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: