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