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#ifndef DATAPROV_H
#define DATAPROV_H
#include<Eigen/Dense>
#include "../tools/easylogging++.h"
#include<vector>
// 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{
std::string qid;
double rank;
Eigen::VectorXd feature;
} DataEntry;
class DataList{
private:
int n;
std::vector<DataEntry*> data;
public:
int getSize(){return data.size();}
void addEntry(DataEntry* d){data.push_back(d);}
void setfSize(int fsize){n=fsize;}
int getfSize(){return n;}
int clear(){
for (int i=0;i<data.size();++i)
delete data[i];
data.clear();
}
inline std::vector<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;}
int getAllData(DataList &out){\
out.clear();
DataList buf;
while (!EOFile())
{
getDataSet(buf);
out.getData().insert(out.getData().end(),buf.getData().begin(),buf.getData().end());
}
}
virtual int getDataSet(DataList &out) = 0;
virtual int open()=0;
virtual int close()=0;
};
#endif
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