int main(int argc, char* argv[]) {
if(argc!=5) {
std::cout << "You have the wrong argument count" << std::endl;
}
double C=atof(argv[1]);
int nr_threads=atoi(argv[2]);
char* trainloc = argv[3];
char* testloc = argv[4];
omp_set_num_threads(nr_threads);
std::vector<int> y;
std::vector< std::vector<double> > traindata;
std::vector< std::vector<int> > feattrain;
std::ifstream file;
file.open(trainloc);
std::string line;
std::vector<std::string> vals;
std::vector<std::string> vals2;
std::vector<std::string> feat_val;
std::vector<int> yt;
std::vector< std::vector<double> > testdata;
std::vector< std::vector<int> > feattest;
std::vector<std::string> feat_valt;
if(file.is_open()) {
while(std::getline(file,line)) {
feattrain.push_back(std::vector<int>());
traindata.push_back(std::vector<double>());
vals= split(line, ' ');
y.push_back(std::stoi(vals[0]));
for(int i=1;i<vals.size();i++) {
feat_val = split(vals[i],':');
(feattrain.back()).push_back(std::stoi(feat_val[0])-1);
(traindata.back()).push_back(std::stof(feat_val[1]));
}
}
file.close();
std::ifstream file2;
file2.open(testloc);
std::string line2;
while(std::getline(file2,line2)) {
feattest.push_back(std::vector<int>());
testdata.push_back(std::vector<double>());
vals2= split(line2, ' ');
yt.push_back(std::stoi(vals2[0]));
for(int j=1;j<vals2.size();j++) {
feat_valt = split(vals2[j],':');
(feattest.back()).push_back(std::stoi(feat_valt[0])-1);
(testdata.back()).push_back(std::stof(feat_valt[1]));
}
}
file2.close();
}
else {
std::cout << "File didn't open" << std::endl;
}
std::cout << "Data has been loaded" << std::endl;
int sizet=yt.size();
int size=y.size();
std::vector<int> permutes(size);
std::vector<int> used(size);
#pragma omp parallel for
for(int i=0;i<size;i++) {
permutes[i]=i;
}
// permuting the vector and finding the max feature number
int maxn=0;
#pragma omp parallel for
for(int t=0;t<size;t++) {
int h=traindata[t].size();
#pragma omp parallel for
for(int ins=0;ins<h;ins++) {
if(feattrain[t][ins]>maxn){
maxn=feattrain[t][ins];
}
}
}
std::cout << "Max feature count: " << maxn << std::endl;
std::vector<double> alpha(size), w(maxn+1);
std::fill(alpha.begin(),alpha.end(),0);
std::fill(w.begin(),w.end(),0);
double delt=0;
for(int outer=1;outer<=20;outer++){
std::srand(std::time(0));
std::random_shuffle(permutes.begin(),permutes.end());
double itertime1 = omp_get_wtime();
#pragma omp parallel for private(delt) shared(alpha,w,traindata,feattrain) schedule(dynamic)
for(int iter=0;iter<size;iter++) {
int i=permutes[iter];
int tempsize=traindata[i].size();
// x w inner product
double inprodxw=0;
double inprodxx=0;
for(int xal=0;xal<tempsize;xal++){
inprodxw+=traindata[i][xal]*w[feattrain[i][xal]];
inprodxx+=traindata[i][xal]*traindata[i][xal];
}
delt=(1-y[i]*inprodxw-((double)alpha[i]/(2*C)))/(y[i]*y[i]*inprodxx + ((double)1/(2*C)));
double gz = std::max(delt,-alpha[i]);
alpha[i]+=gz;
for(int xal2=0;xal2<tempsize;xal2++){
#pragma omp atomic
w[feattrain[i][xal2]] = w[feattrain[i][xal2]] + y[i]*gz*traindata[i][xal2];
}
}
double itertime2 = omp_get_wtime() - itertime1;
double norms = wnorm(y,alpha,traindata,maxn,w,feattrain);
double primvals = primval(C,y,w,traindata,feattrain);
double dualvals = dualval(C,alpha,w);
double midaccuracy=prederror(w,yt,testdata,feattest);
std::cout<< outer << "- "<< "Accuracy: "<<midaccuracy <<", Wtime: " << itertime2<< ", Primal: "<<primvals<<", W norm: "<<norms<<", Dual: "<< dualvals<<std::endl;
}
std::cout<< "Finished" << std::endl;
double accuracy=prederror(w,yt,testdata,feattest);
std::cout<< accuracy << std::endl;
return 0;
}
static int install_white(SINGLE_QSP_ARG_DECL)
{
int j,k;
float wc[3]; /* white chromaticity */
float uv[3]; /* unit vector */
float *ptr;
if( init_matrices(SINGLE_QSP_ARG) < 0 ) return(-1);
/* we assume we already have the white point */
if( ! know_white )
error1("install_white: white point not defined!?");
for(j=0;j<3;j++) wc[j] = _white[j];
showvec(wc);
rgb2rb(wc); /* now we have the chromaticity of the white point */
advise("white transformed to opponent space");
showvec(wc);
for(j=0;j<3;j++) uv[j] = wc[j];
uv[0]+= 0.1; /* take a step in the red cone direction */
rb2rgb(uv);
for(j=0;j<3;j++) uv[j] -= _white[j];
/* now have an rgb vector for an red cone step */
/* normalize this relative to the white point */
/* this is to guarantee that amp. of +-1 won't overflow */
advise("red cone vector");
showvec(wc);
wnorm(uv);
/* now set the entries of the o2p matrix */
for(j=0;j<3;j++) o2p_mat[j][0] = uv[j];
for(j=0;j<3;j++) uv[j] = wc[j];
uv[1]+= 0.1; /* take a step in the blue cone direction */
rb2rgb(uv);
for(j=0;j<3;j++) uv[j] -= _white[j];
/* now have an rgb vector for a blue cone step */
wnorm(uv);
for(j=0;j<3;j++) o2p_mat[j][1] = uv[j];
for(j=0;j<3;j++) uv[j] = _white[j];
wnorm(uv);
for(j=0;j<3;j++) o2p_mat[j][2] = uv[j];
ptr = (float *)OBJ_DATA_PTR(o2p_dp);
for(j=0;j<3;j++)
for(k=0;k<3;k++)
*ptr++ = o2p_mat[j][k];
dp_copy(p2o_dp,o2p_dp);
dt_invert(p2o_dp);
ptr = (float *)OBJ_DATA_PTR(p2o_dp);
for(j=0;j<3;j++)
for(k=0;k<3;k++)
p2o_mat[j][k] = *ptr++;
return(0);
}
