/// <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 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 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> Method for building a pruneable classifier tree.
///
/// </summary>
/// <exception cref="Exception">if something goes wrong
/// </exception>
public override void buildClassifier(Instances data)
{
if (data.classAttribute().Numeric)
throw new Exception("Class is numeric!");
if (data.checkForStringAttributes())
{
throw new Exception("Cannot handle string attributes!");
}
data = new Instances(data);
data.deleteWithMissingClass();
buildTree(data, m_subtreeRaising);
collapse();
if (m_pruneTheTree)
{
prune();
}
if (m_cleanup)
{
cleanup(new Instances(data, 0));
}
}
/// <summary> Performs a (stratified if class is nominal) cross-validation
/// for a classifier on a set of instances. Now performs
/// a deep copy of the classifier before each call to
/// buildClassifier() (just in case the classifier is not
/// initialized properly).
///
/// </summary>
/// <param name="classifier">the classifier with any options set.
/// </param>
/// <param name="data">the data on which the cross-validation is to be
/// performed
/// </param>
/// <param name="numFolds">the number of folds for the cross-validation
/// </param>
/// <param name="random">random number generator for randomization
/// </param>
/// <throws> Exception if a classifier could not be generated </throws>
/// <summary> successfully or the class is not defined
/// </summary>
public virtual void crossValidateModel(Classifier classifier, Instances data, int numFolds, System.Random random)
{
// Make a copy of the data we can reorder
data = new Instances(data);
data.randomize(random);
if (data.classAttribute().Nominal)
{
data.stratify(numFolds);
}
// Do the folds
for (int i = 0; i < numFolds; i++)
{
Instances train = data.trainCV(numFolds, i, random);
Priors = train;
Classifier copiedClassifier = Classifier.makeCopy(classifier);
copiedClassifier.buildClassifier(train);
Instances test = data.testCV(numFolds, i);
evaluateModel(copiedClassifier, test);
}
m_NumFolds = numFolds;
}
/// <summary> Initializes all the counters for the evaluation and also takes a
/// cost matrix as parameter.
/// Use <code>useNoPriors()</code> if the dataset is the test set and you
/// can't initialize with the priors from the training set via
/// <code>setPriors(Instances)</code>.
///
/// </summary>
/// <param name="data"> set of training instances, to get some header
/// information and prior class distribution information
/// </param>
/// <param name="costMatrix"> the cost matrix---if null, default costs will be used
/// </param>
/// <throws> Exception if cost matrix is not compatible with </throws>
/// <summary> data, the class is not defined or the class is numeric
/// </summary>
/// <seealso cref="useNoPriors()">
/// </seealso>
/// <seealso cref="setPriors(Instances)">
/// </seealso>
public Evaluation(Instances data, CostMatrix costMatrix)
{
m_NumClasses = data.numClasses();
m_NumFolds = 1;
m_ClassIsNominal = data.classAttribute().Nominal;
if (m_ClassIsNominal)
{
double[][] tmpArray = new double[m_NumClasses][];
for (int i = 0; i < m_NumClasses; i++)
{
tmpArray[i] = new double[m_NumClasses];
}
m_ConfusionMatrix = tmpArray;
m_ClassNames = new System.String[m_NumClasses];
for (int i = 0; i < m_NumClasses; i++)
{
m_ClassNames[i] = data.classAttribute().value_Renamed(i);
}
}
m_CostMatrix = costMatrix;
if (m_CostMatrix != null)
{
if (!m_ClassIsNominal)
{
throw new System.Exception("Class has to be nominal if cost matrix " + "given!");
}
if (m_CostMatrix.size() != m_NumClasses)
{
throw new System.Exception("Cost matrix not compatible with data!");
}
}
m_ClassPriors = new double[m_NumClasses];
Priors = data;
m_MarginCounts = new double[k_MarginResolution + 1];
}
/// <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();
}
public override void buildClassifier(Instances insts)
{
if (insts.checkForStringAttributes())
{
throw new Exception("Cannot handle string attributes!");
}
if (insts.numClasses() > 2)
{
throw new System.Exception("Can only handle two-class datasets!");
}
if (insts.classAttribute().Numeric)
{
throw new Exception("Can't handle a numeric class!");
}
// Filter data
m_Train = new Instances(insts);
m_Train.deleteWithMissingClass();
m_ReplaceMissingValues = new ReplaceMissingValues();
m_ReplaceMissingValues.setInputFormat(m_Train);
m_Train = Filter.useFilter(m_Train, m_ReplaceMissingValues);
m_NominalToBinary = new NominalToBinary();
m_NominalToBinary.setInputFormat(m_Train);
m_Train = Filter.useFilter(m_Train, m_NominalToBinary);
/** Randomize 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'"
m_Train.randomize(new System.Random((System.Int32) m_Seed));
/** Make space to store perceptrons */
m_Additions = new int[m_MaxK + 1];
m_IsAddition = new bool[m_MaxK + 1];
m_Weights = new int[m_MaxK + 1];
/** Compute perceptrons */
m_K = 0;
for (int it = 0; it < m_NumIterations; it++)
{
for (int i = 0; i < m_Train.numInstances(); i++)
{
Instance inst = m_Train.instance(i);
if (!inst.classIsMissing())
{
int prediction = makePrediction(m_K, inst);
//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'"
int classValue = (int) inst.classValue();
if (prediction == classValue)
{
m_Weights[m_K]++;
}
else
{
m_IsAddition[m_K] = (classValue == 1);
m_Additions[m_K] = i;
m_K++;
m_Weights[m_K]++;
}
if (m_K == m_MaxK)
{
//UPGRADE_NOTE: Labeled break statement was changed to a goto statement. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1012'"
goto out_brk;
}
}
}
}
//UPGRADE_NOTE: Label 'out_brk' was added. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1011'"
out_brk: ;
}
/// <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);
}
}
public static double classifyTrain_Test(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);
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();
List <Result> eachResults = new List <Result>();
int dataIndex = 0;
for (int i = trainSize; i < numnerOfInst; i++)
{
dataIndex++;
Result eachRow = new Result();
eachRow.lineIndex = 0;
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)
{
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);
}
/// <summary> Builds the boosted classifier</summary>
public virtual void buildClassifier(Instances data)
{
m_RandomInstance = new Random(m_Seed);
Instances boostData;
int classIndex = data.classIndex();
if (data.classAttribute().Numeric)
{
throw new Exception("LogitBoost can't handle a numeric class!");
}
if (m_Classifier == null)
{
throw new System.Exception("A base classifier has not been specified!");
}
if (!(m_Classifier is WeightedInstancesHandler) && !m_UseResampling)
{
m_UseResampling = true;
}
if (data.checkForStringAttributes())
{
throw new Exception("Cannot handle string attributes!");
}
if (m_Debug)
{
System.Console.Error.WriteLine("Creating copy of the training data");
}
m_NumClasses = data.numClasses();
m_ClassAttribute = data.classAttribute();
// Create a copy of the data
data = new Instances(data);
data.deleteWithMissingClass();
// Create the base classifiers
if (m_Debug)
{
System.Console.Error.WriteLine("Creating base classifiers");
}
m_Classifiers = new Classifier[m_NumClasses][];
for (int j = 0; j < m_NumClasses; j++)
{
m_Classifiers[j] = Classifier.makeCopies(m_Classifier, this.NumIterations);
}
// Do we want to select the appropriate number of iterations
// using cross-validation?
int bestNumIterations = this.NumIterations;
if (m_NumFolds > 1)
{
if (m_Debug)
{
System.Console.Error.WriteLine("Processing first fold.");
}
// Array for storing the results
double[] results = new double[this.NumIterations];
// Iterate throught the cv-runs
for (int r = 0; r < m_NumRuns; r++)
{
// Stratify the data
data.randomize(m_RandomInstance);
data.stratify(m_NumFolds);
// Perform the cross-validation
for (int i = 0; i < m_NumFolds; i++)
{
// Get train and test folds
Instances train = data.trainCV(m_NumFolds, i, m_RandomInstance);
Instances test = data.testCV(m_NumFolds, i);
// Make class numeric
Instances trainN = new Instances(train);
trainN.ClassIndex = - 1;
trainN.deleteAttributeAt(classIndex);
trainN.insertAttributeAt(new weka.core.Attribute("'pseudo class'"), classIndex);
trainN.ClassIndex = classIndex;
m_NumericClassData = new Instances(trainN, 0);
// Get class values
int numInstances = train.numInstances();
double[][] tmpArray = new double[numInstances][];
for (int i2 = 0; i2 < numInstances; i2++)
{
tmpArray[i2] = new double[m_NumClasses];
}
double[][] trainFs = tmpArray;
double[][] tmpArray2 = new double[numInstances][];
for (int i3 = 0; i3 < numInstances; i3++)
{
tmpArray2[i3] = new double[m_NumClasses];
}
double[][] trainYs = tmpArray2;
for (int j = 0; j < m_NumClasses; j++)
{
for (int k = 0; k < numInstances; k++)
{
trainYs[k][j] = (train.instance(k).classValue() == j)?1.0 - m_Offset:0.0 + (m_Offset / (double) m_NumClasses);
}
}
// Perform iterations
double[][] probs = initialProbs(numInstances);
m_NumGenerated = 0;
double sumOfWeights = train.sumOfWeights();
for (int j = 0; j < this.NumIterations; j++)
{
performIteration(trainYs, trainFs, probs, trainN, sumOfWeights);
Evaluation eval = new Evaluation(train);
eval.evaluateModel(this, test);
results[j] += eval.correct();
}
}
}
// Find the number of iterations with the lowest error
//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 bestResult = - System.Double.MaxValue;
for (int j = 0; j < this.NumIterations; j++)
{
if (results[j] > bestResult)
{
bestResult = results[j];
bestNumIterations = j;
}
}
if (m_Debug)
{
System.Console.Error.WriteLine("Best result for " + bestNumIterations + " iterations: " + bestResult);
}
}
// Build classifier on all the data
int numInstances2 = data.numInstances();
double[][] trainFs2 = new double[numInstances2][];
for (int i4 = 0; i4 < numInstances2; i4++)
{
trainFs2[i4] = new double[m_NumClasses];
}
double[][] trainYs2 = new double[numInstances2][];
for (int i5 = 0; i5 < numInstances2; i5++)
{
trainYs2[i5] = new double[m_NumClasses];
}
for (int j = 0; j < m_NumClasses; j++)
{
for (int i = 0, k = 0; i < numInstances2; i++, k++)
{
trainYs2[i][j] = (data.instance(k).classValue() == j)?1.0 - m_Offset:0.0 + (m_Offset / (double) m_NumClasses);
}
}
// Make class numeric
data.ClassIndex = - 1;
data.deleteAttributeAt(classIndex);
data.insertAttributeAt(new weka.core.Attribute("'pseudo class'"), classIndex);
data.ClassIndex = classIndex;
m_NumericClassData = new Instances(data, 0);
// Perform iterations
double[][] probs2 = initialProbs(numInstances2);
double logLikelihood = CalculateLogLikelihood(trainYs2, probs2);
m_NumGenerated = 0;
if (m_Debug)
{
System.Console.Error.WriteLine("Avg. log-likelihood: " + logLikelihood);
}
double sumOfWeights2 = data.sumOfWeights();
for (int j = 0; j < bestNumIterations; j++)
{
double previousLoglikelihood = logLikelihood;
performIteration(trainYs2, trainFs2, probs2, data, sumOfWeights2);
logLikelihood = CalculateLogLikelihood(trainYs2, probs2);
if (m_Debug)
{
System.Console.Error.WriteLine("Avg. log-likelihood: " + logLikelihood);
}
if (System.Math.Abs(previousLoglikelihood - logLikelihood) < m_Precision)
{
return ;
}
}
}
/// <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);
}
