Example usage for org.apache.commons.math.stat.regression SimpleRegression addData

Introduction

In this page you can find the example usage for org.apache.commons.math.stat.regression SimpleRegression addData.

Prototype

public void addData(double[][] data) 

Source Link

Document

Adds the observations represented by the elements in data.

Usage

From source file:emlab.util.TrendEstimator.java

public static double[] estimateLinearTrend(double[][] input, double[] predictionYears) {
    ;/*w  w w . java2  s. c  om*/
    //Get logarithm of second trend
    SimpleRegression sr = new SimpleRegression();
    sr.addData(input);
    double result[] = new double[predictionYears.length];
    for (int i = 0; i < result.length; i++) {
        result[i] = sr.predict(predictionYears[i]);
    }
    return result;

}

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From source file:emlab.util.TrendEstimator.java

public static double[] estimateGeometricTrend(double[][] input, double[] predictionYears) {
    //Get logarithm of second trend
    for (int i = 0; i < input.length; i++) {
        input[i][1] = Math.log(input[i][1]);
    }/*from  ww  w .j  av a 2s  .co  m*/
    //input[1]=log;
    SimpleRegression sr = new SimpleRegression();
    sr.addData(input);
    double result[] = new double[predictionYears.length];
    for (int i = 0; i < result.length; i++) {
        result[i] = Math.exp(sr.predict(predictionYears[i]));
    }
    return result;

}

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From source file:emlab.util.GeometricTrendRegressionTest.java

@Test
public void testLinearTrendEstimation() {
    double[][] input = { { 0, 1 }, { 1, 1.1 }, { 2, 1.2 }, { 3, 1.3 }, { 4, 1.4 } };
    double[] predictionYears = { 5, 6, 7, 8 };
    double[] expectedResults = { 1.5, 1.6, 1.7, 1.8 };
    SimpleRegression sr = new SimpleRegression();
    sr.addData(input);
    for (int i = 0; i < predictionYears.length; i++) {
        assert (expectedResults[i] == sr.predict(predictionYears[i]));
    }//from   ww  w . ja va 2s .  c  o m
}

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From source file:com.netxforge.netxstudio.common.math.NativeFunctions.java

/**
 * Return a {@link GenericsTuple tuple} with a key being the slope and the
 * value being the intercept of the trendline.
 * //from  w w w  .  j av  a2 s .co  m
 */
public GenericsTuple<Double, Double> trend(double[][] dataPair) {

    SimpleRegression regression = new SimpleRegression();
    regression.addData(dataPair);
    double slope = regression.getSlope();
    double intercept = regression.getIntercept();

    return new GenericsTuple<Double, Double>(slope, intercept);
}

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From source file:org.noisemap.core.ProgressionOrbisGisManager.java

@Override
public void run() {
    while (enabled) {
        double progression = (getMainProgression() * 100);
        if (monitor != null) {
            monitor.progressTo((long) progression);
            if (monitor.isCancelled()) {
                break;
            }/*from w  w  w .j av a  2 s .  c o  m*/
        }
        // Evaluate computation time and print it in console.
        if (progressionHistoryTime.isEmpty()) {
            if (progression > 0) {
                progressionHistoryTime.push(System.currentTimeMillis());
                progressionHistoryValue.push(progression);
            }
        } else {
            if (lastPushedProgress < System.currentTimeMillis() - historyTimeStep) {
                //reg.addData(progression,System.currentTimeMillis());
                lastPushedProgress = System.currentTimeMillis();
                if ((int) (progression - progressionHistoryValue.lastElement()) >= 1) {
                    progressionHistoryTime.push(System.currentTimeMillis());
                    progressionHistoryValue.push(progression);
                }
                //Estimate end of computation
                SimpleRegression reg = new SimpleRegression();
                double prog[][] = new double[progressionHistoryTime.size() + 1][2];
                for (int t = 0; t < progressionHistoryTime.size(); t++) {
                    prog[t][0] = progressionHistoryValue.get(t);
                    prog[t][1] = progressionHistoryTime.get(t);
                }
                prog[progressionHistoryTime.size()][0] = progression;
                prog[progressionHistoryTime.size()][1] = System.currentTimeMillis();
                reg.addData(prog);
                lastEstimation = reg.predict(100);
                //If estimation fails, use simple estimation
                if (lastEstimation < System.currentTimeMillis() && progressionHistoryTime.size() > 1) {
                    lastEstimation = System.currentTimeMillis()
                            + (System.currentTimeMillis() - progressionHistoryTime.get(1)) * 100 / progression;
                }
            }
        }

        //Round
        progression = (((int) (progression * 100000)) / 100000.);
        if (lastEstimation > 0) {
            System.out.println(progression + " % remaining "
                    + getHumanTime((long) lastEstimation - System.currentTimeMillis()));
        } else {
            System.out.println(progression + " %");
        }

        try {
            Thread.sleep(updateInterval);
        } catch (InterruptedException e) {
            break;
        }
    }
}

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