public ArticleComparer(IArticleProvider articleProvider, ISimilarityCalculator similarityCalculator,
IArticleProcessor articleProcessor)
{
_articleProvider = articleProvider;
_similarityCalculator = similarityCalculator;
_articleProcessor = articleProcessor;
}
private void CalculateSimilarities(ISolverResult solverResult, ISimilarityCalculator similarityCalculator)
{
foreach (var strategy in _calculatedSimilarities)
{
SimilairityValues[$"{strategy.GetType().Name}|{similarityCalculator.GetType().Name}"] =
similarityCalculator.CalculatePathsSimilarities(strategy, solverResult)
.Zip(solverResult.Paths, (similarity, path) => new SimilaritySolverResult(similarity, path.Cost))
.ToList();
}
}
static void Main(string[] args)
{
calculator = new PearsonCoefficient();
predictor = new Predictor(calculator);
int k = 3;
float threshold = 0.35f;
//Dictionary<int, Vector> vectors = DataParser.DataToVectors(DataParser.ParseData(USER_DATA));
//PrettyPrint(vectors);
//Console.WriteLine();
//Console.WriteLine("Euclidean: {0}", new EuclideanSimilarity().CalculateSimilarity(vectors[3], vectors[4]));
//Console.WriteLine("Manhattan: {0}", new ManhattanSimilary().CalculateSimilarity(vectors[3], vectors[4]));
//Console.WriteLine("Pearson: {0}", new PearsonCoefficient().CalculateSimilarity(vectors[3], vectors[4]));
//Console.WriteLine("Cosine: {0}", new CosineSimilarity().CalculateSimilarity(vectors[3], vectors[4]));
//Console.Read();
//int targetUser = 7;
//int[] idsToPredict = new int[] { 101, 103 };
//List<ArticleRating> results = predictor.PredictRatings(targetUser, k, threshold, vectors, idsToPredict);
//PrettyPrint(targetUser, results);
//Console.WriteLine();
//int targetUser = 4;
//int[] idsToPredict = new int[] { 101 };
//List<ArticleRating> results = predictor.PredictRatings(targetUser, k, threshold, vectors, idsToPredict);
//PrettyPrint(targetUser, results);
Dictionary <int, Vector> vectors = DataParser.DataToVectors(DataParser.ParseData(MOVIELENS_DATA));
int targetUser = 186;
int n = 8;
List <ArticleRating> topNratings = predictor.GetTopNpredictions(n, predictor.PredictRatings(targetUser, k, threshold, vectors, DataParser.ids.ToArray()));
PrettyPrint(targetUser, topNratings);
Console.Read();
}
public static List <Record> NearestNeighbors(int targetUser, Dictionary <int, Vector> dataSet, int k, float threshold, ISimilarityCalculator similarityCalculator)
{
runner = new UserItemRunner(similarityCalculator);
List <Record> nearestNeighbors = new List <Record>();
int n = 0;
for (int i = 1; i <= dataSet.Count; i++)
{
if (i != targetUser)
{
Record r = new Record(dataSet[i], runner.CalculateSimilarity(dataSet[i], dataSet[targetUser]));
if (r.Similarity > threshold)
{
if (n < k)
{
nearestNeighbors.Add(r);
n++;
}
else
{
if (r.V.NumberOfRatings > dataSet[targetUser].NumberOfRatings)
{
nearestNeighbors.Remove(nearestNeighbors.OrderBy(x => x.Similarity).First());
nearestNeighbors.Add(r);
threshold = nearestNeighbors.Min(x => x.Similarity);
}
}
}
}
}
return(nearestNeighbors);
}
public SimilarityCalculatorTimer(ISimilarityCalculator calculator, StreamWriter streamWriter)
{
Calculator = calculator;
StreamWriter = streamWriter;
}
public UserItemRunner(ISimilarityCalculator similarityCalculator)
{
this.similarityCalculator = similarityCalculator;
}
public Predictor(ISimilarityCalculator similarityCalcluator)
{
SimilarityCalculator = similarityCalcluator;
}
