pydpf.distributions.vonMises.VonMises

class pydpf.distributions.vonMises.VonMises(mean: Tensor, concentration: Tensor, generator: Generator)

Bases: Distribution

von Mises distribution in radians

Parameters:

mean: Tensor

The mean of the distribution.

concentration: Tensor

The concentration of the distribution. All entries must be positive.

generator: torch.Generator

The generator object used to control the RNG.

Notes

This distribution is not reparameterisable as implemented. There is no way to correlate the dimensions of the output sample. Each dimension is sampled from a separate von Mises distribution.

__init__(mean: Tensor, concentration: Tensor, generator: Generator)

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

Methods

__init__(mean, concentration, generator)	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)	Get the density of a Von-Mises distribution.
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([sample_size])	Sample a Von-Mises distribution.
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
concentration	The concentration of the distribution, constrained to be positive
conditional
dump_patches
mean	The mean of the distribution, constrained to be within \([-\pi, \pi]\).
training

concentration

The concentration of the distribution, constrained to be positive

conditional = False

log_density(sample: Tensor) → Tensor

Get the density of a Von-Mises distribution.

pi and -pi are identified.

Parameters:

sample: Tensor

The sample to get the density of.

Returns:

sample log_density: Tensor

The log density of each datum in the sample.

mean

The mean of the distribution, constrained to be within \([-\pi, \pi]\).

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

Sample a Von-Mises distribution.

Parameters:

sample_size: tuple[int,…]|None

The size of the sample to draw. Draw a single sample without a sample dimension if None.

Returns:

sample: Tensor

A multivariate Von-Mises sample.