List of usage examples for org.apache.commons.math3.stat.descriptive DescriptiveStatistics getN
public long getN()
From source file:org.asoem.test.Statistics.java
/** * Assert that the mean of the measurements of the {@code treatment} are significantly less high than those of the * {@code control}.//from w ww .j av a2s . co m * * @param control the measurements of the control * @param treatment the measurements of the treatment * @param alpha the required significance level (like {@link org.asoem.greyfish.utils.math.SignificanceLevel#getAlpha()}) */ public static void assertSignificantDecrease(final DescriptiveStatistics control, final DescriptiveStatistics treatment, final double alpha) { // Is it faster? logger.info("Treatment vs. Control: {}, {}", treatment, control); assertThat("The mean of the treatment " + "is not less than the mean of the control", treatment.getMean(), is(lessThan(control.getMean()))); // Is it also significantly faster? Make a t-test. // Test assumptions for t-test: normality assertThat("The treatment is not normal distributed", StatisticalTests.shapiroWilk(treatment.getValues()).p(), is(lessThan(alpha))); assertThat("The control is not normal distributed", StatisticalTests.shapiroWilk(control.getValues()).p(), is(lessThan(alpha))); // Perform the t-test final double t = new TTest().t(treatment, control); final double p = new TTest().tTest(treatment, control); logger.info("t-test: t={}, p={}", t, p); double qt = new TDistribution(treatment.getN() - 1 + control.getN() - 1) .inverseCumulativeProbability(1 - alpha / 2); assertThat("The means are not significantly different", Math.abs(t), is(greaterThan(qt))); }
From source file:org.bml.util.rt.telemetry.SecondBasedCounterTest.java
/** * Test of getLastMinutesTelemetry method, of class SecondBasedCounter. */// ww w . java 2 s . c om public void testGetLastMinutesTelemetry() { System.out.println("getLastMinutesTelemetry"); DescriptiveStatistics stats = counter.getLastMinutesTelemetry(); long max = stats.getN(); AtomicInteger[] counterArray = counter.getCounterArray(); for (int index = 0; index < max; index++) { System.out.println(stats.getElement(index) + " -- " + counterArray[index].get()); } }
From source file:org.hawkular.client.test.metrics.openshift.CollectionRateDetailTest.java
private void getData(String metricID, String testID, long start, long end, Duration timeBucket) { Reporter.log("Fetching large data set... may take a couple minutes", true); List<DataPoint<Double>> rawData = client().metrics().gauge() .findGaugeDataWithId(metricID, String.valueOf(start), String.valueOf(end), null, null, null) .getEntity();/*from w ww .j av a 2s . c om*/ Assert.assertNotNull(rawData, testID); Reporter.log("raw datapoints: " + rawData.size(), true); List<Long> zeroList = findZeroValues(rawData); Assert.assertTrue(zeroList == null || zeroList.size() == 0, testID); Map<Long, Integer> hist = OpenshiftBaseTest.makeHistogram(rawData, timeBucket); Double[] result = hist.entrySet().stream().map(x -> new Double(x.getValue())) .toArray(size -> new Double[size]); double[] d = ArrayUtils.toPrimitive(result); // drop the first and last as they are usually outliers double[] samples = Arrays.copyOfRange(d, 1, d.length - 1); DescriptiveStatistics stats = new DescriptiveStatistics(samples); Reporter.log(hist.toString(), true); Reporter.log("size: " + stats.getN(), true); Reporter.log("min/max: " + stats.getMin() + "/" + stats.getMax(), true); Reporter.log("mean: " + stats.getMean(), true); Reporter.log("variance: " + stats.getVariance(), true); Reporter.log("stddev: " + stats.getStandardDeviation(), true); }
From source file:org.lightjason.agentspeak.action.buildin.math.statistic.EStatisticValue.java
/** * returns a statistic value//from w w w . ja va 2 s. c om * * @param p_statistic statistic object * @return statistic value */ public final double value(final DescriptiveStatistics p_statistic) { switch (this) { case GEOMETRICMEAN: return p_statistic.getGeometricMean(); case MAX: return p_statistic.getMax(); case MIN: return p_statistic.getMin(); case COUNT: return p_statistic.getN(); case POPULATIONVARIANCE: return p_statistic.getPopulationVariance(); case QUADRATICMEAN: return p_statistic.getQuadraticMean(); case STANDARDDEVIATION: return p_statistic.getStandardDeviation(); case SUM: return p_statistic.getSum(); case SUMSQUARE: return p_statistic.getSumsq(); case VARIANCE: return p_statistic.getVariance(); case MEAN: return p_statistic.getMean(); case KURTIOSIS: return p_statistic.getKurtosis(); default: throw new CIllegalStateException( org.lightjason.agentspeak.common.CCommon.languagestring(this, "unknown", this)); } }
From source file:org.lightjason.agentspeak.action.builtin.math.statistic.EStatisticValue.java
/** * returns a statistic value// w w w .j a va2s .c o m * * @param p_statistic statistic object * @return statistic value */ public final double value(@Nonnull final DescriptiveStatistics p_statistic) { switch (this) { case GEOMETRICMEAN: return p_statistic.getGeometricMean(); case MAX: return p_statistic.getMax(); case MIN: return p_statistic.getMin(); case COUNT: return p_statistic.getN(); case POPULATIONVARIANCE: return p_statistic.getPopulationVariance(); case QUADRATICMEAN: return p_statistic.getQuadraticMean(); case STANDARDDEVIATION: return p_statistic.getStandardDeviation(); case SUM: return p_statistic.getSum(); case SUMSQUARE: return p_statistic.getSumsq(); case VARIANCE: return p_statistic.getVariance(); case MEAN: return p_statistic.getMean(); case KURTIOSIS: return p_statistic.getKurtosis(); default: throw new CIllegalStateException( org.lightjason.agentspeak.common.CCommon.languagestring(this, "unknown", this)); } }
From source file:org.matsim.contrib.drt.analysis.DynModeTripsAnalyser.java
public static void analyseWaitTimes(String fileName, List<DynModeTrip> trips, int binsize_s) { Collections.sort(trips);/*from w w w . j ava2 s . co m*/ if (trips.size() == 0) return; int startTime = ((int) (trips.get(0).getDepartureTime() / binsize_s)) * binsize_s; int endTime = ((int) (trips.get(trips.size() - 1).getDepartureTime() / binsize_s) + binsize_s) * binsize_s; Map<Double, List<DynModeTrip>> splitTrips = splitTripsIntoBins(trips, startTime, endTime, binsize_s); DecimalFormat format = new DecimalFormat(); format.setDecimalFormatSymbols(new DecimalFormatSymbols(Locale.US)); format.setMinimumIntegerDigits(1); format.setMaximumFractionDigits(2); format.setGroupingUsed(false); SimpleDateFormat sdf2 = new SimpleDateFormat("HH:mm:ss"); BufferedWriter bw = IOUtils.getBufferedWriter(fileName + ".csv"); TimeSeriesCollection dataset = new TimeSeriesCollection(); TimeSeriesCollection datasetrequ = new TimeSeriesCollection(); TimeSeries averageWaitC = new TimeSeries("average"); TimeSeries medianWait = new TimeSeries("median"); TimeSeries p_5Wait = new TimeSeries("5th percentile"); TimeSeries p_95Wait = new TimeSeries("95th percentile"); TimeSeries requests = new TimeSeries("Ride requests"); try { bw.write("timebin;trips;average_wait;min;p_5;p_25;median;p_75;p_95;max"); for (Entry<Double, List<DynModeTrip>> e : splitTrips.entrySet()) { long rides = 0; double averageWait = 0; double min = 0; double p_5 = 0; double p_25 = 0; double median = 0; double p_75 = 0; double p_95 = 0; double max = 0; if (!e.getValue().isEmpty()) { DescriptiveStatistics stats = new DescriptiveStatistics(); for (DynModeTrip t : e.getValue()) { stats.addValue(t.getWaitTime()); } rides = stats.getN(); averageWait = stats.getMean(); min = stats.getMin(); p_5 = stats.getPercentile(5); p_25 = stats.getPercentile(25); median = stats.getPercentile(50); p_75 = stats.getPercentile(75); p_95 = stats.getPercentile(95); max = stats.getMax(); } Minute h = new Minute(sdf2.parse(Time.writeTime(e.getKey()))); medianWait.addOrUpdate(h, Double.valueOf(median)); averageWaitC.addOrUpdate(h, Double.valueOf(averageWait)); p_5Wait.addOrUpdate(h, Double.valueOf(p_5)); p_95Wait.addOrUpdate(h, Double.valueOf(p_95)); requests.addOrUpdate(h, rides * 3600. / binsize_s);// normalised [req/h] bw.newLine(); bw.write(Time.writeTime(e.getKey()) + ";" + rides + ";" + format.format(averageWait) + ";" + format.format(min) + ";" + format.format(p_5) + ";" + format.format(p_25) + ";" + format.format(median) + ";" + format.format(p_75) + ";" + format.format(p_95) + ";" + format.format(max)); } bw.flush(); bw.close(); dataset.addSeries(averageWaitC); dataset.addSeries(medianWait); dataset.addSeries(p_5Wait); dataset.addSeries(p_95Wait); datasetrequ.addSeries(requests); JFreeChart chart = chartProfile(splitTrips.size(), dataset, "Waiting times", "Wait time (s)"); JFreeChart chart2 = chartProfile(splitTrips.size(), datasetrequ, "Ride requests per hour", "Requests per hour (req/h)"); ChartSaveUtils.saveAsPNG(chart, fileName, 1500, 1000); ChartSaveUtils.saveAsPNG(chart2, fileName + "_requests", 1500, 1000); } catch (IOException | ParseException e) { e.printStackTrace(); } }
From source file:org.obiba.magma.math.summary.ContinuousVariableSummaryTest.java
@Test public void test_compute_integerType() { Variable mockVariable = Variable.Builder.newVariable("mock", IntegerType.get(), "mock").build(); ContinuousVariableSummary summary = computeFromTable(mockVariable, Values.asValues(IntegerType.get(), 1, 2, 3)); DescriptiveStatistics descriptiveStats = summary.getDescriptiveStats(); assertThat(descriptiveStats.getMin()).isEqualTo(1.0); assertThat(descriptiveStats.getMax()).isEqualTo(3.0); assertThat(descriptiveStats.getMean()).isEqualTo(2.0); assertThat(descriptiveStats.getN()).isEqualTo(3l); }
From source file:org.obiba.magma.math.summary.ContinuousVariableSummaryTest.java
@Test public void test_compute_integerTypeWithNull() { Variable mockVariable = Variable.Builder.newVariable("mock", IntegerType.get(), "mock").build(); ContinuousVariableSummary summary = computeFromTable(mockVariable, Values.asValues(IntegerType.get(), 1, 2, 3, null, null)); DescriptiveStatistics descriptiveStats = summary.getDescriptiveStats(); assertThat(descriptiveStats.getMin()).isEqualTo(1.0); assertThat(descriptiveStats.getMax()).isEqualTo(3.0); assertThat(descriptiveStats.getMean()).isEqualTo(2.0); assertThat(descriptiveStats.getN()).isEqualTo(3l); }
From source file:org.obiba.magma.math.summary.ContinuousVariableSummaryTest.java
@Test public void test_compute_integerTypeMissingCategories() { Variable mockVariable = Variable.Builder.newVariable("mock", IntegerType.get(), "mock") .addCategory("888", "", true).addCategory("999", "", true).build(); ContinuousVariableSummary summary = computeFromTable(mockVariable, Values.asValues(IntegerType.get(), 1, 2, 3, 888, 999)); DescriptiveStatistics descriptiveStats = summary.getDescriptiveStats(); assertThat(descriptiveStats.getMin()).isEqualTo(1.0); assertThat(descriptiveStats.getMax()).isEqualTo(3.0); assertThat(descriptiveStats.getMean()).isEqualTo(2.0); assertThat(descriptiveStats.getN()).isEqualTo(3l); }
From source file:org.obiba.opal.web.magma.Dtos.java
@SuppressWarnings({ "OverlyLongMethod", "PMD.NcssMethodCount" })
public static Math.ContinuousSummaryDto.Builder asDto(ContinuousVariableSummary summary) {
DescriptiveStatistics descriptiveStats = summary.getDescriptiveStats();
Math.DescriptiveStatsDto.Builder descriptiveBuilder = Math.DescriptiveStatsDto.newBuilder()
.setN(descriptiveStats.getN()).addAllPercentiles(summary.getPercentiles());
if (isNumeric(descriptiveStats.getMin()))
descriptiveBuilder.setMin(descriptiveStats.getMin());
if (isNumeric(descriptiveStats.getMax()))
descriptiveBuilder.setMax(descriptiveStats.getMax());
if (isNumeric(descriptiveStats.getMean()))
descriptiveBuilder.setMean(descriptiveStats.getMean());
if (isNumeric(descriptiveStats.getSum()))
descriptiveBuilder.setSum(descriptiveStats.getSum());
if (isNumeric(descriptiveStats.getSumsq()))
descriptiveBuilder.setSumsq(descriptiveStats.getSumsq());
if (isNumeric(descriptiveStats.getStandardDeviation())) {
descriptiveBuilder.setStdDev(descriptiveStats.getStandardDeviation());
}/* w w w . ja va 2 s . com*/
if (isNumeric(descriptiveStats.getVariance()))
descriptiveBuilder.setVariance(descriptiveStats.getVariance());
if (isNumeric(descriptiveStats.getSkewness()))
descriptiveBuilder.setSkewness(descriptiveStats.getSkewness());
if (isNumeric(descriptiveStats.getGeometricMean())) {
descriptiveBuilder.setGeometricMean(descriptiveStats.getGeometricMean());
}
if (isNumeric(descriptiveStats.getKurtosis()))
descriptiveBuilder.setKurtosis(descriptiveStats.getKurtosis());
double median = descriptiveStats.apply(new Median());
if (isNumeric(median))
descriptiveBuilder.setMedian(median);
if (isNumeric(descriptiveStats.getVariance()))
descriptiveBuilder.setVariance(descriptiveStats.getVariance());
Math.ContinuousSummaryDto.Builder continuousBuilder = Math.ContinuousSummaryDto.newBuilder()
.addAllDistributionPercentiles(summary.getDistributionPercentiles());
for (IntervalFrequency.Interval interval : summary.getIntervalFrequencies()) {
Math.IntervalFrequencyDto.Builder freqBuilder = Math.IntervalFrequencyDto.newBuilder()
.setFreq(interval.getFreq());
if (isNumeric(interval.getLower()))
freqBuilder.setLower(interval.getLower());
if (isNumeric(interval.getUpper()))
freqBuilder.setUpper(interval.getUpper());
if (isNumeric(interval.getDensity()))
freqBuilder.setDensity(interval.getDensity());
if (isNumeric(interval.getDensityPct()))
freqBuilder.setDensityPct(interval.getDensityPct());
continuousBuilder.addIntervalFrequency(freqBuilder);
}
for (ContinuousVariableSummary.Frequency frequency : summary.getFrequencies()) {
Math.FrequencyDto.Builder freqBuilder = Math.FrequencyDto.newBuilder() //
.setValue(frequency.getValue()) //
.setFreq(frequency.getFreq())//
.setMissing(frequency.isMissing());
if (isNumeric(frequency.getPct()))
freqBuilder.setPct(frequency.getPct());
continuousBuilder.addFrequencies(freqBuilder);
}
return continuousBuilder.setSummary(descriptiveBuilder);
}