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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.bern/rand.dist.bern.bin/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 binomial_distribution
// binomial_distribution& operator=(const binomial_distribution&);
#include <random>
#include <cassert>
void
test1()
{
typedef std::binomial_distribution<> D;
D d1(2, 0.75);
D d2;
assert(d1 != d2);
d2 = d1;
assert(d1 == d2);
}
int main()
{
test1();
}

32
test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bin/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 binomial_distribution
// binomial_distribution(const binomial_distribution&);
#include <random>
#include <cassert>
void
test1()
{
typedef std::binomial_distribution<> D;
D d1(2, 0.75);
D d2 = d1;
assert(d1 == d2);
}
int main()
{
test1();
}

40
test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bin/ctor_int_double.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 binomial_distribution
// explicit binomial_distribution(IntType t = 1, double p = 0.5);
#include <random>
#include <cassert>
int main()
{
{
typedef std::binomial_distribution<> D;
D d;
assert(d.t() == 1);
assert(d.p() == 0.5);
}
{
typedef std::binomial_distribution<> D;
D d(3);
assert(d.t() == 3);
assert(d.p() == 0.5);
}
{
typedef std::binomial_distribution<> D;
D d(3, 0.75);
assert(d.t() == 3);
assert(d.p() == 0.75);
}
}

30
test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bin/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 binomial_distribution
// explicit binomial_distribution(const param_type& parm);
#include <random>
#include <cassert>
int main()
{
{
typedef std::binomial_distribution<> D;
typedef D::param_type P;
P p(5, 0.25);
D d(p);
assert(d.t() == 5);
assert(d.p() == 0.25);
}
}

43
test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bin/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 binomial_distribution
// bool operator=(const binomial_distribution& x,
// const binomial_distribution& y);
// bool operator!(const binomial_distribution& x,
// const binomial_distribution& y);
#include <random>
#include <cassert>
int main()
{
{
typedef std::binomial_distribution<> D;
D d1(3, .25);
D d2(3, .25);
assert(d1 == d2);
}
{
typedef std::binomial_distribution<> D;
D d1(3, .28);
D d2(3, .25);
assert(d1 != d2);
}
{
typedef std::binomial_distribution<> D;
D d1(3, .25);
D d2(4, .25);
assert(d1 != d2);
}
}

475
test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bin/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 binomial_distribution
// template<class _URNG> result_type operator()(_URNG& g);
#include <random>
#include <numeric>
#include <vector>
#include <cassert>
template <class T>
inline
T
sqr(T x)
{
return x * x;
}
int main()
{
{
typedef std::binomial_distribution<> D;
typedef std::mt19937_64 G;
G g;
D d(5, .75);
const int N = 1000000;
std::vector<D::result_type> u;
for (int i = 0; i < N; ++i)
{
D::result_type v = d(g);
assert(d.min() <= v && v <= d.max());
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 = d.t() * d.p();
double x_var = x_mean*(1-d.p());
double x_skew = (1-2*d.p()) / std::sqrt(x_var);
double x_kurtosis = (1-6*d.p()*(1-d.p())) / x_var;
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) < 0.04);
}
{
typedef std::binomial_distribution<> D;
typedef std::mt19937 G;
G g;
D d(30, .03125);
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.min() <= v && v <= d.max());
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 = d.t() * d.p();
double x_var = x_mean*(1-d.p());
double x_skew = (1-2*d.p()) / std::sqrt(x_var);
double x_kurtosis = (1-6*d.p()*(1-d.p())) / x_var;
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) < 0.01);
}
{
typedef std::binomial_distribution<> D;
typedef std::mt19937 G;
G g;
D d(40, .25);
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.min() <= v && v <= d.max());
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 = d.t() * d.p();
double x_var = x_mean*(1-d.p());
double x_skew = (1-2*d.p()) / std::sqrt(x_var);
double x_kurtosis = (1-6*d.p()*(1-d.p())) / x_var;
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) < 0.03);
assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.3);
}
{
typedef std::binomial_distribution<> D;
typedef std::mt19937 G;
G g;
D d(40, 0);
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.min() <= v && v <= d.max());
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);
// In this case:
// skew computes to 0./0. == nan
// kurtosis computes to 0./0. == nan
// x_skew == inf
// x_kurtosis == inf
// These tests are commented out because UBSan warns about division by 0
// skew /= u.size() * dev * var;
// kurtosis /= u.size() * var * var;
// kurtosis -= 3;
double x_mean = d.t() * d.p();
double x_var = x_mean*(1-d.p());
// double x_skew = (1-2*d.p()) / std::sqrt(x_var);
// double x_kurtosis = (1-6*d.p()*(1-d.p())) / x_var;
assert(mean == x_mean);
assert(var == x_var);
// assert(skew == x_skew);
// assert(kurtosis == x_kurtosis);
}
{
typedef std::binomial_distribution<> D;
typedef std::mt19937 G;
G g;
D d(40, 1);
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.min() <= v && v <= d.max());
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);
// In this case:
// skew computes to 0./0. == nan
// kurtosis computes to 0./0. == nan
// x_skew == -inf
// x_kurtosis == inf
// These tests are commented out because UBSan warns about division by 0
// skew /= u.size() * dev * var;
// kurtosis /= u.size() * var * var;
// kurtosis -= 3;
double x_mean = d.t() * d.p();
double x_var = x_mean*(1-d.p());
// double x_skew = (1-2*d.p()) / std::sqrt(x_var);
// double x_kurtosis = (1-6*d.p()*(1-d.p())) / x_var;
assert(mean == x_mean);
assert(var == x_var);
// assert(skew == x_skew);
// assert(kurtosis == x_kurtosis);
}
{
typedef std::binomial_distribution<> D;
typedef std::mt19937 G;
G g;
D d(400, 0.5);
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.min() <= v && v <= d.max());
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 = d.t() * d.p();
double x_var = x_mean*(1-d.p());
double x_skew = (1-2*d.p()) / std::sqrt(x_var);
double x_kurtosis = (1-6*d.p()*(1-d.p())) / x_var;
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) < 0.01);
}
{
typedef std::binomial_distribution<> D;
typedef std::mt19937 G;
G g;
D d(1, 0.5);
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.min() <= v && v <= d.max());
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 = d.t() * d.p();
double x_var = x_mean*(1-d.p());
double x_skew = (1-2*d.p()) / std::sqrt(x_var);
double x_kurtosis = (1-6*d.p()*(1-d.p())) / x_var;
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);
}
{
const int N = 100000;
std::mt19937 gen1;
std::mt19937 gen2;
std::binomial_distribution<> dist1(5, 0.1);
std::binomial_distribution<unsigned> dist2(5, 0.1);
for(int i = 0; i < N; ++i)
assert(dist1(gen1) == dist2(gen2));
}
{
typedef std::binomial_distribution<> D;
typedef std::mt19937 G;
G g;
D d(0, 0.005);
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.min() <= v && v <= d.max());
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);
// In this case:
// skew computes to 0./0. == nan
// kurtosis computes to 0./0. == nan
// x_skew == inf
// x_kurtosis == inf
// These tests are commented out because UBSan warns about division by 0
// skew /= u.size() * dev * var;
// kurtosis /= u.size() * var * var;
// kurtosis -= 3;
double x_mean = d.t() * d.p();
double x_var = x_mean*(1-d.p());
// double x_skew = (1-2*d.p()) / std::sqrt(x_var);
// double x_kurtosis = (1-6*d.p()*(1-d.p())) / x_var;
assert(mean == x_mean);
assert(var == x_var);
// assert(skew == x_skew);
// assert(kurtosis == x_kurtosis);
}
{
typedef std::binomial_distribution<> D;
typedef std::mt19937 G;
G g;
D d(0, 0);
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.min() <= v && v <= d.max());
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);
// In this case:
// skew computes to 0./0. == nan
// kurtosis computes to 0./0. == nan
// x_skew == inf
// x_kurtosis == inf
// These tests are commented out because UBSan warns about division by 0
// skew /= u.size() * dev * var;
// kurtosis /= u.size() * var * var;
// kurtosis -= 3;
double x_mean = d.t() * d.p();
double x_var = x_mean*(1-d.p());
// double x_skew = (1-2*d.p()) / std::sqrt(x_var);
// double x_kurtosis = (1-6*d.p()*(1-d.p())) / x_var;
assert(mean == x_mean);
assert(var == x_var);
// assert(skew == x_skew);
// assert(kurtosis == x_kurtosis);
}
{
typedef std::binomial_distribution<> D;
typedef std::mt19937 G;
G g;
D d(0, 1);
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.min() <= v && v <= d.max());
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);
// In this case:
// skew computes to 0./0. == nan
// kurtosis computes to 0./0. == nan
// x_skew == -inf
// x_kurtosis == inf
// These tests are commented out because UBSan warns about division by 0
// skew /= u.size() * dev * var;
// kurtosis /= u.size() * var * var;
// kurtosis -= 3;
double x_mean = d.t() * d.p();
double x_var = x_mean*(1-d.p());
// double x_skew = (1-2*d.p()) / std::sqrt(x_var);
// double x_kurtosis = (1-6*d.p()*(1-d.p())) / x_var;
assert(mean == x_mean);
assert(var == x_var);
// assert(skew == x_skew);
// assert(kurtosis == x_kurtosis);
}
}

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test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bin/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.
//
//===----------------------------------------------------------------------===//
//
// REQUIRES: long_tests
// <random>
// template<class IntType = int>
// class binomial_distribution
// template<class _URNG> result_type operator()(_URNG& g, const param_type& parm);
#include <random>
#include <numeric>
#include <vector>
#include <cassert>
template <class T>
inline
T
sqr(T x)
{
return x * x;
}
int main()
{
{
typedef std::binomial_distribution<> D;
typedef D::param_type P;
typedef std::mt19937_64 G;
G g;
D d(16, .75);
P p(5, .75);
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(0 <= v && v <= p.t());
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 = p.t() * p.p();
double x_var = x_mean*(1-p.p());
double x_skew = (1-2*p.p()) / std::sqrt(x_var);
double x_kurtosis = (1-6*p.p()*(1-p.p())) / x_var;
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) < 0.04);
}
{
typedef std::binomial_distribution<> D;
typedef D::param_type P;
typedef std::mt19937 G;
G g;
D d(16, .75);
P p(30, .03125);
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(0 <= v && v <= p.t());
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 = p.t() * p.p();
double x_var = x_mean*(1-p.p());
double x_skew = (1-2*p.p()) / std::sqrt(x_var);
double x_kurtosis = (1-6*p.p()*(1-p.p())) / x_var;
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) < 0.01);
}
{
typedef std::binomial_distribution<> D;
typedef D::param_type P;
typedef std::mt19937 G;
G g;
D d(16, .75);
P p(40, .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(0 <= v && v <= p.t());
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 = p.t() * p.p();
double x_var = x_mean*(1-p.p());
double x_skew = (1-2*p.p()) / std::sqrt(x_var);
double x_kurtosis = (1-6*p.p()*(1-p.p())) / x_var;
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) < 0.04);
assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.3);
}
}

29
test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bin/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 binomial_distribution
// param_type param() const;
#include <random>
#include <cassert>
int main()
{
{
typedef std::binomial_distribution<> D;
typedef D::param_type P;
P p(5, .125);
D d(p);
assert(d.param() == p);
}
}

41
test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bin/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 binomial_distribution
// template <class charT, class traits>
// basic_ostream<charT, traits>&
// operator<<(basic_ostream<charT, traits>& os,
// const binomial_distribution& x);
//
// template <class charT, class traits>
// basic_istream<charT, traits>&
// operator>>(basic_istream<charT, traits>& is,
// binomial_distribution& x);
#include <random>
#include <sstream>
#include <cassert>
int main()
{
{
typedef std::binomial_distribution<> D;
D d1(7, .25);
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.bern/rand.dist.bern.bin/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 binomial_distribution
// result_type max() const;
#include <random>
#include <cassert>
int main()
{
{
typedef std::binomial_distribution<> D;
D d(4, .25);
assert(d.max() == 4);
}
}

27
test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bin/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 binomial_distribution
// result_type min() const;
#include <random>
#include <cassert>
int main()
{
{
typedef std::binomial_distribution<> D;
D d(4, .5);
assert(d.min() == 0);
}
}

32
test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bin/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 binomial_distribution
// {
// class param_type;
#include <random>
#include <limits>
#include <cassert>
int main()
{
{
typedef std::binomial_distribution<> D;
typedef D::param_type param_type;
param_type p0(6, .7);
param_type p;
p = p0;
assert(p.t() == 6);
assert(p.p() == .7);
}
}

31
test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bin/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 binomial_distribution
// {
// class param_type;
#include <random>
#include <limits>
#include <cassert>
int main()
{
{
typedef std::binomial_distribution<> D;
typedef D::param_type param_type;
param_type p0(10, .125);
param_type p = p0;
assert(p.t() == 10);
assert(p.p() == .125);
}
}

44
test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bin/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 binomial_distribution
// {
// class param_type;
#include <random>
#include <limits>
#include <cassert>
int main()
{
{
typedef std::binomial_distribution<> D;
typedef D::param_type param_type;
param_type p;
assert(p.t() == 1);
assert(p.p() == 0.5);
}
{
typedef std::binomial_distribution<> D;
typedef D::param_type param_type;
param_type p(10);
assert(p.t() == 10);
assert(p.p() == 0.5);
}
{
typedef std::binomial_distribution<> D;
typedef D::param_type param_type;
param_type p(10, 0.25);
assert(p.t() == 10);
assert(p.p() == 0.25);
}
}

37
test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bin/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 binomial_distribution
// {
// class param_type;
#include <random>
#include <limits>
#include <cassert>
int main()
{
{
typedef std::binomial_distribution<> D;
typedef D::param_type param_type;
param_type p1(3, 0.75);
param_type p2(3, 0.75);
assert(p1 == p2);
}
{
typedef std::binomial_distribution<> D;
typedef D::param_type param_type;
param_type p1(3, 0.75);
param_type p2(3, 0.5);
assert(p1 != p2);
}
}

28
test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bin/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 binomial_distribution
// {
// class param_type;
#include <random>
#include <type_traits>
int main()
{
{
typedef std::binomial_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/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bin/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 binomial_distribution
// void param(const param_type& parm);
#include <random>
#include <cassert>
int main()
{
{
typedef std::binomial_distribution<> D;
typedef D::param_type P;
P p(10, 0.25);
D d(8, 0.75);
d.param(p);
assert(d.param() == p);
}
}

32
test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bin/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 binomial_distribution
// {
// typedef bool result_type;
#include <random>
#include <type_traits>
int main()
{
{
typedef std::binomial_distribution<> D;
typedef D::result_type result_type;
static_assert((std::is_same<result_type, int>::value), "");
}
{
typedef std::binomial_distribution<long> D;
typedef D::result_type result_type;
static_assert((std::is_same<result_type, long>::value), "");
}
}