pyhgf.model.Network

class pyhgf.model.Network

A predictive coding neural network.

This is the core class to define and manipulate neural networks, that consists in 1. attributes, 2. structure and 3. update sequences.

Attributes:

attributes

The attributes of the probabilistic nodes.

edges

The edges of the probabilistic nodes as a tuple of pyhgf.typing.AdjacencyLists. The tuple has the same length as the node number. For each node, the index lists the value/volatility parents/children.

inputs

Information on the input nodes.

node_trajectories

The dynamic of the node’s beliefs after updating.

update_sequence

The sequence of update functions that are applied during the belief propagation step.

scan_fn

The function that is passed to jax.lax.scan(). This is a pre- parametrized version of pyhgf.networks.beliefs_propagation().

__init__() → None

Initialize an empty neural network.

Methods

__init__()	Initialize an empty neural network.
add_edges([kind, parent_idxs, ...])	Add a value or volatility coupling link between a set of nodes.
add_nodes([kind, n_nodes, node_parameters, ...])	Add new input/state node(s) to the neural network.
create_belief_propagation_fn([overwrite, ...])	Create the belief propagation function.
get_network()	Return the attributes, edges and update sequence defining the network.
input_custom_sequence(update_branches, ...)	Add new observations with custom update sequences.
input_data(input_data[, time_steps, ...])	Add new observations.
plot_correlations()	Plot the heatmap of cross-trajectories correlation.
plot_network()	Visualization of node network using GraphViz.
plot_nodes(node_idxs, **kwargs)	Plot the node(s) beliefs trajectories.
plot_trajectories(**kwargs)	Plot the parameters trajectories.
surprise(response_function[, ...])	Surprise of the model conditioned by the response function.
to_pandas()	Export the nodes trajectories and surprise as a Pandas data frame.

Attributes

input_idxs

Idexes of state nodes that can observe new data points by default.