## page was renamed from AdmovelNeuralNetwork
<<TableOfContents>>

== Preparation ==
 * Follow the instructions from the assignment [[http://wiki.id.tue.nl/creapro|Creative Programming]] to prepare the SoftwareEnvironment, including Processing, Arduino and the AdMoVeo Robot. If you are not planning to use the GUI components from the Control``P5 library, you can skip the Control``P5 part.
 * Install [[http://neuroph.sourceforge.net/|Neuroph]], a lightweight Java neural network framework. 
  * Download [[http://garr.dl.sourceforge.net/project/neuroph/neuroph%202.5/neurophstudio-windows.exe | Neuroph Studio]] and install it.
  * Download [[http://sourceforge.net/projects/neuroph/files/neuroph%202.5/neuroph-2.5b.zip/download | neuroph-2.5b.zip]]. Unzip it into C:\Programs. In C:\Programs\neuroph-2.5b you shall be able to find neuroph-2.5b.jar. (You can unzip it to anywhere you want, but then remember where it is for later reference).
  * In "Processing Sketchbook location"\libraries, create a sub-directory "neuroph".
  * In "Processing Sketchbook location"\libraries\neuroph, create a sub-directory "library".
  * From C:\Programs\neuroph-2.5b copy neuroph-2.5b.jar to  "Processing Sketchbook location"\libraries\neuroph\library. Rename neuroph-2.5b.jar to neuroph.jar.
  * From C:\Programs\neuroph-2.5b\lib copy two jar files found there to "Processing Sketchbook location"\libraries\neuroph\library.
 * If you are confident enough, you can also try to use Eclipse to program in Java. Then try to follow the instructions from the assignment [[http://http://wiki.id.tue.nl/processing2java|Processing2Java]]. Remember you shall include the aforementioned jar files in the projects if you want to use neuroph.

== References ==
 * CreaproProcessingLibrary
 * [[http://neuroph.sourceforge.net/documentation.html|Neuroph Documentation]]
 * [[http://wiki.id.tue.nl/creapro|Assignment Creative Programming]]
 * [[http://wiki.id.tue.nl/processing2java|Assignment Processing to java]]
 * [[MaxJavaArduino | Workshop on how to interface Max/msp, Java, a neural network engine and Arduino]]

== Examples ==
=== Java -> Processing ===
{{{
#!java
import org.neuroph.core.NeuralNetwork;
import org.neuroph.nnet.MultiLayerPerceptron;
import org.neuroph.core.learning.TrainingSet;
import org.neuroph.core.learning.TrainingElement;
import org.neuroph.core.learning.SupervisedTrainingElement;
import java.util.Vector;
import org.neuroph.util.TransferFunctionType;

/**
 * This sample shows how to create, train, save and load simple Multi Layer Perceptron
 */

void setup() {

    // create training set (logical XOR function)
    TrainingSet trainingSet = new TrainingSet();
    trainingSet.addElement(new SupervisedTrainingElement(new double[] {
      0, 0
    }
    , new double[] {
      0
    }
    ));
    trainingSet.addElement(new SupervisedTrainingElement(new double[] {
      0, 1
    }
    , new double[] {
      1
    }
    ));
    trainingSet.addElement(new SupervisedTrainingElement(new double[] {
      1, 0
    }
    , new double[] {
      1
    }
    ));
    trainingSet.addElement(new SupervisedTrainingElement(new double[] {
      1, 1
    }
    , new double[] {
      0
    }
    ));

    // create multi layer perceptron
    MultiLayerPerceptron myMlPerceptron = new MultiLayerPerceptron(TransferFunctionType.TANH, 2, 3, 1);
    // learn the training set
    myMlPerceptron.learnInSameThread(trainingSet);

    // test perceptron
    System.out.println("Testing trained neural network");
    testNeuralNetwork(myMlPerceptron, trainingSet);

    // save trained neural network
    myMlPerceptron.save("myMlPerceptron.nnet");

    // load saved neural network
    NeuralNetwork loadedMlPerceptron = NeuralNetwork.load("myMlPerceptron.nnet");

    // test loaded neural network
    System.out.println("Testing loaded neural network");
    testNeuralNetwork(loadedMlPerceptron, trainingSet);
    
    noLoop();
  }

void testNeuralNetwork(NeuralNetwork nnet, TrainingSet tset) {
  
    nnet.setInput(new double[]{1, 0});
    nnet.calculate();
    double networkOutput = nnet.getOutput()[0];
    System.out.println(" Output: " + networkOutput);
  }

}}}

=== AdMoVeo XOR ===
{{{
#!java

import org.neuroph.core.NeuralNetwork;
import org.neuroph.nnet.MultiLayerPerceptron;
import processing.serial.*;
import nl.tue.id.creapro.admoveo.*;

AdMoVeo admoveo;
int bgcolor = 0;
int left = 0, right = 0;
int result = 0;
NeuralNetwork nnet;  

void setup() {
  nnet = NeuralNetwork.load("C:/Users/jhu/Documents/NetBeansProjects/XORProject/Neural Networks/NewNeuralNetwork.nnet");
 
  admoveo = new AdMoVeo(this, "COM3"); 
  admoveo.getLeftDistanceSensor().enable();
  admoveo.getRightDistanceSensor().enable();
}

void draw()
{
  background(0,0,bgcolor);
}

void inputAvailable(Sensor sensor, int oldValue, int newValue){  
    if(sensor == admoveo.getLeftDistanceSensor()){
      println(newValue);
      left = newValue < 900? 1: 0;
    }
    if(sensor == admoveo.getRightDistanceSensor()){
       right = newValue < 900? 1: 0;

    }
    nnet.setInput(new double[] {left, right});
    nnet.calculate();
    int output = nnet.getOutput()[0]>.5?1:0;
    //println(""+ left + " " + right +  " " + output);
    
    if(output!=result){
      bgcolor = output *255;
      admoveo.getBlueLed().setPower(bgcolor);
      result = output;
    }
}


}}}

=== A trained XOR multilayer perceptron network ===
[[attachment:MultiLayerPerceptron.nnet]]