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.
rsnnsObjectFactory(
subclass,
nInputs,
maxit,
initFunc,
initFuncParams,
learnFunc,
learnFuncParams,
updateFunc,
updateFuncParams,
shufflePatterns = TRUE,
computeIterativeError = TRUE,
pruneFunc = NULL,
pruneFuncParams = NULL
)a partly initialized rsnns object
the subclass of rsnns to generate (vector of strings)
the number of inputs the network will have
maximum of iterations to learn
the initialization function to use
the parameters for the initialization function
the learning function to use
the parameters for the learning function
the update function to use
the parameters for the update function
should the patterns be shuffled?
should the error be computed in every iteration?
the pruning function to use
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.
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.
mlp, dlvq, rbf, rbfDDA, elman,
jordan, som, art1, art2, artmap, assoz