def sample(net: BayesNet,
sample_from: Iterable[Vertex],
algo: str = 'metropolis',
proposal_distribution: str = None,
proposal_distribution_sigma: numpy_types = None,
proposal_listeners=[],
draws: int = 500,
drop: int = 0,
down_sample_interval: int = 1,
plot: bool = False,
ax: Any = None) -> sample_types:
sampling_algorithm: JavaObject = build_sampling_algorithm(
algo, proposal_distribution, proposal_distribution_sigma,
proposal_listeners)
vertices_unwrapped: JavaList = k.to_java_object_list(sample_from)
network_samples: JavaObject = sampling_algorithm.getPosteriorSamples(
net.unwrap(), vertices_unwrapped,
draws).drop(drop).downSample(down_sample_interval)
vertex_samples = {
Vertex._get_python_label(vertex_unwrapped): list(
map(Tensor._to_ndarray,
network_samples.get(vertex_unwrapped).asList()))
for vertex_unwrapped in vertices_unwrapped
}
if plot:
traceplot(vertex_samples, ax=ax)
return vertex_samples
def _samples_generator(sample_iterator: JavaObject,
vertices_unwrapped: JavaList, live_plot: bool,
refresh_every: int, ax: Any) -> sample_generator_types:
traces = []
x0 = 0
while (True):
network_sample = sample_iterator.next()
sample = {
Vertex._get_python_label(vertex_unwrapped):
Tensor._to_ndarray(network_sample.get(vertex_unwrapped))
for vertex_unwrapped in vertices_unwrapped
}
if live_plot:
traces.append(sample)
if len(traces) % refresh_every == 0:
joined_trace = {
k: [t[k] for t in traces]
for k in sample.keys()
}
if ax is None:
ax = traceplot(joined_trace, x0=x0)
else:
traceplot(joined_trace, ax=ax, x0=x0)
x0 += refresh_every
traces = []
yield sample