public Stereotypes newRandomCentroids(Users users, Items items, int cStereotypes)
{
Stereotypes stereotypes = new Stereotypes();
//create random initial stereotype
List <User> centroids = new List <User>();
var subsetUsers = users.Where(x => x.GetRatedItems().Count > minimumRatingThreshold).ToList();
centroids.Add(randomGenerator.getRandomUser(subsetUsers));
double sumSquaredDistanceFromStereotypes;
//create cStereotypes
for (int i = 1; i < cStereotypes; i++)
{
double farestDistanceFromStereotypes = 0.0;
User farestUser = null;
//find farest user from stereotypes
foreach (User user in subsetUsers)
{
if (centroids.Contains(user))
{
continue;
}
// calculate similarites and update farest centroid accordingly
sumSquaredDistanceFromStereotypes = 0.0;
List <KeyValuePair <User, double> > disSimilarities = similarityEngine.calculateSimilarity(similarityMethod, user, centroids, true);
foreach (KeyValuePair <User, double> disSimilarity in disSimilarities)
{
sumSquaredDistanceFromStereotypes += Math.Pow(Math.Abs(disSimilarity.Value), 2);
}
if (sumSquaredDistanceFromStereotypes > farestDistanceFromStereotypes)
{
farestDistanceFromStereotypes = sumSquaredDistanceFromStereotypes;
farestUser = user;
}
}
if (farestUser == null)
{
throw new ArgumentNullException();
}
centroids.Add(farestUser);
}
foreach (User user in centroids)
{
stereotypes.addStereotype(new Stereotype(user));
}
return(stereotypes);
}
public void Train()
{
Console.WriteLine("***************** Train Stereotypes Model *********************");
stereotypes = newRandomCentroids(users, items, cStereotypes);
Users usersCopy = new Users(users);
bool isConverged = false;
int iteration = 1;
while (!isConverged && iteration <= MAX_ITERATION)
{
Console.WriteLine("Iteration: {0}", iteration);
List <User> prevCentroids = stereotypes.getStereotypesCentroids();
stereotypes.initStereotypesUsers();
List <User> usersWithNoSimilarity = new List <User>();;
foreach (User user in usersCopy)
{
//Calculate a users similarity to the stereotypes
List <KeyValuePair <User, double> > similarities = similarityEngine.calculateSimilarity(similarityMethod, user, prevCentroids, false, true); //TODO - check the similarity threshold for stereotype algorithm
//Determine users that don't have a correlation with non of the stereotypes
if (similarities.Count == 0)
{
usersWithNoSimilarity.Add(user);
continue;
}
//Allocate a user to it's most similar stereotype
User mostSimilarCentroid = similarities.Last().Key;
stereotypes.addUserToStereotype(mostSimilarCentroid.GetId(), user);
}
usersCopy.removeUsers(usersWithNoSimilarity);
//Determine convergence
stereotypes.reCalculateCentroids();
List <User> currCentroids = stereotypes.getStereotypesCentroids();
var prevAndCurrCentroidsSimilarity = currCentroids.Zip(prevCentroids, (x, y) => Tuple.Create(x, y)).Select(x => Tuple.Create(x.Item1, x.Item2, similarityEngine.calculateSimilarity(similarityMethod, x.Item1, x.Item2))).ToList();
if (prevAndCurrCentroidsSimilarity.All(x => x.Item3 > CENTROIDS_SIMILARITY_THRESHOLD))
{
isConverged = true;
}
else
{
iteration++;
}
}
}