Examples of org.apache.commons.math3.stat.descriptive.UnivariateStatistic

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

Examples of org.apache.commons.math3.stat.descriptive.UnivariateStatistic

org.apache.commons.math3.util.IterationManager
Base interface implemented by all statistics.

    @Test
    public void testAllTechniquesEmpty() {
        final double[] singletonArray = new double[] { };
        for (final Percentile.EstimationType e : Percentile.EstimationType.values()) {
            reset (50, e);
            final UnivariateStatistic percentile = getUnivariateStatistic();
            Assert.assertEquals(Double.NaN, percentile.evaluate(singletonArray),
                    0);
            Assert.assertEquals(Double.NaN, percentile.evaluate(singletonArray,
                    0, 0),
                    0);
            Assert.assertEquals(Double.NaN,
                    new Percentile().evaluate(singletonArray, 0, 0, 5), 0);
            Assert.assertEquals(Double.NaN,
                    new Percentile().evaluate(singletonArray, 0, 0, 100), 0);
            Assert.assertTrue(Double.isNaN(percentile.evaluate(singletonArray,
                    0, 0)));
        }
    }

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    }


    @Test
    public void testAllTechniquesSpecialValues() {
        reset(50d, Percentile.EstimationType.LEGACY);
        final UnivariateStatistic percentile = getUnivariateStatistic();
        double[] specialValues =
                new double[] { 0d, 1d, 2d, 3d, 4d, Double.NaN };
        Assert.assertEquals(2.5d, percentile.evaluate(specialValues), 0);


        testAssertMappedValues(specialValues, new Object[][] {
                { Percentile.EstimationType.LEGACY, 2.5d }, { Percentile.EstimationType.R_1, 2.0 }, { Percentile.EstimationType.R_2, 2.0 }, { Percentile.EstimationType.R_3, 1.0 },
                { Percentile.EstimationType.R_4, 1.5 }, { Percentile.EstimationType.R_5, 2.0 }, { Percentile.EstimationType.R_6, 2.0 },
                { Percentile.EstimationType.R_7, 2.0 }, { Percentile.EstimationType.R_8, 2.0 }, { Percentile.EstimationType.R_9, 2.0 }}, 50d, 0d);


        specialValues =
                new double[] { Double.NEGATIVE_INFINITY, 1d, 2d, 3d,
                        Double.NaN, Double.POSITIVE_INFINITY };
        Assert.assertEquals(2.5d, percentile.evaluate(specialValues), 0);


        testAssertMappedValues(specialValues, new Object[][] {
                { Percentile.EstimationType.LEGACY, 2.5d }, { Percentile.EstimationType.R_1, 2.0 }, { Percentile.EstimationType.R_2, 2.0 }, { Percentile.EstimationType.R_3, 1.0 },
                { Percentile.EstimationType.R_4, 1.5 }, { Percentile.EstimationType.R_5, 2.0 }, { Percentile.EstimationType.R_7, 2.0 }, { Percentile.EstimationType.R_7, 2.0 },
                { Percentile.EstimationType.R_8, 2.0 }, { Percentile.EstimationType.R_9, 2.0 } }, 50d, 0d);


        specialValues =
                new double[] { 1d, 1d, Double.POSITIVE_INFINITY,
                        Double.POSITIVE_INFINITY };
        Assert.assertTrue(Double.isInfinite(percentile.evaluate(specialValues)));


        testAssertMappedValues(specialValues, new Object[][] {
                // This is one test not matching with R results.
                { Percentile.EstimationType.LEGACY, Double.POSITIVE_INFINITY },
                { Percentile.EstimationType.R_1,/* 1.0 */Double.NaN },
                { Percentile.EstimationType.R_2, /* Double.POSITIVE_INFINITY */Double.NaN },
                { Percentile.EstimationType.R_3, /* 1.0 */Double.NaN }, { Percentile.EstimationType.R_4, /* 1.0 */Double.NaN },
                { Percentile.EstimationType.R_5, Double.POSITIVE_INFINITY },
                { Percentile.EstimationType.R_6, Double.POSITIVE_INFINITY },
                { Percentile.EstimationType.R_7, Double.POSITIVE_INFINITY },
                { Percentile.EstimationType.R_8, Double.POSITIVE_INFINITY },
                { Percentile.EstimationType.R_9, Double.POSITIVE_INFINITY }, }, 50d, 0d);


        specialValues = new double[] { 1d, 1d, Double.NaN, Double.NaN };
        Assert.assertTrue(Double.isNaN(percentile.evaluate(specialValues)));
        testAssertMappedValues(specialValues, new Object[][] {
                { Percentile.EstimationType.LEGACY, Double.NaN }, { Percentile.EstimationType.R_1, 1.0 }, { Percentile.EstimationType.R_2, 1.0 }, { Percentile.EstimationType.R_3, 1.0 },
                { Percentile.EstimationType.R_4, 1.0 }, { Percentile.EstimationType.R_5, 1.0 },{ Percentile.EstimationType.R_6, 1.0 },{ Percentile.EstimationType.R_7, 1.0 },
                { Percentile.EstimationType.R_8, 1.0 }, { Percentile.EstimationType.R_9, 1.0 },}, 50d, 0d);

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


        // Make sure that if stat == statImpl in source, same

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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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     * <p>Double.NaN is returned if no values have been added.</p>
     *
     * @return the population variance
     */
    public double getPopulationVariance() {
        Variance populationVariance = new Variance(secondMoment);
        populationVariance.setBiasCorrected(false);
        return populationVariance.getResult();
    }

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        dest.secondMoment = source.secondMoment.copy();
        dest.n = source.n;


        // Keep commons-math supplied statistics with embedded moments in synch
        if (source.getVarianceImpl() instanceof Variance) {
            dest.varianceImpl = new Variance(dest.secondMoment);
        } else {
            dest.varianceImpl = source.varianceImpl.copy();
        }
        if (source.meanImpl instanceof Mean) {
            dest.meanImpl = new Mean(dest.secondMoment);

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        for (int i = 0; i < k; ++i) {
            sumImpl[i]     = new Sum();
            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 =

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        geoMeanImpl = new StorelessUnivariateStatistic[k];
        meanImpl    = new StorelessUnivariateStatistic[k];


        for (int i = 0; i < k; ++i) {
            sumImpl[i]     = new Sum();
            sumSqImpl[i]   = new SumOfSquares();
            minImpl[i]     = new Min();
            maxImpl[i]     = new Max();
            sumLogImpl[i]  = new SumOfLogs();
            geoMeanImpl[i] = new GeometricMean();
            meanImpl[i]    = new Mean();

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     * @param checker Convergence checker.
     */
    protected BaseOptimizer(ConvergenceChecker<PAIR> checker) {
        this.checker = checker;


        evaluations = new Incrementor(0, new MaxEvalCallback());
        iterations = new Incrementor(0, new MaxIterCallback());
    }

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        DimensionMismatchException, NonSelfAdjointOperatorException,
        NonPositiveDefiniteOperatorException, IllConditionedOperatorException,
        MaxCountExceededException {
        checkParameters(a, m, b, x);


        final IterationManager manager = getIterationManager();
        /* Initialization counts as an iteration. */
        manager.resetIterationCount();
        manager.incrementIterationCount();


        final State state;
        state = new State(a, m, b, goodb, shift, delta, check);
        state.init();
        state.refineSolution(x);
        IterativeLinearSolverEvent event;
        event = new DefaultIterativeLinearSolverEvent(this,
                                                      manager.getIterations(),
                                                      x,
                                                      b,
                                                      state.getNormOfResidual());
        if (state.bEqualsNullVector()) {
            /* If b = 0 exactly, stop with x = 0. */
            manager.fireTerminationEvent(event);
            return x;
        }
        /* Cause termination if beta is essentially zero. */
        final boolean earlyStop;
        earlyStop = state.betaEqualsZero() || state.hasConverged();
        manager.fireInitializationEvent(event);
        if (!earlyStop) {
            do {
                manager.incrementIterationCount();
                event = new DefaultIterativeLinearSolverEvent(this,
                                                              manager.getIterations(),
                                                              x,
                                                              b,
                                                              state.getNormOfResidual());
                manager.fireIterationStartedEvent(event);
                state.update();
                state.refineSolution(x);
                event = new DefaultIterativeLinearSolverEvent(this,
                                                              manager.getIterations(),
                                                              x,
                                                              b,
                                                              state.getNormOfResidual());
                manager.fireIterationPerformedEvent(event);
            } while (!state.hasConverged());
        }
        event = new DefaultIterativeLinearSolverEvent(this,
                                                      manager.getIterations(),
                                                      x,
                                                      b,
                                                      state.getNormOfResidual());
        manager.fireTerminationEvent(event);
        return x;
    }

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Related Classes of org.apache.commons.math3.stat.descriptive.UnivariateStatistic

org.apache.commons.math3.analysis.differentiation.DerivativeStructure

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

org.apache.commons.math3.complex.Complex

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