public void Select_Lambda(List <T> X, List <T> Xval, int max_iterations, int num_features, out double best_lambda)
{
double[] lambda_candidates = new double[] { 0.01, 0.03, 0.1, 0.3, 1, 3, 10, 30, 100 };
int C_length = lambda_candidates.Length;
double min_prediction_error = double.MaxValue;
best_lambda = -1;
for (int i = 0; i < C_length; ++i)
{
CollaborativeFilteringRS <T> svm = new CollaborativeFilteringRS <T>();
svm.RegularizationLambda = lambda_candidates[i];
svm.MaxSolverIteration = max_iterations;
svm.Compute(X, num_features);
double cost = svm.ComputeCost(Xval, num_features);
if (min_prediction_error > cost)
{
min_prediction_error = cost;
best_lambda = lambda_candidates[i];
}
}
}
public static void Test_Compute()
{
List <string> movie_titles = LoadMovies();
int num_movies = movie_titles.Count;
// Step 1: create my ratings with missing entries
double[] my_ratings = new double[num_movies];
int[] my_ratings_r = new int[num_movies];
for (int i = 0; i < num_movies; ++i)
{
my_ratings[i] = 0;
}
my_ratings[1] = 4;
my_ratings[98] = 2;
my_ratings[7] = 3;
my_ratings[12] = 5;
my_ratings[54] = 4;
my_ratings[64] = 5;
my_ratings[66] = 3;
my_ratings[69] = 5;
my_ratings[183] = 4;
my_ratings[226] = 5;
my_ratings[355] = 5;
for (int i = 0; i < num_movies; ++i)
{
my_ratings_r[i] = my_ratings[i] > 0 ? 1 : 0;
}
// Step 2: load the current ratings of all users, i.e., Y and R
List <List <double> > Y = DblDataTableUtil.LoadDataSet("Y.txt");
List <List <int> > R = IntDataTableUtil.LoadDataSet("R.txt");
int num_users;
DblDataTableUtil.GetSize(Y, out num_movies, out num_users);
// Step 3: insert my ratings into the existing Y and R (as the first column)
num_users++;
List <RatedItem> records = new List <RatedItem>();
for (int i = 0; i < num_movies; ++i)
{
double[] rec_Y = new double[num_users];
bool[] rec_R = new bool[num_users];
for (int j = 0; j < num_users; ++j)
{
if (j == 0)
{
rec_Y[j] = my_ratings[i];
rec_R[j] = my_ratings_r[i] == 1;
}
else
{
rec_Y[j] = Y[i][j - 1];
rec_R[j] = R[i][j - 1] == 1;
}
}
RatedItem rec = new RatedItem(null, rec_Y, rec_R);
records.Add(rec);
}
int num_features = 10;
double lambda = 10;
CollaborativeFilteringRS <RatedItem> algorithm = new CollaborativeFilteringRS <RatedItem>();
algorithm.Stepped += (s, step) =>
{
Console.WriteLine("#{0}: {1}", step, s.Cost);
};
algorithm.RegularizationLambda = lambda;
algorithm.MaxLocalSearchIteration = 100;
GradientDescent local_search = algorithm.LocalSearch as GradientDescent;
local_search.Alpha = 0.005;
double[] Ymean;
algorithm.DoMeanNormalization(records, out Ymean);
algorithm.Compute(records, num_features);
algorithm.UndoMeanNormalization(records, Ymean);
int userId = 0;
int topK = 10;
List <RatedItem> highest_ranks = algorithm.SelectHigestRanked(userId, records, topK);
for (int i = 0; i < highest_ranks.Count; ++i)
{
RatedItem rec = highest_ranks[i];
Console.WriteLine("#{0}: ({1}) {2}", i + 1, rec.UserRanks[0], movie_titles[rec.ItemIndex]);
}
}
