List of usage examples for org.apache.commons.math.stat.descriptive SummaryStatistics addValue
public void addValue(double value)
From source file:it.univpm.deit.semedia.musicuri.utils.experimental.LambdaCalculator.java
public static void main(String[] args) throws Exception { //***************************************************************************** //************************* F I L E I N P U T *************************** //***************************************************************************** if ((args.length == 1) && (new File(args[0]).exists())) { // get the file's canonical path File givenHandle = new File(args[0]); String queryAudioCanonicalPath = givenHandle.getCanonicalPath(); System.out.println("Input: " + queryAudioCanonicalPath); //PerformanceStatistic tempStat; SummaryStatistics lambdaSummary = SummaryStatistics.newInstance(); if (givenHandle.isDirectory()) { File[] list = givenHandle.listFiles(); if (list.length == 0) { System.out.println("Directory is empty"); return; } else { ArrayList allStats = new ArrayList(); File currentFile; for (int i = 0; i < list.length; i++) { currentFile = list[i]; try { if (Toolset.isSupportedAudioFile(currentFile)) { System.out.println("\nCalculating optimal lambda : " + currentFile.getName()); lambdaSummary.addValue(getBestLambda(new MusicURIQuery(currentFile))); }/*www . j a v a2 s. com*/ } catch (Exception e) { e.printStackTrace(); } } // System.out.println("\n\nStatistics for Test Case: " + queryAudioCanonicalPath); // mergeStatistics(allStats); } } if (givenHandle.isFile()) { if (Toolset.isSupportedAudioFile(givenHandle)) { // tempStat = getBestLambda (new MusicURIQuery(givenHandle)); // if (tempStat!=null) // { // //tempStat.printStatistics(); // ArrayList allStats = new ArrayList(); // allStats.add(tempStat); // mergeStatistics(allStats); // } // else // System.out.println("Error in identification "); } } } //end if else { System.err.println("LambdaCalculator"); System.err.println("Usage: java tester.LambdaCalculator {directory}"); } }
From source file:com.netflix.dyno.connectionpool.impl.lb.CircularListTest.java
private static double checkValues(List<Integer> values) { System.out.println("Values: " + values); SummaryStatistics ss = new SummaryStatistics(); for (int i = 0; i < values.size(); i++) { ss.addValue(values.get(i)); }/*w ww .ja v a2s. c o m*/ double mean = ss.getMean(); double stddev = ss.getStandardDeviation(); double p = ((stddev * 100) / mean); System.out.println("Percentage diff: " + p); Assert.assertTrue("" + p + " " + values, p < 0.1); return p; }
From source file:it.univpm.deit.semedia.musicuri.utils.experimental.LambdaCalculator.java
public static void mergeStatistics(ArrayList allStats) { PerformanceStatistic tempStat;//from w w w.j a va2s . c o m int truePositives = 0; int falsePositives = 0; int trueNegatives = 0; int falseNegatives = 0; SummaryStatistics TPBestMatchSummary = SummaryStatistics.newInstance(); SummaryStatistics TPSecondBestSummary = SummaryStatistics.newInstance(); SummaryStatistics FPBestMatchSummary = SummaryStatistics.newInstance(); SummaryStatistics BothTP_FPBestMatchSummary = SummaryStatistics.newInstance(); SummaryStatistics TNSummary = SummaryStatistics.newInstance(); SummaryStatistics FNSummary = SummaryStatistics.newInstance(); SummaryStatistics pruningSpeedSummary = SummaryStatistics.newInstance(); SummaryStatistics matchingSpeedSummary = SummaryStatistics.newInstance(); SummaryStatistics totalSpeedSummary = SummaryStatistics.newInstance(); for (int i = 0; i < allStats.size(); i++) { tempStat = (PerformanceStatistic) allStats.get(i); if (tempStat.isTruePositive()) truePositives++; if (tempStat.isFalsePositive()) falsePositives++; if (tempStat.isTrueNegative()) trueNegatives++; if (tempStat.isFalseNegative()) falseNegatives++; // accurate results only //if (tempStat.isTruePositive() || tempStat.isTrueNegative()) pruningSpeedSummary.addValue(tempStat.getPruningTime()); matchingSpeedSummary.addValue(tempStat.getMatchingTime()); totalSpeedSummary.addValue(tempStat.getPruningTime() + tempStat.getMatchingTime()); if (tempStat.isTruePositive()) { TPBestMatchSummary.addValue(tempStat.getBestMatchDistance()); TPSecondBestSummary.addValue(tempStat.getSecondBestMatchDistance()); } if (tempStat.isFalsePositive()) { FPBestMatchSummary.addValue(tempStat.getBestMatchDistance()); } BothTP_FPBestMatchSummary.addValue(tempStat.getBestMatchDistance()); } System.out.println("---------------------------------------------------------"); System.out.println("\nTrue Positives : " + truePositives + "/" + allStats.size()); System.out.println("False Positives : " + falsePositives + "/" + allStats.size()); System.out.println("True Negatives : " + trueNegatives + "/" + allStats.size()); System.out.println("False Negatives : " + falseNegatives + "/" + allStats.size()); System.out.println("\nTrue Positive Best Match Statistics"); System.out.println("Distance Min : " + TPBestMatchSummary.getMin()); System.out.println("Distance Max : " + TPBestMatchSummary.getMax()); System.out.println("Distance Mean : " + TPBestMatchSummary.getMean()); System.out.println("Distance Variance : " + TPBestMatchSummary.getVariance()); System.out.println("Distance StdDev : " + TPBestMatchSummary.getStandardDeviation()); System.out.println("Confidence Mean : " + (100 - (100 * (TPBestMatchSummary.getMean()))) + " %"); System.out.println("\nTrue Positive Second Best Statistics"); System.out.println("Distance Min : " + TPSecondBestSummary.getMin()); System.out.println("Distance Max : " + TPSecondBestSummary.getMax()); System.out.println("Distance Mean : " + TPSecondBestSummary.getMean()); System.out.println("Confidence Mean : " + (100 - (100 * (TPSecondBestSummary.getMean()))) + " %"); System.out.println("\nFalse Positive Best Match Statistics"); System.out.println("Distance Min : " + FPBestMatchSummary.getMin()); System.out.println("Distance Max : " + FPBestMatchSummary.getMax()); System.out.println("Distance Mean : " + FPBestMatchSummary.getMean()); System.out.println("Distance Variance : " + FPBestMatchSummary.getVariance()); System.out.println("Distance StdDev : " + FPBestMatchSummary.getStandardDeviation()); System.out.println("Confidence Mean : " + (100 - (100 * (FPBestMatchSummary.getMean()))) + " %"); System.out.println("\nBest Match Statistics (Regardless being False or True Positive) "); System.out.println("Distance Min : " + BothTP_FPBestMatchSummary.getMin()); System.out.println("Distance Max : " + BothTP_FPBestMatchSummary.getMax()); System.out.println("Distance Mean : " + BothTP_FPBestMatchSummary.getMean()); System.out.println("Distance Variance : " + BothTP_FPBestMatchSummary.getVariance()); System.out.println("Distance StdDev : " + BothTP_FPBestMatchSummary.getStandardDeviation()); System.out.println("Confidence Mean : " + (100 - (100 * (BothTP_FPBestMatchSummary.getMean()))) + " %"); System.out.println("\n\nPruning Speed Statistics"); System.out.println("Speed Min : " + (pruningSpeedSummary.getMin() / 1000) + " sec"); System.out.println("Speed Max : " + (pruningSpeedSummary.getMax() / 1000) + " sec"); System.out.println("Speed Mean : " + (pruningSpeedSummary.getMean() / 1000) + " sec"); System.out.println("\nMatching Speed Statistics"); System.out.println("Speed Min : " + (matchingSpeedSummary.getMin() / 1000) + " sec"); System.out.println("Speed Max : " + (matchingSpeedSummary.getMax() / 1000) + " sec"); System.out.println("Speed Mean : " + (matchingSpeedSummary.getMean() / 1000) + " sec"); System.out.println("\nOverall Speed Statistics"); System.out.println("Speed Min : " + (totalSpeedSummary.getMin() / 1000) + " sec"); System.out.println("Speed Max : " + (totalSpeedSummary.getMax() / 1000) + " sec"); System.out.println("Speed Mean : " + (totalSpeedSummary.getMean() / 1000) + " sec"); }
From source file:com.userweave.module.methoden.rrt.page.report.MeanAndStdDeviation.java
public MeanAndStdDeviation(List<Double> values) { SummaryStatistics stats = new SummaryStatistics(); for (Double value : values) { stats.addValue(value); }//from w w w . j a v a 2 s . c o m this.mean = stats.getMean(); this.stdDeviation = stats.getStandardDeviation(); }
From source file:hmp.Read.java
private void addToHash(HashMap<String, SummaryStatistics> hash, String key, double value) { if (hash.containsKey(key)) { SummaryStatistics stat = hash.get(key); stat.addValue(value); } else {//from ww w . ja v a 2s . co m SummaryStatistics stat = new SummaryStatistics(); stat.addValue(value); hash.put(key, stat); } }
From source file:de.escidoc.core.om.performance.Statistics.java
/** * @param key the name of package.class.method * @param value the execution time of the method *//*from ww w. ja v a 2 s . co m*/ public void addValueToStatistics(final String key, final long value) { final SummaryStatistics statistics = getStatistics(key); statistics.addValue((double) value); }
From source file:de.tudarmstadt.ukp.dkpro.core.decompounding.ranking.FrequencyGeometricMeanRanker.java
/** * Calculates the weight for a split/*from www . j a va 2s. c o m*/ */ private double calcRank(DecompoundedWord aSplit) { SummaryStatistics stats = new SummaryStatistics(); for (Fragment elem : aSplit.getSplits()) { stats.addValue(freq(elem).doubleValue()); } return stats.getGeometricMean(); }
From source file:com.netflix.curator.x.discovery.TestStrategies.java
@Test public void testRandom() throws Exception { final int QTY = 10; final int ITERATIONS = 1000; TestInstanceProvider instanceProvider = new TestInstanceProvider(QTY, 0); ProviderStrategy<Void> strategy = new RandomStrategy<Void>(); long[] counts = new long[QTY]; for (int i = 0; i < ITERATIONS; ++i) { ServiceInstance<Void> instance = strategy.getInstance(instanceProvider); int id = Integer.parseInt(instance.getId()); counts[id]++;/*from w w w.j av a 2 s . c o m*/ } SummaryStatistics statistic = new SummaryStatistics(); for (int i = 0; i < QTY; ++i) { statistic.addValue(counts[i]); } Assert.assertTrue(statistic.getStandardDeviation() <= (QTY * 2), "" + statistic.getStandardDeviation()); // meager check for even distribution }
From source file:geogebra.kernel.AlgoNormalQuantilePlot.java
private GeoSegment getQQLineSegment() { SummaryStatistics stats = new SummaryStatistics(); for (int i = 0; i < sortedData.length; i++) { stats.addValue(sortedData[i]); }/* w w w .j a va 2s .co m*/ double sd = stats.getStandardDeviation(); double mean = stats.getMean(); double min = stats.getMin(); double max = stats.getMax(); // qq line: y = (1/sd)x - mean/sd GeoPoint startPoint = new GeoPoint(cons); startPoint.setCoords(min, (min / sd) - mean / sd, 1.0); GeoPoint endPoint = new GeoPoint(cons); endPoint.setCoords(max, (max / sd) - mean / sd, 1.0); GeoSegment seg = new GeoSegment(cons, startPoint, endPoint); seg.calcLength(); return seg; }
From source file:geogebra.common.kernel.statistics.AlgoNormalQuantilePlot.java
private GeoSegment getQQLineSegment() { SummaryStatistics stats = new SummaryStatistics(); for (int i = 0; i < sortedData.length; i++) { stats.addValue(sortedData[i]); }/* ww w . j av a2s .com*/ double sd = stats.getStandardDeviation(); double mean = stats.getMean(); double min = stats.getMin(); double max = stats.getMax(); // qq line: y = (1/sd)x - mean/sd GeoPoint startPoint = new GeoPoint(cons); startPoint.setCoords(min, (min / sd) - mean / sd, 1.0); GeoPoint endPoint = new GeoPoint(cons); endPoint.setCoords(max, (max / sd) - mean / sd, 1.0); GeoSegment seg = new GeoSegment(cons, startPoint, endPoint); seg.calcLength(); return seg; }