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#ifndef DATAPROV_H
#define DATAPROV_H
#include<Eigen/Dense>
#include "../tools/easylogging++.h"
#include<vector>
#include<math.h>
// 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:
unsigned long getSize(){return data.size();}
void addEntry(DataEntry* d){data.push_back(d);}
void setfSize(int fsize){n=fsize;}
int getfSize(){return n;}
void clear(){
for (int i=0;i<data.size();++i)
delete data[i];
data.clear();
}
static DataEntry* copyEntry(DataEntry* d)
{
DataEntry* dat = new DataEntry;
dat->rank = d->rank;
dat->qid = d->qid;
dat->feature.resize(d->feature.rows());
for (int i=0;i<d->feature.rows();++i)
{
dat->feature(i)=d->feature(i);
}
return dat;
}
inline std::vector<DataEntry*>& getData(){
return data;
}
~DataList(){
clear();
}
};
class RidList{
private:
int n;
std::vector<DataEntry*> uniq;
std::vector<DataEntry*> other;
std::vector<DataEntry*> all;
public:
static bool single;
void clear(){
uniq.clear();
other.clear();
all.clear();
}
void setfSize(int fsize){n=fsize;}
inline int getfSize(){return n;}
void addEntry(DataEntry* d){
int ext=false;
if (d->qid=="-1")
other.push_back(d);
for (int i=0;i<uniq.size();++i)
if (uniq[i]->qid==d->qid)
{
ext = true;
d->rank = i;
}
if (ext)
other.push_back(d);
else
uniq.push_back(d);
all.push_back(d);
}
inline DataEntry* getU(int x)
{
return uniq[x];
}
inline DataEntry* getO(int x)
{
return other[x];
}
inline DataEntry* getAll(int x)
{
return all[x];
}
inline std::string getQid(int x)
{
int a,b,q=getqSize();
a=x/q;
if (single)
return getU(a)->qid;
return getAll(a)->qid;
}
inline int getqSize()
{
if (single)
return (int)other.size();
return (int)(all.size()-1);
}
inline int getuSize()
{
if (single)
return (int)uniq.size();
return (int)all.size();
}
inline int getSize()
{
return getuSize()*getqSize();
}
inline Eigen::VectorXd getVec(int x){
int a,b,q=getqSize();
a=x/q;
b=x%q;
Eigen::VectorXd *id,*oth;
id = &(uniq[a]->feature);
if (single)
oth = &(other[b]->feature);
else if (b<a)
oth = &(all[b]->feature);
else
oth = &(all[b+1]->feature);
return (*id-*oth).cwiseAbs();
};
inline double getVecDot(int x,const Eigen::VectorXd &w)
{
int a,b,q=getqSize();
a=x/q;
b=x%q;
double res = 0;
Eigen::VectorXd *id,*oth;
if (single)
{
id = &(uniq[a]->feature);
oth = &(other[b]->feature);
}
else {
id = &(all[a]->feature);
if (b<a)
oth = &(all[b]->feature);
else
oth = &(all[b+1]->feature);
}
for (int i=0;i<n;++i)
res += fabs((*id)[i] - (*oth)[i])*w[i];
return res;
}
inline void addVecw(int x,double w,Eigen::VectorXd &X)
{
int a,b,q=getqSize();
a=x/q;
b=x%q;
Eigen::VectorXd *id,*oth;
if (single)
{
id = &(uniq[a]->feature);
oth = &(other[b]->feature);
}
else {
id = &(all[a]->feature);
if (b<a)
oth = &(all[b]->feature);
else
oth = &(all[b+1]->feature);
}
for (int i=0;i<n;++i)
X[i] += fabs((*id)[i] - (*oth)[i])*w;
}
inline double getL(int x){
int a,b,q=getqSize();
a=x/q;
b=x%q;
if (single)
{
if (std::fabs(other[b]->rank - a) < 1e-5)
return 1;
return -1;
}
double id,oth;
id = all[a]->rank;
if (b<a)
oth = all[b]->rank;
else
oth = all[b+1]->rank;
if (fabs(oth - id) < 1e-5)
return 1;
return -1;
};
};
class DataProvider //Virtual base class for data input
{
protected:
bool eof;
public:
DataProvider():eof(false){};
bool EOFile(){return eof;}
virtual void getAllDataSet(RidList &out) = 0;
virtual int getDataSet(DataList &out) = 0;
virtual int open()=0;
virtual int close()=0;
};
#endif
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