#############################################
dasvm_libsvm = DomainAdaptationSVM(1.0, wdk, lab, presvm_libsvm, B)
dasvm_libsvm.set_bias_enabled(False)
dasvm_libsvm.train()
dasvm_liblinear = DomainAdaptationSVMLinear(1.0, feat, lab, presvm_liblinear,
B)
dasvm_liblinear.io.set_loglevel(MSG_DEBUG)
dasvm_liblinear.set_bias_enabled(False)
dasvm_liblinear.train()
print "##############"
alphas = []
sv_ids = dasvm_libsvm.get_support_vectors()
for (j, sv_id) in enumerate(sv_ids):
alpha = dasvm_libsvm.get_alphas()[j]
#get rid of label
alpha = alpha * labels[sv_id]
alphas.append(alpha)
print "alphas libsvm", alphas
#w = presvm_liblinear.get_w()
#print "prew", w[0:10]
#print "labels", labels
#############################################
# checking linear term
#############################################
dasvm_libsvm = DomainAdaptationSVM(1.0, wdk, lab, presvm_libsvm, B)
dasvm_libsvm.set_bias_enabled(False)
dasvm_libsvm.train()
dasvm_liblinear = DomainAdaptationSVMLinear(1.0, feat, lab, presvm_liblinear, B)
dasvm_liblinear.io.set_loglevel(MSG_DEBUG)
dasvm_liblinear.set_bias_enabled(False)
dasvm_liblinear.train()
print "##############"
alphas = []
sv_ids = dasvm_libsvm.get_support_vectors()
for (j, sv_id) in enumerate(sv_ids):
alpha = dasvm_libsvm.get_alphas()[j]
#get rid of label
alpha = alpha*labels[sv_id]
alphas.append(alpha)
print "alphas libsvm", alphas
#w = presvm_liblinear.get_w()
#print "prew", w[0:10]
#print "labels", labels
#############################################
# checking linear term
#############################################
#dasvm = SVMLight(1.0, wdk, lab)
Math_init_random(1)
dasvm.train()
#dasvm = SVMLight(1.0, wdk, lab)
#dasvm.set_linear_term(numpy.double(p))
#dasvm.train()
lin_da = dasvm.get_linear_term()
daobj = dasvm.get_objective()
sv_idx_da = dasvm.get_support_vectors()
alphas_da = dasvm.get_alphas()
alphas_full_da = numpy.zeros(N)
alphas_full_da[sv_idx_da] = alphas_da
################
#checking linear term
presvm.set_bias(0.0)
tmp_out = -B*presvm.classify(feat).get_labels()*tmp_lab - 1
for i in xrange(len(examples)):
print lin_da[i], tmp_out[i]
#############################################
dasvm = DomainAdaptationSVM(1.0, wdk, lab, presvm, B)
#dasvm = SVMLight(1.0, wdk, lab)
Math_init_random(1)
dasvm.train()
#dasvm = SVMLight(1.0, wdk, lab)
#dasvm.set_linear_term(numpy.double(p))
#dasvm.train()
lin_da = dasvm.get_linear_term()
daobj = dasvm.get_objective()
sv_idx_da = dasvm.get_support_vectors()
alphas_da = dasvm.get_alphas()
alphas_full_da = numpy.zeros(N)
alphas_full_da[sv_idx_da] = alphas_da
################
#checking linear term
presvm.set_bias(0.0)
tmp_out = -B * presvm.classify(feat).get_labels() * tmp_lab - 1
for i in xrange(len(examples)):
print lin_da[i], tmp_out[i]
assert (abs(lin_da[i] - tmp_out[i]) <= 0.001)
