
<<TableOfContents>>

== Preparation ==
 * Follow the instructions from the assignment [[http://wiki.id.tue.nl/creapro|Creative Programming]] to install Processing and if needed, Arduino software environment.
 * Install [[http://neuroph.sourceforge.net/|Neuroph]], a lightweight Java neural network framework. 
  * Download neurophstudio '''2.8''' installer for your platform (for Windows: [[http://sourceforge.net/projects/neuroph/files/neuroph-2.8/neurophstudio-windows-2.8.exe/download | neurophstudio-windows-2.8.exe]]). We have difficulties in getting the latest version to work with Processing, and all the tutorials and documentation are not yet updated for the latest version. 
  * In "Processing Sketchbook location"\libraries, create a sub-directory "neuroph".
  * In "Processing Sketchbook location"\libraries\neuroph, create a sub-directory "library".
  * From `<neurophstudio directory>\neurophstudio\modules\ext` copy `neuroph-core-<version>.jar` to  `<Processing Sketchbook location>\libraries\neuroph\library`. Rename `neuroph-core-<version>.jar` to `neuroph.jar`.
 * 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://wiki.id.tue.nl/processing2java|Processing2Java]]. Remember you shall include the aforementioned jar files in the projects if you want to use neuroph.

== References ==
 * [[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 ==
Try the Perceptron example first with Neuroph Studio:
This is the Perceptron example from http://neuroph.sourceforge.net/tutorials/Perceptron.html:

=== Java code ===
{{{
#!java
package org.neuroph.samples;

import org.neuroph.core.NeuralNetwork;
import org.neuroph.nnet.Perceptron;
import org.neuroph.core.learning.DataSet;
import org.neuroph.core.learning.TrainingElement;
import org.neuroph.core.learning.SupervisedTrainingElement;
import java.util.Vector;

/**
 * This sample shows how to create, train, save and load simple Perceptron neural network 
 */
public class PerceptronSample {

  public static void main(String args[]) {

    // create training set (logical AND function)
    DataSet trainingSet = new DataSet(2, 1);
    trainingSet.addRow(new DataSetRow(new double[] {
      0, 0
    }
    , new double[] {
      0
    }
    ));
    trainingSet.addRow(new DataSetRow(new double[] {
      0, 1
    }
    , new double[] {
      0
    }
    ));
    trainingSet.addRow(new DataSetRow(new double[] {
      1, 0
    }
    , new double[] {
      0
    }
    ));
    trainingSet.addRow(new DataSetRow(new double[] {
      1, 1
    }
    , new double[] {
      1
    }
    ));

    // create perceptron neural network
    NeuralNetwork myPerceptron = new Perceptron(2, 1);

    // learn the training set
    myPerceptron.learn(trainingSet);

    // test perceptron
    System.out.println("Testing trained perceptron");
    testNeuralNetwork(myPerceptron, trainingSet);

    // save trained perceptron
    myPerceptron.save("mySamplePerceptron.nnet");

    // load saved neural network
    NeuralNetwork loadedPerceptron = NeuralNetwork.load("mySamplePerceptron.nnet");

    // test loaded neural network
    System.out.println("Testing loaded perceptron");
    testNeuralNetwork(loadedPerceptron, trainingSet);
  }

  public static void testNeuralNetwork(NeuralNetwork nnet, DataSet tset) {

    for (DataSetRow dataRow : tset.getRows()) {

      nnet.setInput(dataRow.getInput());
      nnet.calculate();
      double[ ] networkOutput = nnet.getOutput();
      System.out.print("Input: " + Arrays.toString(dataRow.getInput()) );
      System.out.println(" Output: " + Arrays.toString(networkOutput) );
    }
  }
}

}}}

=== Processing code ===
{{{
#!java
import org.neuroph.util.plugins.*;
import org.neuroph.core.transfer.*;
import org.neuroph.util.*;
import org.neuroph.nnet.comp.layer.*;
import org.neuroph.nnet.*;
import org.neuroph.core.events.*;
import org.neuroph.core.learning.error.*;
import org.neuroph.core.data.sample.*;
import org.neuroph.util.io.*;
import org.neuroph.core.exceptions.*;
import org.neuroph.core.learning.*;
import org.neuroph.core.input.*;
import org.neuroph.nnet.learning.*;
import org.neuroph.util.benchmark.*;
import org.neuroph.util.random.*;
import org.neuroph.nnet.comp.*;
import org.neuroph.core.learning.stop.*;
import org.neuroph.core.data.*;
import org.neuroph.core.*;
import org.neuroph.nnet.comp.neuron.*;
import org.neuroph.core.data.norm.*;

import java.util.*;

/**
 * This sample shows how to create, train, save and load simple Perceptron neural network 
 */
void setup() {

  // create training set (logical AND function)
  DataSet trainingSet = new DataSet(2, 1);
  trainingSet.addRow(new DataSetRow(new double[] {
    0, 0
  }
  , new double[] {
    0
  }
  ));
  trainingSet.addRow(new DataSetRow(new double[] {
    0, 1
  }
  , new double[] {
    0
  }
  ));
  trainingSet.addRow(new DataSetRow(new double[] {
    1, 0
  }
  , new double[] {
    0
  }
  ));
  trainingSet.addRow(new DataSetRow(new double[] {
    1, 1
  }
  , new double[] {
    1
  }
  ));

  // create perceptron neural network
  NeuralNetwork myPerceptron = new Perceptron(2, 1);

  // learn the training set
  myPerceptron.learn(trainingSet);

  // test perceptron
  System.out.println("Testing trained perceptron");
  testNeuralNetwork(myPerceptron, trainingSet);

  // save trained perceptron
  myPerceptron.save("mySamplePerceptron.nnet");

  // load saved neural network
  NeuralNetwork loadedPerceptron = NeuralNetwork.load("mySamplePerceptron.nnet");

  // test loaded neural network
  System.out.println("Testing loaded perceptron");
  testNeuralNetwork(loadedPerceptron, trainingSet);
}

void testNeuralNetwork(NeuralNetwork nnet, DataSet tset) {

  for (DataSetRow dataRow : tset.getRows()) {

    nnet.setInput(dataRow.getInput());
    nnet.calculate();
    double[ ] networkOutput = nnet.getOutput();
    System.out.print("Input: " + Arrays.toString(dataRow.getInput()) );
    System.out.println(" Output: " + Arrays.toString(networkOutput) );
  }
}

}}}



=== A trained XOR multilayer perceptron network ===
[[attachment:MultiLayerPerceptron.nnet]]