protected void Button2_Click(object sender, EventArgs e)
{
weka.core.Instances data = new weka.core.Instances(new java.io.FileReader("d:\\train.arff"));
data.setClassIndex(data.numAttributes() - 1);
weka.classifiers.Classifier cls = new weka.classifiers.bayes.NaiveBayes();
// weka.classifiers.functions.supportVector.SMOset();
int runs = 1;
int folds = 10;
//string sq = "delete from nbresults";
//dbc.execfn(sq);
// perform cross-validation
for (int i = 0; i < runs; i++)
{
// randomize data
int seed = i + 1;
java.util.Random rand = new java.util.Random(seed);
weka.core.Instances randData = new weka.core.Instances(data);
randData.randomize(rand);
if (randData.classAttribute().isNominal())
{
randData.stratify(folds);
}
// weka.classifiers.trees.j48 jj;
weka.classifiers.Evaluation eval = new weka.classifiers.Evaluation(randData);
for (int n = 0; n < folds; n++)
{
weka.core.Instances train = randData.trainCV(folds, n);
weka.core.Instances test = randData.testCV(folds, n);
// build and evaluate classifier
weka.classifiers.Classifier clsCopy = weka.classifiers.Classifier.makeCopy(cls);
clsCopy.buildClassifier(train);
eval.evaluateModel(clsCopy, test);
}
preci_value.Text = eval.precision(0).ToString();
recall_value.Text = eval.recall(0).ToString();
acc_value.Text = eval.fMeasure(0).ToString();
string s = "NB";
// string str = "insert into evaluation values('" + instid.Text + "','" + courid.Text.ToString() + "','" + preci_value.Text.ToString() + "','" + recall_value.Text.ToString() + "','" + acc_value.Text.ToString() + "','" + s + "' )";
// db.execfn(str);
// MessageBox.Show("saved");
}
}