ConfusionMatrix.hh

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#ifndef _CONFUSIONMATRIXHH_
#define _CONFUSIONMATRIXHH_

#include <openmodeller/om_defs.hh>
#include <openmodeller/Configuration.hh>

#include <openmodeller/Model.hh>

#include <openmodeller/Occurrences.hh>
#include <openmodeller/Environment.hh>
#include <openmodeller/Sampler.hh>

#define CONF_MATRIX_DEFAULT_THRESHOLD 0.5

class dllexp ConfusionMatrix
{
public:
  ConfusionMatrix(Scalar predictionThreshold = CONF_MATRIX_DEFAULT_THRESHOLD, bool ignoreAbsences=false);

  ~ConfusionMatrix();

  void reset(Scalar predictionThreshold = CONF_MATRIX_DEFAULT_THRESHOLD, bool ignoreAbsences=false);

  void setLowestTrainingThreshold(const Model& model, const SamplerPtr& sampler);

  void calculate(const EnvironmentPtr & env, const Model& model, 
     const OccurrencesPtr& presences, const OccurrencesPtr& absences = OccurrencesPtr());
  
  void calculate(const Model& model, const SamplerPtr& sampler);
  
  int getValue(Scalar predictionValue, Scalar actualValue) const;

  double getAccuracy() const;

  double getOmissionError() const;

  double getCommissionError() const;

  double getThreshold() const { return _predictionThreshold; }

  bool ready() const { return _ready; }

  ConfigurationPtr getConfiguration() const;

private:
  /* 
   * Representation of the confusion matrix on _confMatrix:
   *  1st row is predicted absent
   *  2nd row is predicted present
   *  1st column is actual absent
   *  2nd column is actual present
   * 
   * Note: it is inverted from the matrix representation above
   *        so that the indexes match the actual or predicted values,
   *        i.e., index[0][1] corresponds to predicted absence (0)
   *        and actual presence (1).
   */
  int _confMatrix[2][2];

  Scalar _predictionThreshold;

  bool _ignoreAbsences;

  bool _ready;
};

#endif