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

Example 11 with DoubleValue

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

the class StatisticsTable method calculateAllMoments.

/**
 * Calculates <b>all the statistical moments in one pass </b>. After the
 * call of this operation, the statistical moments can be obtained very fast
 * from all the other methods.
 *
 * @param rowCount Row count of table for progress, may be NaN if unknown.
 * @param exec object to check with if user canceled the operation
 * @throws CanceledExecutionException if user canceled
 * @throws IllegalArgumentException if rowCount argument < 0
 */
protected void calculateAllMoments(final double rowCount, final ExecutionMonitor exec) throws CanceledExecutionException {
    if (rowCount < 0.0) {
        throw new IllegalArgumentException("rowCount argument must not < 0: " + rowCount);
    }
    DataTableSpec origSpec = m_table.getDataTableSpec();
    int numOfCols = origSpec.getNumColumns();
    // the number of non-missing cells in each column
    int[] validCount = new int[numOfCols];
    double[] sumsquare = new double[numOfCols];
    final DataValueComparator[] comp = new DataValueComparator[numOfCols];
    for (int i = 0; i < numOfCols; i++) {
        sumsquare[i] = 0.0;
        validCount[i] = 0;
        comp[i] = origSpec.getColumnSpec(i).getType().getComparator();
        assert comp[i] != null;
    }
    int nrRows = 0;
    for (RowIterator rowIt = m_table.iterator(); rowIt.hasNext(); nrRows++) {
        DataRow row = rowIt.next();
        if (exec != null) {
            double prog = Double.isNaN(rowCount) ? 0.0 : nrRows / rowCount;
            exec.setProgress(prog, "Calculating statistics, processing row " + (nrRows + 1) + " (\"" + row.getKey() + "\")");
            // throws exception if user canceled
            exec.checkCanceled();
        }
        for (int c = 0; c < numOfCols; c++) {
            final DataCell cell = row.getCell(c);
            if (!(cell.isMissing())) {
                // keep the min and max for each column
                if ((m_minValues[c] == null) || (comp[c].compare(cell, m_minValues[c]) < 0)) {
                    m_minValues[c] = cell;
                }
                if ((m_maxValues[c] == null) || (comp[c].compare(m_maxValues[c], cell) < 0)) {
                    m_maxValues[c] = cell;
                }
                // for double columns we calc the sum (for the mean calc)
                DataType type = origSpec.getColumnSpec(c).getType();
                if (type.isCompatible(DoubleValue.class)) {
                    double d = ((DoubleValue) cell).getDoubleValue();
                    if (Double.isNaN(m_sum[c])) {
                        m_sum[c] = d;
                    } else {
                        m_sum[c] += d;
                    }
                    sumsquare[c] += d * d;
                    validCount[c]++;
                }
            } else {
                m_missingValueCnt[c]++;
            }
        }
        calculateMomentInSubClass(row);
    }
    m_nrRows = nrRows;
    for (int j = 0; j < numOfCols; j++) {
        // missing values
        if (validCount[j] == 0 || m_minValues[j] == null) {
            DataCell mc = DataType.getMissingCell();
            m_minValues[j] = mc;
            m_maxValues[j] = mc;
            m_meanValues[j] = Double.NaN;
            m_varianceValues[j] = Double.NaN;
        } else {
            m_meanValues[j] = m_sum[j] / validCount[j];
            if (validCount[j] > 1) {
                m_varianceValues[j] = (sumsquare[j] - ((m_sum[j] * m_sum[j]) / validCount[j])) / (validCount[j] - 1);
            } else {
                m_varianceValues[j] = 0.0;
            }
            // round-off errors resulting in negative variance values
            if (m_varianceValues[j] < 0.0 && m_varianceValues[j] > -1.0E8) {
                m_varianceValues[j] = 0.0;
            }
            assert m_varianceValues[j] >= 0.0 : "Variance cannot be negative (column \"" + origSpec.getColumnSpec(j).getName() + "\": " + m_varianceValues[j];
        }
    }
    // compute resulting table spec
    int nrCols = m_table.getDataTableSpec().getNumColumns();
    DataColumnSpec[] cSpec = new DataColumnSpec[nrCols];
    for (int c = 0; c < nrCols; c++) {
        DataColumnSpec s = m_table.getDataTableSpec().getColumnSpec(c);
        // we create domains with our bounds.
        Set<DataCell> values = (s.getDomain() == null ? null : s.getDomain().getValues());
        DataColumnDomain newDomain = new DataColumnDomainCreator(values, (m_minValues[c] == null || m_minValues[c].isMissing()) ? null : m_minValues[c], (m_maxValues[c] == null || m_maxValues[c].isMissing()) ? null : m_maxValues[c]).createDomain();
        DataColumnSpecCreator creator = new DataColumnSpecCreator(s);
        creator.setDomain(newDomain);
        cSpec[c] = creator.createSpec();
    }
    m_tSpec = new DataTableSpec(cSpec);
}

Example 12 with DoubleValue

use of org.knime.core.data.DoubleValue 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 13 with DoubleValue

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

the class AutoBinner method calcDomainBoundsIfNeccessary.

/**
 * Determines the per column min/max values of the given data if not already
 * present in the domain.
 * @param data the data
 * @param exec the execution context
 * @param recalcValuesFor The columns
 * @return The data with extended domain information
 * @throws InvalidSettingsException
 * @throws CanceledExecutionException
 */
public BufferedDataTable calcDomainBoundsIfNeccessary(final BufferedDataTable data, final ExecutionContext exec, final List<String> recalcValuesFor) throws InvalidSettingsException, CanceledExecutionException {
    if (null == recalcValuesFor || recalcValuesFor.isEmpty()) {
        return data;
    }
    List<Integer> valuesI = new ArrayList<Integer>();
    for (String colName : recalcValuesFor) {
        DataColumnSpec colSpec = data.getDataTableSpec().getColumnSpec(colName);
        if (!colSpec.getType().isCompatible(DoubleValue.class)) {
            throw new InvalidSettingsException("Can only process numeric " + "data. The column \"" + colSpec.getName() + "\" is not numeric.");
        }
        if (recalcValuesFor.contains(colName) && !colSpec.getDomain().hasBounds()) {
            valuesI.add(data.getDataTableSpec().findColumnIndex(colName));
        }
    }
    if (valuesI.isEmpty()) {
        return data;
    }
    Map<Integer, Double> min = new HashMap<Integer, Double>();
    Map<Integer, Double> max = new HashMap<Integer, Double>();
    for (int col : valuesI) {
        min.put(col, Double.MAX_VALUE);
        max.put(col, Double.MIN_VALUE);
    }
    int c = 0;
    for (DataRow row : data) {
        c++;
        exec.checkCanceled();
        exec.setProgress(c / (double) data.getRowCount());
        for (int col : valuesI) {
            double val = ((DoubleValue) row.getCell(col)).getDoubleValue();
            if (min.get(col) > val) {
                min.put(col, val);
            }
            if (max.get(col) < val) {
                min.put(col, val);
            }
        }
    }
    List<DataColumnSpec> newColSpecList = new ArrayList<DataColumnSpec>();
    int cc = 0;
    for (DataColumnSpec columnSpec : data.getDataTableSpec()) {
        if (recalcValuesFor.contains(columnSpec.getName())) {
            DataColumnSpecCreator specCreator = new DataColumnSpecCreator(columnSpec);
            DataColumnDomainCreator domainCreator = new DataColumnDomainCreator(new DoubleCell(min.get(cc)), new DoubleCell(max.get(cc)));
            specCreator.setDomain(domainCreator.createDomain());
            DataColumnSpec newColSpec = specCreator.createSpec();
            newColSpecList.add(newColSpec);
        } else {
            newColSpecList.add(columnSpec);
        }
        cc++;
    }
    DataTableSpec spec = new DataTableSpec(newColSpecList.toArray(new DataColumnSpec[0]));
    BufferedDataTable newDataTable = exec.createSpecReplacerTable(data, spec);
    return newDataTable;
}

Example 14 with DoubleValue

use of org.knime.core.data.DoubleValue 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 15 with DoubleValue

use of org.knime.core.data.DoubleValue 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;
}