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RSNNS (version 0.4-3)

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

0.4-3

License

LGPL (>= 2)

Maintainer

Christoph Bergmeir

Last Published

January 10th, 2012

Functions in RSNNS (0.4-3)

SnnsRObject$getAllOutputUnits

Get all output units of the net.
getSnnsRDefine

Get a define of the SNNS kernel
normalizeData

Data normalization
SnnsRObjectMethodCaller

Method caller for SnnsR objects
SnnsRObject$getAllInputUnits

Get all input units of the net
som

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

Create and train an RBF network with the DDA algorithm
SnnsRObject$somPredictComponentMaps

Calculate the som component maps
jordan

Create and train a Jordan network
summary.rsnns

Generic summary function for rsnns objects
toNumericClassLabels

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

Generic print function for rsnns objects
predict.rsnns

Generic predict function for rsnns object
resolveSnnsRDefine

Resolve a define of the SNNS kernel
encodeClassLabels

Encode a matrix of (decoded) class labels
SnnsRObjectFactory

SnnsR object factory
SnnsRObject$train

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

Predict values with a trained net
outputColumns

Get the columns that are targets
weightMatrix

Function to extract the weight matrix of an rsnns object
rsnnsObjectFactory

Object factory for generating rsnns objects
rbf

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

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

Function to normalize training and test set
readResFile

Rudimentary parser for res files.
plotIterativeError

Plot iterative errors of an rsnns object
SnnsRObject$getWeightMatrix

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

Get the complete weight matrix.
confusionMatrix

Computes a confusion matrix
SnnsRObject$createPatSet

Create a pattern set
art2

Create and train an art2 network
matrixToActMapList

Convert matrix of activations to activation map list
RSNNS-package

Getting started with the RSNNS package
extractNetInfo

Extract information from a network
elman

Create and train an Elman network
SnnsRObject$resetRSNNS

Reset the SnnsR object.
SnnsRObject$setTTypeUnitsActFunc

Set the activation function for all units of a certain ttype.
SnnsR-class

The main class of the package
analyzeClassification

Converts continuous outputs to class labels
art1

Create and train an art1 network
SnnsRObject$whereAreResults

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

Get most of the relevant results from a som
inputColumns

Get the columns that are inputs
getNormParameters

Get normalization parameters of the input data
train

Internal generic train function for rsnns objects
savePatFile

Save data to a pat file
setSnnsRSeedValue

Set the SnnsR seed value
SnnsRObject$getAllUnits

Get all units present in the net.
SnnsRObject$createNet

Create a layered network
SnnsRObject$getInfoHeader

Get an info header of the network.
SnnsRObject$getAllUnitsTType

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

Predict values with a trained net
SnnsRObject$getSiteDefinitions

Get the sites definitions of the network.
exportToSnnsNetFile

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

Get the FType definitions of the network.
dlvq

Create and train a dlvq network
SnnsRObject$getUnitDefinitions

Get the unit definitions of the network.
getSnnsRFunctionTable

Get SnnsR function table
decodeClassLabels

Decode class labels to a binary matrix
vectorToActMap

Convert a vector to an activation map
mlp

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

Plot activation map
plotROC

Plot a ROC curve
denormalizeData

Revert data normalization
plotRegressionError

Plot a regression error plot
readPatFile

Load data from a pat file
SnnsRObject$extractPatterns

Extract the current pattern set to a matrix
SnnsRObject$setUnitDefaults

Set the unit defaults
SnnsRObject$somPredictCurrPatSetWinnersSpanTree

Get the spanning tree of the SOM
SnnsRObject$initializeNet

Initialize the network
assoz

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

Get characteristics of the network.
SnnsRObject$getAllHiddenUnits

Get all hidden units of the net
artmap

Create and train an artmap network
snnsData

Example data of the package
splitForTrainingAndTest

Function to split data into training and test set