def compute_dist_stats(config, id_distance, id_stream, delta): distance = config.distances.instance(id_distance) stream = config.streams.instance(id_stream) it = stream.read_all() results = [] for logitem in iterate_testcases(it, delta): assert_allclose(len(logitem.u), delta) y0 = UncertainImage(logitem.y0) y1 = UncertainImage(logitem.y1) d = distance.distance(y0, y1) results.append(d) logger.info('%s: found %d of %d steps in %s' % (id_distance, len(results), delta, id_stream)) return results
def compute_predstats(config, id_discdds, id_stream, delta, id_distances): dds = config.discdds.instance(id_discdds) stream = config.streams.instance(id_stream) distances = dict(map(lambda x: (x, config.distances.instance(x)), id_distances)) dtype = [(x, 'float32') for x in id_distances] results = [] for logitem in iterate_testcases(stream.read_all(), delta): assert_allclose(len(logitem.u), delta) y0 = UncertainImage(logitem.y0) y1 = UncertainImage(logitem.y1) py0 = dds.predict(y0, dds.commands_to_indices(logitem.u)) ds = [] for name in id_distances: d = distances[name].distance(y1, py0) # d0 = distances[name].distance(y1, y0) ds.append(d) a = np.array(tuple(ds), dtype=dtype) results.append(a) # pdb.set_trace() return results
def compute_predstats(config, id_discdds, id_stream, delta, id_distances): dds = config.discdds.instance(id_discdds) stream = config.streams.instance(id_stream) distances = dict( map(lambda x: (x, config.distances.instance(x)), id_distances)) dtype = [(x, 'float32') for x in id_distances] results = [] for logitem in iterate_testcases(stream.read_all(), delta): assert_allclose(len(logitem.u), delta) y0 = UncertainImage(logitem.y0) y1 = UncertainImage(logitem.y1) py0 = dds.predict(y0, dds.commands_to_indices(logitem.u)) ds = [] for name in id_distances: d = distances[name].distance(y1, py0) # d0 = distances[name].distance(y1, y0) ds.append(d) a = np.array(tuple(ds), dtype=dtype) results.append(a) # pdb.set_trace() return results