///
public override void LoadModel(string filename)
{
global_effects.LoadModel(filename + "-global-effects");
global_effects.Ratings = Ratings;
using (StreamReader reader = Recommender.GetReader(filename, this.GetType()))
{
var global_bias = double.Parse(reader.ReadLine(), CultureInfo.InvariantCulture);
var num_learned_factors = int.Parse(reader.ReadLine(), CultureInfo.InvariantCulture);
var user_factors = (Matrix <double>)IMatrixUtils.ReadMatrix(reader, new Matrix <double>(0, 0));
var item_factors = (Matrix <double>)IMatrixUtils.ReadMatrix(reader, new Matrix <double>(0, 0));
if (user_factors.NumberOfColumns != item_factors.NumberOfColumns)
{
throw new Exception(
string.Format("Number of user and item factors must match: {0} != {1}",
user_factors.NumberOfColumns, item_factors.NumberOfColumns));
}
this.MaxUserID = user_factors.NumberOfRows - 1;
this.MaxItemID = item_factors.NumberOfRows - 1;
// assign new model
this.global_bias = global_bias;
this.num_learned_factors = num_learned_factors;
if (this.NumFactors != user_factors.NumberOfColumns)
{
Console.Error.WriteLine("Set num_factors to {0}", user_factors.NumberOfColumns);
this.NumFactors = (uint)user_factors.NumberOfColumns;
}
this.user_factors = user_factors;
this.item_factors = item_factors;
}
}
///
public override void LoadModel(string filename)
{
baseline_predictor.LoadModel(filename + "-global-effects");
using (StreamReader reader = Model.GetReader(filename, this.GetType()))
{
Correlation = (RatingCorrelationType)Enum.Parse(typeof(RatingCorrelationType), reader.ReadLine());
InitModel();
if (correlation is SymmetricCorrelationMatrix)
{
((SymmetricCorrelationMatrix)correlation).ReadSymmetricCorrelationMatrix(reader);
}
else if (correlation is AsymmetricCorrelationMatrix)
{
((AsymmetricCorrelationMatrix)correlation).ReadAsymmetricCorrelationMatrix(reader);
}
else
{
throw new NotSupportedException("Unsupported correlation type: " + correlation.GetType());
}
}
}