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); }