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Move test into test/std subdirectory.

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git-svn-id: https://llvm.org/svn/llvm-project/libcxx/trunk@224658 91177308-0d34-0410-b5e6-96231b3b80d8
This commit is contained in:
Eric Fiselier
2014-12-20 01:40:03 +00:00
parent 669a8a5a19
commit a90c6dd460
4817 changed files with 13 additions and 0 deletions
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34
test/std/numerics/rand/rand.dis/rand.dist.uni/rand.dist.uni.int/assign.pass.cpp Normal file
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//===----------------------------------------------------------------------===//
//
// The LLVM Compiler Infrastructure
//
// This file is dual licensed under the MIT and the University of Illinois Open
// Source Licenses. See LICENSE.TXT for details.
//
//===----------------------------------------------------------------------===//
// <random>
// template<class _IntType = int>
// class uniform_int_distribution
// uniform_int_distribution& operator=(const uniform_int_distribution&);
#include <random>
#include <cassert>
void
test1()
{
typedef std::uniform_int_distribution<long> D;
D d1(2, 5);
D d2;
assert(d1 != d2);
d2 = d1;
assert(d1 == d2);
}
int main()
{
test1();
}

32
test/std/numerics/rand/rand.dis/rand.dist.uni/rand.dist.uni.int/copy.pass.cpp Normal file
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//===----------------------------------------------------------------------===//
//
// The LLVM Compiler Infrastructure
//
// This file is dual licensed under the MIT and the University of Illinois Open
// Source Licenses. See LICENSE.TXT for details.
//
//===----------------------------------------------------------------------===//
// <random>
// template<class _IntType = int>
// class uniform_int_distribution
// uniform_int_distribution(const uniform_int_distribution&);
#include <random>
#include <cassert>
void
test1()
{
typedef std::uniform_int_distribution<long> D;
D d1(2, 5);
D d2 = d1;
assert(d1 == d2);
}
int main()
{
test1();
}

41
test/std/numerics/rand/rand.dis/rand.dist.uni/rand.dist.uni.int/ctor_int_int.pass.cpp Normal file
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//===----------------------------------------------------------------------===//
//
// The LLVM Compiler Infrastructure
//
// This file is dual licensed under the MIT and the University of Illinois Open
// Source Licenses. See LICENSE.TXT for details.
//
//===----------------------------------------------------------------------===//
// <random>
// template<class _IntType = int>
// class uniform_int_distribution
// explicit uniform_int_distribution(IntType a = 0,
// IntType b = numeric_limits<IntType>::max());
#include <random>
#include <cassert>
int main()
{
{
typedef std::uniform_int_distribution<> D;
D d;
assert(d.a() == 0);
assert(d.b() == std::numeric_limits<int>::max());
}
{
typedef std::uniform_int_distribution<> D;
D d(-6);
assert(d.a() == -6);
assert(d.b() == std::numeric_limits<int>::max());
}
{
typedef std::uniform_int_distribution<> D;
D d(-6, 106);
assert(d.a() == -6);
assert(d.b() == 106);
}
}

30
test/std/numerics/rand/rand.dis/rand.dist.uni/rand.dist.uni.int/ctor_param.pass.cpp Normal file
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//===----------------------------------------------------------------------===//
//
// The LLVM Compiler Infrastructure
//
// This file is dual licensed under the MIT and the University of Illinois Open
// Source Licenses. See LICENSE.TXT for details.
//
//===----------------------------------------------------------------------===//
// <random>
// template<class _IntType = int>
// class uniform_int_distribution
// explicit uniform_int_distribution(const param_type& parm);
#include <random>
#include <cassert>
int main()
{
{
typedef std::uniform_int_distribution<> D;
typedef D::param_type P;
P p(3, 8);
D d(p);
assert(d.a() == 3);
assert(d.b() == 8);
}
}

37
test/std/numerics/rand/rand.dis/rand.dist.uni/rand.dist.uni.int/eq.pass.cpp Normal file
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//===----------------------------------------------------------------------===//
//
// The LLVM Compiler Infrastructure
//
// This file is dual licensed under the MIT and the University of Illinois Open
// Source Licenses. See LICENSE.TXT for details.
//
//===----------------------------------------------------------------------===//
// <random>
// template<class _IntType = int>
// class uniform_int_distribution
// bool operator=(const uniform_int_distribution& x,
// const uniform_int_distribution& y);
// bool operator!(const uniform_int_distribution& x,
// const uniform_int_distribution& y);
#include <random>
#include <cassert>
int main()
{
{
typedef std::uniform_int_distribution<> D;
D d1(3, 8);
D d2(3, 8);
assert(d1 == d2);
}
{
typedef std::uniform_int_distribution<> D;
D d1(3, 8);
D d2(3, 9);
assert(d1 != d2);
}
}

455
test/std/numerics/rand/rand.dis/rand.dist.uni/rand.dist.uni.int/eval.pass.cpp Normal file
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//===----------------------------------------------------------------------===//
//
// The LLVM Compiler Infrastructure
//
// This file is dual licensed under the MIT and the University of Illinois Open
// Source Licenses. See LICENSE.TXT for details.
//
//===----------------------------------------------------------------------===//
//
// REQUIRES: long_tests
// <random>
// template<class _IntType = int>
// class uniform_int_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::uniform_int_distribution<> D;
typedef std::minstd_rand0 G;
G g;
D d;
const int N = 100000;
std::vector<D::result_type> u;
for (int i = 0; i < N; ++i)
{
D::result_type v = d(g);
assert(d.a() <= v && v <= d.b());
u.push_back(v);
}
double mean = std::accumulate(u.begin(), u.end(),
double(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 = ((double)d.a() + d.b()) / 2;
double x_var = (sqr((double)d.b() - d.a() + 1) - 1) / 12;
double x_skew = 0;
double x_kurtosis = -6. * (sqr((double)d.b() - d.a() + 1) + 1) /
(5. * (sqr((double)d.b() - d.a() + 1) - 1));
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) < 0.01);
assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
}
{
typedef std::uniform_int_distribution<> D;
typedef std::minstd_rand G;
G g;
D d;
const int N = 100000;
std::vector<D::result_type> u;
for (int i = 0; i < N; ++i)
{
D::result_type v = d(g);
assert(d.a() <= v && v <= d.b());
u.push_back(v);
}
double mean = std::accumulate(u.begin(), u.end(),
double(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 = ((double)d.a() + d.b()) / 2;
double x_var = (sqr((double)d.b() - d.a() + 1) - 1) / 12;
double x_skew = 0;
double x_kurtosis = -6. * (sqr((double)d.b() - d.a() + 1) + 1) /
(5. * (sqr((double)d.b() - d.a() + 1) - 1));
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) < 0.01);
assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
}
{
typedef std::uniform_int_distribution<> D;
typedef std::mt19937 G;
G g;
D d;
const int N = 100000;
std::vector<D::result_type> u;
for (int i = 0; i < N; ++i)
{
D::result_type v = d(g);
assert(d.a() <= v && v <= d.b());
u.push_back(v);
}
double mean = std::accumulate(u.begin(), u.end(),
double(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 = ((double)d.a() + d.b()) / 2;
double x_var = (sqr((double)d.b() - d.a() + 1) - 1) / 12;
double x_skew = 0;
double x_kurtosis = -6. * (sqr((double)d.b() - d.a() + 1) + 1) /
(5. * (sqr((double)d.b() - d.a() + 1) - 1));
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) < 0.01);
assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
}
{
typedef std::uniform_int_distribution<> D;
typedef std::mt19937_64 G;
G g;
D d;
const int N = 100000;
std::vector<D::result_type> u;
for (int i = 0; i < N; ++i)
{
D::result_type v = d(g);
assert(d.a() <= v && v <= d.b());
u.push_back(v);
}
double mean = std::accumulate(u.begin(), u.end(),
double(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 = ((double)d.a() + d.b()) / 2;
double x_var = (sqr((double)d.b() - d.a() + 1) - 1) / 12;
double x_skew = 0;
double x_kurtosis = -6. * (sqr((double)d.b() - d.a() + 1) + 1) /
(5. * (sqr((double)d.b() - d.a() + 1) - 1));
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) < 0.01);
assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
}
{
typedef std::uniform_int_distribution<> D;
typedef std::ranlux24_base G;
G g;
D d;
const int N = 100000;
std::vector<D::result_type> u;
for (int i = 0; i < N; ++i)
{
D::result_type v = d(g);
assert(d.a() <= v && v <= d.b());
u.push_back(v);
}
double mean = std::accumulate(u.begin(), u.end(),
double(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 = ((double)d.a() + d.b()) / 2;
double x_var = (sqr((double)d.b() - d.a() + 1) - 1) / 12;
double x_skew = 0;
double x_kurtosis = -6. * (sqr((double)d.b() - d.a() + 1) + 1) /
(5. * (sqr((double)d.b() - d.a() + 1) - 1));
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) < 0.01);
assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
}
{
typedef std::uniform_int_distribution<> D;
typedef std::ranlux48_base G;
G g;
D d;
const int N = 100000;
std::vector<D::result_type> u;
for (int i = 0; i < N; ++i)
{
D::result_type v = d(g);
assert(d.a() <= v && v <= d.b());
u.push_back(v);
}
double mean = std::accumulate(u.begin(), u.end(),
double(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 = ((double)d.a() + d.b()) / 2;
double x_var = (sqr((double)d.b() - d.a() + 1) - 1) / 12;
double x_skew = 0;
double x_kurtosis = -6. * (sqr((double)d.b() - d.a() + 1) + 1) /
(5. * (sqr((double)d.b() - d.a() + 1) - 1));
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) < 0.01);
assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
}
{
typedef std::uniform_int_distribution<> D;
typedef std::ranlux24 G;
G g;
D d;
const int N = 100000;
std::vector<D::result_type> u;
for (int i = 0; i < N; ++i)
{
D::result_type v = d(g);
assert(d.a() <= v && v <= d.b());
u.push_back(v);
}
double mean = std::accumulate(u.begin(), u.end(),
double(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 = ((double)d.a() + d.b()) / 2;
double x_var = (sqr((double)d.b() - d.a() + 1) - 1) / 12;
double x_skew = 0;
double x_kurtosis = -6. * (sqr((double)d.b() - d.a() + 1) + 1) /
(5. * (sqr((double)d.b() - d.a() + 1) - 1));
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) < 0.01);
assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
}
{
typedef std::uniform_int_distribution<> D;
typedef std::ranlux48 G;
G g;
D d;
const int N = 100000;
std::vector<D::result_type> u;
for (int i = 0; i < N; ++i)
{
D::result_type v = d(g);
assert(d.a() <= v && v <= d.b());
u.push_back(v);
}
double mean = std::accumulate(u.begin(), u.end(),
double(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 = ((double)d.a() + d.b()) / 2;
double x_var = (sqr((double)d.b() - d.a() + 1) - 1) / 12;
double x_skew = 0;
double x_kurtosis = -6. * (sqr((double)d.b() - d.a() + 1) + 1) /
(5. * (sqr((double)d.b() - d.a() + 1) - 1));
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) < 0.01);
assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
}
{
typedef std::uniform_int_distribution<> D;
typedef std::knuth_b G;
G g;
D d;
const int N = 100000;
std::vector<D::result_type> u;
for (int i = 0; i < N; ++i)
{
D::result_type v = d(g);
assert(d.a() <= v && v <= d.b());
u.push_back(v);
}
double mean = std::accumulate(u.begin(), u.end(),
double(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 = ((double)d.a() + d.b()) / 2;
double x_var = (sqr((double)d.b() - d.a() + 1) - 1) / 12;
double x_skew = 0;
double x_kurtosis = -6. * (sqr((double)d.b() - d.a() + 1) + 1) /
(5. * (sqr((double)d.b() - d.a() + 1) - 1));
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) < 0.01);
assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
}
{
typedef std::uniform_int_distribution<> D;
typedef std::minstd_rand0 G;
G g;
D d(-6, 106);
for (int i = 0; i < 10000; ++i)
{
int u = d(g);
assert(-6 <= u && u <= 106);
}
}
{
typedef std::uniform_int_distribution<> D;
typedef std::minstd_rand G;
G g;
D d(5, 100);
const int N = 100000;
std::vector<D::result_type> u;
for (int i = 0; i < N; ++i)
{
D::result_type v = d(g);
assert(d.a() <= v && v <= d.b());
u.push_back(v);
}
double mean = std::accumulate(u.begin(), u.end(),
double(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 = ((double)d.a() + d.b()) / 2;
double x_var = (sqr((double)d.b() - d.a() + 1) - 1) / 12;
double x_skew = 0;
double x_kurtosis = -6. * (sqr((double)d.b() - d.a() + 1) + 1) /
(5. * (sqr((double)d.b() - d.a() + 1) - 1));
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) < 0.01);
assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
}
}

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test/std/numerics/rand/rand.dis/rand.dist.uni/rand.dist.uni.int/eval_param.pass.cpp Normal file
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//===----------------------------------------------------------------------===//
//
// The LLVM Compiler Infrastructure
//
// This file is dual licensed under the MIT and the University of Illinois Open
// Source Licenses. See LICENSE.TXT for details.
//
//===----------------------------------------------------------------------===//
// <random>
// template<class _IntType = int>
// class uniform_int_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::uniform_int_distribution<> D;
typedef std::minstd_rand G;
typedef D::param_type P;
G g;
D d(5, 100);
P p(-10, 20);
const int N = 100000;
std::vector<D::result_type> u;
for (int i = 0; i < N; ++i)
{
D::result_type v = d(g, p);
assert(p.a() <= v && v <= p.b());
u.push_back(v);
}
double mean = std::accumulate(u.begin(), u.end(),
double(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 = ((double)p.a() + p.b()) / 2;
double x_var = (sqr((double)p.b() - p.a() + 1) - 1) / 12;
double x_skew = 0;
double x_kurtosis = -6. * (sqr((double)p.b() - p.a() + 1) + 1) /
(5. * (sqr((double)p.b() - p.a() + 1) - 1));
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) < 0.01);
assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
}
}

29
test/std/numerics/rand/rand.dis/rand.dist.uni/rand.dist.uni.int/get_param.pass.cpp Normal file
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//===----------------------------------------------------------------------===//
//
// The LLVM Compiler Infrastructure
//
// This file is dual licensed under the MIT and the University of Illinois Open
// Source Licenses. See LICENSE.TXT for details.
//
//===----------------------------------------------------------------------===//
// <random>
// template<class _IntType = int>
// class uniform_int_distribution
// param_type param() const;
#include <random>
#include <cassert>
int main()
{
{
typedef std::uniform_int_distribution<> D;
typedef D::param_type P;
P p(3, 8);
D d(p);
assert(d.param() == p);
}
}

41
test/std/numerics/rand/rand.dis/rand.dist.uni/rand.dist.uni.int/io.pass.cpp Normal file
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//===----------------------------------------------------------------------===//
//
// The LLVM Compiler Infrastructure
//
// This file is dual licensed under the MIT and the University of Illinois Open
// Source Licenses. See LICENSE.TXT for details.
//
//===----------------------------------------------------------------------===//
// <random>
// template<class _IntType = int>
// class uniform_int_distribution
// template <class charT, class traits>
// basic_ostream<charT, traits>&
// operator<<(basic_ostream<charT, traits>& os,
// const uniform_int_distribution& x);
//
// template <class charT, class traits>
// basic_istream<charT, traits>&
// operator>>(basic_istream<charT, traits>& is,
// uniform_int_distribution& x);
#include <random>
#include <sstream>
#include <cassert>
int main()
{
{
typedef std::uniform_int_distribution<> D;
D d1(3, 8);
std::ostringstream os;
os << d1;
std::istringstream is(os.str());
D d2;
is >> d2;
assert(d1 == d2);
}
}

27
test/std/numerics/rand/rand.dis/rand.dist.uni/rand.dist.uni.int/max.pass.cpp Normal file
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//===----------------------------------------------------------------------===//
//
// The LLVM Compiler Infrastructure
//
// This file is dual licensed under the MIT and the University of Illinois Open
// Source Licenses. See LICENSE.TXT for details.
//
//===----------------------------------------------------------------------===//
// <random>
// template<class _IntType = int>
// class uniform_int_distribution
// result_type max() const;
#include <random>
#include <cassert>
int main()
{
{
typedef std::uniform_int_distribution<> D;
D d(3, 8);
assert(d.max() == 8);
}
}

27
test/std/numerics/rand/rand.dis/rand.dist.uni/rand.dist.uni.int/min.pass.cpp Normal file
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//===----------------------------------------------------------------------===//
//
// The LLVM Compiler Infrastructure
//
// This file is dual licensed under the MIT and the University of Illinois Open
// Source Licenses. See LICENSE.TXT for details.
//
//===----------------------------------------------------------------------===//
// <random>
// template<class _IntType = int>
// class uniform_int_distribution
// result_type min() const;
#include <random>
#include <cassert>
int main()
{
{
typedef std::uniform_int_distribution<> D;
D d(3, 8);
assert(d.min() == 3);
}
}

32
test/std/numerics/rand/rand.dis/rand.dist.uni/rand.dist.uni.int/param_assign.pass.cpp Normal file
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//===----------------------------------------------------------------------===//
//
// The LLVM Compiler Infrastructure
//
// This file is dual licensed under the MIT and the University of Illinois Open
// Source Licenses. See LICENSE.TXT for details.
//
//===----------------------------------------------------------------------===//
// <random>
// template<class _IntType = int>
// class uniform_int_distribution
// {
// class param_type;
#include <random>
#include <limits>
#include <cassert>
int main()
{
{
typedef std::uniform_int_distribution<long> D;
typedef D::param_type param_type;
param_type p0(5, 10);
param_type p;
p = p0;
assert(p.a() == 5);
assert(p.b() == 10);
}
}

31
test/std/numerics/rand/rand.dis/rand.dist.uni/rand.dist.uni.int/param_copy.pass.cpp Normal file
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//===----------------------------------------------------------------------===//
//
// The LLVM Compiler Infrastructure
//
// This file is dual licensed under the MIT and the University of Illinois Open
// Source Licenses. See LICENSE.TXT for details.
//
//===----------------------------------------------------------------------===//
// <random>
// template<class _IntType = int>
// class uniform_int_distribution
// {
// class param_type;
#include <random>
#include <limits>
#include <cassert>
int main()
{
{
typedef std::uniform_int_distribution<long> D;
typedef D::param_type param_type;
param_type p0(5, 10);
param_type p = p0;
assert(p.a() == 5);
assert(p.b() == 10);
}
}

44
test/std/numerics/rand/rand.dis/rand.dist.uni/rand.dist.uni.int/param_ctor.pass.cpp Normal file
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//===----------------------------------------------------------------------===//
//
// The LLVM Compiler Infrastructure
//
// This file is dual licensed under the MIT and the University of Illinois Open
// Source Licenses. See LICENSE.TXT for details.
//
//===----------------------------------------------------------------------===//
// <random>
// template<class _IntType = int>
// class uniform_int_distribution
// {
// class param_type;
#include <random>
#include <limits>
#include <cassert>
int main()
{
{
typedef std::uniform_int_distribution<long> D;
typedef D::param_type param_type;
param_type p;
assert(p.a() == 0);
assert(p.b() == std::numeric_limits<long>::max());
}
{
typedef std::uniform_int_distribution<long> D;
typedef D::param_type param_type;
param_type p(5);
assert(p.a() == 5);
assert(p.b() == std::numeric_limits<long>::max());
}
{
typedef std::uniform_int_distribution<long> D;
typedef D::param_type param_type;
param_type p(5, 10);
assert(p.a() == 5);
assert(p.b() == 10);
}
}

37
test/std/numerics/rand/rand.dis/rand.dist.uni/rand.dist.uni.int/param_eq.pass.cpp Normal file
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//===----------------------------------------------------------------------===//
//
// The LLVM Compiler Infrastructure
//
// This file is dual licensed under the MIT and the University of Illinois Open
// Source Licenses. See LICENSE.TXT for details.
//
//===----------------------------------------------------------------------===//
// <random>
// template<class _IntType = int>
// class uniform_int_distribution
// {
// class param_type;
#include <random>
#include <limits>
#include <cassert>
int main()
{
{
typedef std::uniform_int_distribution<long> D;
typedef D::param_type param_type;
param_type p1(5, 10);
param_type p2(5, 10);
assert(p1 == p2);
}
{
typedef std::uniform_int_distribution<long> D;
typedef D::param_type param_type;
param_type p1(5, 10);
param_type p2(6, 10);
assert(p1 != p2);
}
}

28
test/std/numerics/rand/rand.dis/rand.dist.uni/rand.dist.uni.int/param_types.pass.cpp Normal file
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//===----------------------------------------------------------------------===//
//
// The LLVM Compiler Infrastructure
//
// This file is dual licensed under the MIT and the University of Illinois Open
// Source Licenses. See LICENSE.TXT for details.
//
//===----------------------------------------------------------------------===//
// <random>
// template<class _IntType = int>
// class uniform_int_distribution
// {
// class param_type;
#include <random>
#include <type_traits>
int main()
{
{
typedef std::uniform_int_distribution<long> 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/std/numerics/rand/rand.dis/rand.dist.uni/rand.dist.uni.int/set_param.pass.cpp Normal file
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//===----------------------------------------------------------------------===//
//
// The LLVM Compiler Infrastructure
//
// This file is dual licensed under the MIT and the University of Illinois Open
// Source Licenses. See LICENSE.TXT for details.
//
//===----------------------------------------------------------------------===//
// <random>
// template<class _IntType = int>
// class uniform_int_distribution
// void param(const param_type& parm);
#include <random>
#include <cassert>
int main()
{
{
typedef std::uniform_int_distribution<> D;
typedef D::param_type P;
P p(3, 8);
D d(6, 7);
d.param(p);
assert(d.param() == p);
}
}

27
test/std/numerics/rand/rand.dis/rand.dist.uni/rand.dist.uni.int/types.pass.cpp Normal file
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//===----------------------------------------------------------------------===//
//
// The LLVM Compiler Infrastructure
//
// This file is dual licensed under the MIT and the University of Illinois Open
// Source Licenses. See LICENSE.TXT for details.
//
//===----------------------------------------------------------------------===//
// <random>
// template<class _IntType = int>
// class uniform_int_distribution
// {
// typedef IntType result_type;
#include <random>
#include <type_traits>
int main()
{
{
typedef std::uniform_int_distribution<long> D;
typedef D::result_type result_type;
static_assert((std::is_same<result_type, long>::value), "");
}
}