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