Examples with DoubleCell org.knime.core.data.def.DoubleCell used on opensource projects

Example 56 with DoubleCell

use of org.knime.core.data.def.DoubleCell in project knime-core by knime.

the class AdapterNodeModel method execute.

/**
 * {@inheritDoc}
 */
@Override
protected PortObject[] execute(final PortObject[] inObjects, final ExecutionContext exec) throws Exception {
    if (getNrInPorts() == 0 && getNrOutPorts() == 1) {
        // assume simple source node with one table output
        BufferedDataContainer cnt = exec.createDataContainer(createDefaultOutputSpec());
        cnt.addRowToTable(new DefaultRow(RowKey.createRowKey(0), new DataCell[] { new StringCell("Cell-1.1"), new IntCell(12), new DoubleCell(1.3) }));
        cnt.addRowToTable(new DefaultRow(RowKey.createRowKey(1), new DataCell[] { new StringCell("Cell-2.1"), new IntCell(22), new DoubleCell(2.3) }));
        cnt.addRowToTable(new DefaultRow(RowKey.createRowKey(2), new DataCell[] { new StringCell("Cell-3.1"), new IntCell(32), new DoubleCell(3.3) }));
        cnt.close();
        return new BufferedDataTable[] { cnt.getTable() };
    }
    return inObjects;
}

Example 57 with DoubleCell

use of org.knime.core.data.def.DoubleCell in project knime-core by knime.

the class SubgroupMinerModel2 method createAssociationRulesOutput.

private BufferedDataTable createAssociationRulesOutput(final DataTableSpec inputSpec, final ExecutionContext exec, final AprioriAlgorithm apriori, final List<DataCell> nameMapping) {
    DataTableSpec outSpec = createAssociationRulesSpec(inputSpec);
    BufferedDataContainer ruleRows = exec.createDataContainer(outSpec);
    assert nameMapping != null;
    List<AssociationRule> associationRules = apriori.getAssociationRules(m_confidence.getDoubleValue());
    // for every association rule
    int rowKeyCounter = 0;
    for (AssociationRule r : associationRules) {
        // get the support
        double support = r.getSupport();
        // get the confidence
        double confidence = r.getConfidence();
        // get lift
        double lift = r.getLift();
        // get the antecedence (which is one item) -> cell
        FrequentItemSet antecedent = r.getAntecedent();
        // get the consequence
        FrequentItemSet consequent = r.getConsequent();
        DataCell[] allCells = new DataCell[6];
        allCells[0] = new DoubleCell(support);
        allCells[1] = new DoubleCell(confidence);
        allCells[2] = new DoubleCell(lift);
        // consequent is always only one item -> access with get(0) ok
        if (nameMapping.size() > consequent.getItems().get(0)) {
            allCells[3] = nameMapping.get(consequent.getItems().get(0));
        } else {
            allCells[3] = new StringCell("Item" + consequent.getItems().get(0));
        }
        allCells[4] = new StringCell("<---");
        Set<DataCell> allcells = new HashSet<DataCell>();
        for (int i = 0; i < antecedent.getItems().size() && i < m_maxItemSetLength.getIntValue() + 5; i++) {
            if (nameMapping.size() > antecedent.getItems().get(i)) {
                allcells.add(nameMapping.get(antecedent.getItems().get(i)));
            } else {
                allcells.add(new StringCell("Item" + antecedent.getItems().get(i)));
            }
        }
        allCells[5] = CollectionCellFactory.createSetCell(allcells);
        if (antecedent.getItems().size() > 0) {
            DataRow row = new DefaultRow("rule" + (rowKeyCounter++), allCells);
            ruleRows.addRowToTable(row);
        }
    }
    ruleRows.close();
    return ruleRows.getTable();
}

Example 58 with DoubleCell

use of org.knime.core.data.def.DoubleCell in project knime-core by knime.

the class SVMPredictor method getCells.

/**
 * {@inheritDoc}
 */
@Override
public DataCell[] getCells(final DataRow row) {
    ArrayList<Double> values = new ArrayList<Double>();
    for (int i = 0; i < m_colindices.length; i++) {
        if (row.getCell(m_colindices[i]).isMissing()) {
            if (m_appendProbabilities) {
                DataCell[] ret = new DataCell[1 + m_svms.length];
                Arrays.fill(ret, new MissingCell("Missing value in input data."));
                return ret;
            }
            return new DataCell[] { DataType.getMissingCell() };
        }
        DoubleValue dv = (DoubleValue) row.getCell(m_colindices[i]);
        values.add(dv.getDoubleValue());
    }
    String classvalue = doPredict(values);
    if (m_appendProbabilities) {
        DataCell[] ret = new DataCell[m_svms.length + 1];
        double[] probabilities = computeProbabilities(values);
        assert ret.length == probabilities.length + 1 : ret.length + " vs. " + (probabilities.length + 1);
        for (int i = ret.length - 1; i-- > 0; ) {
            ret[i] = new DoubleCell(probabilities[i]);
        }
        ret[probabilities.length] = new StringCell(classvalue);
        return ret;
    }
    return new DataCell[] { new StringCell(classvalue) };
}

Example 59 with DoubleCell

use of org.knime.core.data.def.DoubleCell in project knime-core by knime.

the class SVMPredictor method getColumnSpecs.

/**
 * {@inheritDoc}
 */
@Override
public DataColumnSpec[] getColumnSpecs() {
    DataColumnSpecCreator colspeccreator = new DataColumnSpecCreator(m_predictionColumnName, StringCell.TYPE);
    if (m_appendProbabilities) {
        final DataColumnSpec[] ret = new DataColumnSpec[m_svms.length + 1];
        PredictorHelper ph = PredictorHelper.getInstance();
        final DataColumnSpecCreator creator = new DataColumnSpecCreator("Dummy", DoubleCell.TYPE);
        creator.setDomain(new DataColumnDomainCreator(new DoubleCell(0), new DoubleCell(1)).createDomain());
        for (int i = m_svms.length; i-- > 0; ) {
            String name = ph.probabilityColumnName(m_trainingColumn, m_svms[i].getPositive(), m_suffix);
            creator.setName(name);
            ret[i] = creator.createSpec();
        }
        ret[m_svms.length] = colspeccreator.createSpec();
        return ret;
    }
    return new DataColumnSpec[] { colspeccreator.createSpec() };
}

Example 60 with DoubleCell

use of org.knime.core.data.def.DoubleCell in project knime-core by knime.

the class PredictorHelper method createOutTableSpec.

/**
 * Computes the output table's specifaction based on common node settings.
 *
 * @param dataSpec The input table {@link DataColumnSpec}.
 * @param modelSpec The model {@link PMMLPortObjectSpec}.
 * @param addProbs Add the probability columns?
 * @param predictionCol Custom name of the prediction column.
 * @param shouldOverride Should we use that name?
 * @param suffix Suffix for probability columns.
 * @return The output table {@link DataTableSpec}.
 * @throws InvalidSettingsException Invalid settings for the prediction column name.
 */
public DataTableSpec createOutTableSpec(final PortObjectSpec dataSpec, final PortObjectSpec modelSpec, final boolean addProbs, final String predictionCol, final boolean shouldOverride, final String suffix) throws InvalidSettingsException {
    CheckUtils.checkSettingNotNull(predictionCol, "Prediction column name cannot be null");
    CheckUtils.checkSetting(!predictionCol.trim().isEmpty(), "Prediction column name cannot be empty");
    List<DataCell> predValues = null;
    if (addProbs) {
        predValues = getPredictionValues((PMMLPortObjectSpec) modelSpec);
        if (predValues == null) {
            // no out spec can be determined
            return null;
        }
    }
    int numCols = (predValues == null ? 0 : predValues.size()) + 1;
    DataTableSpec inSpec = (DataTableSpec) dataSpec;
    DataColumnSpec[] newCols = new DataColumnSpec[numCols];
    /* Set bar renderer and domain [0,1] as default for the double cells
         * containing the distribution */
    // DataColumnProperties propsRendering = new DataColumnProperties(
    // Collections.singletonMap(
    // DataValueRenderer.PROPERTY_PREFERRED_RENDERER,
    // DoubleBarRenderer.DESCRIPTION));
    DataColumnDomain domain = new DataColumnDomainCreator(new DoubleCell(0.0), new DoubleCell(1.0)).createDomain();
    String trainingColumnName = ((PMMLPortObjectSpec) modelSpec).getTargetFields().iterator().next();
    // add all distribution columns
    for (int i = 0; i < numCols - 1; i++) {
        assert predValues != null;
        DataColumnSpecCreator colSpecCreator = new DataColumnSpecCreator(probabilityColumnName(trainingColumnName, predValues.get(i).toString(), suffix), DoubleCell.TYPE);
        // colSpecCreator.setProperties(propsRendering);
        colSpecCreator.setDomain(domain);
        newCols[i] = colSpecCreator.createSpec();
    }
    // add the prediction column
    String predictionColumnName = computePredictionColumnName(predictionCol, shouldOverride, trainingColumnName);
    newCols[numCols - 1] = new DataColumnSpecCreator(predictionColumnName, StringCell.TYPE).createSpec();
    DataTableSpec newColSpec = new DataTableSpec(newCols);
    return new DataTableSpec(inSpec, newColSpec);
}