protected override AlternatingLeastSquares CreateRecommender(Dictionary <string, Dictionary <string, double> > data)
{
var parameters = new AlternatingLeastSquaresParameters(factors: 3, regularization: 0, iterations: 15, useConjugateGradient: true);
var recommender = AlternatingLeastSquares.Fit(DataMatrix.Load(data), parameters);
return(recommender);
}
public void Factorize()
{
var matrix = new double[][]
{
new double[] { 1, 1, 0, 1, 0, 0 },
new double[] { 0, 1, 1, 1, 0, 0 },
new double[] { 1, 0, 1, 0, 0, 0 },
new double[] { 1, 1, 0, 0, 0, 0 },
new double[] { 0, 0, 1, 1, 0, 1 },
new double[] { 0, 1, 0, 0, 0, 1 },
new double[] { 0, 0, 0, 0, 1, 1 },
};
var data = Enumerable.Range(0, 7)
.SelectMany(
o => Enumerable.Range(0, 6),
(o, p) => new { UserId = o.ToString(), ItemId = p.ToString(), Confidence = matrix[o][p] })
.Where(o => o.Confidence > 0)
.GroupBy(o => o.UserId)
.ToDictionary(o => o.Key, o => o.ToDictionary(p => p.ItemId, p => p.Confidence * 2));
var parametersScenarios = new[]
{
new AlternatingLeastSquaresParameters(factors: 6, regularization: 0, iterations: 15, useConjugateGradient: true),
new AlternatingLeastSquaresParameters(factors: 6, regularization: 0, iterations: 15, useConjugateGradient: false),
};
foreach (var parameters in parametersScenarios)
{
var recommender = AlternatingLeastSquares.Fit(DataMatrix.Load(data), parameters);
Assert.True(recommender.Loss < 0.00001);
}
}
public void Setup()
{
switch (this.Provider)
{
case ProviderId.Managed:
Control.UseManaged();
break;
case ProviderId.NativeMKL:
MklControl.UseNativeMKL(MklConsistency.Auto, MklPrecision.Double, MklAccuracy.High);
break;
}
this.data = DataMatrix.Load(DataFactory.GetLastFm360k());
}
