Random.h

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/*
 GRT MIT License
 Copyright (c) <2012> <Nicholas Gillian, Media Lab, MIT>
 
 Permission is hereby granted, free of charge, to any person obtaining a copy of this software
 and associated documentation files (the "Software"), to deal in the Software without restriction,
 including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense,
 and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so,
 subject to the following conditions:
 
 The above copyright notice and this permission notice shall be included in all copies or substantial
 portions of the Software.
 
 THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT
 LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
 IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
 WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
 SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
 */

#ifndef GRT_RANDOM_HEADER
#define GRT_RANDOM_HEADER

#include <assert.h>
#include <cstring>
#include "../Util/GRTVersionInfo.h"
#include "Timer.h"

GRT_BEGIN_NAMESPACE

class Random{
public:
 Random(unsigned long long seed = 0):v(4101842887655102017LL), w(1), storedval(0.0){
 if( seed == 0 ){
 Timer t;
 seed = (unsigned long long)t.getSystemTime();
 }
 setSeed( seed );
 }

 ~Random(){
 }
 
 void setSeed(unsigned long long seed = 0){
 if( seed == 0 ){
 Timer t;
 seed = (unsigned long long)t.getSystemTime();
 }
 v = 4101842887655102017LL;
 w = 1;
 storedval = 0;
 u = seed ^ v; int64();
 v = u; int64();
 w = v; int64();
 }
 
 inline int getRandomNumberInt(int minRange,int maxRange){
 return int( floor(getRandomNumberUniform(minRange,maxRange)) );
 }
 
 int getRandomNumberWeighted(const Vector< int > &values,const VectorFloat &weights){
 
 if( values.size() != weights.size() ) return 0;
 
 unsigned int N = (unsigned int)values.size();
 Vector< IndexedDouble > weightedValues( N );
 for(unsigned int i=0; i<N; i++){
 weightedValues[i].index = values[i];
 weightedValues[i].value = weights[i];
 }
 
 return getRandomNumberWeighted( weightedValues );
 }
 
 int getRandomNumberWeighted(Vector< IndexedDouble > weightedValues){
 
 unsigned int N = (unsigned int)weightedValues.size();
 
 if( N == 0 ) return 0;
 if( N == 1 ) return weightedValues[0].index;
 
 //Sort the weighted values by value in ascending order (so the least likely value is first, the second most likely is second, etc...
 sort(weightedValues.begin(),weightedValues.end(),IndexedDouble::sortIndexedDoubleByValueAscending);
 
 //Create the accumulated sum lookup table
 Vector< Float > x(N);
 x[0] = weightedValues[0].value;
 for(unsigned int i=1; i<N; i++){
 x[i] = x[i-1] + weightedValues[i].value;
 }
 
 //Generate a random value between min and the max weighted Float values
 Float randValue = getRandomNumberUniform(0,x[N-1]);
 
 //Find which bin the rand value falls into, return the index of that bin
 for(unsigned int i=0; i<N; i++){
 if( randValue <= x[i] ){
 return weightedValues[ i ].index;
 }
 }
 return 0;
 }
 
 int getRandomNumberWeighted(Vector< IndexedDouble > &weightedValues, VectorFloat &x){
 
 unsigned int N = (unsigned int)weightedValues.size();
 
 if( weightedValues.size() != x.size() ) return 0;
 
 //Generate a random value between min and the max weighted Float values
 Float randValue = getRandomNumberUniform(0,x[N-1]);
 
 //Find which bin the rand value falls into, return the index of that bin
 for(unsigned int i=0; i<N; i++){
 if( randValue <= x[i] ){
 return weightedValues[ i ].index;
 }
 }
 return 0;
 }
 
 inline Float getRandomNumberUniform(Float minRange=0.0,Float maxRange=1.0){
 return (doub()*(maxRange-minRange))+minRange;
 }
 
 Float getRandomNumberGauss(Float mu=0.0,Float sigma=1.0){
 Float v1,v2,rsq,fac;
 
 if (storedval == 0.){
 do {
 v1=2.0*doub()-1.0;
 v2=2.0*doub()-1.0;
 rsq=v1*v1+v2*v2;
 } while (rsq >= 1.0 || rsq == 0.0);
 fac=sqrt(-2.0*log(rsq)/rsq);
 storedval = v1*fac;
 return mu + sigma*v2*fac;
 } else {
 fac = storedval;
 storedval = 0.;
 return mu + sigma*fac;
 }
 }
 
 VectorFloat getRandomVectorUniform(UINT numDimensions,Float minRange=0.0,Float maxRange=1.0){
 VectorFloat randomValues(numDimensions);
 for(UINT i=0; i<numDimensions; i++){
 randomValues[i] = getRandomNumberUniform(minRange,maxRange);
 }
 return randomValues;
 }
 
 VectorFloat getRandomVectorGauss(UINT numDimensions,Float mu=0.0,Float sigma=1.0){
 VectorFloat randomValues(numDimensions);
 for(UINT i=0; i<numDimensions; i++){
 randomValues[i] = getRandomNumberGauss(mu,sigma);
 }
 return randomValues;
 }
 
 Vector< unsigned int > getRandomSubset( const unsigned int startRange, const unsigned int endRange, const unsigned int subsetSize ){
 
 unsigned int i = 0;
 const unsigned int rangeSize = endRange - startRange;
 
 grt_assert( rangeSize > 0 );
 grt_assert( endRange > startRange );
 grt_assert( subsetSize <= rangeSize );
 
 Vector< unsigned int > indexs( rangeSize );
 Vector< unsigned int > subset ( subsetSize );
 
 //Fill up the range buffer and the randomly suffle it
 for(i=startRange; i<endRange; i++){
 indexs[i] = i;
 }
 std::random_shuffle(indexs.begin(), indexs.end());
 
 //Select the first X values from the randomly shuffled range buffer as the subset
 for(i=0; i<subsetSize; i++){
 subset[i] = indexs[i];
 }
 
 return subset;
 }
 
private:
 inline unsigned long long int64() {
 u = u * 2862933555777941757LL + 7046029254386353087LL;
 v ^= v >> 17; v ^= v << 31; v ^= v >> 8;
 w = 4294957665U*(w & 0xffffffff) + (w >> 32);
 unsigned long long x = u ^ (u << 21); x ^= x >> 35; x ^= x << 4;
 return (x + v) ^ w;
 }
 inline Float doub() { return 5.42101086242752217E-20 * int64(); }
 inline unsigned int int32() { return (unsigned int)int64(); } 
 
 unsigned long long u;
 unsigned long long v;
 unsigned long long w;
 Float storedval; //This is for the Gauss Box-Muller 
};

GRT_END_NAMESPACE

#endif //GRT_RANDOM_HEADER