Compare commits

...

3 Commits

Author SHA1 Message Date
Giulio Tantaro
8576a2dc96
Merge 5f66061be5 into fcf82a1eda 2024-10-06 01:30:29 +02:00
Vineet Kumar
fcf82a1eda
Implemented Exponential Search with binary search for improved perfor… (#11666)
* 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>
2024-10-05 10:34:48 -07:00
lorduke22
5f66061be5 add doctest for quick_sort_3_partition. Contributes to #9943 2024-10-05 17:05:08 +02:00
2 changed files with 136 additions and 0 deletions

View File

@ -0,0 +1,113 @@
#!/usr/bin/env python3
"""
Pure Python implementation of exponential search algorithm
For more information, see the Wikipedia page:
https://en.wikipedia.org/wiki/Exponential_search
For doctests run the following command:
python3 -m doctest -v exponential_search.py
For manual testing run:
python3 exponential_search.py
"""
from __future__ import annotations
def binary_search_by_recursion(
sorted_collection: list[int], item: int, left: int = 0, right: int = -1
) -> int:
"""Pure implementation of binary search algorithm in Python using recursion
Be careful: the collection must be ascending sorted otherwise, the result will be
unpredictable.
:param sorted_collection: some ascending sorted collection with comparable items
:param item: item value to search
:param left: starting index for the search
:param right: ending index for the search
:return: index of the found item or -1 if the item is not found
Examples:
>>> binary_search_by_recursion([0, 5, 7, 10, 15], 0, 0, 4)
0
>>> binary_search_by_recursion([0, 5, 7, 10, 15], 15, 0, 4)
4
>>> binary_search_by_recursion([0, 5, 7, 10, 15], 5, 0, 4)
1
>>> binary_search_by_recursion([0, 5, 7, 10, 15], 6, 0, 4)
-1
"""
if right < 0:
right = len(sorted_collection) - 1
if list(sorted_collection) != sorted(sorted_collection):
raise ValueError("sorted_collection must be sorted in ascending order")
if right < left:
return -1
midpoint = left + (right - left) // 2
if sorted_collection[midpoint] == item:
return midpoint
elif sorted_collection[midpoint] > item:
return binary_search_by_recursion(sorted_collection, item, left, midpoint - 1)
else:
return binary_search_by_recursion(sorted_collection, item, midpoint + 1, right)
def exponential_search(sorted_collection: list[int], item: int) -> int:
"""
Pure implementation of an exponential search algorithm in Python.
For more information, refer to:
https://en.wikipedia.org/wiki/Exponential_search
Be careful: the collection must be ascending sorted, otherwise the result will be
unpredictable.
:param sorted_collection: some ascending sorted collection with comparable items
:param item: item value to search
:return: index of the found item or -1 if the item is not found
The time complexity of this algorithm is O(log i) where i is the index of the item.
Examples:
>>> exponential_search([0, 5, 7, 10, 15], 0)
0
>>> exponential_search([0, 5, 7, 10, 15], 15)
4
>>> exponential_search([0, 5, 7, 10, 15], 5)
1
>>> exponential_search([0, 5, 7, 10, 15], 6)
-1
"""
if list(sorted_collection) != sorted(sorted_collection):
raise ValueError("sorted_collection must be sorted in ascending order")
if sorted_collection[0] == item:
return 0
bound = 1
while bound < len(sorted_collection) and sorted_collection[bound] < item:
bound *= 2
left = bound // 2
right = min(bound, len(sorted_collection) - 1)
return binary_search_by_recursion(sorted_collection, item, left, right)
if __name__ == "__main__":
import doctest
doctest.testmod()
# Manual testing
user_input = input("Enter numbers separated by commas: ").strip()
collection = sorted(int(item) for item in user_input.split(","))
target = int(input("Enter a number to search for: "))
result = exponential_search(sorted_collection=collection, item=target)
if result == -1:
print(f"{target} was not found in {collection}.")
else:
print(f"{target} was found at index {result} in {collection}.")

View File

@ -1,4 +1,27 @@
def quick_sort_3partition(sorting: list, left: int, right: int) -> None:
""" "
Python implementation of quick sort algorithm with 3-way partition.
The idea of 3-way quick sort is based on "Dutch National Flag algorithm".
:param sorting: sort list
:param left: left endpoint of sorting
:param right: right endpoint of sorting
:return: None
Examples:
>>> array1 = [5, -1, -1, 5, 5, 24, 0]
>>> quick_sort_3partition(array1, 0, 6)
>>> array1
[-1, -1, 0, 5, 5, 5, 24]
>>> array2 = [9, 0, 2, 6]
>>> quick_sort_3partition(array2, 0, 3)
>>> array2
[0, 2, 6, 9]
>>> array3 = []
>>> quick_sort_3partition(array3, 0, 0)
>>> array3
[]
"""
if right <= left:
return
a = i = left