content: /*
   *    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 University of Waikato, Hamilton, New Zealand
   *
   */
  
  package weka.estimators;
  
  import weka.core.Capabilities.Capability;
  import weka.core.Capabilities;
  import weka.core.RevisionUtils;
  import weka.core.Statistics;
  import weka.core.Utils;
  
  /** 
   * Simple probability estimator that places a single normal distribution
   * over the observed values.
   *
   * @author Len Trigg (trigg@cs.waikato.ac.nz)
   * @version $Revision: 5490 $
   */
  public class NormalEstimator
    extends Estimator
    implements IncrementalEstimator {
  
    /** for serialization */
    private static final long serialVersionUID = 93584379632315841L;
  
    /** 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";
    }
  
    /**
     * Returns default capabilities of the classifier.
     *
     * @return      the capabilities of this classifier
     */
    public Capabilities getCapabilities() {
      Capabilities result = super.getCapabilities();
      result.disableAll();
      
      // class
      if (!m_noClass) {
        result.enable(Capability.NOMINAL_CLASS);
        result.enable(Capability.MISSING_CLASS_VALUES);
      } else {
        result.enable(Capability.NO_CLASS);
      }
      
      // attributes
      result.enable(Capability.NUMERIC_ATTRIBUTES);
      return result;
    }
  
    /**
     * Return the value of the mean of this normal estimator.
     *
     * @return the mean
     */
    public double getMean() {
      return m_Mean;
    }
  
    /**
     * Return the value of the standard deviation of this normal estimator.
     *
     * @return the standard deviation
     */
    public double getStdDev() {
      return m_StandardDev;
    }
  
    /**
     * Return the value of the precision of this normal estimator.
     *
     * @return the precision
     */
    public double getPrecision() {
      return m_Precision;
    }
  
    /**
     * Return the sum of the weights for this normal estimator.
     *
     * @return the sum of the weights
     */
    public double getSumOfWeights() {
      return m_SumOfWeights;
    }
    
    /**
     * Returns the revision string.
     * 
     * @return		the revision
     */
    public String getRevision() {
      return RevisionUtils.extract("$Revision: 5490 $");
    }
  
    /**
     * 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());
      }
    }
  }
  