mirror of
https://github.com/TheAlgorithms/Python.git
synced 2024-12-18 17:20:16 +00:00
bc8df6de31
* [pre-commit.ci] pre-commit autoupdate updates: - [github.com/astral-sh/ruff-pre-commit: v0.2.2 → v0.3.2](https://github.com/astral-sh/ruff-pre-commit/compare/v0.2.2...v0.3.2) - [github.com/pre-commit/mirrors-mypy: v1.8.0 → v1.9.0](https://github.com/pre-commit/mirrors-mypy/compare/v1.8.0...v1.9.0) * [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>
47 lines
1.4 KiB
Python
47 lines
1.4 KiB
Python
"""
|
|
Given an array of integer elements and an integer 'k', we are required to find the
|
|
maximum sum of 'k' consecutive elements in the array.
|
|
|
|
Instead of using a nested for loop, in a Brute force approach we will use a technique
|
|
called 'Window sliding technique' where the nested loops can be converted to a single
|
|
loop to reduce time complexity.
|
|
"""
|
|
|
|
from __future__ import annotations
|
|
|
|
|
|
def max_sum_in_array(array: list[int], k: int) -> int:
|
|
"""
|
|
Returns the maximum sum of k consecutive elements
|
|
>>> arr = [1, 4, 2, 10, 2, 3, 1, 0, 20]
|
|
>>> k = 4
|
|
>>> max_sum_in_array(arr, k)
|
|
24
|
|
>>> k = 10
|
|
>>> max_sum_in_array(arr,k)
|
|
Traceback (most recent call last):
|
|
...
|
|
ValueError: Invalid Input
|
|
>>> arr = [1, 4, 2, 10, 2, 13, 1, 0, 2]
|
|
>>> k = 4
|
|
>>> max_sum_in_array(arr, k)
|
|
27
|
|
"""
|
|
if len(array) < k or k < 0:
|
|
raise ValueError("Invalid Input")
|
|
max_sum = current_sum = sum(array[:k])
|
|
for i in range(len(array) - k):
|
|
current_sum = current_sum - array[i] + array[i + k]
|
|
max_sum = max(max_sum, current_sum)
|
|
return max_sum
|
|
|
|
|
|
if __name__ == "__main__":
|
|
from doctest import testmod
|
|
from random import randint
|
|
|
|
testmod()
|
|
array = [randint(-1000, 1000) for i in range(100)]
|
|
k = randint(0, 110)
|
|
print(f"The maximum sum of {k} consecutive elements is {max_sum_in_array(array,k)}")
|