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-rw-r--r--libcxx/test/numerics/rand/rand.dis/rand.dist.uni/rand.dist.uni.int/eval.pass.cpp420
1 files changed, 420 insertions, 0 deletions
diff --git a/libcxx/test/numerics/rand/rand.dis/rand.dist.uni/rand.dist.uni.int/eval.pass.cpp b/libcxx/test/numerics/rand/rand.dis/rand.dist.uni/rand.dist.uni.int/eval.pass.cpp
index 192242a16b4..3443e16f26f 100644
--- a/libcxx/test/numerics/rand/rand.dis/rand.dist.uni/rand.dist.uni.int/eval.pass.cpp
+++ b/libcxx/test/numerics/rand/rand.dis/rand.dist.uni/rand.dist.uni.int/eval.pass.cpp
@@ -16,6 +16,16 @@
#include <random>
#include <cassert>
+#include <vector>
+#include <numeric>
+
+template <class T>
+inline
+T
+sqr(T x)
+{
+ return x * x;
+}
int main()
{
@@ -23,6 +33,375 @@ int main()
typedef std::uniform_int_distribution<> D;
typedef std::minstd_rand0 G;
G g;
+ D d;
+ 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.a() <= v && v <= d.b());
+ 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 = ((double)d.a() + d.b()) / 2;
+ double x_var = (sqr((double)d.b() - d.a() + 1) - 1) / 12;
+ double x_skew = 0;
+ double x_kurtosis = -6. * (sqr((double)d.b() - d.a() + 1) + 1) /
+ (5. * (sqr((double)d.b() - d.a() + 1) - 1));
+ 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);
+ }
+ {
+ typedef std::uniform_int_distribution<> D;
+ typedef std::minstd_rand G;
+ G g;
+ D d;
+ 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.a() <= v && v <= d.b());
+ 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 = ((double)d.a() + d.b()) / 2;
+ double x_var = (sqr((double)d.b() - d.a() + 1) - 1) / 12;
+ double x_skew = 0;
+ double x_kurtosis = -6. * (sqr((double)d.b() - d.a() + 1) + 1) /
+ (5. * (sqr((double)d.b() - d.a() + 1) - 1));
+ 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);
+ }
+ {
+ typedef std::uniform_int_distribution<> D;
+ typedef std::mt19937 G;
+ G g;
+ D d;
+ 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.a() <= v && v <= d.b());
+ 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 = ((double)d.a() + d.b()) / 2;
+ double x_var = (sqr((double)d.b() - d.a() + 1) - 1) / 12;
+ double x_skew = 0;
+ double x_kurtosis = -6. * (sqr((double)d.b() - d.a() + 1) + 1) /
+ (5. * (sqr((double)d.b() - d.a() + 1) - 1));
+ 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);
+ }
+ {
+ typedef std::uniform_int_distribution<> D;
+ typedef std::mt19937_64 G;
+ G g;
+ D d;
+ 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.a() <= v && v <= d.b());
+ 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 = ((double)d.a() + d.b()) / 2;
+ double x_var = (sqr((double)d.b() - d.a() + 1) - 1) / 12;
+ double x_skew = 0;
+ double x_kurtosis = -6. * (sqr((double)d.b() - d.a() + 1) + 1) /
+ (5. * (sqr((double)d.b() - d.a() + 1) - 1));
+ 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);
+ }
+ {
+ typedef std::uniform_int_distribution<> D;
+ typedef std::ranlux24_base G;
+ G g;
+ D d;
+ 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.a() <= v && v <= d.b());
+ 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 = ((double)d.a() + d.b()) / 2;
+ double x_var = (sqr((double)d.b() - d.a() + 1) - 1) / 12;
+ double x_skew = 0;
+ double x_kurtosis = -6. * (sqr((double)d.b() - d.a() + 1) + 1) /
+ (5. * (sqr((double)d.b() - d.a() + 1) - 1));
+ 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);
+ }
+ {
+ typedef std::uniform_int_distribution<> D;
+ typedef std::ranlux48_base G;
+ G g;
+ D d;
+ 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.a() <= v && v <= d.b());
+ 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 = ((double)d.a() + d.b()) / 2;
+ double x_var = (sqr((double)d.b() - d.a() + 1) - 1) / 12;
+ double x_skew = 0;
+ double x_kurtosis = -6. * (sqr((double)d.b() - d.a() + 1) + 1) /
+ (5. * (sqr((double)d.b() - d.a() + 1) - 1));
+ 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);
+ }
+ {
+ typedef std::uniform_int_distribution<> D;
+ typedef std::ranlux24 G;
+ G g;
+ D d;
+ 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.a() <= v && v <= d.b());
+ 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 = ((double)d.a() + d.b()) / 2;
+ double x_var = (sqr((double)d.b() - d.a() + 1) - 1) / 12;
+ double x_skew = 0;
+ double x_kurtosis = -6. * (sqr((double)d.b() - d.a() + 1) + 1) /
+ (5. * (sqr((double)d.b() - d.a() + 1) - 1));
+ 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);
+ }
+ {
+ typedef std::uniform_int_distribution<> D;
+ typedef std::ranlux48 G;
+ G g;
+ D d;
+ 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.a() <= v && v <= d.b());
+ 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 = ((double)d.a() + d.b()) / 2;
+ double x_var = (sqr((double)d.b() - d.a() + 1) - 1) / 12;
+ double x_skew = 0;
+ double x_kurtosis = -6. * (sqr((double)d.b() - d.a() + 1) + 1) /
+ (5. * (sqr((double)d.b() - d.a() + 1) - 1));
+ 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);
+ }
+ {
+ typedef std::uniform_int_distribution<> D;
+ typedef std::knuth_b G;
+ G g;
+ D d;
+ 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.a() <= v && v <= d.b());
+ 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 = ((double)d.a() + d.b()) / 2;
+ double x_var = (sqr((double)d.b() - d.a() + 1) - 1) / 12;
+ double x_skew = 0;
+ double x_kurtosis = -6. * (sqr((double)d.b() - d.a() + 1) + 1) /
+ (5. * (sqr((double)d.b() - d.a() + 1) - 1));
+ 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);
+ }
+ {
+ typedef std::uniform_int_distribution<> D;
+ typedef std::minstd_rand0 G;
+ G g;
D d(-6, 106);
for (int i = 0; i < 10000; ++i)
{
@@ -30,4 +409,45 @@ int main()
assert(-6 <= u && u <= 106);
}
}
+ {
+ typedef std::uniform_int_distribution<> D;
+ typedef std::minstd_rand G;
+ G g;
+ D d(5, 100);
+ 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.a() <= v && v <= d.b());
+ 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 = ((double)d.a() + d.b()) / 2;
+ double x_var = (sqr((double)d.b() - d.a() + 1) - 1) / 12;
+ double x_skew = 0;
+ double x_kurtosis = -6. * (sqr((double)d.b() - d.a() + 1) + 1) /
+ (5. * (sqr((double)d.b() - d.a() + 1) - 1));
+ 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);
+ }
}
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