python-modelselection.py

import numpy as np 
import shogun as sg
from sklearn import datasets

X, y = datasets.load_breast_cancer(True)

y[y==0] = -1
feat = sg.features(X.T)

kernel1 = sg.kernel("GaussianKernel")  
kernel2 = sg.kernel("PolyKernel") 
svm = sg.machine("LibSVM", epsilon=1, kernel=kernel1, labels=sg.BinaryLabels(y))

node = sg.GridSearch(svm)
node.attach("kernel::log_width", np.array([1., 2.]))
node.attach("C1", np.array([1., 2., 3.])) 
node.attach("C2", np.array([1., 2., 3.])) 

node2=sg.GridParameters(kernel2)
node2.attach("degree", np.array([1,2], np.int32))

node.attach("kernel", node2)

node.train(feat)