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.statistics;
  
  import java.util.HashSet;
  import java.util.Map;
  import java.util.Set;
  import org.apache.commons.math3.stat.descriptive.SummaryStatistics;
  
  /**
   *
   * Class used to compute the standard error of an algorithm with respect to the
   * baseline.
   *
   * @author <a href="http://github.com/abellogin">Alejandro</a>
   *
   * @param <V> generic type for users
   */
  public class StandardError<V> {
  
      /**
       * Baseline metric for each dimension (users).
       */
      private Map<V, Double> baselineMetricPerDimension;
      /**
       * Test metric for each dimension (users).
       */
      private Map<V, Double> testMetricPerDimension;
  
      /**
       * Default constructor.
       *
       * @param theBaselineMetricPerDimension map for the baseline method, one
       * value for each user (dimension)
       * @param theTestMetricPerDimension map for the test method, one value for
       * each user (dimension)
       */
      public StandardError(final Map<V, Double> theBaselineMetricPerDimension, final Map<V, Double> theTestMetricPerDimension) {
          this.baselineMetricPerDimension = theBaselineMetricPerDimension;
          this.testMetricPerDimension = theTestMetricPerDimension;
      }
  
      /**
       * Implements equation (8.13) from "Elementary Statistics: A Problem Solving
       * Approach 4th Edition", Andrew L. Comrey, Howard B. Lee
       *
       * @return the standard error as the ratio of the standard deviation divided
       * by the sqrt(number of users) of the distribution of difference scores.
       */
      public double getStandardError() {
          Set<V> overlap = new HashSet<V>(baselineMetricPerDimension.keySet());
          overlap.retainAll(testMetricPerDimension.keySet());
  
          // paired or matched samples --> analyse distribution of difference scores
          SummaryStatistics differences = new SummaryStatistics();
          for (V key : overlap) {
              double diff = baselineMetricPerDimension.get(key) - testMetricPerDimension.get(key);
              differences.addValue(diff);
          }
  
          double e = differences.getStandardDeviation() / Math.sqrt(differences.getN());
          return e;
      }
  
      /**
       * {@inheritDoc}
       */
      @Override
      public String toString() {
          return "StandardError";
      }
  }
  