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Merge branch 'master' of https://github.com/SilverDragonOfR/Python
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DIRECTORY.md
23
DIRECTORY.md
@ -54,13 +54,12 @@
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* [Largest Pow Of Two Le Num](bit_manipulation/largest_pow_of_two_le_num.py)
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* [Missing Number](bit_manipulation/missing_number.py)
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* [Numbers Different Signs](bit_manipulation/numbers_different_signs.py)
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* [Power Of 4](bit_manipulation/power_of_4.py)
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* [Reverse Bits](bit_manipulation/reverse_bits.py)
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* [Single Bit Manipulation Operations](bit_manipulation/single_bit_manipulation_operations.py)
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## Blockchain
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* [Chinese Remainder Theorem](blockchain/chinese_remainder_theorem.py)
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* [Diophantine Equation](blockchain/diophantine_equation.py)
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* [Modular Division](blockchain/modular_division.py)
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## Boolean Algebra
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* [And Gate](boolean_algebra/and_gate.py)
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@ -101,11 +100,13 @@
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* [Diffie Hellman](ciphers/diffie_hellman.py)
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* [Elgamal Key Generator](ciphers/elgamal_key_generator.py)
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* [Enigma Machine2](ciphers/enigma_machine2.py)
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* [Fractionated Morse Cipher](ciphers/fractionated_morse_cipher.py)
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* [Hill Cipher](ciphers/hill_cipher.py)
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* [Mixed Keyword Cypher](ciphers/mixed_keyword_cypher.py)
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* [Mono Alphabetic Ciphers](ciphers/mono_alphabetic_ciphers.py)
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* [Morse Code](ciphers/morse_code.py)
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* [Onepad Cipher](ciphers/onepad_cipher.py)
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* [Permutation Cipher](ciphers/permutation_cipher.py)
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* [Playfair Cipher](ciphers/playfair_cipher.py)
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* [Polybius](ciphers/polybius.py)
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* [Porta Cipher](ciphers/porta_cipher.py)
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@ -172,6 +173,7 @@
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## Data Structures
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* Arrays
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* [Equilibrium Index In Array](data_structures/arrays/equilibrium_index_in_array.py)
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* [Median Two Array](data_structures/arrays/median_two_array.py)
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* [Permutations](data_structures/arrays/permutations.py)
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* [Prefix Sum](data_structures/arrays/prefix_sum.py)
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@ -352,6 +354,7 @@
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* [Smith Waterman](dynamic_programming/smith_waterman.py)
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* [Subset Generation](dynamic_programming/subset_generation.py)
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* [Sum Of Subset](dynamic_programming/sum_of_subset.py)
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* [Trapped Water](dynamic_programming/trapped_water.py)
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* [Tribonacci](dynamic_programming/tribonacci.py)
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* [Viterbi](dynamic_programming/viterbi.py)
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* [Word Break](dynamic_programming/word_break.py)
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@ -360,6 +363,7 @@
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* [Apparent Power](electronics/apparent_power.py)
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* [Builtin Voltage](electronics/builtin_voltage.py)
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* [Carrier Concentration](electronics/carrier_concentration.py)
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* [Charging Capacitor](electronics/charging_capacitor.py)
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* [Circular Convolution](electronics/circular_convolution.py)
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* [Coulombs Law](electronics/coulombs_law.py)
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* [Electric Conductivity](electronics/electric_conductivity.py)
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@ -466,6 +470,8 @@
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* [Test Min Spanning Tree Prim](graphs/tests/test_min_spanning_tree_prim.py)
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## Greedy Methods
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* [Best Time To Buy And Sell Stock](greedy_methods/best_time_to_buy_and_sell_stock.py)
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* [Fractional Cover Problem](greedy_methods/fractional_cover_problem.py)
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* [Fractional Knapsack](greedy_methods/fractional_knapsack.py)
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* [Fractional Knapsack 2](greedy_methods/fractional_knapsack_2.py)
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* [Gas Station](greedy_methods/gas_station.py)
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@ -524,6 +530,10 @@
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* Local Weighted Learning
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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
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* [Binary Cross Entropy](machine_learning/loss_functions/binary_cross_entropy.py)
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* [Huber Loss](machine_learning/loss_functions/huber_loss.py)
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* [Mean Squared Error](machine_learning/loss_functions/mean_squared_error.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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@ -565,6 +575,7 @@
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* [Catalan Number](maths/catalan_number.py)
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* [Ceil](maths/ceil.py)
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* [Check Polygon](maths/check_polygon.py)
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* [Chinese Remainder Theorem](maths/chinese_remainder_theorem.py)
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* [Chudnovsky Algorithm](maths/chudnovsky_algorithm.py)
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* [Collatz Sequence](maths/collatz_sequence.py)
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* [Combinations](maths/combinations.py)
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@ -591,6 +602,7 @@
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* [Gaussian](maths/gaussian.py)
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* [Gaussian Error Linear Unit](maths/gaussian_error_linear_unit.py)
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* [Gcd Of N Numbers](maths/gcd_of_n_numbers.py)
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* [Germain Primes](maths/germain_primes.py)
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* [Greatest Common Divisor](maths/greatest_common_divisor.py)
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* [Greedy Coin Change](maths/greedy_coin_change.py)
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* [Hamming Numbers](maths/hamming_numbers.py)
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@ -619,6 +631,7 @@
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* [Max Sum Sliding Window](maths/max_sum_sliding_window.py)
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* [Median Of Two Arrays](maths/median_of_two_arrays.py)
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* [Mobius Function](maths/mobius_function.py)
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* [Modular Division](maths/modular_division.py)
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* [Modular Exponential](maths/modular_exponential.py)
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* [Monte Carlo](maths/monte_carlo.py)
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* [Monte Carlo Dice](maths/monte_carlo_dice.py)
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@ -720,12 +733,16 @@
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## Neural Network
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* [2 Hidden Layers Neural Network](neural_network/2_hidden_layers_neural_network.py)
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* Activation Functions
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* [Binary Step](neural_network/activation_functions/binary_step.py)
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* [Exponential Linear Unit](neural_network/activation_functions/exponential_linear_unit.py)
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* [Leaky Rectified Linear Unit](neural_network/activation_functions/leaky_rectified_linear_unit.py)
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* [Mish](neural_network/activation_functions/mish.py)
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* [Rectified Linear Unit](neural_network/activation_functions/rectified_linear_unit.py)
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* [Scaled Exponential Linear Unit](neural_network/activation_functions/scaled_exponential_linear_unit.py)
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* [Sigmoid Linear Unit](neural_network/activation_functions/sigmoid_linear_unit.py)
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* [Soboleva Modified Hyperbolic Tangent](neural_network/activation_functions/soboleva_modified_hyperbolic_tangent.py)
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* [Softplus](neural_network/activation_functions/softplus.py)
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* [Squareplus](neural_network/activation_functions/squareplus.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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* [Perceptron](neural_network/perceptron.py)
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@ -1101,6 +1118,7 @@
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* [Interpolation Search](searches/interpolation_search.py)
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* [Jump Search](searches/jump_search.py)
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* [Linear Search](searches/linear_search.py)
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* [Median Of Medians](searches/median_of_medians.py)
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* [Quick Select](searches/quick_select.py)
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* [Sentinel Linear Search](searches/sentinel_linear_search.py)
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* [Simple Binary Search](searches/simple_binary_search.py)
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* [Snake Case To Camel Pascal Case](strings/snake_case_to_camel_pascal_case.py)
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* [Split](strings/split.py)
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* [String Switch Case](strings/string_switch_case.py)
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* [Strip](strings/strip.py)
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* [Text Justification](strings/text_justification.py)
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* [Top K Frequent Words](strings/top_k_frequent_words.py)
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* [Upper](strings/upper.py)
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142
ciphers/permutation_cipher.py
Normal file
142
ciphers/permutation_cipher.py
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"""
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The permutation cipher, also called the transposition cipher, is a simple encryption
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technique that rearranges the characters in a message based on a secret key. It
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divides the message into blocks and applies a permutation to the characters within
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each block according to the key. The key is a sequence of unique integers that
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determine the order of character rearrangement.
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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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def generate_valid_block_size(message_length: int) -> int:
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"""
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Generate a valid block size that is a factor of the message length.
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Args:
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message_length (int): The length of the message.
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Returns:
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int: A valid block size.
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Example:
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>>> random.seed(1)
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>>> generate_valid_block_size(12)
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3
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"""
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block_sizes = [
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block_size
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for block_size in range(2, message_length + 1)
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if message_length % block_size == 0
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]
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return random.choice(block_sizes)
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def generate_permutation_key(block_size: int) -> list[int]:
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"""
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Generate a random permutation key of a specified block size.
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Args:
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block_size (int): The size of each permutation block.
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Returns:
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list[int]: A list containing a random permutation of digits.
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Example:
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>>> random.seed(0)
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>>> generate_permutation_key(4)
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[2, 0, 1, 3]
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"""
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digits = list(range(block_size))
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random.shuffle(digits)
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return digits
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def encrypt(
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message: str, key: list[int] | None = None, block_size: int | None = None
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) -> tuple[str, list[int]]:
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"""
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Encrypt a message using a permutation cipher with block rearrangement using a key.
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Args:
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message (str): The plaintext message to be encrypted.
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key (list[int]): The permutation key for decryption.
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block_size (int): The size of each permutation block.
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Returns:
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tuple: A tuple containing the encrypted message and the encryption key.
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Example:
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>>> encrypted_message, key = encrypt("HELLO WORLD")
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>>> decrypted_message = decrypt(encrypted_message, key)
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>>> decrypted_message
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'HELLO WORLD'
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"""
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message = message.upper()
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message_length = len(message)
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if key is None or block_size is None:
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block_size = generate_valid_block_size(message_length)
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key = generate_permutation_key(block_size)
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encrypted_message = ""
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for i in range(0, message_length, block_size):
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block = message[i : i + block_size]
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rearranged_block = [block[digit] for digit in key]
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encrypted_message += "".join(rearranged_block)
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return encrypted_message, key
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def decrypt(encrypted_message: str, key: list[int]) -> str:
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"""
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Decrypt an encrypted message using a permutation cipher with block rearrangement.
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Args:
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encrypted_message (str): The encrypted message.
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key (list[int]): The permutation key for decryption.
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Returns:
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str: The decrypted plaintext message.
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Example:
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>>> encrypted_message, key = encrypt("HELLO WORLD")
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>>> decrypted_message = decrypt(encrypted_message, key)
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>>> decrypted_message
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'HELLO WORLD'
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"""
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key_length = len(key)
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decrypted_message = ""
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for i in range(0, len(encrypted_message), key_length):
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block = encrypted_message[i : i + key_length]
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original_block = [""] * key_length
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for j, digit in enumerate(key):
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original_block[digit] = block[j]
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decrypted_message += "".join(original_block)
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return decrypted_message
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def main() -> None:
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"""
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Driver function to pass message to get encrypted, then decrypted.
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Example:
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>>> main()
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Decrypted message: HELLO WORLD
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"""
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message = "HELLO WORLD"
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encrypted_message, key = encrypt(message)
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decrypted_message = decrypt(encrypted_message, key)
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print(f"Decrypted message: {decrypted_message}")
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if __name__ == "__main__":
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import doctest
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doctest.testmod()
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main()
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