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‎.github/workflows/ruff.yml

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steps:
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- uses: actions/checkout@v4
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- run: pip install --user ruff
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- run: ruff --output-format=github .
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- run: ruff check --output-format=github .

‎.pre-commit-config.yaml

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- id: auto-walrus
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- repo: https://github.com/astral-sh/ruff-pre-commit
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rev: v0.2.2
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rev: v0.3.4
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hooks:
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- id: ruff
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- id: ruff-format
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- id: validate-pyproject
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- repo: https://github.com/pre-commit/mirrors-mypy
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rev: v1.8.0
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rev: v1.9.0
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hooks:
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- id: mypy
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args:

‎DIRECTORY.md

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* [Run Length Encoding](compression/run_length_encoding.py)
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## Computer Vision
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* [Cnn Classification](computer_vision/cnn_classification.py)
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* [Flip Augmentation](computer_vision/flip_augmentation.py)
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* [Haralick Descriptors](computer_vision/haralick_descriptors.py)
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* [Harris Corner](computer_vision/harris_corner.py)
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* [Floyd Warshall](dynamic_programming/floyd_warshall.py)
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* [Integer Partition](dynamic_programming/integer_partition.py)
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* [Iterating Through Submasks](dynamic_programming/iterating_through_submasks.py)
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* [K Means Clustering Tensorflow](dynamic_programming/k_means_clustering_tensorflow.py)
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* [Knapsack](dynamic_programming/knapsack.py)
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* [Largest Divisible Subset](dynamic_programming/largest_divisible_subset.py)
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* [Longest Common Subsequence](dynamic_programming/longest_common_subsequence.py)
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* [Koch Snowflake](fractals/koch_snowflake.py)
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* [Mandelbrot](fractals/mandelbrot.py)
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* [Sierpinski Triangle](fractals/sierpinski_triangle.py)
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* [Vicsek](fractals/vicsek.py)
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## Fuzzy Logic
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* [Fuzzy Operations](fuzzy_logic/fuzzy_operations.py)
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* [Local Weighted Learning](machine_learning/local_weighted_learning/local_weighted_learning.py)
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* [Logistic Regression](machine_learning/logistic_regression.py)
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* [Loss Functions](machine_learning/loss_functions.py)
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* Lstm
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* [Lstm Prediction](machine_learning/lstm/lstm_prediction.py)
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* [Mfcc](machine_learning/mfcc.py)
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* [Multilayer Perceptron Classifier](machine_learning/multilayer_perceptron_classifier.py)
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* [Polynomial Regression](machine_learning/polynomial_regression.py)
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* [Newton Forward Interpolation](maths/numerical_analysis/newton_forward_interpolation.py)
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* [Newton Raphson](maths/numerical_analysis/newton_raphson.py)
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* [Numerical Integration](maths/numerical_analysis/numerical_integration.py)
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* [Proper Fractions](maths/numerical_analysis/proper_fractions.py)
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* [Runge Kutta](maths/numerical_analysis/runge_kutta.py)
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* [Runge Kutta Fehlberg 45](maths/numerical_analysis/runge_kutta_fehlberg_45.py)
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* [Runge Kutta Gills](maths/numerical_analysis/runge_kutta_gills.py)
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* [Swish](neural_network/activation_functions/swish.py)
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* [Back Propagation Neural Network](neural_network/back_propagation_neural_network.py)
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* [Convolution Neural Network](neural_network/convolution_neural_network.py)
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* [Input Data](neural_network/input_data.py)
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* [Simple Neural Network](neural_network/simple_neural_network.py)
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## Other

‎backtracking/all_combinations.py

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"""
2-
In this problem, we want to determine all possible combinations of k
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numbers out of 1 ... n. We use backtracking to solve this problem.
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In this problem, we want to determine all possible combinations of k
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numbers out of 1 ... n. We use backtracking to solve this problem.
44
5-
Time complexity: O(C(n,k)) which is O(n choose k) = O((n!/(k! * (n - k)!))),
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Time complexity: O(C(n,k)) which is O(n choose k) = O((n!/(k! * (n - k)!))),
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"""
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from __future__ import annotations
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from itertools import combinations

‎backtracking/all_permutations.py

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"""
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In this problem, we want to determine all possible permutations
3-
of the given sequence. We use backtracking to solve this problem.
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In this problem, we want to determine all possible permutations
3+
of the given sequence. We use backtracking to solve this problem.
44
5-
Time complexity: O(n! * n),
6-
where n denotes the length of the given sequence.
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Time complexity: O(n! * n),
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where n denotes the length of the given sequence.
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"""
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from __future__ import annotations
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‎backtracking/all_subsequences.py

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Time complexity: O(2^n),
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where n denotes the length of the given sequence.
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"""
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from __future__ import annotations
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from typing import Any

‎backtracking/coloring.py

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"""
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Graph Coloring also called "m coloring problem"
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consists of coloring a given graph with at most m colors
4-
such that no adjacent vertices are assigned the same color
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Graph Coloring also called "m coloring problem"
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consists of coloring a given graph with at most m colors
4+
such that no adjacent vertices are assigned the same color
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Wikipedia: https://en.wikipedia.org/wiki/Graph_coloring
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Wikipedia: https://en.wikipedia.org/wiki/Graph_coloring
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"""
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‎backtracking/hamiltonian_cycle.py

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"""
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A Hamiltonian cycle (Hamiltonian circuit) is a graph cycle
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through a graph that visits each node exactly once.
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Determining whether such paths and cycles exist in graphs
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is the 'Hamiltonian path problem', which is NP-complete.
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A Hamiltonian cycle (Hamiltonian circuit) is a graph cycle
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through a graph that visits each node exactly once.
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Determining whether such paths and cycles exist in graphs
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is the 'Hamiltonian path problem', which is NP-complete.
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Wikipedia: https://en.wikipedia.org/wiki/Hamiltonian_path
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Wikipedia: https://en.wikipedia.org/wiki/Hamiltonian_path
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"""
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‎backtracking/minimax.py

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leaves of game tree is stored in scores[]
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height is maximum height of Game tree
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"""
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from __future__ import annotations
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import math

‎backtracking/n_queens.py

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"""
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The nqueens problem is of placing N queens on a N * N
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chess board such that no queen can attack any other queens placed
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on that chess board.
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This means that one queen cannot have any other queen on its horizontal, vertical and
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diagonal lines.
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The nqueens problem is of placing N queens on a N * N
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chess board such that no queen can attack any other queens placed
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on that chess board.
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This means that one queen cannot have any other queen on its horizontal, vertical and
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diagonal lines.
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"""
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from __future__ import annotations
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solution = []

‎backtracking/n_queens_math.py

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for another one or vice versa.
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"""
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‎backtracking/sudoku.py

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have solved the puzzle. else, we backtrack and place another number
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in that cell and repeat this process.
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"""
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Matrix = list[list[int]]

‎backtracking/sum_of_subsets.py

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"""
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The sum-of-subsetsproblem states that a set of non-negative integers, and a
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value M, determine all possible subsets of the given set whose summation sum
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equal to given M.
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The sum-of-subsetsproblem states that a set of non-negative integers, and a
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value M, determine all possible subsets of the given set whose summation sum
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equal to given M.
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Summation of the chosen numbers must be equal to given number M and one number
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can be used only once.
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Summation of the chosen numbers must be equal to given number M and one number
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can be used only once.
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"""
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from __future__ import annotations
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‎boolean_algebra/nor_gate.py

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Code provided by Akshaj Vishwanathan
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https://www.geeksforgeeks.org/logic-gates-in-python
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"""
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from collections.abc import Callable
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‎cellular_automata/conways_game_of_life.py

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Conway's Game of Life implemented in Python.
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https://en.wikipedia.org/wiki/Conway%27s_Game_of_Life
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"""
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from PIL import Image

‎cellular_automata/game_of_life.py

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4.
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Any dead cell with exactly three live neighbours be-
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comes a live cell, as if by reproduction.
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"""
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"""
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import random
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import sys
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‎cellular_automata/nagel_schrekenberg.py

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>>> simulate(construct_highway(5, 2, -2), 3, 0, 2)
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[[0, -1, 0, -1, 0], [0, -1, 0, -1, -1], [0, -1, -1, 1, -1], [-1, 1, -1, 0, -1]]
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"""
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from random import randint, random
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‎ciphers/a1z26.py

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https://www.dcode.fr/letter-number-cipher
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http://bestcodes.weebly.com/a1z26.html
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"""
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‎ciphers/atbash.py

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""" https://en.wikipedia.org/wiki/Atbash """
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"""https://en.wikipedia.org/wiki/Atbash"""
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import string
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‎ciphers/base32.py

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https://en.wikipedia.org/wiki/Base32
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"""
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B32_CHARSET = "ABCDEFGHIJKLMNOPQRSTUVWXYZ234567"
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‎ciphers/enigma_machine2.py

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Created by TrapinchO
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"""
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from __future__ import annotations
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RotorPositionT = tuple[int, int, int]

‎ciphers/fractionated_morse_cipher.py

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http://practicalcryptography.com/ciphers/fractionated-morse-cipher/
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"""
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import string
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MORSE_CODE_DICT = {

‎ciphers/hill_cipher.py

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https://www.youtube.com/watch?v=4RhLNDqcjpA
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"""
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import numpy

‎ciphers/permutation_cipher.py

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For more info: https://www.nku.edu/~christensen/1402%20permutation%20ciphers.pdf
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"""
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import random
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‎ciphers/rail_fence_cipher.py

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""" https://en.wikipedia.org/wiki/Rail_fence_cipher """
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"""https://en.wikipedia.org/wiki/Rail_fence_cipher"""
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def encrypt(input_string: str, key: int) -> str:

‎ciphers/rsa_cipher.py

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key_size, n, e = read_key_file(key_filename)
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if key_size < block_size * 8:
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sys.exit(
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"ERROR: Block size is {} bits and key size is {} bits. The RSA cipher "
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"requires the block size to be equal to or greater than the key size. "
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"Either decrease the block size or use different keys.".format(
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block_size * 8, key_size
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)
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f"ERROR: Block size is {block_size * 8} bits and key size is {key_size} "
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"bits. The RSA cipher requires the block size to be equal to or greater "
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"than the key size. Either decrease the block size or use different keys."
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)
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encrypted_blocks = [str(i) for i in encrypt_message(message, (n, e), block_size)]
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103101
if key_size < block_size * 8:
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sys.exit(
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"ERROR: Block size is {} bits and key size is {} bits. The RSA cipher "
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"requires the block size to be equal to or greater than the key size. "
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"Did you specify the correct key file and encrypted file?".format(
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block_size * 8, key_size
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)
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f"ERROR: Block size is {block_size * 8} bits and key size is {key_size} "
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"bits. The RSA cipher requires the block size to be equal to or greater "
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"than the key size. Were the correct key file and encrypted file specified?"
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)
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encrypted_blocks = []

‎ciphers/rsa_factorization.py

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More readable source: https://www.di-mgt.com.au/rsa_factorize_n.html
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large number can take minutes to factor, therefore are not included in doctest.
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"""
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from __future__ import annotations
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import math

‎ciphers/xor_cipher.py

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"""
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author: Christian Bender
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date: 21.12.2017
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class: XORCipher
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This class implements the XOR-cipher algorithm and provides
7-
some useful methods for encrypting and decrypting strings and
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files.
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Overview about methods
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- encrypt : list of char
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- decrypt : list of char
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- encrypt_string : str
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- decrypt_string : str
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- encrypt_file : boolean
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- decrypt_file : boolean
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author: Christian Bender
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date: 21.12.2017
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class: XORCipher
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This class implements the XOR-cipher algorithm and provides
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some useful methods for encrypting and decrypting strings and
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files.
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Overview about methods
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- encrypt : list of char
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- decrypt : list of char
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- encrypt_string : str
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- decrypt_string : str
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- encrypt_file : boolean
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- decrypt_file : boolean
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"""
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from __future__ import annotations
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‎compression/burrows_wheeler.py

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original character. The BWT is thus a "free" method of improving the efficiency
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of text compression algorithms, costing only some extra computation.
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"""
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from __future__ import annotations
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from typing import TypedDict

‎compression/lempel_ziv.py

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"""
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One of the several implementations of Lempel–Ziv–Welch compression algorithm
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https://en.wikipedia.org/wiki/Lempel%E2%80%93Ziv%E2%80%93Welch
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One of the several implementations of Lempel–Ziv–Welch compression algorithm
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https://en.wikipedia.org/wiki/Lempel%E2%80%93Ziv%E2%80%93Welch
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"""
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import math

‎compression/lempel_ziv_decompress.py

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"""
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One of the several implementations of Lempel–Ziv–Welch decompression algorithm
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https://en.wikipedia.org/wiki/Lempel%E2%80%93Ziv%E2%80%93Welch
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One of the several implementations of Lempel–Ziv–Welch decompression algorithm
3+
https://en.wikipedia.org/wiki/Lempel%E2%80%93Ziv%E2%80%93Welch
44
"""
55

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import math

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