public IEnumerable <(Instance, double)> GetAllBetaValues()
{
Dictionary <string, int> homoCount = new Dictionary <string, int>();
TrainingInstances.ForEach(i =>
{
if (!homoCount.ContainsKey(i.LabelValue ?? throw new NullReferenceException("Cannot compute beta value for an unlabeled instance. ")))
{
homoCount.Add(i.LabelValue, TrainingInstances.Count(instance => instance.LabelValue == i.LabelValue));
}
});
double[,] distStats = new double[TrainingInstances.Count, TrainingInstances.Count];
for (int i = 1; i < TrainingInstances.Count; i++)
{
for (int j = 0; j < i; j++)
{
distStats[i, j] = distStats[j, i] = EuclideanDistance(TrainingInstances[i], TrainingInstances[j]);
}
}
for (int i = 0; i < TrainingInstances.Count; i++)
{
Instance currInstance = TrainingInstances[i];
Dictionary <Instance, double> distToOtherInstances = new Dictionary <Instance, double>(Enumerable.Range(0, TrainingInstances.Count).Select(j => new KeyValuePair <Instance, double>(TrainingInstances[j], distStats[i, j])));
distToOtherInstances.Remove(TrainingInstances[i]);
yield return(currInstance,
distToOtherInstances.OrderBy(kvp => kvp.Value).Take(homoCount[currInstance.LabelValue ?? throw new NullReferenceException("Cannot compute beta value for an unlabeled instance. ")] - 1)
public override void Train()
{
if (UseLaplaceCorrection)
{
priorProb = new Dictionary <string, double>();
IEnumerable <string?> distinctLabels = TrainingInstances.Select(i => i.LabelValue).Distinct();
foreach (string?labelValue in distinctLabels)
{
priorProb.Add(labelValue ?? throw new NullReferenceException("Unlabeled instance in training instances. "), TrainingInstances.Count(i => i.LabelValue == labelValue) / (double)TrainingInstances.Count);
}
}
string splitFeatureName = GetSplitFeature(TrainingInstances, out double threshold);
rootNode = new Node(TrainingInstances, splitFeatureName, threshold);
SplitRecursive(rootNode, -1);
}
public double GetBetaValue(Instance testingInstance) => GetNeighbors(testingInstance, TrainingInstances.Count(i => i.LabelValue == testingInstance.LabelValue) - 1) .Where(i => i.LabelValue == testingInstance.LabelValue).Sum(i => 1.0 / (1.0 + EuclideanDistance(i, testingInstance))) / TrainingInstances.Where(i => i != testingInstance).Sum(i => 1.0 / (1.0 + EuclideanDistance(i, testingInstance)));
/// <summary>
/// For experimental use. Alpha measures the ratio of agreeing neighbors. In this case every instance in the TrainingInstances is considered a neighbour.
/// </summary>
/// <param name="testingInstance">The instance to be calculated alpha value on. </param>
/// <returns>The value of alpha. </returns>
public double GetAlphaValue(Instance testingInstance)
{
int homoCount = TrainingInstances.Count(i => i.LabelValue == testingInstance.LabelValue);
return(GetNeighbors(testingInstance, homoCount - 1).Count(i => i.LabelValue == testingInstance.LabelValue) / (double)homoCount);
}
public override Dictionary <string, double> GetProbDist(Instance testingInstance)
{
Dictionary <string, double> distStats = new Dictionary <string, double>();
foreach (Instance neighborInstance in GetNeighbors(testingInstance, NeighboringMethod == NeighboringOption.SqrtNeighbors ? (int)Sqrt(TrainingInstances.Count) : TrainingInstances.Count - 1))
{
if (neighborInstance.LabelValue is null)
{
throw new NullReferenceException("Unlabeled instance is used as training instance. ");
}
if (!distStats.ContainsKey(neighborInstance.LabelValue))
{
distStats.Add(neighborInstance.LabelValue, 0);
}
distStats[neighborInstance.LabelValue] += EuclideanDistance(neighborInstance, testingInstance);
}
Dictionary <string, double> distStatsInverted = new Dictionary <string, double>();
foreach (KeyValuePair <string, double> kvp in distStats)
{
distStatsInverted.Add(kvp.Key, 1.0 / kvp.Value * (NeighboringMethod == NeighboringOption.AllNeighborsWithReweighting ? TrainingInstances.Count(i => i.LabelValue == kvp.Key) / (double)TrainingInstances.Count : 1.0));
}
return(OrderedNormalized(distStatsInverted));
}