001/*
002 * Java Genetic Algorithm Library (jenetics-8.1.0).
003 * Copyright (c) 2007-2024 Franz Wilhelmstötter
004 *
005 * Licensed under the Apache License, Version 2.0 (the "License");
006 * you may not use this file except in compliance with the License.
007 * You may obtain a copy of the License at
008 *
009 *      http://www.apache.org/licenses/LICENSE-2.0
010 *
011 * Unless required by applicable law or agreed to in writing, software
012 * distributed under the License is distributed on an "AS IS" BASIS,
013 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
014 * See the License for the specific language governing permissions and
015 * limitations under the License.
016 *
017 * Author:
018 *    Franz Wilhelmstötter (franz.wilhelmstoetter@gmail.com)
019 */
020package io.jenetics.stat;
021
022import java.util.random.RandomGenerator;
023
024import io.jenetics.util.DoubleRange;
025import io.jenetics.util.IntRange;
026
027/**
028 * Interface for creating <em>continuous</em> random samples, with a given
029 * <em>distribution</em>. This interface isn't responsible for creating the
030 * random numbers itself. It uses a {@link RandomGenerator} generator, which is
031 * given by the caller.
032 * {@snippet lang = java:
033 * final var random = RandomGenerator.getDefault();
034 * final var range = DoubleRange.of(0, 1);
035 * final var sampler = Sampler.linear(0.1);
036 * // Create a new sample point, which obeys the given distribution.
037 * // The random generator is responsible for the base randomness.
038 * final double value = sampler.sample(random, range);
039 *}
040 *
041 * @see Samplers
042 * @see <a href="https://en.wikipedia.org/wiki/Inverse_transform_sampling">
043 *     Inverse transform sampling</a>
044 *
045 * @author <a href="mailto:franz.wilhelmstoetter@gmail.com">Franz Wilhelmstötter</a>
046 * @version 8.0
047 * @since 8.0
048 */
049@FunctionalInterface
050public interface Sampler {
051
052        /**
053         * Default uniform distribution by calling
054         * {@link RandomGenerator#nextDouble(double, double)}.
055         */
056        Sampler UNIFORM = (random, range) -> random.nextDouble(range.min(), range.max());
057
058        /**
059         * Create a new {@code double} sample point within the given range
060         * {@code [min, max)}.
061         *
062         * @param random the random generator used for creating a sample point of
063         *        the defined distribution
064         * @param range the range of the sample point: {@code [min, max)}
065         * @return a new sample point between {@code [min, max)}
066         */
067        double sample(RandomGenerator random, DoubleRange range);
068
069        /**
070         * Create a new {@code int} sample point within the given range
071         * {@code [min, max)}.
072         *
073         * @param random the random generator used for creating a sample point of
074         *        the defined distribution
075         * @param range the range of the sample point: {@code [min, max)}
076         * @return a new sample point between {@code [min, max)}
077         */
078        default int sample(RandomGenerator random, IntRange range) {
079                return (int)sample(random, DoubleRange.of(range.min(), range.max()));
080        }
081
082}