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/* ---------------------------------------------------------------------
* Numenta Platform for Intelligent Computing (NuPIC)
* Copyright (C) 2016, Numenta, Inc. Unless you have an agreement
* with Numenta, Inc., for a separate license for this software code, the
* following terms and conditions apply:
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU Affero Public License version 3 as
* published by the Free Software Foundation.
*
* This program 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 Affero Public License for more details.
*
* You should have received a copy of the GNU Affero Public License
* along with this program. If not, see http://www.gnu.org/licenses.
*
* http://numenta.org/licenses/
* ---------------------------------------------------------------------
*/
package org.numenta.nupic.util;
import java.math.BigDecimal;
import java.math.BigInteger;
import java.math.MathContext;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
import java.util.Random;
import java.util.stream.Collectors;
import java.util.stream.IntStream;
import gnu.trove.list.array.TIntArrayList;
import gnu.trove.set.hash.TIntHashSet;
/**
*
* This also has a Python version which is guaranteed to output the same random
* numbers if given the same initial seed value.
*
* Implementation of George Marsaglia's elegant Xorshift random generator
* 30% faster and better quality than the built-in java.util.random.
*
* see http://www.javamex.com/tutorials/random_numbers/xorshift.shtml.
* @author cogmission
*/
public class UniversalRandom extends Random {
/** serial version */
private static final long serialVersionUID = 1L;
private static final MathContext MATH_CONTEXT = new MathContext(9);
long seed;
static final String BadBound = "bound must be positive";
public UniversalRandom(long seed) {
this.seed = seed;
}
/**
* Sets the long value used as the initial seed
*
* @param seed the value with which to be initialized
*/
@Override
public void setSeed(long seed) {
this.seed = seed;
}
/**
* Returns the long value used as the initial seed
*
* @return the initial seed value
*/
public long getSeed() {
return seed;
}
/*
* Internal method used for testing
*/
private int[] sampleWithPrintout(TIntArrayList choices, int[] selectedIndices, List collectedRandoms) {
TIntArrayList choiceSupply = new TIntArrayList(choices);
int upperBound = choices.size();
for (int i = 0; i < selectedIndices.length; i++) {
int randomIdx = nextInt(upperBound);
//System.out.println("randomIdx: " + randomIdx);
collectedRandoms.add(randomIdx);
selectedIndices[i] = (choiceSupply.removeAt(randomIdx));
upperBound--;
}
Arrays.sort(selectedIndices);
return selectedIndices;
}
/**
* Returns a random, sorted, and unique list of the specified sample size of
* selections from the specified list of choices.
*
* @param choices
* @param selectedIndices
* @return an array containing a sampling of the specified choices
*/
public int[] sample(TIntArrayList choices, int[] selectedIndices) {
TIntArrayList choiceSupply = new TIntArrayList(choices);
int upperBound = choices.size();
for (int i = 0; i < selectedIndices.length; i++) {
int randomIdx = nextInt(upperBound);
selectedIndices[i] = (choiceSupply.removeAt(randomIdx));
upperBound--;
}
Arrays.sort(selectedIndices);
//System.out.println("sample: " + Arrays.toString(selectedIndices));
return selectedIndices;
}
/**
* Fisher-Yates implementation which shuffles the array contents.
*
* @param array the array of ints to shuffle.
* @return shuffled array
*/
public int[] shuffle(int[] array) {
int index;
for (int i = array.length - 1; i > 0; i--) {
index = nextInt(i + 1);
if (index != i) {
array[index] ^= array[i];
array[i] ^= array[index];
array[index] ^= array[i];
}
}
//System.out.println("shuffle: " + Arrays.toString(array));
return array;
}
/**
* Returns an array of floating point values of the specified shape
*
* @param rows the number of rows
* @param cols the number of cols
* @return
*/
public double[][] rand(int rows, int cols) {
double[][] retval = new double[rows][cols];
for(int i = 0;i < rows;i++) {
for(int j = 0;j < cols;j++) {
retval[i][j] = nextDouble();
}
}
return retval;
}
/**
* Returns an array of binary values of the specified shape whose
* total number of "1's" will reflect the sparsity specified.
*
* @param rows the number of rows
* @param cols the number of cols
* @param sparsity number between 0 and 1, indicating percentage
* of "on" bits
* @return
*/
public int[][] binDistrib(int rows, int cols, double sparsity) {
double[][] rand = rand(rows, cols);
for(int i = 0;i < rand.length;i++) {
TIntArrayList sub = new TIntArrayList(
ArrayUtils.where(rand[i], new Condition.Adapter() {
@Override public boolean eval(double d) {
return d >= sparsity;
}
}));
int sublen = sub.size();
int target = (int)(sparsity * cols);
if(sublen < target) {
int[] full = IntStream.range(0, cols).toArray();
TIntHashSet subSet = new TIntHashSet(sub);
TIntArrayList toFill = new TIntArrayList(
Arrays.stream(full)
.filter(d -> !subSet.contains(d))
.toArray());
int cnt = toFill.size();
for(int x = 0;x < target - sublen;x++, cnt--) {
int ind = nextInt(cnt);
int item = toFill.removeAt(ind);
rand[i][item] = sparsity;
}
}else if(sublen > target) {
int cnt = sublen;
for(int x = 0;x < sublen - target;x++, cnt--) {
int ind = nextInt(cnt);
int item = sub.removeAt(ind);
rand[i][item] = 0.0;
}
}
}
int[][] retval = Arrays.stream(rand)
.map(da -> Arrays.stream(da).mapToInt(d -> d >= sparsity ? 1 : 0).toArray())
.toArray(int[][]::new);
return retval;
}
@Override
public double nextDouble() {
int nd = nextInt(10000);
double retVal = new BigDecimal(nd * .0001d, MATH_CONTEXT).doubleValue();
//System.out.println("nextDouble: " + retVal);
return retVal;
}
@Override
public int nextInt() {
int retVal = nextInt(Integer.MAX_VALUE);
//System.out.println("nextIntNB: " + retVal);
return retVal;
}
@Override
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 {
r = r % bound;
/*
THIS CODE IS COMMENTED TO WORK IDENTICALLY WITH THE PYTHON VERSION
for (int u = r;
u - (r = u % bound) + m < 0;
u = next(31))
;
*/
}
//System.out.println("nextInt(" + bound + "): " + r);
return r;
}
/**
* Implementation of George Marsaglia's elegant Xorshift random generator
* 30% faster and better quality than the built-in java.util.random see also
* see http://www.javamex.com/tutorials/random_numbers/xorshift.shtml
*/
protected int next(int nbits) {
long x = seed;
x ^= (x << 21) & 0xffffffffffffffffL;
x ^= (x >>> 35);
x ^= (x << 4);
seed = x;
x &= ((1L << nbits) - 1);
return (int) x;
}
BigInteger bigSeed;
/**
* PYTHON COMPATIBLE (Protected against overflows)
*
* Implementation of George Marsaglia's elegant Xorshift random generator
* 30% faster and better quality than the built-in java.util.random see also
* see http://www.javamex.com/tutorials/random_numbers/xorshift.shtml
*/
protected int nextX(int nbits) {
long x = seed;
BigInteger bigX = bigSeed == null ? BigInteger.valueOf(seed) : bigSeed;
bigX = bigX.shiftLeft(21).xor(bigX).and(new BigInteger("ffffffffffffffff", 16));
bigX = bigX.shiftRight(35).xor(bigX).and(new BigInteger("ffffffffffffffff", 16));
bigX = bigX.shiftLeft(4).xor(bigX).and(new BigInteger("ffffffffffffffff", 16));
bigSeed = bigX;
bigX = bigX.and(BigInteger.valueOf(1L).shiftLeft(nbits).subtract(BigInteger.valueOf(1)));
x = bigX.intValue();
//System.out.println("x = " + x + ", seed = " + seed);
return (int)x;
}
public static void main(String[] args) {
UniversalRandom random = new UniversalRandom(42);
long s = 2858730232218250L;
long e = (s >>> 35);
System.out.println("e = " + e);
int x = random.nextInt(50);
System.out.println("x = " + x);
x = random.nextInt(50);
System.out.println("x = " + x);
x = random.nextInt(50);
System.out.println("x = " + x);
x = random.nextInt(50);
System.out.println("x = " + x);
x = random.nextInt(50);
System.out.println("x = " + x);
for(int i = 0;i < 10;i++) {
int o = random.nextInt(50);
System.out.println("x = " + o);
}
random = new UniversalRandom(42);
for(int i = 0;i < 10;i++) {
double o = random.nextDouble();
System.out.println("d = " + o);
}
///////////////////////////////////
// Values Seen in Python //
///////////////////////////////////
/*
* e = 83200
x = 0
x = 26
x = 14
x = 15
x = 38
x = 47
x = 13
x = 9
x = 15
x = 31
x = 6
x = 3
x = 0
x = 21
x = 45
d = 0.945
d = 0.2426
d = 0.5214
d = 0.0815
d = 0.0988
d = 0.5497
d = 0.4013
d = 0.4559
d = 0.5415
d = 0.2381
*/
random = new UniversalRandom(42);
TIntArrayList choices = new TIntArrayList(new int[] { 1,2,3,4,5,6,7,8,9 });
int sampleSize = 6;
int[] selectedIndices = new int[sampleSize];
List collectedRandoms = new ArrayList<>();
int[] expectedSample = {1,2,3,7,8,9};
List expectedRandoms = Arrays.stream(new int[] {0,0,0,5,3,3}).boxed().collect(Collectors.toList());
random.sampleWithPrintout(choices, selectedIndices, collectedRandoms);
System.out.println("samples are equal ? " + Arrays.equals(expectedSample, selectedIndices));
System.out.println("used randoms are equal ? " + collectedRandoms.equals(expectedRandoms));
random = new UniversalRandom(42);
int[] coll = ArrayUtils.range(0, 10);
int[] before = Arrays.copyOf(coll, coll.length);
random.shuffle(coll);
System.out.println("collection before: " + Arrays.toString(before));
System.out.println("collection shuffled: " + Arrays.toString(coll));
int[] expected = { 5, 1, 8, 6, 2, 4, 7, 3, 9, 0 };
System.out.println(Arrays.equals(expected, coll));
System.out.println(!Arrays.equals(expected, before)); // not equal
}
}