print 'Generated Results Array'
for i in xrange(E.R.shape[0]): # for each alg version
for j in xrange(E.R.shape[1]): # for each parameter set for the alg version
for k in xrange(E.R.shape[2]): # for each data set parameter set
anomalies_list = []
gt_list = []
for ic in xrange(D.shape[0]): # for each initial condition
# Fetch alg
F = E.F_dict[E.R[i,j,k]['key']]
# Fetch data
data = np.load(path + '/' + D[ic,k]['file'])
#data = data.reshape(E.R[i,j,k]['params']['dat']['T'], E.R[i,j,k]['params']['dat']['N'])
data = zscore_win(data, F.p['z_win'])
'''Initialise'''
z_iter = iter(data)
numStreams = data.shape[1]
F.re_init(numStreams)
'''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 '''
for i in xrange(E.R.shape[0]): # for each alg version
for j in xrange(
E.R.shape[1]): # for each parameter set for the alg version
for k in xrange(E.R.shape[2]): # for each data set parameter set
anomalies_list = []
gt_list = []
for ic in xrange(D.shape[0]): # for each initial condition
# Fetch alg
F = E.F_dict[E.R[i, j, k]['key']]
# Fetch data
data = np.load(path + '/' + D[ic, k]['file'])
#data = data.reshape(E.R[i,j,k]['params']['dat']['T'], E.R[i,j,k]['params']['dat']['N'])
data = zscore_win(data, F.p['z_win'])
'''Initialise'''
z_iter = iter(data)
numStreams = data.shape[1]
F.re_init(numStreams)
'''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)
# Calculate reconstructed data if needed
step=gen_a_step,
step_n_back=gen_a_step_n_back,
trend=gen_a_periodic_shift)
'''Setup Data Structure'''
time_results = np.array([0.0] * dat_change_count)
for k, n in enumerate(dat_changes['N']):
# set time sample buffer
time_sample_list = np.array([0.0] * initial_conditions)
for i in range(initial_conditions):
'''Generate Data Set'''
a['N'] = n
D = gen_funcs[anomaly_type](**a)
data = D['data']
''' Mean Centering '''
data = zscore_win(data, 100)
#data = zscore(data)
data = np.nan_to_num(data)
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
trend = gen_a_periodic_shift)
'''Setup Data Structure'''
time_results = np.array([0.0]* dat_change_count)
for k,n in enumerate(dat_changes['N']):
# set time sample buffer
time_sample_list = np.array([0.0]*initial_conditions)
for i in range(initial_conditions):
'''Generate Data Set'''
a['N'] = n
D = gen_funcs[anomaly_type](**a)
data = D['data']
''' Mean Centering '''
data = zscore_win(data, 100)
#data = zscore(data)
data = np.nan_to_num(data)
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:
