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])