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

Examples of org.apache.commons.math3.stat.descriptive.rank.PSquarePercentile.PSquareMarkers

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    }

    @Test
    public void testMarkers2() {
        double p = 0.5;
        PSquareMarkers markers =
                PSquarePercentile.newMarkers(
                        Arrays.asList(new Double[] { 0.02, 1.18, 9.15, 21.91,
                                38.62 }), p);

        PSquareMarkers markersNew =
                PSquarePercentile.newMarkers(
                        Arrays.asList(new Double[] { 0.02, 1.18, 9.15, 21.91,
                                38.62 }), p);

        Assert.assertTrue(markers.equals(markersNew));
        // If just one element of markers got changed then its still false.
        markersNew.processDataPoint(39);
        Assert.assertFalse(markers.equals(markersNew));

    }
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        s.add(p);
        s.add(p2);
        Assert.assertEquals(1, s.size());
        Assert.assertEquals(p, s.iterator().next());

        PSquareMarkers m1 =
                PSquarePercentile.newMarkers(
                        Arrays.asList(new Double[] { 95.1772, 95.1567, 95.1937,
                                95.1959, 95.1442, 95.0610, 95.1591, 95.1195,
                                95.1772, 95.0925, 95.1990, 95.1682 }), 0.0);
        PSquareMarkers m2 =
                PSquarePercentile.newMarkers(
                        Arrays.asList(new Double[] { 95.1772, 95.1567, 95.1937,
                                95.1959, 95.1442, 95.0610, 95.1591, 95.1195,
                                95.1772, 95.0925, 95.1990, 95.1682 }), 0.0);
        Assert.assertTrue(m1.equals(m2));
        Set<PSquareMarkers> setMarkers = new LinkedHashSet<PSquareMarkers>();
        Assert.assertTrue(setMarkers.add(m1));
        Assert.assertFalse(setMarkers.add(m2));
        Assert.assertEquals(1, setMarkers.size());

        PSquareMarkers mThis =
                PSquarePercentile.newMarkers(
                        Arrays.asList(new Double[] { 195.1772, 195.1567,
                                195.1937, 195.1959, 95.1442, 195.0610,
                                195.1591, 195.1195, 195.1772, 95.0925, 95.1990,
                                195.1682 }), 0.50);
        PSquareMarkers mThat =
                PSquarePercentile.newMarkers(
                        Arrays.asList(new Double[] { 95.1772, 95.1567, 95.1937,
                                95.1959, 95.1442, 95.0610, 95.1591, 95.1195,
                                95.1772, 95.0925, 95.1990, 95.1682 }), 0.50);
        Assert.assertTrue(mThis.equals(mThis));
        Assert.assertFalse(mThis.equals(mThat));
        String s1="";
        Assert.assertFalse(mThis.equals(s1));
        for (int i = 0; i < testArray.length; i++) {
            mThat.processDataPoint(testArray[i]);
        }
        setMarkers.add(mThat);
        setMarkers.add(mThis);
        Assert.assertTrue(mThat.equals(mThat));
        Assert.assertTrue(setMarkers.contains(mThat));
        Assert.assertTrue(setMarkers.contains(mThis));
        Assert.assertEquals(3, setMarkers.size());
        Iterator<PSquareMarkers> iterator=setMarkers.iterator();
        Assert.assertEquals(m1, iterator.next());
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        Assert.assertEquals(mThis, iterator.next());
    }

    @Test(expected = OutOfRangeException.class)
    public void testMarkersWithLowerIndex() {
        PSquareMarkers mThat =
                PSquarePercentile.newMarkers(
                        Arrays.asList(new Double[] { 95.1772, 95.1567, 95.1937,
                                95.1959, 95.1442, 95.0610, 95.1591, 95.1195,
                                95.1772, 95.0925, 95.1990, 95.1682 }), 0.50);
        for (int i = 0; i < testArray.length; i++) {
            mThat.processDataPoint(testArray[i]);
        }
        mThat.estimate(0);
    }
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        mThat.estimate(0);
    }

    @Test(expected = OutOfRangeException.class)
    public void testMarkersWithHigherIndex() {
        PSquareMarkers mThat =
                PSquarePercentile.newMarkers(
                        Arrays.asList(new Double[] { 95.1772, 95.1567, 95.1937,
                                95.1959, 95.1442, 95.0610, 95.1591, 95.1195,
                                95.1772, 95.0925, 95.1990, 95.1682 }), 0.50);
        for (int i = 0; i < testArray.length; i++) {
            mThat.processDataPoint(testArray[i]);
        }
        mThat.estimate(6);
    }
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        mThat.estimate(6);
    }

    @Test(expected = OutOfRangeException.class)
    public void testMarkerHeightWithLowerIndex() {
        PSquareMarkers mThat =
                PSquarePercentile.newMarkers(
                        Arrays.asList(new Double[] { 95.1772, 95.1567, 95.1937,
                                95.1959, 95.1442, 95.0610, 95.1591, 95.1195,
                                95.1772, 95.0925, 95.1990, 95.1682 }), 0.50);
        mThat.height(0);
    }
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        mThat.height(0);
    }

    @Test(expected = OutOfRangeException.class)
    public void testMarkerHeightWithHigherIndex() {
        PSquareMarkers mThat =
                PSquarePercentile.newMarkers(
                        Arrays.asList(new Double[] { 95.1772, 95.1567, 95.1937,
                                95.1959, 95.1442, 95.0610, 95.1591, 95.1195,
                                95.1772, 95.0925, 95.1990, 95.1682 }), 0.50);
        mThat.height(6);
    }
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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.rank.PSquarePercentile.PSquareMarkers

  • 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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