code: /*
   * Copyright 2015 recommenders.net.
   *
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   *      http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   */
  package net.recommenders.rival.evaluation.strategy;
  
  import java.util.Set;
  import net.recommenders.rival.core.DataModelIF;
  
  /**
   * An evaluation strategy where all the items are used as candidates.
   *
   * @author <a href="http://github.com/abellogin">Alejandro</a>
   */
  public class AllItems extends AbstractStrategy {
  
      /**
       * Default constructor.
       *
       * @see
       * AbstractStrategy#AbstractStrategy(net.recommenders.rival.core.DataModelIF,
       * net.recommenders.rival.core.DataModelIF, double)
       *
       * @param training The training set.
       * @param test The test set.
       * @param threshold The relevance threshold.
       */
      public AllItems(final DataModelIF<Long, Long> training, final DataModelIF<Long, Long> test, final double threshold) {
          super(training, test, threshold);
      }
  
      /**
       * {@inheritDoc}
       */
      @Override
      public Set<Long> getCandidateItemsToRank(final Long user) {
          final Set<Long> items = getModelTrainingDifference(getTraining(), user);
          items.addAll(getModelTrainingDifference(getTest(), user));
          return items;
      }
  
      /**
       * {@inheritDoc}
       */
      @Override
      public String toString() {
          return "AllItems_" + getThreshold();
      }
  }
  