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RSNNS (version 0.3-1)

Neural Networks in R using the Stuttgart Neural Network Simulator (SNNS)

Description

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, nearly all of the 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.

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Version

Install

install.packages('RSNNS')

Monthly Downloads

4,371

Version

0.3-1

License

LGPL (>= 2)

Maintainer

Christoph Bergmeir

Last Published

November 17th, 2010

Functions in RSNNS (0.3-1)

SnnsRObject$predictCurrPatSet

Predict values with a trained net.
art1

Create and train an art1 network.
SnnsRObject$getAllOutputUnits

Get all output units of the net.
art2

Create and train an art2 network.
matrixToActMapList

Organize a matrix containing 1d vectors of network activations as 2d maps.
toNumericClassLabels

Converts a vector (of class labels) to a numeric vector.
SnnsRObject$createPatSet

Create a pattern set.
decodeClassLabels

Decode class labels from a numerical or levels vector to a binary matrix.
SnnsRObject$somPredictComponentMaps

Calculate the som component maps.
SnnsRObject$train

Train a network and test it in every training iteration.
encodeClassLabels

Applies analyzeClassification row-wise to a matrix.
SnnsRObject$getAllUnitsTType

Get all units in the net of a certain ttype.
SnnsRObject$getAllHiddenUnits

Get all hidden units of the net.
RSNNS-package

Getting started with the RSNNS package
splitForTrainingAndTest

Function to split data into training and test set.
elman

Create and train an elman network.
getSnnsRDefine

Get a define of the SNNS kernel.
rbf

Create and train a radial basis function (rbf) network.
rbfDDA

Create and train a rbf network with the DDA algorithm.
jordan

Create and train a jordan network.
confusionMatrix

Computes a confusion matrix.
outputColumns

Function to get the columns that are targets.
SnnsRObject$createNet

Create a layered network.
SnnsRObject$resetRSNNS

Reset the SnnsR-class object.
plotRegressionError

Plot a regression error plot.
train

Generic train function.
vectorToActMap

Organize network activation as 2d map.
setSnnsRSeedValue

Set the seed value used in all SnnsR objects.
mlp

Create and train a multi-layer perceptron (mlp).
SnnsRObject$getAllInputUnits

Get all input units of the net.
SnnsRObject$setTTypeUnitsActFunc

Set the activation function for all units of a certain ttype.
summary.rsnns

Generic summary function for rsnns objects.
plotActMap

Plot an activation map as a heatmap.
assoz

Create and train an (auto-)associative memory.
savePatFile

Save data to a pat file.
resolveSnnsRDefine

Resolve a define of the SNNS kernel.
readPatFile

Load data from a pat file.
rsnnsObjectFactory

Object factory for generating rsnns objects.
SnnsRObject$somPredictCurrPatSetWinnersSpanTree

Get the spanning tree of the som, calculated directly by SNNS.
analyzeClassification

Converts continuous outputs to class labels.
SnnsR-class

The main class of the package.
SnnsRObject$extractPatterns

Extract the current pattern set to a matrix.
SnnsRObject$somPredictCurrPatSetWinners

Get most of the relevant results from a som.
SnnsRObjectFactory

Object factory to create a new object of type SnnsR-class.
predict.rsnns

Generic predict function for rsnns object.
SnnsRObjectMethodCaller

Enable calling of C++ functions as methods of SnnsR-class objects.
SnnsRObject$initializeNet

Initialize the network.
SnnsRObject$setUnitDefaults

Set the unit defaults.
dlvq

Create and train a dlvq network.
inputColumns

Function to get the columns that are inputs.
normalizeData

Data normalization.
plotIterativeError

Plot the iterative training and test error of the net of this rsnns object.
snnsData

Example data of the package.
plotROC

Plot a ROC curve.
readResFile

Rudimentary parser for res files.
SnnsRObject$genericPredictCurrPatSet

Predict values with a trained net.
SnnsRObject$getUnitsByName

Find all units whose name begins with a given prefix.
getSnnsRFunctionTable

Get the function table of available SNNS functions.
print.rsnns

Generic print function for rsnns objects.
som

Create and train a self-organizing map (som).
SnnsRObject$whereAreResults

Get a list of output units of a net.