お９９９ - 経済統計練習帳

package mono;
 
import org.encog.Encog;
import org.encog.engine.network.activation.ActivationSigmoid;
import org.encog.ml.data.MLData;
import org.encog.ml.data.MLDataPair;
import org.encog.ml.data.MLDataSet;
import org.encog.ml.data.basic.BasicMLDataSet;
import org.encog.neural.networks.BasicNetwork;
import org.encog.neural.networks.layers.BasicLayer;
import org.encog.neural.networks.training.propagation.resilient.ResilientPropagation;
 
public class pro{
 
public static void main(String[]args){
pro t=new pro();
}
 
pro(){
 
 
	double XOR_INPUT[][] = { { 0.0, 0.0 }, { 1.0, 0.0 },
			{ 0.0, 1.0 }, { 1.0, 1.0 } };
 
 
	 double XOR_IDEAL[][] = { { 0.0 }, { 1.0 }, { 1.0 }, { 0.0 } };
 
 
		BasicNetwork network = new BasicNetwork();
		network.addLayer(new BasicLayer(null,true,2));
		network.addLayer(new BasicLayer(new ActivationSigmoid(),true,3));
		network.addLayer(new BasicLayer(new ActivationSigmoid(),false,1));
		network.getStructure().finalizeStructure();
		network.reset();
 
		// create training data
		MLDataSet trainingSet = new BasicMLDataSet(XOR_INPUT, XOR_IDEAL);
 
		// train the neural network
		final ResilientPropagation train = new ResilientPropagation(network, trainingSet);
 
		int epoch = 1;
 
		do {
			train.iteration();
			System.out.println("Epoch #" + epoch + " Error:" + train.getError());
			epoch++;
		} while(train.getError() > 0.01);
		train.finishTraining();
 
		// test the neural network
		System.out.println("Neural Network Results:");
		for(MLDataPair pair: trainingSet ) {
			final MLData output = network.compute(pair.getInput());
			System.out.println(pair.getInput().getData(0) + "," + pair.getInput().getData(1)
					+ ", actual=" + output.getData(0) + ",ideal=" + pair.getIdeal().getData(0));
		}
 
		Encog.getInstance().shutdown();
	}
}