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The Apache Commons RNG Sampling module provides samplers
for various distributions.
/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.commons.rng.sampling.distribution;
import org.apache.commons.rng.UniformRandomProvider;
/**
* Implementation of the Zipf distribution.
*
* Sampling uses {@link UniformRandomProvider#nextDouble()}.
*
* @since 1.0
*/
public class RejectionInversionZipfSampler
extends SamplerBase
implements SharedStateDiscreteSampler {
/** The implementation of the sample method. */
private final SharedStateDiscreteSampler delegate;
/**
* Implements the rejection-inversion method for the Zipf distribution.
*/
private static class RejectionInversionZipfSamplerImpl implements SharedStateDiscreteSampler {
/** Threshold below which Taylor series will be used. */
private static final double TAYLOR_THRESHOLD = 1e-8;
/** 1/2. */
private static final double F_1_2 = 0.5;
/** 1/3. */
private static final double F_1_3 = 1d / 3;
/** 1/4. */
private static final double F_1_4 = 0.25;
/** Number of elements. */
private final int numberOfElements;
/** Exponent parameter of the distribution. */
private final double exponent;
/** {@code hIntegral(1.5) - 1}. */
private final double hIntegralX1;
/** {@code hIntegral(numberOfElements + 0.5)}. */
private final double hIntegralNumberOfElements;
/** {@code hIntegralX1 - hIntegralNumberOfElements}. */
private final double r;
/** {@code 2 - hIntegralInverse(hIntegral(2.5) - h(2)}. */
private final double s;
/** Underlying source of randomness. */
private final UniformRandomProvider rng;
/**
* @param rng Generator of uniformly distributed random numbers.
* @param numberOfElements Number of elements (must be > 0).
* @param exponent Exponent (must be > 0).
*/
RejectionInversionZipfSamplerImpl(UniformRandomProvider rng,
int numberOfElements,
double exponent) {
this.rng = rng;
this.numberOfElements = numberOfElements;
this.exponent = exponent;
this.hIntegralX1 = hIntegral(1.5) - 1;
this.hIntegralNumberOfElements = hIntegral(numberOfElements + F_1_2);
this.r = hIntegralX1 - hIntegralNumberOfElements;
this.s = 2 - hIntegralInverse(hIntegral(2.5) - h(2));
}
/**
* @param rng Generator of uniformly distributed random numbers.
* @param source Source to copy.
*/
private RejectionInversionZipfSamplerImpl(UniformRandomProvider rng,
RejectionInversionZipfSamplerImpl source) {
this.rng = rng;
this.numberOfElements = source.numberOfElements;
this.exponent = source.exponent;
this.hIntegralX1 = source.hIntegralX1;
this.hIntegralNumberOfElements = source.hIntegralNumberOfElements;
this.r = source.r;
this.s = source.s;
}
@Override
public int sample() {
// The paper describes an algorithm for exponents larger than 1
// (Algorithm ZRI).
// The original method uses
// H(x) = (v + x)^(1 - q) / (1 - q)
// as the integral of the hat function.
// This function is undefined for q = 1, which is the reason for
// the limitation of the exponent.
// If instead the integral function
// H(x) = ((v + x)^(1 - q) - 1) / (1 - q)
// is used,
// for which a meaningful limit exists for q = 1, the method works
// for all positive exponents.
// The following implementation uses v = 0 and generates integral
// number in the range [1, numberOfElements].
// This is different to the original method where v is defined to
// be positive and numbers are taken from [0, i_max].
// This explains why the implementation looks slightly different.
while (true) {
final double u = hIntegralNumberOfElements + rng.nextDouble() * r;
// u is uniformly distributed in (hIntegralX1, hIntegralNumberOfElements]
final double x = hIntegralInverse(u);
int k = (int) (x + F_1_2);
// Limit k to the range [1, numberOfElements] if it would be outside
// due to numerical inaccuracies.
if (k < 1) {
k = 1;
} else if (k > numberOfElements) {
k = numberOfElements;
}
// Here, the distribution of k is given by:
//
// P(k = 1) = C * (hIntegral(1.5) - hIntegralX1) = C
// P(k = m) = C * (hIntegral(m + 1/2) - hIntegral(m - 1/2)) for m >= 2
//
// where C = 1 / (hIntegralNumberOfElements - hIntegralX1)
if (k - x <= s || u >= hIntegral(k + F_1_2) - h(k)) {
// Case k = 1:
//
// The right inequality is always true, because replacing k by 1 gives
// u >= hIntegral(1.5) - h(1) = hIntegralX1 and u is taken from
// (hIntegralX1, hIntegralNumberOfElements].
//
// Therefore, the acceptance rate for k = 1 is P(accepted | k = 1) = 1
// and the probability that 1 is returned as random value is
// P(k = 1 and accepted) = P(accepted | k = 1) * P(k = 1) = C = C / 1^exponent
//
// Case k >= 2:
//
// The left inequality (k - x <= s) is just a short cut
// to avoid the more expensive evaluation of the right inequality
// (u >= hIntegral(k + 0.5) - h(k)) in many cases.
//
// If the left inequality is true, the right inequality is also true:
// Theorem 2 in the paper is valid for all positive exponents, because
// the requirements h'(x) = -exponent/x^(exponent + 1) < 0 and
// (-1/hInverse'(x))'' = (1+1/exponent) * x^(1/exponent-1) >= 0
// are both fulfilled.
// Therefore, f(x) = x - hIntegralInverse(hIntegral(x + 0.5) - h(x))
// is a non-decreasing function. If k - x <= s holds,
// k - x <= s + f(k) - f(2) is obviously also true which is equivalent to
// -x <= -hIntegralInverse(hIntegral(k + 0.5) - h(k)),
// -hIntegralInverse(u) <= -hIntegralInverse(hIntegral(k + 0.5) - h(k)),
// and finally u >= hIntegral(k + 0.5) - h(k).
//
// Hence, the right inequality determines the acceptance rate:
// P(accepted | k = m) = h(m) / (hIntegrated(m+1/2) - hIntegrated(m-1/2))
// The probability that m is returned is given by
// P(k = m and accepted) = P(accepted | k = m) * P(k = m) = C * h(m) = C / m^exponent.
//
// In both cases the probabilities are proportional to the probability mass function
// of the Zipf distribution.
return k;
}
}
}
/** {@inheritDoc} */
@Override
public String toString() {
return "Rejection inversion Zipf deviate [" + rng.toString() + "]";
}
@Override
public SharedStateDiscreteSampler withUniformRandomProvider(UniformRandomProvider rng) {
return new RejectionInversionZipfSamplerImpl(rng, this);
}
/**
* {@code H(x)} is defined as
*
* - {@code (x^(1 - exponent) - 1) / (1 - exponent)}, if {@code exponent != 1}
* - {@code log(x)}, if {@code exponent == 1}
*
* H(x) is an integral function of h(x), the derivative of H(x) is h(x).
*
* @param x Free parameter.
* @return {@code H(x)}.
*/
private double hIntegral(final double x) {
final double logX = Math.log(x);
return helper2((1 - exponent) * logX) * logX;
}
/**
* {@code h(x) = 1 / x^exponent}.
*
* @param x Free parameter.
* @return {@code h(x)}.
*/
private double h(final double x) {
return Math.exp(-exponent * Math.log(x));
}
/**
* The inverse function of {@code H(x)}.
*
* @param x Free parameter
* @return y for which {@code H(y) = x}.
*/
private double hIntegralInverse(final double x) {
double t = x * (1 - exponent);
if (t < -1) {
// Limit value to the range [-1, +inf).
// t could be smaller than -1 in some rare cases due to numerical errors.
t = -1;
}
return Math.exp(helper1(t) * x);
}
/**
* Helper function that calculates {@code log(1 + x) / x}.
*
* A Taylor series expansion is used, if x is close to 0.
*
*
* @param x A value larger than or equal to -1.
* @return {@code log(1 + x) / x}.
*/
private static double helper1(final double x) {
if (Math.abs(x) > TAYLOR_THRESHOLD) {
return Math.log1p(x) / x;
}
return 1 - x * (F_1_2 - x * (F_1_3 - F_1_4 * x));
}
/**
* Helper function to calculate {@code (exp(x) - 1) / x}.
*
* A Taylor series expansion is used, if x is close to 0.
*
*
* @param x Free parameter.
* @return {@code (exp(x) - 1) / x} if x is non-zero, or 1 if x = 0.
*/
private static double helper2(final double x) {
if (Math.abs(x) > TAYLOR_THRESHOLD) {
return Math.expm1(x) / x;
}
return 1 + x * F_1_2 * (1 + x * F_1_3 * (1 + F_1_4 * x));
}
}
/**
* This instance delegates sampling. Use the factory method
* {@link #of(UniformRandomProvider, int, double)} to create an optimal sampler.
*
* @param rng Generator of uniformly distributed random numbers.
* @param numberOfElements Number of elements.
* @param exponent Exponent.
* @throws IllegalArgumentException if {@code numberOfElements <= 0}
* or {@code exponent < 0}.
*/
public RejectionInversionZipfSampler(UniformRandomProvider rng,
int numberOfElements,
double exponent) {
super(null);
// Delegate all work to specialised samplers.
this.delegate = of(rng, numberOfElements, exponent);
}
/**
* Rejection inversion sampling method for a discrete, bounded Zipf
* distribution that is based on the method described in
*
* Wolfgang Hörmann and Gerhard Derflinger.
* "Rejection-inversion to generate variates from monotone discrete
* distributions",
* ACM Transactions on Modeling and Computer Simulation (TOMACS) 6.3 (1996): 169-184.
*
*/
@Override
public int sample() {
return delegate.sample();
}
/** {@inheritDoc} */
@Override
public String toString() {
return delegate.toString();
}
/**
* {@inheritDoc}
*
* @since 1.3
*/
@Override
public SharedStateDiscreteSampler withUniformRandomProvider(UniformRandomProvider rng) {
return delegate.withUniformRandomProvider(rng);
}
/**
* Creates a new Zipf distribution sampler.
*
* Note when {@code exponent = 0} the Zipf distribution reduces to a
* discrete uniform distribution over the interval {@code [1, n]} with {@code n}
* the number of elements.
*
* @param rng Generator of uniformly distributed random numbers.
* @param numberOfElements Number of elements.
* @param exponent Exponent.
* @return the sampler
* @throws IllegalArgumentException if {@code numberOfElements <= 0} or
* {@code exponent < 0}.
* @since 1.3
*/
public static SharedStateDiscreteSampler of(UniformRandomProvider rng,
int numberOfElements,
double exponent) {
if (numberOfElements <= 0) {
throw new IllegalArgumentException("number of elements is not strictly positive: " + numberOfElements);
}
if (exponent < 0) {
throw new IllegalArgumentException("exponent is not positive: " + exponent);
}
// When the exponent is at the limit of 0 the distribution PMF reduces to 1 / n
// and sampling can use a discrete uniform sampler.
return exponent > 0 ?
new RejectionInversionZipfSamplerImpl(rng, numberOfElements, exponent) :
DiscreteUniformSampler.of(rng, 1, numberOfElements);
}
}