From f45f7a68b221ec5b619b8fd8de797815a1eecf43 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Sep 2018 14:30:44 +0200 Subject: pep8 --- ot/gpu/da.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) (limited to 'ot/gpu/da.py') diff --git a/ot/gpu/da.py b/ot/gpu/da.py index 8bcc2aa..8c63870 100644 --- a/ot/gpu/da.py +++ b/ot/gpu/da.py @@ -10,15 +10,16 @@ Domain adaptation with optimal transport with GPU implementation # # License: MIT License -import cupy as np # np used for matrix computation -import cupy as cp # cp used for cupy specific operations +import cupy as np # np used for matrix computation +import cupy as cp # cp used for cupy specific operations import numpy as npp from . import utils from .bregman import sinkhorn + def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10, numInnerItermax=200, stopInnerThr=1e-9, verbose=False, - log=False,to_numpy=True): + log=False, to_numpy=True): """ Solve the entropic regularization optimal transport problem with nonconvex group lasso regularization @@ -101,15 +102,14 @@ def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10, ot.optim.cg : General regularized OT """ - + a, labels_a, b, M = utils.to_gpu(a, labels_a, b, M) - - + p = 0.5 epsilon = 1e-3 indices_labels = [] - labels_a2=cp.asnumpy(labels_a) + labels_a2 = cp.asnumpy(labels_a) classes = npp.unique(labels_a2) for c in classes: idxc, = utils.to_gpu(npp.where(labels_a2 == c)) @@ -120,7 +120,7 @@ def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10, for cpt in range(numItermax): Mreg = M + eta * W transp = sinkhorn(a, b, Mreg, reg, numItermax=numInnerItermax, - stopThr=stopInnerThr,to_numpy=False) + stopThr=stopInnerThr, to_numpy=False) # the transport has been computed. Check if classes are really # separated W = np.ones(M.shape) -- cgit v1.2.3