public void Build()
{
this.featureIndex = new int[this.features.Count];
for(int i=0;(i< this.features.Count);i++)
{
for (int j = 0; (j < Extractor.ArffAttributeLabels.Length); j++)
if (((string)this.features[i]).Equals(Extractor.ArffAttributeLabels[j]))
{
this.featureIndex[i] = j;
break;
}
}
instances = new Instances(new StreamReader(this.filename));
instances.Class = instances.attribute(this.features.Count);
classifier = new J48();
classifier.buildClassifier(instances);
//setup the feature vector
//fvWekaAttributes = new FastVector(this.features.Count + 1);
//for (i = 0; (i < this.features.Count); i++)
// fvWekaAttributes.addElement(new weka.core.Attribute(this.features[i]));
//Setup the class attribute
//FastVector fvClassVal = new FastVector();
//for (i = 0; (i < this.classes.Count); i++)
// fvClassVal.addElement(this.classes[i]);
//weka.core.Attribute ClassAttribute = new weka.core.Attribute("activity", fvClassVal);
}
public ID3Node Train(string arffFilePath, double confidenceLevel, int maxDepth = 0)
{
// Load the examples into S
Instances S = new weka.core.Instances(new java.io.FileReader(arffFilePath));
return(this.Train(S, confidenceLevel, maxDepth));
}
public void EvaluateIncrementalBatches(int batchSize)
{
//Randomize Filter
Randomize randomizeFilter = new Randomize();
randomizeFilter.setInputFormat(this.data);
//RemoveRange Filter
//number of classes
int numClasses = this.data.attribute(this.data.numAttributes() - 1).numValues();
Instances[] classInstances = new Instances[numClasses];
for (int i = 1; (i <= numClasses); i++)
{
//RemoveWithValues Filter
RemoveWithValues removeValuesFilter = new RemoveWithValues();
removeValuesFilter.setInputFormat(this.data);
// removeValuesFilter.set_AttributeIndex("last");
// removeValuesFilter.
removeValuesFilter.set_MatchMissingValues(false);
removeValuesFilter.set_NominalIndices("1-1");
classInstances[i] = Filter.useFilter(this.data, removeValuesFilter);
}
}
/// <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 inputFormat can't be set successfully
/// </exception>
public override bool setInputFormat(Instances instanceInfo)
{
base.setInputFormat(instanceInfo);
m_removeFilter = null;
return false;
}
/// <summary>
/// Dummy attribute
/// </summary>
/// <param name="instances"></param>
/// <returns></returns>
public void NominalToBinary(weka.core.Instances instances)
{
weka.filters.Filter nominalToBinary = new weka.filters.unsupervised.attribute.NominalToBinary();
nominalToBinary.setInputFormat(instances);
instances = weka.filters.Filter.useFilter(instances, nominalToBinary);
this.Instance = instances;
}
public void Test(string arffFilePath, ID3Node root)
{
// Load the examples into S
Instances S = new weka.core.Instances(new java.io.FileReader(arffFilePath));
this.Test(S, root);
}
/// <summary>
/// Randomize select random data from data set
/// </summary>
/// <param name="instances"></param>
/// <returns></returns>
public void Randomize(weka.core.Instances instances)
{
weka.filters.Filter randomize = new weka.filters.unsupervised.instance.Randomize();
randomize.setInputFormat(this.Instance);
instances = weka.filters.Filter.useFilter(instances, randomize);
this.Instance = instances;
}
/// <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)
{
double sumOfWeights = 0;
m_Class = instances.classAttribute();
m_ClassValue = 0;
switch (instances.classAttribute().type())
{
case weka.core.Attribute.NUMERIC:
m_Counts = null;
break;
case weka.core.Attribute.NOMINAL:
m_Counts = new double[instances.numClasses()];
for (int i = 0; i < m_Counts.Length; i++)
{
m_Counts[i] = 1;
}
sumOfWeights = instances.numClasses();
break;
default:
throw new System.Exception("ZeroR can only handle nominal and numeric class" + " attributes.");
}
System.Collections.IEnumerator enu = instances.enumerateInstances();
//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'"
Instance instance = (Instance) enu.Current;
if (!instance.classIsMissing())
{
if (instances.classAttribute().Nominal)
{
//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'"
m_Counts[(int) instance.classValue()] += instance.weight();
}
else
{
m_ClassValue += instance.weight() * instance.classValue();
}
sumOfWeights += instance.weight();
}
}
if (instances.classAttribute().Numeric)
{
if (Utils.gr(sumOfWeights, 0))
{
m_ClassValue /= sumOfWeights;
}
}
else
{
m_ClassValue = Utils.maxIndex(m_Counts);
Utils.normalize(m_Counts, sumOfWeights);
}
}
public static string Classify(bool useRubine, float duration, bool righthandedness, List<float> SpeakerAngles, PointCollection pointHist, StylusPointCollection S, List<List<int>> hist, List<List<int>> ihist)
{
// Convert all parameters to format used in GestureTests
List<Vector2> InterpretedPoints = new List<Vector2>();
List<Vector2> StylusPoints = new List<Vector2>();
List<Vector2> VelocityHistory = new List<Vector2>();
List<Vector2> InverseVelocityHistory = new List<Vector2>();
foreach(Point P in pointHist)
InterpretedPoints.Add(new Vector2((float)P.X,(float)P.Y));
foreach(StylusPoint P in S)
StylusPoints.Add(new Vector2((float)P.X,(float)P.Y));
for (int i = 0; i < hist[0].Count; i++)
{
VelocityHistory.Add(new Vector2(hist[0][i], hist[1][i]));
InverseVelocityHistory.Add(new Vector2(ihist[0][i], ihist[1][i]));
}
// Create a new Sample, compute the features, and classify
GS = new GestureSample(GestureTests.Types.GestureType.unknown, righthandedness,duration,SpeakerAngles,InterpretedPoints,StylusPoints,VelocityHistory,InverseVelocityHistory);
GS.ComputeFeatures(GestureFeatures.PointsStroke);
if (useRubine)
return EC.Recognizer.Classify(GS).ToString();
WriteARFF();
Instances test = new Instances(new java.io.FileReader("outfile.arff"));
test.setClassIndex(0);
double clsLabel = cls.classifyInstance(test.instance(0));
test.instance(0).setClassValue(clsLabel);
// Return the appropriate label
return ((GestureType2D)((int)clsLabel+1)).ToString();
}
/// <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;
}
/// <summary>
/// Normalize to numeric instance
/// </summary>
/// <param name="instances"></param>
/// <returns></returns>
public void Normalization(weka.core.Instances instances)
{
weka.filters.Filter normalized = new weka.filters.unsupervised.instance.Normalize();
normalized.setInputFormat(instances);
instances = weka.filters.Filter.useFilter(instances, normalized);
this.Instance = instances;
}
/// <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 virtual bool setInputFormat(Instances instanceInfo)
{
base.setInputFormat(instanceInfo);
setOutputFormat(instanceInfo);
m_ModesAndMeans = null;
return true;
}
public void Discreatization(weka.core.Instances instances)
{
weka.filters.unsupervised.attribute.Discretize discretized = new weka.filters.unsupervised.attribute.Discretize();
discretized.setInputFormat(instances);
//discretize.setFindNumBins(true);
instances = weka.filters.Filter.useFilter(instances, discretized);
this.Instance = instances;
}
public static void SaveInstances(weka.core.Instances instances, string fileName)
{
var file = new java.io.File(fileName);
//m_arffSaver.setDestination(file);
m_arffSaver.setInstances(instances);
m_arffSaver.setFile(file);
m_arffSaver.writeBatch();
}
/// <summary>
/// Discreatize instance if the instance is numeric
/// </summary>
/// <param name="insts"></param>
private void Discreatization(weka.core.Instances insts)
{
weka.filters.unsupervised.attribute.Discretize myDiscretize = new
weka.filters.unsupervised.attribute.Discretize();
myDiscretize.setInputFormat(insts);
myDiscretize.setFindNumBins(true);
insts = weka.filters.Filter.useFilter(insts, myDiscretize);
DataPreparation(insts);
}
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 AddInstanceQuickly(weka.core.Instances instances, IList <weka.core.Instance> listInstances)
{
java.util.ArrayList arrayListTrainInstances = Feng.Utils.ReflectionHelper.GetObjectValue(instances, "m_Instances") as java.util.ArrayList;
foreach (var i in listInstances)
{
i.setDataset(instances);
arrayListTrainInstances.add(i);
}
}
// 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");
}
/// <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);
}
public ArffStore parse()
{
ArffStore arffStore = new ArffStore();
instances = new Instances(new StreamReader(this.filename));
//foreach (weka.core.Attribute attribute in instances.enumerateAttributes())
// arffStore.Features.Add(attribute.name);
return arffStore;
}
public BinC45ModelSelection(XmlNode model,Instances allData)
{
foreach (XmlAttribute xAttribute in model.Attributes)
{
if (xAttribute.Name == Constants.MIN_NO_OBJ_ATTRIBUTE)
this.m_minNoObj = Convert.ToInt32(xAttribute.Value);
}
m_allData = allData;
}
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();
}
public Evaluator(string arffFile)
{
this.data = new Instances(new StreamReader(arffFile));
this.data.ClassIndex = this.data.numAttributes() - 1;
this.numExamples = this.data.m_Instances.size();
this.classCount = this.data.attribute(this.data.numAttributes() - 1).numValues();
// this.trainingSizeMatrix=new double[
}
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 VectorClassif(int nbTags)
{
tagsNb = nbTags;
ArrayList nomi = new ArrayList();
nomi.add("0");
nomi.add("1");
ArrayList attr = new ArrayList();
weka.core.Attribute stringAttr = new weka.core.Attribute("todoString", (List)null);
attr.add(stringAttr);
for (int i = 1; i <= nbTags; i++) {
attr.add(new weka.core.Attribute("label" + i, nomi));
}
oDataSet = new Instances("Todo-Instances", attr, 500);
}
/// <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 Build(weka.core.Instances instances)
{
WekaUtils.DebugAssert(instances.numClasses() == 3, "instance's numClasses should be 3.");
m_counts = new int[instances.numClasses()];
for (int i = 0; i < m_counts.Length; i++)
{
m_counts[i] = 0;
}
foreach (Instance instance in instances)
{
int v = (int)instance.classValue();
m_counts[v]++;
}
}
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 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
}
}
private void BtnBrowse_Click(object sender, EventArgs e)
{
categories = new List <string[]>();
txtInfo.Text = "";
btnTwoing.Enabled = false;
btnGini.Enabled = false;
DialogResult result = openFileDialog.ShowDialog();
if (result == DialogResult.OK)
{
txtPath.Text = openFileDialog.FileName;
btnTwoing.Enabled = true;
btnGini.Enabled = true;
}
weka.core.Instances insts = new weka.core.Instances(new java.io.FileReader(txtPath.Text));
Discreatization(insts);
}
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 EndTrainingSession()
{
Console.WriteLine("End");
stv = new StringToWordVector();
stv.setAttributeNamePrefix("#");
stv.setLowerCaseTokens(true);
stv.setOutputWordCounts(true);
stv.setInputFormat(oDataSet);
stv.setStemmer(new weka.core.stemmers.LovinsStemmer());
stv.setIDFTransform(true);
dataSet = Filter.useFilter(oDataSet, stv);
MultiLabelInstances mli = new MultiLabelInstances(dataSet, loadLabelsMeta(dataSet, tagsNb));
BinaryRelevance br = new mulan.classifier.transformation.BinaryRelevance(new NaiveBayes());
lps = new mulan.classifier.meta.RAkEL(br);
br.setDebug(true);
lps.setDebug(true);
lps.build(mli);
}
/// <summary>
/// Train
/// </summary>
/// <param name="instances"></param>
/// <returns></returns>
public TrainModel Train(weka.core.Instances instances, Classifier classifier)
{
const int percentSplit = 66;
int trainSize = instances.numInstances() * percentSplit / 100;
int testSize = instances.numInstances() - trainSize;
weka.core.Instances train = new weka.core.Instances(instances, 0, trainSize);
classifier.buildClassifier(train);
return(this.Classifier = new TrainModel()
{
PercentSplit = percentSplit,
classifier = classifier,
TestSize = testSize,
TrainSize = trainSize,
Instance = instances
});
}
/// <summary> Creates a distribution with only one bag according
/// to instances in source.
///
/// </summary>
/// <exception cref="Exception">if something goes wrong
/// </exception>
public Distribution(Instances source)
{
m_perClassPerBag = new double[1][];
for (int i = 0; i < 1; i++)
{
m_perClassPerBag[i] = new double[0];
}
m_perBag = new double[1];
totaL = 0;
m_perClass = new double[source.numClasses()];
m_perClassPerBag[0] = new double[source.numClasses()];
System.Collections.IEnumerator enu = source.enumerateInstances();
//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'"
add(0, (Instance) enu.Current);
}
}
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);
}
/// <summary>
/// Calculates Pleft, Prigth, rigthTargets , PclassDivideTleft, PclassDivideTleft and teta numbers for each inctance
/// Then calling the creating dataset function
/// </summary>
/// <param name="insts"></param>
/// <param name="categoryTypeNumber"></param>
/// <param name="categoryTypeTargetNumber"></param>
private void Twoing(weka.core.Instances insts, List <int[]> categoryTypeNumber, List <int[, ]> categoryTypeTargetNumber)
{
List <double[]> categoryTetaNumber = new List <double[]>();
for (int i = 0; i < categoryTypeNumber.Count; i++)
{
categoryTetaNumber.Add(new double[categoryTypeNumber[i].Length]);
}
for (int i = 0; i < categoryTypeNumber.Count - 1; i++)
{
for (int j = 0; j < categoryTypeNumber[i].Length; j++)
{
Double pLeft = Convert.ToDouble(categoryTypeNumber[i][j]) / Convert.ToDouble(insts.numInstances());
Double pRight = 1 - pLeft;
Double sumFunction = 0;
for (int k = 0; k < categoryTypeNumber[categoryTypeNumber.Count - 1].Length; k++)
{
Double PclassDivideTleft = Convert.ToDouble(categoryTypeTargetNumber[i][j, k]) / Convert.ToDouble(categoryTypeNumber[i][j]);
int sagtarafıntargetları = 0;
for (int h = 0; h < categoryTypeNumber[i].Length; h++)
{
if (h != j)
{
sagtarafıntargetları += categoryTypeTargetNumber[i][h, k];
}
}
Double PclassDivideTRigt = Convert.ToDouble(sagtarafıntargetları) / Convert.ToDouble((insts.numInstances() - categoryTypeNumber[i][j]));
sumFunction += Math.Abs(PclassDivideTleft - PclassDivideTRigt);
}
categoryTetaNumber[i][j] = 2 * pLeft * pRight * sumFunction;
}
}
Instances fileInst = new Instances(insts);
CreateNewDataset(fileInst, categoryTetaNumber, twoingPath);
}
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);
}
/* 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 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;
}
/// <summary> Selects a model for the given train data using the given test data
///
/// </summary>
/// <exception cref="Exception">if model can't be selected
/// </exception>
public virtual ClassifierSplitModel selectModel(Instances train, Instances test)
{
throw new System.Exception("Model selection method not implemented");
}
/// <summary> Selects a model for the given dataset. /// /// </summary> /// <exception cref="Exception">if model can't be selected /// </exception> public abstract ClassifierSplitModel selectModel(Instances data);
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;
}
// ---- 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);
}
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;
}
public ClassifierManager(weka.core.Instances insts)
{
this.Instance = insts;
}
/// <summary> Creates a "no-split"-split for a given set of instances.
///
/// </summary>
/// <exception cref="Exception">if split can't be built successfully
/// </exception>
public override void buildClassifier(Instances instances)
{
m_distribution = new Distribution(instances);
m_numSubsets = 1;
}
/// <summary> Does nothing because no condition has to be satisfied.</summary>
public override System.String leftSide(Instances instances)
{
return "";
}
/// <summary> Does nothing because no condition has to be satisfied.</summary>
public override System.String rightSide(int index, Instances instances)
{
return "";
}
/// <summary> Selects C4.5-type split for the given dataset.</summary>
public override ClassifierSplitModel selectModel(Instances train, Instances test)
{
return selectModel(train);
}
/// <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;
}
/// <summary> Returns a string containing java source code equivalent to the test
/// made at this node. The instance being tested is called "i".
///
/// </summary>
/// <param name="index">index of the nominal value tested
/// </param>
/// <param name="data">the data containing instance structure info
/// </param>
/// <returns> a value of type 'String'
/// </returns>
public override System.String sourceExpression(int index, Instances data)
{
return "true"; // or should this be false??
}
/// <summary> Initializes the split selection method with the given parameters.
///
/// </summary>
/// <param name="minNoObj">minimum number of instances that have to occur in
/// at least two subsets induced by split
/// </param>
/// <param name="allData">FULL training dataset (necessary for selection of
/// split points).
/// </param>
public BinC45ModelSelection(int minNoObj, Instances allData)
{
m_minNoObj = minNoObj;
m_allData = allData;
}
/// <summary> Sets reference to training data to null.</summary>
public virtual void cleanup()
{
m_allData = null;
}
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));
//}
}