private void button1_Click(object sender, EventArgs e)
{
weka.core.Instances insts = new weka.core.Instances(new java.io.FileReader(file));
double[] Data = new double[insts.numAttributes()];
for (int i = 0; i < list.Count; i++)
{
if (list[i].GetType() == typeof(TextBox))
{
TextBox txt = (TextBox)list[i];
string value = txt.Text.Replace('.', ',');
Data[i] = Convert.ToDouble(value);
}
else
{
ComboBox combobox = (ComboBox)list[i];
Data[i] = Convert.ToDouble(combobox.SelectedIndex);
}
}
// Data[(insts.numAttributes() - 1] = 0;
insts.setClassIndex(insts.numAttributes() - 1);
Instance newInsts = new Instance(1.0, Data);
insts.add(newInsts);
string type = model.GetType().ToString();
if (type == "weka.classifiers.bayes.NaiveBayes")
{
weka.filters.Filter myDiscretize = new weka.filters.unsupervised.attribute.Discretize();
myDiscretize.setInputFormat(insts);
insts = weka.filters.Filter.useFilter(insts, myDiscretize);
}
else if (type == "weka.classifiers.lazy.IBk")
{
weka.filters.Filter myDummy = new weka.filters.unsupervised.attribute.NominalToBinary();
myDummy.setInputFormat(insts);
insts = weka.filters.Filter.useFilter(insts, myDummy);
weka.filters.Filter myNormalize = new weka.filters.unsupervised.instance.Normalize();
myNormalize.setInputFormat(insts);
insts = weka.filters.Filter.useFilter(insts, myNormalize);
}
double index = model.classifyInstance(insts.lastInstance());
string result = insts.attribute(insts.numAttributes() - 1).value(Convert.ToInt16(index));
MessageBox.Show(result);
}
private void btnDiscover_Click(object sender, EventArgs e)
{
string type = model.GetType().ToString();
bool flag = false;
bool flag2 = false;
//input kontrolleri
if (nominal != null)
{
for (int i = 0; i < nominal.Length; i++)
{
if (nominal[i].SelectedIndex == -1)
{
flag = true;
break;
}
}
}
if (numeric != null)
{
for (int i = 0; i < numeric.Length; i++)
{
if (String.IsNullOrEmpty(numeric[i].Text))
{
flag2 = true;
break;
}
}
}
if (numAtt == numeric.Length && flag2 == true)
{
MessageBox.Show("Please select value!", "Error Message!");
}
else if (numAtt == nominal.Length && flag == true)
{
MessageBox.Show("Please select value!", "Error Message!");
}
else if ((nominal.Length + numeric.Length) == numAtt && (flag == true || flag2 == true))
{
MessageBox.Show("Please select value!", "Error Message!");
}
else
{
weka.core.Instance newIns = new weka.core.Instance(numAtt + 1);
newIns.setDataset(insts);
int i1 = 0, i2 = 0;
for (int i = 0; i < numAtt; i++)
{
//nominal
if (typeAtt[i])
{
newIns.setValue(i, nominal[i1].SelectedItem.ToString());
i1++;
}
//numeric
else
{
newIns.setValue(i, double.Parse(numeric[i2].Text));
i2++;
}
}
weka.core.Instances insts2 = new weka.core.Instances(insts);
insts2.add(newIns);
if (type == "weka.classifiers.bayes.NaiveBayes")
{
weka.filters.Filter myDiscretize = new weka.filters.unsupervised.attribute.Discretize();
myDiscretize.setInputFormat(insts2);
insts2 = weka.filters.Filter.useFilter(insts2, myDiscretize);
}
else if (type == "weka.classifiers.functions.Logistic")
{
weka.filters.Filter myDummy = new weka.filters.unsupervised.attribute.NominalToBinary();
myDummy.setInputFormat(insts2);
insts2 = weka.filters.Filter.useFilter(insts2, myDummy);
weka.filters.Filter myNormalize = new weka.filters.unsupervised.instance.Normalize();
myNormalize.setInputFormat(insts2);
insts2 = weka.filters.Filter.useFilter(insts2, myNormalize);
}
else if (type == "new weka.classifiers.lazy.IBk")
{
weka.filters.Filter myDummy = new weka.filters.unsupervised.attribute.NominalToBinary();
myDummy.setInputFormat(insts2);
insts2 = weka.filters.Filter.useFilter(insts2, myDummy);
weka.filters.Filter myNormalize = new weka.filters.unsupervised.instance.Normalize();
myNormalize.setInputFormat(insts2);
insts2 = weka.filters.Filter.useFilter(insts2, myNormalize);
}
else if (type == "weka.classifiers.trees.J48")
{
}
else if (type == "weka.classifiers.trees.RandomForest")
{
}
else if (type == "weka.classifiers.trees.RandomTree")
{
}
else if (type == "weka.classifiers.functions.MultilayerPerceptron")
{
weka.filters.Filter myDummy = new weka.filters.unsupervised.attribute.NominalToBinary();
myDummy.setInputFormat(insts2);
insts2 = weka.filters.Filter.useFilter(insts2, myDummy);
weka.filters.Filter myNormalize = new weka.filters.unsupervised.instance.Normalize();
myNormalize.setInputFormat(insts2);
insts2 = weka.filters.Filter.useFilter(insts2, myNormalize);
}
else if (type == "weka.classifiers.functions.SMO")
{
weka.filters.Filter myDummy = new weka.filters.unsupervised.attribute.NominalToBinary();
myDummy.setInputFormat(insts2);
insts2 = weka.filters.Filter.useFilter(insts2, myDummy);
weka.filters.Filter myNormalize = new weka.filters.unsupervised.instance.Normalize();
myNormalize.setInputFormat(insts2);
insts2 = weka.filters.Filter.useFilter(insts2, myNormalize);
}
double index = model.classifyInstance(insts2.lastInstance());
//Model okuma kısmı
weka.classifiers.Classifier cls = (weka.classifiers.Classifier)weka.core.SerializationHelper.read("models/mdl.model");
lblResult2.Text = "Result= " + insts2.attribute(insts2.numAttributes() - 1).value(Convert.ToInt16(index));
}
}