R: Neural Networks using the Stuttgart Neural Network Simulator (SNNS)

# Neural Networks using the Stuttgart Neural Network Simulator (SNNS)

## Documentation for package ‘RSNNS’ version 0.4-18

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Code demos. Use demo() to run them.

## Help Pages

A C D E G I J M N O P R S T V W misc

RSNNS-package | Getting started with the RSNNS package

## -- A --

analyzeClassification | Converts continuous outputs to class labels
art1 | Create and train an art1 network
art1.default | Create and train an art1 network
art2 | Create and train an art2 network
art2.default | Create and train an art2 network
artmap | Create and train an artmap network
artmap.default | Create and train an artmap network
assoz | Create and train an (auto-)associative memory
assoz.default | Create and train an (auto-)associative memory

## -- C --

confusionMatrix | Computes a confusion matrix
createNet-method | Create a layered network
createPatSet-method | Create a pattern set

## -- D --

decodeClassLabels | Decode class labels to a binary matrix
denormalizeData | Revert data normalization
dlvq | Create and train a dlvq network
dlvq.default | Create and train a dlvq network

## -- E --

elman | Create and train an Elman network
elman.default | Create and train an Elman network
encodeClassLabels | Encode a matrix of (decoded) class labels
exportToSnnsNetFile | Export the net to a file in the original SNNS file format
extractNetInfo | Extract information from a network
extractNetInfo-method | Get characteristics of the network.
extractPatterns-method | Extract the current pattern set to a matrix

## -- G --

getAllHiddenUnits-method | Get all hidden units of the net
getAllInputUnits-method | Get all input units of the net
getAllOutputUnits-method | Get all output units of the net.
getAllUnits-method | Get all units present in the net.
getAllUnitsTType-method | Get all units in the net of a certain 'ttype'.
getCompleteWeightMatrix-method | Get the complete weight matrix.
getInfoHeader-method | Get an info header of the network.
getNormParameters | Get normalization parameters of the input data
getSiteDefinitions-method | Get the sites definitions of the network.
getSnnsRDefine | Get a define of the SNNS kernel
getSnnsRFunctionTable | Get SnnsR function table
getTypeDefinitions-method | Get the FType definitions of the network.
getUnitDefinitions-method | Get the unit definitions of the network.
getUnitsByName-method | Find all units whose name begins with a given prefix.
getWeightMatrix-method | Get the weight matrix between two sets of units

## -- I --

initializeNet-method | Initialize the network
inputColumns | Get the columns that are inputs

## -- J --

jordan | Create and train a Jordan network
jordan.default | Create and train a Jordan network

## -- M --

matrixToActMapList | Convert matrix of activations to activation map list
mlp | Create and train a multi-layer perceptron (MLP)
mlp.default | Create and train a multi-layer perceptron (MLP)

## -- N --

normalizeData | Data normalization
normTrainingAndTestSet | Function to normalize training and test set

## -- O --

outputColumns | Get the columns that are targets

## -- P --

plotActMap | Plot activation map
plotIterativeError | Plot iterative errors of an rsnns object
plotIterativeError.rsnns | Plot iterative errors of an rsnns object
plotRegressionError | Plot a regression error plot
plotROC | Plot a ROC curve
predict.rsnns | Generic predict function for rsnns object
predictCurrPatSet-method | Predict values with a trained net
print.rsnns | Generic print function for rsnns objects

## -- R --

rbf | Create and train a radial basis function (RBF) network
rbf.default | Create and train a radial basis function (RBF) network
rbfDDA | Create and train an RBF network with the DDA algorithm
rbfDDA.default | Create and train an RBF network with the DDA algorithm
readPatFile | Load data from a pat file
readResFile | Rudimentary parser for res files.
resetRSNNS-method | Reset the SnnsR object.
resolveSnnsRDefine | Resolve a define of the SNNS kernel
RSNNS | Getting started with the RSNNS package
rsnns | Object factory for generating rsnns objects
rsnnsObjectFactory | Object factory for generating rsnns objects

## -- S --

savePatFile | Save data to a pat file
setSnnsRSeedValue | DEPRECATED, Set the SnnsR seed value
setTTypeUnitsActFunc-method | Set the activation function for all units of a certain ttype.
setUnitDefaults-method | Set the unit defaults
snnsData | Example data of the package
SnnsR-class | The main class of the package
SnnsRObject$createNet | Create a layered network
SnnsRObject$createPatSet | Create a pattern set
SnnsRObject$extractNetInfo | Get characteristics of the network.
SnnsRObject$extractPatterns | Extract the current pattern set to a matrix
SnnsRObject$getAllHiddenUnits | Get all hidden units of the net
SnnsRObject$getAllInputUnits | Get all input units of the net
SnnsRObject$getAllOutputUnits | Get all output units of the net.
SnnsRObject$getAllUnits | Get all units present in the net.
SnnsRObject$getAllUnitsTType | Get all units in the net of a certain 'ttype'.
SnnsRObject$getCompleteWeightMatrix | Get the complete weight matrix.
SnnsRObject$getInfoHeader | Get an info header of the network.
SnnsRObject$getSiteDefinitions | Get the sites definitions of the network.
SnnsRObject$getTypeDefinitions | Get the FType definitions of the network.
SnnsRObject$getUnitDefinitions | Get the unit definitions of the network.
SnnsRObject$getUnitsByName | Find all units whose name begins with a given prefix.
SnnsRObject$getWeightMatrix | Get the weight matrix between two sets of units
SnnsRObject$initializeNet | Initialize the network
SnnsRObject$predictCurrPatSet | Predict values with a trained net
SnnsRObject$resetRSNNS | Reset the SnnsR object.
SnnsRObject$setTTypeUnitsActFunc | Set the activation function for all units of a certain ttype.
SnnsRObject$setUnitDefaults | Set the unit defaults
SnnsRObject$somPredictComponentMaps | Calculate the som component maps
SnnsRObject$somPredictCurrPatSetWinners | Get most of the relevant results from a som
SnnsRObject$somPredictCurrPatSetWinnersSpanTree | Get the spanning tree of the SOM
SnnsRObject$train | Train a network and test it in every training iteration
SnnsRObject$whereAreResults | Get a list of output units of a net
SnnsRObjectFactory | SnnsR object factory
SnnsRObjectMethodCaller | Method caller for SnnsR objects
SnnsR__createNet | Create a layered network
SnnsR__createPatSet | Create a pattern set
SnnsR__extractNetInfo | Get characteristics of the network.
SnnsR__extractPatterns | Extract the current pattern set to a matrix
SnnsR__getAllHiddenUnits | Get all hidden units of the net
SnnsR__getAllInputUnits | Get all input units of the net
SnnsR__getAllOutputUnits | Get all output units of the net.
SnnsR__getAllUnits | Get all units present in the net.
SnnsR__getAllUnitsTType | Get all units in the net of a certain 'ttype'.
SnnsR__getCompleteWeightMatrix | Get the complete weight matrix.
SnnsR__getInfoHeader | Get an info header of the network.
SnnsR__getSiteDefinitions | Get the sites definitions of the network.
SnnsR__getTypeDefinitions | Get the FType definitions of the network.
SnnsR__getUnitDefinitions | Get the unit definitions of the network.
SnnsR__getUnitsByName | Find all units whose name begins with a given prefix.
SnnsR__getWeightMatrix | Get the weight matrix between two sets of units
SnnsR__initializeNet | Initialize the network
SnnsR__predictCurrPatSet | Predict values with a trained net
SnnsR__resetRSNNS | Reset the SnnsR object.
SnnsR__setTTypeUnitsActFunc | Set the activation function for all units of a certain ttype.
SnnsR__setUnitDefaults | Set the unit defaults
SnnsR__somPredictComponentMaps | Calculate the som component maps
SnnsR__somPredictCurrPatSetWinners | Get most of the relevant results from a som
SnnsR__somPredictCurrPatSetWinnersSpanTree | Get the spanning tree of the SOM
SnnsR__train | Train a network and test it in every training iteration
SnnsR__whereAreResults | Get a list of output units of a net
som | Create and train a self-organizing map (SOM)
som.default | Create and train a self-organizing map (SOM)
somPredictComponentMaps-method | Calculate the som component maps
somPredictCurrPatSetWinners-method | Get most of the relevant results from a som
somPredictCurrPatSetWinnersSpanTree-method | Get the spanning tree of the SOM
splitForTrainingAndTest | Function to split data into training and test set
summary.rsnns | Generic summary function for rsnns objects

## -- T --

toNumericClassLabels | Convert a vector (of class labels) to a numeric vector
train | Internal generic train function for rsnns objects
train-method | Train a network and test it in every training iteration
train.rsnns | Internal generic train function for rsnns objects

## -- V --

vectorToActMap | Convert a vector to an activation map

## -- W --

weightMatrix | Function to extract the weight matrix of an rsnns object
weightMatrix.rsnns | Function to extract the weight matrix of an rsnns object
whereAreResults-method | Get a list of output units of a net

## -- misc --

$ | Method caller for SnnsR objects
$-method | Method caller for SnnsR objects