public double GetCosineDistance(BagOfWords bag1, BagOfWords bag2, bool useLabels)
{
var words1 = useLabels ? bag1.Labels : bag1.WordsFrequency;
var words2 = useLabels ? bag2.Labels : bag2.WordsFrequency;
double numerator = 0;
double denumerator1 = 0;
foreach (var word1 in words1)
{
if (words2.ContainsKey(word1.Key))
{
numerator += word1.Value.Frequency * words2[word1.Key].Frequency;
}
denumerator1 += word1.Value.Frequency * word1.Value.Frequency;
}
denumerator1 = Math.Sqrt(denumerator1);
double denumerator2 = 0;
foreach (var word in words2)
{
denumerator2 += word.Value.Frequency * word.Value.Frequency;
}
denumerator2 = Math.Sqrt(denumerator2);
double result = numerator / (denumerator1 * denumerator2);
return(result);
}
public List <DocumentRating> CalculateRatings(List <BagOfWords> bags)
{
Clustering clustering = new Clustering();
var ratings = new List <DocumentRating>();
for (int i = 0; i < bags.Count - 1; i++)
{
BagOfWords bag = bags[i];
if (Interest != null && Interest.LikedDocs.Contains(bag.DocumentID))
{
continue;
}
double sim = clustering.GetCosineDistance(this, bag, true);
ratings.Add(new DocumentRating()
{
ID = bag.DocumentID, Name = bag.Name, Type = bag.Type, Rating = sim
});
}
return(ratings.OrderByDescending(rating => rating.Rating).ToList());
}
