Example usage for org.apache.commons.lang ArrayUtils toObject

Introduction

In this page you can find the example usage for org.apache.commons.lang ArrayUtils toObject.

Prototype

public static Boolean[] toObject(boolean[] array)

Source Link

Document

Converts an array of primitive booleans to objects.

Usage

From source file:org.uncertml.distribution.multivariate.DirichletDistribution.java

/**
 * Constructor that takes an array of doubles for the concentration parameter.
 * /*from w  ww.j a  v  a2s  .co m*/
 * @param concentration an array of doubles representing the concentration
 * parameter.
 */
public DirichletDistribution(double[] concentration) {
    this(Arrays.asList(ArrayUtils.toObject(concentration)));
}

From source file:org.uncertml.distribution.multivariate.MultinomialDistribution.java

/**
 * Constructor taking a single number of trials and an array of doubles for
 * the probabilities./*from  w w  w.  j av  a2 s.  co  m*/
 * 
 * @param numberOfTrials the number of trials parameter.
 * @param probabilities an array of doubles representing the probabilities parameter.
 */
public MultinomialDistribution(int numberOfTrials, double[] probabilities) {
    this(numberOfTrials, Arrays.asList(ArrayUtils.toObject(probabilities)));
}

From source file:org.uncertml.distribution.multivariate.MultivariateNormalDistribution.java

/**
 * Constructor that takes an array of doubles representing the mean parameters
 * and a covariance matrix. Each mean parameter represents a marginal normal
 * distribution. The covariance matrix should contain n^2 elements where n is
 * the size of the array./*ww w  .  j  av  a2  s  . co m*/
 * 
 * @param mean an array of doubles representing the mean parameter of n marginal
 * distributions.
 * @param covarianceMatrix a covariance matrix.
 */
public MultivariateNormalDistribution(double[] mean, CovarianceMatrix covarianceMatrix) {
    this(Arrays.asList(ArrayUtils.toObject(mean)), covarianceMatrix);
}

From source file:org.uncertml.distribution.multivariate.MultivariateStudentTDistribution.java

/**
 * Constructor that takes an array of doubles for the mean parameter, a covariance
 * matrix and an array of integers for the degrees of freedom parameter. Each
 * mean and degrees of freedom pair represents a marginal distribution. The 
 * arrays must be of equal length and the covariance matrix must contain n^2
 * values where n is the length of the arrays.
 * //ww w. j  a  v a  2s  . c om
 * @param mean an array of doubles representing the mean parameter of n marginal
 * Student T distributions.
 * @param covarianceMatrix a covariance matrix.
 * @param degreesOfFreedom an array of integers representing the degrees of
 * freedom of n marginal Student T distributions.
 */
public MultivariateStudentTDistribution(double[] mean, CovarianceMatrix covarianceMatrix,
        int[] degreesOfFreedom) {
    this(Arrays.asList(ArrayUtils.toObject(mean)), covarianceMatrix,
            Arrays.asList(ArrayUtils.toObject(degreesOfFreedom)));
}

From source file:org.uncertml.sample.ContinuousRealisation.java

/**
 * Constructor that takes an array of doubles as the values of
 * the realisation.//  www.jav a 2 s  .c  o  m
 * 
 * @param values the numeric values of a single realisation.
 */
public ContinuousRealisation(double[] values) {
    this(Arrays.asList(ArrayUtils.toObject(values)));
}

From source file:org.uncertml.sample.ContinuousRealisation.java

/**
 * Constructor that takes an array of doubles as the values of
 * the realisation and a weight, used in weighted samples.
 * //from  ww w .ja v  a 2s  . c  o  m
 * @param values the numeric values of a single realisation.
 * @param weight the weight of this realisation, between 0 - 1.
 */
public ContinuousRealisation(double[] values, double weight) {
    this(Arrays.asList(ArrayUtils.toObject(values)), weight);
}

From source file:org.uncertml.sample.ContinuousRealisation.java

/**
 * Constructor that takes an array of doubles as the values of
 * the realisation,a weight used in weighted samples and an ID.
 * /*  w  ww . j  ava 2  s .  co m*/
 * @param values the numeric values of a single realisation.
 * @param weight the weight of this realisation, between 0 - 1.
 * @param id a unique identifier for this realisation. Used to track realisations
 * through processing chains.
 */
public ContinuousRealisation(double[] values, double weight, String id) {
    this(Arrays.asList(ArrayUtils.toObject(values)), weight, id);
}

From source file:org.uncertml.statistic.CentredMoment.java

/**
 * Constructor that takes a single integer order and an array of
 * doubles for the value of a centred moment statistic. Each value represents a single
 * centred moment statistic. This is in line with the UncertML syntax whereby a collection
 * of types can be represented by a single entity.
 * //  w  w  w .  j  ava 2s  . c om
 * @param order the order of the centred moment, e.g. 1st order.
 * @param values an array of doubles representing the value of n
 * centred moment statistics.
 */
public CentredMoment(int order, double[] values) {
    this(order, Arrays.asList(ArrayUtils.toObject(values)));
}

From source file:org.uncertml.statistic.ConfusionMatrix.java

/**
 * Constructor that takes an array of categories and counts. The source and
 * target categories are assumed to the same. There should be categories^2 count values.
 * /*from w  w  w.j  a va  2s .c om*/
 * @param categories the categories (both source and target) of the confusion matrix.
 * @param counts the counts for each transition from source to target categories.
 */
public ConfusionMatrix(String[] categories, int[] counts) {
    this(Arrays.asList(categories), Arrays.asList(ArrayUtils.toObject(counts)));
}

From source file:org.uncertml.statistic.ConfusionMatrix.java

/**
 * Constructor that takes an array of source and target categories and counts. 
 * There should be source categories * target categories count values.
 * /*from  w ww . ja v a 2  s .  c o m*/
 * @param categories the categories (both source and target) of the confusion matrix.
 * @param counts the counts for each transition from source to target categories.
 */
public ConfusionMatrix(String[] sourceCategories, String[] targetCategories, int[] counts) {
    this(Arrays.asList(sourceCategories), Arrays.asList(targetCategories),
            Arrays.asList(ArrayUtils.toObject(counts)));
}