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-rw-r--r--libcxx/test/numerics/rand/rand.dis/rand.dist.uni/rand.dist.uni.real/eval.pass.cpp474
1 files changed, 0 insertions, 474 deletions
diff --git a/libcxx/test/numerics/rand/rand.dis/rand.dist.uni/rand.dist.uni.real/eval.pass.cpp b/libcxx/test/numerics/rand/rand.dis/rand.dist.uni/rand.dist.uni.real/eval.pass.cpp
deleted file mode 100644
index 2663b2683bb..00000000000
--- a/libcxx/test/numerics/rand/rand.dis/rand.dist.uni/rand.dist.uni.real/eval.pass.cpp
+++ /dev/null
@@ -1,474 +0,0 @@
-//===----------------------------------------------------------------------===//
-//
-// 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 RealType = double>
-// class uniform_real_distribution
-
-// template<class _URNG> result_type operator()(_URNG& g);
-
-#include <random>
-#include <cassert>
-#include <vector>
-#include <numeric>
-
-template <class T>
-inline
-T
-sqr(T x)
-{
- return x * x;
-}
-
-int main()
-{
- {
- typedef std::uniform_real_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);
- }
- D::result_type mean = std::accumulate(u.begin(), u.end(),
- D::result_type(0)) / u.size();
- D::result_type var = 0;
- D::result_type skew = 0;
- D::result_type kurtosis = 0;
- for (int i = 0; i < u.size(); ++i)
- {
- D::result_type d = (u[i] - mean);
- D::result_type d2 = sqr(d);
- var += d2;
- skew += d * d2;
- kurtosis += d2 * d2;
- }
- var /= u.size();
- D::result_type dev = std::sqrt(var);
- skew /= u.size() * dev * var;
- kurtosis /= u.size() * var * var;
- kurtosis -= 3;
- D::result_type x_mean = (d.a() + d.b()) / 2;
- D::result_type x_var = sqr(d.b() - d.a()) / 12;
- D::result_type x_skew = 0;
- D::result_type x_kurtosis = -6./5;
- 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_real_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);
- }
- D::result_type mean = std::accumulate(u.begin(), u.end(),
- D::result_type(0)) / u.size();
- D::result_type var = 0;
- D::result_type skew = 0;
- D::result_type kurtosis = 0;
- for (int i = 0; i < u.size(); ++i)
- {
- D::result_type d = (u[i] - mean);
- D::result_type d2 = sqr(d);
- var += d2;
- skew += d * d2;
- kurtosis += d2 * d2;
- }
- var /= u.size();
- D::result_type dev = std::sqrt(var);
- skew /= u.size() * dev * var;
- kurtosis /= u.size() * var * var;
- kurtosis -= 3;
- D::result_type x_mean = (d.a() + d.b()) / 2;
- D::result_type x_var = sqr(d.b() - d.a()) / 12;
- D::result_type x_skew = 0;
- D::result_type x_kurtosis = -6./5;
- 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_real_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);
- }
- D::result_type mean = std::accumulate(u.begin(), u.end(),
- D::result_type(0)) / u.size();
- D::result_type var = 0;
- D::result_type skew = 0;
- D::result_type kurtosis = 0;
- for (int i = 0; i < u.size(); ++i)
- {
- D::result_type d = (u[i] - mean);
- D::result_type d2 = sqr(d);
- var += d2;
- skew += d * d2;
- kurtosis += d2 * d2;
- }
- var /= u.size();
- D::result_type dev = std::sqrt(var);
- skew /= u.size() * dev * var;
- kurtosis /= u.size() * var * var;
- kurtosis -= 3;
- D::result_type x_mean = (d.a() + d.b()) / 2;
- D::result_type x_var = sqr(d.b() - d.a()) / 12;
- D::result_type x_skew = 0;
- D::result_type x_kurtosis = -6./5;
- 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_real_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);
- }
- D::result_type mean = std::accumulate(u.begin(), u.end(),
- D::result_type(0)) / u.size();
- D::result_type var = 0;
- D::result_type skew = 0;
- D::result_type kurtosis = 0;
- for (int i = 0; i < u.size(); ++i)
- {
- D::result_type d = (u[i] - mean);
- D::result_type d2 = sqr(d);
- var += d2;
- skew += d * d2;
- kurtosis += d2 * d2;
- }
- var /= u.size();
- D::result_type dev = std::sqrt(var);
- skew /= u.size() * dev * var;
- kurtosis /= u.size() * var * var;
- kurtosis -= 3;
- D::result_type x_mean = (d.a() + d.b()) / 2;
- D::result_type x_var = sqr(d.b() - d.a()) / 12;
- D::result_type x_skew = 0;
- D::result_type x_kurtosis = -6./5;
- 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_real_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);
- }
- D::result_type mean = std::accumulate(u.begin(), u.end(),
- D::result_type(0)) / u.size();
- D::result_type var = 0;
- D::result_type skew = 0;
- D::result_type kurtosis = 0;
- for (int i = 0; i < u.size(); ++i)
- {
- D::result_type d = (u[i] - mean);
- D::result_type d2 = sqr(d);
- var += d2;
- skew += d * d2;
- kurtosis += d2 * d2;
- }
- var /= u.size();
- D::result_type dev = std::sqrt(var);
- skew /= u.size() * dev * var;
- kurtosis /= u.size() * var * var;
- kurtosis -= 3;
- D::result_type x_mean = (d.a() + d.b()) / 2;
- D::result_type x_var = sqr(d.b() - d.a()) / 12;
- D::result_type x_skew = 0;
- D::result_type x_kurtosis = -6./5;
- 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) < 0.02);
- assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
- }
- {
- typedef std::uniform_real_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);
- }
- D::result_type mean = std::accumulate(u.begin(), u.end(),
- D::result_type(0)) / u.size();
- D::result_type var = 0;
- D::result_type skew = 0;
- D::result_type kurtosis = 0;
- for (int i = 0; i < u.size(); ++i)
- {
- D::result_type d = (u[i] - mean);
- D::result_type d2 = sqr(d);
- var += d2;
- skew += d * d2;
- kurtosis += d2 * d2;
- }
- var /= u.size();
- D::result_type dev = std::sqrt(var);
- skew /= u.size() * dev * var;
- kurtosis /= u.size() * var * var;
- kurtosis -= 3;
- D::result_type x_mean = (d.a() + d.b()) / 2;
- D::result_type x_var = sqr(d.b() - d.a()) / 12;
- D::result_type x_skew = 0;
- D::result_type x_kurtosis = -6./5;
- 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_real_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);
- }
- D::result_type mean = std::accumulate(u.begin(), u.end(),
- D::result_type(0)) / u.size();
- D::result_type var = 0;
- D::result_type skew = 0;
- D::result_type kurtosis = 0;
- for (int i = 0; i < u.size(); ++i)
- {
- D::result_type d = (u[i] - mean);
- D::result_type d2 = sqr(d);
- var += d2;
- skew += d * d2;
- kurtosis += d2 * d2;
- }
- var /= u.size();
- D::result_type dev = std::sqrt(var);
- skew /= u.size() * dev * var;
- kurtosis /= u.size() * var * var;
- kurtosis -= 3;
- D::result_type x_mean = (d.a() + d.b()) / 2;
- D::result_type x_var = sqr(d.b() - d.a()) / 12;
- D::result_type x_skew = 0;
- D::result_type x_kurtosis = -6./5;
- 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_real_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);
- }
- D::result_type mean = std::accumulate(u.begin(), u.end(),
- D::result_type(0)) / u.size();
- D::result_type var = 0;
- D::result_type skew = 0;
- D::result_type kurtosis = 0;
- for (int i = 0; i < u.size(); ++i)
- {
- D::result_type d = (u[i] - mean);
- D::result_type d2 = sqr(d);
- var += d2;
- skew += d * d2;
- kurtosis += d2 * d2;
- }
- var /= u.size();
- D::result_type dev = std::sqrt(var);
- skew /= u.size() * dev * var;
- kurtosis /= u.size() * var * var;
- kurtosis -= 3;
- D::result_type x_mean = (d.a() + d.b()) / 2;
- D::result_type x_var = sqr(d.b() - d.a()) / 12;
- D::result_type x_skew = 0;
- D::result_type x_kurtosis = -6./5;
- 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_real_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);
- }
- D::result_type mean = std::accumulate(u.begin(), u.end(),
- D::result_type(0)) / u.size();
- D::result_type var = 0;
- D::result_type skew = 0;
- D::result_type kurtosis = 0;
- for (int i = 0; i < u.size(); ++i)
- {
- D::result_type d = (u[i] - mean);
- D::result_type d2 = sqr(d);
- var += d2;
- skew += d * d2;
- kurtosis += d2 * d2;
- }
- var /= u.size();
- D::result_type dev = std::sqrt(var);
- skew /= u.size() * dev * var;
- kurtosis /= u.size() * var * var;
- kurtosis -= 3;
- D::result_type x_mean = (d.a() + d.b()) / 2;
- D::result_type x_var = sqr(d.b() - d.a()) / 12;
- D::result_type x_skew = 0;
- D::result_type x_kurtosis = -6./5;
- 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_real_distribution<> D;
- typedef std::minstd_rand G;
- G g;
- D d(-1, 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.a() <= v && v < d.b());
- u.push_back(v);
- }
- D::result_type mean = std::accumulate(u.begin(), u.end(),
- D::result_type(0)) / u.size();
- D::result_type var = 0;
- D::result_type skew = 0;
- D::result_type kurtosis = 0;
- for (int i = 0; i < u.size(); ++i)
- {
- D::result_type d = (u[i] - mean);
- D::result_type d2 = sqr(d);
- var += d2;
- skew += d * d2;
- kurtosis += d2 * d2;
- }
- var /= u.size();
- D::result_type dev = std::sqrt(var);
- skew /= u.size() * dev * var;
- kurtosis /= u.size() * var * var;
- kurtosis -= 3;
- D::result_type x_mean = (d.a() + d.b()) / 2;
- D::result_type x_var = sqr(d.b() - d.a()) / 12;
- D::result_type x_skew = 0;
- D::result_type x_kurtosis = -6./5;
- assert(std::abs(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_real_distribution<> D;
- typedef std::minstd_rand G;
- G g;
- D d(5.5, 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.a() <= v && v < d.b());
- u.push_back(v);
- }
- D::result_type mean = std::accumulate(u.begin(), u.end(),
- D::result_type(0)) / u.size();
- D::result_type var = 0;
- D::result_type skew = 0;
- D::result_type kurtosis = 0;
- for (int i = 0; i < u.size(); ++i)
- {
- D::result_type d = (u[i] - mean);
- D::result_type d2 = sqr(d);
- var += d2;
- skew += d * d2;
- kurtosis += d2 * d2;
- }
- var /= u.size();
- D::result_type dev = std::sqrt(var);
- skew /= u.size() * dev * var;
- kurtosis /= u.size() * var * var;
- kurtosis -= 3;
- D::result_type x_mean = (d.a() + d.b()) / 2;
- D::result_type x_var = sqr(d.b() - d.a()) / 12;
- D::result_type x_skew = 0;
- D::result_type x_kurtosis = -6./5;
- 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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