323",
"331",
"332",
"333",
)
character2Number = {}
number2Character = {}
for letter, number in zip(alphabet, numbers):
character2Number[letter] = number
number2Character[number] = letter
return message, alphabet, character2Number, number2Character
def encryptMessage(
message: str, alphabet: str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ.", period: int = 5
) -> str:
message, alphabet, character2Number, number2Character = __prepare(message, alphabet)
encrypted, encrypted_numeric = "", ""
for i in range(0, len(message) + 1, period):
encrypted_numeric += __encryptPart(message[i : i + period], character2Number)
for i in range(0, len(encrypted_numeric), 3):
encrypted += number2Character[encrypted_numeric[i : i + 3]]
return encrypted
def decryptMessage(
message: str, alphabet: str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ.", period: int = 5
) -> str:
message, alphabet, character2Number, number2Character = __prepare(message, alphabet)
decrypted_numeric = []
decrypted = ""
for i in range(0, len(message) + 1, period):
a, b, c = __decryptPart(message[i : i + period], character2Number)
for j in range(0, len(a)):
decrypted_numeric.append(a[j] + b[j] + c[j])
for each in decrypted_numeric:
decrypted += number2Character[each]
return decrypted
if __name__ == "__main__":
msg = "DEFEND THE EAST WALL OF THE CASTLE."
encrypted = encryptMessage(msg, "EPSDUCVWYM.ZLKXNBTFGORIJHAQ")
decrypted = decryptMessage(encrypted, "EPSDUCVWYM.ZLKXNBTFGORIJHAQ")
print(f"Encrypted: {encrypted}\nDecrypted: {decrypted}")
| 1 |
TheAlgorithms/Python | 4,763 | from __future__ import annotations | ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2021-09-14T22:54:26Z" | "2021-09-22T21:11:51Z" | dc07a850763d8154e012c9d1be7f8fe78326e8fb | 15d1cfabb15903c5e9d4d103e2390876efb3f85f | from __future__ import annotations. ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
author: Christian Bender
date: 21.12.2017
class: XORCipher
This class implements the XOR-cipher algorithm and provides
some useful methods for encrypting and decrypting strings and
files.
Overview about methods
- encrypt : list of char
- decrypt : list of char
- encrypt_string : str
- decrypt_string : str
- encrypt_file : boolean
- decrypt_file : boolean
"""
class XORCipher:
def __init__(self, key: int = 0):
"""
simple constructor that receives a key or uses
default key = 0
"""
# private field
self.__key = key
def encrypt(self, content: str, key: int) -> list[str]:
"""
input: 'content' of type string and 'key' of type int
output: encrypted string 'content' as a list of chars
if key not passed the method uses the key by the constructor.
otherwise key = 1
"""
# precondition
assert isinstance(key, int) and isinstance(content, str)
key = key or self.__key or 1
# make sure key can be any size
while key > 255:
key -= 255
# This will be returned
ans = []
for ch in content:
ans.append(chr(ord(ch) ^ key))
return ans
def decrypt(self, content: str, key: int) -> list[str]:
"""
input: 'content' of type list and 'key' of type int
output: decrypted string 'content' as a list of chars
if key not passed the method uses the key by the constructor.
otherwise key = 1
"""
# precondition
assert isinstance(key, int) and isinstance(content, list)
key = key or self.__key or 1
# make sure key can be any size
while key > 255:
key -= 255
# This will be returned
ans = []
for ch in content:
ans.append(chr(ord(ch) ^ key))
return ans
def encrypt_string(self, content: str, key: int = 0) -> str:
"""
input: 'content' of type string and 'key' of type int
output: encrypted string 'content'
if key not passed the method uses the key by the constructor.
otherwise key = 1
"""
# precondition
assert isinstance(key, int) and isinstance(content, str)
key = key or self.__key or 1
# make sure key can be any size
while key > 255:
key -= 255
# This will be returned
ans = ""
for ch in content:
ans += chr(ord(ch) ^ key)
return ans
def decrypt_string(self, content: str, key: int = 0) -> str:
"""
input: 'content' of type string and 'key' of type int
output: decrypted string 'content'
if key not passed the method uses the key by the constructor.
otherwise key = 1
"""
# precondition
assert isinstance(key, int) and isinstance(content, str)
key = key or self.__key or 1
# make sure key can be any size
while key > 255:
key -= 255
# This will be returned
ans = ""
for ch in content:
ans += chr(ord(ch) ^ key)
return ans
def encrypt_file(self, file: str, key: int = 0) -> bool:
"""
input: filename (str) and a key (int)
output: returns true if encrypt process was
successful otherwise false
if key not passed the method uses the key by the constructor.
otherwise key = 1
"""
# precondition
assert isinstance(file, str) and isinstance(key, int)
try:
with open(file) as fin:
with open("encrypt.out", "w+") as fout:
# actual encrypt-process
for line in fin:
fout.write(self.encrypt_string(line, key))
except OSError:
return False
return True
def decrypt_file(self, file: str, key: int) -> bool:
"""
input: filename (str) and a key (int)
output: returns true if decrypt process was
successful otherwise false
if key not passed the method uses the key by the constructor.
otherwise key = 1
"""
# precondition
assert isinstance(file, str) and isinstance(key, int)
try:
with open(file) as fin:
with open("decrypt.out", "w+") as fout:
# actual encrypt-process
for line in fin:
fout.write(self.decrypt_string(line, key))
except OSError:
return False
return True
# Tests
# crypt = XORCipher()
# key = 67
# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))
# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))
# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))
# if (crypt.encrypt_file("test.txt",key)):
# print("encrypt successful")
# else:
# print("encrypt unsuccessful")
# if (crypt.decrypt_file("encrypt.out",key)):
# print("decrypt successful")
# else:
# print("decrypt unsuccessful")
| """
author: Christian Bender
date: 21.12.2017
class: XORCipher
This class implements the XOR-cipher algorithm and provides
some useful methods for encrypting and decrypting strings and
files.
Overview about methods
- encrypt : list of char
- decrypt : list of char
- encrypt_string : str
- decrypt_string : str
- encrypt_file : boolean
- decrypt_file : boolean
"""
from __future__ import annotations
class XORCipher:
def __init__(self, key: int = 0):
"""
simple constructor that receives a key or uses
default key = 0
"""
# private field
self.__key = key
def encrypt(self, content: str, key: int) -> list[str]:
"""
input: 'content' of type string and 'key' of type int
output: encrypted string 'content' as a list of chars
if key not passed the method uses the key by the constructor.
otherwise key = 1
"""
# precondition
assert isinstance(key, int) and isinstance(content, str)
key = key or self.__key or 1
# make sure key is an appropriate size
key %= 255
return [chr(ord(ch) ^ key) for ch in content]
def decrypt(self, content: str, key: int) -> list[str]:
"""
input: 'content' of type list and 'key' of type int
output: decrypted string 'content' as a list of chars
if key not passed the method uses the key by the constructor.
otherwise key = 1
"""
# precondition
assert isinstance(key, int) and isinstance(content, list)
key = key or self.__key or 1
# make sure key is an appropriate size
key %= 255
return [chr(ord(ch) ^ key) for ch in content]
def encrypt_string(self, content: str, key: int = 0) -> str:
"""
input: 'content' of type string and 'key' of type int
output: encrypted string 'content'
if key not passed the method uses the key by the constructor.
otherwise key = 1
"""
# precondition
assert isinstance(key, int) and isinstance(content, str)
key = key or self.__key or 1
# make sure key can be any size
while key > 255:
key -= 255
# This will be returned
ans = ""
for ch in content:
ans += chr(ord(ch) ^ key)
return ans
def decrypt_string(self, content: str, key: int = 0) -> str:
"""
input: 'content' of type string and 'key' of type int
output: decrypted string 'content'
if key not passed the method uses the key by the constructor.
otherwise key = 1
"""
# precondition
assert isinstance(key, int) and isinstance(content, str)
key = key or self.__key or 1
# make sure key can be any size
while key > 255:
key -= 255
# This will be returned
ans = ""
for ch in content:
ans += chr(ord(ch) ^ key)
return ans
def encrypt_file(self, file: str, key: int = 0) -> bool:
"""
input: filename (str) and a key (int)
output: returns true if encrypt process was
successful otherwise false
if key not passed the method uses the key by the constructor.
otherwise key = 1
"""
# precondition
assert isinstance(file, str) and isinstance(key, int)
try:
with open(file) as fin:
with open("encrypt.out", "w+") as fout:
# actual encrypt-process
for line in fin:
fout.write(self.encrypt_string(line, key))
except OSError:
return False
return True
def decrypt_file(self, file: str, key: int) -> bool:
"""
input: filename (str) and a key (int)
output: returns true if decrypt process was
successful otherwise false
if key not passed the method uses the key by the constructor.
otherwise key = 1
"""
# precondition
assert isinstance(file, str) and isinstance(key, int)
try:
with open(file) as fin:
with open("decrypt.out", "w+") as fout:
# actual encrypt-process
for line in fin:
fout.write(self.decrypt_string(line, key))
except OSError:
return False
return True
# Tests
# crypt = XORCipher()
# key = 67
# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))
# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))
# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))
# if (crypt.encrypt_file("test.txt",key)):
# print("encrypt successful")
# else:
# print("encrypt unsuccessful")
# if (crypt.decrypt_file("encrypt.out",key)):
# print("decrypt successful")
# else:
# print("decrypt unsuccessful")
| 1 |
TheAlgorithms/Python | 4,763 | from __future__ import annotations | ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2021-09-14T22:54:26Z" | "2021-09-22T21:11:51Z" | dc07a850763d8154e012c9d1be7f8fe78326e8fb | 15d1cfabb15903c5e9d4d103e2390876efb3f85f | from __future__ import annotations. ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,763 | from __future__ import annotations | ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2021-09-14T22:54:26Z" | "2021-09-22T21:11:51Z" | dc07a850763d8154e012c9d1be7f8fe78326e8fb | 15d1cfabb15903c5e9d4d103e2390876efb3f85f | from __future__ import annotations. ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from __future__ import annotations
class Node:
def __init__(self, data=None):
self.data = data
self.next = None
def __repr__(self):
"""Returns a visual representation of the node and all its following nodes."""
string_rep = []
temp = self
while temp:
string_rep.append(f"{temp.data}")
temp = temp.next
return "->".join(string_rep)
def make_linked_list(elements_list: list):
"""Creates a Linked List from the elements of the given sequence
(list/tuple) and returns the head of the Linked List.
>>> make_linked_list([])
Traceback (most recent call last):
...
Exception: The Elements List is empty
>>> make_linked_list([7])
7
>>> make_linked_list(['abc'])
abc
>>> make_linked_list([7, 25])
7->25
"""
if not elements_list:
raise Exception("The Elements List is empty")
current = head = Node(elements_list[0])
for i in range(1, len(elements_list)):
current.next = Node(elements_list[i])
current = current.next
return head
def print_reverse(head_node: Node) -> None:
"""Prints the elements of the given Linked List in reverse order
>>> print_reverse([])
>>> linked_list = make_linked_list([69, 88, 73])
>>> print_reverse(linked_list)
73
88
69
"""
if head_node is not None and isinstance(head_node, Node):
print_reverse(head_node.next)
print(head_node.data)
def main():
from doctest import testmod
testmod()
linked_list = make_linked_list([14, 52, 14, 12, 43])
print("Linked List:")
print(linked_list)
print("Elements in Reverse:")
print_reverse(linked_list)
if __name__ == "__main__":
main()
| from __future__ import annotations
class Node:
def __init__(self, data=None):
self.data = data
self.next = None
def __repr__(self):
"""Returns a visual representation of the node and all its following nodes."""
string_rep = []
temp = self
while temp:
string_rep.append(f"{temp.data}")
temp = temp.next
return "->".join(string_rep)
def make_linked_list(elements_list: list):
"""Creates a Linked List from the elements of the given sequence
(list/tuple) and returns the head of the Linked List.
>>> make_linked_list([])
Traceback (most recent call last):
...
Exception: The Elements List is empty
>>> make_linked_list([7])
7
>>> make_linked_list(['abc'])
abc
>>> make_linked_list([7, 25])
7->25
"""
if not elements_list:
raise Exception("The Elements List is empty")
current = head = Node(elements_list[0])
for i in range(1, len(elements_list)):
current.next = Node(elements_list[i])
current = current.next
return head
def print_reverse(head_node: Node) -> None:
"""Prints the elements of the given Linked List in reverse order
>>> print_reverse([])
>>> linked_list = make_linked_list([69, 88, 73])
>>> print_reverse(linked_list)
73
88
69
"""
if head_node is not None and isinstance(head_node, Node):
print_reverse(head_node.next)
print(head_node.data)
def main():
from doctest import testmod
testmod()
linked_list = make_linked_list([14, 52, 14, 12, 43])
print("Linked List:")
print(linked_list)
print("Elements in Reverse:")
print_reverse(linked_list)
if __name__ == "__main__":
main()
| -1 |
TheAlgorithms/Python | 4,763 | from __future__ import annotations | ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2021-09-14T22:54:26Z" | "2021-09-22T21:11:51Z" | dc07a850763d8154e012c9d1be7f8fe78326e8fb | 15d1cfabb15903c5e9d4d103e2390876efb3f85f | from __future__ import annotations. ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$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)
exponentVector = np.exp(vector)
# Add up the all the exponentials
sumOfExponents = np.sum(exponentVector)
# Divide every exponent by the sum of all exponents
softmax_vector = exponentVector / sumOfExponents
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)
exponentVector = np.exp(vector)
# Add up the all the exponentials
sumOfExponents = np.sum(exponentVector)
# Divide every exponent by the sum of all exponents
softmax_vector = exponentVector / sumOfExponents
return softmax_vector
if __name__ == "__main__":
print(softmax((0,)))
| -1 |
TheAlgorithms/Python | 4,763 | from __future__ import annotations | ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2021-09-14T22:54:26Z" | "2021-09-22T21:11:51Z" | dc07a850763d8154e012c9d1be7f8fe78326e8fb | 15d1cfabb15903c5e9d4d103e2390876efb3f85f | from __future__ import annotations. ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch("socket.socket")
@patch("builtins.open")
def test_send_file_running_as_expected(file, sock):
# ===== initialization =====
conn = Mock()
sock.return_value.accept.return_value = conn, Mock()
f = iter([1, None])
file.return_value.__enter__.return_value.read.side_effect = lambda _: next(f)
# ===== invoke =====
send_file(filename="mytext.txt", testing=True)
# ===== ensurance =====
sock.assert_called_once()
sock.return_value.bind.assert_called_once()
sock.return_value.listen.assert_called_once()
sock.return_value.accept.assert_called_once()
conn.recv.assert_called_once()
file.return_value.__enter__.assert_called_once()
file.return_value.__enter__.return_value.read.assert_called()
conn.send.assert_called_once()
conn.close.assert_called_once()
sock.return_value.shutdown.assert_called_once()
sock.return_value.close.assert_called_once()
| from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch("socket.socket")
@patch("builtins.open")
def test_send_file_running_as_expected(file, sock):
# ===== initialization =====
conn = Mock()
sock.return_value.accept.return_value = conn, Mock()
f = iter([1, None])
file.return_value.__enter__.return_value.read.side_effect = lambda _: next(f)
# ===== invoke =====
send_file(filename="mytext.txt", testing=True)
# ===== ensurance =====
sock.assert_called_once()
sock.return_value.bind.assert_called_once()
sock.return_value.listen.assert_called_once()
sock.return_value.accept.assert_called_once()
conn.recv.assert_called_once()
file.return_value.__enter__.assert_called_once()
file.return_value.__enter__.return_value.read.assert_called()
conn.send.assert_called_once()
conn.close.assert_called_once()
sock.return_value.shutdown.assert_called_once()
sock.return_value.close.assert_called_once()
| -1 |
TheAlgorithms/Python | 4,763 | from __future__ import annotations | ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2021-09-14T22:54:26Z" | "2021-09-22T21:11:51Z" | dc07a850763d8154e012c9d1be7f8fe78326e8fb | 15d1cfabb15903c5e9d4d103e2390876efb3f85f | from __future__ import annotations. ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,763 | from __future__ import annotations | ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2021-09-14T22:54:26Z" | "2021-09-22T21:11:51Z" | dc07a850763d8154e012c9d1be7f8fe78326e8fb | 15d1cfabb15903c5e9d4d103e2390876efb3f85f | from __future__ import annotations. ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Problem 72 Counting fractions: https://projecteuler.net/problem=72
Description:
Consider the fraction, n/d, where n and d are positive integers. If n<d and HCF(n,d)=1,
it is called a reduced proper fraction.
If we list the set of reduced proper fractions for d ≤ 8 in ascending order of size, we
get: 1/8, 1/7, 1/6, 1/5, 1/4, 2/7, 1/3, 3/8, 2/5, 3/7, 1/2, 4/7, 3/5, 5/8, 2/3, 5/7,
3/4, 4/5, 5/6, 6/7, 7/8
It can be seen that there are 21 elements in this set.
How many elements would be contained in the set of reduced proper fractions for
d ≤ 1,000,000?
Solution:
Number of numbers between 1 and n that are coprime to n is given by the Euler's Totient
function, phi(n). So, the answer is simply the sum of phi(n) for 2 <= n <= 1,000,000
Sum of phi(d), for all d|n = n. This result can be used to find phi(n) using a sieve.
Time: 3.5 sec
"""
def solution(limit: int = 1_000_000) -> int:
"""
Returns an integer, the solution to the problem
>>> solution(10)
31
>>> solution(100)
3043
>>> solution(1_000)
304191
"""
phi = [i - 1 for i in range(limit + 1)]
for i in range(2, limit + 1):
for j in range(2 * i, limit + 1, i):
phi[j] -= phi[i]
return sum(phi[2 : limit + 1])
if __name__ == "__main__":
print(solution())
| """
Problem 72 Counting fractions: https://projecteuler.net/problem=72
Description:
Consider the fraction, n/d, where n and d are positive integers. If n<d and HCF(n,d)=1,
it is called a reduced proper fraction.
If we list the set of reduced proper fractions for d ≤ 8 in ascending order of size, we
get: 1/8, 1/7, 1/6, 1/5, 1/4, 2/7, 1/3, 3/8, 2/5, 3/7, 1/2, 4/7, 3/5, 5/8, 2/3, 5/7,
3/4, 4/5, 5/6, 6/7, 7/8
It can be seen that there are 21 elements in this set.
How many elements would be contained in the set of reduced proper fractions for
d ≤ 1,000,000?
Solution:
Number of numbers between 1 and n that are coprime to n is given by the Euler's Totient
function, phi(n). So, the answer is simply the sum of phi(n) for 2 <= n <= 1,000,000
Sum of phi(d), for all d|n = n. This result can be used to find phi(n) using a sieve.
Time: 3.5 sec
"""
def solution(limit: int = 1_000_000) -> int:
"""
Returns an integer, the solution to the problem
>>> solution(10)
31
>>> solution(100)
3043
>>> solution(1_000)
304191
"""
phi = [i - 1 for i in range(limit + 1)]
for i in range(2, limit + 1):
for j in range(2 * i, limit + 1, i):
phi[j] -= phi[i]
return sum(phi[2 : limit + 1])
if __name__ == "__main__":
print(solution())
| -1 |
TheAlgorithms/Python | 4,763 | from __future__ import annotations | ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2021-09-14T22:54:26Z" | "2021-09-22T21:11:51Z" | dc07a850763d8154e012c9d1be7f8fe78326e8fb | 15d1cfabb15903c5e9d4d103e2390876efb3f85f | from __future__ import annotations. ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Normalization Wikipedia: https://en.wikipedia.org/wiki/Normalization
Normalization is the process of converting numerical data to a standard range of values.
This range is typically between [0, 1] or [-1, 1]. The equation for normalization is
x_norm = (x - x_min)/(x_max - x_min) where x_norm is the normalized value, x is the
value, x_min is the minimum value within the column or list of data, and x_max is the
maximum value within the column or list of data. Normalization is used to speed up the
training of data and put all of the data on a similar scale. This is useful because
variance in the range of values of a dataset can heavily impact optimization
(particularly Gradient Descent).
Standardization Wikipedia: https://en.wikipedia.org/wiki/Standardization
Standardization is the process of converting numerical data to a normally distributed
range of values. This range will have a mean of 0 and standard deviation of 1. This is
also known as z-score normalization. The equation for standardization is
x_std = (x - mu)/(sigma) where mu is the mean of the column or list of values and sigma
is the standard deviation of the column or list of values.
Choosing between Normalization & Standardization is more of an art of a science, but it
is often recommended to run experiments with both to see which performs better.
Additionally, a few rules of thumb are:
1. gaussian (normal) distributions work better with standardization
2. non-gaussian (non-normal) distributions work better with normalization
3. If a column or list of values has extreme values / outliers, use standardization
"""
from statistics import mean, stdev
def normalization(data: list, ndigits: int = 3) -> list:
"""
Returns a normalized list of values
@params: data, a list of values to normalize
@returns: a list of normalized values (rounded to ndigits decimal places)
@examples:
>>> normalization([2, 7, 10, 20, 30, 50])
[0.0, 0.104, 0.167, 0.375, 0.583, 1.0]
>>> normalization([5, 10, 15, 20, 25])
[0.0, 0.25, 0.5, 0.75, 1.0]
"""
# variables for calculation
x_min = min(data)
x_max = max(data)
# normalize data
return [round((x - x_min) / (x_max - x_min), ndigits) for x in data]
def standardization(data: list, ndigits: int = 3) -> list:
"""
Returns a standardized list of values
@params: data, a list of values to standardize
@returns: a list of standardized values (rounded to ndigits decimal places)
@examples:
>>> standardization([2, 7, 10, 20, 30, 50])
[-0.999, -0.719, -0.551, 0.009, 0.57, 1.69]
>>> standardization([5, 10, 15, 20, 25])
[-1.265, -0.632, 0.0, 0.632, 1.265]
"""
# variables for calculation
mu = mean(data)
sigma = stdev(data)
# standardize data
return [round((x - mu) / (sigma), ndigits) for x in data]
| """
Normalization Wikipedia: https://en.wikipedia.org/wiki/Normalization
Normalization is the process of converting numerical data to a standard range of values.
This range is typically between [0, 1] or [-1, 1]. The equation for normalization is
x_norm = (x - x_min)/(x_max - x_min) where x_norm is the normalized value, x is the
value, x_min is the minimum value within the column or list of data, and x_max is the
maximum value within the column or list of data. Normalization is used to speed up the
training of data and put all of the data on a similar scale. This is useful because
variance in the range of values of a dataset can heavily impact optimization
(particularly Gradient Descent).
Standardization Wikipedia: https://en.wikipedia.org/wiki/Standardization
Standardization is the process of converting numerical data to a normally distributed
range of values. This range will have a mean of 0 and standard deviation of 1. This is
also known as z-score normalization. The equation for standardization is
x_std = (x - mu)/(sigma) where mu is the mean of the column or list of values and sigma
is the standard deviation of the column or list of values.
Choosing between Normalization & Standardization is more of an art of a science, but it
is often recommended to run experiments with both to see which performs better.
Additionally, a few rules of thumb are:
1. gaussian (normal) distributions work better with standardization
2. non-gaussian (non-normal) distributions work better with normalization
3. If a column or list of values has extreme values / outliers, use standardization
"""
from statistics import mean, stdev
def normalization(data: list, ndigits: int = 3) -> list:
"""
Returns a normalized list of values
@params: data, a list of values to normalize
@returns: a list of normalized values (rounded to ndigits decimal places)
@examples:
>>> normalization([2, 7, 10, 20, 30, 50])
[0.0, 0.104, 0.167, 0.375, 0.583, 1.0]
>>> normalization([5, 10, 15, 20, 25])
[0.0, 0.25, 0.5, 0.75, 1.0]
"""
# variables for calculation
x_min = min(data)
x_max = max(data)
# normalize data
return [round((x - x_min) / (x_max - x_min), ndigits) for x in data]
def standardization(data: list, ndigits: int = 3) -> list:
"""
Returns a standardized list of values
@params: data, a list of values to standardize
@returns: a list of standardized values (rounded to ndigits decimal places)
@examples:
>>> standardization([2, 7, 10, 20, 30, 50])
[-0.999, -0.719, -0.551, 0.009, 0.57, 1.69]
>>> standardization([5, 10, 15, 20, 25])
[-1.265, -0.632, 0.0, 0.632, 1.265]
"""
# variables for calculation
mu = mean(data)
sigma = stdev(data)
# standardize data
return [round((x - mu) / (sigma), ndigits) for x in data]
| -1 |
TheAlgorithms/Python | 4,763 | from __future__ import annotations | ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2021-09-14T22:54:26Z" | "2021-09-22T21:11:51Z" | dc07a850763d8154e012c9d1be7f8fe78326e8fb | 15d1cfabb15903c5e9d4d103e2390876efb3f85f | from __future__ import annotations. ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """Implementation of Basic Math in Python."""
import math
def prime_factors(n: int) -> list:
"""Find Prime Factors.
>>> prime_factors(100)
[2, 2, 5, 5]
>>> prime_factors(0)
Traceback (most recent call last):
...
ValueError: Only positive integers have prime factors
>>> prime_factors(-10)
Traceback (most recent call last):
...
ValueError: Only positive integers have prime factors
"""
if n <= 0:
raise ValueError("Only positive integers have prime factors")
pf = []
while n % 2 == 0:
pf.append(2)
n = int(n / 2)
for i in range(3, int(math.sqrt(n)) + 1, 2):
while n % i == 0:
pf.append(i)
n = int(n / i)
if n > 2:
pf.append(n)
return pf
def number_of_divisors(n: int) -> int:
"""Calculate Number of Divisors of an Integer.
>>> number_of_divisors(100)
9
>>> number_of_divisors(0)
Traceback (most recent call last):
...
ValueError: Only positive numbers are accepted
>>> number_of_divisors(-10)
Traceback (most recent call last):
...
ValueError: Only positive numbers are accepted
"""
if n <= 0:
raise ValueError("Only positive numbers are accepted")
div = 1
temp = 1
while n % 2 == 0:
temp += 1
n = int(n / 2)
div *= temp
for i in range(3, int(math.sqrt(n)) + 1, 2):
temp = 1
while n % i == 0:
temp += 1
n = int(n / i)
div *= temp
return div
def sum_of_divisors(n: int) -> int:
"""Calculate Sum of Divisors.
>>> sum_of_divisors(100)
217
>>> sum_of_divisors(0)
Traceback (most recent call last):
...
ValueError: Only positive numbers are accepted
>>> sum_of_divisors(-10)
Traceback (most recent call last):
...
ValueError: Only positive numbers are accepted
"""
if n <= 0:
raise ValueError("Only positive numbers are accepted")
s = 1
temp = 1
while n % 2 == 0:
temp += 1
n = int(n / 2)
if temp > 1:
s *= (2 ** temp - 1) / (2 - 1)
for i in range(3, int(math.sqrt(n)) + 1, 2):
temp = 1
while n % i == 0:
temp += 1
n = int(n / i)
if temp > 1:
s *= (i ** temp - 1) / (i - 1)
return int(s)
def euler_phi(n: int) -> int:
"""Calculate Euler's Phi Function.
>>> euler_phi(100)
40
"""
s = n
for x in set(prime_factors(n)):
s *= (x - 1) / x
return int(s)
if __name__ == "__main__":
import doctest
doctest.testmod()
| """Implementation of Basic Math in Python."""
import math
def prime_factors(n: int) -> list:
"""Find Prime Factors.
>>> prime_factors(100)
[2, 2, 5, 5]
>>> prime_factors(0)
Traceback (most recent call last):
...
ValueError: Only positive integers have prime factors
>>> prime_factors(-10)
Traceback (most recent call last):
...
ValueError: Only positive integers have prime factors
"""
if n <= 0:
raise ValueError("Only positive integers have prime factors")
pf = []
while n % 2 == 0:
pf.append(2)
n = int(n / 2)
for i in range(3, int(math.sqrt(n)) + 1, 2):
while n % i == 0:
pf.append(i)
n = int(n / i)
if n > 2:
pf.append(n)
return pf
def number_of_divisors(n: int) -> int:
"""Calculate Number of Divisors of an Integer.
>>> number_of_divisors(100)
9
>>> number_of_divisors(0)
Traceback (most recent call last):
...
ValueError: Only positive numbers are accepted
>>> number_of_divisors(-10)
Traceback (most recent call last):
...
ValueError: Only positive numbers are accepted
"""
if n <= 0:
raise ValueError("Only positive numbers are accepted")
div = 1
temp = 1
while n % 2 == 0:
temp += 1
n = int(n / 2)
div *= temp
for i in range(3, int(math.sqrt(n)) + 1, 2):
temp = 1
while n % i == 0:
temp += 1
n = int(n / i)
div *= temp
return div
def sum_of_divisors(n: int) -> int:
"""Calculate Sum of Divisors.
>>> sum_of_divisors(100)
217
>>> sum_of_divisors(0)
Traceback (most recent call last):
...
ValueError: Only positive numbers are accepted
>>> sum_of_divisors(-10)
Traceback (most recent call last):
...
ValueError: Only positive numbers are accepted
"""
if n <= 0:
raise ValueError("Only positive numbers are accepted")
s = 1
temp = 1
while n % 2 == 0:
temp += 1
n = int(n / 2)
if temp > 1:
s *= (2 ** temp - 1) / (2 - 1)
for i in range(3, int(math.sqrt(n)) + 1, 2):
temp = 1
while n % i == 0:
temp += 1
n = int(n / i)
if temp > 1:
s *= (i ** temp - 1) / (i - 1)
return int(s)
def euler_phi(n: int) -> int:
"""Calculate Euler's Phi Function.
>>> euler_phi(100)
40
"""
s = n
for x in set(prime_factors(n)):
s *= (x - 1) / x
return int(s)
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 4,763 | from __future__ import annotations | ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2021-09-14T22:54:26Z" | "2021-09-22T21:11:51Z" | dc07a850763d8154e012c9d1be7f8fe78326e8fb | 15d1cfabb15903c5e9d4d103e2390876efb3f85f | from __future__ import annotations. ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Given a partially filled 9×9 2D array, the objective is to fill a 9×9
square grid with digits numbered 1 to 9, so that every row, column, and
and each of the nine 3×3 sub-grids contains all of the digits.
This can be solved using Backtracking and is similar to n-queens.
We check to see if a cell is safe or not and recursively call the
function on the next column to see if it returns True. if yes, we
have solved the puzzle. else, we backtrack and place another number
in that cell and repeat this process.
"""
from __future__ import annotations
Matrix = list[list[int]]
# assigning initial values to the grid
initial_grid: Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
# a grid with no solution
no_solution: Matrix = [
[5, 0, 6, 5, 0, 8, 4, 0, 3],
[5, 2, 0, 0, 0, 0, 0, 0, 2],
[1, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
def is_safe(grid: Matrix, row: int, column: int, n: int) -> bool:
"""
This function checks the grid to see if each row,
column, and the 3x3 subgrids contain the digit 'n'.
It returns False if it is not 'safe' (a duplicate digit
is found) else returns True if it is 'safe'
"""
for i in range(9):
if grid[row][i] == n or grid[i][column] == n:
return False
for i in range(3):
for j in range(3):
if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
return False
return True
def find_empty_location(grid: Matrix) -> tuple[int, int] | None:
"""
This function finds an empty location so that we can assign a number
for that particular row and column.
"""
for i in range(9):
for j in range(9):
if grid[i][j] == 0:
return i, j
return None
def sudoku(grid: Matrix) -> Matrix | None:
"""
Takes a partially filled-in grid and attempts to assign values to
all unassigned locations in such a way to meet the requirements
for Sudoku solution (non-duplication across rows, columns, and boxes)
>>> sudoku(initial_grid) # doctest: +NORMALIZE_WHITESPACE
[[3, 1, 6, 5, 7, 8, 4, 9, 2],
[5, 2, 9, 1, 3, 4, 7, 6, 8],
[4, 8, 7, 6, 2, 9, 5, 3, 1],
[2, 6, 3, 4, 1, 5, 9, 8, 7],
[9, 7, 4, 8, 6, 3, 1, 2, 5],
[8, 5, 1, 7, 9, 2, 6, 4, 3],
[1, 3, 8, 9, 4, 7, 2, 5, 6],
[6, 9, 2, 3, 5, 1, 8, 7, 4],
[7, 4, 5, 2, 8, 6, 3, 1, 9]]
>>> sudoku(no_solution) is None
True
"""
if location := find_empty_location(grid):
row, column = location
else:
# If the location is ``None``, then the grid is solved.
return grid
for digit in range(1, 10):
if is_safe(grid, row, column, digit):
grid[row][column] = digit
if sudoku(grid) is not None:
return grid
grid[row][column] = 0
return None
def print_solution(grid: Matrix) -> None:
"""
A function to print the solution in the form
of a 9x9 grid
"""
for row in grid:
for cell in row:
print(cell, end=" ")
print()
if __name__ == "__main__":
# make a copy of grid so that you can compare with the unmodified grid
for example_grid in (initial_grid, no_solution):
print("\nExample grid:\n" + "=" * 20)
print_solution(example_grid)
print("\nExample grid solution:")
solution = sudoku(example_grid)
if solution is not None:
print_solution(solution)
else:
print("Cannot find a solution.")
| """
Given a partially filled 9×9 2D array, the objective is to fill a 9×9
square grid with digits numbered 1 to 9, so that every row, column, and
and each of the nine 3×3 sub-grids contains all of the digits.
This can be solved using Backtracking and is similar to n-queens.
We check to see if a cell is safe or not and recursively call the
function on the next column to see if it returns True. if yes, we
have solved the puzzle. else, we backtrack and place another number
in that cell and repeat this process.
"""
from __future__ import annotations
Matrix = list[list[int]]
# assigning initial values to the grid
initial_grid: Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
# a grid with no solution
no_solution: Matrix = [
[5, 0, 6, 5, 0, 8, 4, 0, 3],
[5, 2, 0, 0, 0, 0, 0, 0, 2],
[1, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
def is_safe(grid: Matrix, row: int, column: int, n: int) -> bool:
"""
This function checks the grid to see if each row,
column, and the 3x3 subgrids contain the digit 'n'.
It returns False if it is not 'safe' (a duplicate digit
is found) else returns True if it is 'safe'
"""
for i in range(9):
if grid[row][i] == n or grid[i][column] == n:
return False
for i in range(3):
for j in range(3):
if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
return False
return True
def find_empty_location(grid: Matrix) -> tuple[int, int] | None:
"""
This function finds an empty location so that we can assign a number
for that particular row and column.
"""
for i in range(9):
for j in range(9):
if grid[i][j] == 0:
return i, j
return None
def sudoku(grid: Matrix) -> Matrix | None:
"""
Takes a partially filled-in grid and attempts to assign values to
all unassigned locations in such a way to meet the requirements
for Sudoku solution (non-duplication across rows, columns, and boxes)
>>> sudoku(initial_grid) # doctest: +NORMALIZE_WHITESPACE
[[3, 1, 6, 5, 7, 8, 4, 9, 2],
[5, 2, 9, 1, 3, 4, 7, 6, 8],
[4, 8, 7, 6, 2, 9, 5, 3, 1],
[2, 6, 3, 4, 1, 5, 9, 8, 7],
[9, 7, 4, 8, 6, 3, 1, 2, 5],
[8, 5, 1, 7, 9, 2, 6, 4, 3],
[1, 3, 8, 9, 4, 7, 2, 5, 6],
[6, 9, 2, 3, 5, 1, 8, 7, 4],
[7, 4, 5, 2, 8, 6, 3, 1, 9]]
>>> sudoku(no_solution) is None
True
"""
if location := find_empty_location(grid):
row, column = location
else:
# If the location is ``None``, then the grid is solved.
return grid
for digit in range(1, 10):
if is_safe(grid, row, column, digit):
grid[row][column] = digit
if sudoku(grid) is not None:
return grid
grid[row][column] = 0
return None
def print_solution(grid: Matrix) -> None:
"""
A function to print the solution in the form
of a 9x9 grid
"""
for row in grid:
for cell in row:
print(cell, end=" ")
print()
if __name__ == "__main__":
# make a copy of grid so that you can compare with the unmodified grid
for example_grid in (initial_grid, no_solution):
print("\nExample grid:\n" + "=" * 20)
print_solution(example_grid)
print("\nExample grid solution:")
solution = sudoku(example_grid)
if solution is not None:
print_solution(solution)
else:
print("Cannot find a solution.")
| -1 |
TheAlgorithms/Python | 4,763 | from __future__ import annotations | ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2021-09-14T22:54:26Z" | "2021-09-22T21:11:51Z" | dc07a850763d8154e012c9d1be7f8fe78326e8fb | 15d1cfabb15903c5e9d4d103e2390876efb3f85f | from __future__ import annotations. ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,763 | from __future__ import annotations | ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2021-09-14T22:54:26Z" | "2021-09-22T21:11:51Z" | dc07a850763d8154e012c9d1be7f8fe78326e8fb | 15d1cfabb15903c5e9d4d103e2390876efb3f85f | from __future__ import annotations. ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| #!/usr/bin/env python
#
# Sort large text files in a minimum amount of memory
#
import argparse
import os
class FileSplitter:
BLOCK_FILENAME_FORMAT = "block_{0}.dat"
def __init__(self, filename):
self.filename = filename
self.block_filenames = []
def write_block(self, data, block_number):
filename = self.BLOCK_FILENAME_FORMAT.format(block_number)
with open(filename, "w") as file:
file.write(data)
self.block_filenames.append(filename)
def get_block_filenames(self):
return self.block_filenames
def split(self, block_size, sort_key=None):
i = 0
with open(self.filename) as file:
while True:
lines = file.readlines(block_size)
if lines == []:
break
if sort_key is None:
lines.sort()
else:
lines.sort(key=sort_key)
self.write_block("".join(lines), i)
i += 1
def cleanup(self):
map(lambda f: os.remove(f), self.block_filenames)
class NWayMerge:
def select(self, choices):
min_index = -1
min_str = None
for i in range(len(choices)):
if min_str is None or choices[i] < min_str:
min_index = i
return min_index
class FilesArray:
def __init__(self, files):
self.files = files
self.empty = set()
self.num_buffers = len(files)
self.buffers = {i: None for i in range(self.num_buffers)}
def get_dict(self):
return {
i: self.buffers[i] for i in range(self.num_buffers) if i not in self.empty
}
def refresh(self):
for i in range(self.num_buffers):
if self.buffers[i] is None and i not in self.empty:
self.buffers[i] = self.files[i].readline()
if self.buffers[i] == "":
self.empty.add(i)
self.files[i].close()
if len(self.empty) == self.num_buffers:
return False
return True
def unshift(self, index):
value = self.buffers[index]
self.buffers[index] = None
return value
class FileMerger:
def __init__(self, merge_strategy):
self.merge_strategy = merge_strategy
def merge(self, filenames, outfilename, buffer_size):
buffers = FilesArray(self.get_file_handles(filenames, buffer_size))
with open(outfilename, "w", buffer_size) as outfile:
while buffers.refresh():
min_index = self.merge_strategy.select(buffers.get_dict())
outfile.write(buffers.unshift(min_index))
def get_file_handles(self, filenames, buffer_size):
files = {}
for i in range(len(filenames)):
files[i] = open(filenames[i], "r", buffer_size)
return files
class ExternalSort:
def __init__(self, block_size):
self.block_size = block_size
def sort(self, filename, sort_key=None):
num_blocks = self.get_number_blocks(filename, self.block_size)
splitter = FileSplitter(filename)
splitter.split(self.block_size, sort_key)
merger = FileMerger(NWayMerge())
buffer_size = self.block_size / (num_blocks + 1)
merger.merge(splitter.get_block_filenames(), filename + ".out", buffer_size)
splitter.cleanup()
def get_number_blocks(self, filename, block_size):
return (os.stat(filename).st_size / block_size) + 1
def parse_memory(string):
if string[-1].lower() == "k":
return int(string[:-1]) * 1024
elif string[-1].lower() == "m":
return int(string[:-1]) * 1024 * 1024
elif string[-1].lower() == "g":
return int(string[:-1]) * 1024 * 1024 * 1024
else:
return int(string)
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"-m", "--mem", help="amount of memory to use for sorting", default="100M"
)
parser.add_argument(
"filename", metavar="<filename>", nargs=1, help="name of file to sort"
)
args = parser.parse_args()
sorter = ExternalSort(parse_memory(args.mem))
sorter.sort(args.filename[0])
if __name__ == "__main__":
main()
| #!/usr/bin/env python
#
# Sort large text files in a minimum amount of memory
#
import argparse
import os
class FileSplitter:
BLOCK_FILENAME_FORMAT = "block_{0}.dat"
def __init__(self, filename):
self.filename = filename
self.block_filenames = []
def write_block(self, data, block_number):
filename = self.BLOCK_FILENAME_FORMAT.format(block_number)
with open(filename, "w") as file:
file.write(data)
self.block_filenames.append(filename)
def get_block_filenames(self):
return self.block_filenames
def split(self, block_size, sort_key=None):
i = 0
with open(self.filename) as file:
while True:
lines = file.readlines(block_size)
if lines == []:
break
if sort_key is None:
lines.sort()
else:
lines.sort(key=sort_key)
self.write_block("".join(lines), i)
i += 1
def cleanup(self):
map(lambda f: os.remove(f), self.block_filenames)
class NWayMerge:
def select(self, choices):
min_index = -1
min_str = None
for i in range(len(choices)):
if min_str is None or choices[i] < min_str:
min_index = i
return min_index
class FilesArray:
def __init__(self, files):
self.files = files
self.empty = set()
self.num_buffers = len(files)
self.buffers = {i: None for i in range(self.num_buffers)}
def get_dict(self):
return {
i: self.buffers[i] for i in range(self.num_buffers) if i not in self.empty
}
def refresh(self):
for i in range(self.num_buffers):
if self.buffers[i] is None and i not in self.empty:
self.buffers[i] = self.files[i].readline()
if self.buffers[i] == "":
self.empty.add(i)
self.files[i].close()
if len(self.empty) == self.num_buffers:
return False
return True
def unshift(self, index):
value = self.buffers[index]
self.buffers[index] = None
return value
class FileMerger:
def __init__(self, merge_strategy):
self.merge_strategy = merge_strategy
def merge(self, filenames, outfilename, buffer_size):
buffers = FilesArray(self.get_file_handles(filenames, buffer_size))
with open(outfilename, "w", buffer_size) as outfile:
while buffers.refresh():
min_index = self.merge_strategy.select(buffers.get_dict())
outfile.write(buffers.unshift(min_index))
def get_file_handles(self, filenames, buffer_size):
files = {}
for i in range(len(filenames)):
files[i] = open(filenames[i], "r", buffer_size)
return files
class ExternalSort:
def __init__(self, block_size):
self.block_size = block_size
def sort(self, filename, sort_key=None):
num_blocks = self.get_number_blocks(filename, self.block_size)
splitter = FileSplitter(filename)
splitter.split(self.block_size, sort_key)
merger = FileMerger(NWayMerge())
buffer_size = self.block_size / (num_blocks + 1)
merger.merge(splitter.get_block_filenames(), filename + ".out", buffer_size)
splitter.cleanup()
def get_number_blocks(self, filename, block_size):
return (os.stat(filename).st_size / block_size) + 1
def parse_memory(string):
if string[-1].lower() == "k":
return int(string[:-1]) * 1024
elif string[-1].lower() == "m":
return int(string[:-1]) * 1024 * 1024
elif string[-1].lower() == "g":
return int(string[:-1]) * 1024 * 1024 * 1024
else:
return int(string)
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"-m", "--mem", help="amount of memory to use for sorting", default="100M"
)
parser.add_argument(
"filename", metavar="<filename>", nargs=1, help="name of file to sort"
)
args = parser.parse_args()
sorter = ExternalSort(parse_memory(args.mem))
sorter.sort(args.filename[0])
if __name__ == "__main__":
main()
| -1 |
TheAlgorithms/Python | 4,763 | from __future__ import annotations | ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2021-09-14T22:54:26Z" | "2021-09-22T21:11:51Z" | dc07a850763d8154e012c9d1be7f8fe78326e8fb | 15d1cfabb15903c5e9d4d103e2390876efb3f85f | from __future__ import annotations. ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| #!/usr/bin/env python3
"""
Deutsch-Josza Algorithm is one of the first examples of a quantum
algorithm that is exponentially faster than any possible deterministic
classical algorithm
Premise:
We are given a hidden Boolean function f,
which takes as input a string of bits, and returns either 0 or 1:
f({x0,x1,x2,...}) -> 0 or 1, where xn is 0 or 1
The property of the given Boolean function is that it is guaranteed to
either be balanced or constant. A constant function returns all 0's
or all 1's for any input, while a balanced function returns 0's for
exactly half of all inputs and 1's for the other half. Our task is to
determine whether the given function is balanced or constant.
References:
- https://en.wikipedia.org/wiki/Deutsch-Jozsa_algorithm
- https://qiskit.org/textbook/ch-algorithms/deutsch-jozsa.html
"""
import numpy as np
import qiskit as q
def dj_oracle(case: str, num_qubits: int) -> q.QuantumCircuit:
"""
Returns a Quantum Circuit for the Oracle function.
The circuit returned can represent balanced or constant function,
according to the arguments passed
"""
# This circuit has num_qubits+1 qubits: the size of the input,
# plus one output qubit
oracle_qc = q.QuantumCircuit(num_qubits + 1)
# First, let's deal with the case in which oracle is balanced
if case == "balanced":
# First generate a random number that tells us which CNOTs to
# wrap in X-gates:
b = np.random.randint(1, 2 ** num_qubits)
# Next, format 'b' as a binary string of length 'n', padded with zeros:
b_str = format(b, f"0{num_qubits}b")
# Next, we place the first X-gates. Each digit in our binary string
# correspopnds to a qubit, if the digit is 0, we do nothing, if it's 1
# we apply an X-gate to that qubit:
for index, bit in enumerate(b_str):
if bit == "1":
oracle_qc.x(index)
# Do the controlled-NOT gates for each qubit, using the output qubit
# as the target:
for index in range(num_qubits):
oracle_qc.cx(index, num_qubits)
# Next, place the final X-gates
for index, bit in enumerate(b_str):
if bit == "1":
oracle_qc.x(index)
# Case in which oracle is constant
if case == "constant":
# First decide what the fixed output of the oracle will be
# (either always 0 or always 1)
output = np.random.randint(2)
if output == 1:
oracle_qc.x(num_qubits)
oracle_gate = oracle_qc.to_gate()
oracle_gate.name = "Oracle" # To show when we display the circuit
return oracle_gate
def dj_algorithm(oracle: q.QuantumCircuit, num_qubits: int) -> q.QuantumCircuit:
"""
Returns the complete Deustch-Jozsa Quantum Circuit,
adding Input & Output registers and Hadamard & Measurement Gates,
to the Oracle Circuit passed in arguments
"""
dj_circuit = q.QuantumCircuit(num_qubits + 1, num_qubits)
# Set up the output qubit:
dj_circuit.x(num_qubits)
dj_circuit.h(num_qubits)
# And set up the input register:
for qubit in range(num_qubits):
dj_circuit.h(qubit)
# Let's append the oracle gate to our circuit:
dj_circuit.append(oracle, range(num_qubits + 1))
# Finally, perform the H-gates again and measure:
for qubit in range(num_qubits):
dj_circuit.h(qubit)
for i in range(num_qubits):
dj_circuit.measure(i, i)
return dj_circuit
def deutsch_jozsa(case: str, num_qubits: int) -> q.result.counts.Counts:
"""
Main function that builds the circuit using other helper functions,
runs the experiment 1000 times & returns the resultant qubit counts
>>> deutsch_jozsa("constant", 3)
{'000': 1000}
>>> deutsch_jozsa("balanced", 3)
{'111': 1000}
"""
# Use Aer's qasm_simulator
simulator = q.Aer.get_backend("qasm_simulator")
oracle_gate = dj_oracle(case, num_qubits)
dj_circuit = dj_algorithm(oracle_gate, num_qubits)
# Execute the circuit on the qasm simulator
job = q.execute(dj_circuit, simulator, shots=1000)
# Return the histogram data of the results of the experiment.
return job.result().get_counts(dj_circuit)
if __name__ == "__main__":
print(f"Deutsch Jozsa - Constant Oracle: {deutsch_jozsa('constant', 3)}")
print(f"Deutsch Jozsa - Balanced Oracle: {deutsch_jozsa('balanced', 3)}")
| #!/usr/bin/env python3
"""
Deutsch-Josza Algorithm is one of the first examples of a quantum
algorithm that is exponentially faster than any possible deterministic
classical algorithm
Premise:
We are given a hidden Boolean function f,
which takes as input a string of bits, and returns either 0 or 1:
f({x0,x1,x2,...}) -> 0 or 1, where xn is 0 or 1
The property of the given Boolean function is that it is guaranteed to
either be balanced or constant. A constant function returns all 0's
or all 1's for any input, while a balanced function returns 0's for
exactly half of all inputs and 1's for the other half. Our task is to
determine whether the given function is balanced or constant.
References:
- https://en.wikipedia.org/wiki/Deutsch-Jozsa_algorithm
- https://qiskit.org/textbook/ch-algorithms/deutsch-jozsa.html
"""
import numpy as np
import qiskit as q
def dj_oracle(case: str, num_qubits: int) -> q.QuantumCircuit:
"""
Returns a Quantum Circuit for the Oracle function.
The circuit returned can represent balanced or constant function,
according to the arguments passed
"""
# This circuit has num_qubits+1 qubits: the size of the input,
# plus one output qubit
oracle_qc = q.QuantumCircuit(num_qubits + 1)
# First, let's deal with the case in which oracle is balanced
if case == "balanced":
# First generate a random number that tells us which CNOTs to
# wrap in X-gates:
b = np.random.randint(1, 2 ** num_qubits)
# Next, format 'b' as a binary string of length 'n', padded with zeros:
b_str = format(b, f"0{num_qubits}b")
# Next, we place the first X-gates. Each digit in our binary string
# correspopnds to a qubit, if the digit is 0, we do nothing, if it's 1
# we apply an X-gate to that qubit:
for index, bit in enumerate(b_str):
if bit == "1":
oracle_qc.x(index)
# Do the controlled-NOT gates for each qubit, using the output qubit
# as the target:
for index in range(num_qubits):
oracle_qc.cx(index, num_qubits)
# Next, place the final X-gates
for index, bit in enumerate(b_str):
if bit == "1":
oracle_qc.x(index)
# Case in which oracle is constant
if case == "constant":
# First decide what the fixed output of the oracle will be
# (either always 0 or always 1)
output = np.random.randint(2)
if output == 1:
oracle_qc.x(num_qubits)
oracle_gate = oracle_qc.to_gate()
oracle_gate.name = "Oracle" # To show when we display the circuit
return oracle_gate
def dj_algorithm(oracle: q.QuantumCircuit, num_qubits: int) -> q.QuantumCircuit:
"""
Returns the complete Deustch-Jozsa Quantum Circuit,
adding Input & Output registers and Hadamard & Measurement Gates,
to the Oracle Circuit passed in arguments
"""
dj_circuit = q.QuantumCircuit(num_qubits + 1, num_qubits)
# Set up the output qubit:
dj_circuit.x(num_qubits)
dj_circuit.h(num_qubits)
# And set up the input register:
for qubit in range(num_qubits):
dj_circuit.h(qubit)
# Let's append the oracle gate to our circuit:
dj_circuit.append(oracle, range(num_qubits + 1))
# Finally, perform the H-gates again and measure:
for qubit in range(num_qubits):
dj_circuit.h(qubit)
for i in range(num_qubits):
dj_circuit.measure(i, i)
return dj_circuit
def deutsch_jozsa(case: str, num_qubits: int) -> q.result.counts.Counts:
"""
Main function that builds the circuit using other helper functions,
runs the experiment 1000 times & returns the resultant qubit counts
>>> deutsch_jozsa("constant", 3)
{'000': 1000}
>>> deutsch_jozsa("balanced", 3)
{'111': 1000}
"""
# Use Aer's qasm_simulator
simulator = q.Aer.get_backend("qasm_simulator")
oracle_gate = dj_oracle(case, num_qubits)
dj_circuit = dj_algorithm(oracle_gate, num_qubits)
# Execute the circuit on the qasm simulator
job = q.execute(dj_circuit, simulator, shots=1000)
# Return the histogram data of the results of the experiment.
return job.result().get_counts(dj_circuit)
if __name__ == "__main__":
print(f"Deutsch Jozsa - Constant Oracle: {deutsch_jozsa('constant', 3)}")
print(f"Deutsch Jozsa - Balanced Oracle: {deutsch_jozsa('balanced', 3)}")
| -1 |
TheAlgorithms/Python | 4,763 | from __future__ import annotations | ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2021-09-14T22:54:26Z" | "2021-09-22T21:11:51Z" | dc07a850763d8154e012c9d1be7f8fe78326e8fb | 15d1cfabb15903c5e9d4d103e2390876efb3f85f | from __future__ import annotations. ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Heap's algorithm returns the list of all permutations possible from a list.
It minimizes movement by generating each permutation from the previous one
by swapping only two elements.
More information:
https://en.wikipedia.org/wiki/Heap%27s_algorithm.
"""
def heaps(arr: list) -> list:
"""
Pure python implementation of the Heap's algorithm (recursive version),
returning all permutations of a list.
>>> heaps([])
[()]
>>> heaps([0])
[(0,)]
>>> heaps([-1, 1])
[(-1, 1), (1, -1)]
>>> heaps([1, 2, 3])
[(1, 2, 3), (2, 1, 3), (3, 1, 2), (1, 3, 2), (2, 3, 1), (3, 2, 1)]
>>> from itertools import permutations
>>> sorted(heaps([1,2,3])) == sorted(permutations([1,2,3]))
True
>>> all(sorted(heaps(x)) == sorted(permutations(x))
... for x in ([], [0], [-1, 1], [1, 2, 3]))
True
"""
if len(arr) <= 1:
return [tuple(arr)]
res = []
def generate(k: int, arr: list):
if k == 1:
res.append(tuple(arr[:]))
return
generate(k - 1, arr)
for i in range(k - 1):
if k % 2 == 0: # k is even
arr[i], arr[k - 1] = arr[k - 1], arr[i]
else: # k is odd
arr[0], arr[k - 1] = arr[k - 1], arr[0]
generate(k - 1, arr)
generate(len(arr), arr)
return res
if __name__ == "__main__":
user_input = input("Enter numbers separated by a comma:\n").strip()
arr = [int(item) for item in user_input.split(",")]
print(heaps(arr))
| """
Heap's algorithm returns the list of all permutations possible from a list.
It minimizes movement by generating each permutation from the previous one
by swapping only two elements.
More information:
https://en.wikipedia.org/wiki/Heap%27s_algorithm.
"""
def heaps(arr: list) -> list:
"""
Pure python implementation of the Heap's algorithm (recursive version),
returning all permutations of a list.
>>> heaps([])
[()]
>>> heaps([0])
[(0,)]
>>> heaps([-1, 1])
[(-1, 1), (1, -1)]
>>> heaps([1, 2, 3])
[(1, 2, 3), (2, 1, 3), (3, 1, 2), (1, 3, 2), (2, 3, 1), (3, 2, 1)]
>>> from itertools import permutations
>>> sorted(heaps([1,2,3])) == sorted(permutations([1,2,3]))
True
>>> all(sorted(heaps(x)) == sorted(permutations(x))
... for x in ([], [0], [-1, 1], [1, 2, 3]))
True
"""
if len(arr) <= 1:
return [tuple(arr)]
res = []
def generate(k: int, arr: list):
if k == 1:
res.append(tuple(arr[:]))
return
generate(k - 1, arr)
for i in range(k - 1):
if k % 2 == 0: # k is even
arr[i], arr[k - 1] = arr[k - 1], arr[i]
else: # k is odd
arr[0], arr[k - 1] = arr[k - 1], arr[0]
generate(k - 1, arr)
generate(len(arr), arr)
return res
if __name__ == "__main__":
user_input = input("Enter numbers separated by a comma:\n").strip()
arr = [int(item) for item in user_input.split(",")]
print(heaps(arr))
| -1 |
TheAlgorithms/Python | 4,763 | from __future__ import annotations | ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2021-09-14T22:54:26Z" | "2021-09-22T21:11:51Z" | dc07a850763d8154e012c9d1be7f8fe78326e8fb | 15d1cfabb15903c5e9d4d103e2390876efb3f85f | from __future__ import annotations. ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Implementation of median filter algorithm
"""
from cv2 import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import divide, int8, multiply, ravel, sort, zeros_like
def median_filter(gray_img, mask=3):
"""
:param gray_img: gray image
:param mask: mask size
:return: image with median filter
"""
# set image borders
bd = int(mask / 2)
# copy image size
median_img = zeros_like(gray_img)
for i in range(bd, gray_img.shape[0] - bd):
for j in range(bd, gray_img.shape[1] - bd):
# get mask according with mask
kernel = ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1])
# calculate mask median
median = sort(kernel)[int8(divide((multiply(mask, mask)), 2) + 1)]
median_img[i, j] = median
return median_img
if __name__ == "__main__":
# read original image
img = imread("../image_data/lena.jpg")
# turn image in gray scale value
gray = cvtColor(img, COLOR_BGR2GRAY)
# get values with two different mask size
median3x3 = median_filter(gray, 3)
median5x5 = median_filter(gray, 5)
# show result images
imshow("median filter with 3x3 mask", median3x3)
imshow("median filter with 5x5 mask", median5x5)
waitKey(0)
| """
Implementation of median filter algorithm
"""
from cv2 import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import divide, int8, multiply, ravel, sort, zeros_like
def median_filter(gray_img, mask=3):
"""
:param gray_img: gray image
:param mask: mask size
:return: image with median filter
"""
# set image borders
bd = int(mask / 2)
# copy image size
median_img = zeros_like(gray_img)
for i in range(bd, gray_img.shape[0] - bd):
for j in range(bd, gray_img.shape[1] - bd):
# get mask according with mask
kernel = ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1])
# calculate mask median
median = sort(kernel)[int8(divide((multiply(mask, mask)), 2) + 1)]
median_img[i, j] = median
return median_img
if __name__ == "__main__":
# read original image
img = imread("../image_data/lena.jpg")
# turn image in gray scale value
gray = cvtColor(img, COLOR_BGR2GRAY)
# get values with two different mask size
median3x3 = median_filter(gray, 3)
median5x5 = median_filter(gray, 5)
# show result images
imshow("median filter with 3x3 mask", median3x3)
imshow("median filter with 5x5 mask", median5x5)
waitKey(0)
| -1 |
TheAlgorithms/Python | 4,763 | from __future__ import annotations | ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2021-09-14T22:54:26Z" | "2021-09-22T21:11:51Z" | dc07a850763d8154e012c9d1be7f8fe78326e8fb | 15d1cfabb15903c5e9d4d103e2390876efb3f85f | from __future__ import annotations. ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # flake8: noqa
"""
Binomial Heap
Reference: Advanced Data Structures, Peter Brass
"""
class Node:
"""
Node in a doubly-linked binomial tree, containing:
- value
- size of left subtree
- link to left, right and parent nodes
"""
def __init__(self, val):
self.val = val
# Number of nodes in left subtree
self.left_tree_size = 0
self.left = None
self.right = None
self.parent = None
def mergeTrees(self, other):
"""
In-place merge of two binomial trees of equal size.
Returns the root of the resulting tree
"""
assert self.left_tree_size == other.left_tree_size, "Unequal Sizes of Blocks"
if self.val < other.val:
other.left = self.right
other.parent = None
if self.right:
self.right.parent = other
self.right = other
self.left_tree_size = self.left_tree_size * 2 + 1
return self
else:
self.left = other.right
self.parent = None
if other.right:
other.right.parent = self
other.right = self
other.left_tree_size = other.left_tree_size * 2 + 1
return other
class BinomialHeap:
r"""
Min-oriented priority queue implemented with the Binomial Heap data
structure implemented with the BinomialHeap class. It supports:
- Insert element in a heap with n elements: Guaranteed logn, amoratized 1
- Merge (meld) heaps of size m and n: O(logn + logm)
- Delete Min: O(logn)
- Peek (return min without deleting it): O(1)
Example:
Create a random permutation of 30 integers to be inserted and 19 of them deleted
>>> import numpy as np
>>> permutation = np.random.permutation(list(range(30)))
Create a Heap and insert the 30 integers
__init__() test
>>> first_heap = BinomialHeap()
30 inserts - insert() test
>>> for number in permutation:
... first_heap.insert(number)
Size test
>>> print(first_heap.size)
30
Deleting - delete() test
>>> for i in range(25):
... print(first_heap.deleteMin(), end=" ")
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Create a new Heap
>>> second_heap = BinomialHeap()
>>> vals = [17, 20, 31, 34]
>>> for value in vals:
... second_heap.insert(value)
The heap should have the following structure:
17
/ \
# 31
/ \
20 34
/ \ / \
# # # #
preOrder() test
>>> print(second_heap.preOrder())
[(17, 0), ('#', 1), (31, 1), (20, 2), ('#', 3), ('#', 3), (34, 2), ('#', 3), ('#', 3)]
printing Heap - __str__() test
>>> print(second_heap)
17
-#
-31
--20
---#
---#
--34
---#
---#
mergeHeaps() test
>>> merged = second_heap.mergeHeaps(first_heap)
>>> merged.peek()
17
values in merged heap; (merge is inplace)
>>> while not first_heap.isEmpty():
... print(first_heap.deleteMin(), end=" ")
17 20 25 26 27 28 29 31 34
"""
def __init__(self, bottom_root=None, min_node=None, heap_size=0):
self.size = heap_size
self.bottom_root = bottom_root
self.min_node = min_node
def mergeHeaps(self, other):
"""
In-place merge of two binomial heaps.
Both of them become the resulting merged heap
"""
# Empty heaps corner cases
if other.size == 0:
return
if self.size == 0:
self.size = other.size
self.bottom_root = other.bottom_root
self.min_node = other.min_node
return
# Update size
self.size = self.size + other.size
# Update min.node
if self.min_node.val > other.min_node.val:
self.min_node = other.min_node
# Merge
# Order roots by left_subtree_size
combined_roots_list = []
i, j = self.bottom_root, other.bottom_root
while i or j:
if i and ((not j) or i.left_tree_size < j.left_tree_size):
combined_roots_list.append((i, True))
i = i.parent
else:
combined_roots_list.append((j, False))
j = j.parent
# Insert links between them
for i in range(len(combined_roots_list) - 1):
if combined_roots_list[i][1] != combined_roots_list[i + 1][1]:
combined_roots_list[i][0].parent = combined_roots_list[i + 1][0]
combined_roots_list[i + 1][0].left = combined_roots_list[i][0]
# Consecutively merge roots with same left_tree_size
i = combined_roots_list[0][0]
while i.parent:
if (
(i.left_tree_size == i.parent.left_tree_size) and (not i.parent.parent)
) or (
i.left_tree_size == i.parent.left_tree_size
and i.left_tree_size != i.parent.parent.left_tree_size
):
# Neighbouring Nodes
previous_node = i.left
next_node = i.parent.parent
# Merging trees
i = i.mergeTrees(i.parent)
# Updating links
i.left = previous_node
i.parent = next_node
if previous_node:
previous_node.parent = i
if next_node:
next_node.left = i
else:
i = i.parent
# Updating self.bottom_root
while i.left:
i = i.left
self.bottom_root = i
# Update other
other.size = self.size
other.bottom_root = self.bottom_root
other.min_node = self.min_node
# Return the merged heap
return self
def insert(self, val):
"""
insert a value in the heap
"""
if self.size == 0:
self.bottom_root = Node(val)
self.size = 1
self.min_node = self.bottom_root
else:
# Create new node
new_node = Node(val)
# Update size
self.size += 1
# update min_node
if val < self.min_node.val:
self.min_node = new_node
# Put new_node as a bottom_root in heap
self.bottom_root.left = new_node
new_node.parent = self.bottom_root
self.bottom_root = new_node
# Consecutively merge roots with same left_tree_size
while (
self.bottom_root.parent
and self.bottom_root.left_tree_size
== self.bottom_root.parent.left_tree_size
):
# Next node
next_node = self.bottom_root.parent.parent
# Merge
self.bottom_root = self.bottom_root.mergeTrees(self.bottom_root.parent)
# Update Links
self.bottom_root.parent = next_node
self.bottom_root.left = None
if next_node:
next_node.left = self.bottom_root
def peek(self):
"""
return min element without deleting it
"""
return self.min_node.val
def isEmpty(self):
return self.size == 0
def deleteMin(self):
"""
delete min element and return it
"""
# assert not self.isEmpty(), "Empty Heap"
# Save minimal value
min_value = self.min_node.val
# Last element in heap corner case
if self.size == 1:
# Update size
self.size = 0
# Update bottom root
self.bottom_root = None
# Update min_node
self.min_node = None
return min_value
# No right subtree corner case
# The structure of the tree implies that this should be the bottom root
# and there is at least one other root
if self.min_node.right is None:
# Update size
self.size -= 1
# Update bottom root
self.bottom_root = self.bottom_root.parent
self.bottom_root.left = None
# Update min_node
self.min_node = self.bottom_root
i = self.bottom_root.parent
while i:
if i.val < self.min_node.val:
self.min_node = i
i = i.parent
return min_value
# General case
# Find the BinomialHeap of the right subtree of min_node
bottom_of_new = self.min_node.right
bottom_of_new.parent = None
min_of_new = bottom_of_new
size_of_new = 1
# Size, min_node and bottom_root
while bottom_of_new.left:
size_of_new = size_of_new * 2 + 1
bottom_of_new = bottom_of_new.left
if bottom_of_new.val < min_of_new.val:
min_of_new = bottom_of_new
# Corner case of single root on top left path
if (not self.min_node.left) and (not self.min_node.parent):
self.size = size_of_new
self.bottom_root = bottom_of_new
self.min_node = min_of_new
# print("Single root, multiple nodes case")
return min_value
# Remaining cases
# Construct heap of right subtree
newHeap = BinomialHeap(
bottom_root=bottom_of_new, min_node=min_of_new, heap_size=size_of_new
)
# Update size
self.size = self.size - 1 - size_of_new
# Neighbour nodes
previous_node = self.min_node.left
next_node = self.min_node.parent
# Initialize new bottom_root and min_node
self.min_node = previous_node or next_node
self.bottom_root = next_node
# Update links of previous_node and search below for new min_node and
# bottom_root
if previous_node:
previous_node.parent = next_node
# Update bottom_root and search for min_node below
self.bottom_root = previous_node
self.min_node = previous_node
while self.bottom_root.left:
self.bottom_root = self.bottom_root.left
if self.bottom_root.val < self.min_node.val:
self.min_node = self.bottom_root
if next_node:
next_node.left = previous_node
# Search for new min_node above min_node
i = next_node
while i:
if i.val < self.min_node.val:
self.min_node = i
i = i.parent
# Merge heaps
self.mergeHeaps(newHeap)
return min_value
def preOrder(self):
"""
Returns the Pre-order representation of the heap including
values of nodes plus their level distance from the root;
Empty nodes appear as #
"""
# Find top root
top_root = self.bottom_root
while top_root.parent:
top_root = top_root.parent
# preorder
heap_preOrder = []
self.__traversal(top_root, heap_preOrder)
return heap_preOrder
def __traversal(self, curr_node, preorder, level=0):
"""
Pre-order traversal of nodes
"""
if curr_node:
preorder.append((curr_node.val, level))
self.__traversal(curr_node.left, preorder, level + 1)
self.__traversal(curr_node.right, preorder, level + 1)
else:
preorder.append(("#", level))
def __str__(self):
"""
Overwriting str for a pre-order print of nodes in heap;
Performance is poor, so use only for small examples
"""
if self.isEmpty():
return ""
preorder_heap = self.preOrder()
return "\n".join(("-" * level + str(value)) for value, level in preorder_heap)
# Unit Tests
if __name__ == "__main__":
import doctest
doctest.testmod()
| # flake8: noqa
"""
Binomial Heap
Reference: Advanced Data Structures, Peter Brass
"""
class Node:
"""
Node in a doubly-linked binomial tree, containing:
- value
- size of left subtree
- link to left, right and parent nodes
"""
def __init__(self, val):
self.val = val
# Number of nodes in left subtree
self.left_tree_size = 0
self.left = None
self.right = None
self.parent = None
def mergeTrees(self, other):
"""
In-place merge of two binomial trees of equal size.
Returns the root of the resulting tree
"""
assert self.left_tree_size == other.left_tree_size, "Unequal Sizes of Blocks"
if self.val < other.val:
other.left = self.right
other.parent = None
if self.right:
self.right.parent = other
self.right = other
self.left_tree_size = self.left_tree_size * 2 + 1
return self
else:
self.left = other.right
self.parent = None
if other.right:
other.right.parent = self
other.right = self
other.left_tree_size = other.left_tree_size * 2 + 1
return other
class BinomialHeap:
r"""
Min-oriented priority queue implemented with the Binomial Heap data
structure implemented with the BinomialHeap class. It supports:
- Insert element in a heap with n elements: Guaranteed logn, amoratized 1
- Merge (meld) heaps of size m and n: O(logn + logm)
- Delete Min: O(logn)
- Peek (return min without deleting it): O(1)
Example:
Create a random permutation of 30 integers to be inserted and 19 of them deleted
>>> import numpy as np
>>> permutation = np.random.permutation(list(range(30)))
Create a Heap and insert the 30 integers
__init__() test
>>> first_heap = BinomialHeap()
30 inserts - insert() test
>>> for number in permutation:
... first_heap.insert(number)
Size test
>>> print(first_heap.size)
30
Deleting - delete() test
>>> for i in range(25):
... print(first_heap.deleteMin(), end=" ")
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Create a new Heap
>>> second_heap = BinomialHeap()
>>> vals = [17, 20, 31, 34]
>>> for value in vals:
... second_heap.insert(value)
The heap should have the following structure:
17
/ \
# 31
/ \
20 34
/ \ / \
# # # #
preOrder() test
>>> print(second_heap.preOrder())
[(17, 0), ('#', 1), (31, 1), (20, 2), ('#', 3), ('#', 3), (34, 2), ('#', 3), ('#', 3)]
printing Heap - __str__() test
>>> print(second_heap)
17
-#
-31
--20
---#
---#
--34
---#
---#
mergeHeaps() test
>>> merged = second_heap.mergeHeaps(first_heap)
>>> merged.peek()
17
values in merged heap; (merge is inplace)
>>> while not first_heap.isEmpty():
... print(first_heap.deleteMin(), end=" ")
17 20 25 26 27 28 29 31 34
"""
def __init__(self, bottom_root=None, min_node=None, heap_size=0):
self.size = heap_size
self.bottom_root = bottom_root
self.min_node = min_node
def mergeHeaps(self, other):
"""
In-place merge of two binomial heaps.
Both of them become the resulting merged heap
"""
# Empty heaps corner cases
if other.size == 0:
return
if self.size == 0:
self.size = other.size
self.bottom_root = other.bottom_root
self.min_node = other.min_node
return
# Update size
self.size = self.size + other.size
# Update min.node
if self.min_node.val > other.min_node.val:
self.min_node = other.min_node
# Merge
# Order roots by left_subtree_size
combined_roots_list = []
i, j = self.bottom_root, other.bottom_root
while i or j:
if i and ((not j) or i.left_tree_size < j.left_tree_size):
combined_roots_list.append((i, True))
i = i.parent
else:
combined_roots_list.append((j, False))
j = j.parent
# Insert links between them
for i in range(len(combined_roots_list) - 1):
if combined_roots_list[i][1] != combined_roots_list[i + 1][1]:
combined_roots_list[i][0].parent = combined_roots_list[i + 1][0]
combined_roots_list[i + 1][0].left = combined_roots_list[i][0]
# Consecutively merge roots with same left_tree_size
i = combined_roots_list[0][0]
while i.parent:
if (
(i.left_tree_size == i.parent.left_tree_size) and (not i.parent.parent)
) or (
i.left_tree_size == i.parent.left_tree_size
and i.left_tree_size != i.parent.parent.left_tree_size
):
# Neighbouring Nodes
previous_node = i.left
next_node = i.parent.parent
# Merging trees
i = i.mergeTrees(i.parent)
# Updating links
i.left = previous_node
i.parent = next_node
if previous_node:
previous_node.parent = i
if next_node:
next_node.left = i
else:
i = i.parent
# Updating self.bottom_root
while i.left:
i = i.left
self.bottom_root = i
# Update other
other.size = self.size
other.bottom_root = self.bottom_root
other.min_node = self.min_node
# Return the merged heap
return self
def insert(self, val):
"""
insert a value in the heap
"""
if self.size == 0:
self.bottom_root = Node(val)
self.size = 1
self.min_node = self.bottom_root
else:
# Create new node
new_node = Node(val)
# Update size
self.size += 1
# update min_node
if val < self.min_node.val:
self.min_node = new_node
# Put new_node as a bottom_root in heap
self.bottom_root.left = new_node
new_node.parent = self.bottom_root
self.bottom_root = new_node
# Consecutively merge roots with same left_tree_size
while (
self.bottom_root.parent
and self.bottom_root.left_tree_size
== self.bottom_root.parent.left_tree_size
):
# Next node
next_node = self.bottom_root.parent.parent
# Merge
self.bottom_root = self.bottom_root.mergeTrees(self.bottom_root.parent)
# Update Links
self.bottom_root.parent = next_node
self.bottom_root.left = None
if next_node:
next_node.left = self.bottom_root
def peek(self):
"""
return min element without deleting it
"""
return self.min_node.val
def isEmpty(self):
return self.size == 0
def deleteMin(self):
"""
delete min element and return it
"""
# assert not self.isEmpty(), "Empty Heap"
# Save minimal value
min_value = self.min_node.val
# Last element in heap corner case
if self.size == 1:
# Update size
self.size = 0
# Update bottom root
self.bottom_root = None
# Update min_node
self.min_node = None
return min_value
# No right subtree corner case
# The structure of the tree implies that this should be the bottom root
# and there is at least one other root
if self.min_node.right is None:
# Update size
self.size -= 1
# Update bottom root
self.bottom_root = self.bottom_root.parent
self.bottom_root.left = None
# Update min_node
self.min_node = self.bottom_root
i = self.bottom_root.parent
while i:
if i.val < self.min_node.val:
self.min_node = i
i = i.parent
return min_value
# General case
# Find the BinomialHeap of the right subtree of min_node
bottom_of_new = self.min_node.right
bottom_of_new.parent = None
min_of_new = bottom_of_new
size_of_new = 1
# Size, min_node and bottom_root
while bottom_of_new.left:
size_of_new = size_of_new * 2 + 1
bottom_of_new = bottom_of_new.left
if bottom_of_new.val < min_of_new.val:
min_of_new = bottom_of_new
# Corner case of single root on top left path
if (not self.min_node.left) and (not self.min_node.parent):
self.size = size_of_new
self.bottom_root = bottom_of_new
self.min_node = min_of_new
# print("Single root, multiple nodes case")
return min_value
# Remaining cases
# Construct heap of right subtree
newHeap = BinomialHeap(
bottom_root=bottom_of_new, min_node=min_of_new, heap_size=size_of_new
)
# Update size
self.size = self.size - 1 - size_of_new
# Neighbour nodes
previous_node = self.min_node.left
next_node = self.min_node.parent
# Initialize new bottom_root and min_node
self.min_node = previous_node or next_node
self.bottom_root = next_node
# Update links of previous_node and search below for new min_node and
# bottom_root
if previous_node:
previous_node.parent = next_node
# Update bottom_root and search for min_node below
self.bottom_root = previous_node
self.min_node = previous_node
while self.bottom_root.left:
self.bottom_root = self.bottom_root.left
if self.bottom_root.val < self.min_node.val:
self.min_node = self.bottom_root
if next_node:
next_node.left = previous_node
# Search for new min_node above min_node
i = next_node
while i:
if i.val < self.min_node.val:
self.min_node = i
i = i.parent
# Merge heaps
self.mergeHeaps(newHeap)
return min_value
def preOrder(self):
"""
Returns the Pre-order representation of the heap including
values of nodes plus their level distance from the root;
Empty nodes appear as #
"""
# Find top root
top_root = self.bottom_root
while top_root.parent:
top_root = top_root.parent
# preorder
heap_preOrder = []
self.__traversal(top_root, heap_preOrder)
return heap_preOrder
def __traversal(self, curr_node, preorder, level=0):
"""
Pre-order traversal of nodes
"""
if curr_node:
preorder.append((curr_node.val, level))
self.__traversal(curr_node.left, preorder, level + 1)
self.__traversal(curr_node.right, preorder, level + 1)
else:
preorder.append(("#", level))
def __str__(self):
"""
Overwriting str for a pre-order print of nodes in heap;
Performance is poor, so use only for small examples
"""
if self.isEmpty():
return ""
preorder_heap = self.preOrder()
return "\n".join(("-" * level + str(value)) for value, level in preorder_heap)
# Unit Tests
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 4,763 | from __future__ import annotations | ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2021-09-14T22:54:26Z" | "2021-09-22T21:11:51Z" | dc07a850763d8154e012c9d1be7f8fe78326e8fb | 15d1cfabb15903c5e9d4d103e2390876efb3f85f | from __future__ import annotations. ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| import math
def perfect_square(num: int) -> bool:
"""
Check if a number is perfect square number or not
:param num: the number to be checked
:return: True if number is square number, otherwise False
>>> perfect_square(9)
True
>>> perfect_square(16)
True
>>> perfect_square(1)
True
>>> perfect_square(0)
True
>>> perfect_square(10)
False
"""
return math.sqrt(num) * math.sqrt(num) == num
def perfect_square_binary_search(n: int) -> bool:
"""
Check if a number is perfect square using binary search.
Time complexity : O(Log(n))
Space complexity: O(1)
>>> perfect_square_binary_search(9)
True
>>> perfect_square_binary_search(16)
True
>>> perfect_square_binary_search(1)
True
>>> perfect_square_binary_search(0)
True
>>> perfect_square_binary_search(10)
False
>>> perfect_square_binary_search(-1)
False
>>> perfect_square_binary_search(1.1)
False
>>> perfect_square_binary_search("a")
Traceback (most recent call last):
...
TypeError: '<=' not supported between instances of 'int' and 'str'
>>> perfect_square_binary_search(None)
Traceback (most recent call last):
...
TypeError: '<=' not supported between instances of 'int' and 'NoneType'
>>> perfect_square_binary_search([])
Traceback (most recent call last):
...
TypeError: '<=' not supported between instances of 'int' and 'list'
"""
left = 0
right = n
while left <= right:
mid = (left + right) // 2
if mid ** 2 == n:
return True
elif mid ** 2 > n:
right = mid - 1
else:
left = mid + 1
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| import math
def perfect_square(num: int) -> bool:
"""
Check if a number is perfect square number or not
:param num: the number to be checked
:return: True if number is square number, otherwise False
>>> perfect_square(9)
True
>>> perfect_square(16)
True
>>> perfect_square(1)
True
>>> perfect_square(0)
True
>>> perfect_square(10)
False
"""
return math.sqrt(num) * math.sqrt(num) == num
def perfect_square_binary_search(n: int) -> bool:
"""
Check if a number is perfect square using binary search.
Time complexity : O(Log(n))
Space complexity: O(1)
>>> perfect_square_binary_search(9)
True
>>> perfect_square_binary_search(16)
True
>>> perfect_square_binary_search(1)
True
>>> perfect_square_binary_search(0)
True
>>> perfect_square_binary_search(10)
False
>>> perfect_square_binary_search(-1)
False
>>> perfect_square_binary_search(1.1)
False
>>> perfect_square_binary_search("a")
Traceback (most recent call last):
...
TypeError: '<=' not supported between instances of 'int' and 'str'
>>> perfect_square_binary_search(None)
Traceback (most recent call last):
...
TypeError: '<=' not supported between instances of 'int' and 'NoneType'
>>> perfect_square_binary_search([])
Traceback (most recent call last):
...
TypeError: '<=' not supported between instances of 'int' and 'list'
"""
left = 0
right = n
while left <= right:
mid = (left + right) // 2
if mid ** 2 == n:
return True
elif mid ** 2 > n:
right = mid - 1
else:
left = mid + 1
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 4,763 | from __future__ import annotations | ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2021-09-14T22:54:26Z" | "2021-09-22T21:11:51Z" | dc07a850763d8154e012c9d1be7f8fe78326e8fb | 15d1cfabb15903c5e9d4d103e2390876efb3f85f | from __future__ import annotations. ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Convert International System of Units (SI) and Binary prefixes
"""
from __future__ import annotations
from enum import Enum
class SI_Unit(Enum):
yotta = 24
zetta = 21
exa = 18
peta = 15
tera = 12
giga = 9
mega = 6
kilo = 3
hecto = 2
deca = 1
deci = -1
centi = -2
milli = -3
micro = -6
nano = -9
pico = -12
femto = -15
atto = -18
zepto = -21
yocto = -24
class Binary_Unit(Enum):
yotta = 8
zetta = 7
exa = 6
peta = 5
tera = 4
giga = 3
mega = 2
kilo = 1
def convert_si_prefix(
known_amount: float,
known_prefix: str | SI_Unit,
unknown_prefix: str | SI_Unit,
) -> float:
"""
Wikipedia reference: https://en.wikipedia.org/wiki/Binary_prefix
Wikipedia reference: https://en.wikipedia.org/wiki/International_System_of_Units
>>> convert_si_prefix(1, SI_Unit.giga, SI_Unit.mega)
1000
>>> convert_si_prefix(1, SI_Unit.mega, SI_Unit.giga)
0.001
>>> convert_si_prefix(1, SI_Unit.kilo, SI_Unit.kilo)
1
>>> convert_si_prefix(1, 'giga', 'mega')
1000
>>> convert_si_prefix(1, 'gIGa', 'mEGa')
1000
"""
if isinstance(known_prefix, str):
known_prefix = SI_Unit[known_prefix.lower()]
if isinstance(unknown_prefix, str):
unknown_prefix = SI_Unit[unknown_prefix.lower()]
unknown_amount: float = known_amount * (
10 ** (known_prefix.value - unknown_prefix.value)
)
return unknown_amount
def convert_binary_prefix(
known_amount: float,
known_prefix: str | Binary_Unit,
unknown_prefix: str | Binary_Unit,
) -> float:
"""
Wikipedia reference: https://en.wikipedia.org/wiki/Metric_prefix
>>> convert_binary_prefix(1, Binary_Unit.giga, Binary_Unit.mega)
1024
>>> convert_binary_prefix(1, Binary_Unit.mega, Binary_Unit.giga)
0.0009765625
>>> convert_binary_prefix(1, Binary_Unit.kilo, Binary_Unit.kilo)
1
>>> convert_binary_prefix(1, 'giga', 'mega')
1024
>>> convert_binary_prefix(1, 'gIGa', 'mEGa')
1024
"""
if isinstance(known_prefix, str):
known_prefix = Binary_Unit[known_prefix.lower()]
if isinstance(unknown_prefix, str):
unknown_prefix = Binary_Unit[unknown_prefix.lower()]
unknown_amount: float = known_amount * (
2 ** ((known_prefix.value - unknown_prefix.value) * 10)
)
return unknown_amount
if __name__ == "__main__":
import doctest
doctest.testmod()
| """
Convert International System of Units (SI) and Binary prefixes
"""
from __future__ import annotations
from enum import Enum
class SI_Unit(Enum):
yotta = 24
zetta = 21
exa = 18
peta = 15
tera = 12
giga = 9
mega = 6
kilo = 3
hecto = 2
deca = 1
deci = -1
centi = -2
milli = -3
micro = -6
nano = -9
pico = -12
femto = -15
atto = -18
zepto = -21
yocto = -24
class Binary_Unit(Enum):
yotta = 8
zetta = 7
exa = 6
peta = 5
tera = 4
giga = 3
mega = 2
kilo = 1
def convert_si_prefix(
known_amount: float,
known_prefix: str | SI_Unit,
unknown_prefix: str | SI_Unit,
) -> float:
"""
Wikipedia reference: https://en.wikipedia.org/wiki/Binary_prefix
Wikipedia reference: https://en.wikipedia.org/wiki/International_System_of_Units
>>> convert_si_prefix(1, SI_Unit.giga, SI_Unit.mega)
1000
>>> convert_si_prefix(1, SI_Unit.mega, SI_Unit.giga)
0.001
>>> convert_si_prefix(1, SI_Unit.kilo, SI_Unit.kilo)
1
>>> convert_si_prefix(1, 'giga', 'mega')
1000
>>> convert_si_prefix(1, 'gIGa', 'mEGa')
1000
"""
if isinstance(known_prefix, str):
known_prefix = SI_Unit[known_prefix.lower()]
if isinstance(unknown_prefix, str):
unknown_prefix = SI_Unit[unknown_prefix.lower()]
unknown_amount: float = known_amount * (
10 ** (known_prefix.value - unknown_prefix.value)
)
return unknown_amount
def convert_binary_prefix(
known_amount: float,
known_prefix: str | Binary_Unit,
unknown_prefix: str | Binary_Unit,
) -> float:
"""
Wikipedia reference: https://en.wikipedia.org/wiki/Metric_prefix
>>> convert_binary_prefix(1, Binary_Unit.giga, Binary_Unit.mega)
1024
>>> convert_binary_prefix(1, Binary_Unit.mega, Binary_Unit.giga)
0.0009765625
>>> convert_binary_prefix(1, Binary_Unit.kilo, Binary_Unit.kilo)
1
>>> convert_binary_prefix(1, 'giga', 'mega')
1024
>>> convert_binary_prefix(1, 'gIGa', 'mEGa')
1024
"""
if isinstance(known_prefix, str):
known_prefix = Binary_Unit[known_prefix.lower()]
if isinstance(unknown_prefix, str):
unknown_prefix = Binary_Unit[unknown_prefix.lower()]
unknown_amount: float = known_amount * (
2 ** ((known_prefix.value - unknown_prefix.value) * 10)
)
return unknown_amount
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 4,763 | from __future__ import annotations | ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2021-09-14T22:54:26Z" | "2021-09-22T21:11:51Z" | dc07a850763d8154e012c9d1be7f8fe78326e8fb | 15d1cfabb15903c5e9d4d103e2390876efb3f85f | from __future__ import annotations. ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
The following undirected network consists of seven vertices and twelve edges
with a total weight of 243.

The same network can be represented by the matrix below.
A B C D E F G
A - 16 12 21 - - -
B 16 - - 17 20 - -
C 12 - - 28 - 31 -
D 21 17 28 - 18 19 23
E - 20 - 18 - - 11
F - - 31 19 - - 27
G - - - 23 11 27 -
However, it is possible to optimise the network by removing some edges and still
ensure that all points on the network remain connected. The network which achieves
the maximum saving is shown below. It has a weight of 93, representing a saving of
243 - 93 = 150 from the original network.
Using network.txt (right click and 'Save Link/Target As...'), a 6K text file
containing a network with forty vertices, and given in matrix form, find the maximum
saving which can be achieved by removing redundant edges whilst ensuring that the
network remains connected.
Solution:
We use Prim's algorithm to find a Minimum Spanning Tree.
Reference: https://en.wikipedia.org/wiki/Prim%27s_algorithm
"""
from __future__ import annotations
import os
from typing import Mapping
EdgeT = tuple[int, int]
class Graph:
"""
A class representing an undirected weighted graph.
"""
def __init__(self, vertices: set[int], edges: Mapping[EdgeT, int]) -> None:
self.vertices: set[int] = vertices
self.edges: dict[EdgeT, int] = {
(min(edge), max(edge)): weight for edge, weight in edges.items()
}
def add_edge(self, edge: EdgeT, weight: int) -> None:
"""
Add a new edge to the graph.
>>> graph = Graph({1, 2}, {(2, 1): 4})
>>> graph.add_edge((3, 1), 5)
>>> sorted(graph.vertices)
[1, 2, 3]
>>> sorted([(v,k) for k,v in graph.edges.items()])
[(4, (1, 2)), (5, (1, 3))]
"""
self.vertices.add(edge[0])
self.vertices.add(edge[1])
self.edges[(min(edge), max(edge))] = weight
def prims_algorithm(self) -> Graph:
"""
Run Prim's algorithm to find the minimum spanning tree.
Reference: https://en.wikipedia.org/wiki/Prim%27s_algorithm
>>> graph = Graph({1,2,3,4},{(1,2):5, (1,3):10, (1,4):20, (2,4):30, (3,4):1})
>>> mst = graph.prims_algorithm()
>>> sorted(mst.vertices)
[1, 2, 3, 4]
>>> sorted(mst.edges)
[(1, 2), (1, 3), (3, 4)]
"""
subgraph: Graph = Graph({min(self.vertices)}, {})
min_edge: EdgeT
min_weight: int
edge: EdgeT
weight: int
while len(subgraph.vertices) < len(self.vertices):
min_weight = max(self.edges.values()) + 1
for edge, weight in self.edges.items():
if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
if weight < min_weight:
min_edge = edge
min_weight = weight
subgraph.add_edge(min_edge, min_weight)
return subgraph
def solution(filename: str = "p107_network.txt") -> int:
"""
Find the maximum saving which can be achieved by removing redundant edges
whilst ensuring that the network remains connected.
>>> solution("test_network.txt")
150
"""
script_dir: str = os.path.abspath(os.path.dirname(__file__))
network_file: str = os.path.join(script_dir, filename)
adjacency_matrix: list[list[str]]
edges: dict[EdgeT, int] = dict()
data: list[str]
edge1: int
edge2: int
with open(network_file) as f:
data = f.read().strip().split("\n")
adjaceny_matrix = [line.split(",") for line in data]
for edge1 in range(1, len(adjaceny_matrix)):
for edge2 in range(edge1):
if adjaceny_matrix[edge1][edge2] != "-":
edges[(edge2, edge1)] = int(adjaceny_matrix[edge1][edge2])
graph: Graph = Graph(set(range(len(adjaceny_matrix))), edges)
subgraph: Graph = graph.prims_algorithm()
initial_total: int = sum(graph.edges.values())
optimal_total: int = sum(subgraph.edges.values())
return initial_total - optimal_total
if __name__ == "__main__":
print(f"{solution() = }")
| """
The following undirected network consists of seven vertices and twelve edges
with a total weight of 243.

The same network can be represented by the matrix below.
A B C D E F G
A - 16 12 21 - - -
B 16 - - 17 20 - -
C 12 - - 28 - 31 -
D 21 17 28 - 18 19 23
E - 20 - 18 - - 11
F - - 31 19 - - 27
G - - - 23 11 27 -
However, it is possible to optimise the network by removing some edges and still
ensure that all points on the network remain connected. The network which achieves
the maximum saving is shown below. It has a weight of 93, representing a saving of
243 - 93 = 150 from the original network.
Using network.txt (right click and 'Save Link/Target As...'), a 6K text file
containing a network with forty vertices, and given in matrix form, find the maximum
saving which can be achieved by removing redundant edges whilst ensuring that the
network remains connected.
Solution:
We use Prim's algorithm to find a Minimum Spanning Tree.
Reference: https://en.wikipedia.org/wiki/Prim%27s_algorithm
"""
from __future__ import annotations
import os
from typing import Mapping
EdgeT = tuple[int, int]
class Graph:
"""
A class representing an undirected weighted graph.
"""
def __init__(self, vertices: set[int], edges: Mapping[EdgeT, int]) -> None:
self.vertices: set[int] = vertices
self.edges: dict[EdgeT, int] = {
(min(edge), max(edge)): weight for edge, weight in edges.items()
}
def add_edge(self, edge: EdgeT, weight: int) -> None:
"""
Add a new edge to the graph.
>>> graph = Graph({1, 2}, {(2, 1): 4})
>>> graph.add_edge((3, 1), 5)
>>> sorted(graph.vertices)
[1, 2, 3]
>>> sorted([(v,k) for k,v in graph.edges.items()])
[(4, (1, 2)), (5, (1, 3))]
"""
self.vertices.add(edge[0])
self.vertices.add(edge[1])
self.edges[(min(edge), max(edge))] = weight
def prims_algorithm(self) -> Graph:
"""
Run Prim's algorithm to find the minimum spanning tree.
Reference: https://en.wikipedia.org/wiki/Prim%27s_algorithm
>>> graph = Graph({1,2,3,4},{(1,2):5, (1,3):10, (1,4):20, (2,4):30, (3,4):1})
>>> mst = graph.prims_algorithm()
>>> sorted(mst.vertices)
[1, 2, 3, 4]
>>> sorted(mst.edges)
[(1, 2), (1, 3), (3, 4)]
"""
subgraph: Graph = Graph({min(self.vertices)}, {})
min_edge: EdgeT
min_weight: int
edge: EdgeT
weight: int
while len(subgraph.vertices) < len(self.vertices):
min_weight = max(self.edges.values()) + 1
for edge, weight in self.edges.items():
if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
if weight < min_weight:
min_edge = edge
min_weight = weight
subgraph.add_edge(min_edge, min_weight)
return subgraph
def solution(filename: str = "p107_network.txt") -> int:
"""
Find the maximum saving which can be achieved by removing redundant edges
whilst ensuring that the network remains connected.
>>> solution("test_network.txt")
150
"""
script_dir: str = os.path.abspath(os.path.dirname(__file__))
network_file: str = os.path.join(script_dir, filename)
adjacency_matrix: list[list[str]]
edges: dict[EdgeT, int] = dict()
data: list[str]
edge1: int
edge2: int
with open(network_file) as f:
data = f.read().strip().split("\n")
adjaceny_matrix = [line.split(",") for line in data]
for edge1 in range(1, len(adjaceny_matrix)):
for edge2 in range(edge1):
if adjaceny_matrix[edge1][edge2] != "-":
edges[(edge2, edge1)] = int(adjaceny_matrix[edge1][edge2])
graph: Graph = Graph(set(range(len(adjaceny_matrix))), edges)
subgraph: Graph = graph.prims_algorithm()
initial_total: int = sum(graph.edges.values())
optimal_total: int = sum(subgraph.edges.values())
return initial_total - optimal_total
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 4,763 | from __future__ import annotations | ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2021-09-14T22:54:26Z" | "2021-09-22T21:11:51Z" | dc07a850763d8154e012c9d1be7f8fe78326e8fb | 15d1cfabb15903c5e9d4d103e2390876efb3f85f | from __future__ import annotations. ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Minimax helps to achieve maximum score in a game by checking all possible moves
depth is current depth in game tree.
nodeIndex is index of current node in scores[].
if move is of maximizer return true else false
leaves of game tree is stored in scores[]
height is maximum height of Game tree
"""
from __future__ import annotations
import math
def minimax(
depth: int, node_index: int, is_max: bool, scores: list[int], height: float
) -> int:
"""
>>> import math
>>> scores = [90, 23, 6, 33, 21, 65, 123, 34423]
>>> height = math.log(len(scores), 2)
>>> minimax(0, 0, True, scores, height)
65
>>> minimax(-1, 0, True, scores, height)
Traceback (most recent call last):
...
ValueError: Depth cannot be less than 0
>>> minimax(0, 0, True, [], 2)
Traceback (most recent call last):
...
ValueError: Scores cannot be empty
>>> scores = [3, 5, 2, 9, 12, 5, 23, 23]
>>> height = math.log(len(scores), 2)
>>> minimax(0, 0, True, scores, height)
12
"""
if depth < 0:
raise ValueError("Depth cannot be less than 0")
if len(scores) == 0:
raise ValueError("Scores cannot be empty")
if depth == height:
return scores[node_index]
if is_max:
return max(
minimax(depth + 1, node_index * 2, False, scores, height),
minimax(depth + 1, node_index * 2 + 1, False, scores, height),
)
return min(
minimax(depth + 1, node_index * 2, True, scores, height),
minimax(depth + 1, node_index * 2 + 1, True, scores, height),
)
def main() -> None:
scores = [90, 23, 6, 33, 21, 65, 123, 34423]
height = math.log(len(scores), 2)
print("Optimal value : ", end="")
print(minimax(0, 0, True, scores, height))
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| """
Minimax helps to achieve maximum score in a game by checking all possible moves
depth is current depth in game tree.
nodeIndex is index of current node in scores[].
if move is of maximizer return true else false
leaves of game tree is stored in scores[]
height is maximum height of Game tree
"""
from __future__ import annotations
import math
def minimax(
depth: int, node_index: int, is_max: bool, scores: list[int], height: float
) -> int:
"""
>>> import math
>>> scores = [90, 23, 6, 33, 21, 65, 123, 34423]
>>> height = math.log(len(scores), 2)
>>> minimax(0, 0, True, scores, height)
65
>>> minimax(-1, 0, True, scores, height)
Traceback (most recent call last):
...
ValueError: Depth cannot be less than 0
>>> minimax(0, 0, True, [], 2)
Traceback (most recent call last):
...
ValueError: Scores cannot be empty
>>> scores = [3, 5, 2, 9, 12, 5, 23, 23]
>>> height = math.log(len(scores), 2)
>>> minimax(0, 0, True, scores, height)
12
"""
if depth < 0:
raise ValueError("Depth cannot be less than 0")
if len(scores) == 0:
raise ValueError("Scores cannot be empty")
if depth == height:
return scores[node_index]
if is_max:
return max(
minimax(depth + 1, node_index * 2, False, scores, height),
minimax(depth + 1, node_index * 2 + 1, False, scores, height),
)
return min(
minimax(depth + 1, node_index * 2, True, scores, height),
minimax(depth + 1, node_index * 2 + 1, True, scores, height),
)
def main() -> None:
scores = [90, 23, 6, 33, 21, 65, 123, 34423]
height = math.log(len(scores), 2)
print("Optimal value : ", end="")
print(minimax(0, 0, True, scores, height))
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| -1 |
TheAlgorithms/Python | 4,763 | from __future__ import annotations | ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2021-09-14T22:54:26Z" | "2021-09-22T21:11:51Z" | dc07a850763d8154e012c9d1be7f8fe78326e8fb | 15d1cfabb15903c5e9d4d103e2390876efb3f85f | from __future__ import annotations. ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,763 | from __future__ import annotations | ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2021-09-14T22:54:26Z" | "2021-09-22T21:11:51Z" | dc07a850763d8154e012c9d1be7f8fe78326e8fb | 15d1cfabb15903c5e9d4d103e2390876efb3f85f | from __future__ import annotations. ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Chinese Remainder Theorem:
GCD ( Greatest Common Divisor ) or HCF ( Highest Common Factor )
If GCD(a,b) = 1, then for any remainder ra modulo a and any remainder rb modulo b
there exists integer n, such that n = ra (mod a) and n = ra(mod b). If n1 and n2 are
two such integers, then n1=n2(mod ab)
Algorithm :
1. Use extended euclid algorithm to find x,y such that a*x + b*y = 1
2. Take n = ra*by + rb*ax
"""
from __future__ import annotations
# Extended Euclid
def extended_euclid(a: int, b: int) -> tuple[int, int]:
"""
>>> extended_euclid(10, 6)
(-1, 2)
>>> extended_euclid(7, 5)
(-2, 3)
"""
if b == 0:
return (1, 0)
(x, y) = extended_euclid(b, a % b)
k = a // b
return (y, x - k * y)
# Uses ExtendedEuclid to find inverses
def chinese_remainder_theorem(n1: int, r1: int, n2: int, r2: int) -> int:
"""
>>> chinese_remainder_theorem(5,1,7,3)
31
Explanation : 31 is the smallest number such that
(i) When we divide it by 5, we get remainder 1
(ii) When we divide it by 7, we get remainder 3
>>> chinese_remainder_theorem(6,1,4,3)
14
"""
(x, y) = extended_euclid(n1, n2)
m = n1 * n2
n = r2 * x * n1 + r1 * y * n2
return (n % m + m) % m
# ----------SAME SOLUTION USING InvertModulo instead ExtendedEuclid----------------
# This function find the inverses of a i.e., a^(-1)
def invert_modulo(a: int, n: int) -> int:
"""
>>> invert_modulo(2, 5)
3
>>> invert_modulo(8,7)
1
"""
(b, x) = extended_euclid(a, n)
if b < 0:
b = (b % n + n) % n
return b
# Same a above using InvertingModulo
def chinese_remainder_theorem2(n1: int, r1: int, n2: int, r2: int) -> int:
"""
>>> chinese_remainder_theorem2(5,1,7,3)
31
>>> chinese_remainder_theorem2(6,1,4,3)
14
"""
x, y = invert_modulo(n1, n2), invert_modulo(n2, n1)
m = n1 * n2
n = r2 * x * n1 + r1 * y * n2
return (n % m + m) % m
if __name__ == "__main__":
from doctest import testmod
testmod(name="chinese_remainder_theorem", verbose=True)
testmod(name="chinese_remainder_theorem2", verbose=True)
testmod(name="invert_modulo", verbose=True)
testmod(name="extended_euclid", verbose=True)
| """
Chinese Remainder Theorem:
GCD ( Greatest Common Divisor ) or HCF ( Highest Common Factor )
If GCD(a,b) = 1, then for any remainder ra modulo a and any remainder rb modulo b
there exists integer n, such that n = ra (mod a) and n = ra(mod b). If n1 and n2 are
two such integers, then n1=n2(mod ab)
Algorithm :
1. Use extended euclid algorithm to find x,y such that a*x + b*y = 1
2. Take n = ra*by + rb*ax
"""
from __future__ import annotations
# Extended Euclid
def extended_euclid(a: int, b: int) -> tuple[int, int]:
"""
>>> extended_euclid(10, 6)
(-1, 2)
>>> extended_euclid(7, 5)
(-2, 3)
"""
if b == 0:
return (1, 0)
(x, y) = extended_euclid(b, a % b)
k = a // b
return (y, x - k * y)
# Uses ExtendedEuclid to find inverses
def chinese_remainder_theorem(n1: int, r1: int, n2: int, r2: int) -> int:
"""
>>> chinese_remainder_theorem(5,1,7,3)
31
Explanation : 31 is the smallest number such that
(i) When we divide it by 5, we get remainder 1
(ii) When we divide it by 7, we get remainder 3
>>> chinese_remainder_theorem(6,1,4,3)
14
"""
(x, y) = extended_euclid(n1, n2)
m = n1 * n2
n = r2 * x * n1 + r1 * y * n2
return (n % m + m) % m
# ----------SAME SOLUTION USING InvertModulo instead ExtendedEuclid----------------
# This function find the inverses of a i.e., a^(-1)
def invert_modulo(a: int, n: int) -> int:
"""
>>> invert_modulo(2, 5)
3
>>> invert_modulo(8,7)
1
"""
(b, x) = extended_euclid(a, n)
if b < 0:
b = (b % n + n) % n
return b
# Same a above using InvertingModulo
def chinese_remainder_theorem2(n1: int, r1: int, n2: int, r2: int) -> int:
"""
>>> chinese_remainder_theorem2(5,1,7,3)
31
>>> chinese_remainder_theorem2(6,1,4,3)
14
"""
x, y = invert_modulo(n1, n2), invert_modulo(n2, n1)
m = n1 * n2
n = r2 * x * n1 + r1 * y * n2
return (n % m + m) % m
if __name__ == "__main__":
from doctest import testmod
testmod(name="chinese_remainder_theorem", verbose=True)
testmod(name="chinese_remainder_theorem2", verbose=True)
testmod(name="invert_modulo", verbose=True)
testmod(name="extended_euclid", verbose=True)
| -1 |
TheAlgorithms/Python | 4,763 | from __future__ import annotations | ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2021-09-14T22:54:26Z" | "2021-09-22T21:11:51Z" | dc07a850763d8154e012c9d1be7f8fe78326e8fb | 15d1cfabb15903c5e9d4d103e2390876efb3f85f | from __future__ import annotations. ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| #!/usr/bin/env python3
"""
Build a simple bare-minimum quantum circuit that starts with a single
qubit (by default, in state 0) and inverts it. Run the experiment 1000
times and print the total count of the states finally observed.
Qiskit Docs: https://qiskit.org/documentation/getting_started.html
"""
import qiskit as q
def single_qubit_measure(qubits: int, classical_bits: int) -> q.result.counts.Counts:
"""
>>> single_qubit_measure(2, 2)
{'11': 1000}
>>> single_qubit_measure(4, 4)
{'0011': 1000}
"""
# Use Aer's qasm_simulator
simulator = q.Aer.get_backend("qasm_simulator")
# Create a Quantum Circuit acting on the q register
circuit = q.QuantumCircuit(qubits, classical_bits)
# Apply X (NOT) Gate to Qubits 0 & 1
circuit.x(0)
circuit.x(1)
# Map the quantum measurement to the classical bits
circuit.measure([0, 1], [0, 1])
# Execute the circuit on the qasm simulator
job = q.execute(circuit, simulator, shots=1000)
# Return the histogram data of the results of the experiment.
return job.result().get_counts(circuit)
if __name__ == "__main__":
counts = single_qubit_measure(2, 2)
print(f"Total count for various states are: {counts}")
| #!/usr/bin/env python3
"""
Build a simple bare-minimum quantum circuit that starts with a single
qubit (by default, in state 0) and inverts it. Run the experiment 1000
times and print the total count of the states finally observed.
Qiskit Docs: https://qiskit.org/documentation/getting_started.html
"""
import qiskit as q
def single_qubit_measure(qubits: int, classical_bits: int) -> q.result.counts.Counts:
"""
>>> single_qubit_measure(2, 2)
{'11': 1000}
>>> single_qubit_measure(4, 4)
{'0011': 1000}
"""
# Use Aer's qasm_simulator
simulator = q.Aer.get_backend("qasm_simulator")
# Create a Quantum Circuit acting on the q register
circuit = q.QuantumCircuit(qubits, classical_bits)
# Apply X (NOT) Gate to Qubits 0 & 1
circuit.x(0)
circuit.x(1)
# Map the quantum measurement to the classical bits
circuit.measure([0, 1], [0, 1])
# Execute the circuit on the qasm simulator
job = q.execute(circuit, simulator, shots=1000)
# Return the histogram data of the results of the experiment.
return job.result().get_counts(circuit)
if __name__ == "__main__":
counts = single_qubit_measure(2, 2)
print(f"Total count for various states are: {counts}")
| -1 |
TheAlgorithms/Python | 4,763 | from __future__ import annotations | ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2021-09-14T22:54:26Z" | "2021-09-22T21:11:51Z" | dc07a850763d8154e012c9d1be7f8fe78326e8fb | 15d1cfabb15903c5e9d4d103e2390876efb3f85f | from __future__ import annotations. ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,763 | from __future__ import annotations | ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2021-09-14T22:54:26Z" | "2021-09-22T21:11:51Z" | dc07a850763d8154e012c9d1be7f8fe78326e8fb | 15d1cfabb15903c5e9d4d103e2390876efb3f85f | from __future__ import annotations. ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| def binary_recursive(decimal: int) -> str:
"""
Take a positive integer value and return its binary equivalent.
>>> binary_recursive(1000)
'1111101000'
>>> binary_recursive("72")
'1001000'
>>> binary_recursive("number")
Traceback (most recent call last):
...
ValueError: invalid literal for int() with base 10: 'number'
"""
decimal = int(decimal)
if decimal in (0, 1): # Exit cases for the recursion
return str(decimal)
div, mod = divmod(decimal, 2)
return binary_recursive(div) + str(mod)
def main(number: str) -> str:
"""
Take an integer value and raise ValueError for wrong inputs,
call the function above and return the output with prefix "0b" & "-0b"
for positive and negative integers respectively.
>>> main(0)
'0b0'
>>> main(40)
'0b101000'
>>> main(-40)
'-0b101000'
>>> main(40.8)
Traceback (most recent call last):
...
ValueError: Input value is not an integer
>>> main("forty")
Traceback (most recent call last):
...
ValueError: Input value is not an integer
"""
number = str(number).strip()
if not number:
raise ValueError("No input value was provided")
negative = "-" if number.startswith("-") else ""
number = number.lstrip("-")
if not number.isnumeric():
raise ValueError("Input value is not an integer")
return f"{negative}0b{binary_recursive(int(number))}"
if __name__ == "__main__":
from doctest import testmod
testmod()
| def binary_recursive(decimal: int) -> str:
"""
Take a positive integer value and return its binary equivalent.
>>> binary_recursive(1000)
'1111101000'
>>> binary_recursive("72")
'1001000'
>>> binary_recursive("number")
Traceback (most recent call last):
...
ValueError: invalid literal for int() with base 10: 'number'
"""
decimal = int(decimal)
if decimal in (0, 1): # Exit cases for the recursion
return str(decimal)
div, mod = divmod(decimal, 2)
return binary_recursive(div) + str(mod)
def main(number: str) -> str:
"""
Take an integer value and raise ValueError for wrong inputs,
call the function above and return the output with prefix "0b" & "-0b"
for positive and negative integers respectively.
>>> main(0)
'0b0'
>>> main(40)
'0b101000'
>>> main(-40)
'-0b101000'
>>> main(40.8)
Traceback (most recent call last):
...
ValueError: Input value is not an integer
>>> main("forty")
Traceback (most recent call last):
...
ValueError: Input value is not an integer
"""
number = str(number).strip()
if not number:
raise ValueError("No input value was provided")
negative = "-" if number.startswith("-") else ""
number = number.lstrip("-")
if not number.isnumeric():
raise ValueError("Input value is not an integer")
return f"{negative}0b{binary_recursive(int(number))}"
if __name__ == "__main__":
from doctest import testmod
testmod()
| -1 |
TheAlgorithms/Python | 4,763 | from __future__ import annotations | ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2021-09-14T22:54:26Z" | "2021-09-22T21:11:51Z" | dc07a850763d8154e012c9d1be7f8fe78326e8fb | 15d1cfabb15903c5e9d4d103e2390876efb3f85f | from __future__ import annotations. ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
This is pure Python implementation of counting sort algorithm
For doctests run following command:
python -m doctest -v counting_sort.py
or
python3 -m doctest -v counting_sort.py
For manual testing run:
python counting_sort.py
"""
def counting_sort(collection):
"""Pure implementation of counting sort algorithm in Python
:param collection: some mutable ordered collection with heterogeneous
comparable items inside
:return: the same collection ordered by ascending
Examples:
>>> counting_sort([0, 5, 3, 2, 2])
[0, 2, 2, 3, 5]
>>> counting_sort([])
[]
>>> counting_sort([-2, -5, -45])
[-45, -5, -2]
"""
# if the collection is empty, returns empty
if collection == []:
return []
# get some information about the collection
coll_len = len(collection)
coll_max = max(collection)
coll_min = min(collection)
# create the counting array
counting_arr_length = coll_max + 1 - coll_min
counting_arr = [0] * counting_arr_length
# count how much a number appears in the collection
for number in collection:
counting_arr[number - coll_min] += 1
# sum each position with it's predecessors. now, counting_arr[i] tells
# us how many elements <= i has in the collection
for i in range(1, counting_arr_length):
counting_arr[i] = counting_arr[i] + counting_arr[i - 1]
# create the output collection
ordered = [0] * coll_len
# place the elements in the output, respecting the original order (stable
# sort) from end to begin, updating counting_arr
for i in reversed(range(0, coll_len)):
ordered[counting_arr[collection[i] - coll_min] - 1] = collection[i]
counting_arr[collection[i] - coll_min] -= 1
return ordered
def counting_sort_string(string):
"""
>>> counting_sort_string("thisisthestring")
'eghhiiinrsssttt'
"""
return "".join([chr(i) for i in counting_sort([ord(c) for c in string])])
if __name__ == "__main__":
# Test string sort
assert "eghhiiinrsssttt" == counting_sort_string("thisisthestring")
user_input = input("Enter numbers separated by a comma:\n").strip()
unsorted = [int(item) for item in user_input.split(",")]
print(counting_sort(unsorted))
| """
This is pure Python implementation of counting sort algorithm
For doctests run following command:
python -m doctest -v counting_sort.py
or
python3 -m doctest -v counting_sort.py
For manual testing run:
python counting_sort.py
"""
def counting_sort(collection):
"""Pure implementation of counting sort algorithm in Python
:param collection: some mutable ordered collection with heterogeneous
comparable items inside
:return: the same collection ordered by ascending
Examples:
>>> counting_sort([0, 5, 3, 2, 2])
[0, 2, 2, 3, 5]
>>> counting_sort([])
[]
>>> counting_sort([-2, -5, -45])
[-45, -5, -2]
"""
# if the collection is empty, returns empty
if collection == []:
return []
# get some information about the collection
coll_len = len(collection)
coll_max = max(collection)
coll_min = min(collection)
# create the counting array
counting_arr_length = coll_max + 1 - coll_min
counting_arr = [0] * counting_arr_length
# count how much a number appears in the collection
for number in collection:
counting_arr[number - coll_min] += 1
# sum each position with it's predecessors. now, counting_arr[i] tells
# us how many elements <= i has in the collection
for i in range(1, counting_arr_length):
counting_arr[i] = counting_arr[i] + counting_arr[i - 1]
# create the output collection
ordered = [0] * coll_len
# place the elements in the output, respecting the original order (stable
# sort) from end to begin, updating counting_arr
for i in reversed(range(0, coll_len)):
ordered[counting_arr[collection[i] - coll_min] - 1] = collection[i]
counting_arr[collection[i] - coll_min] -= 1
return ordered
def counting_sort_string(string):
"""
>>> counting_sort_string("thisisthestring")
'eghhiiinrsssttt'
"""
return "".join([chr(i) for i in counting_sort([ord(c) for c in string])])
if __name__ == "__main__":
# Test string sort
assert "eghhiiinrsssttt" == counting_sort_string("thisisthestring")
user_input = input("Enter numbers separated by a comma:\n").strip()
unsorted = [int(item) for item in user_input.split(",")]
print(counting_sort(unsorted))
| -1 |
TheAlgorithms/Python | 4,763 | from __future__ import annotations | ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2021-09-14T22:54:26Z" | "2021-09-22T21:11:51Z" | dc07a850763d8154e012c9d1be7f8fe78326e8fb | 15d1cfabb15903c5e9d4d103e2390876efb3f85f | from __future__ import annotations. ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Implementation of gaussian filter algorithm
"""
from itertools import product
from cv2 import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uint8, zeros
def gen_gaussian_kernel(k_size, sigma):
center = k_size // 2
x, y = mgrid[0 - center : k_size - center, 0 - center : k_size - center]
g = 1 / (2 * pi * sigma) * exp(-(square(x) + square(y)) / (2 * square(sigma)))
return g
def gaussian_filter(image, k_size, sigma):
height, width = image.shape[0], image.shape[1]
# dst image height and width
dst_height = height - k_size + 1
dst_width = width - k_size + 1
# im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
image_array = zeros((dst_height * dst_width, k_size * k_size))
row = 0
for i, j in product(range(dst_height), range(dst_width)):
window = ravel(image[i : i + k_size, j : j + k_size])
image_array[row, :] = window
row += 1
# turn the kernel into shape(k*k, 1)
gaussian_kernel = gen_gaussian_kernel(k_size, sigma)
filter_array = ravel(gaussian_kernel)
# reshape and get the dst image
dst = dot(image_array, filter_array).reshape(dst_height, dst_width).astype(uint8)
return dst
if __name__ == "__main__":
# read original image
img = imread(r"../image_data/lena.jpg")
# turn image in gray scale value
gray = cvtColor(img, COLOR_BGR2GRAY)
# get values with two different mask size
gaussian3x3 = gaussian_filter(gray, 3, sigma=1)
gaussian5x5 = gaussian_filter(gray, 5, sigma=0.8)
# show result images
imshow("gaussian filter with 3x3 mask", gaussian3x3)
imshow("gaussian filter with 5x5 mask", gaussian5x5)
waitKey()
| """
Implementation of gaussian filter algorithm
"""
from itertools import product
from cv2 import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uint8, zeros
def gen_gaussian_kernel(k_size, sigma):
center = k_size // 2
x, y = mgrid[0 - center : k_size - center, 0 - center : k_size - center]
g = 1 / (2 * pi * sigma) * exp(-(square(x) + square(y)) / (2 * square(sigma)))
return g
def gaussian_filter(image, k_size, sigma):
height, width = image.shape[0], image.shape[1]
# dst image height and width
dst_height = height - k_size + 1
dst_width = width - k_size + 1
# im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
image_array = zeros((dst_height * dst_width, k_size * k_size))
row = 0
for i, j in product(range(dst_height), range(dst_width)):
window = ravel(image[i : i + k_size, j : j + k_size])
image_array[row, :] = window
row += 1
# turn the kernel into shape(k*k, 1)
gaussian_kernel = gen_gaussian_kernel(k_size, sigma)
filter_array = ravel(gaussian_kernel)
# reshape and get the dst image
dst = dot(image_array, filter_array).reshape(dst_height, dst_width).astype(uint8)
return dst
if __name__ == "__main__":
# read original image
img = imread(r"../image_data/lena.jpg")
# turn image in gray scale value
gray = cvtColor(img, COLOR_BGR2GRAY)
# get values with two different mask size
gaussian3x3 = gaussian_filter(gray, 3, sigma=1)
gaussian5x5 = gaussian_filter(gray, 5, sigma=0.8)
# show result images
imshow("gaussian filter with 3x3 mask", gaussian3x3)
imshow("gaussian filter with 5x5 mask", gaussian5x5)
waitKey()
| -1 |
TheAlgorithms/Python | 4,763 | from __future__ import annotations | ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2021-09-14T22:54:26Z" | "2021-09-22T21:11:51Z" | dc07a850763d8154e012c9d1be7f8fe78326e8fb | 15d1cfabb15903c5e9d4d103e2390876efb3f85f | from __future__ import annotations. ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """Password generator allows you to generate a random password of length N."""
from random import choice, shuffle
from string import ascii_letters, digits, punctuation
def password_generator(length=8):
"""
>>> len(password_generator())
8
>>> len(password_generator(length=16))
16
>>> len(password_generator(257))
257
>>> len(password_generator(length=0))
0
>>> len(password_generator(-1))
0
"""
chars = ascii_letters + digits + punctuation
return "".join(choice(chars) for x in range(length))
# ALTERNATIVE METHODS
# ctbi= characters that must be in password
# i= how many letters or characters the password length will be
def alternative_password_generator(ctbi, i):
# Password generator = full boot with random_number, random_letters, and
# random_character FUNCTIONS
# Put your code here...
i = i - len(ctbi)
quotient = int(i / 3)
remainder = i % 3
# chars = ctbi + random_letters(ascii_letters, i / 3 + remainder) +
# random_number(digits, i / 3) + random_characters(punctuation, i / 3)
chars = (
ctbi
+ random(ascii_letters, quotient + remainder)
+ random(digits, quotient)
+ random(punctuation, quotient)
)
chars = list(chars)
shuffle(chars)
return "".join(chars)
# random is a generalised function for letters, characters and numbers
def random(ctbi, i):
return "".join(choice(ctbi) for x in range(i))
def random_number(ctbi, i):
pass # Put your code here...
def random_letters(ctbi, i):
pass # Put your code here...
def random_characters(ctbi, i):
pass # Put your code here...
def main():
length = int(input("Please indicate the max length of your password: ").strip())
ctbi = input(
"Please indicate the characters that must be in your password: "
).strip()
print("Password generated:", password_generator(length))
print(
"Alternative Password generated:", alternative_password_generator(ctbi, length)
)
print("[If you are thinking of using this passsword, You better save it.]")
if __name__ == "__main__":
main()
| """Password generator allows you to generate a random password of length N."""
from random import choice, shuffle
from string import ascii_letters, digits, punctuation
def password_generator(length=8):
"""
>>> len(password_generator())
8
>>> len(password_generator(length=16))
16
>>> len(password_generator(257))
257
>>> len(password_generator(length=0))
0
>>> len(password_generator(-1))
0
"""
chars = ascii_letters + digits + punctuation
return "".join(choice(chars) for x in range(length))
# ALTERNATIVE METHODS
# ctbi= characters that must be in password
# i= how many letters or characters the password length will be
def alternative_password_generator(ctbi, i):
# Password generator = full boot with random_number, random_letters, and
# random_character FUNCTIONS
# Put your code here...
i = i - len(ctbi)
quotient = int(i / 3)
remainder = i % 3
# chars = ctbi + random_letters(ascii_letters, i / 3 + remainder) +
# random_number(digits, i / 3) + random_characters(punctuation, i / 3)
chars = (
ctbi
+ random(ascii_letters, quotient + remainder)
+ random(digits, quotient)
+ random(punctuation, quotient)
)
chars = list(chars)
shuffle(chars)
return "".join(chars)
# random is a generalised function for letters, characters and numbers
def random(ctbi, i):
return "".join(choice(ctbi) for x in range(i))
def random_number(ctbi, i):
pass # Put your code here...
def random_letters(ctbi, i):
pass # Put your code here...
def random_characters(ctbi, i):
pass # Put your code here...
def main():
length = int(input("Please indicate the max length of your password: ").strip())
ctbi = input(
"Please indicate the characters that must be in your password: "
).strip()
print("Password generated:", password_generator(length))
print(
"Alternative Password generated:", alternative_password_generator(ctbi, length)
)
print("[If you are thinking of using this passsword, You better save it.]")
if __name__ == "__main__":
main()
| -1 |
TheAlgorithms/Python | 4,763 | from __future__ import annotations | ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2021-09-14T22:54:26Z" | "2021-09-22T21:11:51Z" | dc07a850763d8154e012c9d1be7f8fe78326e8fb | 15d1cfabb15903c5e9d4d103e2390876efb3f85f | from __future__ import annotations. ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
This is pure Python implementation of sentinel linear search algorithm
For doctests run following command:
python -m doctest -v sentinel_linear_search.py
or
python3 -m doctest -v sentinel_linear_search.py
For manual testing run:
python sentinel_linear_search.py
"""
def sentinel_linear_search(sequence, target):
"""Pure implementation of sentinel linear search algorithm in Python
:param sequence: some sequence with comparable items
:param target: item value to search
:return: index of found item or None if item is not found
Examples:
>>> sentinel_linear_search([0, 5, 7, 10, 15], 0)
0
>>> sentinel_linear_search([0, 5, 7, 10, 15], 15)
4
>>> sentinel_linear_search([0, 5, 7, 10, 15], 5)
1
>>> sentinel_linear_search([0, 5, 7, 10, 15], 6)
"""
sequence.append(target)
index = 0
while sequence[index] != target:
index += 1
sequence.pop()
if index == len(sequence):
return None
return index
if __name__ == "__main__":
user_input = input("Enter numbers separated by comma:\n").strip()
sequence = [int(item) for item in user_input.split(",")]
target_input = input("Enter a single number to be found in the list:\n")
target = int(target_input)
result = sentinel_linear_search(sequence, target)
if result is not None:
print(f"{target} found at positions: {result}")
else:
print("Not found")
| """
This is pure Python implementation of sentinel linear search algorithm
For doctests run following command:
python -m doctest -v sentinel_linear_search.py
or
python3 -m doctest -v sentinel_linear_search.py
For manual testing run:
python sentinel_linear_search.py
"""
def sentinel_linear_search(sequence, target):
"""Pure implementation of sentinel linear search algorithm in Python
:param sequence: some sequence with comparable items
:param target: item value to search
:return: index of found item or None if item is not found
Examples:
>>> sentinel_linear_search([0, 5, 7, 10, 15], 0)
0
>>> sentinel_linear_search([0, 5, 7, 10, 15], 15)
4
>>> sentinel_linear_search([0, 5, 7, 10, 15], 5)
1
>>> sentinel_linear_search([0, 5, 7, 10, 15], 6)
"""
sequence.append(target)
index = 0
while sequence[index] != target:
index += 1
sequence.pop()
if index == len(sequence):
return None
return index
if __name__ == "__main__":
user_input = input("Enter numbers separated by comma:\n").strip()
sequence = [int(item) for item in user_input.split(",")]
target_input = input("Enter a single number to be found in the list:\n")
target = int(target_input)
result = sentinel_linear_search(sequence, target)
if result is not None:
print(f"{target} found at positions: {result}")
else:
print("Not found")
| -1 |
TheAlgorithms/Python | 4,763 | from __future__ import annotations | ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2021-09-14T22:54:26Z" | "2021-09-22T21:11:51Z" | dc07a850763d8154e012c9d1be7f8fe78326e8fb | 15d1cfabb15903c5e9d4d103e2390876efb3f85f | from __future__ import annotations. ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| import math
"""
In cryptography, the TRANSPOSITION cipher is a method of encryption where the
positions of plaintext are shifted a certain number(determined by the key) that
follows a regular system that results in the permuted text, known as the encrypted
text. The type of transposition cipher demonstrated under is the ROUTE cipher.
"""
def main() -> None:
message = input("Enter message: ")
key = int(input("Enter key [2-%s]: " % (len(message) - 1)))
mode = input("Encryption/Decryption [e/d]: ")
if mode.lower().startswith("e"):
text = encryptMessage(key, message)
elif mode.lower().startswith("d"):
text = decryptMessage(key, message)
# Append pipe symbol (vertical bar) to identify spaces at the end.
print("Output:\n%s" % (text + "|"))
def encryptMessage(key: int, message: str) -> str:
"""
>>> encryptMessage(6, 'Harshil Darji')
'Hlia rDsahrij'
"""
cipherText = [""] * key
for col in range(key):
pointer = col
while pointer < len(message):
cipherText[col] += message[pointer]
pointer += key
return "".join(cipherText)
def decryptMessage(key: int, message: str) -> str:
"""
>>> decryptMessage(6, 'Hlia rDsahrij')
'Harshil Darji'
"""
numCols = math.ceil(len(message) / key)
numRows = key
numShadedBoxes = (numCols * numRows) - len(message)
plainText = [""] * numCols
col = 0
row = 0
for symbol in message:
plainText[col] += symbol
col += 1
if (
(col == numCols)
or (col == numCols - 1)
and (row >= numRows - numShadedBoxes)
):
col = 0
row += 1
return "".join(plainText)
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| import math
"""
In cryptography, the TRANSPOSITION cipher is a method of encryption where the
positions of plaintext are shifted a certain number(determined by the key) that
follows a regular system that results in the permuted text, known as the encrypted
text. The type of transposition cipher demonstrated under is the ROUTE cipher.
"""
def main() -> None:
message = input("Enter message: ")
key = int(input("Enter key [2-%s]: " % (len(message) - 1)))
mode = input("Encryption/Decryption [e/d]: ")
if mode.lower().startswith("e"):
text = encryptMessage(key, message)
elif mode.lower().startswith("d"):
text = decryptMessage(key, message)
# Append pipe symbol (vertical bar) to identify spaces at the end.
print("Output:\n%s" % (text + "|"))
def encryptMessage(key: int, message: str) -> str:
"""
>>> encryptMessage(6, 'Harshil Darji')
'Hlia rDsahrij'
"""
cipherText = [""] * key
for col in range(key):
pointer = col
while pointer < len(message):
cipherText[col] += message[pointer]
pointer += key
return "".join(cipherText)
def decryptMessage(key: int, message: str) -> str:
"""
>>> decryptMessage(6, 'Hlia rDsahrij')
'Harshil Darji'
"""
numCols = math.ceil(len(message) / key)
numRows = key
numShadedBoxes = (numCols * numRows) - len(message)
plainText = [""] * numCols
col = 0
row = 0
for symbol in message:
plainText[col] += symbol
col += 1
if (
(col == numCols)
or (col == numCols - 1)
and (row >= numRows - numShadedBoxes)
):
col = 0
row += 1
return "".join(plainText)
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| -1 |
TheAlgorithms/Python | 4,763 | from __future__ import annotations | ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2021-09-14T22:54:26Z" | "2021-09-22T21:11:51Z" | dc07a850763d8154e012c9d1be7f8fe78326e8fb | 15d1cfabb15903c5e9d4d103e2390876efb3f85f | from __future__ import annotations. ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| def exchange_sort(numbers: list[int]) -> list[int]:
"""
Uses exchange sort to sort a list of numbers.
Source: https://en.wikipedia.org/wiki/Sorting_algorithm#Exchange_sort
>>> exchange_sort([5, 4, 3, 2, 1])
[1, 2, 3, 4, 5]
>>> exchange_sort([-1, -2, -3])
[-3, -2, -1]
>>> exchange_sort([1, 2, 3, 4, 5])
[1, 2, 3, 4, 5]
>>> exchange_sort([0, 10, -2, 5, 3])
[-2, 0, 3, 5, 10]
>>> exchange_sort([])
[]
"""
numbers_length = len(numbers)
for i in range(numbers_length):
for j in range(i + 1, numbers_length):
if numbers[j] < numbers[i]:
numbers[i], numbers[j] = numbers[j], numbers[i]
return numbers
if __name__ == "__main__":
user_input = input("Enter numbers separated by a comma:\n").strip()
unsorted = [int(item) for item in user_input.split(",")]
print(exchange_sort(unsorted))
| def exchange_sort(numbers: list[int]) -> list[int]:
"""
Uses exchange sort to sort a list of numbers.
Source: https://en.wikipedia.org/wiki/Sorting_algorithm#Exchange_sort
>>> exchange_sort([5, 4, 3, 2, 1])
[1, 2, 3, 4, 5]
>>> exchange_sort([-1, -2, -3])
[-3, -2, -1]
>>> exchange_sort([1, 2, 3, 4, 5])
[1, 2, 3, 4, 5]
>>> exchange_sort([0, 10, -2, 5, 3])
[-2, 0, 3, 5, 10]
>>> exchange_sort([])
[]
"""
numbers_length = len(numbers)
for i in range(numbers_length):
for j in range(i + 1, numbers_length):
if numbers[j] < numbers[i]:
numbers[i], numbers[j] = numbers[j], numbers[i]
return numbers
if __name__ == "__main__":
user_input = input("Enter numbers separated by a comma:\n").strip()
unsorted = [int(item) for item in user_input.split(",")]
print(exchange_sort(unsorted))
| -1 |
TheAlgorithms/Python | 4,763 | from __future__ import annotations | ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2021-09-14T22:54:26Z" | "2021-09-22T21:11:51Z" | dc07a850763d8154e012c9d1be7f8fe78326e8fb | 15d1cfabb15903c5e9d4d103e2390876efb3f85f | from __future__ import annotations. ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
The A* algorithm combines features of uniform-cost search and pure
heuristic search to efficiently compute optimal solutions.
A* algorithm is a best-first search algorithm in which the cost
associated with a node is f(n) = g(n) + h(n),
where g(n) is the cost of the path from the initial state to node n and
h(n) is the heuristic estimate or the cost or a path
from node n to a goal.A* algorithm introduces a heuristic into a
regular graph-searching algorithm,
essentially planning ahead at each step so a more optimal decision
is made.A* also known as the algorithm with brains
"""
import numpy as np
class Cell:
"""
Class cell represents a cell in the world which have the property
position : The position of the represented by tupleof x and y
coordinates initially set to (0,0)
parent : This contains the parent cell object which we visited
before arrinving this cell
g,h,f : The parameters for constructing the heuristic function
which can be any function. for simplicity used line
distance
"""
def __init__(self):
self.position = (0, 0)
self.parent = None
self.g = 0
self.h = 0
self.f = 0
"""
overrides equals method because otherwise cell assign will give
wrong results
"""
def __eq__(self, cell):
return self.position == cell.position
def showcell(self):
print(self.position)
class Gridworld:
"""
Gridworld class represents the external world here a grid M*M
matrix
world_size: create a numpy array with the given world_size default is 5
"""
def __init__(self, world_size=(5, 5)):
self.w = np.zeros(world_size)
self.world_x_limit = world_size[0]
self.world_y_limit = world_size[1]
def show(self):
print(self.w)
def get_neigbours(self, cell):
"""
Return the neighbours of cell
"""
neughbour_cord = [
(-1, -1),
(-1, 0),
(-1, 1),
(0, -1),
(0, 1),
(1, -1),
(1, 0),
(1, 1),
]
current_x = cell.position[0]
current_y = cell.position[1]
neighbours = []
for n in neughbour_cord:
x = current_x + n[0]
y = current_y + n[1]
if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
c = Cell()
c.position = (x, y)
c.parent = cell
neighbours.append(c)
return neighbours
def astar(world, start, goal):
"""
Implementation of a start algorithm
world : Object of the world object
start : Object of the cell as start position
stop : Object of the cell as goal position
>>> p = Gridworld()
>>> start = Cell()
>>> start.position = (0,0)
>>> goal = Cell()
>>> goal.position = (4,4)
>>> astar(p, start, goal)
[(0, 0), (1, 1), (2, 2), (3, 3), (4, 4)]
"""
_open = []
_closed = []
_open.append(start)
while _open:
min_f = np.argmin([n.f for n in _open])
current = _open[min_f]
_closed.append(_open.pop(min_f))
if current == goal:
break
for n in world.get_neigbours(current):
for c in _closed:
if c == n:
continue
n.g = current.g + 1
x1, y1 = n.position
x2, y2 = goal.position
n.h = (y2 - y1) ** 2 + (x2 - x1) ** 2
n.f = n.h + n.g
for c in _open:
if c == n and c.f < n.f:
continue
_open.append(n)
path = []
while current.parent is not None:
path.append(current.position)
current = current.parent
path.append(current.position)
return path[::-1]
if __name__ == "__main__":
world = Gridworld()
# stat position and Goal
start = Cell()
start.position = (0, 0)
goal = Cell()
goal.position = (4, 4)
print(f"path from {start.position} to {goal.position}")
s = astar(world, start, goal)
# Just for visual reasons
for i in s:
world.w[i] = 1
print(world.w)
| """
The A* algorithm combines features of uniform-cost search and pure
heuristic search to efficiently compute optimal solutions.
A* algorithm is a best-first search algorithm in which the cost
associated with a node is f(n) = g(n) + h(n),
where g(n) is the cost of the path from the initial state to node n and
h(n) is the heuristic estimate or the cost or a path
from node n to a goal.A* algorithm introduces a heuristic into a
regular graph-searching algorithm,
essentially planning ahead at each step so a more optimal decision
is made.A* also known as the algorithm with brains
"""
import numpy as np
class Cell:
"""
Class cell represents a cell in the world which have the property
position : The position of the represented by tupleof x and y
coordinates initially set to (0,0)
parent : This contains the parent cell object which we visited
before arrinving this cell
g,h,f : The parameters for constructing the heuristic function
which can be any function. for simplicity used line
distance
"""
def __init__(self):
self.position = (0, 0)
self.parent = None
self.g = 0
self.h = 0
self.f = 0
"""
overrides equals method because otherwise cell assign will give
wrong results
"""
def __eq__(self, cell):
return self.position == cell.position
def showcell(self):
print(self.position)
class Gridworld:
"""
Gridworld class represents the external world here a grid M*M
matrix
world_size: create a numpy array with the given world_size default is 5
"""
def __init__(self, world_size=(5, 5)):
self.w = np.zeros(world_size)
self.world_x_limit = world_size[0]
self.world_y_limit = world_size[1]
def show(self):
print(self.w)
def get_neigbours(self, cell):
"""
Return the neighbours of cell
"""
neughbour_cord = [
(-1, -1),
(-1, 0),
(-1, 1),
(0, -1),
(0, 1),
(1, -1),
(1, 0),
(1, 1),
]
current_x = cell.position[0]
current_y = cell.position[1]
neighbours = []
for n in neughbour_cord:
x = current_x + n[0]
y = current_y + n[1]
if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
c = Cell()
c.position = (x, y)
c.parent = cell
neighbours.append(c)
return neighbours
def astar(world, start, goal):
"""
Implementation of a start algorithm
world : Object of the world object
start : Object of the cell as start position
stop : Object of the cell as goal position
>>> p = Gridworld()
>>> start = Cell()
>>> start.position = (0,0)
>>> goal = Cell()
>>> goal.position = (4,4)
>>> astar(p, start, goal)
[(0, 0), (1, 1), (2, 2), (3, 3), (4, 4)]
"""
_open = []
_closed = []
_open.append(start)
while _open:
min_f = np.argmin([n.f for n in _open])
current = _open[min_f]
_closed.append(_open.pop(min_f))
if current == goal:
break
for n in world.get_neigbours(current):
for c in _closed:
if c == n:
continue
n.g = current.g + 1
x1, y1 = n.position
x2, y2 = goal.position
n.h = (y2 - y1) ** 2 + (x2 - x1) ** 2
n.f = n.h + n.g
for c in _open:
if c == n and c.f < n.f:
continue
_open.append(n)
path = []
while current.parent is not None:
path.append(current.position)
current = current.parent
path.append(current.position)
return path[::-1]
if __name__ == "__main__":
world = Gridworld()
# stat position and Goal
start = Cell()
start.position = (0, 0)
goal = Cell()
goal.position = (4, 4)
print(f"path from {start.position} to {goal.position}")
s = astar(world, start, goal)
# Just for visual reasons
for i in s:
world.w[i] = 1
print(world.w)
| -1 |
TheAlgorithms/Python | 4,763 | from __future__ import annotations | ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2021-09-14T22:54:26Z" | "2021-09-22T21:11:51Z" | dc07a850763d8154e012c9d1be7f8fe78326e8fb | 15d1cfabb15903c5e9d4d103e2390876efb3f85f | from __future__ import annotations. ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$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 | 4,763 | from __future__ import annotations | ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2021-09-14T22:54:26Z" | "2021-09-22T21:11:51Z" | dc07a850763d8154e012c9d1be7f8fe78326e8fb | 15d1cfabb15903c5e9d4d103e2390876efb3f85f | from __future__ import annotations. ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from __future__ import annotations
from collections import deque
class Automaton:
def __init__(self, keywords: list[str]):
self.adlist: list[dict] = list()
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] = list()
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()
| -1 |
TheAlgorithms/Python | 4,763 | from __future__ import annotations | ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2021-09-14T22:54:26Z" | "2021-09-22T21:11:51Z" | dc07a850763d8154e012c9d1be7f8fe78326e8fb | 15d1cfabb15903c5e9d4d103e2390876efb3f85f | from __future__ import annotations. ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,763 | from __future__ import annotations | ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2021-09-14T22:54:26Z" | "2021-09-22T21:11:51Z" | dc07a850763d8154e012c9d1be7f8fe78326e8fb | 15d1cfabb15903c5e9d4d103e2390876efb3f85f | from __future__ import annotations. ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,763 | from __future__ import annotations | ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2021-09-14T22:54:26Z" | "2021-09-22T21:11:51Z" | dc07a850763d8154e012c9d1be7f8fe78326e8fb | 15d1cfabb15903c5e9d4d103e2390876efb3f85f | from __future__ import annotations. ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,763 | from __future__ import annotations | ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2021-09-14T22:54:26Z" | "2021-09-22T21:11:51Z" | dc07a850763d8154e012c9d1be7f8fe78326e8fb | 15d1cfabb15903c5e9d4d103e2390876efb3f85f | from __future__ import annotations. ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| import math
def rearrange(bitString32):
"""[summary]
Regroups the given binary string.
Arguments:
bitString32 {[string]} -- [32 bit binary]
Raises:
ValueError -- [if the given string not are 32 bit binary string]
Returns:
[string] -- [32 bit binary string]
>>> rearrange('1234567890abcdfghijklmnopqrstuvw')
'pqrstuvwhijklmno90abcdfg12345678'
"""
if len(bitString32) != 32:
raise ValueError("Need length 32")
newString = ""
for i in [3, 2, 1, 0]:
newString += bitString32[8 * i : 8 * i + 8]
return newString
def reformatHex(i):
"""[summary]
Converts the given integer into 8-digit hex number.
Arguments:
i {[int]} -- [integer]
>>> reformatHex(666)
'9a020000'
"""
hexrep = format(i, "08x")
thing = ""
for i in [3, 2, 1, 0]:
thing += hexrep[2 * i : 2 * i + 2]
return thing
def pad(bitString):
"""[summary]
Fills up the binary string to a 512 bit binary string
Arguments:
bitString {[string]} -- [binary string]
Returns:
[string] -- [binary string]
"""
startLength = len(bitString)
bitString += "1"
while len(bitString) % 512 != 448:
bitString += "0"
lastPart = format(startLength, "064b")
bitString += rearrange(lastPart[32:]) + rearrange(lastPart[:32])
return bitString
def getBlock(bitString):
"""[summary]
Iterator:
Returns by each call a list of length 16 with the 32 bit
integer blocks.
Arguments:
bitString {[string]} -- [binary string >= 512]
"""
currPos = 0
while currPos < len(bitString):
currPart = bitString[currPos : currPos + 512]
mySplits = []
for i in range(16):
mySplits.append(int(rearrange(currPart[32 * i : 32 * i + 32]), 2))
yield mySplits
currPos += 512
def not32(i):
"""
>>> not32(34)
4294967261
"""
i_str = format(i, "032b")
new_str = ""
for c in i_str:
new_str += "1" if c == "0" else "0"
return int(new_str, 2)
def sum32(a, b):
return (a + b) % 2 ** 32
def leftrot32(i, s):
return (i << s) ^ (i >> (32 - s))
def md5me(testString):
"""[summary]
Returns a 32-bit hash code of the string 'testString'
Arguments:
testString {[string]} -- [message]
"""
bs = ""
for i in testString:
bs += format(ord(i), "08b")
bs = pad(bs)
tvals = [int(2 ** 32 * abs(math.sin(i + 1))) for i in range(64)]
a0 = 0x67452301
b0 = 0xEFCDAB89
c0 = 0x98BADCFE
d0 = 0x10325476
s = [
7,
12,
17,
22,
7,
12,
17,
22,
7,
12,
17,
22,
7,
12,
17,
22,
5,
9,
14,
20,
5,
9,
14,
20,
5,
9,
14,
20,
5,
9,
14,
20,
4,
11,
16,
23,
4,
11,
16,
23,
4,
11,
16,
23,
4,
11,
16,
23,
6,
10,
15,
21,
6,
10,
15,
21,
6,
10,
15,
21,
6,
10,
15,
21,
]
for m in getBlock(bs):
A = a0
B = b0
C = c0
D = d0
for i in range(64):
if i <= 15:
# f = (B & C) | (not32(B) & D)
f = D ^ (B & (C ^ D))
g = i
elif i <= 31:
# f = (D & B) | (not32(D) & C)
f = C ^ (D & (B ^ C))
g = (5 * i + 1) % 16
elif i <= 47:
f = B ^ C ^ D
g = (3 * i + 5) % 16
else:
f = C ^ (B | not32(D))
g = (7 * i) % 16
dtemp = D
D = C
C = B
B = sum32(B, leftrot32((A + f + tvals[i] + m[g]) % 2 ** 32, s[i]))
A = dtemp
a0 = sum32(a0, A)
b0 = sum32(b0, B)
c0 = sum32(c0, C)
d0 = sum32(d0, D)
digest = reformatHex(a0) + reformatHex(b0) + reformatHex(c0) + reformatHex(d0)
return digest
def test():
assert md5me("") == "d41d8cd98f00b204e9800998ecf8427e"
assert (
md5me("The quick brown fox jumps over the lazy dog")
== "9e107d9d372bb6826bd81d3542a419d6"
)
print("Success.")
if __name__ == "__main__":
test()
import doctest
doctest.testmod()
| import math
def rearrange(bitString32):
"""[summary]
Regroups the given binary string.
Arguments:
bitString32 {[string]} -- [32 bit binary]
Raises:
ValueError -- [if the given string not are 32 bit binary string]
Returns:
[string] -- [32 bit binary string]
>>> rearrange('1234567890abcdfghijklmnopqrstuvw')
'pqrstuvwhijklmno90abcdfg12345678'
"""
if len(bitString32) != 32:
raise ValueError("Need length 32")
newString = ""
for i in [3, 2, 1, 0]:
newString += bitString32[8 * i : 8 * i + 8]
return newString
def reformatHex(i):
"""[summary]
Converts the given integer into 8-digit hex number.
Arguments:
i {[int]} -- [integer]
>>> reformatHex(666)
'9a020000'
"""
hexrep = format(i, "08x")
thing = ""
for i in [3, 2, 1, 0]:
thing += hexrep[2 * i : 2 * i + 2]
return thing
def pad(bitString):
"""[summary]
Fills up the binary string to a 512 bit binary string
Arguments:
bitString {[string]} -- [binary string]
Returns:
[string] -- [binary string]
"""
startLength = len(bitString)
bitString += "1"
while len(bitString) % 512 != 448:
bitString += "0"
lastPart = format(startLength, "064b")
bitString += rearrange(lastPart[32:]) + rearrange(lastPart[:32])
return bitString
def getBlock(bitString):
"""[summary]
Iterator:
Returns by each call a list of length 16 with the 32 bit
integer blocks.
Arguments:
bitString {[string]} -- [binary string >= 512]
"""
currPos = 0
while currPos < len(bitString):
currPart = bitString[currPos : currPos + 512]
mySplits = []
for i in range(16):
mySplits.append(int(rearrange(currPart[32 * i : 32 * i + 32]), 2))
yield mySplits
currPos += 512
def not32(i):
"""
>>> not32(34)
4294967261
"""
i_str = format(i, "032b")
new_str = ""
for c in i_str:
new_str += "1" if c == "0" else "0"
return int(new_str, 2)
def sum32(a, b):
return (a + b) % 2 ** 32
def leftrot32(i, s):
return (i << s) ^ (i >> (32 - s))
def md5me(testString):
"""[summary]
Returns a 32-bit hash code of the string 'testString'
Arguments:
testString {[string]} -- [message]
"""
bs = ""
for i in testString:
bs += format(ord(i), "08b")
bs = pad(bs)
tvals = [int(2 ** 32 * abs(math.sin(i + 1))) for i in range(64)]
a0 = 0x67452301
b0 = 0xEFCDAB89
c0 = 0x98BADCFE
d0 = 0x10325476
s = [
7,
12,
17,
22,
7,
12,
17,
22,
7,
12,
17,
22,
7,
12,
17,
22,
5,
9,
14,
20,
5,
9,
14,
20,
5,
9,
14,
20,
5,
9,
14,
20,
4,
11,
16,
23,
4,
11,
16,
23,
4,
11,
16,
23,
4,
11,
16,
23,
6,
10,
15,
21,
6,
10,
15,
21,
6,
10,
15,
21,
6,
10,
15,
21,
]
for m in getBlock(bs):
A = a0
B = b0
C = c0
D = d0
for i in range(64):
if i <= 15:
# f = (B & C) | (not32(B) & D)
f = D ^ (B & (C ^ D))
g = i
elif i <= 31:
# f = (D & B) | (not32(D) & C)
f = C ^ (D & (B ^ C))
g = (5 * i + 1) % 16
elif i <= 47:
f = B ^ C ^ D
g = (3 * i + 5) % 16
else:
f = C ^ (B | not32(D))
g = (7 * i) % 16
dtemp = D
D = C
C = B
B = sum32(B, leftrot32((A + f + tvals[i] + m[g]) % 2 ** 32, s[i]))
A = dtemp
a0 = sum32(a0, A)
b0 = sum32(b0, B)
c0 = sum32(c0, C)
d0 = sum32(d0, D)
digest = reformatHex(a0) + reformatHex(b0) + reformatHex(c0) + reformatHex(d0)
return digest
def test():
assert md5me("") == "d41d8cd98f00b204e9800998ecf8427e"
assert (
md5me("The quick brown fox jumps over the lazy dog")
== "9e107d9d372bb6826bd81d3542a419d6"
)
print("Success.")
if __name__ == "__main__":
test()
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 4,763 | from __future__ import annotations | ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2021-09-14T22:54:26Z" | "2021-09-22T21:11:51Z" | dc07a850763d8154e012c9d1be7f8fe78326e8fb | 15d1cfabb15903c5e9d4d103e2390876efb3f85f | from __future__ import annotations. ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,763 | from __future__ import annotations | ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2021-09-14T22:54:26Z" | "2021-09-22T21:11:51Z" | dc07a850763d8154e012c9d1be7f8fe78326e8fb | 15d1cfabb15903c5e9d4d103e2390876efb3f85f | from __future__ import annotations. ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Create a Long Short Term Memory (LSTM) network model
An LSTM is a type of Recurrent Neural Network (RNN) as discussed at:
* http://colah.github.io/posts/2015-08-Understanding-LSTMs
* https://en.wikipedia.org/wiki/Long_short-term_memory
"""
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
"""
First part of building a model is to get the data and prepare
it for our model. You can use any dataset for stock prediction
make sure you set the price column on line number 21. Here we
use a dataset which have the price on 3rd column.
"""
df = pd.read_csv("sample_data.csv", header=None)
len_data = df.shape[:1][0]
# If you're using some other dataset input the target column
actual_data = df.iloc[:, 1:2]
actual_data = actual_data.values.reshape(len_data, 1)
actual_data = MinMaxScaler().fit_transform(actual_data)
look_back = 10
forward_days = 5
periods = 20
division = len_data - periods * look_back
train_data = actual_data[:division]
test_data = actual_data[division - look_back :]
train_x, train_y = [], []
test_x, test_y = [], []
for i in range(0, len(train_data) - forward_days - look_back + 1):
train_x.append(train_data[i : i + look_back])
train_y.append(train_data[i + look_back : i + look_back + forward_days])
for i in range(0, len(test_data) - forward_days - look_back + 1):
test_x.append(test_data[i : i + look_back])
test_y.append(test_data[i + look_back : i + look_back + forward_days])
x_train = np.array(train_x)
x_test = np.array(test_x)
y_train = np.array([list(i.ravel()) for i in train_y])
y_test = np.array([list(i.ravel()) for i in test_y])
model = Sequential()
model.add(LSTM(128, input_shape=(look_back, 1), return_sequences=True))
model.add(LSTM(64, input_shape=(128, 1)))
model.add(Dense(forward_days))
model.compile(loss="mean_squared_error", optimizer="adam")
history = model.fit(
x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
)
pred = model.predict(x_test)
| """
Create a Long Short Term Memory (LSTM) network model
An LSTM is a type of Recurrent Neural Network (RNN) as discussed at:
* http://colah.github.io/posts/2015-08-Understanding-LSTMs
* https://en.wikipedia.org/wiki/Long_short-term_memory
"""
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
"""
First part of building a model is to get the data and prepare
it for our model. You can use any dataset for stock prediction
make sure you set the price column on line number 21. Here we
use a dataset which have the price on 3rd column.
"""
df = pd.read_csv("sample_data.csv", header=None)
len_data = df.shape[:1][0]
# If you're using some other dataset input the target column
actual_data = df.iloc[:, 1:2]
actual_data = actual_data.values.reshape(len_data, 1)
actual_data = MinMaxScaler().fit_transform(actual_data)
look_back = 10
forward_days = 5
periods = 20
division = len_data - periods * look_back
train_data = actual_data[:division]
test_data = actual_data[division - look_back :]
train_x, train_y = [], []
test_x, test_y = [], []
for i in range(0, len(train_data) - forward_days - look_back + 1):
train_x.append(train_data[i : i + look_back])
train_y.append(train_data[i + look_back : i + look_back + forward_days])
for i in range(0, len(test_data) - forward_days - look_back + 1):
test_x.append(test_data[i : i + look_back])
test_y.append(test_data[i + look_back : i + look_back + forward_days])
x_train = np.array(train_x)
x_test = np.array(test_x)
y_train = np.array([list(i.ravel()) for i in train_y])
y_test = np.array([list(i.ravel()) for i in test_y])
model = Sequential()
model.add(LSTM(128, input_shape=(look_back, 1), return_sequences=True))
model.add(LSTM(64, input_shape=(128, 1)))
model.add(Dense(forward_days))
model.compile(loss="mean_squared_error", optimizer="adam")
history = model.fit(
x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
)
pred = model.predict(x_test)
| -1 |
TheAlgorithms/Python | 4,763 | from __future__ import annotations | ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2021-09-14T22:54:26Z" | "2021-09-22T21:11:51Z" | dc07a850763d8154e012c9d1be7f8fe78326e8fb | 15d1cfabb15903c5e9d4d103e2390876efb3f85f | from __future__ import annotations. ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| -1 |
||
TheAlgorithms/Python | 4,763 | from __future__ import annotations | ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2021-09-14T22:54:26Z" | "2021-09-22T21:11:51Z" | dc07a850763d8154e012c9d1be7f8fe78326e8fb | 15d1cfabb15903c5e9d4d103e2390876efb3f85f | from __future__ import annotations. ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| #
| #
| -1 |
TheAlgorithms/Python | 4,763 | from __future__ import annotations | ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2021-09-14T22:54:26Z" | "2021-09-22T21:11:51Z" | dc07a850763d8154e012c9d1be7f8fe78326e8fb | 15d1cfabb15903c5e9d4d103e2390876efb3f85f | from __future__ import annotations. ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| # https://en.wikipedia.org/wiki/Hill_climbing
import math
class SearchProblem:
"""
An interface to define search problems.
The interface will be illustrated using the example of mathematical function.
"""
def __init__(self, x: int, y: int, step_size: int, function_to_optimize):
"""
The constructor of the search problem.
x: the x coordinate of the current search state.
y: the y coordinate of the current search state.
step_size: size of the step to take when looking for neighbors.
function_to_optimize: a function to optimize having the signature f(x, y).
"""
self.x = x
self.y = y
self.step_size = step_size
self.function = function_to_optimize
def score(self) -> int:
"""
Returns the output of the function called with current x and y coordinates.
>>> def test_function(x, y):
... return x + y
>>> SearchProblem(0, 0, 1, test_function).score() # 0 + 0 = 0
0
>>> SearchProblem(5, 7, 1, test_function).score() # 5 + 7 = 12
12
"""
return self.function(self.x, self.y)
def get_neighbors(self):
"""
Returns a list of coordinates of neighbors adjacent to the current coordinates.
Neighbors:
| 0 | 1 | 2 |
| 3 | _ | 4 |
| 5 | 6 | 7 |
"""
step_size = self.step_size
return [
SearchProblem(x, y, step_size, self.function)
for x, y in (
(self.x - step_size, self.y - step_size),
(self.x - step_size, self.y),
(self.x - step_size, self.y + step_size),
(self.x, self.y - step_size),
(self.x, self.y + step_size),
(self.x + step_size, self.y - step_size),
(self.x + step_size, self.y),
(self.x + step_size, self.y + step_size),
)
]
def __hash__(self):
"""
hash the string representation of the current search state.
"""
return hash(str(self))
def __eq__(self, obj):
"""
Check if the 2 objects are equal.
"""
if isinstance(obj, SearchProblem):
return hash(str(self)) == hash(str(obj))
return False
def __str__(self):
"""
string representation of the current search state.
>>> str(SearchProblem(0, 0, 1, None))
'x: 0 y: 0'
>>> str(SearchProblem(2, 5, 1, None))
'x: 2 y: 5'
"""
return f"x: {self.x} y: {self.y}"
def hill_climbing(
search_prob,
find_max: bool = True,
max_x: float = math.inf,
min_x: float = -math.inf,
max_y: float = math.inf,
min_y: float = -math.inf,
visualization: bool = False,
max_iter: int = 10000,
) -> SearchProblem:
"""
Implementation of the hill climbling algorithm.
We start with a given state, find all its neighbors,
move towards the neighbor which provides the maximum (or minimum) change.
We keep doing this until we are at a state where we do not have any
neighbors which can improve the solution.
Args:
search_prob: The search state at the start.
find_max: If True, the algorithm should find the maximum else the minimum.
max_x, min_x, max_y, min_y: the maximum and minimum bounds of x and y.
visualization: If True, a matplotlib graph is displayed.
max_iter: number of times to run the iteration.
Returns a search state having the maximum (or minimum) score.
"""
current_state = search_prob
scores = [] # list to store the current score at each iteration
iterations = 0
solution_found = False
visited = set()
while not solution_found and iterations < max_iter:
visited.add(current_state)
iterations += 1
current_score = current_state.score()
scores.append(current_score)
neighbors = current_state.get_neighbors()
max_change = -math.inf
min_change = math.inf
next_state = None # to hold the next best neighbor
for neighbor in neighbors:
if neighbor in visited:
continue # do not want to visit the same state again
if (
neighbor.x > max_x
or neighbor.x < min_x
or neighbor.y > max_y
or neighbor.y < min_y
):
continue # neighbor outside our bounds
change = neighbor.score() - current_score
if find_max: # finding max
# going to direction with greatest ascent
if change > max_change and change > 0:
max_change = change
next_state = neighbor
else: # finding min
# to direction with greatest descent
if change < min_change and change < 0:
min_change = change
next_state = neighbor
if next_state is not None:
# we found at least one neighbor which improved the current state
current_state = next_state
else:
# since we have no neighbor that improves the solution we stop the search
solution_found = True
if visualization:
from matplotlib import pyplot as plt
plt.plot(range(iterations), scores)
plt.xlabel("Iterations")
plt.ylabel("Function values")
plt.show()
return current_state
if __name__ == "__main__":
import doctest
doctest.testmod()
def test_f1(x, y):
return (x ** 2) + (y ** 2)
# starting the problem with initial coordinates (3, 4)
prob = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_f1)
local_min = hill_climbing(prob, find_max=False)
print(
"The minimum score for f(x, y) = x^2 + y^2 found via hill climbing: "
f"{local_min.score()}"
)
# starting the problem with initial coordinates (12, 47)
prob = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_f1)
local_min = hill_climbing(
prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
)
print(
"The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 "
f"and 50 > y > - 5 found via hill climbing: {local_min.score()}"
)
def test_f2(x, y):
return (3 * x ** 2) - (6 * y)
prob = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_f1)
local_min = hill_climbing(prob, find_max=True)
print(
"The maximum score for f(x, y) = x^2 + y^2 found via hill climbing: "
f"{local_min.score()}"
)
| # https://en.wikipedia.org/wiki/Hill_climbing
import math
class SearchProblem:
"""
An interface to define search problems.
The interface will be illustrated using the example of mathematical function.
"""
def __init__(self, x: int, y: int, step_size: int, function_to_optimize):
"""
The constructor of the search problem.
x: the x coordinate of the current search state.
y: the y coordinate of the current search state.
step_size: size of the step to take when looking for neighbors.
function_to_optimize: a function to optimize having the signature f(x, y).
"""
self.x = x
self.y = y
self.step_size = step_size
self.function = function_to_optimize
def score(self) -> int:
"""
Returns the output of the function called with current x and y coordinates.
>>> def test_function(x, y):
... return x + y
>>> SearchProblem(0, 0, 1, test_function).score() # 0 + 0 = 0
0
>>> SearchProblem(5, 7, 1, test_function).score() # 5 + 7 = 12
12
"""
return self.function(self.x, self.y)
def get_neighbors(self):
"""
Returns a list of coordinates of neighbors adjacent to the current coordinates.
Neighbors:
| 0 | 1 | 2 |
| 3 | _ | 4 |
| 5 | 6 | 7 |
"""
step_size = self.step_size
return [
SearchProblem(x, y, step_size, self.function)
for x, y in (
(self.x - step_size, self.y - step_size),
(self.x - step_size, self.y),
(self.x - step_size, self.y + step_size),
(self.x, self.y - step_size),
(self.x, self.y + step_size),
(self.x + step_size, self.y - step_size),
(self.x + step_size, self.y),
(self.x + step_size, self.y + step_size),
)
]
def __hash__(self):
"""
hash the string representation of the current search state.
"""
return hash(str(self))
def __eq__(self, obj):
"""
Check if the 2 objects are equal.
"""
if isinstance(obj, SearchProblem):
return hash(str(self)) == hash(str(obj))
return False
def __str__(self):
"""
string representation of the current search state.
>>> str(SearchProblem(0, 0, 1, None))
'x: 0 y: 0'
>>> str(SearchProblem(2, 5, 1, None))
'x: 2 y: 5'
"""
return f"x: {self.x} y: {self.y}"
def hill_climbing(
search_prob,
find_max: bool = True,
max_x: float = math.inf,
min_x: float = -math.inf,
max_y: float = math.inf,
min_y: float = -math.inf,
visualization: bool = False,
max_iter: int = 10000,
) -> SearchProblem:
"""
Implementation of the hill climbling algorithm.
We start with a given state, find all its neighbors,
move towards the neighbor which provides the maximum (or minimum) change.
We keep doing this until we are at a state where we do not have any
neighbors which can improve the solution.
Args:
search_prob: The search state at the start.
find_max: If True, the algorithm should find the maximum else the minimum.
max_x, min_x, max_y, min_y: the maximum and minimum bounds of x and y.
visualization: If True, a matplotlib graph is displayed.
max_iter: number of times to run the iteration.
Returns a search state having the maximum (or minimum) score.
"""
current_state = search_prob
scores = [] # list to store the current score at each iteration
iterations = 0
solution_found = False
visited = set()
while not solution_found and iterations < max_iter:
visited.add(current_state)
iterations += 1
current_score = current_state.score()
scores.append(current_score)
neighbors = current_state.get_neighbors()
max_change = -math.inf
min_change = math.inf
next_state = None # to hold the next best neighbor
for neighbor in neighbors:
if neighbor in visited:
continue # do not want to visit the same state again
if (
neighbor.x > max_x
or neighbor.x < min_x
or neighbor.y > max_y
or neighbor.y < min_y
):
continue # neighbor outside our bounds
change = neighbor.score() - current_score
if find_max: # finding max
# going to direction with greatest ascent
if change > max_change and change > 0:
max_change = change
next_state = neighbor
else: # finding min
# to direction with greatest descent
if change < min_change and change < 0:
min_change = change
next_state = neighbor
if next_state is not None:
# we found at least one neighbor which improved the current state
current_state = next_state
else:
# since we have no neighbor that improves the solution we stop the search
solution_found = True
if visualization:
from matplotlib import pyplot as plt
plt.plot(range(iterations), scores)
plt.xlabel("Iterations")
plt.ylabel("Function values")
plt.show()
return current_state
if __name__ == "__main__":
import doctest
doctest.testmod()
def test_f1(x, y):
return (x ** 2) + (y ** 2)
# starting the problem with initial coordinates (3, 4)
prob = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_f1)
local_min = hill_climbing(prob, find_max=False)
print(
"The minimum score for f(x, y) = x^2 + y^2 found via hill climbing: "
f"{local_min.score()}"
)
# starting the problem with initial coordinates (12, 47)
prob = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_f1)
local_min = hill_climbing(
prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
)
print(
"The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 "
f"and 50 > y > - 5 found via hill climbing: {local_min.score()}"
)
def test_f2(x, y):
return (3 * x ** 2) - (6 * y)
prob = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_f1)
local_min = hill_climbing(prob, find_max=True)
print(
"The maximum score for f(x, y) = x^2 + y^2 found via hill climbing: "
f"{local_min.score()}"
)
| -1 |
TheAlgorithms/Python | 4,763 | from __future__ import annotations | ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2021-09-14T22:54:26Z" | "2021-09-22T21:11:51Z" | dc07a850763d8154e012c9d1be7f8fe78326e8fb | 15d1cfabb15903c5e9d4d103e2390876efb3f85f | from __future__ import annotations. ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Problem 16: https://projecteuler.net/problem=16
2^15 = 32768 and the sum of its digits is 3 + 2 + 7 + 6 + 8 = 26.
What is the sum of the digits of the number 2^1000?
"""
def solution(power: int = 1000) -> int:
"""Returns the sum of the digits of the number 2^power.
>>> solution(1000)
1366
>>> solution(50)
76
>>> solution(20)
31
>>> solution(15)
26
"""
num = 2 ** power
string_num = str(num)
list_num = list(string_num)
sum_of_num = 0
for i in list_num:
sum_of_num += int(i)
return sum_of_num
if __name__ == "__main__":
power = int(input("Enter the power of 2: ").strip())
print("2 ^ ", power, " = ", 2 ** power)
result = solution(power)
print("Sum of the digits is: ", result)
| """
Problem 16: https://projecteuler.net/problem=16
2^15 = 32768 and the sum of its digits is 3 + 2 + 7 + 6 + 8 = 26.
What is the sum of the digits of the number 2^1000?
"""
def solution(power: int = 1000) -> int:
"""Returns the sum of the digits of the number 2^power.
>>> solution(1000)
1366
>>> solution(50)
76
>>> solution(20)
31
>>> solution(15)
26
"""
num = 2 ** power
string_num = str(num)
list_num = list(string_num)
sum_of_num = 0
for i in list_num:
sum_of_num += int(i)
return sum_of_num
if __name__ == "__main__":
power = int(input("Enter the power of 2: ").strip())
print("2 ^ ", power, " = ", 2 ** power)
result = solution(power)
print("Sum of the digits is: ", result)
| -1 |
TheAlgorithms/Python | 4,763 | from __future__ import annotations | ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2021-09-14T22:54:26Z" | "2021-09-22T21:11:51Z" | dc07a850763d8154e012c9d1be7f8fe78326e8fb | 15d1cfabb15903c5e9d4d103e2390876efb3f85f | from __future__ import annotations. ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """Borůvka's algorithm.
Determines the minimum spanning tree (MST) of a graph using the Borůvka's algorithm.
Borůvka's algorithm is a greedy algorithm for finding a minimum spanning tree in a
connected graph, or a minimum spanning forest if a graph that is not connected.
The time complexity of this algorithm is O(ELogV), where E represents the number
of edges, while V represents the number of nodes.
O(number_of_edges Log number_of_nodes)
The space complexity of this algorithm is O(V + E), since we have to keep a couple
of lists whose sizes are equal to the number of nodes, as well as keep all the
edges of a graph inside of the data structure itself.
Borůvka's algorithm gives us pretty much the same result as other MST Algorithms -
they all find the minimum spanning tree, and the time complexity is approximately
the same.
One advantage that Borůvka's algorithm has compared to the alternatives is that it
doesn't need to presort the edges or maintain a priority queue in order to find the
minimum spanning tree.
Even though that doesn't help its complexity, since it still passes the edges logE
times, it is a bit simpler to code.
Details: https://en.wikipedia.org/wiki/Bor%C5%AFvka%27s_algorithm
"""
class Graph:
def __init__(self, num_of_nodes: int) -> None:
"""
Arguments:
num_of_nodes - the number of nodes in the graph
Attributes:
m_num_of_nodes - the number of nodes in the graph.
m_edges - the list of edges.
m_component - the dictionary which stores the index of the component which
a node belongs to.
"""
self.m_num_of_nodes = num_of_nodes
self.m_edges = []
self.m_component = {}
def add_edge(self, u_node: int, v_node: int, weight: int) -> None:
"""Adds an edge in the format [first, second, edge weight] to graph."""
self.m_edges.append([u_node, v_node, weight])
def find_component(self, u_node: int) -> int:
"""Propagates a new component throughout a given component."""
if self.m_component[u_node] == u_node:
return u_node
return self.find_component(self.m_component[u_node])
def set_component(self, u_node: int) -> None:
"""Finds the component index of a given node"""
if self.m_component[u_node] != u_node:
for k in self.m_component:
self.m_component[k] = self.find_component(k)
def union(self, component_size: list, u_node: int, v_node: int) -> None:
"""Union finds the roots of components for two nodes, compares the components
in terms of size, and attaches the smaller one to the larger one to form
single component"""
if component_size[u_node] <= component_size[v_node]:
self.m_component[u_node] = v_node
component_size[v_node] += component_size[u_node]
self.set_component(u_node)
elif component_size[u_node] >= component_size[v_node]:
self.m_component[v_node] = self.find_component(u_node)
component_size[u_node] += component_size[v_node]
self.set_component(v_node)
def boruvka(self) -> None:
"""Performs Borůvka's algorithm to find MST."""
# Initialize additional lists required to algorithm.
component_size = []
mst_weight = 0
minimum_weight_edge = [-1] * self.m_num_of_nodes
# A list of components (initialized to all of the nodes)
for node in range(self.m_num_of_nodes):
self.m_component.update({node: node})
component_size.append(1)
num_of_components = self.m_num_of_nodes
while num_of_components > 1:
for edge in self.m_edges:
u, v, w = edge
u_component = self.m_component[u]
v_component = self.m_component[v]
if u_component != v_component:
"""If the current minimum weight edge of component u doesn't
exist (is -1), or if it's greater than the edge we're
observing right now, we will assign the value of the edge
we're observing to it.
If the current minimum weight edge of component v doesn't
exist (is -1), or if it's greater than the edge we're
observing right now, we will assign the value of the edge
we're observing to it"""
for component in (u_component, v_component):
if (
minimum_weight_edge[component] == -1
or minimum_weight_edge[component][2] > w
):
minimum_weight_edge[component] = [u, v, w]
for edge in minimum_weight_edge:
if edge != -1:
u, v, w = edge
u_component = self.m_component[u]
v_component = self.m_component[v]
if u_component != v_component:
mst_weight += w
self.union(component_size, u_component, v_component)
print(f"Added edge [{u} - {v}]\nAdded weight: {w}\n")
num_of_components -= 1
minimum_weight_edge = [-1] * self.m_num_of_nodes
print(f"The total weight of the minimal spanning tree is: {mst_weight}")
def test_vector() -> None:
"""
>>> g = Graph(8)
>>> for u_v_w in ((0, 1, 10), (0, 2, 6), (0, 3, 5), (1, 3, 15), (2, 3, 4),
... (3, 4, 8), (4, 5, 10), (4, 6, 6), (4, 7, 5), (5, 7, 15), (6, 7, 4)):
... g.add_edge(*u_v_w)
>>> g.boruvka()
Added edge [0 - 3]
Added weight: 5
<BLANKLINE>
Added edge [0 - 1]
Added weight: 10
<BLANKLINE>
Added edge [2 - 3]
Added weight: 4
<BLANKLINE>
Added edge [4 - 7]
Added weight: 5
<BLANKLINE>
Added edge [4 - 5]
Added weight: 10
<BLANKLINE>
Added edge [6 - 7]
Added weight: 4
<BLANKLINE>
Added edge [3 - 4]
Added weight: 8
<BLANKLINE>
The total weight of the minimal spanning tree is: 46
"""
if __name__ == "__main__":
import doctest
doctest.testmod()
| """Borůvka's algorithm.
Determines the minimum spanning tree (MST) of a graph using the Borůvka's algorithm.
Borůvka's algorithm is a greedy algorithm for finding a minimum spanning tree in a
connected graph, or a minimum spanning forest if a graph that is not connected.
The time complexity of this algorithm is O(ELogV), where E represents the number
of edges, while V represents the number of nodes.
O(number_of_edges Log number_of_nodes)
The space complexity of this algorithm is O(V + E), since we have to keep a couple
of lists whose sizes are equal to the number of nodes, as well as keep all the
edges of a graph inside of the data structure itself.
Borůvka's algorithm gives us pretty much the same result as other MST Algorithms -
they all find the minimum spanning tree, and the time complexity is approximately
the same.
One advantage that Borůvka's algorithm has compared to the alternatives is that it
doesn't need to presort the edges or maintain a priority queue in order to find the
minimum spanning tree.
Even though that doesn't help its complexity, since it still passes the edges logE
times, it is a bit simpler to code.
Details: https://en.wikipedia.org/wiki/Bor%C5%AFvka%27s_algorithm
"""
class Graph:
def __init__(self, num_of_nodes: int) -> None:
"""
Arguments:
num_of_nodes - the number of nodes in the graph
Attributes:
m_num_of_nodes - the number of nodes in the graph.
m_edges - the list of edges.
m_component - the dictionary which stores the index of the component which
a node belongs to.
"""
self.m_num_of_nodes = num_of_nodes
self.m_edges = []
self.m_component = {}
def add_edge(self, u_node: int, v_node: int, weight: int) -> None:
"""Adds an edge in the format [first, second, edge weight] to graph."""
self.m_edges.append([u_node, v_node, weight])
def find_component(self, u_node: int) -> int:
"""Propagates a new component throughout a given component."""
if self.m_component[u_node] == u_node:
return u_node
return self.find_component(self.m_component[u_node])
def set_component(self, u_node: int) -> None:
"""Finds the component index of a given node"""
if self.m_component[u_node] != u_node:
for k in self.m_component:
self.m_component[k] = self.find_component(k)
def union(self, component_size: list, u_node: int, v_node: int) -> None:
"""Union finds the roots of components for two nodes, compares the components
in terms of size, and attaches the smaller one to the larger one to form
single component"""
if component_size[u_node] <= component_size[v_node]:
self.m_component[u_node] = v_node
component_size[v_node] += component_size[u_node]
self.set_component(u_node)
elif component_size[u_node] >= component_size[v_node]:
self.m_component[v_node] = self.find_component(u_node)
component_size[u_node] += component_size[v_node]
self.set_component(v_node)
def boruvka(self) -> None:
"""Performs Borůvka's algorithm to find MST."""
# Initialize additional lists required to algorithm.
component_size = []
mst_weight = 0
minimum_weight_edge = [-1] * self.m_num_of_nodes
# A list of components (initialized to all of the nodes)
for node in range(self.m_num_of_nodes):
self.m_component.update({node: node})
component_size.append(1)
num_of_components = self.m_num_of_nodes
while num_of_components > 1:
for edge in self.m_edges:
u, v, w = edge
u_component = self.m_component[u]
v_component = self.m_component[v]
if u_component != v_component:
"""If the current minimum weight edge of component u doesn't
exist (is -1), or if it's greater than the edge we're
observing right now, we will assign the value of the edge
we're observing to it.
If the current minimum weight edge of component v doesn't
exist (is -1), or if it's greater than the edge we're
observing right now, we will assign the value of the edge
we're observing to it"""
for component in (u_component, v_component):
if (
minimum_weight_edge[component] == -1
or minimum_weight_edge[component][2] > w
):
minimum_weight_edge[component] = [u, v, w]
for edge in minimum_weight_edge:
if edge != -1:
u, v, w = edge
u_component = self.m_component[u]
v_component = self.m_component[v]
if u_component != v_component:
mst_weight += w
self.union(component_size, u_component, v_component)
print(f"Added edge [{u} - {v}]\nAdded weight: {w}\n")
num_of_components -= 1
minimum_weight_edge = [-1] * self.m_num_of_nodes
print(f"The total weight of the minimal spanning tree is: {mst_weight}")
def test_vector() -> None:
"""
>>> g = Graph(8)
>>> for u_v_w in ((0, 1, 10), (0, 2, 6), (0, 3, 5), (1, 3, 15), (2, 3, 4),
... (3, 4, 8), (4, 5, 10), (4, 6, 6), (4, 7, 5), (5, 7, 15), (6, 7, 4)):
... g.add_edge(*u_v_w)
>>> g.boruvka()
Added edge [0 - 3]
Added weight: 5
<BLANKLINE>
Added edge [0 - 1]
Added weight: 10
<BLANKLINE>
Added edge [2 - 3]
Added weight: 4
<BLANKLINE>
Added edge [4 - 7]
Added weight: 5
<BLANKLINE>
Added edge [4 - 5]
Added weight: 10
<BLANKLINE>
Added edge [6 - 7]
Added weight: 4
<BLANKLINE>
Added edge [3 - 4]
Added weight: 8
<BLANKLINE>
The total weight of the minimal spanning tree is: 46
"""
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
TheAlgorithms/Python | 4,763 | from __future__ import annotations | ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2021-09-14T22:54:26Z" | "2021-09-22T21:11:51Z" | dc07a850763d8154e012c9d1be7f8fe78326e8fb | 15d1cfabb15903c5e9d4d103e2390876efb3f85f | from __future__ import annotations. ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
One of the several implementations of Lempel–Ziv–Welch decompression algorithm
https://en.wikipedia.org/wiki/Lempel%E2%80%93Ziv%E2%80%93Welch
"""
import math
import sys
def read_file_binary(file_path: str) -> str:
"""
Reads given file as bytes and returns them as a long string
"""
result = ""
try:
with open(file_path, "rb") as binary_file:
data = binary_file.read()
for dat in data:
curr_byte = f"{dat:08b}"
result += curr_byte
return result
except OSError:
print("File not accessible")
sys.exit()
def decompress_data(data_bits: str) -> str:
"""
Decompresses given data_bits using Lempel–Ziv–Welch compression algorithm
and returns the result as a string
"""
lexicon = {"0": "0", "1": "1"}
result, curr_string = "", ""
index = len(lexicon)
for i in range(len(data_bits)):
curr_string += data_bits[i]
if curr_string not in lexicon:
continue
last_match_id = lexicon[curr_string]
result += last_match_id
lexicon[curr_string] = last_match_id + "0"
if math.log2(index).is_integer():
newLex = {}
for curr_key in list(lexicon):
newLex["0" + curr_key] = lexicon.pop(curr_key)
lexicon = newLex
lexicon[bin(index)[2:]] = last_match_id + "1"
index += 1
curr_string = ""
return result
def write_file_binary(file_path: str, to_write: str) -> None:
"""
Writes given to_write string (should only consist of 0's and 1's) as bytes in the
file
"""
byte_length = 8
try:
with open(file_path, "wb") as opened_file:
result_byte_array = [
to_write[i : i + byte_length]
for i in range(0, len(to_write), byte_length)
]
if len(result_byte_array[-1]) % byte_length == 0:
result_byte_array.append("10000000")
else:
result_byte_array[-1] += "1" + "0" * (
byte_length - len(result_byte_array[-1]) - 1
)
for elem in result_byte_array[:-1]:
opened_file.write(int(elem, 2).to_bytes(1, byteorder="big"))
except OSError:
print("File not accessible")
sys.exit()
def remove_prefix(data_bits: str) -> str:
"""
Removes size prefix, that compressed file should have
Returns the result
"""
counter = 0
for letter in data_bits:
if letter == "1":
break
counter += 1
data_bits = data_bits[counter:]
data_bits = data_bits[counter + 1 :]
return data_bits
def compress(source_path: str, destination_path: str) -> None:
"""
Reads source file, decompresses it and writes the result in destination file
"""
data_bits = read_file_binary(source_path)
data_bits = remove_prefix(data_bits)
decompressed = decompress_data(data_bits)
write_file_binary(destination_path, decompressed)
if __name__ == "__main__":
compress(sys.argv[1], sys.argv[2])
| """
One of the several implementations of Lempel–Ziv–Welch decompression algorithm
https://en.wikipedia.org/wiki/Lempel%E2%80%93Ziv%E2%80%93Welch
"""
import math
import sys
def read_file_binary(file_path: str) -> str:
"""
Reads given file as bytes and returns them as a long string
"""
result = ""
try:
with open(file_path, "rb") as binary_file:
data = binary_file.read()
for dat in data:
curr_byte = f"{dat:08b}"
result += curr_byte
return result
except OSError:
print("File not accessible")
sys.exit()
def decompress_data(data_bits: str) -> str:
"""
Decompresses given data_bits using Lempel–Ziv–Welch compression algorithm
and returns the result as a string
"""
lexicon = {"0": "0", "1": "1"}
result, curr_string = "", ""
index = len(lexicon)
for i in range(len(data_bits)):
curr_string += data_bits[i]
if curr_string not in lexicon:
continue
last_match_id = lexicon[curr_string]
result += last_match_id
lexicon[curr_string] = last_match_id + "0"
if math.log2(index).is_integer():
newLex = {}
for curr_key in list(lexicon):
newLex["0" + curr_key] = lexicon.pop(curr_key)
lexicon = newLex
lexicon[bin(index)[2:]] = last_match_id + "1"
index += 1
curr_string = ""
return result
def write_file_binary(file_path: str, to_write: str) -> None:
"""
Writes given to_write string (should only consist of 0's and 1's) as bytes in the
file
"""
byte_length = 8
try:
with open(file_path, "wb") as opened_file:
result_byte_array = [
to_write[i : i + byte_length]
for i in range(0, len(to_write), byte_length)
]
if len(result_byte_array[-1]) % byte_length == 0:
result_byte_array.append("10000000")
else:
result_byte_array[-1] += "1" + "0" * (
byte_length - len(result_byte_array[-1]) - 1
)
for elem in result_byte_array[:-1]:
opened_file.write(int(elem, 2).to_bytes(1, byteorder="big"))
except OSError:
print("File not accessible")
sys.exit()
def remove_prefix(data_bits: str) -> str:
"""
Removes size prefix, that compressed file should have
Returns the result
"""
counter = 0
for letter in data_bits:
if letter == "1":
break
counter += 1
data_bits = data_bits[counter:]
data_bits = data_bits[counter + 1 :]
return data_bits
def compress(source_path: str, destination_path: str) -> None:
"""
Reads source file, decompresses it and writes the result in destination file
"""
data_bits = read_file_binary(source_path)
data_bits = remove_prefix(data_bits)
decompressed = decompress_data(data_bits)
write_file_binary(destination_path, decompressed)
if __name__ == "__main__":
compress(sys.argv[1], sys.argv[2])
| -1 |
TheAlgorithms/Python | 4,763 | from __future__ import annotations | ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2021-09-14T22:54:26Z" | "2021-09-22T21:11:51Z" | dc07a850763d8154e012c9d1be7f8fe78326e8fb | 15d1cfabb15903c5e9d4d103e2390876efb3f85f | from __future__ import annotations. ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| """
Project Euler Problem 135: https://projecteuler.net/problem=135
Given the positive integers, x, y, and z,
are consecutive terms of an arithmetic progression,
the least value of the positive integer, n,
for which the equation,
x2 − y2 − z2 = n, has exactly two solutions is n = 27:
342 − 272 − 202 = 122 − 92 − 62 = 27
It turns out that n = 1155 is the least value
which has exactly ten solutions.
How many values of n less than one million
have exactly ten distinct solutions?
Taking x,y,z of the form a+d,a,a-d respectively,
the given equation reduces to a*(4d-a)=n.
Calculating no of solutions for every n till 1 million by fixing a
,and n must be multiple of a.
Total no of steps=n*(1/1+1/2+1/3+1/4..+1/n)
,so roughly O(nlogn) time complexity.
"""
def solution(limit: int = 1000000) -> int:
"""
returns the values of n less than or equal to the limit
have exactly ten distinct solutions.
>>> solution(100)
0
>>> solution(10000)
45
>>> solution(50050)
292
"""
limit = limit + 1
frequency = [0] * limit
for first_term in range(1, limit):
for n in range(first_term, limit, first_term):
common_difference = first_term + n / first_term
if common_difference % 4: # d must be divisble by 4
continue
else:
common_difference /= 4
if (
first_term > common_difference
and first_term < 4 * common_difference
): # since x,y,z are positive integers
frequency[n] += 1 # so z>0 and a>d ,also 4d<a
count = sum(1 for x in frequency[1:limit] if x == 10)
return count
if __name__ == "__main__":
print(f"{solution() = }")
| """
Project Euler Problem 135: https://projecteuler.net/problem=135
Given the positive integers, x, y, and z,
are consecutive terms of an arithmetic progression,
the least value of the positive integer, n,
for which the equation,
x2 − y2 − z2 = n, has exactly two solutions is n = 27:
342 − 272 − 202 = 122 − 92 − 62 = 27
It turns out that n = 1155 is the least value
which has exactly ten solutions.
How many values of n less than one million
have exactly ten distinct solutions?
Taking x,y,z of the form a+d,a,a-d respectively,
the given equation reduces to a*(4d-a)=n.
Calculating no of solutions for every n till 1 million by fixing a
,and n must be multiple of a.
Total no of steps=n*(1/1+1/2+1/3+1/4..+1/n)
,so roughly O(nlogn) time complexity.
"""
def solution(limit: int = 1000000) -> int:
"""
returns the values of n less than or equal to the limit
have exactly ten distinct solutions.
>>> solution(100)
0
>>> solution(10000)
45
>>> solution(50050)
292
"""
limit = limit + 1
frequency = [0] * limit
for first_term in range(1, limit):
for n in range(first_term, limit, first_term):
common_difference = first_term + n / first_term
if common_difference % 4: # d must be divisble by 4
continue
else:
common_difference /= 4
if (
first_term > common_difference
and first_term < 4 * common_difference
): # since x,y,z are positive integers
frequency[n] += 1 # so z>0 and a>d ,also 4d<a
count = sum(1 for x in frequency[1:limit] if x == 10)
return count
if __name__ == "__main__":
print(f"{solution() = }")
| -1 |
TheAlgorithms/Python | 4,763 | from __future__ import annotations | ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2021-09-14T22:54:26Z" | "2021-09-22T21:11:51Z" | dc07a850763d8154e012c9d1be7f8fe78326e8fb | 15d1cfabb15903c5e9d4d103e2390876efb3f85f | from __future__ import annotations. ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| #!/usr/bin/python
"""
A Framework of Back Propagation Neural Network（BP） model
Easy to use:
* add many layers as you want ！！！
* clearly see how the loss decreasing
Easy to expand:
* more activation functions
* more loss functions
* more optimization method
Author: Stephen Lee
Github : https://github.com/RiptideBo
Date: 2017.11.23
"""
import numpy as np
from matplotlib import pyplot as plt
def sigmoid(x):
return 1 / (1 + np.exp(-1 * x))
class DenseLayer:
"""
Layers of BP neural network
"""
def __init__(
self, units, activation=None, learning_rate=None, is_input_layer=False
):
"""
common connected layer of bp network
:param units: numbers of neural units
:param activation: activation function
:param learning_rate: learning rate for paras
:param is_input_layer: whether it is input layer or not
"""
self.units = units
self.weight = None
self.bias = None
self.activation = activation
if learning_rate is None:
learning_rate = 0.3
self.learn_rate = learning_rate
self.is_input_layer = is_input_layer
def initializer(self, back_units):
self.weight = np.asmatrix(np.random.normal(0, 0.5, (self.units, back_units)))
self.bias = np.asmatrix(np.random.normal(0, 0.5, self.units)).T
if self.activation is None:
self.activation = sigmoid
def cal_gradient(self):
# activation function may be sigmoid or linear
if self.activation == sigmoid:
gradient_mat = np.dot(self.output, (1 - self.output).T)
gradient_activation = np.diag(np.diag(gradient_mat))
else:
gradient_activation = 1
return gradient_activation
def forward_propagation(self, xdata):
self.xdata = xdata
if self.is_input_layer:
# input layer
self.wx_plus_b = xdata
self.output = xdata
return xdata
else:
self.wx_plus_b = np.dot(self.weight, self.xdata) - self.bias
self.output = self.activation(self.wx_plus_b)
return self.output
def back_propagation(self, gradient):
gradient_activation = self.cal_gradient() # i * i 维
gradient = np.asmatrix(np.dot(gradient.T, gradient_activation))
self._gradient_weight = np.asmatrix(self.xdata)
self._gradient_bias = -1
self._gradient_x = self.weight
self.gradient_weight = np.dot(gradient.T, self._gradient_weight.T)
self.gradient_bias = gradient * self._gradient_bias
self.gradient = np.dot(gradient, self._gradient_x).T
# upgrade: the Negative gradient direction
self.weight = self.weight - self.learn_rate * self.gradient_weight
self.bias = self.bias - self.learn_rate * self.gradient_bias.T
# updates the weights and bias according to learning rate (0.3 if undefined)
return self.gradient
class BPNN:
"""
Back Propagation Neural Network model
"""
def __init__(self):
self.layers = []
self.train_mse = []
self.fig_loss = plt.figure()
self.ax_loss = self.fig_loss.add_subplot(1, 1, 1)
def add_layer(self, layer):
self.layers.append(layer)
def build(self):
for i, layer in enumerate(self.layers[:]):
if i < 1:
layer.is_input_layer = True
else:
layer.initializer(self.layers[i - 1].units)
def summary(self):
for i, layer in enumerate(self.layers[:]):
print("------- layer %d -------" % i)
print("weight.shape ", np.shape(layer.weight))
print("bias.shape ", np.shape(layer.bias))
def train(self, xdata, ydata, train_round, accuracy):
self.train_round = train_round
self.accuracy = accuracy
self.ax_loss.hlines(self.accuracy, 0, self.train_round * 1.1)
x_shape = np.shape(xdata)
for round_i in range(train_round):
all_loss = 0
for row in range(x_shape[0]):
_xdata = np.asmatrix(xdata[row, :]).T
_ydata = np.asmatrix(ydata[row, :]).T
# forward propagation
for layer in self.layers:
_xdata = layer.forward_propagation(_xdata)
loss, gradient = self.cal_loss(_ydata, _xdata)
all_loss = all_loss + loss
# back propagation: the input_layer does not upgrade
for layer in self.layers[:0:-1]:
gradient = layer.back_propagation(gradient)
mse = all_loss / x_shape[0]
self.train_mse.append(mse)
self.plot_loss()
if mse < self.accuracy:
print("----达到精度----")
return mse
def cal_loss(self, ydata, ydata_):
self.loss = np.sum(np.power((ydata - ydata_), 2))
self.loss_gradient = 2 * (ydata_ - ydata)
# vector (shape is the same as _ydata.shape)
return self.loss, self.loss_gradient
def plot_loss(self):
if self.ax_loss.lines:
self.ax_loss.lines.remove(self.ax_loss.lines[0])
self.ax_loss.plot(self.train_mse, "r-")
plt.ion()
plt.xlabel("step")
plt.ylabel("loss")
plt.show()
plt.pause(0.1)
def example():
x = np.random.randn(10, 10)
y = np.asarray(
[
[0.8, 0.4],
[0.4, 0.3],
[0.34, 0.45],
[0.67, 0.32],
[0.88, 0.67],
[0.78, 0.77],
[0.55, 0.66],
[0.55, 0.43],
[0.54, 0.1],
[0.1, 0.5],
]
)
model = BPNN()
for i in (10, 20, 30, 2):
model.add_layer(DenseLayer(i))
model.build()
model.summary()
model.train(xdata=x, ydata=y, train_round=100, accuracy=0.01)
if __name__ == "__main__":
example()
| #!/usr/bin/python
"""
A Framework of Back Propagation Neural Network（BP） model
Easy to use:
* add many layers as you want ！！！
* clearly see how the loss decreasing
Easy to expand:
* more activation functions
* more loss functions
* more optimization method
Author: Stephen Lee
Github : https://github.com/RiptideBo
Date: 2017.11.23
"""
import numpy as np
from matplotlib import pyplot as plt
def sigmoid(x):
return 1 / (1 + np.exp(-1 * x))
class DenseLayer:
"""
Layers of BP neural network
"""
def __init__(
self, units, activation=None, learning_rate=None, is_input_layer=False
):
"""
common connected layer of bp network
:param units: numbers of neural units
:param activation: activation function
:param learning_rate: learning rate for paras
:param is_input_layer: whether it is input layer or not
"""
self.units = units
self.weight = None
self.bias = None
self.activation = activation
if learning_rate is None:
learning_rate = 0.3
self.learn_rate = learning_rate
self.is_input_layer = is_input_layer
def initializer(self, back_units):
self.weight = np.asmatrix(np.random.normal(0, 0.5, (self.units, back_units)))
self.bias = np.asmatrix(np.random.normal(0, 0.5, self.units)).T
if self.activation is None:
self.activation = sigmoid
def cal_gradient(self):
# activation function may be sigmoid or linear
if self.activation == sigmoid:
gradient_mat = np.dot(self.output, (1 - self.output).T)
gradient_activation = np.diag(np.diag(gradient_mat))
else:
gradient_activation = 1
return gradient_activation
def forward_propagation(self, xdata):
self.xdata = xdata
if self.is_input_layer:
# input layer
self.wx_plus_b = xdata
self.output = xdata
return xdata
else:
self.wx_plus_b = np.dot(self.weight, self.xdata) - self.bias
self.output = self.activation(self.wx_plus_b)
return self.output
def back_propagation(self, gradient):
gradient_activation = self.cal_gradient() # i * i 维
gradient = np.asmatrix(np.dot(gradient.T, gradient_activation))
self._gradient_weight = np.asmatrix(self.xdata)
self._gradient_bias = -1
self._gradient_x = self.weight
self.gradient_weight = np.dot(gradient.T, self._gradient_weight.T)
self.gradient_bias = gradient * self._gradient_bias
self.gradient = np.dot(gradient, self._gradient_x).T
# upgrade: the Negative gradient direction
self.weight = self.weight - self.learn_rate * self.gradient_weight
self.bias = self.bias - self.learn_rate * self.gradient_bias.T
# updates the weights and bias according to learning rate (0.3 if undefined)
return self.gradient
class BPNN:
"""
Back Propagation Neural Network model
"""
def __init__(self):
self.layers = []
self.train_mse = []
self.fig_loss = plt.figure()
self.ax_loss = self.fig_loss.add_subplot(1, 1, 1)
def add_layer(self, layer):
self.layers.append(layer)
def build(self):
for i, layer in enumerate(self.layers[:]):
if i < 1:
layer.is_input_layer = True
else:
layer.initializer(self.layers[i - 1].units)
def summary(self):
for i, layer in enumerate(self.layers[:]):
print("------- layer %d -------" % i)
print("weight.shape ", np.shape(layer.weight))
print("bias.shape ", np.shape(layer.bias))
def train(self, xdata, ydata, train_round, accuracy):
self.train_round = train_round
self.accuracy = accuracy
self.ax_loss.hlines(self.accuracy, 0, self.train_round * 1.1)
x_shape = np.shape(xdata)
for round_i in range(train_round):
all_loss = 0
for row in range(x_shape[0]):
_xdata = np.asmatrix(xdata[row, :]).T
_ydata = np.asmatrix(ydata[row, :]).T
# forward propagation
for layer in self.layers:
_xdata = layer.forward_propagation(_xdata)
loss, gradient = self.cal_loss(_ydata, _xdata)
all_loss = all_loss + loss
# back propagation: the input_layer does not upgrade
for layer in self.layers[:0:-1]:
gradient = layer.back_propagation(gradient)
mse = all_loss / x_shape[0]
self.train_mse.append(mse)
self.plot_loss()
if mse < self.accuracy:
print("----达到精度----")
return mse
def cal_loss(self, ydata, ydata_):
self.loss = np.sum(np.power((ydata - ydata_), 2))
self.loss_gradient = 2 * (ydata_ - ydata)
# vector (shape is the same as _ydata.shape)
return self.loss, self.loss_gradient
def plot_loss(self):
if self.ax_loss.lines:
self.ax_loss.lines.remove(self.ax_loss.lines[0])
self.ax_loss.plot(self.train_mse, "r-")
plt.ion()
plt.xlabel("step")
plt.ylabel("loss")
plt.show()
plt.pause(0.1)
def example():
x = np.random.randn(10, 10)
y = np.asarray(
[
[0.8, 0.4],
[0.4, 0.3],
[0.34, 0.45],
[0.67, 0.32],
[0.88, 0.67],
[0.78, 0.77],
[0.55, 0.66],
[0.55, 0.43],
[0.54, 0.1],
[0.1, 0.5],
]
)
model = BPNN()
for i in (10, 20, 30, 2):
model.add_layer(DenseLayer(i))
model.build()
model.summary()
model.train(xdata=x, ydata=y, train_round=100, accuracy=0.01)
if __name__ == "__main__":
example()
| -1 |
TheAlgorithms/Python | 4,763 | from __future__ import annotations | ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2021-09-14T22:54:26Z" | "2021-09-22T21:11:51Z" | dc07a850763d8154e012c9d1be7f8fe78326e8fb | 15d1cfabb15903c5e9d4d103e2390876efb3f85f | from __future__ import annotations. ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from __future__ import annotations
def find_primitive(n: int) -> int | None:
for r in range(1, n):
li = []
for x in range(n - 1):
val = pow(r, x, n)
if val in li:
break
li.append(val)
else:
return r
return None
if __name__ == "__main__":
q = int(input("Enter a prime number q: "))
a = find_primitive(q)
if a is None:
print(f"Cannot find the primitive for the value: {a!r}")
else:
a_private = int(input("Enter private key of A: "))
a_public = pow(a, a_private, q)
b_private = int(input("Enter private key of B: "))
b_public = pow(a, b_private, q)
a_secret = pow(b_public, a_private, q)
b_secret = pow(a_public, b_private, q)
print("The key value generated by A is: ", a_secret)
print("The key value generated by B is: ", b_secret)
| from __future__ import annotations
def find_primitive(n: int) -> int | None:
for r in range(1, n):
li = []
for x in range(n - 1):
val = pow(r, x, n)
if val in li:
break
li.append(val)
else:
return r
return None
if __name__ == "__main__":
q = int(input("Enter a prime number q: "))
a = find_primitive(q)
if a is None:
print(f"Cannot find the primitive for the value: {a!r}")
else:
a_private = int(input("Enter private key of A: "))
a_public = pow(a, a_private, q)
b_private = int(input("Enter private key of B: "))
b_public = pow(a, b_private, q)
a_secret = pow(b_public, a_private, q)
b_secret = pow(a_public, b_private, q)
print("The key value generated by A is: ", a_secret)
print("The key value generated by B is: ", b_secret)
| -1 |
TheAlgorithms/Python | 4,763 | from __future__ import annotations | ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| cclauss | "2021-09-14T22:54:26Z" | "2021-09-22T21:11:51Z" | dc07a850763d8154e012c9d1be7f8fe78326e8fb | 15d1cfabb15903c5e9d4d103e2390876efb3f85f | from __future__ import annotations. ### **Describe your change:**
from __future__ import annotations
* [ ] Add an algorithm?
* [x] Fix a bug or typo in an existing algorithm?
* [ ] Documentation change?
### **Checklist:**
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [ ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python [type hints](https://docs.python.org/3/library/typing.html).
* [ ] All functions have [doctests](https://docs.python.org/3/library/doctest.html) that pass the automated testing.
* [ ] All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
* [ ] If this pull request resolves one or more open issues then the commit message contains `Fixes: #{$ISSUE_NO}`.
| from sklearn.neural_network import MLPClassifier
X = [[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
y = [0, 1, 0, 0]
clf = MLPClassifier(
solver="lbfgs", alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1
)
clf.fit(X, y)
test = [[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
Y = clf.predict(test)
def wrapper(Y):
"""
>>> wrapper(Y)
[0, 0, 1]
"""
return list(Y)
if __name__ == "__main__":
import doctest
doctest.testmod()
| from sklearn.neural_network import MLPClassifier
X = [[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
y = [0, 1, 0, 0]
clf = MLPClassifier(
solver="lbfgs", alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1
)
clf.fit(X, y)
test = [[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
Y = clf.predict(test)
def wrapper(Y):
"""
>>> wrapper(Y)
[0, 0, 1]
"""
return list(Y)
if __name__ == "__main__":
import doctest
doctest.testmod()
| -1 |
Subsets and Splits