# 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 No Results!