RSNNS v0.4-9


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Neural Networks in R 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
elman Create and train an Elman network
encodeClassLabels Encode a matrix of (decoded) class labels
dlvq Create and train a dlvq network
art2 Create and train an art2 network
denormalizeData Revert data normalization
assoz Create and train an (auto-)associative memory
artmap Create and train an artmap network
art1 Create and train an art1 network
confusionMatrix Computes a confusion matrix
decodeClassLabels Decode class labels to a binary matrix
inputColumns Get the columns that are inputs
getSnnsRFunctionTable Get SnnsR function table
jordan Create and train a Jordan network
mlp Create and train a multi-layer perceptron (MLP)
matrixToActMapList Convert matrix of activations to activation map list
extractNetInfo Extract information from a network
exportToSnnsNetFile Export the net to a file in the original SNNS file format
normTrainingAndTestSet Function to normalize training and test set
getSnnsRDefine Get a define of the SNNS kernel
getNormParameters Get normalization parameters of the input data
plotRegressionError Plot a regression error plot
plotIterativeError Plot iterative errors of an rsnns object
readResFile Rudimentary parser for res files.
plotROC Plot a ROC curve
resolveSnnsRDefine Resolve a define of the SNNS kernel
rsnnsObjectFactory Object factory for generating rsnns objects
RSNNS-package Getting started with the RSNNS package
predict.rsnns Generic predict function for rsnns object
outputColumns Get the columns that are targets
plotActMap Plot activation map
snnsData Example data of the package
SnnsRObject$createNet Create a layered network
SnnsRObject$getCompleteWeightMatrix Get the complete weight matrix.
print.rsnns Generic print function for rsnns objects
SnnsRObject$resetRSNNS Reset the SnnsR object.
SnnsRObject$getInfoHeader Get an info header of the network.
SnnsRObject$predictCurrPatSet Predict values with a trained net
savePatFile Save data to a pat file
rbf Create and train a radial basis function (RBF) network
setSnnsRSeedValue DEPRECATED, Set the SnnsR seed value
SnnsRObject$getSiteDefinitions Get the sites definitions of the network.
SnnsRObject$getTypeDefinitions Get the FType definitions of the network.
SnnsRObject$getWeightMatrix Get the weight matrix between two sets of units
SnnsRObject$getUnitDefinitions Get the unit definitions of the network.
SnnsRObject$getAllUnitsTType Get all units in the net of a certain ttype.
SnnsRObject$getAllUnits Get all units present in the net.
SnnsRObject$initializeNet Initialize the network
splitForTrainingAndTest Function to split data into training and test set
SnnsRObject$getUnitsByName Find all units whose name begins with a given prefix.
summary.rsnns Generic summary function for rsnns objects
som Create and train a self-organizing map (SOM)
SnnsRObjectMethodCaller Method caller for SnnsR objects
SnnsRObject$extractPatterns Extract the current pattern set to a matrix
toNumericClassLabels Convert a vector (of class labels) to a numeric vector
SnnsRObject$setUnitDefaults Set the unit defaults
SnnsRObject$setTTypeUnitsActFunc Set the activation function for all units of a certain ttype.
SnnsRObject$getAllHiddenUnits Get all hidden units of the net
rbfDDA Create and train an RBF network with the DDA algorithm
train Internal generic train function for rsnns objects
readPatFile Load data from a pat file
SnnsRObject$createPatSet Create a pattern set
SnnsRObject$extractNetInfo Get characteristics of the network.
SnnsRObject$somPredictComponentMaps Calculate the som component maps
SnnsRObject$somPredictCurrPatSetWinners Get most of the relevant results from a som
vectorToActMap Convert a vector to an activation map
SnnsRObjectFactory SnnsR object factory
SnnsRObject$whereAreResults Get a list of output units of a net
SnnsRObject$getAllInputUnits Get all input units of the net
weightMatrix Function to extract the weight matrix of an rsnns object
SnnsRObject$getAllOutputUnits Get all output units of the net.
SnnsRObject$train Train a network and test it in every training iteration
SnnsRObject$somPredictCurrPatSetWinnersSpanTree Get the spanning tree of the SOM
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License LGPL (>= 2) | file LICENSE
LinkingTo Rcpp
Type Package
LazyLoad yes
Date 2016-12-16
Encoding UTF-8
RoxygenNote 5.0.1
NeedsCompilation yes
Packaged 2016-12-16 06:07:33 UTC; bergmeir
Repository CRAN
Date/Publication 2016-12-16 08:33:39

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