repo stringlengths 7 90 | file_url stringlengths 81 315 | file_path stringlengths 4 228 | content stringlengths 0 32.8k | language stringclasses 1
value | license stringclasses 7
values | commit_sha stringlengths 40 40 | retrieved_at stringdate 2026-01-04 14:38:15 2026-01-05 02:33:18 | truncated bool 2
classes |
|---|---|---|---|---|---|---|---|---|
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/neural_network/activation_functions/exponential_linear_unit.py | neural_network/activation_functions/exponential_linear_unit.py | """
Implements the Exponential Linear Unit or ELU function.
The function takes a vector of K real numbers and a real number alpha as
input and then applies the ELU function to each element of the vector.
Script inspired from its corresponding Wikipedia article
https://en.wikipedia.org/wiki/Rectifier_(neural_networks)... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/neural_network/activation_functions/__init__.py | neural_network/activation_functions/__init__.py | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false | |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/neural_network/activation_functions/swish.py | neural_network/activation_functions/swish.py | """
This script demonstrates the implementation of the Sigmoid Linear Unit (SiLU)
or swish function.
* https://en.wikipedia.org/wiki/Rectifier_(neural_networks)
* https://en.wikipedia.org/wiki/Swish_function
The function takes a vector x of K real numbers as input and returns x * sigmoid(x).
Swish is a smooth, non-mon... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/neural_network/activation_functions/rectified_linear_unit.py | neural_network/activation_functions/rectified_linear_unit.py | """
This script demonstrates the implementation of the ReLU function.
It's a kind of activation function defined as the positive part of its argument in the
context of neural network.
The function takes a vector of K real numbers as input and then argmax(x, 0).
After through ReLU, the element of the vector always 0 or... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/neural_network/activation_functions/leaky_rectified_linear_unit.py | neural_network/activation_functions/leaky_rectified_linear_unit.py | """
Leaky Rectified Linear Unit (Leaky ReLU)
Use Case: Leaky ReLU addresses the problem of the vanishing gradient.
For more detailed information, you can refer to the following link:
https://en.wikipedia.org/wiki/Rectifier_(neural_networks)#Leaky_ReLU
"""
import numpy as np
def leaky_rectified_linear_unit(vector: n... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/dynamic_programming/tribonacci.py | dynamic_programming/tribonacci.py | # Tribonacci sequence using Dynamic Programming
def tribonacci(num: int) -> list[int]:
"""
Given a number, return first n Tribonacci Numbers.
>>> tribonacci(5)
[0, 0, 1, 1, 2]
>>> tribonacci(8)
[0, 0, 1, 1, 2, 4, 7, 13]
"""
dp = [0] * num
dp[2] = 1
for i in range(3, num):
... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/dynamic_programming/wildcard_matching.py | dynamic_programming/wildcard_matching.py | """
Author : ilyas dahhou
Date : Oct 7, 2023
Task:
Given an input string and a pattern, implement wildcard pattern matching with support
for '?' and '*' where:
'?' matches any single character.
'*' matches any sequence of characters (including the empty sequence).
The matching should cover the entire input string ... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/dynamic_programming/longest_common_substring.py | dynamic_programming/longest_common_substring.py | """
Longest Common Substring Problem Statement:
Given two sequences, find the
longest common substring present in both of them. A substring is
necessarily continuous.
Example:
``abcdef`` and ``xabded`` have two longest common substrings, ``ab`` or ``de``.
Therefore, algorithm should return any one ... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/dynamic_programming/abbreviation.py | dynamic_programming/abbreviation.py | """
https://www.hackerrank.com/challenges/abbr/problem
You can perform the following operation on some string, :
1. Capitalize zero or more of 's lowercase letters at some index i
(i.e., make them uppercase).
2. Delete all of the remaining lowercase letters in .
Example:
a=daBcd and b="ABC"
daBcd -> capitalize a a... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/dynamic_programming/optimal_binary_search_tree.py | dynamic_programming/optimal_binary_search_tree.py | #!/usr/bin/env python3
# This Python program implements an optimal binary search tree (abbreviated BST)
# building dynamic programming algorithm that delivers O(n^2) performance.
#
# The goal of the optimal BST problem is to build a low-cost BST for a
# given set of nodes, each with its own key and frequency. The freq... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/dynamic_programming/word_break.py | dynamic_programming/word_break.py | """
Author : Alexander Pantyukhin
Date : December 12, 2022
Task:
Given a string and a list of words, return true if the string can be
segmented into a space-separated sequence of one or more words.
Note that the same word may be reused
multiple times in the segmentation.
Implementation notes: Trie + Dynamic prog... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/dynamic_programming/regex_match.py | dynamic_programming/regex_match.py | """
Regex matching check if a text matches pattern or not.
Pattern:
1. ``.`` Matches any single character.
2. ``*`` Matches zero or more of the preceding element.
More info:
https://medium.com/trick-the-interviwer/regular-expression-matching-9972eb74c03
"""
def recursive_match(text: str, pattern: str) -... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/dynamic_programming/rod_cutting.py | dynamic_programming/rod_cutting.py | """
This module provides two implementations for the rod-cutting problem:
1. A naive recursive implementation which has an exponential runtime
2. Two dynamic programming implementations which have quadratic runtime
The rod-cutting problem is the problem of finding the maximum possible revenue
obtainable from a rod... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/dynamic_programming/fast_fibonacci.py | dynamic_programming/fast_fibonacci.py | #!/usr/bin/env python3
"""
This program calculates the nth Fibonacci number in O(log(n)).
It's possible to calculate F(1_000_000) in less than a second.
"""
from __future__ import annotations
import sys
def fibonacci(n: int) -> int:
"""
return F(n)
>>> [fibonacci(i) for i in range(13)]
[0, 1, 1, 2,... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/dynamic_programming/longest_increasing_subsequence.py | dynamic_programming/longest_increasing_subsequence.py | """
Author : Mehdi ALAOUI
This is a pure Python implementation of Dynamic Programming solution to the longest
increasing subsequence of a given sequence.
The problem is:
Given an array, to find the longest and increasing sub-array in that given array and
return it.
Example:
``[10, 22, 9, 33, 21, 50, 41,... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/dynamic_programming/viterbi.py | dynamic_programming/viterbi.py | from typing import Any
def viterbi(
observations_space: list,
states_space: list,
initial_probabilities: dict,
transition_probabilities: dict,
emission_probabilities: dict,
) -> list:
"""
Viterbi Algorithm, to find the most likely path of
states from the start and the expected output.
... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/dynamic_programming/fibonacci.py | dynamic_programming/fibonacci.py | """
This is a pure Python implementation of Dynamic Programming solution to the fibonacci
sequence problem.
"""
class Fibonacci:
def __init__(self) -> None:
self.sequence = [0, 1]
def get(self, index: int) -> list:
"""
Get the Fibonacci number of `index`. If the number does not exist,... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/dynamic_programming/knapsack.py | dynamic_programming/knapsack.py | """
Given weights and values of n items, put these items in a knapsack of
capacity W to get the maximum total value in the knapsack.
Note that only the integer weights 0-1 knapsack problem is solvable
using dynamic programming.
"""
def mf_knapsack(i, wt, val, j):
"""
This code involves the concept of memory ... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/dynamic_programming/minimum_squares_to_represent_a_number.py | dynamic_programming/minimum_squares_to_represent_a_number.py | import math
import sys
def minimum_squares_to_represent_a_number(number: int) -> int:
"""
Count the number of minimum squares to represent a number
>>> minimum_squares_to_represent_a_number(25)
1
>>> minimum_squares_to_represent_a_number(37)
2
>>> minimum_squares_to_represent_a_number(21)... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/dynamic_programming/longest_palindromic_subsequence.py | dynamic_programming/longest_palindromic_subsequence.py | """
author: Sanket Kittad
Given a string s, find the longest palindromic subsequence's length in s.
Input: s = "bbbab"
Output: 4
Explanation: One possible longest palindromic subsequence is "bbbb".
Leetcode link: https://leetcode.com/problems/longest-palindromic-subsequence/description/
"""
def longest_palindromic_su... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/dynamic_programming/smith_waterman.py | dynamic_programming/smith_waterman.py | """
https://en.wikipedia.org/wiki/Smith%E2%80%93Waterman_algorithm
The Smith-Waterman algorithm is a dynamic programming algorithm used for sequence
alignment. It is particularly useful for finding similarities between two sequences,
such as DNA or protein sequences. In this implementation, gaps are penalized
linearly,... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/dynamic_programming/all_construct.py | dynamic_programming/all_construct.py | """
Program to list all the ways a target string can be
constructed from the given list of substrings
"""
from __future__ import annotations
def all_construct(target: str, word_bank: list[str] | None = None) -> list[list[str]]:
"""
returns the list containing all the possible
combinations a string(`targe... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/dynamic_programming/edit_distance.py | dynamic_programming/edit_distance.py | """
Author : Turfa Auliarachman
Date : October 12, 2016
This is a pure Python implementation of Dynamic Programming solution to the edit
distance problem.
The problem is :
Given two strings A and B. Find the minimum number of operations to string B such that
A = B. The permitted operations are removal, insertion... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/dynamic_programming/longest_common_subsequence.py | dynamic_programming/longest_common_subsequence.py | """
LCS Problem Statement: Given two sequences, find the length of longest subsequence
present in both of them. A subsequence is a sequence that appears in the same relative
order, but not necessarily continuous.
Example:"abc", "abg" are subsequences of "abcdefgh".
"""
def longest_common_subsequence(x: str, y: str):... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/dynamic_programming/climbing_stairs.py | dynamic_programming/climbing_stairs.py | #!/usr/bin/env python3
def climb_stairs(number_of_steps: int) -> int:
"""
LeetCdoe No.70: Climbing Stairs
Distinct ways to climb a number_of_steps staircase where each time you can either
climb 1 or 2 steps.
Args:
number_of_steps: number of steps on the staircase
Returns:
Dis... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/dynamic_programming/palindrome_partitioning.py | dynamic_programming/palindrome_partitioning.py | """
Given a string s, partition s such that every substring of the
partition is a palindrome.
Find the minimum cuts needed for a palindrome partitioning of s.
Time Complexity: O(n^2)
Space Complexity: O(n^2)
For other explanations refer to: https://www.youtube.com/watch?v=_H8V5hJUGd0
"""
def find_minimum_partitions(... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/dynamic_programming/factorial.py | dynamic_programming/factorial.py | # Factorial of a number using memoization
from functools import lru_cache
@lru_cache
def factorial(num: int) -> int:
"""
>>> factorial(7)
5040
>>> factorial(-1)
Traceback (most recent call last):
...
ValueError: Number should not be negative.
>>> [factorial(i) for i in range(10)]
... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/dynamic_programming/catalan_numbers.py | dynamic_programming/catalan_numbers.py | """
Print all the Catalan numbers from 0 to n, n being the user input.
* The Catalan numbers are a sequence of positive integers that
* appear in many counting problems in combinatorics [1]. Such
* problems include counting [2]:
* - The number of Dyck words of length 2n
* - The number well-formed expressions with... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/dynamic_programming/max_product_subarray.py | dynamic_programming/max_product_subarray.py | def max_product_subarray(numbers: list[int]) -> int:
"""
Returns the maximum product that can be obtained by multiplying a
contiguous subarray of the given integer list `numbers`.
Example:
>>> max_product_subarray([2, 3, -2, 4])
6
>>> max_product_subarray((-2, 0, -1))
0
>>> max_pro... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/dynamic_programming/min_distance_up_bottom.py | dynamic_programming/min_distance_up_bottom.py | """
Author : Alexander Pantyukhin
Date : October 14, 2022
This is an implementation of the up-bottom approach to find edit distance.
The implementation was tested on Leetcode: https://leetcode.com/problems/edit-distance/
Levinstein distance
Dynamic Programming: up -> down.
"""
import functools
def min_distance_... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/dynamic_programming/minimum_cost_path.py | dynamic_programming/minimum_cost_path.py | # Youtube Explanation: https://www.youtube.com/watch?v=lBRtnuxg-gU
from __future__ import annotations
def minimum_cost_path(matrix: list[list[int]]) -> int:
"""
Find the minimum cost traced by all possible paths from top left to bottom right in
a given matrix
>>> minimum_cost_path([[2, 1], [3, 1], [... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/dynamic_programming/range_sum_query.py | dynamic_programming/range_sum_query.py | """
Author: Sanjay Muthu <https://github.com/XenoBytesX>
This is an implementation of the Dynamic Programming solution to the Range Sum Query.
The problem statement is:
Given an array and q queries,
each query stating you to find the sum of elements from l to r (inclusive)
Example:
arr = [1, 4, 6, 2, 61,... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/dynamic_programming/trapped_water.py | dynamic_programming/trapped_water.py | """
Given an array of non-negative integers representing an elevation map where the width
of each bar is 1, this program calculates how much rainwater can be trapped.
Example - height = (0, 1, 0, 2, 1, 0, 1, 3, 2, 1, 2, 1)
Output: 6
This problem can be solved using the concept of "DYNAMIC PROGRAMMING".
We calculate t... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/dynamic_programming/minimum_coin_change.py | dynamic_programming/minimum_coin_change.py | """
You have m types of coins available in infinite quantities
where the value of each coins is given in the array S=[S0,... Sm-1]
Can you determine number of ways of making change for n units using
the given types of coins?
https://www.hackerrank.com/challenges/coin-change/problem
"""
def dp_count(s, n):
"""
... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/dynamic_programming/integer_partition.py | dynamic_programming/integer_partition.py | """
The number of partitions of a number n into at least k parts equals the number of
partitions into exactly k parts plus the number of partitions into at least k-1 parts.
Subtracting 1 from each part of a partition of n into k parts gives a partition of n-k
into k parts. These two facts together are used for this alg... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/dynamic_programming/k_means_clustering_tensorflow.py | dynamic_programming/k_means_clustering_tensorflow.py | from random import shuffle
import tensorflow as tf
from numpy import array
def tf_k_means_cluster(vectors, noofclusters):
"""
K-Means Clustering using TensorFlow.
'vectors' should be a n*k 2-D NumPy array, where n is the number
of vectors of dimensionality k.
'noofclusters' should be an integer.
... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/dynamic_programming/iterating_through_submasks.py | dynamic_programming/iterating_through_submasks.py | """
Author : Syed Faizan (3rd Year Student IIIT Pune)
github : faizan2700
You are given a bitmask m and you want to efficiently iterate through all of
its submasks. The mask s is submask of m if only bits that were included in
bitmask are set
"""
from __future__ import annotations
def list_of_submasks(mask: int) -> ... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/dynamic_programming/matrix_chain_multiplication.py | dynamic_programming/matrix_chain_multiplication.py | """
| Find the minimum number of multiplications needed to multiply chain of matrices.
| Reference: https://www.geeksforgeeks.org/matrix-chain-multiplication-dp-8/
The algorithm has interesting real-world applications.
Example:
1. Image transformations in Computer Graphics as images are composed of matrix.
2. Sol... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/dynamic_programming/matrix_chain_order.py | dynamic_programming/matrix_chain_order.py | import sys
"""
Dynamic Programming
Implementation of Matrix Chain Multiplication
Time Complexity: O(n^3)
Space Complexity: O(n^2)
Reference: https://en.wikipedia.org/wiki/Matrix_chain_multiplication
"""
def matrix_chain_order(array: list[int]) -> tuple[list[list[int]], list[list[int]]]:
"""
>>> matrix_chain... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/dynamic_programming/fizz_buzz.py | dynamic_programming/fizz_buzz.py | # https://en.wikipedia.org/wiki/Fizz_buzz#Programming
def fizz_buzz(number: int, iterations: int) -> str:
"""
| Plays FizzBuzz.
| Prints Fizz if number is a multiple of ``3``.
| Prints Buzz if its a multiple of ``5``.
| Prints FizzBuzz if its a multiple of both ``3`` and ``5`` or ``15``.
| Els... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/dynamic_programming/minimum_tickets_cost.py | dynamic_programming/minimum_tickets_cost.py | """
Author : Alexander Pantyukhin
Date : November 1, 2022
Task:
Given a list of days when you need to travel. Each day is integer from 1 to 365.
You are able to use tickets for 1 day, 7 days and 30 days.
Each ticket has a cost.
Find the minimum cost you need to travel every day in the given list of days.
Impleme... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/dynamic_programming/floyd_warshall.py | dynamic_programming/floyd_warshall.py | import math
class Graph:
def __init__(self, n=0): # a graph with Node 0,1,...,N-1
self.n = n
self.w = [
[math.inf for j in range(n)] for i in range(n)
] # adjacency matrix for weight
self.dp = [
[math.inf for j in range(n)] for i in range(n)
] # d... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/dynamic_programming/minimum_partition.py | dynamic_programming/minimum_partition.py | """
Partition a set into two subsets such that the difference of subset sums is minimum
"""
def find_min(numbers: list[int]) -> int:
"""
>>> find_min([1, 2, 3, 4, 5])
1
>>> find_min([5, 5, 5, 5, 5])
5
>>> find_min([5, 5, 5, 5])
0
>>> find_min([3])
3
>>> find_min([])
0
>... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/dynamic_programming/__init__.py | dynamic_programming/__init__.py | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false | |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/dynamic_programming/longest_increasing_subsequence_iterative.py | dynamic_programming/longest_increasing_subsequence_iterative.py | """
Author : Sanjay Muthu <https://github.com/XenoBytesX>
This is a pure Python implementation of Dynamic Programming solution to the longest
increasing subsequence of a given sequence.
The problem is:
Given an array, to find the longest and increasing sub-array in that given array and
return it.
Example:
... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/dynamic_programming/bitmask.py | dynamic_programming/bitmask.py | """
This is a Python implementation for questions involving task assignments between people.
Here Bitmasking and DP are used for solving this.
Question :-
We have N tasks and M people. Each person in M can do only certain of these tasks. Also
a person can do only one task and a task is performed only by one person.
F... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/dynamic_programming/minimum_steps_to_one.py | dynamic_programming/minimum_steps_to_one.py | """
YouTube Explanation: https://www.youtube.com/watch?v=f2xi3c1S95M
Given an integer n, return the minimum steps from n to 1
AVAILABLE STEPS:
* Decrement by 1
* if n is divisible by 2, divide by 2
* if n is divisible by 3, divide by 3
Example 1: n = 10
10 -> 9 -> 3 -> 1
Result: 3 steps
Example 2: n = ... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/dynamic_programming/sum_of_subset.py | dynamic_programming/sum_of_subset.py | def is_sum_subset(arr: list[int], required_sum: int) -> bool:
"""
>>> is_sum_subset([2, 4, 6, 8], 5)
False
>>> is_sum_subset([2, 4, 6, 8], 14)
True
"""
# a subset value says 1 if that subset sum can be formed else 0
# initially no subsets can be formed hence False/0
arr_len = len(arr... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/dynamic_programming/longest_increasing_subsequence_o_nlogn.py | dynamic_programming/longest_increasing_subsequence_o_nlogn.py | #############################
# Author: Aravind Kashyap
# File: lis.py
# comments: This programme outputs the Longest Strictly Increasing Subsequence in
# O(NLogN) Where N is the Number of elements in the list
#############################
from __future__ import annotations
def ceil_index(v, left, right, ke... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/dynamic_programming/largest_divisible_subset.py | dynamic_programming/largest_divisible_subset.py | from __future__ import annotations
def largest_divisible_subset(items: list[int]) -> list[int]:
"""
Algorithm to find the biggest subset in the given array such that for any 2 elements
x and y in the subset, either x divides y or y divides x.
>>> largest_divisible_subset([1, 16, 7, 8, 4])
[16, 8, ... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/dynamic_programming/minimum_size_subarray_sum.py | dynamic_programming/minimum_size_subarray_sum.py | import sys
def minimum_subarray_sum(target: int, numbers: list[int]) -> int:
"""
Return the length of the shortest contiguous subarray in a list of numbers whose sum
is at least target. Reference: https://stackoverflow.com/questions/8269916
>>> minimum_subarray_sum(7, [2, 3, 1, 2, 4, 3])
2
>... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/dynamic_programming/max_subarray_sum.py | dynamic_programming/max_subarray_sum.py | """
The maximum subarray sum problem is the task of finding the maximum sum that can be
obtained from a contiguous subarray within a given array of numbers. For example, given
the array [-2, 1, -3, 4, -1, 2, 1, -5, 4], the contiguous subarray with the maximum sum
is [4, -1, 2, 1], so the maximum subarray sum is 6.
Kad... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/dynamic_programming/combination_sum_iv.py | dynamic_programming/combination_sum_iv.py | """
Question:
You are given an array of distinct integers and you have to tell how many
different ways of selecting the elements from the array are there such that
the sum of chosen elements is equal to the target number tar.
Example
Input:
* N = 3
* target = 5
* array = [1, 2, 5]
Output:
... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/dynamic_programming/subset_generation.py | dynamic_programming/subset_generation.py | def subset_combinations(elements: list[int], n: int) -> list:
"""
Compute n-element combinations from a given list using dynamic programming.
Args:
* `elements`: The list of elements from which combinations will be generated.
* `n`: The number of elements in each combination.
Returns:
... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/dynamic_programming/max_non_adjacent_sum.py | dynamic_programming/max_non_adjacent_sum.py | # Video Explanation: https://www.youtube.com/watch?v=6w60Zi1NtL8&feature=emb_logo
from __future__ import annotations
def maximum_non_adjacent_sum(nums: list[int]) -> int:
"""
Find the maximum non-adjacent sum of the integers in the nums input list
>>> maximum_non_adjacent_sum([1, 2, 3])
4
>>> ma... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/machine_learning/logistic_regression.py | machine_learning/logistic_regression.py | #!/usr/bin/python
# Logistic Regression from scratch
# In[62]:
# In[63]:
# importing all the required libraries
"""
Implementing logistic regression for classification problem
Helpful resources:
Coursera ML course
https://medium.com/@martinpella/logistic-regression-from-scratch-in-python-124c5636b8ac
"""
import n... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/machine_learning/automatic_differentiation.py | machine_learning/automatic_differentiation.py | """
Demonstration of the Automatic Differentiation (Reverse mode).
Reference: https://en.wikipedia.org/wiki/Automatic_differentiation
Author: Poojan Smart
Email: smrtpoojan@gmail.com
"""
from __future__ import annotations
from collections import defaultdict
from enum import Enum
from types import TracebackType
from... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/machine_learning/xgboost_regressor.py | machine_learning/xgboost_regressor.py | # XGBoost Regressor Example
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def data_handling(data: dict) -> tuple:
# Split dataset int... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/machine_learning/similarity_search.py | machine_learning/similarity_search.py | """
Similarity Search : https://en.wikipedia.org/wiki/Similarity_search
Similarity search is a search algorithm for finding the nearest vector from
vectors, used in natural language processing.
In this algorithm, it calculates distance with euclidean distance and
returns a list containing two data for each vector:
... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/machine_learning/multilayer_perceptron_classifier.py | machine_learning/multilayer_perceptron_classifier.py | 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):
... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/machine_learning/loss_functions.py | machine_learning/loss_functions.py | import numpy as np
def binary_cross_entropy(
y_true: np.ndarray, y_pred: np.ndarray, epsilon: float = 1e-15
) -> float:
"""
Calculate the mean binary cross-entropy (BCE) loss between true labels and predicted
probabilities.
BCE loss quantifies dissimilarity between true labels (0 or 1) and predic... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/machine_learning/t_stochastic_neighbour_embedding.py | machine_learning/t_stochastic_neighbour_embedding.py | """
t-distributed stochastic neighbor embedding (t-SNE)
For more details, see:
https://en.wikipedia.org/wiki/T-distributed_stochastic_neighbor_embedding
"""
import doctest
import numpy as np
from numpy import ndarray
from sklearn.datasets import load_iris
def collect_dataset() -> tuple[ndarray, ndarray]:
"""
... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/machine_learning/word_frequency_functions.py | machine_learning/word_frequency_functions.py | import string
from math import log10
"""
tf-idf Wikipedia: https://en.wikipedia.org/wiki/Tf%E2%80%93idf
tf-idf and other word frequency algorithms are often used
as a weighting factor in information retrieval and text
mining. 83% of text-based recommender systems use
tf-idf for term weighting. In L... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/machine_learning/support_vector_machines.py | machine_learning/support_vector_machines.py | import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def norm_squared(vector: ndarray) -> float:
"""
Return the squared second norm of vector
norm_squared(v) = sum(x * x for x in v)
Args:
vector (ndarray): input vector
Returns:
... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/machine_learning/dimensionality_reduction.py | machine_learning/dimensionality_reduction.py | # Copyright (c) 2023 Diego Gasco (diego.gasco99@gmail.com), Diegomangasco on GitHub
"""
Requirements:
- numpy version 1.21
- scipy version 1.3.3
Notes:
- Each column of the features matrix corresponds to a class item
"""
import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.ba... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/machine_learning/data_transformations.py | machine_learning/data_transformations.py | """
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 val... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/machine_learning/linear_discriminant_analysis.py | machine_learning/linear_discriminant_analysis.py | """
Linear Discriminant Analysis
Assumptions About Data :
1. The input variables has a gaussian distribution.
2. The variance calculated for each input variables by class grouping is the
same.
3. The mix of classes in your training set is representative of the problem.
Learning The Model :
T... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/machine_learning/self_organizing_map.py | machine_learning/self_organizing_map.py | """
https://en.wikipedia.org/wiki/Self-organizing_map
"""
import math
class SelfOrganizingMap:
def get_winner(self, weights: list[list[float]], sample: list[int]) -> int:
"""
Compute the winning vector by Euclidean distance
>>> SelfOrganizingMap().get_winner([[1, 2, 3], [4, 5, 6]], [1, 2... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/machine_learning/k_means_clust.py | machine_learning/k_means_clust.py | """README, Author - Anurag Kumar(mailto:anuragkumarak95@gmail.com)
Requirements:
- sklearn
- numpy
- matplotlib
Python:
- 3.5
Inputs:
- X , a 2D numpy array of features.
- k , number of clusters to create.
- initial_centroids , initial centroid values generated by utility function(mentioned
in usage).... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/machine_learning/gradient_boosting_classifier.py | machine_learning/gradient_boosting_classifier.py | import numpy as np
from sklearn.datasets import load_iris
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
class GradientBoostingClassifier:
def __init__(self, n_estimators: int = 100, learning_rate: float = 0.1) -> None... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/machine_learning/scoring_functions.py | machine_learning/scoring_functions.py | import numpy as np
""" Here I implemented the scoring functions.
MAE, MSE, RMSE, RMSLE are included.
Those are used for calculating differences between
predicted values and actual values.
Metrics are slightly differentiated. Sometimes squared, rooted,
even log is used.
Using log and roots ca... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/machine_learning/gradient_descent.py | machine_learning/gradient_descent.py | """
Implementation of gradient descent algorithm for minimizing cost of a linear hypothesis
function.
"""
import numpy as np
# List of input, output pairs
train_data = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
test_data = (((515, 22, 13), 555), (... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/machine_learning/decision_tree.py | machine_learning/decision_tree.py | """
Implementation of a basic regression decision tree.
Input data set: The input data set must be 1-dimensional with continuous labels.
Output: The decision tree maps a real number input to a real number output.
"""
import numpy as np
class DecisionTree:
def __init__(self, depth=5, min_leaf_size=5):
sel... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/machine_learning/sequential_minimum_optimization.py | machine_learning/sequential_minimum_optimization.py | """
Sequential minimal optimization (SMO) for support vector machines (SVM)
Sequential minimal optimization (SMO) is an algorithm for solving the quadratic
programming (QP) problem that arises during the training of SVMs. It was invented by
John Platt in 1998.
Input:
0: type: numpy.ndarray.
1: first column of... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/machine_learning/xgboost_classifier.py | machine_learning/xgboost_classifier.py | # XGBoost Classifier Example
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def data_handling(data: dict) -> tuple:
# Split data... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/machine_learning/apriori_algorithm.py | machine_learning/apriori_algorithm.py | """
Apriori Algorithm is a Association rule mining technique, also known as market basket
analysis, aims to discover interesting relationships or associations among a set of
items in a transactional or relational database.
For example, Apriori Algorithm states: "If a customer buys item A and item B, then they
are like... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/machine_learning/__init__.py | machine_learning/__init__.py | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false | |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/machine_learning/principle_component_analysis.py | machine_learning/principle_component_analysis.py | """
Principal Component Analysis (PCA) is a dimensionality reduction technique
used in machine learning. It transforms high-dimensional data into a lower-dimensional
representation while retaining as much variance as possible.
This implementation follows best practices, including:
- Standardizing the dataset.
- Comput... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/machine_learning/astar.py | machine_learning/astar.py | """
The A* algorithm combines features of uniform-cost search and pure heuristic search to
efficiently compute optimal solutions.
The 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... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/machine_learning/frequent_pattern_growth.py | machine_learning/frequent_pattern_growth.py | """
The Frequent Pattern Growth algorithm (FP-Growth) is a widely used data mining
technique for discovering frequent itemsets in large transaction databases.
It overcomes some of the limitations of traditional methods such as Apriori by
efficiently constructing the FP-Tree
WIKI: https://athena.ecs.csus.edu/~mei/asso... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/machine_learning/mfcc.py | machine_learning/mfcc.py | """
Mel Frequency Cepstral Coefficients (MFCC) Calculation
MFCC is an algorithm widely used in audio and speech processing to represent the
short-term power spectrum of a sound signal in a more compact and
discriminative way. It is particularly popular in speech and audio processing
tasks such as speech recognition an... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/machine_learning/polynomial_regression.py | machine_learning/polynomial_regression.py | """
Polynomial regression is a type of regression analysis that models the relationship
between a predictor x and the response y as an mth-degree polynomial:
y = β₀ + β₁x + β₂x² + ... + βₘxᵐ + ε
By treating x, x², ..., xᵐ as distinct variables, we see that polynomial regression is a
special case of multiple linear re... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/machine_learning/k_nearest_neighbours.py | machine_learning/k_nearest_neighbours.py | """
k-Nearest Neighbours (kNN) is a simple non-parametric supervised learning
algorithm used for classification. Given some labelled training data, a given
point is classified using its k nearest neighbours according to some distance
metric. The most commonly occurring label among the neighbours becomes the label
of th... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/machine_learning/linear_regression.py | machine_learning/linear_regression.py | """
Linear regression is the most basic type of regression commonly used for
predictive analysis. The idea is pretty simple: we have a dataset and we have
features associated with it. Features should be chosen very cautiously
as they determine how much our model will be able to make future predictions.
We try to set th... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/machine_learning/lstm/lstm_prediction.py | machine_learning/lstm/lstm_prediction.py | """
Create a Long Short Term Memory (LSTM) network model
An LSTM is a type of Recurrent Neural Network (RNN) as discussed at:
* https://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 keras.layers import LSTM, Dens... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/machine_learning/lstm/__init__.py | machine_learning/lstm/__init__.py | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false | |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/machine_learning/local_weighted_learning/local_weighted_learning.py | machine_learning/local_weighted_learning/local_weighted_learning.py | """
Locally weighted linear regression, also called local regression, is a type of
non-parametric linear regression that prioritizes data closest to a given
prediction point. The algorithm estimates the vector of model coefficients β
using weighted least squares regression:
β = (XᵀWX)⁻¹(XᵀWy),
where X is the design m... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/machine_learning/local_weighted_learning/__init__.py | machine_learning/local_weighted_learning/__init__.py | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false | |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/machine_learning/forecasting/run.py | machine_learning/forecasting/run.py | """
this is code for forecasting
but I modified it and used it for safety checker of data
for ex: you have an online shop and for some reason some data are
missing (the amount of data that u expected are not supposed to be)
then we can use it
*ps : 1. ofc we can use normal statistic method but in this case
... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/machine_learning/forecasting/__init__.py | machine_learning/forecasting/__init__.py | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false | |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/data_structures/__init__.py | data_structures/__init__.py | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false | |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/data_structures/heap/heap_generic.py | data_structures/heap/heap_generic.py | 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... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/data_structures/heap/binomial_heap.py | data_structures/heap/binomial_heap.py | """
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 ... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/data_structures/heap/heap.py | data_structures/heap/heap.py | 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... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/data_structures/heap/randomized_heap.py | data_structures/heap/randomized_heap.py | #!/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.
"""
... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/data_structures/heap/min_heap.py | data_structures/heap/min_heap.py | # 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.... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/data_structures/heap/max_heap.py | data_structures/heap/max_heap.py | 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
... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/data_structures/heap/__init__.py | data_structures/heap/__init__.py | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false | |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/data_structures/heap/skew_heap.py | data_structures/heap/skew_heap.py | #!/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... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
TheAlgorithms/Python | https://github.com/TheAlgorithms/Python/blob/2c15b8c54eb8130e83640fe1d911c10eb6cd70d4/data_structures/kd_tree/build_kdtree.py | data_structures/kd_tree/build_kdtree.py | # 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... | python | MIT | 2c15b8c54eb8130e83640fe1d911c10eb6cd70d4 | 2026-01-04T14:38:15.231112Z | false |
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