source: issm/trunk-jpl/src/c/shared/Random/random.cpp@ 26656

/*!\file: random
 * \brief random number generating functions
 */ 

/*Headers*/
/*{{{*/
#include <stdio.h>
#include <sys/types.h>
#include <math.h>
#include <float.h>    /*  DBL_EPSILON  */
#include <cstdarg>
#include <iostream>

#include "../Matrix/matrix.h"
#include "../Exceptions/exceptions.h"
#include "../MemOps/MemOps.h"
#include "../io/io.h"
#include "./randomgenerator.h"
/*}}}*/

void univariateNormal(IssmPDouble* prand, IssmPDouble mean, IssmPDouble sdev, int seed=-1) { /*{{{*/

        /*Seed the pseudo-random number generator*/
        rnd::linear_congruential_engine randomengine;
        randomengine.seed(seed);
        /*Normal distribution*/
        rnd::normal_distribution distriNormal(mean,sdev);
        *prand = distriNormal.generator(randomengine);
} /*}}}*/
void multivariateNormal(IssmDouble** prand, int dim, IssmDouble mean, IssmDouble* covariancematrix, int seed=-1) { /*{{{*/
   
        IssmPDouble* sampleStandardNormal    = xNew<IssmPDouble>(dim);
   IssmDouble* sampleMultivariateNormal = xNew<IssmDouble>(dim);
   IssmDouble* Lchol                    = xNewZeroInit<IssmDouble>(dim*dim);

        /*True randomness if seed<0, otherwise random seed is fixed at seed*/
        /*Seed the pseudo-random number generator, repeatedly calling univariateNormal does not ensure randomness*/
        rnd::linear_congruential_engine randomengine;
        randomengine.seed(seed);
        /*Normal distribution*/
        rnd::normal_distribution distriNormal(0.0,1.0);
        for(int i=0;i<dim;i++){
                sampleStandardNormal[i] = distriNormal.generator(randomengine);
        }

        /*Cholsesky decomposition of the covariance matrix*/
        CholeskyRealPositiveDefinite(Lchol,covariancematrix,dim);
   
        /*Matrix by vector multiplication*/
        for(int i=0;i<dim;i++){ 
      /*Entry-by-entry multiplication along matrix row*/
      IssmDouble sum=0.;
      for(int j=0;j<dim;j++) sum += sampleStandardNormal[j]*Lchol[i*dim+j]; 
      sampleMultivariateNormal[i] = mean+sum;
   }

   /*Assign output pointer and cleanup*/
   *prand = sampleMultivariateNormal;
   xDelete<IssmPDouble>(sampleStandardNormal);
   xDelete<IssmDouble>(Lchol);
} /*}}}*/
void multivariateNormal(IssmDouble** prand, int dim, IssmDouble* mean, IssmDouble* covariancematrix, int seed=-1) { /*{{{*/
        
        IssmPDouble* sampleStandardNormal    = xNew<IssmPDouble>(dim);
        IssmDouble* sampleMultivariateNormal = xNew<IssmDouble>(dim);
        IssmDouble* Lchol                    = xNewZeroInit<IssmDouble>(dim*dim);
        
        /*True randomness if seed<0, otherwise random seed is fixed at seed*/
        /*Seed the pseudo-random number generator, repeatedly calling univariateNormal does not ensure randomness*/
        rnd::linear_congruential_engine randomengine;
        randomengine.seed(seed);
        /*Normal distribution*/
        rnd::normal_distribution distriNormal(0.0,1.0);
        for(int i=0;i<dim;i++){
                sampleStandardNormal[i] = distriNormal.generator(randomengine);
        }

        /*Cholsesky decomposition of the covariance matrix*/
        CholeskyRealPositiveDefinite(Lchol,covariancematrix,dim);

        /*Matrix by vector multiplication*/
        for(int i=0;i<dim;i++){
                IssmDouble sum = 0.;
      for(int j=0;j<dim;j++) sum += sampleStandardNormal[j]*Lchol[i*dim+j]; 
      sampleMultivariateNormal[i] = mean[i]+sum;
        }
   
        /*Assign output pointer and cleanup*/
        *prand = sampleMultivariateNormal;
        xDelete<IssmPDouble>(sampleStandardNormal);
        xDelete<IssmDouble>(Lchol);
} /*}}}*/