reservoirnet (version 0.3.0)

createNode: Function to create some node

Description

Function to create some node

Usage

createNode(
  nodeType = c("Ridge"),
  units = NULL,
  lr = 1,
  sr = NULL,
  otputDim = NULL,
  inputDim = NULL,
  name = NULL,
  ridge = 0,
  inputBias = TRUE,
  input_scaling = TRUE,
  input_connectivity = 0.1,
  rc_connectivity = 0.1,
  activation = "tanh",
  dtype = "float64",
  seed = NULL,
  ...
)

Value

A node generated by reservoirpy python module.

Arguments

nodeType

Type of node. Default is "Ridge".

units

(int) optional Number of reservoir units. If None, the number of units will be infered from the W matrix shape.

lr

(float) default to 1.0 Neurons leak rate. Must be in :math:[0, 1].

sr

(float) optional Spectral radius of recurrent weight matrix.

otputDim

Output dimension of the Node. Dimension of its state.

inputDim

Input dimension of the Node.

name

Name of the Node. It must be a unique identifier.

ridge

float, default to 0.0. L2 regularization parameter.

inputBias

bool, default to TRUE. If TRUE, then a bias parameter will be learned along with output weights.

input_scaling

float or array-like of shapes (features), default to 1.0. Input gain. An array of the same dimension as the inputs can be used to set up different input scaling for each feature.

input_connectivity

float, default to 0.1. Connectivity of input neurons, i.e. ratio of input neurons connected to reservoir neurons. Must be between 0 and 1.

rc_connectivity

float, default to 0.1. Connectivity of recurrent weight matrix, i.e. ratio of reservoir neurons connected to other reservoir neurons, including themselves. Must be between 0 and 1.

activation

str 'tanh'. Reservoir units activation function. Should be a activationsfunc function name ('tanh', 'identity', 'sigmoid', 'relu', 'softmax', 'softplus').

dtype

Numerical type for node parameters

seed

set random seed

...

Others params

Examples

if(interactive()){
readout <- reservoirnet::createNode("Ridge")
}