Package org.encog.neural.pattern

Source Code of org.encog.neural.pattern.BAMPattern

/*
* 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.neural.pattern;

import org.encog.engine.network.activation.ActivationFunction;
import org.encog.ml.MLMethod;
import org.encog.neural.bam.BAM;

/**
* Construct a Bidirectional Access Memory (BAM) neural network. This neural
* network type learns to associate one pattern with another. The two patterns
* do not need to be of the same length. This network has two that are connected
* to each other. Though they are labeled as input and output layers to Encog,
* they are both equal, and should simply be thought of as the two layers that
* make up the net.
*
*/
public class BAMPattern implements NeuralNetworkPattern {

  /**
   * The number of neurons in the first layer.
   */
  private int f1Neurons;

  /**
   * The number of neurons in the second layer.
   */
  private int f2Neurons;

  /**
   * Unused, a BAM has no hidden layers.
   *
   * @param count
   *            Not used.
   */
  public void addHiddenLayer(final int count) {
    throw new PatternError("A BAM network has no hidden layers.");
  }

  /**
   * Clear any settings on the pattern.
   */
  public void clear() {
    this.f1Neurons = 0;
    this.f2Neurons = 0;

  }

  /**
   * @return The generated network.
   */
  public MLMethod generate() {
    BAM bam = new BAM(this.f1Neurons,this.f2Neurons);
    return bam;
  }

  /**
   * Not used, the BAM uses a bipoloar activation function.
   *
   * @param activation
   *            Not used.
   */
  public void setActivationFunction(final ActivationFunction activation) {
    throw new PatternError("A BAM network can't specify a custom activation function.");
  }

  /**
   * Set the F1 neurons. The BAM really does not have an input and output
   * layer, so this is simply setting the number of neurons that are in the
   * first layer.
   *
   * @param count
   *            The number of neurons in the first layer.
   */
  public void setF1Neurons(final int count) {
    this.f1Neurons = count;
  }

  /**
   * Set the output neurons. The BAM really does not have an input and output
   * layer, so this is simply setting the number of neurons that are in the
   * second layer.
   *
   * @param count
   *            The number of neurons in the second layer.
   */
  public void setF2Neurons(final int count) {
    this.f2Neurons = count;
  }

  /**
   * Set the number of input neurons.
   *
   * @param count
   *            The number of input neurons.
   */
  public void setInputNeurons(final int count) {
    throw new PatternError( "A BAM network has no input layer, consider setting F1 layer.");
  }

  /**
   * Set the number of output neurons.
   *
   * @param count
   *            The output neuron count.
   */
  public void setOutputNeurons(final int count) {
    throw new PatternError("A BAM network has no output layer, consider setting F2 layer.");
  }

}
TOP

Related Classes of org.encog.neural.pattern.BAMPattern

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.