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();
}
}
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();
}
}
}
