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* Add word ladder algorithm in backtracking * Improve comments and implement ruff checks * updating DIRECTORY.md * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Change BFS to Backtracking * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Incorporate PR Changes * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Add type hints for backtrack function * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: Hardvan <Hardvan@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
101 lines
3.6 KiB
Python
101 lines
3.6 KiB
Python
"""
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Word Ladder is a classic problem in computer science.
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The problem is to transform a start word into an end word
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by changing one letter at a time.
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Each intermediate word must be a valid word from a given list of words.
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The goal is to find a transformation sequence
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from the start word to the end word.
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Wikipedia: https://en.wikipedia.org/wiki/Word_ladder
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"""
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import string
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def backtrack(
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current_word: str, path: list[str], end_word: str, word_set: set[str]
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) -> list[str]:
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"""
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Helper function to perform backtracking to find the transformation
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from the current_word to the end_word.
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Parameters:
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current_word (str): The current word in the transformation sequence.
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path (list[str]): The list of transformations from begin_word to current_word.
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end_word (str): The target word for transformation.
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word_set (set[str]): The set of valid words for transformation.
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Returns:
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list[str]: The list of transformations from begin_word to end_word.
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Returns an empty list if there is no valid
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transformation from current_word to end_word.
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Example:
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>>> backtrack("hit", ["hit"], "cog", {"hot", "dot", "dog", "lot", "log", "cog"})
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['hit', 'hot', 'dot', 'lot', 'log', 'cog']
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>>> backtrack("hit", ["hit"], "cog", {"hot", "dot", "dog", "lot", "log"})
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[]
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>>> backtrack("lead", ["lead"], "gold", {"load", "goad", "gold", "lead", "lord"})
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['lead', 'lead', 'load', 'goad', 'gold']
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>>> backtrack("game", ["game"], "code", {"came", "cage", "code", "cade", "gave"})
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['game', 'came', 'cade', 'code']
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"""
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# Base case: If the current word is the end word, return the path
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if current_word == end_word:
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return path
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# Try all possible single-letter transformations
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for i in range(len(current_word)):
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for c in string.ascii_lowercase: # Try changing each letter
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transformed_word = current_word[:i] + c + current_word[i + 1 :]
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if transformed_word in word_set:
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word_set.remove(transformed_word)
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# Recur with the new word added to the path
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result = backtrack(
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transformed_word, [*path, transformed_word], end_word, word_set
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)
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if result: # valid transformation found
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return result
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word_set.add(transformed_word) # backtrack
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return [] # No valid transformation found
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def word_ladder(begin_word: str, end_word: str, word_set: set[str]) -> list[str]:
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"""
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Solve the Word Ladder problem using Backtracking and return
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the list of transformations from begin_word to end_word.
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Parameters:
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begin_word (str): The word from which the transformation starts.
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end_word (str): The target word for transformation.
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word_list (list[str]): The list of valid words for transformation.
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Returns:
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list[str]: The list of transformations from begin_word to end_word.
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Returns an empty list if there is no valid transformation.
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Example:
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>>> word_ladder("hit", "cog", ["hot", "dot", "dog", "lot", "log", "cog"])
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['hit', 'hot', 'dot', 'lot', 'log', 'cog']
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>>> word_ladder("hit", "cog", ["hot", "dot", "dog", "lot", "log"])
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[]
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>>> word_ladder("lead", "gold", ["load", "goad", "gold", "lead", "lord"])
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['lead', 'lead', 'load', 'goad', 'gold']
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>>> word_ladder("game", "code", ["came", "cage", "code", "cade", "gave"])
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['game', 'came', 'cade', 'code']
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"""
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if end_word not in word_set: # no valid transformation possible
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return []
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# Perform backtracking starting from the begin_word
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return backtrack(begin_word, [begin_word], end_word, word_set)
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