/// <summary>
/// Perform one iteration of the mapping learning process
/// </summary>
public override void IterateMapping()
{
_MapToLatentFactorSpace = __MapToLatentFactorSpace; // make sure we don't memoize during training
// stochastic gradient descent
int item_id = SampleItem();
float[] est_factors = MapToLatentFactorSpace(item_id);
for (int j = 0; j < num_factors; j++)
{
// TODO do we need an absolute term here???
double diff = est_factors[j] - item_factors[item_id, j];
if (diff > 0)
{
foreach (int attribute in item_attributes[item_id])
{
double w = attribute_to_factor[attribute, j];
double deriv = diff * w + reg_mapping * w;
MatrixExtensions.Inc(attribute_to_factor, attribute, j, learn_rate_mapping * -deriv);
}
// bias term
double w_bias = attribute_to_factor[NumItemAttributes, j];
double deriv_bias = diff * w_bias + reg_mapping * w_bias;
MatrixExtensions.Inc(attribute_to_factor, NumItemAttributes, j, learn_rate_mapping * -deriv_bias);
}
}
}
///
public override void IterateMapping()
{
// stochastic gradient descent
int user_id = SampleUserWithAttributes();
double[] est_factors = MapUserToLatentFactorSpace(user_attributes[user_id]);
for (int j = 0; j < num_factors; j++)
{
// TODO do we need an absolute term here???
double diff = est_factors[j] - user_factors[user_id, j];
if (diff > 0)
{
foreach (int attribute in user_attributes[user_id])
{
double w = attribute_to_factor[attribute, j];
double deriv = diff * w + reg_mapping * w;
MatrixExtensions.Inc(attribute_to_factor, attribute, j, learn_rate_mapping * -deriv);
}
// bias term
double w_bias = attribute_to_factor[NumUserAttributes, j];
double deriv_bias = diff * w_bias + reg_mapping * w_bias;
MatrixExtensions.Inc(attribute_to_factor, NumUserAttributes, j, learn_rate_mapping * -deriv_bias);
}
}
}
[Test()] public void TestScalarProductWithRowDifference()
{
var matrix1 = new Matrix <float>(5, 5);
float[] row = { 1, 2, 3, 4, 5 };
for (int i = 0; i < 5; i++)
{
matrix1.SetRow(i, row);
}
var matrix2 = new Matrix <float>(5, 5);
for (int i = 0; i < 5; i++)
{
matrix2.SetRow(i, row);
}
var matrix3 = new Matrix <float>(5, 5);
MatrixExtensions.Inc(matrix3, 1.0f);
Assert.AreEqual(40, MatrixExtensions.RowScalarProductWithRowDifference(matrix1, 2, matrix2, 3, matrix3, 1));
}
