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/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);
+    }
+}