/** * Main method for testing this class. * * @param argv the options for the learner */ public static void main(String [] argv){ runClassifier(new ClassificationViaRegression(), argv); } }
/** Creates a default ClassificationViaRegression */ public Classifier getClassifier() { return new ClassificationViaRegression(); }
/** * Returns a string describing classifier * @return a description suitable for * displaying in the explorer/experimenter gui */ public String globalInfo() { return "Class for doing classification using regression methods. Class is " + "binarized and one regression model is built for each class value. For more " + "information, see, for example\n\n" + getTechnicalInformation().toString(); }
/** * Builds the classifiers. * * @param insts the training data. * @throws Exception if a classifier can't be built */ public void buildClassifier(Instances insts) throws Exception { Instances newInsts; // can classifier handle the data? getCapabilities().testWithFail(insts); // remove instances with missing class insts = new Instances(insts); insts.deleteWithMissingClass(); m_Classifiers = AbstractClassifier.makeCopies(m_Classifier, insts.numClasses()); m_ClassFilters = new MakeIndicator[insts.numClasses()]; for (int i = 0; i < insts.numClasses(); i++) { m_ClassFilters[i] = new MakeIndicator(); m_ClassFilters[i].setAttributeIndex("" + (insts.classIndex() + 1)); m_ClassFilters[i].setValueIndex(i); m_ClassFilters[i].setNumeric(true); m_ClassFilters[i].setInputFormat(insts); newInsts = Filter.useFilter(insts, m_ClassFilters[i]); m_Classifiers[i].buildClassifier(newInsts); } }
/** Creates a default ClassificationViaRegression */ public Classifier getClassifier() { return new ClassificationViaRegression(); }
/** * Returns a string describing classifier * @return a description suitable for * displaying in the explorer/experimenter gui */ public String globalInfo() { return "Class for doing classification using regression methods. Class is " + "binarized and one regression model is built for each class value. For more " + "information, see, for example\n\n" + getTechnicalInformation().toString(); }
/** * Builds the classifiers. * * @param insts the training data. * @throws Exception if a classifier can't be built */ public void buildClassifier(Instances insts) throws Exception { Instances newInsts; // can classifier handle the data? getCapabilities().testWithFail(insts); // remove instances with missing class insts = new Instances(insts); insts.deleteWithMissingClass(); m_Classifiers = AbstractClassifier.makeCopies(m_Classifier, insts.numClasses()); m_ClassFilters = new MakeIndicator[insts.numClasses()]; for (int i = 0; i < insts.numClasses(); i++) { m_ClassFilters[i] = new MakeIndicator(); m_ClassFilters[i].setAttributeIndex("" + (insts.classIndex() + 1)); m_ClassFilters[i].setValueIndex(i); m_ClassFilters[i].setNumeric(true); m_ClassFilters[i].setInputFormat(insts); newInsts = Filter.useFilter(insts, m_ClassFilters[i]); m_Classifiers[i].buildClassifier(newInsts); } }
/** * Main method for testing this class. * * @param argv the options for the learner */ public static void main(String [] argv){ runClassifier(new ClassificationViaRegression(), argv); } }