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"""
domain adaptation with optimal transport
"""
import numpy as np
from .bregman import sinkhorn
def indices(a, func):
return [i for (i, val) in enumerate(a) if func(val)]
def sinkhorn_lpl1_mm(a,labels_a, b, M, reg, eta=0.1):
p=0.5
epsilon = 1e-3
# init data
Nini = len(a)
Nfin = len(b)
indices_labels = []
idx_begin = np.min(labels_a)
for c in range(idx_begin,np.max(labels_a)+1):
idxc = indices(labels_a, lambda x: x==c)
indices_labels.append(idxc)
W=np.zeros(M.shape)
for cpt in range(10):
Mreg = M + eta*W
transp=sinkhorn(a,b,Mreg,reg,numItermax = 200)
# the transport has been computed. Check if classes are really separated
W = np.ones((Nini,Nfin))
for t in range(Nfin):
column = transp[:,t]
all_maj = []
for c in range(idx_begin,np.max(labels_a)+1):
col_c = column[indices_labels[c-idx_begin]]
if c!=-1:
maj = p*((sum(col_c)+epsilon)**(p-1))
W[indices_labels[c-idx_begin],t]=maj
all_maj.append(maj)
# now we majorize the unlabelled by the min of the majorizations
# do it only for unlabbled data
if idx_begin==-1:
W[indices_labels[0],t]=np.min(all_maj)
return transp
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