Examples with DataCell org.knime.core.data.DataCell used on opensource projects

Example 76 with DataCell

use of org.knime.core.data.DataCell 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()) {
            return new DataCell[] { DataType.getMissingCell() };
        }
        DoubleValue dv = (DoubleValue) row.getCell(m_colindices[i]);
        values.add(dv.getDoubleValue());
    }
    String classvalue = doPredict(values);
    return new DataCell[] { new StringCell(classvalue) };
}

Example 77 with DataCell

use of org.knime.core.data.DataCell in project knime-core by knime.

the class AutoBinner method createEdgesFromQuantiles.

private static double[] createEdgesFromQuantiles(final BufferedDataTable data, final ExecutionContext exec, final double[] sampleQuantiles) throws CanceledExecutionException {
    double[] edges = new double[sampleQuantiles.length];
    long n = data.size();
    long c = 0;
    int cc = 0;
    RowIterator iter = data.iterator();
    DataRow rowQ = null;
    DataRow rowQ1 = null;
    if (iter.hasNext()) {
        rowQ1 = iter.next();
        rowQ = rowQ1;
    }
    for (double p : sampleQuantiles) {
        double h = (n - 1) * p + 1;
        int q = (int) Math.floor(h);
        while ((1.0 == p || c < q) && iter.hasNext()) {
            rowQ = rowQ1;
            rowQ1 = iter.next();
            c++;
            exec.setProgress(c / (double) n);
            exec.checkCanceled();
        }
        rowQ = 1.0 != p ? rowQ : rowQ1;
        final DataCell xqCell = rowQ.getCell(0);
        final DataCell xq1Cell = rowQ1.getCell(0);
        // data first?)
        if (xqCell.isMissing() || xq1Cell.isMissing()) {
            throw new RuntimeException("Missing values not support for " + "quantile calculation (error in row \"" + rowQ1.getKey() + "\")");
        }
        // for quantile calculation see also
        // http://en.wikipedia.org/wiki/
        // Quantile#Estimating_the_quantiles_of_a_population.
        // this implements R-7
        double xq = ((DoubleValue) xqCell).getDoubleValue();
        double xq1 = ((DoubleValue) xq1Cell).getDoubleValue();
        double quantile = xq + (h - q) * (xq1 - xq);
        edges[cc] = quantile;
        cc++;
    }
    return edges;
}

Example 78 with DataCell

use of org.knime.core.data.DataCell in project knime-core by knime.

the class ColCombineNodeModel method createColumnRearranger.

private ColumnRearranger createColumnRearranger(final DataTableSpec spec) {
    ColumnRearranger result = new ColumnRearranger(spec);
    DataColumnSpec append = new DataColumnSpecCreator(m_newColName, StringCell.TYPE).createSpec();
    final int[] indices = new int[m_columns.length];
    List<String> colNames = Arrays.asList(m_columns);
    int j = 0;
    for (int k = 0; k < spec.getNumColumns(); k++) {
        DataColumnSpec cs = spec.getColumnSpec(k);
        if (colNames.contains(cs.getName())) {
            indices[j] = k;
            j++;
        }
    }
    // ", " -> ","
    // "  " -> "  " (do not let the resulting string be empty)
    // " bla bla " -> "bla bla"
    final String delimTrim = trimDelimString(m_delimString);
    result.append(new SingleCellFactory(append) {

        @Override
        public DataCell getCell(final DataRow row) {
            String[] cellContents = new String[indices.length];
            for (int i = 0; i < indices.length; i++) {
                DataCell c = row.getCell(indices[i]);
                String s = c instanceof StringValue ? ((StringValue) c).getStringValue() : c.toString();
                cellContents[i] = s;
            }
            return new StringCell(handleContent(cellContents, delimTrim));
        }
    });
    return result;
}

Example 79 with DataCell

use of org.knime.core.data.DataCell in project knime-core by knime.

the class AutoBinner method createEdgesFromQuantiles.

@SuppressWarnings("null")
private static double[] createEdgesFromQuantiles(final BufferedDataTable data, final ExecutionContext exec, final double[] sampleQuantiles) throws CanceledExecutionException {
    double[] edges = new double[sampleQuantiles.length];
    long n = data.size();
    long c = 0;
    int cc = 0;
    RowIterator iter = data.iterator();
    DataRow rowQ = null;
    DataRow rowQ1 = null;
    if (iter.hasNext()) {
        rowQ1 = iter.next();
        rowQ = rowQ1;
    }
    for (double p : sampleQuantiles) {
        double h = (n - 1) * p + 1;
        int q = (int) Math.floor(h);
        while ((1.0 == p || c < q) && iter.hasNext()) {
            rowQ = rowQ1;
            rowQ1 = iter.next();
            c++;
            exec.setProgress(c / (double) n);
            exec.checkCanceled();
        }
        rowQ = 1.0 != p ? rowQ : rowQ1;
        final DataCell xqCell = rowQ.getCell(0);
        final DataCell xq1Cell = rowQ1.getCell(0);
        // data first?)
        if (xqCell.isMissing() || xq1Cell.isMissing()) {
            throw new RuntimeException("Missing values not support for " + "quantile calculation (error in row \"" + rowQ1.getKey() + "\")");
        }
        // for quantile calculation see also
        // http://en.wikipedia.org/wiki/
        // Quantile#Estimating_the_quantiles_of_a_population.
        // this implements R-7
        double xq = ((DoubleValue) xqCell).getDoubleValue();
        double xq1 = ((DoubleValue) xq1Cell).getDoubleValue();
        double quantile = xq + (h - q) * (xq1 - xq);
        edges[cc] = quantile;
        cc++;
    }
    return edges;
}

Example 80 with DataCell

use of org.knime.core.data.DataCell in project knime-core by knime.

the class TreeEnsembleClassificationPredictorCellFactory method getCells.

/**
 * {@inheritDoc}
 */
@Override
public DataCell[] getCells(final DataRow row) {
    TreeEnsembleModelPortObject modelObject = m_predictor.getModelObject();
    TreeEnsemblePredictorConfiguration cfg = m_predictor.getConfiguration();
    final TreeEnsembleModel ensembleModel = modelObject.getEnsembleModel();
    int size = 1;
    final boolean appendConfidence = cfg.isAppendPredictionConfidence();
    if (appendConfidence) {
        size += 1;
    }
    final boolean appendClassConfidences = cfg.isAppendClassConfidences();
    if (appendClassConfidences) {
        size += m_targetValueMap.size();
    }
    final boolean appendModelCount = cfg.isAppendModelCount();
    if (appendModelCount) {
        size += 1;
    }
    final boolean hasOutOfBagFilter = m_predictor.hasOutOfBagFilter();
    DataCell[] result = new DataCell[size];
    DataRow filterRow = new FilterColumnRow(row, m_learnColumnInRealDataIndices);
    PredictorRecord record = ensembleModel.createPredictorRecord(filterRow, m_learnSpec);
    if (record == null) {
        // missing value
        Arrays.fill(result, DataType.getMissingCell());
        return result;
    }
    final Voting voting = m_votingFactory.createVoting();
    final int nrModels = ensembleModel.getNrModels();
    int nrValidModels = 0;
    for (int i = 0; i < nrModels; i++) {
        if (hasOutOfBagFilter && m_predictor.isRowPartOfTrainingData(row.getKey(), i)) {
        // ignore, row was used to train the model
        } else {
            TreeModelClassification m = ensembleModel.getTreeModelClassification(i);
            TreeNodeClassification match = m.findMatchingNode(record);
            voting.addVote(match);
            nrValidModels += 1;
        }
    }
    final NominalValueRepresentation[] targetVals = ((TreeTargetNominalColumnMetaData) ensembleModel.getMetaData().getTargetMetaData()).getValues();
    String majorityClass = voting.getMajorityClass();
    int index = 0;
    if (majorityClass == null) {
        assert nrValidModels == 0;
        Arrays.fill(result, DataType.getMissingCell());
        index = size - 1;
    } else {
        result[index++] = m_targetValueMap.get(majorityClass);
        // final float[] distribution = voting.getClassProbabilities();
        if (appendConfidence) {
            result[index++] = new DoubleCell(voting.getClassProbabilityForClass(majorityClass));
        }
        if (appendClassConfidences) {
            for (String targetValue : m_targetValueMap.keySet()) {
                result[index++] = new DoubleCell(voting.getClassProbabilityForClass(targetValue));
            }
        }
    }
    if (appendModelCount) {
        result[index++] = new IntCell(voting.getNrVotes());
    }
    return result;
}