Question

我一直在玩RcppParallel并编写了一个相当简单的例子来弄清楚事情的运作方式。代码显示如下。

函数 float pdf（double x，double sigma）计算具有均值0和标准差sigma的高斯分布的缩放版本。

Struct_1是一个结构，它创建一个工作程序来执行一些计算。我填充一个矩阵来弄清楚为什么某些东西不能正常工作。

void Struct_check（）执行计算。

该功能似乎有效，但每次都不会按预期工作。我认为它与用于在函数pdf中执行计算的类型有关！

示例运行显示在代码下方。

感谢任何帮助！

#include <RcppParallel.h>
#include <RcppArmadillo.h>
#include <RcppArmadilloExtensions/sample.h>
#include <math.h>

#define pi           3.14159265358979323846  /* pi */
using namespace arma;
using namespace Rcpp;
using namespace R;
using namespace sugar;
using namespace std;
using namespace RcppParallel;

// Enable C++11 via this plugin (Rcpp 0.10.3 or later)
// [[Rcpp::plugins(cpp11)]]
// [[Rcpp::depends(RcppParallel)]]


// Returns the probability of x, given the distribution described by mu and sigma.
float pdf(double x,  double sigma)
{
     return exp( -1 * x * x / (2 * sigma * sigma)) / sigma;
}

struct Struct_1 : public Worker
{
     arma::vec wr;
     arma::vec sr; 
     NumericVector w2;

     // source matrix
     const RVector<double> input;

     // destination matrix
     RMatrix<double> output;

     // initialize with source and destination
     Struct_1(const NumericMatrix input, NumericMatrix output) 
          : input(input), output(output) {}

     //what is done. 
     void operator()(std::size_t begin, std::size_t end) {

          for (std::size_t i=begin; i<end; i++){ //the processor loop!

               NumericVector w2(3); 

               for (int comp_j=0; comp_j<3; ++comp_j){
                    w2(comp_j) = wr(comp_j) * pdf( input[i], sr(comp_j) ) ;
               }

               double sw1 = sum(w2);

               output(i,0) = w2(0); 
               output(i,1) = w2(1); 
               output(i,2) = w2(2); 
               output(i,3) = sw1; 

               w2 = w2/sw1;

               output(i,4) = w2(0); 
               output(i,5) = w2(1); 
               output(i,6) = w2(2); 

               double sw2 = sum(w2);

               output(i,7) = sw2;

          }//end of i loop
     }//end of operator
};


// [[Rcpp::depends("RcppArmadillo")]]
// [[Rcpp::export]]
void Struct_check(){

     //Some vecs defined
     arma::vec wr = {0.2522, 0.58523, 0.16257};
     arma::vec s2r = {1.2131, 2.9955, 7.5458}; 
     arma::vec sr = sqrt(s2r);

     //an arma mat that will be used in the struct 
     arma::mat arb_mat;
     arb_mat.randn(20);
     Rcout<<"Arb_mat=\n"<<arb_mat<<endl;

     NumericMatrix r_i_x_NM = as<NumericMatrix>(wrap( arb_mat )); //convert to NumericMatrix
     NumericMatrix output( r_i_x_NM.nrow() , 8 ); //define the output matrix

     Struct_1 struct_1( r_i_x_NM , output);
     struct_1.wr = wr;
     struct_1.sr = sr;

     Rcout<<"nrow output = "<<output.nrow()<<endl;
     Rcout<<"ncol output = "<<output.ncol()<<endl;

     parallelFor(0, r_i_x_NM.length(), struct_1);
     Rcout<<"completed Parallell calculations"<<endl;
     Rcout<<"output = \n"<<output<<endl;

}

从Rstudio中运行。如果那样的话，我正在运行OS X El Capitan。

Struct_check()
Arb_mat=
  -0.4539
   0.7915
   0.2581
   1.5917
   0.3718
   0.4452
   0.1230
  -1.4719
   0.0024
   2.6166
  -0.4839
  -1.2865
   2.0492
  -1.5980
  -0.7531
  -0.7312
  -1.4482
   0.0202
   0.4434
  -0.0224

nrow output = 20
ncol output = 8
completed Parallell calculations
output = 
0.210336 0.326704 0.0583792 0.595419 0.353256 0.548696 0.0980473 1.00000
0.176872 0.304564 0.0567753 0.538211 0.328629 0.565882 0.105489 1.00000
0.222778 0.334398 0.0589211 0.616097 0.361596 0.542768 0.0956361 1.00000
0.0805904 0.221529 0.0500356 0.352155 0.228849 0.629067 0.142084 1.00000
0.216296 0.330423 0.0586421 0.605361 0.357301 0.545827 0.0968712 1.00000
0.211018 0.327133 0.0584096 0.596561 0.353724 0.548365 0.0979106 1.00000
0.227556 0.337284 0.0591224 0.623962 0.364695 0.540551 0.0947533 1.00000
0.0937487 0.235521 0.0512670 0.380537 0.246359 0.618918 0.134723 1.00000
0.228979 0.338136 0.0591817 0.626297 0.365608 0.539897 0.0944947 1.00000
0.0136216 0.107837 0.0375975 0.159056 0.0856401 0.677981 0.236379 1.00000
0.207911 0.325174 0.0582705 0.591355 0.351584 0.549879 0.0985372 1.00000
0.115751 0.256513 0.0530344 0.425298 0.272164 0.603137 0.124699 1.00000
0.0405607 0.167755 0.0448066 0.253123 0.160241 0.662743 0.177015 1.00000
0.0799309 0.220793 0.0499695 0.350694 0.227922 0.629590 0.142488 1.00000
0.181248 0.307594 0.0569989 0.545841 0.332053 0.563523 0.104424 1.00000
0.183689 0.309265 0.0571216 0.550075 0.333934 0.562222 0.103843 1.00000
**0.228941 0.338113 0.0591801 0.618557 0.591026 0.872861 0.152777 1.61666**
0.228941 0.338113 0.0591801 0.626234 0.365583 0.539915 0.0945016 1.61666
0.211153 0.327218 0.0584156 0.596786 0.353816 0.548300 0.0978837 1.00000
0.228932 0.338108 0.0591798 0.626220 0.365578 0.539919 0.0945032 1.00000

当评估 -1.4482 以产生以下行时，会发生错误 0.228941 0.338113 0.0591801 0.618557 0.591026 0.872861 0.152777 1.61666

在R - 检查中我得到：

wr <- c(0.2522, 0.58523, 0.16257)
s2r <- c(1.2131, 2.9955, 7.5458)
sr <- sqrt(s2r)

w<-NULL
for (i in 1:3){
     w[i] = wr[i]*exp( -0.5*((-1.4482/sr[i])^ 2))/(sr[i])
}

w
[1] 0.09646706 0.23826346 0.05150315

sum(w)
[1] 0.3862337

w = w/sum(w)

w
[1] 0.2497635 0.6168894 0.1333471

RcppParallel和C ++。结果不一致

0 个答案: