neuralinput.java

一个数据挖掘软件ALPHAMINERR的整个过程的JAVA版源代码

/*
 *    This program is free software; you can redistribute it and/or modify
 *    it under the terms of the GNU General Public License as published by
 *    the Free Software Foundation; either version 2 of the License, or
 *    (at your option) any later version.
 *
 *    This program is distributed in the hope that it will be useful,
 *    but WITHOUT ANY WARRANTY; without even the implied warranty of
 *    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *    GNU General Public License for more details.
 *
 *    You should have received a copy of the GNU General Public License
 *    along with this program; if not, write to the Free Software
 *    Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
 */

/**
 * Title: XELOPES Data Mining Library
 * Description: The XELOPES library is an open platform-independent and data-source-independent library for Embedded Data Mining.
 * Copyright: Copyright (c) 2002 Prudential Systems Software GmbH
 * Company: ZSoft (www.zsoft.ru), Prudsys (www.prudsys.com)
 * @author Michael Thess
 * @version 1.2
 */

package com.prudsys.pdm.Models.Regression.NeuralNetwork;

import com.prudsys.pdm.Adapters.PmmlVersion20.DerivedField;
import com.prudsys.pdm.Core.Category;
import com.prudsys.pdm.Core.MiningException;
import com.prudsys.pdm.Transform.OneToOneMapping;

/**
 * An input unit of a Neural Network. Just runs a transformation of the
 * input attribute. <p>
 *
 * Corresponds to PMML element NeuralInput.
 *
 * @see com.prudsys.pdm.Adapters.PmmlVersion20.NeuralInput
 */
public class NeuralInput extends NeuralNode {

  // -----------------------------------------------------------------------
  //  Variables declarations
  // -----------------------------------------------------------------------
  /** Transformation object of this class. */
  protected OneToOneMapping oneToOneMapping = null;

  // -----------------------------------------------------------------------
  //  Constructor
  // -----------------------------------------------------------------------
  public NeuralInput() {
  }

  // -----------------------------------------------------------------------
  //  Getter and setter methods
  // -----------------------------------------------------------------------
  /**
   * Returns input value.
   *
   * @return input value
   */
  public double getInput() {
    return input;
  }

  /**
   * Sets input value.
   *
   * @param input new input value
   */
  public void setInput(double input) {
    this.input = input;
  }

  /**
   * Returns input one-to-one mapping.
   *
   * @return input mapping
   */
  public OneToOneMapping getOneToOneMapping() {
    return oneToOneMapping;
  }

  /**
   * Sets input one-to-one mapping.
   *
   * @param oneToOneMapping new mapping
   */
  public void setOneToOneMapping(OneToOneMapping oneToOneMapping) {
    this.oneToOneMapping = oneToOneMapping;
  }

  // -----------------------------------------------------------------------
  //  Calculation methods
  // -----------------------------------------------------------------------
  /**
   * Reset all internal calculation values.
   */
  public void resetValues() {

    if ( !Category.isMissingValue(output) || !Category.isMissingValue(error) ) {
      output = Category.MISSING_VALUE;
      error  = Category.MISSING_VALUE;
      weightsUpdated = false;
    }
  }

  /**
   * Calculates input of the neural input. This is just the input value itself.
   *
   * @param newcalc calculate the input new
   * @return input value, missing if not calculated
   * @exception MiningException error while calculating input value
   */
  public double inputValue(boolean newcalc) throws MiningException {

    return input;
  }

  /**
   * Calculates output of the neural input. Just applies the
   * transformation to the input value.
   *
   * @param newcalc calculate the output new
   * @return output value, missing if not calculated
   * @exception MiningException error while calculating output value
   */
  public double outputValue(boolean newcalc) throws MiningException {

    if ( Category.isMissingValue(output) && newcalc ) {

      // Transformation:
      output = oneToOneMapping.transformAttributeValue(input);
    }

    return output;
  }

  /**
   * Calculate error of neuron.
   *
   * @param newcalc calculate the error new
   * @return error value, missing if not calculated
   * @exception MiningException error while calculating error value
   */
  public double errorValue(boolean newcalc) throws MiningException {

    if ( Category.isMissingValue(error) && !Category.isMissingValue(input) && newcalc ) {

        // Error back propagation:
        error = 0.0;
        for (int i = 0; i < getChildCount(); i++) {
          NeuralNode NN = (NeuralNode) getChildAt(i);
          double weight = NN.getWeightAt( NN.getParentIndex(this) );
          error = error + NN.errorValue(true)*weight;
        }

        // Identity:
        error = error*1.0;
    }

    return error;
  }

  // -----------------------------------------------------------------------
  //  Methods of PMML handling
  // -----------------------------------------------------------------------
  /**
   * Write neural input to PMML element.
   *
   * @return PMML element of neural input
   * @exception MiningException
   */
  public Object createPmmlObject() throws MiningException
  {
    // Create neural input:
    com.prudsys.pdm.Adapters.PmmlVersion20.NeuralInput ninput =
      new com.prudsys.pdm.Adapters.PmmlVersion20.NeuralInput();

    // Set neural input ID:
    ninput.setId(id);

    // Set derived field if one-to-one mapping:
    if (oneToOneMapping != null)
      ninput.setDerivedField( (DerivedField) oneToOneMapping.createPmmlObject() );

    return ninput;
  }

  /**
   * Read neural input from PMML element.
   *
   * @param pmmlObject PMML element to read in
   * @exception MiningException always thrown
   */
  public void parsePmmlObject( Object pmmlObject ) throws MiningException
  {
    // Get neural input:
    com.prudsys.pdm.Adapters.PmmlVersion20.NeuralInput ninput =
      (com.prudsys.pdm.Adapters.PmmlVersion20.NeuralInput) pmmlObject;

    // Get neural input ID:
    this.id = ninput.getId();

    // Get one-to-one mapping field:
    DerivedField derivedField =  ninput.getDerivedField();
    oneToOneMapping = OneToOneMapping.createOneToOneMappingFromPmml( derivedField );
    if ( oneToOneMapping != null )
      oneToOneMapping.parsePmmlObject( derivedField );

  }

  // -----------------------------------------------------------------------
  //  Further methods
  // -----------------------------------------------------------------------
  /**
   * Delivers string representation of neural input.
   *
   * @return string representation of neural input
   */
  public String toString() {

    String s = "Neural Input, " + super.toString() + ", input value = " + input;

    return s;
  }

}