mirror of https://github.com/jlizier/jidt
226 lines
7.4 KiB
Java
226 lines
7.4 KiB
Java
/*
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* Java Information Dynamics Toolkit (JIDT)
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* Copyright (C) 2017, Joseph T. Lizier
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*
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* This program is free software: you can redistribute it and/or modify
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* it under the terms of the GNU General Public License as published by
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* the Free Software Foundation, either version 3 of the License, or
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* (at your option) any later version.
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*
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* This program is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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* GNU General Public License for more details.
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*
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* You should have received a copy of the GNU General Public License
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* along with this program. If not, see <http://www.gnu.org/licenses/>.
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*/
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/*
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* This class was originally distributed as part of the Apache Commons
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* Math3 library (3.6.1), under the Apache License Version 2.0, which is
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* copied below. This Apache 2 software is now included as a derivative
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* work in the GPLv3 licensed JIDT project, as per:
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* http://www.apache.org/licenses/GPL-compatibility.html
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*
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* The original Apache source code has been modified as follows:
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* -- We have modified package names to sit inside the JIDT structure.
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*/
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/*
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* Licensed to the Apache Software Foundation (ASF) under one or more
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* contributor license agreements. See the NOTICE file distributed with
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* this work for additional information regarding copyright ownership.
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* The ASF licenses this file to You under the Apache License, Version 2.0
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* (the "License"); you may not use this file except in compliance with
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* the License. You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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package infodynamics.utils.commonsmath3.distribution;
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import infodynamics.utils.commonsmath3.random.RandomGenerator;
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import infodynamics.utils.commonsmath3.random.Well19937c;
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/**
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* Implementation of the chi-squared distribution.
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*
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* @see <a href="http://en.wikipedia.org/wiki/Chi-squared_distribution">Chi-squared distribution (Wikipedia)</a>
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* @see <a href="http://mathworld.wolfram.com/Chi-SquaredDistribution.html">Chi-squared Distribution (MathWorld)</a>
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*/
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public class ChiSquaredDistribution extends AbstractRealDistribution {
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/**
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* Default inverse cumulative probability accuracy
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* @since 2.1
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*/
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public static final double DEFAULT_INVERSE_ABSOLUTE_ACCURACY = 1e-9;
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/** Serializable version identifier */
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private static final long serialVersionUID = -8352658048349159782L;
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/** Internal Gamma distribution. */
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private final GammaDistribution gamma;
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/** Inverse cumulative probability accuracy */
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private final double solverAbsoluteAccuracy;
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/**
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* Create a Chi-Squared distribution with the given degrees of freedom.
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*
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* @param degreesOfFreedom Degrees of freedom.
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*/
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public ChiSquaredDistribution(double degreesOfFreedom) {
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this(degreesOfFreedom, DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
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}
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/**
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* Create a Chi-Squared distribution with the given degrees of freedom and
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* inverse cumulative probability accuracy.
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* <p>
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* <b>Note:</b> this constructor will implicitly create an instance of
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* {@link Well19937c} as random generator to be used for sampling only (see
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* {@link #sample()} and {@link #sample(int)}). In case no sampling is
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* needed for the created distribution, it is advised to pass {@code null}
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* as random generator via the appropriate constructors to avoid the
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* additional initialisation overhead.
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*
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* @param degreesOfFreedom Degrees of freedom.
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* @param inverseCumAccuracy the maximum absolute error in inverse
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* cumulative probability estimates (defaults to
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* {@link #DEFAULT_INVERSE_ABSOLUTE_ACCURACY}).
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* @since 2.1
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*/
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public ChiSquaredDistribution(double degreesOfFreedom,
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double inverseCumAccuracy) {
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this(new Well19937c(), degreesOfFreedom, inverseCumAccuracy);
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}
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/**
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* Create a Chi-Squared distribution with the given degrees of freedom.
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*
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* @param rng Random number generator.
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* @param degreesOfFreedom Degrees of freedom.
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* @since 3.3
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*/
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public ChiSquaredDistribution(RandomGenerator rng, double degreesOfFreedom) {
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this(rng, degreesOfFreedom, DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
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}
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/**
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* Create a Chi-Squared distribution with the given degrees of freedom and
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* inverse cumulative probability accuracy.
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*
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* @param rng Random number generator.
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* @param degreesOfFreedom Degrees of freedom.
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* @param inverseCumAccuracy the maximum absolute error in inverse
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* cumulative probability estimates (defaults to
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* {@link #DEFAULT_INVERSE_ABSOLUTE_ACCURACY}).
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* @since 3.1
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*/
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public ChiSquaredDistribution(RandomGenerator rng,
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double degreesOfFreedom,
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double inverseCumAccuracy) {
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super(rng);
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gamma = new GammaDistribution(degreesOfFreedom / 2, 2);
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solverAbsoluteAccuracy = inverseCumAccuracy;
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}
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/**
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* Access the number of degrees of freedom.
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*
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* @return the degrees of freedom.
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*/
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public double getDegreesOfFreedom() {
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return gamma.getShape() * 2.0;
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}
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/** {@inheritDoc} */
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public double density(double x) {
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return gamma.density(x);
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}
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/** {@inheritDoc} **/
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@Override
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public double logDensity(double x) {
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return gamma.logDensity(x);
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}
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/** {@inheritDoc} */
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public double cumulativeProbability(double x) {
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return gamma.cumulativeProbability(x);
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}
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/** {@inheritDoc} */
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@Override
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protected double getSolverAbsoluteAccuracy() {
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return solverAbsoluteAccuracy;
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}
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/**
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* {@inheritDoc}
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*
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* For {@code k} degrees of freedom, the mean is {@code k}.
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*/
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public double getNumericalMean() {
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return getDegreesOfFreedom();
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}
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/**
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* {@inheritDoc}
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*
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* @return {@code 2 * k}, where {@code k} is the number of degrees of freedom.
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*/
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public double getNumericalVariance() {
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return 2 * getDegreesOfFreedom();
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}
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/**
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* {@inheritDoc}
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*
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* The lower bound of the support is always 0 no matter the
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* degrees of freedom.
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*
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* @return zero.
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*/
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public double getSupportLowerBound() {
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return 0;
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}
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/**
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* {@inheritDoc}
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*
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* The upper bound of the support is always positive infinity no matter the
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* degrees of freedom.
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*
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* @return {@code Double.POSITIVE_INFINITY}.
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*/
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public double getSupportUpperBound() {
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return Double.POSITIVE_INFINITY;
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}
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/** {@inheritDoc} */
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public boolean isSupportLowerBoundInclusive() {
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return true;
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}
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/** {@inheritDoc} */
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public boolean isSupportUpperBoundInclusive() {
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return false;
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}
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/**
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* {@inheritDoc}
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*
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* The support of this distribution is connected.
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*
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* @return {@code true}
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*/
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public boolean isSupportConnected() {
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return true;
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}
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}
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