torch_mist.estimators.generative.implementations

Submodules

Package Contents

Classes

BA

CLUB

DoE

GM

L1Out

DummyGenerativeMIEstimator

class torch_mist.estimators.generative.implementations.BA(q_Y_given_X: pyro.distributions.ConditionalDistribution, entropy_y: torch.Tensor | None = None)

Bases: torch_mist.estimators.generative.base.ConditionalGenerativeMIEstimator

lower_bound: bool = True
infomax_gradient: Dict[str, bool]
mutual_information(x: torch.Tensor, y: torch.Tensor) → torch.Tensor
class torch_mist.estimators.generative.implementations.CLUB(q_Y_given_X: pyro.distributions.ConditionalDistribution, neg_samples: int = 0)

Bases: torch_mist.estimators.generative.implementations.l1out.L1Out

infomax_gradient: Dict[str, bool]
approx_log_p_y(x: torch.Tensor, y: torch.Tensor) → torch.Tensor
class torch_mist.estimators.generative.implementations.DoE(q_Y_given_X: pyro.distributions.ConditionalDistribution, q_Y: torch.distributions.Distribution)

Bases: torch_mist.estimators.generative.base.ConditionalGenerativeMIEstimator

infomax_gradient: Dict[str, bool]
approx_log_p_y(y: torch.Tensor, x: torch.Tensor | None = None) → torch.Tensor
batch_loss(x: torch.Tensor, y: torch.Tensor) → torch.Tensor
__repr__()
class torch_mist.estimators.generative.implementations.GM(q_XY: torch_mist.distributions.joint.base.JointDistribution, q_Y: torch.distributions.Distribution | torch_mist.distributions.joint.base.JointDistribution, q_X: torch.distributions.Distribution | torch_mist.distributions.joint.base.JointDistribution)

Bases: torch_mist.estimators.generative.base.JointGenerativeMIEstimator

property q_X
property q_Y
batch_loss(x: torch.Tensor, y: torch.Tensor) → torch.Tensor
class torch_mist.estimators.generative.implementations.L1Out(q_Y_given_X: pyro.distributions.ConditionalDistribution, neg_samples: int = -1)

Bases: torch_mist.estimators.generative.base.ConditionalGenerativeMIEstimator

infomax_gradient: Dict[str, bool]
_broadcast_log_p_y_given_x(x: torch.Tensor, y: torch.Tensor) → torch.Tensor
approx_log_p_y(x: torch.Tensor, y: torch.Tensor) → torch.Tensor
class torch_mist.estimators.generative.implementations.DummyGenerativeMIEstimator

Bases: torch_mist.estimators.generative.base.ConditionalGenerativeMIEstimator

lower_bound: bool = True
infomax_gradient: Dict[str, bool]
log_ratio(x: torch.Tensor, y: torch.Tensor) → torch.Tensor
batch_loss(x: torch.Tensor, y: torch.Tensor) → torch.Tensor