Examples with Gaussian edu.cmu.tetrad.annotation.Gaussian used on opensource projects

Example 1 with Gaussian

use of edu.cmu.tetrad.annotation.Gaussian in project tetrad by cmu-phil.

the class GeneralAlgorithmEditor method refreshScoreList.

private void refreshScoreList() {
    updatingScoreModels = true;
    scoreComboBox.removeAllItems();
    AlgorithmModel algoModel = algorithmList.getSelectedValue();
    if (algoModel != null && algoModel.isRequiredScore()) {
        boolean linear = linearVarChkBox.isSelected();
        boolean gaussian = gaussianVarChkBox.isSelected();
        List<ScoreModel> models = ScoreModels.getInstance().getModels(dataType);
        List<ScoreModel> scoreModels = new LinkedList<>();
        if (linear && gaussian) {
            models.stream().filter(e -> e.getScore().getClazz().isAnnotationPresent(Linear.class)).filter(e -> e.getScore().getClazz().isAnnotationPresent(Gaussian.class)).forEach(e -> scoreModels.add(e));
        } else if (linear) {
            models.stream().filter(e -> e.getScore().getClazz().isAnnotationPresent(Linear.class)).filter(e -> !e.getScore().getClazz().isAnnotationPresent(Gaussian.class)).forEach(e -> scoreModels.add(e));
        } else if (gaussian) {
            models.stream().filter(e -> !e.getScore().getClazz().isAnnotationPresent(Linear.class)).filter(e -> e.getScore().getClazz().isAnnotationPresent(Gaussian.class)).forEach(e -> scoreModels.add(e));
        } else {
            models.stream().forEach(e -> scoreModels.add(e));
        }
        // or BDeu score for discrete data
        if (TsImages.class.equals(algoModel.getAlgorithm().getClazz())) {
            switch(dataType) {
                case Continuous:
                    scoreModels.stream().filter(e -> e.getScore().getClazz().equals(SemBicScore.class)).forEach(e -> scoreComboBox.addItem(e));
                    break;
                case Discrete:
                    scoreModels.stream().filter(e -> e.getScore().getClazz().equals(BdeuScore.class)).forEach(e -> scoreComboBox.addItem(e));
                    break;
            }
        } else {
            scoreModels.forEach(e -> scoreComboBox.addItem(e));
        }
    }
    updatingScoreModels = false;
    if (scoreComboBox.getItemCount() > 0) {
        scoreComboBox.setEnabled(true);
        Map<DataType, ScoreModel> map = defaultScoreModels.get(algoModel);
        if (map == null) {
            map = new EnumMap<>(DataType.class);
            defaultScoreModels.put(algoModel, map);
        }
        ScoreModel scoreModel = map.get(dataType);
        if (scoreModel == null) {
            scoreModel = ScoreModels.getInstance().getDefaultModel(dataType);
            if (scoreModel == null) {
                scoreModel = scoreComboBox.getItemAt(0);
            }
        }
        scoreComboBox.setSelectedItem(scoreModel);
    } else {
        scoreComboBox.setEnabled(false);
    }
}

Example 2 with Gaussian

use of edu.cmu.tetrad.annotation.Gaussian in project tetrad by cmu-phil.

the class GeneralAlgorithmEditor method refreshTestList.

private void refreshTestList() {
    updatingTestModels = true;
    indTestComboBox.removeAllItems();
    AlgorithmModel algoModel = algorithmList.getSelectedValue();
    if (algoModel != null && algoModel.isRequiredTest()) {
        boolean linear = linearVarChkBox.isSelected();
        boolean gaussian = gaussianVarChkBox.isSelected();
        List<IndependenceTestModel> models = IndependenceTestModels.getInstance().getModels(dataType);
        if (linear && gaussian) {
            models.stream().filter(e -> e.getIndependenceTest().getClazz().isAnnotationPresent(Linear.class)).filter(e -> e.getIndependenceTest().getClazz().isAnnotationPresent(Gaussian.class)).forEach(e -> indTestComboBox.addItem(e));
        } else if (linear) {
            models.stream().filter(e -> e.getIndependenceTest().getClazz().isAnnotationPresent(Linear.class)).filter(e -> !e.getIndependenceTest().getClazz().isAnnotationPresent(Gaussian.class)).forEach(e -> indTestComboBox.addItem(e));
        } else if (gaussian) {
            models.stream().filter(e -> !e.getIndependenceTest().getClazz().isAnnotationPresent(Linear.class)).filter(e -> e.getIndependenceTest().getClazz().isAnnotationPresent(Gaussian.class)).forEach(e -> indTestComboBox.addItem(e));
        } else {
            models.stream().forEach(e -> indTestComboBox.addItem(e));
        }
    }
    updatingTestModels = false;
    if (indTestComboBox.getItemCount() > 0) {
        indTestComboBox.setEnabled(true);
        Map<DataType, IndependenceTestModel> map = defaultIndTestModels.get(algoModel);
        if (map == null) {
            map = new EnumMap<>(DataType.class);
            defaultIndTestModels.put(algoModel, map);
        }
        IndependenceTestModel testModel = map.get(dataType);
        if (testModel == null) {
            testModel = IndependenceTestModels.getInstance().getDefaultModel(dataType);
            if (testModel == null) {
                testModel = indTestComboBox.getItemAt(0);
            }
        }
        indTestComboBox.setSelectedItem(testModel);
    } else {
        indTestComboBox.setEnabled(false);
    }
}