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Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\binary_tree\floor_and_ceiling.py | python | Python | """
In a binary search tree (BST):
* The floor of key 'k' is the maximum value that is smaller than or equal to 'k'.
* The ceiling of key 'k' is the minimum value that is greater than or equal to 'k'.
Reference:
https://bit.ly/46uB0a2
Author : Arunkumar
Date : 14th October 2023
"""
from __future__ import annotations... | 2,198 | 89 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\binary_tree\inorder_tree_traversal_2022.py | python | Python | """
Illustrate how to implement inorder traversal in binary search tree.
Author: Gurneet Singh
https://www.geeksforgeeks.org/tree-traversals-inorder-preorder-and-postorder/
"""
class BinaryTreeNode:
"""Defining the structure of BinaryTreeNode"""
def __init__(self, data: int) -> None:
self.data = data... | 2,354 | 83 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\binary_tree\is_sorted.py | python | Python | """
Given the root of a binary tree, determine if it is a valid binary search tree (BST).
A valid binary search tree is defined as follows:
- The left subtree of a node contains only nodes with keys less than the node's key.
- The right subtree of a node contains only nodes with keys greater than the node's key.
- Bot... | 3,142 | 99 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\binary_tree\is_sum_tree.py | python | Python | """
Is a binary tree a sum tree where the value of every non-leaf node is equal to the sum
of the values of its left and right subtrees?
https://www.geeksforgeeks.org/check-if-a-given-binary-tree-is-sumtree
"""
from __future__ import annotations
from collections.abc import Iterator
from dataclasses import dataclass
... | 4,288 | 163 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\binary_tree\lazy_segment_tree.py | python | Python | from __future__ import annotations
import math
class SegmentTree:
def __init__(self, size: int) -> None:
self.size = size
# approximate the overall size of segment tree with given value
self.segment_tree = [0 for i in range(4 * size)]
# create array to store lazy update
se... | 4,983 | 137 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\binary_tree\lowest_common_ancestor.py | python | Python | # https://en.wikipedia.org/wiki/Lowest_common_ancestor
# https://en.wikipedia.org/wiki/Breadth-first_search
from __future__ import annotations
from queue import Queue
def swap(a: int, b: int) -> tuple[int, int]:
"""
Return a tuple (b, a) when given two integers a and b
>>> swap(2,3)
(3, 2)
>>> s... | 5,170 | 172 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\binary_tree\maximum_fenwick_tree.py | python | Python | class MaxFenwickTree:
"""
Maximum Fenwick Tree
More info: https://cp-algorithms.com/data_structures/fenwick.html
---------
>>> ft = MaxFenwickTree(5)
>>> ft.query(0, 5)
0
>>> ft.update(4, 100)
>>> ft.query(0, 5)
100
>>> ft.update(4, 0)
>>> ft.update(2, 20)
>>> ft.que... | 2,867 | 115 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\binary_tree\maximum_sum_bst.py | python | Python | from __future__ import annotations
import sys
from dataclasses import dataclass
INT_MIN = -sys.maxsize + 1
INT_MAX = sys.maxsize - 1
@dataclass
class TreeNode:
val: int = 0
left: TreeNode | None = None
right: TreeNode | None = None
def max_sum_bst(root: TreeNode | None) -> int:
"""
The solutio... | 2,221 | 79 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\binary_tree\merge_two_binary_trees.py | python | Python | #!/usr/local/bin/python3
"""
Problem Description: Given two binary tree, return the merged tree.
The rule for merging is that if two nodes overlap, then put the value sum of
both nodes to the new value of the merged node. Otherwise, the NOT null node
will be used as the node of new tree.
"""
from __future__ import ann... | 2,379 | 95 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\binary_tree\mirror_binary_tree.py | python | Python | """
Given the root of a binary tree, mirror the tree, and return its root.
Leetcode problem reference: https://leetcode.com/problems/mirror-binary-tree/
"""
from __future__ import annotations
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class Node:
"""
A Node has value ... | 3,654 | 161 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\binary_tree\non_recursive_segment_tree.py | python | Python | """
A non-recursive Segment Tree implementation with range query and single element update,
works virtually with any list of the same type of elements with a "commutative"
combiner.
Explanation:
https://www.geeksforgeeks.org/iterative-segment-tree-range-minimum-query/
https://www.geeksforgeeks.org/segment-tree-efficie... | 4,909 | 164 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\binary_tree\number_of_possible_binary_trees.py | python | Python | """
Hey, we are going to find an exciting number called Catalan number which is use to find
the number of possible binary search trees from tree of a given number of nodes.
We will use the formula: t(n) = SUMMATION(i = 1 to n)t(i-1)t(n-i)
Further details at Wikipedia: https://en.wikipedia.org/wiki/Catalan_number
"""
... | 2,991 | 103 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\binary_tree\README.md | readme | Markdown | # Binary Tree Traversal
## Overview
The combination of binary trees being data structures and traversal being an algorithm relates to classic problems, either directly or indirectly.
> If you can grasp the traversal of binary trees, the traversal of other complicated trees will be easy for you.
The following are so... | 5,613 | 112 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\binary_tree\red_black_tree.py | python | Python | from __future__ import annotations
from collections.abc import Iterator
class RedBlackTree:
"""
A Red-Black tree, which is a self-balancing BST (binary search
tree).
This tree has similar performance to AVL trees, but the balancing is
less strict, so it will perform faster for writing/deleting no... | 25,467 | 717 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\binary_tree\segment_tree.py | python | Python | import math
class SegmentTree:
def __init__(self, a):
self.A = a
self.N = len(self.A)
self.st = [0] * (
4 * self.N
) # approximate the overall size of segment tree with array N
if self.N:
self.build(1, 0, self.N - 1)
def left(self, idx):
... | 3,392 | 117 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\binary_tree\segment_tree_other.py | python | Python | """
Segment_tree creates a segment tree with a given array and function,
allowing queries to be done later in log(N) time
function takes 2 values and returns a same type value
"""
from collections.abc import Sequence
from queue import Queue
class SegmentTreeNode:
def __init__(self, start, end, val, left=None, ri... | 7,781 | 238 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\binary_tree\serialize_deserialize_binary_tree.py | python | Python | from __future__ import annotations
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class TreeNode:
"""
A binary tree node has a value, left child, and right child.
Props:
value: The value of the node.
left: The left child of the node.
right: The ... | 3,701 | 141 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\binary_tree\symmetric_tree.py | python | Python | """
Given the root of a binary tree, check whether it is a mirror of itself
(i.e., symmetric around its center).
Leetcode reference: https://leetcode.com/problems/symmetric-tree/
"""
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class Node:
"""
A Node represents an element... | 3,795 | 160 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\binary_tree\treap.py | python | Python | from __future__ import annotations
from random import random
class Node:
"""
Treap's node
Treap is a binary tree by value and heap by priority
"""
def __init__(self, value: int | None = None):
self.value = value
self.prior = random()
self.left: Node | None = None
... | 4,930 | 180 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\binary_tree\wavelet_tree.py | python | Python | """
Wavelet tree is a data-structure designed to efficiently answer various range queries
for arrays. Wavelets trees are different from other binary trees in the sense that
the nodes are split based on the actual values of the elements and not on indices,
such as the with segment trees or fenwick trees. You can read mo... | 6,275 | 211 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\disjoint_set\alternate_disjoint_set.py | python | Python | """
Implements a disjoint set using Lists and some added heuristics for efficiency
Union by Rank Heuristic and Path Compression
"""
class DisjointSet:
def __init__(self, set_counts: list) -> None:
"""
Initialize with a list of the number of items in each set
and with rank = 1 for each set
... | 2,260 | 69 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\disjoint_set\disjoint_set.py | python | Python | """
Disjoint set.
Reference: https://en.wikipedia.org/wiki/Disjoint-set_data_structure
"""
class Node:
def __init__(self, data: int) -> None:
self.data = data
self.rank: int
self.parent: Node
def make_set(x: Node) -> None:
"""
Make x as a set.
"""
# rank is the distance f... | 1,920 | 86 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\hashing\bloom_filter.py | python | Python | """
See https://en.wikipedia.org/wiki/Bloom_filter
The use of this data structure is to test membership in a set.
Compared to Python's built-in set() it is more space-efficient.
In the following example, only 8 bits of memory will be used:
>>> bloom = Bloom(size=8)
Initially, the filter contains all zeros:
>>> bloom.... | 2,903 | 107 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\hashing\double_hash.py | python | Python | #!/usr/bin/env python3
"""
Double hashing is a collision resolving technique in Open Addressed Hash tables.
Double hashing uses the idea of applying a second hash function to key when a collision
occurs. The advantage of Double hashing is that it is one of the best form of probing,
producing a uniform distribution of ... | 2,822 | 87 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\hashing\hash_map.py | python | Python | """
Hash map with open addressing.
https://en.wikipedia.org/wiki/Hash_table
Another hash map implementation, with a good explanation.
Modern Dictionaries by Raymond Hettinger
https://www.youtube.com/watch?v=p33CVV29OG8
"""
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from ty... | 9,057 | 328 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\hashing\hash_table.py | python | Python | #!/usr/bin/env python3
from abc import abstractmethod
from .number_theory.prime_numbers import next_prime
class HashTable:
"""
Basic Hash Table example with open addressing and linear probing
"""
def __init__(
self,
size_table: int,
charge_factor: int | None = None,
l... | 8,320 | 284 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\hashing\hash_table_with_linked_list.py | python | Python | from collections import deque
from .hash_table import HashTable
class HashTableWithLinkedList(HashTable):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def _set_value(self, key, data):
self.values[key] = deque() if self.values[key] is None else self.values[key]
... | 871 | 28 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\hashing\number_theory\prime_numbers.py | python | Python | #!/usr/bin/env python3
"""
module to operations with prime numbers
"""
import math
def is_prime(number: int) -> bool:
"""Checks to see if a number is a prime in O(sqrt(n)).
A number is prime if it has exactly two factors: 1 and itself.
>>> is_prime(0)
False
>>> is_prime(1)
False
>>> is_... | 1,344 | 60 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\hashing\quadratic_probing.py | python | Python | #!/usr/bin/env python3
from .hash_table import HashTable
class QuadraticProbing(HashTable):
"""
Basic Hash Table example with open addressing using Quadratic Probing
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def _collision_resolution(self, key, data=None... | 2,449 | 85 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\hashing\tests\test_hash_map.py | test | Python | from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def _get(k):
return getitem, k
def _set(k, v):
return setitem, k, v
def _del(k):
return delitem, k
def _run_operation(obj, fun, *args):
try:
return fun(obj, *args), None
... | 2,384 | 98 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\heap\binomial_heap.py | python | Python | """
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 ... | 12,648 | 402 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\heap\heap.py | python | Python | from __future__ import annotations
from abc import abstractmethod
from collections.abc import Iterable
from typing import Protocol, TypeVar
class Comparable(Protocol):
@abstractmethod
def __lt__(self: T, other: T) -> bool:
pass
@abstractmethod
def __gt__(self: T, other: T) -> bool:
p... | 7,666 | 277 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\heap\heap_generic.py | python | Python | from collections.abc import Callable
class Heap:
"""
A generic Heap class, can be used as min or max by passing the key function
accordingly.
"""
def __init__(self, key: Callable | None = None) -> None:
# Stores actual heap items.
self.arr: list = []
# Stores indexes of ea... | 6,000 | 175 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\heap\max_heap.py | python | Python | 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
... | 2,499 | 87 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\heap\min_heap.py | python | Python | # Min heap data structure
# with decrease key functionality - in O(log(n)) time
class Node:
def __init__(self, name, val):
self.name = name
self.val = val
def __str__(self):
return f"{self.__class__.__name__}({self.name}, {self.val})"
def __lt__(self, other):
return self.... | 4,677 | 171 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\heap\randomized_heap.py | python | Python | #!/usr/bin/env python3
from __future__ import annotations
import random
from collections.abc import Iterable
from typing import Any, TypeVar
T = TypeVar("T", bound=bool)
class RandomizedHeapNode[T: bool]:
"""
One node of the randomized heap. Contains the value and references to
two children.
"""
... | 5,519 | 223 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\heap\skew_heap.py | python | Python | #!/usr/bin/env python3
from __future__ import annotations
from collections.abc import Iterable, Iterator
from typing import Any, TypeVar
T = TypeVar("T", bound=bool)
class SkewNode[T: bool]:
"""
One node of the skew heap. Contains the value and references to
two children.
"""
def __init__(self... | 5,869 | 238 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\kd_tree\build_kdtree.py | python | Python | # Created by: Ramy-Badr-Ahmed (https://github.com/Ramy-Badr-Ahmed)
# in Pull Request: #11532
# https://github.com/TheAlgorithms/Python/pull/11532
#
# Please mention me (@Ramy-Badr-Ahmed) in any issue or pull request
# addressing bugs/corrections to this file.
# Thank you!
from data_structures.kd_tree.kd_node imp... | 1,346 | 44 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\kd_tree\example\example_usage.py | python | Python | # Created by: Ramy-Badr-Ahmed (https://github.com/Ramy-Badr-Ahmed)
# in Pull Request: #11532
# https://github.com/TheAlgorithms/Python/pull/11532
#
# Please mention me (@Ramy-Badr-Ahmed) in any issue or pull request
# addressing bugs/corrections to this file.
# Thank you!
import numpy as np
from data_structures... | 1,621 | 47 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\kd_tree\example\hypercube_points.py | python | Python | # Created by: Ramy-Badr-Ahmed (https://github.com/Ramy-Badr-Ahmed)
# in Pull Request: #11532
# https://github.com/TheAlgorithms/Python/pull/11532
#
# Please mention me (@Ramy-Badr-Ahmed) in any issue or pull request
# addressing bugs/corrections to this file.
# Thank you!
import numpy as np
def hypercube_point... | 938 | 30 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\kd_tree\kd_node.py | python | Python | # Created by: Ramy-Badr-Ahmed (https://github.com/Ramy-Badr-Ahmed)
# in Pull Request: #11532
# https://github.com/TheAlgorithms/Python/pull/11532
#
# Please mention me (@Ramy-Badr-Ahmed) in any issue or pull request
# addressing bugs/corrections to this file.
# Thank you!
from __future__ import annotations
cla... | 1,072 | 39 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\kd_tree\nearest_neighbour_search.py | python | Python | # Created by: Ramy-Badr-Ahmed (https://github.com/Ramy-Badr-Ahmed)
# in Pull Request: #11532
# https://github.com/TheAlgorithms/Python/pull/11532
#
# Please mention me (@Ramy-Badr-Ahmed) in any issue or pull request
# addressing bugs/corrections to this file.
# Thank you!
from data_structures.kd_tree.kd_node imp... | 2,836 | 80 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\kd_tree\tests\test_kdtree.py | test | Python | # Created by: Ramy-Badr-Ahmed (https://github.com/Ramy-Badr-Ahmed)
# in Pull Request: #11532
# https://github.com/TheAlgorithms/Python/pull/11532
#
# Please mention me (@Ramy-Badr-Ahmed) in any issue or pull request
# addressing bugs/corrections to this file.
# Thank you!
import numpy as np
import pytest
from d... | 3,332 | 109 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\linked_list\__init__.py | python | Python | """
Linked Lists consists of Nodes.
Nodes contain data and also may link to other nodes:
- Head Node: First node, the address of the
head node gives us access of the complete list
- Last node: points to null
"""
from __future__ import annotations
from typing import Any
class Node:
def _... | 3,893 | 134 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\linked_list\circular_linked_list.py | python | Python | from __future__ import annotations
from collections.abc import Iterator
from dataclasses import dataclass
from typing import Any
@dataclass
class Node:
data: Any
next_node: Node | None = None
@dataclass
class CircularLinkedList:
head: Node | None = None # Reference to the head (first node)
tail: N... | 7,067 | 211 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\linked_list\deque_doubly.py | python | Python | """
Implementing Deque using DoublyLinkedList ...
Operations:
1. insertion in the front -> O(1)
2. insertion in the end -> O(1)
3. remove from the front -> O(1)
4. remove from the end -> O(1)
"""
class _DoublyLinkedBase:
"""A Private class (to be inherited)"""
class _Node:
__slots__ =... | 4,216 | 144 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\linked_list\doubly_linked_list.py | python | Python | """
https://en.wikipedia.org/wiki/Doubly_linked_list
"""
class Node:
def __init__(self, data):
self.data = data
self.previous = None
self.next = None
def __str__(self):
return f"{self.data}"
class DoublyLinkedList:
def __init__(self):
self.head = None
sel... | 6,949 | 231 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\linked_list\doubly_linked_list_two.py | python | Python | """
- A linked list is similar to an array, it holds values. However, links in a linked
list do not have indexes.
- This is an example of a double ended, doubly linked list.
- Each link references the next link and the previous one.
- A Doubly Linked List (DLL) contains an extra pointer, typically called previous
... | 7,169 | 264 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\linked_list\floyds_cycle_detection.py | python | Python | """
Floyd's cycle detection algorithm is a popular algorithm used to detect cycles
in a linked list. It uses two pointers, a slow pointer and a fast pointer,
to traverse the linked list. The slow pointer moves one node at a time while the fast
pointer moves two nodes at a time. If there is a cycle in the linked list,
t... | 4,370 | 151 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\linked_list\from_sequence.py | python | Python | """
Recursive Program to create a Linked List from a sequence and
print a string representation of it.
"""
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."""
... | 1,738 | 58 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\linked_list\has_loop.py | python | Python | from __future__ import annotations
from typing import Any
class ContainsLoopError(Exception):
pass
class Node:
def __init__(self, data: Any) -> None:
self.data: Any = data
self.next_node: Node | None = None
def __iter__(self):
node = self
visited = set()
while n... | 1,764 | 63 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\linked_list\is_palindrome.py | python | Python | from __future__ import annotations
from dataclasses import dataclass
@dataclass
class ListNode:
val: int = 0
next_node: ListNode | None = None
def is_palindrome(head: ListNode | None) -> bool:
"""
Check if a linked list is a palindrome.
Args:
head: The head of the linked list.
Ret... | 4,818 | 186 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\linked_list\merge_two_lists.py | python | Python | """
Algorithm that merges two sorted linked lists into one sorted linked list.
"""
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
test_data_odd = (3, 9, -11, 0, 7, 5, 1, -1)
test_data_even = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class Node:
d... | 2,293 | 84 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\linked_list\middle_element_of_linked_list.py | python | Python | from __future__ import annotations
class Node:
def __init__(self, data: int) -> None:
self.data = data
self.next = None
class LinkedList:
def __init__(self):
self.head = None
def push(self, new_data: int) -> int:
new_node = Node(new_data)
new_node.next = self.hea... | 1,611 | 69 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\linked_list\print_reverse.py | python | Python | from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
@dataclass
class Node:
data: int
next_node: Node | None = None
class LinkedList:
"""A class to represent a Linked List.
Use a tail pointer to speed up the append() operation.
"""
... | 3,737 | 127 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\linked_list\reverse_k_group.py | python | Python | from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
@dataclass
class Node:
data: int
next_node: Node | None = None
class LinkedList:
def __init__(self, ints: Iterable[int]) -> None:
self.head: Node | None = None
for i in in... | 3,194 | 119 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\linked_list\rotate_to_the_right.py | python | Python | from __future__ import annotations
from dataclasses import dataclass
@dataclass
class Node:
data: int
next_node: Node | None = None
def print_linked_list(head: Node | None) -> None:
"""
Print the entire linked list iteratively.
This function prints the elements of a linked list separat... | 4,260 | 157 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\linked_list\singly_linked_list.py | python | Python | from __future__ import annotations
from collections.abc import Iterator
from dataclasses import dataclass
from typing import Any
@dataclass
class Node:
"""
Create and initialize Node class instance.
>>> Node(20)
Node(20)
>>> Node("Hello, world!")
Node(Hello, world!)
>>> Node(None)
Nod... | 16,564 | 530 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\linked_list\skip_list.py | python | Python | """
Based on "Skip Lists: A Probabilistic Alternative to Balanced Trees" by William Pugh
https://epaperpress.com/sortsearch/download/skiplist.pdf
"""
from __future__ import annotations
from itertools import pairwise
from random import random
from typing import TypeVar
KT = TypeVar("KT")
VT = TypeVar("VT")
class No... | 13,049 | 449 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\linked_list\swap_nodes.py | python | Python | from __future__ import annotations
from collections.abc import Iterator
from dataclasses import dataclass
from typing import Any
@dataclass
class Node:
data: Any
next_node: Node | None = None
@dataclass
class LinkedList:
head: Node | None = None
def __iter__(self) -> Iterator:
"""
... | 4,305 | 149 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\queues\circular_queue.py | python | Python | # Implementation of Circular Queue (using Python lists)
class CircularQueue:
"""Circular FIFO queue with a fixed capacity"""
def __init__(self, n: int):
self.n = n
self.array = [None] * self.n
self.front = 0 # index of the first element
self.rear = 0
self.size = 0
... | 3,220 | 110 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\queues\circular_queue_linked_list.py | python | Python | # Implementation of Circular Queue using linked lists
# https://en.wikipedia.org/wiki/Circular_buffer
from __future__ import annotations
from typing import Any
class CircularQueueLinkedList:
"""
Circular FIFO list with the given capacity (default queue length : 6)
>>> cq = CircularQueueLinkedList(2)
... | 4,394 | 162 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\queues\double_ended_queue.py | python | Python | """
Implementation of double ended queue.
"""
from __future__ import annotations
from collections.abc import Iterable
from dataclasses import dataclass
from typing import Any
class Deque:
"""
Deque data structure.
Operations
----------
append(val: Any) -> None
appendleft(val: Any) -> None
... | 14,341 | 464 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\queues\linked_queue.py | python | Python | """A Queue using a linked list like structure"""
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class Node:
def __init__(self, data: Any) -> None:
self.data: Any = data
self.next: Node | None = None
def __str__(self) -> str:
return f"{... | 3,864 | 157 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\queues\priority_queue_using_list.py | python | Python | """
Pure Python implementations of a Fixed Priority Queue and an Element Priority Queue
using Python lists.
"""
class OverFlowError(Exception):
pass
class UnderFlowError(Exception):
pass
class FixedPriorityQueue:
"""
Tasks can be added to a Priority Queue at any time and in any order but when Task... | 5,949 | 233 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\queues\queue_by_list.py | python | Python | """Queue represented by a Python list"""
from collections.abc import Iterable
class QueueByList[T]:
def __init__(self, iterable: Iterable[T] | None = None) -> None:
"""
>>> QueueByList()
Queue(())
>>> QueueByList([10, 20, 30])
Queue((10, 20, 30))
>>> QueueByList((i... | 3,175 | 139 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\queues\queue_by_two_stacks.py | python | Python | """Queue implementation using two stacks"""
from collections.abc import Iterable
class QueueByTwoStacks[T]:
def __init__(self, iterable: Iterable[T] | None = None) -> None:
"""
>>> QueueByTwoStacks()
Queue(())
>>> QueueByTwoStacks([10, 20, 30])
Queue((10, 20, 30))
... | 2,726 | 113 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\queues\queue_on_pseudo_stack.py | python | Python | """Queue represented by a pseudo stack (represented by a list with pop and append)"""
from typing import Any
class Queue:
def __init__(self):
self.stack = []
self.length = 0
def __str__(self):
printed = "<" + str(self.stack)[1:-1] + ">"
return printed
"""Enqueues {@code ... | 1,547 | 60 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\stacks\balanced_parentheses.py | python | Python | from .stack import Stack
def balanced_parentheses(parentheses: str) -> bool:
"""Use a stack to check if a string of parentheses is balanced.
>>> balanced_parentheses("([]{})")
True
>>> balanced_parentheses("[()]{}{[()()]()}")
True
>>> balanced_parentheses("[(])")
False
>>> balanced_par... | 1,117 | 39 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\stacks\dijkstras_two_stack_algorithm.py | python | Python | """
Author: Alexander Joslin
GitHub: github.com/echoaj
Explanation: https://medium.com/@haleesammar/implemented-in-js-dijkstras-2-stack-
algorithm-for-evaluating-mathematical-expressions-fc0837dae1ea
We can use Dijkstra's two stack algorithm to solve an equation
such as: (5 + ((4 * 2) * (2 + 3)))
THES... | 2,727 | 85 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\stacks\infix_to_postfix_conversion.py | python | Python | """
https://en.wikipedia.org/wiki/Infix_notation
https://en.wikipedia.org/wiki/Reverse_Polish_notation
https://en.wikipedia.org/wiki/Shunting-yard_algorithm
"""
from typing import Literal
from .balanced_parentheses import balanced_parentheses
from .stack import Stack
PRECEDENCES: dict[str, int] = {
"+": 1,
"... | 3,186 | 114 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\stacks\infix_to_prefix_conversion.py | python | Python | """
Output:
Enter an Infix Equation = a + b ^c
Symbol | Stack | Postfix
----------------------------
c | | c
^ | ^ | c
b | ^ | cb
+ | + | cb^
a | + | cb^a
| | cb^a+
a+b^c (Infix) -> +a^bc (Prefix)
"""
def infix_2_postf... | 6,010 | 193 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\stacks\largest_rectangle_histogram.py | python | Python | def largest_rectangle_area(heights: list[int]) -> int:
"""
Inputs an array of integers representing the heights of bars,
and returns the area of the largest rectangle that can be formed
>>> largest_rectangle_area([2, 1, 5, 6, 2, 3])
10
>>> largest_rectangle_area([2, 4])
4
>>> largest_... | 1,113 | 40 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\stacks\lexicographical_numbers.py | python | Python | from collections.abc import Iterator
def lexical_order(max_number: int) -> Iterator[int]:
"""
Generate numbers in lexical order from 1 to max_number.
>>> " ".join(map(str, lexical_order(13)))
'1 10 11 12 13 2 3 4 5 6 7 8 9'
>>> list(lexical_order(1))
[1]
>>> " ".join(map(str, lexical_orde... | 1,004 | 39 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\stacks\next_greater_element.py | python | Python | from __future__ import annotations
arr = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
expect = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]
def next_greatest_element_slow(arr: list[float]) -> list[float]:
"""
Get the Next Greatest Element (NGE) for each element in the array
b... | 4,095 | 134 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\stacks\postfix_evaluation.py | python | Python | """
Reverse Polish Nation is also known as Polish postfix notation or simply postfix
notation.
https://en.wikipedia.org/wiki/Reverse_Polish_notation
Classic examples of simple stack implementations.
Valid operators are +, -, *, /.
Each operand may be an integer or another expression.
Output:
Enter a Postfix Equation ... | 6,204 | 201 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\stacks\prefix_evaluation.py | python | Python | """
Program to evaluate a prefix expression.
https://en.wikipedia.org/wiki/Polish_notation
"""
operators = {
"+": lambda x, y: x + y,
"-": lambda x, y: x - y,
"*": lambda x, y: x * y,
"/": lambda x, y: x / y,
}
def is_operand(c):
"""
Return True if the given char c is an operand, e.g. it is a... | 2,078 | 93 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\stacks\stack.py | python | Python | from __future__ import annotations
from typing import TypeVar
T = TypeVar("T")
class StackOverflowError(BaseException):
pass
class StackUnderflowError(BaseException):
pass
class Stack[T]:
"""A stack is an abstract data type that serves as a collection of
elements with two principal operations: p... | 4,940 | 216 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\stacks\stack_using_two_queues.py | python | Python | from __future__ import annotations
from collections import deque
from dataclasses import dataclass, field
@dataclass
class StackWithQueues:
"""
https://www.geeksforgeeks.org/implement-stack-using-queue/
>>> stack = StackWithQueues()
>>> stack.push(1)
>>> stack.push(2)
>>> stack.push(3)
>... | 2,336 | 86 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\stacks\stack_with_doubly_linked_list.py | python | Python | # A complete working Python program to demonstrate all
# stack operations using a doubly linked list
from __future__ import annotations
from typing import TypeVar
T = TypeVar("T")
class Node[T]:
def __init__(self, data: T):
self.data = data # Assign data
self.next: Node[T] | None = None # Ini... | 3,301 | 131 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\stacks\stack_with_singly_linked_list.py | python | Python | """A Stack using a linked list like structure"""
from __future__ import annotations
from collections.abc import Iterator
from typing import TypeVar
T = TypeVar("T")
class Node[T]:
def __init__(self, data: T):
self.data = data
self.next: Node[T] | None = None
def __str__(self) -> str:
... | 3,862 | 166 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\stacks\stock_span_problem.py | python | Python | """
The stock span problem is a financial problem where we have a series of n daily
price quotes for a stock and we need to calculate span of stock's price for all n days.
The span Si of the stock's price on a given day i is defined as the maximum
number of consecutive days just before the given day, for which the pri... | 2,242 | 74 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\suffix_tree\example\example_usage.py | python | Python | # Created by: Ramy-Badr-Ahmed (https://github.com/Ramy-Badr-Ahmed)
# in Pull Request: #11554
# https://github.com/TheAlgorithms/Python/pull/11554
#
# Please mention me (@Ramy-Badr-Ahmed) in any issue or pull request
# addressing bugs/corrections to this file.
# Thank you!
from data_structures.suffix_tree.suffix_... | 1,118 | 38 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\suffix_tree\suffix_tree.py | python | Python | # Created by: Ramy-Badr-Ahmed (https://github.com/Ramy-Badr-Ahmed)
# in Pull Request: #11554
# https://github.com/TheAlgorithms/Python/pull/11554
#
# Please mention me (@Ramy-Badr-Ahmed) in any issue or pull request
# addressing bugs/corrections to this file.
# Thank you!
from data_structures.suffix_tree.suffix_... | 2,071 | 67 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\suffix_tree\suffix_tree_node.py | python | Python | # Created by: Ramy-Badr-Ahmed (https://github.com/Ramy-Badr-Ahmed)
# in Pull Request: #11554
# https://github.com/TheAlgorithms/Python/pull/11554
#
# Please mention me (@Ramy-Badr-Ahmed) in any issue or pull request
# addressing bugs/corrections to this file.
# Thank you!
from __future__ import annotations
cla... | 1,342 | 37 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\suffix_tree\tests\test_suffix_tree.py | test | Python | # Created by: Ramy-Badr-Ahmed (https://github.com/Ramy-Badr-Ahmed)
# in Pull Request: #11554
# https://github.com/TheAlgorithms/Python/pull/11554
#
# Please mention me (@Ramy-Badr-Ahmed) in any issue or pull request
# addressing bugs/corrections to this file.
# Thank you!
import unittest
from data_structures.su... | 2,259 | 60 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\trie\radix_tree.py | python | Python | """
A Radix Tree is a data structure that represents a space-optimized
trie (prefix tree) in whicheach node that is the only child is merged
with its parent [https://en.wikipedia.org/wiki/Radix_tree]
"""
class RadixNode:
def __init__(self, prefix: str = "", is_leaf: bool = False) -> None:
# Mapping from t... | 7,864 | 230 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | data_structures\trie\trie.py | python | Python | """
A Trie/Prefix Tree is a kind of search tree used to provide quick lookup
of words/patterns in a set of words. A basic Trie however has O(n^2) space complexity
making it impractical in practice. It however provides O(max(search_string, length of
longest word)) lookup time making it an optimal approach when space is ... | 3,741 | 128 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | digital_image_processing\change_brightness.py | python | Python | from PIL import Image
def change_brightness(img: Image, level: float) -> Image:
"""
Change the brightness of a PIL Image to a given level.
"""
def brightness(c: int) -> float:
"""
Fundamental Transformation/Operation that'll be performed on
every bit.
"""
retur... | 777 | 27 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | digital_image_processing\change_contrast.py | python | Python | """
Changing contrast with PIL
This algorithm is used in
https://noivce.pythonanywhere.com/ Python web app.
psf/black: True
ruff : True
"""
from PIL import Image
def change_contrast(img: Image, level: int) -> Image:
"""
Function to change contrast
"""
factor = (259 * (level + 255)) / (255 * (259 - ... | 834 | 36 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | digital_image_processing\convert_to_negative.py | python | Python | """
Implemented an algorithm using opencv to convert a colored image into its negative
"""
from cv2 import destroyAllWindows, imread, imshow, waitKey
def convert_to_negative(img):
# getting number of pixels in the image
pixel_h, pixel_v = img.shape[0], img.shape[1]
# converting each pixel's color to its... | 764 | 31 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | digital_image_processing\dithering\burkes.py | python | Python | """
Implementation Burke's algorithm (dithering)
"""
import numpy as np
from cv2 import destroyAllWindows, imread, imshow, waitKey
class Burkes:
"""
Burke's algorithm is using for converting grayscale image to black and white version
Source: Source: https://en.wikipedia.org/wiki/Dither
Note:
... | 3,735 | 99 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | digital_image_processing\edge_detection\canny.py | python | Python | import cv2
import numpy as np
from digital_image_processing.filters.convolve import img_convolve
from digital_image_processing.filters.sobel_filter import sobel_filter
PI = 180
def gen_gaussian_kernel(k_size, sigma):
center = k_size // 2
x, y = np.mgrid[0 - center : k_size - center, 0 - center : k_size - ce... | 5,549 | 144 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | digital_image_processing\filters\bilateral_filter.py | python | Python | """
Implementation of Bilateral filter
Inputs:
img: A 2d image with values in between 0 and 1
varS: variance in space dimension.
varI: variance in Intensity.
N: Kernel size(Must be an odd number)
Output:
img:A 2d zero padded image with values in between 0 and 1
"""
import math
import sys
import c... | 2,971 | 89 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | digital_image_processing\filters\convolve.py | python | Python | # @Author : lightXu
# @File : convolve.py
# @Time : 2019/7/8 0008 下午 16:13
from cv2 import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import array, dot, pad, ravel, uint8, zeros
def im2col(image, block_size):
rows, cols = image.shape
dst_height = cols - block_size[1] + 1
dst_width... | 1,678 | 50 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | digital_image_processing\filters\gabor_filter.py | python | Python | # Implementation of the Gaborfilter
# https://en.wikipedia.org/wiki/Gabor_filter
import numpy as np
from cv2 import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filter2D, imread, imshow, waitKey
def gabor_filter_kernel(
ksize: int, sigma: int, theta: int, lambd: int, gamma: int, psi: int
) -> np.ndarray:
"""
:param... | 2,705 | 86 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | digital_image_processing\filters\gaussian_filter.py | python | Python | """
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... | 1,801 | 54 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | digital_image_processing\filters\laplacian_filter.py | python | Python | # @Author : ojas-wani
# @File : laplacian_filter.py
# @Date : 10/04/2023
import numpy as np
from cv2 import (
BORDER_DEFAULT,
COLOR_BGR2GRAY,
CV_64F,
cvtColor,
filter2D,
imread,
imshow,
waitKey,
)
from digital_image_processing.filters.gaussian_filter import gaussian_filter
def... | 2,319 | 82 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | digital_image_processing\filters\local_binary_pattern.py | python | Python | import cv2
import numpy as np
def get_neighbors_pixel(
image: np.ndarray, x_coordinate: int, y_coordinate: int, center: int
) -> int:
"""
Comparing local neighborhood pixel value with threshold value of centre pixel.
Exception is required when neighborhood value of a center pixel value is null.
i.... | 3,026 | 81 |
Python | TheAlgorithms/Python | TheAlgorithms | 220,221 | MIT | All Algorithms implemented in Python | digital_image_processing\filters\median_filter.py | python | Python | """
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 ... | 1,329 | 43 |
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