Examples of org.encog.neural.cpn.CPN

org.encog.neural.cpn.CPN
Counterpropagation Neural Networks (CPN) were developed by Professor Robert Hecht-Nielsen in 1987. CPN neural networks are a hybrid neural network, employing characteristics of both a feedforward neural network and a self-organzing map (SOM). The CPN is composed of three layers, the input, the instar and the outstar. The connection from the input to the instar layer is competitive, with only one neuron being allowed to win. The connection between the instar and outstar is feedforward. The layers are trained separately, using instar training and outstar training. The CPN network is good at regression.

  public final TempDir TEMP_DIR = new TempDir();
  public final File EG_FILENAME = TEMP_DIR.createFile("encogtest.eg");
  public final File SERIAL_FILENAME = TEMP_DIR.createFile("encogtest.ser");
  
  private CPN create() {
    CPN result = new CPN(5, 4, 3, 2);
    return result;
  }

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    return result;
  }
  
  public void testPersistEG()
  {
    CPN network = create();


    EncogDirectoryPersistence.saveObject(EG_FILENAME, network);
    CPN network2 = (CPN)EncogDirectoryPersistence.loadObject(EG_FILENAME);


    validate(network2);
  }

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    validate(network2);
  }
  
  public void testPersistSerial() throws IOException, ClassNotFoundException
  {
    CPN network = create();
    
    SerializeObject.save(SERIAL_FILENAME, network);
    CPN network2 = (CPN)SerializeObject.load(SERIAL_FILENAME);
        
    validate(network2);
  }

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   * Generate the network.
   * 
   * @return The generated network.
   */
  public final MLMethod generate() {
    return new CPN(inputCount,instarCount,outstarCount,1);
  }

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    }
  }


  public CPN createNetwork()
  {    
    CPN result = new CPN(this.inputNeurons, this.instarNeurons, this.outstarNeurons,1);
        return result;
  }

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  public void run()
  {
    prepareInput();
    normalizeInput();
    CPN network = createNetwork();
    MLDataSet training = generateTraining(this.input1,this.ideal1);
    trainInstar(network,training);
    trainOutstar(network,training);
    test(network,PATTERN1,this.input1);
  }

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      report.tablePair("Output Activation Function", neat.getOutputActivationFunction().getClass().getSimpleName());
      report.tablePair("NEAT Activation Function", neat.getActivationFunction().getClass().getSimpleName());
    }
    
    if( method instanceof CPN ) {
      CPN cpn = (CPN)method;
      report.tablePair("Instar Count", Format.formatInteger(cpn.getInstarCount()));
      report.tablePair("Outstar Count", Format.formatInteger(cpn.getOutstarCount()));      
    }
    
    report.endTable();
    
    if (this.method instanceof RBFNetwork) {

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   * Generate the network.
   * 
   * @return The generated network.
   */
  public MLMethod generate() {
    return new CPN(inputCount,instarCount,outstarCount,1);
  }

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  public final TempDir TEMP_DIR = new TempDir();
  public final File EG_FILENAME = TEMP_DIR.createFile("encogtest.eg");
  public final File SERIAL_FILENAME = TEMP_DIR.createFile("encogtest.ser");
  
  private CPN create() {
    CPN result = new CPN(5, 4, 3, 2);
    return result;
  }

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    return result;
  }
  
  public void testPersistEG()
  {
    CPN network = create();


    EncogDirectoryPersistence.saveObject(EG_FILENAME, network);
    CPN network2 = (CPN)EncogDirectoryPersistence.loadObject(EG_FILENAME);


    validate(network2);
  }

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