int
Transform::
AutoScaleData()
{
double min=stat.minValue();
addScalar(-1*min);
double max=stat.maxValue();
if (max>0.0) ScaleData(1/max);
return 0;
}
void LogisticRegression::Test()
{
cout << "Test ......." << endl;
ifstream fin(this->str_test_file_.c_str());
if(!fin)
{
cerr << "Can not read test data file !" << endl;
exit(0);
}
string line;
while(getline(fin, line))
{
vector<double> vd_data = split2double(line, "\t");
int label = (int)vd_data.back();
vd_data.pop_back();
this->vec_test_label_.push_back(label);
this->vec_test_data_.push_back(vd_data);
}
fin.close();
ScaleData(this->vec_test_data_);
//test
int i_error_count = 0;
for(int i = 0; i < this->vec_test_data_.size(); i++)
{
double dou_prob = Sigmoid(this->vec_test_data_[i]);
int i_ret_label = dou_prob > 0.5 ? 1 : 0;
if(i_ret_label != this->vec_test_label_[i])
{
i_error_count ++;
}
}
cout << "the error rate of this test is " << 1.0 * i_error_count / this->vec_test_data_.size() << endl;
}
void LogisticRegression::TrainModel()
{
cout << "Train ...." << endl;
//load data
string line;
ifstream fin(this->str_train_data_file_.c_str());
if(!fin)
{
cerr << "Can not read train data file !" << endl;
exit(0);
}
while(getline(fin, line))
{
vector<double> vd_data = split2double(line, "\t");
int label = (int)vd_data.back();
vd_data.pop_back();
this->vec_train_label_.push_back(label);
this->vec_train_data_.push_back(vd_data);
}
fin.close();
//data scale
InitScale(this->vec_train_data_);
ScaleData(this->vec_train_data_);
//train
size_t i_round = 0;
while(i_round < this->i_max_round)
{
cout << "Rount [" << i_round << "]....." << endl;
vector<double> douVec_gradient(this->vec_train_data_[0].size(), 0);
double dou_bias_gradient = 0;
double dou_cost = 0;
for(int i = 0; i < this->vec_train_data_.size(); i++)
{
double dou_h = Sigmoid(this->vec_train_data_[i]);
//calculate cost for each data
dou_cost += (this->vec_train_label_[i]*log(dou_h) + (1-this->vec_train_label_[i])*log(1-dou_h));
//calculate gradient for each data
for(int j = 0; j < douVec_gradient.size(); j++)
{
douVec_gradient[j] += (this->vec_train_label_[i] - dou_h)*this->vec_train_data_[i][j];
}
dou_bias_gradient += (this->vec_train_label_[i] - dou_h);
}
//L2 regularization
double dou_regularization = 0;
for(int i = 0; i < this->douVec_weights_.size(); i++)
{
dou_regularization += pow(this->douVec_weights_[i], 2);
}
//final cost
dou_cost = -1.0 * dou_cost / this->douVec_weights_.size() + this->dou_lambda_*dou_regularization/(2*this->douVec_weights_.size());
cout << "Cost J = " << dou_cost << endl;
//update
string str_new_weights("new weights is [ ");
string str_gradient("gradient is [");
for(int k = 0; k < douVec_gradient.size(); k++)
{
this->douVec_weights_[k] += this->dou_step_*(douVec_gradient[k]+this->dou_lambda_*this->douVec_weights_[k])/this->vec_train_data_.size();
str_new_weights = str_new_weights + double2string(this->douVec_weights_[k]) + " ";
str_gradient = str_gradient + double2string(douVec_gradient[k]) + " ";
}
this->dou_bias_ += this->dou_step_*dou_bias_gradient/this->vec_train_data_.size();
str_new_weights = str_new_weights + double2string(this->dou_bias_) + "]";
str_gradient = str_gradient + double2string(dou_bias_gradient) + "]";
cout << str_new_weights << endl;
cout << str_gradient << endl;
i_round += 1;
}
//save model
cout << "save model ..." << endl;
ofstream fout("./logreg_model.txt");
cout << this->douVec_weights_.size() << endl;
for(int i = 0; i < this->douVec_weights_.size(); i++)
{
fout << this->douVec_weights_[i] << " ";
}
fout << this->dou_bias_ << endl;
fout.close();
}
