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 string WEKA_GETMLP(weka.core.Instances insts)
{
string THEOUTPUT = " ";
try
{
insts.setClassIndex(insts.numAttributes() - 1);
weka.classifiers.functions.MultilayerPerceptron mlp = new weka.classifiers.functions.MultilayerPerceptron();
//SETTING PARAMETERS
weka.core.Utils.splitOptions("-L 0.3 -M 0.2 -N 500 -V 0 -S 0 -E 20 -H 3");
mlp.buildClassifier(insts);
THEOUTPUT = mlp.ToString();
}
catch (java.lang.Exception ex)
{
ex.printStackTrace();
}
return(THEOUTPUT);
//new Program().Method3
}
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);
}
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);
}
}
/// <summary>
/// Creates a classifier of the desired type from an .arff file
/// </summary>
/// <param name="ARFFfile">The arff file to read from. Should be a full path.</param>
/// <param name="classifier">The type of classifier you want to make.</param>
/// <returns>The classifier you created</returns>
public void createModel(string ARFFfile, Classifier myClassifier)
{
if (debug)
{
Console.WriteLine("Loading ARFF file " + ARFFfile);
}
_classifier = GetClassifier(myClassifier);
try
{
_dataSet = new weka.core.Instances(new java.io.FileReader(ARFFfile));
if (debug)
{
Console.WriteLine("You have " + _dataSet.numAttributes() + " attributes.");
}
_dataSet.setClassIndex(_dataSet.numAttributes() - 1);
_classifier.buildClassifier(_dataSet);
if (debug)
{
Console.WriteLine(_classifier.toString());
}
}
catch (Exception e)
{
Console.WriteLine("You failed. End of Game. Poor Weka.");
Console.WriteLine(e);
}
}
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();
}
}
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();
}
}
}
//**************************************************************************************
/// <summary>
/// Returns classifier prediction based on provided parameters.
/// </summary>
/// <param name="iParameters"></param>
public Prediction Predict(float[] iParameters)
{
if (iParameters is null)
{
throw new ArgumentNullException(nameof(iParameters));
}
WaitTillModelReady();
// Create instances object
var instances = new weka.core.Instances("Test", Attributes, 1);
instances.setClassIndex(instances.numAttributes() - 1);
// Create single instance
var instance = new weka.core.DenseInstance(instances.numAttributes() - 1);
instance.setDataset(instances);
// Fill instance with data
for (int i = 0; i < iParameters.Length; i++)
{
instance.setValue(i, iParameters[i]);
}
// Get prediction
double prediction = Model.classifyInstance(instance);
// Convert prediction to decision
return((Prediction)Enum.Parse(typeof(Prediction), instances.classAttribute().value((int)prediction)));
}
//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));
}
//void report_write(){
// Evaluation eval = new Evaluation(trainCases);
// eval.crossValidateModel(tree, trainCases, 3, new Random(1));
// System.out.println(eval.toSummaryString("\nResults\n======\n", false));
// System.out.println(eval.toClassDetailsString());
// System.out.println(eval.toMatrixString());
// System.out.println("accuracy:"+eval.pctCorrect());
// System.out.println("Specificity:"+eval.truePositiveRate(0));
// System.out.println("sensitivity:"+eval.trueNegativeRate(0));
//}
//======================================================================
//--------- Seperate Part -------
public static weka.core.Instances getInst(string path_file, int colIndex)
{
try
{
weka.core.Instances ins = new weka.core.Instances(new java.io.FileReader(path_file));
ins.setClassIndex(colIndex);
return(ins);
}
catch (Exception ex)
{
TheSys.showError("Err: " + ex.ToString(), true);
return(null);
}
}
/// <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!");
}
}
//**************************************************************************************
/// <summary>
/// Creates and fills in weka instances object.
/// </summary>
protected static weka.core.Instances CreateInstances(CandlestickCollection iCandlesticks, List <int> iTrainingPoints, java.util.ArrayList iAttributes, List <ClassifierParameter> iParameters, Candlestick.Period iPeriod, int iMaxProfitTime)
{
int dataSize = iTrainingPoints.Count;
// Create instance object
var instances = new weka.core.Instances("Data", iAttributes, dataSize);
// Set class index
instances.setClassIndex(instances.numAttributes() - 1);
// Fill instances
FillInstances(instances, CalculateDecisions(iCandlesticks[kTrainingPeriod], iTrainingPoints, iMaxProfitTime), CalculateParameters(iParameters, iCandlesticks, iTrainingPoints, iPeriod));
return(instances);
}
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);
}
}
private void initialiseClassifier()
{
// Loads the file to grab the filename.
GameObject player = GameObject.Find("Player");
PlayerBehaviour playerBehaviourScript = player.GetComponent <PlayerBehaviour>();
//loads training and test data, builds new classifier and trains it
insts = new weka.core.Instances(new java.io.FileReader(playerBehaviourScript.stringToEdit));
insts.setClassIndex(4);
j48Classifier = new weka.classifiers.trees.J48();
//Optionals methods currently testing for improvements
//j48Classifier.setUnpruned (true);
//j48Classifier.setUseLaplace (true);
j48Classifier.buildClassifier(insts);
//NO CURRENT TEST SET READY TO USE
//weka.core.Instances testData = new weka.core.Instances(new java.io.FileReader("TestSet.arff"));
//testData.setClassIndex(5);
//weka.classifiers.Evaluation eval = new weka.classifiers.Evaluation(insts);
//eval.evaluateModel (j48Classifier,testData);
Debug.Log(j48Classifier.toSummaryString());
}
/* 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());
}
}
static public List <string> do_Classification_bySerialClassfier_standAlone
(SerializedClassifier serialClassifier, weka.core.Instances instances, int colClass)
{
instances.setClassIndex(colClass);
List <string> listPredictClass = new List <string>();
double predictedClass; double actualClass; predict_numCorrect = 0;
weka.core.Instance each;
for (int i = 0; i < instances.numInstances(); i++)
{
each = instances.instance(i);
predictedClass = serialClassifier.classifyInstance(each);
actualClass = instances.instance(i).classValue();
if (predictedClass == actualClass)
{
predict_numCorrect++;
}
//Get Class Name
listPredictClass.Add(instances.classAttribute().value((int)predictedClass));
}
return(listPredictClass);
}
public static void do_Classification_full(Boolean loadData)
{
try
{
if (loadData == true)
{
step_loadInstance();
}
insts.setClassIndex(colClass);//Which Column is classified
if (random_sort == true)
{
step_randomInstanceOrder();
}
//------ Train & Test ----------
classifier = step_buildClassifier(classify_model);
txtResult_Reset();
step_train_test();
}
catch (Exception ex)
{
TheSys.showError("Err: " + ex.ToString(), true);
}
}
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);
}
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)
{
}
}
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 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 object Clone() {
// MemoryStream ms = new MemoryStream(500000000);
// System.Runtime.Serialization.Formatters.Binary.BinaryFormatter bf =
// new System.Runtime.Serialization.Formatters.Binary.BinaryFormatter(null,
// new System.Runtime.Serialization.StreamingContext(System.Runtime.Serialization.StreamingContextStates.Clone));
// bf.Serialize(ms, this);
// ms.Seek(0, SeekOrigin.Begin);
// object obj = bf.Deserialize(ms);
// ms.Close();
// return obj;
//}
public void train(bool LoadingFromFile)
{
if (!LoadingFromFile) {
DisplayMessage("Begin training on Efigi galaxies...");
Console.Write("Begin training on Efigi galaxies...");
}
else {
DisplayImage(0);
DisplayMessage("Load from file...");
Console.Write("Load from file...");
frV = new GeneralMatrix(ReadFVMatrix(0));
fgV = new GeneralMatrix(ReadFVMatrix(1));
fbV = new GeneralMatrix(ReadFVMatrix(2));
}
weka.classifiers.trees.M5P tree = new weka.classifiers.trees.M5P();
String[] options = new String[1];
weka.core.converters.ConverterUtils.DataSource source = new weka.core.converters.ConverterUtils.DataSource(OutputDir + "Results/" + "resultsGalaxy.arff");
data = source.getDataSet();
if (data == null) {
DisplayMessage("Cannot load from file.");
throw new Exception("Arff File not valid");
}
data.setClassIndex(0);
tree.buildClassifier(data);
StreamWriter output = new StreamWriter(OutputDir + "Results/" + "classification.txt");
rmse = 0.0;
int classifiedCount = 0;
weka.filters.unsupervised.attribute.Remove rm = new weka.filters.unsupervised.attribute.Remove();
rm.setInputFormat(data);
fc = new FilteredClassifier();
fc.setFilter(rm);
fc.setClassifier(tree);
for (int i = 0; i < data.numInstances(); i++) {
int classPrediction = (int)Math.Round(fc.classifyInstance(data.instance(i)));
if (classPrediction < -6) {
classPrediction = -6;
}
else if (classPrediction > 11) {
classPrediction = 11;
}
int actualClass = (int)Math.Round(data.instance(i).classValue());
int error = Math.Abs(classPrediction - actualClass);
rmse += error * error;
classifiedCount++;
output.WriteLine("\n" + classPrediction + ", " + error);
if (i % 10 == 0 && !LoadingFromFile)
DisplayImage(i);
}
rmse = Math.Sqrt(rmse / classifiedCount);
output.WriteLine("\nRMSE: " + rmse);
DisplayMessage("RMSE: " + rmse);
output.Flush();
output.Close();
output.Dispose();
readyToClassify = true;
Console.WriteLine("Finished training on Efigi galaxies; RMSE: " + rmse.ToString());
}
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)
{
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 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
}
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();
}
}
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;
}
// 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");
}
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);
}
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();
}
}
}
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;
}
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;
}
