Move test into test/std subdirectory.

llvm-svn: 224658

1 files changed, 0 insertions, 272 deletions

diff --git a/libcxx/test/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.negbin/eval.pass.cpp b/libcxx/test/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.negbin/eval.pass.cpp
deleted file mode 100644
index 853161e9f9b..00000000000
--- a/libcxx/test/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.negbin/eval.pass.cpp
+++ /dev/null
@@ -1,272 +0,0 @@
-//===----------------------------------------------------------------------===//
-//
-//                     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);
-    }
-}