std :: normal_distribution& lt; double& gt;导致顺序错误的Windows与Linux? [英] std::normal_distribution<double> results in wrong order windows versus linux?
问题描述
有人来访问此问题吗?根据 1 ，实现不需要生成相同的数据.实际情况如何?arm，x86，免费和商业编译器之间在STL实现方面有很多区别吗?
Anyone came access this issue? Per 1 the implementations are not required to produce same data. What about in practice - are there many differences in STL implementations among arm, x86, free and commercial compilers?
// g++ --std=c++11 -o a minimal.cpp && ./a
#include <iostream>
#include <random>
using namespace std;
int
main()
{
std::mt19937_64 gen;
gen.seed(17);
cout << "\nNormal\n";
normal_distribution<double> distr1;
for (int i = 0; i < 2; i++) {
double delay = distr1(gen);
printf(" Value = %15.15g\n", delay);
}
return(0);
}
/*
Results
(1) gcc-4.8.0 linux 64b version result
Normal
Value = 1.03167351251536
Value = 1.21967569130525
(2) Microsoft Visual Studio Community 2015 Version 14.0.23107.0 D14REL
or
Microsoft Visual Studio Professional 2012 Version 11.0.60610.01 Update 3
Normal
Value = 1.21967569130525
Value = 1.03167351251536 // same values in wrong (different) order
*/
我可以理解在特殊的HW平台上为生成器或发行版使用了不同的算法，但是这种差异似乎更多是一个错误.
I could understand use of a different algorithm for either the generator or the distribution on some special HW platform but this difference seems more as a bug.
这里有更多我用来诊断差异来源的代码，并解决该差异:-生成器和统一分发在Win和Linux上匹配.-除成对顺序外，正态分布在数值上匹配
Here is some more code I used to diagnose where does the difference come from and workaround it: - Generator and uniform distribution match on win and linux. - The normal distribution matches numerically except for pair-wise order
// g++ --std=c++11 -o a workaround.cpp && ./a
#include <iostream>
#include <random>
#include <stack>
using namespace std;
typedef std::mt19937_64 RandGenLowType;
// Helper wrapper - it did confirm that the differences
// do NOT come from the generator
class RandGenType : public RandGenLowType {
public:
result_type operator()() {
result_type val = RandGenLowType::operator()();
printf(" Gen pulled %20llu\n", val);
return(val);
}
};
typedef normal_distribution<double> NormalDistrLowType;
// Workaround wrapper to swap the output data stream pairwise
class NormalDistrType : NormalDistrLowType {
public:
result_type operator()(RandGenType &pGen) {
// Keep single flow (used variables, includes) same for all platforms
if (win64WaStack.empty()) {
win64WaStack.push(NormalDistrLowType::operator()(pGen));
#ifdef _MSC_VER
win64WaStack.push(NormalDistrLowType::operator()(pGen));
#endif
}
result_type lResult = win64WaStack.top();
win64WaStack.pop();
return(lResult);
}
private:
std::stack<result_type> win64WaStack;
};
int
main()
{
RandGenType gen;
gen.seed(17);
// No platform issue, no workaround used
cout << "\nUniform\n";
uniform_real_distribution<double> distr;
for (int i = 0; i < 4; i++) {
double delay = distr(gen);
printf(" Delay = %15.15g\n", delay);
}
// Requires the workaround
#ifdef _MSC_VER
cout << "Workaround code is active, swapping the output stream pairwise\n";
#endif
cout << "\nNormal\n";
//normal_distribution<float> distr1;
NormalDistrType distr1;
for (int i = 0; i < 10; i++) {
double delay = distr1(gen);
printf(" Value = %15.15g\n", delay);
}
return(0);
}
推荐答案
生成正态分布的随机数的几种常用方法，例如 Marsaglia极坐标方法，一次生成两个随机数.使用这些方法之一的分发对象将生成两个随机数，返回其中一个，然后将另一个保存下来，以备下次调用.
Several common methods of generating normally distributed random numbers, such as the Box-Muller transform and the Marsaglia polar method, generate two random numbers at once. A distribution object using one of those methods would generate two random numbers, return one of them, and save the other one for the next time it's called.
返回哪一个和存储哪一个完全取决于库作者.看起来libstdc ++和MSVC使用相同的算法，但是碰巧选择不同.
Which one is returned and which one is stored is, of course, completely up to the library author. It looks like libstdc++ and MSVC use the same algorithm, but happen to choose differently.
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