pydpf.distributions.Gaussian.ConditionalGaussian#

class pydpf.distributions.Gaussian.ConditionalGaussian(mean: Callable | Tensor, cholesky_covariance: Callable | Tensor, force_diagonal_cov: bool = False, dim: int | None = None, device: device | None = None, generator: Generator = <torch._C.Generator object>)#

Bases: Distribution

A general conditional Gaussian distribution, where the mean and covariance can be given as arbitrary functions of some conditioning tensor.

Both the mean and cholesky_covariance can be either a Tensor or a function from a Tensor to a Tensor.

If the mean is a function, it should take an arbitrary conditioning Tensor and output a BxD Tensor where the mean vectors are along the last dimension, and B is zero or more batch dimensions.

If cholesky_covariance is a fuction and force_diagonal is False, it should take an arbitrary conditioning Tensor and output a BxDxD Tensor where the lower triangular cholesky covariance matricies are the last two dimension, and B is zero or more batch dimensions. If the matrix is not a valid lower triangular form it will be mapped as so by setting the diagonal to be positive and all elements above the diagonal to be zero.

If cholesky_covariance is a fuction and force_diagonal is True, it should take an arbitrary conditioning Tensor and output a BxD Tensor where the last dimension contains the standard deviations of the sample dimensions, and B is zero or more batch dimensions.

Parameters:

mean: Callable|Tensor: The means or a function to calculate them
cholesky_covariance: Callable|Tensor: The lower cholesky decomposition of the covariance, or a function to calculate it.
force_diagonal_cov: bool: Whether to force the covariance matrix to be diagonal or not.
dim: int|None: If both the mean and the covariance are given as functions, then the dimension of the distribution must be provided, otherwise it is inferred from the constant parameters.
devicetorch.device|None: If both the mean and the covariance are given as functions, then the device of the distribution must be provided, otherwise it is inferred from the constant parameters.
generatortorch.Generator: The generator to control the rng when sampling.

Notes

In the most general case, repeatedly computing large batches of matrix determinants and inverses is slow. We implement optimised routines for the special cases that the covariance is constant or that it is diagonal. If the user’s problem does not fit these cases but has other structure that can be taken advantage of it is recommended that they implement a custom sampling proceedure rather than use this.

__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`(args, *kwargs)
`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`(args, *kwargs)
`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`

abstractmethod log_density(*args, **kwargs) → Tensor#

abstractmethod sample(*args, **kwargs) → Tensor#