chi_squared_test.hpp

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//============================================================================
// MCKL/include/mckl/random/chi_squared_test.hpp
//----------------------------------------------------------------------------
// MCKL: Monte Carlo Kernel Library
//----------------------------------------------------------------------------
// Copyright (c) 2013-2018, Yan Zhou
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#ifndef MCKL_RANDOM_CHI_SQUARED_TEST_HPP
#define MCKL_RANDOM_CHI_SQUARED_TEST_HPP

#include <mckl/random/internal/common.hpp>

namespace mckl {

template <typename Derived>
class ChiSquaredTest
{
  public:
    using result_type = double;

    bool pass(double alpha, result_type s) const
    {
        double p = cdf(s);

        return std::min(p, 1 - p) > 0.5 * alpha;
    }

    double pdf(result_type s) const
    {
        double k =
            0.5 * static_cast<const Derived *>(this)->degree_of_freedom();
        double lpdf = -k * const_ln_2<double>() - std::lgamma(k) +
            (k - 1) * std::log(s) - 0.5 * s;

        return std::exp(lpdf);
    }

    double cdf(result_type s) const
    {
        return gammap(
            0.5 * static_cast<const Derived *>(this)->degree_of_freedom(),
            0.5 * s);
    }

  protected:
    double stat(std::size_t m, const double *count, const double *np) const
    {
        return stat_dispatch(m, count, np);
    }

    double stat(std::size_t m, const double *count, double np) const
    {
        return stat_dispatch(m, count, np);
    }

  private:
    template <typename NPType>
    double stat_dispatch(std::size_t m, const double *count, NPType np) const
    {
        Vector<double> tmp(m);
        sub(m, count, np, tmp.data());
        sqr(m, tmp.data(), tmp.data());
        div(m, tmp.data(), np, tmp.data());

        return std::accumulate(tmp.begin(), tmp.end(), 0.0);
    }
}; // class ChiSquaredTest

} // namespace mckl

#endif // MCKL_RANDOM_CHI_SQUARED_TEST_HPP