brainpy.nn.nodes.RC.NVAR

class brainpy.nn.nodes.RC.NVAR(delay, order=None, stride=1, constant=False, trainable=False, **kwargs)

Nonlinear vector auto-regression (NVAR) node.

This class has the following features:

• it supports batch size,

• it supports multiple orders,

Parameters

• delay (int) – The number of delay step.

• order (int, sequence of int) – The nonlinear order.

• stride (int) – The stride to sample linear part vector in the delays.

• constant (optional, float) – The constant value.

References

1

Gauthier, D.J., Bollt, E., Griffith, A. et al. Next generation reservoir computing. Nat Commun 12, 5564 (2021). https://doi.org/10.1038/s41467-021-25801-2

__init__(delay, order=None, stride=1, constant=False, trainable=False, **kwargs)

Methods

__init__(delay[, order, stride, constant, ...])
copy([name, shallow])	Returns a copy of the Node.
feedback(ff_output, **shared_kwargs)	The feedback computation function of a node.
forward(ff[, fb])	The feedforward computation function of a node.
get_feature_names()	Get output feature names for transformation.
get_feature_names_for_plot()	Get output feature names for matplotlib plotting.
init_fb_conn()	Initialize the feedback connections.
init_fb_output([num_batch])	Set the initial node feedback state.
init_ff_conn()	Initialize feedforward connections.
init_state([num_batch])	Initialize the node state which depends on batch size.
initialize([num_batch])	Initialize the node.
load_states(filename[, verbose])	Load the model states.
nodes([method, level, include_self])	Collect all children nodes.
offline_fit(targets, ffs[, fbs])	Offline training interface.
online_fit(target, ff[, fb])	Online training fitting interface.
online_init()	Online training initialization interface.
register_implicit_nodes(nodes)
register_implicit_vars(variables)
save_states(filename[, variables])	Save the model states.
set_fb_output(state)	Safely set the feedback state of the node.
set_feedback_shapes(fb_shapes)
set_feedforward_shapes(feedforward_shapes)
set_output_shape(shape)
set_state(state)	Safely set the state of the node.
train_vars([method, level, include_self])	The shortcut for retrieving all trainable variables.
unique_name([name, type_])	Get the unique name for this object.
vars([method, level, include_self])	Collect all variables in this node and the children nodes.

Attributes

data_pass	Offline fitting method.
fb_output	rtype Optional[TypeVar(Tensor, JaxArray, ndarray)]
feedback_shapes	Output data size.
feedforward_shapes	Input data size.
is_feedback_input_supported
is_feedback_supported
is_initialized	rtype bool
name
output_shape	Output data size.
state	Node current internal state.
state_trainable	Returns if the Node can be trained.
train_state
trainable	Returns if the Node can be trained.