StatFeature.hpp

#ifndef OPALS_STAT_FEATURE_HPP_INCLUDED
#define OPALS_STAT_FEATURE_HPP_INCLUDED

#pragma once

#include "opals/config.hpp"

namespace opals {

  /// Enumerator defining different cell feature types (used by opalsCell)
  namespace StatFeatureType {
    enum Type {
      unknown = -1,
      min = 0,        ///< lowest
      max,            ///< highest
      range,          ///< difference between highest and lowest
      nmin,           ///< n lowest
      nmax,           ///< n highest
      mean,           ///< mean
      median,         ///< median
      sum,            ///< sum
      stdDev,         ///< standard deviation \f$\sqrt{\sum_{i=1}^{N}(r_i - \bar{r})^2 / (N-1) }\f$
      variance,       ///< (sample) variance \f$\sum_{i=1}^{N}(r_i - \bar{r})^2 / (N-1)\f$ 
      rms,            ///< root mean square \f$\sqrt{\sum_{i=1}^{N} r_i^2 / N}\f$ 
      stdDevMAD,      ///< robust standard deviation estimator computed from the median of absolute deviations from the median \f$1.4826 \cdot median_i(\left|r_i - median_j(r_j)\right|)\f$.
                      ///< In literature this robust estimator is often referred to as normalized MAD (NMAD)
      minority,       ///< the class with the lowest relative frequency (histogram based)
      majority,       ///< the class with the highest relative frequency (histogram based)
      pdens,          ///< point density
      pcount,         ///< point count
      quantile,       ///< p-quantile (p=0..1)
      entropy,        ///< (shannon) entropy, measure of information content
      center,         ///< select closest to cell center
      rank,           ///< quantile rank [0..100]
      posOpenness,    ///< positive openness
      negOpenness,    ///< negative openness
      pdist,          ///< average linear point distance (see DM::ILinearDistance2D for details)
      null,           ///< respresenting void feature
      Count           ///< number of available StatFeatures
    };
  };

  class OPALS_API StatFeature
  {
  public:
    StatFeature();
    StatFeature(StatFeatureType::Type type);
    StatFeature(StatFeatureType::Type type, unsigned n);      ///< only for nmin and nmax
    StatFeature(StatFeatureType::Type type, double value);    ///< only for quantile, minority and majority
    virtual ~StatFeature();

  private:
    StatFeatureType::Type e_featureType;
    unsigned u_n; ///< value for n in case of "n lowest" or "n highest" (only relevant if e_featureType is StatFeatureType::nmin or StatFeatureType::nmax)
    double   d_p; ///< p-value for quantile estimation (StatFeatureType::quantile)
    double   d_binWidth;  ///< bin width for the minority and majority feature (default value 1)

  public:
    StatFeatureType::Type e_getFeatureType() const;
    unsigned u_getN() const;
    double d_getP() const;
    double d_getBinWidth() const;
    void   v_setFeatureType(const StatFeatureType::Type &Ire_featureType);
    void   v_setN(const unsigned &Iru_n);
    void   v_setP(const double &Ird_p);
    void   v_setBinWidth(const double &Ird_binWidth);

    ///returns minimum number of elements needed to be computable
    unsigned  u_minNumberOfElement() const;
  };
}

#endif //OPALS_STAT_FEATURE_HPP_INCLUDED