List of usage examples for org.apache.commons.math3.stat.descriptive DescriptiveStatistics getN
public long getN()
From source file:com.intuit.tank.vm.common.util.ReportUtil.java
public static final String[] getSummaryData(String key, DescriptiveStatistics stats) { String[] ret = new String[ReportUtil.SUMMARY_HEADERS.length + PERCENTILES.length]; int i = 0;/*from ww w. ja v a2 s.c o m*/ ret[i++] = key;// Page ID ret[i++] = INT_NF.format(stats.getN());// Sample Size ret[i++] = DOUBLE_NF.format(stats.getMean());// Mean ret[i++] = INT_NF.format(stats.getPercentile(50));// Meadian ret[i++] = INT_NF.format(stats.getMin());// Min ret[i++] = INT_NF.format(stats.getMax());// Max ret[i++] = DOUBLE_NF.format(stats.getStandardDeviation());// Std Dev ret[i++] = DOUBLE_NF.format(stats.getKurtosis());// Kurtosis ret[i++] = DOUBLE_NF.format(stats.getSkewness());// Skewness ret[i++] = DOUBLE_NF.format(stats.getVariance());// Varience for (int n = 0; n < PERCENTILES.length; n++) { ret[i++] = INT_NF.format(stats.getPercentile((Integer) PERCENTILES[n][1]));// Percentiles } return ret; }
From source file:com.intuit.tank.service.impl.v1.report.SummaryReportRunner.java
/** * @param jobId/*from w ww. j a v a 2 s. c o m*/ * @param key * @param stats * @return */ private static PeriodicData getBucketData(int jobId, String key, BucketDataItem bucketItem) { DescriptiveStatistics stats = bucketItem.getStats(); PeriodicData ret = PeriodicDataBuilder.periodicData().withJobId(jobId).withMax(stats.getMax()) .withMean(stats.getMean()).withMin(stats.getMin()).withPageId(key) .withSampleSize((int) stats.getN()).withPeriod(bucketItem.getPeriod()) .withTimestamp(bucketItem.getStartTime()).build(); return ret; }
From source file:com.github.aptd.simulation.core.statistic.local.CStatistic.java
/** * write data/* w w w . j a v a2s.c o m*/ * * @param p_writer writer instance * @param p_name section name * @param p_statistic statistic value */ private static void apply(final IWriter p_writer, final String p_name, final DescriptiveStatistics p_statistic) { p_writer.section(1, p_name); p_writer.value("geometricmean", p_statistic.getGeometricMean()); p_writer.value("kurtosis", p_statistic.getKurtosis()); p_writer.value("max", p_statistic.getMax()); p_writer.value("min", p_statistic.getMin()); p_writer.value("mean", p_statistic.getMean()); p_writer.value("count", p_statistic.getN()); p_writer.value("25-percentile", p_statistic.getPercentile(0.25)); p_writer.value("75-percentile", p_statistic.getPercentile(0.75)); p_writer.value("populationvariance", p_statistic.getPopulationVariance()); p_writer.value("quadraticmean", p_statistic.getQuadraticMean()); p_writer.value("standdeviation", p_statistic.getStandardDeviation()); p_writer.value("skewness", p_statistic.getSkewness()); p_writer.value("sum", p_statistic.getSum()); p_writer.value("sumsequared", p_statistic.getSumsq()); p_writer.value("variance", p_statistic.getVariance()); }
From source file:com.intuit.tank.service.impl.v1.report.SummaryReportRunner.java
/** * @param key/*from www.ja v a 2 s .c om*/ * @param value * @return */ private static SummaryData getSummaryData(int jobId, String key, DescriptiveStatistics stats) { SummaryData ret = SummaryDataBuilder.summaryData().withJobId(jobId) .withKurtosis(!Double.isNaN(stats.getKurtosis()) ? stats.getKurtosis() : 0).withMax(stats.getMax()) .withMean(stats.getMean()).withMin(stats.getMin()).withPageId(key) .withPercentile10(stats.getPercentile(10)).withPercentile20(stats.getPercentile(20)) .withPercentile30(stats.getPercentile(30)).withPercentile40(stats.getPercentile(40)) .withPercentile50(stats.getPercentile(50)).withPercentile60(stats.getPercentile(60)) .withPercentile70(stats.getPercentile(70)).withPercentile80(stats.getPercentile(80)) .withPercentile90(stats.getPercentile(90)).withPercentile95(stats.getPercentile(95)) .withPercentile99(stats.getPercentile(99)).withSampleSize((int) stats.getN()) .withSkewness(!Double.isNaN(stats.getSkewness()) ? stats.getSkewness() : 0) .withSttDev(!Double.isNaN(stats.getStandardDeviation()) ? stats.getStandardDeviation() : 0) .withVarience(!Double.isNaN(stats.getVariance()) ? stats.getVariance() : 0).build(); return ret; }
From source file:me.datamining.bandwidth.ScottsRule.java
public double bandWidth(double variance, int dimensions, DescriptiveStatistics data) { return this.bandWidth(variance, dimensions, data.getN()); }
From source file:io.atomix.cluster.impl.PhiAccrualFailureDetector.java
/** * Compute phi for the specified node id. * * @return phi value/* w w w.j av a2s . co m*/ */ public double phi() { long latestHeartbeat = history.latestHeartbeatTime(); DescriptiveStatistics samples = history.samples(); if (samples.getN() < minSamples) { return 0.0; } return computePhi(samples, latestHeartbeat, System.currentTimeMillis()); }
From source file:com.insightml.data.features.stats.FeatureStatistics.java
public int getN(final String feature) { final DescriptiveStatistics stat = stats.get(feature); return (int) ((stat == null ? 0 : stat.getN()) + getNull(feature)); }
From source file:io.atomix.cluster.impl.PhiAccrualFailureDetector.java
/** * Computes the phi value from the given samples. * <p>//w w w .j a v a 2s. c o m * The original phi value in Hayashibara's paper is calculated based on a normal distribution. * Here, we calculate it based on an exponential distribution. * * @param samples the samples from which to compute phi * @param lastHeartbeat the last heartbeat * @param currentTime the current time * @return phi */ private double computePhi(DescriptiveStatistics samples, long lastHeartbeat, long currentTime) { long size = samples.getN(); long t = currentTime - lastHeartbeat; return (size > 0) ? phiFactor * t / samples.getMean() : 100; }
From source file:com.datatorrent.netlet.benchmark.util.BenchmarkResults.java
private String getResults() { DescriptiveStatistics statistics = getDescriptiveStatistics(); final StringBuilder sb = new StringBuilder(); sb.append("Iterations: ").append(statistics.getN()); sb.append(" | Avg Time: ").append(fromNanoTime(statistics.getMean())); sb.append(" | Min Time: ").append(fromNanoTime(statistics.getMin())); sb.append(" | Max Time: ").append(fromNanoTime(statistics.getMax())); sb.append(" | 75% Time: ").append(fromNanoTime(statistics.getPercentile(75d))); sb.append(" | 90% Time: ").append(fromNanoTime(statistics.getPercentile(90d))); sb.append(" | 99% Time: ").append(fromNanoTime(statistics.getPercentile(99d))); sb.append(" | 99.9% Time: ").append(fromNanoTime(statistics.getPercentile(99.9d))); sb.append(" | 99.99% Time: ").append(fromNanoTime(statistics.getPercentile(99.99d))); sb.append(" | 99.999% Time: ").append(fromNanoTime(statistics.getPercentile(99.999d))); return sb.toString(); }
From source file:me.datamining.bandwidth.MesureOfSpread.java
public double bandWidth(double variance, int dimensions, DescriptiveStatistics data) { double q1 = data.getPercentile(q1_); double q3 = data.getPercentile(q3_); return 0.9 * Math.min(variance, (q3 - q1) / 1.34) * Math.pow(data.getN(), -(1.0 / 5.0)); }