RSNNS

Getting started with the RSNNS package

The main class of the package

Create a layered network

Create a pattern set

Get characteristics of the network.

Extract the current pattern set to a matrix

Get all hidden units of the net

Get all input units of the net

Get all output units of the net.

Get all units present in the net.

Get all units in the net of a certain ttype.

Get the complete weight matrix.

Get an info header of the network.

Get the sites definitions of the network.

Get the FType definitions of the network.

Get the unit definitions of the network.

Find all units whose name begins with a given prefix.

Get the weight matrix between two sets of units

Initialize the network

Predict values with a trained net

Reset the SnnsR object.

Set the activation function for all units of a certain ttype.

Set the unit defaults

Calculate the som component maps

Get most of the relevant results from a som

Get the spanning tree of the SOM

Train a network and test it in every training iteration

Get a list of output units of a net

SnnsR object factory

Method caller for SnnsR objects

Converts continuous outputs to class labels

Create and train an art1 network

Create and train an art2 network

Create and train an artmap network

Create and train an (auto-)associative memory

Computes a confusion matrix

Decode class labels to a binary matrix

Revert data normalization

Create and train a dlvq network

Create and train an Elman network

Encode a matrix of (decoded) class labels

Export the net to a file in the original SNNS file format

Extract information from a network

Get normalization parameters of the input data

Get a define of the SNNS kernel

Get SnnsR function table

Get the columns that are inputs

Create and train a Jordan network

Convert matrix of activations to activation map list

Create and train a multi-layer perceptron (MLP)

Function to normalize training and test set

Data normalization

Get the columns that are targets

Plot activation map

Plot iterative errors of an rsnns object

Plot a ROC curve

Plot a regression error plot

Generic predict function for rsnns object

Generic print function for rsnns objects

Create and train a radial basis function (RBF) network

Create and train an RBF network with the DDA algorithm

Load data from a pat file

Rudimentary parser for res files.

Resolve a define of the SNNS kernel

Object factory for generating rsnns objects

Save data to a pat file

DEPRECATED, Set the SnnsR seed value

Example data of the package

Create and train a self-organizing map (SOM)

Function to split data into training and test set

Generic summary function for rsnns objects

Convert a vector (of class labels) to a numeric vector

Internal generic train function for rsnns objects

Convert a vector to an activation map

Function to extract the weight matrix of an rsnns object