static void trainSeverityModel()
{
MLService service = new MLService();
var chain = service.GetAllSegments_SegmentChain7_AnomalySpreaded_OnlyANomaly();
double[][] inputs = chain.Select(sc => new double[] { sc.MaxSpeedChange.Value, sc.MinSpeedChange.Value }).ToArray(); //
KMeans kmeans = new KMeans(k: 3);
//kmeans.Centroids = new double [2][];
//kmeans.Centroids[0] = new double[] {-2.76384235537843, 0.608515794829327 };
//kmeans.Centroids[1] = new double[] {-0.579246221664498,0.121793585323178};
// Compute and retrieve the data centroids
var clusters = kmeans.Learn(inputs);
Serializer.Save(clusters, AnomalyClassifierBySeverityModelFileName);
// Use the centroids to parition all the data
int[] labels = clusters.Decide(inputs);
var zeros = labels.Where(l => l == 0).ToList();
var ones = labels.Where(l => l == 1).ToList();
var twos = labels.Where(l => l == 2).ToList();
ScatterplotBox.Show("Severity Clusters", inputs, labels);
}
static void trainAnomalyDetecorDTModel()
{
MLService service = new MLService();
var chain = service.GetAllSegments_SegmentChain7_AnomalySpreaded_OnlyANomaly().ToList();
double[][] inputs = chain.Select(sc => new double[] { sc.MaxSpeed.Value, sc.MinSpeed.Value, sc.SpeedRange.Value, sc.TotalTime.Value, sc.MinTime.Value, sc.MaxTime.Value, sc.TimeRange.Value, sc.MaxSpeedVar.Value, sc.MinSpeedVar.Value, sc.SpeedVarRange.Value, sc.MaxTimeVar.Value, sc.MinTimeVar.Value, sc.TimeVarRange.Value }).ToArray(); //
int[] outputs = chain.Select(sc => sc.AnomalyType).ToArray();
DecisionTree_crossValidation(inputs, outputs);
Console.ReadKey();
}