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modification with std::vector and openmp for speedup #1

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32 changes: 18 additions & 14 deletions lr_sgd.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -78,13 +78,17 @@ double sigmoid(double x){
return 1.0/(1.0 + exp(-x));
}

double classify(map<int,double>& features, map<int,double>& weights){
double classify(vector<pair<int,double> >& features, map<int,double>& weights){

double logit = 0.0;
for(auto it = features.begin(); it != features.end(); it++){
if(it->first != 0){
logit += it->second * weights[it->first];
}

int i = 0;
int num = features.size();

#pragma omp parallel for private(i) \
shared(num, features, weights) reduction(+:logit)
for (i = 1; i < num; i++) {
logit += (features[i]).second * weights[(features[i]).first];
}
return sigmoid(logit);
}
Expand Down Expand Up @@ -174,7 +178,7 @@ int main(int argc, const char* argv[]){
if(test_file.length()) cout << "# test data: " << test_file << endl;
if(predict_file.length()) cout << "# predictions: " << predict_file << endl;

vector<map<int,double> > data;
vector<vector<pair<int,double> > > data;
map<int,double> weights;
map<int,double> total_l1;
random_device rd;
Expand Down Expand Up @@ -210,17 +214,17 @@ int main(int argc, const char* argv[]){
if(line.length()){
if(line[0] != '#' && line[0] != ' '){
vector<string> tokens = split(line,' ');
map<int,double> example;
vector<pair<int,double> > example;
if(atoi(tokens[0].c_str()) == 1){
example[0] = 1;
example.push_back(pair<int, double>(0, 1));
}else{
example[0] = 0;
example.push_back(pair<int, double>(0, 0));
}
for(unsigned int i = 1; i < tokens.size(); i++){
//if(strstr (tokens[i],"#") == NULL){
vector<string> feat_val = split(tokens[i],':');
if(feat_val.size() == 2){
example[atoi(feat_val[0].c_str())] = atof(feat_val[1].c_str());
example.push_back(pair<int, double>(atoi(feat_val[0].c_str()), atof(feat_val[1].c_str())));
if(randw){
weights[atoi(feat_val[0].c_str())] = -1.0+2.0*(double)rd()/rd.max();
}else{
Expand Down Expand Up @@ -254,7 +258,7 @@ int main(int argc, const char* argv[]){

for (unsigned int i = 0; i < data.size(); i++){
mu += (l1*alpha);
int label = data[index[i]][0];
int label = data[index[i]][0].second;
double predicted = classify(data[index[i]],weights);
for(auto it = data[index[i]].begin(); it != data[index[i]].end(); it++){
if(it->first != 0){
Expand All @@ -270,7 +274,7 @@ int main(int argc, const char* argv[]){
weights[it->first] = min(0.0,(double)(weights[it->first] + (mu - total_l1[it->first])));
}
total_l1[it->first] += (weights[it->first] - z);
}
}
}
}
}
Expand Down Expand Up @@ -318,11 +322,11 @@ int main(int argc, const char* argv[]){
if(line.length()){
if(line[0] != '#' && line[0] != ' '){
vector<string> tokens = split(line,' ');
map<int,double> example;
vector<pair<int,double> > example;
int label = atoi(tokens[0].c_str());
for(unsigned int i = 1; i < tokens.size(); i++){
vector<string> feat_val = split(tokens[i],':');
example[atoi(feat_val[0].c_str())] = atof(feat_val[1].c_str());
example.push_back(pair<int, double>(atoi(feat_val[0].c_str()), atof(feat_val[1].c_str())));
}
double predicted = classify(example,weights);
if(verbose){
Expand Down