summaryrefslogtreecommitdiff
path: root/test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bin/eval.pass.cpp
diff options
context:
space:
mode:
Diffstat (limited to 'test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bin/eval.pass.cpp')
-rw-r--r--test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bin/eval.pass.cpp880
1 files changed, 462 insertions, 418 deletions
diff --git a/test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bin/eval.pass.cpp b/test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bin/eval.pass.cpp
index 43c6b546bdb27..88004ba4a741f 100644
--- a/test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bin/eval.pass.cpp
+++ b/test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bin/eval.pass.cpp
@@ -29,447 +29,491 @@ sqr(T x)
return x * x;
}
-int main()
+void
+test1()
{
+ typedef std::binomial_distribution<> D;
+ typedef std::mt19937_64 G;
+ G g;
+ D d(5, .75);
+ const int N = 1000000;
+ std::vector<D::result_type> u;
+ for (int i = 0; i < N; ++i)
{
- typedef std::binomial_distribution<> D;
- typedef std::mt19937_64 G;
- G g;
- D d(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);
- 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.t() * d.p();
- double x_var = x_mean*(1-d.p());
- double x_skew = (1-2*d.p()) / std::sqrt(x_var);
- double x_kurtosis = (1-6*d.p()*(1-d.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);
+ 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::binomial_distribution<> D;
- typedef std::mt19937 G;
- G g;
- D d(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);
- 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.t() * d.p();
- double x_var = x_mean*(1-d.p());
- double x_skew = (1-2*d.p()) / std::sqrt(x_var);
- double x_kurtosis = (1-6*d.p()*(1-d.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);
+ 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.t() * d.p();
+ double x_var = x_mean*(1-d.p());
+ double x_skew = (1-2*d.p()) / std::sqrt(x_var);
+ double x_kurtosis = (1-6*d.p()*(1-d.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);
+}
+
+void
+test2()
+{
+ typedef std::binomial_distribution<> D;
+ typedef std::mt19937 G;
+ G g;
+ D d(30, .03125);
+ const int N = 100000;
+ std::vector<D::result_type> u;
+ for (int i = 0; i < N; ++i)
{
- typedef std::binomial_distribution<> D;
- typedef std::mt19937 G;
- G g;
- D d(40, .25);
- const int N = 100000;
- 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.t() * d.p();
- double x_var = x_mean*(1-d.p());
- double x_skew = (1-2*d.p()) / std::sqrt(x_var);
- double x_kurtosis = (1-6*d.p()*(1-d.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) < 0.03);
- assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.3);
+ 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::binomial_distribution<> D;
- typedef std::mt19937 G;
- G g;
- D d(40, 0);
- const int N = 100000;
- 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);
- // In this case:
- // skew computes to 0./0. == nan
- // kurtosis computes to 0./0. == nan
- // x_skew == inf
- // x_kurtosis == inf
- // These tests are commented out because UBSan warns about division by 0
-// skew /= u.size() * dev * var;
-// kurtosis /= u.size() * var * var;
-// kurtosis -= 3;
- double x_mean = d.t() * d.p();
- double x_var = x_mean*(1-d.p());
-// double x_skew = (1-2*d.p()) / std::sqrt(x_var);
-// double x_kurtosis = (1-6*d.p()*(1-d.p())) / x_var;
- assert(mean == x_mean);
- assert(var == x_var);
-// assert(skew == x_skew);
-// assert(kurtosis == x_kurtosis);
+ 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.t() * d.p();
+ double x_var = x_mean*(1-d.p());
+ double x_skew = (1-2*d.p()) / std::sqrt(x_var);
+ double x_kurtosis = (1-6*d.p()*(1-d.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);
+}
+
+void
+test3()
+{
+ typedef std::binomial_distribution<> D;
+ typedef std::mt19937 G;
+ G g;
+ D d(40, .25);
+ const int N = 100000;
+ std::vector<D::result_type> u;
+ for (int i = 0; i < N; ++i)
{
- typedef std::binomial_distribution<> D;
- typedef std::mt19937 G;
- G g;
- D d(40, 1);
- const int N = 100000;
- 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);
- // In this case:
- // skew computes to 0./0. == nan
- // kurtosis computes to 0./0. == nan
- // x_skew == -inf
- // x_kurtosis == inf
- // These tests are commented out because UBSan warns about division by 0
-// skew /= u.size() * dev * var;
-// kurtosis /= u.size() * var * var;
-// kurtosis -= 3;
- double x_mean = d.t() * d.p();
- double x_var = x_mean*(1-d.p());
-// double x_skew = (1-2*d.p()) / std::sqrt(x_var);
-// double x_kurtosis = (1-6*d.p()*(1-d.p())) / x_var;
- assert(mean == x_mean);
- assert(var == x_var);
-// assert(skew == x_skew);
-// assert(kurtosis == x_kurtosis);
+ 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::binomial_distribution<> D;
- typedef std::mt19937 G;
- G g;
- D d(400, 0.5);
- const int N = 100000;
- 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.t() * d.p();
- double x_var = x_mean*(1-d.p());
- double x_skew = (1-2*d.p()) / std::sqrt(x_var);
- double x_kurtosis = (1-6*d.p()*(1-d.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) < 0.01);
- assert(std::abs(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.t() * d.p();
+ double x_var = x_mean*(1-d.p());
+ double x_skew = (1-2*d.p()) / std::sqrt(x_var);
+ double x_kurtosis = (1-6*d.p()*(1-d.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) < 0.03);
+ assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.3);
+}
+
+void
+test4()
+{
+ typedef std::binomial_distribution<> D;
+ typedef std::mt19937 G;
+ G g;
+ D d(40, 0);
+ const int N = 100000;
+ std::vector<D::result_type> u;
+ for (int i = 0; i < N; ++i)
{
- typedef std::binomial_distribution<> D;
- typedef std::mt19937 G;
- G g;
- D d(1, 0.5);
- const int N = 100000;
- 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.t() * d.p();
- double x_var = x_mean*(1-d.p());
- double x_skew = (1-2*d.p()) / std::sqrt(x_var);
- double x_kurtosis = (1-6*d.p()*(1-d.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) < 0.01);
- assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
+ 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)
{
- const int N = 100000;
- std::mt19937 gen1;
- std::mt19937 gen2;
+ 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);
+ // In this case:
+ // skew computes to 0./0. == nan
+ // kurtosis computes to 0./0. == nan
+ // x_skew == inf
+ // x_kurtosis == inf
+ // These tests are commented out because UBSan warns about division by 0
+// skew /= u.size() * dev * var;
+// kurtosis /= u.size() * var * var;
+// kurtosis -= 3;
+ double x_mean = d.t() * d.p();
+ double x_var = x_mean*(1-d.p());
+// double x_skew = (1-2*d.p()) / std::sqrt(x_var);
+// double x_kurtosis = (1-6*d.p()*(1-d.p())) / x_var;
+ assert(mean == x_mean);
+ assert(var == x_var);
+// assert(skew == x_skew);
+// assert(kurtosis == x_kurtosis);
+}
- std::binomial_distribution<> dist1(5, 0.1);
- std::binomial_distribution<unsigned> dist2(5, 0.1);
+void
+test5()
+{
+ typedef std::binomial_distribution<> D;
+ typedef std::mt19937 G;
+ G g;
+ D d(40, 1);
+ const int N = 100000;
+ 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);
+ // In this case:
+ // skew computes to 0./0. == nan
+ // kurtosis computes to 0./0. == nan
+ // x_skew == -inf
+ // x_kurtosis == inf
+ // These tests are commented out because UBSan warns about division by 0
+// skew /= u.size() * dev * var;
+// kurtosis /= u.size() * var * var;
+// kurtosis -= 3;
+ double x_mean = d.t() * d.p();
+ double x_var = x_mean*(1-d.p());
+// double x_skew = (1-2*d.p()) / std::sqrt(x_var);
+// double x_kurtosis = (1-6*d.p()*(1-d.p())) / x_var;
+ assert(mean == x_mean);
+ assert(var == x_var);
+// assert(skew == x_skew);
+// assert(kurtosis == x_kurtosis);
+}
- for(int i = 0; i < N; ++i)
- assert(dist1(gen1) == dist2(gen2));
+void
+test6()
+{
+ typedef std::binomial_distribution<> D;
+ typedef std::mt19937 G;
+ G g;
+ D d(400, 0.5);
+ const int N = 100000;
+ 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)
{
- typedef std::binomial_distribution<> D;
- typedef std::mt19937 G;
- G g;
- D d(0, 0.005);
- const int N = 100000;
- 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);
- // In this case:
- // skew computes to 0./0. == nan
- // kurtosis computes to 0./0. == nan
- // x_skew == inf
- // x_kurtosis == inf
- // These tests are commented out because UBSan warns about division by 0
-// skew /= u.size() * dev * var;
-// kurtosis /= u.size() * var * var;
-// kurtosis -= 3;
- double x_mean = d.t() * d.p();
- double x_var = x_mean*(1-d.p());
-// double x_skew = (1-2*d.p()) / std::sqrt(x_var);
-// double x_kurtosis = (1-6*d.p()*(1-d.p())) / x_var;
- assert(mean == x_mean);
- assert(var == x_var);
-// assert(skew == x_skew);
-// assert(kurtosis == x_kurtosis);
+ 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.t() * d.p();
+ double x_var = x_mean*(1-d.p());
+ double x_skew = (1-2*d.p()) / std::sqrt(x_var);
+ double x_kurtosis = (1-6*d.p()*(1-d.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) < 0.01);
+ assert(std::abs(kurtosis - x_kurtosis) < 0.01);
+}
+
+void
+test7()
+{
+ typedef std::binomial_distribution<> D;
+ typedef std::mt19937 G;
+ G g;
+ D d(1, 0.5);
+ const int N = 100000;
+ std::vector<D::result_type> u;
+ for (int i = 0; i < N; ++i)
{
- typedef std::binomial_distribution<> D;
- typedef std::mt19937 G;
- G g;
- D d(0, 0);
- const int N = 100000;
- 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);
- // In this case:
- // skew computes to 0./0. == nan
- // kurtosis computes to 0./0. == nan
- // x_skew == inf
- // x_kurtosis == inf
- // These tests are commented out because UBSan warns about division by 0
-// skew /= u.size() * dev * var;
-// kurtosis /= u.size() * var * var;
-// kurtosis -= 3;
- double x_mean = d.t() * d.p();
- double x_var = x_mean*(1-d.p());
-// double x_skew = (1-2*d.p()) / std::sqrt(x_var);
-// double x_kurtosis = (1-6*d.p()*(1-d.p())) / x_var;
- assert(mean == x_mean);
- assert(var == x_var);
-// assert(skew == x_skew);
-// assert(kurtosis == x_kurtosis);
+ 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::binomial_distribution<> D;
- typedef std::mt19937 G;
- G g;
- D d(0, 1);
- const int N = 100000;
- 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);
- // In this case:
- // skew computes to 0./0. == nan
- // kurtosis computes to 0./0. == nan
- // x_skew == -inf
- // x_kurtosis == inf
- // These tests are commented out because UBSan warns about division by 0
-// skew /= u.size() * dev * var;
-// kurtosis /= u.size() * var * var;
-// kurtosis -= 3;
- double x_mean = d.t() * d.p();
- double x_var = x_mean*(1-d.p());
-// double x_skew = (1-2*d.p()) / std::sqrt(x_var);
-// double x_kurtosis = (1-6*d.p()*(1-d.p())) / x_var;
- assert(mean == x_mean);
- assert(var == x_var);
-// assert(skew == x_skew);
-// assert(kurtosis == x_kurtosis);
+ 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.t() * d.p();
+ double x_var = x_mean*(1-d.p());
+ double x_skew = (1-2*d.p()) / std::sqrt(x_var);
+ double x_kurtosis = (1-6*d.p()*(1-d.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) < 0.01);
+ assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
+}
+
+void
+test8()
+{
+ const int N = 100000;
+ std::mt19937 gen1;
+ std::mt19937 gen2;
+
+ std::binomial_distribution<> dist1(5, 0.1);
+ std::binomial_distribution<unsigned> dist2(5, 0.1);
+
+ for(int i = 0; i < N; ++i)
+ assert(dist1(gen1) == dist2(gen2));
+}
+
+void
+test9()
+{
+ typedef std::binomial_distribution<> D;
+ typedef std::mt19937 G;
+ G g;
+ D d(0, 0.005);
+ const int N = 100000;
+ 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);
+ // In this case:
+ // skew computes to 0./0. == nan
+ // kurtosis computes to 0./0. == nan
+ // x_skew == inf
+ // x_kurtosis == inf
+ // These tests are commented out because UBSan warns about division by 0
+// skew /= u.size() * dev * var;
+// kurtosis /= u.size() * var * var;
+// kurtosis -= 3;
+ double x_mean = d.t() * d.p();
+ double x_var = x_mean*(1-d.p());
+// double x_skew = (1-2*d.p()) / std::sqrt(x_var);
+// double x_kurtosis = (1-6*d.p()*(1-d.p())) / x_var;
+ assert(mean == x_mean);
+ assert(var == x_var);
+// assert(skew == x_skew);
+// assert(kurtosis == x_kurtosis);
+}
+
+void
+test10()
+{
+ typedef std::binomial_distribution<> D;
+ typedef std::mt19937 G;
+ G g;
+ D d(0, 0);
+ const int N = 100000;
+ 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);
+ // In this case:
+ // skew computes to 0./0. == nan
+ // kurtosis computes to 0./0. == nan
+ // x_skew == inf
+ // x_kurtosis == inf
+ // These tests are commented out because UBSan warns about division by 0
+// skew /= u.size() * dev * var;
+// kurtosis /= u.size() * var * var;
+// kurtosis -= 3;
+ double x_mean = d.t() * d.p();
+ double x_var = x_mean*(1-d.p());
+// double x_skew = (1-2*d.p()) / std::sqrt(x_var);
+// double x_kurtosis = (1-6*d.p()*(1-d.p())) / x_var;
+ assert(mean == x_mean);
+ assert(var == x_var);
+// assert(skew == x_skew);
+// assert(kurtosis == x_kurtosis);
+}
+
+void
+test11()
+{
+ typedef std::binomial_distribution<> D;
+ typedef std::mt19937 G;
+ G g;
+ D d(0, 1);
+ const int N = 100000;
+ 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);
+ // In this case:
+ // skew computes to 0./0. == nan
+ // kurtosis computes to 0./0. == nan
+ // x_skew == -inf
+ // x_kurtosis == inf
+ // These tests are commented out because UBSan warns about division by 0
+// skew /= u.size() * dev * var;
+// kurtosis /= u.size() * var * var;
+// kurtosis -= 3;
+ double x_mean = d.t() * d.p();
+ double x_var = x_mean*(1-d.p());
+// double x_skew = (1-2*d.p()) / std::sqrt(x_var);
+// double x_kurtosis = (1-6*d.p()*(1-d.p())) / x_var;
+ assert(mean == x_mean);
+ assert(var == x_var);
+// assert(skew == x_skew);
+// assert(kurtosis == x_kurtosis);
+}
+
+int main()
+{
+ test1();
+ test2();
+ test3();
+ test4();
+ test5();
+ test6();
+ test7();
+ test8();
+ test9();
+ test10();
+ test11();
}
generated by cgit v1.2.3 (git 2.25.1) at 2025-11-04 05:40:22 +0000