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* 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,
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* 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;
import java.util.Arrays;
/**
* Distribution sampler that uses the Alias method. It can be used to
* sample from {@code n} values each with an associated probability. If all unique items
* are assigned the same probability it is more efficient to use the {@link DiscreteUniformSampler}.
*
* This implementation is based on the detailed explanation of the alias method by
* Keith Schartz and implements Vose's algorithm.
*
*
* -
*
* Vose, M.D.,
* A linear algorithm for generating random numbers with a given distribution,
* IEEE Transactions on Software Engineering, 17, 972-975, 1991.
*
*
*
*
* The algorithm will sample values in {@code O(1)} time after a pre-processing step of
* {@code O(n)} time.
*
* The alias tables are constructed using fraction probabilities with an assumed denominator
* of 253. In the generic case sampling uses {@link UniformRandomProvider#nextInt(int)}
* and the upper 53-bits from {@link UniformRandomProvider#nextLong()}.
*
* Zero padding the input probabilities can be used to make more sampling more efficient.
* Any zero entry will always be aliased removing the requirement to compute a {@code long}.
* Increased sampling speed comes at the cost of increased storage space. The algorithm requires
* approximately 12 bytes of storage per input probability, that is {@code n * 12} for size
* {@code n}. Zero-padding only requires 4 bytes of storage per padded value as the probability is
* known to be zero. A table can be padded to a power of 2 using the utility function
* {@link #of(UniformRandomProvider, double[], int)} to construct the sampler.
*
* An optimisation is performed for small table sizes that are a power of 2. In this case the
* sampling uses 1 or 2 calls from {@link UniformRandomProvider#nextInt()} to generate up to
* 64-bits for creation of an 11-bit index and 53-bits for the {@code long}. This optimisation
* requires a generator with a high cycle length for the lower order bits.
*
* Larger table sizes that are a power of 2 will benefit from fast algorithms for
* {@link UniformRandomProvider#nextInt(int)} that exploit the power of 2.
*
* @see Alias Method
* @see Darts, Dice, and Coins:
* Sampling from a Discrete Distribution by Keith Schwartz
* @see Vose (1991) IEEE Transactions
* on Software Engineering 17, 972-975.
* @since 1.3
*/
public class AliasMethodDiscreteSampler
implements SharedStateDiscreteSampler {
/**
* The default alpha factor for zero-padding an input probability table. The default
* value will pad the probabilities by to the next power-of-2.
*/
private static final int DEFAULT_ALPHA = 0;
/** The value zero for a {@code double}. */
private static final double ZERO = 0.0;
/** The value 1.0 represented as the numerator of a fraction with denominator 253. */
private static final long ONE_AS_NUMERATOR = 1L << 53;
/**
* The multiplier to convert a {@code double} probability in the range {@code [0, 1]}
* to the numerator of a fraction with denominator 253.
*/
private static final double CONVERT_TO_NUMERATOR = ONE_AS_NUMERATOR;
/**
* The maximum size of the small alias table. This is 211.
*/
private static final int MAX_SMALL_POWER_2_SIZE = 1 << 11;
/** Underlying source of randomness. */
protected final UniformRandomProvider rng;
/**
* The probability table. During sampling a random index into this table is selected.
* A random probability is compared to the value at this index: if lower then the sample is the
* index; if higher then the sample uses the corresponding entry in the alias table.
*
* This has entries up to the last non-zero element since there is no need to store
* probabilities of zero. This is an optimisation for zero-padded input. Any zero value will
* always be aliased so any look-up index outside this table always uses the alias.
*
* Note that a uniform double in the range [0,1) can be generated using 53-bits from a long
* to sample all the dyadic rationals with a denominator of 253
* (e.g. see org.apache.commons.rng.core.utils.NumberFactory.makeDouble(long)). To avoid
* computation of a double and comparison to the probability as a double the probabilities are
* stored as 53-bit longs to use integer arithmetic. This is the equivalent of storing the
* numerator of a fraction with the denominator of 253.
*
* During conversion of the probability to a double it is rounded up to the next integer
* value. This ensures the functionality of comparing a uniform deviate distributed evenly on
* the interval 1/2^53 to the unevenly distributed probability is equivalent, i.e. a uniform
* deviate is either below the probability or above it:
*
*
* Uniform deviate
* 1/2^53 2/2^53 3/2^53 4/2^53
* --|---------|---------|---------|---
* ^
* |
* probability
* ^
* |
* rounded up
*
*
* Round-up ensures a non-zero probability is always non-zero and zero probability remains
* zero. Thus any item with a non-zero input probability can always be sampled, and a zero
* input probability cannot be sampled.
*
* @see Dyadic rational
*/
protected final long[] probability;
/**
* The alias table. During sampling if the random probability is not below the entry in the
* probability table then the sample is the alias.
*/
protected final int[] alias;
/**
* Sample from the computed tables exploiting the small power-of-two table size.
* This implements a variant of the optimised algorithm as per Vose (1991):
*
*
* bits = obtained required number of random bits
* v = (some of the bits) * constant1
* j = (rest of the bits) * constant2
* if v < prob[j] then
* return j
* else
* return alias[j]
*
*
* This is a variant because the bits are not multiplied by constants. In the case of
* {@code v} the constant is a scale that is pre-applied to the probability table. In the
* case of {@code j} the constant is not used to scale a deviate to an index; the index is
* from a power-of-2 range and so the bits are used directly.
*
* This is implemented using up to 64 bits from the random generator.
* The index for the table is computed using a mask to extract up to 11 of the lower bits
* from an integer. The probability is computed using a second integer combined with the
* remaining bits to create 53-bits for the numerator of a fraction with denominator
* 253. This is only computed on demand.
*
* Note: This supports a table size of up to 2^11, or 2048, exclusive. Any larger requires
* consuming more than 64-bits and the algorithm is not more efficient than the
* {@link AliasMethodDiscreteSampler}.
*
* Sampling uses 1 or 2 calls to {@link UniformRandomProvider#nextInt()}.
*/
private static final class SmallTableAliasMethodDiscreteSampler extends AliasMethodDiscreteSampler {
/** The mask to isolate the lower bits. */
private final int mask;
/**
* Create a new instance.
*
* @param rng Generator of uniformly distributed random numbers.
* @param probability Probability table.
* @param alias Alias table.
*/
SmallTableAliasMethodDiscreteSampler(final UniformRandomProvider rng,
final long[] probability,
final int[] alias) {
super(rng, probability, alias);
// Assume the table size is a power of 2 and create the mask
mask = alias.length - 1;
}
@Override
public int sample() {
final int bits = rng.nextInt();
// Isolate lower bits
final int j = bits & mask;
// Optimisation for zero-padded input tables
if (j >= probability.length) {
// No probability must use the alias
return alias[j];
}
// Create a uniform random deviate as a long.
// This replicates functionality from the o.a.c.rng.core.utils.NumberFactory.makeLong
final long longBits = (((long) rng.nextInt()) << 32) | (bits & 0xffffffffL);
// Choose between the two. Use a 53-bit long for the probability.
return (longBits >>> 11) < probability[j] ? j : alias[j];
}
/** {@inheritDoc} */
@Override
public SharedStateDiscreteSampler withUniformRandomProvider(UniformRandomProvider rng) {
return new SmallTableAliasMethodDiscreteSampler(rng, probability, alias);
}
}
/**
* Creates a sampler.
*
* The input parameters are not validated and must be correctly computed alias tables.
*
* @param rng Generator of uniformly distributed random numbers.
* @param probability Probability table.
* @param alias Alias table.
*/
AliasMethodDiscreteSampler(final UniformRandomProvider rng,
final long[] probability,
final int[] alias) {
this.rng = rng;
// Deliberate direct storage of input arrays
this.probability = probability;
this.alias = alias;
}
/** {@inheritDoc} */
@Override
public int sample() {
// This implements the algorithm as per Vose (1991):
// v = uniform() in [0, 1)
// j = uniform(n) in [0, n)
// if v < prob[j] then
// return j
// else
// return alias[j]
final int j = rng.nextInt(alias.length);
// Optimisation for zero-padded input tables
if (j >= probability.length) {
// No probability must use the alias
return alias[j];
}
// Note: We could check the probability before computing a deviate.
// p(j) == 0 => alias[j]
// p(j) == 1 => j
// However it is assumed these edge cases are rare:
//
// The probability table will be 1 for approximately 1/n samples, i.e. only the
// last unpaired probability. This is only worth checking for when the table size (n)
// is small. But in that case the user should zero-pad the table for performance.
//
// The probability table will be 0 when an input probability was zero. We
// will assume this is also rare if modelling a discrete distribution where
// all samples are possible. The edge case for zero-padded tables is handled above.
// Choose between the two. Use a 53-bit long for the probability.
return (rng.nextLong() >>> 11) < probability[j] ? j : alias[j];
}
/** {@inheritDoc} */
@Override
public String toString() {
return "Alias method [" + rng.toString() + "]";
}
/** {@inheritDoc} */
@Override
public SharedStateDiscreteSampler withUniformRandomProvider(UniformRandomProvider rng) {
return new AliasMethodDiscreteSampler(rng, probability, alias);
}
/**
* Creates a sampler.
*
* The probabilities will be normalised using their sum. The only requirement
* is the sum is strictly positive.
*
* Where possible this method zero-pads the probabilities so the length is the next
* power-of-two. Padding is bounded by the upper limit on the size of an array.
*
* To avoid zero-padding use the
* {@link #of(UniformRandomProvider, double[], int)} method with a negative
* {@code alpha} factor.
*
* @param rng Generator of uniformly distributed random numbers.
* @param probabilities The list of probabilities.
* @return the sampler
* @throws IllegalArgumentException if {@code probabilities} is null or empty, a
* probability is negative, infinite or {@code NaN}, or the sum of all
* probabilities is not strictly positive.
* @see #of(UniformRandomProvider, double[], int)
*/
public static SharedStateDiscreteSampler of(final UniformRandomProvider rng,
final double[] probabilities) {
return of(rng, probabilities, DEFAULT_ALPHA);
}
/**
* Creates a sampler.
*
* The probabilities will be normalised using their sum. The only requirement
* is the sum is strictly positive.
*
* Where possible this method zero-pads the probabilities to improve sampling
* efficiency. Padding is bounded by the upper limit on the size of an array and
* controlled by the {@code alpha} argument. Set to negative to disable
* padding.
*
* For each zero padded value an entry is added to the tables which is always
* aliased. This can be sampled with fewer bits required from the
* {@link UniformRandomProvider}. Increasing the padding of zeros increases the
* chance of using this fast path to selecting a sample. The penalty is
* two-fold: initialisation is bounded by {@code O(n)} time with {@code n} the
* size after padding; an additional memory cost of 4 bytes per
* padded value.
*
* Zero padding to any length improves performance; using a power of 2 allows
* the index into the tables to be more efficiently generated. The argument
* {@code alpha} controls the level of padding. Positive values of {@code alpha}
* represent a scale factor in powers of 2. The size of the input array will be
* increased by a factor of 2alpha and then rounded-up to the next
* power of 2. Padding is bounded by the upper limit on the size of an
* array.
*
* The chance of executing the slow path is upper bounded at
* 2-alpha when padding is enabled. Each successive doubling of
* padding will have diminishing performance gains.
*
* @param rng Generator of uniformly distributed random numbers.
* @param probabilities The list of probabilities.
* @param alpha The alpha factor controlling the zero padding.
* @return the sampler
* @throws IllegalArgumentException if {@code probabilities} is null or empty, a
* probability is negative, infinite or {@code NaN}, or the sum of all
* probabilities is not strictly positive.
*/
public static SharedStateDiscreteSampler of(final UniformRandomProvider rng,
final double[] probabilities,
int alpha) {
// The Alias method balances N categories with counts around the mean into N sections,
// each allocated 'mean' observations.
//
// Consider 4 categories with counts 6,3,2,1. The histogram can be balanced into a
// 2D array as 4 sections with a height of the mean:
//
// 6
// 6
// 6
// 63 => 6366 --
// 632 6326 |-- mean
// 6321 6321 --
//
// section abcd
//
// Each section is divided as:
// a: 6=1/1
// b: 3=1/1
// c: 2=2/3; 6=1/3 (6 is the alias)
// d: 1=1/3; 6=2/3 (6 is the alias)
//
// The sample is obtained by randomly selecting a section, then choosing which category
// from the pair based on a uniform random deviate.
final double sumProb = InternalUtils.validateProbabilities(probabilities);
// Allow zero-padding
final int n = computeSize(probabilities.length, alpha);
// Partition into small and large by splitting on the average.
final double mean = sumProb / n;
// The cardinality of smallSize + largeSize = n.
// So fill the same array from either end.
final int[] indices = new int[n];
int large = n;
int small = 0;
for (int i = 0; i < probabilities.length; i++) {
if (probabilities[i] >= mean) {
indices[--large] = i;
} else {
indices[small++] = i;
}
}
small = fillRemainingIndices(probabilities.length, indices, small);
// This may be smaller than the input length if the probabilities were already padded.
final int nonZeroIndex = findLastNonZeroIndex(probabilities);
// The probabilities are modified so use a copy.
// Note: probabilities are required only up to last nonZeroIndex
final double[] remainingProbabilities = Arrays.copyOf(probabilities, nonZeroIndex + 1);
// Allocate the final tables.
// Probability table may be truncated (when zero padded).
// The alias table is full length.
final long[] probability = new long[remainingProbabilities.length];
final int[] alias = new int[n];
// This loop uses each large in turn to fill the alias table for small probabilities that
// do not reach the requirement to fill an entire section alone (i.e. p < mean).
// Since the sum of the small should be less than the sum of the large it should use up
// all the small first. However floating point round-off can result in
// misclassification of items as small or large. The Vose algorithm handles this using
// a while loop conditioned on the size of both sets and a subsequent loop to use
// unpaired items.
while (large != n && small != 0) {
// Index of the small and the large probabilities.
final int j = indices[--small];
final int k = indices[large++];
// Optimisation for zero-padded input:
// p(j) = 0 above the last nonZeroIndex
if (j > nonZeroIndex) {
// The entire amount for the section is taken from the alias.
remainingProbabilities[k] -= mean;
} else {
final double pj = remainingProbabilities[j];
// Item j is a small probability that is below the mean.
// Compute the weight of the section for item j: pj / mean.
// This is scaled by 2^53 and the ceiling function used to round-up
// the probability to a numerator of a fraction in the range [1,2^53].
// Ceiling ensures non-zero values.
probability[j] = (long) Math.ceil(CONVERT_TO_NUMERATOR * (pj / mean));
// The remaining amount for the section is taken from the alias.
// Effectively: probabilities[k] -= (mean - pj)
remainingProbabilities[k] += pj - mean;
}
// If not j then the alias is k
alias[j] = k;
// Add the remaining probability from large to the appropriate list.
if (remainingProbabilities[k] >= mean) {
indices[--large] = k;
} else {
indices[small++] = k;
}
}
// Final loop conditions to consume unpaired items.
// Note: The large set should never be non-empty but this can occur due to round-off
// error so consume from both.
fillTable(probability, alias, indices, 0, small);
fillTable(probability, alias, indices, large, n);
// Change the algorithm for small power of 2 sized tables
return isSmallPowerOf2(n) ?
new SmallTableAliasMethodDiscreteSampler(rng, probability, alias) :
new AliasMethodDiscreteSampler(rng, probability, alias);
}
/**
* Allocate the remaining indices from zero padding as small probabilities. The
* number to add is from the length of the probability array to the length of
* the padded probability array (which is the same length as the indices array).
*
* @param length Length of probability array.
* @param indices Indices.
* @param small Number of small indices.
* @return the updated number of small indices
*/
private static int fillRemainingIndices(final int length, final int[] indices, int small) {
int updatedSmall = small;
for (int i = length; i < indices.length; i++) {
indices[updatedSmall++] = i;
}
return updatedSmall;
}
/**
* Find the last non-zero index in the probabilities. This may be smaller than
* the input length if the probabilities were already padded.
*
* @param probabilities The list of probabilities.
* @return the index
*/
private static int findLastNonZeroIndex(final double[] probabilities) {
// No bounds check is performed when decrementing as the array contains at least one
// value above zero.
int nonZeroIndex = probabilities.length - 1;
while (probabilities[nonZeroIndex] == ZERO) {
nonZeroIndex--;
}
return nonZeroIndex;
}
/**
* Compute the size after padding. A value of {@code alpha < 0} disables
* padding. Otherwise the length will be increased by 2alpha
* rounded-up to the next power of 2.
*
* @param length Length of probability array.
* @param alpha The alpha factor controlling the zero padding.
* @return the padded size
*/
private static int computeSize(int length, int alpha) {
if (alpha < 0) {
// No padding
return length;
}
// Use the number of leading zeros function to find the next power of 2,
// i.e. ceil(log2(x))
int pow2 = 32 - Integer.numberOfLeadingZeros(length - 1);
// Increase by the alpha. Clip this to limit to a positive integer (2^30)
pow2 = Math.min(30, pow2 + alpha);
// Use max to handle a length above the highest possible power of 2
return Math.max(length, 1 << pow2);
}
/**
* Fill the tables using unpaired items that are in the range between {@code start} inclusive
* and {@code end} exclusive.
*
* Anything left must fill the entire section so the probability table is set
* to 1 and there is no alias. This will occur for 1/n samples, i.e. the last
* remaining unpaired probability. Note: When the tables are zero-padded the
* remaining indices are from an input probability that is above zero so the
* index will be allowed in the truncated probability array and no
* index-out-of-bounds exception will occur.
*
* @param probability Probability table.
* @param alias Alias table.
* @param indices Unpaired indices.
* @param start Start position.
* @param end End position.
*/
private static void fillTable(long[] probability, int[] alias, int[] indices, int start, int end) {
for (int i = start; i < end; i++) {
final int index = indices[i];
probability[index] = ONE_AS_NUMERATOR;
alias[index] = index;
}
}
/**
* Checks if the size is a small power of 2 so can be supported by the
* {@link SmallTableAliasMethodDiscreteSampler}.
*
* @param n Size of the alias table.
* @return true if supported by {@link SmallTableAliasMethodDiscreteSampler}
*/
private static boolean isSmallPowerOf2(int n) {
return n <= MAX_SMALL_POWER_2_SIZE && (n & (n - 1)) == 0;
}
}