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RSNNS (version 0.4-1)

Neural Networks in R 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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Version

Install

install.packages('RSNNS')

Monthly Downloads

4,371

Version

0.4-1

License

LGPL (>= 2)

Maintainer

Christoph Bergmeir

Last Published

July 28th, 2011

Functions in RSNNS (0.4-1)

RSNNS-package

Getting started with the RSNNS package
SnnsRObject$createPatSet

Create a pattern set
SnnsRObject$initializeNet

Initialize the network
analyzeClassification

Converts continuous outputs to class labels
SnnsRObject$whereAreResults

Get a list of output units of a net
getSnnsRFunctionTable

Get SnnsR function table
elman

Create and train an Elman network
weightMatrix

Function to extract the weight matrix of an rsnns object
plotIterativeError

Plot iterative errors of an rsnns object
SnnsRObject$somPredictCurrPatSetWinnersSpanTree

Get the spanning tree of the SOM
SnnsRObject$resetRSNNS

Reset the SnnsR object.
denormalizeData

Revert data normalization
snnsData

Example data of the package
encodeClassLabels

Encode a matrix of (decoded) class labels
som

Create and train a self-organizing map (SOM)
SnnsRObject$getAllInputUnits

Get all input units of the net
confusionMatrix

Computes a confusion matrix
jordan

Create and train a Jordan network
SnnsRObject$createNet

Create a layered network
SnnsRObject$train

Train a network and test it in every training iteration
plotRegressionError

Plot a regression error plot
resolveSnnsRDefine

Resolve a define of the SNNS kernel
print.rsnns

Generic print function for rsnns objects
rbfDDA

Create and train an RBF network with the DDA algorithm
vectorToActMap

Convert a vector to an activation map
SnnsRObject$predictCurrPatSet

Predict values with a trained net
normalizeData

Data normalization
SnnsRObject$somPredictComponentMaps

Calculate the som component maps
SnnsRObject$getSiteDefinitions

Get the sites definitions of the network.
SnnsRObject$genericPredictCurrPatSet

Predict values with a trained net
toNumericClassLabels

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

Convert matrix of activations to activation map list
artmap

Create and train an artmap network
SnnsRObject$extractPatterns

Extract the current pattern set to a matrix
SnnsRObject$getAllUnits

Get all units present in the net.
SnnsRObject$setTTypeUnitsActFunc

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

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

Load data from a pat file
plotActMap

Plot activation map
SnnsRObjectFactory

SnnsR object factory
decodeClassLabels

Decode class labels to a binary matrix
SnnsRObject$getWeightMatrix

Get the weight matrix between two sets of units
plotROC

Plot a ROC curve
normTrainingAndTestSet

Function to normalize training and test set
SnnsRObject$getUnitsByName

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

Get the unit definitions of the network.
SnnsRObject$getCompleteWeightMatrix

Get the complete weight matrix.
SnnsRObject$somPredictCurrPatSetWinners

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

Get characteristics of the network.
SnnsR-class

The main class of the package
mlp

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

Extract information from a network
savePatFile

Save data to a pat file
predict.rsnns

Generic predict function for rsnns object
setSnnsRSeedValue

Set the SnnsR seed value
art2

Create and train an art2 network
SnnsRObject$getAllUnitsTType

Get all units in the net of a certain ttype.
inputColumns

Get the columns that are inputs
SnnsRObjectMethodCaller

Method caller for SnnsR objects
SnnsRObject$setUnitDefaults

Set the unit defaults
summary.rsnns

Generic summary function for rsnns objects
SnnsRObject$getAllOutputUnits

Get all output units of the net.
getSnnsRDefine

Get a define of the SNNS kernel
rsnnsObjectFactory

Object factory for generating rsnns objects
outputColumns

Get the columns that are targets
SnnsRObject$getAllHiddenUnits

Get all hidden units of the net
splitForTrainingAndTest

Function to split data into training and test set
assoz

Create and train an (auto-)associative memory
SnnsRObject$getInfoHeader

Get an info header of the network.
rbf

Create and train a radial basis function (RBF) network
SnnsRObject$getTypeDefinitions

Get the FType definitions of the network.
readResFile

Rudimentary parser for res files.
train

Internal generic train function for rsnns objects
art1

Create and train an art1 network
dlvq

Create and train a dlvq network
getNormParameters

Get normalization parameters of the input data