classifier = train( new PipeExtendedIterator( new LineIterator (trainingData, instanceFormat, 2, 1, -1), preprocess), gaussianVarianceOption.value, modelOption.value); test(classifier, new PipeExtendedIterator( new LineIterator (testData, instanceFormat, 2, 1, -1), preprocess))); getReader(classifyOption.value, encodingOption.value); Classification[] cl = classify(classifier, new PipeExtendedIterator( new LineIterator(unlabeledData, unlabeledInstanceFormat, 1, -1, -1),
classifier = train( new PipeExtendedIterator( new LineIterator (trainingData, instanceFormat, 2, 1, -1), preprocess), gaussianVarianceOption.value, modelOption.value); test(classifier, new PipeExtendedIterator( new LineIterator (testData, instanceFormat, 2, 1, -1), preprocess))); getReader(classifyOption.value, encodingOption.value); Classification[] cl = classify(classifier, new PipeExtendedIterator( new LineIterator(unlabeledData, unlabeledInstanceFormat, 1, -1, -1),
classifier = train( new PipeExtendedIterator( new LineIterator (trainingData, instanceFormat, 2, 1, -1), preprocess), gaussianVarianceOption.value, modelOption.value); test(classifier, new PipeExtendedIterator( new LineIterator (testData, instanceFormat, 2, 1, -1), preprocess))); getReader(classifyOption.value, encodingOption.value); Classification[] cl = classify(classifier, new PipeExtendedIterator( new LineIterator(unlabeledData, unlabeledInstanceFormat, 1, -1, -1),