private SymbolicNearestNeighbourClassificationModel(SymbolicNearestNeighbourClassificationModel original, Cloner cloner)
: base(original, cloner)
{
k = original.k;
frequencyComparer = new ClassFrequencyComparer(original.frequencyComparer);
trainedEstimatedValues = new List <double>(original.trainedEstimatedValues);
trainedClasses = new List <double>(original.trainedClasses);
}
public override void RecalculateModelParameters(IClassificationProblemData problemData, IEnumerable <int> rows)
{
var estimatedValues = Interpreter.GetSymbolicExpressionTreeValues(SymbolicExpressionTree, problemData.Dataset, rows)
.LimitToRange(LowerEstimationLimit, UpperEstimationLimit);
var targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
var trainedClasses = targetValues.ToArray();
var trainedEstimatedValues = estimatedValues.ToArray();
Array.Sort(trainedEstimatedValues, trainedClasses);
this.trainedClasses = new List <double>(trainedClasses);
this.trainedEstimatedValues = new List <double>(trainedEstimatedValues);
var freq = trainedClasses
.GroupBy(c => c)
.ToDictionary(g => g.Key, g => g.Count());
this.frequencyComparer = new ClassFrequencyComparer(freq);
}
public SymbolicNearestNeighbourClassificationModel(string targetVariable, int k, ISymbolicExpressionTree tree, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, double lowerEstimationLimit = double.MinValue, double upperEstimationLimit = double.MaxValue)
: base(targetVariable, tree, interpreter, lowerEstimationLimit, upperEstimationLimit)
{
this.k = k;
frequencyComparer = new ClassFrequencyComparer();
}
public ClassFrequencyComparer(ClassFrequencyComparer original)
{
classFrequencies = new Dictionary <double, int>(original.classFrequencies);
}
