Python LibSVR.apply Examples

Example #1

File: regression_libsvr_modular.py Project: theoryno3/shogun

def regression_libsvr_modular (svm_c=1, svr_param=0.1, n=100,n_test=100, \
		x_range=6,x_range_test=10,noise_var=0.5,width=1, seed=1):

	from shogun.Features import RegressionLabels, RealFeatures
	from shogun.Kernel import GaussianKernel
	from shogun.Regression import LibSVR, LIBSVR_NU_SVR, LIBSVR_EPSILON_SVR

	# reproducable results
	random.seed(seed)
	
	# easy regression data: one dimensional noisy sine wave
	n=15
	n_test=100
	x_range_test=10
	noise_var=0.5;
	X=random.rand(1,n)*x_range
	
	X_test=array([[float(i)/n_test*x_range_test for i in range(n_test)]])
	Y_test=sin(X_test)
	Y=sin(X)+random.randn(n)*noise_var
	
	# shogun representation
	labels=RegressionLabels(Y[0])
	feats_train=RealFeatures(X)
	feats_test=RealFeatures(X_test)

	kernel=GaussianKernel(feats_train, feats_train, width)
	
	# two svr models: epsilon and nu
	svr_epsilon=LibSVR(svm_c, svr_param, kernel, labels, LIBSVR_EPSILON_SVR)
	svr_epsilon.train()
	svr_nu=LibSVR(svm_c, svr_param, kernel, labels, LIBSVR_NU_SVR)
	svr_nu.train()

	# predictions
	kernel.init(feats_train, feats_test)
	out1_epsilon=svr_epsilon.apply().get_labels()
	out2_epsilon=svr_epsilon.apply(feats_test).get_labels()
	out1_nu=svr_epsilon.apply().get_labels()
	out2_nu=svr_epsilon.apply(feats_test).get_labels()

	return out1_epsilon,out2_epsilon,out1_nu,out2_nu ,kernel

Example #2

File: regression_libsvr_modular.py Project: pombredanne/rqpersonalsvn

def regression_libsvr_modular (fm_train=traindat,fm_test=testdat,label_train=label_traindat,\
           width=2.1,C=1,epsilon=1e-5,tube_epsilon=1e-2):

    from shogun.Features import Labels, RealFeatures
    from shogun.Kernel import GaussianKernel
    from shogun.Regression import LibSVR

    feats_train = RealFeatures(fm_train)
    feats_test = RealFeatures(fm_test)

    kernel = GaussianKernel(feats_train, feats_train, width)
    labels = Labels(label_train)

    svr = LibSVR(C, tube_epsilon, kernel, labels)
    svr.set_epsilon(epsilon)
    svr.train()

    kernel.init(feats_train, feats_test)
    out1 = svr.apply().get_labels()
    out2 = svr.apply(feats_test).get_labels()

    return out1, out2, kernel

Example #3

File: regression_libsvr_modular.py Project: harshitsyal/shogun

def regression_libsvr_modular (fm_train=traindat,fm_test=testdat,label_train=label_traindat,\
				       width=2.1,C=1,epsilon=1e-5,tube_epsilon=1e-2):

	from shogun.Features import Labels, RealFeatures
	from shogun.Kernel import GaussianKernel
	from shogun.Regression import LibSVR

	feats_train=RealFeatures(fm_train)
	feats_test=RealFeatures(fm_test)

	kernel=GaussianKernel(feats_train, feats_train, width)
	labels=Labels(label_train)

	svr=LibSVR(C, tube_epsilon, kernel, labels)
	svr.set_epsilon(epsilon)
	svr.train()

	kernel.init(feats_train, feats_test)
	out1=svr.apply().get_labels()
	out2=svr.apply(feats_test).get_labels()

	return out1,out2,kernel