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[rand.dist.norm.lognormal]

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git-svn-id: https://llvm.org/svn/llvm-project/libcxx/trunk@103957 91177308-0d34-0410-b5e6-96231b3b80d8
This commit is contained in:
Howard Hinnant 2010-05-17 18:31:53 +00:00
parent c2b0dc7e33
commit 2bc36fcff3
19 changed files with 1185 additions and 1 deletions
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162
include/random
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@ -1124,7 +1124,62 @@ public:
};
template<class RealType = double>
class lognormal_distribution;
class lognormal_distribution
{
public:
// types
typedef RealType result_type;
class param_type
{
public:
typedef lognormal_distribution distribution_type;
explicit param_type(result_type m = 0, result_type s = 1);
result_type m() const;
result_type s() const;
friend bool operator==(const param_type& x, const param_type& y);
friend bool operator!=(const param_type& x, const param_type& y);
};
// constructor and reset functions
explicit lognormal_distribution(result_type m = 0, result_type s = 1);
explicit lognormal_distribution(const param_type& parm);
void reset();
// generating functions
template<class URNG> result_type operator()(URNG& g);
template<class URNG> result_type operator()(URNG& g, const param_type& parm);
// property functions
result_type m() const;
result_type s() const;
param_type param() const;
void param(const param_type& parm);
result_type min() const;
result_type max() const;
friend bool operator==(const lognormal_distribution& x,
const lognormal_distribution& y);
friend bool operator!=(const lognormal_distribution& x,
const lognormal_distribution& y);
template <class charT, class traits>
friend
basic_ostream<charT, traits>&
operator<<(basic_ostream<charT, traits>& os,
const lognormal_distribution& x);
template <class charT, class traits>
friend
basic_istream<charT, traits>&
operator>>(basic_istream<charT, traits>& is,
lognormal_distribution& x);
};
template<class RealType = double>
class chi_squared_distribution
@ -3796,6 +3851,111 @@ operator>>(basic_istream<_CharT, _Traits>& __is,
return __is;
}
// lognormal_distribution
template<class _RealType = double>
class lognormal_distribution
{
public:
// types
typedef _RealType result_type;
class param_type
{
normal_distribution<result_type> __nd_;
public:
typedef lognormal_distribution distribution_type;
explicit param_type(result_type __m = 0, result_type __s = 1)
: __nd_(__m, __s) {}
result_type m() const {return __nd_.mean();}
result_type s() const {return __nd_.stddev();}
friend bool operator==(const param_type& __x, const param_type& __y)
{return __x.__nd_ == __y.__nd_;}
friend bool operator!=(const param_type& __x, const param_type& __y)
{return !(__x == __y);}
friend class lognormal_distribution;
template <class _CharT, class _Traits, class _RT>
friend
basic_ostream<_CharT, _Traits>&
operator<<(basic_ostream<_CharT, _Traits>& __os,
const lognormal_distribution<_RT>& __x);
template <class _CharT, class _Traits, class _RT>
friend
basic_istream<_CharT, _Traits>&
operator>>(basic_istream<_CharT, _Traits>& __is,
lognormal_distribution<_RT>& __x);
};
private:
param_type __p_;
public:
// constructor and reset functions
explicit lognormal_distribution(result_type __m = 0, result_type __s = 1)
: __p_(param_type(__m, __s)) {}
explicit lognormal_distribution(const param_type& __p)
: __p_(__p) {}
void reset() {__p_.__nd_.reset();}
// generating functions
template<class _URNG> result_type operator()(_URNG& __g)
{return (*this)(__g, __p_);}
template<class _URNG> result_type operator()(_URNG& __g, const param_type& __p)
{return _STD::exp(const_cast<normal_distribution<result_type>&>(__p.__nd_)(__g));}
// property functions
result_type m() const {return __p_.m();}
result_type s() const {return __p_.s();}
param_type param() const {return __p_;}
void param(const param_type& __p) {return __p_ = __p;}
result_type min() const {return 0;}
result_type max() const {return numeric_limits<result_type>::infinity();}
friend bool operator==(const lognormal_distribution& __x,
const lognormal_distribution& __y)
{return __x.__p_ == __y.__p_;}
friend bool operator!=(const lognormal_distribution& __x,
const lognormal_distribution& __y)
{return !(__x == __y);}
template <class _CharT, class _Traits, class _RT>
friend
basic_ostream<_CharT, _Traits>&
operator<<(basic_ostream<_CharT, _Traits>& __os,
const lognormal_distribution<_RT>& __x);
template <class _CharT, class _Traits, class _RT>
friend
basic_istream<_CharT, _Traits>&
operator>>(basic_istream<_CharT, _Traits>& __is,
lognormal_distribution<_RT>& __x);
};
template <class _CharT, class _Traits, class _RT>
inline
basic_ostream<_CharT, _Traits>&
operator<<(basic_ostream<_CharT, _Traits>& __os,
const lognormal_distribution<_RT>& __x)
{
return __os << __x.__p_.__nd_;
}
template <class _CharT, class _Traits, class _RT>
inline
basic_istream<_CharT, _Traits>&
operator>>(basic_istream<_CharT, _Traits>& __is,
lognormal_distribution<_RT>& __x)
{
return __is >> __x.__p_.__nd_;
}
// poisson_distribution
template<class _IntType = int>

34
test/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.lognormal/assign.pass.cpp Normal file
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@ -0,0 +1,34 @@
//===----------------------------------------------------------------------===//
//
// The LLVM Compiler Infrastructure
//
// This file is distributed under the University of Illinois Open Source
// License. See LICENSE.TXT for details.
//
//===----------------------------------------------------------------------===//
// <random>
// template<class RealType = double>
// class lognormal_distribution
// lognormal_distribution& operator=(const lognormal_distribution&);
#include <random>
#include <cassert>
void
test1()
{
typedef std::lognormal_distribution<> D;
D d1(20, 0.75);
D d2;
assert(d1 != d2);
d2 = d1;
assert(d1 == d2);
}
int main()
{
test1();
}

32
test/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.lognormal/copy.pass.cpp Normal file
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@ -0,0 +1,32 @@
//===----------------------------------------------------------------------===//
//
// The LLVM Compiler Infrastructure
//
// This file is distributed under the University of Illinois Open Source
// License. See LICENSE.TXT for details.
//
//===----------------------------------------------------------------------===//
// <random>
// template<class RealType = double>
// class lognormal_distribution
// lognormal_distribution(const lognormal_distribution&);
#include <random>
#include <cassert>
void
test1()
{
typedef std::lognormal_distribution<> D;
D d1(20, 1.75);
D d2 = d1;
assert(d1 == d2);
}
int main()
{
test1();
}

40
test/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.lognormal/ctor_double_double.pass.cpp Normal file
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@ -0,0 +1,40 @@
//===----------------------------------------------------------------------===//
//
// The LLVM Compiler Infrastructure
//
// This file is distributed under the University of Illinois Open Source
// License. See LICENSE.TXT for details.
//
//===----------------------------------------------------------------------===//
// <random>
// template<class RealType = double>
// class lognormal_distribution
// explicit lognormal_distribution(result_type mean = 0, result_type stddev = 1);
#include <random>
#include <cassert>
int main()
{
{
typedef std::lognormal_distribution<> D;
D d;
assert(d.m() == 0);
assert(d.s() == 1);
}
{
typedef std::lognormal_distribution<> D;
D d(14.5);
assert(d.m() == 14.5);
assert(d.s() == 1);
}
{
typedef std::lognormal_distribution<> D;
D d(14.5, 5.25);
assert(d.m() == 14.5);
assert(d.s() == 5.25);
}
}

30
test/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.lognormal/ctor_param.pass.cpp Normal file
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@ -0,0 +1,30 @@
//===----------------------------------------------------------------------===//
//
// The LLVM Compiler Infrastructure
//
// This file is distributed under the University of Illinois Open Source
// License. See LICENSE.TXT for details.
//
//===----------------------------------------------------------------------===//
// <random>
// template<class RealType = double>
// class lognormal_distribution
// explicit lognormal_distribution(const param_type& parm);
#include <random>
#include <cassert>
int main()
{
{
typedef std::lognormal_distribution<> D;
typedef D::param_type P;
P p(0.25, 10);
D d(p);
assert(d.m() == 0.25);
assert(d.s() == 10);
}
}

37
test/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.lognormal/eq.pass.cpp Normal file
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@ -0,0 +1,37 @@
//===----------------------------------------------------------------------===//
//
// The LLVM Compiler Infrastructure
//
// This file is distributed under the University of Illinois Open Source
// License. See LICENSE.TXT for details.
//
//===----------------------------------------------------------------------===//
// <random>
// template<class RealType = double>
// class lognormal_distribution
// bool operator=(const lognormal_distribution& x,
// const lognormal_distribution& y);
// bool operator!(const lognormal_distribution& x,
// const lognormal_distribution& y);
#include <random>
#include <cassert>
int main()
{
{
typedef std::lognormal_distribution<> D;
D d1(2.5, 4);
D d2(2.5, 4);
assert(d1 == d2);
}
{
typedef std::lognormal_distribution<> D;
D d1(2.5, 4);
D d2(2.5, 4.5);
assert(d1 != d2);
}
}

242
test/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.lognormal/eval.pass.cpp Normal file
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@ -0,0 +1,242 @@
//===----------------------------------------------------------------------===//
//
// The LLVM Compiler Infrastructure
//
// This file is distributed under the University of Illinois Open Source
// License. See LICENSE.TXT for details.
//
//===----------------------------------------------------------------------===//
// <random>
// template<class RealType = double>
// class lognormal_distribution
// template<class _URNG> result_type operator()(_URNG& g);
#include <random>
#include <cassert>
#include <vector>
#include <numeric>
template <class T>
inline
T
sqr(T x)
{
return x * x;
}
int main()
{
{
typedef std::lognormal_distribution<> D;
typedef D::param_type P;
typedef std::mt19937 G;
G g;
D d(-1./8192, 0.015625);
const int N = 1000000;
std::vector<D::result_type> u;
for (int i = 0; i < N; ++i)
{
D::result_type v = d(g);
assert(v > 0);
u.push_back(v);
}
double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size();
double var = 0;
double skew = 0;
double kurtosis = 0;
for (int i = 0; i < u.size(); ++i)
{
double d = (u[i] - mean);
double d2 = sqr(d);
var += d2;
skew += d * d2;
kurtosis += d2 * d2;
}
var /= u.size();
double dev = std::sqrt(var);
skew /= u.size() * dev * var;
kurtosis /= u.size() * var * var;
kurtosis -= 3;
double x_mean = std::exp(d.m() + sqr(d.s())/2);
double x_var = (std::exp(sqr(d.s())) - 1) * std::exp(2*d.m() + sqr(d.s()));
double x_skew = (std::exp(sqr(d.s())) + 2) *
std::sqrt((std::exp(sqr(d.s())) - 1));
double x_kurtosis = std::exp(4*sqr(d.s())) + 2*std::exp(3*sqr(d.s())) +
3*std::exp(2*sqr(d.s())) - 6;
assert(std::abs(mean - x_mean) / x_mean < 0.01);
assert(std::abs(var - x_var) / x_var < 0.01);
assert(std::abs(skew - x_skew) / x_skew < 0.05);
assert(std::abs(kurtosis - x_kurtosis) / x_kurtosis < 0.25);
}
{
typedef std::lognormal_distribution<> D;
typedef D::param_type P;
typedef std::mt19937 G;
G g;
D d(-1./32, 0.25);
const int N = 1000000;
std::vector<D::result_type> u;
for (int i = 0; i < N; ++i)
{
D::result_type v = d(g);
assert(v > 0);
u.push_back(v);
}
double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size();
double var = 0;
double skew = 0;
double kurtosis = 0;
for (int i = 0; i < u.size(); ++i)
{
double d = (u[i] - mean);
double d2 = sqr(d);
var += d2;
skew += d * d2;
kurtosis += d2 * d2;
}
var /= u.size();
double dev = std::sqrt(var);
skew /= u.size() * dev * var;
kurtosis /= u.size() * var * var;
kurtosis -= 3;
double x_mean = std::exp(d.m() + sqr(d.s())/2);
double x_var = (std::exp(sqr(d.s())) - 1) * std::exp(2*d.m() + sqr(d.s()));
double x_skew = (std::exp(sqr(d.s())) + 2) *
std::sqrt((std::exp(sqr(d.s())) - 1));
double x_kurtosis = std::exp(4*sqr(d.s())) + 2*std::exp(3*sqr(d.s())) +
3*std::exp(2*sqr(d.s())) - 6;
assert(std::abs(mean - x_mean) / x_mean < 0.01);
assert(std::abs(var - x_var) / x_var < 0.01);
assert(std::abs(skew - x_skew) / x_skew < 0.01);
assert(std::abs(kurtosis - x_kurtosis) / x_kurtosis < 0.03);
}
{
typedef std::lognormal_distribution<> D;
typedef D::param_type P;
typedef std::mt19937 G;
G g;
D d(-1./8, 0.5);
const int N = 1000000;
std::vector<D::result_type> u;
for (int i = 0; i < N; ++i)
{
D::result_type v = d(g);
assert(v > 0);
u.push_back(v);
}
double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size();
double var = 0;
double skew = 0;
double kurtosis = 0;
for (int i = 0; i < u.size(); ++i)
{
double d = (u[i] - mean);
double d2 = sqr(d);
var += d2;
skew += d * d2;
kurtosis += d2 * d2;
}
var /= u.size();
double dev = std::sqrt(var);
skew /= u.size() * dev * var;
kurtosis /= u.size() * var * var;
kurtosis -= 3;
double x_mean = std::exp(d.m() + sqr(d.s())/2);
double x_var = (std::exp(sqr(d.s())) - 1) * std::exp(2*d.m() + sqr(d.s()));
double x_skew = (std::exp(sqr(d.s())) + 2) *
std::sqrt((std::exp(sqr(d.s())) - 1));
double x_kurtosis = std::exp(4*sqr(d.s())) + 2*std::exp(3*sqr(d.s())) +
3*std::exp(2*sqr(d.s())) - 6;
assert(std::abs(mean - x_mean) / x_mean < 0.01);
assert(std::abs(var - x_var) / x_var < 0.01);
assert(std::abs(skew - x_skew) / x_skew < 0.02);
assert(std::abs(kurtosis - x_kurtosis) / x_kurtosis < 0.05);
}
{
typedef std::lognormal_distribution<> D;
typedef D::param_type P;
typedef std::mt19937 G;
G g;
D d;
const int N = 1000000;
std::vector<D::result_type> u;
for (int i = 0; i < N; ++i)
{
D::result_type v = d(g);
assert(v > 0);
u.push_back(v);
}
double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size();
double var = 0;
double skew = 0;
double kurtosis = 0;
for (int i = 0; i < u.size(); ++i)
{
double d = (u[i] - mean);
double d2 = sqr(d);
var += d2;
skew += d * d2;
kurtosis += d2 * d2;
}
var /= u.size();
double dev = std::sqrt(var);
skew /= u.size() * dev * var;
kurtosis /= u.size() * var * var;
kurtosis -= 3;
double x_mean = std::exp(d.m() + sqr(d.s())/2);
double x_var = (std::exp(sqr(d.s())) - 1) * std::exp(2*d.m() + sqr(d.s()));
double x_skew = (std::exp(sqr(d.s())) + 2) *
std::sqrt((std::exp(sqr(d.s())) - 1));
double x_kurtosis = std::exp(4*sqr(d.s())) + 2*std::exp(3*sqr(d.s())) +
3*std::exp(2*sqr(d.s())) - 6;
assert(std::abs(mean - x_mean) / x_mean < 0.01);
assert(std::abs(var - x_var) / x_var < 0.02);
assert(std::abs(skew - x_skew) / x_skew < 0.08);
assert(std::abs(kurtosis - x_kurtosis) / x_kurtosis < 0.4);
}
{
typedef std::lognormal_distribution<> D;
typedef D::param_type P;
typedef std::mt19937 G;
G g;
D d(-0.78125, 1.25);
const int N = 1000000;
std::vector<D::result_type> u;
for (int i = 0; i < N; ++i)
{
D::result_type v = d(g);
assert(v > 0);
u.push_back(v);
}
double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size();
double var = 0;
double skew = 0;
double kurtosis = 0;
for (int i = 0; i < u.size(); ++i)
{
double d = (u[i] - mean);
double d2 = sqr(d);
var += d2;
skew += d * d2;
kurtosis += d2 * d2;
}
var /= u.size();
double dev = std::sqrt(var);
skew /= u.size() * dev * var;
kurtosis /= u.size() * var * var;
kurtosis -= 3;
double x_mean = std::exp(d.m() + sqr(d.s())/2);
double x_var = (std::exp(sqr(d.s())) - 1) * std::exp(2*d.m() + sqr(d.s()));
double x_skew = (std::exp(sqr(d.s())) + 2) *
std::sqrt((std::exp(sqr(d.s())) - 1));
double x_kurtosis = std::exp(4*sqr(d.s())) + 2*std::exp(3*sqr(d.s())) +
3*std::exp(2*sqr(d.s())) - 6;
assert(std::abs(mean - x_mean) / x_mean < 0.01);
assert(std::abs(var - x_var) / x_var < 0.04);
assert(std::abs(skew - x_skew) / x_skew < 0.2);
assert(std::abs(kurtosis - x_kurtosis) / x_kurtosis < 0.7);
}
}

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test/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.lognormal/eval_param.pass.cpp Normal file
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@ -0,0 +1,248 @@
//===----------------------------------------------------------------------===//
//
// The LLVM Compiler Infrastructure
//
// This file is distributed under the University of Illinois Open Source
// License. See LICENSE.TXT for details.
//
//===----------------------------------------------------------------------===//
// <random>
// template<class RealType = double>
// class lognormal_distribution
// template<class _URNG> result_type operator()(_URNG& g, const param_type& parm);
#include <random>
#include <cassert>
#include <vector>
#include <numeric>
template <class T>
inline
T
sqr(T x)
{
return x * x;
}
int main()
{
{
typedef std::lognormal_distribution<> D;
typedef D::param_type P;
typedef std::mt19937 G;
G g;
D d;
P p(-1./8192, 0.015625);
const int N = 1000000;
std::vector<D::result_type> u;
for (int i = 0; i < N; ++i)
{
D::result_type v = d(g, p);
assert(v > 0);
u.push_back(v);
}
double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size();
double var = 0;
double skew = 0;
double kurtosis = 0;
for (int i = 0; i < u.size(); ++i)
{
double d = (u[i] - mean);
double d2 = sqr(d);
var += d2;
skew += d * d2;
kurtosis += d2 * d2;
}
var /= u.size();
double dev = std::sqrt(var);
skew /= u.size() * dev * var;
kurtosis /= u.size() * var * var;
kurtosis -= 3;
double x_mean = std::exp(p.m() + sqr(p.s())/2);
double x_var = (std::exp(sqr(p.s())) - 1) * std::exp(2*p.m() + sqr(p.s()));
double x_skew = (std::exp(sqr(p.s())) + 2) *
std::sqrt((std::exp(sqr(p.s())) - 1));
double x_kurtosis = std::exp(4*sqr(p.s())) + 2*std::exp(3*sqr(p.s())) +
3*std::exp(2*sqr(p.s())) - 6;
assert(std::abs(mean - x_mean) / x_mean < 0.01);
assert(std::abs(var - x_var) / x_var < 0.01);
assert(std::abs(skew - x_skew) / x_skew < 0.05);
assert(std::abs(kurtosis - x_kurtosis) / x_kurtosis < 0.25);
}
{
typedef std::lognormal_distribution<> D;
typedef D::param_type P;
typedef std::mt19937 G;
G g;
D d;
P p(-1./32, 0.25);
const int N = 1000000;
std::vector<D::result_type> u;
for (int i = 0; i < N; ++i)
{
D::result_type v = d(g, p);
assert(v > 0);
u.push_back(v);
}
double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size();
double var = 0;
double skew = 0;
double kurtosis = 0;
for (int i = 0; i < u.size(); ++i)
{
double d = (u[i] - mean);
double d2 = sqr(d);
var += d2;
skew += d * d2;
kurtosis += d2 * d2;
}
var /= u.size();
double dev = std::sqrt(var);
skew /= u.size() * dev * var;
kurtosis /= u.size() * var * var;
kurtosis -= 3;
double x_mean = std::exp(p.m() + sqr(p.s())/2);
double x_var = (std::exp(sqr(p.s())) - 1) * std::exp(2*p.m() + sqr(p.s()));
double x_skew = (std::exp(sqr(p.s())) + 2) *
std::sqrt((std::exp(sqr(p.s())) - 1));
double x_kurtosis = std::exp(4*sqr(p.s())) + 2*std::exp(3*sqr(p.s())) +
3*std::exp(2*sqr(p.s())) - 6;
assert(std::abs(mean - x_mean) / x_mean < 0.01);
assert(std::abs(var - x_var) / x_var < 0.01);
assert(std::abs(skew - x_skew) / x_skew < 0.01);
assert(std::abs(kurtosis - x_kurtosis) / x_kurtosis < 0.03);
}
{
typedef std::lognormal_distribution<> D;
typedef D::param_type P;
typedef std::mt19937 G;
G g;
D d;
P p(-1./8, 0.5);
const int N = 1000000;
std::vector<D::result_type> u;
for (int i = 0; i < N; ++i)
{
D::result_type v = d(g, p);
assert(v > 0);
u.push_back(v);
}
double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size();
double var = 0;
double skew = 0;
double kurtosis = 0;
for (int i = 0; i < u.size(); ++i)
{
double d = (u[i] - mean);
double d2 = sqr(d);
var += d2;
skew += d * d2;
kurtosis += d2 * d2;
}
var /= u.size();
double dev = std::sqrt(var);
skew /= u.size() * dev * var;
kurtosis /= u.size() * var * var;
kurtosis -= 3;
double x_mean = std::exp(p.m() + sqr(p.s())/2);
double x_var = (std::exp(sqr(p.s())) - 1) * std::exp(2*p.m() + sqr(p.s()));
double x_skew = (std::exp(sqr(p.s())) + 2) *
std::sqrt((std::exp(sqr(p.s())) - 1));
double x_kurtosis = std::exp(4*sqr(p.s())) + 2*std::exp(3*sqr(p.s())) +
3*std::exp(2*sqr(p.s())) - 6;
assert(std::abs(mean - x_mean) / x_mean < 0.01);
assert(std::abs(var - x_var) / x_var < 0.01);
assert(std::abs(skew - x_skew) / x_skew < 0.02);
assert(std::abs(kurtosis - x_kurtosis) / x_kurtosis < 0.05);
}
{
typedef std::lognormal_distribution<> D;
typedef D::param_type P;
typedef std::mt19937 G;
G g;
D d(3, 4);
P p;
const int N = 1000000;
std::vector<D::result_type> u;
for (int i = 0; i < N; ++i)
{
D::result_type v = d(g, p);
assert(v > 0);
u.push_back(v);
}
double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size();
double var = 0;
double skew = 0;
double kurtosis = 0;
for (int i = 0; i < u.size(); ++i)
{
double d = (u[i] - mean);
double d2 = sqr(d);
var += d2;
skew += d * d2;
kurtosis += d2 * d2;
}
var /= u.size();
double dev = std::sqrt(var);
skew /= u.size() * dev * var;
kurtosis /= u.size() * var * var;
kurtosis -= 3;
double x_mean = std::exp(p.m() + sqr(p.s())/2);
double x_var = (std::exp(sqr(p.s())) - 1) * std::exp(2*p.m() + sqr(p.s()));
double x_skew = (std::exp(sqr(p.s())) + 2) *
std::sqrt((std::exp(sqr(p.s())) - 1));
double x_kurtosis = std::exp(4*sqr(p.s())) + 2*std::exp(3*sqr(p.s())) +
3*std::exp(2*sqr(p.s())) - 6;
assert(std::abs(mean - x_mean) / x_mean < 0.01);
assert(std::abs(var - x_var) / x_var < 0.02);
assert(std::abs(skew - x_skew) / x_skew < 0.08);
assert(std::abs(kurtosis - x_kurtosis) / x_kurtosis < 0.4);
}
{
typedef std::lognormal_distribution<> D;
typedef D::param_type P;
typedef std::mt19937 G;
G g;
D d;
P p(-0.78125, 1.25);
const int N = 1000000;
std::vector<D::result_type> u;
for (int i = 0; i < N; ++i)
{
D::result_type v = d(g, p);
assert(v > 0);
u.push_back(v);
}
double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size();
double var = 0;
double skew = 0;
double kurtosis = 0;
for (int i = 0; i < u.size(); ++i)
{
double d = (u[i] - mean);
double d2 = sqr(d);
var += d2;
skew += d * d2;
kurtosis += d2 * d2;
}
var /= u.size();
double dev = std::sqrt(var);
skew /= u.size() * dev * var;
kurtosis /= u.size() * var * var;
kurtosis -= 3;
double x_mean = std::exp(p.m() + sqr(p.s())/2);
double x_var = (std::exp(sqr(p.s())) - 1) * std::exp(2*p.m() + sqr(p.s()));
double x_skew = (std::exp(sqr(p.s())) + 2) *
std::sqrt((std::exp(sqr(p.s())) - 1));
double x_kurtosis = std::exp(4*sqr(p.s())) + 2*std::exp(3*sqr(p.s())) +
3*std::exp(2*sqr(p.s())) - 6;
assert(std::abs(mean - x_mean) / x_mean < 0.01);
assert(std::abs(var - x_var) / x_var < 0.04);
assert(std::abs(skew - x_skew) / x_skew < 0.2);
assert(std::abs(kurtosis - x_kurtosis) / x_kurtosis < 0.7);
}
}

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test/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.lognormal/get_param.pass.cpp Normal file
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//===----------------------------------------------------------------------===//
//
// The LLVM Compiler Infrastructure
//
// This file is distributed under the University of Illinois Open Source
// License. See LICENSE.TXT for details.
//
//===----------------------------------------------------------------------===//
// <random>
// template<class RealType = double>
// class lognormal_distribution
// param_type param() const;
#include <random>
#include <cassert>
int main()
{
{
typedef std::lognormal_distribution<> D;
typedef D::param_type P;
P p(.125, .5);
D d(p);
assert(d.param() == p);
}
}

41
test/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.lognormal/io.pass.cpp Normal file
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//===----------------------------------------------------------------------===//
//
// The LLVM Compiler Infrastructure
//
// This file is distributed under the University of Illinois Open Source
// License. See LICENSE.TXT for details.
//
//===----------------------------------------------------------------------===//
// <random>
// template<class RealType = double>
// class lognormal_distribution
// template <class CharT, class Traits, class RealType>
// basic_ostream<CharT, Traits>&
// operator<<(basic_ostream<CharT, Traits>& os,
// const lognormal_distribution<RealType>& x);
// template <class CharT, class Traits, class RealType>
// basic_istream<CharT, Traits>&
// operator>>(basic_istream<CharT, Traits>& is,
// lognormal_distribution<RealType>& x);
#include <random>
#include <sstream>
#include <cassert>
int main()
{
{
typedef std::lognormal_distribution<> D;
D d1(7, 5);
std::ostringstream os;
os << d1;
std::istringstream is(os.str());
D d2;
is >> d2;
assert(d1 == d2);
}
}

28
test/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.lognormal/max.pass.cpp Normal file
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//===----------------------------------------------------------------------===//
//
// The LLVM Compiler Infrastructure
//
// This file is distributed under the University of Illinois Open Source
// License. See LICENSE.TXT for details.
//
//===----------------------------------------------------------------------===//
// <random>
// template<class RealType = double>
// class lognormal_distribution
// result_type max() const;
#include <random>
#include <cassert>
int main()
{
{
typedef std::lognormal_distribution<> D;
D d(5, .25);
D::result_type m = d.max();
assert(m == INFINITY);
}
}

27
test/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.lognormal/min.pass.cpp Normal file
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//===----------------------------------------------------------------------===//
//
// The LLVM Compiler Infrastructure
//
// This file is distributed under the University of Illinois Open Source
// License. See LICENSE.TXT for details.
//
//===----------------------------------------------------------------------===//
// <random>
// template<class RealType = double>
// class lognormal_distribution
// result_type min() const;
#include <random>
#include <cassert>
int main()
{
{
typedef std::lognormal_distribution<> D;
D d(.5, .5);
assert(d.min() == 0);
}
}

32
test/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.lognormal/param_assign.pass.cpp Normal file
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//===----------------------------------------------------------------------===//
//
// The LLVM Compiler Infrastructure
//
// This file is distributed under the University of Illinois Open Source
// License. See LICENSE.TXT for details.
//
//===----------------------------------------------------------------------===//
// <random>
// template<class RealType = double>
// class lognormal_distribution
// {
// class param_type;
#include <random>
#include <limits>
#include <cassert>
int main()
{
{
typedef std::lognormal_distribution<> D;
typedef D::param_type param_type;
param_type p0(.75, 6);
param_type p;
p = p0;
assert(p.m() == .75);
assert(p.s() == 6);
}
}

31
test/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.lognormal/param_copy.pass.cpp Normal file
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//===----------------------------------------------------------------------===//
//
// The LLVM Compiler Infrastructure
//
// This file is distributed under the University of Illinois Open Source
// License. See LICENSE.TXT for details.
//
//===----------------------------------------------------------------------===//
// <random>
// template<class RealType = double>
// class lognormal_distribution
// {
// class param_type;
#include <random>
#include <limits>
#include <cassert>
int main()
{
{
typedef std::lognormal_distribution<> D;
typedef D::param_type param_type;
param_type p0(10, .125);
param_type p = p0;
assert(p.m() == 10);
assert(p.s() == .125);
}
}

44
test/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.lognormal/param_ctor.pass.cpp Normal file
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//===----------------------------------------------------------------------===//
//
// The LLVM Compiler Infrastructure
//
// This file is distributed under the University of Illinois Open Source
// License. See LICENSE.TXT for details.
//
//===----------------------------------------------------------------------===//
// <random>
// template<class RealType = double>
// class lognormal_distribution
// {
// class param_type;
#include <random>
#include <limits>
#include <cassert>
int main()
{
{
typedef std::lognormal_distribution<> D;
typedef D::param_type param_type;
param_type p;
assert(p.m() == 0);
assert(p.s() == 1);
}
{
typedef std::lognormal_distribution<> D;
typedef D::param_type param_type;
param_type p(10);
assert(p.m() == 10);
assert(p.s() == 1);
}
{
typedef std::lognormal_distribution<> D;
typedef D::param_type param_type;
param_type p(10, 5);
assert(p.m() == 10);
assert(p.s() == 5);
}
}

37
test/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.lognormal/param_eq.pass.cpp Normal file
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//===----------------------------------------------------------------------===//
//
// The LLVM Compiler Infrastructure
//
// This file is distributed under the University of Illinois Open Source
// License. See LICENSE.TXT for details.
//
//===----------------------------------------------------------------------===//
// <random>
// template<class RealType = double>
// class lognormal_distribution
// {
// class param_type;
#include <random>
#include <limits>
#include <cassert>
int main()
{
{
typedef std::lognormal_distribution<> D;
typedef D::param_type param_type;
param_type p1(0.75, .5);
param_type p2(0.75, .5);
assert(p1 == p2);
}
{
typedef std::lognormal_distribution<> D;
typedef D::param_type param_type;
param_type p1(0.75, .5);
param_type p2(0.5, .5);
assert(p1 != p2);
}
}

28
test/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.lognormal/param_types.pass.cpp Normal file
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//===----------------------------------------------------------------------===//
//
// The LLVM Compiler Infrastructure
//
// This file is distributed under the University of Illinois Open Source
// License. See LICENSE.TXT for details.
//
//===----------------------------------------------------------------------===//
// <random>
// template<class RealType = double>
// class lognormal_distribution
// {
// class param_type;
#include <random>
#include <type_traits>
int main()
{
{
typedef std::lognormal_distribution<> D;
typedef D::param_type param_type;
typedef param_type::distribution_type distribution_type;
static_assert((std::is_same<D, distribution_type>::value), "");
}
}

30
test/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.lognormal/set_param.pass.cpp Normal file
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//===----------------------------------------------------------------------===//
//
// The LLVM Compiler Infrastructure
//
// This file is distributed under the University of Illinois Open Source
// License. See LICENSE.TXT for details.
//
//===----------------------------------------------------------------------===//
// <random>
// template<class RealType = double>
// class lognormal_distribution;
// void param(const param_type& parm);
#include <random>
#include <cassert>
int main()
{
{
typedef std::lognormal_distribution<> D;
typedef D::param_type P;
P p(0.25, 5.5);
D d(0.75, 4);
d.param(p);
assert(d.param() == p);
}
}

34
test/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.lognormal/types.pass.cpp Normal file
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//===----------------------------------------------------------------------===//
//
// The LLVM Compiler Infrastructure
//
// This file is distributed under the University of Illinois Open Source
// License. See LICENSE.TXT for details.
//
//===----------------------------------------------------------------------===//
// <random>
// template<class RealType = double>
// class lognormal_distribution
// {
// public:
// // types
// typedef RealType result_type;
#include <random>
#include <type_traits>
int main()
{
{
typedef std::lognormal_distribution<> D;
typedef D::result_type result_type;
static_assert((std::is_same<result_type, double>::value), "");
}
{
typedef std::lognormal_distribution<float> D;
typedef D::result_type result_type;
static_assert((std::is_same<result_type, float>::value), "");
}
}