/** * loadDataset - load a dataset, given command line option '-t' specifying an arff file. * @param options command line options, specifying dataset filename * @return the dataset */ public static Instances loadDataset(String options[]) throws Exception { return loadDataset(options,'t'); }
/** * loadDataset - load a dataset, given command line option '-t' specifying an arff file. * @param options command line options, specifying dataset filename * @return the dataset */ public static Instances loadDataset(String options[]) throws Exception { return loadDataset(options,'t'); }
/** * Main - do some tests. */ public static void main(String args[]) throws Exception { Instances D = Evaluation.loadDataset(args); MLUtils.prepareData(D); int L = D.classIndex(); double CD[][] = null; if (args[2].equals("L")) { String I = "I"; if (args.length >= 3) I = args[3]; CD = StatUtils.LEAD(D, new SMO(), new Random(), I); } else { CD = StatUtils.margDepMatrix(D,args[2]); } System.out.println(MatrixUtils.toString(CD, "M" + args[2])); }
/** * Main - do some tests. */ public static void main(String args[]) throws Exception { Instances D = Evaluation.loadDataset(args); MLUtils.prepareData(D); int L = D.classIndex(); double CD[][] = null; if (args[2].equals("L")) { String I = "I"; if (args.length >= 3) I = args[3]; CD = StatUtils.LEAD(D, new SMO(), new Random(), I); } else { CD = StatUtils.margDepMatrix(D,args[2]); } System.out.println(MatrixUtils.toString(CD, "M" + args[2])); }
test = Evaluation.loadDataset(options, 'T'); MLUtils.prepareData(test); needPrebuiltModel = true; train = Evaluation.loadDataset(options, 't'); MLUtils.prepareData(train); needPrebuiltModel = false; // we can build a model with training data
test = Evaluation.loadDataset(options, 'T'); MLUtils.prepareData(test); needPrebuiltModel = true; train = Evaluation.loadDataset(options, 't'); MLUtils.prepareData(train); needPrebuiltModel = false; // we can build a model with training data
Instances D_train = loadDataset(options); D_test = loadDataset(options,'T'); MLUtils.prepareData(D_test); } catch(Exception e) {
Instances D_train = loadDataset(options); D_test = loadDataset(options,'T'); MLUtils.prepareData(D_test); } catch(Exception e) {