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