@Override public Double summarize(NumericColumn<?> column) { return StatUtils.max(removeMissing(column)); } };
@Override public Double summarize(NumericColumn<?> column) { double[] data = removeMissing(column); return StatUtils.max(data) - StatUtils.min(data); } };
@Override public Double summarize(NumericColumn<?> data) { return StatUtils.max(data.asDoubleArray()) + 1000; } };
@Override public double reduce(double[] data) { return StatUtils.max(data); } };
@Override public Double summarize(NumericColumn<?> column) { return StatUtils.max(removeMissing(column)); } };
@Override public Double summarize(NumericColumn<?> column) { double[] data = removeMissing(column); return StatUtils.max(data) - StatUtils.min(data); } };
@Override public double reduce(double[] data) { return StatUtils.max(data) - StatUtils.min(data); } };
validateFit(); values = fCounts; max = StatUtils.max(values); } else { max = getMaxCount(fittedData);
private void setLUT(final ArrayList<Roi> rois, final String property, final ColorTable ct, final int alpha) throws IllegalArgumentException { String fProperty = COUNT; if (property != null && property.toLowerCase().startsWith("radi")) // radi[i|us] fProperty = RADIUS; final double[] mappingValues = (RADIUS.equals(fProperty)) ? profile.radiiAsArray() : profile.countsAsArray(); final double min = StatUtils.min(mappingValues); final double max = StatUtils.max(mappingValues); for (final Roi roi : rois) { final double value = Double.parseDouble(roi.getProperty(fProperty)); final int idx = (int) Math.round((ct.getLength() - 1) * (value - min) / (max - min)); final Color color = new Color(ct.get(ColorTable.RED, idx), ct.get(ColorTable.GREEN, idx), ct.get(ColorTable.BLUE, idx), alpha); roi.setStrokeColor(color); } }
log.info("Normalising percentiles of {} over {} subjects", singleValueField.getLabel(), percentileSubjects.size()); log.info("Min value: {}", StatUtils.min(values)); log.info("Max value: {}", StatUtils.max(values)); log.info("Median: {}", StatUtils.mean(values)); log.info("Mean: {}", percentile.evaluate(50d));
@Test public void acceptReverseMinMax() { final Random random = RandomRegistry.getRandom(); final double[] numbers = random.doubles().limit(1000).toArray(); final MinMax<Double> minMax = MinMax.of((a, b) -> b.compareTo(a)); Arrays.stream(numbers) .mapToObj(Double::valueOf) .forEach(minMax); Assert.assertEquals(minMax.getMin(), StatUtils.max(numbers)); Assert.assertEquals(minMax.getMax(), StatUtils.min(numbers)); }
@Test public void toMinMaxReverse() { final Random random = RandomRegistry.getRandom(); final double[] numbers = random.doubles().limit(1000).toArray(); final MinMax<Double> minMax = Arrays.stream(numbers) .mapToObj(Double::valueOf) .collect(MinMax.toMinMax((a, b) -> b.compareTo(a))); Assert.assertEquals(minMax.getMin(), StatUtils.max(numbers)); Assert.assertEquals(minMax.getMax(), StatUtils.min(numbers)); }
@Test public void toMinMaxNormal() { final Random random = RandomRegistry.getRandom(); final double[] numbers = random.doubles().limit(1000).toArray(); final MinMax<Double> minMax = Arrays.stream(numbers) .mapToObj(Double::valueOf) .collect(MinMax.toMinMax()); Assert.assertEquals(minMax.getMin(), StatUtils.min(numbers)); Assert.assertEquals(minMax.getMax(), StatUtils.max(numbers)); }
@Test public void acceptNormalMinMax() { final Random random = RandomRegistry.getRandom(); final double[] numbers = random.doubles().limit(1000).toArray(); final MinMax<Double> minMax = MinMax.of(); Arrays.stream(numbers) .mapToObj(Double::valueOf) .forEach(minMax); Assert.assertEquals(minMax.getMin(), StatUtils.min(numbers)); Assert.assertEquals(minMax.getMax(), StatUtils.max(numbers)); }
private void summarizeSkeleton(final SkeletonResult sr) { final String TABLE_TITLE = "Summary of Rendered Paths"; final ResultsTable rt = getTable(TABLE_TITLE); try { double sumLength = 0d; final int[] branches = sr.getBranches(); final double[] avgLengths = sr.getAverageBranchLength(); for (int i = 0; i < sr.getNumOfTrees(); i++) sumLength += avgLengths[i] * branches[i]; rt.incrementCounter(); rt.addValue("N. Rendered Paths", renderingPaths.size()); rt.addValue("Unit", imp.getCalibration().getUnits()); rt.addValue("Total length", sumLength); rt.addValue("Mean branch length", StatUtils.mean(avgLengths)); rt.addValue("Length of longest branch", StatUtils.max(sr.getMaximumBranchLength())); rt.addValue("# Branches", IntStream.of(sr.getBranches()).sum()); rt.addValue("# Junctions", IntStream.of(sr.getJunctions()).sum()); rt.addValue("# End-points", IntStream.of(sr.getEndPoints()).sum()); rt.addValue("Fitering", getFilterString()); if (restrictByRoi && roi != null && roi.isArea()) rt.addValue("ROI Name", roi.getName() == null ? "Unammed ROI" : roi.getName()); } catch (final Exception ignored) { SNT.error("Some statistics could not be calculated."); } finally { rt.show(TABLE_TITLE); } }
return StatUtils.geometricMean(aggregationValues); case MAX: return StatUtils.max(aggregationValues); case MEAN: return StatUtils.mean(aggregationValues);
return StatUtils.geometricMean(aggregationValues); case MAX: return StatUtils.max(aggregationValues); case MEAN: return StatUtils.mean(aggregationValues);