sv_id = int(old_svm.get_support_vectors()[j])
alpha = old_svm.get_alpha(j)
inner_sum = inner_sum + alpha * kv.kernel(sv_id, idx)
inner.append(inner_sum)
#general case
linterm_manual[idx] = B * tmp_lab[idx] * inner_sum - 1.0
################
# compare pre-svms
assert (presvm_liblinear.get_bias() == 0.0)
assert (presvm_libsvm.get_bias() == 0.0)
tmp_out = presvm_liblinear.classify(feat).get_labels()
tmp_out2 = presvm_libsvm.classify(feat).get_labels()
# compare outputs
for i in xrange(N):
try:
assert (abs(inner[i] - tmp_out[i]) <= 0.001)
assert (abs(inner[i] - tmp_out2[i]) <= 0.001)
except Exception, message:
print "difference in outputs: (%.4f, %.4f, %.4f)" % (tmp_out[i],
tmp_out2[i])
###############
alpha = old_svm.get_alpha(j)
inner_sum = inner_sum + alpha * kv.kernel(sv_id, idx)
inner.append(inner_sum)
#general case
linterm_manual[idx] = B *tmp_lab[idx] * inner_sum - 1.0
################
# compare pre-svms
assert(presvm_liblinear.get_bias() == 0.0)
assert(presvm_libsvm.get_bias() == 0.0)
tmp_out = presvm_liblinear.classify(feat).get_labels()
tmp_out2 = presvm_libsvm.classify(feat).get_labels()
# compare outputs
for i in xrange(N):
try:
assert(abs(inner[i]-tmp_out[i])<= 0.001)
assert(abs(inner[i]-tmp_out2[i])<= 0.001)
except Exception, message:
print "difference in outputs: (%.4f, %.4f, %.4f)" % (tmp_out[i], tmp_out2[i])
