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