[rand.dist.bern.negbin]

llvm-svn: 103916

1 files changed, 270 insertions, 0 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
new file mode 100644
index 00000000000..96b4c180261
--- /dev/null
+++ b/libcxx/test/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.negbin/eval.pass.cpp
@@ -0,0 +1,270 @@
+//===----------------------------------------------------------------------===//
+//
+//                     The LLVM Compiler Infrastructure
+//
+// This file is distributed under the University of Illinois Open Source
+// License. See LICENSE.TXT for details.
+//
+//===----------------------------------------------------------------------===//
+
+// <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 < 0.01);
+    }
+    {
+        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 < 0.02);
+    }
+}