Package org.encog.neural.pattern

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

/*
* 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.ml.svm.KernelType;
import org.encog.ml.svm.SVM;
import org.encog.ml.svm.SVMType;

/**
* A pattern to create support vector machines.
*
*/
public class SVMPattern implements NeuralNetworkPattern {
  /**
   * The number of neurons in the first layer.
   */
  private int inputNeurons;

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

  /**
   * True, if using regression.
   */
  private boolean regression = true;

  /**
   * The kernel type.
   */
  private KernelType kernelType = KernelType.RadialBasisFunction;
 
  /**
   * The SVM type.
   */
  private SVMType svmType = SVMType.EpsilonSupportVectorRegression;

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

  /**
   * Clear any settings on the pattern.
   */
  @Override
  public void clear() {
    this.inputNeurons = 0;
    this.outputNeurons = 0;

  }

  /**
   * @return The generated network.
   */
  @Override
  public MLMethod generate() {
    if (this.outputNeurons != 1) {
      throw new PatternError("A SVM may only have one output.");
    }
    final SVM network = new SVM(this.inputNeurons, this.svmType,
        this.kernelType);
    return network;
  }

  /**
   * @return The input neuron count.
   */
  public int getInputNeurons() {
    return this.inputNeurons;
  }

  /**
   * @return The input output count.
   */
  public int getOutputNeurons() {
    return this.outputNeurons;
  }

  /**
   * @return True, if this is regression.
   */
  public boolean isRegression() {
    return this.regression;
  }

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

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

  /**
   * Set the kernel type.
   * @param kernelType The kernel type.
   */
  public void setKernelType(final KernelType kernelType) {
    this.kernelType = kernelType;
  }

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

  /**
   * Set if regression is used.
   * @param regression True if regression is used.
   */
  public void setRegression(final boolean regression) {
    this.regression = regression;
  }

  /**
   * Set the SVM type.
   * @param svmType The SVM type.
   */
  public void setSVMType(final SVMType svmType) {
    this.svmType = svmType;
  }
}
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