machineLearning.h

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#ifndef machineLearning_h
#define machineLearning_h

#include "rapidMix.h"

#include "classification.h"
#include "regression.h"
#include "seriesClassification.h"
#include "rapidXMM.h"
#include "rapidGVF.h"

namespace rapidmix {
    
    // forward declaration
    class trainingData;
    
    typedef struct runResults_t {
        std::vector<double> likelihoods;
        std::vector<double> regression;
        std::vector<double> progressions;
        std::string likeliest;
    } runResults;
    
    template <typename MachineLearningModule>
    class machineLearning : public MachineLearningModule {
    public:
        
        //* Constructors */
        machineLearning() : MachineLearningModule() {};
        
        template<class T>
        machineLearning(T type) : MachineLearningModule(type) {};
        
        bool train(const trainingData &newTrainingData);
        
        //* this function is not being specialized
        std::vector<double> run(const std::vector<double> &inputVector) {
            return MachineLearningModule::run(inputVector);
        }
        
        // This is a hack while I think about how to do this. -MZ //
        std::string run(const std::vector<double> &inputVector, const std::string &label);
        
        //* This is the one I'm using for DTW */
        std::string run(const std::vector<std::vector<double> > &inputSeries);
        
        bool reset() {
            return MachineLearningModule::reset();
        }
        
    private:
        MachineLearningModule module;
        
        //this holds string labels
        std::vector<std::string> labels; //FIXME: This probably should be pushed down into rapidLib?
        std::string getLabel(int value);
        
    };
    
    
    
    typedef machineLearning<classification<double> > staticClassification;
    
    typedef machineLearning<regression<double> > staticRegression;
    
    typedef machineLearning<seriesClassification<double> > dtwTemporalClassification;
    
    
    typedef xmmToolConfig xmmConfig;
    
    typedef machineLearning<rapidXmmGmm> xmmStaticClassification;
    
    typedef machineLearning<rapidXmmGmr> xmmStaticRegression;
    
    typedef machineLearning<rapidXmmHmm> xmmTemporalClassification;
    
    typedef machineLearning<rapidXmmHmr> xmmTemporalRegression;
    
    
    typedef machineLearning<rapidGVF> gvfTemporalVariation;
    
    
}

#endif