Package org.encog.neural.pattern

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

/*
* 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.mathutil.rbf.RBFEnum;
import org.encog.ml.MLMethod;
import org.encog.neural.rbf.RBFNetwork;

/**
* A radial basis function (RBF) network uses several radial basis functions to
* provide a more dynamic hidden layer activation function than many other types
* of neural network. It consists of a input, output and hidden layer.
*
* @author jheaton
*
*/
public class RadialBasisPattern implements NeuralNetworkPattern {

  private RBFEnum rbfType = RBFEnum.Gaussian;
 
  /**
   * The number of input neurons to use. Must be set, default to invalid -1
   * value.
   */
  private int inputNeurons = -1;

  /**
   * The number of hidden neurons to use. Must be set, default to invalid -1
   * value.
   */
  private int outputNeurons = -1;

  /**
   * The number of hidden neurons to use. Must be set, default to invalid -1
   * value.
   */
  private int hiddenNeurons = -1;

  /**
   * Add the hidden layer, this should be called once, as a RBF has a single
   * hidden layer.
   *
   * @param count
   *            The number of neurons in the hidden layer.
   */
  public void addHiddenLayer(final int count) {
    if (this.hiddenNeurons != -1) {
      throw new PatternError("A RBF network usually has a single "
          + "hidden layer.");

    } else {
      this.hiddenNeurons = count;
    }
  }

  /**
   * Clear out any hidden neurons.
   */
  public void clear() {
    this.hiddenNeurons = -1;
  }

  /**
   * Generate the RBF network.
   *
   * @return The neural network.
   */
  public MLMethod generate() {

    RBFNetwork result = new RBFNetwork(inputNeurons, this.hiddenNeurons ,outputNeurons,this.rbfType);
    return result;
  }

  /**
   * Set the activation function, this is an error. The activation function
   * may not be set on a RBF layer.
   *
   * @param activation
   *            The new activation function.
   */
  public void setActivationFunction(final ActivationFunction activation) {
    throw new PatternError( "Can't set the activation function for "
        + "a radial basis function network.");
  }

  /**
   * Set the number of input neurons.
   *
   * @param count
   *            The number of input neurons.
   */
  public void setInputNeurons(final int count) {
    this.inputNeurons = count;
  }

  /**
   * Set the number of output neurons.
   *
   * @param count
   *            The number of output neurons.
   */
  public void setOutputNeurons(final int count) {
    this.outputNeurons = count;
  }

  public void setRBF(RBFEnum type) {
    this.rbfType = type;
   
  }
}
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