Examples of Mean

de.lmu.ifi.dbs.elki.math.Mean
Compute the mean using a numerically stable online algorithm. This class can repeatedly be fed with data using the add() methods, the resulting values for mean can be queried at any time using getMean(). Trivial code, but replicated a lot. The class is final so it should come at low cost. Related Literature:
B. P. Welford
Note on a method for calculating corrected sums of squares and products
in: Technometrics 4(3)

D.H.D. West
Updating Mean and Variance Estimates: An Improved Method
In: Communications of the ACM, Volume 22 Issue 9
@author Erich Schubert
jscicalc.pobject.Mean
Container to hold most recently evaluated mean value. @author J. D. Lamb @version $Revision: 1.6 $
opennlp.tools.util.eval.Mean
Calculates the arithmetic mean of values added with the {@link #add(double)} method.
org.apache.commons.math.stat.descriptive.moment.Mean
Computes the arithmetic mean of a set of values. Uses the definitional formula:

mean = sum(x_i) / n

where n is the number of observations.

When {@link #increment(double)} is used to add data incrementally from astream of (unstored) values, the value of the statistic that {@link #getResult()} returns is computed using the following recursiveupdating algorithm:
1. Initialize m = the first value
2. For each additional value, update using
  m = m + (new value - m) / (number of observations)
If {@link #evaluate(double[])} is used to compute the mean of an arrayof stored values, a two-pass, corrected algorithm is used, starting with the definitional formula computed using the array of stored values and then correcting this by adding the mean deviation of the data values from the arithmetic mean. See, e.g. "Comparison of Several Algorithms for Computing Sample Means and Variances," Robert F. Ling, Journal of the American Statistical Association, Vol. 69, No. 348 (Dec., 1974), pp. 859-866.

Returns Double.NaN if the dataset is empty.
Note that this implementation is not synchronized. If multiple threads access an instance of this class concurrently, and at least one of the threads invokes the increment() or clear() method, it must be synchronized externally. @version $Revision: 1006299 $ $Date: 2010-10-10 16:47:17 +0200 (dim. 10 oct. 2010) $
org.apache.commons.math.stat.univariate.moment.Mean
ycoon.com/arithmetic_mean.htm"> arithmetic mean of the available values. @version $Revision: 1.13 $ $Date: 2003/11/19 03:28:24 $
org.apache.commons.math3.stat.descriptive.moment.Mean
Computes the arithmetic mean of a set of values. Uses the definitional formula:

mean = sum(x_i) / n

where n is the number of observations.

When {@link #increment(double)} is used to add data incrementally from astream of (unstored) values, the value of the statistic that {@link #getResult()} returns is computed using the following recursiveupdating algorithm:
1. Initialize m = the first value
2. For each additional value, update using
  m = m + (new value - m) / (number of observations)
If {@link #evaluate(double[])} is used to compute the mean of an arrayof stored values, a two-pass, corrected algorithm is used, starting with the definitional formula computed using the array of stored values and then correcting this by adding the mean deviation of the data values from the arithmetic mean. See, e.g. "Comparison of Several Algorithms for Computing Sample Means and Variances," Robert F. Ling, Journal of the American Statistical Association, Vol. 69, No. 348 (Dec., 1974), pp. 859-866.

Returns Double.NaN if the dataset is empty.
Note that this implementation is not synchronized. If multiple threads access an instance of this class concurrently, and at least one of the threads invokes the increment() or clear() method, it must be synchronized externally.
tv.floe.metronome.clustering.kmeans.Mean
zdenekdrahos.Statistics.Characteristics.Mean

Examples of org.apache.commons.math3.stat.descriptive.moment.Mean

     * @throws  MathIllegalArgumentException if the arrays lengths do not match or
     * there is insufficient data
     */
    public double covariance(final double[] xArray, final double[] yArray, boolean biasCorrected)
        throws MathIllegalArgumentException {
        Mean mean = new Mean();
        double result = 0d;
        int length = xArray.length;
        if (length != yArray.length) {
            throw new MathIllegalArgumentException(
                  LocalizedFormats.DIMENSIONS_MISMATCH_SIMPLE, length, yArray.length);
        } else if (length < 2) {
            throw new MathIllegalArgumentException(
                  LocalizedFormats.INSUFFICIENT_OBSERVED_POINTS_IN_SAMPLE, length, 2);
        } else {
            double xMean = mean.evaluate(xArray);
            double yMean = mean.evaluate(yArray);
            for (int i = 0; i < length; i++) {
                double xDev = xArray[i] - xMean;
                double yDev = yArray[i] - yMean;
                result += (xDev * yDev - result) / (i + 1);
            }

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Examples of org.apache.commons.math3.stat.descriptive.moment.Mean

            sumSqImpl[i]   = new SumOfSquares();
            minImpl[i]     = new Min();
            maxImpl[i]     = new Max();
            sumLogImpl[i]  = new SumOfLogs();
            geoMeanImpl[i] = new GeometricMean();
            meanImpl[i]    = new Mean();
        }


        covarianceImpl =
            new VectorialCovariance(k, isCovarianceBiasCorrected);

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Examples of org.apache.commons.math3.stat.descriptive.moment.Mean

        u.clear();
        u.addValue(1);
        u.addValue(2);
        Assert.assertEquals(3, u.getMean(), 1E-14);
        u.clear();
        u.setMeanImpl(new Mean()); // OK after clear
    }

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Examples of org.apache.commons.math3.stat.descriptive.moment.Mean

    
    @Test
    public void testOverrideMeanWithMathClass() {
        double[] scores = {1, 2, 3, 4};
        SummaryStatistics stats = new SummaryStatistics();
        stats.setMeanImpl(new Mean()); 
        for(double i : scores) {
          stats.addValue(i);
        }
        Assert.assertEquals((new Mean()).evaluate(scores),stats.getMean(), 0); 
    }

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Examples of org.apache.commons.math3.stat.descriptive.moment.Mean

            double[] values = dstats.getValues();
            for (int j = 0; j < values.length; j++) {
                sstats.addValue(values[j]);
            }
            TestUtils.assertEquals(dstats.getMean(), sstats.getMean(), tol);
            TestUtils.assertEquals(new Mean().evaluate(values), dstats.getMean(), tol);
            TestUtils.assertEquals(dstats.getMax(), sstats.getMax(), tol);
            TestUtils.assertEquals(new Max().evaluate(values), dstats.getMax(), tol);
            TestUtils.assertEquals(dstats.getGeometricMean(), sstats.getGeometricMean(), tol);
            TestUtils.assertEquals(new GeometricMean().evaluate(values), dstats.getGeometricMean(), tol);
            TestUtils.assertEquals(dstats.getMin(), sstats.getMin(), tol);

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Examples of org.apache.commons.math3.stat.descriptive.moment.Mean

        u.addValue(new double[] { 3, 4 });
        Assert.assertEquals(4, u.getMean()[0], 1E-14);
        Assert.assertEquals(6, u.getMean()[1], 1E-14);
        u.clear();
        u.setMeanImpl(new StorelessUnivariateStatistic[] {
                        new Mean(), new Mean()
                      }); // OK after clear
        u.addValue(new double[] { 1, 2 });
        u.addValue(new double[] { 3, 4 });
        Assert.assertEquals(2, u.getMean()[0], 1E-14);
        Assert.assertEquals(3, u.getMean()[1], 1E-14);

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Examples of org.apache.commons.math3.stat.descriptive.moment.Mean

            sumSqImpl[i]   = new SumOfSquares();
            minImpl[i]     = new Min();
            maxImpl[i]     = new Max();
            sumLogImpl[i]  = new SumOfLogs();
            geoMeanImpl[i] = new GeometricMean();
            meanImpl[i]    = new Mean();
        }


        covarianceImpl =
            new VectorialCovariance(k, isCovarianceBiasCorrected);

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Examples of org.apache.commons.math3.stat.descriptive.moment.Mean

            dest.varianceImpl = new Variance(dest.secondMoment);
        } else {
            dest.varianceImpl = source.varianceImpl.copy();
        }
        if (source.meanImpl instanceof Mean) {
            dest.meanImpl = new Mean(dest.secondMoment);
        } else {
            dest.meanImpl = source.meanImpl.copy();
        }
        if (source.getGeoMeanImpl() instanceof GeometricMean) {
            dest.geoMeanImpl = new GeometricMean((SumOfLogs) dest.sumLogImpl);

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Examples of org.apache.commons.math3.stat.descriptive.moment.Mean

     * @throws  IllegalArgumentException if the arrays lengths do not match or
     * there is insufficient data
     */
    public double covariance(final double[] xArray, final double[] yArray, boolean biasCorrected)
        throws IllegalArgumentException {
        Mean mean = new Mean();
        double result = 0d;
        int length = xArray.length;
        if (length != yArray.length) {
            throw new MathIllegalArgumentException(
                  LocalizedFormats.DIMENSIONS_MISMATCH_SIMPLE, length, yArray.length);
        } else if (length < 2) {
            throw new MathIllegalArgumentException(
                  LocalizedFormats.INSUFFICIENT_OBSERVED_POINTS_IN_SAMPLE, length, 2);
        } else {
            double xMean = mean.evaluate(xArray);
            double yMean = mean.evaluate(yArray);
            for (int i = 0; i < length; i++) {
                double xDev = xArray[i] - xMean;
                double yDev = yArray[i] - yMean;
                result += (xDev * yDev - result) / (i + 1);
            }

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Examples of org.apache.commons.math3.stat.descriptive.moment.Mean

            dest.varianceImpl = new Variance(dest.secondMoment);
        } else {
            dest.varianceImpl = source.varianceImpl.copy();
        }
        if (source.meanImpl instanceof Mean) {
            dest.meanImpl = new Mean(dest.secondMoment);
        } else {
            dest.meanImpl = source.meanImpl.copy();
        }
        if (source.getGeoMeanImpl() instanceof GeometricMean) {
            dest.geoMeanImpl = new GeometricMean((SumOfLogs) dest.sumLogImpl);

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