def inner_product(example1, example2):
return example1.h_add * example2.h_add + example1.h_max * example2.h_max + example1.h_ff * example2.h_ff
def gram_matrix(examples1, examples2):
gram = zeros((len(examples1), len(examples2)))
for i in range(len(examples1)):
for j in range(len(examples2)):
gram[i, j] = inner_product(examples1[i], examples2[j])
return gram
print examples[1].h_ff
from sklearn.svm import SVR, NuSVR
model = SVR(C=1000, epsilon=0.1, kernel="precomputed") # model = SVR(kernel=kernel)
print gram_matrix(examples, examples)
model.fit(gram_matrix(examples, examples), array([example.cost for example in examples]))
print array([example.cost for example in examples])
print model.predict(gram_matrix(examples[:1], examples))
from mlpy import KernelRidge
model = KernelRidge(lmb=0.01)
model.learn(gram_matrix(examples, examples), array([example.cost for example in examples]))
print model.pred(gram_matrix(examples[:1], examples))
