Neural Networks using the Stuttgart Neural Network Simulator (SNNS)
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)
Data normalization
Function to normalize training and test set
Get the columns that are targets
Plot activation map
Plot iterative errors of an rsnns object
Plot a regression error plot
Plot a ROC curve
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
Getting started with the RSNNS package
Object factory for generating rsnns objects
Save data to a pat file
DEPRECATED, Set the SnnsR seed value
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
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
The Stuttgart Neural Network Simulator (SNNS) is a library containing many standard implementations of neural networks. This package wraps the SNNS functionality to make it available from within R. Using the 'RSNNS' low-level interface, all of the algorithmic functionality and flexibility of SNNS can be accessed. Furthermore, the package contains a convenient high-level interface, so that the most common neural network topologies and learning algorithms integrate seamlessly into R.