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 No Results!