public static void main(String[] args) {
Main m = new Main();
if (args.length > 0) {
if (args[0].toLowerCase().contains("v")) {
m.showVersion();
System.exit(0);
}
m.showUsage();
System.exit(0);
}
File curDir = new File(".");
File[] vectors = curDir.listFiles(m);
if (vectors.length != 3) {
m.showUsage();
System.exit(1);
}
for (File file : vectors) {
VectorParser parser = new VectorParser();
String fileName = file.getName().toLowerCase();
if (fileName.endsWith("x.txt")) {
parser.setVectorType(VectorParser.VectorType.X);
} else if (fileName.endsWith("y.txt")) {
parser.setVectorType(VectorParser.VectorType.Y);
} else if (fileName.endsWith("z.txt")) {
parser.setVectorType(VectorParser.VectorType.Z);
}
try {
parser.parse(file);
} catch (IOException ex) {
m.showError(ex);
System.exit(1);
}
}
try {
W.getInstace().write();
} catch (IOException ex) {
m.showError(ex);
System.exit(1);
}
}
/**
* 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;
}
