def forward_sample(X,
n_iters,
Zv=None,
Zrcv=None,
n_grid=30,
n_chains=1,
ct_kernel=0):
total_iters = n_chains * n_iters
n_cols = len(X)
cctypes = ['normal'] * n_cols
distargs = [None] * n_cols
forward_samples = dict()
stats = []
i = 0
for chain in range(n_chains):
forward_samples[chain] = []
for itr in range(n_iters):
i += 1
state = cc_state.cc_state(X,
cctypes,
distargs,
Zv=Zv,
Zrcv=Zrcv,
n_grid=n_grid,
ct_kernel=ct_kernel)
Y = su.resample_data(state)
forward_samples[chain].append(Y)
stats.append(get_data_stats(Y, state))
string = "\r%1.2f " % (i * 100.0 / float(total_iters))
sys.stdout.write(string)
sys.stdout.flush()
return stats, forward_samples
def posterior_sample(X, n_iters, kernels=_all_kernels, Zv=None, Zrcv=None, n_grid=30, n_chains=1, ct_kernel=0):
n_cols = len(X)
cctypes = ['normal']*n_cols
distargs = [None]*n_cols
stats = []
posterior_samples = dict()
i = 0.0;
total_iters = n_chains*n_iters
for chain in range(n_chains):
state = cc_state.cc_state(X, cctypes, distargs, Zv=Zv, Zrcv=Zrcv, n_grid=n_grid, ct_kernel=ct_kernel)
Y = su.resample_data(state)
posterior_samples[chain] = Y
for _ in range(n_iters):
state.transition(kernel_list=kernels)
Y = su.resample_data(state)
stats.append(get_data_stats(Y, state))
posterior_samples[chain].append(Y)
i += 1.0
string = "\r%1.2f " % (i*100.0/float(total_iters))
sys.stdout.write(string)
sys.stdout.flush()
return stats, posterior_samples
def posterior_sample(X,
n_iters,
kernels=_all_kernels,
Zv=None,
Zrcv=None,
n_grid=30,
n_chains=1,
ct_kernel=0):
n_cols = len(X)
cctypes = ['normal'] * n_cols
distargs = [None] * n_cols
stats = []
posterior_samples = dict()
i = 0.0
total_iters = n_chains * n_iters
for chain in range(n_chains):
state = cc_state.cc_state(X,
cctypes,
distargs,
Zv=Zv,
Zrcv=Zrcv,
n_grid=n_grid,
ct_kernel=ct_kernel)
Y = su.resample_data(state)
posterior_samples[chain] = Y
for _ in range(n_iters):
state.transition(kernel_list=kernels)
Y = su.resample_data(state)
stats.append(get_data_stats(Y, state))
posterior_samples[chain].append(Y)
i += 1.0
string = "\r%1.2f " % (i * 100.0 / float(total_iters))
sys.stdout.write(string)
sys.stdout.flush()
return stats, posterior_samples
def forward_sample(X, n_iters, Zv=None, Zrcv=None, n_grid=30, n_chains=1, ct_kernel=0):
total_iters = n_chains*n_iters
n_cols = len(X)
cctypes = ['normal']*n_cols
distargs = [None]*n_cols
forward_samples = dict()
stats = []
i = 0
for chain in range(n_chains):
forward_samples[chain] = []
for itr in range(n_iters):
i += 1
state = cc_state.cc_state(X, cctypes, distargs, Zv=Zv, Zrcv=Zrcv, n_grid=n_grid, ct_kernel=ct_kernel)
Y = su.resample_data(state)
forward_samples[chain].append(Y)
stats.append(get_data_stats(Y, state))
string = "\r%1.2f " % (i*100.0/float(total_iters))
sys.stdout.write(string)
sys.stdout.flush()
return stats, forward_samples
