# rsnnsObjectFactory

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##### Object factory for generating rsnns objects

The object factory generates an rsnns object and initializes its member variables with the values given as parameters. Furthermore, it generates an object of SnnsR-class. Later, this information is to be used to train the network.

##### Usage
rsnnsObjectFactory(subclass, nInputs, maxit, initFunc, initFuncParams,
learnFunc, learnFuncParams, updateFunc, updateFuncParams,
shufflePatterns = TRUE, computeIterativeError = TRUE,
pruneFunc = NULL, pruneFuncParams = NULL)
##### Arguments
subclass

the subclass of rsnns to generate (vector of strings)

nInputs

the number of inputs the network will have

maxit

maximum of iterations to learn

initFunc

the initialization function to use

initFuncParams

the parameters for the initialization function

learnFunc

the learning function to use

learnFuncParams

the parameters for the learning function

updateFunc

the update function to use

updateFuncParams

the parameters for the update function

shufflePatterns

should the patterns be shuffled?

computeIterativeError

should the error be computed in every iteration?

pruneFunc

the pruning function to use

pruneFuncParams

the parameters for the pruning function. Unlike the other functions, these have to be given in a named list. See the pruning demos for further explanation.

##### Details

The typical procedure implemented in rsnns subclasses is the following:

• generate the rsnns object with this object factory

• generate the network according to the architecture needed

• train the network (with train)

In every rsnns object, the iterative error is the summed squared error (SSE) of all patterns. If the SSE is computed on the test set, then it is weighted to take care of the different amount of patterns in the sets.

##### Value

a partly initialized rsnns object

mlp, dlvq, rbf, rbfDDA, elman, jordan, som, art1, art2, artmap, assoz