/**
* Iteratively train each feature on the entire data set
* Once sufficient progress has been made, move on
*/
void SvdOrder::calculateFeatures()
{
qDebug() << "Training";
// Keep looping until you have passed the maximum number
// of epochs or have stopped making significant progress
double prevRMSE = 1e8;
double RMSE = 1e7;
Movie movie(currDb);
double averageRating = currDb->getAverageRating();
for (unsigned int i = 0;
i < MIN_EPOCHS ||
(i < MAX_EPOCHS && (prevRMSE - RMSE) > MIN_IMPROVEMENT);
i++)
{
prevRMSE = RMSE;
RMSE = 0;
User user(currDb,6);
int numUsers = currDb->totalUsers();
int totalSamples = 0;
for (int j = 0; j < numUsers; j++)
{
for (int k = 0; k < user.votes(); k++)
{
int movieId = user.movie(k);
float rating = user.score(k) - averageRating;
int userIndex = j;
int movieIndex = movieId - 1;
float predict = predictRating(movieIndex, userIndex);
float diff = predict - rating;
RMSE += diff * diff;
// Update all the feature vectors here
for (unsigned int l = 0; l < NUM_FEATURES; l++)
{
float oldUF = userFeatures[userIndex][l];
float oldMF = movieFeatures[movieIndex][l];
userFeatures[userIndex][l] -= LRATE *
(diff * oldMF * 2 + REGULARIZE * oldUF);
movieFeatures[movieIndex][l] -= LRATE *
(diff * oldUF * 2 + REGULARIZE * oldMF);
}
totalSamples++;
}
user.next();
}
RMSE = sqrt(RMSE / totalSamples);
qDebug() << "Epoch" << i + 1 << "RMSE: " << RMSE;
}
qDebug() << "Done with training";
}
/**
* Loop through the entire list of finished features
*/
double SvdOrder::determine(int user)
{
int movieIndex = currentMovie - 1;
int userIndex = currDb->mapUser(user);
return currDb->getAverageRating() + predictRating(movieIndex, userIndex);
}
