F_ref = None
for i in range(len(X_ref)):
if F_ref is None:
F_ref = X_ref[i]
else:
F_ref = np.concatenate((F_ref, X_ref[i]), axis=0)
u_Kref, s_kref, v_Kref = LSA.LSA_training(F_ref)
# 测试
print("---------------- testing phase ----------------")
Y_test = data_processing(test_data, window_len)
X_test = []
for i in range(len(Y_test)):
X_ktest = sc.SR_testing(Y_test[i], B_ref[i], num_bases, beta)
X_test.append(X_ktest)
F_test = None
for i in range(len(X_test)):
if F_test is None:
F_test = X_test[i]
else:
F_test = np.concatenate((F_test, X_test[i]), axis=0)
anomaly_score_m = LSA.LSA_testing(F_test, u_Kref, s_kref)
print(anomaly_score_m.shape)
anomaly_score_per_win = np.sum(anomaly_score_m, axis=0)
anomaly_score = np.zeros(shape=(test_data.shape[0]))
for i in range(test_data.shape[0]):
if i < window_len:
F_ref = None
for i in range(len(X_ref)):
if F_ref is None:
F_ref = X_ref[i]
else:
F_ref = np.concatenate((F_ref, X_ref[i]), axis=0)
u_Kref, s_kref, v_Kref = LSA.LSA_training(F_ref)
# 测试
print("---------------- testing phase ----------------")
Y_test = data_processing(test_data, window_len)
X_test = []
for i in range(len(Y_test)):
X_ktest = sc.SR_testing(Y_test[i], B_ref[i], num_bases, gamma)
X_test.append(X_ktest)
F_test = None
for i in range(len(X_test)):
if F_test is None:
F_test = X_test[i]
else:
F_test = np.concatenate((F_test, X_test[i]), axis=0)
anomaly_score_m = LSA.LSA_testing(F_test, u_Kref, s_kref)
print(anomaly_score_m.shape)
anomaly_score_per_win = np.sum(anomaly_score_m, axis=0)
anomaly_score = np.zeros(shape=(test_data.shape[0]))
for i in range(test_data.shape[0]):
if i < window_len: