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bob
bob.learn.em
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ff64a296749e552a9a73691450b5c898301831e1
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Add support for fitting on dask bags in ISV/JFA
Merge branch 'isv-dask' into 'master'
Finalize the dask implementation of ISV/JFA
Merge branch 'fix-init' into 'master'
Add super().__init__() to classes
Change the placement of super().__init__()
Add super().__init__() to GMMMachine
Update pre-commit
Merge branch 'isv-2.0' into 'master'
revert gmm_class option
small fixes
rename seed to random_state
Check for consistent length between X and y
[doc] fix doctests for ISV and JFA
[factor_analysis] fix the prior in tests to get the old reference results
[doc] fix JFA plot script
[factor_analysis] make sure state changes work through dask as well
[factor_analysis] somehow it works with dask all of a sudden
Refactor factor_analysis to accept n_classes
[factor_analysis] initial support for fitting with dask arrays
[factor_analysis] test fit without prior
[factor_analysis] implement fit with ubm priors and data
[factor_analysis] add a fit method using data not stats
[gmm] in-place summation
[factor_analysis] make main score using data not stats
remove duplicate tests
[factor_analysis] merge tests into one file
[factor_analysis] Still allow fit and init using gmm stats
[factor_analysis] fix tests and remove boilerplate code
Creating the GMM UBM in factor_analysis.initialize
Try speeding up transform by making e_step delayed
Add the estimate_ux method
[precommit] Fixes
Created enrollment and scoring functions that accept arrays as input
[sphinx] Update the documentation
Optimized some functions
Implemented JFA
Implemented the Factor analysis base class and the ISVMachine
Merge branch 'mean-variance-kmeans' into 'master'
[pre-commit] autoupdate
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