garage.tf.distributions.categorical module¶

class Categorical(dim, name=None)[source]¶

cross_entropy_sym(old_dist_info_vars, new_dist_info_vars, name='cross_entropy_sym')[source]¶

dim¶

Dimension of this distribution.

Type:	int

dist_info_specs¶

Specification of the parameter of a distribution.

Type:	list

entropy(info)[source]¶

Entropy of a distribution.

Parameters:	dist_info (dict) – Parameters of a distribution.
Returns:	Entropy of the distribution.
Return type:	float

entropy_sym(dist_info_vars)[source]¶

Symbolic entropy of a distribution.

Parameters:	dist_info_vars (dict) – Symbolic parameters of a distribution. name (str) – TensorFlow scope name.
Returns:	Symbolic entropy of the distribution.
Return type:	tf.Tensor

kl(old_dist_info, new_dist_info)[source]¶: Compute the KL divergence of two categorical distributions

kl_sym(old_dist_info_vars, new_dist_info_vars, name='kl_sym')[source]¶: Compute the symbolic KL divergence of two categorical distributions

likelihood_ratio_sym(x_var, old_dist_info_vars, new_dist_info_vars, name='likelihood_ratio_sym')[source]¶

Symbolic likelihood ratio.

Parameters:	x_var (tf.Tensor) – Input placeholder. old_dist_info_vars (dict) – Old distribution tensors. new_dist_info_vars (dict) – New distribution tensors. name (str) – TensorFlow scope name.
Returns:	Symbolic likelihood ratio.
Return type:	tf.Tensor

log_likelihood(xs, dist_info)[source]¶

Log likelihood of a sample under a distribution.

Parameters:	xs (np.ndarray) – Input value. dist_info (dict) – Parameters of a distribution.
Returns:	Log likelihood of a sample under the distribution.
Return type:	float

log_likelihood_sym(x_var, dist_info_vars, name='log_likelihood_sym')[source]¶

Symbolic log likelihood.

Parameters:	x_var (tf.Tensor) – Input placeholder. dist_info_vars (dict) – Parameters of a distribution. name (str) – TensorFlow scope name.
Returns:	Symbolic log likelihood.
Return type:	tf.Tensor