えｘ０２＠ - 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 x[][] = { { 0.0, 0.0 ,20}, { 1.0, 0.0,20},{ 0.0, 5.0,20},{1.0,5,20}};
 
 
	 double y[][] = { { 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();
 
		MLDataSet ts = new BasicMLDataSet(x, y);
 
		final ResilientPropagation train = new ResilientPropagation(network, ts);
 
		int epoch = 1;
 
		do {
			train.iteration();
			epoch++;
		} while(train.getError() > 0.01);
		train.finishTraining();
 
		System.out.println("結果");
		for(MLDataPair pair: ts ) {
			final MLData output = network.compute(pair.getInput());
		System.out.println(output.getData(0));
		}
 
		Encog.getInstance().shutdown();
	}
}