torch_mist.estimators.generative.base

Module Contents

Classes

GenerativeMIEstimator
ConditionalGenerativeMIEstimator
JointGenerativeMIEstimator

class torch_mist.estimators.generative.base.GenerativeMIEstimator

Bases: torch_mist.estimators.base.MIEstimator

unnormalized_log_ratio(x: torch.Tensor, y: torch.Tensor) → torch.Tensor

abstract approx_log_p_y_given_x(x: torch.Tensor, y: torch.Tensor) → torch.Tensor

abstract approx_log_p_y(x: torch.Tensor, y: torch.Tensor) → torch.Tensor

log_ratio(x: torch.Tensor, y: torch.Tensor) → torch.Tensor

class torch_mist.estimators.generative.base.ConditionalGenerativeMIEstimator(q_Y_given_X: pyro.distributions.ConditionalDistribution)

Bases: GenerativeMIEstimator

approx_log_p_y_given_x(x: torch.Tensor, y: torch.Tensor) → torch.Tensor

batch_loss(x: torch.Tensor, y: torch.Tensor) → torch.Tensor

__repr__()

class torch_mist.estimators.generative.base.JointGenerativeMIEstimator(q_XY: torch_mist.distributions.JointDistribution)

Bases: GenerativeMIEstimator

property q_X

property q_Y

approx_log_p_xy(x: torch.Tensor, y: torch.Tensor) → torch.Tensor

approx_log_p_x(x: torch.Tensor, y: torch.Tensor) → torch.Tensor

approx_log_p_y(x: torch.Tensor, y: torch.Tensor) → torch.Tensor

approx_log_p_y_given_x(x: torch.Tensor, y: torch.Tensor) → torch.Tensor

batch_loss(x: torch.Tensor, y: torch.Tensor) → torch.Tensor

__repr__()