/** * Get median value for given statistics. * @param data collected datapoints. * @return median value. */ private static double median(@NotNull DescriptiveStatistics data) { return new Median().evaluate(data.getValues()); }
public Stat(double[] values) { mean = new Mean().evaluate(values); standardDeviation = new StandardDeviation().evaluate(values); median = new Median().evaluate(values); }
/** * Calculates Transit center from median of coordinates of all transitStops if graph * has transit. If it doesn't it isn't calculated. (mean walue of min, max latitude and longitudes are used) * * Transit center is saved in center variable * * This speeds up calculation, but problem is that median needs to have all of latitudes/longitudes * in memory, this can become problematic in large installations. It works without a problem on New York State. */ public void calculateTransitCenter() { if (hasTransit) { TDoubleList latitudes = new TDoubleLinkedList(); TDoubleList longitudes = new TDoubleLinkedList(); Median median = new Median(); getVertices().stream() .filter(v -> v instanceof TransitStop) .forEach(v -> { latitudes.add(v.getLat()); longitudes.add(v.getLon()); }); median.setData(latitudes.toArray()); double medianLatitude = median.evaluate(); median = new Median(); median.setData(longitudes.toArray()); double medianLongitude = median.evaluate(); this.center = new Coordinate(medianLongitude, medianLatitude); } }
public MetaScores(List<Double> scores) { // A meta alert could be entirely alerts with no values. DoubleSummaryStatistics stats = scores .stream() .mapToDouble(a -> a) .summaryStatistics(); metaScores.put("max", stats.getMax()); metaScores.put("min", stats.getMin()); metaScores.put("average", stats.getAverage()); metaScores.put("count", stats.getCount()); metaScores.put("sum", stats.getSum()); // median isn't in the stats summary double[] arr = scores .stream() .mapToDouble(d -> d) .toArray(); metaScores.put("median", new Median().evaluate(arr)); }
/** * Gets the median value of all elements in this vector. * * @return The median value of all elements in this vector. */ public double median() { final Median median = new Median(); return median.evaluate(this.vector); }
import org.apache.commons.math3.*; ..... ...... ........ //calculate median public double getMedian(double[] values){ Median median = new Median(); double medianValue = median.evaluate(values); return medianValue; } .......
import org.apache.commons.math3.*; ..... ...... ........ //calculate median public double getMedian(double[] values){ Median median = new Median(); double medianValue = median.evaluate(values); return medianValue; } .......
/** * Gets the median value of all elements in this vector. * * @return The median value of all elements in this vector. */ public double median() { final Median median = new Median(); return median.evaluate(this.vector); }
Median median = new Median(); median.evaluate(values);
/** * * @param offsets a list of integers * @param median median of the list offsets. * @return median absolute deviation (median of the list of deviations from the median) */ private double calculateMAD(final List<Integer> offsets, final double median) { // This code is concise but somehow leads to ClusteredReadPosition class being removed from ClassMap. // mapToDouble() seems to be the trigger // return new Median().evaluate(offsets.stream().mapToDouble(x -> Math.abs(x - median)).toArray()); double[] medianAbsoluteDeviations = new double[offsets.size()]; for (int i = 0; i < offsets.size(); i++){ medianAbsoluteDeviations[i] = Math.abs(offsets.get(i) - median); } return new Median().evaluate(medianAbsoluteDeviations); } }
@Nonnull Double calculateMz() { if (mzValues.isEmpty()) throw new MSDKRuntimeException("Cannot calculate the m/z value of an empty chromatogram"); // Convert the m/z values to an array double mzDoubleValues[] = Doubles.toArray(mzValues); // Calculate the final m/z value as a median of all m/z values Median median = new Median(); double medianValue = median.evaluate(mzDoubleValues); return medianValue; }
@Nonnull Double calculateMz() { if (mzValues.isEmpty()) throw new MSDKRuntimeException("Cannot calculate the m/z value of an empty chromatogram"); // Convert the m/z values to an array double mzDoubleValues[] = Doubles.toArray(mzValues); // Calculate the final m/z value as a median of all m/z values Median median = new Median(); double medianValue = median.evaluate(mzDoubleValues); return medianValue; }
@Nonnull Double calculateMz() { if (mzValues.isEmpty()) throw new MSDKRuntimeException( "Cannot calculate the m/z value of an empty chromatogram"); // Convert the m/z values to an array double mzDoubleValues[] = Doubles.toArray(mzValues); // Calculate the final m/z value as a median of all m/z values Median median = new Median(); double medianValue = median.evaluate(mzDoubleValues); return medianValue; }
@Nonnull Double calculateMz() { if (mzValues.isEmpty()) throw new MSDKRuntimeException("Cannot calculate the m/z value of an empty chromatogram"); // Convert the m/z values to an array double mzDoubleValues[] = Doubles.toArray(mzValues); // Calculate the final m/z value as a median of all m/z values Median median = new Median(); double medianValue = median.evaluate(mzDoubleValues); return medianValue; }
private void print(int indent, String header, TimingResults timings) { String msg = String.format("" + "Mean: % 7.2f ms\n" + "Median: % 7.2f ms\n" + "StdDev: % 7.2f ms", timings.mean.evaluate() * 1000.0, timings.median.evaluate() * 1000.0, timings.stddev.evaluate() * 1000.0); System.out.println(StringTools.indent(header + ":", StringTools.strrep(' ', indent))); System.out.println(StringTools.indent(msg, StringTools.strrep(' ', indent + 2))); }
public static void main(String[] args) { final double[] values = new java.util.Random().doubles(5000).toArray(); final Percentile stat1 = new Percentile(0.5); final Median stat2 = new Median(); for (double value : values) stat1.add(value); final double result1 = stat1.getValue(); final double result2 = stat2.evaluate(values); if (result1 != result2) { throw new RuntimeException("Error: " + result1 + " != " + result2); } }
public Stat(double[] values) { mean = new Mean().evaluate(values); standardDeviation = new StandardDeviation().evaluate(values); median = new Median().evaluate(values); }
final double nonRefLikelihood = medianCalculator.evaluate(qualifiedAlleleLikelihoods, 0, numberOfQualifiedAlleleLikelihoods);
qualifylingLikelihoods.add(originalLikelihoods[s][a][r]); final double medianLikelihood = median.evaluate(qualifylingLikelihoods.stream().mapToDouble(d -> d).toArray());