stats.java

一个数据挖掘系统的源码

/**
 *   
 *   AgentAcademy - an open source Data Mining framework for
 *   training intelligent agents
 *
 *   Copyright (C)   2001-2003 AA Consortium.
 *
 *   This library is open source software; you can redistribute it 
 *   and/or modify it under the terms of the GNU Lesser General 
 *   Public License as published by the Free Software Foundation;   
 *   either version 2.0 of the License, or (at your option) any later 
 *   version.
 *
 *   This library 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 Lesser General Public
 *   License along with this library; if not, write to the Free 
 *   Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, 
 *   MA  02111-1307 USA
 * 
 */

package org.agentacademy.modules.dataminer.experiment;

/**
 * <p>Title: The Data Miner prototype</p>
 * <p>Description: A prototype for the DataMiner (DM), the Agent Academy (AA) module responsible for performing data mining on the contents of the Agent Use Repository (AUR). The extracted knowledge is to be sent back to the AUR in the form of a PMML document.</p>
 * <p>Copyright: Copyright (c) 2002</p>
 * <p>Company: CERTH</p>
 * @author asymeon
 * @version 0.3
 */

import org.agentacademy.modules.dataminer.core.Utils;
import java.io.Serializable;

/**
 * A class to store simple statistics
 *
 * @author Len Trigg ([e-mail])
 * @version $Revision: 1.1.1.1 $
 */
public class Stats implements Serializable {

  /** The number of values seen */
  public double count = 0;

  /** The sum of values seen */
  public double sum = 0;

  /** The sum of values squared seen */
  public double sumSq = 0;

  /** The std deviation of values at the last calculateDerived() call */
  public double stdDev = Double.NaN;

  /** The mean of values at the last calculateDerived() call */
  public double mean = Double.NaN;

  /** The minimum value seen, or Double.NaN if no values seen */
  public double min = Double.NaN;

  /** The maximum value seen, or Double.NaN if no values seen */
  public double max = Double.NaN;

  /**
   * Adds a value to the observed values
   *
   * @param value the observed value
   */
  public void add(double value) {

    add(value, 1);
  }

  /**
   * Adds a value that has been seen n times to the observed values
   *
   * @param value the observed value
   * @param n the number of times to add value
   */
  public void add(double value, double n) {

    sum += value * n;
    sumSq += value * value * n;
    count += n;
    if (Double.isNaN(min)) {
      min = max = value;
    } else if (value < min) {
      min = value;
    } else if (value > max) {
      max = value;
    }
  }

  /**
   * Removes a value to the observed values (no checking is done
   * that the value being removed was actually added).
   *
   * @param value the observed value
   */
  public void subtract(double value) {
    subtract(value, 1);
  }

  /**
   * Subtracts a value that has been seen n times from the observed values
   *
   * @param value the observed value
   * @param n the number of times to subtract value
   */
  public void subtract(double value, double n) {
    sum -= value * n;
    sumSq -= value * value * n;
    count -= n;
  }

  /**
   * Tells the object to calculate any statistics that don't have their
   * values automatically updated during add. Currently updates the mean
   * and standard deviation.
   */
  public void calculateDerived() {

    mean = Double.NaN;
    stdDev = Double.NaN;
    if (count > 0) {
      mean = sum / count;
      stdDev = Double.POSITIVE_INFINITY;
      if (count > 1) {
	stdDev = sumSq - (sum * sum) / count;
	stdDev /= (count - 1);
        if (stdDev < 0) {
	  //          System.err.println("Warning: stdDev value = " + stdDev
	  //                             + " -- rounded to zero.");
          stdDev = 0;
        }
	stdDev = Math.sqrt(stdDev);
      }
    }
  }

  /**
   * Returns a string summarising the stats so far.
   *
   * @return the summary string
   */
  public String toString() {

    calculateDerived();
    return
      "Count   " + Utils.doubleToString(count, 8) + '\n'
      + "Min     " + Utils.doubleToString(min, 8) + '\n'
      + "Max     " + Utils.doubleToString(max, 8) + '\n'
      + "Sum     " + Utils.doubleToString(sum, 8) + '\n'
      + "SumSq   " + Utils.doubleToString(sumSq, 8) + '\n'
      + "Mean    " + Utils.doubleToString(mean, 8) + '\n'
      + "StdDev  " + Utils.doubleToString(stdDev, 8) + '\n';
  }

  /**
   * Tests the paired stats object from the command line.
   * reads line from stdin, expecting two values per line.
   *
   * @param args ignored.
   */
  public static void main(String [] args) {

    try {
      Stats ps = new Stats();
      java.io.LineNumberReader r = new java.io.LineNumberReader(
				   new java.io.InputStreamReader(System.in));
      String line;
      while ((line = r.readLine()) != null) {
        line = line.trim();
        if (line.equals("") || line.startsWith("@") || line.startsWith("%")) {
          continue;
        }
	java.util.StringTokenizer s
          = new java.util.StringTokenizer(line, " ,\t\n\r\f");
	int count = 0;
	double v1 = 0;
	while (s.hasMoreTokens()) {
	  double val = (new Double(s.nextToken())).doubleValue();
	  if (count == 0) {
	    v1 = val;
	  } else {
            System.err.println("MSG: Too many values in line \""
                               + line + "\", skipped.");
	    break;
	  }
	  count++;
	}
        if (count == 1) {
          ps.add(v1);
        }
      }
      ps.calculateDerived();
      System.err.println(ps);
    } catch (Exception ex) {
      ex.printStackTrace();
      System.err.println(ex.getMessage());
    }
  }

} // Stats