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Created with Raphaël 2.2.022Sep21201011May13Apr24Mar11Nov10651Oct30Apr252414Feb729Oct6Sep23Aug22Jul20Jun614May87614Feb26Sep17Jul611Apr911Feb522Jan21201615Sep543Aug219Jul76512Jun96130May281714Feb111016Jan1411435Dec15Oct5426Sep2315Aug13Jul31May2611Apr54Mar26Feb23Jan22212018Dec161110330Nov261928Aug16Jul151413116Jun22May212018121187624Mar131296543128Feb26241918171110765432131Jan3029282726232219181514131226Dec1511954327Nov21206429Oct2415Sep141242128Aug27222124Jun2327May262322Remove GMM Trainers classesFix a typo in hdf5, versioning of hdf5 file.Rename Statistics to GMMStats, tests for GMMStats.Remove Multi- and Gaussian, use Gaussians instead.Fixed test_gaussian not passingMAP GMM fix and testsML GMM testEpsilon as default variance thresholdVariance threshold application at init for GMMAdd gaussian tests backAdded and adapted old GMM Machine testsAdded MAP GMM implementation in pythonAdded setters and getters for machine mean and varRemove the steps in GMM plot exampleFix the GMM log_likelihood and add related tests.[doc] Plot code example of GMMGMM port progressReset to accept dask arraysFix Dask array not supporting str as indexAdding GMM testsAdding the ML GMM m_step and machine's log_weightsGMM stats now has an add operatorTweaks to weights calculationFix Gaussian mean naming, use structuredarrayse_step donee_step continued and tests [wip]GMM e step progressGaussian and Statistics classesGMMMachine as TransformerFinalize GMM Machine log likelihood computationFix the picklability test for GMMSum the likelihood along the gaussian means firstImplement GMM log_likelihood computation in pythonFirst draw for GMM machine and trainersMerge branch 'py-kmeans' into 'pure-python'clean-up the testsremove test_kmeans_trainer.pyImprove tests error messagesremove dask from requirements.txt to fix a possible issueAdd dask and dask-ml to requirements
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