reservoirpy.nodes.Output#

class reservoirpy.nodes.Output(name: str | None = None)[source]#

Convenience node which can be used to add an output to a model.

For instance, this node can be connected to a reservoir within a model to inspect its states.

Parameters:: name (str, optional) – Node name.

Example

We can use the Output node to probe the hidden states of Reservoir in an Echo State Network:

>>> import numpy as np
>>> from reservoirpy.nodes import Reservoir, Ridge, Output
>>> reservoir = Reservoir(100)
>>> readout = Ridge(name="readout")
>>> probe = Output(name="reservoir-states")
>>> esn = reservoir >> readout & reservoir >> probe
>>> _ = esn.initialize(np.ones((1,1)), np.ones((1,1)))

When running the model, states can then be retrieved as an output:

>>> data = np.ones((10, 1))
>>> outputs = esn.run(data)
>>> states = outputs["reservoir-states"]

Methods

`__init__`([name])
`initialize`(x)	Define input and output dimensions, and instantiate variables.
`predict`([x, iters, workers])	Alias for `run()`
`reset`()	Reset all Node state
`run`([x, iters, workers])	Run the Node on a sequence of data.
`step`([x])	Call the Node function on a single step of data and update the state of the Node.

Attributes

`input_dim`	Expected dimension of the Node input.
`name`	Optional name of the Node.
`output_dim`	Expected dimension of the Node input.
`initialized`	True if the Node has been initialized
`state`	Current state of the Node.

initialize( x: array(t, d) | array(s, t, d) | ~typing.Sequence[array(t, d)] | array(d), )[source]#

Define input and output dimensions, and instantiate variables.

Only called once, before fitting or running the node.

Parameters:

x (array of shape (input_dim,) or (timestep, input_dim)) – Input data to the node.
y (None) – Training data to the node. As it is not a trainable node, y is expected to be None.

initialized: bool#: True if the Node has been initialized

input_dim: int = None#: Expected dimension of the Node input. Can be None before initialization

name: str | None = None#: Optional name of the Node.

output_dim: int = None#: Expected dimension of the Node input. Can be None before initialization

Alias for run()

Run the Node on a sequence of data. Can update the state of the Node several times.

Parameters:

x (array-like of shape ([n_inputs,] timesteps, input_dim) or list of) – arrays of shape (timesteps, input_dim), optional A sequence of data of shape (timesteps, features).
iters (int, optional) – If x is None, a dimensionless timeseries of length iters is used instead.

Returns:

A sequence of output vectors.

Return type:

array of shape ([n_inputs,] timesteps, output_dim) or list of arrays

reset() → dict[str, ndarray][source]#

Reset all Node state

Returns:: dict[str, np.array]
Return type:: previous state of the Node.

Run the Node on a sequence of data. Can update the state of the Node several times.

Parameters:

x (array-like of shape ([n_inputs,] timesteps, input_dim) or list of arrays of shape (timesteps, input_dim), optional) – A timeseries, array of shape (timesteps, features), or a sequence of timeseries. Input of the Node.
iters (int, optional) – If x is None, a dimensionless timeseries of length iters is used instead.
workers (int, default to 1) – Number of workers used for parallelization. If set to -1, all available workers (threads or processes) are used.

Returns:

A sequence of output vectors.

Return type:

array of shape ([n_inputs,] timesteps, output_dim) or list of arrays

state: dict[str, ndarray]#: Current state of the Node. Must have “out” as one of the keys.

step(x: array(d) | None = None)[source]#

Call the Node function on a single step of data and update the state of the Node.

Parameters:: x (array of shape (input_dim,), optional) – One single step of input data. If None, an empty array is used instead and the Node is assumed to have an input_dim of 0
Returns:: An output vector.
Return type:: array of shape (output_dim,)