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); }
static void Main(string[] args) { //double trainingToTestDataRatio = 0.6; int stopListWordNumber = 100; bool IdfOn = true; LoadAllDocumentArticles(new DocumentReader()); LoadAllCustomArticles(new CustomReader()); IMetric metric = new EuclideanMetric(); Console.WriteLine(metric.GetType().Name); RunFor3Sets(0.2, stopListWordNumber, metric, IdfOn); RunFor3Sets(0.6, stopListWordNumber, metric, IdfOn); RunFor3Sets(0.8, stopListWordNumber, metric, IdfOn); metric = new ChebyshevMetric(); Console.WriteLine(metric.GetType().Name); RunFor3Sets(0.2, stopListWordNumber, metric, IdfOn); RunFor3Sets(0.6, stopListWordNumber, metric, IdfOn); RunFor3Sets(0.8, stopListWordNumber, metric, IdfOn); metric = new ManhattanMetric(); Console.WriteLine(metric.GetType().Name); RunFor3Sets(0.2, stopListWordNumber, metric, IdfOn); RunFor3Sets(0.6, stopListWordNumber, metric, IdfOn); RunFor3Sets(0.8, stopListWordNumber, metric, IdfOn); IdfOn = false; metric = new EuclideanMetric(); Console.WriteLine(metric.GetType().Name); RunFor3Sets(0.2, stopListWordNumber, metric, IdfOn); RunFor3Sets(0.6, stopListWordNumber, metric, IdfOn); RunFor3Sets(0.8, stopListWordNumber, metric, IdfOn); metric = new ChebyshevMetric(); Console.WriteLine(metric.GetType().Name); RunFor3Sets(0.2, stopListWordNumber, metric, IdfOn); RunFor3Sets(0.6, stopListWordNumber, metric, IdfOn); RunFor3Sets(0.8, stopListWordNumber, metric, IdfOn); metric = new ManhattanMetric(); Console.WriteLine(metric.GetType().Name); RunFor3Sets(0.2, stopListWordNumber, metric, IdfOn); RunFor3Sets(0.6, stopListWordNumber, metric, IdfOn); RunFor3Sets(0.8, stopListWordNumber, metric, IdfOn); Console.ReadKey(); }
public async void ChooseExtract(string[] path) { Reuters = await Model.Reuter.GetReutersFromFileAsync(path, ChosenExtractFeature); AllReuters = TrainingPatterns.SetTrainingAndTestSet(TrainingSetPercent, Reuters); if (ChosenMetricFeature.Equals("Euclidean Metric")) { _percent = await EuclideanMetric.CalculateAsync(AllReuters, getK); Percent = (Math.Round(_percent, 2) * 100).ToString(); MessageBox.Show("Done"); } else if (ChosenMetricFeature.Equals("Manhattan Metric")) { _percent = await ManhattanMetric.CalculateAsync(AllReuters, getK); Percent = (Math.Round(_percent, 2) * 100).ToString(); MessageBox.Show("Done"); } }
static void Main(string[] argv) { modshogun.init_shogun_with_defaults(); DoubleMatrix traindata_real = Load.load_numbers("../data/fm_train_real.dat"); DoubleMatrix testdata_real = Load.load_numbers("../data/fm_test_real.dat"); RealFeatures feats_train = new RealFeatures(traindata_real); RealFeatures feats_test = new RealFeatures(testdata_real); ManhattanMetric distance = new ManhattanMetric(feats_train, feats_train); DoubleMatrix dm_train = distance.get_distance_matrix(); distance.init(feats_train, feats_test); DoubleMatrix dm_test = distance.get_distance_matrix(); Console.WriteLine(dm_train.ToString()); Console.WriteLine(dm_test.ToString()); modshogun.exit_shogun(); }
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); }
internal static HandleRef getCPtr(ManhattanMetric obj) { return((obj == null) ? new HandleRef(null, IntPtr.Zero) : obj.swigCPtr); }
internal static HandleRef getCPtr(ManhattanMetric obj) { return (obj == null) ? new HandleRef(null, IntPtr.Zero) : obj.swigCPtr; }