def krr_short ():
print('KRR_short')
from shogun.Features import Labels, RealFeatures
from shogun.Kernel import GaussianKernel
from shogun.Regression import KernelRidgeRegression
width=0.8; tau=1e-6
krr=KernelRidgeRegression(tau, GaussianKernel(0, width), Labels(label_train))
krr.train(RealFeatures(fm_train))
out = krr.apply(RealFeatures(fm_test)).get_labels()
return krr,out
def krr_short():
print('KRR_short')
from shogun.Features import RegressionLabels, RealFeatures
from shogun.Kernel import GaussianKernel
from shogun.Regression import KernelRidgeRegression
width = 0.8
tau = 1e-6
krr = KernelRidgeRegression(tau, GaussianKernel(0, width),
RegressionLabels(label_train))
krr.train(RealFeatures(fm_train))
out = krr.apply(RealFeatures(fm_test)).get_labels()
return krr, out
def regression_kernel_ridge_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 KernelRidgeRegression feats_train=RealFeatures(fm_train) feats_test=RealFeatures(fm_test) kernel=GaussianKernel(feats_train, feats_train, width) labels=Labels(label_train) krr=KernelRidgeRegression(tau, kernel, labels) krr.train(feats_train) kernel.init(feats_train, feats_test) out = krr.apply().get_labels() return out,kernel,krr
def regression_kernel_ridge_modular (n=100,n_test=100, \ x_range=6,x_range_test=10,noise_var=0.5,width=1, tau=1e-6, seed=1): from shogun.Features import RegressionLabels, RealFeatures from shogun.Kernel import GaussianKernel from shogun.Regression import KernelRidgeRegression # 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) krr=KernelRidgeRegression(tau, kernel, labels) krr.train(feats_train) kernel.init(feats_train, feats_test) out = krr.apply().get_labels() # plot results #plot(X[0],Y[0],'x') # training observations #plot(X_test[0],Y_test[0],'-') # ground truth of test #plot(X_test[0],out, '-') # mean predictions of test #legend(["training", "ground truth", "mean predictions"]) #show() return out,kernel,krr
def regression_kernel_ridge_modular(fm_train=traindat,
fm_test=testdat,
label_train=label_traindat,
width=0.8,
tau=1e-6):
from shogun.Features import RegressionLabels, RealFeatures
from shogun.Kernel import GaussianKernel
from shogun.Regression import KernelRidgeRegression
feats_train = RealFeatures(fm_train)
feats_test = RealFeatures(fm_test)
kernel = GaussianKernel(feats_train, feats_train, width)
labels = RegressionLabels(label_train)
krr = KernelRidgeRegression(tau, kernel, labels)
krr.train(feats_train)
kernel.init(feats_train, feats_test)
out = krr.apply().get_labels()
return out, kernel, krr
