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Copy pathlfw_unrest_API.cpp
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149 lines (143 loc) · 4.7 KB
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#include "lfw_unrest_API.h"
bool UnRestLFW(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;
int Nfold;
getline(fin, str_Nfold);
Nfold = atoi(str_Nfold.c_str());
// Get the name of the file
vector<dict> per_fea_num_train;
vector<dict> per_fea_num_test;
for( i = 0; i < Nfold; i++){
vector<dict> per_fea_num_unit;
string str_person_num;
int person_num;
vector<int> sample_num;
getline(fin, str_person_num);
person_num = atoi(str_person_num.c_str());
vector<string> person;
for ( j = 0; j < person_num; j++) {
string line;
string per, str_num;
int num;
getline(fin, line);
per.assign(line.begin(), line.begin() + line.find_first_of(" "));
str_num.assign(line.begin() + line.find_last_of(" ") + 1, line.end());
num = atoi(str_num.c_str());
person.push_back(per);
sample_num.push_back(num);
}
assert(sample_num.size() == person.size()); // QA
for ( k = 0; k < person.size(); k++) {
per_fea_num_unit.push_back(dict(person[k], sample_num[k]));
}
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 it;
int it_num;
if (axis == 0) {
feature_train = MatrixXd(sum_sample_num(per_fea_num_train),
features[0].fea.cols());
feature_test = MatrixXd(sum_sample_num(per_fea_num_test),
features[0].fea.cols());
label_train = VectorXd(sum_sample_num(per_fea_num_train));
label_test = VectorXd(sum_sample_num(per_fea_num_test));
k = 0;
for ( i = 0; i < per_fea_num_train.size(); i++) {
it = find(totalname.begin(), totalname.end(), per_fea_num_train[i].name);
it_num = it - totalname.begin();
for ( j = 0; j < per_fea_num_train[i].num; j++) {
feature_train.row(k) = features[it_num].fea.row(j);
label_train(k) = i;
k++;
}
}
k = 0;
for ( i = 0; i < per_fea_num_test.size(); i++) {
it = find(totalname.begin(), totalname.end(), per_fea_num_test[i].name);
it_num = it - totalname.begin();
for ( j = 0; j < per_fea_num_test[i].num; j++) {
feature_test.row(k) = features[it_num].fea.row(j);
label_test(k) = i;
k++;
}
}
return true;
}
else{
feature_train = MatrixXd(features[0].fea.rows(),
sum_sample_num(per_fea_num_train));
feature_test = MatrixXd(features[0].fea.rows(),
sum_sample_num(per_fea_num_test));
label_train = VectorXd(sum_sample_num(per_fea_num_train));
label_test = VectorXd(sum_sample_num(per_fea_num_test));
k = 0;
for ( i = 0; i < per_fea_num_train.size(); i++) {
it = find(totalname.begin(), totalname.end(), per_fea_num_train[i].name);
it_num = it - totalname.begin();
for ( j = 0; j < per_fea_num_train[i].num; j++) {
feature_train.col(k) = features[it_num].fea.col(j);
label_train(k) = i;
k++;
}
}
k = 0;
for ( i = 0; i < per_fea_num_test.size(); i++) {
it = find(totalname.begin(), totalname.end(), per_fea_num_test[i].name);
it_num = it - totalname.begin();
for ( j = 0; j < per_fea_num_test[i].num; j++) {
feature_test.col(k) = features[it_num].fea.col(j);
label_test(k) = i;
k++;
}
}
return true;
}
}