Create pearson correlation (Hacktoberfest)#13082
Create pearson correlation (Hacktoberfest)#13082LuisOfL wants to merge 5 commits intoTheAlgorithms:masterfrom
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| import numpy as np | ||
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| def pearson_correlation(x: np.ndarray, y: np.ndarray) -> float: |
There was a problem hiding this comment.
As there is no test file in this pull request nor any test function or class in the file machine_learning/pearson_correlation.py, please provide doctest for the function pearson_correlation
Please provide descriptive name for the parameter: x
Please provide descriptive name for the parameter: y
for more information, see https://pre-commit.ci
There was a problem hiding this comment.
Click here to look at the relevant links ⬇️
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Repository:
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Automated review generated by algorithms-keeper. If there's any problem regarding this review, please open an issue about it.
algorithms-keeper commands and options
algorithms-keeper actions can be triggered by commenting on this PR:
@algorithms-keeper reviewto trigger the checks for only added pull request files@algorithms-keeper review-allto trigger the checks for all the pull request files, including the modified files. As we cannot post review comments on lines not part of the diff, this command will post all the messages in one comment.NOTE: Commands are in beta and so this feature is restricted only to a member or owner of the organization.
| import numpy as np | ||
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|
||
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| def pearson_correlation(data_x: np.ndarray, data_y: np.ndarray) -> float: |
There was a problem hiding this comment.
As there is no test file in this pull request nor any test function or class in the file machine_learning/pearson_correlation.py, please provide doctest for the function pearson_correlation
for more information, see https://pre-commit.ci
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Closing require_tests PRs to prepare for Hacktoberfest |
Describe your change:
Description:
Added the Pearson correlation coefficient method.
References:
https://en.wikipedia.org/wiki/Pearson_correlation_coefficient
Checklist: