# 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]) ############### # compare to LibSVM dasvm_manual_libsvm = LibSVM(1.0, wdk, lab) dasvm_manual_libsvm.set_linear_term(linterm_manual) dasvm_manual_libsvm.set_bias_enabled(False) Math_init_random(1) dasvm_manual_libsvm.train() ############### # compare to LibLinear dasvm_manual_liblinear = LibLinear(1.0, feat, lab) dasvm_manual_liblinear.set_linear_term(linterm_manual) dasvm_manual_liblinear.set_bias_enabled(False) dasvm_manual_liblinear.train() ############################################# # compute DA-SVMs in shogun (kernelized AND linear) #############################################
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]) ############### # compare to LibSVM dasvm_manual_libsvm = LibSVM(1.0, wdk, lab) dasvm_manual_libsvm.set_linear_term(linterm_manual) dasvm_manual_libsvm.set_bias_enabled(False) Math_init_random(1) dasvm_manual_libsvm.train() ############### # compare to LibLinear dasvm_manual_liblinear = LibLinear(1.0, feat, lab) dasvm_manual_liblinear.set_linear_term(linterm_manual) dasvm_manual_liblinear.set_bias_enabled(False) dasvm_manual_liblinear.train()
#print inner[i], tmp_out[i] assert(abs(inner[i]-tmp_out[i])<= 0.001) svm = SVMLight(1.0, wdk, lab) svm.set_linear_term(p) Math_init_random(1) svm.train() ############### #compare to LibSVM svm2 = LibSVM(1.0, wdk, lab) svm2.set_linear_term(p) Math_init_random(1) svm2.train() svm3 = LibSVM(1.0, wdk, lab) Math_init_random(1) svm3.train() print "SVMLight linear:", svm.get_objective() print "LibSVM linear:", svm2.get_objective() print "LibSVM:", svm3.get_objective() print svm.get_objective(), svm2.get_objective() assert(abs(svm.get_objective()-svm2.get_objective())<= 0.001)
for i in xrange(len(examples)): #print inner[i], tmp_out[i] assert (abs(inner[i] - tmp_out[i]) <= 0.001) svm = SVMLight(1.0, wdk, lab) svm.set_linear_term(p) Math_init_random(1) svm.train() ############### #compare to LibSVM svm2 = LibSVM(1.0, wdk, lab) svm2.set_linear_term(p) Math_init_random(1) svm2.train() svm3 = LibSVM(1.0, wdk, lab) Math_init_random(1) svm3.train() print "SVMLight linear:", svm.get_objective() print "LibSVM linear:", svm2.get_objective() print "LibSVM:", svm3.get_objective() print svm.get_objective(), svm2.get_objective() assert (abs(svm.get_objective() - svm2.get_objective()) <= 0.001) sv_idx = svm.get_support_vectors()