/// <summary>
/// Calculates the distance.
/// </summary>
/// <param name="metric">The metric.</param>
/// <param name="bmu">The bmu.</param>
/// <param name="gridNeuron">The grid neuron.</param>
/// <returns>System.Double.</returns>
public double CalculateDistance(IMetric metric, IBestMatchingUnit bmu, IGridNeuron gridNeuron)
{
var distanceToBmu = metric.CalculateDistance(bmu.GridCoordinates, gridNeuron.GridCoordinates);
if (!IsToroidal)
{
return(distanceToBmu);
}
// on spherical grids, check the wrap-around distances
//
//
// X 1 2 2 1
// 1 2 3 3 2
// 2 3 4 4 3
// 1 2 3 3 2
var coordinates = gridNeuron.GridCoordinates;
var coordinatesX = new[] { coordinates[0] - Width + 2, coordinates[1] };
var coordinatesY = new[] { coordinates[0], coordinates[1] - Height + 2 };
var coordinatesXy = new[] { coordinatesX[0], coordinatesY[1] };
distanceToBmu = Math.Min(distanceToBmu, metric.CalculateDistance(bmu.GridCoordinates, coordinatesX));
distanceToBmu = Math.Min(distanceToBmu, metric.CalculateDistance(bmu.GridCoordinates, coordinatesY));
distanceToBmu = Math.Min(distanceToBmu, metric.CalculateDistance(bmu.GridCoordinates, coordinatesXy));
return(distanceToBmu);
}
private void QueryHelper(IMetric <T> metric, int radius, IDictionary <BKTreeNode <T>, int> acc)
{
int distanceToTargetElement = metric.CalculateDistance(_data);
if (distanceToTargetElement <= radius)
{
acc.Add(this, distanceToTargetElement);
}
int lowerBound = distanceToTargetElement - radius;
int upperBound = distanceToTargetElement + radius;
foreach (int distance in _children.Keys)
{
if (distance > lowerBound || distance <= upperBound)
{
_children[distance].QueryHelper(metric, radius, acc);
}
}
}
public int Classify(ArticleRepo trainingSet, ArticleRepo testedSet, int neighboursNumber, IMetric metric)
{
Dictionary <Article[], double> distancesDict = new Dictionary <Article[], double>();
List <string> trainingLabels = new List <string>();
int truePositiveCounter = 0;
foreach (Article testedArticle in testedSet.articles)
{
foreach (Article trainingArticle in trainingSet.articles)
{
double distance = metric.CalculateDistance(trainingArticle.AllCharacteristicValues, testedArticle.AllCharacteristicValues);
Article[] articleIDPair = { testedArticle, trainingArticle };
distancesDict[articleIDPair] = distance;
}
var sortedDict = distancesDict.OrderBy(x => x.Value).Take(neighboursNumber);
foreach (var item in sortedDict)
{
string trainingLabel = item.Key.ElementAt(1).Label;
trainingLabels.Add(trainingLabel);
}
string testedLabel = trainingLabels.GroupBy(x => x).OrderByDescending(x => x.Count()).First().Key;
if (testedLabel == testedArticle.Label)
{
truePositiveCounter++;
}
trainingLabels.Clear();
distancesDict.Clear();
}
return(truePositiveCounter);
}