List of usage examples for org.apache.commons.math.stat.descriptive.moment StandardDeviation evaluate
@Override public double evaluate(final double[] values)
Double.NaN if the array is empty. From source file:com.discursive.jccook.math.StatExample.java
public static void main(String[] args) { double[] values = new double[] { 2.3, 5.4, 6.2, 7.3, 23.3 }; System.out.println("min: " + StatUtils.min(values)); System.out.println("max: " + StatUtils.max(values)); System.out.println("mean: " + StatUtils.mean(values)); System.out.println("product: " + StatUtils.product(values)); System.out.println("sum: " + StatUtils.sum(values)); System.out.println("variance: " + StatUtils.variance(values)); // Measures from previous example Min min = new Min(); System.out.println("min: " + min.evaluate(values)); Max max = new Max(); System.out.println("max: " + max.evaluate(values)); Mean mean = new Mean(); System.out.println("mean: " + mean.evaluate(values)); Product product = new Product(); System.out.println("product: " + product.evaluate(values)); Sum sum = new Sum(); System.out.println("sum: " + sum.evaluate(values)); Variance variance = new Variance(); System.out.println("variance: " + variance.evaluate(values)); // New measures Percentile percentile = new Percentile(); System.out.println("80 percentile value: " + percentile.evaluate(values, 80.0)); GeometricMean geoMean = new GeometricMean(); System.out.println("geometric mean: " + geoMean.evaluate(values)); StandardDeviation stdDev = new StandardDeviation(); System.out.println("standard dev: " + stdDev.evaluate(values)); Skewness skewness = new Skewness(); System.out.println("skewness: " + skewness.evaluate(values)); Kurtosis kurtosis = new Kurtosis(); System.out.println("kurtosis: " + kurtosis.evaluate(values)); }
From source file:jCloisterZone.CarcassonneEnvironment.java
public static void main(String[] args) { int repetitions = 100; double[] scores = new double[repetitions]; RRLJCloisterClient client = new LocalCarcassonneClient("config.ini"); ServerIF server = null;/*from w w w . j ava 2 s .c o m*/ Game game = client.getGame(); Player firstPlayer = null; ArrayList<PlayerSlot> slots = new ArrayList<PlayerSlot>(); for (int r = 0; r < repetitions; r++) { client.createGame(); if (game == null) { server = new LocalCarcassonneServer(client.getGame()); PlayerSlot slot = new PlayerSlot(0, PlayerSlot.SlotType.AI, "RANDOM" + 0, client.getClientId()); slot.setAiClassName(RandomAIPlayer.class.getName()); slots.add(slot); for (int j = 1; j < Integer.parseInt(args[0]); j++) { slot = new PlayerSlot(j, PlayerSlot.SlotType.AI, "AI" + j, client.getClientId()); slot.setAiClassName(LegacyAiPlayer.class.getName()); slots.add(slot); } game = client.getGame(); } else { // Reset the UIs server.stopGame(); game.clearUserInterface(); // Clear the slots and re-add them. for (int i = 0; i < PlayerSlot.COUNT; i++) { server.updateSlot(new PlayerSlot(i), null); } } Collections.shuffle(slots); for (int i = 0; i < slots.size(); i++) { PlayerSlot slot = slots.get(i); PlayerSlot cloneSlot = new PlayerSlot(i, slot.getType(), slot.getNick(), slot.getOwner()); cloneSlot.setAiClassName(slot.getAiClassName()); server.updateSlot(cloneSlot, LegacyAiPlayer.supportedExpansions()); } server.startGame(); Phase phase = game.getPhase(); // Cycle through (probably only once) to keep the game moving. while (phase != null && !phase.isEntered()) { // Modifying phases to proxyless versions if (phase.getClass().equals(CreateGamePhase.class)) phase = game.getPhases().get(ProxylessCreateGamePhase.class); if (phase.getClass().equals(DrawPhase.class)) phase = game.getPhases().get(ProxylessDrawPhase.class); phase.setEntered(true); phase.enter(); phase = game.getPhase(); if (game.getTurnPlayer().getNick().equals("RANDOM0")) firstPlayer = game.getTurnPlayer(); } int score = firstPlayer.getPoints(); scores[r] = score; System.out.println(score); } Mean m = new Mean(); StandardDeviation sd = new StandardDeviation(); System.out.println("Mean: " + m.evaluate(scores) + ", SD: " + sd.evaluate(scores)); }
From source file:jsprit.core.algorithm.acceptor.SchrimpfInitialThresholdGenerator.java
@Override public void informAlgorithmStarts(VehicleRoutingProblem problem, VehicleRoutingAlgorithm algorithm, Collection<VehicleRoutingProblemSolution> solutions) { logger.info("prepare schrimpfAcceptanceFunction, i.e. determine initial threshold"); double now = System.currentTimeMillis(); /*// w ww . j av a 2s . com * randomWalk to determine standardDev */ final double[] results = new double[nOfRandomWalks]; URL resource = Resource.getAsURL("randomWalk.xml"); AlgorithmConfig algorithmConfig = new AlgorithmConfig(); new AlgorithmConfigXmlReader(algorithmConfig).read(resource); VehicleRoutingAlgorithm vra = VehicleRoutingAlgorithms.createAlgorithm(problem, algorithmConfig); vra.setMaxIterations(nOfRandomWalks); vra.getAlgorithmListeners().addListener(new IterationEndsListener() { @Override public void informIterationEnds(int iteration, VehicleRoutingProblem problem, Collection<VehicleRoutingProblemSolution> solutions) { double result = Solutions.bestOf(solutions).getCost(); // logger.info("result={}", result); results[iteration - 1] = result; } }); vra.searchSolutions(); StandardDeviation dev = new StandardDeviation(); double standardDeviation = dev.evaluate(results); double initialThreshold = standardDeviation / 2; schrimpfAcceptance.setInitialThreshold(initialThreshold); logger.info("took {} seconds", ((System.currentTimeMillis() - now) / 1000.0)); logger.debug("initial threshold: {}", initialThreshold); logger.info("---------------------------------------------------------------------"); }
From source file:jsprit.core.algorithm.acceptor.ExperimentalSchrimpfAcceptance.java
@Override public void informAlgorithmStarts(VehicleRoutingProblem problem, VehicleRoutingAlgorithm algorithm, Collection<VehicleRoutingProblemSolution> solutions) { reset();//from ww w. ja va 2 s. co m logger.info("---------------------------------------------------------------------"); logger.info("prepare schrimpfAcceptanceFunction, i.e. determine initial threshold"); logger.info("start random-walk (see randomWalk.xml)"); double now = System.currentTimeMillis(); this.nOfTotalIterations = algorithm.getMaxIterations(); /* * randomWalk to determine standardDev */ final double[] results = new double[nOfRandomWalks]; URL resource = Resource.getAsURL("randomWalk.xml"); AlgorithmConfig algorithmConfig = new AlgorithmConfig(); new AlgorithmConfigXmlReader(algorithmConfig).read(resource); VehicleRoutingAlgorithm vra = VehicleRoutingAlgorithms.createAlgorithm(problem, algorithmConfig); vra.setMaxIterations(nOfRandomWalks); vra.getAlgorithmListeners().addListener(new IterationEndsListener() { @Override public void informIterationEnds(int iteration, VehicleRoutingProblem problem, Collection<VehicleRoutingProblemSolution> solutions) { double result = Solutions.bestOf(solutions).getCost(); // logger.info("result={}", result); results[iteration - 1] = result; } }); vra.searchSolutions(); StandardDeviation dev = new StandardDeviation(); double standardDeviation = dev.evaluate(results); initialThreshold = standardDeviation / 2; logger.info("warmup done"); logger.info("total time: {}s", ((System.currentTimeMillis() - now) / 1000.0)); logger.info("initial threshold: {}", initialThreshold); logger.info("---------------------------------------------------------------------"); }
From source file:cerrla.Performance.java
/** * Records performance scores using sliding windows of results. * /*from ww w . ja va2s .c om*/ * @param currentEpisode * The current episode. */ public void recordPerformanceScore(int currentEpisode) { if (recentScores_.isEmpty()) return; // Transform the queues into arrays double[] vals = new double[recentScores_.size()]; int i = 0; for (Double val : recentScores_) vals[i++] = val.doubleValue(); double[] envSDs = new double[internalSDs_.size()]; i = 0; for (Double envSD : internalSDs_) envSDs[i++] = envSD.doubleValue(); Mean m = new Mean(); StandardDeviation sd = new StandardDeviation(); double mean = m.evaluate(vals); double meanDeviation = sd.evaluate(envSDs) * CONVERGENCE_PERCENT_BUFFER; Double[] details = new Double[PerformanceDetails.values().length]; details[PerformanceDetails.EPISODE.ordinal()] = Double.valueOf(currentEpisode); details[PerformanceDetails.MEAN.ordinal()] = mean; details[PerformanceDetails.SD.ordinal()] = sd.evaluate(vals); performanceDetails_.put(currentEpisode, details); // Output current means if (ProgramArgument.SYSTEM_OUTPUT.booleanValue() && !frozen_) { DecimalFormat formatter = new DecimalFormat("#0.00"); String meanString = formatter.format(mean); String sdString = formatter.format(meanDeviation); System.out.println("Average performance: " + meanString + " " + SD_SYMBOL + " " + sdString); } if (frozen_) { System.out.println(currentEpisode + ": " + details[PerformanceDetails.MEAN.ordinal()]); } }
From source file:de.tudarmstadt.ukp.dkpro.tc.mallet.report.MalletBatchCrossValidationReport.java
@Override public void execute() throws Exception { StorageService store = getContext().getStorageService(); FlexTable<String> table = FlexTable.forClass(String.class); Map<String, List<Double>> key2resultValues = new HashMap<String, List<Double>>(); for (TaskContextMetadata subcontext : getSubtasks()) { String name = BatchTask.class.getSimpleName() + "CrossValidation"; // one CV batch (which internally ran numFolds times) if (subcontext.getLabel().startsWith(name)) { Map<String, String> discriminatorsMap = store .retrieveBinary(subcontext.getId(), Task.DISCRIMINATORS_KEY, new PropertiesAdapter()) .getMap();/*from www .jav a 2 s . com*/ File eval = store.getStorageFolder(subcontext.getId(), EVAL_FILE_NAME + SUFFIX_CSV); Map<String, String> resultMap = new HashMap<String, String>(); String[][] evalMatrix = null; int i = 0; for (String line : FileUtils.readLines(eval)) { String[] tokenizedLine = StrTokenizer.getCSVInstance(line).getTokenArray(); if (evalMatrix == null) { evalMatrix = new String[FileUtils.readLines(eval).size()][tokenizedLine.length]; } evalMatrix[i] = tokenizedLine; i++; } // columns for (int j = 0; j < evalMatrix[0].length; j++) { String header = evalMatrix[0][j]; String[] vals = new String[evalMatrix.length - 1]; // rows for (int k = 1; k < evalMatrix.length; k++) { if (evalMatrix[k][j].equals("null")) { vals[k - 1] = String.valueOf(0.); } else { vals[k - 1] = evalMatrix[k][j]; } } Mean mean = new Mean(); Sum sum = new Sum(); StandardDeviation std = new StandardDeviation(); double[] dVals = new double[vals.length]; Set<String> sVals = new HashSet<String>(); for (int k = 0; k < vals.length; k++) { try { dVals[k] = Double.parseDouble(vals[k]); sVals = null; } catch (NumberFormatException e) { dVals = null; sVals.add(vals[k]); } } if (dVals != null) { if (nonAveragedResultsMeasures.contains(header)) { resultMap.put(header, String.valueOf(sum.evaluate(dVals))); } else { resultMap.put(header, String.valueOf(mean.evaluate(dVals)) + "\u00B1" + String.valueOf(std.evaluate(dVals))); } } else { if (sVals.size() > 1) { resultMap.put(header, "---"); } else { resultMap.put(header, vals[0]); } } } String key = getKey(discriminatorsMap); List<Double> results; if (key2resultValues.get(key) == null) { results = new ArrayList<Double>(); } else { results = key2resultValues.get(key); } key2resultValues.put(key, results); Map<String, String> values = new HashMap<String, String>(); Map<String, String> cleanedDiscriminatorsMap = new HashMap<String, String>(); for (String disc : discriminatorsMap.keySet()) { if (!ReportUtils.containsExcludePattern(disc, discriminatorsToExclude)) { cleanedDiscriminatorsMap.put(disc, discriminatorsMap.get(disc)); } } values.putAll(cleanedDiscriminatorsMap); values.putAll(resultMap); table.addRow(subcontext.getLabel(), values); } } getContext().getLoggingService().message(getContextLabel(), ReportUtils.getPerformanceOverview(table)); // Excel cannot cope with more than 255 columns if (table.getColumnIds().length <= 255) { getContext().storeBinary(EVAL_FILE_NAME + "_compact" + SUFFIX_EXCEL, table.getExcelWriter()); } getContext().storeBinary(EVAL_FILE_NAME + "_compact" + SUFFIX_CSV, table.getCsvWriter()); table.setCompact(false); // Excel cannot cope with more than 255 columns if (table.getColumnIds().length <= 255) { getContext().storeBinary(EVAL_FILE_NAME + SUFFIX_EXCEL, table.getExcelWriter()); } getContext().storeBinary(EVAL_FILE_NAME + SUFFIX_CSV, table.getCsvWriter()); // output the location of the batch evaluation folder // otherwise it might be hard for novice users to locate this File dummyFolder = store.getStorageFolder(getContext().getId(), "dummy"); // TODO can we also do this without creating and deleting the dummy folder? getContext().getLoggingService().message(getContextLabel(), "Storing detailed results in:\n" + dummyFolder.getParent() + "\n"); dummyFolder.delete(); }
From source file:de.tudarmstadt.ukp.dkpro.tc.crfsuite.CRFSuiteBatchCrossValidationReport.java
@Override public void execute() throws Exception { StorageService store = getContext().getStorageService(); FlexTable<String> table = FlexTable.forClass(String.class); Map<String, List<Double>> key2resultValues = new HashMap<String, List<Double>>(); for (TaskContextMetadata subcontext : getSubtasks()) { String name = ExperimentCrossValidation.class.getSimpleName(); // one CV batch (which internally ran numFolds times) if (subcontext.getLabel().startsWith(name)) { Map<String, String> discriminatorsMap = store .retrieveBinary(subcontext.getId(), Task.DISCRIMINATORS_KEY, new PropertiesAdapter()) .getMap();// w w w . j a v a 2 s .c o m File eval = store.getStorageFolder(subcontext.getId(), EVAL_FILE_NAME + SUFFIX_CSV); Map<String, String> resultMap = new HashMap<String, String>(); String[][] evalMatrix = null; int i = 0; for (String line : FileUtils.readLines(eval)) { String[] tokenizedLine = StrTokenizer.getCSVInstance(line).getTokenArray(); if (evalMatrix == null) { evalMatrix = new String[FileUtils.readLines(eval).size()][tokenizedLine.length]; } evalMatrix[i] = tokenizedLine; i++; } // columns for (int j = 0; j < evalMatrix[0].length; j++) { String header = evalMatrix[0][j]; String[] vals = new String[evalMatrix.length - 1]; // rows for (int k = 1; k < evalMatrix.length; k++) { if (evalMatrix[k][j].equals("null")) { vals[k - 1] = String.valueOf(0.); } else { vals[k - 1] = evalMatrix[k][j]; } } Mean mean = new Mean(); Sum sum = new Sum(); StandardDeviation std = new StandardDeviation(); double[] dVals = new double[vals.length]; Set<String> sVals = new HashSet<String>(); for (int k = 0; k < vals.length; k++) { try { dVals[k] = Double.parseDouble(vals[k]); sVals = null; } catch (NumberFormatException e) { dVals = null; sVals.add(vals[k]); } } if (dVals != null) { if (nonAveragedResultsMeasures.contains(header)) { resultMap.put(header + foldSum, String.valueOf(sum.evaluate(dVals))); } else { resultMap.put(header + foldAveraged, String.valueOf( mean.evaluate(dVals) + "\u00B1" + String.valueOf(std.evaluate(dVals)))); } } else { if (sVals.size() > 1) { resultMap.put(header, "---"); } else { resultMap.put(header, vals[0]); } } } String key = getKey(discriminatorsMap); List<Double> results; if (key2resultValues.get(key) == null) { results = new ArrayList<Double>(); } else { results = key2resultValues.get(key); } key2resultValues.put(key, results); Map<String, String> values = new HashMap<String, String>(); Map<String, String> cleanedDiscriminatorsMap = new HashMap<String, String>(); for (String disc : discriminatorsMap.keySet()) { if (!ReportUtils.containsExcludePattern(disc, discriminatorsToExclude)) { cleanedDiscriminatorsMap.put(disc, discriminatorsMap.get(disc)); } } values.putAll(cleanedDiscriminatorsMap); values.putAll(resultMap); table.addRow(subcontext.getLabel(), values); } } getContext().getLoggingService().message(getContextLabel(), ReportUtils.getPerformanceOverview(table)); // Excel cannot cope with more than 255 columns if (table.getColumnIds().length <= 255) { getContext().storeBinary(EVAL_FILE_NAME + "_compact" + SUFFIX_EXCEL, table.getExcelWriter()); } getContext().storeBinary(EVAL_FILE_NAME + "_compact" + SUFFIX_CSV, table.getCsvWriter()); table.setCompact(false); // Excel cannot cope with more than 255 columns if (table.getColumnIds().length <= 255) { getContext().storeBinary(EVAL_FILE_NAME + SUFFIX_EXCEL, table.getExcelWriter()); } getContext().storeBinary(EVAL_FILE_NAME + SUFFIX_CSV, table.getCsvWriter()); // output the location of the batch evaluation folder // otherwise it might be hard for novice users to locate this File dummyFolder = store.getStorageFolder(getContext().getId(), "dummy"); // TODO can we also do this without creating and deleting the dummy folder? getContext().getLoggingService().message(getContextLabel(), "Storing detailed results in:\n" + dummyFolder.getParent() + "\n"); dummyFolder.delete(); }
From source file:org.apache.drill.exec.physical.impl.orderedpartitioner.TestOrderedPartitionExchange.java
/** * Starts two drillbits and runs a physical plan with a Mock scan, project, OrderedParititionExchange, Union Exchange, * and sort. The final sort is done first on the partition column, and verifies that the partitions are correct, in that * all rows in partition 0 should come in the sort order before any row in partition 1, etc. Also verifies that the standard * deviation of the size of the partitions is less than one tenth the mean size of the partitions, because we expect all * the partitions to be roughly equal in size. * @throws Exception/*from ww w.j ava 2 s . c o m*/ */ @Test public void twoBitTwoExchangeRun() throws Exception { RemoteServiceSet serviceSet = RemoteServiceSet.getLocalServiceSet(); try (Drillbit bit1 = new Drillbit(CONFIG, serviceSet); Drillbit bit2 = new Drillbit(CONFIG, serviceSet); DrillClient client = new DrillClient(CONFIG, serviceSet.getCoordinator());) { bit1.run(); bit2.run(); client.connect(); List<QueryDataBatch> results = client.runQuery( org.apache.drill.exec.proto.UserBitShared.QueryType.PHYSICAL, Files.toString(FileUtils.getResourceAsFile("/sender/ordered_exchange.json"), Charsets.UTF_8)); int count = 0; List<Integer> partitionRecordCounts = Lists.newArrayList(); for (QueryDataBatch b : results) { if (b.getData() != null) { int rows = b.getHeader().getRowCount(); count += rows; RecordBatchLoader loader = new RecordBatchLoader( new BootStrapContext(DrillConfig.create()).getAllocator()); loader.load(b.getHeader().getDef(), b.getData()); BigIntVector vv1 = (BigIntVector) loader.getValueAccessorById(BigIntVector.class, loader .getValueVectorId(new SchemaPath("col1", ExpressionPosition.UNKNOWN)).getFieldIds()) .getValueVector(); Float8Vector vv2 = (Float8Vector) loader.getValueAccessorById(Float8Vector.class, loader .getValueVectorId(new SchemaPath("col2", ExpressionPosition.UNKNOWN)).getFieldIds()) .getValueVector(); IntVector pVector = (IntVector) loader.getValueAccessorById(IntVector.class, loader.getValueVectorId(new SchemaPath("partition", ExpressionPosition.UNKNOWN)) .getFieldIds()) .getValueVector(); long previous1 = Long.MIN_VALUE; double previous2 = Double.MIN_VALUE; int partPrevious = -1; long current1 = Long.MIN_VALUE; double current2 = Double.MIN_VALUE; int partCurrent = -1; int partitionRecordCount = 0; for (int i = 0; i < rows; i++) { previous1 = current1; previous2 = current2; partPrevious = partCurrent; current1 = vv1.getAccessor().get(i); current2 = vv2.getAccessor().get(i); partCurrent = pVector.getAccessor().get(i); Assert.assertTrue(current1 >= previous1); if (current1 == previous1) { Assert.assertTrue(current2 <= previous2); } if (partCurrent == partPrevious || partPrevious == -1) { partitionRecordCount++; } else { partitionRecordCounts.add(partitionRecordCount); partitionRecordCount = 0; } } partitionRecordCounts.add(partitionRecordCount); loader.clear(); } b.release(); } double[] values = new double[partitionRecordCounts.size()]; int i = 0; for (Integer rc : partitionRecordCounts) { values[i++] = rc.doubleValue(); } StandardDeviation stdDev = new StandardDeviation(); Mean mean = new Mean(); double std = stdDev.evaluate(values); double m = mean.evaluate(values); System.out.println("mean: " + m + " std dev: " + std); //Assert.assertTrue(std < 0.1 * m); assertEquals(31000, count); } }
From source file:org.coode.distance.entityrelevance.AbstractRankingRelevancePolicy.java
private double computeStandardDeviation() { StandardDeviation sd = new StandardDeviation(); Set<Double> rankingValues = this.getRanking().getValues(); double[] values = new double[rankingValues.size()]; int i = 0;// www . j a va2 s . c o m for (Double d : rankingValues) { values[i] = d; i++; } return sd.evaluate(values); }
From source file:org.graphstream.algorithm.measure.CommunityDistribution.java
@Override /**/*from ww w. jav a 2 s .c om*/ * Computes and update the statistical information on size distribution. * * @complexity O(C), where C is the expected number of communities. */ public void compute() { if (graphChanged) { // Default measure is the number of communities M = (float) communities.size(); // Update the smallest/biggest community // and creates the size distribution int maxSize = 0; int minSize = Integer.MAX_VALUE; double[] distribution = new double[(int) M]; int k = 0; Mean mean = new Mean(); StandardDeviation stdev = new StandardDeviation(); for (Object c : communities.keySet()) { distribution[k++] = (communities.get(c)).size(); if ((communities.get(c)).size() > maxSize) { biggestCommunity = c; maxSize = (communities.get(c)).size(); } if ((communities.get(c)).size() < minSize) { smallestCommunity = c; minSize = (communities.get(c)).size(); } } // Compute the statistical moments avgSize = (float) mean.evaluate(distribution); stdevSize = (float) stdev.evaluate(distribution); graphChanged = false; } }