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174 lines (168 loc) · 6.96 KB
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#include "lfw_rest_API.h"
bool RestLFW(string filename, int fold, vector<dict> features,
MatrixXd& feature_train, MatrixXd& feature_test,
VectorXd& label_train, VectorXd& label_test, int axis){
// axis: INPUT and OUTPUT features are stored column-ordred or row-ordred.
// And axis = 0 meansrow orded, axis = 1 means column-ordred.
// The default setting is axis = 0.
int i, j, k;
// QA
if (axis == 0) {
for (i = 0; i < features.size(); i++) {
if(features[i].num != features[i].fea.rows()){
cout<< "Input feature are bad. Examine it carefully" <<endl;
return false;
}
}
}
else if(axis == 1){
for (i = 0; i < features.size(); i++) {
if(features[i].num != features[i].fea.cols()){
cout<< "Input feature are bad. Examine it carefully" <<endl;
return false;
}
}
}
else{
cout<<"The input parameter 'axis' is limited to 0 and 1"<<endl;
return false;
}
// Read the whole dataset
ifstream fin(filename.c_str());
if(!fin){
cout<< "File not found"<< endl;
exit(-1);
}
// Get the Nfold
string str_Nfold_Npos_neg, str_Nfold, str_Npos_neg;
int Nfold, Npos_neg;
getline(fin, str_Nfold_Npos_neg);
str_Nfold.assign(str_Nfold_Npos_neg.begin(), str_Nfold_Npos_neg.begin() +
str_Nfold_Npos_neg.find_first_of(" "));
str_Npos_neg.assign(str_Nfold_Npos_neg.begin() +
str_Nfold_Npos_neg.find_last_of(" ") + 1,
str_Nfold_Npos_neg.end());
Nfold = atoi(str_Nfold.c_str());
Npos_neg = atoi(str_Npos_neg.c_str());
// Get the name of the file
vector<samplepair> per_fea_num_train, per_fea_num_test;
for (i = 0; i < Nfold; i++) {
vector<samplepair> per_fea_num_unit;
for (j = 0; j < Npos_neg; j++) {
string pos_line, pos_per, str_num;
int position, num1, num2;
getline(fin, pos_line);
pos_per.assign(pos_line.begin(), pos_line.begin() + pos_line.find_first_of(" "));
position = pos_line.find_first_not_of(alphabet);
str_num.assign(str_num.begin() + position, str_num.begin() +
str_num.find_first_of(" ", position));
num1 = atoi(str_num.c_str());
str_num.assign(str_num.begin() + str_num.find_last_of(" ") + 1,
str_num.end());
num2 = atoi(str_num.c_str());
per_fea_num_unit.push_back(samplepair(pos_per, pos_per, num1, num2));
}
for (j = 0; j < Npos_neg; j++) {
string neg_line, neg_per1, neg_per2, str_num;
int position1, position2, num1, num2;
getline(fin, neg_line);
position1 = neg_line.find_first_of(alphabet_pure,
neg_line.find_first_not_of(alphabet));
position2 = neg_line.find_first_not_of(alphabet);
neg_per1.assign(neg_line.begin(), neg_line.begin() +
neg_line.find_first_of(" "));
neg_per2.assign(neg_line.begin() + position1, neg_line.begin() +
neg_line.find_first_of(" ", position1));
str_num.assign(neg_line.begin() + position2, neg_line.begin() +
neg_line.find_first_of(" ", position2));
num1 = atoi(str_num.c_str());
str_num.assign(neg_line.begin() + neg_line.find_first_not_of(alphabet),
neg_line.end());
num2 = atoi(str_num.c_str());
per_fea_num_unit.push_back(samplepair(neg_per1, neg_per2, num1, num2));
}
if (i==fold) {
per_fea_num_test.assign(per_fea_num_unit.begin(), per_fea_num_unit.end());
}
else{
per_fea_num_train.insert(per_fea_num_train.end(),
per_fea_num_unit.begin(),
per_fea_num_unit.end());
}
}
// QA
assert(fin.eof());
fin.close();
vector<string> totalname;
for (i = 0; i < features.size(); i++) {
totalname.push_back(features[i].name);
}
vector<string>::iterator it1, it2;
int it_num1, it_num2;
if (axis == 0) {
feature_train = MatrixXd(per_fea_num_train.size() * 2,
features[0].fea.cols());
feature_test = MatrixXd(per_fea_num_test.size() * 2,
features[0].fea.cols());
label_train = VectorXd(per_fea_num_train.size());
label_test = VectorXd(per_fea_num_test.size());
for ( i = 0; i < per_fea_num_train.size(); i++) {
it1 = find(totalname.begin(), totalname.end(), per_fea_num_train[i].name1);
it_num1 = it1 - totalname.begin();
it2 = find(totalname.begin(), totalname.end(), per_fea_num_train[i].name2);
it_num2 = it2 - totalname.begin();
feature_train.row(2*i) = features[it_num1].fea.row(per_fea_num_train[i].num1);
feature_train.row(2*i+1) = features[it_num2].fea.row(per_fea_num_train[i].num2);
if(per_fea_num_train[i].name1 == per_fea_num_train[i].name2)
label_train[i] = 1;
else
label_train[i] = 0;
}
for ( i = 0; i < per_fea_num_test.size(); i++) {
it1 = find(totalname.begin(), totalname.end(), per_fea_num_test[i].name1);
it_num1 = it1 - totalname.begin();
it2 = find(totalname.begin(), totalname.end(), per_fea_num_test[i].name2);
it_num2 = it2 - totalname.begin();
feature_test.row(2*i) = features[it_num1].fea.row(per_fea_num_test[i].num1);
feature_test.row(2*i+1) = features[it_num2].fea.row(per_fea_num_test[i].num2);
if(per_fea_num_test[i].name1 == per_fea_num_test[i].name2)
label_test[i] = 1;
else
label_test[i] = 0;
}
return true;
}
else {
feature_train = MatrixXd(features[0].fea.rows(),
per_fea_num_train.size() * 2) ;
feature_test = MatrixXd(features[0].fea.rows(),
per_fea_num_test.size() * 2) ;
label_train = VectorXd(per_fea_num_train.size());
label_test = VectorXd(per_fea_num_test.size());
for ( i = 0; i < per_fea_num_train.size(); i++) {
it1 = find(totalname.begin(), totalname.end(), per_fea_num_train[i].name1);
it_num1 = it1 - totalname.begin();
it2 = find(totalname.begin(), totalname.end(), per_fea_num_train[i].name2);
it_num2 = it2 - totalname.begin();
feature_train.col(2*i) = features[it_num1].fea.col(per_fea_num_train[i].num1);
feature_train.col(2*i+1) = features[it_num2].fea.col(per_fea_num_train[i].num2);
if(per_fea_num_train[i].name1 == per_fea_num_train[i].name2)
label_train[i] = 1;
else
label_train[i] = 0;
}
for ( i = 0; i < per_fea_num_test.size(); i++) {
it1 = find(totalname.begin(), totalname.end(), per_fea_num_test[i].name1);
it_num1 = it1 - totalname.begin();
it2 = find(totalname.begin(), totalname.end(), per_fea_num_test[i].name2);
it_num2 = it2 - totalname.begin();
feature_test.col(2*i) = features[it_num1].fea.col(per_fea_num_test[i].num1);
feature_test.col(2*i+1) = features[it_num2].fea.col(per_fea_num_test[i].num2);
if(per_fea_num_test[i].name1 == per_fea_num_test[i].name2)
label_test[i] = 1;
else
label_test[i] = 0;
}
return true;
}
}