Move test into test/std subdirectory.

git-svn-id: https://llvm.org/svn/llvm-project/libcxx/trunk@224658 91177308-0d34-0410-b5e6-96231b3b80d8

test/std/numerics/rand/rand.dis/nothing_to_do.pass.cpp

@@ -0,0 +1,12 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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.
+//
+//===----------------------------------------------------------------------===//
+
+int main()
+{
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/nothing_to_do.pass.cpp

@@ -0,0 +1,12 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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.
+//
+//===----------------------------------------------------------------------===//
+
+int main()
+{
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bernoulli/assign.pass.cpp

@@ -0,0 +1,33 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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>
+
+// class bernoulli_distribution
+
+// bernoulli_distribution& operator=(const bernoulli_distribution&);
+
+#include <random>
+#include <cassert>
+
+void
+test1()
+{
+    typedef std::bernoulli_distribution D;
+    D d1(0.75);
+    D d2;
+    assert(d1 != d2);
+    d2 = d1;
+    assert(d1 == d2);
+}
+
+int main()
+{
+    test1();
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bernoulli/copy.pass.cpp

@@ -0,0 +1,31 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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>
+
+// class bernoulli_distribution
+
+// bernoulli_distribution(const bernoulli_distribution&);
+
+#include <random>
+#include <cassert>
+
+void
+test1()
+{
+    typedef std::bernoulli_distribution D;
+    D d1(0.75);
+    D d2 = d1;
+    assert(d1 == d2);
+}
+
+int main()
+{
+    test1();
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bernoulli/ctor_double.pass.cpp

@@ -0,0 +1,36 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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>
+
+// class bernoulli_distribution
+
+// explicit bernoulli_distribution(double p = 0.5);
+
+#include <random>
+#include <cassert>
+
+int main()
+{
+    {
+        typedef std::bernoulli_distribution D;
+        D d;
+        assert(d.p() == 0.5);
+    }
+    {
+        typedef std::bernoulli_distribution D;
+        D d(0);
+        assert(d.p() == 0);
+    }
+    {
+        typedef std::bernoulli_distribution D;
+        D d(0.75);
+        assert(d.p() == 0.75);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bernoulli/ctor_param.pass.cpp

@@ -0,0 +1,28 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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>
+
+// class bernoulli_distribution
+
+// explicit bernoulli_distribution(const param_type& parm);
+
+#include <random>
+#include <cassert>
+
+int main()
+{
+    {
+        typedef std::bernoulli_distribution D;
+        typedef D::param_type P;
+        P p(0.25);
+        D d(p);
+        assert(d.p() == 0.25);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bernoulli/eq.pass.cpp

@@ -0,0 +1,36 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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>
+
+// class bernoulli_distribution
+
+// bool operator=(const bernoulli_distribution& x,
+//                const bernoulli_distribution& y);
+// bool operator!(const bernoulli_distribution& x,
+//                const bernoulli_distribution& y);
+
+#include <random>
+#include <cassert>
+
+int main()
+{
+    {
+        typedef std::bernoulli_distribution D;
+        D d1(.25);
+        D d2(.25);
+        assert(d1 == d2);
+    }
+    {
+        typedef std::bernoulli_distribution D;
+        D d1(.28);
+        D d2(.25);
+        assert(d1 != d2);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bernoulli/eval.pass.cpp

@@ -0,0 +1,103 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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>
+
+// class bernoulli_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::bernoulli_distribution D;
+        typedef std::minstd_rand G;
+        G g;
+        D d(.75);
+        const int N = 100000;
+        std::vector<D::result_type> u;
+        for (int i = 0; i < N; ++i)
+            u.push_back(d(g));
+        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.p();
+        double x_var = d.p()*(1-d.p());
+        double x_skew = (1 - 2 * d.p())/std::sqrt(x_var);
+        double x_kurtosis = (6 * sqr(d.p()) - 6 * d.p() + 1)/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) < 0.02);
+    }
+    {
+        typedef std::bernoulli_distribution D;
+        typedef std::minstd_rand G;
+        G g;
+        D d(.25);
+        const int N = 100000;
+        std::vector<D::result_type> u;
+        for (int i = 0; i < N; ++i)
+            u.push_back(d(g));
+        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.p();
+        double x_var = d.p()*(1-d.p());
+        double x_skew = (1 - 2 * d.p())/std::sqrt(x_var);
+        double x_kurtosis = (6 * sqr(d.p()) - 6 * d.p() + 1)/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) < 0.02);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bernoulli/eval_param.pass.cpp

@@ -0,0 +1,107 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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>
+
+// class bernoulli_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::bernoulli_distribution D;
+        typedef D::param_type P;
+        typedef std::minstd_rand G;
+        G g;
+        D d(.75);
+        P p(.25);
+        const int N = 100000;
+        std::vector<D::result_type> u;
+        for (int i = 0; i < N; ++i)
+            u.push_back(d(g, p));
+        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.p();
+        double x_var = p.p()*(1-p.p());
+        double x_skew = (1 - 2 * p.p())/std::sqrt(x_var);
+        double x_kurtosis = (6 * sqr(p.p()) - 6 * p.p() + 1)/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) < 0.02);
+    }
+    {
+        typedef std::bernoulli_distribution D;
+        typedef D::param_type P;
+        typedef std::minstd_rand G;
+        G g;
+        D d(.25);
+        P p(.75);
+        const int N = 100000;
+        std::vector<D::result_type> u;
+        for (int i = 0; i < N; ++i)
+            u.push_back(d(g, p));
+        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.p();
+        double x_var = p.p()*(1-p.p());
+        double x_skew = (1 - 2 * p.p())/std::sqrt(x_var);
+        double x_kurtosis = (6 * sqr(p.p()) - 6 * p.p() + 1)/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) < 0.02);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bernoulli/get_param.pass.cpp

@@ -0,0 +1,28 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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>
+
+// class bernoulli_distribution
+
+// param_type param() const;
+
+#include <random>
+#include <cassert>
+
+int main()
+{
+    {
+        typedef std::bernoulli_distribution D;
+        typedef D::param_type P;
+        P p(.125);
+        D d(p);
+        assert(d.param() == p);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bernoulli/io.pass.cpp

@@ -0,0 +1,40 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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>
+
+// class bernoulli_distribution
+
+// template <class charT, class traits>
+// basic_ostream<charT, traits>&
+// operator<<(basic_ostream<charT, traits>& os,
+//            const bernoulli_distribution& x);
+//
+// template <class charT, class traits>
+// basic_istream<charT, traits>&
+// operator>>(basic_istream<charT, traits>& is,
+//            bernoulli_distribution& x);
+
+#include <random>
+#include <sstream>
+#include <cassert>
+
+int main()
+{
+    {
+        typedef std::bernoulli_distribution D;
+        D d1(.25);
+        std::ostringstream os;
+        os << d1;
+        std::istringstream is(os.str());
+        D d2;
+        is >> d2;
+        assert(d1 == d2);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bernoulli/max.pass.cpp

@@ -0,0 +1,26 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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>
+
+// class bernoulli_distribution
+
+// result_type max() const;
+
+#include <random>
+#include <cassert>
+
+int main()
+{
+    {
+        typedef std::bernoulli_distribution D;
+        D d(.25);
+        assert(d.max() == true);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bernoulli/min.pass.cpp

@@ -0,0 +1,26 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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>
+
+// class bernoulli_distribution
+
+// result_type min() const;
+
+#include <random>
+#include <cassert>
+
+int main()
+{
+    {
+        typedef std::bernoulli_distribution D;
+        D d(.5);
+        assert(d.min() == false);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bernoulli/param_assign.pass.cpp

@@ -0,0 +1,30 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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>
+
+// class bernoulli_distribution
+// {
+//     class param_type;
+
+#include <random>
+#include <limits>
+#include <cassert>
+
+int main()
+{
+    {
+        typedef std::bernoulli_distribution D;
+        typedef D::param_type param_type;
+        param_type p0(.7);
+        param_type p;
+        p = p0;
+        assert(p.p() == .7);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bernoulli/param_copy.pass.cpp

@@ -0,0 +1,29 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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>
+
+// class bernoulli_distribution
+// {
+//     class param_type;
+
+#include <random>
+#include <limits>
+#include <cassert>
+
+int main()
+{
+    {
+        typedef std::bernoulli_distribution D;
+        typedef D::param_type param_type;
+        param_type p0(.125);
+        param_type p = p0;
+        assert(p.p() == .125);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bernoulli/param_ctor.pass.cpp

@@ -0,0 +1,34 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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>
+
+// class bernoulli_distribution
+// {
+//     class param_type;
+
+#include <random>
+#include <limits>
+#include <cassert>
+
+int main()
+{
+    {
+        typedef std::bernoulli_distribution D;
+        typedef D::param_type param_type;
+        param_type p;
+        assert(p.p() == 0.5);
+    }
+    {
+        typedef std::bernoulli_distribution D;
+        typedef D::param_type param_type;
+        param_type p(0.25);
+        assert(p.p() == 0.25);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bernoulli/param_eq.pass.cpp

@@ -0,0 +1,36 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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>
+
+// class bernoulli_distribution
+// {
+//     class param_type;
+
+#include <random>
+#include <limits>
+#include <cassert>
+
+int main()
+{
+    {
+        typedef std::bernoulli_distribution D;
+        typedef D::param_type param_type;
+        param_type p1(0.75);
+        param_type p2(0.75);
+        assert(p1 == p2);
+    }
+    {
+        typedef std::bernoulli_distribution D;
+        typedef D::param_type param_type;
+        param_type p1(0.75);
+        param_type p2(0.5);
+        assert(p1 != p2);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bernoulli/param_types.pass.cpp

@@ -0,0 +1,27 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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>
+
+// class bernoulli_distribution
+// {
+//     class param_type;
+
+#include <random>
+#include <type_traits>
+
+int main()
+{
+    {
+        typedef std::bernoulli_distribution D;
+        typedef D::param_type param_type;
+        typedef param_type::distribution_type distribution_type;
+        static_assert((std::is_same<D, distribution_type>::value), "");
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bernoulli/set_param.pass.cpp

@@ -0,0 +1,29 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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>
+
+// class bernoulli_distribution
+
+// void param(const param_type& parm);
+
+#include <random>
+#include <cassert>
+
+int main()
+{
+    {
+        typedef std::bernoulli_distribution D;
+        typedef D::param_type P;
+        P p(0.25);
+        D d(0.75);
+        d.param(p);
+        assert(d.param() == p);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bernoulli/types.pass.cpp

@@ -0,0 +1,26 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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>
+
+// class bernoulli_distribution
+// {
+//     typedef bool result_type;
+
+#include <random>
+#include <type_traits>
+
+int main()
+{
+    {
+        typedef std::bernoulli_distribution D;
+        typedef D::result_type result_type;
+        static_assert((std::is_same<result_type, bool>::value), "");
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bin/assign.pass.cpp

@@ -0,0 +1,34 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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();
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bin/copy.pass.cpp

@@ -0,0 +1,32 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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();
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bin/ctor_int_double.pass.cpp

@@ -0,0 +1,40 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bin/ctor_param.pass.cpp

@@ -0,0 +1,30 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bin/eq.pass.cpp

@@ -0,0 +1,43 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bin/eval.pass.cpp

@@ -0,0 +1,475 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bin/eval_param.pass.cpp

@@ -0,0 +1,160 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bin/get_param.pass.cpp

@@ -0,0 +1,29 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bin/io.pass.cpp

@@ -0,0 +1,41 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bin/max.pass.cpp

@@ -0,0 +1,27 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bin/min.pass.cpp

@@ -0,0 +1,27 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bin/param_assign.pass.cpp

@@ -0,0 +1,32 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bin/param_copy.pass.cpp

@@ -0,0 +1,31 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bin/param_ctor.pass.cpp

@@ -0,0 +1,44 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bin/param_eq.pass.cpp

@@ -0,0 +1,37 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bin/param_types.pass.cpp

@@ -0,0 +1,28 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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), "");
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bin/set_param.pass.cpp

@@ -0,0 +1,30 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bin/types.pass.cpp

@@ -0,0 +1,32 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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), "");
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.geo/assign.pass.cpp

@@ -0,0 +1,34 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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 geometric_distribution
+
+// geometric_distribution& operator=(const geometric_distribution&);
+
+#include <random>
+#include <cassert>
+
+void
+test1()
+{
+    typedef std::geometric_distribution<> D;
+    D d1(0.75);
+    D d2;
+    assert(d1 != d2);
+    d2 = d1;
+    assert(d1 == d2);
+}
+
+int main()
+{
+    test1();
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.geo/copy.pass.cpp

@@ -0,0 +1,32 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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 geometric_distribution
+
+// geometric_distribution(const geometric_distribution&);
+
+#include <random>
+#include <cassert>
+
+void
+test1()
+{
+    typedef std::geometric_distribution<> D;
+    D d1(0.75);
+    D d2 = d1;
+    assert(d1 == d2);
+}
+
+int main()
+{
+    test1();
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.geo/ctor_double.pass.cpp

@@ -0,0 +1,32 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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 geometric_distribution
+
+// explicit geometric_distribution(double p = 0.5);
+
+#include <random>
+#include <cassert>
+
+int main()
+{
+    {
+        typedef std::geometric_distribution<> D;
+        D d;
+        assert(d.p() == 0.5);
+    }
+    {
+        typedef std::geometric_distribution<> D;
+        D d(0.75);
+        assert(d.p() == 0.75);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.geo/ctor_param.pass.cpp

@@ -0,0 +1,29 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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 geometric_distribution
+
+// explicit geometric_distribution(const param_type& parm);
+
+#include <random>
+#include <cassert>
+
+int main()
+{
+    {
+        typedef std::geometric_distribution<> D;
+        typedef D::param_type P;
+        P p(0.25);
+        D d(p);
+        assert(d.p() == 0.25);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.geo/eq.pass.cpp

@@ -0,0 +1,37 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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 geometric_distribution
+
+// bool operator=(const geometric_distribution& x,
+//                const geometric_distribution& y);
+// bool operator!(const geometric_distribution& x,
+//                const geometric_distribution& y);
+
+#include <random>
+#include <cassert>
+
+int main()
+{
+    {
+        typedef std::geometric_distribution<> D;
+        D d1(.25);
+        D d2(.25);
+        assert(d1 == d2);
+    }
+    {
+        typedef std::geometric_distribution<> D;
+        D d1(.28);
+        D d2(.25);
+        assert(d1 != d2);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.geo/eval.pass.cpp

@@ -0,0 +1,274 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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 geometric_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::geometric_distribution<> D;
+        typedef std::mt19937 G;
+        G g;
+        D d(.03125);
+        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 = (1 - d.p()) / d.p();
+        double x_var = x_mean / d.p();
+        double x_skew = (2 - d.p()) / std::sqrt((1 - d.p()));
+        double x_kurtosis = 6 + sqr(d.p()) / (1 - d.p());
+        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::geometric_distribution<> D;
+        typedef std::mt19937 G;
+        G g;
+        D d(0.05);
+        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 = (1 - d.p()) / d.p();
+        double x_var = x_mean / d.p();
+        double x_skew = (2 - d.p()) / std::sqrt((1 - d.p()));
+        double x_kurtosis = 6 + sqr(d.p()) / (1 - d.p());
+        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::geometric_distribution<> D;
+        typedef std::minstd_rand G;
+        G g;
+        D d(.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(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 = (1 - d.p()) / d.p();
+        double x_var = x_mean / d.p();
+        double x_skew = (2 - d.p()) / std::sqrt((1 - d.p()));
+        double x_kurtosis = 6 + sqr(d.p()) / (1 - d.p());
+        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) < 0.02);
+    }
+    {
+        typedef std::geometric_distribution<> D;
+        typedef std::mt19937 G;
+        G g;
+        D d(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(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 = (1 - d.p()) / d.p();
+        double x_var = x_mean / d.p();
+        double x_skew = (2 - d.p()) / std::sqrt((1 - d.p()));
+        double x_kurtosis = 6 + sqr(d.p()) / (1 - d.p());
+        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) < 0.02);
+    }
+    {
+        typedef std::geometric_distribution<> D;
+        typedef std::mt19937 G;
+        G g;
+        D d(0.75);
+        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 = (1 - d.p()) / d.p();
+        double x_var = x_mean / d.p();
+        double x_skew = (2 - d.p()) / std::sqrt((1 - d.p()));
+        double x_kurtosis = 6 + sqr(d.p()) / (1 - d.p());
+        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) < 0.02);
+    }
+    {
+        typedef std::geometric_distribution<> D;
+        typedef std::mt19937 G;
+        G g;
+        D d(0.96875);
+        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 = (1 - d.p()) / d.p();
+        double x_var = x_mean / d.p();
+        double x_skew = (2 - d.p()) / std::sqrt((1 - d.p()));
+        double x_kurtosis = 6 + sqr(d.p()) / (1 - d.p());
+        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) < 0.02);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.geo/eval_param.pass.cpp

@@ -0,0 +1,160 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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 geometric_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::geometric_distribution<> D;
+        typedef D::param_type P;
+        typedef std::mt19937 G;
+        G g;
+        D d(.75);
+        P p(.03125);
+        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(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 = (1 - p.p()) / p.p();
+        double x_var = x_mean / p.p();
+        double x_skew = (2 - p.p()) / std::sqrt((1 - p.p()));
+        double x_kurtosis = 6 + sqr(p.p()) / (1 - p.p());
+        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::geometric_distribution<> D;
+        typedef D::param_type P;
+        typedef std::mt19937 G;
+        G g;
+        D d(.75);
+        P p(.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(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 = (1 - p.p()) / p.p();
+        double x_var = x_mean / p.p();
+        double x_skew = (2 - p.p()) / std::sqrt((1 - p.p()));
+        double x_kurtosis = 6 + sqr(p.p()) / (1 - p.p());
+        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::geometric_distribution<> D;
+        typedef D::param_type P;
+        typedef std::minstd_rand G;
+        G g;
+        D d(.5);
+        P p(.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(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 = (1 - p.p()) / p.p();
+        double x_var = x_mean / p.p();
+        double x_skew = (2 - p.p()) / std::sqrt((1 - p.p()));
+        double x_kurtosis = 6 + sqr(p.p()) / (1 - p.p());
+        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) < 0.02);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.geo/get_param.pass.cpp

@@ -0,0 +1,29 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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 geometric_distribution
+
+// param_type param() const;
+
+#include <random>
+#include <cassert>
+
+int main()
+{
+    {
+        typedef std::geometric_distribution<> D;
+        typedef D::param_type P;
+        P p(.125);
+        D d(p);
+        assert(d.param() == p);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.geo/io.pass.cpp

@@ -0,0 +1,41 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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 geometric_distribution
+
+// template <class charT, class traits>
+// basic_ostream<charT, traits>&
+// operator<<(basic_ostream<charT, traits>& os,
+//            const geometric_distribution& x);
+//
+// template <class charT, class traits>
+// basic_istream<charT, traits>&
+// operator>>(basic_istream<charT, traits>& is,
+//            geometric_distribution& x);
+
+#include <random>
+#include <sstream>
+#include <cassert>
+
+int main()
+{
+    {
+        typedef std::geometric_distribution<> D;
+        D d1(.25);
+        std::ostringstream os;
+        os << d1;
+        std::istringstream is(os.str());
+        D d2;
+        is >> d2;
+        assert(d1 == d2);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.geo/max.pass.cpp

@@ -0,0 +1,27 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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 geometric_distribution
+
+// result_type max() const;
+
+#include <random>
+#include <cassert>
+
+int main()
+{
+    {
+        typedef std::geometric_distribution<> D;
+        D d(.25);
+        assert(d.max() == std::numeric_limits<int>::max());
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.geo/min.pass.cpp

@@ -0,0 +1,27 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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 geometric_distribution
+
+// result_type min() const;
+
+#include <random>
+#include <cassert>
+
+int main()
+{
+    {
+        typedef std::geometric_distribution<> D;
+        D d(.5);
+        assert(d.min() == 0);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.geo/param_assign.pass.cpp

@@ -0,0 +1,31 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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 geometric_distribution
+// {
+//     class param_type;
+
+#include <random>
+#include <limits>
+#include <cassert>
+
+int main()
+{
+    {
+        typedef std::geometric_distribution<> D;
+        typedef D::param_type param_type;
+        param_type p0(.7);
+        param_type p;
+        p = p0;
+        assert(p.p() == .7);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.geo/param_copy.pass.cpp

@@ -0,0 +1,30 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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 geometric_distribution
+// {
+//     class param_type;
+
+#include <random>
+#include <limits>
+#include <cassert>
+
+int main()
+{
+    {
+        typedef std::geometric_distribution<> D;
+        typedef D::param_type param_type;
+        param_type p0(.125);
+        param_type p = p0;
+        assert(p.p() == .125);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.geo/param_ctor.pass.cpp

@@ -0,0 +1,35 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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 geometric_distribution
+// {
+//     class param_type;
+
+#include <random>
+#include <limits>
+#include <cassert>
+
+int main()
+{
+    {
+        typedef std::geometric_distribution<> D;
+        typedef D::param_type param_type;
+        param_type p;
+        assert(p.p() == 0.5);
+    }
+    {
+        typedef std::geometric_distribution<> D;
+        typedef D::param_type param_type;
+        param_type p(0.25);
+        assert(p.p() == 0.25);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.geo/param_eq.pass.cpp

@@ -0,0 +1,37 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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 geometric_distribution
+// {
+//     class param_type;
+
+#include <random>
+#include <limits>
+#include <cassert>
+
+int main()
+{
+    {
+        typedef std::geometric_distribution<> D;
+        typedef D::param_type param_type;
+        param_type p1(0.75);
+        param_type p2(0.75);
+        assert(p1 == p2);
+    }
+    {
+        typedef std::geometric_distribution<> D;
+        typedef D::param_type param_type;
+        param_type p1(0.75);
+        param_type p2(0.5);
+        assert(p1 != p2);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.geo/param_types.pass.cpp

@@ -0,0 +1,28 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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 geometric_distribution
+// {
+//     class param_type;
+
+#include <random>
+#include <type_traits>
+
+int main()
+{
+    {
+        typedef std::geometric_distribution<> D;
+        typedef D::param_type param_type;
+        typedef param_type::distribution_type distribution_type;
+        static_assert((std::is_same<D, distribution_type>::value), "");
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.geo/set_param.pass.cpp

@@ -0,0 +1,30 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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 geometric_distribution
+
+// void param(const param_type& parm);
+
+#include <random>
+#include <cassert>
+
+int main()
+{
+    {
+        typedef std::geometric_distribution<> D;
+        typedef D::param_type P;
+        P p(0.25);
+        D d(0.75);
+        d.param(p);
+        assert(d.param() == p);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.geo/types.pass.cpp

@@ -0,0 +1,32 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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 geometric_distribution
+// {
+//     typedef bool result_type;
+
+#include <random>
+#include <type_traits>
+
+int main()
+{
+    {
+        typedef std::geometric_distribution<> D;
+        typedef D::result_type result_type;
+        static_assert((std::is_same<result_type, int>::value), "");
+    }
+    {
+        typedef std::geometric_distribution<long> D;
+        typedef D::result_type result_type;
+        static_assert((std::is_same<result_type, long>::value), "");
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.negbin/assign.pass.cpp

@@ -0,0 +1,34 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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 negative_binomial_distribution
+
+// negative_binomial_distribution& operator=(const negative_binomial_distribution&);
+
+#include <random>
+#include <cassert>
+
+void
+test1()
+{
+    typedef std::negative_binomial_distribution<> D;
+    D d1(2, 0.75);
+    D d2;
+    assert(d1 != d2);
+    d2 = d1;
+    assert(d1 == d2);
+}
+
+int main()
+{
+    test1();
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.negbin/copy.pass.cpp

@@ -0,0 +1,32 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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 negative_binomial_distribution
+
+// negative_binomial_distribution(const negative_binomial_distribution&);
+
+#include <random>
+#include <cassert>
+
+void
+test1()
+{
+    typedef std::negative_binomial_distribution<> D;
+    D d1(2, 0.75);
+    D d2 = d1;
+    assert(d1 == d2);
+}
+
+int main()
+{
+    test1();
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.negbin/ctor_int_double.pass.cpp

@@ -0,0 +1,40 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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 negative_binomial_distribution
+
+// explicit negative_binomial_distribution(IntType t = 1, double p = 0.5);
+
+#include <random>
+#include <cassert>
+
+int main()
+{
+    {
+        typedef std::negative_binomial_distribution<> D;
+        D d;
+        assert(d.k() == 1);
+        assert(d.p() == 0.5);
+    }
+    {
+        typedef std::negative_binomial_distribution<> D;
+        D d(3);
+        assert(d.k() == 3);
+        assert(d.p() == 0.5);
+    }
+    {
+        typedef std::negative_binomial_distribution<> D;
+        D d(3, 0.75);
+        assert(d.k() == 3);
+        assert(d.p() == 0.75);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.negbin/ctor_param.pass.cpp

@@ -0,0 +1,30 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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 negative_binomial_distribution
+
+// explicit negative_binomial_distribution(const param_type& parm);
+
+#include <random>
+#include <cassert>
+
+int main()
+{
+    {
+        typedef std::negative_binomial_distribution<> D;
+        typedef D::param_type P;
+        P p(5, 0.25);
+        D d(p);
+        assert(d.k() == 5);
+        assert(d.p() == 0.25);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.negbin/eq.pass.cpp

@@ -0,0 +1,43 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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 negative_binomial_distribution
+
+// bool operator=(const negative_binomial_distribution& x,
+//                const negative_binomial_distribution& y);
+// bool operator!(const negative_binomial_distribution& x,
+//                const negative_binomial_distribution& y);
+
+#include <random>
+#include <cassert>
+
+int main()
+{
+    {
+        typedef std::negative_binomial_distribution<> D;
+        D d1(3, .25);
+        D d2(3, .25);
+        assert(d1 == d2);
+    }
+    {
+        typedef std::negative_binomial_distribution<> D;
+        D d1(3, .28);
+        D d2(3, .25);
+        assert(d1 != d2);
+    }
+    {
+        typedef std::negative_binomial_distribution<> D;
+        D d1(3, .25);
+        D d2(4, .25);
+        assert(d1 != d2);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.negbin/eval.pass.cpp

@@ -0,0 +1,272 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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 negative_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::negative_binomial_distribution<> D;
+        typedef std::minstd_rand G;
+        G g;
+        D d(5, .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(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.k() * (1 - d.p()) / d.p();
+        double x_var = x_mean / d.p();
+        double x_skew = (2 - d.p()) / std::sqrt(d.k() * (1 - d.p()));
+        double x_kurtosis = 6. / d.k() + sqr(d.p()) / (d.k() * (1 - d.p()));
+        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) < 0.02);
+    }
+    {
+        typedef std::negative_binomial_distribution<> D;
+        typedef std::mt19937 G;
+        G g;
+        D d(30, .03125);
+        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.k() * (1 - d.p()) / d.p();
+        double x_var = x_mean / d.p();
+        double x_skew = (2 - d.p()) / std::sqrt(d.k() * (1 - d.p()));
+        double x_kurtosis = 6. / d.k() + sqr(d.p()) / (d.k() * (1 - d.p()));
+        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::negative_binomial_distribution<> D;
+        typedef std::mt19937 G;
+        G g;
+        D d(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);
+            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.k() * (1 - d.p()) / d.p();
+        double x_var = x_mean / d.p();
+        double x_skew = (2 - d.p()) / std::sqrt(d.k() * (1 - d.p()));
+        double x_kurtosis = 6. / d.k() + sqr(d.p()) / (d.k() * (1 - d.p()));
+        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::negative_binomial_distribution<> D;
+        typedef std::mt19937 G;
+        G g;
+        D d(40, 1);
+        const int N = 1000;
+        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.k() * (1 - d.p()) / d.p();
+        double x_var = x_mean / d.p();
+        double x_skew = (2 - d.p()) / std::sqrt(d.k() * (1 - d.p()));
+        double x_kurtosis = 6. / d.k() + sqr(d.p()) / (d.k() * (1 - d.p()));
+        assert(mean == x_mean);
+        assert(var == x_var);
+    }
+    {
+        typedef std::negative_binomial_distribution<> D;
+        typedef std::mt19937 G;
+        G g;
+        D d(400, 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(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.k() * (1 - d.p()) / d.p();
+        double x_var = x_mean / d.p();
+        double x_skew = (2 - d.p()) / std::sqrt(d.k() * (1 - d.p()));
+        double x_kurtosis = 6. / d.k() + sqr(d.p()) / (d.k() * (1 - d.p()));
+        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) < 0.05);
+    }
+    {
+        typedef std::negative_binomial_distribution<> D;
+        typedef std::mt19937 G;
+        G g;
+        D d(1, 0.05);
+        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.k() * (1 - d.p()) / d.p();
+        double x_var = x_mean / d.p();
+        double x_skew = (2 - d.p()) / std::sqrt(d.k() * (1 - d.p()));
+        double x_kurtosis = 6. / d.k() + sqr(d.p()) / (d.k() * (1 - d.p()));
+        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);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.negbin/eval_param.pass.cpp

@@ -0,0 +1,160 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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 negative_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::negative_binomial_distribution<> D;
+        typedef D::param_type P;
+        typedef std::minstd_rand 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(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 = p.k() * (1 - p.p()) / p.p();
+        double x_var = x_mean / p.p();
+        double x_skew = (2 - p.p()) / std::sqrt(p.k() * (1 - p.p()));
+        double x_kurtosis = 6. / p.k() + sqr(p.p()) / (p.k() * (1 - p.p()));
+        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::negative_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 = 1000000;
+        std::vector<D::result_type> u;
+        for (int i = 0; i < N; ++i)
+        {
+            D::result_type v = d(g, p);
+            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 = p.k() * (1 - p.p()) / p.p();
+        double x_var = x_mean / p.p();
+        double x_skew = (2 - p.p()) / std::sqrt(p.k() * (1 - p.p()));
+        double x_kurtosis = 6. / p.k() + sqr(p.p()) / (p.k() * (1 - p.p()));
+        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::negative_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(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 = p.k() * (1 - p.p()) / p.p();
+        double x_var = x_mean / p.p();
+        double x_skew = (2 - p.p()) / std::sqrt(p.k() * (1 - p.p()));
+        double x_kurtosis = 6. / p.k() + sqr(p.p()) / (p.k() * (1 - p.p()));
+        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);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.negbin/get_param.pass.cpp

@@ -0,0 +1,29 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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 negative_binomial_distribution
+
+// param_type param() const;
+
+#include <random>
+#include <cassert>
+
+int main()
+{
+    {
+        typedef std::negative_binomial_distribution<> D;
+        typedef D::param_type P;
+        P p(5, .125);
+        D d(p);
+        assert(d.param() == p);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.negbin/io.pass.cpp

@@ -0,0 +1,41 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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 negative_binomial_distribution
+
+// template <class charT, class traits>
+// basic_ostream<charT, traits>&
+// operator<<(basic_ostream<charT, traits>& os,
+//            const negative_binomial_distribution& x);
+//
+// template <class charT, class traits>
+// basic_istream<charT, traits>&
+// operator>>(basic_istream<charT, traits>& is,
+//            negative_binomial_distribution& x);
+
+#include <random>
+#include <sstream>
+#include <cassert>
+
+int main()
+{
+    {
+        typedef std::negative_binomial_distribution<> D;
+        D d1(7, .25);
+        std::ostringstream os;
+        os << d1;
+        std::istringstream is(os.str());
+        D d2;
+        is >> d2;
+        assert(d1 == d2);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.negbin/max.pass.cpp

@@ -0,0 +1,27 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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 negative_binomial_distribution
+
+// result_type max() const;
+
+#include <random>
+#include <cassert>
+
+int main()
+{
+    {
+        typedef std::negative_binomial_distribution<> D;
+        D d(4, .25);
+        assert(d.max() == std::numeric_limits<int>::max());
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.negbin/min.pass.cpp

@@ -0,0 +1,27 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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 negative_binomial_distribution
+
+// result_type min() const;
+
+#include <random>
+#include <cassert>
+
+int main()
+{
+    {
+        typedef std::negative_binomial_distribution<> D;
+        D d(4, .5);
+        assert(d.min() == 0);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.negbin/param_assign.pass.cpp

@@ -0,0 +1,32 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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 negative_binomial_distribution
+// {
+//     class param_type;
+
+#include <random>
+#include <limits>
+#include <cassert>
+
+int main()
+{
+    {
+        typedef std::negative_binomial_distribution<> D;
+        typedef D::param_type param_type;
+        param_type p0(6, .7);
+        param_type p;
+        p = p0;
+        assert(p.k() == 6);
+        assert(p.p() == .7);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.negbin/param_copy.pass.cpp

@@ -0,0 +1,31 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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 negative_binomial_distribution
+// {
+//     class param_type;
+
+#include <random>
+#include <limits>
+#include <cassert>
+
+int main()
+{
+    {
+        typedef std::negative_binomial_distribution<> D;
+        typedef D::param_type param_type;
+        param_type p0(10, .125);
+        param_type p = p0;
+        assert(p.k() == 10);
+        assert(p.p() == .125);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.negbin/param_ctor.pass.cpp

@@ -0,0 +1,44 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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 negative_binomial_distribution
+// {
+//     class param_type;
+
+#include <random>
+#include <limits>
+#include <cassert>
+
+int main()
+{
+    {
+        typedef std::negative_binomial_distribution<> D;
+        typedef D::param_type param_type;
+        param_type p;
+        assert(p.k() == 1);
+        assert(p.p() == 0.5);
+    }
+    {
+        typedef std::negative_binomial_distribution<> D;
+        typedef D::param_type param_type;
+        param_type p(10);
+        assert(p.k() == 10);
+        assert(p.p() == 0.5);
+    }
+    {
+        typedef std::negative_binomial_distribution<> D;
+        typedef D::param_type param_type;
+        param_type p(10, 0.25);
+        assert(p.k() == 10);
+        assert(p.p() == 0.25);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.negbin/param_eq.pass.cpp

@@ -0,0 +1,37 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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 negative_binomial_distribution
+// {
+//     class param_type;
+
+#include <random>
+#include <limits>
+#include <cassert>
+
+int main()
+{
+    {
+        typedef std::negative_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::negative_binomial_distribution<> D;
+        typedef D::param_type param_type;
+        param_type p1(3, 0.75);
+        param_type p2(3, 0.5);
+        assert(p1 != p2);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.negbin/param_types.pass.cpp

@@ -0,0 +1,28 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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 negative_binomial_distribution
+// {
+//     class param_type;
+
+#include <random>
+#include <type_traits>
+
+int main()
+{
+    {
+        typedef std::negative_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), "");
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.negbin/set_param.pass.cpp

@@ -0,0 +1,30 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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 negative_binomial_distribution
+
+// void param(const param_type& parm);
+
+#include <random>
+#include <cassert>
+
+int main()
+{
+    {
+        typedef std::negative_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);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.negbin/types.pass.cpp

@@ -0,0 +1,32 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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 negative_binomial_distribution
+// {
+//     typedef bool result_type;
+
+#include <random>
+#include <type_traits>
+
+int main()
+{
+    {
+        typedef std::negative_binomial_distribution<> D;
+        typedef D::result_type result_type;
+        static_assert((std::is_same<result_type, int>::value), "");
+    }
+    {
+        typedef std::negative_binomial_distribution<long> D;
+        typedef D::result_type result_type;
+        static_assert((std::is_same<result_type, long>::value), "");
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.norm/nothing_to_do.pass.cpp

@@ -0,0 +1,12 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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.
+//
+//===----------------------------------------------------------------------===//
+
+int main()
+{
+}

test/std/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.cauchy/assign.pass.cpp

@@ -0,0 +1,34 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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 RealType = double>
+// class cauchy_distribution
+
+// cauchy_distribution& operator=(const cauchy_distribution&);
+
+#include <random>
+#include <cassert>
+
+void
+test1()
+{
+    typedef std::cauchy_distribution<> D;
+    D d1(.5, 2);
+    D d2;
+    assert(d1 != d2);
+    d2 = d1;
+    assert(d1 == d2);
+}
+
+int main()
+{
+    test1();
+}

test/std/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.cauchy/copy.pass.cpp

@@ -0,0 +1,32 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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 RealType = double>
+// class cauchy_distribution
+
+// cauchy_distribution(const cauchy_distribution&);
+
+#include <random>
+#include <cassert>
+
+void
+test1()
+{
+    typedef std::cauchy_distribution<> D;
+    D d1(.5, 1.75);
+    D d2 = d1;
+    assert(d1 == d2);
+}
+
+int main()
+{
+    test1();
+}

test/std/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.cauchy/ctor_double_double.pass.cpp

@@ -0,0 +1,40 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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 RealType = double>
+// class cauchy_distribution
+
+// explicit cauchy_distribution(result_type a = 0, result_type b = 1);
+
+#include <random>
+#include <cassert>
+
+int main()
+{
+    {
+        typedef std::cauchy_distribution<> D;
+        D d;
+        assert(d.a() == 0);
+        assert(d.b() == 1);
+    }
+    {
+        typedef std::cauchy_distribution<> D;
+        D d(14.5);
+        assert(d.a() == 14.5);
+        assert(d.b() == 1);
+    }
+    {
+        typedef std::cauchy_distribution<> D;
+        D d(14.5, 5.25);
+        assert(d.a() == 14.5);
+        assert(d.b() == 5.25);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.cauchy/ctor_param.pass.cpp

@@ -0,0 +1,30 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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 RealType = double>
+// class cauchy_distribution
+
+// explicit cauchy_distribution(const param_type& parm);
+
+#include <random>
+#include <cassert>
+
+int main()
+{
+    {
+        typedef std::cauchy_distribution<> D;
+        typedef D::param_type P;
+        P p(0.25, 10);
+        D d(p);
+        assert(d.a() == 0.25);
+        assert(d.b() == 10);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.cauchy/eq.pass.cpp

@@ -0,0 +1,37 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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 RealType = double>
+// class cauchy_distribution
+
+// bool operator=(const cauchy_distribution& x,
+//                const cauchy_distribution& y);
+// bool operator!(const cauchy_distribution& x,
+//                const cauchy_distribution& y);
+
+#include <random>
+#include <cassert>
+
+int main()
+{
+    {
+        typedef std::cauchy_distribution<> D;
+        D d1(2.5, 4);
+        D d2(2.5, 4);
+        assert(d1 == d2);
+    }
+    {
+        typedef std::cauchy_distribution<> D;
+        D d1(2.5, 4);
+        D d2(2.5, 4.5);
+        assert(d1 != d2);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.cauchy/eval.pass.cpp

@@ -0,0 +1,80 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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 RealType = double>
+// class cauchy_distribution
+
+// template<class _URNG> result_type operator()(_URNG& g);
+
+#include <random>
+#include <cassert>
+#include <vector>
+#include <algorithm>
+
+double
+f(double x, double a, double b)
+{
+    return 1/3.1415926535897932 * std::atan((x - a)/b) + .5;
+}
+
+int main()
+{
+    {
+        typedef std::cauchy_distribution<> D;
+        typedef D::param_type P;
+        typedef std::mt19937 G;
+        G g;
+        const double a = 10;
+        const double b = .5;
+        D d(a, b);
+        const int N = 1000000;
+        std::vector<D::result_type> u;
+        for (int i = 0; i < N; ++i)
+            u.push_back(d(g));
+        std::sort(u.begin(), u.end());
+        for (int i = 0; i < N; ++i)
+            assert(std::abs(f(u[i], a, b) - double(i)/N) < .001);
+    }
+    {
+        typedef std::cauchy_distribution<> D;
+        typedef D::param_type P;
+        typedef std::mt19937 G;
+        G g;
+        const double a = -1.5;
+        const double b = 1;
+        D d(a, b);
+        const int N = 1000000;
+        std::vector<D::result_type> u;
+        for (int i = 0; i < N; ++i)
+            u.push_back(d(g));
+        std::sort(u.begin(), u.end());
+        for (int i = 0; i < N; ++i)
+            assert(std::abs(f(u[i], a, b) - double(i)/N) < .001);
+    }
+    {
+        typedef std::cauchy_distribution<> D;
+        typedef D::param_type P;
+        typedef std::mt19937 G;
+        G g;
+        const double a = .5;
+        const double b = 2;
+        D d(a, b);
+        const int N = 1000000;
+        std::vector<D::result_type> u;
+        for (int i = 0; i < N; ++i)
+            u.push_back(d(g));
+        std::sort(u.begin(), u.end());
+        for (int i = 0; i < N; ++i)
+            assert(std::abs(f(u[i], a, b) - double(i)/N) < .001);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.cauchy/eval_param.pass.cpp

@@ -0,0 +1,83 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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 RealType = double>
+// class cauchy_distribution
+
+// template<class _URNG> result_type operator()(_URNG& g, const param_type& parm);
+
+#include <random>
+#include <cassert>
+#include <vector>
+#include <algorithm>
+
+double
+f(double x, double a, double b)
+{
+    return 1/3.1415926535897932 * std::atan((x - a)/b) + .5;
+}
+
+int main()
+{
+    {
+        typedef std::cauchy_distribution<> D;
+        typedef D::param_type P;
+        typedef std::mt19937 G;
+        G g;
+        const double a = 10;
+        const double b = .5;
+        D d;
+        P p(a, b);
+        const int N = 1000000;
+        std::vector<D::result_type> u;
+        for (int i = 0; i < N; ++i)
+            u.push_back(d(g, p));
+        std::sort(u.begin(), u.end());
+        for (int i = 0; i < N; ++i)
+            assert(std::abs(f(u[i], a, b) - double(i)/N) < .001);
+    }
+    {
+        typedef std::cauchy_distribution<> D;
+        typedef D::param_type P;
+        typedef std::mt19937 G;
+        G g;
+        const double a = -1.5;
+        const double b = 1;
+        D d;
+        P p(a, b);
+        const int N = 1000000;
+        std::vector<D::result_type> u;
+        for (int i = 0; i < N; ++i)
+            u.push_back(d(g, p));
+        std::sort(u.begin(), u.end());
+        for (int i = 0; i < N; ++i)
+            assert(std::abs(f(u[i], a, b) - double(i)/N) < .001);
+    }
+    {
+        typedef std::cauchy_distribution<> D;
+        typedef D::param_type P;
+        typedef std::mt19937 G;
+        G g;
+        const double a = .5;
+        const double b = 2;
+        D d;
+        P p(a, b);
+        const int N = 1000000;
+        std::vector<D::result_type> u;
+        for (int i = 0; i < N; ++i)
+            u.push_back(d(g, p));
+        std::sort(u.begin(), u.end());
+        for (int i = 0; i < N; ++i)
+            assert(std::abs(f(u[i], a, b) - double(i)/N) < .001);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.cauchy/get_param.pass.cpp

@@ -0,0 +1,29 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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 RealType = double>
+// class cauchy_distribution
+
+// param_type param() const;
+
+#include <random>
+#include <cassert>
+
+int main()
+{
+    {
+        typedef std::cauchy_distribution<> D;
+        typedef D::param_type P;
+        P p(.125, .5);
+        D d(p);
+        assert(d.param() == p);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.cauchy/io.pass.cpp

@@ -0,0 +1,41 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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 RealType = double>
+// class cauchy_distribution
+
+// template <class CharT, class Traits, class RealType>
+// basic_ostream<CharT, Traits>&
+// operator<<(basic_ostream<CharT, Traits>& os,
+//            const cauchy_distribution<RealType>& x);
+
+// template <class CharT, class Traits, class RealType>
+// basic_istream<CharT, Traits>&
+// operator>>(basic_istream<CharT, Traits>& is,
+//            cauchy_distribution<RealType>& x);
+
+#include <random>
+#include <sstream>
+#include <cassert>
+
+int main()
+{
+    {
+        typedef std::cauchy_distribution<> D;
+        D d1(7.5, 5.5);
+        std::ostringstream os;
+        os << d1;
+        std::istringstream is(os.str());
+        D d2;
+        is >> d2;
+        assert(d1 == d2);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.cauchy/max.pass.cpp

@@ -0,0 +1,28 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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 RealType = double>
+// class cauchy_distribution
+
+// result_type max() const;
+
+#include <random>
+#include <cassert>
+
+int main()
+{
+    {
+        typedef std::cauchy_distribution<> D;
+        D d(5, .25);
+        D::result_type m = d.max();
+        assert(m == INFINITY);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.cauchy/min.pass.cpp

@@ -0,0 +1,27 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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 RealType = double>
+// class cauchy_distribution
+
+// result_type min() const;
+
+#include <random>
+#include <cassert>
+
+int main()
+{
+    {
+        typedef std::cauchy_distribution<> D;
+        D d(.5, .5);
+        assert(d.min() == -INFINITY);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.cauchy/param_assign.pass.cpp

@@ -0,0 +1,32 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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 RealType = double>
+// class cauchy_distribution
+// {
+//     class param_type;
+
+#include <random>
+#include <limits>
+#include <cassert>
+
+int main()
+{
+    {
+        typedef std::cauchy_distribution<> D;
+        typedef D::param_type param_type;
+        param_type p0(.75, 6);
+        param_type p;
+        p = p0;
+        assert(p.a() == .75);
+        assert(p.b() == 6);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.cauchy/param_copy.pass.cpp

@@ -0,0 +1,31 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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 RealType = double>
+// class cauchy_distribution
+// {
+//     class param_type;
+
+#include <random>
+#include <limits>
+#include <cassert>
+
+int main()
+{
+    {
+        typedef std::cauchy_distribution<> D;
+        typedef D::param_type param_type;
+        param_type p0(10, .125);
+        param_type p = p0;
+        assert(p.a() == 10);
+        assert(p.b() == .125);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.cauchy/param_ctor.pass.cpp

@@ -0,0 +1,44 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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 RealType = double>
+// class cauchy_distribution
+// {
+//     class param_type;
+
+#include <random>
+#include <limits>
+#include <cassert>
+
+int main()
+{
+    {
+        typedef std::cauchy_distribution<> D;
+        typedef D::param_type param_type;
+        param_type p;
+        assert(p.a() == 0);
+        assert(p.b() == 1);
+    }
+    {
+        typedef std::cauchy_distribution<> D;
+        typedef D::param_type param_type;
+        param_type p(10);
+        assert(p.a() == 10);
+        assert(p.b() == 1);
+    }
+    {
+        typedef std::cauchy_distribution<> D;
+        typedef D::param_type param_type;
+        param_type p(10, 5);
+        assert(p.a() == 10);
+        assert(p.b() == 5);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.cauchy/param_eq.pass.cpp

@@ -0,0 +1,37 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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 RealType = double>
+// class cauchy_distribution
+// {
+//     class param_type;
+
+#include <random>
+#include <limits>
+#include <cassert>
+
+int main()
+{
+    {
+        typedef std::cauchy_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::cauchy_distribution<> D;
+        typedef D::param_type param_type;
+        param_type p1(0.75, .5);
+        param_type p2(0.5, .5);
+        assert(p1 != p2);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.cauchy/param_types.pass.cpp

@@ -0,0 +1,28 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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 RealType = double>
+// class cauchy_distribution
+// {
+//     class param_type;
+
+#include <random>
+#include <type_traits>
+
+int main()
+{
+    {
+        typedef std::cauchy_distribution<> D;
+        typedef D::param_type param_type;
+        typedef param_type::distribution_type distribution_type;
+        static_assert((std::is_same<D, distribution_type>::value), "");
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.cauchy/set_param.pass.cpp

@@ -0,0 +1,30 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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 RealType = double>
+// class cauchy_distribution;
+
+// void param(const param_type& parm);
+
+#include <random>
+#include <cassert>
+
+int main()
+{
+    {
+        typedef std::cauchy_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);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.cauchy/types.pass.cpp

@@ -0,0 +1,34 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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 RealType = double>
+// class cauchy_distribution
+// {
+// public:
+//     // types
+//     typedef RealType result_type;
+
+#include <random>
+#include <type_traits>
+
+int main()
+{
+    {
+        typedef std::cauchy_distribution<> D;
+        typedef D::result_type result_type;
+        static_assert((std::is_same<result_type, double>::value), "");
+    }
+    {
+        typedef std::cauchy_distribution<float> D;
+        typedef D::result_type result_type;
+        static_assert((std::is_same<result_type, float>::value), "");
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.chisq/assign.pass.cpp

@@ -0,0 +1,34 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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 RealType = double>
+// class chi_squared_distribution
+
+// chi_squared_distribution& operator=(const chi_squared_distribution&);
+
+#include <random>
+#include <cassert>
+
+void
+test1()
+{
+    typedef std::chi_squared_distribution<> D;
+    D d1(20.75);
+    D d2;
+    assert(d1 != d2);
+    d2 = d1;
+    assert(d1 == d2);
+}
+
+int main()
+{
+    test1();
+}

test/std/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.chisq/copy.pass.cpp

@@ -0,0 +1,32 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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 RealType = double>
+// class chi_squared_distribution
+
+// chi_squared_distribution(const chi_squared_distribution&);
+
+#include <random>
+#include <cassert>
+
+void
+test1()
+{
+    typedef std::chi_squared_distribution<> D;
+    D d1(21.75);
+    D d2 = d1;
+    assert(d1 == d2);
+}
+
+int main()
+{
+    test1();
+}

test/std/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.chisq/ctor_double.pass.cpp

@@ -0,0 +1,32 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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 RealType = double>
+// class chi_squared_distribution
+
+// explicit chi_squared_distribution(result_type alpha = 0, result_type beta = 1);
+
+#include <random>
+#include <cassert>
+
+int main()
+{
+    {
+        typedef std::chi_squared_distribution<> D;
+        D d;
+        assert(d.n() == 1);
+    }
+    {
+        typedef std::chi_squared_distribution<> D;
+        D d(14.5);
+        assert(d.n() == 14.5);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.chisq/ctor_param.pass.cpp

@@ -0,0 +1,29 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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 RealType = double>
+// class chi_squared_distribution
+
+// explicit chi_squared_distribution(const param_type& parm);
+
+#include <random>
+#include <cassert>
+
+int main()
+{
+    {
+        typedef std::chi_squared_distribution<> D;
+        typedef D::param_type P;
+        P p(0.25);
+        D d(p);
+        assert(d.n() == 0.25);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.chisq/eq.pass.cpp

@@ -0,0 +1,37 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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 RealType = double>
+// class chi_squared_distribution
+
+// bool operator=(const chi_squared_distribution& x,
+//                const chi_squared_distribution& y);
+// bool operator!(const chi_squared_distribution& x,
+//                const chi_squared_distribution& y);
+
+#include <random>
+#include <cassert>
+
+int main()
+{
+    {
+        typedef std::chi_squared_distribution<> D;
+        D d1(2.5);
+        D d2(2.5);
+        assert(d1 == d2);
+    }
+    {
+        typedef std::chi_squared_distribution<> D;
+        D d1(4);
+        D d2(4.5);
+        assert(d1 != d2);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.chisq/eval.pass.cpp

@@ -0,0 +1,154 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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 RealType = double>
+// class chi_squared_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::chi_squared_distribution<> D;
+        typedef D::param_type P;
+        typedef std::minstd_rand G;
+        G g;
+        D d(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(d.min() < v);
+            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 = d.n();
+        double x_var = 2 * d.n();
+        double x_skew = std::sqrt(8 / d.n());
+        double x_kurtosis = 12 / d.n();
+        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::chi_squared_distribution<> D;
+        typedef D::param_type P;
+        typedef std::minstd_rand G;
+        G g;
+        D d(1);
+        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);
+            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 = d.n();
+        double x_var = 2 * d.n();
+        double x_skew = std::sqrt(8 / d.n());
+        double x_kurtosis = 12 / d.n();
+        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::chi_squared_distribution<> D;
+        typedef D::param_type P;
+        typedef std::mt19937 G;
+        G g;
+        D d(2);
+        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);
+            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 = d.n();
+        double x_var = 2 * d.n();
+        double x_skew = std::sqrt(8 / d.n());
+        double x_kurtosis = 12 / d.n();
+        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);
+    }
+}

test/std/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.chisq/eval_param.pass.cpp

@@ -0,0 +1,157 @@
+//===----------------------------------------------------------------------===//
+//
+//                     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 RealType = double>
+// class chi_squared_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::chi_squared_distribution<> D;
+        typedef D::param_type P;
+        typedef std::minstd_rand G;
+        G g;
+        D d(0.5);
+        P p(1);
+        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(d.min() < v);
+            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 = p.n();
+        double x_var = 2 * p.n();
+        double x_skew = std::sqrt(8 / p.n());
+        double x_kurtosis = 12 / p.n();
+        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::chi_squared_distribution<> D;
+        typedef D::param_type P;
+        typedef std::mt19937 G;
+        G g;
+        D d(1);
+        P p(2);
+        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(d.min() < v);
+            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 = p.n();
+        double x_var = 2 * p.n();
+        double x_skew = std::sqrt(8 / p.n());
+        double x_kurtosis = 12 / p.n();
+        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::chi_squared_distribution<> D;
+        typedef D::param_type P;
+        typedef std::minstd_rand G;
+        G g;
+        D d(2);
+        P p(.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(d.min() < v);
+            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 = p.n();
+        double x_var = 2 * p.n();
+        double x_skew = std::sqrt(8 / p.n());
+        double x_kurtosis = 12 / p.n();
+        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);
+    }
+}