/**
* Creates and returns a new instance of Multi Layer Perceptron
*
* @param layersStr space separated number of neurons in layers
* @param transferFunctionType transfer function type for neurons
* @return instance of Multi Layer Perceptron
*/
public static MultiLayerPerceptron createMLPerceptron(
String layersStr,
TransferFunctionType transferFunctionType,
Class learningRule,
boolean useBias,
boolean connectIO) {
ArrayList<Integer> layerSizes = VectorParser.parseInteger(layersStr);
NeuronProperties neuronProperties = new NeuronProperties(transferFunctionType, useBias);
MultiLayerPerceptron nnet = new MultiLayerPerceptron(layerSizes, neuronProperties);
// set learning rule - TODO: use reflection here
if (learningRule.getName().equals(BackPropagation.class.getName())) {
nnet.setLearningRule(new BackPropagation());
} else if (learningRule.getName().equals(MomentumBackpropagation.class.getName())) {
nnet.setLearningRule(new MomentumBackpropagation());
} else if (learningRule.getName().equals(DynamicBackPropagation.class.getName())) {
nnet.setLearningRule(new DynamicBackPropagation());
} else if (learningRule.getName().equals(ResilientPropagation.class.getName())) {
nnet.setLearningRule(new ResilientPropagation());
}
// connect io
if (connectIO) {
nnet.connectInputsToOutputs();
}
return nnet;
}
/**
* Creates and returns a new instance of Multi Layer Perceptron
*
* @param layersStr space separated number of neurons in layers
* @param transferFunctionType transfer function type for neurons
* @return instance of Multi Layer Perceptron
*/
public static MultiLayerPerceptron createMLPerceptron(
String layersStr, TransferFunctionType transferFunctionType) {
ArrayList<Integer> layerSizes = VectorParser.parseInteger(layersStr);
MultiLayerPerceptron nnet = new MultiLayerPerceptron(layerSizes, transferFunctionType);
return nnet;
}