def transform2(self, test_data, mode=None):
if self.norm == True:
t_data = self.normalizer.norm(test_data)
else:
t_data = test_data
if mode == None:
mode = self.mode
if mode == 'v1':
similarity_result = SCCI.get_MD(
t_data, self.node_mean, self.node_sigma)
pred_table = np.zeros([len(test_data), self.num_classes])
for i, sim_result in enumerate(similarity_result):
pred_cls = sim_result.argmax()
pred = self.node_to_class_dict[pred_cls]
pred_table[i][pred] = 1
else:
boundary = 0.0
SCCI_enhance.set_alpha(self.alpha)
node_output = SCCI_enhance.get_MD(
t_data, self.node_mean, self.node_sigma)
node_output = np.append(node_output, np.ones(
(np.size(node_output, 0), 1)), axis=1)
target_output = np.matmul(node_output, self.weights)
pred_table = self.matrix2Binary(target_output, boundary)
ylabel = []
for i in pred_table:
ylabel.append(i.argmax())
ylabel = np.array(ylabel)
return ylabel
def train(self, train_data, train_label):
self.data = self.preprocess_data(train_data, train_label)
self.tar_matrix = self.target2matrix(train_label, self.num_classes)
self.get_hidden_nodes(self.data, train_label)
SCCI_enhance.set_alpha(self.alpha)
node_output = SCCI_enhance.get_MD(
self.data, self.node_mean, self.node_sigma)
self.node_output = np.append(node_output, np.ones(
(np.size(node_output, 0), 1), dtype=float), axis=1)
if self.mode == 'v1':
pass
elif self.mode == 'v2':
self.get_network_weights(self.node_output, self.tar_matrix)
class_output = np.matmul(self.node_output, self.weights)
self.error.append(self.error_estimate(
class_output, self.tar_matrix))
elif self.mode == 'v4':
self.get_network_weights(self.node_output, self.tar_matrix)
class_output = np.matmul(self.node_output, self.weights)
self.error.append(self.error_estimate(
class_output, self.tar_matrix))
# Check train result
pred_table = self.matrix2Binary(class_output)
self.train_target = self.target2matrix(
train_label, self.num_classes, neg_note=0)
self.error_train.append(
1 - accuracy_score(pred_table, self.train_target))
threshold = 0.55
sigma_init = 0.2
alpha = math.sqrt(2)
beta = 2
learning_rate = 1
lambda_i = 0
tra_label = RBF_ISCC.target2matrix(Y_train, 2, neg_note=0)
rbf_iscc = RBF_ISCC_update_once(threshold=threshold, sigma_init=sigma_init,
alpha=alpha, beta=beta, lambda_i=lambda_i,
learning_rate=learning_rate, mode='v4', norm=False)
rbf_iscc.train(X_train, Y_train)
w = rbf_iscc.weights
SCCI_enhance.set_alpha(alpha)
node_output = SCCI_enhance.get_MD(
X_train, rbf_iscc.node_mean, rbf_iscc.node_sigma)
node_output = np.append(node_output, np.ones(
(np.size(node_output, 0), 1), dtype=float), axis=1)
cls_output = np.matmul(node_output, rbf_iscc.weights)
pred_label_tr = rbf_iscc.transform(X_train)
score_tr = performance_measure(pred_label_tr, tra_label)
error_tr = [1 - score_tr["AACC"]]
flag = 0
end_flag = 1
if error_tr[-1] == 0:
flag = end_flag
while flag < end_flag: