RSNNS v0.4-11


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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$getInfoHeader Get an info header of the network.
SnnsRObject$getSiteDefinitions Get the sites definitions of the network.
SnnsRObject$initializeNet Initialize the network
art2 Create and train an art2 network
SnnsRObject$resetRSNNS Reset the SnnsR object.
artmap Create and train an artmap network
SnnsRObject$predictCurrPatSet Predict values with a trained net
print.rsnns Generic print function for rsnns objects
SnnsRObject$getTypeDefinitions Get the FType definitions of the network.
SnnsRObject$setTTypeUnitsActFunc Set the activation function for all units of a certain ttype.
SnnsRObject$train Train a network and test it in every training iteration
SnnsRObject$getAllUnitsTType Get all units in the net of a certain ttype.
SnnsR-class The main class of the package
rbf Create and train a radial basis function (RBF) network
SnnsRObject$somPredictComponentMaps Calculate the som component maps
SnnsRObject$getUnitDefinitions Get the unit definitions of the network.
SnnsRObject$setUnitDefaults Set the unit defaults
readResFile Rudimentary parser for res files.
SnnsRObject$whereAreResults Get a list of output units of a net
analyzeClassification Converts continuous outputs to class labels
dlvq Create and train a dlvq network
elman Create and train an Elman network
resolveSnnsRDefine Resolve a define of the SNNS kernel
decodeClassLabels Decode class labels to a binary matrix
encodeClassLabels Encode a matrix of (decoded) class labels
art1 Create and train an art1 network
denormalizeData Revert data normalization
SnnsRObject$getCompleteWeightMatrix Get the complete weight matrix.
SnnsRObject$getUnitsByName Find all units whose name begins with a given prefix.
SnnsRObject$somPredictCurrPatSetWinners Get most of the relevant results from a som
SnnsRObject$getWeightMatrix Get the weight matrix between two sets of units
inputColumns Get the columns that are inputs
getSnnsRDefine Get a define of the SNNS kernel
exportToSnnsNetFile Export the net to a file in the original SNNS file format
som Create and train a self-organizing map (SOM)
SnnsRObject$somPredictCurrPatSetWinnersSpanTree Get the spanning tree of the SOM
getSnnsRFunctionTable Get SnnsR function table
assoz Create and train an (auto-)associative memory
SnnsRObjectFactory SnnsR object factory
extractNetInfo Extract information from a network
confusionMatrix Computes a confusion matrix
normTrainingAndTestSet Function to normalize training and test set
SnnsRObjectMethodCaller Method caller for SnnsR objects
matrixToActMapList Convert matrix of activations to activation map list
mlp Create and train a multi-layer perceptron (MLP)
jordan Create and train a Jordan network
splitForTrainingAndTest Function to split data into training and test set
plotRegressionError Plot a regression error plot
predict.rsnns Generic predict function for rsnns object
plotIterativeError Plot iterative errors of an rsnns object
train Internal generic train function for rsnns objects
getNormParameters Get normalization parameters of the input data
vectorToActMap Convert a vector to an activation map
plotROC Plot a ROC curve
rsnnsObjectFactory Object factory for generating rsnns objects
savePatFile Save data to a pat file
outputColumns Get the columns that are targets
weightMatrix Function to extract the weight matrix of an rsnns object
plotActMap Plot activation map
normalizeData Data normalization
setSnnsRSeedValue DEPRECATED, Set the SnnsR seed value
snnsData Example data of the package
summary.rsnns Generic summary function for rsnns objects
toNumericClassLabels Convert a vector (of class labels) to a numeric vector
rbfDDA Create and train an RBF network with the DDA algorithm
readPatFile Load data from a pat file
RSNNS-package Getting started with the RSNNS package
SnnsRObject$getAllOutputUnits Get all output units of the net.
SnnsRObject$getAllUnits Get all units present in the net.
SnnsRObject$createNet Create a layered network
SnnsRObject$createPatSet Create a pattern set
SnnsRObject$getAllHiddenUnits Get all hidden units of the net
SnnsRObject$getAllInputUnits Get all input units of the net
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 2018-08-10
Encoding UTF-8
RoxygenNote 5.0.1
NeedsCompilation yes
Packaged 2018-08-10 09:59:17 UTC; bergmeir
Repository CRAN
Date/Publication 2018-08-10 21:50:16 UTC

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