variableLog2ParamComplexity.h

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#ifndef GUM_VARIABLE_LOG2_PARAM_COMPLEXITY_H
#define GUM_VARIABLE_LOG2_PARAM_COMPLEXITY_H
 
#include <cstddef>
#include <fstream>
#include <string>
 
#include <agrum/agrum.h>
 
#include <agrum/base/core/hashTable.h>
 
#include <agrum/base/core/math/math_utils.h>
 
namespace gum {
 
 
  // the CTable cache for log2(C_n^r), with n in {0,...,999} and r in {2,3,4,5}
  extern const double VariableLog2ParamComplexityCTable[4][1000];
 
  // the size in r of the CTable cache
  constexpr std::size_t VariableLog2ParamComplexityCTableRSize{std::size_t(4)};
 
  // the size in n of the CTable cache
  constexpr std::size_t VariableLog2ParamComplexityCTableNSize{std::size_t(1000)};

  class VariableLog2ParamComplexity {
    public:
    // ########################################################################
    // ########################################################################

    VariableLog2ParamComplexity();

    VariableLog2ParamComplexity(const VariableLog2ParamComplexity& from);

    VariableLog2ParamComplexity(VariableLog2ParamComplexity&& from);

    virtual VariableLog2ParamComplexity* clone() const;

    virtual ~VariableLog2ParamComplexity();

 
 
    // ########################################################################
    // ########################################################################

    VariableLog2ParamComplexity& operator=(const VariableLog2ParamComplexity& from);

    VariableLog2ParamComplexity& operator=(VariableLog2ParamComplexity&& from);

 
 
    // ########################################################################
    // ########################################################################

    double log2Cnr(const std::size_t r, const double n);

    void CnrToFile(const std::string& filename);

    void useCache(const bool on_off);

    void clearCache();

 
    private:
    // const double _Szpankowski_threshold_{VariableLog2ParamComplexityCTableNSize};
 
    // constants used to speed-up the computation of the Szpankowski
    // approximation.
    // The formula for the approximation given in Silander, Roos,
    // Kontkanen and Myllymaki (2007) "Factorized Normalized Maximum "
    // Likelihood Criterion for Learning Bayesian network Structures" paper
    // is incorrect. However, the one in Kontkanen, Buntine, Myllymaki,
    // Rissanen and Tirri (2003) "Efficient Computation of Stochastic
    // Complexity" is correct. So we use the latter and simplify it. Thus,
    // the approximation of log2(Cnr) is equal to:
    // 0.5 log2(n) - 0.5 + log2(sqrt(pi)) + (sqrt(2/pi)/3) / sqrt(n) +
    // (3/36 - 4/(9*pi)) / n.
    // So, given the constants below, it is equal to:
    // 0.5 * std::log2 (n) +  _cst1_ +  _cst2_ / std::sqrt(n) +  _cst3_ / n
    const double _cst1_ = -0.5 + std::log2(std::sqrt(M_PI));
    const double _cst2_ = std::sqrt(2.0 / M_PI) / 3.0;
    const double _cst3_ = 3.0 / 36.0 - 4.0 / (9.0 * M_PI);
 
    // indicates whether we should use a cache or not
    bool _use_cache_{true};
 
    // the cache used, eventually, to store the log2Cnr values
    HashTable< std::pair< std::size_t, double >, double > _cache_;
  };
 
} /* namespace gum */
 
 
#ifndef GUM_NO_INLINE
#  include <agrum/base/core/math/variableLog2ParamComplexity_inl.h>
#endif   // GUM_NO_INLINE
 
#endif   /* GUM_VARIABLE_LOG2_PARAM_COMPLEXITY_H */