練習問題６ - 経済統計練習帳

package enc;
 
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=new double[100][2];
 double[][] y=new double[100][1];
 int s;
for(s=0;s<100;s++){
x[s][0]=10*Math.random();
x[s][1]=10*Math.random();
double z=(x[s][0]-5)*(x[s][0]-5)+(x[s][1]-5)*(x[s][1]-5);
y[s][0]=0;
if(z<10)y[s][0]=1;
}	 
 
 
 
 
		BasicNetwork net = new BasicNetwork();
		net.addLayer(new BasicLayer(null ,false ,2 ));
		net.addLayer(new BasicLayer(new ActivationSigmoid( ),true,2));
                net.addLayer(new BasicLayer(new ActivationSigmoid( ),false,2));
		net.addLayer(new BasicLayer(new ActivationSigmoid( ),false,1));
		net.getStructure().finalizeStructure();
		net.reset();
 
		MLDataSet ts = new BasicMLDataSet(x, y);
 
		final ResilientPropagation train = new ResilientPropagation(net, ts);
 
		int epoch = 1;
 
 
                int mode=0;
		do {
			train.iteration();
			System.out.println("(" + epoch + ") =>" + train.getError());
			epoch= epoch+1;
                        if(train.getError()<0.01)epoch=1000;
		} while( epoch<100);
		train.finishTraining();
 
 
                MLDataSet tsx = new BasicMLDataSet(x, y);
 
		for(MLDataPair pair: tsx ) {
          final MLData output = net.compute(pair.getInput());
 
		System.out.print(pair.getIdeal().getData(0));
                      System.out.print("***");  
                System.out.println(output.getData(0));
                }
 
		Encog.getInstance().shutdown();
	}
}