1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
|
#include "low_discrepancy.hpp"
#include <vector>
#include <gsl/gsl_cdf.h>
#include "var_alea.hpp"
double frac_part(double x);
//fonctions de compositions de monte_carlo.hpp
template <class _Result>
struct generator
{
typedef _Result result_type;
};
template <typename Fct, typename VA>
struct compose_t : public generator< typename Fct::result_type >
{
compose_t(Fct f, VA X) : f(f), X(X) {};
typename Fct::result_type operator()() {
return f(X());
};
private:
Fct f; VA X;
};
template <typename Fct, typename VA>
inline compose_t<Fct, VA>
compose(Fct f, VA X) {
return compose_t<Fct, VA>(f, X);
};
template <typename VA>
struct mean_t: public generator<double>
{
mean_t(int n, VA X): n(n), X(X) {};
double operator()(){
double sum = 0;
for(int i=0; i<n; i++){
sum+=X();
}
return sum/n;
}
private :
int n; VA X;
};
template <typename VA>
inline mean_t<VA>
mean(int n, VA X){
return mean_t<VA>(n, X);
};
//Les classes de monte-Carlo
template <typename L>
std::vector<double> monte_carlo(int n, L X)
{
std::vector<double> result(3,0);
double x;
for (int j = 0; j < n; j++) {
x = X();
result[0] += x;
result[1] += x*x;
}
result[0] /= (double) n;
result[1] = (result[1] - n*result[0]*result[0])/(double)(n-1);
result[2] = 1.96*sqrt(result[1]/(double) n);
return result;
};
template <typename L, typename Fct>
std::vector<double> monte_carlo(int n, Fct f, L X)
{
return monte_carlo(n, compose(f, X));
};
//Les classes de quasi-mean
template <typename LDS>
struct quasi_gaussian : public var_alea<std::vector<double> >
{
quasi_gaussian (int d, double mean = 0, double std =1)
: d(d), mean(mean), std(std), s(d), U(0,1), seed(d) {
for(int i=0; i<d; i++) {seed[i]=U();}
};
std::vector<double> operator ()(){
std::vector<double> result = s();
for(int i=0;i<d; i++){
result[i] = mean + gsl_cdf_gaussian_Pinv(frac_part(result[i]+seed[i]), std);
}
return value = result;
}
private:
int d;
double mean, std;
LDS s;
uniform U;
std::vector<double> seed;
};
struct gaussian_d : public var_alea<std::vector<double> >
{
gaussian_d(int d, double mean = 0, double std =1)
: d(d), mean(mean), std(std), G(mean, std) {};
std::vector<double> operator ()(){
std::vector<double> result(d);
for(int i=0;i<d; i++){
result[i] = G();
}
return value = result;
}
private:
int d;
double mean, std;
gaussian G;
};
|
