com.github.explainable.util.RandomSampler.java Source code

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Here is the source code for com.github.explainable.util.RandomSampler.java

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/*
 * The MIT License (MIT)
 *
 * Copyright (c) 2014 Gabriel Bender
 *
 * Permission is hereby granted, free of charge, to any person obtaining a copy
 * of this software and associated documentation files (the "Software"), to deal
 * in the Software without restriction, including without limitation the rights
 * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 * copies of the Software, and to permit persons to whom the Software is
 * furnished to do so, subject to the following conditions:
 *
 * The above copyright notice and this permission notice shall be included in all
 * copies or substantial portions of the Software.
 *
 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 * SOFTWARE.
 */

package com.github.explainable.util;

import com.google.common.base.Preconditions;
import com.google.common.collect.Lists;

import java.util.List;
import java.util.Random;

/**
 * Class that selects a random subset of the elements of a list uniformly at random.
 */
public final class RandomSampler {
    private final Random random;

    private RandomSampler(Random random) {
        this.random = Preconditions.checkNotNull(random);
    }

    public static RandomSampler createAndSeed() {
        return new RandomSampler(new Random());
    }

    public static RandomSampler create(Random random) {
        return new RandomSampler(random);
    }

    public Random random() {
        return random;
    }

    /**
     * Obtain a uniform random sample of the elements in the population. The order of elements in the
     * output list is also uniformly random.
     *
     * @param population the population from which a sample is to be drawn
     * @param n the sample size
     * @return a sample of size {@code n} drawn from {@code population} uniformly at random
     */
    public <T> List<T> sample(List<T> population, int n) {
        Preconditions.checkNotNull(population);
        Preconditions.checkArgument(n <= population.size());

        boolean[] selected = new boolean[population.size()];

        List<T> result = Lists.newArrayListWithCapacity(n);
        for (int i = 0; i < n; i++) {
            int j = random.nextInt(population.size());
            while (selected[j]) {
                j = random.nextInt(population.size());
            }

            selected[j] = true;
            result.add(population.get(j));
        }

        return result;
    }

    /**
     * Select a single item in the specified list uniformly at random.
     */
    public <T> T choice(List<T> population) {
        return population.get(random.nextInt(population.size()));
    }

    /**
     * Select a number in the range [0, n] by clamping a Laplace distribution with expected value
     * lambda.
     */
    public int nextPoisson(double lambda, int n) {
        Preconditions.checkArgument(lambda > 0);
        Preconditions.checkArgument(n > 0);

        // density: Pr[k] = pow(lambda, k) * exp(-lambda) / factorial(k)
        double density = Math.exp(-lambda);
        double cumulativeDensity = 0.0;
        double target = random.nextDouble();

        for (int k = 0; k < n; k++) {
            cumulativeDensity += density;
            if (cumulativeDensity >= target) {
                return k;
            }

            density *= lambda;
            density /= (k + 1);
        }

        return n;
    }
}