def _samples_generator(
sample_iterator: JavaObject, vertices_unwrapped: JavaList,
live_plot: bool, refresh_every: int, ax: Any, all_scalar: bool,
id_to_label: Dict[Tuple[int, ...], str]) -> sample_generator_types:
traces = []
x0 = 0
while (True):
network_sample = sample_iterator.next()
if all_scalar:
sample: sample_generator_dict_type = {
id_to_label[Vertex._get_python_id(vertex_unwrapped)]:
Tensor._to_ndarray(
network_sample.get(vertex_unwrapped)).item()
for vertex_unwrapped in vertices_unwrapped
}
else:
sample = __create_multi_indexed_samples_generated(
vertices_unwrapped, network_sample, id_to_label)
if live_plot:
traces.append(sample)
if len(traces) % refresh_every == 0:
joined_trace: sample_types = {
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
def _samples_generator(sample_iterator: JavaObject,
vertices_unwrapped: JavaList) -> sample_generator_types:
while (True):
network_sample = sample_iterator.next()
sample = {
Vertex._get_python_id(vertex_unwrapped):
Tensor._to_ndarray(network_sample.get(vertex_unwrapped))
for vertex_unwrapped in vertices_unwrapped
}
yield sample
def __create_single_indexed_samples(
network_samples: JavaObject, vertices_unwrapped: JavaList,
id_to_label: Dict[Tuple[int, ...], str]) -> sample_types:
vertex_samples: sample_types = {}
for vertex_unwrapped in vertices_unwrapped:
vertex_label = id_to_label[Vertex._get_python_id(vertex_unwrapped)]
samples_for_vertex = __get_vertex_samples(network_samples,
vertex_unwrapped)
vertex_samples[vertex_label] = samples_for_vertex.tolist()
return vertex_samples
def test_java_collections_to_generator() -> None:
gaussian = Gaussian(0., 1.)
java_collections = gaussian.unwrap().getConnectedGraph()
python_list = list(Vertex._to_generator(java_collections))
java_vertex_ids = [
Vertex._get_python_id(java_vertex) for java_vertex in java_collections
]
assert java_collections.size() == len(python_list)
assert all(
type(element) == Double and element.get_id() in java_vertex_ids
for element in python_list)
def __create_multi_indexed_samples_generated(
vertices_unwrapped: JavaList, network_samples: JavaObject,
id_to_label: Dict[Tuple[int, ...], str]) -> sample_generator_dict_type:
vertex_samples_multi: Dict = {}
for vertex in vertices_unwrapped:
vertex_label = id_to_label[Vertex._get_python_id(vertex)]
vertex_samples_multi[vertex_label] = defaultdict(list)
sample = Tensor._to_ndarray(network_samples.get(vertex))
__add_sample_to_dict(sample, vertex_samples_multi[vertex_label])
tuple_hierarchy: Dict = {
(vertex_label, tensor_index): values
for vertex_label, tensor_index in vertex_samples_multi.items()
for tensor_index, values in tensor_index.items()
}
return tuple_hierarchy
def __create_multi_indexed_samples(
vertices_unwrapped: JavaList, network_samples: JavaObject,
id_to_label: Dict[Tuple[int, ...], str]) -> sample_types:
vertex_samples_multi: Dict = {}
for vertex in vertices_unwrapped:
vertex_label = id_to_label[Vertex._get_python_id(vertex)]
vertex_samples_multi[vertex_label] = defaultdict(list)
samples_for_vertex = __get_vertex_samples(network_samples, vertex)
for sample in samples_for_vertex:
__add_sample_to_dict(sample, vertex_samples_multi[vertex_label])
tuple_hierarchy: Dict = {
(vertex_label, shape_index): values
for vertex_label, samples in vertex_samples_multi.items()
for shape_index, values in samples.items()
}
return tuple_hierarchy
def sample(net: BayesNet,
sample_from: Iterable[Vertex],
algo: str = 'metropolis',
draws: int = 500,
drop: int = 0,
down_sample_interval: int = 1) -> sample_types:
vertices_unwrapped = k.to_java_object_list(sample_from)
network_samples = algorithms[algo].withDefaultConfig().getPosteriorSamples(
net.unwrap(), vertices_unwrapped,
draws).drop(drop).downSample(down_sample_interval)
vertex_samples = {
Vertex._get_python_id(vertex_unwrapped): list(
map(Tensor._to_ndarray,
network_samples.get(vertex_unwrapped).asList()))
for vertex_unwrapped in vertices_unwrapped
}
return vertex_samples
