public static void Main(string[] args)
{
var usersDistinct = new HashSet <int>();
var articlesDistinct = new HashSet <int>();
var userPreferences = CsvReader <UserPref> .MapFromFile("userItem.data", ',', UserPref.Map);
for (var i = 0; i < userPreferences.Count; i++)
{
var userPref = userPreferences[i];
usersDistinct.Add(userPref.Id);
articlesDistinct.Add(userPref.ArticleId);
}
_matrixSmall = new Matrix <float>(usersDistinct.Count, articlesDistinct.Count);
_matrixSmall.ReplaceDefault(float.NaN);
foreach (var pref in userPreferences)
{
_matrixSmall[pref.Id, pref.ArticleId] = pref.Rating;
}
// get neighbours of user 7
var neighboursSeven = KNearest.GetNeighbours(_matrixSmall, 7, 3, 0.35, GenericVector.PearsonCoefficient);
Console.WriteLine("### Neighbours of 7 (Pearson): ");
foreach (var neighbour in neighboursSeven)
{
Console.WriteLine("Neighbour: " + neighbour.Key + " with similarity of: " + neighbour.Value);
}
Console.WriteLine("\n");
Console.WriteLine("### Neighbours of 4 (Pearson): ");
var neighboursFour = KNearest.GetNeighbours(_matrixSmall, 4, 3, 0.35, GenericVector.PearsonCoefficient);
foreach (var neighbour in neighboursFour)
{
Console.WriteLine("Neighbour: " + neighbour.Key + " with similarity of: " + neighbour.Value);
}
Console.WriteLine("\n");
Console.WriteLine("### Predicted ratings 7: ");
Console.WriteLine("Predicted 101: " + Prediction.PredictRating(_matrixSmall, neighboursSeven, 101));
Console.WriteLine("Predicted 103: " + Prediction.PredictRating(_matrixSmall, neighboursSeven, 103));
Console.WriteLine("Predicted 106: " + Prediction.PredictRating(_matrixSmall, neighboursSeven, 106));
Console.WriteLine("\n");
Console.WriteLine("### Predicted ratings 4: ");
Console.WriteLine("Predicted 101: " + Prediction.PredictRating(_matrixSmall, neighboursFour, 101));
_matrixSmall[7, 106] = 2.8f;
var neighboursAfterUpdate = KNearest.GetNeighbours(_matrixSmall, 7, 3, 0.35, GenericVector.PearsonCoefficient);
Console.WriteLine("### Neighbours of 7 after update 2.8 (Pearson): ");
foreach (var neighbour in neighboursAfterUpdate)
{
Console.WriteLine("Neighbour: " + neighbour.Key + " with similarity of: " + neighbour.Value);
}
Console.WriteLine("\n");
Console.WriteLine("### Predicted ratings 7 after update 2.8: ");
Console.WriteLine("Predicted 101: " + Prediction.PredictRating(_matrixSmall, neighboursAfterUpdate, 101));
Console.WriteLine("Predicted 103: " + Prediction.PredictRating(_matrixSmall, neighboursAfterUpdate, 103));
_matrixSmall[7, 106] = 5f;
var neighboursAfterSecondUpdate = KNearest.GetNeighbours(_matrixSmall, 7, 3, 0.35, GenericVector.PearsonCoefficient);
Console.WriteLine("\n");
Console.WriteLine("### Neighbours of 7 after update 5 (Pearson): ");
foreach (var neighbour in neighboursAfterSecondUpdate)
{
Console.WriteLine("Neighbour: " + neighbour.Key + " with similarity of: " + neighbour.Value);
}
Console.WriteLine("\n");
Console.WriteLine("### Predicted ratings 7 after update 5: ");
Console.WriteLine("Predicted 101: " + Prediction.PredictRating(_matrixSmall, neighboursAfterSecondUpdate, 101));
Console.WriteLine("Predicted 103: " + Prediction.PredictRating(_matrixSmall, neighboursAfterSecondUpdate, 103));
var usersBig = new HashSet <int>();
var articlesBig = new HashSet <int>();
var userPreferencesBig = CsvReader <UserPref> .MapFromFile("u.data", '\t', UserPref.Map);
for (var i = 0; i < userPreferencesBig.Count; i++)
{
var userPref = userPreferencesBig[i];
usersBig.Add(userPref.Id);
articlesBig.Add(userPref.ArticleId);
}
var matrixBig = new Matrix <float>(usersBig.Count, articlesBig.Count);
matrixBig.ReplaceDefault(float.NaN);
foreach (var pref in userPreferencesBig)
{
matrixBig[pref.Id, pref.ArticleId] = pref.Rating;
}
var recom = GetRecommendations(matrixBig, 186, articlesBig);
var bestEight = recom.OrderByDescending(x => x.Value).Take(8);
foreach (var rating in bestEight)
{
Console.WriteLine($"Movie Id: {rating.Key} with rating {rating.Value}");
}
}
