[libcxx-commits] [libcxx] 9fe6748 - [libc++] [test] Improve the test for `uniform_int_distribution<T>`.
Arthur O'Dwyer via libcxx-commits
libcxx-commits at lists.llvm.org
Mon Jan 17 07:31:25 PST 2022
Author: Arthur O'Dwyer
Date: 2022-01-17T10:31:10-05:00
New Revision: 9fe67486cc06a02308a169a33ecbbf841017a27d
URL: https://github.com/llvm/llvm-project/commit/9fe67486cc06a02308a169a33ecbbf841017a27d
DIFF: https://github.com/llvm/llvm-project/commit/9fe67486cc06a02308a169a33ecbbf841017a27d.diff
LOG: [libc++] [test] Improve the test for `uniform_int_distribution<T>`.
Extracted from https://reviews.llvm.org/D114920
Added:
Modified:
libcxx/test/std/numerics/rand/rand.dis/rand.dist.uni/rand.dist.uni.int/eval.pass.cpp
Removed:
################################################################################
diff --git a/libcxx/test/std/numerics/rand/rand.dis/rand.dist.uni/rand.dist.uni.int/eval.pass.cpp b/libcxx/test/std/numerics/rand/rand.dis/rand.dist.uni/rand.dist.uni.int/eval.pass.cpp
index e3ab0ab94de18..dd90b793de85c 100644
--- a/libcxx/test/std/numerics/rand/rand.dis/rand.dist.uni/rand.dist.uni.int/eval.pass.cpp
+++ b/libcxx/test/std/numerics/rand/rand.dis/rand.dist.uni/rand.dist.uni.int/eval.pass.cpp
@@ -17,443 +17,125 @@
#include <random>
#include <cassert>
-#include <vector>
-#include <numeric>
+#include <climits>
#include <cstddef>
+#include <limits>
+#include <numeric>
+#include <vector>
#include "test_macros.h"
template <class T>
-inline
-T
-sqr(T x)
+T sqr(T x)
{
return x * x;
}
-int main(int, char**)
+template <class ResultType, class EngineType>
+void test_statistics(ResultType a, ResultType b)
{
- {
- 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 (std::size_t 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 = ((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 (std::size_t 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 = ((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 (std::size_t 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 = ((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 (std::size_t 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 = ((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 (std::size_t 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 = ((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 (std::size_t 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 = ((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 (std::size_t 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 = ((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 (std::size_t 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 = ((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 (std::size_t 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 = ((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);
+ ASSERT_SAME_TYPE(typename std::uniform_int_distribution<ResultType>::result_type, ResultType);
+
+ EngineType g;
+ std::uniform_int_distribution<ResultType> dist(a, b);
+ assert(dist.a() == a);
+ assert(dist.b() == b);
+ std::vector<ResultType> u;
+ for (int i = 0; i < 10000; ++i) {
+ ResultType v = dist(g);
+ assert(a <= v && v <= b);
+ u.push_back(v);
}
- {
- typedef std::uniform_int_distribution<> D;
- typedef std::minstd_rand0 G;
- G g;
- D d(-6, 106);
- for (int i = 0; i < 10000; ++i)
- {
- int u = d(g);
- assert(-6 <= u && u <= 106);
- }
+
+ // Quick check: The chance of getting *no* hits in any given tenth of the range
+ // is (0.9)^10000, or "ultra-astronomically low."
+ bool bottom_tenth = false;
+ bool top_tenth = false;
+ for (std::size_t i = 0; i < u.size(); ++i) {
+ bottom_tenth = bottom_tenth || (u[i] <= (a + (b / 10) - (a / 10)));
+ top_tenth = top_tenth || (u[i] >= (b - (b / 10) + (a / 10)));
}
- {
- 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 (std::size_t 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 = ((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);
+ assert(bottom_tenth); // ...is populated
+ assert(top_tenth); // ...is populated
+
+ // Now do some more involved statistical math.
+ double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size();
+ double var = 0;
+ double skew = 0;
+ double kurtosis = 0;
+ for (std::size_t i = 0; i < u.size(); ++i) {
+ double dbl = (u[i] - mean);
+ double d2 = dbl * 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;
+
+ double expected_mean = double(a) + double(b)/2 - double(a)/2;
+ double expected_var = (sqr(double(b) - double(a) + 1) - 1) / 12;
+
+ double range = double(b) - double(a) + 1.0;
+ assert(range > range / 10); // i.e., it's not infinity
+
+ assert(std::abs(mean - expected_mean) < range / 100);
+ assert(std::abs(var - expected_var) < expected_var / 50);
+ assert(-0.1 < skew && skew < 0.1);
+ assert(1.6 < kurtosis && kurtosis < 2.0);
+}
+
+template <class ResultType, class EngineType>
+void test_statistics()
+{
+ test_statistics<ResultType, EngineType>(0, std::numeric_limits<ResultType>::max());
+}
+
+int main(int, char**)
+{
+ test_statistics<int, std::minstd_rand0>();
+ test_statistics<int, std::minstd_rand>();
+ test_statistics<int, std::mt19937>();
+ test_statistics<int, std::mt19937_64>();
+ test_statistics<int, std::ranlux24_base>();
+ test_statistics<int, std::ranlux48_base>();
+ test_statistics<int, std::ranlux24>();
+ test_statistics<int, std::ranlux48>();
+ test_statistics<int, std::knuth_b>();
+ test_statistics<int, std::minstd_rand0>(-6, 106);
+ test_statistics<int, std::minstd_rand>(5, 100);
+
+ test_statistics<short, std::minstd_rand0>();
+ test_statistics<int, std::minstd_rand0>();
+ test_statistics<long, std::minstd_rand0>();
+ test_statistics<long long, std::minstd_rand0>();
+
+ test_statistics<unsigned short, std::minstd_rand0>();
+ test_statistics<unsigned int, std::minstd_rand0>();
+ test_statistics<unsigned long, std::minstd_rand0>();
+ test_statistics<unsigned long long, std::minstd_rand0>();
+
+ test_statistics<short, std::minstd_rand0>(SHRT_MIN, SHRT_MAX);
+
+ // http://eel.is/c++draft/rand.req#genl-1.5
+ // The effect of instantiating a template that has a parameter
+ // named IntType is undefined unless the corresponding template
+ // argument is cv-unqualified and is one of short, int, long,
+ // long long, unsigned short, unsigned int, unsigned long,
+ // or unsigned long long.
+ // (We support __int128 as an extension.)
+
+#ifndef _LIBCPP_HAS_NO_INT128
+ test_statistics<__int128_t, std::minstd_rand0>();
+ test_statistics<__uint128_t, std::minstd_rand0>();
+
+ test_statistics<__int128_t, std::minstd_rand0>(-100, 900);
+ test_statistics<__int128_t, std::minstd_rand0>(0, UINT64_MAX);
+ test_statistics<__int128_t, std::minstd_rand0>(std::numeric_limits<__int128_t>::min(), std::numeric_limits<__int128_t>::max());
+ test_statistics<__uint128_t, std::minstd_rand0>(0, UINT64_MAX);
+#endif
- return 0;
+ return 0;
}
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