// 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);
}
// 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;
}
