'''Initialise'''
Frahst_alg.re_init(numStreams)
print 'data set ', i
'''Begin Frahst'''
# Main iterative loop.
for zt in z_iter:
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']
z_iter = iter(data)
numStreams = data.shape[1]
# Initialise Algorithm
F = FRAHST('F-7.A-recS.R-static.S-none', p, numStreams)
# Start time Profiling
start = time.time()
'''Begin Frahst'''
# Main iterative loop.
for zt in z_iter:
zt = zt.reshape(zt.shape[0], 1) # Convert to a column Vector
if np.any(F.st['anomaly']):
F.st['anomaly'][:] = False # reset anomaly var
'''Frahst Version '''
F.run(zt)
'''Anomaly Detection method'''
F.detect_anom(zt)
'''Rank adaptation method'''
F.rank_adjust(zt)
'''Store Values'''
F.track_var()
# End of a single Frahst run
time_sample_list[i] = time.time() - start
# End of all initial conditions for N streams
time_results[k] = time_sample_list.mean()
#data = zscore_win(data, 100)
z_iter = iter(data)
numStreams = data.shape[1]
'''Initialise'''
Frahst_alg.re_init(numStreams)
print 'data set ', i
'''Begin Frahst'''
# Main iterative loop.
for zt in z_iter:
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'])
F = FRAHST('F-7.A-recS.R-static.S-none', p, numStreams)
# Start time Profiling
start = time.time()
'''Begin Frahst'''
# Main iterative loop.
for zt in z_iter:
zt = zt.reshape(zt.shape[0],1) # Convert to a column Vector
if np.any(F.st['anomaly']):
F.st['anomaly'][:] = False # reset anomaly var
'''Frahst Version '''
F.run(zt)
'''Anomaly Detection method'''
F.detect_anom(zt)
'''Rank adaptation method'''
F.rank_adjust(zt)
'''Store Values'''
F.track_var()
# End of a single Frahst run
time_sample_list[i] = time.time() - start
# End of all initial conditions for N streams
time_results[k] = time_sample_list.mean()
