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Random.java
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/*
* Copyright (c) 1995, 2013, Oracle and/or its affiliates. All rights reserved.
* DO NOT ALTER OR REMOVE COPYRIGHT NOTICES OR THIS FILE HEADER.
*
* This code is free software; you can redistribute it and/or modify it
* under the terms of the GNU General Public License version 2 only, as
* published by the Free Software Foundation. Oracle designates this
* particular file as subject to the "Classpath" exception as provided
* by Oracle in the LICENSE file that accompanied this code.
*
* This code is distributed in the hope that it will be useful, but WITHOUT
* ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
* FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License
* version 2 for more details (a copy is included in the LICENSE file that
* accompanied this code).
*
* You should have received a copy of the GNU General Public License version
* 2 along with this work; if not, write to the Free Software Foundation,
* Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
*
* Please contact Oracle, 500 Oracle Parkway, Redwood Shores, CA 94065 USA
* or visit www.oracle.com if you need additional information or have any
* questions.
*/
package java.util;
import java.io.IOException;
import java.io.ObjectInputStream;
import java.io.ObjectOutputStream;
import java.io.ObjectStreamField;
import java.io.Serializable;
import java.util.concurrent.atomic.AtomicLong;
import java.util.function.DoubleConsumer;
import java.util.function.IntConsumer;
import java.util.function.LongConsumer;
import java.util.stream.DoubleStream;
import java.util.stream.IntStream;
import java.util.stream.LongStream;
import java.util.stream.StreamSupport;
import jdk.internal.misc.Unsafe;
/**
* An instance of this class is used to generate a stream of
* pseudorandom numbers. The class uses a 48-bit seed, which is
* modified using a linear congruential formula. (See Donald Knuth,
* <i>The Art of Computer Programming, Volume 2</i>, Section 3.2.1.)
* <p>
* If two instances of {@code Random} are created with the same
* seed, and the same sequence of method calls is made for each, they
* will generate and return identical sequences of numbers. In order to
* guarantee this property, particular algorithms are specified for the
* class {@code Random}. Java implementations must use all the algorithms
* shown here for the class {@code Random}, for the sake of absolute
* portability of Java code. However, subclasses of class {@code Random}
* are permitted to use other algorithms, so long as they adhere to the
* general contracts for all the methods.
* <p>
* The algorithms implemented by class {@code Random} use a
* {@code protected} utility method that on each invocation can supply
* up to 32 pseudorandomly generated bits.
* <p>
* Many applications will find the method {@link Math#random} simpler to use.
*
* <p>Instances of {@code java.util.Random} are threadsafe.
* However, the concurrent use of the same {@code java.util.Random}
* instance across threads may encounter contention and consequent
* poor performance. Consider instead using
* {@link java.util.concurrent.ThreadLocalRandom} in multithreaded
* designs.
*
* <p>Instances of {@code java.util.Random} are not cryptographically
* secure. Consider instead using {@link java.security.SecureRandom} to
* get a cryptographically secure pseudo-random number generator for use
* by security-sensitive applications.
*
* @author Frank Yellin
* @since 1.0
*/
/*
* 伪随机数生成器
*
* 线程安全
* 适用于大多数单线程场景
*
* 在多线程中,生成随机数的性能欠佳(存在线程争用)
* 该类更适用于单线程环境,在多线程中可以使用ThreadLocalRandom
*
* 支持使用内置种子计算的原始种子
* 支持自定义原始种子
*/
public class Random implements Serializable {
/** use serialVersionUID from JDK 1.1 for interoperability */
static final long serialVersionUID = 3905348978240129619L;
/**
* Serializable fields for Random.
*
* @serialField seed long
* seed for random computations
* @serialField nextNextGaussian double
* next Gaussian to be returned
* @serialField haveNextNextGaussian boolean
* nextNextGaussian is valid
*/
// 确定哪些字段参与序列化
private static final ObjectStreamField[] serialPersistentFields = {
new ObjectStreamField("seed", Long.TYPE),
new ObjectStreamField("nextNextGaussian", Double.TYPE),
new ObjectStreamField("haveNextNextGaussian", Boolean.TYPE)
};
// IllegalArgumentException messages
static final String BadBound = "bound must be positive";
static final String BadRange = "bound must be greater than origin";
static final String BadSize = "size must be non-negative";
/*▼ 内置种子 ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓┓ */
// 哈希魔数[偶],作为内置种子的初始值
private static final long m0 = 0x5DEECE66DL;
// 哈希魔数[偶],用来更新内置种子
private static final long M = 1181783497276652981L;
/*
* 内置种子,用于为默认的Random实例生成原始种子
*
* 当用户没有显式指定随机数种子时,使用内置种子来推导原始种子的值
* 每创建一个默认的Random实例,内置种子的值就改变一次
*/
private static final AtomicLong seedUniquifier = new AtomicLong(m0); // 初始的种子标记
/*▲ 内置种子 ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓┛ */
/*▼ 原始种子 ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓┓ */
// 哈希魔数,用来更新原始种子
private static final long multiplier = 0x5DEECE66DL;
private static final long addend = 0xBL; // 偏移量
// 更新原始种子时使用的掩码
private static final long mask = (1L << 48) - 1;
/**
* The internal state associated with this pseudorandom number generator.
* (The specs for the methods in this class describe the ongoing computation of this value.)
*/
/*
* 原始种子,Random实例使用该种子生成伪随机数
*
* 原始种子的初值可由系统的内置种子配合系统时间生成,也可由用户指定
* 每生成一个随机数,原始种子的值就改变一次
*
* 如果原始种子被单个线程持有,那么接下来生成的一系列随机数是均匀的
* 如果原始种子被多个线程持有,那么从单个线程的角度观察,其生成的随机数是不均匀的
*/
private final AtomicLong seed;
/*▲ 原始种子 ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓┛ */
// float值的二进制精度
private static final float FLOAT_UNIT = 0x1.0p-24f; // 1.0f/(1 << 24)
// double值的二进制精度
private static final double DOUBLE_UNIT = 0x1.0p-53; // 1.0/(1L << 53)
private double nextNextGaussian;
private boolean haveNextNextGaussian = false;
// Support for resetting seed while deserializing
private static final Unsafe unsafe = Unsafe.getUnsafe();
private static final long seedOffset; // 记录seed属性在JVM内存中的的偏移地址
static {
try {
seedOffset = unsafe.objectFieldOffset(Random.class.getDeclaredField("seed"));
} catch(Exception ex) {
throw new Error(ex);
}
}
/*▼ 构造方法 ████████████████████████████████████████████████████████████████████████████████┓ */
/**
* Creates a new random number generator. This constructor sets
* the seed of the random number generator to a value very likely
* to be distinct from any other invocation of this constructor.
*/
// 构造默认的伪随机数生成器
public Random() {
// 配合当前的系统时间,生成一个内置种子,并进一步计算出原始种子
this(seedUniquifier() ^ System.nanoTime());
}
/**
* Creates a new random number generator using a single {@code long} seed.
* The seed is the initial value of the internal state of the pseudorandom
* number generator which is maintained by method {@link #next}.
*
* <p>The invocation {@code new Random(seed)} is equivalent to:
* <pre> {@code
* Random rnd = new Random();
* rnd.setSeed(seed);}</pre>
*
* @param seed the initial seed
*
* @see #setSeed(long)
*/
// 构造指定种子的伪随机数生成器
public Random(long seed) {
if(getClass() == Random.class) {
// 对指定的种子加工后作为当前Random实例的种子的初始值
this.seed = new AtomicLong(initialScramble(seed));
} else {
// subclass might have overriden setSeed
this.seed = new AtomicLong();
setSeed(seed);
}
}
/*▲ 构造方法 ████████████████████████████████████████████████████████████████████████████████┛ */
/*▼ 种子 ████████████████████████████████████████████████████████████████████████████████┓ */
// 更新内置种子,每初始化一个默认的Random实例就调用一次
private static long seedUniquifier() {
// L'Ecuyer, "Tables of Linear Congruential Generators of Different Sizes and Good Lattice Structure", 1999
for(; ; ) {
long current = seedUniquifier.get();
long next = current * M;
// 更新seedUniquifier为新值next,更新时参考的期望值是current
if(seedUniquifier.compareAndSet(current, next)) {
return next;
}
}
}
/**
* Sets the seed of this random number generator using a single
* {@code long} seed. The general contract of {@code setSeed} is
* that it alters the state of this random number generator object
* so as to be in exactly the same state as if it had just been
* created with the argument {@code seed} as a seed. The method
* {@code setSeed} is implemented by class {@code Random} by
* atomically updating the seed to
* <pre>{@code (seed ^ 0x5DEECE66DL) & ((1L << 48) - 1)}</pre>
* and clearing the {@code haveNextNextGaussian} flag used by {@link
* #nextGaussian}.
*
* <p>The implementation of {@code setSeed} by class {@code Random}
* happens to use only 48 bits of the given seed. In general, however,
* an overriding method may use all 64 bits of the {@code long}
* argument as a seed value.
*
* @param seed the initial seed
*/
// 设置原始种子,该方法可能由子类重写
public synchronized void setSeed(long seed) {
this.seed.set(initialScramble(seed));
haveNextNextGaussian = false;
}
// 加工原始种子
private static long initialScramble(long seed) {
return (seed ^ multiplier) & mask;
}
// 重置原始种子为seedVal
private void resetSeed(long seedVal) {
unsafe.putObjectVolatile(this, seedOffset, new AtomicLong(seedVal));
}
/*▲ 种子 ████████████████████████████████████████████████████████████████████████████████┛ */
/*▼ 生成伪随机数 ████████████████████████████████████████████████████████████████████████████████┓ */
/**
* 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 method {@code nextBytes} is implemented by class {@code Random}
* as if by:
* <pre> {@code
* public void nextBytes(byte[] bytes) {
* for (int i = 0; i < bytes.length; )
* for (int rnd = nextInt(), n = Math.min(bytes.length - i, 4);
* n-- > 0; rnd >>= 8)
* bytes[i++] = (byte)rnd;
* }}</pre>
*
* @param bytes the byte array to fill with random bytes
*
* @throws NullPointerException if the byte array is null
* @since 1.1
*/
// 随机填充一个byte数组,有正有负
public void nextBytes(byte[] bytes) {
for(int i = 0, len = bytes.length; i<len; ) {
for(int rnd = nextInt(), n = Math.min(len - i, Integer.SIZE / Byte.SIZE); n-->0; rnd >>= Byte.SIZE) {
bytes[i++] = (byte) rnd;
}
}
}
/**
* Returns the next pseudorandom, uniformly distributed {@code int}
* value from this random number generator's sequence. The general
* contract of {@code nextInt} is that one {@code int} value is
* pseudorandomly generated and returned. All 2<sup>32</sup> possible
* {@code int} values are produced with (approximately) equal probability.
*
* <p>The method {@code nextInt} is implemented by class {@code Random}
* as if by:
* <pre> {@code
* public int nextInt() {
* return next(32);
* }}</pre>
*
* @return the next pseudorandom, uniformly distributed {@code int}
* value from this random number generator's sequence
*/
// 随机生成一个int值,有正有负
public int nextInt() {
return next(32);
}
/**
* Returns a pseudorandom, uniformly distributed {@code int} value
* between 0 (inclusive) and the specified value (exclusive), drawn from
* this random number generator's sequence. The general contract of
* {@code nextInt} is that one {@code int} value in the specified range
* is pseudorandomly generated and returned. All {@code bound} possible
* {@code int} values are produced with (approximately) equal
* probability. The method {@code nextInt(int bound)} is implemented by
* class {@code Random} as if by:
* <pre> {@code
* public int nextInt(int bound) {
* if (bound <= 0)
* throw new IllegalArgumentException("bound must be positive");
*
* if ((bound & -bound) == bound) // i.e., bound is a power of 2
* return (int)((bound * (long)next(31)) >> 31);
*
* int bits, val;
* do {
* bits = next(31);
* val = bits % bound;
* } while (bits - val + (bound-1) < 0);
* return val;
* }}</pre>
*
* <p>The hedge "approximately" is used in the foregoing description only
* because the next method is only approximately an unbiased source of
* independently chosen bits. If it were a perfect source of randomly
* chosen bits, then the algorithm shown would choose {@code int}
* values from the stated range with perfect uniformity.
* <p>
* The algorithm is slightly tricky. It rejects values that would result
* in an uneven distribution (due to the fact that 2^31 is not divisible
* by n). The probability of a value being rejected depends on n. The
* worst case is n=2^30+1, for which the probability of a reject is 1/2,
* and the expected number of iterations before the loop terminates is 2.
* <p>
* The algorithm treats the case where n is a power of two specially: it
* returns the correct number of high-order bits from the underlying
* pseudo-random number generator. In the absence of special treatment,
* the correct number of <i>low-order</i> bits would be returned. Linear
* congruential pseudo-random number generators such as the one
* implemented by this class are known to have short periods in the
* sequence of values of their low-order bits. Thus, this special case
* greatly increases the length of the sequence of values returned by
* successive calls to this method if n is a small power of two.
*
* @param bound the upper bound (exclusive). Must be positive.
*
* @return the next pseudorandom, uniformly distributed {@code int}
* value between zero (inclusive) and {@code bound} (exclusive)
* from this random number generator's sequence
*
* @throws IllegalArgumentException if bound is not positive
* @since 1.2
*/
// 随机生成一个[0, bound)之内的int值
public int nextInt(int bound) {
if(bound<=0) {
throw new IllegalArgumentException(BadBound);
}
int r = next(31);
int m = bound - 1;
if((bound & m) == 0) // i.e., bound is a power of 2
r = (int) ((bound * (long) r) >> 31);
else {
for(int u = r; u - (r = u % bound) + m<0; u = next(31))
;
}
return r;
}
/**
* Returns the next pseudorandom, uniformly distributed {@code long}
* value from this random number generator's sequence. The general
* contract of {@code nextLong} is that one {@code long} value is
* pseudorandomly generated and returned.
*
* <p>The method {@code nextLong} is implemented by class {@code Random}
* as if by:
* <pre> {@code
* public long nextLong() {
* return ((long)next(32) << 32) + next(32);
* }}</pre>
*
* Because class {@code Random} uses a seed with only 48 bits,
* this algorithm will not return all possible {@code long} values.
*
* @return the next pseudorandom, uniformly distributed {@code long}
* value from this random number generator's sequence
*/
// 随机生成一个long值,有正有负
public long nextLong() {
// it's okay that the bottom word remains signed.
return ((long) (next(32)) << 32) + next(32);
}
/**
* 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 general contract of {@code nextFloat} is that one
* {@code float} value, chosen (approximately) uniformly from the
* range {@code 0.0f} (inclusive) to {@code 1.0f} (exclusive), is
* pseudorandomly generated and returned. All 2<sup>24</sup> possible
* {@code float} values of the form <i>m x </i>2<sup>-24</sup>,
* where <i>m</i> is a positive integer less than 2<sup>24</sup>, are
* produced with (approximately) equal probability.
*
* <p>The method {@code nextFloat} is implemented by class {@code Random}
* as if by:
* <pre> {@code
* public float nextFloat() {
* return next(24) / ((float)(1 << 24));
* }}</pre>
*
* <p>The hedge "approximately" is used in the foregoing description only
* because the next method is only approximately an unbiased source of
* independently chosen bits. If it were a perfect source of randomly
* chosen bits, then the algorithm shown would choose {@code float}
* values from the stated range with perfect uniformity.<p>
* [In early versions of Java, the result was incorrectly calculated as:
* <pre> {@code
* return next(30) / ((float)(1 << 30));}</pre>
* This might seem to be equivalent, if not better, but in fact it
* introduced a slight nonuniformity because of the bias in the rounding
* of floating-point numbers: it was slightly more likely that the
* low-order bit of the significand would be 0 than that it would be 1.]
*
* @return the next pseudorandom, uniformly distributed {@code float}
* value between {@code 0.0} and {@code 1.0} from this
* random number generator's sequence
*/
// 随机生成一个[0, 1)之内的double值
public float nextFloat() {
return next(24) * FLOAT_UNIT;
}
/**
* 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>The general contract of {@code nextDouble} is that one
* {@code double} value, chosen (approximately) uniformly from the
* range {@code 0.0d} (inclusive) to {@code 1.0d} (exclusive), is
* pseudorandomly generated and returned.
*
* <p>The method {@code nextDouble} is implemented by class {@code Random}
* as if by:
* <pre> {@code
* public double nextDouble() {
* return (((long)next(26) << 27) + next(27))
* / (double)(1L << 53);
* }}</pre>
*
* <p>The hedge "approximately" is used in the foregoing description only
* because the {@code next} method is only approximately an unbiased
* source of independently chosen bits. If it were a perfect source of
* randomly chosen bits, then the algorithm shown would choose
* {@code double} values from the stated range with perfect uniformity.
* <p>[In early versions of Java, the result was incorrectly calculated as:
* <pre> {@code
* return (((long)next(27) << 27) + next(27))
* / (double)(1L << 54);}</pre>
* This might seem to be equivalent, if not better, but in fact it
* introduced a large nonuniformity because of the bias in the rounding
* of floating-point numbers: it was three times as likely that the
* low-order bit of the significand would be 0 than that it would be 1!
* This nonuniformity probably doesn't matter much in practice, but we
* strive for perfection.]
*
* @return the next pseudorandom, uniformly distributed {@code double}
* value between {@code 0.0} and {@code 1.0} from this
* random number generator's sequence
*
* @see Math#random
*/
// 随机生成一个[0, bound)之内的double值
public double nextDouble() {
return (((long) (next(26)) << 27) + next(27)) * DOUBLE_UNIT;
}
/**
* Returns the next pseudorandom, uniformly distributed
* {@code boolean} value from this random number generator's
* sequence. The general contract of {@code nextBoolean} is that one
* {@code boolean} value is pseudorandomly generated and returned. The
* values {@code true} and {@code false} are produced with
* (approximately) equal probability.
*
* <p>The method {@code nextBoolean} is implemented by class {@code Random}
* as if by:
* <pre> {@code
* public boolean nextBoolean() {
* return next(1) != 0;
* }}</pre>
*
* @return the next pseudorandom, uniformly distributed
* {@code boolean} value from this random number generator's
* sequence
*
* @since 1.2
*/
// 随机生成一个boolean值
public boolean nextBoolean() {
return next(1) != 0;
}
/**
* 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 general contract of {@code nextGaussian} is that one
* {@code double} value, chosen from (approximately) the usual
* normal distribution with mean {@code 0.0} and standard deviation
* {@code 1.0}, is pseudorandomly generated and returned.
*
* <p>The method {@code nextGaussian} is implemented by class
* {@code Random} as if by a threadsafe version of the following:
* <pre> {@code
* private double nextNextGaussian;
* private boolean haveNextNextGaussian = false;
*
* public double nextGaussian() {
* if (haveNextNextGaussian) {
* haveNextNextGaussian = false;
* return nextNextGaussian;
* } else {
* double v1, v2, s;
* do {
* v1 = 2 * nextDouble() - 1; // between -1.0 and 1.0
* v2 = 2 * nextDouble() - 1; // between -1.0 and 1.0
* s = v1 * v1 + v2 * v2;
* } while (s >= 1 || s == 0);
* double multiplier = StrictMath.sqrt(-2 * StrictMath.log(s)/s);
* nextNextGaussian = v2 * multiplier;
* haveNextNextGaussian = true;
* return v1 * multiplier;
* }
* }}</pre>
* This uses the <i>polar method</i> of G. E. P. Box, M. E. Muller, and
* G. Marsaglia, as described by Donald E. Knuth in <i>The Art of
* Computer Programming</i>, Volume 2: <i>Seminumerical Algorithms</i>,
* section 3.4.1, subsection C, algorithm P. Note that it generates two
* independent values at the cost of only one call to {@code StrictMath.log}
* and one call to {@code StrictMath.sqrt}.
*
* @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
*/
// 随机生成一个double值,有正有负。所有生成的double值符合标准正态分布
public synchronized double nextGaussian() {
// See Knuth, ACP, Section 3.4.1 Algorithm C.
if(haveNextNextGaussian) {
haveNextNextGaussian = false;
return nextNextGaussian;
} else {
double v1, v2, s;
do {
v1 = 2 * nextDouble() - 1; // between -1 and 1
v2 = 2 * nextDouble() - 1; // between -1 and 1
s = v1 * v1 + v2 * v2;
} while(s >= 1 || s == 0);
double multiplier = StrictMath.sqrt(-2 * StrictMath.log(s) / s);
nextNextGaussian = v2 * multiplier;
haveNextNextGaussian = true;
return v1 * multiplier;
}
}
/**
* Generates the next pseudorandom number. Subclasses should
* override this, as this is used by all other methods.
*
* <p>The general contract of {@code next} is that it returns an
* {@code int} value and if the argument {@code bits} is between
* {@code 1} and {@code 32} (inclusive), then that many low-order
* bits of the returned value will be (approximately) independently
* chosen bit values, each of which is (approximately) equally
* likely to be {@code 0} or {@code 1}. The method {@code next} is
* implemented by class {@code Random} by atomically updating the seed to
* <pre>{@code (seed * 0x5DEECE66DL + 0xBL) & ((1L << 48) - 1)}</pre>
* and returning
* <pre>{@code (int)(seed >>> (48 - bits))}.</pre>
*
* This is a linear congruential pseudorandom number generator, as
* defined by D. H. Lehmer and described by Donald E. Knuth in
* <i>The Art of Computer Programming,</i> Volume 2:
* <i>Seminumerical Algorithms</i>, section 3.2.1.
*
* @param bits random bits
*
* @return the next pseudorandom value from this random number
* generator's sequence
*
* @since 1.1
*/
// 随机生成一个int值,该值范围是[0, 2^bits -1)
protected int next(int bits) {
long oldseed, nextseed;
AtomicLong seed = this.seed;
// 原子地更新原始种子,该种子取值范围是[0, mask]
do {
oldseed = seed.get();
nextseed = (oldseed * multiplier + addend) & mask;
} while(!seed.compareAndSet(oldseed, nextseed));
// 由原始种子计算出哈希值,此时的哈希值与之前的哈希值可能重复
return (int) (nextseed >>> (48 - bits));
}
/*▲ 生成伪随机数 ████████████████████████████████████████████████████████████████████████████████┛ */
/*▼ 流 ████████████████████████████████████████████████████████████████████████████████┓ */
/**
* Returns an effectively unlimited stream of pseudorandom {@code int}
* values.
*
* <p>A pseudorandom {@code int} value is generated as if it's the result of
* calling the method {@link #nextInt()}.
*
* @return a stream of pseudorandom {@code int} values
*
* @implNote This method is implemented to be equivalent to {@code
* ints(Long.MAX_VALUE)}.
* @since 1.8
*/
// 返回的流可以生成Long.MAX_VALUE个随机int值
public IntStream ints() {
return StreamSupport.intStream(new RandomIntsSpliterator(this, 0L, Long.MAX_VALUE, Integer.MAX_VALUE, 0), false);
}
/**
* Returns a stream producing the given {@code streamSize} number of
* pseudorandom {@code int} values.
*
* <p>A pseudorandom {@code int} value is generated as if it's the result of
* calling the method {@link #nextInt()}.
*
* @param streamSize the number of values to generate
*
* @return a stream of pseudorandom {@code int} values
*
* @throws IllegalArgumentException if {@code streamSize} is
* less than zero
* @since 1.8
*/
// 返回的流可以生成streamSize个随机int值
public IntStream ints(long streamSize) {
if(streamSize<0L) {
throw new IllegalArgumentException(BadSize);
}
return StreamSupport.intStream(new RandomIntsSpliterator(this, 0L, streamSize, Integer.MAX_VALUE, 0), false);
}
/**
* Returns an effectively unlimited stream of pseudorandom {@code
* int} values, each conforming to the given origin (inclusive) and bound
* (exclusive).
*
* <p>A pseudorandom {@code int} value is generated as if it's the result of
* calling the following method with the origin and bound:
* <pre> {@code
* int nextInt(int origin, int bound) {
* int n = bound - origin;
* if (n > 0) {
* return nextInt(n) + origin;
* }
* else { // range not representable as int
* int r;
* do {
* r = nextInt();
* } while (r < origin || r >= bound);
* return r;
* }
* }}</pre>
*
* @param randomNumberOrigin the origin (inclusive) of each random value
* @param randomNumberBound the bound (exclusive) of each random value
*
* @return a stream of pseudorandom {@code int} values,
* each with the given origin (inclusive) and bound (exclusive)
*
* @throws IllegalArgumentException if {@code randomNumberOrigin}
* is greater than or equal to {@code randomNumberBound}
* @implNote This method is implemented to be equivalent to {@code
* ints(Long.MAX_VALUE, randomNumberOrigin, randomNumberBound)}.
* @since 1.8
*/
// 返回的流可以生成Long.MAX_VALUE个随机int值,取值范围是[randomNumberOrigin, randomNumberBound)
public IntStream ints(int randomNumberOrigin, int randomNumberBound) {
if(randomNumberOrigin >= randomNumberBound) {
throw new IllegalArgumentException(BadRange);
}
return StreamSupport.intStream(new RandomIntsSpliterator(this, 0L, Long.MAX_VALUE, randomNumberOrigin, randomNumberBound), false);
}
/**
* Returns a stream producing the given {@code streamSize} number
* of pseudorandom {@code int} values, each conforming to the given
* origin (inclusive) and bound (exclusive).
*
* <p>A pseudorandom {@code int} value is generated as if it's the result of
* calling the following method with the origin and bound:
* <pre> {@code
* int nextInt(int origin, int bound) {
* int n = bound - origin;
* if (n > 0) {
* return nextInt(n) + origin;
* }
* else { // range not representable as int
* int r;
* do {
* r = nextInt();
* } while (r < origin || r >= bound);
* return r;
* }
* }}</pre>
*
* @param streamSize the number of values to generate
* @param randomNumberOrigin the origin (inclusive) of each random value
* @param randomNumberBound the bound (exclusive) of each random value
*
* @return a stream of pseudorandom {@code int} values,
* each with the given origin (inclusive) and bound (exclusive)
*
* @throws IllegalArgumentException if {@code streamSize} is
* less than zero, or {@code randomNumberOrigin}
* is greater than or equal to {@code randomNumberBound}
* @since 1.8
*/
// 返回的流可以生成streamSize个随机int值,取值范围是[randomNumberOrigin, randomNumberBound)
public IntStream ints(long streamSize, int randomNumberOrigin, int randomNumberBound) {
if(streamSize<0L) {
throw new IllegalArgumentException(BadSize);
}
if(randomNumberOrigin >= randomNumberBound) {
throw new IllegalArgumentException(BadRange);
}
return StreamSupport.intStream(new RandomIntsSpliterator(this, 0L, streamSize, randomNumberOrigin, randomNumberBound), false);
}
/**
* Returns an effectively unlimited stream of pseudorandom {@code long}
* values.
*
* <p>A pseudorandom {@code long} value is generated as if it's the result
* of calling the method {@link #nextLong()}.
*
* @return a stream of pseudorandom {@code long} values
*
* @implNote This method is implemented to be equivalent to {@code
* longs(Long.MAX_VALUE)}.
* @since 1.8
*/
// 返回的流可以生成Long.MAX_VALUE个随机long值
public LongStream longs() {
return StreamSupport.longStream(new RandomLongsSpliterator(this, 0L, Long.MAX_VALUE, Long.MAX_VALUE, 0L), false);
}
/**
* Returns a stream producing the given {@code streamSize} number of
* pseudorandom {@code long} values.
*
* <p>A pseudorandom {@code long} value is generated as if it's the result
* of calling the method {@link #nextLong()}.
*
* @param streamSize the number of values to generate
*
* @return a stream of pseudorandom {@code long} values
*
* @throws IllegalArgumentException if {@code streamSize} is
* less than zero
* @since 1.8
*/
// 返回的流可以生成streamSize个随机long值
public LongStream longs(long streamSize) {
if(streamSize<0L) {
throw new IllegalArgumentException(BadSize);
}
return StreamSupport.longStream(new RandomLongsSpliterator(this, 0L, streamSize, Long.MAX_VALUE, 0L), false);
}
/**
* Returns an effectively unlimited stream of pseudorandom {@code
* long} values, each conforming to the given origin (inclusive) and bound
* (exclusive).
*
* <p>A pseudorandom {@code long} value is generated as if it's the result
* of calling the following method with the origin and bound:
* <pre> {@code
* long nextLong(long origin, long bound) {
* long r = nextLong();
* long n = bound - origin, m = n - 1;
* if ((n & m) == 0L) // power of two
* r = (r & m) + origin;
* else if (n > 0L) { // reject over-represented candidates
* for (long u = r >>> 1; // ensure nonnegative
* u + m - (r = u % n) < 0L; // rejection check
* u = nextLong() >>> 1) // retry
* ;
* r += origin;
* }
* else { // range not representable as long
* while (r < origin || r >= bound)
* r = nextLong();
* }
* return r;
* }}</pre>
*
* @param randomNumberOrigin the origin (inclusive) of each random value
* @param randomNumberBound the bound (exclusive) of each random value
*
* @return a stream of pseudorandom {@code long} values,
* each with the given origin (inclusive) and bound (exclusive)
*
* @throws IllegalArgumentException if {@code randomNumberOrigin}
* is greater than or equal to {@code randomNumberBound}
* @implNote This method is implemented to be equivalent to {@code
* longs(Long.MAX_VALUE, randomNumberOrigin, randomNumberBound)}.
* @since 1.8
*/
// 返回的流可以生成Long.MAX_VALUE个随机long值,取值范围是[randomNumberOrigin, randomNumberBound)
public LongStream longs(long randomNumberOrigin, long randomNumberBound) {
if(randomNumberOrigin >= randomNumberBound) {
throw new IllegalArgumentException(BadRange);
}
return StreamSupport.longStream(new RandomLongsSpliterator(this, 0L, Long.MAX_VALUE, randomNumberOrigin, randomNumberBound), false);
}
/**
* Returns a stream producing the given {@code streamSize} number of
* pseudorandom {@code long}, each conforming to the given origin
* (inclusive) and bound (exclusive).
*
* <p>A pseudorandom {@code long} value is generated as if it's the result
* of calling the following method with the origin and bound:
* <pre> {@code
* long nextLong(long origin, long bound) {
* long r = nextLong();
* long n = bound - origin, m = n - 1;
* if ((n & m) == 0L) // power of two
* r = (r & m) + origin;
* else if (n > 0L) { // reject over-represented candidates
* for (long u = r >>> 1; // ensure nonnegative
* u + m - (r = u % n) < 0L; // rejection check
* u = nextLong() >>> 1) // retry
* ;
* r += origin;
* }
* else { // range not representable as long
* while (r < origin || r >= bound)
* r = nextLong();
* }
* return r;
* }}</pre>
*
* @param streamSize the number of values to generate
* @param randomNumberOrigin the origin (inclusive) of each random value
* @param randomNumberBound the bound (exclusive) of each random value
*
* @return a stream of pseudorandom {@code long} values,
* each with the given origin (inclusive) and bound (exclusive)
*
* @throws IllegalArgumentException if {@code streamSize} is
* less than zero, or {@code randomNumberOrigin}
* is greater than or equal to {@code randomNumberBound}
* @since 1.8
*/
// 返回的流可以生成streamSize个随机long值,取值范围是[randomNumberOrigin, randomNumberBound)
public LongStream longs(long streamSize, long randomNumberOrigin, long randomNumberBound) {
if(streamSize<0L) {
throw new IllegalArgumentException(BadSize);
}
if(randomNumberOrigin >= randomNumberBound) {
throw new IllegalArgumentException(BadRange);
}
return StreamSupport.longStream(new RandomLongsSpliterator(this, 0L, streamSize, randomNumberOrigin, randomNumberBound), false);
}
/**
* Returns an effectively unlimited stream of pseudorandom {@code
* double} values, each between zero (inclusive) and one
* (exclusive).
*
* <p>A pseudorandom {@code double} value is generated as if it's the result
* of calling the method {@link #nextDouble()}.
*
* @return a stream of pseudorandom {@code double} values
*
* @implNote This method is implemented to be equivalent to {@code
* doubles(Long.MAX_VALUE)}.
* @since 1.8
*/
// 返回的流可以生成Long.MAX_VALUE个随机double值,取值范围是[0, 1)
public DoubleStream doubles() {
return StreamSupport.doubleStream(new RandomDoublesSpliterator(this, 0L, Long.MAX_VALUE, Double.MAX_VALUE, 0.0), false);
}
/**
* Returns a stream producing the given {@code streamSize} number of
* pseudorandom {@code double} values, each between zero
* (inclusive) and one (exclusive).
*
* <p>A pseudorandom {@code double} value is generated as if it's the result
* of calling the method {@link #nextDouble()}.
*
* @param streamSize the number of values to generate
*
* @return a stream of {@code double} values
*
* @throws IllegalArgumentException if {@code streamSize} is
* less than zero
* @since 1.8
*/
// 返回的流可以生成streamSize个随机double值,取值范围是[0, 1)
public DoubleStream doubles(long streamSize) {
if(streamSize<0L) {
throw new IllegalArgumentException(BadSize);
}
return StreamSupport.doubleStream(new RandomDoublesSpliterator(this, 0L, streamSize, Double.MAX_VALUE, 0.0), false);
}
/**
* Returns an effectively unlimited stream of pseudorandom {@code
* double} values, each conforming to the given origin (inclusive) and bound
* (exclusive).
*
* <p>A pseudorandom {@code double} value is generated as if it's the result
* of calling the following method with the origin and bound:
* <pre> {@code
* double nextDouble(double origin, double bound) {
* double r = nextDouble();
* r = r * (bound - origin) + origin;
* if (r >= bound) // correct for rounding
* r = Math.nextDown(bound);
* return r;
* }}</pre>
*
* @param randomNumberOrigin the origin (inclusive) of each random value
* @param randomNumberBound the bound (exclusive) of each random value
*
* @return a stream of pseudorandom {@code double} values,
* each with the given origin (inclusive) and bound (exclusive)
*
* @throws IllegalArgumentException if {@code randomNumberOrigin}
* is greater than or equal to {@code randomNumberBound}
* @implNote This method is implemented to be equivalent to {@code
* doubles(Long.MAX_VALUE, randomNumberOrigin, randomNumberBound)}.
* @since 1.8
*/
// 返回的流可以生成Long.MAX_VALUE个随机double值,取值范围是[randomNumberOrigin, randomNumberBound)
public DoubleStream doubles(double randomNumberOrigin, double randomNumberBound) {
if(!(randomNumberOrigin<randomNumberBound)) {
throw new IllegalArgumentException(BadRange);
}
return StreamSupport.doubleStream(new RandomDoublesSpliterator(this, 0L, Long.MAX_VALUE, randomNumberOrigin, randomNumberBound), false);
}