/// <summary> Sets the format of the input instances.
///
/// </summary>
/// <param name="instanceInfo">an Instances object containing the input instance
/// structure (any instances contained in the object are ignored - only the
/// structure is required).
/// </param>
/// <returns> true if the outputFormat may be collected immediately
/// </returns>
/// <exception cref="Exception">if the format couldn't be set successfully
/// </exception>
public override bool setInputFormat(Instances instanceInfo)
{
base.setInputFormat(instanceInfo);
m_SelectCols.Upper = instanceInfo.numAttributes() - 1;
// Create the output buffer
FastVector attributes = new FastVector();
int outputClass = - 1;
m_SelectedAttributes = m_SelectCols.Selection;
int inStrKeepLen = 0;
int[] inStrKeep = new int[m_SelectedAttributes.Length];
for (int i = 0; i < m_SelectedAttributes.Length; i++)
{
int current = m_SelectedAttributes[i];
if (instanceInfo.classIndex() == current)
{
outputClass = attributes.size();
}
Attribute keep = (Attribute) instanceInfo.attribute(current).copy();
if (keep.type() == Attribute.STRING)
{
inStrKeep[inStrKeepLen++] = current;
}
attributes.addElement(keep);
}
m_InputStringIndex = new int[inStrKeepLen];
Array.Copy(inStrKeep, 0, m_InputStringIndex, 0, inStrKeepLen);
Instances outputFormat = new Instances(instanceInfo.relationName(), attributes, 0);
outputFormat.ClassIndex = outputClass;
setOutputFormat(outputFormat);
return true;
}
/// <summary>
/// Loads a classifier from a model file.
/// </summary>
/// <param name="filename">The filename (full path) that you want to load. Should be an .arff file and a .model file in your working directory.</param>
public void loadModel(string filename)
{
if (debug)
{
Console.WriteLine("Model loading...");
}
_classifier = (weka.classifiers.Classifier)weka.core.SerializationHelper.read(filename + MODEL);
_dataSet = new weka.core.Instances(new java.io.FileReader(filename + ARFF));
_dataSet.setClassIndex(_dataSet.numAttributes() - 1);
if (debug)
{
Console.WriteLine("Model locked and loaded!");
}
}
private weka.core.Instances ReadFile(string path)
{
try
{
weka.core.Instances insts = new weka.core.Instances(new java.io.FileReader(path));
insts.setClassIndex(insts.numAttributes() - 1);
return(insts);
}
catch (java.lang.Exception ex)
{
ex.printStackTrace();
MessageBox.Show(ex.ToString(), "Error reading file", MessageBoxButtons.OK, MessageBoxIcon.Error);
return(null);
}
}
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");
}
}
private void btnLoadStep_Click(object sender, EventArgs e)
{
if (m_loadStepInstances == null)
{
using (OpenFileDialog d = new OpenFileDialog())
{
d.Filter = "Arff File|*.arff";
if (d.ShowDialog() == System.Windows.Forms.DialogResult.OK)
{
m_loadStepInstances = new weka.core.Instances(new java.io.BufferedReader(new java.io.FileReader(d.FileName)));
m_loadStepInstances.setClassIndex(m_loadStepInstances.numAttributes() - 1);
clear_all();
}
}
}
else
{
for (int i = m_loadStepIdx; i < m_loadStepInstances.numInstances(); ++i)
{
var ins = m_loadStepInstances.instance(i);
var p = new valuePoint(ins.value(0), ins.value(1), (int)ins.classValue());
if (p.x < 0 || p.x >= 1 || p.y < 0 || p.y >= 1)
{
continue;
}
point_list.Add(p);
draw_point(p);
m_loadStepIdx = i + 1;
if (i % 1000 == 0)
{
break;
}
}
pictureBox1.Invalidate();
if (m_loadStepIdx == m_loadStepInstances.numInstances())
{
m_loadStepIdx = 0;
m_loadStepInstances = null;
}
}
}
//Knn
public static double Knn(weka.core.Instances insts)
{
try
{
insts.setClassIndex(insts.numAttributes() - 1);
Knncl = new 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);
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);
Knncl.buildClassifier(train);
int numCorrect = 0;
for (int i = trainSize; i < insts.numInstances(); i++)
{
weka.core.Instance currentInst = insts.instance(i);
double predictedClass = Knncl.classifyInstance(currentInst);
if (predictedClass == insts.instance(i).classValue())
{
numCorrect++;
}
}
return((double)numCorrect / (double)testSize * 100.0);
}
catch (java.lang.Exception ex)
{
ex.printStackTrace();
return(0);
}
}
public void Test2()
{
java.io.ObjectInputStream ois = new java.io.ObjectInputStream(new java.io.FileInputStream("D:\\android_analysis\\som_model.model"));
weka.classifiers.Classifier cl = (weka.classifiers.Classifier)ois.readObject();
ois.close();
weka.core.Instances insts = new weka.core.Instances(new java.io.FileReader("D:\\android_analysis\\test1.arff"));
insts.setClassIndex(insts.numAttributes() - 1);
for (int i = 0; i < insts.numInstances(); i++)
{
weka.core.Instance currentInst = insts.instance(i);
double predictedClass = cl.classifyInstance(currentInst);
double[] distrs = cl.distributionForInstance(currentInst);
//string actual = insts.classAttribute().value((int)currentInst.classValue());
//string predicted = insts.classAttribute().value((int)predictedClass);
// System.Console.WriteLine("ID: " + (i + 1) + ", " + predicted);
}
}
public static string classifyTest()
{
try
{
String result = "";
weka.core.Instances insts = new weka.core.Instances(new java.io.FileReader("C:\\Program Files\\Weka-3-7\\data\\iris.arff"));
insts.setClassIndex(insts.numAttributes() - 1);
weka.classifiers.Classifier cl = new weka.classifiers.trees.J48();
// Console.WriteLine("Performing " + percentSplit + "% split evaluation.");
result += "Performing " + percentSplit + "% split evaluation.\n";
//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);
cl.buildClassifier(train);
int numCorrect = 0;
for (int i = trainSize; i < insts.numInstances(); i++)
{
weka.core.Instance currentInst = insts.instance(i);
double predictedClass = cl.classifyInstance(currentInst);
if (predictedClass == insts.instance(i).classValue())
{
numCorrect++;
}
}
//Console.WriteLine(numCorrect + " out of " + testSize + " correct (" + (double)((double)numCorrect / (double)testSize * 100.0) + "%)");
result += (numCorrect + " out of " + testSize + " correct (" + (double)((double)numCorrect / (double)testSize * 100.0) + "%)");
return(result);
}
catch (Exception ex)
{
MessageBox.Show(ex.Message);
return(ex.Message);
}
}
public void flatten_attribute()
{
FlattenClass[] rows = new[] {
new FlattenClass {
CLASS = 0, Att1 = new [] { 1, 2, 3, 4, 5 }
},
new FlattenClass {
CLASS = 1, Att1 = new [] { 6, 7, 8, 9, 10 }
}
};
InstancesBuilder <FlattenClass> builder = new InstancesBuilder <FlattenClass>(rows, 0);
weka.core.Instances instances = builder.Build();
Assert.AreEqual(2, instances.numInstances());
Assert.AreEqual(6, instances.numAttributes());
CollectionAssert.AreEqual(new[] { "1", "2" }, instances.GetAttrStrings(1));
CollectionAssert.AreEqual(new[] { "1.2", "2.2" }, instances.GetAttrStrings(2));
}
public void test_ignore_attributes()
{
TestingRow6[] rows = new[] {
new TestingRow6 {
CLASS = 1.0, ATT_1 = "1", ATT_2 = "1.1", ATT_3 = "1.2"
},
new TestingRow6 {
CLASS = 2.0, ATT_1 = "2", ATT_2 = "2.1", ATT_3 = "2.2"
}
};
InstancesBuilder <TestingRow6> builder = new InstancesBuilder <TestingRow6>(rows, 0);
weka.core.Instances instances = builder.Build();
Assert.AreEqual(3, instances.numAttributes()); // 1 is ignored
Assert.AreEqual(2, instances.numInstances());
CollectionAssert.AreEqual(new[] { "1", "2" }, instances.GetAttrStrings(1));
CollectionAssert.AreEqual(new[] { "1.2", "2.2" }, instances.GetAttrStrings(2));
}
/// <summary>
/// Uses the classifier to classify an instance (from its featureValues).
/// </summary>
/// <param name="featureValues">An array of doubles that describe the instance.</param>
/// <returns>The string name of the classification of the instance.</returns>
public string classify(double[] featureValues)
{
//if (!classifierBuilt) { _classifier.buildClassifier(_dataSet); classifierBuilt = true; }
weka.core.Instance inst = new weka.core.Instance(1, featureValues);
inst.setDataset(_dataSet);
double result = _classifier.classifyInstance(inst);
weka.core.Attribute attribute = _dataSet.attribute(_dataSet.numAttributes() - 1);
string resultName = attribute.value((int)result);
// Get rid of this line once ARFF files are rewritten
if (resultName == "Label")
{
resultName = "Text";
}
//Console.WriteLine(resultName);
return(resultName);
}
/* Use when the player logs in to initially create the classifier with data from server */
public void InitializeClassifier(String dataString)
{
try {
java.io.StringReader stringReader = new java.io.StringReader(dataString);
java.io.BufferedReader buffReader = new java.io.BufferedReader(stringReader);
playerData = new weka.core.Instances(buffReader);
/* State where in each Instance the class attribute is, if its not already specified by the file */
if (playerData.classIndex() == -1)
{
playerData.setClassIndex(playerData.numAttributes() - 1);
}
/* NAIVE BAYES */
//classifier = new weka.classifiers.bayes.NaiveBayes();
/* NEURAL NET */
//classifier = new weka.classifiers.functions.MultilayerPerceptron();
//((weka.classifiers.functions.MultilayerPerceptron)classifier).setHiddenLayers("12");
/* J48 TREE */
//classifier = new weka.classifiers.trees.J48();
/* IB1 NEAREST NEIGHBOUR */
//classifier = new weka.classifiers.lazy.IB1();
/* RANDOM FOREST */
classifier = new weka.classifiers.trees.RandomForest();
classifier.buildClassifier(playerData);
Debug.Log("Initialized Classifier");
}
catch (java.lang.Exception ex)
{
Debug.LogError(ex.getMessage());
}
}
public static void classifyTest()
{
try
{
Console.WriteLine("Hello Java, from C#!");
weka.core.Instances insts = new weka.core.Instances(new java.io.FileReader("D:/iris.arff"));
insts.setClassIndex(insts.numAttributes() - 1);
weka.classifiers.Classifier cl = new weka.classifiers.trees.J48();
Console.WriteLine("Performing " + 33 + "% 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() * 33 / 100;
int testSize = insts.numInstances() - trainSize;
weka.core.Instances train = new weka.core.Instances(insts, 0, trainSize);
cl.buildClassifier(train);
int numCorrect = 0;
for (int i = trainSize; i < insts.numInstances(); i++)
{
weka.core.Instance currentInst = insts.instance(i);
double predictedClass = cl.classifyInstance(currentInst);
if (predictedClass == insts.instance(i).classValue())
numCorrect++;
}
Console.WriteLine(numCorrect + " out of " + testSize + " correct (" +
(double)((double)numCorrect / (double)testSize * 100.0) + "%)");
}
catch (Exception ex)
{
}
}
public weka.core.Instances[] GenerateRegressionInstances(int rIdx)
{
var instances = new weka.core.Instances[3];
string dataName = ((Rounds)rIdx).ToString().ToLower() + "_{0}_data";
for(int i = 0; i < 3; i++)
{
var atts = generateStateAttributes(rIdx);
string className = i == 0 ? "Fold" : i == 1 ? "Call" : "Raise";
atts.addElement(new weka.core.Attribute(className));
var data = new weka.core.Instances(string.Format(dataName, className.ToLower()), atts, 0);
data.setClassIndex(data.numAttributes() - 1);
instances[i] = data;
}
return instances;
}
public weka.core.Instances GenerateClassifierInstances(int rIdx)
{
var atts = generateStateAttributes(rIdx);
var classVals = new weka.core.FastVector();
classVals.addElement("Fold");
classVals.addElement("Call");
classVals.addElement("Raise");
atts.addElement(new weka.core.Attribute("Action", classVals));
var data = new weka.core.Instances(((Rounds)rIdx).ToString().ToLower() + "_data", atts, 0);
data.setClassIndex(data.numAttributes() - 1);
return data;
}
/// <summary> Method for testing filters ability to process multiple batches.
///
/// </summary>
/// <param name="argv">should contain the following arguments:<br>
/// -i (first) input file <br>
/// -o (first) output file <br>
/// -r (second) input file <br>
/// -s (second) output file <br>
/// -c class_index <br>
/// or -h for help on options
/// </param>
/// <exception cref="Exception">if something goes wrong or the user requests help on
/// command options
/// </exception>
public static void batchFilterFile(Filter filter, System.String[] options)
{
Instances firstData = null;
Instances secondData = null;
//UPGRADE_ISSUE: Class hierarchy differences between 'java.io.Reader' and 'System.IO.StreamReader' may cause compilation errors. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1186'"
System.IO.StreamReader firstInput = null;
//UPGRADE_ISSUE: Class hierarchy differences between 'java.io.Reader' and 'System.IO.StreamReader' may cause compilation errors. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1186'"
System.IO.StreamReader secondInput = null;
System.IO.StreamWriter firstOutput = null;
System.IO.StreamWriter secondOutput = null;
bool helpRequest;
try
{
helpRequest = Utils.getFlag('h', options);
System.String fileName = Utils.getOption('i', options);
if (fileName.Length != 0)
{
//UPGRADE_TODO: The differences in the expected value of parameters for constructor 'java.io.BufferedReader.BufferedReader' may cause compilation errors. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1092'"
//UPGRADE_WARNING: At least one expression was used more than once in the target code. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1181'"
//UPGRADE_TODO: Constructor 'java.io.FileReader.FileReader' was converted to 'System.IO.StreamReader' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073'"
firstInput = new System.IO.StreamReader(new System.IO.StreamReader(fileName, System.Text.Encoding.Default).BaseStream, new System.IO.StreamReader(fileName, System.Text.Encoding.Default).CurrentEncoding);
}
else
{
throw new System.Exception("No first input file given.\n");
}
fileName = Utils.getOption('r', options);
if (fileName.Length != 0)
{
//UPGRADE_TODO: The differences in the expected value of parameters for constructor 'java.io.BufferedReader.BufferedReader' may cause compilation errors. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1092'"
//UPGRADE_WARNING: At least one expression was used more than once in the target code. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1181'"
//UPGRADE_TODO: Constructor 'java.io.FileReader.FileReader' was converted to 'System.IO.StreamReader' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073'"
secondInput = new System.IO.StreamReader(new System.IO.StreamReader(fileName, System.Text.Encoding.Default).BaseStream, new System.IO.StreamReader(fileName, System.Text.Encoding.Default).CurrentEncoding);
}
else
{
throw new System.Exception("No second input file given.\n");
}
fileName = Utils.getOption('o', options);
if (fileName.Length != 0)
{
//UPGRADE_TODO: Constructor 'java.io.FileOutputStream.FileOutputStream' was converted to 'System.IO.FileStream.FileStream' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javaioFileOutputStreamFileOutputStream_javalangString'"
firstOutput = new System.IO.StreamWriter(new System.IO.FileStream(fileName, System.IO.FileMode.Create), System.Text.Encoding.Default);
}
else
{
firstOutput = new System.IO.StreamWriter(System.Console.OpenStandardOutput(), System.Text.Encoding.Default);
}
fileName = Utils.getOption('s', options);
if (fileName.Length != 0)
{
//UPGRADE_TODO: Constructor 'java.io.FileOutputStream.FileOutputStream' was converted to 'System.IO.FileStream.FileStream' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javaioFileOutputStreamFileOutputStream_javalangString'"
secondOutput = new System.IO.StreamWriter(new System.IO.FileStream(fileName, System.IO.FileMode.Create), System.Text.Encoding.Default);
}
else
{
secondOutput = new System.IO.StreamWriter(System.Console.OpenStandardOutput(), System.Text.Encoding.Default);
}
System.String classIndex = Utils.getOption('c', options);
// if (filter instanceof OptionHandler)
// {
// ((OptionHandler)filter).setOptions(options);
// }
Utils.checkForRemainingOptions(options);
if (helpRequest)
{
throw new System.Exception("Help requested.\n");
}
firstData = new Instances(firstInput, 1);
secondData = new Instances(secondInput, 1);
if (!secondData.equalHeaders(firstData))
{
throw new System.Exception("Input file formats differ.\n");
}
if (classIndex.Length != 0)
{
if (classIndex.Equals("first"))
{
firstData.ClassIndex = 0;
secondData.ClassIndex = 0;
}
else if (classIndex.Equals("last"))
{
firstData.ClassIndex = firstData.numAttributes() - 1;
secondData.ClassIndex = secondData.numAttributes() - 1;
}
else
{
firstData.ClassIndex = System.Int32.Parse(classIndex) - 1;
secondData.ClassIndex = System.Int32.Parse(classIndex) - 1;
}
}
}
catch (System.Exception ex)
{
System.String filterOptions = "";
// Output the error and also the valid options
// if (filter instanceof OptionHandler)
// {
// filterOptions += "\nFilter options:\n\n";
// Enumeration enu = ((OptionHandler)filter).listOptions();
// while (enu.hasMoreElements())
// {
// Option option = (Option) enu.nextElement();
// filterOptions += option.synopsis() + '\n'
// + option.description() + "\n";
// }
// }
System.String genericOptions = "\nGeneral options:\n\n" + "-h\n" + "\tGet help on available options.\n" + "-i <filename>\n" + "\tThe file containing first input instances.\n" + "-o <filename>\n" + "\tThe file first output instances will be written to.\n" + "-r <filename>\n" + "\tThe file containing second input instances.\n" + "-s <filename>\n" + "\tThe file second output instances will be written to.\n" + "-c <class index>\n" + "\tThe number of the attribute to use as the class.\n" + "\t\"first\" and \"last\" are also valid entries.\n" + "\tIf not supplied then no class is assigned.\n";
//UPGRADE_TODO: The equivalent in .NET for method 'java.lang.Throwable.getMessage' may return a different value. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1043'"
throw new System.Exception('\n' + ex.Message + filterOptions + genericOptions);
}
bool printedHeader = false;
if (filter.setInputFormat(firstData))
{
//UPGRADE_TODO: Method 'java.io.PrintWriter.println' was converted to 'System.IO.TextWriter.WriteLine' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javaioPrintWriterprintln_javalangString'"
firstOutput.WriteLine(filter.getOutputFormat().ToString());
printedHeader = true;
}
// Pass all the instances to the filter
while (firstData.readInstance(firstInput))
{
if (filter.input(firstData.instance(0)))
{
if (!printedHeader)
{
throw new System.ApplicationException("Filter didn't return true from setInputFormat() " + "earlier!");
}
//UPGRADE_TODO: Method 'java.io.PrintWriter.println' was converted to 'System.IO.TextWriter.WriteLine' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javaioPrintWriterprintln_javalangString'"
firstOutput.WriteLine(filter.output().ToString());
}
firstData.delete(0);
}
// Say that input has finished, and print any pending output instances
if (filter.batchFinished())
{
if (!printedHeader)
{
//UPGRADE_TODO: Method 'java.io.PrintWriter.println' was converted to 'System.IO.TextWriter.WriteLine' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javaioPrintWriterprintln_javalangString'"
firstOutput.WriteLine(filter.getOutputFormat().ToString());
}
while (filter.numPendingOutput() > 0)
{
//UPGRADE_TODO: Method 'java.io.PrintWriter.println' was converted to 'System.IO.TextWriter.WriteLine' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javaioPrintWriterprintln_javalangString'"
firstOutput.WriteLine(filter.output().ToString());
}
}
if (firstOutput != null)
{
//UPGRADE_NOTE: Exceptions thrown by the equivalent in .NET of method 'java.io.PrintWriter.close' may be different. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1099'"
firstOutput.Close();
}
printedHeader = false;
if (filter.OutputFormatDefined)
{
//UPGRADE_TODO: Method 'java.io.PrintWriter.println' was converted to 'System.IO.TextWriter.WriteLine' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javaioPrintWriterprintln_javalangString'"
secondOutput.WriteLine(filter.getOutputFormat().ToString());
printedHeader = true;
}
// Pass all the second instances to the filter
while (secondData.readInstance(secondInput))
{
if (filter.input(secondData.instance(0)))
{
if (!printedHeader)
{
throw new System.ApplicationException("Filter didn't return true from" + " isOutputFormatDefined() earlier!");
}
//UPGRADE_TODO: Method 'java.io.PrintWriter.println' was converted to 'System.IO.TextWriter.WriteLine' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javaioPrintWriterprintln_javalangString'"
secondOutput.WriteLine(filter.output().ToString());
}
secondData.delete(0);
}
// Say that input has finished, and print any pending output instances
if (filter.batchFinished())
{
if (!printedHeader)
{
//UPGRADE_TODO: Method 'java.io.PrintWriter.println' was converted to 'System.IO.TextWriter.WriteLine' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javaioPrintWriterprintln_javalangString'"
secondOutput.WriteLine(filter.getOutputFormat().ToString());
}
while (filter.numPendingOutput() > 0)
{
//UPGRADE_TODO: Method 'java.io.PrintWriter.println' was converted to 'System.IO.TextWriter.WriteLine' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javaioPrintWriterprintln_javalangString'"
secondOutput.WriteLine(filter.output().ToString());
}
}
if (secondOutput != null)
{
//UPGRADE_NOTE: Exceptions thrown by the equivalent in .NET of method 'java.io.PrintWriter.close' may be different. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1099'"
secondOutput.Close();
}
}
/// <summary> Gets an array containing the indices of all string attributes.
///
/// </summary>
/// <param name="insts">the Instances to scan for string attributes.
/// </param>
/// <returns> an array containing the indices of string attributes in
/// the input structure. Will be zero-length if there are no
/// string attributes
/// </returns>
protected internal virtual int[] getStringIndices(Instances insts)
{
// Scan through getting the indices of String attributes
int[] index = new int[insts.numAttributes()];
int indexSize = 0;
for (int i = 0; i < insts.numAttributes(); i++)
{
if (insts.attribute(i).type() == Attribute.STRING)
{
index[indexSize++] = i;
}
}
int[] result = new int[indexSize];
Array.Copy(index, 0, result, 0, indexSize);
return result;
}
/// <summary> Copies string values contained in the instance copied to a new
/// dataset. The Instance must already be assigned to a dataset. This
/// dataset and the destination dataset must have the same structure.
///
/// </summary>
/// <param name="instance">the Instance containing the string values to copy.
/// </param>
/// <param name="destDataset">the destination set of Instances
/// </param>
/// <param name="strAtts">an array containing the indices of any string attributes
/// in the dataset.
/// </param>
private void copyStringValues(Instance inst, Instances destDataset, int[] strAtts)
{
if (strAtts.Length == 0)
{
return ;
}
if (inst.dataset() == null)
{
throw new System.ArgumentException("Instance has no dataset assigned!!");
}
else if (inst.dataset().numAttributes() != destDataset.numAttributes())
{
throw new System.ArgumentException("Src and Dest differ in # of attributes!!");
}
copyStringValues(inst, true, inst.dataset(), strAtts, destDataset, strAtts);
}
public bool PrepareDataset()
{
try
{
weka.filters.Filter missingFilter = new weka.filters.unsupervised.attribute.ReplaceMissingValues(); // missing values handled
missingFilter.setInputFormat(insts);
insts = weka.filters.Filter.useFilter(insts, missingFilter);
bool isTargetNumeric = insts.attribute(insts.numAttributes() - 1).isNumeric();
List <bool> isNumeric = new List <bool>();
List <bool> is2Categorical = new List <bool>();
List <List <string> > numericColumns = new List <List <string> >();
List <string> atrNames = new List <string>();
for (int i = 0; i < insts.numAttributes(); i++)
{
atrNames.Add(insts.attribute(i).name());
bool isNum = insts.attribute(i).isNumeric();
isNumeric.Add(isNum);
if (isNum == true)
{
numericColumns.Add(new List <string>());
for (int j = 0; j < insts.numInstances(); j++)
{
numericColumns[numericColumns.Count - 1].Add(insts.instance(j).toString(i));
}
}
}
weka.filters.unsupervised.attribute.Discretize myDiscretize = new weka.filters.unsupervised.attribute.Discretize();
myDiscretize.setInputFormat(insts);
myDiscretize.setFindNumBins(true);
insts = weka.filters.Filter.useFilter(insts, myDiscretize);
List <List <string> > atrs = new List <List <string> >();
for (int i = 0; i < insts.numAttributes(); i++)
{
atrs.Add(new List <string>());
for (int j = 0; j < insts.attribute(i).numValues(); j++)
{
string sub_category = insts.attribute(i).value(j);
string temp = sub_category.Replace("'", string.Empty);
atrs[atrs.Count - 1].Add(temp);
}
if (atrs[atrs.Count - 1].Count == 2)
{
is2Categorical.Add(true);
}
else
{
is2Categorical.Add(false);
}
}
List <List <string> > lst = new List <List <string> >();
for (int i = 0; i < insts.numInstances(); i++)
{
lst.Add(new List <string>());
for (int j = 0; j < insts.instance(i).numValues(); j++)
{
string temp = insts.instance(i).toString(j);
temp = temp.Replace("\\", string.Empty);
temp = temp.Replace("'", string.Empty);
lst[lst.Count - 1].Add(temp);
}
}
List <string> targetValues = atrs[insts.numAttributes() - 1];
List <List <string> > giniDataset = ConvertToNumericWithGini(lst, atrs);
giniDataset = Arrange2CategoricalColumns(giniDataset, lst, is2Categorical);
giniDataset = ChangeBackNumericalColumns(giniDataset, numericColumns, isNumeric);
WriteFile(giniDataset, filename + "-numeric-gini.arff", atrNames, targetValues, isTargetNumeric);
List <List <string> > twoingDataset = ConvertToNumericWithTwoing(lst, atrs);
twoingDataset = Arrange2CategoricalColumns(twoingDataset, lst, is2Categorical);
twoingDataset = ChangeBackNumericalColumns(twoingDataset, numericColumns, isNumeric);
WriteFile(twoingDataset, filename + "-numeric-twoing.arff", atrNames, targetValues, isTargetNumeric);
return(true);
}
catch (Exception e)
{
return(false);
}
}
private void btnLoad_Click(object sender, EventArgs e)
{
using (OpenFileDialog d = new OpenFileDialog())
{
d.Filter = "Arff File|*.arff";
if (d.ShowDialog() == System.Windows.Forms.DialogResult.OK)
{
var fileReader = new java.io.FileReader(d.FileName);
var instances = new weka.core.Instances(new java.io.BufferedReader(fileReader));
instances.setClassIndex(instances.numAttributes() - 1);
fileReader.close();
clear_all();
foreach (weka.core.Instance i in instances)
{
var p = new valuePoint(i.value(0), i.value(1), (int)i.classValue());
if (p.x < 0 || p.x >= 1 || p.y < 0 || p.y >= 1)
continue;
point_list.Add(p);
}
draw_all_points();
this.pictureBox1.Invalidate();
}
}
}
public static string classifyTest(string file, string classifier)
{
string data = "No data";
try
{
//weka.core.Instances insts = new weka.core.Instances(new java.io.FileReader("C:\\Users\\kinli\\source\\repos\\WebApplication2\\WebApplication2\\iris.arff"));
weka.core.Instances insts = new weka.core.Instances(new java.io.FileReader(file));
insts.setClassIndex(insts.numAttributes() - 1);
weka.classifiers.Classifier cl = new weka.classifiers.trees.J48();
if (classifier == "J48")
{
cl = new weka.classifiers.trees.J48();
}
else if (classifier == "MLP")
{
cl = new weka.classifiers.functions.MultilayerPerceptron();
}
else if (classifier == "NaiveBayes")
{
cl = new weka.classifiers.bayes.NaiveBayes();
}
//data = ("Performing " + percentSplit + "% split evaluation.\n");
data = ("Performing use training set evaluation.\n");
//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);
*
* cl.buildClassifier(train);
* int numCorrect = 0;
* for (int i = trainSize; i < insts.numInstances(); i++)
* {
* weka.core.Instance currentInst = insts.instance(i);
* double predictedClass = cl.classifyInstance(currentInst);
* if (predictedClass == insts.instance(i).classValue())
* numCorrect++;
* }*/
cl.buildClassifier(insts);
int numCorrect = 0;
for (int i = 0; i < insts.numInstances(); i++)
{
weka.core.Instance currentInst = insts.instance(i);
double predictedClass = cl.classifyInstance(currentInst);
if (predictedClass == insts.instance(i).classValue())
{
numCorrect++;
}
}
data = data + (numCorrect + " out of " + insts.numInstances() + " correct (" +
(double)((double)numCorrect / (double)insts.numInstances() * 100.0) + "%)");
}
catch (java.lang.Exception ex)
{
data = "Error";
ex.printStackTrace();
}
return(data);
}
public override double classifyInstance(weka.core.Instance instance)
{
if (m_instances.numInstances() == 0)
{
return(2);
}
if (m_instances.numAttributes() != instance.numAttributes())
{
throw new AssertException("different attribute.");
}
int n = (instance.numAttributes() - 1) / 2;
List <Tuple <int, int> > dist = new List <Tuple <int, int> >();
for (int i = 0; i < m_instances.numInstances(); ++i)
{
int d1 = 0, d2 = 0;
weka.core.Instance instanceI = m_instances.instance(i);
for (int j = 0; j < n; ++j)
{
//d += (int)((instanceI.value(j) - instance.value(j)) * (instanceI.value(j) - instance.value(j)));
if (instanceI.value(j) != instance.value(j))
{
if (instance.value(j) == 2 || instanceI.value(j) == 2)
{
d1++;
}
else
{
d1 += 4;
}
}
}
for (int j = n; j < 2 * n; ++j)
{
//d += (int)((instanceI.value(j) - instance.value(j)) * (instanceI.value(j) - instance.value(j)));
if (instanceI.value(j) != instance.value(j))
{
if (instance.value(j) == 2 || instanceI.value(j) == 2)
{
d2++;
}
else
{
d2 += 4;
}
}
}
int c = (int)instanceI.classValue();
//if (c == 0)
//{
// if (d1 < n / 4 && d1 < d2)
// {
// dist.Add(new Tuple<int, int>(d1, c));
// }
//}
//else if (c == 1)
//{
// if (d2 < n / 4 && d2 < d1)
// {
// dist.Add(new Tuple<int, int>(d2, c));
// }
//}
//else
//{
// throw new AssertException("");
//}
dist.Add(new Tuple <int, int>(d1 + d2, c));
}
if (dist.Count == 0)
{
return(2);
}
dist.Sort(new Comparison <Tuple <int, int> >((x, y) =>
{
return(x.Item1.CompareTo(y.Item1));
}));
int sum = 0, count = 0;
for (int i = 0; i < dist.Count; ++i)
{
if (dist[i].Item1 < n / 4 * 2 * 4)
{
if (dist[i].Item2 != 2 && dist[i].Item2 != 3)
{
sum += dist[i].Item2;
count++;
}
else
{
}
}
else
{
break;
}
}
if (count == 0)
{
return(2);
}
if (count < m_instances.numInstances() / 30)
{
return(2);
}
return((int)Math.Round((double)sum / count));
}
private void button1_Click(object sender, EventArgs e)
{
string fname = "";
OpenFileDialog dialog = new OpenFileDialog();
dialog.Filter =
"Weka Files (*.arff)|*.arff|All files (*.*)|*.*";
dialog.InitialDirectory = Application.StartupPath;
dialog.Title = "Select a .arff file";
if (dialog.ShowDialog() == DialogResult.OK)
{
fname = dialog.FileName;
//label5.Text = System.IO.Directory.;
}
if (fname == "")
{
return;
}
try
{
weka.core.Instances insts = new weka.core.Instances(new java.io.FileReader(fname.ToString()));
insts.setClassIndex(insts.numAttributes() - 1);
Classifier cl = new weka.classifiers.functions.SMO();
//label1.Text = "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);
cl.buildClassifier(train);
int numCorrect = 0;
for (int i = trainSize; i < insts.numInstances(); i++)
{
weka.core.Instance currentInst = insts.instance(i);
double predictedClass = cl.classifyInstance(currentInst);
if (predictedClass == insts.instance(i).classValue())
{
numCorrect++;
}
}
//label1.Text = numCorrect + " out of " + testSize + " correct (" +
//(double)((double)numCorrect / (double)testSize * 100.0) + "%)";
label6.Text = testSize.ToString();
label7.Text = numCorrect.ToString();
label8.Text = (double)((double)numCorrect / (double)testSize * 100.0) + "%";
double result_perc = (double)((double)numCorrect / (double)testSize * 100.0);
result_perc = Math.Truncate(result_perc);
try
{
// Send Data On Serial port
SerialPort serialPort = new SerialPort("COM" + textBox1.Text + "", Int32.Parse(textBox2.Text), Parity.None, 8);
serialPort.Open();
if (result_perc <= 75)
{
serialPort.WriteLine("1");
}
serialPort.WriteLine("a");
serialPort.Close();
}
catch (Exception ex)
{
MessageBox.Show(ex.Message);
}
}
catch (java.lang.Exception ex)
{
MessageBox.Show(ex.getMessage().ToString(), "");
}
}
//private void ConvertNorminalToString(string fileName)
//{
// List<string> list = new List<string>();
// using (System.IO.StreamReader sr = new System.IO.StreamReader(fileName))
// {
// while (true)
// {
// if (sr.EndOfStream)
// break;
// string s = sr.ReadLine();
// if (string.IsNullOrEmpty(s))
// continue;
// int idx = s.IndexOf(' ');
// string c = idx == -1 ? s : s.Substring(0, idx);
// if (Convert.ToDouble(c) == 0)
// {
// list.Add("-1.0 " + (idx == -1 ? string.Empty : s.Substring(idx + 1)));
// }
// else if (Convert.ToDouble(c) == 1)
// {
// list.Add("0.0 " + (idx == -1 ? string.Empty : s.Substring(idx + 1)));
// }
// else if (Convert.ToDouble(c) == 2)
// {
// list.Add("+1.0 " + (idx == -1 ? string.Empty : s.Substring(idx + 1)));
// }
// else
// {
// list.Add(s);
// }
// }
// }
// using (System.IO.StreamWriter sw = new System.IO.StreamWriter(fileName))
// {
// foreach (string s in list)
// {
// sw.WriteLine(s);
// }
// }
//}
//private Random m_randomGenerator;
private void AddInstancesAccordWeight(Instances instances)
{
// 0, 2
double[] weights = MincostLiblinearClassifier.GetCount(instances);
if (weights == null)
return;
double c = m_tp / m_sl;
if (c == 1 && weights[0] == weights[1])
return;
int n = 0;
int toCopyClass = 0;
if (c >= 1)
{
int shouldWeight1 = (int)(c * weights[1]);
n = (int)(shouldWeight1 - weights[1]);
toCopyClass = 2;
}
else
{
int shouldShouldWeight0 = (int)(1 / c * weights[0]);
n = (int)(weights[1] - weights[0]);
toCopyClass = 0;
}
//m_randomGenerator = new Random((int)System.DateTime.Now.Ticks);
List<Instance> copyInstances = new List<Instance>();
for (int i = 0; i < instances.numInstances(); ++i)
{
if (instances.instance(i).classValue() == toCopyClass)
{
copyInstances.Add(instances.instance(i));
}
}
int nAll = n / copyInstances.Count;
for (int j = 0; j < nAll; ++j)
{
for (int i = 0; i < copyInstances.Count; ++i)
{
Instance newInstance = new weka.core.DenseInstance(copyInstances[i]);
instances.add(newInstance);
newInstance.setDataset(instances);
}
}
//for (int j = 0; j < n - nAll * copyInstances.Count; ++j)
//{
// int idx = (int)(m_randomGenerator.NextDouble() * copyInstances.Count);
// idx = Math.Min(idx, copyInstances.Count - 1);
// Instance newInstance = new weka.core.DenseInstance(copyInstances[idx]);
// instances.add(newInstance);
// newInstance.setDataset(instances);
//}
if (n - nAll * copyInstances.Count > 0)
{
Instance avgInstance = new weka.core.DenseInstance(instances.numAttributes());
for (int i = 0; i < avgInstance.numAttributes(); ++i)
{
double sum = 0;
for (int j = 0; j < copyInstances.Count; ++j)
{
sum += copyInstances[j].value(i);
}
avgInstance.setValue(i, sum / copyInstances.Count);
}
for (int j = 0; j < n - nAll * copyInstances.Count; ++j)
{
Instance newInstance = new weka.core.DenseInstance(avgInstance);
instances.add(newInstance);
}
}
}
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));
}
}
//Dosya seçim bölümü ve yüzde hesabı bölümü
private void btnBrowse_Click(object sender, EventArgs e)
{
clears();
OpenFileDialog file = new OpenFileDialog();
file.Filter = "Files (ARFF)|*.ARFF";
file.Multiselect = false;
file.Title = "Please select a dataset file!";
if (file.ShowDialog() == DialogResult.OK)
{
txtPath.Text = file.FileName;
fileName = file.SafeFileName;
//dosya seçildikten sonra işlemi gerçekleştiriyor.
try
{
if (txtPath.Text.Length < 1)
{
MessageBox.Show("Please select file!", "Error Message!");
txtPath.Text = "";
}
else
{
this.Text = "Processing...";
insts = new weka.core.Instances(new java.io.FileReader(txtPath.Text));
//naive bayes
double max_value = NaiveBayesTest(insts);
model = NaiveBayescl;
name = "Naïve Bayes";
//logistic regression
double LogRegressionvalue = LogRegressionTest(insts);
if (LogRegressionvalue > max_value)
{
max_value = LogRegressionvalue;
model = LogRegressioncl;
name = "Logistic Regression";
}
//knn
double KnnValue = Knn(insts);
if (KnnValue > max_value)
{
max_value = KnnValue;
model = Knncl;
name = "K-Nearest Neighbour";
}
//J48
double J48Value = J48classifyTest(insts);
if (J48Value > max_value)
{
max_value = J48Value;
model = J48cl;
name = "Decision Tree(J48)";
}
//Random forest
double RFvalue = RandomForestTest(insts);
if (RFvalue > max_value)
{
max_value = RFvalue;
model = RandomForestcl;
name = "Decision Tree(Random Forest)";
}
//Random Tree
double RTvalue = RandomTreeTest(insts);
if (RTvalue > max_value)
{
max_value = RTvalue;
model = RandomTreecl;
name = "Decision Tree(Random Tree)";
}
//Artificial nn
double AnnValue = ArtificialNN(insts);
if (AnnValue > max_value)
{
max_value = AnnValue;
model = Anncl;
name = "Artificial Neural Network";
}
//Svm
double SvmValue = SVM(insts);
if (SvmValue > max_value)
{
max_value = SvmValue;
model = Svmcl;
name = "Support Vector Machine";
}
//Model kaydetme kısmı
weka.core.SerializationHelper.write("models/mdl.model", model);
lblResult.Text = name + " is the most successful algorithm for this data set (%" + string.Format("{0:0.00}", max_value) + ")";
this.Text = "DEUCENG - ML Classification Tool";
//seçme işlemleri
numAtt = insts.numAttributes() - 1;
int x = 30, y = 130, t = 35, l = 110;
int txt = 0, cmb = 0, r1 = 0, r2 = 0;
labels = new Label[insts.numAttributes()];
for (int i = 0; i < numAtt; i++)
{
if (insts.attribute(i).isNumeric())
{
txt++;
}
else if (insts.attribute(i).isNominal())
{
cmb++;
}
}
nominal = new ComboBox[cmb];
numeric = new TextBox[txt];
typeAtt = new bool[numAtt];
this.Height += (numAtt + 1) * t;
for (int i = 0; i < numAtt; i++)
{
if (insts.attribute(i).isNominal())
{
string[] s1 = insts.attribute(i).toString().Split('{');
string[] s2 = s1[1].Split('}');
string[] s3 = s2[0].Split(',');
nominal[r1] = new ComboBox();
labels[i] = new Label();
for (int j = 0; j < s3.Length; j++)
{
nominal[r1].Items.Add(s3[j].Replace('\'', ' ').Trim());
}
labels[i].Text = insts.attribute(i).name();
labels[i].Left = x;
labels[i].Top = y;
nominal[r1].Left = x + l;
nominal[r1].Top = y;
nominal[r1].DropDownStyle = ComboBoxStyle.DropDownList;
y += t;
Controls.Add(nominal[r1]);
Controls.Add(labels[i]);
r1++;
typeAtt[i] = true;
}
else if (insts.attribute(i).isNumeric())
{
numeric[r2] = new TextBox();
labels[i] = new Label();
labels[i].Text = insts.attribute(i).name();
labels[i].Left = x;
labels[i].Top = y;
numeric[r2].Left = x + l;
numeric[r2].Top = y;
y += t;
Controls.Add(numeric[r2]);
Controls.Add(labels[i]);
r2++;
typeAtt[i] = false;
}
btnDiscover.Enabled = true;
}
}
}
catch (Exception e2)
{
MessageBox.Show(e2.Message, "Error Message!");
}
}
}
/// <summary> Sets the format of the input instances.
///
/// </summary>
/// <param name="instanceInfo">an Instances object containing the input instance
/// structure (any instances contained in the object are ignored - only the
/// structure is required).
/// </param>
/// <exception cref="UnsupportedAttributeTypeException">if the specified attribute
/// is neither numeric or nominal.
/// </exception>
public override bool setInputFormat(Instances instanceInfo)
{
base.setInputFormat(instanceInfo);
m_AttIndex.Upper=instanceInfo.numAttributes() - 1;
if (!Numeric && !Nominal)
{
throw new Exception("Can only handle numeric " + "or nominal attributes.");
}
m_Values.Upper=instanceInfo.attribute(m_AttIndex.Index).numValues() - 1;
if (Nominal && m_ModifyHeader)
{
instanceInfo = new Instances(instanceInfo, 0); // copy before modifying
Attribute oldAtt = instanceInfo.attribute(m_AttIndex.Index);
int[] selection = m_Values.Selection;
FastVector newVals = new FastVector();
for (int i = 0; i < selection.Length; i++)
{
newVals.addElement(oldAtt.value_Renamed(selection[i]));
}
instanceInfo.deleteAttributeAt(m_AttIndex.Index);
instanceInfo.insertAttributeAt(new Attribute(oldAtt.name(), newVals), m_AttIndex.Index);
m_NominalMapping = new int[oldAtt.numValues()];
for (int i = 0; i < m_NominalMapping.Length; i++)
{
bool found = false;
for (int j = 0; j < selection.Length; j++)
{
if (selection[j] == i)
{
m_NominalMapping[i] = j;
found = true;
break;
}
}
if (!found)
{
m_NominalMapping[i] = - 1;
}
}
}
setOutputFormat(instanceInfo);
return true;
}
public void classifyTest()
{
try
{
CSV2Arff();
java.io.FileReader arrfFile = new java.io.FileReader("D:/final_version/Gesture-Gis-master/GestureGis2/ComparisonFeaturefile.arff");
weka.core.Instances insts = new weka.core.Instances(arrfFile);
//weka.core.Instances insts2 = new weka.core.Instances(new java.io.FileReader("D:/Gesture-Gis-master/GestureGis2/ComparisonFeaturefile.arff"));
insts.setClassIndex(insts.numAttributes() - 1);
//int percentSplit = 66;
weka.classifiers.Classifier cl = new weka.classifiers.trees.J48();
//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 count = insts.numInstances();
int trainSize = count - 1;
int testSize = count - trainSize;
weka.core.Instances train = new weka.core.Instances(insts, 0, trainSize);
cl.buildClassifier(train);
//weka.core.Instance current = insts2.instance(0);
int numCorrect = 0;
/*for (int i = trainSize; i < insts.numInstances(); i++)
* {
* weka.core.Instance currentInst = insts.instance(i);
* double predictedClass = cl.classifyInstance(currentInst);
* if (predictedClass == insts.instance(i).classValue())
* numCorrect++;
* }*/
int index = count - 1;
weka.core.Instance currentInst = insts.instance(index);
double predictedClass = cl.classifyInstance(currentInst);
int pre = (int)predictedClass;
if (predictedClass == insts.instance(index).classValue())
{
numCorrect++;
}
//insts.instance(index).classAttribute();
//insts.attribute(11);
string s = insts.toString();
s = s.Substring(s.IndexOf("{") + 1);
s = s.Substring(0, s.IndexOf("}"));
s = s.Substring(0, s.Length);
string[] ae = s.Split(',');
/*ArrayList arr = new ArrayList();
* string path_class = @"D:\final_version\Gesture-Gis-master\GestureGis2\Classfile.txt";
* using (StreamReader reader = new StreamReader(path_class))
* {
* while (!reader.EndOfStream)
* {
* arr.Add(reader.ReadLine());
* }
* reader.Close();
* }*/
PredictedClassbyWeka = (string)(ae[pre]);
arrfFile.close();
//insts.instance(index).attribute(3);
/*System.Diagnostics.Debug.WriteLine(numCorrect + " out of " + testSize + " correct (" +
* (double)((double)numCorrect / (double)testSize * 100.0) + "%)");
* Console.WriteLine(numCorrect + " out of " + testSize + " correct (" +
* (double)((double)numCorrect / (double)testSize * 100.0) + "%)");*/
}
catch (java.lang.Exception ex)
{
ex.printStackTrace();
}
}
//static public void classifyLastColumn(){
// try { colClass = insts.numAttributes() - 1; }
// catch { colClass = 0; }
//}
static public void step_loadInstance()
{
insts = new weka.core.Instances(new java.io.FileReader(TheURL.dm_path_file));
count_feature = insts.numAttributes() - 1;
}
public override double classifyInstance(weka.core.Instance instance)
{
if (m_instances.numInstances() == 0)
{
return(2);
}
if (m_instances.numAttributes() != instance.numAttributes())
{
throw new AssertException("different attribute.");
}
int n = instance.numAttributes();
List <Tuple <int, int> > dist = new List <Tuple <int, int> >();
for (int i = 0; i < m_instances.numInstances(); ++i)
{
int d1 = 0, d2 = 0;
weka.core.Instance instanceI = m_instances.instance(i);
for (int j = 0; j < n; ++j)
{
//d += (int)((instanceI.value(j) - instance.value(j)) * (instanceI.value(j) - instance.value(j)));
if (instanceI.value(j) != instance.value(j))
{
d1++;
}
if (instance.value(j) != 0)
{
d2++;
}
}
int c = (int)instanceI.classValue();
dist.Add(new Tuple <int, int>(d1, c));
}
if (dist.Count == 0)
{
return(2);
}
dist.Sort(new Comparison <Tuple <int, int> >((x, y) =>
{
return(x.Item1.CompareTo(y.Item1));
}));
int sum = 0, count = 0;
for (int i = 0; i < dist.Count; ++i)
{
if (dist[i].Item1 < 4)
{
sum += dist[i].Item2;
count++;
}
else
{
break;
}
}
if (count == 0)
{
return(2);
}
if (count < m_instances.numInstances() / 70)
{
return(2);
}
return((int)Math.Round((double)sum / count));
}
private void result_Click(object sender, EventArgs e)
{
ArrayList algorithms = new ArrayList();
algorithms.Add("Naive Bayes");
algorithms.Add("K Nearest Neighbor");
algorithms.Add("Decision Tree");
algorithms.Add("Neural Network");
algorithms.Add("Support Vector Machine");
ArrayList successPercent = new ArrayList();
double res_Naive, res_KNN, res_NN, res_Tree, res_SVM = 0.0;
string nameOfAlgo = "";
//NAIVE BAYES ALGORITHM
weka.core.Instances insts = new weka.core.Instances(new java.io.FileReader(fileDirectory));
//CREATIING DYNAMIC GRIDVIEW FOR ADDING NEW INSTANCE
dataGridView1.ColumnCount = 2;
dataGridView1.RowCount = insts.numAttributes();
String[,] matrixOfInstances = new String[insts.numInstances(), insts.numAttributes()];
for (int y = 0; y < insts.numAttributes() - 1; y++)
{
dataGridView1.Rows[y].Cells[0].Value = insts.attribute(y).name();
if (insts.attribute(y).isNominal())
{
//nominalDataValues.Add(insts.attribute(y).toString());
string phrase = insts.attribute(y).toString();
string[] first = phrase.Split('{');
string[] second = first[1].Split('}');
string[] attributeValues = second[0].Split(',');
DataGridViewComboBoxCell comboColumn = new DataGridViewComboBoxCell();
foreach (var a in attributeValues)
{
comboColumn.Items.Add(a);
}
dataGridView1.Rows[y].Cells[1] = comboColumn;
}
}
insts.setClassIndex(insts.numAttributes() - 1);
cl_Naive = new weka.classifiers.bayes.NaiveBayes();
weka.filters.Filter myNominalData = new weka.filters.unsupervised.attribute.Discretize();
myNominalData.setInputFormat(insts);
insts = weka.filters.Filter.useFilter(insts, myNominalData);
//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);
cl_Naive.buildClassifier(train);
string str = cl_Naive.toString();
int numCorrect = 0;
for (int i = trainSize; i < insts.numInstances(); i++)
{
weka.core.Instance currentInst = insts.instance(i);
double predictedClass = cl_Naive.classifyInstance(currentInst);
if (predictedClass == insts.instance(i).classValue())
{
numCorrect++;
}
}
res_Naive = (double)((double)numCorrect / (double)testSize * 100.0);
successPercent.Add(res_Naive);
//kNN
weka.core.Instances insts2 = new weka.core.Instances(new java.io.FileReader(fileDirectory));
insts2.setClassIndex(insts2.numAttributes() - 1);
cl_Knn = new weka.classifiers.lazy.IBk();
//Nominal to Binary
weka.filters.Filter myBinaryData = new weka.filters.unsupervised.attribute.NominalToBinary();
myBinaryData.setInputFormat(insts2);
insts2 = weka.filters.Filter.useFilter(insts2, myBinaryData);
//Normalization
weka.filters.Filter myNormalized = new weka.filters.unsupervised.instance.Normalize();
myNormalized.setInputFormat(insts2);
insts2 = weka.filters.Filter.useFilter(insts2, myNormalized);
//randomize the order of the instances in the dataset.
weka.filters.Filter myRandom2 = new weka.filters.unsupervised.instance.Randomize();
myRandom2.setInputFormat(insts2);
insts2 = weka.filters.Filter.useFilter(insts2, myRandom2);
int trainSize2 = insts2.numInstances() * percentSplit / 100;
int testSize2 = insts2.numInstances() - trainSize2;
weka.core.Instances train2 = new weka.core.Instances(insts2, 0, trainSize2);
cl_Knn.buildClassifier(train2);
string str2 = cl_Knn.toString();
int numCorrect2 = 0;
for (int i = trainSize2; i < insts2.numInstances(); i++)
{
weka.core.Instance currentInst2 = insts2.instance(i);
double predictedClass = cl_Knn.classifyInstance(currentInst2);
if (predictedClass == insts2.instance(i).classValue())
{
numCorrect2++;
}
}
res_KNN = (double)((double)numCorrect2 / (double)testSize2 * 100.0);
successPercent.Add(res_KNN);
//Decision tree
weka.core.Instances insts3 = new weka.core.Instances(new java.io.FileReader(fileDirectory));
insts3.setClassIndex(insts3.numAttributes() - 1);
cl_Tree = new weka.classifiers.trees.J48();
weka.filters.Filter myNormalized2 = new weka.filters.unsupervised.instance.Normalize();
myNormalized2.setInputFormat(insts3);
insts3 = weka.filters.Filter.useFilter(insts3, myNormalized2);
//randomize the order of the instances in the dataset.
weka.filters.Filter myRandom3 = new weka.filters.unsupervised.instance.Randomize();
myRandom3.setInputFormat(insts3);
insts3 = weka.filters.Filter.useFilter(insts3, myRandom3);
int trainSize3 = insts3.numInstances() * percentSplit / 100;
int testSize3 = insts3.numInstances() - trainSize3;
weka.core.Instances train3 = new weka.core.Instances(insts3, 0, trainSize3);
cl_Tree.buildClassifier(train3);
string str3 = cl_Tree.toString();
int numCorrect3 = 0;
for (int i = trainSize3; i < insts3.numInstances(); i++)
{
weka.core.Instance currentInst3 = insts3.instance(i);
double predictedClass = cl_Tree.classifyInstance(currentInst3);
if (predictedClass == insts3.instance(i).classValue())
{
numCorrect3++;
}
}
res_Tree = (double)((double)numCorrect3 / (double)testSize3 * 100.0);
successPercent.Add(res_Tree);
//Neural Network
weka.core.Instances insts4 = new weka.core.Instances(new java.io.FileReader(fileDirectory));
insts4.setClassIndex(insts4.numAttributes() - 1);
cl_NN = new weka.classifiers.functions.MultilayerPerceptron();
//Nominal to Binary
weka.filters.Filter myBinaryData2 = new weka.filters.unsupervised.attribute.NominalToBinary();
myBinaryData2.setInputFormat(insts4);
insts4 = weka.filters.Filter.useFilter(insts4, myBinaryData2);
//Normalization
weka.filters.Filter myNormalized3 = new weka.filters.unsupervised.instance.Normalize();
myNormalized3.setInputFormat(insts4);
insts4 = weka.filters.Filter.useFilter(insts4, myNormalized3);
//randomize the order of the instances in the dataset.
weka.filters.Filter myRandom4 = new weka.filters.unsupervised.instance.Randomize();
myRandom4.setInputFormat(insts4);
insts4 = weka.filters.Filter.useFilter(insts4, myRandom4);
int trainSize4 = insts4.numInstances() * percentSplit / 100;
int testSize4 = insts4.numInstances() - trainSize4;
weka.core.Instances train4 = new weka.core.Instances(insts4, 0, trainSize4);
cl_NN.buildClassifier(train4);
string str4 = cl_NN.toString();
int numCorrect4 = 0;
for (int i = trainSize4; i < insts4.numInstances(); i++)
{
weka.core.Instance currentInst4 = insts4.instance(i);
double predictedClass = cl_NN.classifyInstance(currentInst4);
if (predictedClass == insts4.instance(i).classValue())
{
numCorrect4++;
}
}
res_NN = (double)((double)numCorrect4 / (double)testSize4 * 100.0);
successPercent.Add(res_NN);
//SVM
weka.core.Instances insts5 = new weka.core.Instances(new java.io.FileReader(fileDirectory));
insts5.setClassIndex(insts5.numAttributes() - 1);
cl_SVM = new weka.classifiers.functions.SMO();
//Nominal to Binary
weka.filters.Filter myBinaryData3 = new weka.filters.unsupervised.attribute.NominalToBinary();
myBinaryData3.setInputFormat(insts5);
insts5 = weka.filters.Filter.useFilter(insts5, myBinaryData3);
//Normalization
weka.filters.Filter myNormalized4 = new weka.filters.unsupervised.instance.Normalize();
myNormalized4.setInputFormat(insts5);
insts5 = weka.filters.Filter.useFilter(insts5, myNormalized4);
//randomize the order of the instances in the dataset.
weka.filters.Filter myRandom5 = new weka.filters.unsupervised.instance.Randomize();
myRandom5.setInputFormat(insts5);
insts5 = weka.filters.Filter.useFilter(insts5, myRandom5);
int trainSize5 = insts5.numInstances() * percentSplit / 100;
int testSize5 = insts5.numInstances() - trainSize5;
weka.core.Instances train5 = new weka.core.Instances(insts5, 0, trainSize5);
cl_SVM.buildClassifier(train5);
string str5 = cl_SVM.toString();
int numCorrect5 = 0;
for (int i = trainSize5; i < insts5.numInstances(); i++)
{
weka.core.Instance currentInst5 = insts5.instance(i);
double predictedClass = cl_SVM.classifyInstance(currentInst5);
if (predictedClass == insts5.instance(i).classValue())
{
numCorrect5++;
}
}
res_SVM = (double)((double)numCorrect5 / (double)testSize5 * 100.0);
successPercent.Add(res_SVM);
for (int i = 0; i < successPercent.Count; i++)
{
if ((double)successPercent[i] > max)
{
max = (double)successPercent[i];
count = i + 1;
}
}
for (int i = 0; i < count; i++)
{
nameOfAlgo = (string)algorithms[i];
}
textBox1.Text = nameOfAlgo + " is the most successful algorithm for this data set." + "(" + max + "%)\n";
}
private void button1_Click(object sender, EventArgs e)
{
OpenFileDialog file = new OpenFileDialog();
if (file.ShowDialog() == DialogResult.OK)
{
string filename = file.FileName;
string filee = Path.GetFileName(filename);
bool attributeType;
string attributeName = " ";
int numAttributeValue = 0;
string attributeValueName = " ";
textBox1.Text = filee + " chosen succesfully!";
///////Decision Tree
weka.core.Instances insts = new weka.core.Instances(new java.io.FileReader(filename));
insts.setClassIndex(insts.numAttributes() - 1);
//find nominal or numeric attributes and create dropbox or textbox
int numofAttributes = insts.numAttributes() - 1;
for (int i = 0; i < numofAttributes; i++)
{
attributeType = insts.attribute(i).isNumeric();
attributeName = insts.attribute(i).name();
dataGridView1.Rows.Add(attributeName);
if (attributeType == true)
{
}
else
{
numAttributeValue = insts.attribute(i).numValues();
string[] name = new string[numAttributeValue];
for (int j = 0; j < numAttributeValue; j++)
{
attributeValueName = insts.attribute(i).value(j);
name[j] += attributeValueName;
}
DataGridViewComboBoxCell combo = new DataGridViewComboBoxCell();
combo.DataSource = name.ToList();
dataGridView1.Rows[i].Cells[1] = combo;
}
}
cl = new weka.classifiers.trees.J48();
textBox2.Text = "Performing " + percentSplit + "% split evaluation.";
//filling missing values
weka.filters.Filter missingval = new weka.filters.unsupervised.attribute.ReplaceMissingValues();
missingval.setInputFormat(insts);
insts = weka.filters.Filter.useFilter(insts, missingval);
weka.filters.Filter myNormalized = new weka.filters.unsupervised.instance.Normalize();
myNormalized.setInputFormat(insts);
insts = weka.filters.Filter.useFilter(insts, myNormalized);
//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);
cl.buildClassifier(train);
string str = cl.toString();
int numCorrect = 0;
for (int i = trainSize; i < insts.numInstances(); i++)
{
weka.core.Instance currentInst = insts.instance(i);
double predictedClass = cl.classifyInstance(currentInst);
if (predictedClass == insts.instance(i).classValue())
{
numCorrect++;
}
}
textBox3.Text = numCorrect + " out of " + testSize + " correct (" +
(double)((double)numCorrect / (double)testSize * 100.0) + "%)";
//////////Naive Bayes
//dosya okuma
weka.core.Instances insts2 = new weka.core.Instances(new java.io.FileReader(filename));
insts2.setClassIndex(insts2.numAttributes() - 1);
//naive bayes
cl2 = new weka.classifiers.bayes.NaiveBayes();
//filling missing values
weka.filters.Filter missingval2 = new weka.filters.unsupervised.attribute.ReplaceMissingValues();
missingval2.setInputFormat(insts2);
insts2 = weka.filters.Filter.useFilter(insts2, missingval2);
//for naive bayes
weka.filters.Filter discrete2 = new weka.filters.unsupervised.attribute.Discretize();
discrete2.setInputFormat(insts2);
insts2 = weka.filters.Filter.useFilter(insts2, discrete2);
//randomize the order of the instances in the dataset. -ortak
weka.filters.Filter myRandom2 = new weka.filters.unsupervised.instance.Randomize();
myRandom2.setInputFormat(insts2);
insts2 = weka.filters.Filter.useFilter(insts2, myRandom2);
//ortak
int trainSize2 = insts2.numInstances() * percentSplit / 100;
int testSize2 = insts2.numInstances() - trainSize2;
weka.core.Instances train2 = new weka.core.Instances(insts2, 0, trainSize2);
cl2.buildClassifier(train2);
string str2 = cl2.toString();
int numCorrect2 = 0;
for (int i = trainSize2; i < insts2.numInstances(); i++)
{
weka.core.Instance currentInst2 = insts2.instance(i);
double predictedClass2 = cl2.classifyInstance(currentInst2);
if (predictedClass2 == insts2.instance(i).classValue())
{
numCorrect2++;
}
}
textBox4.Text = numCorrect2 + " out of " + testSize2 + " correct (" +
(double)((double)numCorrect2 / (double)testSize2 * 100.0) + "%)";
/////////K-Nearest Neigbour
//dosya okuma
weka.core.Instances insts3 = new weka.core.Instances(new java.io.FileReader(filename));
insts3.setClassIndex(insts3.numAttributes() - 1);
cl3 = new weka.classifiers.lazy.IBk();
//filling missing values
weka.filters.Filter missingval3 = new weka.filters.unsupervised.attribute.ReplaceMissingValues();
missingval3.setInputFormat(insts3);
insts3 = weka.filters.Filter.useFilter(insts3, missingval3);
//Convert to dummy attribute knn,svm,neural network
weka.filters.Filter dummy3 = new weka.filters.unsupervised.attribute.NominalToBinary();
dummy3.setInputFormat(insts3);
insts3 = weka.filters.Filter.useFilter(insts3, dummy3);
//normalize numeric attribute
weka.filters.Filter myNormalized3 = new weka.filters.unsupervised.instance.Normalize();
myNormalized3.setInputFormat(insts3);
insts3 = weka.filters.Filter.useFilter(insts3, myNormalized3);
//randomize the order of the instances in the dataset.
weka.filters.Filter myRandom3 = new weka.filters.unsupervised.instance.Randomize();
myRandom3.setInputFormat(insts3);
insts3 = weka.filters.Filter.useFilter(insts3, myRandom3);
int trainSize3 = insts3.numInstances() * percentSplit / 100;
int testSize3 = insts3.numInstances() - trainSize3;
weka.core.Instances train3 = new weka.core.Instances(insts3, 0, trainSize3);
cl3.buildClassifier(train3);
string str3 = cl3.toString();
int numCorrect3 = 0;
for (int i = trainSize3; i < insts3.numInstances(); i++)
{
weka.core.Instance currentInst3 = insts3.instance(i);
double predictedClass3 = cl3.classifyInstance(currentInst3);
if (predictedClass3 == insts3.instance(i).classValue())
{
numCorrect3++;
}
}
textBox5.Text = numCorrect3 + " out of " + testSize3 + " correct (" +
(double)((double)numCorrect3 / (double)testSize3 * 100.0) + "%)";
//////////Artificial neural network
//dosya okuma
weka.core.Instances insts4 = new weka.core.Instances(new java.io.FileReader(filename));
insts4.setClassIndex(insts4.numAttributes() - 1);
cl4 = new weka.classifiers.functions.MultilayerPerceptron();
//filling missing values
weka.filters.Filter missingval4 = new weka.filters.unsupervised.attribute.ReplaceMissingValues();
missingval4.setInputFormat(insts4);
insts4 = weka.filters.Filter.useFilter(insts4, missingval4);
//Convert to dummy attribute
weka.filters.Filter dummy4 = new weka.filters.unsupervised.attribute.NominalToBinary();
dummy4.setInputFormat(insts4);
insts4 = weka.filters.Filter.useFilter(insts4, dummy4);
//normalize numeric attribute
weka.filters.Filter myNormalized4 = new weka.filters.unsupervised.instance.Normalize();
myNormalized4.setInputFormat(insts4);
insts4 = weka.filters.Filter.useFilter(insts4, myNormalized4);
//randomize the order of the instances in the dataset.
weka.filters.Filter myRandom4 = new weka.filters.unsupervised.instance.Randomize();
myRandom4.setInputFormat(insts4);
insts4 = weka.filters.Filter.useFilter(insts4, myRandom4);
int trainSize4 = insts4.numInstances() * percentSplit / 100;
int testSize4 = insts4.numInstances() - trainSize4;
weka.core.Instances train4 = new weka.core.Instances(insts4, 0, trainSize4);
cl4.buildClassifier(train4);
string str4 = cl4.toString();
int numCorrect4 = 0;
for (int i = trainSize4; i < insts4.numInstances(); i++)
{
weka.core.Instance currentInst4 = insts4.instance(i);
double predictedClass4 = cl4.classifyInstance(currentInst4);
if (predictedClass4 == insts4.instance(i).classValue())
{
numCorrect4++;
}
}
textBox6.Text = numCorrect4 + " out of " + testSize4 + " correct (" +
(double)((double)numCorrect4 / (double)testSize4 * 100.0) + "%)";
///////Support Vector Machine
// dosya okuma
weka.core.Instances insts5 = new weka.core.Instances(new java.io.FileReader(filename));
insts5.setClassIndex(insts5.numAttributes() - 1);
cl5 = new weka.classifiers.functions.SMO();
//filling missing values
weka.filters.Filter missingval5 = new weka.filters.unsupervised.attribute.ReplaceMissingValues();
missingval5.setInputFormat(insts5);
insts5 = weka.filters.Filter.useFilter(insts5, missingval5);
//Convert to dummy attribute
weka.filters.Filter dummy5 = new weka.filters.unsupervised.attribute.NominalToBinary();
dummy5.setInputFormat(insts5);
insts5 = weka.filters.Filter.useFilter(insts5, dummy5);
//normalize numeric attribute
weka.filters.Filter myNormalized5 = new weka.filters.unsupervised.instance.Normalize();
myNormalized5.setInputFormat(insts5);
insts5 = weka.filters.Filter.useFilter(insts5, myNormalized5);
//randomize the order of the instances in the dataset.
weka.filters.Filter myRandom5 = new weka.filters.unsupervised.instance.Randomize();
myRandom5.setInputFormat(insts5);
insts5 = weka.filters.Filter.useFilter(insts5, myRandom5);
int trainSize5 = insts5.numInstances() * percentSplit / 100;
int testSize5 = insts5.numInstances() - trainSize5;
weka.core.Instances train5 = new weka.core.Instances(insts5, 0, trainSize5);
cl5.buildClassifier(train5);
string str5 = cl5.toString();
int numCorrect5 = 0;
for (int i = trainSize5; i < insts5.numInstances(); i++)
{
weka.core.Instance currentInst5 = insts5.instance(i);
double predictedClass5 = cl5.classifyInstance(currentInst5);
if (predictedClass5 == insts5.instance(i).classValue())
{
numCorrect5++;
}
}
textBox7.Text = numCorrect5 + " out of " + testSize5 + " correct (" +
(double)((double)numCorrect5 / (double)testSize5 * 100.0) + "%)";
string result1 = textBox3.Text;
string output1 = result1.Split('(', ')')[1];
output1 = output1.Remove(output1.Length - 1);
double r1 = Convert.ToDouble(output1);
string result2 = textBox4.Text;
string output2 = result2.Split('(', ')')[1];
output2 = output2.Remove(output2.Length - 1);
double r2 = Convert.ToDouble(output2);
string result3 = textBox5.Text;
string output3 = result3.Split('(', ')')[1];
output3 = output3.Remove(output3.Length - 1);
double r3 = Convert.ToDouble(output3);
string result4 = textBox6.Text;
string output4 = result4.Split('(', ')')[1];
output4 = output4.Remove(output4.Length - 1);
double r4 = Convert.ToDouble(output4);
string result5 = textBox7.Text;
string output5 = result5.Split('(', ')')[1];
output5 = output5.Remove(output5.Length - 1);
double r5 = Convert.ToDouble(output5);
double[] max_array = new double[] { r1, r2, r3, r4, r5 };
double max = max_array.Max();
if (r1 == max)
{
textBox8.Text = "Best Algoritm is Decision Tree Algorithm ";
}
else if (r2 == max)
{
textBox8.Text = "Best Algoritm is Naive Bayes Algorithm ";
}
else if (r3 == max)
{
textBox8.Text = "Best Algoritm is K-Nearest Neighbour Algorithm ";
}
else if (r4 == max)
{
textBox8.Text = "Best Algoritm is Artificial Neural Network Algorithm ";
}
else if (r5 == max)
{
textBox8.Text = "Best Algoritm is Support Vector Machine Algorithm ";
}
}
}
// Test the classification result of each map that a user played,
// with the data available as if they were playing through it
public static void classifyTest(String dataString, String playerID)
{
String results = "";
try {
java.io.StringReader stringReader = new java.io.StringReader(dataString);
java.io.BufferedReader buffReader = new java.io.BufferedReader(stringReader);
/* NOTE THAT FOR NAIVE BAYES ALL WEIGHTS CAN BE = 1*/
//weka.core.converters.ConverterUtils.DataSource source = new weka.core.converters.ConverterUtils.DataSource("iris.arff");
weka.core.Instances data = new weka.core.Instances(buffReader); //source.getDataSet();
// setting class attribute if the data format does not provide this information
// For example, the XRFF format saves the class attribute information as well
if (data.classIndex() == -1)
{
data.setClassIndex(data.numAttributes() - 1);
}
weka.classifiers.Classifier cl;
for (int i = 3; i < data.numInstances(); i++)
{
cl = new weka.classifiers.bayes.NaiveBayes();
//cl = new weka.classifiers.trees.J48();
//cl = new weka.classifiers.lazy.IB1();
//cl = new weka.classifiers.functions.MultilayerPerceptron();
((weka.classifiers.functions.MultilayerPerceptron)cl).setHiddenLayers("12");
weka.core.Instances subset = new weka.core.Instances(data, 0, i);
cl.buildClassifier(subset);
weka.classifiers.Evaluation eval = new weka.classifiers.Evaluation(subset);
eval.crossValidateModel(cl, subset, 3, new java.util.Random(1));
results = results + eval.pctCorrect(); // For accuracy measurement
/* For Mathews Correlation Coefficient */
//double TP = eval.numTruePositives(1);
//double FP = eval.numFalsePositives(1);
//double TN = eval.numTrueNegatives(1);
//double FN = eval.numFalseNegatives(1);
//double correlationCoeff = ((TP*TN)-(FP*FN))/Math.Sqrt((TP+FP)*(TP+FN)*(TN+FP)*(TN+FN));
//results = results + correlationCoeff;
if (i != data.numInstances() - 1)
{
results = results + ", ";
}
if (i == data.numInstances() - 1)
{
Debug.Log("Player: " + playerID + ", Num Maps: " + data.numInstances() + ", AUC: " + eval.areaUnderROC(1));
}
}
} catch (java.lang.Exception ex)
{
Debug.LogError(ex.getMessage());
}
// Write values to file for a matlab read
// For accuracy
StreamWriter writer = new StreamWriter("DataForMatlab/" + playerID + "_CrossFoldValidations_NeuralNet.txt");
//StreamWriter writer = new StreamWriter("DataForMatlab/"+playerID+"_CrossFoldCorrCoeff.txt"); // For mathews cc
writer.WriteLine(results);
writer.Close();
Debug.Log(playerID + " has been written to file");
}
private void Classify(string path)
{
readyToTest = false; // initialize flag
// Try reading file, if failed exit function
insts = ReadFile(path);
if (insts == null)
{
// Error occured reading file, display error message
MessageBox.Show("Instances are null!", "Error", MessageBoxButtons.OK, MessageBoxIcon.Error);
return;
}
var form = Form.ActiveForm as Form1; // get the current form object
// Reset UI and lists
succesRates.Clear();
classifiers.Clear();
form.inputPanel.Controls.Clear();
inputObjects.Clear();
form.textMostSuccessful.Text = "";
form.testResult.Text = "";
// Place attribute inputs on UI, max 18, numeric and nominal
int offsetV = 60;
int offsetH = 10;
int width = 75;
int height = 30;
for (int i = 0; i < insts.numAttributes() - 1; i++)
{
// Create and place label
Label label = new Label();
label.Width = width;
label.Height = height;
label.Text = insts.attribute(i).name();
label.Parent = form.inputPanel;
label.Location = new Point((width * (i % 8)) + offsetH, (height * (i / 8)) + (offsetV * (i / 8)));
// NumericUpDown for numeric and ComboBox for nominal values
if (insts.attribute(i).isNumeric())
{
NumericUpDown nud = new NumericUpDown();
nud.Width = width - 10;
nud.Height = height;
nud.Parent = form.inputPanel;
nud.Location = new Point((width * (i % 8)) + offsetH, (height * (i / 8)) + (offsetV * (i / 8)) + height);
inputObjects.Add(new UserInput(nud));
}
else
{
string[] values = insts.attribute(i).toString().Split('{', '}')[1].Split(',');
ComboBox comboBox = new ComboBox();
comboBox.DataSource = values;
comboBox.Width = width - 10;
comboBox.Height = height;
comboBox.Parent = form.inputPanel;
comboBox.Location = new Point((width * (i % 8)) + offsetH, (height * (i / 8)) + (offsetV * (i / 8)) + height);
inputObjects.Add(new UserInput(comboBox));
}
}
// Set train and test sizes
trainSize = insts.numInstances() * percentSplit / 100;
testSize = insts.numInstances() - trainSize;
// Set target attribute
insts.setClassIndex(insts.numAttributes() - 1);
// Randomize
weka.filters.Filter rndFilter = new weka.filters.unsupervised.instance.Randomize();
rndFilter.setInputFormat(insts);
insts = weka.filters.Filter.useFilter(insts, rndFilter);
// Start threads for each method
Thread t_SuccessNb = new Thread(() => CalculateSuccessForNb(insts));
t_SuccessNb.Start();
Thread t_SuccessKn = new Thread(() => CalculateSuccessForKn(insts));
t_SuccessKn.Start();
Thread t_SuccessDt = new Thread(() => CalculateSuccessForDt(insts));
t_SuccessDt.Start();
Thread t_SuccessAnn = new Thread(() => CalculateSuccessForAnn(insts));
t_SuccessAnn.Start();
Thread t_SuccessSvm = new Thread(() => CalculateSuccessForSvm(insts));
t_SuccessSvm.Start();
// Wait for threads
t_SuccessNb.Join();
t_SuccessKn.Join();
t_SuccessDt.Join();
t_SuccessAnn.Join();
t_SuccessSvm.Join();
// Find out which algorithm has the best success rate
foreach (var item in succesRates)
{
if (highestSuccessRate.Equals(default(KeyValuePair <Classifier, double>)) || highestSuccessRate.Value < item.Value)
{
highestSuccessRate = item;
}
}
form.textMostSuccessful.Text = "Most successful algorithm is " + highestSuccessRate.Key + " and it will be used for testing.";
readyToTest = true; // switch flag
}
/// <summary> Gets the index of the instance with the closest threshold value to the
/// desired target
///
/// </summary>
/// <param name="tcurve">a set of instances that have been generated by this class
/// </param>
/// <param name="threshold">the target threshold
/// </param>
/// <returns> the index of the instance that has threshold closest to
/// the target, or -1 if this could not be found (i.e. no data, or
/// bad threshold target)
/// </returns>
public static int getThresholdInstance(Instances tcurve, double threshold)
{
if (!RELATION_NAME.Equals(tcurve.relationName()) || (tcurve.numInstances() == 0) || (threshold < 0) || (threshold > 1.0))
{
return - 1;
}
if (tcurve.numInstances() == 1)
{
return 0;
}
double[] tvals = tcurve.attributeToDoubleArray(tcurve.numAttributes() - 1);
int[] sorted = Utils.sort(tvals);
return binarySearch(sorted, tvals, threshold);
}
// Test the classification result of each map that a user played,
// with the data available as if they were playing through it
public static void classifyTest(String dataString, String playerID)
{
try {
java.io.StringReader stringReader = new java.io.StringReader(dataString);
java.io.BufferedReader buffReader = new java.io.BufferedReader(stringReader);
/* NOTE THAT FOR NAIVE BAYES ALL WEIGHTS CAN BE = 1*/
//weka.core.converters.ConverterUtils.DataSource source = new weka.core.converters.ConverterUtils.DataSource("iris.arff");
weka.core.Instances thisData = new weka.core.Instances(buffReader); //source.getDataSet();
if (thisData.classIndex() == -1)
{
thisData.setClassIndex(thisData.numAttributes() - 1);
}
weka.core.Instances thisUniqueData = new weka.core.Instances(thisData);
if (thisUniqueData.classIndex() == -1)
{
thisUniqueData.setClassIndex(thisUniqueData.numAttributes() - 1);
}
thisUniqueData.delete();
if (allUniqueData == null)
{
allUniqueData = new weka.core.Instances(thisData);
if (allUniqueData.classIndex() == -1)
{
allUniqueData.setClassIndex(allUniqueData.numAttributes() - 1);
}
allUniqueData.delete();
}
weka.core.InstanceComparator com = new weka.core.InstanceComparator(false);
for (int i = 0; i < thisData.numInstances(); i++)
{
bool dup = false;
for (int j = 0; j < allUniqueData.numInstances(); j++)
{
if (com.compare(thisData.instance(i), allUniqueData.instance(j)) == 0)
{
Debug.Log("Duplicate found!");
dup = true;
break;
}
}
if (!dup)
{
allUniqueData.add(thisData.instance(i));
}
else
{
dupInstances++;
}
}
for (int i = 0; i < thisData.numInstances(); i++)
{
bool dup = false;
for (int j = 0; j < thisUniqueData.numInstances(); j++)
{
if (com.compare(thisData.instance(i), thisUniqueData.instance(j)) == 0)
{
Debug.Log("Duplicate found!");
dup = true;
break;
}
}
if (!dup)
{
thisUniqueData.add(thisData.instance(i));
}
else
{
dupInstancesSamePlayer++;
}
}
//Debug.Log("All Data Instance Count = " + thisData.numInstances());
//Debug.Log("Unique Data Instance Count = " + thisUniqueData.numInstances());
//Debug.Log("Done!");
} catch (java.lang.Exception ex)
{
Debug.LogError(ex.getMessage());
}
}
private void btnLoadStep_Click(object sender, EventArgs e)
{
if (m_loadStepInstances == null)
{
using (OpenFileDialog d = new OpenFileDialog())
{
d.Filter = "Arff File|*.arff";
if (d.ShowDialog() == System.Windows.Forms.DialogResult.OK)
{
m_loadStepInstances = new weka.core.Instances(new java.io.BufferedReader(new java.io.FileReader(d.FileName)));
m_loadStepInstances.setClassIndex(m_loadStepInstances.numAttributes() - 1);
clear_all();
}
}
}
else
{
for (int i = m_loadStepIdx; i < m_loadStepInstances.numInstances(); ++i)
{
var ins = m_loadStepInstances.instance(i);
var p = new valuePoint(ins.value(0), ins.value(1), (int)ins.classValue());
if (p.x < 0 || p.x >= 1 || p.y < 0 || p.y >= 1)
continue;
point_list.Add(p);
draw_point(p);
m_loadStepIdx = i + 1;
if (i % 1000 == 0)
{
break;
}
}
pictureBox1.Invalidate();
if (m_loadStepIdx == m_loadStepInstances.numInstances())
{
m_loadStepIdx = 0;
m_loadStepInstances = null;
}
}
}
private void btnBrowse_Click(object sender, EventArgs e)
{
panel1.Controls.Clear();
panel2.Controls.Clear();
testValues = new List <object>();
classes = new List <string>();
algoritmName = String.Empty;
SuccessfulAlgorithm = new List <AlgorithmModel>();
labelNames = new List <string>();
DialogResult result = openFileDialog.ShowDialog();
if (result == DialogResult.OK)
{
txtPath.Text = openFileDialog.FileName;
}
staticInsts = new weka.core.Instances(new java.io.FileReader(txtPath.Text));
for (int i = 0; i < staticInsts.attribute(staticInsts.numAttributes() - 1).numValues(); i++)
{
classes.Add(staticInsts.attribute(staticInsts.numAttributes() - 1).value(i));
}
AlgoritmAccurancy(staticInsts, new weka.classifiers.bayes.NaiveBayes(), "Naive Bayes", true);
AlgoritmAccurancy(staticInsts, new weka.classifiers.lazy.IBk(3), "KNN with k = 3", false);
AlgoritmAccurancy(staticInsts, new weka.classifiers.trees.RandomForest(), "Random Forest");
AlgoritmAccurancy(staticInsts, new weka.classifiers.trees.RandomTree(), "Random Tree");
AlgoritmAccurancy(staticInsts, new weka.classifiers.trees.J48(), "J48");
pointY = 20;
pointX = 20;
for (int i = 0; i < staticInsts.numAttributes() - 1; i++)
{
if (staticInsts.attribute(i).numValues() == 0)
{
pointX = 0;
string attName = staticInsts.attribute(i).name(); // Bunlar numeric textbox aç
Label attributeName = new Label();
attributeName.Size = new Size(70, 20);
attributeName.Text = attName + "\t :";
labelNames.Add(attributeName.Text);
attributeName.Location = new Point(pointX, pointY);
panel1.Controls.Add(attributeName);
pointX += 70;
TextBox txtValue = new TextBox();
txtValue.Location = new Point(pointX, pointY);
panel1.Controls.Add(txtValue);
panel1.Show();
pointY += 30;
testValues.Add(txtValue);
}
else
{
pointX = 0;
string attName = staticInsts.attribute(i).name(); // Bunlar numeric textbox aç
Label attributeName = new Label();
attributeName.Size = new Size(70, 20);
attributeName.Text = attName + "\t :";
labelNames.Add(attributeName.Text);
attributeName.Location = new Point(pointX, pointY);
panel1.Controls.Add(attributeName);
pointX += 70;
ComboBox cb = new ComboBox();
cb.DropDownStyle = ComboBoxStyle.DropDownList;
cb.Location = new Point(pointX, pointY);
List <string> items = new List <string>();
for (int j = 0; j < staticInsts.attribute(i).numValues(); j++)
{
items.Add(staticInsts.attribute(i).value(j).ToString()); // Bu gelen valueları dropdowna koy
}
cb.Items.AddRange(items.ToArray());
cb.SelectedIndex = 0;
panel1.Controls.Add(cb);
panel1.Show();
pointY += 30;
testValues.Add(cb);
}
}
double maxRatio = Double.MinValue;
foreach (var item in SuccessfulAlgorithm)
{
if (item.SuccessRatio > maxRatio)
{
maxRatio = item.SuccessRatio;
algoritmName = item.AlgorithName;
predictor = item.TrainModel.classifier;
}
}
string _maxRatio = string.Format("{0:0.00}", maxRatio);
lblSuccessulAlgorithm.Text = "The most Successful Algoritm is " + algoritmName + " and the ratio of accurancy is %" + _maxRatio;
Button btn = new Button();
btn.Click += Btn_Click;
btn.Location = new Point(pointX, pointY);
btn.Size = new Size(80, 20);
btn.Text = "DISCOVER";
btn.BackColor = Color.White;
panel1.Controls.Add(btn);
panel1.Show();
}
private weka.core.Instances CreateEmptyInstances()
{
var atts = new java.util.ArrayList();
atts.add(new weka.core.Attribute("x"));
atts.add(new weka.core.Attribute("y"));
if (!ckbClassIsNominal.Checked)
{
atts.add(new weka.core.Attribute("v"));
}
else
{
// - nominal
var attVals = new java.util.ArrayList();
//for(int i=0; i<MAXCLASSNUM; ++i)
// attVals.add(i.ToString());
attVals.add("0");
attVals.add("1");
atts.add(new weka.core.Attribute("v", attVals));
}
weka.core.Instances data = new weka.core.Instances("MyRelation", atts, 0);
data.setClassIndex(data.numAttributes() - 1);
return data;
}
private void button_Discover_Click(object sender, EventArgs e)
{
String s_newInstance = "";
StreamReader sr = new StreamReader(fileDirectory);
StreamWriter sw = new StreamWriter(@"test.arff", true);
String newDirectory = "test.arff"; // for algortihms
string line = "";
string comp = "@data";
string comp2 = "@DATA";
line = sr.ReadLine();
do
{
sw.WriteLine(line);
if (line == comp || line == comp2)
{
break;
}
} while ((line = sr.ReadLine()) != null);
for (int i = 0; i < dataGridView1.Rows.Count - 1; i++)
{
s_newInstance += (String)dataGridView1.Rows[i].Cells[1].Value + ","; //değiştir
}
s_newInstance += "?";
sw.WriteLine(s_newInstance);
sr.Close();
sw.Close();
switch (count)
{
case 1:
weka.core.Instances insts = new weka.core.Instances(new java.io.FileReader(newDirectory));
insts.setClassIndex(insts.numAttributes() - 1);
weka.filters.Filter myNominalData = new weka.filters.unsupervised.attribute.Discretize();
myNominalData.setInputFormat(insts);
insts = weka.filters.Filter.useFilter(insts, myNominalData);
//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);
double predictedClass = cl_Naive.classifyInstance(insts.instance(0));
Console.WriteLine("hey", insts.instance(0));
textBox3.Text = insts.classAttribute().value(Convert.ToInt32(predictedClass));
break;
case 2:
weka.core.Instances insts2 = new weka.core.Instances(new java.io.FileReader(fileDirectory));
insts2.setClassIndex(insts2.numAttributes() - 1);
//Nominal to Binary
weka.filters.Filter myBinaryData = new weka.filters.unsupervised.attribute.NominalToBinary();
myBinaryData.setInputFormat(insts2);
insts2 = weka.filters.Filter.useFilter(insts2, myBinaryData);
//Normalization
weka.filters.Filter myNormalized = new weka.filters.unsupervised.instance.Normalize();
myNormalized.setInputFormat(insts2);
insts2 = weka.filters.Filter.useFilter(insts2, myNormalized);
//randomize the order of the instances in the dataset.
weka.filters.Filter myRandom2 = new weka.filters.unsupervised.instance.Randomize();
myRandom2.setInputFormat(insts2);
insts2 = weka.filters.Filter.useFilter(insts2, myRandom2);
double predictedClass2 = cl_Knn.classifyInstance(insts2.instance(0));
textBox3.Text = insts2.classAttribute().value(Convert.ToInt32(predictedClass2));
break;
case 3:
weka.core.Instances insts3 = new weka.core.Instances(new java.io.FileReader(newDirectory));
insts3.setClassIndex(insts3.numAttributes() - 1);
weka.filters.Filter myNormalized2 = new weka.filters.unsupervised.instance.Normalize();
myNormalized2.setInputFormat(insts3);
insts3 = weka.filters.Filter.useFilter(insts3, myNormalized2);
//randomize the order of the instances in the dataset.
weka.filters.Filter myRandom3 = new weka.filters.unsupervised.instance.Randomize();
myRandom3.setInputFormat(insts3);
insts3 = weka.filters.Filter.useFilter(insts3, myRandom3);
double predictedClass3 = cl_Tree.classifyInstance(insts3.instance(0));
textBox3.Text = insts3.classAttribute().value(Convert.ToInt32(predictedClass3));
break;
case 4:
weka.core.Instances insts4 = new weka.core.Instances(new java.io.FileReader(newDirectory));
insts4.setClassIndex(insts4.numAttributes() - 1);
//cl = new weka.classifiers.functions.MultilayerPerceptron();
//Nominal to Binary
weka.filters.Filter myBinaryData2 = new weka.filters.unsupervised.attribute.NominalToBinary();
myBinaryData2.setInputFormat(insts4);
insts4 = weka.filters.Filter.useFilter(insts4, myBinaryData2);
//Normalization
weka.filters.Filter myNormalized3 = new weka.filters.unsupervised.instance.Normalize();
myNormalized3.setInputFormat(insts4);
insts4 = weka.filters.Filter.useFilter(insts4, myNormalized3);
//randomize the order of the instances in the dataset.
weka.filters.Filter myRandom4 = new weka.filters.unsupervised.instance.Randomize();
myRandom4.setInputFormat(insts4);
insts4 = weka.filters.Filter.useFilter(insts4, myRandom4);
double predictedClass4 = cl_NN.classifyInstance(insts4.instance(0));
textBox3.Text = insts4.classAttribute().value(Convert.ToInt32(predictedClass4));
break;
case 5:
weka.core.Instances insts5 = new weka.core.Instances(new java.io.FileReader(newDirectory));
insts5.setClassIndex(insts5.numAttributes() - 1);
//Nominal to Binary
weka.filters.Filter myBinaryData3 = new weka.filters.unsupervised.attribute.NominalToBinary();
myBinaryData3.setInputFormat(insts5);
insts5 = weka.filters.Filter.useFilter(insts5, myBinaryData3);
//Normalization
weka.filters.Filter myNormalized4 = new weka.filters.unsupervised.instance.Normalize();
myNormalized4.setInputFormat(insts5);
insts5 = weka.filters.Filter.useFilter(insts5, myNormalized4);
//randomize the order of the instances in the dataset.
weka.filters.Filter myRandom5 = new weka.filters.unsupervised.instance.Randomize();
myRandom5.setInputFormat(insts5);
insts5 = weka.filters.Filter.useFilter(insts5, myRandom5);
double predictedClass5 = cl_SVM.classifyInstance(insts5.instance(0));
textBox3.Text = insts5.classAttribute().value(Convert.ToInt32(predictedClass5));
break;
default:
textBox3.Text = "Error!";
break;
}
}