Java Code Examples for weka.core.SerializationHelper#read()

public NERTool(){
		try {
//	        URL realPath = Thread.currentThread().getContextClassLoader().getResource("");
//	        System.out.println("realPath:"+realPath);
//	        String decoded = URLDecoder.decode(realPath.getFile(), "UTF-8");
//	        File fileRource1 = new File(decoded, GlobalSetting.rule_base_acdat_model);
//			File fileRource2 = new File(decoded, GlobalSetting.rule_base_dict_model);
//			System.out.println("f1="+fileRource1.getAbsolutePath());
//			System.out.println("f2="+fileRource2.getAbsolutePath());
			Resource fileRource = new ClassPathResource(rule_based_model);
			this.rbm =(RuleBasedModels) SerializationHelper.read(new GZIPInputStream(fileRource.getInputStream()));	
			
		} catch (Exception e) {
			// TODO Auto-generated catch block
			e.printStackTrace();
		}
	}
 

public static void initialise(String browserModelFilePath, String osModelFilePath, String fontsPath) throws Exception{
	browserAttributes = new ArrayList<Attribute>();
	osAttributes = new ArrayList<Attribute>();
	browserClassAttribute = new Attribute("className", browserGroupsWeCareAbout);
	osClassAttribute = new Attribute("className", osGroupsWeCareAbout);
	browserAttributes.add(browserClassAttribute);
	osAttributes.add(osClassAttribute);
	for(int i = 1; i <= 5300; ++i){
		browserAttributes.add(new Attribute(Integer.toString(i)));
		osAttributes.add(new Attribute(Integer.toString(i)));
	}
	
	browserClassifier = (Classifier) SerializationHelper.read(browserModelFilePath);
	osClassifier = (Classifier) SerializationHelper.read(osModelFilePath);
	
	BrowserOsGuessFingerprintNumericRepresentation.initialise(fontsPath);
}
 

public static void main(String[] args) throws Exception {
    Classifier j48 = new J48();
    Instances trainingData = GenerateTestVessels.getData();
    j48.buildClassifier(trainingData);
    System.out.println(j48);



    double[] vesselUnderTest = GenerateTestVessels.getBarco(5);

    DenseInstance inst = new DenseInstance(1.0,vesselUnderTest);
    inst.setDataset(trainingData);
    inst.setClassMissing();
    System.out.println(inst);

    double result = j48.classifyInstance(inst);
    System.out.println(GenerateTestVessels.types[(int)result]);

    SerializationHelper.write(new FileOutputStream("tmp"), j48);
    J48 j48Read = (J48)SerializationHelper.read(new FileInputStream("tmp"));
}
 

/**
 * Reads the statistics.
 *
 * @return              the statistics that were read
 */
@Override
public List<EvaluationStatistics> read() {
	List<EvaluationStatistics>  result;

	try {
		result = (List<EvaluationStatistics>) SerializationHelper.read(m_File.getAbsolutePath());
	}
	catch (Exception e) {
		result = null;
		handleException("Failed to read serialized statistics from: " + m_File, e);
	}

	return result;
}
 

/**
 * Reads an experiment from disk.
 *
 * @param file      the file to load
 * @return          the experiment, null if failed to load
 */
@Override
public Experiment read(File file) {
	try {
		return (Experiment) SerializationHelper.read(file.getAbsolutePath());
	}
	catch (Exception e) {
		handleException("Failed to read experiment from: " + file, e);
		return null;
	}
}
 

@Override
public boolean initialize() {
	try {
		cls_act = (Classifier) SerializationHelper.read(Environment.getExternalStorageDirectory().getAbsolutePath()
			+ "/Android/data/tinygsn/" + fileName);
	} catch (Exception e) {
		return false;
	}
	return true;
}
 

public static void main(String[] args) throws Exception {
	
	if (args.length != 2) {
		System.out.println("Usage: WekaSpeechActClassifier <train_set_input_file> <test_set_input_file>");
		System.exit(0);
	}
	
	String arffFileTrain = args[0];
	String arffFileTest = args[1];

	LibSVM wekaClassifier = new LibSVM();
	wekaClassifier.setOptions(new String[] {"-B", "-H"});

	Instances preparedData = (Instances) SerializationHelper.read(arffFileTrain);
	Instances preparedTest = (Instances) SerializationHelper.read(arffFileTest);
	
	System.out.println("Reading train set and test set done!");

	System.out.print("\nTraining...");
	wekaClassifier.buildClassifier(preparedData);
	
	System.out.println("\nTraining...done!");
	
	Evaluation evalTrain = new Evaluation(preparedData);
	evalTrain.evaluateModel(wekaClassifier, preparedData);

	DecimalFormat formatter = new DecimalFormat("#0.0");
	
	System.out.println("\nEvaluating on trainSet...");
	System.out.println(evalTrain.toSummaryString());
	
	System.out.println("\nResult on trainSet...");
	System.out.println("Precision:" + formatter.format(100*evalTrain.precision(0)) + "%" +
			" - Recal: " + formatter.format(100*evalTrain.recall(0)) + "%" +
			" - F1: " + formatter.format(evalTrain.fMeasure(0)) + "%");
	
	Evaluation eval = new Evaluation(preparedTest);
	eval.evaluateModel(wekaClassifier, preparedTest);

	System.out.println("\nEvaluating on testSet...");
	System.out.println(eval.toSummaryString());
	
	System.out.println("\nResult on testSet...");
	System.out.println("Precision:" + formatter.format(100*eval.precision(0)) + "%" +
			" - Recal: " + formatter.format(100*eval.recall(0)) + "%" +
			" - F1: " + formatter.format(100*eval.fMeasure(0)) + "%");

	System.out.println("True positive rate: " + formatter.format(100*eval.truePositiveRate(0)) + "%" + 
			" - True negative rate: "  + formatter.format(100*eval.trueNegativeRate(0)) + "%");
	System.out.println("Accuracy: " + formatter.format(100*((eval.truePositiveRate(0) + eval.trueNegativeRate(0)) / 2)) + "%");
	
	System.out.println("\nDone!");
}