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

Example 41 with DataCell

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

the class OneWayANOVANodeModel method getGroups.

private List<String> getGroups(final BufferedDataTable inData, final ExecutionMonitor exec) throws InvalidSettingsException, CanceledExecutionException {
    DataTableSpec spec = inData.getSpec();
    int gIndex = spec.findColumnIndex(m_settings.getGroupingColumn());
    LinkedHashSet<String> groups = new LinkedHashSet<String>();
    if (gIndex < 0) {
        throw new InvalidSettingsException("Grouping column not found.");
    }
    final int rowCount = inData.getRowCount();
    int rowIndex = 0;
    for (DataRow row : inData) {
        exec.checkCanceled();
        exec.setProgress(rowIndex++ / (double) rowCount, rowIndex + "/" + rowCount + " (\"" + row.getKey() + "\")");
        DataCell group = row.getCell(gIndex);
        if (!group.isMissing()) {
            groups.add(group.toString());
        }
    }
    return Arrays.asList(groups.toArray(new String[groups.size()]));
}

Example 42 with DataCell

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

the class LeveneTestStatistics method getLeveneTestTwoGroupsCells.

/**
 * Get the test result of the Levene test. This is an optimized version for
 * two groups.
 * @return the Levene test
 */
public List<List<DataCell>> getLeveneTestTwoGroupsCells() {
    SummaryStatistics statsX = m_denStats.get(0);
    SummaryStatistics statsY = m_denStats.get(1);
    // overall sample mean
    double m = m_lstats.getMean();
    // first sample mean
    double m1 = statsX.getMean();
    // second sample mean
    double m2 = statsY.getMean();
    // first sample variance
    double v1 = statsX.getVariance();
    // second sample variance
    double v2 = statsY.getVariance();
    // first sample count
    double n1 = statsX.getN();
    // second sample count
    double n2 = statsY.getN();
    // Levene's test
    double num = n1 * (m1 - m) * (m1 - m) + n2 * (m2 - m) * (m2 - m);
    double den = (n1 - 1) * v1 + (n2 - 1) * v2;
    double L = (n1 + n2 - 2) / den * num;
    long df1 = 1;
    long df2 = (long) n1 + (long) n2 - 2;
    FDistribution distribution = new FDistribution(df1, df2);
    double pValue = 1 - distribution.cumulativeProbability(L);
    List<DataCell> cells = new ArrayList<DataCell>();
    cells.add(new StringCell(m_column));
    cells.add(new DoubleCell(L));
    cells.add(new IntCell((int) df1));
    cells.add(new IntCell((int) df2));
    cells.add(new DoubleCell(pValue));
    return Collections.singletonList(cells);
}

Example 43 with DataCell

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

the class LeveneTestStatistics method getTTestTable.

/**
 * Get the test result of the t-test, for the assumption of equal
 * variance and the assumption of unequal variances.
 * @param exec the execution context
 * @return the t-test results
 */
public BufferedDataTable getTTestTable(final ExecutionContext exec) {
    DataTableSpec outSpec = getTableSpec();
    BufferedDataContainer cont = exec.createDataContainer(outSpec);
    int r = 0;
    for (List<DataCell> cells : getTTestCells()) {
        cont.addRowToTable(new DefaultRow(RowKey.createRowKey(r), cells));
        r++;
    }
    cont.close();
    BufferedDataTable outTable = cont.getTable();
    return outTable;
}

Example 44 with DataCell

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

the class NodeViewUtil method renderDataTable.

/**
 * Create HTML from the given table using column rowHeader as row headers.
 * @param table the table
 * @param rowHeader the column with row headers
 * @param exclude columns to exclude
 * @param colHeaders override column headers
 * @param buffer append to this buffer
 */
public static void renderDataTable(final BufferedDataTable table, final String rowHeader, final Collection<String> exclude, final Map<String, String> colHeaders, final StringBuilder buffer) {
    int rowHeaderI = table.getDataTableSpec().findColumnIndex(rowHeader);
    Set<Integer> excludeI = new HashSet<Integer>();
    for (String toExclude : exclude) {
        excludeI.add(table.getDataTableSpec().findColumnIndex(toExclude));
    }
    buffer.append("<table>\n");
    buffer.append("<tr>");
    buffer.append("<th class=\"left\"></th>");
    for (DataColumnSpec colSpec : table.getDataTableSpec()) {
        String colName = colSpec.getName();
        if (!exclude.contains(colName)) {
            String value = colHeaders.containsKey(colName) ? colHeaders.get(colName) : colName;
            buffer.append("<th>");
            buffer.append(escapeHtml(value));
            buffer.append("</th>");
        }
    }
    buffer.append("</tr>");
    int r = 0;
    for (Iterator<DataRow> it = table.iteratorFailProve(); it.hasNext(); ) {
        DataRow row = it.next();
        buffer.append("<tr class=\"");
        buffer.append(r % 2 == 0 ? "odd" : "even");
        buffer.append("\">");
        renderDataCell(row.getCell(rowHeaderI), buffer);
        for (int i = 0; i < row.getNumCells(); i++) {
            if (excludeI.contains(i)) {
                continue;
            }
            DataCell cell = row.getCell(i);
            renderDataCell(cell, buffer);
        }
        buffer.append("</tr>");
        r++;
    }
    buffer.append("</table>\n");
}

Example 45 with DataCell

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

the class OneWayANOVAStatistics method getGroupStatistics.

/**
 * Get descriptive statistics for the given Group.
 * @param groupIndex the index of the group
 * @return the descriptive statistics for this group.
 */
public List<DataCell> getGroupStatistics(final int groupIndex) {
    List<DataCell> cells = new ArrayList<DataCell>();
    cells.add(new StringCell(m_column));
    cells.add(new StringCell(m_groups.get(groupIndex)));
    SummaryStatistics stats = m_gstats.get(groupIndex);
    cells.add(new IntCell((int) stats.getN()));
    cells.add(new IntCell(m_missing.get(groupIndex).intValue()));
    cells.add(new IntCell(m_missingGroup.intValue()));
    cells.add(new DoubleCell(stats.getMean()));
    cells.add(new DoubleCell(stats.getStandardDeviation()));
    cells.add(new DoubleCell(StatsUtil.getStandardError(stats)));
    cells.add(new DoubleCell(m_confidenceIntervalProp));
    long df = stats.getN() - 1;
    TDistribution distribution = new TDistribution(df);
    double tValue = FastMath.abs(distribution.inverseCumulativeProbability((1 - m_confidenceIntervalProp) / 2));
    double confidenceDelta = tValue * StatsUtil.getStandardError(stats);
    double confidenceLowerBound = stats.getMean() - confidenceDelta;
    double confidenceUpperBound = stats.getMean() + confidenceDelta;
    cells.add(new DoubleCell(confidenceLowerBound));
    cells.add(new DoubleCell(confidenceUpperBound));
    cells.add(new DoubleCell(stats.getMin()));
    cells.add(new DoubleCell(stats.getMax()));
    return cells;
}