Examples with SettingsModelIntegerBounded org.knime.core.node.defaultnodesettings.SettingsModelIntegerBounded used on opensource projects

Example 1 with SettingsModelIntegerBounded

use of org.knime.core.node.defaultnodesettings.SettingsModelIntegerBounded in project knime-core by knime.

the class NaiveBayesLearnerNodeModel2 method validateSettings.

/**
 * {@inheritDoc}
 */
@Override
protected void validateSettings(final NodeSettingsRO settings) throws InvalidSettingsException {
    final SettingsModelString colName = m_classifyColumnName.createCloneWithValidatedValue(settings);
    if (colName == null || colName.getStringValue() == null || colName.getStringValue().trim().length() < 1) {
        throw new InvalidSettingsException("No class column selected");
    }
    final SettingsModelIntegerBounded maxNoOfNomVals = m_maxNoOfNominalVals.createCloneWithValidatedValue(settings);
    if (maxNoOfNomVals.getIntValue() < 0) {
        throw new InvalidSettingsException("Maximum number of unique nominal values should be a positive number");
    }
    m_pmmlCompatible.validateSettings(settings);
    m_threshold.validateSettings(settings);
}

Example 2 with SettingsModelIntegerBounded

use of org.knime.core.node.defaultnodesettings.SettingsModelIntegerBounded in project knime-core by knime.

the class CreateBitVectorNodeModel method createMeanPercentageModel.

/**
 * @return the mean percentage model from 0 to 100.
 */
@SuppressWarnings("unused")
static SettingsModelInteger createMeanPercentageModel() {
    final SettingsModelIntegerBounded model = new SettingsModelIntegerBounded("singleNumericMeanThreshold", 100, 0, Integer.MAX_VALUE);
    // disable the model since we use threshold by default
    model.setEnabled(DEFAULT_USE_MEAN && ColumnType.getDefault().equals(ColumnType.MULTI_NUMERICAL));
    return model;
}

Example 3 with SettingsModelIntegerBounded

use of org.knime.core.node.defaultnodesettings.SettingsModelIntegerBounded in project knime-core by knime.

the class RoundDoubleNodeModel method validateSettings.

/**
 * {@inheritDoc}
 */
@Override
protected void validateSettings(final NodeSettingsRO settings) throws InvalidSettingsException {
    m_filterDoubleColModel.validateSettings(settings);
    m_numberPrecisionModel.validateSettings(settings);
    m_appendColumnsModel.validateSettings(settings);
    m_columnSuffixModel.validateSettings(settings);
    m_roundingModeModel.validateSettings(settings);
    try {
        // added in 2.8
        m_outputTypeModel.validateSettings(settings);
    } catch (InvalidSettingsException ise) {
        RoundDoubleNodeDialog.getOutputAsStringModel().validateSettings(settings);
    }
    m_numberModeModel.validateSettings(settings);
    // additional sanity checks
    StringBuffer errMsgBuffer = new StringBuffer();
    boolean err = false;
    // precision number has to be between 0 and inf
    int precision = ((SettingsModelIntegerBounded) m_numberPrecisionModel.createCloneWithValidatedValue(settings)).getIntValue();
    if (precision < MIN_PRECISION || precision > MAX_PRECISION) {
        errMsgBuffer.append("Rounding precision has to be between " + MIN_PRECISION + " and " + MAX_PRECISION + "\n");
        err = true;
    }
    // if rounded values have to be appended, check for valid column suffix
    boolean append = ((SettingsModelBoolean) m_appendColumnsModel.createCloneWithValidatedValue(settings)).getBooleanValue();
    if (append) {
        String suffix = ((SettingsModelString) m_columnSuffixModel.createCloneWithValidatedValue(settings)).getStringValue();
        if (suffix.length() <= 0) {
            errMsgBuffer.append("Column suffix may not be empty if append " + "columns is set!\n");
            err = true;
        }
    }
    // rounding mode string needs to be a valid round mode
    String roundingModeString = ((SettingsModelString) m_roundingModeModel.createCloneWithValidatedValue(settings)).getStringValue();
    try {
        RoundingMode.valueOf(roundingModeString);
    } catch (Exception e) {
        errMsgBuffer.append("Specified round mode is not valid!\n");
        err = true;
    }
    // number mode string needs to be a valid number mode
    String numberModeString = ((SettingsModelString) m_numberModeModel.createCloneWithValidatedValue(settings)).getStringValue();
    try {
        NumberMode.valueByDescription(numberModeString);
    } catch (Exception e) {
        errMsgBuffer.append("Specified number mode is not valid!\n");
        err = true;
    // throw exception when at least one settings is invalid
    } finally {
        if (err) {
            throw new InvalidSettingsException(errMsgBuffer.toString());
        }
    }
}

Example 4 with SettingsModelIntegerBounded

use of org.knime.core.node.defaultnodesettings.SettingsModelIntegerBounded in project knime-core by knime.

the class DecisionTreeLearnerNodeDialog method createSettingsBinaryMaxNominalValues.

/**
 * @return max number nominal values for complete subset calculation for
 *         binary nominal splits
 */
static SettingsModelIntegerBounded createSettingsBinaryMaxNominalValues() {
    SettingsModelIntegerBounded model = new SettingsModelIntegerBounded(DecisionTreeLearnerNodeModel.KEY_BINARY_MAX_NUM_NOMINAL_VALUES, DecisionTreeLearnerNodeModel.DEFAULT_MAX_BIN_NOMINAL_SPLIT_COMPUTATION, 1, Integer.MAX_VALUE);
    model.setEnabled(DecisionTreeLearnerNodeModel.DEFAULT_BINARY_NOMINAL_SPLIT_MODE);
    return model;
}

Example 5 with SettingsModelIntegerBounded

use of org.knime.core.node.defaultnodesettings.SettingsModelIntegerBounded in project knime-core by knime.

the class DecisionTreeLearnerNodeDialog2 method createSettingsBinaryMaxNominalValues.

/**
 * @return max number nominal values for complete subset calculation for
 *         binary nominal splits
 */
static SettingsModelIntegerBounded createSettingsBinaryMaxNominalValues() {
    SettingsModelIntegerBounded model = new SettingsModelIntegerBounded(DecisionTreeLearnerNodeModel2.KEY_BINARY_MAX_NUM_NOMINAL_VALUES, DecisionTreeLearnerNodeModel2.DEFAULT_MAX_BIN_NOMINAL_SPLIT_COMPUTATION, 1, Integer.MAX_VALUE);
    model.setEnabled(DecisionTreeLearnerNodeModel2.DEFAULT_BINARY_NOMINAL_SPLIT_MODE);
    return model;
}