/* *    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. *//* *    NormalEstimator.java *    Copyright (C) 1999 Len Trigg * */package weka.estimators;import java.util.*;import weka.core.*;/**  * Simple probability estimator that places a single normal distribution * over the observed values. * * @author Len Trigg (trigg@cs.waikato.ac.nz) * @version $Revision: 1.5 $ */public class NormalEstimator implements Estimator {  /** The sum of the weights */  private double m_SumOfWeights;  /** The sum of the values seen */  private double m_SumOfValues;  /** The sum of the values squared */  private double m_SumOfValuesSq;  /** The current mean */  private double m_Mean;  /** The current standard deviation */  private double m_StandardDev;  /** The precision of numeric values ( = minimum std dev permitted) */  private double m_Precision;  /**   * Round a data value using the defined precision for this estimator   *   * @param data the value to round   * @return the rounded data value   */  private double round(double data) {    return Math.rint(data / m_Precision) * m_Precision;  }    // ===============  // Public methods.  // ===============    /**   * Constructor that takes a precision argument.   *   * @param precision the precision to which numeric values are given. For   * example, if the precision is stated to be 0.1, the values in the   * interval (0.25,0.35] are all treated as 0.3.    */  public NormalEstimator(double precision) {    m_Precision = precision;    // Allow at most 3 sd's within one interval    m_StandardDev = m_Precision / (2 * 3);  }  /**   * Add a new data value to the current estimator.   *   * @param data the new data value    * @param weight the weight assigned to the data value    */  public void addValue(double data, double weight) {    if (weight == 0) {      return;    }    data = round(data);    m_SumOfWeights += weight;    m_SumOfValues += data * weight;    m_SumOfValuesSq += data * data * weight;    if (m_SumOfWeights > 0) {      m_Mean = m_SumOfValues / m_SumOfWeights;      double stdDev = Math.sqrt(Math.abs(m_SumOfValuesSq 					  - m_Mean * m_SumOfValues) 					 / m_SumOfWeights);      // If the stdDev ~= 0, we really have no idea of scale yet,       // so stick with the default. Otherwise...      if (stdDev > 1e-10) {	m_StandardDev = Math.max(m_Precision / (2 * 3), 				 // allow at most 3sd's within one interval 				 stdDev);      }    }  }  /**   * Get a probability estimate for a value   *   * @param data the value to estimate the probability of   * @return the estimated probability of the supplied value   */  public double getProbability(double data) {    data = round(data);    double zLower = (data - m_Mean - (m_Precision / 2)) / m_StandardDev;    double zUpper = (data - m_Mean + (m_Precision / 2)) / m_StandardDev;        double pLower = Statistics.normalProbability(zLower);    double pUpper = Statistics.normalProbability(zUpper);    return pUpper - pLower;  }  /**   * Display a representation of this estimator   */  public String toString() {    return "Normal Distribution. Mean = " + Utils.doubleToString(m_Mean, 4)      + " StandardDev = " + Utils.doubleToString(m_StandardDev, 4)      + " WeightSum = " + Utils.doubleToString(m_SumOfWeights, 4)      + " Precision = " + m_Precision + "\n";  }  /**   * Main method for testing this class.   *   * @param argv should contain a sequence of numeric values   */  public static void main(String [] argv) {    try {      if (argv.length == 0) {	System.out.println("Please specify a set of instances.");	return;      }      NormalEstimator newEst = new NormalEstimator(0.01);      for(int i = 0; i < argv.length; i++) {	double current = Double.valueOf(argv[i]).doubleValue();	System.out.println(newEst);	System.out.println("Prediction for " + current 			   + " = " + newEst.getProbability(current));	newEst.addValue(current, 1);      }    } catch (Exception e) {      System.out.println(e.getMessage());    }  }}