weka.classifiers.evaluation.NumericPrediction.java Source code

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Here is the source code for weka.classifiers.evaluation.NumericPrediction.java

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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 <http://www.gnu.org/licenses/>.
 */

/*
 *    NumericPrediction.java
 *    Copyright (C) 2002-2012 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.classifiers.evaluation;

import java.io.Serializable;

import weka.classifiers.IntervalEstimator;
import weka.core.RevisionHandler;
import weka.core.RevisionUtils;

/**
 * Encapsulates an evaluatable numeric prediction: the predicted class value
 * plus the actual class value.
 *
 * @author Len Trigg (len@reeltwo.com)
 * @version $Revision$
 */
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("$Revision$");
    }
}