pydpf.distributions.Gaussian.LinearGaussian

class pydpf.distributions.Gaussian.LinearGaussian(weight: Tensor, bias: Tensor, cholesky_covariance: Tensor, diagonal_cov: bool = False, constrain_spectral_radius: float | None = None, generator: Generator | None = None)

Bases: Distribution

A Gaussian conditional distribution, where the means of the Gaussian are conditional on a supplied variable, X, through the linear map \(WX + B\). Where W is the weights and B is the bias.

Parameters:

weight: Tensor

2D tensor specifying the weight matrix, W.

bias: Tensor

1D tensor specifying the bias, B.

cholesky_covariance: Tensor

2D tensor specifying the (lower) Cholesky decomposition of the covariance matrix. If the upper triangular section has non-zero values these will be ignored.

diagonal_cov: bool

Whether to force the covariance matrix to be diagonal or not.

constrain_spectral_radius: Union[int, None]

If constrain_spectral_radius is an integer, then the weight matrix will be scaled so that it’s spectral radius never exceed the passed value. If constrain_spectral_radius is None, no scaling is applied.

generatorUnion[torch.Generator, None]

The generator to control the rng when sampling kernels from the mixture.

__init__(generator: Generator, *args, **kwargs) → None

Initialize internal Module state, shared by both nn.Module and ScriptModule.

Methods

__init__(generator, *args, **kwargs)	Initialize internal Module state, shared by both nn.Module and ScriptModule.
add_module(name, module)	Add a child module to the current module.
apply(fn)	Apply fn recursively to every submodule (as returned by .children()) as well as self.
bfloat16()	Casts all floating point parameters and buffers to bfloat16 datatype.
buffers([recurse])	Return an iterator over module buffers.
check_sample(sample)	Check that the sample matches the defined dimension of the distribution.
children()	Return an iterator over immediate children modules.
compile(*args, **kwargs)	Compile this Module's forward using torch.compile().
cpu()	Move all model parameters and buffers to the CPU.
cuda([device])	Move all model parameters and buffers to the GPU.
double()	Casts all floating point parameters and buffers to double datatype.
eval()	Set the module in evaluation mode.
extra_repr()	Return the extra representation of the module.
float()	Casts all floating point parameters and buffers to float datatype.
forward(*args, **kwargs)	Do not implement a forward method for a Distribution
get_batch_size(size, data_dims)	Get the size of a tensor excluding the last (data_size) dimensions.
get_buffer(target)	Return the buffer given by target if it exists, otherwise throw an error.
get_extra_state()	Return any extra state to include in the module's state_dict.
get_parameter(target)	Return the parameter given by target if it exists, otherwise throw an error.
get_submodule(target)	Return the submodule given by target if it exists, otherwise throw an error.
half()	Casts all floating point parameters and buffers to half datatype.
ipu([device])	Move all model parameters and buffers to the IPU.
load_state_dict(state_dict[, strict, assign])	Copy parameters and buffers from state_dict into this module and its descendants.
log_density(sample, condition_on)	Evaluate the log density of a sample.
modules([remove_duplicate])	Return an iterator over all modules in the network.
mtia([device])	Move all model parameters and buffers to the MTIA.
named_buffers([prefix, recurse, ...])	Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
named_children()	Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
named_modules([memo, prefix, remove_duplicate])	Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
named_parameters([prefix, recurse, ...])	Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
parameters([recurse])	Return an iterator over module parameters.
register_backward_hook(hook)	Register a backward hook on the module.
register_buffer(name, tensor[, persistent])	Add a buffer to the module.
register_forward_hook(hook, *[, prepend, ...])	Register a forward hook on the module.
register_forward_pre_hook(hook, *[, ...])	Register a forward pre-hook on the module.
register_full_backward_hook(hook[, prepend])	Register a backward hook on the module.
register_full_backward_pre_hook(hook[, prepend])	Register a backward pre-hook on the module.
register_load_state_dict_post_hook(hook)	Register a post-hook to be run after module's load_state_dict() is called.
register_load_state_dict_pre_hook(hook)	Register a pre-hook to be run before module's load_state_dict() is called.
register_module(name, module)	Alias for add_module().
register_parameter(name, param)	Add a parameter to the module.
register_state_dict_post_hook(hook)	Register a post-hook for the state_dict() method.
register_state_dict_pre_hook(hook)	Register a pre-hook for the state_dict() method.
requires_grad_([requires_grad])	Change if autograd should record operations on parameters in this module.
sample(condition_on[, sample_size])	Sample a multivariate Linear Gaussian.
set_extra_state(state)	Set extra state contained in the loaded state_dict.
set_submodule(target, module[, strict])	Set the submodule given by target if it exists, otherwise throw an error.
share_memory()	See torch.Tensor.share_memory_().
state_dict(*args[, destination, prefix, ...])	Return a dictionary containing references to the whole state of the module.
to(*args, **kwargs)	Move and/or cast the parameters and buffers.
to_empty(*, device[, recurse])	Move the parameters and buffers to the specified device without copying storage.
train([mode])	Set the module in training mode.
type(dst_type)	Casts all parameters and buffers to dst_type.
update()	Update all constrained_parameters and cached_properties belonging to this Module.
xpu([device])	Move all model parameters and buffers to the XPU.
zero_grad([set_to_none])	Reset gradients of all model parameters.

Attributes

T_destination
call_super_init
conditional
dump_patches
training

conditional = True

log_density(sample: Tensor, condition_on: Tensor) → Tensor

Evaluate the log density of a sample. The means of the Gaussian are calculated as condition_on @ self.weight + self.bias.

Parameters:

sample: Tensor

The sample to get the density of.

condition_on: Tensor

The vector to condition the distribution on.

Returns:

sample log_density: Tensor

The log density of each datum in the sample.

sample(condition_on: Tensor, sample_size: tuple[int, ...] | None = None) → Tensor

Sample a multivariate Linear Gaussian. The means of the Gaussian are calculated as condition_on @ self.weight + self.bias.

Parameters:

condition_on: Tensor

The vector to condition the distribution on.

sample_size: Union[Tuple[int, …], None]

The size of the sample to draw. If None then a single sample is drawn per batch dimension and no sample dimension is used.

Returns:

sample: Tensor

A sample of a multivariate Linear Gaussian, conditioned on condition_on.