diff options
Diffstat (limited to 'ot/da.py')
| -rw-r--r-- | ot/da.py | 37 |
1 files changed, 11 insertions, 26 deletions
@@ -11,7 +11,7 @@ from .optim import cg from .optim import gcg -def sinkhorn_lpl1_mm(a,labels_a, b, M, reg, eta=0.1,numItermax = 10,numInnerItermax = 200,stopInnerThr=1e-9,unlabelledValue=-99,verbose=False,log=False): +def sinkhorn_lpl1_mm(a,labels_a, b, M, reg, eta=0.1,numItermax = 10,numInnerItermax = 200,stopInnerThr=1e-9,verbose=False,log=False): """ Solve the entropic regularization optimal transport problem with nonconvex group lasso regularization @@ -55,8 +55,6 @@ def sinkhorn_lpl1_mm(a,labels_a, b, M, reg, eta=0.1,numItermax = 10,numInnerIter Max number of iterations (inner sinkhorn solver) stopInnerThr : float, optional Stop threshold on error (inner sinkhorn solver) (>0) - unlabelledValue : int, optional - this value in array labels_a means this is an unlabelled example verbose : bool, optional Print information along iterations log : bool, optional @@ -84,41 +82,28 @@ def sinkhorn_lpl1_mm(a,labels_a, b, M, reg, eta=0.1,numItermax = 10,numInnerIter ot.optim.cg : General regularized OT """ - p=0.5 + p = 0.5 epsilon = 1e-3 - # init data - Nini = len(a) - Nfin = len(b) - indices_labels = [] classes = np.unique(labels_a) for c in classes: idxc, = np.where(labels_a == c) indices_labels.append(idxc) - W=np.zeros(M.shape) + W = np.zeros(M.shape) for cpt in range(numItermax): Mreg = M + eta*W - transp=sinkhorn(a,b,Mreg,reg,numItermax=numInnerItermax, stopThr=stopInnerThr) - # the transport has been computed. Check if classes are really separated - W = np.ones((Nini,Nfin)) - all_majs = [] - idx_unlabelled = -1 + transp = sinkhorn(a, b, Mreg, reg, numItermax=numInnerItermax, + stopThr=stopInnerThr) + # the transport has been computed. Check if classes are really + # separated + W = np.ones(M.shape) for (i, c) in enumerate(classes): - if c != unlabelledValue: - majs = np.sum(transp[indices_labels[i]], axis=0) - majs = p*((majs+epsilon)**(p-1)) - W[indices_labels[i]] = majs - all_majs.append(majs) - else: - idx_unlabelled = i - - # now we majorize the unlabelled (if there are any) by the min of - # the majorizations. do it only for unlabbled data - if idx_unlabelled != -1: - W[indices_labels[idx_unlabelled]] = np.min(all_majs, axis=0) + majs = np.sum(transp[indices_labels[i]], axis=0) + majs = p*((majs+epsilon)**(p-1)) + W[indices_labels[i]] = majs return transp |