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Add static typing to backtracking algorithms (#2684)
* Added static typing to backtracking algorithms * Ran psf/black to fix some minor issues. * updating DIRECTORY.md * updating DIRECTORY.md Co-authored-by: github-actions <${GITHUB_ACTOR}@users.noreply.github.com> Co-authored-by: John Law <johnlaw.po@gmail.com>
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DIRECTORY.md
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DIRECTORY.md
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@ -41,6 +41,7 @@
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* [Quine Mc Cluskey](https://github.com/TheAlgorithms/Python/blob/master/boolean_algebra/quine_mc_cluskey.py)
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## Cellular Automata
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* [Conways Game Of Life](https://github.com/TheAlgorithms/Python/blob/master/cellular_automata/conways_game_of_life.py)
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* [One Dimensional](https://github.com/TheAlgorithms/Python/blob/master/cellular_automata/one_dimensional.py)
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## Ciphers
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@ -107,6 +108,7 @@
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* [Prefix Conversions](https://github.com/TheAlgorithms/Python/blob/master/conversions/prefix_conversions.py)
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* [Roman To Integer](https://github.com/TheAlgorithms/Python/blob/master/conversions/roman_to_integer.py)
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* [Temperature Conversions](https://github.com/TheAlgorithms/Python/blob/master/conversions/temperature_conversions.py)
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* [Weight Conversion](https://github.com/TheAlgorithms/Python/blob/master/conversions/weight_conversion.py)
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## Data Structures
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* Binary Tree
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@ -321,10 +323,6 @@
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* [Test Min Spanning Tree Kruskal](https://github.com/TheAlgorithms/Python/blob/master/graphs/tests/test_min_spanning_tree_kruskal.py)
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* [Test Min Spanning Tree Prim](https://github.com/TheAlgorithms/Python/blob/master/graphs/tests/test_min_spanning_tree_prim.py)
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## Greedy Method
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* [Greedy Knapsack](https://github.com/TheAlgorithms/Python/blob/master/greedy_method/greedy_knapsack.py)
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* [Test Knapsack](https://github.com/TheAlgorithms/Python/blob/master/greedy_method/test_knapsack.py)
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## Hashes
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* [Adler32](https://github.com/TheAlgorithms/Python/blob/master/hashes/adler32.py)
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* [Chaos Machine](https://github.com/TheAlgorithms/Python/blob/master/hashes/chaos_machine.py)
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* [Sha1](https://github.com/TheAlgorithms/Python/blob/master/hashes/sha1.py)
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## Knapsack
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* [Greedy Knapsack](https://github.com/TheAlgorithms/Python/blob/master/knapsack/greedy_knapsack.py)
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* [Knapsack](https://github.com/TheAlgorithms/Python/blob/master/knapsack/knapsack.py)
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* [Test Knapsack](https://github.com/TheAlgorithms/Python/blob/master/knapsack/test_knapsack.py)
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* Tests
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* [Test Greedy Knapsack](https://github.com/TheAlgorithms/Python/blob/master/knapsack/tests/test_greedy_knapsack.py)
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* [Test Knapsack](https://github.com/TheAlgorithms/Python/blob/master/knapsack/tests/test_knapsack.py)
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## Linear Algebra
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* Src
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* [Combinations](https://github.com/TheAlgorithms/Python/blob/master/maths/combinations.py)
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* [Decimal Isolate](https://github.com/TheAlgorithms/Python/blob/master/maths/decimal_isolate.py)
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* [Entropy](https://github.com/TheAlgorithms/Python/blob/master/maths/entropy.py)
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* [Euclidean Distance](https://github.com/TheAlgorithms/Python/blob/master/maths/euclidean_distance.py)
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* [Eulers Totient](https://github.com/TheAlgorithms/Python/blob/master/maths/eulers_totient.py)
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* [Explicit Euler](https://github.com/TheAlgorithms/Python/blob/master/maths/explicit_euler.py)
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* [Extended Euclidean Algorithm](https://github.com/TheAlgorithms/Python/blob/master/maths/extended_euclidean_algorithm.py)
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* [Get Imdbtop](https://github.com/TheAlgorithms/Python/blob/master/web_programming/get_imdbtop.py)
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* [Instagram Crawler](https://github.com/TheAlgorithms/Python/blob/master/web_programming/instagram_crawler.py)
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* [Instagram Pic](https://github.com/TheAlgorithms/Python/blob/master/web_programming/instagram_pic.py)
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* [Instagram Video](https://github.com/TheAlgorithms/Python/blob/master/web_programming/instagram_video.py)
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* [Recaptcha Verification](https://github.com/TheAlgorithms/Python/blob/master/web_programming/recaptcha_verification.py)
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* [Slack Message](https://github.com/TheAlgorithms/Python/blob/master/web_programming/slack_message.py)
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* [Test Fetch Github Info](https://github.com/TheAlgorithms/Python/blob/master/web_programming/test_fetch_github_info.py)
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@ -16,7 +16,13 @@ def generate_all_combinations(n: int, k: int) -> [[int]]:
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return result
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def create_all_state(increment, total_number, level, current_list, total_list):
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def create_all_state(
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increment: int,
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total_number: int,
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level: int,
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current_list: [int],
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total_list: [int],
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) -> None:
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if level == 0:
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total_list.append(current_list[:])
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return
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current_list.pop()
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def print_all_state(total_list):
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def print_all_state(total_list: [int]) -> None:
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for i in total_list:
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print(*i)
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"""
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def generate_all_permutations(sequence):
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def generate_all_permutations(sequence: [int]) -> None:
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create_state_space_tree(sequence, [], 0, [0 for i in range(len(sequence))])
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def create_state_space_tree(sequence, current_sequence, index, index_used):
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def create_state_space_tree(
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sequence: [int], current_sequence: [int], index: int, index_used: int
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) -> None:
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"""
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Creates a state space tree to iterate through each branch using DFS.
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We know that each state has exactly len(sequence) - index children.
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solution = []
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def isSafe(board, row, column):
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def isSafe(board: [[int]], row: int, column: int) -> bool:
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"""
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This function returns a boolean value True if it is safe to place a queen there
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considering the current state of the board.
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return True
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def solve(board, row):
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def solve(board: [[int]], row: int) -> bool:
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"""
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It creates a state space tree and calls the safe function until it receives a
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False Boolean and terminates that branch and backtracks to the next
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return False
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def printboard(board):
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def printboard(board: [[int]]) -> None:
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"""
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Prints the boards that have a successful combination.
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"""
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def solve_maze(maze: list) -> bool:
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def solve_maze(maze: [[int]]) -> bool:
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"""
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This method solves the "rat in maze" problem.
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In this problem we have some n by n matrix, a start point and an end point.
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return solved
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def run_maze(maze, i, j, solutions):
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def run_maze(maze: [[int]], i: int, j: int, solutions: [[int]]) -> bool:
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"""
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This method is recursive starting from (i, j) and going in one of four directions:
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up, down, left, right.
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"""
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def generate_sum_of_subsets_soln(nums, max_sum):
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def generate_sum_of_subsets_soln(nums: [int], max_sum: [int]) -> [int]:
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result = []
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path = []
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num_index = 0
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return result
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def create_state_space_tree(nums, max_sum, num_index, path, result, remaining_nums_sum):
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def create_state_space_tree(
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nums: [int],
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max_sum: int,
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num_index: int,
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path: [int],
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result: [int],
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remaining_nums_sum: int,
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) -> None:
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"""
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Creates a state space tree to iterate through each branch using DFS.
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It terminates the branching of a node when any of the two conditions
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