linearnormalstream.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 1.0
  */
package com.prudsys.pdm.Transform.Special;

import com.prudsys.pdm.Core.MiningAttribute;
import com.prudsys.pdm.Core.MiningDataSpecification;
import com.prudsys.pdm.Core.MiningException;
import com.prudsys.pdm.Core.NumericAttribute;
import com.prudsys.pdm.Input.MiningInputStream;
import com.prudsys.pdm.Models.Statistics.SimpleStats;
import com.prudsys.pdm.Transform.MiningTransformationFactory;
import com.prudsys.pdm.Transform.MiningTransformationStep;
import com.prudsys.pdm.Transform.OneToOne.Identity;
import com.prudsys.pdm.Transform.OneToOne.LinearNormal;

/**
 * Realization of normalization for a given mining input
 * stream. Only numeric attributes are normalized. The parameters
 * lowerBound and upperBound of the normalization range apply
 * to all numeric attributes.
 */
public class LinearNormalStream extends VectorTransformationStream
{
  // -----------------------------------------------------------------------
  //  Variables declarations
  // -----------------------------------------------------------------------
  /** Array of minimum values of all attributes. */
  private double[] minValues = null;

  /** Array of maximum values of all attributes. */
  private double[] maxValues = null;

  /** Lower bound of normalization valus (default = 0). */
  private double lowerBound = 0.0;

  /** Upper bound of normalization valus (default = 1). */
  private double upperBound = 1.0;

  // -----------------------------------------------------------------------
  //  Constructors
  // -----------------------------------------------------------------------
  /**
   * Empty constructor.
   */
  public LinearNormalStream()
  {
  }

  /**
   * Constructor for given stream.
   *
   * @param inputStream mining input stream for normalization
   */
  public LinearNormalStream(MiningInputStream inputStream) {

    this.inputStream = inputStream;
  }

  // -----------------------------------------------------------------------
  //  Getter and setter methods
  // -----------------------------------------------------------------------
  /**
   * Sets lower bound of normalized values (default 0).
   *
   * @param lowerBound new lower bound of normalized values
   */
  public void setLowerBound(double lowerBound)
  {
    this.lowerBound = lowerBound;
  }

  /**
   * Returns lower bound of normalized values (default 0).
   *
   * @return lower bound of normalized values
   */
  public double getLowerBound()
  {
    return lowerBound;
  }

  /**
   * Sets upper bound of normalized values (default 1).
   *
   * @param upperBound new upper bound of normalized values
   */
  public void setUpperBound(double upperBound)
  {
    this.upperBound = upperBound;
  }

  /**
   * Returns upper bound of normalized values (default 1).
   *
   * @return upper bound of normalized values
   */
  public double getUpperBound()
  {
    return upperBound;
  }

  /**
   * Returns array of minimum values.
   *
   * @return array of minimum values
   */
  public double[] getMinValues()
  {
    return minValues;
  }

  /**
   * Returns array of maximum values.
   *
   * @return array of maximum values
   */
  public double[] getMaxValues()
  {
    return maxValues;
  }

  // -----------------------------------------------------------------------
  //  Transformation methods
  // -----------------------------------------------------------------------
  /**
   * Calculates minimum and maximum values for numeric attributes.
   *
   * @exception MiningException error while calculating minmax values
   */
  private void calcMinMaxValues() throws MiningException {

    // Calculate simple statistics:
    SimpleStats sist = new SimpleStats();
    sist.setInputStream(inputStream);
    sist.runCalculation();

    // Fill arrays of minimum and maximum values:
    MiningDataSpecification metaData = inputStream.getMetaData();
    int nAtt  = metaData.getAttributesNumber();
    minValues = new double[nAtt];
    maxValues = new double[nAtt];
    for (int i = 0; i < nAtt; i++) {
      MiningAttribute att = metaData.getMiningAttribute(i);
      if (att instanceof NumericAttribute) {
        minValues[i] = sist.getCalculatedValue(att, SimpleStats.STAT_MIN);
        maxValues[i] = sist.getCalculatedValue(att, SimpleStats.STAT_MAX);
      };
    };
  }

  /**
   * Creates mining transformation step for linearization of numeric attributs.
   *
   * @return mining transformation step
   * @exception MiningException no input stream defined
   */
  public MiningTransformationStep createMiningTransformationStep() throws MiningException  {

      // No mining input stream defined => exception:
      if (inputStream == null)
        throw new MiningException("No mining input stream defined");

      // Get minimum and maximum values of all numericattributes:
      calcMinMaxValues();

      // Mining transformation factory:
      MiningTransformationFactory mtf = new MiningTransformationFactory();

      boolean notrans = true;
      MiningDataSpecification metaData = inputStream.getMetaData();
      for (int i = 0; i < metaData.getAttributesNumber(); i++) {
        // Get attribute and name:
        MiningAttribute mAtt = metaData.getMiningAttribute(i);
        String attName = mAtt.getName();

        // Don't use excluded attributes, if defined:
        if ( excludedAttributeNames != null && excludedAttributeNames.indexOf(attName) > -1)
          continue;

        // Add linear normalization if numeric attribute:
        if (mAtt instanceof NumericAttribute) {
          LinearNormal ln = new LinearNormal();
          ln.setSourceName( attName );
          ln.setTargetName( "n_" + attName );
          ln.setMin( minValues[i] );
          ln.setMax( maxValues[i] );
          ln.setLowerBound( lowerBound );
          ln.setUpperBound( upperBound );
          mtf.addOneToOneMapping(ln);
          notrans = false;
        };
      };

      // No transformations at all => just 1 required, use first attribute:
      if (notrans) {
        MiningAttribute mAtt = metaData.getMiningAttribute(0);
        Identity id          = new Identity();
        id.setSourceName( mAtt.getName() );
        mtf.addOneToOneMapping(id);
      };

      // Create transformation step for normalization:
      mts = mtf.createMiningTransformationStep();

      return mts;
  }

  // -----------------------------------------------------------------------
  //  Other methods
  // -----------------------------------------------------------------------
  /**
   * Returns normalization description.
   *
   * @returns description of normalization
   */
  public String toString() {

    String mess = "Linear normalization stream. Transform in following interval:";
    mess = mess + " LowerBound = " + lowerBound + " UpperBound = " + upperBound;

    return mess;
  }

}