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Serializable pseudo-random number generators and distributions.
/*
* Copyright (c) 2022-2023 See AUTHORS file.
*
* Licensed 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 com.github.tommyettinger.random;
import com.github.tommyettinger.digital.Distributor;
import com.github.tommyettinger.digital.Hasher;
import java.util.Random;
/**
* Not actually a pseudo-random number generator, but a quasi-random number generator, this is an extremely simple
* way to produce random-seeming numbers with a high distance between one number and the next. This has a period of
* 2 to the 64. It does not pass any tests for randomness. This is simply a counter with a specific large increment:
* 2 to the 64 divided by the golden ratio.
*
* Useful traits of this generator are that it has exactly one {@code long} of state, that all values are
* permitted for that state, and that you can {@link #skip(long)} the state forwards or backwards in constant time.
* It is also extremely fast, though it shouldn't be compared to pseudo-random number generators. It implements
* {@link #nextGaussian()} and its overload specially; these methods advance the state differently and don't return
* quasi-random output (it's much closer to pseudo-random, and is similar to {@link DistinctRandom}'s approach). The
* Gaussian methods needed this treatment because anything that requested multiple Gaussian-distributed variables each
* time it produced one output (such as a Chi or Beta distribution) would have extremely noticeable, severe artifacts.
* Because there's always a strong separation between subsequent results of {@link #nextDouble()}, that made the
* Gaussian doubles have large gaps in their output range, because some combinations were impossible.
*
* This class is an {@link EnhancedRandom} from juniper and is also a JDK {@link Random} as a result.
*
* This doesn't randomize the seed when given one with {@link #setSeed(long)}, and it doesn't do anything else to
* randomize the output, so sequential seeds will produce extremely similar sequences. You can randomize sequential
* seeds using something like {@link Hasher#randomize3(long)}, if you want random starting points.
*
* This implements all methods from {@link EnhancedRandom}, including the optional {@link #skip(long)} and
* {@link #previousLong()} methods.
*/
public class GoldenQuasiRandom extends EnhancedRandom {
/**
* The only long state variable; can be any {@code long}.
*/
public long state;
/**
* Creates a new GoldenQuasiRandom with a random state.
*/
public GoldenQuasiRandom() {
this(EnhancedRandom.seedFromMath());
}
/**
* Creates a new GoldenQuasiRandom with the given state; all {@code long} values are permitted.
*
* @param state any {@code long} value
*/
public GoldenQuasiRandom(long state) {
super(state);
this.state = state;
}
@Override
public String getTag() {
return "GoQR";
}
/**
* This has one long state.
*
* @return 1 (one)
*/
@Override
public int getStateCount () {
return 1;
}
/**
* Gets the only state, which can be any long value.
*
* @param selection ignored; this always returns the same, only state
* @return the only state's exact value
*/
@Override
public long getSelectedState (int selection) {
return state;
}
/**
* Sets the only state, which can be given any long value. The selection
* can be anything and is ignored.
*
* @param selection ignored; this always sets the same, only state
* @param value the exact value to use for the state; all longs are valid
*/
@Override
public void setSelectedState (int selection, long value) {
state = value;
}
/**
* Sets the only state, which can be given any long value; this seed value
* will not be altered. Equivalent to {@link #setSelectedState(int, long)}
* with any selection and {@code seed} passed as the {@code value}.
*
* @param seed the exact value to use for the state; all longs are valid
*/
@Override
public void setSeed (long seed) {
state = seed;
}
/**
* Gets the current state; it's already public, but I guess this could still
* be useful. The state can be any {@code long}.
*
* @return the current state, as a long
*/
public long getState () {
return state;
}
/**
* Sets each state variable to the given {@code state}. This implementation
* simply sets the one state variable to {@code state}.
*
* @param state the long value to use for the state variable
*/
@Override
public void setState (long state) {
this.state = state;
}
@Override
public long nextLong () {
return (state += 0x9E3779B97F4A7C15L);
}
/**
* Skips the state forward or backwards by the given {@code advance}, then returns the result of {@link #nextLong()}
* at the same point in the sequence. If advance is 1, this is equivalent to nextLong(). If advance is 0, this
* returns the same {@code long} as the previous call to the generator (if it called nextLong()), and doesn't change
* the state. If advance is -1, this moves the state backwards and produces the {@code long} before the last one
* generated by nextLong(). More positive numbers move the state further ahead, and more negative numbers move the
* state further behind; all of these take constant time.
*
* @param advance how many steps to advance the state before generating a {@code long}
* @return a random {@code long} by the same algorithm as {@link #nextLong()}, using the appropriately-advanced state
*/
@Override
public long skip (long advance) {
return (state += 0x9E3779B97F4A7C15L * advance);
}
@Override
public long previousLong () {
final long s = state;
state -= 0x9E3779B97F4A7C15L;
return s;
}
@Override
public int next (int bits) {
return (int) ((state += 0x9E3779B97F4A7C15L) >>> 64 - bits);
}
@Override
public int nextInt() {
return (int) ((state += 0x9E3779B97F4A7C15L) >>> 32);
}
@Override
public int nextInt(int bound) {
return (int)(bound * ((state += 0x9E3779B97F4A7C15L) >>> 32) >> 32) & ~(bound >> 31);
}
@Override
public int nextSignedInt(int outerBound) {
outerBound = (int)(outerBound * ((state += 0x9E3779B97F4A7C15L) >>> 32) >> 32);
return outerBound + (outerBound >>> 31);
}
@Override
public double nextExclusiveDouble () {
final double n = ((state += 0x9E3779B97F4A7C15L) >>> 11) * 0x1p-53;
return n == 0.0 ? 0x1.0p-54 : n;
}
@Override
public double nextExclusiveSignedDouble() {
final long bits = (state += 0x9E3779B97F4A7C15L);
final double n = (bits >>> 11) * 0x1p-53;
return Math.copySign(n == 0.0 ? 0x1.0p-54 : n, bits << 54);
}
@Override
public float nextExclusiveFloat() {
final float n = ((state += 0x9E3779B97F4A7C15L) >>> 40) * 0x1p-24f;
return n == 0f ? 0x1p-25f : n;
}
@Override
public float nextExclusiveSignedFloat() {
final long bits = (state += 0x9E3779B97F4A7C15L);
final float n = (bits >>> 40) * 0x1p-24f;
return Math.copySign(n == 0f ? 0x1p-25f : n, bits << 25);
}
@Override
public double nextGaussian() {
// return super.nextGaussian();
// return probit(nextDouble());
// return probit(((state & 0x1FFF_FFFFF_FFFFFL) ^ nextLong() >>> 11) * 0x1p-53);
return Distributor.linearNormal(state += 0x9E3779B97F4A7C15L);
// return Ziggurat.normal(state += 0x9E3779B97F4A7C15L); // has severe problems when getting tuples of Gaussians
}
@Override
public GoldenQuasiRandom copy () {
return new GoldenQuasiRandom(state);
}
@Override
public boolean equals (Object o) {
if (this == o)
return true;
if (o == null || getClass() != o.getClass())
return false;
GoldenQuasiRandom that = (GoldenQuasiRandom)o;
return state == that.state;
}
@Override
public String toString () {
return "GoldenQuasiRandom{state=" + (state) + "L}";
}
}