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RSNNS (version 0.4-18)

Neural Networks using the Stuttgart Neural Network Simulator (SNNS)

Description

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.

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Install

install.packages('RSNNS')

Monthly Downloads

8,361

Version

0.4-18

License

LGPL (>= 2) | file LICENSE

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Maintainer

Christoph Bergmeir

Last Published

January 30th, 2026

Functions in RSNNS (0.4-18)

SnnsRObject$initializeNet

Initialize the network
SnnsRObjectFactory

SnnsR object factory
elman

Create and train an Elman network
SnnsRObject$predictCurrPatSet

Predict values with a trained net
dlvq

Create and train a dlvq network
SnnsRObjectMethodCaller

Method caller for SnnsR objects
denormalizeData

Revert data normalization
decodeClassLabels

Decode class labels to a binary matrix
SnnsRObject$somPredictCurrPatSetWinnersSpanTree

Get the spanning tree of the SOM
matrixToActMapList

Convert matrix of activations to activation map list
mlp

Create and train a multi-layer perceptron (MLP)
inputColumns

Get the columns that are inputs
jordan

Create and train a Jordan network
SnnsRObject$somPredictComponentMaps

Calculate the som component maps
encodeClassLabels

Encode a matrix of (decoded) class labels
art2

Create and train an art2 network
exportToSnnsNetFile

Export the net to a file in the original SNNS file format
SnnsRObject$train

Train a network and test it in every training iteration
SnnsRObject$whereAreResults

Get a list of output units of a net
artmap

Create and train an artmap network
print.rsnns

Generic print function for rsnns objects
rbf

Create and train a radial basis function (RBF) network
normTrainingAndTestSet

Function to normalize training and test set
getSnnsRDefine

Get a define of the SNNS kernel
getSnnsRFunctionTable

Get SnnsR function table
confusionMatrix

Computes a confusion matrix
plotROC

Plot a ROC curve
assoz

Create and train an (auto-)associative memory
plotIterativeError

Plot iterative errors of an rsnns object
plotRegressionError

Plot a regression error plot
SnnsRObject$getSiteDefinitions

Get the sites definitions of the network.
rbfDDA

Create and train an RBF network with the DDA algorithm
readPatFile

Load data from a pat file
snnsData

Example data of the package
weightMatrix

Function to extract the weight matrix of an rsnns object
normalizeData

Data normalization
setSnnsRSeedValue

DEPRECATED, Set the SnnsR seed value
vectorToActMap

Convert a vector to an activation map
plotActMap

Plot activation map
som

Create and train a self-organizing map (SOM)
outputColumns

Get the columns that are targets
train

Internal generic train function for rsnns objects
splitForTrainingAndTest

Function to split data into training and test set
predict.rsnns

Generic predict function for rsnns object
rsnnsObjectFactory

Object factory for generating rsnns objects
savePatFile

Save data to a pat file
SnnsRObject$setTTypeUnitsActFunc

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

Reset the SnnsR object.
analyzeClassification

Converts continuous outputs to class labels
art1

Create and train an art1 network
extractNetInfo

Extract information from a network
getNormParameters

Get normalization parameters of the input data
readResFile

Rudimentary parser for res files.
resolveSnnsRDefine

Resolve a define of the SNNS kernel
toNumericClassLabels

Convert a vector (of class labels) to a numeric vector
summary.rsnns

Generic summary function for rsnns objects
SnnsRObject$getAllOutputUnits

Get all output units of the net.
RSNNS-package

Getting started with the RSNNS package
SnnsRObject$extractPatterns

Extract the current pattern set to a matrix
SnnsRObject$getAllUnits

Get all units present in the net.
SnnsRObject$createPatSet

Create a pattern set
SnnsRObject$createNet

Create a layered network
SnnsR-class

The main class of the package
SnnsRObject$extractNetInfo

Get characteristics of the network.
SnnsRObject$getAllHiddenUnits

Get all hidden units of the net
SnnsRObject$getAllInputUnits

Get all input units of the net
SnnsRObject$getUnitDefinitions

Get the unit definitions of the network.
SnnsRObject$getTypeDefinitions

Get the FType definitions of the network.
SnnsRObject$getCompleteWeightMatrix

Get the complete weight matrix.
SnnsRObject$getAllUnitsTType

Get all units in the net of a certain ttype.
SnnsRObject$getUnitsByName

Find all units whose name begins with a given prefix.
SnnsRObject$getInfoHeader

Get an info header of the network.
SnnsRObject$somPredictCurrPatSetWinners

Get most of the relevant results from a som
SnnsRObject$getWeightMatrix

Get the weight matrix between two sets of units
SnnsRObject$setUnitDefaults

Set the unit defaults