def _calculate_log_probability(self, x, **parameters):
"""
One line description
Parameters
----------
Returns
-------
"""
vector_shape = parameters[
"p"].shape if "p" in parameters else parameters["softmax_p"].shape
if x.shape == vector_shape and tensor_range(x) == {0, 1}:
dist = distributions.one_hot_categorical.OneHotCategorical
else:
dist = distributions.categorical.Categorical
if "p" in parameters:
log_prob = dist(probs=parameters["p"]).log_prob(x[:, 0])
elif "softmax_p" in parameters:
log_prob = dist(logits=parameters["softmax_p"]).log_prob(x[:, 0])
else:
raise ValueError("Either p or " +
"softmax_p needs to be provided as input")
return log_prob
def _calculate_log_probability(self, x, **parameters):
"""
One line description
Parameters
----------
Returns
-------
"""
vector_shape = parameters["probs"].shape if "probs" in parameters else parameters["logits"].shape
if x.shape == vector_shape and tensor_range(x) == {0, 1}:
dist = self.torchdist
else:
dist = distributions.categorical.Categorical
log_prob = dist(**parameters).log_prob(x[:, 0])
return log_prob