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2018-09-24correct reference number in docRémi Flamary
2018-09-24Merge readme with masterRémi Flamary
2018-09-24Merge pull request #64 from rflamary/convolutionRémi Flamary
[MRG] Wasserstein convolutional barycenter This PR closes Issue #51
2018-09-24remove i+1Rémi Flamary
2018-09-24implement for loopRémi Flamary
2018-09-24remove unused variableRémi Flamary
2018-09-24add contributorRémi Flamary
2018-09-24remove @ for python compatibility+ comments alexandreRémi Flamary
2018-09-24correct if error bugRémi Flamary
2018-09-24adding greenkhornalain
2018-09-14Merge pull request #65 from kilianFatras/stochastic_OTRémi Flamary
better implementation on stocjastic gradient updates
2018-09-13better implementation on gradient updatesKilian Fatras
2018-09-07whitetrail pep8Nicolas Courty
2018-09-07stabThr and pep8Nicolas Courty
2018-09-07Merge branch 'master' into convolutionNicolas Courty
2018-09-07pep8 fixed (contd)Nicolas Courty
2018-09-07pep8 normalizationNicolas Courty
2018-09-07Wasserstein convolutional barycenterNicolas Courty
2018-08-29Merge branch 'master' into stochastic_OTKilian
2018-08-29replaced marginal testsKilian Fatras
2018-08-29Merge pull request #48 from rflamary/remove_otda_v05Rémi Flamary
Remove deprecated OTDA Classes
2018-08-28fixed argument functionsKilian Fatras
2018-08-28fixed pep8Kilian Fatras
2018-08-28debug sgd dualKilian Fatras
2018-08-28fixed bug in sgd dualKilian Fatras
2018-07-24ensum tets marginals sinkhornRémi Flamary
2018-07-24final makefile benchRémi Flamary
2018-07-24cancel einsumRémi Flamary
2018-07-24pb indexRémi Flamary
2018-07-24correction sizeRémi Flamary
2018-07-24test eisum instead of dotRémi Flamary
2018-07-24pep8 all the wayRémi Flamary
2018-07-24speedup einsum constraint violationRémi Flamary
2018-07-18Merge pull request #57 from LeoGautheron/masterRémi Flamary
Speed-up Sinkhorn
2018-07-16Add comment & fix flake8 errorLeoGautheron
2018-07-16Remove dependency sklearnLeoGautheron
2018-07-11Speed-up SinkhornLeoGautheron
Speed-up in 3 places: - the computation of pairwise distance is faster with sklearn.metrics.pairwise.euclidean_distances - faster computation of K = np.exp(-M / reg) - faster computation of the error every 10 iterations Example with this little script: import time import numpy as np import ot rng = np.random.RandomState(0) transport = ot.da.SinkhornTransport() time1 = time.time() Xs, ys, Xt = rng.randn(10000, 100), rng.randint(0, 2, size=10000), rng.randn(10000, 100) transport.fit(Xs=Xs, Xt=Xt) time2 = time.time() print("OT Computation Time {:6.2f} sec".format(time2-time1)) transport = ot.da.SinkhornLpl1Transport() transport.fit(Xs=Xs, ys=ys, Xt=Xt) time3 = time.time() print("OT LpL1 Computation Time {:6.2f} sec".format(time3-time2)) Before OT Computation Time 19.93 sec OT LpL1 Computation Time 133.43 sec After OT Computation Time 7.55 sec OT LpL1 Computation Time 82.25 sec
2018-07-09return log dict in free support barycenter functionVivien Seguy
2018-07-09add test free support barycenter algorithm + cleaningVivien Seguy
2018-07-06add free support barycenter algorithmVivien Seguy
2018-07-05free support barycentervivienseguy
2018-07-05free support barycentervivienseguy
2018-07-05free support barycentervivienseguy
2018-06-26Merge branch 'master' into stochastic_OTKilian
2018-06-25fix math operator and log bugsKilian Fatras
2018-06-21pep8Kilian Fatras
2018-06-21fixed bugKilian Fatras
2018-06-21gave better step size ASGD & SAGKilian Fatras
2018-06-19remove if in test and cleaned codeKilian Fatras
2018-06-19change grad function namesKilian Fatras
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