public List <double> testMLPUsingWeka(string[] attributeArray, string[] classNames, double[] dataValues, string classHeader, string defaultclass, string modelName, int hiddelLayers = 7, double learningRate = 0.03, double momentum = 0.4, int decimalPlaces = 2, int trainingTime = 1000)
{
java.util.ArrayList classLabel = new java.util.ArrayList();
foreach (string className in classNames)
{
classLabel.Add(className);
}
weka.core.Attribute classHeaderName = new weka.core.Attribute(classHeader, classLabel);
java.util.ArrayList attributeList = new java.util.ArrayList();
foreach (string attribute in attributeArray)
{
weka.core.Attribute newAttribute = new weka.core.Attribute(attribute);
attributeList.Add(newAttribute);
}
attributeList.add(classHeaderName);
weka.core.Instances data = new weka.core.Instances("TestInstances", attributeList, 0);
data.setClassIndex(data.numAttributes() - 1);
// Set instance's values for the attributes
weka.core.Instance inst_co = new DenseInstance(data.numAttributes());
for (int i = 0; i < data.numAttributes() - 1; i++)
{
inst_co.setValue(i, dataValues.ElementAt(i));
}
inst_co.setValue(classHeaderName, defaultclass);
data.add(inst_co);
java.io.File path = new java.io.File("/models/");
weka.classifiers.functions.MultilayerPerceptron clRead = loadModel(modelName, path);
clRead.setHiddenLayers(hiddelLayers.ToString());
clRead.setLearningRate(learningRate);
clRead.setMomentum(momentum);
clRead.setNumDecimalPlaces(decimalPlaces);
clRead.setTrainingTime(trainingTime);
weka.filters.Filter myRandom = new weka.filters.unsupervised.instance.Randomize();
myRandom.setInputFormat(data);
data = weka.filters.Filter.useFilter(data, myRandom);
double classValue = clRead.classifyInstance(data.get(0));
double[] predictionDistribution = clRead.distributionForInstance(data.get(0));
List <double> predictionDistributions = new List <double>();
for (int predictionDistributionIndex = 0;
predictionDistributionIndex < predictionDistribution.Count();
predictionDistributionIndex++)
{
string classValueString1 = classLabel.get(predictionDistributionIndex).ToString();
double prob = predictionDistribution[predictionDistributionIndex] * 100;
predictionDistributions.Add(prob);
}
List <double> prediction = new List <double>();
prediction.Add(classValue);
prediction.AddRange(predictionDistributions);
return(prediction);
}
public static void classifierTwo(string classifierFileName, string predictionModel)
{
FileReader javaFileReader = new FileReader(classifierFileName);
weka.core.Instances wekaInsts = new weka.core.Instances(javaFileReader);
javaFileReader.close();
wekaInsts.setClassIndex(wekaInsts.numAttributes() - 1);
//Classifier nbTree = (Classifier)SerializationHelper.read(Model) as J48;
Instances testDataSet = new Instances(new BufferedReader(new FileReader(classifierFileName)));
testDataSet.setClassIndex(wekaInsts.numAttributes() - 1);
//testDataSet.setClassIndex(10);
Evaluation evaluation = new Evaluation(testDataSet);
J48 model = new J48();
//Classifier myClassifier = (Classifier)SerializationHelper.read(Model) as NaiveBayes;
//Classifier myClassifier = new NaiveBayes();
for (int i = 0; i < testDataSet.numInstances(); i++)
{
Instance instance = testDataSet.instance(i);
//evaluation.evaluateModelOnceAndRecordPrediction(myClassifier, instance);
//evaluation.evaluateModelOnce(myClassifier, instance);
}
foreach (object o in evaluation.predictions().toArray())
{
NominalPrediction prediction = o as NominalPrediction;
if (prediction != null)
{
double[] distribution = prediction.distribution();
double predicted = prediction.predicted();
for (int i = 0; i < distribution.Length; i++)
{
System.Console.WriteLine(distribution[i]);
}
System.Console.WriteLine(predicted);
}
}
System.Console.WriteLine(evaluation);
System.Console.ReadKey();
}
/// <summary>
/// Determines all categories --> categories
/// Determines category numbers of each attributes -->> categoryTypeNumber
/// Determines target numbers and amounts of each categories of each attributes -->> categoryTypeTargetNumber
/// [i][j][k] i means attributes, j means categories, k means targets
/// </summary>
/// <param name="insts"></param>
private void DataPreparation(weka.core.Instances insts)
{
for (int i = 0; i < insts.numAttributes(); i++)
{
string[] categoryType = new string[insts.attribute(i).numValues()];
for (int j = 0; j < insts.attribute(i).numValues(); j++)
{
categoryType[j] = insts.attribute(i).value(j).ToString();
}
categories.Add(categoryType);
}
List <List <string> > lst = new List <List <string> >();
for (int i = 0; i < insts.numInstances(); i++)
{
lst.Add(new List <string>());
for (int j = 0; j < insts.instance(i).numValues(); j++)
{
lst[lst.Count - 1].Add(insts.instance(i).toString(j));
}
}
List <int[]> categoryTypeNumber = new List <int[]>();
List <int[, ]> categoryTypeTargetNumber = new List <int[, ]>();
for (int i = 0; i < categories.Count; i++)
{
categoryTypeNumber.Add(new int[categories[i].Length]);
categoryTypeTargetNumber.Add(new int[categories[i].Length, categories[categories.Count - 1].Length]);
}
for (int i = 0; i < lst.Count; i++) //Satır
{
for (int j = 0; j < lst[i].Count; j++) //Sütün
{
for (int k = 0; k < categories[j].Length; k++) //Kategori Sayısı
{
string targetValue = lst[i][lst[i].Count - 1];
if (lst[i][j].Contains(categories[j][k]))
{
categoryTypeNumber[j][k] += 1;
for (int trgt = 0; trgt < categories[categories.Count - 1].Length; trgt++)
{
if (targetValue == categories[categories.Count - 1][trgt])
{
categoryTypeTargetNumber[j][k, trgt] += 1;
}
}
}
}
}
}
Twoing(insts, categoryTypeNumber, categoryTypeTargetNumber);
Gini(insts, categoryTypeNumber, categoryTypeTargetNumber);
LogInfo("Dataset is saved.\r\n\r\n");
LogInfo("TWOING : " + twoingPath + "\r\n\r\n");
LogInfo("GINI : " + giniPath + "\r\n");
}
public void EndTrainingSession()
{
Stream s = new MemoryStream ();
TextWriter tw = new StreamWriter (s);
AbstractBasicTextVector.WriteInstancesArff (tw, vectors, "c45recommender", tags, results);
tw.Flush ();
s.Position = 0;
Instances source = new Instances (new InputStreamReader (new InputStreamWrapper (s)));
tw.Close ();
s.Close ();
Instances[] derived = new Instances[this.not];
classifiers = new AbstractClassifier[this.not];
int[] args = new int[this.not - 1];
int l = source.numAttributes () - this.not;
for (int i = 0; i < this.not-1; i++) {
args [i] = i + l + 1;
}
for (int i = 0; i < this.not; i++) {
Remove rem = new Remove ();
rem.setAttributeIndicesArray (args);
rem.setInputFormat (source);
derived [i] = Filter.useFilter (source, rem);
classifiers [i] = GenerateClassifier ();
derived [i].setClassIndex (derived [i].numAttributes () - 1);
classifiers [i].buildClassifier (derived [i]);
if (i < this.not - 1) {
args [i] = l + i;
}
}
datasets = derived;
}
private void button1_Click(object sender, EventArgs e)
{
weka.core.Instances insts = new weka.core.Instances(new java.io.FileReader(file));
double[] Data = new double[insts.numAttributes()];
for (int i = 0; i < list.Count; i++)
{
if (list[i].GetType() == typeof(TextBox))
{
TextBox txt = (TextBox)list[i];
string value = txt.Text.Replace('.', ',');
Data[i] = Convert.ToDouble(value);
}
else
{
ComboBox combobox = (ComboBox)list[i];
Data[i] = Convert.ToDouble(combobox.SelectedIndex);
}
}
// Data[(insts.numAttributes() - 1] = 0;
insts.setClassIndex(insts.numAttributes() - 1);
Instance newInsts = new Instance(1.0, Data);
insts.add(newInsts);
string type = model.GetType().ToString();
if (type == "weka.classifiers.bayes.NaiveBayes")
{
weka.filters.Filter myDiscretize = new weka.filters.unsupervised.attribute.Discretize();
myDiscretize.setInputFormat(insts);
insts = weka.filters.Filter.useFilter(insts, myDiscretize);
}
else if (type == "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);
}
double index = model.classifyInstance(insts.lastInstance());
string result = insts.attribute(insts.numAttributes() - 1).value(Convert.ToInt16(index));
MessageBox.Show(result);
}
public static void Main(string[] args) {
try {
int runs = 1;
string algo = "";
string data = "";
if(args.Length>0) runs = Convert.ToInt32(args[0]);
if(args.Length>1) algo = args[1];
if(args.Length>2) data = args[2];
Stopwatch read = new Stopwatch(),
build = new Stopwatch(),
classify = new Stopwatch();
for (int cnt=0; cnt<runs; cnt++) {
read.Start();
Instances train = new Instances(new java.io.FileReader(data+"train.arff"));
train.setClassIndex(train.numAttributes() - 1);
Instances test = new Instances(new java.io.FileReader(data+"test.arff"));
test.setClassIndex(test.numAttributes() - 1);
read.Stop();
Classifier[] clList = {
new weka.classifiers.bayes.NaiveBayes(),
new weka.classifiers.trees.RandomForest(),
new weka.classifiers.trees.J48(),
new weka.classifiers.functions.MultilayerPerceptron(),
new weka.classifiers.rules.ConjunctiveRule(),
new weka.classifiers.functions.SMO()
};
build.Start();
foreach (Classifier classifier in clList) {
if(algo.Equals("") || algo.Equals("All") || classifier.getClass().getSimpleName().Equals(algo))
classifier.buildClassifier(train);
}
build.Stop();
classify.Start();
foreach (Classifier classifier in clList) {
if(algo.Equals("") || algo.Equals("All") || classifier.getClass().getSimpleName().Equals(algo)) {
int numCorrect = 0;
for (int i = 0; i < test.numInstances(); i++)
{
if (classifier.classifyInstance(test.instance(i)) == test.instance(i).classValue())
numCorrect++;
}
//Console.Write(classifier.getClass().getSimpleName() + "\t" + numCorrect + " out of " + test.numInstances() + " correct (" +(100.0 * numCorrect / test.numInstances()) + "%)");
}
}
classify.Stop();
}
Console.WriteLine("{\""+ algo + "\"," + read.ElapsedMilliseconds + "," + build.ElapsedMilliseconds + "," + classify.ElapsedMilliseconds + "," + (read.ElapsedMilliseconds+build.ElapsedMilliseconds+classify.ElapsedMilliseconds)+"};");
if(args.Length>3) Console.ReadLine();
} catch (java.lang.Exception e){
e.printStackTrace();
}
}
/// <summary> Sets the format of the input instances.
///
/// </summary>
/// <param name="instanceInfo">an Instances object containing the input
/// instance structure (any instances contained in the object are
/// ignored - only the structure is required).
/// </param>
/// <returns> true if the outputFormat may be collected immediately
/// </returns>
/// <exception cref="Exception">if the input format can't be set
/// successfully
/// </exception>
public override bool setInputFormat(Instances instanceInfo)
{
base.setInputFormat(instanceInfo);
m_Columns.Upper = instanceInfo.numAttributes() - 1;
setOutputFormat();
m_Indices = null;
return true;
}
public static double classifyTrain_Test(string classifierFileName, Classifier _classifier)
{
double performance = 0.0;
try
{
FileReader javaFileReader = new FileReader(classifierFileName);
weka.core.Instances insts = new weka.core.Instances(javaFileReader);
javaFileReader.close();
insts.setClassIndex(insts.numAttributes() - 1);
System.Console.WriteLine("Performing " + percentSplit + "% split evaluation.");
int trainSize = insts.numInstances() * percentSplit / 100;
int testSize = insts.numInstances() - trainSize;
weka.core.Instances train = new weka.core.Instances(insts, 0, trainSize);
_classifier.buildClassifier(train);
int numCorrect = 0;
var numnerOfInst = insts.numInstances();
int dataIndex = 0;
for (int i = trainSize; i < numnerOfInst; i++)
{
dataIndex++;
weka.core.Instance currentInst = insts.instance(i);
double predictClass = _classifier.classifyInstance(currentInst);
double[] dist = _classifier.distributionForInstance(currentInst);
string actualClass = insts.classAttribute().value((int)insts.instance(i).classValue());
string predictedClass = insts.classAttribute().value((int)predictClass);
var abcd = _classifier.getClass();
if (predictedClass == actualClass)
{
numCorrect++;
}
}
performance = (double)((double)numCorrect / (double)testSize) * 100;
System.Console.WriteLine(numCorrect + " out of " + testSize + " correct (" + performance.toString() + "%)");
}
catch (java.lang.Exception ex)
{
ex.printStackTrace();
}
return(performance);
}
// 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 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);
}
}
public static void classifierOne(string classifierFileName, string predictionModel)
{
FileReader javaFileReader = new FileReader(classifierFileName);
weka.core.Instances wekaInsts = new weka.core.Instances(javaFileReader);
javaFileReader.close();
wekaInsts.setClassIndex(wekaInsts.numAttributes() - 1);
Classifier cl = new SMO();
//Classifier cl = new NaiveBayes();
java.util.Random random = new java.util.Random(1);
Evaluation evaluation = new Evaluation(wekaInsts);
evaluation.crossValidateModel(cl, wekaInsts, 10, random);
foreach (object o in evaluation.getMetricsToDisplay().toArray())
{
}
int count = 0;
StringBuilder sb = new StringBuilder();
foreach (object o in evaluation.predictions().toArray())
{
NominalPrediction prediction = o as NominalPrediction;
if (prediction != null)
{
double[] distribution = prediction.distribution();
double predicted = prediction.predicted();
double actual = prediction.actual();
string revision = prediction.getRevision();
double weight = prediction.weight();
double margine = prediction.margin();
//bool equals = prediction.@equals();
string distributions = String.Empty;
for (int i = 0; i < distribution.Length; i++)
{
//System.Console.WriteLine(distribution[i]);
distributions += distribution[i];
}
var predictionLine = String.Format("{0} - {1} - {2} - {3} - {4} - {5}\n", actual, predicted, revision, weight, margine, distributions);
sb.Append(predictionLine);
//System.Console.WriteLine(predicted);
}
count++;
}
File_Helper.WriteToFile(sb, predictionModel + "NbCl.txt");
System.Console.WriteLine(count);
System.Console.ReadKey();
}
private async Task loadFileAndMakeElements(string location)
{
if (location.EndsWith(".csv"))
{
weka.core.converters.CSVLoader csvLoader = new weka.core.converters.CSVLoader();
csvLoader.setSource(new java.io.File(location));
insts = csvLoader.getDataSet();
insts.setClassIndex(insts.numAttributes() - 1);
}
else
{
insts = new weka.core.Instances(new java.io.FileReader(location));
insts.setClassIndex(insts.numAttributes() - 1);
}
flowLayoutPanel1.Controls.Clear();
for (int i = 0; i < insts.numAttributes() - 1; i++)
{
if (insts.attribute(i).isNominal() == true)
{
if (insts.attribute(i).numValues() > 0)
{
Label lbl = new Label();
lbl.Text = insts.attribute(i).name().Trim();
lbl.Enabled = true;
ComboBox cmbBox = new ComboBox();
cmbBox.Name = insts.attribute(i).name();
for (int m = 0; m < insts.attribute(i).numValues(); m++)
{
cmbBox.Items.Add(insts.attribute(i).value(m));
}
cmbBox.DropDownStyle = ComboBoxStyle.DropDownList;
cmbBox.Enabled = true;
flowLayoutPanel1.Controls.Add(lbl);
flowLayoutPanel1.Controls.Add(cmbBox);
}
else
{
}
}
else if (insts.attribute(i).isNumeric() == true)
{
Label lbl = new Label();
lbl.Text = insts.attribute(i).name().Trim();
TextBox txtBox = new TextBox();
txtBox.Name = insts.attribute(i).name();
txtBox.KeyPress += new KeyPressEventHandler(txtBox_Keypress);
flowLayoutPanel1.Controls.Add(lbl);
flowLayoutPanel1.Controls.Add(txtBox);
}
}
}
public string testHybridEmotionUsingWeka(string[] attributeArray, string[] classNames, double[] dataValues, string classHeader, string defaultclass, string modelName)
{
java.util.ArrayList classLabel = new java.util.ArrayList();
foreach (string className in classNames)
{
classLabel.Add(className);
}
weka.core.Attribute classHeaderName = new weka.core.Attribute(classHeader, classLabel);
java.util.ArrayList attributeList = new java.util.ArrayList();
foreach (string attribute in attributeArray)
{
weka.core.Attribute newAttribute = new weka.core.Attribute(attribute);
attributeList.Add(newAttribute);
}
attributeList.add(classHeaderName);
weka.core.Instances data = new weka.core.Instances("TestInstances", attributeList, 0);
data.setClassIndex(data.numAttributes() - 1);
// Set instance's values for the attributes
weka.core.Instance inst_co = new DenseInstance(data.numAttributes());
for (int i = 0; i < data.numAttributes() - 1; i++)
{
inst_co.setValue(i, dataValues.ElementAt(i));
}
inst_co.setValue(classHeaderName, defaultclass);
data.add(inst_co);
java.io.File path = new java.io.File("/models/");
weka.classifiers.meta.Bagging clRead = loadBaggingModel(modelName, path);
weka.filters.Filter myRandom = new weka.filters.unsupervised.instance.Randomize();
myRandom.setInputFormat(data);
data = weka.filters.Filter.useFilter(data, myRandom);
double classValue = clRead.classifyInstance(data.get(0));
string classValueString = classLabel.get(Int32.Parse(classValue.ToString())).ToString();
return(classValueString);
}
/// <summary>
/// Adds teta results of gini results to the list
/// Change the attributes of the arff file
/// Adds the attributes to arff file
/// </summary>
/// <param name="insts"></param>
/// <param name="result"></param>
/// <param name="path"></param>
private void CreateNewDataset(weka.core.Instances insts, List <double[]> result, string path)
{
//Tetaları Listeye Ekle
List <List <string> > lst = new List <List <string> >();
for (int i = 0; i < insts.numInstances(); i++)
{
lst.Add(new List <string>());
for (int j = 0; j < insts.instance(i).numValues() - 1; j++)
{
string value = insts.instance(i).toString(j);
for (int k = 0; k < categories[j].Length; k++)
{
if (insts.instance(i).toString(j) == categories[j][k])
{
lst[lst.Count - 1].Add(String.Format("{0:0.00}", result[j][k]));
break;
}
}
}
}
//Attiribute Değiştir
for (int i = 0; i < insts.numAttributes() - 1; i++)
{
string name = insts.attribute(i).name().ToString();
insts.deleteAttributeAt(i);
weka.core.Attribute att = new weka.core.Attribute(name);
insts.insertAttributeAt(att, i);
}
//Attiributeları yaz
for (int i = 0; i < insts.numInstances(); i++)
{
for (int j = 0; j < insts.instance(i).numValues() - 1; j++)
{
insts.instance(i).setValue(j, Convert.ToDouble(lst[i][j]));
}
}
if (File.Exists(path))
{
File.Delete(path);
}
var saver = new ArffSaver();
saver.setInstances(insts);
saver.setFile(new java.io.File(path));
saver.writeBatch();
}
public void trainMachineForEmotionUsingWeka(string wekaFile, string modelName, int hiddelLayers = 7, double learningRate = 0.03, double momentum = 0.4, int decimalPlaces = 2, int trainingTime = 1000)
{
//"C:\\Users\\Gulraiz\\Desktop\\Genereted2.arff" "MLP"
try
{
weka.core.Instances insts = new weka.core.Instances(new java.io.FileReader(wekaFile));
insts.setClassIndex(insts.numAttributes() - 1);
weka.classifiers.functions.MultilayerPerceptron cl;
cl = new weka.classifiers.functions.MultilayerPerceptron();
cl.setHiddenLayers(hiddelLayers.ToString());
cl.setLearningRate(learningRate);
cl.setMomentum(momentum);
cl.setNumDecimalPlaces(decimalPlaces);
cl.setTrainingTime(trainingTime);
System.Console.WriteLine("Performing " + percentSplit + "% split evaluation.");
//randomize the order of the instances in the dataset.
weka.filters.Filter myRandom = new weka.filters.unsupervised.instance.Randomize();
myRandom.setInputFormat(insts);
insts = weka.filters.Filter.useFilter(insts, myRandom);
int trainSize = insts.numInstances() * percentSplit / 100;
int testSize = insts.numInstances() - trainSize;
weka.core.Instances train = new weka.core.Instances(insts, 0, trainSize);
java.io.File path = new java.io.File("/models/");
cl.buildClassifier(train);
saveModel(cl, modelName, path);
#region test whole set
//int numCorrect = 0;
//for (int i = trainSize; i < insts.numInstances(); i++)
//{
// weka.core.Instance currentInst = insts.instance(i);
// double predictedClass = cl.classifyInstance(currentInst);
// if (predictedClass == insts.instance(i).classValue())
// numCorrect++;
//}
//System.Console.WriteLine(numCorrect + " out of " + testSize + " correct (" +
// (double)((double)numCorrect / (double)testSize * 100.0) + "%)");
#endregion
}
catch (java.lang.Exception ex)
{
ex.printStackTrace();
}
}
public static void JackKnife_Test_prepare(string classifierFileName, int baseClasses, Classifier _classifie)
{
for (int singleClass = 1; singleClass <= baseClasses; singleClass++)
{
string eachFileName = String.Format("{0}_{1}.arff", classifierFileName, singleClass);
FileReader javaFileReader = new FileReader(eachFileName);
weka.core.Instances insts = new weka.core.Instances(javaFileReader);
javaFileReader.close();
insts.setClassIndex(insts.numAttributes() - 1);
var totalnstances = insts.numInstances();
//insts.re
}
}
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);
}
}
public void trainMachineForHybridUsingWeka(string wekaFile, string modelName)
{
weka.core.Instances insts = new weka.core.Instances(new java.io.FileReader(wekaFile));
insts.setClassIndex(insts.numAttributes() - 1);
weka.classifiers.Classifier bagging = new weka.classifiers.meta.Bagging();
System.Console.WriteLine("Performing " + percentSplit + "% split evaluation.");
//randomize the order of the instances in the dataset.
weka.filters.Filter myRandom = new weka.filters.unsupervised.instance.Randomize();
myRandom.setInputFormat(insts);
insts = weka.filters.Filter.useFilter(insts, myRandom);
int trainSize = insts.numInstances() * percentSplit / 100;
int testSize = insts.numInstances() - trainSize;
weka.core.Instances train = new weka.core.Instances(insts, 0, trainSize);
java.io.File path = new java.io.File("/models/");
bagging.buildClassifier(train);
saveModel(bagging, modelName, path);
}
public List <DrugBrandInfo> AssociateDrugs(int?drugId, string dataPath)
{
List <DrugBrandInfo> drugList = new List <DrugBrandInfo>();
weka.core.Instances data = new weka.core.Instances(new java.io.FileReader(dataPath));
data.setClassIndex(data.numAttributes() - 1);
Apriori apriori = new Apriori();
apriori.setClassIndex(data.classIndex());
apriori.buildAssociations(data);
FastVector[] vector = apriori.getAllTheRules();
for (int i = 0; i < vector[0].size(); i++)
{
string value1 = ((AprioriItemSet)vector[0].elementAt(i)).toString(data);
string value2 = ((AprioriItemSet)vector[1].elementAt(i)).toString(data);
string[] set1 = value1.Split(' ', '=');
string[] set2 = value2.Split(' ', '=');
if (set1[0].Equals(drugId.ToString()))
{
if (set1[1] == "1" && set2[1] == "1")
{
int brandId = Convert.ToInt32(set2[0]);
var drug = db.DrugBrandInfos.SingleOrDefault(c => c.Id == brandId);
drugList.Add(drug);
}
break;
}
}
return(drugList);
}
/* 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());
}
}
/// <summary>
/// Create an instances structure with classes for supervised methods
/// </summary>
/// <param name="NumClass"></param>
/// <returns></returns>
public Instances CreateInstancesWithClasses(List<bool> ListClassSelected)
{
weka.core.FastVector atts = new FastVector();
int columnNo = 0;
for (int i = 0; i < ParentScreening.ListDescriptors.Count; i++)
{
if (ParentScreening.ListDescriptors[i].IsActive() == false) continue;
atts.addElement(new weka.core.Attribute(ParentScreening.ListDescriptors[i].GetName()));
columnNo++;
}
weka.core.FastVector attVals = new FastVector();
foreach (var item in cGlobalInfo.ListWellClasses)
{
attVals.addElement(item.Name);
}
atts.addElement(new weka.core.Attribute("ClassAttribute", attVals));
Instances data1 = new Instances("MyRelation", atts, 0);
int IdxWell = 0;
foreach (cWell CurrentWell in this.ListActiveWells)
{
if (!ListClassSelected[CurrentWell.GetCurrentClassIdx()]) continue;
double[] vals = new double[data1.numAttributes()];
int IdxCol = 0;
for (int Col = 0; Col < ParentScreening.ListDescriptors.Count; Col++)
{
if (ParentScreening.ListDescriptors[Col].IsActive() == false) continue;
vals[IdxCol++] = CurrentWell.ListSignatures[Col].GetValue();
}
vals[columnNo] = CurrentWell.GetCurrentClassIdx();
data1.add(new DenseInstance(1.0, vals));
IdxWell++;
}
data1.setClassIndex((data1.numAttributes() - 1));
return data1;
}
/// <summary> Merges two sets of Instances together. The resulting set will have
/// all the attributes of the first set plus all the attributes of the
/// second set. The number of instances in both sets must be the same.
///
/// </summary>
/// <param name="first">the first set of Instances
/// </param>
/// <param name="second">the second set of Instances
/// </param>
/// <returns> the merged set of Instances
/// </returns>
/// <exception cref="IllegalArgumentException">if the datasets are not the same size
/// </exception>
public static Instances mergeInstances(Instances first, Instances second)
{
if (first.numInstances() != second.numInstances())
{
throw new System.ArgumentException("Instance sets must be of the same size");
}
// Create the vector of merged attributes
FastVector newAttributes = new FastVector();
for (int i = 0; i < first.numAttributes(); i++)
{
newAttributes.addElement(first.attribute(i));
}
for (int i = 0; i < second.numAttributes(); i++)
{
newAttributes.addElement(second.attribute(i));
}
// Create the set of Instances
Instances merged = new Instances(first.relationName() + '_' + second.relationName(), newAttributes, first.numInstances());
// Merge each instance
for (int i = 0; i < first.numInstances(); i++)
{
merged.add(first.instance(i).mergeInstance(second.instance(i)));
}
return merged;
}
/// <summary>
/// Create an instances structure without classes for unsupervised methods
/// </summary>
/// <returns>a weka Instances object</returns>
public Instances CreateInstancesWithoutClass()
{
weka.core.FastVector atts = new FastVector();
int columnNo = 0;
// Descriptors loop
for (int i = 0; i < ParentScreening.ListDescriptors.Count; i++)
{
if (ParentScreening.ListDescriptors[i].IsActive() == false) continue;
atts.addElement(new weka.core.Attribute(ParentScreening.ListDescriptors[i].GetName()));
columnNo++;
}
weka.core.FastVector attVals = new FastVector();
Instances data1 = new Instances("MyRelation", atts, 0);
foreach (cWell CurrentWell in this.ListActiveWells)
{
double[] vals = new double[data1.numAttributes()];
int IdxRealCol = 0;
for (int Col = 0; Col < ParentScreening.ListDescriptors.Count; Col++)
{
if (ParentScreening.ListDescriptors[Col].IsActive() == false) continue;
vals[IdxRealCol++] = CurrentWell.ListSignatures[Col].GetValue();
}
data1.add(new DenseInstance(1.0, vals));
}
return data1;
}
/// <summary> Evaluates a classifier with the options given in an array of
/// strings. <p/>
///
/// Valid options are: <p/>
///
/// -t name of training file <br/>
/// Name of the file with the training data. (required) <p/>
///
/// -T name of test file <br/>
/// Name of the file with the test data. If missing a cross-validation
/// is performed. <p/>
///
/// -c class index <br/>
/// Index of the class attribute (1, 2, ...; default: last). <p/>
///
/// -x number of folds <br/>
/// The number of folds for the cross-validation (default: 10). <p/>
///
/// -s random number seed <br/>
/// Random number seed for the cross-validation (default: 1). <p/>
///
/// -m file with cost matrix <br/>
/// The name of a file containing a cost matrix. <p/>
///
/// -l name of model input file <br/>
/// Loads classifier from the given file. <p/>
///
/// -d name of model output file <br/>
/// Saves classifier built from the training data into the given file. <p/>
///
/// -v <br/>
/// Outputs no statistics for the training data. <p/>
///
/// -o <br/>
/// Outputs statistics only, not the classifier. <p/>
///
/// -i <br/>
/// Outputs detailed information-retrieval statistics per class. <p/>
///
/// -k <br/>
/// Outputs information-theoretic statistics. <p/>
///
/// -p <br/>
/// Outputs predictions for test instances (and nothing else). <p/>
///
/// -r <br/>
/// Outputs cumulative margin distribution (and nothing else). <p/>
///
/// -g <br/>
/// Only for classifiers that implement "Graphable." Outputs
/// the graph representation of the classifier (and nothing
/// else). <p/>
///
/// </summary>
/// <param name="classifier">machine learning classifier
/// </param>
/// <param name="options">the array of string containing the options
/// </param>
/// <throws> Exception if model could not be evaluated successfully </throws>
/// <returns> a string describing the results
/// </returns>
public static System.String evaluateModel(Classifier classifier, System.String[] options)
{
Instances train = null, tempTrain, test = null, template = null;
int seed = 1, folds = 10, classIndex = - 1;
System.String trainFileName, testFileName, sourceClass, classIndexString, seedString, foldsString, objectInputFileName, objectOutputFileName, attributeRangeString;
bool noOutput = false, printClassifications = false, trainStatistics = true, printMargins = false, printComplexityStatistics = false, printGraph = false, classStatistics = false, printSource = false;
System.Text.StringBuilder text = new System.Text.StringBuilder();
System.IO.StreamReader trainReader = null, testReader = null;
//UPGRADE_TODO: Class 'java.io.ObjectInputStream' was converted to 'System.IO.BinaryReader' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javaioObjectInputStream'"
System.IO.BinaryReader objectInputStream = null;
System.IO.Stream objectStream=null;
CostMatrix costMatrix = null;
System.Text.StringBuilder schemeOptionsText = null;
Range attributesToOutput = null;
long trainTimeStart = 0, trainTimeElapsed = 0, testTimeStart = 0, testTimeElapsed = 0;
Classifier classifierBackup;
try
{
// Get basic options (options the same for all schemes)
classIndexString = Utils.getOption('c', options);
if (classIndexString.Length != 0)
{
if (classIndexString.Equals("first"))
classIndex = 1;
else if (classIndexString.Equals("last"))
classIndex = - 1;
else
classIndex = System.Int32.Parse(classIndexString);
}
trainFileName = Utils.getOption('t', options);
objectInputFileName = Utils.getOption('l', options);
objectOutputFileName = Utils.getOption('d', options);
testFileName = Utils.getOption('T', options);
if (trainFileName.Length == 0)
{
if (objectInputFileName.Length == 0)
{
throw new System.Exception("No training file and no object " + "input file given.");
}
if (testFileName.Length == 0)
{
throw new System.Exception("No training file and no test " + "file given.");
}
}
else if ((objectInputFileName.Length != 0) && ((!(classifier is UpdateableClassifier)) || (testFileName.Length == 0)))
{
throw new System.Exception("Classifier not incremental, or no " + "test file provided: can't " + "use both train and model file.");
}
try
{
if (trainFileName.Length != 0)
{
//UPGRADE_TODO: The differences in the expected value of parameters for constructor 'java.io.BufferedReader.BufferedReader' may cause compilation errors. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1092'"
//UPGRADE_WARNING: At least one expression was used more than once in the target code. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1181'"
//UPGRADE_TODO: Constructor 'java.io.FileReader.FileReader' was converted to 'System.IO.StreamReader' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073'"
trainReader = new System.IO.StreamReader(new System.IO.StreamReader(trainFileName, System.Text.Encoding.Default).BaseStream, new System.IO.StreamReader(trainFileName, System.Text.Encoding.Default).CurrentEncoding);
}
if (testFileName.Length != 0)
{
//UPGRADE_TODO: The differences in the expected value of parameters for constructor 'java.io.BufferedReader.BufferedReader' may cause compilation errors. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1092'"
//UPGRADE_WARNING: At least one expression was used more than once in the target code. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1181'"
//UPGRADE_TODO: Constructor 'java.io.FileReader.FileReader' was converted to 'System.IO.StreamReader' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073'"
testReader = new System.IO.StreamReader(new System.IO.StreamReader(testFileName, System.Text.Encoding.Default).BaseStream, new System.IO.StreamReader(testFileName, System.Text.Encoding.Default).CurrentEncoding);
}
if (objectInputFileName.Length != 0)
{
//UPGRADE_TODO: Constructor 'java.io.FileInputStream.FileInputStream' was converted to 'System.IO.FileStream.FileStream' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javaioFileInputStreamFileInputStream_javalangString'"
objectStream= new System.IO.FileStream(objectInputFileName, System.IO.FileMode.Open, System.IO.FileAccess.Read);
if (objectInputFileName.EndsWith(".gz"))
{
//UPGRADE_ISSUE: Constructor 'java.util.zip.GZIPInputStream.GZIPInputStream' was not converted. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1000_javautilzipGZIPInputStream'"
objectStream= new ICSharpCode.SharpZipLib.GZip.GZipInputStream(objectStream);
}
//UPGRADE_TODO: Class 'java.io.ObjectInputStream' was converted to 'System.IO.BinaryReader' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javaioObjectInputStream'"
objectInputStream = new System.IO.BinaryReader(objectStream);
}
}
catch (System.Exception e)
{
//UPGRADE_TODO: The equivalent in .NET for method 'java.lang.Throwable.getMessage' may return a different value. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1043'"
throw new System.Exception("Can't open file " + e.Message + '.');
}
if (testFileName.Length != 0)
{
template = test = new Instances(testReader, 1);
if (classIndex != - 1)
{
test.ClassIndex = classIndex - 1;
}
else
{
test.ClassIndex = test.numAttributes() - 1;
}
if (classIndex > test.numAttributes())
{
throw new System.Exception("Index of class attribute too large.");
}
}
if (trainFileName.Length != 0)
{
if ((classifier is UpdateableClassifier) && (testFileName.Length != 0))
{
train = new Instances(trainReader, 1);
}
else
{
train = new Instances(trainReader);
}
template = train;
if (classIndex != - 1)
{
train.ClassIndex = classIndex - 1;
}
else
{
train.ClassIndex = train.numAttributes() - 1;
}
if ((testFileName.Length != 0) && !test.equalHeaders(train))
{
throw new System.ArgumentException("Train and test file not compatible!");
}
if (classIndex > train.numAttributes())
{
throw new System.Exception("Index of class attribute too large.");
}
}
if (template == null)
{
throw new System.Exception("No actual dataset provided to use as template");
}
seedString = Utils.getOption('s', options);
if (seedString.Length != 0)
{
seed = System.Int32.Parse(seedString);
}
foldsString = Utils.getOption('x', options);
if (foldsString.Length != 0)
{
folds = System.Int32.Parse(foldsString);
}
costMatrix = handleCostOption(Utils.getOption('m', options), template.numClasses());
classStatistics = Utils.getFlag('i', options);
noOutput = Utils.getFlag('o', options);
trainStatistics = !Utils.getFlag('v', options);
printComplexityStatistics = Utils.getFlag('k', options);
printMargins = Utils.getFlag('r', options);
printGraph = Utils.getFlag('g', options);
sourceClass = Utils.getOption('z', options);
printSource = (sourceClass.Length != 0);
// Check -p option
try
{
attributeRangeString = Utils.getOption('p', options);
}
catch (System.Exception e)
{
//UPGRADE_TODO: The equivalent in .NET for method 'java.lang.Throwable.getMessage' may return a different value. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1043'"
throw new System.Exception(e.Message + "\nNOTE: the -p option has changed. " + "It now expects a parameter specifying a range of attributes " + "to list with the predictions. Use '-p 0' for none.");
}
if (attributeRangeString.Length != 0)
{
// if no test file given, we cannot print predictions
if (testFileName.Length == 0)
throw new System.Exception("Cannot print predictions ('-p') without test file ('-T')!");
printClassifications = true;
if (!attributeRangeString.Equals("0"))
attributesToOutput = new Range(attributeRangeString);
}
// if no training file given, we don't have any priors
if ((trainFileName.Length == 0) && (printComplexityStatistics))
throw new System.Exception("Cannot print complexity statistics ('-k') without training file ('-t')!");
// If a model file is given, we can't process
// scheme-specific options
if (objectInputFileName.Length != 0)
{
Utils.checkForRemainingOptions(options);
}
else
{
// Set options for classifier
// if (classifier instanceof OptionHandler)
// {
// for (int i = 0; i < options.length; i++)
// {
// if (options[i].length() != 0)
// {
// if (schemeOptionsText == null)
// {
// schemeOptionsText = new StringBuffer();
// }
// if (options[i].indexOf(' ') != -1)
// {
// schemeOptionsText.append('"' + options[i] + "\" ");
// }
// else
// {
// schemeOptionsText.append(options[i] + " ");
// }
// }
// }
// ((OptionHandler)classifier).setOptions(options);
// }
}
Utils.checkForRemainingOptions(options);
}
catch (System.Exception e)
{
//UPGRADE_TODO: The equivalent in .NET for method 'java.lang.Throwable.getMessage' may return a different value. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1043'"
throw new System.Exception("\nWeka exception: " + e.Message + makeOptionString(classifier));
}
// Setup up evaluation objects
Evaluation trainingEvaluation = new Evaluation(new Instances(template, 0), costMatrix);
Evaluation testingEvaluation = new Evaluation(new Instances(template, 0), costMatrix);
if (objectInputFileName.Length != 0)
{
testingEvaluation.useNoPriors();
// Load classifier from file
//UPGRADE_WARNING: Method 'java.io.ObjectInputStream.readObject' was converted to 'SupportClass.Deserialize' which may throw an exception. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1101'"
//classifier = (Classifier) SupportClass.Deserialize(objectInputStream);
//FileStream fs = new FileStream("DataFile.dat", FileMode.Open);
try
{
BinaryFormatter formatter = new BinaryFormatter();
// Deserialize the hashtable from the file and
// assign the reference to the local variable.
// addresses = (Hashtable)formatter.Deserialize(fs);
classifier = (Classifier)formatter.Deserialize(objectStream);
}
catch (Exception e)
{
Console.WriteLine("Failed to deserialize. Reason: " + e.Message);
throw;
}
finally
{
objectStream.Close();
//fs.Close();
}
objectInputStream.Close();
}
// backup of fully setup classifier for cross-validation
classifierBackup = Classifier.makeCopy(classifier);
// Build the classifier if no object file provided
if ((classifier is UpdateableClassifier) && (testFileName.Length != 0) && (costMatrix == null) && (trainFileName.Length != 0))
{
// Build classifier incrementally
trainingEvaluation.Priors = train;
testingEvaluation.Priors = train;
trainTimeStart = (System.DateTime.Now.Ticks - 621355968000000000) / 10000;
if (objectInputFileName.Length == 0)
{
classifier.buildClassifier(train);
}
while (train.readInstance(trainReader))
{
trainingEvaluation.updatePriors(train.instance(0));
testingEvaluation.updatePriors(train.instance(0));
((UpdateableClassifier) classifier).updateClassifier(train.instance(0));
train.delete(0);
}
trainTimeElapsed = (System.DateTime.Now.Ticks - 621355968000000000) / 10000 - trainTimeStart;
trainReader.Close();
}
else if (objectInputFileName.Length == 0)
{
// Build classifier in one go
tempTrain = new Instances(train);
trainingEvaluation.Priors = tempTrain;
testingEvaluation.Priors = tempTrain;
trainTimeStart = (System.DateTime.Now.Ticks - 621355968000000000) / 10000;
classifier.buildClassifier(tempTrain);
trainTimeElapsed = (System.DateTime.Now.Ticks - 621355968000000000) / 10000 - trainTimeStart;
}
// Save the classifier if an object output file is provided
if (objectOutputFileName.Length != 0)
{
//UPGRADE_TODO: Constructor 'java.io.FileOutputStream.FileOutputStream' was converted to 'System.IO.FileStream.FileStream' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javaioFileOutputStreamFileOutputStream_javalangString'"
System.IO.Stream os = new System.IO.FileStream(objectOutputFileName, System.IO.FileMode.Create);
if (objectOutputFileName.EndsWith(".gz"))
{
//UPGRADE_ISSUE: Constructor 'java.util.zip.GZIPOutputStream.GZIPOutputStream' was not converted. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1000_javautilzipGZIPOutputStream'"
os = new ICSharpCode.SharpZipLib.GZip.GZipOutputStream(os);
}
//UPGRADE_TODO: Class 'java.io.ObjectOutputStream' was converted to 'System.IO.BinaryWriter' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javaioObjectOutputStream'"
System.IO.BinaryWriter objectOutputStream = new System.IO.BinaryWriter(os);
//UPGRADE_TODO: Method 'java.io.ObjectOutputStream.writeObject' was converted to 'SupportClass.Serialize' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javaioObjectOutputStreamwriteObject_javalangObject'"
//SupportClass.Serialize(objectOutputStream, classifier);
BinaryFormatter bformatter = new BinaryFormatter();
bformatter.Serialize(os, classifier);
objectOutputStream.Flush();
objectOutputStream.Close();
}
// If classifier is drawable output string describing graph
if ((classifier is Drawable) && (printGraph))
{
return ((Drawable) classifier).graph();
}
// Output the classifier as equivalent source
if ((classifier is Sourcable) && (printSource))
{
return wekaStaticWrapper((Sourcable) classifier, sourceClass);
}
// Output test instance predictions only
if (printClassifications)
{
return toPrintClassifications(classifier, new Instances(template, 0), testFileName, classIndex, attributesToOutput);
}
// Output model
if (!(noOutput || printMargins))
{
// if (classifier instanceof OptionHandler)
// {
// if (schemeOptionsText != null)
// {
// text.append("\nOptions: "+schemeOptionsText);
// text.append("\n");
// }
// }
//UPGRADE_TODO: The equivalent in .NET for method 'java.lang.Object.toString' may return a different value. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1043'"
text.Append("\n" + classifier.ToString() + "\n");
}
if (!printMargins && (costMatrix != null))
{
text.Append("\n=== Evaluation Cost Matrix ===\n\n").Append(costMatrix.ToString());
}
// Compute error estimate from training data
if ((trainStatistics) && (trainFileName.Length != 0))
{
if ((classifier is UpdateableClassifier) && (testFileName.Length != 0) && (costMatrix == null))
{
// Classifier was trained incrementally, so we have to
// reopen the training data in order to test on it.
//UPGRADE_TODO: The differences in the expected value of parameters for constructor 'java.io.BufferedReader.BufferedReader' may cause compilation errors. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1092'"
//UPGRADE_WARNING: At least one expression was used more than once in the target code. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1181'"
//UPGRADE_TODO: Constructor 'java.io.FileReader.FileReader' was converted to 'System.IO.StreamReader' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073'"
trainReader = new System.IO.StreamReader(new System.IO.StreamReader(trainFileName, System.Text.Encoding.Default).BaseStream, new System.IO.StreamReader(trainFileName, System.Text.Encoding.Default).CurrentEncoding);
// Incremental testing
train = new Instances(trainReader, 1);
if (classIndex != - 1)
{
train.ClassIndex = classIndex - 1;
}
else
{
train.ClassIndex = train.numAttributes() - 1;
}
testTimeStart = (System.DateTime.Now.Ticks - 621355968000000000) / 10000;
while (train.readInstance(trainReader))
{
trainingEvaluation.evaluateModelOnce((Classifier) classifier, train.instance(0));
train.delete(0);
}
testTimeElapsed = (System.DateTime.Now.Ticks - 621355968000000000) / 10000 - testTimeStart;
trainReader.Close();
}
else
{
testTimeStart = (System.DateTime.Now.Ticks - 621355968000000000) / 10000;
trainingEvaluation.evaluateModel(classifier, train);
testTimeElapsed = (System.DateTime.Now.Ticks - 621355968000000000) / 10000 - testTimeStart;
}
// Print the results of the training evaluation
if (printMargins)
{
return trainingEvaluation.toCumulativeMarginDistributionString();
}
else
{
text.Append("\nTime taken to build model: " + Utils.doubleToString(trainTimeElapsed / 1000.0, 2) + " seconds");
text.Append("\nTime taken to test model on training data: " + Utils.doubleToString(testTimeElapsed / 1000.0, 2) + " seconds");
text.Append(trainingEvaluation.toSummaryString("\n\n=== Error on training" + " data ===\n", printComplexityStatistics));
if (template.classAttribute().Nominal)
{
if (classStatistics)
{
text.Append("\n\n" + trainingEvaluation.toClassDetailsString());
}
text.Append("\n\n" + trainingEvaluation.toMatrixString());
}
}
}
// Compute proper error estimates
if (testFileName.Length != 0)
{
// Testing is on the supplied test data
while (test.readInstance(testReader))
{
testingEvaluation.evaluateModelOnce((Classifier) classifier, test.instance(0));
test.delete(0);
}
testReader.Close();
text.Append("\n\n" + testingEvaluation.toSummaryString("=== Error on test data ===\n", printComplexityStatistics));
}
else if (trainFileName.Length != 0)
{
// Testing is via cross-validation on training data
//UPGRADE_TODO: The differences in the expected value of parameters for constructor 'java.util.Random.Random' may cause compilation errors. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1092'"
System.Random random = new System.Random((System.Int32) seed);
// use untrained (!) classifier for cross-validation
classifier = Classifier.makeCopy(classifierBackup);
testingEvaluation.crossValidateModel(classifier, train, folds, random);
if (template.classAttribute().Numeric)
{
text.Append("\n\n\n" + testingEvaluation.toSummaryString("=== Cross-validation ===\n", printComplexityStatistics));
}
else
{
text.Append("\n\n\n" + testingEvaluation.toSummaryString("=== Stratified " + "cross-validation ===\n", printComplexityStatistics));
}
}
if (template.classAttribute().Nominal)
{
if (classStatistics)
{
text.Append("\n\n" + testingEvaluation.toClassDetailsString());
}
text.Append("\n\n" + testingEvaluation.toMatrixString());
}
return text.ToString();
}
/// <summary> Prints the predictions for the given dataset into a String variable.
///
/// </summary>
/// <param name="classifier the">classifier to use
/// </param>
/// <param name="train the">training data
/// </param>
/// <param name="testFileName the">name of the test file
/// </param>
/// <param name="classIndex the">class index
/// </param>
/// <param name="attributesToOutput the">indices of the attributes to output
/// </param>
/// <returns> the generated predictions for the attribute range
/// </returns>
/// <throws> Exception if test file cannot be opened </throws>
protected internal static System.String toPrintClassifications(Classifier classifier, Instances train, System.String testFileName, int classIndex, Range attributesToOutput)
{
System.Text.StringBuilder text = new System.Text.StringBuilder();
if (testFileName.Length != 0)
{
System.IO.StreamReader testReader = null;
try
{
//UPGRADE_TODO: The differences in the expected value of parameters for constructor 'java.io.BufferedReader.BufferedReader' may cause compilation errors. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1092'"
//UPGRADE_WARNING: At least one expression was used more than once in the target code. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1181'"
//UPGRADE_TODO: Constructor 'java.io.FileReader.FileReader' was converted to 'System.IO.StreamReader' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073'"
testReader = new System.IO.StreamReader(new System.IO.StreamReader(testFileName, System.Text.Encoding.Default).BaseStream, new System.IO.StreamReader(testFileName, System.Text.Encoding.Default).CurrentEncoding);
}
catch (System.Exception e)
{
//UPGRADE_TODO: The equivalent in .NET for method 'java.lang.Throwable.getMessage' may return a different value. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1043'"
throw new System.Exception("Can't open file " + e.Message + '.');
}
Instances test = new Instances(testReader, 1);
if (classIndex != - 1)
{
test.ClassIndex = classIndex - 1;
}
else
{
test.ClassIndex = test.numAttributes() - 1;
}
int i = 0;
while (test.readInstance(testReader))
{
Instance instance = test.instance(0);
Instance withMissing = (Instance) instance.copy();
withMissing.Dataset = test;
double predValue = ((Classifier) classifier).classifyInstance(withMissing);
if (test.classAttribute().Numeric)
{
if (Instance.isMissingValue(predValue))
{
text.Append(i + " missing ");
}
else
{
text.Append(i + " " + predValue + " ");
}
if (instance.classIsMissing())
{
text.Append("missing");
}
else
{
text.Append(instance.classValue());
}
text.Append(" " + attributeValuesString(withMissing, attributesToOutput) + "\n");
}
else
{
if (Instance.isMissingValue(predValue))
{
text.Append(i + " missing ");
}
else
{
//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'"
text.Append(i + " " + test.classAttribute().value_Renamed((int) predValue) + " ");
}
if (Instance.isMissingValue(predValue))
{
text.Append("missing ");
}
else
{
//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'"
text.Append(classifier.distributionForInstance(withMissing)[(int) predValue] + " ");
}
text.Append(instance.toString(instance.classIndex()) + " " + attributeValuesString(withMissing, attributesToOutput) + "\n");
}
test.delete(0);
i++;
}
testReader.Close();
}
return text.ToString();
}
/// <summary> Returns the minsAndMaxs of the index.th subset.</summary>
public double[][] minsAndMaxs(Instances data, double[][] minsAndMaxs, int index)
{
double[][] newMinsAndMaxs = new double[data.numAttributes()][];
for (int i = 0; i < data.numAttributes(); i++)
{
newMinsAndMaxs[i] = new double[2];
}
for (int i = 0; i < data.numAttributes(); i++)
{
newMinsAndMaxs[i][0] = minsAndMaxs[i][0];
newMinsAndMaxs[i][1] = minsAndMaxs[i][1];
if (i == m_attIndex)
if (data.attribute(m_attIndex).Nominal)
newMinsAndMaxs[m_attIndex][1] = 1;
else
newMinsAndMaxs[m_attIndex][1 - index] = m_splitPoint;
}
return newMinsAndMaxs;
}
public void trainSMOUsingWeka(string wekaFile, string modelName)
{
try
{
weka.core.converters.CSVLoader csvLoader = new weka.core.converters.CSVLoader();
csvLoader.setSource(new java.io.File(wekaFile));
weka.core.Instances insts = csvLoader.getDataSet();
//weka.core.Instances insts = new weka.core.Instances(new java.io.FileReader(wekaFile));
insts.setClassIndex(insts.numAttributes() - 1);
cl = new weka.classifiers.functions.SMO();
cl.setBatchSize("100");
cl.setCalibrator(new weka.classifiers.functions.Logistic());
cl.setKernel(new weka.classifiers.functions.supportVector.PolyKernel());
cl.setEpsilon(1.02E-12);
cl.setC(1.0);
cl.setDebug(false);
cl.setChecksTurnedOff(false);
cl.setFilterType(new SelectedTag(weka.classifiers.functions.SMO.FILTER_NORMALIZE, weka.classifiers.functions.SMO.TAGS_FILTER));
System.Console.WriteLine("Performing " + percentSplit + "% split evaluation.");
//randomize the order of the instances in the dataset.
// weka.filters.Filter myRandom = new weka.filters.unsupervised.instance.Randomize();
//myRandom.setInputFormat(insts);
// insts = weka.filters.Filter.useFilter(insts, myRandom);
int trainSize = insts.numInstances() * percentSplit / 100;
int testSize = insts.numInstances() - trainSize;
weka.core.Instances train = new weka.core.Instances(insts, 0, trainSize);
java.io.File path = new java.io.File("/models/");
cl.buildClassifier(train);
saveModel(cl, modelName, path);
#region test whole set
int numCorrect = 0;
for (int i = 0; i < insts.numInstances(); i++)
{
weka.core.Instance currentInst = insts.instance(i);
if (i == 12)
{
array = new List <float>();
foreach (float value in currentInst.toDoubleArray())
{
array.Add(value);
}
}
double predictedClass = cl.classifyInstance(currentInst);
if (predictedClass == insts.instance(i).classValue())
{
numCorrect++;
}
}
System.Console.WriteLine(numCorrect + " out of " + testSize + " correct (" +
(double)((double)numCorrect / (double)testSize * 100.0) + "%)");
#endregion
}
catch (java.lang.Exception ex)
{
ex.printStackTrace();
}
}
public static void Test()
{
weka.core.Instances data = new weka.core.Instances(new java.io.FileReader("./data/Classification/Communication.arff"));
data.setClassIndex(data.numAttributes() - 1);
weka.classifiers.Classifier cls = new weka.classifiers.bayes.BayesNet();
//Save BayesNet results in .txt file
using (System.IO.StreamWriter file = new System.IO.StreamWriter("./data/Classification/Communication_Report.txt"))
{
file.WriteLine("Performing " + percentSplit + "% split evaluation.");
int runs = 1;
// 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);
//weka.classifiers.Evaluation eval = new weka.classifiers.Evaluation(randData);
int trainSize = (int)Math.Round((double)data.numInstances() * percentSplit / 100);
int testSize = data.numInstances() - trainSize;
weka.core.Instances train = new weka.core.Instances(data, 0, 0);
weka.core.Instances test = new weka.core.Instances(data, 0, 0);
train.setClassIndex(train.numAttributes() - 1);
test.setClassIndex(test.numAttributes() - 1);
//Print classifier analytics for all the dataset
file.WriteLine("EVALUATION OF TEST DATASET.");
//int numCorrect = 0;
for (int j = 0; j < data.numInstances(); j++)
{
weka.core.Instance currentInst = randData.instance(j);
if (j < trainSize)
{
train.add(currentInst);
}
else
{
test.add(currentInst);
/*
* double predictedClass = cls.classifyInstance(currentInst);
*
* double[] prediction = cls.distributionForInstance(currentInst);
*
* for (int p = 0; p < prediction.Length; p++)
* {
* file.WriteLine("Probability of class [{0}] for [{1}] is: {2}", currentInst.classAttribute().value(p), currentInst, Math.Round(prediction[p], 4));
* }
* file.WriteLine();
*
* file.WriteLine();
* if (predictedClass == data.instance(j).classValue())
* numCorrect++;*/
}
}
// build and evaluate classifier
cls.buildClassifier(train);
// Test the model
weka.classifiers.Evaluation eval = new weka.classifiers.Evaluation(randData);
eval.evaluateModel(cls, test);
// Print the results as in Weka explorer:
//Print statistics
String strSummaryTest = eval.toSummaryString();
file.WriteLine(strSummaryTest);
file.WriteLine();
//Print detailed class statistics
file.WriteLine(eval.toClassDetailsString());
file.WriteLine();
//Print confusion matrix
file.WriteLine(eval.toMatrixString());
file.WriteLine();
// Get the confusion matrix
double[][] cmMatrixTest = eval.confusionMatrix();
System.Console.WriteLine("Bayesian Network results saved in Communication_Report.txt file successfully.");
}
}
}
public static void cvdTest()
{
weka.core.Instances data = new weka.core.Instances(new java.io.FileReader("./data/Classification/Communication.arff"));
data.setClassIndex(data.numAttributes() - 1);
weka.classifiers.Classifier cls = new weka.classifiers.bayes.NaiveBayes();
//Save BayesNet results in .txt file
using (System.IO.StreamWriter file = new System.IO.StreamWriter("./data/Classification/Communication_Report.txt"))
{
int runs = 1;
int folds = 10;
// perform cross-validation
for (int i = 0; i < runs; i++)
{
// randomize data
int seed = i + 1;
java.util.Random rand = new java.util.Random(seed);
weka.core.Instances randData = new weka.core.Instances(data);
randData.randomize(rand);
if (randData.classAttribute().isNominal())
{
randData.stratify(folds);
}
weka.classifiers.Evaluation eval = new weka.classifiers.Evaluation(randData);
for (int n = 0; n < folds; n++)
{
weka.core.Instances train = randData.trainCV(folds, n);
weka.core.Instances test = randData.testCV(folds, n);
// build and evaluate classifier
//weka.classifiers.Classifier clsCopy = weka.classifiers.Classifier.makeCopy(cls);
cls.buildClassifier(train);
//eval.evaluateModel(cls, test);
//Print classifier analytics for all the dataset
file.WriteLine("EVALUATION OF TEST DATASET.");
// Test the model
weka.classifiers.Evaluation eTest = new weka.classifiers.Evaluation(test);
eTest.evaluateModel(cls, test);
// Print the results as in Weka explorer:
//Print statistics
String strSummaryTest = eTest.toSummaryString();
file.WriteLine(strSummaryTest);
file.WriteLine();
//Print detailed class statistics
file.WriteLine(eTest.toClassDetailsString());
file.WriteLine();
//Print confusion matrix
file.WriteLine(eTest.toMatrixString());
file.WriteLine();
// Get the confusion matrix
double[][] cmMatrixTest = eTest.confusionMatrix();
System.Console.WriteLine("Bayesian Network results saved in Communication_Report.txt file successfully.");
}
//Print classifier analytics for all the dataset
file.WriteLine("EVALUATION OF ALL DATASET.");
cls.buildClassifier(data);
// Train the model
weka.classifiers.Evaluation eAlldata = new weka.classifiers.Evaluation(data);
eAlldata.evaluateModel(cls, data);
// Print the results as in Weka explorer:
//Print statistics
String strSummaryAlldata = eAlldata.toSummaryString();
file.WriteLine(strSummaryAlldata);
file.WriteLine();
//Print detailed class statistics
file.WriteLine(eAlldata.toClassDetailsString());
file.WriteLine();
//Print confusion matrix
file.WriteLine(eAlldata.toMatrixString());
file.WriteLine("----------------");
//print model
file.WriteLine(cls);
file.WriteLine();
}
}
}
/// <summary> Generates the classifier.
///
/// </summary>
/// <param name="instances">set of instances serving as training data
/// </param>
/// <exception cref="Exception">if the classifier has not been generated successfully
/// </exception>
public override void buildClassifier(Instances instances)
{
//UPGRADE_TODO: The equivalent in .NET for field 'java.lang.Double.MAX_VALUE' may return a different value. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1043'"
double bestVal = System.Double.MaxValue, currVal;
//UPGRADE_TODO: The equivalent in .NET for field 'java.lang.Double.MAX_VALUE' may return a different value. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1043'"
double bestPoint = - System.Double.MaxValue, sum;
int bestAtt = - 1, numClasses;
if (instances.checkForStringAttributes())
{
throw new Exception("Can't handle string attributes!");
}
double[][] bestDist = new double[3][];
for (int i = 0; i < 3; i++)
{
bestDist[i] = new double[instances.numClasses()];
}
m_Instances = new Instances(instances);
m_Instances.deleteWithMissingClass();
if (m_Instances.numInstances() == 0)
{
throw new System.ArgumentException("No instances without missing " + "class values in training file!");
}
if (instances.numAttributes() == 1)
{
throw new System.ArgumentException("Attribute missing. Need at least one " + "attribute other than class attribute!");
}
if (m_Instances.classAttribute().Nominal)
{
numClasses = m_Instances.numClasses();
}
else
{
numClasses = 1;
}
// For each attribute
bool first = true;
for (int i = 0; i < m_Instances.numAttributes(); i++)
{
if (i != m_Instances.classIndex())
{
// Reserve space for distribution.
double[][] tmpArray = new double[3][];
for (int i2 = 0; i2 < 3; i2++)
{
tmpArray[i2] = new double[numClasses];
}
m_Distribution = tmpArray;
// Compute value of criterion for best split on attribute
if (m_Instances.attribute(i).Nominal)
{
currVal = findSplitNominal(i);
}
else
{
currVal = findSplitNumeric(i);
}
if ((first) || (currVal < bestVal))
{
bestVal = currVal;
bestAtt = i;
bestPoint = m_SplitPoint;
for (int j = 0; j < 3; j++)
{
Array.Copy(m_Distribution[j], 0, bestDist[j], 0, numClasses);
}
}
// First attribute has been investigated
first = false;
}
}
// Set attribute, split point and distribution.
m_AttIndex = bestAtt;
m_SplitPoint = bestPoint;
m_Distribution = bestDist;
if (m_Instances.classAttribute().Nominal)
{
for (int i = 0; i < m_Distribution.Length; i++)
{
double sumCounts = Utils.sum(m_Distribution[i]);
if (sumCounts == 0)
{
// This means there were only missing attribute values
Array.Copy(m_Distribution[2], 0, m_Distribution[i], 0, m_Distribution[2].Length);
Utils.normalize(m_Distribution[i]);
}
else
{
Utils.normalize(m_Distribution[i], sumCounts);
}
}
}
// Save memory
m_Instances = new Instances(m_Instances, 0);
}
/// <summary>
/// Build the learning model for classification
/// </summary>
/// <param name="InstancesList">list of instances </param>
/// <param name="NumberofClusters">Number of Clusters</param>
/// <param name="TextBoxForFeedback">Text box for the results (can be NULL)</param>
/// <param name="PanelForVisualFeedback">Panel to display visual results if avalaible (can be NULL)</param>
public Classifier PerformTraining(FormForClassificationInfo WindowForClassificationParam, Instances InstancesList, /*int NumberofClusters,*/ RichTextBox TextBoxForFeedback,
Panel PanelForVisualFeedback, out weka.classifiers.Evaluation ModelEvaluation, bool IsCellular)
{
// weka.classifiers.Evaluation ModelEvaluation = null;
// FormForClassificationInfo WindowForClassificationParam = new FormForClassificationInfo(GlobalInfo);
ModelEvaluation = null;
// if (WindowForClassificationParam.ShowDialog() != System.Windows.Forms.DialogResult.OK) return null;
// weka.classifiers.Evaluation ModelEvaluation = new Evaluation(
cParamAlgo ClassifAlgoParams = WindowForClassificationParam.GetSelectedAlgoAndParameters();
if (ClassifAlgoParams == null) return null;
//this.Cursor = Cursors.WaitCursor;
// cParamAlgo ClassificationAlgo = WindowForClassificationParam.GetSelectedAlgoAndParameters();
cListValuesParam Parameters = ClassifAlgoParams.GetListValuesParam();
//Classifier this.CurrentClassifier = null;
// -------------------------- Classification -------------------------------
// create the instances
// InstancesList = this.ListInstances;
this.attValsWithoutClasses = new FastVector();
if (IsCellular)
for (int i = 0; i < cGlobalInfo.ListCellularPhenotypes.Count; i++)
this.attValsWithoutClasses.addElement(cGlobalInfo.ListCellularPhenotypes[i].Name);
else
for (int i = 0; i < cGlobalInfo.ListWellClasses.Count; i++)
this.attValsWithoutClasses.addElement(cGlobalInfo.ListWellClasses[i].Name);
InstancesList.insertAttributeAt(new weka.core.Attribute("Class", this.attValsWithoutClasses), InstancesList.numAttributes());
//int A = Classes.Count;
for (int i = 0; i < Classes.Count; i++)
InstancesList.get(i).setValue(InstancesList.numAttributes() - 1, Classes[i]);
InstancesList.setClassIndex(InstancesList.numAttributes() - 1);
weka.core.Instances train = new weka.core.Instances(InstancesList, 0, InstancesList.numInstances());
if (PanelForVisualFeedback != null)
PanelForVisualFeedback.Controls.Clear();
#region List classifiers
#region J48
if (ClassifAlgoParams.Name == "J48")
{
this.CurrentClassifier = new weka.classifiers.trees.J48();
((J48)this.CurrentClassifier).setMinNumObj((int)Parameters.ListDoubleValues.Get("numericUpDownMinInstLeaf").Value);
((J48)this.CurrentClassifier).setConfidenceFactor((float)Parameters.ListDoubleValues.Get("numericUpDownConfFactor").Value);
((J48)this.CurrentClassifier).setNumFolds((int)Parameters.ListDoubleValues.Get("numericUpDownNumFolds").Value);
((J48)this.CurrentClassifier).setUnpruned((bool)Parameters.ListCheckValues.Get("checkBoxUnPruned").Value);
((J48)this.CurrentClassifier).setUseLaplace((bool)Parameters.ListCheckValues.Get("checkBoxLaplacianSmoothing").Value);
((J48)this.CurrentClassifier).setSeed((int)Parameters.ListDoubleValues.Get("numericUpDownSeedNumber").Value);
((J48)this.CurrentClassifier).setSubtreeRaising((bool)Parameters.ListCheckValues.Get("checkBoxSubTreeRaising").Value);
// CurrentClassif.SetJ48Tree((J48)this.CurrentClassifier, Classes.Length);
this.CurrentClassifier.buildClassifier(train);
// display results training
// display tree
if (PanelForVisualFeedback != null)
{
GViewer GraphView = DisplayTree(GlobalInfo, ((J48)this.CurrentClassifier), IsCellular).gViewerForTreeClassif;
GraphView.Size = new System.Drawing.Size(PanelForVisualFeedback.Width, PanelForVisualFeedback.Height);
GraphView.Anchor = (AnchorStyles.Bottom | AnchorStyles.Top | AnchorStyles.Left | AnchorStyles.Right);
PanelForVisualFeedback.Controls.Clear();
PanelForVisualFeedback.Controls.Add(GraphView);
}
}
#endregion
#region Random Tree
else if (ClassifAlgoParams.Name == "RandomTree")
{
this.CurrentClassifier = new weka.classifiers.trees.RandomTree();
if ((bool)Parameters.ListCheckValues.Get("checkBoxMaxDepthUnlimited").Value)
((RandomTree)this.CurrentClassifier).setMaxDepth(0);
else
((RandomTree)this.CurrentClassifier).setMaxDepth((int)Parameters.ListDoubleValues.Get("numericUpDownMaxDepth").Value);
((RandomTree)this.CurrentClassifier).setSeed((int)Parameters.ListDoubleValues.Get("numericUpDownSeed").Value);
((RandomTree)this.CurrentClassifier).setMinNum((double)Parameters.ListDoubleValues.Get("numericUpDownMinWeight").Value);
if ((bool)Parameters.ListCheckValues.Get("checkBoxIsBackfitting").Value)
{
((RandomTree)this.CurrentClassifier).setNumFolds((int)Parameters.ListDoubleValues.Get("numericUpDownBackFittingFolds").Value);
}
else
{
((RandomTree)this.CurrentClassifier).setNumFolds(0);
}
this.CurrentClassifier.buildClassifier(train);
//string StringForTree = ((RandomTree)this.CurrentClassifier).graph().Remove(0, ((RandomTree)this.CurrentClassifier).graph().IndexOf("{") + 2);
//Microsoft.Msagl.GraphViewerGdi.GViewer GraphView = new GViewer();
//GraphView.Graph = GlobalInfo.WindowHCSAnalyzer.ComputeAndDisplayGraph(StringForTree);//.Remove(StringForTree.Length - 3, 3));
//GraphView.Size = new System.Drawing.Size(panelForGraphicalResults.Width, panelForGraphicalResults.Height);
//GraphView.Anchor = (AnchorStyles.Bottom | AnchorStyles.Top | AnchorStyles.Left | AnchorStyles.Right);
//this.panelForGraphicalResults.Controls.Clear();
//this.panelForGraphicalResults.Controls.Add(GraphView);
}
#endregion
#region Random Forest
else if (ClassifAlgoParams.Name == "RandomForest")
{
this.CurrentClassifier = new weka.classifiers.trees.RandomForest();
if ((bool)Parameters.ListCheckValues.Get("checkBoxMaxDepthUnlimited").Value)
((RandomForest)this.CurrentClassifier).setMaxDepth(0);
else
((RandomForest)this.CurrentClassifier).setMaxDepth((int)Parameters.ListDoubleValues.Get("numericUpDownMaxDepth").Value);
((RandomForest)this.CurrentClassifier).setNumTrees((int)Parameters.ListDoubleValues.Get("numericUpDownNumTrees").Value);
((RandomForest)this.CurrentClassifier).setSeed((int)Parameters.ListDoubleValues.Get("numericUpDownSeed").Value);
this.CurrentClassifier.buildClassifier(train);
}
#endregion
#region KStar
else if (ClassifAlgoParams.Name == "KStar")
{
this.CurrentClassifier = new weka.classifiers.lazy.KStar();
((KStar)this.CurrentClassifier).setGlobalBlend((int)Parameters.ListDoubleValues.Get("numericUpDownGlobalBlend").Value);
((KStar)this.CurrentClassifier).setEntropicAutoBlend((bool)Parameters.ListCheckValues.Get("checkBoxBlendAuto").Value);
this.CurrentClassifier.buildClassifier(train);
}
#endregion
#region SVM
else if (ClassifAlgoParams.Name == "SVM")
{
this.CurrentClassifier = new weka.classifiers.functions.SMO();
((SMO)this.CurrentClassifier).setC((double)Parameters.ListDoubleValues.Get("numericUpDownC").Value);
((SMO)this.CurrentClassifier).setKernel(WindowForClassificationParam.GeneratedKernel);
((SMO)this.CurrentClassifier).setRandomSeed((int)Parameters.ListDoubleValues.Get("numericUpDownSeed").Value);
this.CurrentClassifier.buildClassifier(train);
}
#endregion
#region KNN
else if (ClassifAlgoParams.Name == "KNN")
{
this.CurrentClassifier = new weka.classifiers.lazy.IBk();
string OptionDistance = " -K " + (int)Parameters.ListDoubleValues.Get("numericUpDownKNN").Value + " -W 0 ";
string WeightType = (string)Parameters.ListTextValues.Get("comboBoxDistanceWeight").Value;
switch (WeightType)
{
case "No Weighting":
OptionDistance += "";
break;
case "1/Distance":
OptionDistance += "-I";
break;
case "1-Distance":
OptionDistance += "-F";
break;
default:
break;
}
OptionDistance += " -A \"weka.core.neighboursearch.LinearNNSearch -A \\\"weka.core.";
string DistanceType = (string)Parameters.ListTextValues.Get("comboBoxDistance").Value;
// OptionDistance += " -A \"weka.core.";
switch (DistanceType)
{
case "Euclidean":
OptionDistance += "EuclideanDistance";
break;
case "Manhattan":
OptionDistance += "ManhattanDistance";
break;
case "Chebyshev":
OptionDistance += "ChebyshevDistance";
break;
default:
break;
}
if (!(bool)Parameters.ListCheckValues.Get("checkBoxNormalize").Value)
OptionDistance += " -D";
OptionDistance += " -R ";
OptionDistance += "first-last\\\"\"";
((IBk)this.CurrentClassifier).setOptions(weka.core.Utils.splitOptions(OptionDistance));
//((IBk)this.CurrentClassifier).setKNN((int)Parameters.ListDoubleValues.Get("numericUpDownKNN").Value);
this.CurrentClassifier.buildClassifier(train);
}
#endregion
#region Multilayer Perceptron
else if (ClassifAlgoParams.Name == "Perceptron")
{
this.CurrentClassifier = new weka.classifiers.functions.MultilayerPerceptron();
((MultilayerPerceptron)this.CurrentClassifier).setMomentum((double)Parameters.ListDoubleValues.Get("numericUpDownMomentum").Value);
((MultilayerPerceptron)this.CurrentClassifier).setLearningRate((double)Parameters.ListDoubleValues.Get("numericUpDownLearningRate").Value);
((MultilayerPerceptron)this.CurrentClassifier).setSeed((int)Parameters.ListDoubleValues.Get("numericUpDownSeed").Value);
((MultilayerPerceptron)this.CurrentClassifier).setTrainingTime((int)Parameters.ListDoubleValues.Get("numericUpDownTrainingTime").Value);
((MultilayerPerceptron)this.CurrentClassifier).setNormalizeAttributes((bool)Parameters.ListCheckValues.Get("checkBoxNormAttribute").Value);
((MultilayerPerceptron)this.CurrentClassifier).setNormalizeNumericClass((bool)Parameters.ListCheckValues.Get("checkBoxNormNumericClasses").Value);
this.CurrentClassifier.buildClassifier(train);
}
#endregion
#region ZeroR
else if (ClassifAlgoParams.Name == "ZeroR")
{
this.CurrentClassifier = new weka.classifiers.rules.OneR();
this.CurrentClassifier.buildClassifier(train);
}
#endregion
#region OneR
else if (ClassifAlgoParams.Name == "OneR")
{
this.CurrentClassifier = new weka.classifiers.rules.OneR();
((OneR)this.CurrentClassifier).setMinBucketSize((int)Parameters.ListDoubleValues.Get("numericUpDownMinBucketSize").Value);
this.CurrentClassifier.buildClassifier(train);
}
#endregion
#region Naive Bayes
else if (ClassifAlgoParams.Name == "NaiveBayes")
{
this.CurrentClassifier = new weka.classifiers.bayes.NaiveBayes();
((NaiveBayes)this.CurrentClassifier).setUseKernelEstimator((bool)Parameters.ListCheckValues.Get("checkBoxKernelEstimator").Value);
this.CurrentClassifier.buildClassifier(train);
}
#endregion
#region Logistic
else if (ClassifAlgoParams.Name == "Logistic")
{
this.CurrentClassifier = new weka.classifiers.functions.Logistic();
((Logistic)this.CurrentClassifier).setUseConjugateGradientDescent((bool)Parameters.ListCheckValues.Get("checkBoxUseConjugateGradientDescent").Value);
((Logistic)this.CurrentClassifier).setRidge((double)Parameters.ListDoubleValues.Get("numericUpDownRidge").Value);
this.CurrentClassifier.buildClassifier(train);
}
#endregion
//weka.classifiers.functions.SMO
//BayesNet
#endregion
if (TextBoxForFeedback != null)
{
TextBoxForFeedback.Clear();
TextBoxForFeedback.AppendText(this.CurrentClassifier.ToString());
}
TextBoxForFeedback.AppendText("\n" + (InstancesList.numAttributes() - 1) + " attributes:\n\n");
for (int IdxAttributes = 0; IdxAttributes < InstancesList.numAttributes() - 1; IdxAttributes++)
{
TextBoxForFeedback.AppendText(IdxAttributes + "\t: " + InstancesList.attribute(IdxAttributes).name() + "\n");
}
#region evaluation of the model and results display
if ((WindowForClassificationParam.numericUpDownFoldNumber.Enabled) && (TextBoxForFeedback != null))
{
TextBoxForFeedback.AppendText("\n-----------------------------\nModel validation\n-----------------------------\n");
ModelEvaluation = new weka.classifiers.Evaluation(InstancesList);
ModelEvaluation.crossValidateModel(this.CurrentClassifier, InstancesList, (int)WindowForClassificationParam.numericUpDownFoldNumber.Value, new java.util.Random(1));
TextBoxForFeedback.AppendText(ModelEvaluation.toSummaryString());
TextBoxForFeedback.AppendText("\n-----------------------------\nConfusion Matrix:\n-----------------------------\n");
double[][] ConfusionMatrix = ModelEvaluation.confusionMatrix();
string NewLine = "";
for (int i = 0; i < ConfusionMatrix[0].Length; i++)
{
NewLine += "c" + i + "\t";
}
TextBoxForFeedback.AppendText(NewLine + "\n\n");
for (int j = 0; j < ConfusionMatrix.Length; j++)
{
NewLine = "";
for (int i = 0; i < ConfusionMatrix[0].Length; i++)
{
NewLine += ConfusionMatrix[j][i] + "\t";
}
// if
TextBoxForFeedback.AppendText(NewLine + "| c" + j + " <=> " + cGlobalInfo.ListCellularPhenotypes[j].Name + "\n");
}
}
#endregion
return this.CurrentClassifier;
}
/// <summary>
/// Evalute and display a WEKA clusterer
/// </summary>
/// <param name="SelectedClusterer">weka clusterer</param>
/// <param name="InstancesList">list of instances for the validation</param>
/// <param name="RichTextBoxToDisplayResults">Text box for the results (can be NULL)</param>
/// <param name="PanelTodisplayGraphicalResults">Panel to display visual results if avalaible (can be NULL)</param>
/// <returns></returns>
public ClusterEvaluation EvaluteAndDisplayClusterer(RichTextBox RichTextBoxToDisplayResults,
Panel PanelTodisplayGraphicalResults, Instances ListInstanceForValid)
{
ClusterEvaluation eval = new ClusterEvaluation();
eval.setClusterer(SelectedClusterer);
eval.evaluateClusterer(ListInstanceForValid);
if (RichTextBoxToDisplayResults != null)
{
if ((RichTextBoxToDisplayResults != null) && (eval.getNumClusters() > cGlobalInfo.ListCellularPhenotypes.Count))
{
RichTextBoxToDisplayResults.Clear();
RichTextBoxToDisplayResults.AppendText("Error: " + eval.getNumClusters() + " clusters identifed.");
RichTextBoxToDisplayResults.AppendText("The maximum number of cluster is " + cGlobalInfo.ListCellularPhenotypes.Count + ".");
return null;
}
if (RichTextBoxToDisplayResults != null)
{
RichTextBoxToDisplayResults.Clear();
RichTextBoxToDisplayResults.AppendText(eval.clusterResultsToString());
}
RichTextBoxToDisplayResults.AppendText("\n" + ListInstanceForValid.numAttributes() + " attributes:\n\n");
for (int IdxAttributes = 0; IdxAttributes < ListInstanceForValid.numAttributes(); IdxAttributes++)
{
RichTextBoxToDisplayResults.AppendText(IdxAttributes + "\t: " + ListInstanceForValid.attribute(IdxAttributes).name() + "\n");
}
}
if (PanelTodisplayGraphicalResults != null) PanelTodisplayGraphicalResults.Controls.Clear();
if ((PanelTodisplayGraphicalResults != null) && (SelectedClusterer.GetType().Name == "HierarchicalClusterer"))
{
Button ButtonToDisplayHierarchicalClustering = new Button();
ButtonToDisplayHierarchicalClustering.Text = "Display Hierarchical Tree";
ButtonToDisplayHierarchicalClustering.Width *= 2;
ButtonToDisplayHierarchicalClustering.Location = new System.Drawing.Point((PanelTodisplayGraphicalResults.Width - ButtonToDisplayHierarchicalClustering.Width) / 2,
(PanelTodisplayGraphicalResults.Height - ButtonToDisplayHierarchicalClustering.Height) / 2);
ButtonToDisplayHierarchicalClustering.Anchor = AnchorStyles.None;
ButtonToDisplayHierarchicalClustering.Click += new EventHandler(ClickToDisplayHierarchicalTree);
PanelTodisplayGraphicalResults.Controls.Add(ButtonToDisplayHierarchicalClustering);
}
return eval;
}
public Instances CreateInstancesWithoutClass(cExtendedTable Input)
{
weka.core.FastVector atts = new FastVector();
int columnNo = 0;
// Descriptors loop
for (int i = 0; i < Input.Count; i++)
{
//if (ParentScreening.ListDescriptors[i].IsActive() == false) continue;
atts.addElement(new weka.core.Attribute(Input[i].Name));
columnNo++;
}
// weka.core.FastVector attVals = new FastVector();
Instances data1 = new Instances("MyRelation", atts, 0);
for (int IdxRow = 0; IdxRow < Input[0].Count; IdxRow++)
{
double[] vals = new double[data1.numAttributes()];
for (int Col = 0; Col < columnNo; Col++)
{
// if (Glo .ListDescriptors[Col].IsActive() == false) continue;
vals[Col] = Input[Col][IdxRow];// double.Parse(dt.Rows[IdxRow][Col].ToString());
}
data1.add(new DenseInstance(1.0, vals));
}
return data1;
}
/// <summary>
/// Create an instances structure with classes for supervised methods
/// </summary>
/// <param name="NumClass"></param>
/// <returns></returns>
public Instances CreateInstancesWithClassesWithPlateBasedDescriptor(int NumberOfClass)
{
weka.core.FastVector atts = new FastVector();
int columnNo = 0;
for (int i = 0; i < ParentScreening.ListPlateBaseddescriptorNames.Count; i++)
{
atts.addElement(new weka.core.Attribute(ParentScreening.ListPlateBaseddescriptorNames[i]));
columnNo++;
}
weka.core.FastVector attVals = new FastVector();
for (int i = 0; i < NumberOfClass; i++)
attVals.addElement("Class" + (i).ToString());
atts.addElement(new weka.core.Attribute("Class", attVals));
Instances data1 = new Instances("MyRelation", atts, 0);
int IdxWell = 0;
foreach (cWell CurrentWell in this.ListActiveWells)
{
if (CurrentWell.GetCurrentClassIdx() == -1) continue;
double[] vals = new double[data1.numAttributes()];
int IdxCol = 0;
for (int Col = 0; Col < ParentScreening.ListPlateBaseddescriptorNames.Count; Col++)
{
vals[IdxCol++] = CurrentWell.ListPlateBasedDescriptors[Col].GetValue();
}
vals[columnNo] = CurrentWell.GetCurrentClassIdx();
data1.add(new DenseInstance(1.0, vals));
IdxWell++;
}
data1.setClassIndex((data1.numAttributes() - 1));
return data1;
}
public List <double> testSMOUsingWeka(string[] attributeArray, string[] classNames, double[] dataValues, string classHeader, string defaultclass, string modelName, int hiddelLayers = 7, double learningRate = 0.03, double momentum = 0.4, int decimalPlaces = 2, int trainingTime = 1000)
{
java.util.ArrayList classLabel = new java.util.ArrayList();
foreach (string className in classNames)
{
classLabel.Add(className);
}
weka.core.Attribute classHeaderName = new weka.core.Attribute(classHeader, classLabel);
java.util.ArrayList attributeList = new java.util.ArrayList();
foreach (string attribute in attributeArray)
{
weka.core.Attribute newAttribute = new weka.core.Attribute(attribute);
attributeList.Add(newAttribute);
}
attributeList.add(classHeaderName);
weka.core.Instances data = new weka.core.Instances("TestInstances", attributeList, 0);
data.setClassIndex(data.numAttributes() - 1);
// Set instance's values for the attributes
weka.core.Instance inst_co = new DenseInstance(data.numAttributes());
for (int i = 0; i < data.numAttributes() - 1; i++)
{
inst_co.setValue(i, Math.Round(dataValues.ElementAt(i), 5));
}
inst_co.setValue(classHeaderName, defaultclass);
data.add(inst_co);
weka.core.Instance currentInst = data.get(0);
int j = 0;
//foreach (float value in dataValues)
//{
// // double roundedValue = Math.Round(value);
// //var rounded = Math.Floor(value * 100) / 100;
// if (array.ElementAt(j) != value)
// {
// System.Console.WriteLine("Masla occur");
// }
// j++;
//}
//double predictedClass = cl.classifyInstance(data.get(0));
weka.classifiers.functions.SMO clRead = new weka.classifiers.functions.SMO();
try
{
java.io.File path = new java.io.File("/models/");
clRead = loadSMOModel(modelName, path);
}
catch (Exception e)
{
//string p1 = Assembly.GetExecutingAssembly().Location;
string ClassifierName = Path.GetFileName(Path.GetFileName(modelName));
string Path1 = HostingEnvironment.MapPath(@"~//libs//models//" + ClassifierName);
//string Path1 = HostingEnvironment.MapPath(@"~//libs//models//FusionCustomized.model");
clRead = (weka.classifiers.functions.SMO)weka.core.SerializationHelper.read(modelName);
}
// weka.classifiers.functions.SMO clRead = loadSMOModel(modelName, path);
clRead.setBatchSize("100");
clRead.setCalibrator(new weka.classifiers.functions.Logistic());
clRead.setKernel(new weka.classifiers.functions.supportVector.PolyKernel());
clRead.setEpsilon(1.02E-12);
clRead.setC(1.0);
clRead.setDebug(false);
clRead.setChecksTurnedOff(false);
clRead.setFilterType(new SelectedTag(weka.classifiers.functions.SMO.FILTER_NORMALIZE, weka.classifiers.functions.SMO.TAGS_FILTER));
double classValue = clRead.classifyInstance(data.get(0));
double[] predictionDistribution = clRead.distributionForInstance(data.get(0));
//for (int predictionDistributionIndex = 0;
// predictionDistributionIndex < predictionDistribution.Count();
// predictionDistributionIndex++)
//{
// string classValueString1 = classLabel.get(predictionDistributionIndex).ToString();
// double prob= predictionDistribution[predictionDistributionIndex]*100;
// System.Console.WriteLine(classValueString1 + ":" + prob);
//}
List <double> prediction = new List <double>();
prediction.Add(classValue);
//prediction.AddRange(predictionDistribution);
return(prediction);
}
private void Browse_Click(object sender, EventArgs e)
{
OpenFileDialog ofd = new OpenFileDialog();
ofd.ShowDialog();
file = ofd.SafeFileName;
label2.Text = "Wait process in progress";
weka.core.Instances insts = new weka.core.Instances(new java.io.FileReader(file));
double max_value = J48classifyTest(insts);
model = J48cl;
string name = "J48cl";
double NBvalue = NaiveBayesTest(insts);
if (NBvalue > max_value)
{
max_value = NBvalue;
model = NaiveBayescl;
name = "NaiveBayes";
}
double RFvalue = RandomForestTest(insts);
if (RFvalue > max_value)
{
max_value = RFvalue;
model = RandomForestcl;
name = "RandomForest";
}
double RTvalue = RandomTreeTest(insts);
if (RTvalue > max_value)
{
max_value = RTvalue;
model = RandomTreecl;
name = "RandomTree";
}
double _5IBKvalue = _5IBkTest(insts);
if (_5IBKvalue > max_value)
{
max_value = _5IBKvalue;
model = _5IBKcl;
name = " _5IBK";
}
double _7IBKvalue = _7IBkTest(insts);
if (_7IBKvalue > max_value)
{
max_value = _7IBKvalue;
model = _7IBKcl;
name = " _7IBk";
}
double _9IBKvalue = _9IBkTest(insts);
if (_9IBKvalue > max_value)
{
max_value = _9IBKvalue;
model = _9IBKcl;
name = " _9IBk";
}
double LogRegressionvalue = LogRegressionTest(insts);
if (LogRegressionvalue > max_value)
{
max_value = LogRegressionvalue;
model = LogRegressioncl;
name = "LogRegression";
}
double SVM = SupportVectorMachineTest(insts);
if (SVM > max_value)
{
max_value = SVM;
model = SupportVectorMachine;
name = "SupportVectorMachine";
}
double ArtNN = ArtNeuralNetworkTest(insts);
if (ArtNN > max_value)
{
max_value = ArtNN;
model = ArtNeuralNetwork;
name = "ArtNeuralNetwork";
}
label2.Text = name + " is the most successful algorithm for this data set " + "(%" + Math.Round(max_value, 2) + ")";
for (int i = 0; i < insts.numAttributes() - 1; i++)
{
if (insts.attribute(i).isNominal())
{
Label l = new Label();
flowLayoutPanel2.Controls.Add(l);
l.Top = i * 30 + 175;
l.Left = 100;
l.Text = insts.attribute(i).name().ToString() + ": ";
ComboBox mybox = new ComboBox();
for (int j = 0; j < insts.attribute(i).numValues(); j++)
{
mybox.Items.Add(insts.attribute(i).value(j));
}
// Creating and setting the properties of comboBox
mybox.DropDownStyle = ComboBoxStyle.DropDownList;
mybox.Size = new Size(100, 30);
mybox.Top = i * 30 + 175;
l.Left = 200;
mybox.Tag = i;
flowLayoutPanel2.Controls.Add(mybox);
list.Add(mybox);
}
else
{
Label l = new Label();
flowLayoutPanel2.Controls.Add(l);
l.Text = insts.attribute(i).name().ToString() + ": ";
TextBox txt = new TextBox();
txt.Tag = i;
list.Add(txt);
flowLayoutPanel2.Controls.Add(txt);
}
}
Button button = new Button();
button.Name = "Discover";
button.Text = "Find";
button.Location = new Point(468, 72);
button.Size = new Size(60, 30);
button.BackColor = Color.Red;
button.Font = new Font(button.Font.Name, button.Font.Size, FontStyle.Bold);
button.Click += new EventHandler(button1_Click);
Controls.Add(button);
}
/// <summary>
/// Create an instances structure with classes for supervised methods
/// </summary>
/// <param name="NumClass"></param>
/// <returns></returns>
public Instances CreateInstancesWithClasses(cInfoClass InfoClass, int NeutralClass)
{
weka.core.FastVector atts = new FastVector();
int columnNo = 0;
for (int i = 0; i < ParentScreening.ListDescriptors.Count; i++)
{
if (ParentScreening.ListDescriptors[i].IsActive() == false) continue;
atts.addElement(new weka.core.Attribute(ParentScreening.ListDescriptors[i].GetName()));
columnNo++;
}
weka.core.FastVector attVals = new FastVector();
for (int i = 0; i < InfoClass.NumberOfClass; i++)
attVals.addElement("Class__" + (i).ToString());
atts.addElement(new weka.core.Attribute("Class__", attVals));
Instances data1 = new Instances("MyRelation", atts, 0);
int IdxWell = 0;
foreach (cWell CurrentWell in this.ListActiveWells)
{
if (CurrentWell.GetCurrentClassIdx() == NeutralClass) continue;
double[] vals = new double[data1.numAttributes()];
int IdxCol = 0;
for (int Col = 0; Col < ParentScreening.ListDescriptors.Count; Col++)
{
if (ParentScreening.ListDescriptors[Col].IsActive() == false) continue;
vals[IdxCol++] = CurrentWell.ListSignatures[Col].GetValue();
}
vals[columnNo] = InfoClass.CorrespondanceTable[CurrentWell.GetCurrentClassIdx()];
data1.add(new DenseInstance(1.0, vals));
IdxWell++;
}
data1.setClassIndex((data1.numAttributes() - 1));
return data1;
}
public static void BayesTest()
{
try
{
weka.core.Instances insts = new weka.core.Instances(new java.io.FileReader("iris.arff"));
insts.setClassIndex(insts.numAttributes() - 1);
weka.classifiers.Classifier cl = new weka.classifiers.bayes.BayesNet();
System.Console.WriteLine("Performing " + percentSplit + "% split evaluation.");
//randomize the order of the instances in the dataset.
weka.filters.Filter myRandom = new weka.filters.unsupervised.instance.Randomize();
myRandom.setInputFormat(insts);
insts = weka.filters.Filter.useFilter(insts, myRandom);
int trainSize = insts.numInstances() * percentSplit / 100;
int testSize = insts.numInstances() - trainSize;
weka.core.Instances train = new weka.core.Instances(insts, 0, trainSize);
weka.core.Instances test = new weka.core.Instances(insts, 0, 0);
cl.buildClassifier(train);
//print model
System.Console.WriteLine(cl);
int numCorrect = 0;
for (int i = trainSize; i < insts.numInstances(); i++)
{
weka.core.Instance currentInst = insts.instance(i);
double predictedClass = cl.classifyInstance(currentInst);
test.add(currentInst);
double[] prediction = cl.distributionForInstance(currentInst);
for (int x = 0; x < prediction.Length; x++)
{
System.Console.WriteLine("Probability of class [{0}] for [{1}] is: {2}", currentInst.classAttribute().value(x), currentInst, Math.Round(prediction[x], 4));
}
System.Console.WriteLine();
if (predictedClass == insts.instance(i).classValue())
{
numCorrect++;
}
}
System.Console.WriteLine(numCorrect + " out of " + testSize + " correct (" +
(double)((double)numCorrect / (double)testSize * 100.0) + "%)");
// Train the model
weka.classifiers.Evaluation eTrain = new weka.classifiers.Evaluation(train);
eTrain.evaluateModel(cl, train);
// Print the results as in Weka explorer:
//Print statistics
String strSummaryTrain = eTrain.toSummaryString();
System.Console.WriteLine(strSummaryTrain);
//Print detailed class statistics
System.Console.WriteLine(eTrain.toClassDetailsString());
//Print confusion matrix
System.Console.WriteLine(eTrain.toMatrixString());
// Get the confusion matrix
double[][] cmMatrixTrain = eTrain.confusionMatrix();
// Test the model
weka.classifiers.Evaluation eTest = new weka.classifiers.Evaluation(test);
eTest.evaluateModel(cl, test);
// Print the results as in Weka explorer:
//Print statistics
String strSummaryTest = eTest.toSummaryString();
System.Console.WriteLine(strSummaryTest);
//Print detailed class statistics
System.Console.WriteLine(eTest.toClassDetailsString());
//Print confusion matrix
System.Console.WriteLine(eTest.toMatrixString());
// Get the confusion matrix
double[][] cmMatrixTest = eTest.confusionMatrix();
}
catch (java.lang.Exception ex)
{
ex.printStackTrace();
}
}
/// <summary> Method for testing this class.
///
/// </summary>
/// <param name="argv">should contain one element: the name of an ARFF file
/// </param>
//@ requires argv != null;
//@ requires argv.length == 1;
//@ requires argv[0] != null;
public static void test(System.String[] argv)
{
Instances instances, secondInstances, train, test, empty;
//Instance instance;
//UPGRADE_TODO: The differences in the expected value of parameters for constructor 'java.util.Random.Random' may cause compilation errors. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1092'"
System.Random random = new System.Random((System.Int32) 2);
//UPGRADE_ISSUE: Class hierarchy differences between 'java.io.Reader' and 'System.IO.StreamReader' may cause compilation errors. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1186'"
System.IO.StreamReader reader;
int start, num;
//double newWeight;
FastVector testAtts, testVals;
int i, j;
try
{
if (argv.Length > 1)
{
throw (new System.Exception("Usage: Instances [<filename>]"));
}
// Creating set of instances from scratch
testVals = new FastVector(2);
testVals.addElement("first_value");
testVals.addElement("second_value");
testAtts = new FastVector(2);
testAtts.addElement(new Attribute("nominal_attribute", testVals));
testAtts.addElement(new Attribute("numeric_attribute"));
instances = new Instances("test_set", testAtts, 10);
instances.add(new Instance(instances.numAttributes()));
instances.add(new Instance(instances.numAttributes()));
instances.add(new Instance(instances.numAttributes()));
instances.ClassIndex = 0;
System.Console.Out.WriteLine("\nSet of instances created from scratch:\n");
//UPGRADE_TODO: Method 'java.io.PrintStream.println' was converted to 'System.Console.Out.WriteLine' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javaioPrintStreamprintln_javalangObject'"
System.Console.Out.WriteLine(instances);
if (argv.Length == 1)
{
System.String filename = argv[0];
//UPGRADE_TODO: Constructor 'java.io.FileReader.FileReader' was converted to 'System.IO.StreamReader' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073'"
reader = new System.IO.StreamReader(filename, System.Text.Encoding.Default);
// Read first five instances and print them
System.Console.Out.WriteLine("\nFirst five instances from file:\n");
instances = new Instances(reader, 1);
instances.ClassIndex = instances.numAttributes() - 1;
i = 0;
while ((i < 5) && (instances.readInstance(reader)))
{
i++;
}
//UPGRADE_TODO: Method 'java.io.PrintStream.println' was converted to 'System.Console.Out.WriteLine' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javaioPrintStreamprintln_javalangObject'"
System.Console.Out.WriteLine(instances);
// Read all the instances in the file
//UPGRADE_TODO: Constructor 'java.io.FileReader.FileReader' was converted to 'System.IO.StreamReader' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073'"
reader = new System.IO.StreamReader(filename, System.Text.Encoding.Default);
instances = new Instances(reader);
// Make the last attribute be the class
instances.ClassIndex = instances.numAttributes() - 1;
// Print header and instances.
System.Console.Out.WriteLine("\nDataset:\n");
//UPGRADE_TODO: Method 'java.io.PrintStream.println' was converted to 'System.Console.Out.WriteLine' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javaioPrintStreamprintln_javalangObject'"
System.Console.Out.WriteLine(instances);
System.Console.Out.WriteLine("\nClass index: " + instances.classIndex());
}
// Test basic methods based on class index.
System.Console.Out.WriteLine("\nClass name: " + instances.classAttribute().name());
System.Console.Out.WriteLine("\nClass index: " + instances.classIndex());
System.Console.Out.WriteLine("\nClass is nominal: " + instances.classAttribute().Nominal);
System.Console.Out.WriteLine("\nClass is numeric: " + instances.classAttribute().Numeric);
System.Console.Out.WriteLine("\nClasses:\n");
for (i = 0; i < instances.numClasses(); i++)
{
System.Console.Out.WriteLine(instances.classAttribute().value_Renamed(i));
}
System.Console.Out.WriteLine("\nClass values and labels of instances:\n");
for (i = 0; i < instances.numInstances(); i++)
{
Instance inst = instances.instance(i);
System.Console.Out.Write(inst.classValue() + "\t");
System.Console.Out.Write(inst.toString(inst.classIndex()));
if (instances.instance(i).classIsMissing())
{
System.Console.Out.WriteLine("\tis missing");
}
else
{
System.Console.Out.WriteLine();
}
}
// Create random weights.
System.Console.Out.WriteLine("\nCreating random weights for instances.");
for (i = 0; i < instances.numInstances(); i++)
{
instances.instance(i).Weight = random.NextDouble();
}
// Print all instances and their weights (and the sum of weights).
System.Console.Out.WriteLine("\nInstances and their weights:\n");
System.Console.Out.WriteLine(instances.instancesAndWeights());
System.Console.Out.Write("\nSum of weights: ");
System.Console.Out.WriteLine(instances.sumOfWeights());
// Insert an attribute
secondInstances = new Instances(instances);
Attribute testAtt = new Attribute("Inserted");
secondInstances.insertAttributeAt(testAtt, 0);
System.Console.Out.WriteLine("\nSet with inserted attribute:\n");
//UPGRADE_TODO: Method 'java.io.PrintStream.println' was converted to 'System.Console.Out.WriteLine' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javaioPrintStreamprintln_javalangObject'"
System.Console.Out.WriteLine(secondInstances);
System.Console.Out.WriteLine("\nClass name: " + secondInstances.classAttribute().name());
// Delete the attribute
secondInstances.deleteAttributeAt(0);
System.Console.Out.WriteLine("\nSet with attribute deleted:\n");
//UPGRADE_TODO: Method 'java.io.PrintStream.println' was converted to 'System.Console.Out.WriteLine' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javaioPrintStreamprintln_javalangObject'"
System.Console.Out.WriteLine(secondInstances);
System.Console.Out.WriteLine("\nClass name: " + secondInstances.classAttribute().name());
// Test if headers are equal
System.Console.Out.WriteLine("\nHeaders equal: " + instances.equalHeaders(secondInstances) + "\n");
// Print data in internal format.
System.Console.Out.WriteLine("\nData (internal values):\n");
for (i = 0; i < instances.numInstances(); i++)
{
for (j = 0; j < instances.numAttributes(); j++)
{
if (instances.instance(i).isMissing(j))
{
System.Console.Out.Write("? ");
}
else
{
System.Console.Out.Write(instances.instance(i).value_Renamed(j) + " ");
}
}
System.Console.Out.WriteLine();
}
// Just print header
System.Console.Out.WriteLine("\nEmpty dataset:\n");
empty = new Instances(instances, 0);
//UPGRADE_TODO: Method 'java.io.PrintStream.println' was converted to 'System.Console.Out.WriteLine' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javaioPrintStreamprintln_javalangObject'"
System.Console.Out.WriteLine(empty);
System.Console.Out.WriteLine("\nClass name: " + empty.classAttribute().name());
// Create copy and rename an attribute and a value (if possible)
if (empty.classAttribute().Nominal)
{
Instances copy = new Instances(empty, 0);
copy.renameAttribute(copy.classAttribute(), "new_name");
copy.renameAttributeValue(copy.classAttribute(), copy.classAttribute().value_Renamed(0), "new_val_name");
System.Console.Out.WriteLine("\nDataset with names changed:\n" + copy);
System.Console.Out.WriteLine("\nOriginal dataset:\n" + empty);
}
// Create and prints subset of instances.
start = instances.numInstances() / 4;
num = instances.numInstances() / 2;
System.Console.Out.Write("\nSubset of dataset: ");
System.Console.Out.WriteLine(num + " instances from " + (start + 1) + ". instance");
secondInstances = new Instances(instances, start, num);
System.Console.Out.WriteLine("\nClass name: " + secondInstances.classAttribute().name());
// Print all instances and their weights (and the sum of weights).
System.Console.Out.WriteLine("\nInstances and their weights:\n");
System.Console.Out.WriteLine(secondInstances.instancesAndWeights());
System.Console.Out.Write("\nSum of weights: ");
System.Console.Out.WriteLine(secondInstances.sumOfWeights());
// Create and print training and test sets for 3-fold
// cross-validation.
System.Console.Out.WriteLine("\nTrain and test folds for 3-fold CV:");
if (instances.classAttribute().Nominal)
{
instances.stratify(3);
}
for (j = 0; j < 3; j++)
{
//UPGRADE_TODO: The differences in the expected value of parameters for constructor 'java.util.Random.Random' may cause compilation errors. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1092'"
train = instances.trainCV(3, j, new System.Random((System.Int32) 1));
test = instances.testCV(3, j);
// Print all instances and their weights (and the sum of weights).
System.Console.Out.WriteLine("\nTrain: ");
System.Console.Out.WriteLine("\nInstances and their weights:\n");
System.Console.Out.WriteLine(train.instancesAndWeights());
System.Console.Out.Write("\nSum of weights: ");
System.Console.Out.WriteLine(train.sumOfWeights());
System.Console.Out.WriteLine("\nClass name: " + train.classAttribute().name());
System.Console.Out.WriteLine("\nTest: ");
System.Console.Out.WriteLine("\nInstances and their weights:\n");
System.Console.Out.WriteLine(test.instancesAndWeights());
System.Console.Out.Write("\nSum of weights: ");
System.Console.Out.WriteLine(test.sumOfWeights());
System.Console.Out.WriteLine("\nClass name: " + test.classAttribute().name());
}
// Randomize instances and print them.
System.Console.Out.WriteLine("\nRandomized dataset:");
instances.randomize(random);
// Print all instances and their weights (and the sum of weights).
System.Console.Out.WriteLine("\nInstances and their weights:\n");
System.Console.Out.WriteLine(instances.instancesAndWeights());
System.Console.Out.Write("\nSum of weights: ");
System.Console.Out.WriteLine(instances.sumOfWeights());
// Sort instances according to first attribute and
// print them.
System.Console.Out.Write("\nInstances sorted according to first attribute:\n ");
instances.sort(0);
// Print all instances and their weights (and the sum of weights).
System.Console.Out.WriteLine("\nInstances and their weights:\n");
System.Console.Out.WriteLine(instances.instancesAndWeights());
System.Console.Out.Write("\nSum of weights: ");
System.Console.Out.WriteLine(instances.sumOfWeights());
}
catch (System.Exception)
{
//.WriteStackTrace(e, Console.Error);
}
}
/// <summary> Selects C4.5-type split for the given dataset.</summary>
public override ClassifierSplitModel selectModel(Instances data)
{
double minResult;
//double currentResult;
BinC45Split[] currentModel;
BinC45Split bestModel = null;
NoSplit noSplitModel = null;
double averageInfoGain = 0;
int validModels = 0;
bool multiVal = true;
Distribution checkDistribution;
double sumOfWeights;
int i;
try
{
// Check if all Instances belong to one class or if not
// enough Instances to split.
checkDistribution = new Distribution(data);
noSplitModel = new NoSplit(checkDistribution);
if (Utils.sm(checkDistribution.total(), 2 * m_minNoObj) || Utils.eq(checkDistribution.total(), checkDistribution.perClass(checkDistribution.maxClass())))
return noSplitModel;
// Check if all attributes are nominal and have a
// lot of values.
System.Collections.IEnumerator enu = data.enumerateAttributes();
//UPGRADE_TODO: Method 'java.util.Enumeration.hasMoreElements' was converted to 'System.Collections.IEnumerator.MoveNext' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javautilEnumerationhasMoreElements'"
while (enu.MoveNext())
{
//UPGRADE_TODO: Method 'java.util.Enumeration.nextElement' was converted to 'System.Collections.IEnumerator.Current' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javautilEnumerationnextElement'"
weka.core.Attribute attribute = (weka.core.Attribute)enu.Current;
if ((attribute.Numeric) || (Utils.sm((double) attribute.numValues(), (0.3 * (double) m_allData.numInstances()))))
{
multiVal = false;
break;
}
}
currentModel = new BinC45Split[data.numAttributes()];
sumOfWeights = data.sumOfWeights();
// For each attribute.
for (i = 0; i < data.numAttributes(); i++)
{
// Apart from class attribute.
if (i != (data).classIndex())
{
// Get models for current attribute.
currentModel[i] = new BinC45Split(i, m_minNoObj, sumOfWeights);
currentModel[i].buildClassifier(data);
// Check if useful split for current attribute
// exists and check for enumerated attributes with
// a lot of values.
if (currentModel[i].checkModel())
if ((data.attribute(i).Numeric) || (multiVal || Utils.sm((double) data.attribute(i).numValues(), (0.3 * (double) m_allData.numInstances()))))
{
averageInfoGain = averageInfoGain + currentModel[i].infoGain();
validModels++;
}
}
else
currentModel[i] = null;
}
// Check if any useful split was found.
if (validModels == 0)
return noSplitModel;
averageInfoGain = averageInfoGain / (double) validModels;
// Find "best" attribute to split on.
minResult = 0;
for (i = 0; i < data.numAttributes(); i++)
{
if ((i != (data).classIndex()) && (currentModel[i].checkModel()))
// Use 1E-3 here to get a closer approximation to the original
// implementation.
if ((currentModel[i].infoGain() >= (averageInfoGain - 1e-3)) && Utils.gr(currentModel[i].gainRatio(), minResult))
{
bestModel = currentModel[i];
minResult = currentModel[i].gainRatio();
}
}
// Check if useful split was found.
if (Utils.eq(minResult, 0))
return noSplitModel;
// Add all Instances with unknown values for the corresponding
// attribute to the distribution for the model, so that
// the complete distribution is stored with the model.
bestModel.distribution().addInstWithUnknown(data, bestModel.attIndex());
// Set the split point analogue to C45 if attribute numeric.
bestModel.SplitPoint = m_allData;
return bestModel;
}
catch (System.Exception e)
{
System.Console.WriteLine(e.StackTrace + " " + e.Message);
}
return null;
}
// ---- OPERATIONS ----
///
/// <summary> * Sets the format of the input instances. If the filter is able to
/// * determine the output format before seeing any input instances, it
/// * does so here. This default implementation clears the output format
/// * and output queue, and the new batch flag is set. Overriders should
/// * call <code>super.setInputFormat(Instances)</code>
/// * </summary>
/// * <param name="instanceInfo"> an Instances object containing the input instance
/// * structure (any instances contained in the object are ignored - only the
/// * structure is required). </param>
/// * <returns> true if the outputFormat may be collected immediately </returns>
/// * <exception cref="Exception"> if the inputFormat can't be set successfully </exception>
///
//JAVA TO VB & C# CONVERTER WARNING: Method 'throws' clauses are not available in .NET:
//ORIGINAL LINE: public boolean setInputFormat(Instances instanceInfo) throws Exception
public override bool setInputFormat(Instances instanceInfo)
{
base.setInputFormat(instanceInfo);
for (int i = 0; i < instanceInfo.numAttributes(); ++i)
{
if (!instanceInfo.attribute(i).isNumeric())
{
throw new UnsupportedAttributeTypeException("All attributes must be numeric");
}
}
// Create the output buffer
setOutputFormat();
return true;
}
// 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());
}
}
/// <summary>
/// Create a single instance for WEKA
/// </summary>
/// <param name="NClasses">Number of classes</param>
/// <returns>the weka instances</returns>
public Instances CreateInstanceForNClasses(cInfoClass InfoClass)
{
List<double> AverageList = new List<double>();
for (int i = 0; i < Parent.ListDescriptors.Count; i++)
if (Parent.ListDescriptors[i].IsActive()) AverageList.Add(GetAverageValuesList()[i]);
weka.core.FastVector atts = new FastVector();
List<string> NameList = Parent.ListDescriptors.GetListNameActives();
for (int i = 0; i < NameList.Count; i++)
atts.addElement(new weka.core.Attribute(NameList[i]));
weka.core.FastVector attVals = new FastVector();
for (int i = 0; i < InfoClass.NumberOfClass; i++)
attVals.addElement("Class" + i);
atts.addElement(new weka.core.Attribute("Class__", attVals));
Instances data1 = new Instances("SingleInstance", atts, 0);
double[] newTable = new double[AverageList.Count + 1];
Array.Copy(AverageList.ToArray(), 0, newTable, 0, AverageList.Count);
//newTable[AverageList.Count] = 1;
data1.add(new DenseInstance(1.0, newTable));
data1.setClassIndex((data1.numAttributes() - 1));
return data1;
}
private LabelsMetaData loadLabelsMeta(Instances data, int numLabels)
{
LabelsMetaDataImpl labelsData = new LabelsMetaDataImpl();
int numAttributes = data.numAttributes();
for (int index = numAttributes - numLabels; index < numAttributes; index++) {
String attrName = data.attribute(index).name();
labelsData.addRootNode(new LabelNodeImpl(attrName));
}
return labelsData;
}
private static Instances CreateInstanceOnFly(double[] a, double[] b)
{
FastVector atts;
Instances data;
double[] vals;
// 1. set up attributes
atts = new FastVector();
// - numeric
atts.addElement(new Attribute("att1"));
atts.addElement(new Attribute("att2"));
// 2. create Instances object
data = new Instances("MyRelation", atts, 0);
for (int i = 0; i < a.Length; ++i)
{
// 3. fill with data
// first instance
vals = new double[data.numAttributes()];
// - numeric
vals[0] = a[i];
// - nominal
vals[1] = b[i];
data.add(new weka.core.DenseInstance(1.0, vals));
}
return data;
}
// ---- OPERATIONS ----
///
/// <summary> * Analyze the time series data. The similarity matrices are created
/// * and filled with euclidean distances based on the tolerance values
/// * for similarity.
/// * </summary>
/// * <param name="data"> data to be analyzed </param>
public override void analyze(Instances data)
{
data.setClassIndex(data.numAttributes() - 1);
m_data = data;
m_rangeTemplates.setUpper(data.numAttributes());
//Date startFT = new Date();
// compute fourier transform
FourierTransform dftFilter = new FourierTransform();
dftFilter.setInputFormat(data);
dftFilter.setNumCoeffs(getNumCoeffs());
dftFilter.setUseFFT(getUseFFT());
Instances fourierdata = Filter.useFilter(data, dftFilter);
Date endFT = new Date();
// time taken for FT
//m_DFTTime = new Date(endFT.getTime() - startFT.getTime());
int numdim = data.numAttributes();
//ORIGINAL LINE: m_distancesFreq = new double[numdim][numdim];
//JAVA TO VB & C# CONVERTER NOTE: The following call to the 'RectangularArrays' helper class reproduces the rectangular array initialization that is automatic in Java:
m_distancesFreq = RectangularArrays.ReturnRectangularDoubleArray(numdim, numdim);
//ORIGINAL LINE: m_distancesTime = new double[numdim][numdim];
//JAVA TO VB & C# CONVERTER NOTE: The following call to the 'RectangularArrays' helper class reproduces the rectangular array initialization that is automatic in Java:
m_distancesTime = RectangularArrays.ReturnRectangularDoubleArray(numdim, numdim);
//long ftDistTime = 0;
//long tDistTime = 0;
// compute similarity matrices
for (int i = 0; i < data.numAttributes(); ++i)
{
for (int j = 0; j < i; j++)
{
// not for template sequences
if (m_rangeTemplates.isInRange(i) && m_rangeTemplates.isInRange(j))
{
continue;
}
//Date startFTDist = new Date();
// Compute the Euclidean distance between 2 dims using FT
double[] reCT = fourierdata.attributeToDoubleArray(2 * i);
double[] imCT = fourierdata.attributeToDoubleArray(2 * i + 1);
double[] reCS = fourierdata.attributeToDoubleArray(2 * j);
double[] imCS = fourierdata.attributeToDoubleArray(2 * j + 1);
m_distancesFreq[i][j] = computeEuclidean(reCT, imCT, reCS, imCS);
// if found similar using FT
if (m_distancesFreq[i][j] <= m_epsilon)
{
// then compute normal Euclidean distances between the 2 dims
double[] x = data.attributeToDoubleArray(i);
double[] y = data.attributeToDoubleArray(j);
m_distancesTime[i][j] = computeEuclidean(x, y);
}
//Date endFTDist = new Date();
// time taken for computing similarity based on FT
//ftDistTime += (endFTDist.getTime() - startFTDist.getTime());
// Date startDist = new Date();
//// compute similarity matrices (brute force)
// double[] x1 = data.attributeToDoubleArray(i);
// double[] y1 = data.attributeToDoubleArray(j);
// computeEuclidean(x1, y1);
// Date endDist = new Date();
//// time taken for computing similarity based brute force method
// tDistTime += (endDist.getTime() - startDist.getTime());
}
}
//m_FTEuclideanTime = new Date(ftDistTime);
//m_EuclideanTime = new Date(tDistTime);
}
public static double classifyCrossFold_Train_Test_onlySelectedClass(string classifierFileName, int baseClasses, Classifier _classifier)
{
double performance = 0.0;
try
{
List <BrResult> results = new List <BrResult>();
for (int singleClass = 1; singleClass <= baseClasses; singleClass++)
{
string eachFileName = String.Format("{0}_{1}.arff", classifierFileName, singleClass);
BrResult result = new BrResult();
result.classNumber = singleClass;
FileReader javaFileReader = new FileReader(eachFileName);
weka.core.Instances insts = new weka.core.Instances(javaFileReader);
javaFileReader.close();
insts.setClassIndex(insts.numAttributes() - 1);
List <Result> eachResults = new List <Result>();
var totalnstances = insts.numInstances();
var foldsInstances = totalnstances / 10;
Instances foldsData = new Instances(insts);
var folds = 10;
int numCorrect = 0;
int dataIndex = 0;
for (int n = 0; n < folds; n++)
{
System.Console.WriteLine("Performing " + n + " folds");
Instances trainFold = foldsData.trainCV(folds, n);
var numnerOfTrainInst = trainFold.numInstances();
Instances testFold = foldsData.testCV(folds, n);
var numnerOfTestInst = testFold.numInstances();
_classifier.buildClassifier(trainFold);
//List<Result> eachResults = new List<Result>();
for (int test = 0; test < numnerOfTestInst; test++)
{
dataIndex++;
Result eachRow = new Result();
eachRow.lineIndex = 0;
weka.core.Instance currentInst = testFold.instance(test);
double predictClass = _classifier.classifyInstance(currentInst);
//double[] dist = _classifier.distributionForInstance(currentInst);
string actualClass = testFold.classAttribute().value((int)testFold.instance(test).classValue());
string predictedClass = testFold.classAttribute().value((int)predictClass);
//var abcd = _classifier.getClass();
if (predictedClass == actualClass)
{
eachRow.correct = "1";
numCorrect++;
}
else
{
eachRow.correct = "0";
}
eachRow.lineIndex = dataIndex;
eachRow.classActual = actualClass;
eachRow.classPredicted = predictedClass;
eachResults.Add(eachRow);
}
}
result.classResult = eachResults;
results.Add(result);
//System.Console.WriteLine(numCorrect + " out of " + testSize + " correct (" + (double)((double)numCorrect / (double)testSize * 100.0) + "%)");
}
#region Evaludation Matrix
var evaluationMatrix = new Dictionary <int, string>();
foreach (var res in results)
{
foreach (var classRes in res.classResult)
{
if (!evaluationMatrix.Keys.Contains(classRes.lineIndex))
{
evaluationMatrix[classRes.lineIndex] = classRes.correct.toString();
}
else
{
evaluationMatrix[classRes.lineIndex] = evaluationMatrix[classRes.lineIndex].toString() + "," + classRes.correct.toString();
}
}
}
#endregion
#region
int correnctlyClassified = 0;
int incorrenctlyClassified = 0;
int totalData = evaluationMatrix.Count;
foreach (var key in evaluationMatrix.Keys)
{
string multiLevelClass = evaluationMatrix[key].ToString();
string[] a = multiLevelClass.Split(',');
int classPredect = 0;
for (int i = 0; i < a.Length; i++)
{
if (a[i] == "0")
{
classPredect++;
}
}
if (classPredect == 0)
{
correnctlyClassified++;
}
else if (classPredect > 0)
{
incorrenctlyClassified++;
}
}
performance = (double)((double)correnctlyClassified / (double)totalData) * 100;
System.Console.WriteLine(performance);
#endregion
}
catch (java.lang.Exception ex)
{
ex.printStackTrace();
}
return(performance);
}
public static void Test_predictClass(string classifierFileName)
{
FileReader javaFileReader = new FileReader(classifierFileName);
weka.core.Instances insts = new weka.core.Instances(javaFileReader);
javaFileReader.close();
insts.setClassIndex(insts.numAttributes() - 1);
weka.classifiers.Classifier cl = new weka.classifiers.trees.J48();
System.Console.WriteLine("Performing " + percentSplit + "% split evaluation.");
#region Manual Cross Fold
Instances foldsData = new Instances(insts);
int folds = 10;
for (int n = 0; n < folds; n++)
{
Instances trainFold = foldsData.trainCV(folds, n);
Instances testFold = foldsData.testCV(folds, n);
}
#endregion
#region
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);
#endregion
//Classifier cls = new J48();
Evaluation eval = new Evaluation(insts);
java.util.Random rand = new java.util.Random(1); // using seed = 1
int fold = 10;
eval.crossValidateModel(cl, insts, fold, rand);
System.Console.WriteLine("toClassDetailsString" + eval.toClassDetailsString());
System.Console.WriteLine("toMatrixString\n" + eval.toMatrixString());
System.Console.WriteLine("toCumulativeMarginDistributionString\n" + eval.toCumulativeMarginDistributionString());
//System.Console.WriteLine("predictions\n" + eval.predictions());
System.Console.ReadKey();
//var numnerOfInst = insts.numInstances();
//for (int i = trainSize; i < numnerOfInst; i++)
//{
// weka.core.Instance currentInst = insts.instance(i);
// double pred = cl.classifyInstance(currentInst);
// System.Console.WriteLine("class Index: " + insts.instance(i).classIndex());
// System.Console.WriteLine(", class value: " + insts.instance(i).classValue());
// System.Console.WriteLine(", ID: " + insts.instance(i).value(0));
// System.Console.WriteLine(", actual: " + insts.classAttribute().value((int)insts.instance(i).classValue()));
// System.Console.WriteLine(", predicted: " + insts.classAttribute().value((int)pred));
//}
}
