Package org.encog.mathutil.randomize

Source Code of org.encog.mathutil.randomize.NguyenWidrowRandomizer

/*
* Encog(tm) Core v3.3 - Java Version
* http://www.heatonresearch.com/encog/
* https://github.com/encog/encog-java-core
* Copyright 2008-2014 Heaton Research, Inc.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
*     http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*  
* For more information on Heaton Research copyrights, licenses
* and trademarks visit:
* http://www.heatonresearch.com/copyright
*/
package org.encog.mathutil.randomize;

import org.encog.EncogError;
import org.encog.engine.network.activation.ActivationFunction;
import org.encog.mathutil.matrices.Matrix;
import org.encog.ml.MLMethod;
import org.encog.neural.networks.BasicNetwork;

/**
* Implementation of <i>Nguyen-Widrow</i> weight initialization. This is the
* default weight initialization used by Encog, as it generally provides the
* most train-able neural network.
*/
public class NguyenWidrowRandomizer extends BasicRandomizer {

  public static String MSG = "This type of randomization is not supported by Nguyen-Widrow";
 
  @Override
  public void randomize(MLMethod method) {
    if( !(method instanceof BasicNetwork) ) {
      throw new EncogError("Nguyen-Widrow only supports BasicNetwork.");
    }
   
    BasicNetwork network = (BasicNetwork)method;
   
    for(int fromLayer=0; fromLayer<network.getLayerCount()-1; fromLayer++) {
      randomizeSynapse(network, fromLayer);
    }
   
  }
 
  private double calculateRange(ActivationFunction af, double r) {
    double[] d = { r };
    af.activationFunction(d, 0, 1);
    return d[0];
  }
 
  private void randomizeSynapse(BasicNetwork network, int fromLayer) {
    int toLayer = fromLayer+1;
    int toCount = network.getLayerNeuronCount(toLayer);
    int fromCount = network.getLayerNeuronCount(fromLayer);
    int fromCountTotalCount = network.getLayerTotalNeuronCount(fromLayer);
    ActivationFunction af = network.getActivation(toLayer);
    double low = calculateRange(af,Double.MIN_VALUE);
    double high = calculateRange(af,Double.MAX_VALUE);

    double b = 0.7d * Math.pow(toCount, (1d / fromCount)) / (high-low);

    for(int toNeuron=0; toNeuron<toCount;toNeuron++) {
      if( fromCount!=fromCountTotalCount ) {
        double w = nextDouble(-b, b);
        network.setWeight(fromLayer, fromCount, toNeuron, w);
      }
      for(int fromNeuron=0; fromNeuron<fromCount;fromNeuron++) {
        double w = nextDouble(0, b);
        network.setWeight(fromLayer, fromNeuron, toNeuron, w)
      }
    }
  }

  @Override
  public double randomize(double d) {
    throw new EncogError(MSG);
  }

  @Override
  public void randomize(double[] d) {
    throw new EncogError(MSG);
  }

  @Override
  public void randomize(double[][] d) {
    throw new EncogError(MSG);
  }

  @Override
  public void randomize(Matrix m) {
    throw new EncogError(MSG);
  }

  @Override
  public void randomize(double[] d, int begin, int size) {
    throw new EncogError(MSG);
  }
}
TOP

Related Classes of org.encog.mathutil.randomize.NguyenWidrowRandomizer

TOP
Copyright © 2018 www.massapi.com. All rights reserved.
All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.