Examples with Problem de.bwaldvogel.liblinear.Problem used on opensource projects

Example 1 with Problem

use of de.bwaldvogel.liblinear.Problem in project dkpro-tc by dkpro.

the class LiblinearLoadModelConnector method runPrediction.

@Override
protected File runPrediction(File infile) throws Exception {
    Problem predictionProblem = Problem.readFromFile(infile, 1.0);
    File tmp = File.createTempFile("libLinearePrediction", ".txt");
    BufferedWriter writer = null;
    try {
        writer = new BufferedWriter(new OutputStreamWriter(new FileOutputStream(tmp), "utf-8"));
        Feature[][] testInstances = predictionProblem.x;
        for (int i = 0; i < testInstances.length; i++) {
            Feature[] instance = testInstances[i];
            Double prediction = Linear.predict(liblinearModel, instance);
            writer.write(prediction.toString() + "\n");
        }
    } finally {
        IOUtils.closeQuietly(writer);
    }
    tmp.deleteOnExit();
    return tmp;
}

Example 2 with Problem

use of de.bwaldvogel.liblinear.Problem in project dkpro-tc by dkpro.

the class LiblinearTestTask method trainModel.

@Override
protected Object trainModel(TaskContext aContext) throws Exception {
    File fileTrain = getTrainFile(aContext);
    // default for bias is -1, documentation says to set it to 1 in order to
    // get results closer
    // to libsvm
    // writer adds bias, so if we de-activate that here for some reason, we
    // need to also
    // deactivate it there
    Problem train = Problem.readFromFile(fileTrain, 1.0);
    SolverType solver = LiblinearUtils.getSolver(classificationArguments);
    double C = LiblinearUtils.getParameterC(classificationArguments);
    double eps = LiblinearUtils.getParameterEpsilon(classificationArguments);
    Linear.setDebugOutput(null);
    Parameter parameter = new Parameter(solver, C, eps);
    Model model = Linear.train(train, parameter);
    return model;
}

Example 3 with Problem

use of de.bwaldvogel.liblinear.Problem in project dkpro-tc by dkpro.

the class LiblinearTestTask method runPrediction.

@Override
protected void runPrediction(TaskContext aContext, Object trainedModel) throws Exception {
    Model model = (Model) trainedModel;
    File fileTest = getTestFile(aContext);
    File predFolder = aContext.getFolder("", AccessMode.READWRITE);
    File predictionsFile = new File(predFolder, Constants.FILENAME_PREDICTIONS);
    BufferedWriter writer = new BufferedWriter(new OutputStreamWriter(new FileOutputStream(predictionsFile), "utf-8"));
    writer.append("#PREDICTION;GOLD" + "\n");
    Problem test = Problem.readFromFile(fileTest, 1.0);
    Feature[][] testInstances = test.x;
    for (int i = 0; i < testInstances.length; i++) {
        Feature[] instance = testInstances[i];
        Double prediction = Linear.predict(model, instance);
        writer.write(prediction + SEPARATOR_CHAR + new Double(test.y[i]));
        writer.write("\n");
    }
    writer.close();
}

Example 4 with Problem

use of de.bwaldvogel.liblinear.Problem in project dkpro-tc by dkpro.

the class LiblinearSerializeModelConnector method trainModel.

@Override
protected void trainModel(TaskContext aContext, File fileTrain) throws Exception {
    SolverType solver = LiblinearUtils.getSolver(classificationArguments);
    double C = LiblinearUtils.getParameterC(classificationArguments);
    double eps = LiblinearUtils.getParameterEpsilon(classificationArguments);
    Linear.setDebugOutput(null);
    Parameter parameter = new Parameter(solver, C, eps);
    Problem train = Problem.readFromFile(fileTrain, 1.0);
    Model model = Linear.train(train, parameter);
    model.save(new File(outputFolder, MODEL_CLASSIFIER));
}

Example 5 with Problem

use of de.bwaldvogel.liblinear.Problem in project dkpro-tc by dkpro.

the class LiblinearDataWriterTest method dataWriterTest.

@Test
public void dataWriterTest() throws Exception {
    List<Instance> fs = new ArrayList<Instance>();
    List<Feature> features1 = new ArrayList<>();
    features1.add(new Feature("feature1", 1.0, FeatureType.NUMERIC));
    features1.add(new Feature("feature2", 0.0, FeatureType.NUMERIC));
    List<Feature> features2 = new ArrayList<>();
    features2.add(new Feature("feature2", 0.5, FeatureType.NUMERIC));
    features2.add(new Feature("feature1", 0.5, FeatureType.NUMERIC));
    Instance instance1 = new Instance(features1, "0");
    Instance instance2 = new Instance(features2, "1");
    fs.add(instance1);
    fs.add(instance2);
    File outputDirectory = folder.newFolder();
    StringBuilder sb = new StringBuilder();
    sb.append("feature1\n");
    sb.append("feature2\n");
    FileUtils.writeStringToFile(new File(outputDirectory, Constants.FILENAME_FEATURES), sb.toString(), "utf-8");
    File outputFile = new File(outputDirectory, Constants.FILENAME_DATA_IN_CLASSIFIER_FORMAT);
    LibsvmDataFormatWriter writer = new LibsvmDataFormatWriter();
    writer.init(outputDirectory, false, Constants.LM_SINGLE_LABEL, false, new String[] { "0", "1" });
    writer.writeClassifierFormat(fs);
    Problem problem = Problem.readFromFile(outputFile, 1.0);
    assertEquals(2, problem.l);
    assertEquals(4, problem.n);
    assertEquals(0.0, problem.y[0], 0.00001);
    assertEquals(1.0, problem.y[1], 0.00001);
}