from shogun.Regression import * from shogun.Kernel import * import util util.set_title('KernelRidgeRegression on Sine') X, Y = util.get_sinedata() width = 1 feat = RealFeatures(X) lab = Labels(Y.flatten()) gk = GaussianKernel(feat, feat, width) krr = KernelRidgeRegression() krr.set_labels(lab) krr.set_kernel(gk) krr.set_tau(1e-6) krr.train() plot(X, Y, '.', label='train data') plot(X[0], krr.apply().get_labels(), hold=True, label='train output') XE, YE = util.compute_output_plot_isolines_sine(krr, gk, feat) YE200 = krr.apply(200) plot(XE[0], YE, hold=True, label='test output') plot([XE[0, 200]], [YE200], '+', hold=True) #print YE[200], YE200 connect('key_press_event', util.quit) show()
from modshogun import * import util util.set_title('KernelRidgeRegression on Sine') X, Y=util.get_sinedata() width=1 feat=RealFeatures(X) lab=RegressionLabels(Y.flatten()) gk=GaussianKernel(feat, feat, width) krr=KernelRidgeRegression() krr.set_labels(lab) krr.set_kernel(gk) krr.set_tau(1e-6) krr.train() plot(X, Y, '.', label='train data') plot(X[0], krr.apply().get_labels(), hold=True, label='train output') XE, YE=util.compute_output_plot_isolines_sine(krr, gk, feat, regression=True) YE200=krr.apply_one(200) plot(XE[0], YE, hold=True, label='test output') plot([XE[0,200]], [YE200], '+', hold=True) #print YE[200], YE200 connect('key_press_event', util.quit) show()
from shogun.Kernel import * import util util.set_title('KRR on Sine') X, Y=util.get_sinedata() width=1 feat=RealFeatures(X) lab=Labels(Y.flatten()) gk=GaussianKernel(feat, feat, width) krr=KRR() krr.set_labels(lab) krr.set_kernel(gk) krr.set_tau(1e-6) krr.train() plot(X, Y, '.', label='train data') plot(X[0], krr.classify().get_labels(), hold=True, label='train output') XE, YE=util.compute_output_plot_isolines_sine(krr, gk, feat) YE200=krr.classify_example(200) plot(XE[0], YE, hold=True, label='test output') plot([XE[0,200]], [YE200], '+', hold=True) #print YE[200], YE200 connect('key_press_event', util.quit) show()