[rand.dist.norm.lognormal]

git-svn-id: https://llvm.org/svn/llvm-project/libcxx/trunk@103957 91177308-0d34-0410-b5e6-96231b3b80d8

include/random

@@ -1124,7 +1124,62 @@ public:
 };
 
 template<class RealType = double>
-    class lognormal_distribution;
+class lognormal_distribution
+{
+public:
+    // types
+    typedef RealType result_type;
+
+    class param_type
+    {
+    public:
+        typedef lognormal_distribution distribution_type;
+
+        explicit param_type(result_type m = 0, result_type s = 1);
+
+        result_type m() const;
+        result_type s() const;
+
+        friend bool operator==(const param_type& x, const param_type& y);
+        friend bool operator!=(const param_type& x, const param_type& y);
+    };
+
+    // constructor and reset functions
+    explicit lognormal_distribution(result_type m = 0, result_type s = 1);
+    explicit lognormal_distribution(const param_type& parm);
+    void reset();
+
+    // generating functions
+    template<class URNG> result_type operator()(URNG& g);
+    template<class URNG> result_type operator()(URNG& g, const param_type& parm);
+
+    // property functions
+    result_type m() const;
+    result_type s() const;
+
+    param_type param() const;
+    void param(const param_type& parm);
+
+    result_type min() const;
+    result_type max() const;
+
+    friend bool operator==(const lognormal_distribution& x,
+                           const lognormal_distribution& y);
+    friend bool operator!=(const lognormal_distribution& x,
+                           const lognormal_distribution& y);
+
+    template <class charT, class traits>
+    friend
+    basic_ostream<charT, traits>&
+    operator<<(basic_ostream<charT, traits>& os,
+               const lognormal_distribution& x);
+    
+    template <class charT, class traits>
+    friend
+    basic_istream<charT, traits>&
+    operator>>(basic_istream<charT, traits>& is,
+               lognormal_distribution& x);
+};
 
 template<class RealType = double>
 class chi_squared_distribution
@@ -3796,6 +3851,111 @@ operator>>(basic_istream<_CharT, _Traits>& __is,
     return __is;
 }
 
+// lognormal_distribution
+
+template<class _RealType = double>
+class lognormal_distribution
+{
+public:
+    // types
+    typedef _RealType result_type;
+
+    class param_type
+    {
+        normal_distribution<result_type> __nd_;
+    public:
+        typedef lognormal_distribution distribution_type;
+
+        explicit param_type(result_type __m = 0, result_type __s = 1)
+            : __nd_(__m, __s) {}
+
+        result_type m() const {return __nd_.mean();}
+        result_type s() const {return __nd_.stddev();}
+
+        friend bool operator==(const param_type& __x, const param_type& __y)
+            {return __x.__nd_ == __y.__nd_;}
+        friend bool operator!=(const param_type& __x, const param_type& __y)
+            {return !(__x == __y);}
+        friend class lognormal_distribution;
+
+        template <class _CharT, class _Traits, class _RT>
+        friend
+        basic_ostream<_CharT, _Traits>&
+        operator<<(basic_ostream<_CharT, _Traits>& __os,
+                   const lognormal_distribution<_RT>& __x);
+        
+        template <class _CharT, class _Traits, class _RT>
+        friend
+        basic_istream<_CharT, _Traits>&
+        operator>>(basic_istream<_CharT, _Traits>& __is,
+                   lognormal_distribution<_RT>& __x);
+    };
+
+private:
+    param_type __p_;
+
+public:
+    // constructor and reset functions
+    explicit lognormal_distribution(result_type __m = 0, result_type __s = 1)
+        : __p_(param_type(__m, __s)) {}
+    explicit lognormal_distribution(const param_type& __p)
+        : __p_(__p) {}
+    void reset() {__p_.__nd_.reset();}
+
+    // generating functions
+    template<class _URNG> result_type operator()(_URNG& __g)
+        {return (*this)(__g, __p_);}
+    template<class _URNG> result_type operator()(_URNG& __g, const param_type& __p)
+        {return _STD::exp(const_cast<normal_distribution<result_type>&>(__p.__nd_)(__g));}
+
+    // property functions
+    result_type m() const {return __p_.m();}
+    result_type s() const {return __p_.s();}
+
+    param_type param() const {return __p_;}
+    void param(const param_type& __p) {return __p_ = __p;}
+
+    result_type min() const {return 0;}
+    result_type max() const {return numeric_limits<result_type>::infinity();}
+
+    friend bool operator==(const lognormal_distribution& __x,
+                           const lognormal_distribution& __y)
+        {return __x.__p_ == __y.__p_;}
+    friend bool operator!=(const lognormal_distribution& __x,
+                           const lognormal_distribution& __y)
+        {return !(__x == __y);}
+
+    template <class _CharT, class _Traits, class _RT>
+    friend
+    basic_ostream<_CharT, _Traits>&
+    operator<<(basic_ostream<_CharT, _Traits>& __os,
+               const lognormal_distribution<_RT>& __x);
+    
+    template <class _CharT, class _Traits, class _RT>
+    friend
+    basic_istream<_CharT, _Traits>&
+    operator>>(basic_istream<_CharT, _Traits>& __is,
+               lognormal_distribution<_RT>& __x);
+};
+
+template <class _CharT, class _Traits, class _RT>
+inline
+basic_ostream<_CharT, _Traits>&
+operator<<(basic_ostream<_CharT, _Traits>& __os,
+           const lognormal_distribution<_RT>& __x)
+{
+    return __os << __x.__p_.__nd_;
+}
+
+template <class _CharT, class _Traits, class _RT>
+inline
+basic_istream<_CharT, _Traits>&
+operator>>(basic_istream<_CharT, _Traits>& __is,
+           lognormal_distribution<_RT>& __x)
+{
+    return __is >> __x.__p_.__nd_;
+}
+
 // poisson_distribution
 
 template<class _IntType = int>

test/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.lognormal/assign.pass.cpp

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

test/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.lognormal/copy.pass.cpp

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

test/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.lognormal/ctor_double_double.pass.cpp

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

test/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.lognormal/ctor_param.pass.cpp

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

test/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.lognormal/eq.pass.cpp

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

test/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.lognormal/eval.pass.cpp

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

test/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.lognormal/eval_param.pass.cpp

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

test/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.lognormal/get_param.pass.cpp

@@ -0,0 +1,29 @@
+//===----------------------------------------------------------------------===//
+//
+//                     The LLVM Compiler Infrastructure
+//
+// This file is distributed under the University of Illinois Open Source
+// License. See LICENSE.TXT for details.
+//
+//===----------------------------------------------------------------------===//
+
+// <random>
+
+// template<class RealType = double>
+// class lognormal_distribution
+
+// param_type param() const;
+
+#include <random>
+#include <cassert>
+
+int main()
+{
+    {
+        typedef std::lognormal_distribution<> D;
+        typedef D::param_type P;
+        P p(.125, .5);
+        D d(p);
+        assert(d.param() == p);
+    }
+}

test/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.lognormal/io.pass.cpp

@@ -0,0 +1,41 @@
+//===----------------------------------------------------------------------===//
+//
+//                     The LLVM Compiler Infrastructure
+//
+// This file is distributed under the University of Illinois Open Source
+// License. See LICENSE.TXT for details.
+//
+//===----------------------------------------------------------------------===//
+
+// <random>
+
+// template<class RealType = double>
+// class lognormal_distribution
+
+// template <class CharT, class Traits, class RealType>
+// basic_ostream<CharT, Traits>&
+// operator<<(basic_ostream<CharT, Traits>& os,
+//            const lognormal_distribution<RealType>& x);
+
+// template <class CharT, class Traits, class RealType>
+// basic_istream<CharT, Traits>&
+// operator>>(basic_istream<CharT, Traits>& is,
+//            lognormal_distribution<RealType>& x);
+
+#include <random>
+#include <sstream>
+#include <cassert>
+
+int main()
+{
+    {
+        typedef std::lognormal_distribution<> D;
+        D d1(7, 5);
+        std::ostringstream os;
+        os << d1;
+        std::istringstream is(os.str());
+        D d2;
+        is >> d2;
+        assert(d1 == d2);
+    }
+}

test/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.lognormal/max.pass.cpp

@@ -0,0 +1,28 @@
+//===----------------------------------------------------------------------===//
+//
+//                     The LLVM Compiler Infrastructure
+//
+// This file is distributed under the University of Illinois Open Source
+// License. See LICENSE.TXT for details.
+//
+//===----------------------------------------------------------------------===//
+
+// <random>
+
+// template<class RealType = double>
+// class lognormal_distribution
+
+// result_type max() const;
+
+#include <random>
+#include <cassert>
+
+int main()
+{
+    {
+        typedef std::lognormal_distribution<> D;
+        D d(5, .25);
+        D::result_type m = d.max();
+        assert(m == INFINITY);
+    }
+}

test/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.lognormal/min.pass.cpp

@@ -0,0 +1,27 @@
+//===----------------------------------------------------------------------===//
+//
+//                     The LLVM Compiler Infrastructure
+//
+// This file is distributed under the University of Illinois Open Source
+// License. See LICENSE.TXT for details.
+//
+//===----------------------------------------------------------------------===//
+
+// <random>
+
+// template<class RealType = double>
+// class lognormal_distribution
+
+// result_type min() const;
+
+#include <random>
+#include <cassert>
+
+int main()
+{
+    {
+        typedef std::lognormal_distribution<> D;
+        D d(.5, .5);
+        assert(d.min() == 0);
+    }
+}

test/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.lognormal/param_assign.pass.cpp

@@ -0,0 +1,32 @@
+//===----------------------------------------------------------------------===//
+//
+//                     The LLVM Compiler Infrastructure
+//
+// This file is distributed under the University of Illinois Open Source
+// License. See LICENSE.TXT for details.
+//
+//===----------------------------------------------------------------------===//
+
+// <random>
+
+// template<class RealType = double>
+// class lognormal_distribution
+// {
+//     class param_type;
+
+#include <random>
+#include <limits>
+#include <cassert>
+
+int main()
+{
+    {
+        typedef std::lognormal_distribution<> D;
+        typedef D::param_type param_type;
+        param_type p0(.75, 6);
+        param_type p;
+        p = p0;
+        assert(p.m() == .75);
+        assert(p.s() == 6);
+    }
+}

test/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.lognormal/param_copy.pass.cpp

@@ -0,0 +1,31 @@
+//===----------------------------------------------------------------------===//
+//
+//                     The LLVM Compiler Infrastructure
+//
+// This file is distributed under the University of Illinois Open Source
+// License. See LICENSE.TXT for details.
+//
+//===----------------------------------------------------------------------===//
+
+// <random>
+
+// template<class RealType = double>
+// class lognormal_distribution
+// {
+//     class param_type;
+
+#include <random>
+#include <limits>
+#include <cassert>
+
+int main()
+{
+    {
+        typedef std::lognormal_distribution<> D;
+        typedef D::param_type param_type;
+        param_type p0(10, .125);
+        param_type p = p0;
+        assert(p.m() == 10);
+        assert(p.s() == .125);
+    }
+}

test/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.lognormal/param_ctor.pass.cpp

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

test/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.lognormal/param_eq.pass.cpp

@@ -0,0 +1,37 @@
+//===----------------------------------------------------------------------===//
+//
+//                     The LLVM Compiler Infrastructure
+//
+// This file is distributed under the University of Illinois Open Source
+// License. See LICENSE.TXT for details.
+//
+//===----------------------------------------------------------------------===//
+
+// <random>
+
+// template<class RealType = double>
+// class lognormal_distribution
+// {
+//     class param_type;
+
+#include <random>
+#include <limits>
+#include <cassert>
+
+int main()
+{
+    {
+        typedef std::lognormal_distribution<> D;
+        typedef D::param_type param_type;
+        param_type p1(0.75, .5);
+        param_type p2(0.75, .5);
+        assert(p1 == p2);
+    }
+    {
+        typedef std::lognormal_distribution<> D;
+        typedef D::param_type param_type;
+        param_type p1(0.75, .5);
+        param_type p2(0.5, .5);
+        assert(p1 != p2);
+    }
+}

test/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.lognormal/param_types.pass.cpp

@@ -0,0 +1,28 @@
+//===----------------------------------------------------------------------===//
+//
+//                     The LLVM Compiler Infrastructure
+//
+// This file is distributed under the University of Illinois Open Source
+// License. See LICENSE.TXT for details.
+//
+//===----------------------------------------------------------------------===//
+
+// <random>
+
+// template<class RealType = double>
+// class lognormal_distribution
+// {
+//     class param_type;
+
+#include <random>
+#include <type_traits>
+
+int main()
+{
+    {
+        typedef std::lognormal_distribution<> D;
+        typedef D::param_type param_type;
+        typedef param_type::distribution_type distribution_type;
+        static_assert((std::is_same<D, distribution_type>::value), "");
+    }
+}

test/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.lognormal/set_param.pass.cpp

@@ -0,0 +1,30 @@
+//===----------------------------------------------------------------------===//
+//
+//                     The LLVM Compiler Infrastructure
+//
+// This file is distributed under the University of Illinois Open Source
+// License. See LICENSE.TXT for details.
+//
+//===----------------------------------------------------------------------===//
+
+// <random>
+
+// template<class RealType = double>
+// class lognormal_distribution;
+
+// void param(const param_type& parm);
+
+#include <random>
+#include <cassert>
+
+int main()
+{
+    {
+        typedef std::lognormal_distribution<> D;
+        typedef D::param_type P;
+        P p(0.25, 5.5);
+        D d(0.75, 4);
+        d.param(p);
+        assert(d.param() == p);
+    }
+}

test/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.lognormal/types.pass.cpp

@@ -0,0 +1,34 @@
+//===----------------------------------------------------------------------===//
+//
+//                     The LLVM Compiler Infrastructure
+//
+// This file is distributed under the University of Illinois Open Source
+// License. See LICENSE.TXT for details.
+//
+//===----------------------------------------------------------------------===//
+
+// <random>
+
+// template<class RealType = double>
+// class lognormal_distribution
+// {
+// public:
+//     // types
+//     typedef RealType result_type;
+
+#include <random>
+#include <type_traits>
+
+int main()
+{
+    {
+        typedef std::lognormal_distribution<> D;
+        typedef D::result_type result_type;
+        static_assert((std::is_same<result_type, double>::value), "");
+    }
+    {
+        typedef std::lognormal_distribution<float> D;
+        typedef D::result_type result_type;
+        static_assert((std::is_same<result_type, float>::value), "");
+    }
+}