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RSNNS (version 0.4-9)

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

3,081

Version

0.4-9

License

LGPL (>= 2) | file LICENSE

Issues

11

Pull Requests

0

Stars

27

Forks

5

Maintainer

Christoph Bergmeir

Last Published

December 16th, 2016

Functions in RSNNS (0.4-9)

elman

Create and train an Elman network
encodeClassLabels

Encode a matrix of (decoded) class labels
dlvq

Create and train a dlvq network
art2

Create and train an art2 network
denormalizeData

Revert data normalization
assoz

Create and train an (auto-)associative memory
artmap

Create and train an artmap network
art1

Create and train an art1 network
confusionMatrix

Computes a confusion matrix
decodeClassLabels

Decode class labels to a binary matrix
inputColumns

Get the columns that are inputs
getSnnsRFunctionTable

Get SnnsR function table
jordan

Create and train a Jordan network
mlp

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

Convert matrix of activations to activation map list
extractNetInfo

Extract information from a network
exportToSnnsNetFile

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

Function to normalize training and test set
getSnnsRDefine

Get a define of the SNNS kernel
getNormParameters

Get normalization parameters of the input data
plotRegressionError

Plot a regression error plot
plotIterativeError

Plot iterative errors of an rsnns object
readResFile

Rudimentary parser for res files.
plotROC

Plot a ROC curve
resolveSnnsRDefine

Resolve a define of the SNNS kernel
rsnnsObjectFactory

Object factory for generating rsnns objects
RSNNS-package

Getting started with the RSNNS package
predict.rsnns

Generic predict function for rsnns object
outputColumns

Get the columns that are targets
plotActMap

Plot activation map
snnsData

Example data of the package
SnnsRObject$createNet

Create a layered network
SnnsRObject$getCompleteWeightMatrix

Get the complete weight matrix.
print.rsnns

Generic print function for rsnns objects
SnnsRObject$resetRSNNS

Reset the SnnsR object.
SnnsRObject$getInfoHeader

Get an info header of the network.
SnnsRObject$predictCurrPatSet

Predict values with a trained net
savePatFile

Save data to a pat file
rbf

Create and train a radial basis function (RBF) network
setSnnsRSeedValue

DEPRECATED, Set the SnnsR seed value
SnnsRObject$getSiteDefinitions

Get the sites definitions of the network.
SnnsRObject$getTypeDefinitions

Get the FType definitions of the network.
SnnsRObject$getWeightMatrix

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

Get the unit definitions of the network.
SnnsRObject$getAllUnitsTType

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

Get all units present in the net.
SnnsRObject$initializeNet

Initialize the network
splitForTrainingAndTest

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

Find all units whose name begins with a given prefix.
summary.rsnns

Generic summary function for rsnns objects
som

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

Method caller for SnnsR objects
SnnsRObject$extractPatterns

Extract the current pattern set to a matrix
toNumericClassLabels

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

Set the unit defaults
SnnsRObject$setTTypeUnitsActFunc

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

Get all hidden units of the net
rbfDDA

Create and train an RBF network with the DDA algorithm
train

Internal generic train function for rsnns objects
readPatFile

Load data from a pat file
SnnsRObject$createPatSet

Create a pattern set
SnnsRObject$extractNetInfo

Get characteristics of the network.
SnnsRObject$somPredictComponentMaps

Calculate the som component maps
SnnsRObject$somPredictCurrPatSetWinners

Get most of the relevant results from a som
vectorToActMap

Convert a vector to an activation map
SnnsRObjectFactory

SnnsR object factory
SnnsRObject$whereAreResults

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

Get all input units of the net
weightMatrix

Function to extract the weight matrix of an rsnns object
SnnsRObject$getAllOutputUnits

Get all output units of the net.
SnnsRObject$train

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

Get the spanning tree of the SOM