private void predictButton_Click(object sender, RoutedEventArgs e)
{
var inputs = new int[questionList.Count];
for (int i = 0; i < questionList.Count; i++)
{
inputs[i] = 3;
if (questionList[i].A0)
{
inputs[i] = 0;
}
if (questionList[i].A1)
{
inputs[i] = 1;
}
if (questionList[i].A2)
{
inputs[i] = 2;
}
if (questionList[i].A3)
{
inputs[i] = 3;
}
if (questionList[i].A4)
{
inputs[i] = 4;
}
}
var dnode = xtree.Items.GetItemAt(0) as DecisionTree.DNode;
var dtree = new DecisionTree();
dtree.rootNode = dnode;
var pred = dtree.Predict(inputs);
predictResultLabel.Foreground = Brushes.White;
if (pred == 0)
{
predictResultLabel.Content = "Class:{0}, Label:Yes";
predictResultLabel.Background = Brushes.Red;
}
else
{
predictResultLabel.Content = "Class:{1}, Label:No";
predictResultLabel.Background = Brushes.Green;
}
}
public List <DecisionTree> KFoldTest(int[][] X, int[] y, int K = 10, bool alert = false)
{
int KSampleSize = (int)Math.Ceiling(y.Length / (float)K);
List <DecisionTree> testTrees = new List <DecisionTree>();
int correctPredicts = 0;
int incorrectPredicts = 0;
for (int i = 0; i < K; i++)
{
var X_validation = X.Skip(i * KSampleSize).Take(KSampleSize).ToArray();
var y_validation = y.Skip(i * KSampleSize).Take(KSampleSize).ToArray();
var x_remain = X.Skip((i + 1) * KSampleSize);
var X_train = X.Take(i * KSampleSize).Concat(x_remain).ToArray();
var y_remain = y.Skip((i + 1) * KSampleSize);
var y_train = y.Take(i * KSampleSize).Concat(y_remain).ToArray();
var testTree = new DecisionTree();
testTree.Train(X_train, y_train);
testTrees.Add(testTree);
for (int j = 0; j < y_validation.Length; j++)
{
if (testTree.Predict(X_validation[j]) == y_validation[j])
{
correctPredicts++;
}
else
{
incorrectPredicts++;
}
}
}
var str = string.Format("Correct Predicts:{0} , IncorrectPredicts:{1} , avg acc:{2}, K-Num:{3}", correctPredicts, incorrectPredicts, correctPredicts / (float)(correctPredicts + incorrectPredicts), K);
Console.WriteLine(str);
if (alert)
{
MessageBox.Show(str, "Result");
}
return(testTrees);
}
