///
public override void LoadModel(string file)
{
using (StreamReader reader = Recommender.GetReader(file, this.GetType()))
{
int numberOfComponents = int.Parse(reader.ReadLine());
var weights = new List <double>();
var recommenders = new List <IRecommender>();
for (int i = 0; i < numberOfComponents; i++)
{
string[] data = reader.ReadLine().Split(' ');
Type t = Type.GetType(data[0]);
recommenders.Add((IRecommender)Activator.CreateInstance(t));
recommenders[i].LoadModel("model-" + i + ".txt");
// make sure the recommenders get their data FIXME
weights.Add(double.Parse(data[1], CultureInfo.InvariantCulture));
}
this.weights = weights;
this.recommenders = recommenders;
}
}
///
public override void LoadModel(string filename)
{
using (StreamReader reader = Recommender.GetReader(filename, this.GetType()))
{
var bias = double.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 = bias;
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)
{
using (StreamReader reader = Recommender.GetReader(filename, this.GetType()))
{
this.global_average = double.Parse(reader.ReadLine(), CultureInfo.InvariantCulture);
this.entity_averages = VectorUtils.ReadVector(reader);
}
}
///
public override void LoadModel(string filename)
{
using (StreamReader reader = Recommender.GetReader(filename, this.GetType()))
{
CorrelationMatrix correlation = CorrelationMatrix.ReadCorrelationMatrix(reader);
base.Train(); // train baseline model
this.correlation = new BinaryCosine(correlation);
}
}
///
public override void LoadModel(string filename)
{
using (StreamReader reader = Recommender.GetReader(filename, this.GetType()))
{
var global_average = double.Parse(reader.ReadLine(), CultureInfo.InvariantCulture);
var user_biases = VectorUtils.ReadVector(reader);
var item_biases = VectorUtils.ReadVector(reader);
this.global_average = global_average;
this.user_biases = user_biases.ToArray();
this.item_biases = item_biases.ToArray();
}
}
///
public override void LoadModel(string filename)
{
using (StreamReader reader = Recommender.GetReader(filename, this.GetType()))
{
var bias = double.Parse(reader.ReadLine(), CultureInfo.InvariantCulture);
IList <double> user_bias = VectorUtils.ReadVector(reader);
var user_factors = (Matrix <double>)IMatrixUtils.ReadMatrix(reader, new Matrix <double>(0, 0));
IList <double> item_bias = VectorUtils.ReadVector(reader);
var item_factors = (Matrix <double>)IMatrixUtils.ReadMatrix(reader, new Matrix <double>(0, 0));
if (user_factors.dim2 != item_factors.dim2)
{
throw new IOException(
string.Format(
"Number of user and item factors must match: {0} != {1}",
user_factors.dim2, item_factors.dim2));
}
if (user_bias.Count != user_factors.dim1)
{
throw new IOException(
string.Format(
"Number of users must be the same for biases and factors: {0} != {1}",
user_bias.Count, user_factors.dim1));
}
if (item_bias.Count != item_factors.dim1)
{
throw new IOException(
string.Format(
"Number of items must be the same for biases and factors: {0} != {1}",
item_bias.Count, item_factors.dim1));
}
this.MaxUserID = user_factors.dim1 - 1;
this.MaxItemID = item_factors.dim1 - 1;
// assign new model
this.global_bias = bias;
if (this.NumFactors != user_factors.dim2)
{
Console.Error.WriteLine("Set num_factors to {0}", user_factors.dim1);
this.NumFactors = (uint)user_factors.dim2;
}
this.user_factors = user_factors;
this.item_factors = item_factors;
this.user_bias = new double[user_factors.dim1];
user_bias.CopyTo(this.user_bias, 0);
this.item_bias = new double[item_factors.dim1];
item_bias.CopyTo(this.item_bias, 0);
}
}
///
public override void LoadModel(string file)
{
InitModel();
using (StreamReader reader = Recommender.GetReader(file, this.GetType()))
{
var global_average = double.Parse(reader.ReadLine(), CultureInfo.InvariantCulture);
var diff_matrix = (SkewSymmetricSparseMatrix)IMatrixUtils.ReadMatrix(reader, this.diff_matrix); // TODO take symmetric matrix into account for smaller model files
var freq_matrix = (SymmetricSparseMatrix <int>)IMatrixUtils.ReadMatrix(reader, this.freq_matrix); // TODO take anti-symmetric matrix into account for smaller model files
// assign new model
this.global_average = global_average;
this.diff_matrix = diff_matrix;
this.freq_matrix = freq_matrix;
}
}
///
public override void LoadModel(string filename)
{
using (StreamReader reader = Recommender.GetReader(filename, this.GetType()))
{
int size = int.Parse(reader.ReadLine());
var view_count = new int[size];
while (!reader.EndOfStream)
{
string[] numbers = reader.ReadLine().Split(' ');
int item_id = int.Parse(numbers[0]);
int count = int.Parse(numbers[1]);
view_count[item_id] = count;
}
this.view_count = view_count;
}
}
///
public override void LoadModel(string filename)
{
using (StreamReader reader = Recommender.GetReader(filename, this.GetType()))
{
int num_users = int.Parse(reader.ReadLine());
var nearest_neighbors = new int[num_users][];
for (int u = 0; u < nearest_neighbors.Length; u++)
{
string[] numbers = reader.ReadLine().Split(' ');
nearest_neighbors[u] = new int[numbers.Length];
for (int i = 0; i < numbers.Length; i++)
{
nearest_neighbors[u][i] = int.Parse(numbers[i]);
}
}
this.correlation = CorrelationMatrix.ReadCorrelationMatrix(reader);
this.k = (uint)nearest_neighbors[0].Length;
this.nearest_neighbors = nearest_neighbors;
}
}
///
public override void LoadModel(string file)
{
InitModel();
using (StreamReader reader = Recommender.GetReader(file, this.GetType()))
{
var global_average = double.Parse(reader.ReadLine(), CultureInfo.InvariantCulture);
var diff_matrix_like = (SkewSymmetricSparseMatrix)IMatrixUtils.ReadMatrix(reader, this.diff_matrix_like);
var freq_matrix_like = (SymmetricSparseMatrix <int>)IMatrixUtils.ReadMatrix(reader, this.freq_matrix_like);
var diff_matrix_dislike = (SkewSymmetricSparseMatrix)IMatrixUtils.ReadMatrix(reader, this.diff_matrix_dislike);
var freq_matrix_dislike = (SymmetricSparseMatrix <int>)IMatrixUtils.ReadMatrix(reader, this.freq_matrix_dislike);
var user_average = VectorUtils.ReadVector(reader);
// assign new model
this.global_average = global_average;
this.diff_matrix_like = diff_matrix_like;
this.freq_matrix_like = freq_matrix_like;
this.diff_matrix_dislike = diff_matrix_dislike;
this.freq_matrix_dislike = freq_matrix_dislike;
this.user_average = user_average;
}
}
///
public override void LoadModel(string file)
{
using (StreamReader reader = Recommender.GetReader(file, this.GetType()))
{
var user_factors = (Matrix <double>)IMatrixUtils.ReadMatrix(reader, new Matrix <double>(0, 0));
IList <double> item_bias = VectorUtils.ReadVector(reader);
var item_factors = (Matrix <double>)IMatrixUtils.ReadMatrix(reader, new Matrix <double>(0, 0));
if (user_factors.NumberOfColumns != item_factors.NumberOfColumns)
{
throw new IOException(
string.Format("Number of user and item factors must match: {0} != {1}",
user_factors.NumberOfColumns, item_factors.NumberOfColumns));
}
if (item_bias.Count != item_factors.dim1)
{
throw new IOException(
string.Format(
"Number of items must be the same for biases and factors: {0} != {1}",
item_bias.Count, item_factors.dim1));
}
this.MaxUserID = user_factors.NumberOfRows - 1;
this.MaxItemID = item_factors.NumberOfRows - 1;
// assign new model
if (this.num_factors != user_factors.NumberOfColumns)
{
Console.Error.WriteLine("Set num_factors to {0}", user_factors.NumberOfColumns);
this.num_factors = user_factors.NumberOfColumns;
}
this.user_factors = user_factors;
this.item_bias = item_bias;
this.item_factors = item_factors;
}
random = Util.Random.GetInstance();
}
///
public override void LoadModel(string filename)
{
using (StreamReader reader = Recommender.GetReader(filename, this.GetType()))
this.item_attribute_weight_by_user = (Matrix <double>)IMatrixUtils.ReadMatrix(reader, new Matrix <double>(0, 0));
}
