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