seriesClassification.h

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#ifndef SERIESCLASSIFICATION_H
#define SERIESCLASSIFICATION_H
 
#include <vector>
#include <string>
#include <map>
#include "fastDTW.h"
#include "trainingExample.h"
 
template<typename T>
class seriesClassificationTemplate final
{
public:
  
  seriesClassificationTemplate();
  ~seriesClassificationTemplate();
  
  bool train(const std::vector<trainingSeriesTemplate<T> > &seriesSet);
  
  void reset();
  
  std::string run(const std::vector<std::vector<T> > &inputSeries);
  
  T run(const std::vector<std::vector<T> >& inputSeries, std::string label);
  
  std::string runParallel(const std::vector<std::vector<T> >& inputSeries);
  
  T runParallel(const std::vector<std::vector<T> > &inputSeries, std::string label);
  
  std::string runContinuous(const std::vector<T> &inputVector);
  
  std::vector<T> getCosts() const;
  
  std::size_t getMinLength() const;
  
  std::size_t getMinLength(std::string label) const;
  
  std::size_t getMaxLength() const;
  
  std::size_t getMaxLength(std::string label) const;
  
  template<typename TT>
  struct minMax {
    TT min;
    TT max;
  };
  
  minMax<T> calculateCosts(std::string label) const;
  
  minMax<T> calculateCosts(std::string label1, std::string label2) const;
  
private:
  std::vector<trainingSeriesTemplate<T> > allTrainingSeries;
  size_t vectorLength;
  std::vector<T> allCosts;
  size_t maxLength;
  size_t minLength;
  std::map<std::string, minMax<size_t> > lengthsPerLabel;
  bool isTraining { false };
  
  std::vector<std::vector<T> > seriesBuffer;
  int hopSize {1};
  int counter {};
  
  size_t findClosestSeries() const;
  void runThread(const std::vector<std::vector<T>> &inputSeries, std::size_t i);
};
 
namespace rapidLib
{
//This is here to keep the old API working
using seriesClassification = seriesClassificationTemplate<double>;
using seriesClassificationFloat = seriesClassificationTemplate<float>;
}
 
#endif