pydpf.distributions.kde.KernelMixture

class pydpf.distributions.kde.KernelMixture(kernel: Distribution | Module, generator: Generator | None, resampler: Module | None = None)

Bases: Distribution

Create a kernel density mixture. The parameter kernel is an unconditional distribution which will be convolved over the kernel density mixture. The resultant distribution is conditional on the locations and weights of the kernels.

Parameters:

kernel: list[tuple[str,int], …]|Distribution

The kernel to convolve over the particles to form the KDE sampling distribution.

generatorUnion[torch.Generator, None]

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

Notes

If the kernel is not a Distribution subclass (i.e. it is a custom implementation) then it must have the following attributes:

dim : the dimension of the distribution.

sample() : method that takes the parameter sample_size and returns a tensor of that size with an extra final dimension of size dim.

log_density() : method that takes the parameter sample and returns a tensor of the same size without the final dimension, assumed to be of size dim.

__init__(kernel: Distribution | Module, generator: Generator | None, resampler: Module | None = None)

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

Methods

__init__(kernel, generator[, resampler])	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, loc, weight)	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(loc, weight, sample_size)	Sample a KDE mixture
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, loc: Tensor, weight: Tensor) → Tensor

Evaluate the log density of a sample.

Parameters:

sample: Tensor

The sample to get the density of.

locTensor

Locations of the Kernels

weightTensor

Weights of the Kernels.

Returns:

Sample: Tensor

The log density of each datum in the sample.

sample(loc: Tensor, weight: Tensor, sample_size: tuple[int, ...] | None) → Tensor

Sample a KDE mixture

Parameters:

locTensor

Locations of the Kernels

weightTensor

Weights of the Kernels

sample_sizetuple[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 from the KDE mixture.