Difference between revisions of "cpp/numeric/random/chi squared distribution"
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The {{tt|chi_squared_distribution}} produces random numbers {{mathjax-or|\(\small x>0\)|x>0}} according to the {{enwiki|Chi-squared distribution}}: | The {{tt|chi_squared_distribution}} produces random numbers {{mathjax-or|\(\small x>0\)|x>0}} according to the {{enwiki|Chi-squared distribution}}: | ||
− | :{{mathjax-or|1=\({\small f(x;n) = }\frac{x^{(n/2)-1}\exp{(-x/2)} }{\Gamma{(n/2)}2^{n/2} }\)|2=f(x;n) = {{mfrac||x{{su|p=(n/2)-1}} | + | :{{mathjax-or|1=\({\small f(x;n) = }\frac{x^{(n/2)-1}\exp{(-x/2)} }{\Gamma{(n/2)}2^{n/2} }\)|2=f(x;n) = {{mfrac||x{{su|p=(n/2)-1}} {{mexp|-x/2}}|Γ(n/2) 2{{su|p=n/2}}}}}} |
{{mathjax-or|\(\small\Gamma\)|Γ}} is the {{enwiki|Gamma function}} (See also {{lc|std::tgamma}}) and {{mathjax-or|\(\small n\)|n}} are the {{enwiki|Degrees_of_freedom_(statistics)|degrees of freedom}} (default 1). | {{mathjax-or|\(\small\Gamma\)|Γ}} is the {{enwiki|Gamma function}} (See also {{lc|std::tgamma}}) and {{mathjax-or|\(\small n\)|n}} are the {{enwiki|Degrees_of_freedom_(statistics)|degrees of freedom}} (default 1). |
Latest revision as of 04:03, 21 December 2023
Defined in header <random>
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template< class RealType = double > class chi_squared_distribution; |
(since C++11) | |
The chi_squared_distribution
produces random numbers x>0 according to the Chi-squared distribution:
- f(x;n) =
x(n/2)-1 e-x/2 Γ(n/2) 2n/2
Γ is the Gamma function (See also std::tgamma) and n are the degrees of freedom (default 1).
std::chi_squared_distribution
satisfies all requirements of RandomNumberDistribution.
Contents |
[edit] Template parameters
RealType | - | The result type generated by the generator. The effect is undefined if this is not one of float, double, or long double. |
[edit] Member types
Member type | Definition |
result_type (C++11)
|
RealType |
param_type (C++11)
|
the type of the parameter set, see RandomNumberDistribution. |
[edit] Member functions
(C++11) |
constructs new distribution (public member function) |
(C++11) |
resets the internal state of the distribution (public member function) |
Generation | |
(C++11) |
generates the next random number in the distribution (public member function) |
Characteristics | |
(C++11) |
returns the degrees of freedom (n) distribution parameter (public member function) |
(C++11) |
gets or sets the distribution parameter object (public member function) |
(C++11) |
returns the minimum potentially generated value (public member function) |
(C++11) |
returns the maximum potentially generated value (public member function) |
[edit] Non-member functions
(C++11)(C++11)(removed in C++20) |
compares two distribution objects (function) |
(C++11) |
performs stream input and output on pseudo-random number distribution (function template) |
[edit] Example
Run this code
#include <algorithm> #include <cmath> #include <iomanip> #include <iostream> #include <map> #include <random> #include <vector> template<int Height = 5, int BarWidth = 1, int Padding = 1, int Offset = 0, class Seq> void draw_vbars(Seq&& s, const bool DrawMinMax = true) { static_assert(0 < Height and 0 < BarWidth and 0 <= Padding and 0 <= Offset); auto cout_n = [](auto&& v, int n = 1) { while (n-- > 0) std::cout << v; }; const auto [min, max] = std::minmax_element(std::cbegin(s), std::cend(s)); std::vector<std::div_t> qr; for (typedef decltype(*std::cbegin(s)) V; V e : s) qr.push_back(std::div(std::lerp(V(0), 8 * Height, (e - *min) / (*max - *min)), 8)); for (auto h{Height}; h-- > 0; cout_n('\n')) { cout_n(' ', Offset); for (auto dv : qr) { const auto q{dv.quot}, r{dv.rem}; unsigned char d[]{0xe2, 0x96, 0x88, 0}; // Full Block: '█' q < h ? d[0] = ' ', d[1] = 0 : q == h ? d[2] -= (7 - r) : 0; cout_n(d, BarWidth), cout_n(' ', Padding); } if (DrawMinMax && Height > 1) Height - 1 == h ? std::cout << "┬ " << *max: h ? std::cout << "│ " : std::cout << "┴ " << *min; } } int main() { std::random_device rd{}; std::mt19937 gen{rd()}; auto χ2 = [&gen](const float dof) { std::chi_squared_distribution<float> d{dof /* n */}; const int norm = 1'00'00; const float cutoff = 0.002f; std::map<int, int> hist{}; for (int n = 0; n != norm; ++n) ++hist[std::round(d(gen))]; std::vector<float> bars; std::vector<int> indices; for (auto const& [n, p] : hist) if (float x = p * (1.0 / norm); cutoff < x) { bars.push_back(x); indices.push_back(n); } std::cout << "dof = " << dof << ":\n"; for (draw_vbars<4, 3>(bars); int n : indices) std::cout << std::setw(2) << n << " "; std::cout << "\n\n"; }; for (float dof : {1.f, 2.f, 3.f, 4.f, 6.f, 9.f}) χ2(dof); }
Possible output:
dof = 1: ███ ┬ 0.5271 ███ │ ███ ███ │ ███ ███ ▇▇▇ ▃▃▃ ▂▂▂ ▁▁▁ ▁▁▁ ▁▁▁ ▁▁▁ ┴ 0.003 0 1 2 3 4 5 6 7 8 dof = 2: ███ ┬ 0.3169 ▆▆▆ ███ ▃▃▃ │ ███ ███ ███ ▄▄▄ │ ███ ███ ███ ███ ▇▇▇ ▄▄▄ ▃▃▃ ▂▂▂ ▁▁▁ ▁▁▁ ▁▁▁ ┴ 0.004 0 1 2 3 4 5 6 7 8 9 10 dof = 3: ███ ▃▃▃ ┬ 0.2439 ███ ███ ▄▄▄ │ ▃▃▃ ███ ███ ███ ▇▇▇ ▁▁▁ │ ███ ███ ███ ███ ███ ███ ▆▆▆ ▄▄▄ ▃▃▃ ▂▂▂ ▁▁▁ ▁▁▁ ▁▁▁ ┴ 0.0033 0 1 2 3 4 5 6 7 8 9 10 11 12 dof = 4: ▂▂▂ ███ ▃▃▃ ┬ 0.1864 ███ ███ ███ ███ ▂▂▂ │ ███ ███ ███ ███ ███ ▅▅▅ ▁▁▁ │ ▅▅▅ ███ ███ ███ ███ ███ ███ ███ ▆▆▆ ▄▄▄ ▃▃▃ ▂▂▂ ▂▂▂ ▁▁▁ ▁▁▁ ▁▁▁ ┴ 0.0026 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 dof = 6: ▅▅▅ ▇▇▇ ███ ▂▂▂ ┬ 0.1351 ▅▅▅ ███ ███ ███ ███ ▇▇▇ ▁▁▁ │ ▁▁▁ ███ ███ ███ ███ ███ ███ ███ ▅▅▅ ▂▂▂ │ ▁▁▁ ███ ███ ███ ███ ███ ███ ███ ███ ███ ███ ███ ▅▅▅ ▄▄▄ ▃▃▃ ▂▂▂ ▁▁▁ ▁▁▁ ▁▁▁ ┴ 0.0031 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 dof = 9: ▅▅▅ ▇▇▇ ███ ███ ▄▄▄ ▂▂▂ ┬ 0.1044 ▃▃▃ ███ ███ ███ ███ ███ ███ ▅▅▅ ▁▁▁ │ ▄▄▄ ███ ███ ███ ███ ███ ███ ███ ███ ███ ▆▆▆ ▃▃▃ │ ▄▄▄ ███ ███ ███ ███ ███ ███ ███ ███ ███ ███ ███ ███ ███ ▆▆▆ ▄▄▄ ▃▃▃ ▂▂▂ ▁▁▁ ▁▁▁ ▁▁▁ ┴ 0.0034 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
[edit] External links
Weisstein, Eric W. "Chi-Squared Distribution." From MathWorld — A Wolfram Web Resource. | |
Chi-squared distribution — From Wikipedia. |