public ReadFile()
{
kNN examplekNN = new kNN().initialiseKNN(1, "teste.txt", true);
List <double> instance2Classify = new List <double> {
54, 16, 36, 31, 63, 51, 89
};
string result = examplekNN.Classify(instance2Classify);
//Console.WriteLine("This instance is classified as: {0}", result);
//Console.ReadLine();S
}
private void Drawer_MouseUp(object sender, MouseButtonEventArgs e)
{
if (e.ChangedButton == MouseButton.Right)
{
Drawer.Strokes.Clear();
tbResult.Text = string.Empty;
}
else if (e.ChangedButton == MouseButton.Left)
{
var sample = CanvasToArray();
var knn = new kNN(Data, 10);
tbResult.Text = Math.Round(knn.Evaluate(sample)).ToString();
}
}
private void bwLoadData_DoWork(object sender, DoWorkEventArgs e)
{
try
{
Classifier classif = null;
Datas.Useable train = null;
Datas.Useable test = null;
ConfusionMatrix conf_train, conf_test;
if (e.Argument is ToBackgroundWorkerArgsTree)
{
var args = e.Argument as ToBackgroundWorkerArgsTree;
train = args._Train;
test = args._Test;
classif = new DecisionTree(args._MaxDepth);
classif.Train(train);
conf_train = new ConfusionMatrix(classif.Compile, train);
conf_test = new ConfusionMatrix(classif.Compile, test);
}
else if (e.Argument is ToBackgroundWorkerArgsForest)
{
var args = e.Argument as ToBackgroundWorkerArgsForest;
train = args._Train;
test = args._Test;
classif = new RandomForest(args._MaxDepth, args._TreeCount);
classif.Train(train);
conf_train = new ConfusionMatrix(classif.Compile, train);
conf_test = new ConfusionMatrix(classif.Compile, test);
}
else if (e.Argument is ToBackgroundWorkerArgsAdaBoost)
{
var args = e.Argument as ToBackgroundWorkerArgsAdaBoost;
train = args._Train;
test = args._Test;
classif = new AdaBoost(args._Factory, args._Boosts);
classif.Train(train);
conf_train = new ConfusionMatrix(classif.Compile, train);
conf_test = new ConfusionMatrix(classif.Compile, test);
}
else if (e.Argument is ToBackgroundWorkerkNN)
{
var args = e.Argument as ToBackgroundWorkerkNN;
train = args._Train;
test = args._Test;
classif = new kNN(args._kNN);
classif.Train(train);
conf_train = null;
conf_test = new ConfusionMatrix(classif.Compile, test);
}
else
{
throw new Exception("Not recognized stuff");
}
if (this.bwLoadData.CancellationPending)
{
e.Result = null;
}
else
{
e.Result = new TrainerReturn(
conf_train,
conf_test,
classif);
}
}
catch (Exception exc)
{
e.Result = exc.ToString();
}
}
