/*
 * 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.math3.random;

import org.apache.commons.math3.exception.NotStrictlyPositiveException;
import org.apache.commons.math3.util.FastMath;

/**
 * Abstract class implementing the {@link RandomGenerator} interface. Default implementations for
 * all methods other than {@link #nextDouble()} and {@link #setSeed(long)} are provided.
 *
 * <p>All data generation methods are based on {@code code nextDouble()}. Concrete implementations
 * <strong>must</strong> override this method and <strong>should</strong> provide better / more
 * performant implementations of the other methods if the underlying PRNG supplies them.
 *
 * @since 1.1
 */
public abstract class AbstractRandomGenerator implements RandomGenerator {

    /**
     * Cached random normal value. The default implementation for {@link #nextGaussian} generates
     * pairs of values and this field caches the second value so that the full algorithm is not
     * executed for every activation. The value {@code Double.NaN} signals that there is no cached
     * value. Use {@link #clear} to clear the cached value.
     */
    private double cachedNormalDeviate = Double.NaN;

    /** Construct a RandomGenerator. */
    public AbstractRandomGenerator() {
        super();
    }

    /**
     * Clears the cache used by the default implementation of {@link #nextGaussian}. Implementations
     * that do not override the default implementation of {@code nextGaussian} should call this
     * method in the implementation of {@link #setSeed(long)}
     */
    public void clear() {
        cachedNormalDeviate = Double.NaN;
    }

    /** {@inheritDoc} */
    public void setSeed(int seed) {
        setSeed((long) seed);
    }

    /** {@inheritDoc} */
    public void setSeed(int[] seed) {
        // the following number is the largest prime that fits in 32 bits (it is 2^32 - 5)
        final long prime = 4294967291l;

        long combined = 0l;
        for (int s : seed) {
            combined = combined * prime + s;
        }
        setSeed(combined);
    }

    /**
     * Sets the seed of the underlying random number generator using a {@code long} seed. Sequences
     * of values generated starting with the same seeds should be identical.
     *
     * <p>Implementations that do not override the default implementation of {@code nextGaussian}
     * should include a call to {@link #clear} in the implementation of this method.
     *
     * @param seed the seed value
     */
    public abstract void setSeed(long seed);

    /**
     * Generates random bytes and places them into a user-supplied byte array. The number of random
     * bytes produced is equal to the length of the byte array.
     *
     * <p>The default implementation fills the array with bytes extracted from random integers
     * generated using {@link #nextInt}.
     *
     * @param bytes the non-null byte array in which to put the random bytes
     */
    public void nextBytes(byte[] bytes) {
        int bytesOut = 0;
        while (bytesOut < bytes.length) {
            int randInt = nextInt();
            for (int i = 0; i < 3; i++) {
                if (i > 0) {
                    randInt >>= 8;
                }
                bytes[bytesOut++] = (byte) randInt;
                if (bytesOut == bytes.length) {
                    return;
                }
            }
        }
    }

    /**
     * Returns the next pseudorandom, uniformly distributed {@code int} value from this random
     * number generator's sequence. All 2<font size="-1"><sup>32</sup></font> possible {@code int}
     * values should be produced with (approximately) equal probability.
     *
     * <p>The default implementation provided here returns
     *
     * <pre>
     * <code>(int) (nextDouble() * Integer.MAX_VALUE)</code>
     * </pre>
     *
     * @return the next pseudorandom, uniformly distributed {@code int} value from this random
     *     number generator's sequence
     */
    public int nextInt() {
        return (int) ((2d * nextDouble() - 1d) * Integer.MAX_VALUE);
    }

    /**
     * Returns a pseudorandom, uniformly distributed {@code int} value between 0 (inclusive) and the
     * specified value (exclusive), drawn from this random number generator's sequence.
     *
     * <p>The default implementation returns
     *
     * <pre>
     * <code>(int) (nextDouble() * n</code>
     * </pre>
     *
     * @param n the bound on the random number to be returned. Must be positive.
     * @return a pseudorandom, uniformly distributed {@code int} value between 0 (inclusive) and n
     *     (exclusive).
     * @throws NotStrictlyPositiveException if {@code n <= 0}.
     */
    public int nextInt(int n) {
        if (n <= 0) {
            throw new NotStrictlyPositiveException(n);
        }
        int result = (int) (nextDouble() * n);
        return result < n ? result : n - 1;
    }

    /**
     * Returns the next pseudorandom, uniformly distributed {@code long} value from this random
     * number generator's sequence. All 2<font size="-1"><sup>64</sup></font> possible {@code long}
     * values should be produced with (approximately) equal probability.
     *
     * <p>The default implementation returns
     *
     * <pre>
     * <code>(long) (nextDouble() * Long.MAX_VALUE)</code>
     * </pre>
     *
     * @return the next pseudorandom, uniformly distributed {@code long} value from this random
     *     number generator's sequence
     */
    public long nextLong() {
        return (long) ((2d * nextDouble() - 1d) * Long.MAX_VALUE);
    }

    /**
     * Returns the next pseudorandom, uniformly distributed {@code boolean} value from this random
     * number generator's sequence.
     *
     * <p>The default implementation returns
     *
     * <pre>
     * <code>nextDouble() <= 0.5</code>
     * </pre>
     *
     * @return the next pseudorandom, uniformly distributed {@code boolean} value from this random
     *     number generator's sequence
     */
    public boolean nextBoolean() {
        return nextDouble() <= 0.5;
    }

    /**
     * Returns the next pseudorandom, uniformly distributed {@code float} value between {@code 0.0}
     * and {@code 1.0} from this random number generator's sequence.
     *
     * <p>The default implementation returns
     *
     * <pre>
     * <code>(float) nextDouble() </code>
     * </pre>
     *
     * @return the next pseudorandom, uniformly distributed {@code float} value between {@code 0.0}
     *     and {@code 1.0} from this random number generator's sequence
     */
    public float nextFloat() {
        return (float) nextDouble();
    }

    /**
     * Returns the next pseudorandom, uniformly distributed {@code double} value between {@code 0.0}
     * and {@code 1.0} from this random number generator's sequence.
     *
     * <p>This method provides the underlying source of random data used by the other methods.
     *
     * @return the next pseudorandom, uniformly distributed {@code double} value between {@code 0.0}
     *     and {@code 1.0} from this random number generator's sequence
     */
    public abstract double nextDouble();

    /**
     * Returns the next pseudorandom, Gaussian ("normally") distributed {@code double} value with
     * mean {@code 0.0} and standard deviation {@code 1.0} from this random number generator's
     * sequence.
     *
     * <p>The default implementation uses the <em>Polar Method</em> due to G.E.P. Box, M.E. Muller
     * and G. Marsaglia, as described in D. Knuth, <u>The Art of Computer Programming</u>, 3.4.1C.
     *
     * <p>The algorithm generates a pair of independent random values. One of these is cached for
     * reuse, so the full algorithm is not executed on each activation. Implementations that do not
     * override this method should make sure to call {@link #clear} to clear the cached value in the
     * implementation of {@link #setSeed(long)}.
     *
     * @return the next pseudorandom, Gaussian ("normally") distributed {@code double} value with
     *     mean {@code 0.0} and standard deviation {@code 1.0} from this random number generator's
     *     sequence
     */
    public double nextGaussian() {
        if (!Double.isNaN(cachedNormalDeviate)) {
            double dev = cachedNormalDeviate;
            cachedNormalDeviate = Double.NaN;
            return dev;
        }
        double v1 = 0;
        double v2 = 0;
        double s = 1;
        while (s >= 1) {
            v1 = 2 * nextDouble() - 1;
            v2 = 2 * nextDouble() - 1;
            s = v1 * v1 + v2 * v2;
        }
        if (s != 0) {
            s = FastMath.sqrt(-2 * FastMath.log(s) / s);
        }
        cachedNormalDeviate = v2 * s;
        return v1 * s;
    }
}