# 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()
