RSNNS v0.4-12


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Neural Networks using the Stuttgart Neural Network Simulator (SNNS)

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.

Functions in RSNNS

Name Description
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
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License LGPL (>= 2) | file LICENSE
LinkingTo Rcpp
Type Package
LazyLoad yes
Copyright Original SNNS software Copyright (C) 1990-1995 SNNS Group, IPVR, Univ. Stuttgart, FRG; 1996-1998 SNNS Group, WSI, Univ. Tuebingen, FRG. R interface Copyright (C) DiCITS Lab, Sci2s group, DECSAI, University of Granada.
Date 2019-09-16
Encoding UTF-8
RoxygenNote 6.1.0
NeedsCompilation yes
Packaged 2019-09-16 23:27:46 UTC; bergmeir
Repository CRAN
Date/Publication 2019-09-17 04:40:13 UTC

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