FIX GaussianMixture now normalizes weights_ directly instead of by n_samples
#23034
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GaussianMixture now normalizes weights_ directly instead of by n_samplesGaussianMixture now normalizes weights_ directly instead of by n_samples
jeremiedbb
reviewed
Apr 4, 2022
|
Note for future reviewers: |
Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>
…into gmix_norm
jeremiedbb
reviewed
Apr 5, 2022
Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>
thomasjpfan
approved these changes
Apr 9, 2022
GaussianMixture now normalizes weights_ directly instead of by n_samplesGaussianMixture now normalizes weights_ directly instead of by n_samples
jjerphan
pushed a commit
to jjerphan/scikit-learn
that referenced
this pull request
Apr 29, 2022
…y `n_samples` (scikit-learn#23034) Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>
This was referenced Aug 5, 2022
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Reference Issues/PRs
Fixes #23032
What does this implement/fix? Explain your changes.
The implementation on
mainnormalizes theweights_attribute (which should form a pmf) byn_sampleswhich lead to floating-point precision errors when fitting a single-component gaussian mixture on few samples (e.g.n_samples < 32). Now theweights_array is normalized directly w.r.t its sum reduction. Added non-regression test.Any other comments?