diff options
| -rw-r--r-- | CMakeLists.txt | 2 | ||||
| -rw-r--r-- | model/ranksvm.cpp | 2 | ||||
| -rw-r--r-- | model/ranksvmtn.cpp | 19 | ||||
| -rw-r--r-- | tools/dataProvider.h | 73 |
4 files changed, 70 insertions, 26 deletions
diff --git a/CMakeLists.txt b/CMakeLists.txt index c579851..4ae8e51 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -6,7 +6,7 @@ INCLUDE_DIRECTORIES ( "/usr/include/eigen3" ) # Use Random Library for Random Number Generation -set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11") +set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11 -fopenmp") FIND_PACKAGE( Boost COMPONENTS program_options REQUIRED ) INCLUDE_DIRECTORIES( ${Boost_INCLUDE_DIR}) diff --git a/model/ranksvm.cpp b/model/ranksvm.cpp index a468729..acc2f4e 100644 --- a/model/ranksvm.cpp +++ b/model/ranksvm.cpp @@ -15,7 +15,7 @@ int maxiter = 1; // max iteration count double prec=1e-10; // precision // conjugate gradient double cg_prec=1e-3; // precision -int cg_maxiter = 500; +int cg_maxiter = 1000; int ls_maxiter = 20; // line search double line_prec=1e-10; // precision diff --git a/model/ranksvmtn.cpp b/model/ranksvmtn.cpp index 7f292f5..ba2d5a3 100644 --- a/model/ranksvmtn.cpp +++ b/model/ranksvmtn.cpp @@ -9,23 +9,26 @@ using namespace Eigen; void cal_Dw(RidList &D,const VectorXd &w, VectorXd &Dw) { int n = D.getSize(); - #pragma omp parallel for + // static chunk size of 1 to interleave the iterations + #pragma omp parallel for schedule(static,1) for (int i=0;i<n;++i) - Dw(i) = D.getVec(i).dot(w); + Dw(i) = D.getVecDot(i,w); } void cal_Dtw(RidList &D,const VectorXd &w, VectorXd &Dw) { int n = D.getSize(); - #pragma omp parallel + int fsize = D.getfSize(); + #pragma omp parallel shared(D,Dw) { VectorXd Dw_private = VectorXd::Zero(D.getfSize()); #pragma omp for nowait for (int i=0;i<n;++i) - Dw_private = Dw_private + D.getVec(i)*w(i); + D.addVecw(i,w(i),Dw_private); #pragma omp critical { - Dw=Dw+Dw_private; + for (int i=0;i<fsize;++i) + Dw(i) = Dw(i) + Dw_private(i); } } } @@ -55,8 +58,10 @@ int cal_Hs(RidList &D,const vector<int> &rank,const VectorXd &corr,const VectorX else g+=Ds[rank[i+j]]; } - VectorXd tmp = alpha.cwiseProduct(Ds)-gamma; - VectorXd res = VectorXd::Zero(D.getSize()); + VectorXd tmp(n); + for (int i=0;i<n;++i) + tmp(i) = alpha(i)*Ds(i)-gamma(i); + VectorXd res = VectorXd::Zero(D.getfSize()); cal_Dtw(D,tmp,res); Hs = s + C*res; return 0; diff --git a/tools/dataProvider.h b/tools/dataProvider.h index 348d15c..4dc9c41 100644 --- a/tools/dataProvider.h +++ b/tools/dataProvider.h @@ -4,6 +4,7 @@ #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: @@ -93,8 +94,8 @@ public: } inline std::string getQid(int x) { - int a,b,n=getqSize(); - a=x/n; + int a,b,q=getqSize(); + a=x/q; return getU(a)->qid; } inline int getqSize() @@ -112,25 +113,63 @@ public: return getuSize()*getqSize(); } inline Eigen::VectorXd getVec(int x){ - int a,b,n=getqSize(); - a=x/n; - b=x%n; - Eigen::VectorXd vec; + int a,b,q=getqSize(); + a=x/q; + b=x%q; + Eigen::VectorXd *id,*oth; + id = &(uniq[a]->feature); if (single) - return (uniq[a]->feature-other[b]->feature).cwiseAbs(); - if (b<a) - vec=uniq[a]->feature-uniq[b]->feature; + oth = &(other[b]->feature); + else if (b<a) + oth = &(uniq[b]->feature); + else if (b<uniq.size()-1) + oth = &(uniq[b+1]->feature); else - if (b<uniq.size()-1) - vec=uniq[a]->feature-uniq[b+1]->feature; - else - vec=uniq[a]->feature-other[b-uniq.size()+1]->feature; - return vec.cwiseAbs(); + oth = &(other[b-uniq.size()+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; + id = &(uniq[a]->feature); + if (single) + oth = &(other[b]->feature); + else if (b<a) + oth = &(uniq[b]->feature); + else if (b<uniq.size()-1) + oth = &(uniq[b+1]->feature); + else + oth = &(other[b-uniq.size()+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; + id = &(uniq[a]->feature); + if (single) + oth = &(other[b]->feature); + else if (b<a) + oth = &(uniq[b]->feature); + else if (b<uniq.size()-1) + oth = &(uniq[b+1]->feature); + else + oth = &(other[b-uniq.size()+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,n=getqSize(); - a=x/n; - b=x%n; + int a,b,q=getqSize(); + a=x/q; + b=x%q; if (single) { if (std::fabs(other[b]->rank - a) < 1e-5) |