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RSNNS (version 0.4-14)

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.packages('RSNNS')

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2,670

Version

0.4-14

License

LGPL (>= 2) | file LICENSE

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Last Published

August 13th, 2021

Functions in RSNNS (0.4-14)

SnnsRObject$getAllHiddenUnits

Get all hidden units of the net
SnnsRObject$createPatSet

Create a pattern set
SnnsRObject$createNet

Create a layered network
SnnsRObject$setTTypeUnitsActFunc

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

Reset the SnnsR object.
SnnsRObject$getAllInputUnits

Get all input units of the net
dlvq

Create and train a dlvq network
SnnsRObject$getInfoHeader

Get an info header of the network.
SnnsR-class

The main class of the package
RSNNS-package

Getting started with the RSNNS package
SnnsRObject$getUnitsByName

Find all units whose name begins with a given prefix.
art2

Create and train an art2 network
SnnsRObject$getSiteDefinitions

Get the sites definitions of the network.
SnnsRObject$whereAreResults

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

Train a network and test it in every training iteration
artmap

Create and train an artmap network
extractNetInfo

Extract information from a network
getNormParameters

Get normalization parameters of the input data
inputColumns

Get the columns that are inputs
elman

Create and train an Elman network
SnnsRObjectFactory

SnnsR object factory
SnnsRObject$getWeightMatrix

Get the weight matrix between two sets of units
jordan

Create and train a Jordan network
print.rsnns

Generic print function for rsnns objects
plotRegressionError

Plot a regression error plot
predict.rsnns

Generic predict function for rsnns object
setSnnsRSeedValue

DEPRECATED, Set the SnnsR seed value
SnnsRObjectMethodCaller

Method caller for SnnsR objects
SnnsRObject$getTypeDefinitions

Get the FType definitions of the network.
SnnsRObject$getUnitDefinitions

Get the unit definitions of the network.
SnnsRObject$initializeNet

Initialize the network
SnnsRObject$getAllOutputUnits

Get all output units of the net.
SnnsRObject$getAllUnits

Get all units present in the net.
assoz

Create and train an (auto-)associative memory
snnsData

Example data of the package
matrixToActMapList

Convert matrix of activations to activation map list
normTrainingAndTestSet

Function to normalize training and test set
mlp

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

Computes a confusion matrix
normalizeData

Data normalization
rbf

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

Calculate the som component maps
SnnsRObject$predictCurrPatSet

Predict values with a trained net
decodeClassLabels

Decode class labels to a binary matrix
SnnsRObject$setUnitDefaults

Set the unit defaults
savePatFile

Save data to a pat file
rsnnsObjectFactory

Object factory for generating rsnns objects
som

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

Get the columns that are targets
train

Internal generic train function for rsnns objects
plotActMap

Plot activation map
vectorToActMap

Convert a vector to an activation map
splitForTrainingAndTest

Function to split data into training and test set
denormalizeData

Revert data normalization
getSnnsRDefine

Get a define of the SNNS kernel
getSnnsRFunctionTable

Get SnnsR function table
rbfDDA

Create and train an RBF network with the DDA algorithm
resolveSnnsRDefine

Resolve a define of the SNNS kernel
readResFile

Rudimentary parser for res files.
weightMatrix

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

Get characteristics of the network.
SnnsRObject$extractPatterns

Extract the current pattern set to a matrix
readPatFile

Load data from a pat file
summary.rsnns

Generic summary function for rsnns objects
SnnsRObject$somPredictCurrPatSetWinnersSpanTree

Get the spanning tree of the SOM
SnnsRObject$getCompleteWeightMatrix

Get the complete weight matrix.
art1

Create and train an art1 network
SnnsRObject$somPredictCurrPatSetWinners

Get most of the relevant results from a som
analyzeClassification

Converts continuous outputs to class labels
SnnsRObject$getAllUnitsTType

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

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

Encode a matrix of (decoded) class labels
exportToSnnsNetFile

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

Plot iterative errors of an rsnns object
plotROC

Plot a ROC curve