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