List of usage examples for org.apache.commons.math3.stat.descriptive SummaryStatistics getMin
public double getMin()
From source file:ijfx.ui.plugin.overlay.OverlayPanel.java
protected XYChart.Series<Double, Double> getOverlayHistogram(Overlay overlay) { Timer timer = timerService.getTimer(this.getClass()); timer.start();// w w w.j a v a 2 s. c o m Double[] valueList = statsService.getValueListFromImageDisplay(currentDisplay(), overlay); timer.elapsed("Getting the stats"); SummaryStatistics sumup = new SummaryStatistics(); for (Double v : valueList) { sumup.addValue(v); } timer.elapsed("Building the sumup"); double min = sumup.getMin(); double max = sumup.getMax(); double range = max - min; int bins = 100;//new Double(max - min).intValue(); EmpiricalDistribution distribution = new EmpiricalDistribution(bins); double[] values = ArrayUtils.toPrimitive(valueList); Arrays.parallelSort(values); distribution.load(values); timer.elapsed("Sort and distrubution repartition up"); XYChart.Series<Double, Double> serie = new XYChart.Series<>(); ArrayList<Data<Double, Double>> data = new ArrayList<>(bins); double k = min; for (SummaryStatistics st : distribution.getBinStats()) { data.add(new Data<Double, Double>(k, new Double(st.getN()))); k += range / bins; } serie.getData().clear(); serie.getData().addAll(data); timer.elapsed("Creating charts"); return serie; }
From source file:com.itemanalysis.psychometrics.irt.estimation.MarginalMaximumLikelihoodEstimation.java
/** * *//*from w w w .j a va 2 s .com*/ public void computeG2ItemFit(int nbins, int minExpectedCount) { IrtExaminee irtExaminee = new IrtExaminee("", irm); double[] eapEstimate = new double[responseVector.length]; SummaryStatistics stats = new SummaryStatistics(); //First loop over response vectors for (int i = 0; i < responseVector.length; i++) { //EAP estimate of ability irtExaminee.setResponseVector(responseVector[i]); eapEstimate[i] = irtExaminee.eapEstimate(latentDistribution); for (int N = 0; N < responseVector[i].getFrequency(); N++) {//Expand response vectors stats.addValue(eapEstimate[i]); } } DefaultLinearTransformation linearTransformation = new DefaultLinearTransformation(stats.getMean(), latentDistribution.getMean(), stats.getStandardDeviation(), latentDistribution.getStandardDeviation()); //Create fit statistic objects itemFit = new ItemFitG2[nItems]; double lower = linearTransformation.transform(stats.getMin()) - .01;//Subtract a small number to ensure lowest theta is counted double upper = linearTransformation.transform(stats.getMax()) + .01;//Add a small number to ensure largest theta is counted Cut thetaCut = new Cut(lower, upper, nbins); for (int j = 0; j < nItems; j++) { itemFit[j] = new ItemFitG2(irm[j], thetaCut, minExpectedCount); } //Second loop over response vectors //Increment fit statistics double A = linearTransformation.getScale(); for (int i = 0; i < responseVector.length; i++) { //Estimate EAP standard deviation should be the same as the standard deviation //of the latent distribution used to estimate the item parameters. eapEstimate[i] = eapEstimate[i] * A; for (int N = 0; N < responseVector[i].getFrequency(); N++) {//Expand table for (int j = 0; j < nItems; j++) { ((ItemFitG2) itemFit[j]).increment(eapEstimate[i], responseVector[i].getResponseAt(j)); } } } // System.out.println(thetaCut.toString()); //Compute Item Fit for (int j = 0; j < nItems; j++) { itemFit[j].compute(); } }
From source file:net.adamjak.thomas.graph.application.run.TestRunner.java
private void save(Map<String, Object> results, boolean rawData) { SnarkTestTypes testType = (SnarkTestTypes) results.get("testType"); if (this.outputFile.getName().split("\\.")[this.outputFile.getName().split("\\.").length - 1].toLowerCase() .equals("ods")) { String[] columnNames;//from w w w . java2s . co m Object[][] data; if (testType == SnarkTestTypes.ALL_ALGORITHMS) { GraphTestResult[][][] graphTestResult = (GraphTestResult[][][]) results.get("resultsData"); columnNames = String.valueOf("Algorithm,Graph ID,Avarage time,Standard deviation,Minimum,Maximum") .split(","); data = new Object[graphTestResult[0].length][6]; for (int cls = 0; cls < graphTestResult[0][0].length; cls++) { Class<?> c = (Class<?>) graphTestResult[0][0][cls].getValue("algorithmClass"); for (int graph = 0; graph < graphTestResult[0].length; graph++) { SummaryStatistics summaryStatistics = new SummaryStatistics(); for (int run = 0; run < graphTestResult.length; run++) { summaryStatistics .addValue((double) graphTestResult[run][graph][cls].getValue("timeInSeconds")); } data[graph][0] = c.getSimpleName(); data[graph][1] = graph; data[graph][2] = summaryStatistics.getMean(); data[graph][3] = summaryStatistics.getStandardDeviation(); data[graph][4] = summaryStatistics.getMin(); data[graph][5] = summaryStatistics.getMax(); } } } else if (testType == SnarkTestTypes.ONE_ALGORITHM_START_IN_EVERY_VERTEX) { GraphTestResult[][][] graphTestResult = (GraphTestResult[][][]) results.get("resultsData"); columnNames = String .valueOf("Graph ID,Start vertex,Avarage time,Standard deviation,Minimum,Maximum") .split(","); data = new Object[graphTestResult[0].length][6]; for (int vid = 0; vid < graphTestResult[0][0].length; vid++) { for (int graph = 0; graph < graphTestResult[0].length; graph++) { SummaryStatistics summaryStatistics = new SummaryStatistics(); for (int run = 0; run < graphTestResult.length; run++) { summaryStatistics .addValue((double) graphTestResult[run][graph][vid].getValue("timeInSeconds")); } data[graph][0] = graph; data[graph][1] = vid; data[graph][2] = summaryStatistics.getMean(); data[graph][3] = summaryStatistics.getStandardDeviation(); data[graph][4] = summaryStatistics.getMin(); data[graph][5] = summaryStatistics.getMax(); } } } else { GraphTestResult[][] graphTestResult = (GraphTestResult[][]) results.get("resultsData"); columnNames = String.valueOf("Graph ID,Avarage time,Standard deviation,Minimum,Maximum").split(","); data = new Object[graphTestResult[0].length][5]; for (int graph = 0; graph < graphTestResult[0].length; graph++) { SummaryStatistics summaryStatistics = new SummaryStatistics(); for (int run = 0; run < graphTestResult.length; run++) { summaryStatistics.addValue((double) graphTestResult[run][graph].getValue("timeInSeconds")); } data[graph][0] = graph; data[graph][1] = summaryStatistics.getMean(); data[graph][2] = summaryStatistics.getStandardDeviation(); data[graph][3] = summaryStatistics.getMin(); data[graph][4] = summaryStatistics.getMax(); } } try { SpreadSheet.createEmpty(new JTable(data, columnNames).getModel()).saveAs(outputFile); } catch (IOException e) { e.printStackTrace(); } if (rawData == true) { if (testType == SnarkTestTypes.ALL_ALGORITHMS) { GraphTestResult[][][] graphTestResult = (GraphTestResult[][][]) results.get("resultsData"); columnNames = String.valueOf("Class,Run,Graph,Time").split(","); data = new Object[graphTestResult.length * graphTestResult[0].length * graphTestResult[0][0].length][4]; int row = 0; for (int i = 0; i < graphTestResult.length; i++) { for (int j = 0; j < graphTestResult[i].length; j++) { for (int k = 0; k < graphTestResult[i][j].length; k++) { data[row][0] = graphTestResult[i][j][k].getValue("algorithmClass"); data[row][1] = i; data[row][2] = j; data[row][3] = graphTestResult[i][j][k].getValue("time"); row++; } } } } else if (testType == SnarkTestTypes.ONE_ALGORITHM_START_IN_EVERY_VERTEX) { GraphTestResult[][][] graphTestResult = (GraphTestResult[][][]) results.get("resultsData"); columnNames = String.valueOf("Run,Graph,Vertex,Time").split(","); data = new Object[graphTestResult.length * graphTestResult[0].length * graphTestResult[0][0].length][4]; int row = 0; for (int i = 0; i < graphTestResult.length; i++) { for (int j = 0; j < graphTestResult[i].length; j++) { for (int k = 0; k < graphTestResult[i][j].length; k++) { data[row][0] = i; data[row][1] = j; data[row][2] = k; data[row][3] = graphTestResult[i][j][k].getValue("time"); row++; } } } } else if (testType == SnarkTestTypes.ALGORITHM_COMPARATION) { GraphTestResult[][] graphTestResult = (GraphTestResult[][]) results.get("resultsData"); columnNames = String.valueOf("Run,Graph,Time,Class").split(","); data = new Object[graphTestResult.length * graphTestResult[0].length][4]; int row = 0; for (int i = 0; i < graphTestResult.length; i++) { for (int j = 0; j < graphTestResult[i].length; j++) { data[row][0] = i; data[row][1] = j; data[row][2] = graphTestResult[i][j].getValue("time"); data[row][3] = ((Class<?>) graphTestResult[i][j] .getValue(GraphTestResult.SNARK_TESTER_CLASS_KEY)).getSimpleName(); row++; } } } else { GraphTestResult[][] graphTestResult = (GraphTestResult[][]) results.get("resultsData"); columnNames = String.valueOf("Run,Graph,Time").split(","); data = new Object[graphTestResult.length * graphTestResult[0].length][3]; int row = 0; for (int i = 0; i < graphTestResult.length; i++) { for (int j = 0; j < graphTestResult[i].length; j++) { data[row][0] = i; data[row][1] = j; data[row][2] = graphTestResult[i][j].getValue("time"); row++; } } } try { SpreadSheet.createEmpty(new JTable(data, columnNames).getModel()).saveAs(outputFile); } catch (IOException e) { e.printStackTrace(); } } } else { StringBuilder sbData = new StringBuilder(); if (testType == SnarkTestTypes.ALL_ALGORITHMS) { GraphTestResult[][][] graphTestResult = (GraphTestResult[][][]) results.get("resultsData"); sbData.append(",,All data,,,,,Data without extremes,,,,,\n"); sbData.append( "Graph ID,Graph ID,Avarage time,Standard deviation,Minimum,Maximum,Confidence Interval,Avarage time,Standard deviation,Minimum,Maximum,Confidence Interval\n"); for (int cls = 0; cls < graphTestResult[0][0].length; cls++) { Class<?> c = (Class<?>) graphTestResult[0][0][cls].getValue("algorithmClass"); for (int graph = 0; graph < graphTestResult[0].length; graph++) { DescriptiveStatistics descriptiveStatistics = new DescriptiveStatistics(); for (int run = 0; run < graphTestResult.length; run++) { descriptiveStatistics .addValue((double) graphTestResult[run][graph][cls].getValue("timeInSeconds")); } DescriptiveStatistics descriptiveStatisticsWithoutExtremes = StatisticsUtils .statisticsWithoutExtremes(descriptiveStatistics, StatisticsUtils.GrubbsLevel.L005); sbData.append(c.getSimpleName()); sbData.append(","); sbData.append(graph); sbData.append(","); sbData.append(descriptiveStatistics.getMean()); sbData.append(","); sbData.append(descriptiveStatistics.getStandardDeviation()); sbData.append(","); sbData.append(descriptiveStatistics.getMin()); sbData.append(","); sbData.append(descriptiveStatistics.getMax()); sbData.append(","); sbData.append(StatisticsUtils.getConfidenceInterval(descriptiveStatistics, StatisticsUtils.NormCritical.U0050)); sbData.append(","); sbData.append(descriptiveStatisticsWithoutExtremes.getMean()); sbData.append(","); sbData.append(descriptiveStatisticsWithoutExtremes.getStandardDeviation()); sbData.append(","); sbData.append(descriptiveStatisticsWithoutExtremes.getMin()); sbData.append(","); sbData.append(descriptiveStatisticsWithoutExtremes.getMax()); sbData.append(","); sbData.append(StatisticsUtils.getConfidenceInterval(descriptiveStatisticsWithoutExtremes, StatisticsUtils.NormCritical.U0050)); sbData.append("\n"); } } } else if (testType == SnarkTestTypes.ONE_ALGORITHM_START_IN_EVERY_VERTEX) { GraphTestResult[][][] graphTestResult = (GraphTestResult[][][]) results.get("resultsData"); sbData.append(",,All data,,,,,Data without extremes,,,,,\n"); sbData.append( "Graph ID,Start vertex,Avarage time,Standard deviation,Minimum,Maximum,Confidence Interval,Avarage time,Standard deviation,Minimum,Maximum,Confidence Interval\n"); for (int vid = 0; vid < graphTestResult[0][0].length; vid++) { for (int graph = 0; graph < graphTestResult[0].length; graph++) { DescriptiveStatistics descriptiveStatistics = new DescriptiveStatistics(); for (int run = 0; run < graphTestResult.length; run++) { descriptiveStatistics .addValue((double) graphTestResult[run][graph][vid].getValue("timeInSeconds")); } DescriptiveStatistics descriptiveStatisticsWithoutExtremes = StatisticsUtils .statisticsWithoutExtremes(descriptiveStatistics, StatisticsUtils.GrubbsLevel.L005); sbData.append(graph); sbData.append(","); sbData.append(vid); sbData.append(","); sbData.append(descriptiveStatistics.getMean()); sbData.append(","); sbData.append(descriptiveStatistics.getStandardDeviation()); sbData.append(","); sbData.append(descriptiveStatistics.getMin()); sbData.append(","); sbData.append(descriptiveStatistics.getMax()); sbData.append(","); sbData.append(StatisticsUtils.getConfidenceInterval(descriptiveStatistics, StatisticsUtils.NormCritical.U0050)); sbData.append(","); sbData.append(descriptiveStatisticsWithoutExtremes.getMean()); sbData.append(","); sbData.append(descriptiveStatisticsWithoutExtremes.getStandardDeviation()); sbData.append(","); sbData.append(descriptiveStatisticsWithoutExtremes.getMin()); sbData.append(","); sbData.append(descriptiveStatisticsWithoutExtremes.getMax()); sbData.append(","); sbData.append(StatisticsUtils.getConfidenceInterval(descriptiveStatisticsWithoutExtremes, StatisticsUtils.NormCritical.U0050)); sbData.append("\n"); } } } else { GraphTestResult[][] graphTestResult = (GraphTestResult[][]) results.get("resultsData"); sbData.append(",All data,,,,,Data without extremes,,,,,\n"); sbData.append( "Graph ID,Avarage time,Standard deviation,Minimum,Maximum,Confidence Interval,Avarage time,Standard deviation,Minimum,Maximum,Confidence Interval\n"); for (int graph = 0; graph < graphTestResult[0].length; graph++) { DescriptiveStatistics descriptiveStatistics = new DescriptiveStatistics(); for (int run = 0; run < graphTestResult.length; run++) { descriptiveStatistics .addValue((double) graphTestResult[run][graph].getValue("timeInSeconds")); } DescriptiveStatistics descriptiveStatisticsWithoutExtremes = StatisticsUtils .statisticsWithoutExtremes(descriptiveStatistics, StatisticsUtils.GrubbsLevel.L005); sbData.append(graph); sbData.append(","); sbData.append(descriptiveStatistics.getMean()); sbData.append(","); sbData.append(descriptiveStatistics.getStandardDeviation()); sbData.append(","); sbData.append(descriptiveStatistics.getMin()); sbData.append(","); sbData.append(descriptiveStatistics.getMax()); sbData.append(","); sbData.append(StatisticsUtils.getConfidenceInterval(descriptiveStatistics, StatisticsUtils.NormCritical.U0050)); sbData.append(","); sbData.append(descriptiveStatisticsWithoutExtremes.getMean()); sbData.append(","); sbData.append(descriptiveStatisticsWithoutExtremes.getStandardDeviation()); sbData.append(","); sbData.append(descriptiveStatisticsWithoutExtremes.getMin()); sbData.append(","); sbData.append(descriptiveStatisticsWithoutExtremes.getMax()); sbData.append(","); sbData.append(StatisticsUtils.getConfidenceInterval(descriptiveStatisticsWithoutExtremes, StatisticsUtils.NormCritical.U0050)); sbData.append("\n"); } } this.saveStringIntoFile(this.outputFile, sbData.toString()); if (rawData == true) { StringBuilder sbRawData = new StringBuilder(); if (testType == SnarkTestTypes.ALL_ALGORITHMS) { GraphTestResult[][][] graphTestResult = (GraphTestResult[][][]) results.get("resultsData"); sbRawData.append("Class,Run,Graph,Time\n"); for (int i = 0; i < graphTestResult.length; i++) { for (int j = 0; j < graphTestResult[i].length; j++) { for (int k = 0; k < graphTestResult[i][j].length; k++) { sbRawData.append(graphTestResult[i][j][k].getValue("algorithmClass")); sbRawData.append(","); sbRawData.append(i); sbRawData.append(","); sbRawData.append(j); sbRawData.append(","); sbRawData.append(graphTestResult[i][j][k].getValue("time")); sbRawData.append("\n"); } } } } else if (testType == SnarkTestTypes.ONE_ALGORITHM_START_IN_EVERY_VERTEX) { GraphTestResult[][][] graphTestResult = (GraphTestResult[][][]) results.get("resultsData"); sbRawData.append("Run,Graph,Vertex,Time\n"); for (int i = 0; i < graphTestResult.length; i++) { for (int j = 0; j < graphTestResult[i].length; j++) { for (int k = 0; k < graphTestResult[i][j].length; k++) { sbRawData.append(i); sbRawData.append(","); sbRawData.append(j); sbRawData.append(","); sbRawData.append(k); sbRawData.append(","); sbRawData.append(graphTestResult[i][j][k].getValue("time")); sbRawData.append("\n"); } } } } else if (testType == SnarkTestTypes.ALGORITHM_COMPARATION) { GraphTestResult[][] graphTestResult = (GraphTestResult[][]) results.get("resultsData"); sbRawData.append("Run,Graph,Time,Class\n"); for (int i = 0; i < graphTestResult.length; i++) { for (int j = 0; j < graphTestResult[i].length; j++) { sbRawData.append(i); sbRawData.append(","); sbRawData.append(j); sbRawData.append(","); sbRawData.append(graphTestResult[i][j].getValue("time")); sbRawData.append(","); sbRawData.append(((Class<?>) graphTestResult[i][j] .getValue(GraphTestResult.SNARK_TESTER_CLASS_KEY)).getSimpleName()); sbRawData.append("\n"); } } } else { GraphTestResult[][] graphTestResult = (GraphTestResult[][]) results.get("resultsData"); sbRawData.append("Run,Graph,Time\n"); for (int i = 0; i < graphTestResult.length; i++) { for (int j = 0; j < graphTestResult[i].length; j++) { sbRawData.append(i); sbRawData.append(","); sbRawData.append(j); sbRawData.append(","); sbRawData.append(graphTestResult[i][j].getValue("time")); sbRawData.append("\n"); } } } this.saveStringIntoFile(new File(this.outputFile.getParent(), "raw_" + this.outputFile.getName()), sbRawData.toString()); } } }
From source file:fr.inria.eventcloud.benchmarks.radix10_conversion.Radix10ConversionBenchmark.java
private final void test(Run run) { System.out.println("Testing precision with NB_QUADS_TO_READ=" + run.getNbQuadsToRead() + " and PRECISION=" + run.getPrecision());/*ww w . j a v a2s . com*/ FileInputStream fis = null; Iterator<Quad> it = null; try { fis = new FileInputStream(this.trigResource); it = RiotReader.createIteratorQuads(fis, Lang.TRIG, null); SummaryStatistics statsWithoutPrefixRemoval = new SummaryStatistics(); SummaryStatistics statsWithPrefixRemoval = new SummaryStatistics(); Stopwatch stopwatch = Stopwatch.createUnstarted(); int i = 1; while (it.hasNext()) { if (i >= run.getNbQuadsToRead()) { break; } Quad quad = it.next(); if (this.enableRdfStats) { // compute stats without applying doping function statsWithoutPrefixRemoval.addValue(size(quad.getGraph())); statsWithoutPrefixRemoval.addValue(size(quad.getSubject())); statsWithoutPrefixRemoval.addValue(size(quad.getPredicate())); statsWithoutPrefixRemoval.addValue(size(quad.getObject())); } String g = SemanticCoordinate.applyDopingFunction(quad.getGraph()); String s = SemanticCoordinate.applyDopingFunction(quad.getSubject()); String p = SemanticCoordinate.applyDopingFunction(quad.getPredicate()); String o = SemanticCoordinate.applyDopingFunction(quad.getObject()); if (this.enableRdfStats) { // compute stats by applying doping function statsWithPrefixRemoval.addValue(g.length()); statsWithPrefixRemoval.addValue(s.length()); statsWithPrefixRemoval.addValue(p.length()); statsWithPrefixRemoval.addValue(o.length()); } long precision = run.getPrecision(); stopwatch.start(); ApfloatUtils.toFloatRadix10(g, precision); ApfloatUtils.toFloatRadix10(s, precision); ApfloatUtils.toFloatRadix10(p, precision); ApfloatUtils.toFloatRadix10(o, precision); stopwatch.stop(); i++; } if (this.enableRdfStats) { System.out.println( " RDF term min size before prefix removal is " + statsWithoutPrefixRemoval.getMin()); System.out.println( " RDF term max size before prefix removal is " + statsWithoutPrefixRemoval.getMax()); System.out.println( " RDF term average size before prefix removal is " + statsWithoutPrefixRemoval.getMean()); System.out .println(" RDF term min size after prefix removal is " + statsWithPrefixRemoval.getMin()); System.out .println(" RDF term max size after prefix removal is " + statsWithPrefixRemoval.getMax()); System.out.println( " RDF term average size after prefix removal is " + statsWithPrefixRemoval.getMean()); } System.out.println("Time to perform radix 10 conversion for " + i + " with precision set to " + run.getPrecision() + " is " + stopwatch.toString() + " --> " + stopwatch.elapsed(TimeUnit.MILLISECONDS) + " ms"); System.out.println(); } catch (FileNotFoundException e) { e.printStackTrace(); } finally { if (fis != null) { try { fis.close(); } catch (IOException e) { e.printStackTrace(); } } } }
From source file:iDynoOptimizer.MOEAFramework26.src.org.moeaframework.analysis.sensitivity.SimpleStatistics.java
@Override public void run(CommandLine commandLine) throws Exception { String mode = null;// ww w . java2s . c om PrintStream out = null; List<double[][]> entries = new ArrayList<double[][]>(); SummaryStatistics statistics = new SummaryStatistics(); OptionCompleter completer = new OptionCompleter("minimum", "maximum", "average", "stdev", "count"); //load data from all input files for (String filename : commandLine.getArgs()) { entries.add(load(new File(filename))); } //validate the inputs if (entries.isEmpty()) { throw new IllegalArgumentException("requires at least one file"); } int numberOfRows = -1; int numberOfColumns = -1; for (int i = 0; i < entries.size(); i++) { if (numberOfRows == -1) { numberOfRows = entries.get(i).length; if (numberOfRows == 0) { throw new IllegalArgumentException("empty file: " + commandLine.getArgs()[i]); } } else if (numberOfRows != entries.get(i).length) { throw new IllegalArgumentException("unbalanced rows: " + commandLine.getArgs()[i]); } if (numberOfColumns == -1) { numberOfColumns = entries.get(i)[0].length; } else if (numberOfColumns != entries.get(i)[0].length) { throw new IllegalArgumentException("unbalanced columns: " + commandLine.getArgs()[i]); } } //setup the mode if (commandLine.hasOption("mode")) { mode = completer.lookup(commandLine.getOptionValue("mode")); if (mode == null) { throw new IllegalArgumentException("invalid mode"); } } else { mode = "average"; } try { //instantiate the writer if (commandLine.hasOption("output")) { out = new PrintStream(commandLine.getOptionValue("output")); } else { out = System.out; } //compute the statistics for (int i = 0; i < numberOfRows; i++) { for (int j = 0; j < numberOfColumns; j++) { statistics.clear(); for (int k = 0; k < entries.size(); k++) { double value = entries.get(k)[i][j]; if (Double.isInfinite(value) && commandLine.hasOption("maximum")) { value = Double.parseDouble(commandLine.getOptionValue("maximum")); } if ((Double.isInfinite(value) || Double.isNaN(value)) && commandLine.hasOption("ignore")) { // ignore infinity or NaN values } else { statistics.addValue(value); } } if (j > 0) { out.print(' '); } if (mode.equals("minimum")) { out.print(statistics.getMin()); } else if (mode.equals("maximum")) { out.print(statistics.getMax()); } else if (mode.equals("average")) { out.print(statistics.getMean()); } else if (mode.equals("stdev")) { out.print(statistics.getStandardDeviation()); } else if (mode.equals("count")) { out.print(statistics.getN()); } else { throw new IllegalArgumentException("unknown mode: " + mode); } } out.println(); } } finally { if ((out != null) && (out != System.out)) { out.close(); } } }
From source file:org.alfresco.bm.report.CSVReporter.java
protected void export(Writer writer) throws Exception { // TODO: Get any nodes String notes = ""; writeTestDetails(writer, notes);/*from w w w .j a v a2s.c o m*/ writer.write(",,"); writer.write("Event Name,Total Count,Success Count,Failure Count,Success Rate (%)," + "Min (ms), Max (ms), Arithmetic Mean (ms), Standard Deviation (ms)"); writer.write(NEW_LINE); TreeMap<String, ResultSummary> summaries = collateResults(true); for (Map.Entry<String, ResultSummary> entry : summaries.entrySet()) { writer.write(",,"); String eventName = entry.getKey(); ResultSummary summary = entry.getValue(); SummaryStatistics statsSuccess = summary.getStats(true); SummaryStatistics statsFail = summary.getStats(false); // Event Name writer.write(String.format("%s,", eventName)); // Total Count writer.write(String.format("%6d,", summary.getTotalResults())); // Success Count writer.write(String.format("%6d,", statsSuccess.getN())); // Failure Count writer.write(String.format("%6d,", statsFail.getN())); // Success Rate (%) writer.write(String.format("%3.1f,", summary.getSuccessPercentage())); // Min (ms) writer.write(String.format("%10d,", (long) statsSuccess.getMin())); // Max (ms) writer.write(String.format("%10d,", (long) statsSuccess.getMax())); // Arithmetic Mean (ms) writer.write(String.format("%10d,", (long) statsSuccess.getMean())); // Standard Deviation (ms) writer.write(String.format("%10d%s", (long) statsSuccess.getStandardDeviation(), NEW_LINE)); } // Done }
From source file:org.alfresco.bm.report.XLSXReporter.java
/** * Create a 'Summary' sheet containing the table of averages *//*from ww w. jav a2 s . com*/ private void createSummarySheet(XSSFWorkbook workbook) throws IOException, NotFoundException { DBObject testRunObj = getTestService().getTestRunMetadata(test, run); // Create the sheet XSSFSheet sheet = workbook.createSheet("Summary"); // Create the fonts we need Font fontBold = workbook.createFont(); fontBold.setBoldweight(Font.BOLDWEIGHT_BOLD); // Create the styles we need XSSFCellStyle summaryDataStyle = sheet.getWorkbook().createCellStyle(); summaryDataStyle.setAlignment(HorizontalAlignment.RIGHT); XSSFCellStyle headerStyle = sheet.getWorkbook().createCellStyle(); headerStyle.setAlignment(HorizontalAlignment.RIGHT); headerStyle.setFont(fontBold); XSSFRow row = null; int rowCount = 0; row = sheet.createRow(rowCount++); { row.getCell(0).setCellValue("Name:"); row.getCell(0).setCellStyle(headerStyle); row.getCell(1).setCellValue(title); row.getCell(1).setCellStyle(summaryDataStyle); } row = sheet.createRow(rowCount++); { String description = (String) testRunObj.get(FIELD_DESCRIPTION); description = description == null ? "" : description; row.getCell(0).setCellValue("Description:"); row.getCell(0).setCellStyle(headerStyle); row.getCell(1).setCellValue(description); row.getCell(1).setCellStyle(summaryDataStyle); } row = sheet.createRow(rowCount++); { row.getCell(0).setCellValue("Progress (%):"); row.getCell(0).setCellStyle(headerStyle); Double progress = (Double) testRunObj.get(FIELD_PROGRESS); progress = progress == null ? 0.0 : progress; row.getCell(1).setCellValue(progress * 100); row.getCell(1).setCellType(XSSFCell.CELL_TYPE_NUMERIC); row.getCell(1).setCellStyle(summaryDataStyle); } row = sheet.createRow(rowCount++); { row.getCell(0).setCellValue("State:"); row.getCell(0).setCellStyle(headerStyle); String state = (String) testRunObj.get(FIELD_STATE); if (state != null) { row.getCell(1).setCellValue(state); row.getCell(1).setCellStyle(summaryDataStyle); } } row = sheet.createRow(rowCount++); { row.getCell(0).setCellValue("Started:"); row.getCell(0).setCellStyle(headerStyle); Long time = (Long) testRunObj.get(FIELD_STARTED); if (time > 0) { row.getCell(1).setCellValue(FastDateFormat .getDateTimeInstance(FastDateFormat.MEDIUM, FastDateFormat.MEDIUM).format(time)); row.getCell(1).setCellStyle(summaryDataStyle); } } row = sheet.createRow(rowCount++); { row.getCell(0).setCellValue("Finished:"); row.getCell(0).setCellStyle(headerStyle); Long time = (Long) testRunObj.get(FIELD_COMPLETED); if (time > 0) { row.getCell(1).setCellValue(FastDateFormat .getDateTimeInstance(FastDateFormat.MEDIUM, FastDateFormat.MEDIUM).format(time)); row.getCell(1).setCellStyle(summaryDataStyle); } } row = sheet.createRow(rowCount++); { row.getCell(0).setCellValue("Duration:"); row.getCell(0).setCellStyle(headerStyle); Long time = (Long) testRunObj.get(FIELD_DURATION); if (time > 0) { row.getCell(1).setCellValue(DurationFormatUtils.formatDurationHMS(time)); row.getCell(1).setCellStyle(summaryDataStyle); } } rowCount++; rowCount++; // Create a header row row = sheet.createRow(rowCount++); // Header row String[] headers = new String[] { "Event Name", "Total Count", "Success Count", "Failure Count", "Success Rate (%)", "Min (ms)", "Max (ms)", "Arithmetic Mean (ms)", "Standard Deviation (ms)" }; int columnCount = 0; for (String header : headers) { XSSFCell cell = row.getCell(columnCount++); cell.setCellStyle(headerStyle); cell.setCellValue(header); } // Grab results and output them columnCount = 0; TreeMap<String, ResultSummary> summaries = collateResults(true); for (Map.Entry<String, ResultSummary> entry : summaries.entrySet()) { // Reset column count columnCount = 0; row = sheet.createRow(rowCount++); String eventName = entry.getKey(); ResultSummary summary = entry.getValue(); SummaryStatistics statsSuccess = summary.getStats(true); SummaryStatistics statsFail = summary.getStats(false); // Event Name row.getCell(columnCount++).setCellValue(eventName); // Total Count row.getCell(columnCount++).setCellValue(summary.getTotalResults()); // Success Count row.getCell(columnCount++).setCellValue(statsSuccess.getN()); // Failure Count row.getCell(columnCount++).setCellValue(statsFail.getN()); // Success Rate (%) row.getCell(columnCount++).setCellValue(summary.getSuccessPercentage()); // Min (ms) row.getCell(columnCount++).setCellValue((long) statsSuccess.getMin()); // Max (ms) row.getCell(columnCount++).setCellValue((long) statsSuccess.getMax()); // Arithmetic Mean (ms) row.getCell(columnCount++).setCellValue((long) statsSuccess.getMean()); // Standard Deviation (ms) row.getCell(columnCount++).setCellValue((long) statsSuccess.getStandardDeviation()); } // Auto-size the columns for (int i = 0; i < 10; i++) { sheet.autoSizeColumn(i); } sheet.setColumnWidth(1, 5120); // Printing PrintSetup ps = sheet.getPrintSetup(); sheet.setAutobreaks(true); ps.setFitWidth((short) 1); ps.setLandscape(true); // Header and footer sheet.getHeader().setCenter(title); }
From source file:org.apache.cassandra.dht.tokenallocator.ReplicationAwareTokenAllocatorTest.java
private static String mms(SummaryStatistics s) { return String.format("max %.2f min %.2f stddev %.4f", s.getMax(), s.getMin(), s.getStandardDeviation()); }
From source file:org.apache.cassandra.dht.tokenallocator.TokenAllocation.java
public static String statToString(SummaryStatistics stat) { return String.format("max %.2f min %.2f stddev %.4f", stat.getMax() / stat.getMean(), stat.getMin() / stat.getMean(), stat.getStandardDeviation()); }
From source file:org.apache.solr.client.solrj.io.eval.HistogramEvaluator.java
@Override public Object doWork(Object... values) throws IOException { if (Arrays.stream(values).anyMatch(item -> null == item)) { return null; }//from w w w. j a va 2 s. c o m List<?> sourceValues; Integer bins = 10; if (values.length >= 1) { sourceValues = values[0] instanceof List<?> ? (List<?>) values[0] : Arrays.asList(values[0]); if (values.length >= 2) { if (values[1] instanceof Number) { bins = ((Number) values[1]).intValue(); } else { throw new IOException(String.format(Locale.ROOT, "Invalid expression %s - if second parameter is provided then it must be a valid number but found %s instead", toExpression(constructingFactory), values[1].getClass().getSimpleName())); } } } else { throw new IOException( String.format(Locale.ROOT, "Invalid expression %s - expecting at least one value but found %d", toExpression(constructingFactory), containedEvaluators.size())); } EmpiricalDistribution distribution = new EmpiricalDistribution(bins); distribution.load( ((List<?>) sourceValues).stream().mapToDouble(value -> ((Number) value).doubleValue()).toArray()); ; List<Tuple> histogramBins = new ArrayList<>(); for (SummaryStatistics binSummary : distribution.getBinStats()) { Map<String, Number> map = new HashMap<>(); map.put("max", binSummary.getMax()); map.put("mean", binSummary.getMean()); map.put("min", binSummary.getMin()); map.put("stdev", binSummary.getStandardDeviation()); map.put("sum", binSummary.getSum()); map.put("N", binSummary.getN()); map.put("var", binSummary.getVariance()); map.put("cumProb", distribution.cumulativeProbability(binSummary.getMean())); map.put("prob", distribution.probability(binSummary.getMin(), binSummary.getMax())); histogramBins.add(new Tuple(map)); } return histogramBins; }