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-rw-r--r--libcxx/test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.negbin/eval.pass.cpp478
1 files changed, 251 insertions, 227 deletions
diff --git a/libcxx/test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.negbin/eval.pass.cpp b/libcxx/test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.negbin/eval.pass.cpp
index b89a16f3172..929e6e7e6c3 100644
--- a/libcxx/test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.negbin/eval.pass.cpp
+++ b/libcxx/test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.negbin/eval.pass.cpp
@@ -29,244 +29,268 @@ sqr(T x)
return x * x;
}
-int main()
+void
+test1()
+{
+ 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 dbl = (u[i] - mean);
+ double d2 = sqr(dbl);
+ var += d2;
+ skew += dbl * 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);
+}
+
+void
+test2()
+{
+ 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 dbl = (u[i] - mean);
+ double d2 = sqr(dbl);
+ var += d2;
+ skew += dbl * 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);
+}
+
+void
+test3()
+{
+ 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 dbl = (u[i] - mean);
+ double d2 = sqr(dbl);
+ var += d2;
+ skew += dbl * 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);
+}
+
+void
+test4()
{
+ 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)
{
- 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 dbl = (u[i] - mean);
- double d2 = sqr(dbl);
- var += d2;
- skew += dbl * 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);
+ 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)
{
- 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 dbl = (u[i] - mean);
- double d2 = sqr(dbl);
- var += d2;
- skew += dbl * 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);
+ double dbl = (u[i] - mean);
+ double d2 = sqr(dbl);
+ var += d2;
+ skew += dbl * 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);
+}
+
+void
+test5()
+{
+ 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)
{
- 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 dbl = (u[i] - mean);
- double d2 = sqr(dbl);
- var += d2;
- skew += dbl * 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);
+ 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)
{
- 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 dbl = (u[i] - mean);
- double d2 = sqr(dbl);
- var += d2;
- skew += dbl * 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);
+ double dbl = (u[i] - mean);
+ double d2 = sqr(dbl);
+ var += d2;
+ skew += dbl * 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);
+}
+
+void
+test6()
+{
+ 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)
{
- 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 dbl = (u[i] - mean);
- double d2 = sqr(dbl);
- var += d2;
- skew += dbl * 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);
+ 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)
{
- 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 dbl = (u[i] - mean);
- double d2 = sqr(dbl);
- var += d2;
- skew += dbl * 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);
+ double dbl = (u[i] - mean);
+ double d2 = sqr(dbl);
+ var += d2;
+ skew += dbl * 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);
+}
+
+int main()
+{
+ test1();
+ test2();
+ test3();
+ test4();
+ test5();
+ test6();
}
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