public List <string> Classify(string model, string test)
{
List <string> ret = new List <string>();
try
{
java.io.ObjectInputStream ois = new java.io.ObjectInputStream(new java.io.FileInputStream(model));
weka.classifiers.Classifier cl = (weka.classifiers.Classifier)ois.readObject();
ois.close();
weka.core.Instances insts = new weka.core.Instances(new java.io.FileReader(test));
insts.setClassIndex(insts.numAttributes() - 1);
for (int i = 0; i < 1; 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);
for (int j = 0; j < distrs.Length; j++)
{
string predicted = insts.classAttribute().value(j);
string distr = distrs[j].ToString("#0.000");
ret.Add(predicted + "," + distr);
}
}
return(ret);
}
catch
{
return(ret);
}
}
public void Test()
{
weka.core.Instances insts = new weka.core.Instances(new java.io.FileReader("D:\\android_analysis\\attributes.arff"));
insts.setClassIndex(insts.numAttributes() - 1);
weka.classifiers.Classifier cl = new weka.classifiers.trees.J48();
cl.buildClassifier(insts);
weka.filters.Filter myRandom = new weka.filters.unsupervised.instance.Randomize();
myRandom.setInputFormat(insts);
insts = weka.filters.Filter.useFilter(insts, myRandom);
int trainSize = (int)(insts.numInstances() * 0.66);
int testSize = insts.numInstances() - trainSize;
weka.core.Instances train = new weka.core.Instances(insts, 0, trainSize);
cl.buildClassifier(train);
for (int i = trainSize; 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) + ", " + actual + " --> " + predicted);
}
}
public static void Main(String[] args)
{
try
{
// Load the model
java.io.ObjectInputStream stream = new java.io.ObjectInputStream(new java.io.FileInputStream("iris_j48.model"));
weka.classifiers.Classifier qhClassifier = (weka.classifiers.Classifier)stream.readObject();
stream.close();
// This model was trained on 66% of instances from the iris dataset. Test the model on remaining 34% instances.
weka.core.Instances insts = new weka.core.Instances(new java.io.FileReader("iris.arff"));
insts.setClassIndex(insts.numAttributes() - 1);
int percentSplit = 66;
int trainSize = insts.numInstances() * percentSplit / 100;
int testSize = insts.numInstances() - trainSize;
int numCorrect = 0;
for (int i = trainSize; i < insts.numInstances(); i++)
{
weka.core.Instance currentInst = insts.instance(i);
double predictedClass = qhClassifier.classifyInstance(currentInst);
if (predictedClass == insts.instance(i).classValue())
{
numCorrect++;
}
}
Console.WriteLine(numCorrect + " out of " + testSize + " correct (" + (double)((double)numCorrect / (double)testSize * 100.0) + "%)");
}
catch (java.lang.Exception e)
{
e.printStackTrace();
}
}
public void UpdateClassifierFromInstanceString(String line)
{
try {
String[] lineSplit = line.Split(',');
double[] fullData = new double[lineSplit.Length - 1];
for (int j = 0; j < lineSplit.Length - 1; j++)
{
fullData[j] = double.Parse(lineSplit[j]);
}
String weightS = lineSplit[lineSplit.Length - 1];
//Debug.Log(weightS);
weightS = weightS.Remove(weightS.Length - 1, 1);
if (weightS[weightS.Length - 1] == '}')
{
weightS = weightS.Remove(weightS.Length - 1, 1);
}
weightS = weightS.Remove(0, 1);
//Debug.Log(weightS);
double weight = double.Parse(weightS);
weka.core.Instance newInstance = new weka.core.Instance(weight, fullData);
playerData.add(newInstance);
classifier.buildClassifier(playerData);
}
catch (java.lang.Exception ex)
{
Debug.LogError(ex.getMessage());
}
}
public void test_ToStrings_gets_the_string_representation_of_the_instance()
{
Runtime rt = TestingHelpers.LoadSmallRuntime <TitanicDataRow2>("titanic_train.csv", 0, 3);
weka.core.Instance instance = rt[0].Impl;
System.Collections.Generic.IEnumerable <string> strs = instance.ToStrings();
Assert.AreEqual(new[] { "0", "3", "Braund, Mr. Owen Harris", "male", "22", "1", "0", "A/5 21171", "7.25", "?", "S" }, strs);
}
public override double classifyInstance(Instance instance)
{
double x = instance.value(idxAttribute);
double p = GetP(x);
if (p < epsilon)
return 1;
else
return 0;
}
//Only 1 output: last instance
static public string do_Classification_bySerialClassfier_1out_standAlone
(SerializedClassifier serialClassifier, weka.core.Instances instances, int colClass)
{
instances.setClassIndex(colClass);
weka.core.Instance each = instances.instance(instances.numInstances() - 1);
double predictedClass = serialClassifier.classifyInstance(each);
return(instances.classAttribute().value((int)predictedClass));
}
public override double classifyInstance(weka.core.Instance instance)
{
var d = base.classifyInstance(instance);
if (d == 3)
{
return(2);
}
return(d);
}
protected override string instanceToLibsvm(weka.core.Instance inst)
{
//StringBuffer.__<clinit>();
//StringBuffer buffer = new StringBuffer(new StringBuilder().append("").append(inst.classValue()).toString());
StringBuffer buffer = new StringBuffer(new StringBuilder().append("").append(inst.classValue() - 1).toString());
for (int i = 0; i < inst.numAttributes(); i++)
{
if ((i != inst.classIndex()) && (inst.value(i) != 0f))
{
buffer.append(new StringBuilder().append(" ").append((int)(i + 1)).append(":").append(inst.value(i)).toString());
}
}
return(buffer.toString());
}
public static void CalculateSuccessForAnn(weka.core.Instances originalInsts)
{
try
{
var form = Form.ActiveForm as Form1;
form.successPrcAnn.Text = "Training...";
form.successRtAnn.Text = "../" + testSize;
weka.core.Instances insts = originalInsts;
// Pre-process
insts = ConvertNominalToNumeric(insts);
insts = Normalize(insts);
// Classify
weka.classifiers.Classifier cl = new weka.classifiers.functions.MultilayerPerceptron();
weka.core.Instances train = new weka.core.Instances(insts, 0, trainSize);
cl.buildClassifier(train);
int numCorrect = 0;
double percentage = 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++;
}
percentage = (double)numCorrect / (double)testSize * 100.0;
form.successRtAnn.Text = numCorrect + "/" + testSize;
form.successPrcAnn.Text = String.Format("{0:0.00}", percentage) + "%";
}
succesRates.Add(Classifier.ANN, percentage);
classifiers.Add(Classifier.ANN, cl);
}
catch (java.lang.Exception ex)
{
ex.printStackTrace();
MessageBox.Show(ex.ToString(), "Error for Neural Network", MessageBoxButtons.OK, MessageBoxIcon.Error);
}
catch (Exception)
{
MessageBox.Show("Error for Neural Network", "Error for Neural Network", MessageBoxButtons.OK, MessageBoxIcon.Error);
}
}
public double GetVolume(weka.core.Instance instance)
{
//double v = m_counts[2] * m_tp - m_counts[0] * m_sl + (m_tp - 1.5 * m_sl) * m_counts[1] / 5;
//if (v < 0)
// return 0;
//v /= 1000;
double sum = (m_counts[0] + m_counts[1] + m_counts[2]);
if (sum == 0)
{
return(0);
}
double v = m_counts[2] / sum;
return(Math.Round(v, 2));
}
//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 void test_binarisation_in_instances_builder()
{
TestingRow[] rows = new[] {
new TestingRow {
CLASS = 1, ATT_1 = 111, ATT_2 = 222, ATT_3 = 333, ATT_4 = 444, ATT_5 = 555, ATT_6 = 666, ATT_7 = 777, ATT_8 = 888
},
new TestingRow {
CLASS = 0, ATT_1 = 1112, ATT_2 = 2222, ATT_3 = 3332, ATT_4 = 4442, ATT_5 = 5552, ATT_6 = 6662, ATT_7 = 7772, ATT_8 = 8882
}
};
weka.core.Instances instances = new InstancesBuilder <TestingRow>(rows, 0).Build();
Assert.AreEqual(25, instances.numAttributes());
weka.core.Instance i1 = instances.instance(0);
weka.core.Instance i2 = instances.instance(1);
// These are the standard properties
Assert.AreEqual(1.0, i1.value(0)); Assert.AreEqual(1.0, i1.classValue());
Assert.AreEqual(0.0, i2.value(0)); Assert.AreEqual(0.0, i2.classValue());
Assert.AreEqual(0.0, i1.value(1)); Assert.AreEqual("111", i1.stringValue(1));
Assert.AreEqual(1.0, i2.value(1)); Assert.AreEqual("1112", i2.stringValue(1));
Assert.AreEqual(0.0, i1.value(2)); Assert.AreEqual("222", i1.stringValue(2));
Assert.AreEqual(1.0, i2.value(2)); Assert.AreEqual("2222", i2.stringValue(2));
Assert.AreEqual(0.0, i1.value(3)); Assert.AreEqual("333", i1.stringValue(3));
Assert.AreEqual(1.0, i2.value(3)); Assert.AreEqual("3332", i2.stringValue(3));
Assert.AreEqual(0.0, i1.value(4)); Assert.AreEqual("444", i1.stringValue(4));
Assert.AreEqual(1.0, i2.value(4)); Assert.AreEqual("4442", i2.stringValue(4));
Assert.AreEqual(0.0, i1.value(5)); Assert.AreEqual("555", i1.stringValue(5));
Assert.AreEqual(1.0, i2.value(5)); Assert.AreEqual("5552", i2.stringValue(5));
Assert.AreEqual(0.0, i1.value(6)); Assert.AreEqual("666", i1.stringValue(6));
Assert.AreEqual(1.0, i2.value(6)); Assert.AreEqual("6662", i2.stringValue(6));
Assert.AreEqual(0.0, i1.value(7)); Assert.AreEqual("777", i1.stringValue(7));
Assert.AreEqual(1.0, i2.value(7)); Assert.AreEqual("7772", i2.stringValue(7));
Assert.AreEqual(0.0, i1.value(8)); Assert.AreEqual("888", i1.stringValue(8));
Assert.AreEqual(1.0, i2.value(8)); Assert.AreEqual("8882", i2.stringValue(8));
// These are the new binarized
for (int i = 9; i < 25; i += 2)
{
Assert.AreEqual(1.0, i1.value(i)); Assert.AreEqual(0.0, i2.value(i));
Assert.AreEqual(0.0, i1.value(i + 1)); Assert.AreEqual(1.0, i2.value(i + 1));
}
}
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 UpdateClassifier(double[] newMapData, int lastRating)
{
try
{
// Copy data to a new array and add the rating (the class of this instance)
double [] fullData = new double[newMapData.Length + 1];
for (int i = 0; i < newMapData.Length; i++)
{
fullData[i] = newMapData[i];
}
//*********fullData[fullData.Length-1] = (double) lastRating;
fullData[fullData.Length - 1] = (double)((lastRating - 1) / 3);
//Debug.LogWarning(fullData[fullData.Length-1]);
double weight = 0;
if (lastRating == 1 || lastRating == 6)
{
weight = 2;
}
else if (lastRating == 2 || lastRating == 5)
{
weight = 1;
}
else
{
weight = 0.5;
}
// Naive Bayes defaults all data to weight of 1, do same for this instance
//*******weka.core.Instance newInstance = new weka.core.Instance(1,fullData);
weka.core.Instance newInstance = new weka.core.Instance(weight, fullData);
playerData.add(newInstance);
// This version of Naive Bayes is not updateable, so just rebuild the classifier
// Updateable version has slightly lower accuracy
classifier.buildClassifier(playerData);
}
catch (java.lang.Exception ex)
{
Debug.LogError(ex.getMessage());
}
}
/// <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);
}
public override double classifyInstance(Instance instance)
{
int r = 2;
if (m_prop <= 0)
{
r = 2;
}
else
{
if (x == y)
{
if (w > z)
r = lastR;
else
r = 2;
}
else if (x > y)
{
return 0;
}
else if (y > x)
{
return 1;
}
else
{
return 2;
}
}
//lastR = r;
return r;
//return (new Random()).NextDouble() > 0.5 ? 1 : 0;
//double v = m_innerClassifier.classifyInstance(instance);
//if (v == 0)
// return 0;
//else if (v == 1)
// return 1;
//else
// return 2;
}
private void testButton_Click(object sender, EventArgs e)
{
var form = Form.ActiveForm as Form1;
if (readyToTest)
{
weka.classifiers.Classifier cl = classifiers[highestSuccessRate.Key];
weka.core.Instance inst = new weka.core.Instance(insts.numAttributes() - 1);
inst.setDataset(insts);
for (int i = 0; i < inputObjects.Count; i++)
{
if (inputObjects[i].numeric)
{
inst.setValue(i, Decimal.ToDouble(inputObjects[i].num.Value));
}
else
{
inst.setValue(i, inputObjects[i].nom.SelectedItem.ToString());
}
}
try
{
string[] values = insts.attribute(insts.numAttributes() - 1).toString().Split('{', '}')[1].Split(',');
double classOfData = cl.classifyInstance(inst);
int idx = Convert.ToInt32(classOfData);
form.testResult.Text = values[idx];
}
catch (Exception ex)
{
MessageBox.Show(ex.Message, "Error", MessageBoxButtons.OK, MessageBoxIcon.Error);
}
}
else
{
MessageBox.Show("Program is not ready to test, probably needs to process data first.", "Not ready", MessageBoxButtons.OK, MessageBoxIcon.Information);
}
}
public int InitializeClassifier(string[] atributes, string[] gestures, string classAttribute, string modelLocation)
{
java.io.ObjectInputStream ois = new java.io.ObjectInputStream(new java.io.FileInputStream(modelLocation));
m_cl = (weka.classifiers.Classifier)ois.readObject();
//Declare the feature vector
weka.core.FastVector fvWekaFeatureVector = new weka.core.FastVector(atributes.Length + 1);
for (int i = 0; i < atributes.Length; i++)
{
weka.core.Attribute aux = new weka.core.Attribute(atributes[i]);
fvWekaFeatureVector.addElement(aux);
}
//Declare the class weka.core.Attribute along with its values
weka.core.FastVector fvClassValues = new weka.core.FastVector(gestures.Length);
for (int i = 0; i < gestures.Length; i++)
{
weka.core.Attribute z1 = new weka.core.Attribute(atributes[i]);
fvClassValues.addElement(gestures[i]);
}
//fvClassValues.addElement("yes");
//fvClassValues.addElement("no");
weka.core.Attribute ClassAttribute = new weka.core.Attribute(classAttribute, fvClassValues);
fvWekaFeatureVector.addElement(ClassAttribute);
dataSet = new weka.core.Instances("TestRel", fvWekaFeatureVector, 10);
dataSet.setClassIndex(atributes.Length);
testInstance = new weka.core.Instance(atributes.Length + 1);
return(1);
}
/// <summary> Input an instance for filtering. Ordinarily the instance is
/// processed and made available for output immediately. Some filters
/// require all instances be read before producing output, in which
/// case output instances should be collected after calling
/// batchFinished(). If the input marks the start of a new batch, the
/// output queue is cleared. This default implementation assumes all
/// instance conversion will occur when batchFinished() is called.
///
/// </summary>
/// <param name="instance">the input instance
/// </param>
/// <returns> true if the filtered instance may now be
/// collected with output().
/// </returns>
/// <exception cref="NullPointerException">if the input format has not been
/// defined.
/// </exception>
/// <exception cref="Exception">if the input instance was not of the correct
/// format or if there was a problem with the filtering.
/// </exception>
public virtual bool input(Instance instance)
{
if (m_InputFormat == null)
{
throw new System.NullReferenceException("No input instance format defined");
}
if (m_NewBatch)
{
m_OutputQueue = new Queue();
m_NewBatch = false;
}
bufferInput(instance);
return false;
}
/// <summary> Takes string values referenced by an Instance and copies them from a
/// source dataset to a destination dataset. The instance references are
/// updated to be valid for the destination dataset. The instance may have the
/// structure (i.e. number and attribute position) of either dataset (this
/// affects where references are obtained from). Only works if the number
/// of string attributes is the same in both indices (implicitly these string
/// attributes should be semantically same but just with shifted positions).
///
/// </summary>
/// <param name="instance">the instance containing references to strings in the source
/// dataset that will have references updated to be valid for the destination
/// dataset.
/// </param>
/// <param name="instSrcCompat">true if the instance structure is the same as the
/// source, or false if it is the same as the destination (i.e. which of the
/// string attribute indices contains the correct locations for this instance).
/// </param>
/// <param name="srcDataset">the dataset for which the current instance string
/// references are valid (after any position mapping if needed)
/// </param>
/// <param name="srcStrAtts">an array containing the indices of string attributes
/// in the source datset.
/// </param>
/// <param name="destDataset">the dataset for which the current instance string
/// references need to be inserted (after any position mapping if needed)
/// </param>
/// <param name="destStrAtts">an array containing the indices of string attributes
/// in the destination datset.
/// </param>
protected internal virtual void copyStringValues(Instance instance, bool instSrcCompat, Instances srcDataset, int[] srcStrAtts, Instances destDataset, int[] destStrAtts)
{
if (srcDataset == destDataset)
{
return ;
}
if (srcStrAtts.Length != destStrAtts.Length)
{
throw new System.ArgumentException("Src and Dest string indices differ in length!!");
}
for (int i = 0; i < srcStrAtts.Length; i++)
{
int instIndex = instSrcCompat?srcStrAtts[i]:destStrAtts[i];
Attribute src = srcDataset.attribute(srcStrAtts[i]);
Attribute dest = destDataset.attribute(destStrAtts[i]);
if (!instance.isMissing(instIndex))
{
//System.err.println(instance.value(srcIndex)
// + " " + src.numValues()
// + " " + dest.numValues());
//UPGRADE_WARNING: Data types in Visual C# might be different. Verify the accuracy of narrowing conversions. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1042'"
int valIndex = dest.addStringValue(src, (int) instance.value_Renamed(instIndex));
// setValue here shouldn't be too slow here unless your dataset has
// squillions of string attributes
instance.setValue(instIndex, (double) valIndex);
}
}
}
public double[] ClassifyNewData(double[] newData)
{
weka.core.Instance newInstance = new weka.core.Instance(1,newData);
newInstance.setDataset(playerData);
return classifier.distributionForInstance(newInstance);
}
public void UpdateClassifier(double[] newMapData, int lastRating)
{
try
{
// Copy data to a new array and add the rating (the class of this instance)
double [] fullData = new double[newMapData.Length+1];
for (int i = 0; i < newMapData.Length; i++)
fullData[i] = newMapData[i];
//*********fullData[fullData.Length-1] = (double) lastRating;
fullData[fullData.Length-1] = (double)((lastRating-1)/3);
//Debug.LogWarning(fullData[fullData.Length-1]);
double weight = 0;
if (lastRating == 1 || lastRating == 6)
weight = 2;
else if (lastRating == 2 || lastRating == 5)
weight = 1;
else
weight = 0.5;
// Naive Bayes defaults all data to weight of 1, do same for this instance
//*******weka.core.Instance newInstance = new weka.core.Instance(1,fullData);
weka.core.Instance newInstance = new weka.core.Instance(weight,fullData);
playerData.add(newInstance);
// This version of Naive Bayes is not updateable, so just rebuild the classifier
// Updateable version has slightly lower accuracy
classifier.buildClassifier(playerData);
}
catch (java.lang.Exception ex)
{
Debug.LogError(ex.getMessage());
}
}
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 ";
}
}
}
/// <summary> Input an instance for filtering. Ordinarily the instance is processed
/// and made available for output immediately. Some filters require all
/// instances be read before producing output.
///
/// </summary>
/// <param name="instance">the input instance
/// </param>
/// <returns> true if the filtered instance may now be
/// collected with output().
/// </returns>
/// <exception cref="IllegalStateException">if no input format has been set.
/// </exception>
public override bool input(Instance instance)
{
if (getInputFormat() == null)
{
throw new System.SystemException("No input instance format defined");
}
if (m_NewBatch)
{
resetQueue();
m_NewBatch = false;
}
if (instance.isMissing(m_AttIndex.Index))
{
if (!get_MatchMissingValues())
{
push((Instance) instance.copy());
return true;
}
else
{
return false;
}
}
if (Numeric)
{
if (!m_Values.Invert)
{
if (instance.value_Renamed(m_AttIndex.Index) < m_Value)
{
push((Instance) instance.copy());
return true;
}
}
else
{
if (instance.value_Renamed(m_AttIndex.Index) >= m_Value)
{
push((Instance) instance.copy());
return true;
}
}
}
if (Nominal)
{
if (m_Values.isInRange((int) instance.value_Renamed(m_AttIndex.Index)))
{
Instance temp = (Instance) instance.copy();
if (get_ModifyHeader())
{
temp.setValue(m_AttIndex.Index, m_NominalMapping[(int) instance.value_Renamed(m_AttIndex.Index)]);
}
push(temp);
return true;
}
}
return false;
}
///
/// <summary> * Classifies a given instance.
/// * </summary>
/// * <param name="instance"> the instance to be classified </param>
/// * <returns> index of the predicted class </returns>
///
public override double classifyInstance(Instance instance)
{
if (randomGenerator == null)
{
randomGenerator = new System.Random((int)System.DateTime.Now.Ticks);
}
var rand = randomGenerator.NextDouble();
for (int i = 0; i < m_normalCounts.Length; ++i)
{
if (rand < m_normalCounts[i])
return i == 1 ? 0 : i;
}
return 0;
}
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));
}
public override double[] distributionForInstance(Instance instance)
{
double n = x + y + z + w;
return new double[] { (x + w / 2) / n, (y + w / 2) / n, z / n, 0 };
}
/// <summary> Input an instance for filtering. Filter requires all
/// training instances be read before producing output.
///
/// </summary>
/// <param name="instance">the input instance
/// </param>
/// <returns> true if the filtered instance may now be
/// collected with output().
/// </returns>
/// <exception cref="IllegalStateException">if no input structure has been defined
/// </exception>
public override bool input(Instance instance)
{
if (getInputFormat() == null)
{
throw new System.SystemException("No input instance format defined");
}
if (m_NewBatch)
{
resetQueue();
m_NewBatch = false;
}
if (m_FirstBatchDone)
{
push(instance);
return true;
}
else
{
bufferInput(instance);
return false;
}
}
public override double classifyInstance(Instance instance)
{
if (m_mustValue.HasValue)
return m_mustValue.Value;
instance.setDataset(m_sampleInstances);
instance.setClassValue(0);
m_sampleInstances.clear();
m_sampleInstances.add(instance);
double[] d = classifyInstances(m_sampleInstances);
return d[0];
}
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));
}
}
public override double classifyInstance(weka.core.Instance instance)
{
return(m_latestClass);
}
public double GetVolume(weka.core.Instance instance)
{
return(m_volume);
}
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 double do_test_single(weka.classifiers.Classifier classifier, weka.core.Instances insts_test)
{
weka.core.Instance currentInst = insts_test.lastInstance();
return(classifier.classifyInstance(currentInst));
}
public weka.core.Instance GenerateFeatures(PokerHand hand, int rIdx, int aIdx, weka.core.Instances data, bool generateClass = true)
{
// Check that we are using limit betting.
Debug.Assert(hand.Context.BettingType == BettingType.FixedLimit);
var results = new weka.core.Instance(data.numAttributes());
results.setDataset(data);
int attIdx = 0;
foreach (var method in typeof(LimitFeatureGenerator).GetMethods())
{
// Get all the features of this class.
var attributes = method.GetCustomAttributes(typeof(Feature), true);
if (attributes.Length == 0)
continue;
// Get the feature attribute on this method.
var attr = ((Feature)attributes[0]);
// Get the name for this attribute
string name = attr.Name;
//Console.WriteLine("Hand: {0} Feature: {1}", hand.Context.ID, name);
// Get the feature only if it's applicable to this situation.
object feature = null;
if(rIdx >= (int)attr.MinRound && rIdx <= (int)attr.MaxRound)
feature = method.Invoke(this, new object[] { hand, rIdx, aIdx });
if (SkipMissingFeatures && (feature == null || feature.ToString() == "?"))
continue;
switch (attr.FType) {
case FeatureType.Continuous:
results.setValue(attIdx, (double)feature);
break;
case FeatureType.Discrete:
results.setValue(attIdx, (int)feature);
break;
case FeatureType.Boolean:
case FeatureType.Nominal:
case FeatureType.Enum:
{
var attribute = data.attribute(attIdx);
var attVal = attribute.indexOfValue(feature.ToString());
if(attVal < 0 || attVal > attribute.numValues())
throw new Exception(string.Format ("Invalid attribute value: {0} for attribute {1} (idx: {2} total values: {3}", feature.ToString(), name, attVal, attribute.numValues()));
results.setValue(attribute, attVal);
}
break;
case FeatureType.String:
{
var attribute = data.attribute(attIdx);
results.setValue(attribute, feature.ToString());
}
break;
default: throw new Exception("Unspecified feature type for feature: " + method.Name);
}
attIdx++;
}
if(generateClass)
{
var classAttr = data.classAttribute();
switch (hand.Rounds[rIdx].Actions[aIdx].Type)
{
case ActionType.Bet:
case ActionType.Raise: results.setClassValue(classAttr.indexOfValue("Raise"));
break;
case ActionType.Call:
case ActionType.Check: results.setClassValue(classAttr.indexOfValue("Call"));
break;
case ActionType.Fold: results.setClassValue(classAttr.indexOfValue("Fold"));;
break;
default:
break;
}
}
return results;
}
public double[] ClassifyNewData(double[] newData)
{
weka.core.Instance newInstance = new weka.core.Instance(1, newData);
newInstance.setDataset(playerData);
return(classifier.distributionForInstance(newInstance));
}
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));
}
/// <summary> Input an instance for filtering. Ordinarily the instance is processed
/// and made available for output immediately. Some filters require all
/// instances be read before producing output.
///
/// </summary>
/// <param name="instance">the input instance
/// </param>
/// <returns> true if the filtered instance may now be
/// collected with output().
/// </returns>
/// <exception cref="IllegalStateException">if no input structure has been defined.
/// </exception>
public override bool input(Instance instance)
{
if (getInputFormat() == null)
{
throw new System.SystemException("No input instance format defined");
}
if (m_NewBatch)
{
resetQueue();
m_NewBatch = false;
}
if (getOutputFormat().numAttributes() == 0)
{
return false;
}
double[] vals = new double[getOutputFormat().numAttributes()];
for (int i = 0; i < m_SelectedAttributes.Length; i++)
{
int current = m_SelectedAttributes[i];
vals[i] = instance.value_Renamed(current);
}
Instance inst = null;
if (instance is SparseInstance)
{
inst = new SparseInstance(instance.weight(), vals);
}
else
{
inst = new Instance(instance.weight(), vals);
}
copyStringValues(inst, false, instance.dataset(), m_InputStringIndex, getOutputFormat(), OutputStringIndex);
inst.Dataset = getOutputFormat();
push(inst);
return true;
}
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";
}
/// <summary> Adds an output instance to the queue. The derived class should use this
/// method for each output instance it makes available.
///
/// </summary>
/// <param name="instance">the instance to be added to the queue.
/// </param>
protected internal virtual void push(Instance instance)
{
if (instance != null)
{
copyStringValues(instance, m_OutputFormat, m_OutputStringAtts);
instance.Dataset = m_OutputFormat;
m_OutputQueue.push(instance);
}
}
/// <summary> Adds the supplied input instance to the inputformat dataset for
/// later processing. Use this method rather than
/// getInputFormat().add(instance). Or else. Note that the provided
/// instance gets copied when buffered.
///
/// </summary>
/// <param name="instance">the <code>Instance</code> to buffer.
/// </param>
protected internal virtual void bufferInput(Instance instance)
{
if (instance != null)
{
copyStringValues(instance, m_InputFormat, m_InputStringAtts);
m_InputFormat.add(instance);
}
}
/// <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 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);
}
/// <summary> Takes string values referenced by an Instance and copies them from a
/// source dataset to a destination dataset. The instance references are
/// updated to be valid for the destination dataset. The instance may have the
/// structure (i.e. number and attribute position) of either dataset (this
/// affects where references are obtained from). The source dataset must
/// have the same structure as the filter input format and the destination
/// must have the same structure as the filter output format.
///
/// </summary>
/// <param name="instance">the instance containing references to strings in the source
/// dataset that will have references updated to be valid for the destination
/// dataset.
/// </param>
/// <param name="instSrcCompat">true if the instance structure is the same as the
/// source, or false if it is the same as the destination
/// </param>
/// <param name="srcDataset">the dataset for which the current instance string
/// references are valid (after any position mapping if needed)
/// </param>
/// <param name="destDataset">the dataset for which the current instance string
/// references need to be inserted (after any position mapping if needed)
/// </param>
protected internal virtual void copyStringValues(Instance instance, bool instSrcCompat, Instances srcDataset, Instances destDataset)
{
copyStringValues(instance, instSrcCompat, srcDataset, m_InputStringAtts, destDataset, m_OutputStringAtts);
}
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(), "");
}
}
public void UpdateClassifierFromInstanceString(String line)
{
try {
String[] lineSplit = line.Split(',');
double[] fullData = new double[lineSplit.Length-1];
for (int j = 0; j < lineSplit.Length-1; j++)
fullData[j] = double.Parse(lineSplit[j]);
String weightS = lineSplit[lineSplit.Length-1];
//Debug.Log(weightS);
weightS = weightS.Remove(weightS.Length-1,1);
if(weightS[weightS.Length-1] == '}')
weightS = weightS.Remove(weightS.Length-1,1);
weightS = weightS.Remove(0,1);
//Debug.Log(weightS);
double weight = double.Parse(weightS);
weka.core.Instance newInstance = new weka.core.Instance(weight,fullData);
playerData.add(newInstance);
classifier.buildClassifier(playerData);
}
catch (java.lang.Exception ex)
{
Debug.LogError(ex.getMessage());
}
}
///
/// <summary> * Classifies a given instance.
/// * </summary>
/// * <param name="instance"> the instance to be classified </param>
/// * <returns> index of the predicted class </returns>
///
public override double classifyInstance(Instance instance)
{
//if (m_counts[1] == 0 && m_counts[2] == 0)
// return 0;
//else if (m_counts[0] == 0 && m_counts[2] == 0)
// return 1;
//else
// return 2;
double a = m_counts[0] / sumOfWeights;
double b = m_counts[1] / sumOfWeights;
if (a >= b && a >= m_delta)
return 0;
else if (b >= a && b >= m_delta)
return 1;
else
return 2;
}
public override double classifyInstance(Instance instance)
{
if (m_mustValue != -1)
return m_mustValue;
double delta = m_delta;
if (delta == -1)
{
delta = 0.5;
}
return m_Classifier.distributionForInstance(instance)[1] < m_delta ? 0 : 2;
}
