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RSNNS (version 0.4-12)

Neural Networks 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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Install

install.packages('RSNNS')

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2,892

Version

0.4-12

License

LGPL (>= 2) | file LICENSE

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Maintainer

Christoph Bergmeir

Last Published

September 17th, 2019

Functions in RSNNS (0.4-12)

SnnsRObject$createPatSet

Create a pattern set
SnnsRObjectMethodCaller

Method caller for SnnsR objects
SnnsRObject$getCompleteWeightMatrix

Get the complete weight matrix.
elman

Create and train an Elman network
SnnsRObject$getAllUnitsTType

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

SnnsR object factory
dlvq

Create and train a dlvq network
encodeClassLabels

Encode a matrix of (decoded) class labels
exportToSnnsNetFile

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

Reset the SnnsR object.
SnnsRObject$setTTypeUnitsActFunc

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

Decode class labels to a binary matrix
readResFile

Rudimentary parser for res files.
train

Internal generic train function for rsnns objects
print.rsnns

Generic print function for rsnns objects
getSnnsRDefine

Get a define of the SNNS kernel
vectorToActMap

Convert a vector to an activation map
resolveSnnsRDefine

Resolve a define of the SNNS kernel
denormalizeData

Revert data normalization
getSnnsRFunctionTable

Get SnnsR function table
rbf

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

Plot a regression error plot
artmap

Create and train an artmap network
SnnsRObject$whereAreResults

Get a list of output units of a net
mlp

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

Predict values with a trained net
art2

Create and train an art2 network
SnnsRObject$initializeNet

Initialize the network
SnnsRObject$train

Train a network and test it in every training iteration
normTrainingAndTestSet

Function to normalize training and test set
matrixToActMapList

Convert matrix of activations to activation map list
normalizeData

Data normalization
SnnsRObject$getTypeDefinitions

Get the FType definitions of the network.
SnnsRObject$somPredictCurrPatSetWinners

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

Get the spanning tree of the SOM
SnnsRObject$getUnitDefinitions

Get the unit definitions of the network.
confusionMatrix

Computes a confusion matrix
SnnsRObject$getInfoHeader

Get an info header of the network.
assoz

Create and train an (auto-)associative memory
som

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

Get the sites definitions of the network.
predict.rsnns

Generic predict function for rsnns object
SnnsRObject$getUnitsByName

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

Get the weight matrix between two sets of units
setSnnsRSeedValue

DEPRECATED, Set the SnnsR seed value
SnnsRObject$setUnitDefaults

Set the unit defaults
SnnsRObject$somPredictComponentMaps

Calculate the som component maps
snnsData

Example data of the package
extractNetInfo

Extract information from a network
art1

Create and train an art1 network
analyzeClassification

Converts continuous outputs to class labels
getNormParameters

Get normalization parameters of the input data
splitForTrainingAndTest

Function to split data into training and test set
inputColumns

Get the columns that are inputs
summary.rsnns

Generic summary function for rsnns objects
savePatFile

Save data to a pat file
rsnnsObjectFactory

Object factory for generating rsnns objects
weightMatrix

Function to extract the weight matrix of an rsnns object
jordan

Create and train a Jordan network
plotActMap

Plot activation map
plotIterativeError

Plot iterative errors of an rsnns object
outputColumns

Get the columns that are targets
plotROC

Plot a ROC curve
rbfDDA

Create and train an RBF network with the DDA algorithm
readPatFile

Load data from a pat file
toNumericClassLabels

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

Get all output units of the net.
SnnsRObject$getAllUnits

Get all units present in the net.
RSNNS-package

Getting started with the RSNNS package
SnnsRObject$getAllInputUnits

Get all input units of the net
SnnsRObject$createNet

Create a layered network
SnnsRObject$getAllHiddenUnits

Get all hidden units of the net
SnnsR-class

The main class of the package
SnnsRObject$extractNetInfo

Get characteristics of the network.
SnnsRObject$extractPatterns

Extract the current pattern set to a matrix