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RSNNS (version 0.4-2)

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

6,879

Version

0.4-2

License

LGPL (>= 2)

Maintainer

Christoph Bergmeir

Last Published

September 30th, 2011

Functions in RSNNS (0.4-2)

SnnsRObject$getAllInputUnits

Get all input units of the net
SnnsRObject$whereAreResults

Get a list of output units of a net
SnnsRObject$getUnitDefinitions

Get the unit definitions of the network.
inputColumns

Get the columns that are inputs
SnnsRObject$getTypeDefinitions

Get the FType definitions of the network.
SnnsRObject$getAllUnits

Get all units present in the net.
SnnsRObject$train

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

Calculate the som component maps
elman

Create and train an Elman network
readResFile

Rudimentary parser for res files.
getSnnsRDefine

Get a define of the SNNS kernel
rbf

Create and train a radial basis function (RBF) network
print.rsnns

Generic print function for rsnns objects
toNumericClassLabels

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

Convert a vector to an activation map
splitForTrainingAndTest

Function to split data into training and test set
SnnsRObject$somPredictCurrPatSetWinnersSpanTree

Get the spanning tree of the SOM
SnnsRObject$extractNetInfo

Get characteristics of the network.
SnnsRObject$predictCurrPatSet

Predict values with a trained net
SnnsRObject$setUnitDefaults

Set the unit defaults
SnnsRObject$getCompleteWeightMatrix

Get the complete weight matrix.
extractNetInfo

Extract information from a network
readPatFile

Load data from a pat file
SnnsRObject$extractPatterns

Extract the current pattern set to a matrix
SnnsRObjectFactory

SnnsR object factory
confusionMatrix

Computes a confusion matrix
exportToSnnsNetFile

Export the net to a file in the original SNNS file format
predict.rsnns

Generic predict function for rsnns object
SnnsRObject$getAllOutputUnits

Get all output units of the net.
SnnsRObject$genericPredictCurrPatSet

Predict values with a trained net
SnnsRObject$getWeightMatrix

Get the weight matrix between two sets of units
art1

Create and train an art1 network
plotActMap

Plot activation map
outputColumns

Get the columns that are targets
SnnsRObject$createNet

Create a layered network
SnnsRObject$getAllHiddenUnits

Get all hidden units of the net
SnnsRObject$setTTypeUnitsActFunc

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

Initialize the network
getSnnsRFunctionTable

Get SnnsR function table
getNormParameters

Get normalization parameters of the input data
rsnnsObjectFactory

Object factory for generating rsnns objects
SnnsRObject$getAllUnitsTType

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

Converts continuous outputs to class labels
mlp

Create and train a multi-layer perceptron (MLP)
SnnsRObject$createPatSet

Create a pattern set
normalizeData

Data normalization
setSnnsRSeedValue

Set the SnnsR seed value
assoz

Create and train an (auto-)associative memory
summary.rsnns

Generic summary function for rsnns objects
SnnsRObject$getInfoHeader

Get an info header of the network.
som

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

Get most of the relevant results from a som
plotROC

Plot a ROC curve
rbfDDA

Create and train an RBF network with the DDA algorithm
weightMatrix

Function to extract the weight matrix of an rsnns object
snnsData

Example data of the package
matrixToActMapList

Convert matrix of activations to activation map list
encodeClassLabels

Encode a matrix of (decoded) class labels
RSNNS-package

Getting started with the RSNNS package
jordan

Create and train a Jordan network
plotRegressionError

Plot a regression error plot
savePatFile

Save data to a pat file
normTrainingAndTestSet

Function to normalize training and test set
art2

Create and train an art2 network
artmap

Create and train an artmap network
SnnsR-class

The main class of the package
SnnsRObject$resetRSNNS

Reset the SnnsR object.
train

Internal generic train function for rsnns objects
denormalizeData

Revert data normalization
SnnsRObject$getUnitsByName

Find all units whose name begins with a given prefix.
SnnsRObjectMethodCaller

Method caller for SnnsR objects
plotIterativeError

Plot iterative errors of an rsnns object
resolveSnnsRDefine

Resolve a define of the SNNS kernel
SnnsRObject$getSiteDefinitions

Get the sites definitions of the network.
dlvq

Create and train a dlvq network
decodeClassLabels

Decode class labels to a binary matrix