/// <summary>
/// User gets number of choices to compute similaries and recommendations
/// </summary>
public static void PickAlgorithm()
{
IDistance iDistance = null;
IReader iReader = null;
Console.WriteLine("Pick 1 for Euclidean");
Console.WriteLine("Pick 2 for Pearson");
Console.WriteLine("Pick 3 for Cosine");
Console.WriteLine("Pick 4 for Recommendation");
Console.WriteLine("Pick 5 for NearestNeighbor");
choice = int.Parse(Console.ReadLine());
Console.WriteLine("Pick 1 for Basic Dataset");
Console.WriteLine("Pick 2 for Advanced Dataset");
choiceData = int.Parse(Console.ReadLine());
Dictionary <int, double[, ]> dictionaryBasic = new Dictionary <int, double[, ]>();
Dictionary <int, double[, ]> dictionaryAdvanced = new Dictionary <int, double[, ]>();
switch (choiceData)
{
case 1:
iReader = new FileReader();
dictionaryBasic = iReader.GetData();
break;
case 2:
iReader = new DataReader();
dictionaryAdvanced = iReader.GetData();
break;
default:
Console.WriteLine("Closed");
Console.ReadLine();
break;
}
switch (choice)
{
case 1:
PickTargetUsers();
Console.WriteLine("You have chosen Euclidian");
if (choiceData == 1)
{
iDistance = new Euclidean();
IterateSimilarity(dictionaryBasic, targetUser, iDistance);
}
else
{
iDistance = new Euclidean();
IterateSimilarity(dictionaryAdvanced, targetUser, iDistance);
}
break;
case 2:
PickTargetUsers();
Console.WriteLine("You have chosen Pearson");
if (choiceData == 1)
{
iDistance = new Pearson();
IterateSimilarity(dictionaryBasic, targetUser, iDistance);
}
else
{
iDistance = new Pearson();
IterateSimilarity(dictionaryAdvanced, targetUser, iDistance);
}
break;
case 3:
PickTargetUsers();
Console.WriteLine("You have chosen Cosine");
if (choiceData == 1)
{
iDistance = new Cosine();
IterateSimilarity(dictionaryBasic, targetUser, iDistance);
}
else
{
iDistance = new Cosine();
IterateSimilarity(dictionaryAdvanced, targetUser, iDistance);
}
break;
case 4:
Console.WriteLine("Select Targeted User");
targetUser = int.Parse(Console.ReadLine());
Console.WriteLine("You have chosen Recommendation");
Console.WriteLine("Select Top numbers of ranking");
int k = int.Parse(Console.ReadLine());
Console.WriteLine("Set up your threshold");
double threshold = double.Parse(Console.ReadLine());
if (choiceData == 1)
{
Recommender.NearestNeighbour.ComputeRecommendations(targetUser, dictionaryBasic, k, threshold);
}
else
{
Recommender.NearestNeighbour.ComputeRecommendations(targetUser, dictionaryAdvanced, k, threshold);
}
break;
case 5:
Console.WriteLine("Select Targeted User");
targetUser = int.Parse(Console.ReadLine());
Console.WriteLine("You have chosen NearestNeighbor");
Console.WriteLine("Select K");
k = int.Parse(Console.ReadLine());
Console.WriteLine("Set up your threshold");
threshold = double.Parse(Console.ReadLine());
iDistance = new Pearson();
if (choiceData == 1)
{
RecommendNearestNeighbour.Run(threshold, targetUser, iDistance, dictionaryBasic, k);
}
else
{
Console.WriteLine("How many users");
int amount = int.Parse(Console.ReadLine());
RecommendNearestNeighbour.Run(threshold, targetUser, iDistance, dictionaryAdvanced.OrderBy(x => x.Key).Take(amount).ToDictionary(pair => pair.Key, pair => pair.Value), k);
}
break;
default:
Console.WriteLine("Closed");
Console.ReadLine();
break;
}
}
private static List <Tuple <double, User> > nearestNeighbours(User selectedUser, List <int> emptyRatings, double smt, int amount)
{
List <Tuple <double, User> > nn_Euclidian = new List <Tuple <double, User> >();
var nn_Pearson = new List <Tuple <double, User> >();
List <Tuple <double, User> > nn_Cosine = new List <Tuple <double, User> >();
//loop through all the users and find the users that are most similar to selecteduser
foreach (var user in userpref)
{
var user2 = userpref[user.Key];
if (selectedUser.id != user.Key)
{
var difference = 0;
foreach (var rating in emptyRatings)
{
if (user2.articleRating[rating] != 0)
{
difference++;
}
}
if (difference <= 0)
{
continue;
}
//TODO refactor this mess, only pearson has treshold atm
Console.WriteLine("Compare user: "******" with user: "******"---------------------");
ed = new EuclidianDistance();
var euclidian_distance = ed.calculate(selectedUser, user2, uniqueArticles);
user2.euclidean_distance = euclidian_distance;
nn_Euclidian.Add(Tuple.Create(euclidian_distance, user2));
Console.WriteLine("Euclidian stuff: " + euclidian_distance);
pe = new Pearsons();
var correlation = pe.calculate(selectedUser, user2, uniqueArticles);
if (correlation >= smt)
{
var neighbour = Tuple.Create(correlation, user2);
if (nn_Pearson.Count < amount)
{
nn_Pearson.Add(neighbour);
if (nn_Pearson.Count() == amount)
{
smt = nn_Pearson.Min(x => x.Item1);
}
}
else
{
nn_Pearson.Add(neighbour);
var lowestSimilarityUser = nn_Pearson.OrderBy(x => x.Item1).First();
nn_Pearson.Remove(lowestSimilarityUser);
smt = nn_Pearson.OrderBy(x => x.Item1).First().Item1;
}
}
Console.WriteLine("Pearsons corr: " + correlation);
co = new Cosine();
var cosine_similarity = co.calculate(selectedUser, user2, uniqueArticles);
user2.cosine = cosine_similarity;
nn_Cosine.Add(Tuple.Create(cosine_similarity, user2));
Console.WriteLine("Cosine similarity: " + cosine_similarity + "\n");
}
else
{
continue;
}
}
//maybe print the first 3 of this stuff
List <Tuple <double, User> > nn_Euclidian_Sorted = nn_Euclidian.OrderByDescending(x => x.Item1).ToList();
List <Tuple <double, User> > nn_Pearson_Sorted = nn_Pearson.OrderByDescending(x => x.Item1).ToList();
List <Tuple <double, User> > nn_Cosine_Sorted = nn_Cosine.OrderByDescending(x => x.Item1).ToList();
for (var i = 0; i < amount_nearestNeighbours; i++)
{
Console.WriteLine("Nearest {0}", i + 1 + "\n----------------");
Console.WriteLine("Euclidian nn: " + nn_Euclidian_Sorted[i].Item2.id);
Console.WriteLine("Pearson nn: " + nn_Pearson_Sorted[i].Item2.id);
Console.WriteLine("consine nn: " + nn_Cosine_Sorted[i].Item2.id + "\n");
}
return(nn_Pearson_Sorted);
}