Example usage for org.apache.commons.math3.exception OutOfRangeException OutOfRangeException

List of usage examples for org.apache.commons.math3.exception OutOfRangeException OutOfRangeException

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Introduction

In this page you can find the example usage for org.apache.commons.math3.exception OutOfRangeException OutOfRangeException.

Prototype

public OutOfRangeException(Localizable specific, Number wrong, Number lo, Number hi)

Source Link

Document

Construct an exception from the mismatched dimensions with a specific context information.

Usage

From source file:eu.tsp.sal.SensorGeneticAlgorithm.java

/**
 * Create a new genetic algorithm.//from   w  ww.  j  a  v  a  2 s. c om
 * @param crossoverPolicy The {@link CrossoverPolicy}
 * @param crossoverRate The crossover rate as a percentage (0-1 inclusive)
 * @param mutationPolicy The {@link MutationPolicy}
 * @param mutationRate The mutation rate as a percentage (0-1 inclusive)
 * @param selectionPolicy The {@link SelectionPolicy}
 * @throws OutOfRangeException if the crossover or mutation rate is outside the [0, 1] range
 */
public SensorGeneticAlgorithm(final CrossoverPolicy crossoverPolicy, final double crossoverRate,
        final MutationPolicy mutationPolicy, final double mutationRate, final SelectionPolicy selectionPolicy)
        throws OutOfRangeException {

    if (crossoverRate < 0 || crossoverRate > 1) {
        throw new OutOfRangeException(LocalizedFormats.CROSSOVER_RATE, crossoverRate, 0, 1);
    }
    if (mutationRate < 0 || mutationRate > 1) {
        throw new OutOfRangeException(LocalizedFormats.MUTATION_RATE, mutationRate, 0, 1);
    }
    this.crossoverPolicy = crossoverPolicy;
    this.crossoverRate = crossoverRate;
    this.mutationPolicy = mutationPolicy;
    this.mutationRate = mutationRate;
    this.selectionPolicy = selectionPolicy;
}

From source file:cz.cuni.lf1.lge.ThunderSTORM.results.ModifiedLoess.java

/**
 * Construct a new {@link LoessInterpolator}
 * with given bandwidth, number of robustness iterations and accuracy.
 *
 * @param bandwidth  when computing the loess fit at
 * a particular point, this fraction of source points closest
 * to the current point is taken into account for computing
 * a least-squares regression.</br>
 * A sensible value is usually 0.25 to 0.5, the default value is
 * {@link #DEFAULT_BANDWIDTH}.//  ww  w  .  j ava  2  s  .  co  m
 * @param robustnessIters This many robustness iterations are done.</br>
 * A sensible value is usually 0 (just the initial fit without any
 * robustness iterations) to 4, the default value is
 * {@link #DEFAULT_ROBUSTNESS_ITERS}.
 * @param accuracy If the median residual at a certain robustness iteration
 * is less than this amount, no more iterations are done.
 * @throws OutOfRangeException if bandwidth does not lie in the interval [0,1].
 * @throws NotPositiveException if {@code robustnessIters} is negative.
 * @see #LoessInterpolator(double, int)
 * @since 2.1
 */
public ModifiedLoess(double bandwidth, int robustnessIters, double accuracy)
        throws OutOfRangeException, NotPositiveException {
    if (bandwidth < 0 || bandwidth > 1) {
        throw new OutOfRangeException(LocalizedFormats.BANDWIDTH, bandwidth, 0, 1);
    }
    this.bandwidth = bandwidth;
    if (robustnessIters < 0) {
        throw new NotPositiveException(LocalizedFormats.ROBUSTNESS_ITERATIONS, robustnessIters);
    }
    this.robustnessIters = robustnessIters;
    this.accuracy = accuracy;
}

From source file:experiment.SimpleRegression_bug.java

/**
 * Returns the half-width of a (100-100*alpha)% confidence interval for
 * the slope estimate.// ww w  .  j a va2  s.c  o m
 * <p>
 * The (100-100*alpha)% confidence interval is </p>
 * <p>
 * <code>(getSlope() - getSlopeConfidenceInterval(),
 * getSlope() + getSlopeConfidenceInterval())</code></p>
 * <p>
 * To request, for example, a 99% confidence interval, use
 * <code>alpha = .01</code></p>
 * <p>
 * <strong>Usage Note</strong>:<br>
 * The validity of this statistic depends on the assumption that the
 * observations included in the model are drawn from a
 * <a href="http://mathworld.wolfram.com/BivariateNormalDistribution.html">
 * Bivariate Normal Distribution</a>.</p>
 * <p>
 * <strong> Preconditions:</strong><ul>
 * <li>If there are fewer that <strong>three</strong> observations in the
 * model, or if there is no variation in x, this returns
 * <code>Double.NaN</code>.
 * </li>
 * <li><code>(0 < alpha < 1)</code>; otherwise an
 * <code>OutOfRangeException</code> is thrown.
 * </li></ul></p>
 *
 * @param alpha the desired significance level
 * @return half-width of 95% confidence interval for the slope estimate
 * @throws OutOfRangeException if the confidence interval can not be computed.
 */
public double getSlopeConfidenceInterval(final double alpha) throws OutOfRangeException {
    if (n < 3) {
        return Double.NaN;
    }
    if (alpha >= 1 || alpha <= 0) {
        throw new OutOfRangeException(LocalizedFormats.SIGNIFICANCE_LEVEL, alpha, 0, 1);
    }
    // No advertised NotStrictlyPositiveException here - will return NaN above
    TDistribution distribution = new TDistribution(n - 2);
    return getSlopeStdErr() * distribution.inverseCumulativeProbability(1d - alpha / 2d);
}

From source file:cz.cuni.mff.spl.evaluator.statistics.KolmogorovSmirnovTestFlag.java

/**
 * Performs a <a href="http://en.wikipedia.org/wiki/Kolmogorov-Smirnov_test"> Kolmogorov-Smirnov
 * test</a> evaluating the null hypothesis that {@code data} conforms to {@code distribution}.
 *
 * @param distribution reference distribution
 * @param data sample being being evaluated
 * @param alpha significance level of the test
 * @return true iff the null hypothesis that {@code data} is a sample from {@code distribution}
 *         can be rejected with confidence 1 - {@code alpha}
 * @throws InsufficientDataException if {@code data} does not have length at least 2
 * @throws NullArgumentException if {@code data} is null
 *//*from  w  ww .  ja  va2s.  c  o m*/
public boolean kolmogorovSmirnovTest(RealDistribution distribution, double[] data, double alpha) {
    if ((alpha <= 0) || (alpha > 0.5)) {
        throw new OutOfRangeException(LocalizedFormats.OUT_OF_BOUND_SIGNIFICANCE_LEVEL, alpha, 0, 0.5);
    }
    return kolmogorovSmirnovTest(distribution, data) < alpha;
}

From source file:embedded2.ESecure.TTest.java

/**
 * Check significance level.//from   w ww.  ja  v  a2s  .co m
 *
 * @param alpha significance level
 * @throws OutOfRangeException if the significance level is out of bounds.
 */
private static void checkSignificanceLevel(final double alpha) throws OutOfRangeException {

    if (alpha <= 0 || alpha > 0.5) {
        throw new OutOfRangeException(LocalizedFormats.SIGNIFICANCE_LEVEL, alpha, 0.0, 0.5);
    }

}

From source file:com.itemanalysis.psychometrics.optimization.BOBYQAOptimizer.java

/**
 * Performs validity checks./* w w  w . j  a  v a2s . c  o  m*/
 *
 * @param lowerBound Lower bounds (constraints) of the objective variables.
 * @param upperBound Upperer bounds (constraints) of the objective variables.
 */
private void setup(double[] lowerBound, double[] upperBound) {
    printMethod(); // XXX

    double[] init = getStartPoint();
    final int dimension = init.length;

    // Check problem dimension.
    if (dimension < MINIMUM_PROBLEM_DIMENSION) {
        throw new NumberIsTooSmallException(dimension, MINIMUM_PROBLEM_DIMENSION, true);
    }
    // Check number of interpolation points.
    final int[] nPointsInterval = { dimension + 2, (dimension + 2) * (dimension + 1) / 2 };
    if (numberOfInterpolationPoints < nPointsInterval[0] || numberOfInterpolationPoints > nPointsInterval[1]) {
        throw new OutOfRangeException(LocalizedFormats.NUMBER_OF_INTERPOLATION_POINTS,
                numberOfInterpolationPoints, nPointsInterval[0], nPointsInterval[1]);
    }

    // Initialize bound differences.
    boundDifference = new double[dimension];

    double requiredMinDiff = 2 * initialTrustRegionRadius;
    double minDiff = Double.POSITIVE_INFINITY;
    for (int i = 0; i < dimension; i++) {
        boundDifference[i] = upperBound[i] - lowerBound[i];
        minDiff = Math.min(minDiff, boundDifference[i]);
    }
    if (minDiff < requiredMinDiff) {
        initialTrustRegionRadius = minDiff / 3.0;
    }

    // Initialize the data structures used by the "bobyqa" method.
    bMatrix = new Array2DRowRealMatrix(dimension + numberOfInterpolationPoints, dimension);
    zMatrix = new Array2DRowRealMatrix(numberOfInterpolationPoints,
            numberOfInterpolationPoints - dimension - 1);
    interpolationPoints = new Array2DRowRealMatrix(numberOfInterpolationPoints, dimension);
    originShift = new ArrayRealVector(dimension);
    fAtInterpolationPoints = new ArrayRealVector(numberOfInterpolationPoints);
    trustRegionCenterOffset = new ArrayRealVector(dimension);
    gradientAtTrustRegionCenter = new ArrayRealVector(dimension);
    lowerDifference = new ArrayRealVector(dimension);
    upperDifference = new ArrayRealVector(dimension);
    modelSecondDerivativesParameters = new ArrayRealVector(numberOfInterpolationPoints);
    newPoint = new ArrayRealVector(dimension);
    alternativeNewPoint = new ArrayRealVector(dimension);
    trialStepPoint = new ArrayRealVector(dimension);
    lagrangeValuesAtNewPoint = new ArrayRealVector(dimension + numberOfInterpolationPoints);
    modelSecondDerivativesValues = new ArrayRealVector(dimension * (dimension + 1) / 2);
}

From source file:org.apache.kylin.cube.cuboid.algorithm.generic.lib.ElitisticListPopulation.java

/**
 * Sets the elitism rate, i.e. how many best chromosomes will be directly transferred to the next generation [in %].
 *
 * @param elitismRate how many best chromosomes will be directly transferred to the next generation [in %]
 * @throws OutOfRangeException if the elitism rate is outside the [0, 1] range
 *///from  w  ww .j  a v  a2  s .  c o m
public void setElitismRate(final double elitismRate) throws OutOfRangeException {
    if (elitismRate < 0 || elitismRate > 1) {
        throw new OutOfRangeException(LocalizedFormats.ELITISM_RATE, elitismRate, 0, 1);
    }
    this.elitismRate = elitismRate;
}

From source file:ro.hasna.ts.math.filter.ExponentialMovingAverageFilter.java

public ExponentialMovingAverageFilter(double smoothingFactor) {
    if (smoothingFactor <= 0 || smoothingFactor >= 1) {
        throw new OutOfRangeException(LocalizableMessages.OUT_OF_RANGE_BOTH_EXCLUSIVE, smoothingFactor, 0, 1);
    }//from w ww.  j  a va 2 s  . co m

    this.smoothingFactor = smoothingFactor;
}

From source file:ro.hasna.ts.math.ml.distance.DynamicTimeWarpingDistance.java

/**
 * Creates a new instance of this class with
 *
 * @param radiusPercentage Sakoe-Chiba Band width used to constraint the warping window
 * @param normalizer       the normalizer (it can be null if the values were normalized)
 * @throws OutOfRangeException if radiusPercentage is outside the interval [0, 1]
 *///from   w w w  . java  2  s. c om
public DynamicTimeWarpingDistance(double radiusPercentage, Normalizer normalizer) {
    if (radiusPercentage < 0 || radiusPercentage > 1) {
        throw new OutOfRangeException(LocalizableMessages.OUT_OF_RANGE_BOTH_INCLUSIVE, radiusPercentage, 0, 1);
    }

    this.radiusPercentage = radiusPercentage;
    this.normalizer = normalizer;
}

From source file:ro.hasna.ts.math.ml.distance.LongestCommonSubsequenceDistance.java

/**
 * Creates a new instance of this class with
 *
 * @param epsilon          the maximum absolute difference between two values that are considered equal
 * @param radiusPercentage Sakoe-Chiba Band width used to constraint the searching window
 * @throws OutOfRangeException if radiusPercentage is outside the interval [0, 1]
 *///from  w w  w .j a  v a 2s. c om
public LongestCommonSubsequenceDistance(double epsilon, double radiusPercentage) {
    this.epsilon = epsilon;
    if (radiusPercentage < 0 || radiusPercentage > 1) {
        throw new OutOfRangeException(LocalizableMessages.OUT_OF_RANGE_BOTH_INCLUSIVE, radiusPercentage, 0, 1);
    }

    this.radiusPercentage = radiusPercentage;
}