/// <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 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());
}
}
public static double SupportVectorMachineTest(weka.core.Instances insts)
{
try
{
//weka.core.Instances insts = new weka.core.Instances(new java.io.FileReader("iris.arff"));
insts.setClassIndex(insts.numAttributes() - 1);
SupportVectorMachine = new weka.classifiers.functions.SMO();
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);
SupportVectorMachine.buildClassifier(train);
int numCorrect = 0;
for (int i = trainSize; i < insts.numInstances(); i++)
{
weka.core.Instance currentInst = insts.instance(i);
double predictedClass = SupportVectorMachine.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);
}
}
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");
}
}
public static double NaiveBayesTest(weka.core.Instances insts)
{
try
{
//weka.core.Instances insts = new weka.core.Instances(new java.io.FileReader("iris.arff"));
insts.setClassIndex(insts.numAttributes() - 1);
NaiveBayescl = new weka.classifiers.bayes.NaiveBayes();
//discretize
weka.filters.Filter myDiscretize = new weka.filters.unsupervised.attribute.Discretize();
myDiscretize.setInputFormat(insts);
insts = weka.filters.Filter.useFilter(insts, myDiscretize);
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);
NaiveBayescl.buildClassifier(train);
int numCorrect = 0;
for (int i = trainSize; i < insts.numInstances(); i++)
{
weka.core.Instance currentInst = insts.instance(i);
double predictedClass = NaiveBayescl.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);
}
}
//Artificial NN
public static double ArtificialNN(weka.core.Instances insts)
{
try
{
insts.setClassIndex(insts.numAttributes() - 1);
Anncl = new weka.classifiers.functions.MultilayerPerceptron();
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);
Anncl.buildClassifier(train);
int numCorrect = 0;
for (int i = trainSize; i < insts.numInstances(); i++)
{
weka.core.Instance currentInst = insts.instance(i);
double predictedClass = Anncl.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);
}
}
/* 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());
}
}
//Random Forest
public static double RandomForestTest(weka.core.Instances insts)
{
try
{
insts.setClassIndex(insts.numAttributes() - 1);
RandomForestcl = new weka.classifiers.trees.RandomForest();
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);
RandomForestcl.buildClassifier(train);
int numCorrect = 0;
for (int i = trainSize; i < insts.numInstances(); i++)
{
weka.core.Instance currentInst = insts.instance(i);
double predictedClass = RandomForestcl.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);
}
}
private void Train(string str)
{
if (string.IsNullOrEmpty(str))
return;
m_cls = CreateClassifier(str);
if (m_cls == null)
{
MessageBox.Show("Can't Create Classifier!");
return;
}
var trainInstances = CreateCurrentInstances();
m_cls.buildClassifier(trainInstances);
// TEST
var data = CreateEmptyInstances();
StringBuilder sb = new StringBuilder();
if (m_cls is MLEA.IBatchClassifier)
{
MLEA.IBatchClassifier batchClassifier = m_cls as MLEA.IBatchClassifier;
for (int i = 0; i < XLEN; i++)
{
for (int j = 0; j < YLEN; j++)
{
var vals = new double[data.numAttributes()];
vals[0] = (double)i / XLEN;
vals[1] = (double)j / YLEN;
var instance = new weka.core.DenseInstance(1.0, vals);
data.add(instance);
instance.setDataset(data);
}
}
double[] ds = batchClassifier.classifyInstances(data);
for (int i = 0; i < XLEN; i++)
{
for (int j = 0; j < YLEN; j++)
{
double d = ds[i * XLEN + j];
if (m_enableLog)
{
string s = string.Format("{0}, {1}: {2}", data.instance(i * XLEN + j).value(0).ToString("N2"), data.instance(i * XLEN + j).value(1).ToString("N2"), d.ToString("N0"));
sb.AppendLine(s);
}
for (int ii = 0; ii < WXLEN / XLEN; ++ii)
for (int jj = 0; jj < WYLEN / YLEN; ++jj)
m_pictureBoxBitmap.SetPixel(i * WXLEN / XLEN + ii, j * WYLEN / YLEN + jj, GetValueColor((int)d, false));
}
}
}
else
{
for (int i = 0; i < XLEN; i++)
{
for (int j = 0; j < YLEN; j++)
{
var vals = new double[data.numAttributes()];
vals[0] = (double)i / XLEN;
vals[1] = (double)j / YLEN;
var instance = new weka.core.DenseInstance(1.0, vals);
data.add(instance);
instance.setDataset(data);
double d = m_cls.classifyInstance(instance);
if (m_enableLog)
{
string s = string.Format("{0}, {1}: {2}", vals[0].ToString("N2"), vals[1].ToString("N2"), d.ToString("N0"));
sb.AppendLine(s);
}
for (int ii = 0; ii < WXLEN / XLEN; ++ii)
for (int jj = 0; jj < WYLEN / YLEN; ++jj)
m_pictureBoxBitmap.SetPixel(i * WXLEN / XLEN + ii, j * WYLEN / YLEN + jj, GetValueColor((int)d, false));
}
}
}
draw_all_points();
this.Invoke(new Action(() =>
{
if (m_enableLog)
{
txtLog.AppendText(sb.ToString());
}
}));
if (m_enableEvaluation)
{
Test(trainInstances);
}
}
private void Train(string str)
{
if (string.IsNullOrEmpty(str))
{
return;
}
m_cls = CreateClassifier(str);
if (m_cls == null)
{
MessageBox.Show("Can't Create Classifier!");
return;
}
var trainInstances = CreateCurrentInstances();
m_cls.buildClassifier(trainInstances);
// TEST
var data = CreateEmptyInstances();
StringBuilder sb = new StringBuilder();
if (m_cls is MLEA.IBatchClassifier)
{
MLEA.IBatchClassifier batchClassifier = m_cls as MLEA.IBatchClassifier;
for (int i = 0; i < XLEN; i++)
{
for (int j = 0; j < YLEN; j++)
{
var vals = new double[data.numAttributes()];
vals[0] = (double)i / XLEN;
vals[1] = (double)j / YLEN;
var instance = new weka.core.DenseInstance(1.0, vals);
data.add(instance);
instance.setDataset(data);
}
}
double[] ds = batchClassifier.classifyInstances(data);
for (int i = 0; i < XLEN; i++)
{
for (int j = 0; j < YLEN; j++)
{
double d = ds[i * XLEN + j];
if (m_enableLog)
{
string s = string.Format("{0}, {1}: {2}", data.instance(i * XLEN + j).value(0).ToString("N2"), data.instance(i * XLEN + j).value(1).ToString("N2"), d.ToString("N0"));
sb.AppendLine(s);
}
for (int ii = 0; ii < WXLEN / XLEN; ++ii)
{
for (int jj = 0; jj < WYLEN / YLEN; ++jj)
{
m_pictureBoxBitmap.SetPixel(i * WXLEN / XLEN + ii, j * WYLEN / YLEN + jj, GetValueColor((int)d, false));
}
}
}
}
}
else
{
for (int i = 0; i < XLEN; i++)
{
for (int j = 0; j < YLEN; j++)
{
var vals = new double[data.numAttributes()];
vals[0] = (double)i / XLEN;
vals[1] = (double)j / YLEN;
var instance = new weka.core.DenseInstance(1.0, vals);
data.add(instance);
instance.setDataset(data);
double d = m_cls.classifyInstance(instance);
if (m_enableLog)
{
string s = string.Format("{0}, {1}: {2}", vals[0].ToString("N2"), vals[1].ToString("N2"), d.ToString("N0"));
sb.AppendLine(s);
}
for (int ii = 0; ii < WXLEN / XLEN; ++ii)
{
for (int jj = 0; jj < WYLEN / YLEN; ++jj)
{
m_pictureBoxBitmap.SetPixel(i * WXLEN / XLEN + ii, j * WYLEN / YLEN + jj, GetValueColor((int)d, false));
}
}
}
}
}
draw_all_points();
this.Invoke(new Action(() =>
{
if (m_enableLog)
{
txtLog.AppendText(sb.ToString());
}
}));
if (m_enableEvaluation)
{
Test(trainInstances);
}
}
//**************************************************************************************
/// <summary>
/// Build cllasifier model and save it to a file.
/// </summary>
public override void Build(CandlestickCollection iCandlestick)
{
List <int> trainingPoints = null;
// Calculate average profit and std dev
if (J48Info.ProfitAverage is null || J48Info.ProfitStdDev is null)
{
trainingPoints = LoadTrainingPoints(iCandlestick, ID, ProfitTime);
float[] profits = FullToTraining(new List <float>(CalculateFutureProfits(iCandlestick[kTrainingPeriod], ProfitTime)), trainingPoints).ToArray();
J48Info.ProfitStdDev = Statistics.StandardDeviation(profits);
J48Info.ProfitAverage = J48Info.ParentID is null ? 0.0f : Statistics.ArithmeticMean(profits);
WekaJ48Info.UpdateDB(J48Info);
}
// Build model
if (!File.Exists(ModelFilename))
{
OutputMessage("Building model");
if (trainingPoints is null)
{
trainingPoints = LoadTrainingPoints(iCandlestick, ID, ProfitTime);
}
Model = new weka.classifiers.trees.J48();
Model.buildClassifier(CreateInstances(iCandlestick, trainingPoints, Attributes, Parameters, Period, ProfitTime));
weka.core.SerializationHelper.write(ModelFilename, Model);
}
// Perfrom crossfold test
if (J48Info.Precision is null)
{
if (Model is null)
{
LoadModel();
}
OutputMessage("Perfroming crossfold");
if (trainingPoints is null)
{
trainingPoints = LoadTrainingPoints(iCandlestick, ID, ProfitTime);
}
var instances = CreateInstances(iCandlestick, trainingPoints, Attributes, Parameters, Period, ProfitTime);
var evaluation = new weka.classifiers.Evaluation(instances);
evaluation.crossValidateModel(Model, instances, 10, new java.util.Random(0));
J48Info.Precision = (float)evaluation.pctCorrect();
WekaJ48Info.UpdateDB(J48Info);
}
// Perfrom singular test
if (J48Info.IsSingular == null)
{
if (Model is null)
{
LoadModel();
}
OutputMessage("Perfroming singular test");
var results = new SortedList <Prediction, List <int> >();
foreach (Prediction p in (Prediction[])Enum.GetValues(typeof(Prediction)))
{
results.Add(p, new List <int>());
}
if (trainingPoints is null)
{
trainingPoints = LoadTrainingPoints(iCandlestick, ID, ProfitTime);
}
var parameters = CalculateParameters(Parameters, iCandlestick, trainingPoints, Period);
for (int k = 0; k < parameters.Count; k++)
{
var pred = Predict(parameters[k]);
results[pred].Add(trainingPoints[k]);
}
J48Info.IsSingular = results.Count(x => x.Value.Count > 0) <= 1;
WekaJ48Info.UpdateDB(J48Info);
}
// Calculating prediction profits
if (J48Info.PredictionProfits.Count(x => x != null) == 0)
{
if (Model is null)
{
LoadModel();
}
OutputMessage("Calculating prediction profits");
if (trainingPoints is null)
{
trainingPoints = LoadTrainingPoints(iCandlestick, ID, ProfitTime);
}
var predictionPoints = GetHistoricalPredictionPoints(iCandlestick, trainingPoints);
foreach (Prediction p in (Prediction[])Enum.GetValues(typeof(Prediction)))
{
float[] profits = FullToTraining(new List <float>(CalculateFutureProfits(iCandlestick[kTrainingPeriod], ProfitTime)), predictionPoints[p]).ToArray();
if (profits.Length < 10)
{
J48Info.PredictionProfits[(int)p] = DecisionToFutureProfit(p, (float)J48Info.ProfitStdDev, (float)J48Info.ProfitAverage);
}
else
{
J48Info.PredictionProfits[(int)p] = Statistics.ArithmeticMean(profits);
}
}
WekaJ48Info.UpdateDB(J48Info);
}
// Create children
if (!J48Info.ReproductionComplete.GetValueOrDefault(false))
{
lock (this)
{
if (J48Info.Precision > 50.0f && !J48Info.IsSingular.GetValueOrDefault(false))
{
OutputMessage("Creating children");
if (trainingPoints is null)
{
trainingPoints = LoadTrainingPoints(iCandlestick, ID, ProfitTime);
}
var predictionPoints = GetHistoricalPredictionPoints(iCandlestick, trainingPoints);
foreach (Prediction p in (Prediction[])Enum.GetValues(typeof(Prediction)))
{
if (predictionPoints[p] != null && predictionPoints[p].Count >= 1000 && J48Info.ChildrenID[(int)p] == null)
{
var child = CreateNew(ParametersID, Parameters, Period, ProfitTime, predictionPoints[p]);
// Set parent
child.J48Info.ParentID = ID;
WekaJ48Info.UpdateDB(child.J48Info);
// Update parent info
J48Info.ChildrenID[(int)p] = (int)child.ID;
WekaJ48Info.UpdateDB(J48Info);
childs[(int)p] = child;
}
}
}
J48Info.ReproductionComplete = true;
WekaJ48Info.UpdateDB(J48Info);
}
}
}
static public void step_train()
{
weka.core.Instances train = new weka.core.Instances(insts, 0, split_trainSize);
classifier.buildClassifier(train);
}
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 ";
}
}
}
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";
}