pyhgf.distribution.HGFLogpGradOp

class pyhgf.distribution.HGFLogpGradOp(input_data: Array | ndarray | bool_ | number | bool | int | float | complex = nan, time_steps: ndarray | Array | bool_ | number | bool | int | float | complex | None = None, model_type: str = 'continous', n_levels: int = 2, response_function: Callable | None = None, response_function_inputs: ndarray | Array | bool_ | number | bool | int | float | complex | None = None)

Gradient Op for the HGF distribution.

__init__(input_data: Array | ndarray | bool_ | number | bool | int | float | complex = nan, time_steps: ndarray | Array | bool_ | number | bool | int | float | complex | None = None, model_type: str = 'continous', n_levels: int = 2, response_function: Callable | None = None, response_function_inputs: ndarray | Array | bool_ | number | bool | int | float | complex | None = None)

Initialize function.

Parameters:

input_data

An array of input time series where the first dimension is the number of models to fit in parallel. By default, the associated time_steps vector is the unit vector. A different time vector can be passed to the time_steps argument.

time_steps

An array with shapes equal to input_data containing the time_steps vectors. If one of the list items is None, or if None is provided instead, the time_steps vector will default to an integer vector starting at 0.

model_type

The model type to use (can be “continuous” or “binary”).

n_levels

The number of hierarchies in the perceptual model (can be 2 or 3). If None, the nodes hierarchy is not created and might be provided afterwards using add_nodes().

response_function

The response function to use to compute the model surprise.

response_function_inputs

A list of tuples with the same length as the number of models. Each tuple contains additional data and parameters that can be accessible to the response functions.

Methods

L_op(inputs, outputs, output_grads)	Construct a graph for the L-operator.
R_op(inputs, eval_points)	Construct a graph for the R-operator.
__init__([input_data, time_steps, ...])	Initialize function.
add_tag_trace(thing[, user_line])	Add tag.trace to a node or variable.
do_constant_folding(fgraph, node)	Determine whether or not constant folding should be performed for the given node.
grad(inputs, output_grads)	Construct a graph for the gradient with respect to each input variable.
make_node([mean_1, mean_2, mean_3, ...])	Initialize node structure.
make_py_thunk(node, storage_map, ...[, debug])	Make a Python thunk.
make_thunk(node, storage_map, compute_map, ...)	Create a thunk.
perform(node, inputs, outputs)	Perform node operations.
prepare_node(node, storage_map, compute_map, ...)	Make any special modifications that the Op needs before doing Op.make_thunk().

Attributes

default_output	An int that specifies which output Op.__call__() should return.
destroy_map	A dict that maps output indices to the input indices upon which they operate in-place.
itypes
otypes
view_map	A dict that maps output indices to the input indices of which they are a view.