@Override protected Serializable reduceValues(final List<Object> slaveValues, final String column, final String measure, final Collection<? extends NumberAnalyzerResult> results, final Class<?> valueClass) { if (SUM_MEASURES.contains(measure)) { return sum(slaveValues); } else if (NumberAnalyzer.MEASURE_HIGHEST_VALUE.equals(measure)) { return maximum(slaveValues); } else if (NumberAnalyzer.MEASURE_LOWEST_VALUE.equals(measure)) { return minimum(slaveValues); } else if (NumberAnalyzer.MEASURE_MEAN.equals(measure)) { final StatisticalSummary summary = getSummary(column, results); return summary.getMean(); } else if (NumberAnalyzer.MEASURE_STANDARD_DEVIATION.equals(measure)) { final StatisticalSummary summary = getSummary(column, results); return summary.getStandardDeviation(); } else if (NumberAnalyzer.MEASURE_VARIANCE.equals(measure)) { final StatisticalSummary summary = getSummary(column, results); return summary.getVariance(); } logger.warn("Encountered non-reduceable measure '{}'. Slave values are: {}", measure, slaveValues); return null; }
@Override protected Serializable reduceValues(List<Object> slaveValues, String column, String measure, Collection<? extends NumberAnalyzerResult> results, Class<?> valueClass) { if (SUM_MEASURES.contains(measure)) { return sum(slaveValues); } else if (NumberAnalyzer.MEASURE_HIGHEST_VALUE.equals(measure)) { return maximum(slaveValues); } else if (NumberAnalyzer.MEASURE_LOWEST_VALUE.equals(measure)) { return minimum(slaveValues); } else if (NumberAnalyzer.MEASURE_MEAN.equals(measure)) { StatisticalSummary summary = getSummary(column, results); return summary.getMean(); } else if (NumberAnalyzer.MEASURE_STANDARD_DEVIATION.equals(measure)) { StatisticalSummary summary = getSummary(column, results); return summary.getStandardDeviation(); } else if (NumberAnalyzer.MEASURE_VARIANCE.equals(measure)) { StatisticalSummary summary = getSummary(column, results); return summary.getVariance(); } logger.warn("Encountered non-reduceable measure '{}'. Slave values are: {}", measure, slaveValues); return null; }
/** * Computes the empirical distribution using values from the file * in <code>valuesFileURL</code> and <code>binCount</code> bins. * <p> * <code>valuesFileURL</code> must exist and be readable by this process * at runtime.</p> * <p> * This method must be called before using <code>getNext()</code> * with <code>mode = DIGEST_MODE</code></p> * * @param binCount the number of bins used in computing the empirical * distribution * @throws IOException if an error occurs reading the input file */ public void computeDistribution(int binCount) throws IOException { empiricalDistribution = new EmpiricalDistributionImpl(binCount); empiricalDistribution.load(valuesFileURL); mu = empiricalDistribution.getSampleStats().getMean(); sigma = empiricalDistribution.getSampleStats().getStandardDeviation(); }
/** * Computes the empirical distribution using values from the file * in <code>valuesFileURL</code> and <code>binCount</code> bins. * <p> * <code>valuesFileURL</code> must exist and be readable by this process * at runtime.</p> * <p> * This method must be called before using <code>getNext()</code> * with <code>mode = DIGEST_MODE</code></p> * * @param binCount the number of bins used in computing the empirical * distribution * @throws IOException if an error occurs reading the input file */ public void computeDistribution(int binCount) throws IOException { empiricalDistribution = new EmpiricalDistributionImpl(binCount); empiricalDistribution.load(valuesFileURL); mu = empiricalDistribution.getSampleStats().getMean(); sigma = empiricalDistribution.getSampleStats().getStandardDeviation(); }
/** * Computes the empirical distribution using values from the file * in <code>valuesFileURL</code> and <code>binCount</code> bins. * <p> * <code>valuesFileURL</code> must exist and be readable by this process * at runtime.</p> * <p> * This method must be called before using <code>getNext()</code> * with <code>mode = DIGEST_MODE</code></p> * * @param binCount the number of bins used in computing the empirical * distribution * @throws IOException if an error occurs reading the input file */ public void computeDistribution(int binCount) throws IOException { empiricalDistribution = new EmpiricalDistributionImpl(binCount); empiricalDistribution.load(valuesFileURL); mu = empiricalDistribution.getSampleStats().getMean(); sigma = empiricalDistribution.getSampleStats().getStandardDeviation(); }
final double sum = s.getSum(); final double mean = s.getMean(); final double standardDeviation = s.getStandardDeviation(); final double variance = s.getVariance();
final double sum = s.getSum(); final double mean = s.getMean(); final double standardDeviation = s.getStandardDeviation(); final double variance = s.getVariance();