Exemplo n.º 1
0
 // Set Operation: Union
 public void unionWith(DataSet otherDataSet)
 {
     foreach (long tweet in otherDataSet.egoLikedTweetsInTimeline)
         this.egoLikedTweetsInTimeline.Add(tweet);
     foreach (long tweet in otherDataSet.egoUnLikedTweetsInTimeline)
         this.egoUnLikedTweetsInTimeline.Add(tweet);
     foreach (long tweet in otherDataSet.timeline)
         this.timeline.Add(tweet);
 }
Exemplo n.º 2
0
        // Return: K-fold 'trainset' and 'testset'
        public void setTrainTestSet(int index)
        {
            this.trainSet = new DataSet();
            this.testSet = dataSets[index]; // TestSet Setting
            this.testSet.clearEgoLikedTweets();

            for (int i = 0; i < this.dataSets.Length; i++)
            {
                if (i != index)
                    trainSet.unionWith(dataSets[i]);
            }
            // Total Ego liked tweets within timebound of the timeline
            egoUser.updateLikedTweets(); // Recharge Liked Tweets: for another kfold validation
            foreach (long tweet in egoUser.getLikedTweets())
            {
                if (testSet.isInTimebound(tweet))
                {
                    testSet.addEgoLikedTweet(tweet);
                    egoUser.deleteLikedTweet(tweet); // Assure candidate tweets are not liked yet by Ego User
                }
                else
                {
                    trainSet.addEgoLikedTweet(tweet);
                }
            }
            // Only Recommend on Ego: |Likes of Ego in TrainSet| >= threshold
            double egoLikeThresholdInTestSet = Program.egoLikeThresholdRatioInTestSet * (trainSet.getCntEgoLikedTweets() + testSet.getCntEgoLikedTweets());
            if (testSet.getCntEgoLikedTweets() < Math.Floor(egoLikeThresholdInTestSet))
                Program.isValidTrainSet = false;
            else
                Program.isValidTrainSet = true;
        }