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.
   */
  
  /*
   *    NumericPrediction.java
   *    Copyright (C) 2002 University of Waikato, Hamilton, New Zealand
   *
   */
  
  package weka.classifiers.evaluation;
  
  import weka.classifiers.IntervalEstimator;
  import weka.core.RevisionHandler;
  import weka.core.RevisionUtils;
  
  import java.io.Serializable;
  
  /**
   * Encapsulates an evaluatable numeric prediction: the predicted class value
   * plus the actual class value.
   *
   * @author Len Trigg (len@reeltwo.com)
   * @version $Revision: 5714 $
   */
  public class NumericPrediction
    implements Prediction, Serializable, RevisionHandler {
  
    /** for serialization. */
    private static final long serialVersionUID = -4880216423674233887L;
  
    /** The actual class value. */
    private double m_Actual = MISSING_VALUE;
  
    /** The predicted class value. */
    private double m_Predicted = MISSING_VALUE;
  
    /** The weight assigned to this prediction. */
    private double m_Weight = 1;
    
    /** the prediction intervals. */
    private double[][] m_PredictionIntervals;
  
    /**
     * Creates the NumericPrediction object with a default weight of 1.0.
     *
     * @param actual the actual value, or MISSING_VALUE.
     * @param predicted the predicted value, or MISSING_VALUE.
     */
    public NumericPrediction(double actual, double predicted) {
      this(actual, predicted, 1);
    }
  
    /**
     * Creates the NumericPrediction object.
     *
     * @param actual the actual value, or MISSING_VALUE.
     * @param predicted the predicted value, or MISSING_VALUE.
     * @param weight the weight assigned to the prediction.
     */
    public NumericPrediction(double actual, double predicted, double weight) {
      this(actual, predicted, weight, new double[0][]);
    }
  
    /**
     * Creates the NumericPrediction object.
     *
     * @param actual the actual value, or MISSING_VALUE.
     * @param predicted the predicted value, or MISSING_VALUE.
     * @param weight the weight assigned to the prediction.
     * @param predInt the prediction intervals from classifiers implementing
     * the <code>IntervalEstimator</code> interface.
     * @see IntervalEstimator
     */
    public NumericPrediction(double actual, double predicted, double weight, double[][] predInt) {
      m_Actual = actual;
      m_Predicted = predicted;
      m_Weight = weight;
      setPredictionIntervals(predInt);
    }
  
    /** 
     * Gets the actual class value.
     *
     * @return the actual class value, or MISSING_VALUE if no
     * prediction was made.  
     */
    public double actual() { 
      return m_Actual; 
    }
  
    /**
     * Gets the predicted class value.
     *
     * @return the predicted class value, or MISSING_VALUE if no
     * prediction was made.  
     */
    public double predicted() { 
      return m_Predicted; 
    }
  
    /** 
     * Gets the weight assigned to this prediction. This is typically the weight
     * of the test instance the prediction was made for.
     *
     * @return the weight assigned to this prediction.
     */
    public double weight() { 
      return m_Weight; 
    }
  
    /**
     * Calculates the prediction error. This is defined as the predicted
     * value minus the actual value.
     *
     * @return the error for this prediction, or
     * MISSING_VALUE if either the actual or predicted value
     * is missing.  
     */
    public double error() {
      if ((m_Actual == MISSING_VALUE) ||
          (m_Predicted == MISSING_VALUE)) {
        return MISSING_VALUE;
      }
      return m_Predicted - m_Actual;
    }
    
    /**
     * Sets the prediction intervals for this prediction.
     * 
     * @param predInt the prediction intervals
     */
    public void setPredictionIntervals(double[][] predInt) {
      m_PredictionIntervals = predInt.clone();
    }
    
    /**
     * Returns the predictions intervals. Only classifiers implementing the
     * <code>IntervalEstimator</code> interface.
     * 
     * @return the prediction intervals.
     * @see IntervalEstimator
     */
    public double[][] predictionIntervals() {
      return m_PredictionIntervals;
    }
  
    /**
     * Gets a human readable representation of this prediction.
     *
     * @return a human readable representation of this prediction.
     */
    public String toString() {
      StringBuffer sb = new StringBuffer();
      sb.append("NUM: ").append(actual()).append(' ').append(predicted());
      sb.append(' ').append(weight());
      return sb.toString();
    }
    
    /**
     * Returns the revision string.
     * 
     * @return		the revision
     */
    public String getRevision() {
      return RevisionUtils.extract("$Revision: 5714 $");
    }
  }
  