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RSNNS (version 0.4-7)

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

0.4-7

License

LGPL (>= 2) | file LICENSE

Maintainer

Christoph Bergmeir

Last Published

June 12th, 2015

Functions in RSNNS (0.4-7)

SnnsRObject$getTypeDefinitions

Get the FType definitions of the network.
plotActMap

Plot activation map
SnnsRObject$genericPredictCurrPatSet

Predict values with a trained net
SnnsRObject$extractNetInfo

Get characteristics of the network.
SnnsRObject$getAllHiddenUnits

Get all hidden units of the net
mlp

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

Get all input units of the net
analyzeClassification

Converts continuous outputs to class labels
getSnnsRDefine

Get a define of the SNNS kernel
matrixToActMapList

Convert matrix of activations to activation map list
SnnsRObjectMethodCaller

Method caller for SnnsR objects
outputColumns

Get the columns that are targets
rbf

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

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

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

Get normalization parameters of the input data
weightMatrix

Function to extract the weight matrix of an rsnns object
inputColumns

Get the columns that are inputs
predict.rsnns

Generic predict function for rsnns object
SnnsR-class

The main class of the package
print.rsnns

Generic print function for rsnns objects
denormalizeData

Revert data normalization
plotIterativeError

Plot iterative errors of an rsnns object
summary.rsnns

Generic summary function for rsnns objects
SnnsRObject$createPatSet

Create a pattern set
SnnsRObject$setTTypeUnitsActFunc

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

Decode class labels to a binary matrix
SnnsRObject$getSiteDefinitions

Get the sites definitions of the network.
SnnsRObject$getCompleteWeightMatrix

Get the complete weight matrix.
elman

Create and train an Elman network
SnnsRObject$getUnitDefinitions

Get the unit definitions of the network.
readResFile

Rudimentary parser for res files.
resolveSnnsRDefine

Resolve a define of the SNNS kernel
jordan

Create and train a Jordan network
normalizeData

Data normalization
train

Internal generic train function for rsnns objects
rsnnsObjectFactory

Object factory for generating rsnns objects
savePatFile

Save data to a pat file
plotRegressionError

Plot a regression error plot
plotROC

Plot a ROC curve
splitForTrainingAndTest

Function to split data into training and test set
RSNNS-package

Getting started with the RSNNS package
SnnsRObject$createNet

Create a layered network
artmap

Create and train an artmap network
assoz

Create and train an (auto-)associative memory
encodeClassLabels

Encode a matrix of (decoded) class labels
SnnsRObject$getWeightMatrix

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

Get all units present in the net.
extractNetInfo

Extract information from a network
confusionMatrix

Computes a confusion matrix
SnnsRObject$getUnitsByName

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

Calculate the som component maps
SnnsRObject$extractPatterns

Extract the current pattern set to a matrix
SnnsRObject$getInfoHeader

Get an info header of the network.
SnnsRObject$getAllOutputUnits

Get all output units of the net.
getSnnsRFunctionTable

Get SnnsR function table
som

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

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

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

Reset the SnnsR object.
SnnsRObject$predictCurrPatSet

Predict values with a trained net
SnnsRObjectFactory

SnnsR object factory
SnnsRObject$setUnitDefaults

Set the unit defaults
art2

Create and train an art2 network
snnsData

Example data of the package
SnnsRObject$train

Train a network and test it in every training iteration
readPatFile

Load data from a pat file
art1

Create and train an art1 network
setSnnsRSeedValue

DEPRECATED, Set the SnnsR seed value
dlvq

Create and train a dlvq network
SnnsRObject$initializeNet

Initialize the network
normTrainingAndTestSet

Function to normalize training and test set
SnnsRObject$somPredictCurrPatSetWinnersSpanTree

Get the spanning tree of the SOM
SnnsRObject$somPredictCurrPatSetWinners

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

Get a list of output units of a net
vectorToActMap

Convert a vector to an activation map