#----------------------------------------------------------------------
# Cross Validation
#----------------------------------------------------------------------
print '...GP prediction (10-fold CV)'
for t in xrange(num_Iteration + 1):
ACC = [] # accuracy
print 'number of kernel iterations =', t
Matrix = K[:, :, t]
# normalize kernel matrix (not useful for MUTAG)
# Matrix = graphUtil.normalizeKernel(Matrix)
# start cross-validation for this t
for index_train, index_test in valid.k_fold_index(N, K=10):
y_train = graph_label[index_train, :]
y_test = graph_label[index_test, :]
n1 = len(index_train)
n2 = len(index_test)
model = pyGPs.GPC()
M1, M2 = graphUtil.formKernelMatrix(Matrix, index_train, index_test)
k = pyGPs.cov.Pre(M1, M2)
model.setPrior(kernel=k)
# gp
x_train = np.zeros((n1, 1))
x_test = np.zeros((n2, 1))
#----------------------------------------------------------------------
# Cross Validation
#----------------------------------------------------------------------
print('...GP prediction (10-fold CV)')
for t in range(num_Iteration+1):
ACC = [] # accuracy
print('number of kernel iterations =', t)
Matrix = K[:,:,t]
# normalize kernel matrix (not useful for MUTAG)
# Matrix = graphUtil.normalizeKernel(Matrix)
# start cross-validation for this t
for index_train, index_test in valid.k_fold_index(N, K=10):
y_train = graph_label[index_train,:]
y_test = graph_label[index_test,:]
n1 = len(index_train)
n2 = len(index_test)
model = pyGPs.GPC()
M1,M2 = graphUtil.formKernelMatrix(Matrix, index_train, index_test)
k = pyGPs.cov.Pre(M1,M2)
model.setPrior(kernel=k)
# gp
x_train = np.zeros((n1,1))
x_test = np.zeros((n2,1))