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

Package org.apache.commons.math3.stat.descriptive.moment

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

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.

     * {@link #DEFAULT_INVERSE_ABSOLUTE_ACCURACY}).
     * @throws NotStrictlyPositiveException if {@code mean <= 0}.
     * @since 2.1
     */
    public ExponentialDistribution(double mean, double inverseCumAccuracy) {
        this(new Well19937c(), mean, inverseCumAccuracy);
    }

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     * @param upper Upper bound (inclusive) of this distribution.
     * @throws NumberIsTooLargeException if {@code lower >= upper}.
     */
    public UniformIntegerDistribution(int lower, int upper)
        throws NumberIsTooLargeException {
        this(new Well19937c(), lower, upper);
    }

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     * @throws NumberIsTooLargeException if {@code a >= b} or if {@code c > b}.
     * @throws NumberIsTooSmallException if {@code c < a}.
     */
    public TriangularDistribution(double a, double c, double b)
        throws NumberIsTooLargeException, NumberIsTooSmallException {
        this(new Well19937c(), a, c, b);
    }

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            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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     * @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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            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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        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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    @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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            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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        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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org.apache.commons.math3.analysis.differentiation.MultivariateDifferentiableVectorFunction

org.apache.commons.math3.analysis.function.HarmonicOscillator

org.apache.commons.math3.analysis.function.Sin

org.apache.commons.math3.analysis.function.Sinc

org.apache.commons.math3.analysis.MultivariateFunction

org.apache.commons.math3.analysis.QuinticFunction

org.apache.commons.math3.analysis.solvers.BrentSolver

org.apache.commons.math3.analysis.UnivariateFunction

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