private void Button_Click_Start_Clasification(object sender, RoutedEventArgs e)
{
int assignedProperly = 0;
int assingedNotProperly = 0;
Utils.DistributeArticles(ChosenSet, ref TrainingSet, ref TestingSet, int.Parse(trainingDataPercentageInput.Text));
for (int j = 0; j < int.Parse(countOfLoopsInput.Text); ++j)
{
Dictionary <string, List <Article> > labelArticlesMap = new Dictionary <string, List <Article> >();
SetExtractor();
PrepareLabelArticleMap(ref labelArticlesMap);
//Taking some Articles from TrainingSet to resolve problem of cold start
List <Article> coldStart = new List <Article>();
foreach (var pair in labelArticlesMap)
{
int amountToTake = TrainingSet.Count / labelArticlesMap.Count / 10;
coldStart.AddRange(pair.Value.Take(amountToTake > pair.Value.Count ? pair.Value.Count : amountToTake).ToList());
}
//Creating mapping from coldstart Articles to characteristics values, assigning actual labels
Dictionary <Article, List <double> > ColdStart = new Dictionary <Article, List <double> >();
foreach (var item in coldStart)
{
ColdStart.Add(item, characteristicExtractor.GetWeights(item));
item.AssignedLabel = item.ActualLabel;
}
//Computing characteristics for first run of KNN - resolving problem of cold start
Dictionary <Article, List <double> > KnnMap = new Dictionary <Article, List <double> >();
foreach (var item in TestingSet)
{
KnnMap.Add(item, characteristicExtractor.GetWeights(item));
}
KNN.ColdStart(ref KnnMap, ref ColdStart, int.Parse(kInput.Text), AssignMetric());
//Actual KNN algorithm
KNN.Testing(ref KnnMap, int.Parse(kInput.Text), Metrics.EuclideanMetricDistance);
assignedProperly += KnnMap.Count(i => i.Key.AssignedLabel == i.Key.ActualLabel);
assingedNotProperly += KnnMap.Count(i => i.Key.AssignedLabel != i.Key.ActualLabel);
}
UpdateStatus("Klasyfikacja zakończona");
succededDisplay.Text = assignedProperly.ToString();
failedDisplay.Text = assingedNotProperly.ToString();
accuracityDisplay.Text = (100.0 * assignedProperly / (assignedProperly + assingedNotProperly)).ToString() + "%";
}
