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RSNNS (version 0.4-6)

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

0.4-6

License

LGPL (>= 2) | file LICENSE

Maintainer

Christoph Bergmeir

Last Published

December 22nd, 2014

Functions in RSNNS (0.4-6)

elman

Create and train an Elman network
plotRegressionError

Plot a regression error plot
normTrainingAndTestSet

Function to normalize training and test set
SnnsRObject$getAllOutputUnits

Get all output units of the net.
SnnsRObjectMethodCaller

Method caller for SnnsR objects
analyzeClassification

Converts continuous outputs to class labels
SnnsRObject$getTypeDefinitions

Get the FType definitions of the network.
SnnsRObject$getUnitsByName

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

Revert data normalization
encodeClassLabels

Encode a matrix of (decoded) class labels
inputColumns

Get the columns that are inputs
SnnsRObject$getAllHiddenUnits

Get all hidden units of the net
SnnsRObject$getAllInputUnits

Get all input units of the net
SnnsRObject$setUnitDefaults

Set the unit defaults
SnnsRObject$getAllUnitsTType

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

Convert matrix of activations to activation map list
SnnsRObject$somPredictComponentMaps

Calculate the som component maps
resolveSnnsRDefine

Resolve a define of the SNNS kernel
SnnsRObject$getCompleteWeightMatrix

Get the complete weight matrix.
dlvq

Create and train a dlvq network
getNormParameters

Get normalization parameters of the input data
SnnsRObject$resetRSNNS

Reset the SnnsR object.
SnnsRObject$genericPredictCurrPatSet

Predict values with a trained net
print.rsnns

Generic print function for rsnns objects
splitForTrainingAndTest

Function to split data into training and test set
artmap

Create and train an artmap network
SnnsRObject$getUnitDefinitions

Get the unit definitions of the network.
confusionMatrix

Computes a confusion matrix
getSnnsRDefine

Get a define of the SNNS kernel
rsnnsObjectFactory

Object factory for generating rsnns objects
SnnsRObject$createNet

Create a layered network
SnnsRObjectFactory

SnnsR object factory
SnnsRObject$getWeightMatrix

Get the weight matrix between two sets of units
weightMatrix

Function to extract the weight matrix of an rsnns object
SnnsR-class

The main class of the package
extractNetInfo

Extract information from a network
plotActMap

Plot activation map
exportToSnnsNetFile

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

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

Plot iterative errors of an rsnns object
SnnsRObject$createPatSet

Create a pattern set
rbfDDA

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

Get most of the relevant results from a som
normalizeData

Data normalization
SnnsRObject$getInfoHeader

Get an info header of the network.
getSnnsRFunctionTable

Get SnnsR function table
savePatFile

Save data to a pat file
summary.rsnns

Generic summary function for rsnns objects
predict.rsnns

Generic predict function for rsnns object
plotROC

Plot a ROC curve
SnnsRObject$somPredictCurrPatSetWinnersSpanTree

Get the spanning tree of the SOM
rbf

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

Initialize the network
readResFile

Rudimentary parser for res files.
SnnsRObject$getSiteDefinitions

Get the sites definitions of the network.
art1

Create and train an art1 network
setSnnsRSeedValue

DEPRECATED, Set the SnnsR seed value
SnnsRObject$extractNetInfo

Get characteristics of the network.
toNumericClassLabels

Convert a vector (of class labels) to a numeric vector
SnnsRObject$getAllUnits

Get all units present in the net.
SnnsRObject$train

Train a network and test it in every training iteration
assoz

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

Extract the current pattern set to a matrix
SnnsRObject$setTTypeUnitsActFunc

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

Decode class labels to a binary matrix
SnnsRObject$predictCurrPatSet

Predict values with a trained net
jordan

Create and train a Jordan network
train

Internal generic train function for rsnns objects
art2

Create and train an art2 network
vectorToActMap

Convert a vector to an activation map
snnsData

Example data of the package
RSNNS-package

Getting started with the RSNNS package
SnnsRObject$whereAreResults

Get a list of output units of a net
outputColumns

Get the columns that are targets
readPatFile

Load data from a pat file
som

Create and train a self-organizing map (SOM)