#ifndef DATAPROV_H
#define DATAPROV_H

#include<Eigen/Dense>
#include "../tools/easylogging++.h"
#include<vector>
#include<list>

// TODO decide how to construct training data
// One possible way for training data:
//  Matrix composed of an array of feature vectors
//  Labels are composed of linked list, such as
//      6,3,4,0,5,0,0
//      =>  0->6 | 1->3 | 2->4->5
//  How to compensate for non exhaustive labeling?
//      Use -1 to indicate not yet labeled data
//      -1s will be excluded from training

typedef Eigen::MatrixXd DataSet;

typedef Eigen::VectorXd Labels;

typedef struct DataEntry{
    int qid;
    double rank;
    Eigen::VectorXd feature;
} DataEntry;

class DataList{
private:
    int n;
    std::list<DataEntry> data;
public:
    int getSize(){return data.size();}
    void addEntry(DataEntry d){data.push_front(d);}
    void setfSize(int fsize){n=fsize;}
    int getfSize(){return n;}
};

class DataProvider  //Virtual base class for data input
{
protected:
    int size;
    int attrSize;
    bool eof;
public:
    DataProvider():eof(false){};
    int getSize(){
        return size;
    }
    int getAttrSize(){
        return attrSize;
    }

    bool EOFile(){return eof;}

    virtual int getDataSet(DataList &out) = 0;
    virtual int open()=0;
    virtual int close()=0;
};

#endif