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, 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
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
