private async Task loadFileAndMakeElements(string location)
{
if (location.EndsWith(".csv"))
{
weka.core.converters.CSVLoader csvLoader = new weka.core.converters.CSVLoader();
csvLoader.setSource(new java.io.File(location));
insts = csvLoader.getDataSet();
insts.setClassIndex(insts.numAttributes() - 1);
}
else
{
insts = new weka.core.Instances(new java.io.FileReader(location));
insts.setClassIndex(insts.numAttributes() - 1);
}
flowLayoutPanel1.Controls.Clear();
for (int i = 0; i < insts.numAttributes() - 1; i++)
{
if (insts.attribute(i).isNominal() == true)
{
if (insts.attribute(i).numValues() > 0)
{
Label lbl = new Label();
lbl.Text = insts.attribute(i).name().Trim();
lbl.Enabled = true;
ComboBox cmbBox = new ComboBox();
cmbBox.Name = insts.attribute(i).name();
for (int m = 0; m < insts.attribute(i).numValues(); m++)
{
cmbBox.Items.Add(insts.attribute(i).value(m));
}
cmbBox.DropDownStyle = ComboBoxStyle.DropDownList;
cmbBox.Enabled = true;
flowLayoutPanel1.Controls.Add(lbl);
flowLayoutPanel1.Controls.Add(cmbBox);
}
else
{
}
}
else if (insts.attribute(i).isNumeric() == true)
{
Label lbl = new Label();
lbl.Text = insts.attribute(i).name().Trim();
TextBox txtBox = new TextBox();
txtBox.Name = insts.attribute(i).name();
txtBox.KeyPress += new KeyPressEventHandler(txtBox_Keypress);
flowLayoutPanel1.Controls.Add(lbl);
flowLayoutPanel1.Controls.Add(txtBox);
}
}
}
public void CSV2Arff()
{
//load csv
weka.core.converters.CSVLoader loader = new weka.core.converters.CSVLoader();
//weka.core.converters.TextDirectoryLoader loader = new weka.core.converters.TextDirectoryLoader();
// C:/ Users / DELL / source / repos / WekafromCtest / WekafromCtest /
loader.setSource(new java.io.File("D:/final_version/Gesture-Gis-master/GestureGis2/ComparisonFeaturefile.txt"));
weka.core.Instances data = loader.getDataSet();
//save arff
weka.core.converters.ArffSaver saver = new weka.core.converters.ArffSaver();
saver.setInstances(data);
//and save as arff file
saver.setFile(new java.io.File("D:/final_version/Gesture-Gis-master/GestureGis2/ComparisonFeaturefile.arff"));
saver.writeBatch();
}
public void EMAlgorithm(string Training, string Test)
{
weka.core.converters.CSVLoader cSVLoader = new weka.core.converters.CSVLoader();
File file = new File(Training);
cSVLoader.setSource(file);
TrainingSet = cSVLoader.getDataSet();
file = new File(Test);
cSVLoader.setSource(file);
TestSet = cSVLoader.getDataSet();
eM.setNumClusters(3);
eM.setSeed(100);
eM.setNumFolds(10);
eM.setMaxIterations(100);
eM.setMaximumNumberOfClusters(-1);
eM.setNumExecutionSlots(1);
eM.setNumKMeansRuns(10);
System.Console.WriteLine("Building Cluster");
eM.buildClusterer(TrainingSet);
System.Console.WriteLine("Done Building Cluster");
}
protected void Button1_Click(object sender, EventArgs e)
{
try
{
string path = Server.MapPath(".") + "\\datasets\\" + dataset.Text + ".csv";
string fname = path;
File.Delete("d:\\train.arff");
weka.core.converters.CSVLoader loader = new weka.core.converters.CSVLoader();
loader.setSource(new java.io.File(fname));
weka.core.Instances inst2 = loader.getDataSet();
weka.core.converters.ArffSaver saver = new weka.core.converters.ArffSaver();
saver.setInstances(inst2);
saver.setFile(new java.io.File("d:\\train.arff"));
saver.writeBatch();
// Response.Write("<html><script>alert('File Converted..');</script></html>");
}
catch (Exception ex)
{
/// Response.Write("<html><script>alert('" + ex.Message.ToString() + "');</script></html>");
}
// 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.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+ "' )";
// dbc.execfn(str);
// MessageBox.Show("saved");
// }
// }
}
public void trainSMOUsingWeka(string wekaFile, string modelName)
{
try
{
weka.core.converters.CSVLoader csvLoader = new weka.core.converters.CSVLoader();
csvLoader.setSource(new java.io.File(wekaFile));
weka.core.Instances insts = csvLoader.getDataSet();
//weka.core.Instances insts = new weka.core.Instances(new java.io.FileReader(wekaFile));
insts.setClassIndex(insts.numAttributes() - 1);
cl = new weka.classifiers.functions.SMO();
cl.setBatchSize("100");
cl.setCalibrator(new weka.classifiers.functions.Logistic());
cl.setKernel(new weka.classifiers.functions.supportVector.PolyKernel());
cl.setEpsilon(1.02E-12);
cl.setC(1.0);
cl.setDebug(false);
cl.setChecksTurnedOff(false);
cl.setFilterType(new SelectedTag(weka.classifiers.functions.SMO.FILTER_NORMALIZE, weka.classifiers.functions.SMO.TAGS_FILTER));
System.Console.WriteLine("Performing " + percentSplit + "% split evaluation.");
//randomize the order of the instances in the dataset.
// weka.filters.Filter myRandom = new weka.filters.unsupervised.instance.Randomize();
//myRandom.setInputFormat(insts);
// insts = weka.filters.Filter.useFilter(insts, myRandom);
int trainSize = insts.numInstances() * percentSplit / 100;
int testSize = insts.numInstances() - trainSize;
weka.core.Instances train = new weka.core.Instances(insts, 0, trainSize);
java.io.File path = new java.io.File("/models/");
cl.buildClassifier(train);
saveModel(cl, modelName, path);
#region test whole set
int numCorrect = 0;
for (int i = 0; i < insts.numInstances(); i++)
{
weka.core.Instance currentInst = insts.instance(i);
if (i == 12)
{
array = new List <float>();
foreach (float value in currentInst.toDoubleArray())
{
array.Add(value);
}
}
double predictedClass = cl.classifyInstance(currentInst);
if (predictedClass == insts.instance(i).classValue())
{
numCorrect++;
}
}
System.Console.WriteLine(numCorrect + " out of " + testSize + " correct (" +
(double)((double)numCorrect / (double)testSize * 100.0) + "%)");
#endregion
}
catch (java.lang.Exception ex)
{
ex.printStackTrace();
}
}
