Python/matrix/matrix_equalization.py
Margaret c026b1952f
adding a matrix equalization algorithm ()
* adding a matrix equalization algorithm

* Adding url for more details

* Implementing suggestions
2024-05-01 12:42:54 +02:00

56 lines
1.7 KiB
Python

from sys import maxsize
def array_equalization(vector: list[int], step_size: int) -> int:
"""
This algorithm equalizes all elements of the input vector
to a common value, by making the minimal number of
"updates" under the constraint of a step size (step_size).
>>> array_equalization([1, 1, 6, 2, 4, 6, 5, 1, 7, 2, 2, 1, 7, 2, 2], 4)
4
>>> array_equalization([22, 81, 88, 71, 22, 81, 632, 81, 81, 22, 92], 2)
5
>>> array_equalization([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 5)
0
>>> array_equalization([22, 22, 22, 33, 33, 33], 2)
2
>>> array_equalization([1, 2, 3], 0)
Traceback (most recent call last):
ValueError: Step size must be positive and non-zero.
>>> array_equalization([1, 2, 3], -1)
Traceback (most recent call last):
ValueError: Step size must be positive and non-zero.
>>> array_equalization([1, 2, 3], 0.5)
Traceback (most recent call last):
ValueError: Step size must be an integer.
>>> array_equalization([1, 2, 3], maxsize)
1
"""
if step_size <= 0:
raise ValueError("Step size must be positive and non-zero.")
if not isinstance(step_size, int):
raise ValueError("Step size must be an integer.")
unique_elements = set(vector)
min_updates = maxsize
for element in unique_elements:
elem_index = 0
updates = 0
while elem_index < len(vector):
if vector[elem_index] != element:
updates += 1
elem_index += step_size
else:
elem_index += 1
min_updates = min(min_updates, updates)
return min_updates
if __name__ == "__main__":
from doctest import testmod
testmod()