private void KNN(List <Tuple <double[], double> > data)
{
Metric metric = new EuclideMetric();
foreach (var item in netMLObject.Options)
{
if (item == "euclidmetric")
{
metric = new EuclideMetric();
}
else if (item == "manhattanmetric")
{
metric = new ManhattanMetric();
}
else if (item == "squaredeuclidmetric")
{
metric = new SquaredEuclideMetric();
}
else if (item == "maximummetric")
{
metric = new MaximumMetric();
}
}
classification = new KNearestNeighborsClassifier(data, 2, metric);
}
private Metric FindMetric()
{
Metric metric = new EuclideMetric();
switch (netMLObject.Options.First())
{
case "euclidmetric":
metric = new EuclideMetric();
break;
case "manhattanmetric":
metric = new ManhattanMetric();
break;
case "maximummetric":
metric = new MaximumMetric();
break;
case "squaredeuclidmetric":
metric = new SquaredEuclideMetric();
break;
}
return(metric);
}
