#include #include #include #include #include /////////////////////////////////////////////////////////////////////////////// /// Probability density function for a normal distribution with specified /// mean and stddev at point `x`. static float pdf (float x, float mean, float stddev) { float const power = cruft::pow2 ((x - mean) / stddev) / -2; float const scale = 1.f / (stddev * std::sqrt (2.f * cruft::pi)); return scale * std::exp (power); } ///---------------------------------------------------------------------------- /// Calculate the maximum difference between a histogram and a PDF for a /// normal distribution with a number of buckets. static float max_histogram_error (int buckets) { // These constants weren't rigorously selected. Eyeballing the generated // values suggested they had some level of precision and didn't explode // the test's runtime. int const BUCKETS = buckets; int const ITERATIONS = BUCKETS * 10'000; float const MEAN = BUCKETS / 2.f; float const STDDEV = BUCKETS * .15f; // Use _our_ normal distribution, not the stdlib one. cruft::rand::distribution::normal g (MEAN, STDDEV); // We use a stdlib generator with reasonable quality just so we're not // testing both our generator and our distributions simultaneously. std::mt19937_64 rand; std::vector counts (BUCKETS, 0); for (int i = 0; i < ITERATIONS; ++i) { auto const val = g (rand); if (val >= BUCKETS || val < 0) continue; counts[int (val)]++; } float max_diff = 0.f; for (int i = 0; i < BUCKETS; ++i) { float expected = pdf (i, MEAN, STDDEV); float actual = counts[i] / float (ITERATIONS); float diff = std::abs (expected - actual); max_diff = std::max (max_diff, diff); } return max_diff; } /////////////////////////////////////////////////////////////////////////////// int main (void) { cruft::TAP::logger tap; tap.expect_lt ( max_histogram_error (500), 1.e-4f, "normal distribution histogram maximum relative error" ); return tap.status (); }