The main class of the package
Create a layered network
Get all units present in the net.
SnnsRObject$somPredictCurrPatSetWinnersSpanTree
Get the spanning tree of the SOM
SnnsRObject$getTypeDefinitions
Get the FType definitions of the network.
Revert data normalization
Create and train an art1 network
Create and train a radial basis function (RBF) network
Function to split data into training and test set
Load data from a pat file
SnnsRObject$getAllHiddenUnits
Get all hidden units of the net
Decode class labels to a binary matrix
Plot a regression error plot
Computes a confusion matrix
Create and train a dlvq network
Get normalization parameters of the input data
Generic summary function for rsnns objects
Set the SnnsR seed value
SnnsRObject$extractNetInfo
Get characteristics of the network.
SnnsRObject$getSiteDefinitions
Get the sites definitions of the network.
Create and train an (auto-)associative memory
SnnsRObject$getUnitsByName
Find all units whose name begins with a given prefix.
Get the columns that are targets
Resolve a define of the SNNS kernel
SnnsRObject$genericPredictCurrPatSet
Predict values with a trained net
SnnsRObject$getAllUnitsTType
Get all units in the net of a certain ttype
.
SnnsRObject$getUnitDefinitions
Get the unit definitions of the network.
Reset the SnnsR object.
Create and train an Elman network
Plot iterative errors of an rsnns object
Object factory for generating rsnns objects
SnnsRObject$setUnitDefaults
Set the unit defaults
Function to normalize training and test set
SnnsRObject$whereAreResults
Get a list of output units of a net
Internal generic train function for rsnns objects
Export the net to a file in the original SNNS file format
Create a pattern set
SnnsRObject$somPredictComponentMaps
Calculate the som component maps
SnnsRObject$extractPatterns
Extract the current pattern set to a matrix
SnnsRObject$predictCurrPatSet
Predict values with a trained net
Create and train an artmap network
Get SnnsR function table
Create and train a multi-layer perceptron (MLP)
Plot a ROC curve
Create and train a Jordan network
Create and train an RBF network with the DDA algorithm
SnnsRObject$somPredictCurrPatSetWinners
Get most of the relevant results from a som
SnnsRObject$getInfoHeader
Get an info header of the network.
SnnsRObject$setTTypeUnitsActFunc
Set the activation function for all units of a certain ttype.
Data normalization
Extract information from a network
Convert a vector to an activation map
Generic predict function for rsnns object
Convert matrix of activations to activation map list
Train a network and test it in every training iteration
SnnsRObject$getAllOutputUnits
Get all output units of the net.
Converts continuous outputs to class labels
Function to extract the weight matrix of an rsnns object
Save data to a pat file
Get a define of the SNNS kernel
Create and train a self-organizing map (SOM)
Get the columns that are inputs
Getting started with the RSNNS package
SnnsRObject$getWeightMatrix
Get the weight matrix between two sets of units
Encode a matrix of (decoded) class labels
SnnsR object factory
Generic print function for rsnns objects
SnnsRObject$getCompleteWeightMatrix
Get the complete weight matrix.
SnnsRObject$initializeNet
Initialize the network
Convert a vector (of class labels) to a numeric vector
SnnsRObject$getAllInputUnits
Get all input units of the net
Create and train an art2 network
Example data of the package
Plot activation map
Method caller for SnnsR objects
Rudimentary parser for res files.