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#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 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;}
int clear(){
for (std::list<DataEntry*>::iterator i=data.begin(),end=data.end();i!=end;++i)
delete *i;
data.clear();
}
std::list<DataEntry*> getData(){
return data;
}
~DataList(){
clear();
}
};
class DataProvider //Virtual base class for data input
{
protected:
bool eof;
public:
DataProvider():eof(false){};
bool EOFile(){return eof;}
virtual int getDataSet(DataList &out) = 0;
virtual int open()=0;
virtual int close()=0;
};
#endif
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