Examples with ModelContentWO org.knime.core.node.ModelContentWO used on opensource projects

Example 11 with ModelContentWO

use of org.knime.core.node.ModelContentWO in project knime-core by knime.

the class BasisFunctionFactory method save.

/**
 * Saves to model content.
 * @param pp the model content this is saved to.
 */
public void save(final ModelContent pp) {
    pp.addInt(CFG_DISTANCE, m_distance);
    ModelContentWO modelSpec = pp.addModelContent(CFG_MODEL_SPEC);
    m_spec.save(modelSpec);
}

Example 12 with ModelContentWO

use of org.knime.core.node.ModelContentWO in project knime-core by knime.

the class FuzzyBasisFunctionPredictorRow method save.

/**
 * {@inheritDoc}
 */
@Override
public void save(final ModelContentWO pp) {
    super.save(pp);
    pp.addInt(Norm.NORM_KEY, m_norm);
    ModelContentWO memParams = pp.addModelContent("membership_functions");
    for (int i = 0; i < m_mem.length; i++) {
        m_mem[i].save(memParams.addModelContent("" + i));
    }
}

Example 13 with ModelContentWO

use of org.knime.core.node.ModelContentWO in project knime-core by knime.

the class CAIMDiscretizationNodeModel method saveInternals.

/**
 * {@inheritDoc}
 */
@Override
protected void saveInternals(final File nodeInternDir, final ExecutionMonitor exec) throws IOException, CanceledExecutionException {
    ModelContent binModel = new ModelContent(SAVE_INTERNALS_FILE_NAME);
    // save the table spec of included names first
    ModelContentWO sub = binModel.addModelContent(CONFIG_KEY_COLUMN_NANES);
    DataTableSpec spec = (DataTableSpec) m_discretizationModel.getSpec();
    spec.save(sub);
    // now save the model.
    m_discretizationModel.saveToModelContent(binModel);
    File internalsFile = new File(nodeInternDir, SAVE_INTERNALS_FILE_NAME);
    BufferedOutputStream out = new BufferedOutputStream(new GZIPOutputStream(new FileOutputStream(internalsFile)));
    binModel.saveToXML(out);
}

Example 14 with ModelContentWO

use of org.knime.core.node.ModelContentWO in project knime-core by knime.

the class AffineTransConfiguration method save.

/**
 * Saves this object to the argument model content.
 * @param model To write to.
 */
protected void save(final ModelContentWO model) {
    ModelContentWO colSub = model.addModelContent(CFG_COLUMNS);
    for (int i = 0; i < m_includeNames.length; i++) {
        String name = m_includeNames[i];
        double scale = m_scales[i];
        double transl = m_translations[i];
        double min = m_min[i];
        double max = m_max[i];
        if (name == null) {
            assert Double.isNaN(scale) && Double.isNaN(transl);
            continue;
        }
        ModelContentWO sub = colSub.addModelContent(name);
        sub.addString(CFG_NAME, name);
        sub.addDouble(CFG_SCALE, scale);
        sub.addDouble(CFG_TRANSLATE, transl);
        sub.addDouble(CFG_MIN, min);
        sub.addDouble(CFG_MAX, max);
    }
    model.addString(CFG_SUMMARY, m_summary);
}

Example 15 with ModelContentWO

use of org.knime.core.node.ModelContentWO in project knime-core by knime.

the class MultiLayerPerceptron method savePredictorParams.

/**
 * Stores this MLP model to config.
 *
 * @param predParams ModelContent to write into.
 */
public void savePredictorParams(final ModelContentWO predParams) {
    predParams.addInt(MODE_KEY, m_mode);
    ModelContentWO layers = predParams.addModelContent(ALLLAYERS_KEY);
    for (int l = 0; l < m_layers.length; l++) {
        Layer mylayer = m_layers[l];
        Perceptron[] neurons = mylayer.getPerceptrons();
        ModelContentWO layerconf = layers.addModelContent("" + l);
        if (l == 0) {
            // Input Layer must be handled seperately.
            for (int n = 0; n < neurons.length; n++) {
                ModelContentWO neuronsconf = layerconf.addModelContent("" + n);
                neuronsconf.addDouble(INPUT_KEY, ((InputPerceptron) neurons[n]).getInput());
                neuronsconf.addString(CLASSVALUE_KEY, neurons[n].getClassValue());
            }
        } else {
            for (int n = 0; n < neurons.length; n++) {
                ModelContentWO neuronsconf = layerconf.addModelContent("" + n);
                neuronsconf.addDoubleArray(WEIGHT_KEY, neurons[n].getWeights());
                neuronsconf.addDouble(THRESHOLD_KEY, neurons[n].getThreshold());
                if (neurons[n].getClassValue() != null) {
                    neuronsconf.addString(CLASSVALUE_KEY, neurons[n].getClassValue());
                }
            }
        }
    }
}