def ramdom_kernels_combination(kernel_indexs,samples,classes,rbf_par,poly_par,scorer):
kernels= []
for indexes in kernel_indexs:
choice = np.random.randint(3, size=1)[0]
if choice ==0:
kernels.append( Kinterface(data=x_train[:,indexes], kernel=linear_kernel))
elif choice ==1:
#print(rbf_par)
length_of_param1 = len(rbf_par["gamma"])
# print(rbf_par["gamma"])
# print(np.random.randint(length_of_param1, size=1)[0])
K = Kinterface(data=x_train[:,indexes], kernel=rbf_kernel,kernel_args={"gamma": rbf_par["gamma"][np.random.randint(length_of_param1, size=1)[0]]})
kernels.append(K)
else:
length_of_param1 = len(poly_par["degree"])
K = Kinterface(data=x_train[:,indexes], kernel=poly_kernel,kernel_args={"degree": poly_par["degree"][np.random.randint(length_of_param1, size=1)[0]]})
kernels.append(K)
#mu = [random.randrange(0,1) for i in range(40)]
model = Alignf(typ="convex")
model.fit(kernels, classes.values)
model.mu # kernel weights (convex combination)
mu = model.mu
#print("numbers:" +str(mu))
combined_k = lambda x,y: \
sum([mu[i]*kernels[i](x[:,kernel_indexs[i]],y[:,kernel_indexs[i]]) for i in range(len(kernels))])
return combined_k
def createKernelCombination(kernel_indexs,samples,classes,rbf_par,poly_par,scorer):
kernels= []
for indexes in kernel_indexs:
svm = tunning_svm(samples[:,indexes],classes,rbf_par,poly_par,scorer)
kernel = svm.get_params()["kernel"]
if kernel=="linear":
kernels.append( Kinterface(data=x_train[:,indexes], kernel=linear_kernel))
elif kernel =="rbf":
gamma=svm.get_params()["gamma"]
K = Kinterface(data=x_train[:,indexes], kernel=rbf_kernel,kernel_args={"gamma": gamma})
kernels.append(K)
else:
degree=svm.get_params()["degree"]
coef0 =degree=svm.get_params()["coef0"]
K = Kinterface(data=x_train[:,indexes], kernel=poly_kernel,kernel_args={"degree": degree})
kernels.append(K)
model = Alignf(typ="convex")
model.fit(kernels, classes.values)
model.mu # kernel weights (convex combination)
mu = model.mu
print(mu)
combined_k = lambda x,y: \
sum([mu[i]*kernels[i](x[:,kernel_indexs[i]],y[:,kernel_indexs[i]]) for i in range(len(kernels))])
return combined_k
