int main(int argc, char *argv[])
{
// Force printf calls to stdout to be printed immediately.
setbuf(stdout, NULL);
QGuiApplication::setAttribute(Qt::AA_EnableHighDpiScaling);
QGuiApplication app(argc, argv);
QFont font("Arial");
font.setWeight(QFont::Light);
app.setFont(font);
// Read configuration arguments from "config.txt" in the same directory as the exectuable.
QString connectionString;
QString devId;
const QString configFilePath = app.applicationDirPath() + QStringLiteral("/config.txt");
QFile file(configFilePath);
if (file.open(QIODevice::ReadOnly)) {
QByteArray contents = file.readAll();
QList<QByteArray> configArgs = contents.split('\n');
bool configFormatError = false;
if (configArgs.size() >= 2) {
connectionString = configArgs.at(0);
devId = configArgs.at(1);
if (connectionString.isEmpty() || devId.isEmpty())
configFormatError = true;
} else {
configFormatError = true;
}
if (configFormatError) {
qWarning() << "Expected <connectionString>\\n<devId> in config.txt, but found:" << contents
<< "\nRunning in offline mode";
}
} else {
qWarning().nospace() << "No config.txt found at " << configFilePath << "; running in offline mode";
}
UserModel userModel(connectionString, devId);
QQmlApplicationEngine engine;
engine.rootContext()->setContextProperty("userModel", &userModel);
engine.load(QUrl(QStringLiteral("qrc:/main.qml")));
return app.exec();
}
void test_Fill_Predictor()
{
string train_data = "E:/data/resys/corpus/1/train/base";
string test_data = "E:/data/resys/corpus/1/test/u1.test";
FileDataReader train_reader = FileDataReader(train_data);
FileDataReader test_reader = FileDataReader(test_data);
int n_user, n_item, n_rating;
n_user = 943;
n_item = 1682;
n_rating = 80000;
UserModel userModel(n_user,n_item,n_rating);
UserModel testModel(n_user,n_item,20000);
UserModel resultModel(n_user,n_item,20000);
ItemModel itemModel(n_user,n_item,n_rating);
if(train_reader.readData(userModel) && train_reader.readData(itemModel)
&& test_reader.readData(testModel)) {
int min_com = 0;
double min_sim = 0.00001;
int shrink_parameter = 25;
Similarity_Shrinking shrinker(min_com,min_sim,shrink_parameter);
Pearson_Similarity similarity(shrinker);
UserBased_Predictor predictor = UserBased_Predictor(similarity,30,0);
min_com = 30;
min_sim = 0.3;
shrink_parameter = 40;
NeighborCollection *item_nbs = new NeighborCollection[n_item+1];
vector<FillObj> *u_fobjs = new vector<FillObj>[n_user+1];
Similarity_Shrinking shrinker2(min_com,min_sim,shrink_parameter);
Pearson_Similarity similarity2(shrinker2);
similarity2.similarity(itemModel,item_nbs,1);
//similarity2.similarity(itemModel,item_nbs,1);
Fill_Predictor fill_predictor;
//int fill_count = fill_predictor.cal_fill_objs(userModel,item_nbs,u_fobjs,min_com);
int fill_count = fill_predictor.cal_fill_objs(userModel,item_nbs,u_fobjs,min_sim);
cout << fill_count << endl;
fill_predictor.fill(predictor,userModel,u_fobjs);
predictor.train(userModel);
predictor.predictAll(userModel,testModel,resultModel);
double rmse = evl_rmse(testModel, resultModel);
cout << "RMSE: " << rmse << endl;
double mae = evl_mae(testModel, resultModel);
cout << "MAE: " << mae << endl;
}
}
void test_UserItemBased_Predictor()
{
clock_t begin,end;
string train_data = "E:/data/resys/corpus/1/train/base";
string test_data = "E:/data/resys/corpus/1/test/u1.test";
FileDataReader train_reader = FileDataReader(train_data);
FileDataReader test_reader = FileDataReader(test_data);
int n_user, n_item, n_rating;
n_user = 943;
n_item = 1682;
n_rating = 80000;
UserModel userModel(n_user,n_item,n_rating);
ItemModel itemModel(n_user,n_item,n_rating);
UserModel testModel(n_user,n_item,20000);
UserModel resultModel(n_user,n_item,20000);
if(train_reader.readData(userModel) && train_reader.readData(itemModel) && test_reader.readData(testModel)) {
int min_com = 0;
double min_sim = 0.05;
int shrink_parameter = 30;
Similarity_Shrinking shrinker(min_com,min_sim,shrink_parameter);
Pearson_Similarity similarity(shrinker);
UserItemBased_Predictor predictor = UserItemBased_Predictor(similarity,15,0);
begin = clock();
predictor.train(userModel,itemModel);
end = clock();
cout << "Train time: " << (end - begin) * 1.0 / CLOCKS_PER_SEC << endl;
begin = end;
predictor.predictAll(userModel,itemModel,testModel,resultModel);
end = clock();
cout << "Predict time: " << (end - begin) * 1.0 / CLOCKS_PER_SEC << endl;
double rmse = evl_rmse(testModel, resultModel);
cout << "RMSE: " << rmse << endl;
double mae = evl_mae(testModel, resultModel);
cout << "MAE: " << mae << endl;
}
}
