/** Creates a default LinearRegression */ public Classifier getClassifier() { return new LinearRegression(); }
/** * Returns the default classifier to use. * * @return the default classifier */ protected Classifier defaultClassifier() { LinearRegression result; result = new LinearRegression(); result.setAttributeSelectionMethod(new SelectedTag(LinearRegression.SELECTION_NONE, LinearRegression.TAGS_SELECTION)); result.setEliminateColinearAttributes(false); return result; }
/** * Generates a linear regression function predictor. * * @param argv the options */ public static void main(String argv[]) { runClassifier(new LinearRegression(), argv); }
LinearRegression temp = new LinearRegression(); temp.setDoNotCheckCapabilities(true); temp.setMinimal(true); temp.buildClassifier(reducedInst); double[] lmCoeffs = temp.coefficients(); double[] coeffs = new double[m_instances.numAttributes()];
setAttributeSelectionMethod(new SelectedTag( Integer.parseInt(selectionString), TAGS_SELECTION)); } else { setAttributeSelectionMethod(new SelectedTag(SELECTION_M5, TAGS_SELECTION)); setRidge(new Double(ridgeString).doubleValue()); } else { setRidge(1.0e-8); setEliminateColinearAttributes(!Utils.getFlag('C', options)); setMinimal(Utils.getFlag("minimal", options)); setOutputAdditionalStats(Utils.getFlag("additional-stats", options)); setUseQRDecomposition(Utils.getFlag("use-qr", options));
m_Coefficients = doRegression(m_SelectedAttributes); } while (m_EliminateColinearAttributes && deselectColinearAttributes(m_SelectedAttributes, m_Coefficients)); double fullMSE = calculateSE(m_SelectedAttributes, m_Coefficients); double akaike = (numInstances - numAttributes) + 2 * numAttributes; if (m_Debug) { double[] currentCoeffs = doRegression(currentSelected); double currentMSE = calculateSE(currentSelected, currentCoeffs); double currentAkaike = currentMSE / fullMSE * (numInstances - numAttributes) + 2 double[] currentCoeffs = doRegression(m_SelectedAttributes); double currentMSE = calculateSE(m_SelectedAttributes, currentCoeffs); double currentAkaike = currentMSE / fullMSE * (numInstances - numAttributes) + 2
getCapabilities().testWithFail(data); findBestModel(); double se = calculateSE(m_SelectedAttributes, m_Coefficients); m_RSquared = RegressionAnalysis.calculateRSquared(m_TransformedData, se); m_RSquaredAdj =
LinearRegression temp = new LinearRegression(); temp.setDoNotCheckCapabilities(true); temp.setMinimal(true); temp.buildClassifier(reducedInst); double[] lmCoeffs = temp.coefficients(); double[] coeffs = new double[m_instances.numAttributes()];
setAttributeSelectionMethod(new SelectedTag( Integer.parseInt(selectionString), TAGS_SELECTION)); } else { setAttributeSelectionMethod(new SelectedTag(SELECTION_M5, TAGS_SELECTION)); setRidge(new Double(ridgeString).doubleValue()); } else { setRidge(1.0e-8); setEliminateColinearAttributes(!Utils.getFlag('C', options)); setMinimal(Utils.getFlag("minimal", options)); setOutputAdditionalStats(Utils.getFlag("additional-stats", options)); setUseQRDecomposition(Utils.getFlag("use-qr", options));
/** * Generates a linear regression function predictor. * * @param argv the options */ public static void main(String argv[]) { runClassifier(new LinearRegression(), argv); }
m_Coefficients = doRegression(m_SelectedAttributes); } while (m_EliminateColinearAttributes && deselectColinearAttributes(m_SelectedAttributes, m_Coefficients)); double fullMSE = calculateSE(m_SelectedAttributes, m_Coefficients); double akaike = (numInstances - numAttributes) + 2 * numAttributes; if (m_Debug) { double[] currentCoeffs = doRegression(currentSelected); double currentMSE = calculateSE(currentSelected, currentCoeffs); double currentAkaike = currentMSE / fullMSE * (numInstances - numAttributes) + 2 double[] currentCoeffs = doRegression(m_SelectedAttributes); double currentMSE = calculateSE(m_SelectedAttributes, currentCoeffs); double currentAkaike = currentMSE / fullMSE * (numInstances - numAttributes) + 2
getCapabilities().testWithFail(data); findBestModel(); double se = calculateSE(m_SelectedAttributes, m_Coefficients); m_RSquared = RegressionAnalysis.calculateRSquared(m_TransformedData, se); m_RSquaredAdj =
/** Creates a default LinearRegression */ public Classifier getClassifier() { return new LinearRegression(); }
/** * Change default classifier to CR with Linear Regression as base as this classifier * uses numeric values in the compressed labels. */ protected Classifier getDefaultClassifier() { CR cr = new CR(); LinearRegression lr = new LinearRegression(); cr.setClassifier(lr); return cr; }
/** * Change default classifier to CR with Linear Regression as base as this classifier * uses numeric values in the compressed labels. */ protected Classifier getDefaultClassifier() { CR cr = new CR(); LinearRegression lr = new LinearRegression(); cr.setClassifier(lr); return cr; }
/** * Change default classifier to CR with Linear Regression as base as this classifier * uses numeric values in the compressed labels. */ protected Classifier getDefaultClassifier() { CR cr = new CR(); LinearRegression lr = new LinearRegression(); cr.setClassifier(lr); return cr; }
/** * Change default classifier to CR with Linear Regression as base as this classifier * uses numeric values in the compressed labels. */ protected Classifier getDefaultClassifier() { CR cr = new CR(); LinearRegression lr = new LinearRegression(); cr.setClassifier(lr); return cr; }
lr = new weka.classifiers.functions.LinearRegression(); generator.setBaseObject(lr);
public void getLinearCombination(List<OWLClassExpression> descriptions){ //get common data Instances data = buildData(descriptions); //compute linear regression model data.setClassIndex(data.numAttributes() - 1); AbstractClassifier model = new LinearRegression(); model = new J48(); try { model.buildClassifier(data); // System.out.println(model); // AddExpression filter = new AddExpression(); // filter.setExpression("a1^2"); // FilteredClassifier filteredClassifier = new FilteredClassifier(); // filteredClassifier.setClassifier(model); // filteredClassifier.setFilter(filter); // filteredClassifier.buildClassifier(data); // logger.debug(filteredClassifier.getClassifier()); Evaluation eval = new Evaluation(data); eval.crossValidateModel(model, data, 10, new Random(1)); System.out.println(eval.toSummaryString(true)); } catch (Exception e) { e.printStackTrace(); } }
protected InputMappedClassifier trainClassifier(Instances data, boolean nominalClass) { InputMappedClassifier toUse = new InputMappedClassifier(); if (nominalClass) { toUse.setClassifier(new weka.classifiers.trees.J48()); } else { toUse.setClassifier(new weka.classifiers.functions.LinearRegression()); } toUse.setSuppressMappingReport(true); try { toUse.buildClassifier(data); } catch (Exception ex) { fail("Training InputMappedClassifier failed: " + ex); return null; } return toUse; }