'''Frahst Version '''
Frahst_alg.run(zt)
# Calculate reconstructed data if needed
st = Frahst_alg.st
Frahst_alg.st['recon'] = np.dot(st['Q'][:,:st['r']],st['ht'][:st['r']])
'''Anomaly Detection method'''
Frahst_alg.detect_anom(zt)
'''Rank adaptation method'''
Frahst_alg.rank_adjust(zt)
'''Store data'''
#tracked_values = ['ht','e_ratio','r','recon', 'pred_err', 'pred_err_norm', 'pred_err_ave', 't_stat', 'pred_dsn', 'pred_zt']
#tracked_values = ['ht','e_ratio','r','recon','recon_err', 'recon_err_norm', 't_stat', 'rec_dsn', 'x_sample']
#tracked_values = ['ht','e_ratio','r','recon', 'h_res', 'h_res_aa', 'h_res_norm']
#Frahst_alg.track_var(tracked_values)
Frahst_alg.track_var(['ht', 'r', 'e_ratio'])
#Frahst_alg.track_var()
anomalies_list.append(Frahst_alg.res['anomalies'][:])
data_list.append(D)
''' Plot Results '''
#Frahst_alg.plot_res([data, 'ht', 't_stat'])
#Frahst_alg.plot_res([data, 'ht', 'rec_dsn', 't_stat'])
Frahst_alg.batch_analysis(data_list, anomalies_list, keep_sets = 0)
zt = zt.reshape(zt.shape[0], 1) # Convert to a column Vector
if Frahst_alg.st['anomaly'] == True:
Frahst_alg.st['anomaly'] = False # reset anomaly var
'''Frahst Version '''
Frahst_alg.run(zt)
# Calculate reconstructed data if needed
st = Frahst_alg.st
Frahst_alg.st['recon'] = np.dot(st['Q'][:, :st['r']],
st['ht'][:st['r']])
'''Anomaly Detection method'''
Frahst_alg.detect_anom(zt)
'''Rank adaptation method'''
Frahst_alg.rank_adjust(zt)
'''Store data'''
#tracked_values = ['ht','e_ratio','r','recon', 'pred_err', 'pred_err_norm', 'pred_err_ave', 't_stat', 'pred_dsn', 'pred_zt']
#tracked_values = ['ht','e_ratio','r','recon','recon_err', 'recon_err_norm', 't_stat', 'rec_dsn', 'x_sample']
#tracked_values = ['ht','e_ratio','r','recon', 'h_res', 'h_res_aa', 'h_res_norm']
#Frahst_alg.track_var(tracked_values)
Frahst_alg.track_var(['ht', 'r', 'e_ratio'])
#Frahst_alg.track_var()
anomalies_list.append(Frahst_alg.res['anomalies'][:])
data_list.append(D)
''' Plot Results '''
#Frahst_alg.plot_res([data, 'ht', 't_stat'])
#Frahst_alg.plot_res([data, 'ht', 'rec_dsn', 't_stat'])
Frahst_alg.batch_analysis(data_list, anomalies_list, keep_sets=0)
