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;
}
