SnnsR low-level function to train a network and test it in every training iteration.
# S4 method for SnnsR
train(inputsTrain, targetsTrain=NULL,
initFunc="Randomize_Weights", initFuncParams=c(1.0, -1.0),
learnFunc="Std_Backpropagation", learnFuncParams=c(0.2, 0),
updateFunc="Topological_Order", updateFuncParams=c(0.0),
outputMethod="reg_class", maxit=100, shufflePatterns=TRUE,
computeError=TRUE, inputsTest=NULL, targetsTest=NULL,
pruneFunc=NULL, pruneFuncParams=NULL)a list containing:
the fitted values, i.e. outputs of the training inputs
The SSE in every iteration/epoch on the training set
the predicted values, i.e. outputs of the test inputs
The SSE in every iteration/epoch on the test set
a matrix with inputs for the network
the corresponding targets
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
the output method of the net
maximum of iterations to learn
should the patterns be shuffled?
should the error be computed in every iteration?
a matrix with inputs to test the network
the corresponding targets for the test input
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.