layer.java

This applet illustrates the prediction capabilities of the multi-layer perceptrons. It allows to def

import java.util.*;

class Layer
{
  Vector neurons;
  int size;

  public Layer(String l,int s)
  {
    String label;
    size    = s;
    neurons = new Vector();
    for(int i=0;i<s;i++)
    {
      label = new String(l) + String.valueOf(i);
      neurons.addElement(new Neuron(label));
    }
  }
  public Neuron getNeuron(int i)
  {
    int         j=0;
    boolean     found=false;
    Neuron      neuron=null;
    Enumeration e = neurons.elements();
    while(e.hasMoreElements())
    {
      neuron = (Neuron)e.nextElement();
      if (i==j)
      {
        found = true;
        break;
      } else j++;
    }
    if (found==false) neuron = null;
    return neuron;
  }
  public void computeOutputs()
  {
    Neuron neuron;
    Enumeration e = neurons.elements();
    while(e.hasMoreElements())
    {
      neuron = (Neuron)e.nextElement();
      neuron.computeOutput();
    }
  }
  public void computeBackpropDeltas(Vector s) // for output neurons
  {
    Neuron neuron;
    Enumeration e = neurons.elements();
    Enumeration d = s.elements();
    while(e.hasMoreElements())
    {
      neuron = (Neuron)e.nextElement();
      neuron.computeBackpropDelta(((Double)d.nextElement()).doubleValue());
    }
  }
  public void computeBackpropDeltas() // for hidden neurons
  {
    Neuron neuron;
    Enumeration e = neurons.elements();
    while(e.hasMoreElements())
    {
      neuron = (Neuron)e.nextElement();
      neuron.computeBackpropDelta();
    }
  }
  public void computeWeights()
  {
    Neuron neuron;
    Enumeration e = neurons.elements();
    while(e.hasMoreElements())
    {
      neuron = (Neuron)e.nextElement();
      neuron.computeWeight();
    }
  }
  public void print()
  {
    Neuron neuron;
    Enumeration e = neurons.elements();
    while(e.hasMoreElements())
    {
      neuron = (Neuron)e.nextElement();
      neuron.print();
    }
  }
}