def _repr_state(state):
string = "\n"
for k, v in state.items():
if k in ["observs", "states"]:
continue
shape = v.shape if hasattr(v, "shape") else None
new_str = "{} shape {} Mean: {:.3f}, Std: {:.3f}, Max: {:.3f} Min: {:.3f}\n".format(
k, shape, *statistics_from_array(v))
string += new_str
return string
def _repr_state(state):
string = "\n"
for k, v in state.items():
if k in ["observs", "states", "id_walkers", "best_id"]:
continue
shape = v.shape if hasattr(v, "shape") else None
new_str = (
"{}: shape {} Mean: {:.3f}, Std: {:.3f}, Max: {:.3f} Min: {:.3f}\n"
.format(k, shape, *statistics_from_array(v))
if isinstance(v, numpy.ndarray) and "best" not in k else
("%s %s\n" %
(k, v if not isinstance(v, numpy.ndarray) else v.flatten())))
string += new_str
return string
def _repr_state(state):
string = "\n"
skip_print = {"observs", "states", "id_walkers", "best_id"}
for k, v in state.items():
if k in skip_print:
continue
elif k == "best_state" and (v.dtype == "O"
or str(v.dtype).startswith("S")):
continue
shape = v.shape if hasattr(v, "shape") else None
new_str = (
"{}: shape {} Mean: {:.3f}, Std: {:.3f}, Max: {:.3f} Min: {:.3f}\n"
.format(k, shape, *statistics_from_array(v))
if dtype.is_tensor(v) and "best" not in k else
("%s %s\n" %
(k, v if not dtype.is_tensor(v) else v.flatten())))
string += new_str
return string