///
public virtual void Iterate()
{
if (num_learned_factors >= NumFactors)
{
return;
}
// compute residuals
for (int index = 0; index < Ratings.Count; index++)
{
int u = Ratings.Users[index];
int i = Ratings.Items[index];
residuals[index] = Ratings[index] - Predict(u, i);
int n_ui = Math.Min(Ratings.ByUser[u].Count, Ratings.ByItem[i].Count); // TODO use less memory
residuals[index] *= n_ui / (n_ui + Shrinkage);
}
// initialize new latent factors
MatrixUtils.ColumnInitNormal(user_factors, InitMean, InitStdev, num_learned_factors);
MatrixUtils.ColumnInitNormal(item_factors, InitMean, InitStdev, num_learned_factors); // TODO make configurable?
// compute the next factor by solving many least squares problems with one variable each
double err = double.MaxValue / 2;
double err_old = double.MaxValue;
while (err / err_old < 1 - Sensibility)
{
{
// TODO create only once?
var user_factors_update_numerator = new double[MaxUserID + 1];
var user_factors_update_denominator = new double[MaxUserID + 1];
// compute updates in one pass over the data
for (int index = 0; index < Ratings.Count; index++)
{
int u = Ratings.Users[index];
int i = Ratings.Items[index];
user_factors_update_numerator[u] += residuals[index] * item_factors[i, num_learned_factors];
user_factors_update_denominator[u] += item_factors[i, num_learned_factors] * item_factors[i, num_learned_factors];
}
// update user factors
for (int u = 0; u <= MaxUserID; u++)
{
if (user_factors_update_numerator[u] != 0)
{
user_factors[u, num_learned_factors] = user_factors_update_numerator[u] / user_factors_update_denominator[u];
}
}
}
{
var item_factors_update_numerator = new double[MaxItemID + 1];
var item_factors_update_denominator = new double[MaxItemID + 1];
// compute updates in one pass over the data
for (int index = 0; index < Ratings.Count; index++)
{
int u = Ratings.Users[index];
int i = Ratings.Items[index];
item_factors_update_numerator[i] += residuals[index] * user_factors[u, num_learned_factors];
item_factors_update_denominator[i] += user_factors[u, num_learned_factors] * user_factors[u, num_learned_factors];
}
// update item factors
for (int i = 0; i <= MaxItemID; i++)
{
if (item_factors_update_numerator[i] != 0)
{
item_factors[i, num_learned_factors] = item_factors_update_numerator[i] / item_factors_update_denominator[i];
}
}
}
err_old = err;
err = ComputeFit();
}
num_learned_factors++;
}
