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* Implemented Exponential Search with binary search for improved performance on large sorted arrays. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Added type hints and doctests for binary_search and exponential_search functions. Improved code documentation and ensured testability. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update and rename Exponential_Search.py to exponential_search.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
114 lines
3.6 KiB
Python
114 lines
3.6 KiB
Python
#!/usr/bin/env python3
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"""
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Pure Python implementation of exponential search algorithm
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For more information, see the Wikipedia page:
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https://en.wikipedia.org/wiki/Exponential_search
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For doctests run the following command:
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python3 -m doctest -v exponential_search.py
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For manual testing run:
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python3 exponential_search.py
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"""
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from __future__ import annotations
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def binary_search_by_recursion(
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sorted_collection: list[int], item: int, left: int = 0, right: int = -1
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) -> int:
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"""Pure implementation of binary search algorithm in Python using recursion
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Be careful: the collection must be ascending sorted otherwise, the result will be
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unpredictable.
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:param sorted_collection: some ascending sorted collection with comparable items
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:param item: item value to search
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:param left: starting index for the search
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:param right: ending index for the search
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:return: index of the found item or -1 if the item is not found
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Examples:
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>>> binary_search_by_recursion([0, 5, 7, 10, 15], 0, 0, 4)
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0
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>>> binary_search_by_recursion([0, 5, 7, 10, 15], 15, 0, 4)
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4
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>>> binary_search_by_recursion([0, 5, 7, 10, 15], 5, 0, 4)
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1
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>>> binary_search_by_recursion([0, 5, 7, 10, 15], 6, 0, 4)
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-1
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"""
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if right < 0:
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right = len(sorted_collection) - 1
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if list(sorted_collection) != sorted(sorted_collection):
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raise ValueError("sorted_collection must be sorted in ascending order")
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if right < left:
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return -1
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midpoint = left + (right - left) // 2
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if sorted_collection[midpoint] == item:
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return midpoint
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elif sorted_collection[midpoint] > item:
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return binary_search_by_recursion(sorted_collection, item, left, midpoint - 1)
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else:
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return binary_search_by_recursion(sorted_collection, item, midpoint + 1, right)
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def exponential_search(sorted_collection: list[int], item: int) -> int:
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"""
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Pure implementation of an exponential search algorithm in Python.
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For more information, refer to:
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https://en.wikipedia.org/wiki/Exponential_search
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Be careful: the collection must be ascending sorted, otherwise the result will be
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unpredictable.
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:param sorted_collection: some ascending sorted collection with comparable items
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:param item: item value to search
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:return: index of the found item or -1 if the item is not found
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The time complexity of this algorithm is O(log i) where i is the index of the item.
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Examples:
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>>> exponential_search([0, 5, 7, 10, 15], 0)
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0
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>>> exponential_search([0, 5, 7, 10, 15], 15)
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4
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>>> exponential_search([0, 5, 7, 10, 15], 5)
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1
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>>> exponential_search([0, 5, 7, 10, 15], 6)
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-1
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"""
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if list(sorted_collection) != sorted(sorted_collection):
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raise ValueError("sorted_collection must be sorted in ascending order")
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if sorted_collection[0] == item:
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return 0
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bound = 1
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while bound < len(sorted_collection) and sorted_collection[bound] < item:
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bound *= 2
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left = bound // 2
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right = min(bound, len(sorted_collection) - 1)
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return binary_search_by_recursion(sorted_collection, item, left, right)
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if __name__ == "__main__":
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import doctest
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doctest.testmod()
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# Manual testing
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user_input = input("Enter numbers separated by commas: ").strip()
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collection = sorted(int(item) for item in user_input.split(","))
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target = int(input("Enter a number to search for: "))
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result = exponential_search(sorted_collection=collection, item=target)
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if result == -1:
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print(f"{target} was not found in {collection}.")
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else:
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print(f"{target} was found at index {result} in {collection}.")
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