[Test()] public void TestGetEntriesByRow()
{
var matrix = new SparseBooleanMatrix();
for (int i = 0; i < 5; i++)
{
if (i != 2 && i != 3)
{
matrix[i, 1] = true;
matrix[i, 4] = true;
}
}
Assert.AreEqual(2, matrix.GetEntriesByRow(0).Count);
Assert.AreEqual(2, matrix.GetEntriesByRow(1).Count);
Assert.AreEqual(0, matrix.GetEntriesByRow(2).Count);
Assert.AreEqual(0, matrix.GetEntriesByRow(3).Count);
Assert.AreEqual(2, matrix.GetEntriesByRow(4).Count);
}
/// <summary>
/// Compute the fit of the mapping
/// </summary>
/// <returns>
/// an array of doubles containing the RMSE on the training data for each latent factor
/// </returns>
protected double[] ComputeMappingFit()
{
double rmse = 0;
double penalty = 0;
var rmse_and_penalty_per_factor = new double[num_factors];
int num_items = 0;
for (int i = 0; i < MaxItemID + 1; i++)
{
var item_users = Feedback.ItemMatrix.GetEntriesByRow(i);
var item_attrs = item_attributes.GetEntriesByRow(i);
if (item_users.Count == 0 || item_attrs.Count == 0)
{
continue;
}
num_items++;
double[] est_factors = MapToLatentFactorSpace(i);
for (int j = 0; j < num_factors; j++)
{
double error = Math.Pow(est_factors[j] - item_factors[i, j], 2);
double reg_term = reg_mapping * VectorUtils.EuclideanNorm(attribute_to_factor.GetColumn(j));
rmse += error;
penalty += reg_term;
rmse_and_penalty_per_factor[j] += error + reg_term;
}
}
for (int i = 0; i < num_factors; i++)
{
rmse_and_penalty_per_factor[i] = (double)rmse_and_penalty_per_factor[i] / num_items;
Console.Error.Write("{0,0:0.####} ", rmse_and_penalty_per_factor[i]);
}
rmse = (double)rmse / (num_factors * num_items);
penalty = (double)penalty / (num_factors * num_items);
Console.Error.WriteLine(" > {0,0:0.####} ({1,0:0.####})", rmse, penalty);
return(rmse_and_penalty_per_factor);
}
