| //===----------------------------------------------------------------------===// |
| // |
| // 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 IntType = int> |
| // class poisson_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; |
| } |
| |
| void test_bad_ranges() { |
| // Test cases where the mean is around the largest representable integer for |
| // `result_type`. These cases don't generate valid poisson distributions, but |
| // at least they don't blow up. |
| std::mt19937 eng; |
| |
| { |
| std::poisson_distribution<std::int16_t> distribution(32710.9); |
| for (int i=0; i < 1000; ++i) { |
| volatile std::int16_t res = distribution(eng); |
| ((void)res); |
| } |
| } |
| { |
| std::poisson_distribution<std::int16_t> distribution(std::numeric_limits<std::int16_t>::max()); |
| for (int i=0; i < 1000; ++i) { |
| volatile std::int16_t res = distribution(eng); |
| ((void)res); |
| } |
| } |
| { |
| std::poisson_distribution<std::int16_t> distribution( |
| static_cast<double>(std::numeric_limits<std::int16_t>::max()) + 10); |
| for (int i=0; i < 1000; ++i) { |
| volatile std::int16_t res = distribution(eng); |
| ((void)res); |
| } |
| } |
| { |
| std::poisson_distribution<std::int16_t> distribution( |
| static_cast<double>(std::numeric_limits<std::int16_t>::max()) * 2); |
| for (int i=0; i < 1000; ++i) { |
| volatile std::int16_t res = distribution(eng); |
| ((void)res); |
| } |
| } |
| { |
| // We convert `INF` to `DBL_MAX` otherwise the distribution will hang. |
| std::poisson_distribution<std::int16_t> distribution(std::numeric_limits<double>::infinity()); |
| for (int i=0; i < 1000; ++i) { |
| volatile std::int16_t res = distribution(eng); |
| ((void)res); |
| } |
| } |
| { |
| std::poisson_distribution<std::int16_t> distribution(0); |
| for (int i=0; i < 1000; ++i) { |
| volatile std::int16_t res = distribution(eng); |
| ((void)res); |
| } |
| } |
| { |
| // We convert `INF` to `DBL_MAX` otherwise the distribution will hang. |
| std::poisson_distribution<std::int16_t> distribution(-100); |
| for (int i=0; i < 1000; ++i) { |
| volatile std::int16_t res = distribution(eng); |
| ((void)res); |
| } |
| } |
| } |
| |
| |
| int main() |
| { |
| { |
| typedef std::poisson_distribution<> D; |
| typedef std::minstd_rand G; |
| G g; |
| D d(2); |
| const int N = 100000; |
| std::vector<double> 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(), 0.0) / u.size(); |
| double var = 0; |
| double skew = 0; |
| double kurtosis = 0; |
| for (unsigned 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 = d.mean(); |
| double x_var = d.mean(); |
| double x_skew = 1 / std::sqrt(x_var); |
| double x_kurtosis = 1 / 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) < 0.03); |
| } |
| { |
| typedef std::poisson_distribution<> D; |
| typedef std::minstd_rand G; |
| G g; |
| D d(0.75); |
| const int N = 100000; |
| std::vector<double> 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(), 0.0) / u.size(); |
| double var = 0; |
| double skew = 0; |
| double kurtosis = 0; |
| for (unsigned 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 = d.mean(); |
| double x_var = d.mean(); |
| double x_skew = 1 / std::sqrt(x_var); |
| double x_kurtosis = 1 / 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); |
| } |
| { |
| typedef std::poisson_distribution<> D; |
| typedef std::mt19937 G; |
| G g; |
| D d(20); |
| const int N = 1000000; |
| std::vector<double> 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(), 0.0) / u.size(); |
| double var = 0; |
| double skew = 0; |
| double kurtosis = 0; |
| for (unsigned 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 = d.mean(); |
| double x_var = d.mean(); |
| double x_skew = 1 / std::sqrt(x_var); |
| double x_kurtosis = 1 / 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); |
| } |
| |
| test_bad_ranges(); |
| } |