public override void Train()
{
List <double[]> FeaturSpace = new List <double[]>();
List <int> FeaturLabel = new List <int>();
List <List <double[]> > DataWindows = new List <List <double[]> >();
List <List <int> > LabelWindows = new List <List <int> >();
FeatureManager featureManager = new FeatureManager();
featureManager.Add(new Mean(1, 2, 3));
featureManager.Add(new StandardDeviation(1, 2, 3));
featureManager.Add(new MeanAbsoluteDeviation(1, 2, 3));
featureManager.Add(new AverageTimeBetweenPeaks(0, 1, 2, 3));
featureManager.Add(new ResultantAcceleration());
featureManager.Add(new BinDistribution(10, 1, 2, 3));
//featureManager.Add(new PreProcessData());
var dataReader = new CSVReader <double[]>("data.txt", false, (dynamic output) =>
{
DataWindows = output;
DataWindows.ForEach(window =>
{
FeaturSpace.Add(featureManager.Generate(window).ToArray());
});
var labelReader = new CSVReader <int>("label.txt", false, (dynamic outputLabels) =>
{
LabelWindows = outputLabels;
LabelWindows.ForEach(row =>
{
FeaturLabel.Add(row.GroupBy(x => x).OrderByDescending(g => g.Count()).Take(1).Select(i => i.Key).First());
});
DecisionTreeClassifier dt = new DecisionTreeClassifier(FusionFramework.Classifiers.DecisionTreeLearningAlgorithms.C45Learning);
dt.Train(FeaturSpace, FeaturLabel, true);
dt.Save("DecisionTreeWSDM");
}, new SlidingWindow <int>(200, 0));
labelReader.Start();
}, new SlidingWindow <double[]>(200, 0));
dataReader.Add(new Normalize());
dataReader.Start();
}