Learn R Programming
⚠️There's a newer version (0.4-18) of this package.Take me there.

RSNNS (version 0.4-5)

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, 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.

Copy Link

Version

Install

install.packages('RSNNS')

Monthly Downloads

4,371

Version

0.4-5

License

LGPL (>= 2) | file LICENSE

Maintainer

Christoph Bergmeir

Last Published

May 23rd, 2014

Functions in RSNNS (0.4-5)

SnnsRObject$extractNetInfo

Get characteristics of the network.
SnnsRObject$somPredictCurrPatSetWinners

Get most of the relevant results from a som
SnnsRObject$setTTypeUnitsActFunc

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

Create and train a dlvq network
inputColumns

Get the columns that are inputs
decodeClassLabels

Decode class labels to a binary matrix
SnnsRObject$getAllHiddenUnits

Get all hidden units of the net
SnnsRObject$somPredictCurrPatSetWinnersSpanTree

Get the spanning tree of the SOM
som

Create and train a self-organizing map (SOM)
SnnsRObject$extractPatterns

Extract the current pattern set to a matrix
RSNNS-package

Getting started with the RSNNS package
SnnsRObject$getAllOutputUnits

Get all output units of the net.
SnnsR-class

The main class of the package
SnnsRObject$predictCurrPatSet

Predict values with a trained net
SnnsRObject$somPredictComponentMaps

Calculate the som component maps
SnnsRObject$getSiteDefinitions

Get the sites definitions of the network.
SnnsRObject$whereAreResults

Get a list of output units of a net
splitForTrainingAndTest

Function to split data into training and test set
SnnsRObject$getAllUnitsTType

Get all units in the net of a certain ttype.
encodeClassLabels

Encode a matrix of (decoded) class labels
extractNetInfo

Extract information from a network
SnnsRObject$createPatSet

Create a pattern set
vectorToActMap

Convert a vector to an activation map
SnnsRObject$getInfoHeader

Get an info header of the network.
confusionMatrix

Computes a confusion matrix
SnnsRObject$train

Train a network and test it in every training iteration
rsnnsObjectFactory

Object factory for generating rsnns objects
snnsData

Example data of the package
denormalizeData

Revert data normalization
predict.rsnns

Generic predict function for rsnns object
assoz

Create and train an (auto-)associative memory
SnnsRObjectMethodCaller

Method caller for SnnsR objects
toNumericClassLabels

Convert a vector (of class labels) to a numeric vector
SnnsRObject$resetRSNNS

Reset the SnnsR object.
art2

Create and train an art2 network
matrixToActMapList

Convert matrix of activations to activation map list
SnnsRObject$getAllInputUnits

Get all input units of the net
SnnsRObject$genericPredictCurrPatSet

Predict values with a trained net
SnnsRObject$getUnitDefinitions

Get the unit definitions of the network.
mlp

Create and train a multi-layer perceptron (MLP)
art1

Create and train an art1 network
SnnsRObject$getWeightMatrix

Get the weight matrix between two sets of units
readPatFile

Load data from a pat file
train

Internal generic train function for rsnns objects
SnnsRObjectFactory

SnnsR object factory
weightMatrix

Function to extract the weight matrix of an rsnns object
SnnsRObject$getCompleteWeightMatrix

Get the complete weight matrix.
normalizeData

Data normalization
print.rsnns

Generic print function for rsnns objects
rbf

Create and train a radial basis function (RBF) network
SnnsRObject$getAllUnits

Get all units present in the net.
SnnsRObject$setUnitDefaults

Set the unit defaults
artmap

Create and train an artmap network
SnnsRObject$createNet

Create a layered network
analyzeClassification

Converts continuous outputs to class labels
getSnnsRFunctionTable

Get SnnsR function table
resolveSnnsRDefine

Resolve a define of the SNNS kernel
SnnsRObject$getUnitsByName

Find all units whose name begins with a given prefix.
SnnsRObject$initializeNet

Initialize the network
summary.rsnns

Generic summary function for rsnns objects
savePatFile

Save data to a pat file
plotRegressionError

Plot a regression error plot
outputColumns

Get the columns that are targets
readResFile

Rudimentary parser for res files.
jordan

Create and train a Jordan network
setSnnsRSeedValue

Set the SnnsR seed value
SnnsRObject$getTypeDefinitions

Get the FType definitions of the network.
elman

Create and train an Elman network
plotROC

Plot a ROC curve
normTrainingAndTestSet

Function to normalize training and test set
exportToSnnsNetFile

Export the net to a file in the original SNNS file format
getNormParameters

Get normalization parameters of the input data
getSnnsRDefine

Get a define of the SNNS kernel
plotActMap

Plot activation map
rbfDDA

Create and train an RBF network with the DDA algorithm
plotIterativeError

Plot iterative errors of an rsnns object