Transpose() public method

Get the transpose of the matrix, i.e. a matrix where rows and columns are interchanged
public Transpose ( ) : IMatrix
return IMatrix
コード例 #1
0
		[Test()] public void TestTranspose()
		{
			var matrix = new SparseBooleanMatrix();
			for (int i = 0; i < 7; i++)
				if(i != 2 && i != 4)
				{
					matrix[i, 1] = true;
					matrix[i, 4] = true;
				}
			matrix[2, 2] = true;
			matrix[2, 5] = true;
			matrix[4, 3] = true;
			// transpose the matrix
			var transposed_matrix = (IBooleanMatrix) matrix.Transpose();
			// test the transposed matrix
			Assert.IsTrue(transposed_matrix[1,0]);
			Assert.IsTrue(transposed_matrix[4, 6]);
			Assert.IsFalse(transposed_matrix[3, 1]);
			Assert.IsFalse(transposed_matrix[5, 4]);
		}
コード例 #2
0
ファイル: ItemsFiltered.cs プロジェクト: zenogantner/MML-KDD
        /// <summary>Evaluation for rankings of filtered items</summary>
        /// <remarks>
        /// </remarks>
        /// <param name="recommender">item recommender</param>
        /// <param name="test">test cases</param>
        /// <param name="train">training data</param>
        /// <param name="item_attributes">the item attributes to be used for filtering</param>
        /// <param name="relevant_users">a collection of integers with all relevant users</param>
        /// <param name="relevant_items">a collection of integers with all relevant items</param>
        /// <returns>a dictionary containing the evaluation results</returns>
        public static Dictionary<string, double> Evaluate(
			IItemRecommender recommender,
			IPosOnlyFeedback test,
			IPosOnlyFeedback train,
		    SparseBooleanMatrix item_attributes,
		    ICollection<int> relevant_users,
			ICollection<int> relevant_items)
        {
            if (train.Overlap(test) > 0)
                Console.Error.WriteLine("WARNING: Overlapping train and test data");

            SparseBooleanMatrix items_by_attribute = (SparseBooleanMatrix) item_attributes.Transpose();

            // compute evaluation measures
            double auc_sum     = 0;
            double map_sum     = 0;
            double prec_5_sum  = 0;
            double prec_10_sum = 0;
            double prec_15_sum = 0;
            double ndcg_sum    = 0;

            // for counting the users and the evaluation lists
            int num_lists = 0;
            int num_users = 0;
            int last_user_id = -1;

            foreach (int user_id in relevant_users)
            {
                var filtered_items = GetFilteredItems(user_id, test, item_attributes);

                foreach (int attribute_id in filtered_items.Keys)
                {
                    // TODO optimize this a bit, currently it is quite naive
                    var relevant_filtered_items = new HashSet<int>(items_by_attribute[attribute_id]);
                    relevant_filtered_items.IntersectWith(relevant_items);

                    var correct_items = new HashSet<int>(filtered_items[attribute_id]);
                    correct_items.IntersectWith(relevant_filtered_items);

                    // the number of items that are really relevant for this user
                    var relevant_items_in_train = new HashSet<int>(train.UserMatrix[user_id]);
                    relevant_items_in_train.IntersectWith(relevant_filtered_items);
                    int num_eval_items = relevant_filtered_items.Count - relevant_items_in_train.Count();

                    // skip all users that have 0 or #relevant_filtered_items test items
                    if (correct_items.Count == 0)
                        continue;
                    if (num_eval_items - correct_items.Count == 0)
                        continue;

                    // counting stats
                    num_lists++;
                    if (last_user_id != user_id)
                    {
                        last_user_id = user_id;
                        num_users++;
                    }

                    // evaluation
                    int[] prediction = Prediction.PredictItems(recommender, user_id, relevant_filtered_items);

                    auc_sum     += Items.AUC(prediction, correct_items, train.UserMatrix[user_id]);
                    map_sum     += Items.MAP(prediction, correct_items, train.UserMatrix[user_id]);
                    ndcg_sum    += Items.NDCG(prediction, correct_items, train.UserMatrix[user_id]);
                    prec_5_sum  += Items.PrecisionAt(prediction, correct_items, train.UserMatrix[user_id],  5);
                    prec_10_sum += Items.PrecisionAt(prediction, correct_items, train.UserMatrix[user_id], 10);
                    prec_15_sum += Items.PrecisionAt(prediction, correct_items, train.UserMatrix[user_id], 15);

                    if (prediction.Length != relevant_filtered_items.Count)
                        throw new Exception("Not all items have been ranked.");

                    if (num_lists % 1000 == 0)
                        Console.Error.Write(".");
                    if (num_lists % 20000 == 0)
                        Console.Error.WriteLine();
                }
            }

            var result = new Dictionary<string, double>();
            result.Add("AUC",     auc_sum / num_lists);
            result.Add("MAP",     map_sum / num_lists);
            result.Add("NDCG",    ndcg_sum / num_lists);
            result.Add("prec@5",  prec_5_sum / num_lists);
            result.Add("prec@10", prec_10_sum / num_lists);
            result.Add("prec@15", prec_15_sum / num_lists);
            result.Add("num_users", num_users);
            result.Add("num_lists", num_lists);
            result.Add("num_items", relevant_items.Count);

            return result;
        }