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The Waikato Environment for Knowledge Analysis (WEKA), a machine learning workbench. This version represents the developer version, the "bleeding edge" of development, you could say. New functionality gets added to this version.
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/*
 *   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 3 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, see .
 */

/*
 *    AttributeStats.java
 *    Copyright (C) 1999-2012 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.core;

import java.io.Serializable;

/**
 * A Utility class that contains summary information on an
 * the values that appear in a dataset for a particular attribute.
 *
 * @author Len Trigg
 * @version $Revision: 8034 $
 */
public class AttributeStats
  implements Serializable, RevisionHandler {

  /** for serialization */
  private static final long serialVersionUID = 4434688832743939380L;
  
  /** The number of int-like values */
  public int intCount = 0;
  
  /** The number of real-like values (i.e. have a fractional part) */
  public int realCount = 0;
  
  /** The number of missing values */
  public int missingCount = 0;
  
  /** The number of distinct values */
  public int distinctCount = 0;
  
  /** The number of values that only appear once */
  public int uniqueCount = 0;
  
  /** The total number of values (i.e. number of instances) */
  public int totalCount = 0;
  
  /** Stats on numeric value distributions */
  // perhaps Stats should be moved from weka.experiment to weka.core
  public weka.experiment.Stats numericStats;
  
  /** Counts of each nominal value */
  public int [] nominalCounts;
  
  /** Weight mass for each nominal value */
  public double[] nominalWeights;
    
  /**
   * Updates the counters for one more observed distinct value.
   *
   * @param value the value that has just been seen
   * @param count the number of times the value appeared
   * @param weight the weight mass of the value
   */
  protected void addDistinct(double value, int count, double weight) {
    
    if (count > 0) {
      if (count == 1) {
	uniqueCount++;
	}
      if (Utils.eq(value, (double)((int)value))) {
	intCount += count;
      } else {
	realCount += count;
      }
      if (nominalCounts != null) {
	nominalCounts[(int)value] = count;
	nominalWeights[(int)value] = weight;
      }
      if (numericStats != null) {
	  //numericStats.add(value, count);
          numericStats.add(value, weight);
	  numericStats.calculateDerived();
      }
    }
    distinctCount++;
  }

  /**
   * Returns a human readable representation of this AttributeStats instance.
   *
   * @return a String represtinging these AttributeStats.
   */
  public String toString() {

    StringBuffer sb = new StringBuffer();
    sb.append(Utils.padLeft("Type", 4)).append(Utils.padLeft("Nom", 5));
    sb.append(Utils.padLeft("Int", 5)).append(Utils.padLeft("Real", 5));
    sb.append(Utils.padLeft("Missing", 12));
    sb.append(Utils.padLeft("Unique", 12));
    sb.append(Utils.padLeft("Dist", 6));
    if (nominalCounts != null) {
      sb.append(' ');
      for (int i = 0; i < nominalCounts.length; i++) {
        sb.append(Utils.padLeft("C[" + i + "]", 5));
      }
    }
    sb.append('\n');

    long percent;
    percent = Math.round(100.0 * intCount / totalCount);
    if (nominalCounts != null) {
      sb.append(Utils.padLeft("Nom", 4)).append(' ');
      sb.append(Utils.padLeft("" + percent, 3)).append("% ");
      sb.append(Utils.padLeft("" + 0, 3)).append("% ");
    } else {
      sb.append(Utils.padLeft("Num", 4)).append(' ');
      sb.append(Utils.padLeft("" + 0, 3)).append("% ");
      sb.append(Utils.padLeft("" + percent, 3)).append("% ");
    }
    percent = Math.round(100.0 * realCount / totalCount);
    sb.append(Utils.padLeft("" + percent, 3)).append("% ");
    sb.append(Utils.padLeft("" + missingCount, 5)).append(" /");
    percent = Math.round(100.0 * missingCount / totalCount);
    sb.append(Utils.padLeft("" + percent, 3)).append("% ");
    sb.append(Utils.padLeft("" + uniqueCount, 5)).append(" /");
    percent = Math.round(100.0 * uniqueCount / totalCount);
    sb.append(Utils.padLeft("" + percent, 3)).append("% ");
    sb.append(Utils.padLeft("" + distinctCount, 5)).append(' ');
    if (nominalCounts != null) {
      for (int i = 0; i < nominalCounts.length; i++) {
        sb.append(Utils.padLeft("" + nominalCounts[i], 5));
      }
    }
    sb.append('\n');
    return sb.toString();
  }
  
  /**
   * Returns the revision string.
   * 
   * @return		the revision
   */
  public String getRevision() {
    return RevisionUtils.extract("$Revision: 8034 $");
  }
}