adding a matrix equalization algorithm #11360
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Thank you, Sanko! @cclauss, if possible, could you please check this pr out and approve a merge? Would be much appreciated :)) |
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Please add a URL to a Wikipedia article or other source that explains what the algorithm does and why it is useful. |
Thank you, @cclauss, just did that! |
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OK... Now that I know what the algorithm does, why would anyone ever want to use it? |
matrix/matrix_equalization.py
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| def array_equalization(vector: list[int], k: int) -> int: |
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Self-documenting variable names, please.
| def array_equalization(vector: list[int], k: int) -> int: | |
| def array_equalization(vector: list[int], step_size: int) -> int: |
matrix/matrix_equalization.py
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| >>> array_equalization([22, 22, 22, 33, 33, 33], 2) | ||
| 2 | ||
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We need tests that have the same vector but different values for step-size including -1, 0, 1.3, and maxsize.
matrix/matrix_equalization.py
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| import sys | |||
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| import sys | |
| from sys import maxsize |
Just added an explanation in the description of this commit:
And thank you very much for the review, @cclauss , just implemented all recommendations suggested :)) |
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With https://en.wikipedia.org/wiki/Single_instruction,_multiple_data and vectorization techniques being so prevalent these daze I doubt this would ever be used but here we go...
* adding a matrix equalization algorithm * Adding url for more details * Implementing suggestions
Describe your change:
Adding a matrix equalization algorithm, which 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 (k).
This algorithm has practical use in scenarios where you need to achieve uniformity across elements of an array under specific constraints and with minimal changes. For example:
Checklist: