def krr_short (): print 'KRR_short' from shogun.Features import Labels, RealFeatures from shogun.Kernel import GaussianKernel from shogun.Regression import KRR width=0.8; tau=1e-6 krr=KRR(tau, GaussianKernel(0, width), Labels(label_train)) krr.train(RealFeatures(fm_train)) out = krr.classify(RealFeatures(fm_test)).get_labels() return out
def krr_short(): print 'KRR_short' from shogun.Features import Labels, RealFeatures from shogun.Kernel import GaussianKernel from shogun.Regression import KRR width = 0.8 tau = 1e-6 krr = KRR(tau, GaussianKernel(0, width), Labels(label_train)) krr.train(RealFeatures(fm_train)) out = krr.apply(RealFeatures(fm_test)).get_labels() return krr, out
def regression_krr_modular (fm_train=traindat,fm_test=testdat,label_train=label_traindat,width=0.8,tau=1e-6): from shogun.Features import Labels, RealFeatures from shogun.Kernel import GaussianKernel from shogun.Regression import KRR feats_train=RealFeatures(fm_train) feats_test=RealFeatures(fm_test) kernel=GaussianKernel(feats_train, feats_train, width) labels=Labels(label_train) krr=KRR(tau, kernel, labels) krr.train(feats_train) kernel.init(feats_train, feats_test) out = krr.apply().get_labels() return out,kernel,krr
def krr (): print 'KRR' from shogun.Features import Labels, RealFeatures from shogun.Kernel import GaussianKernel from shogun.Regression import KRR feats_train=RealFeatures(fm_train) feats_test=RealFeatures(fm_test) width=0.8 kernel=GaussianKernel(feats_train, feats_train, width) tau=1e-6 labels=Labels(label_train) krr=KRR(tau, kernel, labels) krr.train(feats_train) kernel.init(feats_train, feats_test) out = krr.classify().get_labels() return out
def regression_krr_modular(fm_train=traindat, fm_test=testdat, label_train=label_traindat, width=0.8, tau=1e-6): from shogun.Features import Labels, RealFeatures from shogun.Kernel import GaussianKernel from shogun.Regression import KRR feats_train = RealFeatures(fm_train) feats_test = RealFeatures(fm_test) kernel = GaussianKernel(feats_train, feats_train, width) labels = Labels(label_train) krr = KRR(tau, kernel, labels) krr.train(feats_train) kernel.init(feats_train, feats_test) out = krr.apply().get_labels() return out, kernel, krr