///
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 SaveModel(string file)
{
using (StreamWriter writer = Recommender.GetWriter(file, this.GetType()))
{
IMatrixUtils.WriteMatrix(writer, user_factors);
IMatrixUtils.WriteMatrix(writer, item_factors);
}
}
///
public override void SaveModel(string filename)
{
using (StreamWriter writer = Recommender.GetWriter(filename, this.GetType()))
{
writer.WriteLine(global_bias.ToString(CultureInfo.InvariantCulture));
IMatrixUtils.WriteMatrix(writer, user_factors);
IMatrixUtils.WriteMatrix(writer, item_factors);
}
}
///
public override void SaveModel(string file)
{
using (StreamWriter writer = Recommender.GetWriter(file, this.GetType()))
{
writer.WriteLine(global_average.ToString(CultureInfo.InvariantCulture));
IMatrixUtils.WriteSparseMatrix(writer, diff_matrix);
IMatrixUtils.WriteSparseMatrix(writer, freq_matrix);
}
}
///
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 static void Main()
{
XmlConfigurator.Configure();
IMatrix matrix = default(IMatrix);
IMatrixUtils matrixUtils = default(IMatrixUtils);
try
{
int rowsAndCowsInTheMatrix = NumberOfMatrixRowsAndCows;
ICell startingCell = new Cell(StartingPositionOnRowsAndCows, StartingPositionOnRowsAndCows);
matrix = new Matrix(rowsAndCowsInTheMatrix);
matrixUtils = new MatrixUtils(matrix, PossibleDirections.AllDirections, startingCell);
throw new ArgumentException("I Have Error Loging");
}
catch (Exception ex)
{
Logger.Error("Initializing of Matrix failed", ex);
}
matrixUtils.FillMatrix();
Console.WriteLine(matrix.MatrixToString());
}
///
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));
}
///
public override void SaveModel(string filename)
{
using (StreamWriter writer = Recommender.GetWriter(filename, this.GetType()))
IMatrixUtils.WriteMatrix(writer, item_attribute_weight_by_user);
}
