List of usage examples for java.lang String format
public static String format(String format, Object... args)
From source file:examples.cnn.ImagesClassification.java
public static void main(String[] args) { SparkConf conf = new SparkConf(); conf.setAppName("Images CNN Classification"); conf.setMaster(String.format("local[%d]", NUM_CORES)); conf.set(SparkDl4jMultiLayer.AVERAGE_EACH_ITERATION, String.valueOf(true)); try (JavaSparkContext sc = new JavaSparkContext(conf)) { JavaRDD<String> raw = sc.textFile("data/images-data-rgb.csv"); String first = raw.first(); JavaPairRDD<String, String> labelData = raw.filter(f -> f.equals(first) == false).mapToPair(r -> { String[] tab = r.split(";"); return new Tuple2<>(tab[0], tab[1]); });/* w w w . j av a 2 s .c om*/ Map<String, Long> labels = labelData.map(t -> t._1).distinct().zipWithIndex() .mapToPair(t -> new Tuple2<>(t._1, t._2)).collectAsMap(); log.info("Number of labels {}", labels.size()); labels.forEach((a, b) -> log.info("{}: {}", a, b)); NetworkTrainer trainer = new NetworkTrainer.Builder().model(ModelLibrary.net1) .networkToSparkNetwork(net -> new SparkDl4jMultiLayer(sc, net)).numLabels(labels.size()) .cores(NUM_CORES).build(); JavaRDD<Tuple2<INDArray, double[]>> labelsWithData = labelData.map(t -> { INDArray label = FeatureUtil.toOutcomeVector(labels.get(t._1).intValue(), labels.size()); double[] arr = Arrays.stream(t._2.split(" ")).map(normalize1).mapToDouble(Double::doubleValue) .toArray(); return new Tuple2<>(label, arr); }); JavaRDD<Tuple2<INDArray, double[]>>[] splited = labelsWithData.randomSplit(new double[] { .8, .2 }, seed); JavaRDD<DataSet> testDataset = splited[1].map(t -> { INDArray features = Nd4j.create(t._2, new int[] { 1, t._2.length }); return new DataSet(features, t._1); }).cache(); log.info("Number of test images {}", testDataset.count()); JavaRDD<DataSet> plain = splited[0].map(t -> { INDArray features = Nd4j.create(t._2, new int[] { 1, t._2.length }); return new DataSet(features, t._1); }); /* * JavaRDD<DataSet> flipped = splited[0].randomSplit(new double[] { .5, .5 }, seed)[0]. */ JavaRDD<DataSet> flipped = splited[0].map(t -> { double[] arr = t._2; int idx = 0; double[] farr = new double[arr.length]; for (int i = 0; i < arr.length; i += trainer.width) { double[] temp = Arrays.copyOfRange(arr, i, i + trainer.width); ArrayUtils.reverse(temp); for (int j = 0; j < trainer.height; ++j) { farr[idx++] = temp[j]; } } INDArray features = Nd4j.create(farr, new int[] { 1, farr.length }); return new DataSet(features, t._1); }); JavaRDD<DataSet> trainDataset = plain.union(flipped).cache(); log.info("Number of train images {}", trainDataset.count()); trainer.train(trainDataset, testDataset); } }
From source file:de.kaixo.mubi.lists.MubiListsScraper.java
public static void main(String ars[]) throws XMLStreamException, FactoryConfigurationError, IOException { for (int page = 1; page <= 10; page++) { System.out.println("Fetching page " + page); URL url = new URL(MUBI_LISTS_BASE_URL + "&page=" + page); List<MubiListRef> lists = MubiListsReader.getInstance().readMubiLists(url); for (MubiListRef list : lists) { System.out.println(" Fetching list " + list.getTitle()); List<MubiFilmRef> filmList = MubiListsReader.getInstance() .readMubiFilmList(new URL(MUBI_BASE_URL + list.getUrl())); list.addFilms(filmList);/*from w w w. j av a 2s. c o m*/ } File outfile = new File("output", "mubi-lists-page-" + String.format("%04d", page) + ".json"); System.out.println("Writing " + outfile.getName()); mapper.writeValue(outfile, lists); } }
From source file:com.sun.labs.aura.grid.ec2.Ec2Sample.java
public static void main(String[] args) throws Exception { Properties props = new Properties(); props.load(Ec2Sample.class.getResourceAsStream("aws.properties")); Jec2 ec2 = new Jec2(props.getProperty("aws.accessId"), props.getProperty("aws.secretKey")); List<String> params = new ArrayList<String>(); List<ImageDescription> images = ec2.describeImages(params); System.out.println(String.format("%d available images", images.size())); for (ImageDescription img : images) { if (img.getImageState().equals("available")) { System.out.println(img.getImageId() + "\t" + img.getImageLocation() + "\t" + img.getImageOwnerId()); }/*from w ww . ja v a 2 s .co m*/ } // describe instances params = new ArrayList<String>(); List<ReservationDescription> instances = ec2.describeInstances(params); System.out.println(String.format("%d instances", instances.size())); String instanceId = null; for (ReservationDescription res : instances) { System.out.println(res.getOwner() + "\t" + res.getReservationId()); if (res.getInstances() != null) { for (Instance inst : res.getInstances()) { System.out.println("\t" + inst.getImageId() + "\t" + inst.getDnsName() + "\t" + inst.getState() + "\t" + inst.getKeyName()); instanceId = inst.getInstanceId(); } } } // test console output if (instanceId != null) { ConsoleOutput consOutput = ec2.getConsoleOutput(instanceId); System.out.println("Console Output:"); System.out.println(consOutput.getOutput()); } // show keypairs List<KeyPairInfo> info = ec2.describeKeyPairs(new String[] {}); System.out.println("keypair list"); for (KeyPairInfo i : info) { System.out.println("keypair : " + i.getKeyName() + ", " + i.getKeyFingerprint()); } }
From source file:com.sop4j.SimpleStatistics.java
public static void main(String[] args) { final MersenneTwister rng = new MersenneTwister(); // used for RNG... READ THE DOCS!!! final int[] values = new int[NUM_VALUES]; final DescriptiveStatistics descriptiveStats = new DescriptiveStatistics(); // stores values final SummaryStatistics summaryStats = new SummaryStatistics(); // doesn't store values final Frequency frequency = new Frequency(); // add numbers into our stats for (int i = 0; i < NUM_VALUES; ++i) { values[i] = rng.nextInt(MAX_VALUE); descriptiveStats.addValue(values[i]); summaryStats.addValue(values[i]); frequency.addValue(values[i]);//from w w w . ja va 2s .c o m } // print out some standard stats System.out.println("MIN: " + summaryStats.getMin()); System.out.println("AVG: " + String.format("%.3f", summaryStats.getMean())); System.out.println("MAX: " + summaryStats.getMax()); // get some more complex stats only offered by DescriptiveStatistics System.out.println("90%: " + descriptiveStats.getPercentile(90)); System.out.println("MEDIAN: " + descriptiveStats.getPercentile(50)); System.out.println("SKEWNESS: " + String.format("%.4f", descriptiveStats.getSkewness())); System.out.println("KURTOSIS: " + String.format("%.4f", descriptiveStats.getKurtosis())); // quick and dirty stats (need a little help from Guava to convert from int[] to double[]) System.out.println("MIN: " + StatUtils.min(Doubles.toArray(Ints.asList(values)))); System.out.println("AVG: " + String.format("%.4f", StatUtils.mean(Doubles.toArray(Ints.asList(values))))); System.out.println("MAX: " + StatUtils.max(Doubles.toArray(Ints.asList(values)))); // some stats based upon frequencies System.out.println("NUM OF 7s: " + frequency.getCount(7)); System.out.println("CUMULATIVE FREQUENCY OF 7: " + frequency.getCumFreq(7)); System.out.println("PERCENTAGE OF 7s: " + frequency.getPct(7)); }
From source file:com.github.fritaly.svngraph.SvnGraph.java
public static void main(String[] args) throws Exception { if (args.length != 2) { System.out.println(String.format("%s <input-file> <output-file>", SvnGraph.class.getSimpleName())); System.exit(1);//from w w w . ja va2 s.com } final File input = new File(args[0]); if (!input.exists()) { throw new IllegalArgumentException( String.format("The given file '%s' doesn't exist", input.getAbsolutePath())); } final File output = new File(args[1]); final Document document = DocumentBuilderFactory.newInstance().newDocumentBuilder().parse(input); final History history = new History(document); final Set<String> rootPaths = history.getRootPaths(); System.out.println(rootPaths); for (String path : rootPaths) { System.out.println(path); System.out.println(history.getHistory(path).getRevisions()); System.out.println(); } int count = 0; FileWriter fileWriter = null; GraphMLWriter graphWriter = null; try { fileWriter = new FileWriter(output); graphWriter = new GraphMLWriter(fileWriter); final NodeStyle tagStyle = graphWriter.getNodeStyle(); tagStyle.setFillColor(Color.WHITE); graphWriter.graph(); // map associating node labels to their corresponding node id in the graph final Map<String, String> nodeIdsPerLabel = new TreeMap<>(); // the node style associated to each branch final Map<String, NodeStyle> nodeStyles = new TreeMap<>(); for (Revision revision : history.getSignificantRevisions()) { System.out.println(revision.getNumber() + " - " + revision.getMessage()); // TODO Render also the deletion of branches // there should be only 1 significant update per revision (the one with action ADD) for (Update update : revision.getSignificantUpdates()) { if (update.isCopy()) { // a merge is also considered a copy final RevisionPath source = update.getCopySource(); System.out.println(String.format(" > %s %s from %s@%d", update.getAction(), update.getPath(), source.getPath(), source.getRevision())); final String sourceRoot = Utils.getRootName(source.getPath()); if (sourceRoot == null) { // skip the revisions whose associated root is // null (happens whether a branch was created // outside the 'branches' directory for // instance) System.err.println(String.format("Skipped revision %d because of a null root", source.getRevision())); continue; } final String sourceLabel = computeNodeLabel(sourceRoot, source.getRevision()); // create a node for the source (path, revision) final String sourceId; if (nodeIdsPerLabel.containsKey(sourceLabel)) { // retrieve the id of the existing node sourceId = nodeIdsPerLabel.get(sourceLabel); } else { // create the new node if (Utils.isTagPath(source.getPath())) { graphWriter.setNodeStyle(tagStyle); } else { if (!nodeStyles.containsKey(sourceRoot)) { final NodeStyle style = new NodeStyle(); style.setFillColor(randomColor()); nodeStyles.put(sourceRoot, style); } graphWriter.setNodeStyle(nodeStyles.get(sourceRoot)); } sourceId = graphWriter.node(sourceLabel); nodeIdsPerLabel.put(sourceLabel, sourceId); } // and another for the newly created directory final String targetRoot = Utils.getRootName(update.getPath()); if (targetRoot == null) { System.err.println(String.format("Skipped revision %d because of a null root", revision.getNumber())); continue; } final String targetLabel = computeNodeLabel(targetRoot, revision.getNumber()); if (Utils.isTagPath(update.getPath())) { graphWriter.setNodeStyle(tagStyle); } else { if (!nodeStyles.containsKey(targetRoot)) { final NodeStyle style = new NodeStyle(); style.setFillColor(randomColor()); nodeStyles.put(targetRoot, style); } graphWriter.setNodeStyle(nodeStyles.get(targetRoot)); } final String targetId; if (nodeIdsPerLabel.containsKey(targetLabel)) { // retrieve the id of the existing node targetId = nodeIdsPerLabel.get(targetLabel); } else { // create the new node if (Utils.isTagPath(update.getPath())) { graphWriter.setNodeStyle(tagStyle); } else { if (!nodeStyles.containsKey(targetRoot)) { final NodeStyle style = new NodeStyle(); style.setFillColor(randomColor()); nodeStyles.put(targetRoot, style); } graphWriter.setNodeStyle(nodeStyles.get(targetRoot)); } targetId = graphWriter.node(targetLabel); nodeIdsPerLabel.put(targetLabel, targetId); } // create an edge between the 2 nodes graphWriter.edge(sourceId, targetId); } else { System.out.println(String.format(" > %s %s", update.getAction(), update.getPath())); } } System.out.println(); count++; } // Dispatch the revisions per corresponding branch final Map<String, Set<Long>> revisionsPerBranch = new TreeMap<>(); for (String nodeLabel : nodeIdsPerLabel.keySet()) { if (nodeLabel.contains("@")) { final String branchName = StringUtils.substringBefore(nodeLabel, "@"); final long revision = Long.parseLong(StringUtils.substringAfter(nodeLabel, "@")); if (!revisionsPerBranch.containsKey(branchName)) { revisionsPerBranch.put(branchName, new TreeSet<Long>()); } revisionsPerBranch.get(branchName).add(revision); } else { throw new IllegalStateException(nodeLabel); } } // Recreate the missing edges between revisions from a same branch for (String branchName : revisionsPerBranch.keySet()) { final List<Long> branchRevisions = new ArrayList<>(revisionsPerBranch.get(branchName)); for (int i = 0; i < branchRevisions.size() - 1; i++) { final String nodeLabel1 = String.format("%s@%d", branchName, branchRevisions.get(i)); final String nodeLabel2 = String.format("%s@%d", branchName, branchRevisions.get(i + 1)); graphWriter.edge(nodeIdsPerLabel.get(nodeLabel1), nodeIdsPerLabel.get(nodeLabel2)); } } graphWriter.closeGraph(); System.out.println(String.format("Found %d significant revisions", count)); } finally { if (graphWriter != null) { graphWriter.close(); } if (fileWriter != null) { fileWriter.close(); } } System.out.println("Done"); }
From source file:example.client.CamelMongoJmsStockClient.java
@SuppressWarnings("resource") public static void main(final String[] args) throws Exception { systemProps = loadProperties();//from w w w . jav a 2 s .c om AbstractApplicationContext context = new ClassPathXmlApplicationContext("camel-client.xml"); ProducerTemplate camelTemplate = context.getBean("camelTemplate", ProducerTemplate.class); List<Map<String, Object>> stocks = readJsonsFromMongoDB(); for (Map<String, Object> stock : stocks) { stock.remove("_id"); stock.remove("Earnings Date"); camelTemplate.sendBody(String.format("jms:queue:%s", systemProps.getProperty("ticker.queue.name")), ExchangePattern.InOnly, stock); } new Timer().schedule(new TimerTask() { @Override public void run() { Map<String, Object> aRandomStock = stocks.get(RandomUtils.nextInt(0, stocks.size())); aRandomStock.put("Price", ((Double) aRandomStock.get("Price")) + RandomUtils.nextFloat(0.1f, 9.99f)); camelTemplate.sendBody(String.format("jms:queue:%s", systemProps.getProperty("ticker.queue.name")), ExchangePattern.InOnly, aRandomStock); } }, 1000, 2000); }
From source file:com.uber.tchannel.ping.PingServer.java
public static void main(String[] args) throws Exception { Options options = new Options(); options.addOption("p", "port", true, "Server Port to connect to"); options.addOption("?", "help", false, "Usage"); HelpFormatter formatter = new HelpFormatter(); CommandLineParser parser = new DefaultParser(); CommandLine cmd = parser.parse(options, args); if (cmd.hasOption("?")) { formatter.printHelp("PingClient", options, true); return;/*from www .j a v a 2s. com*/ } int port = Integer.parseInt(cmd.getOptionValue("p", "8888")); System.out.println(String.format("Starting server on port: %d", port)); new PingServer(port).run(); System.out.println("Stopping server..."); }
From source file:com.spotify.cassandra.opstools.CountTombstones.java
/** * Counts the number of tombstones, per row, in a given SSTable * * Assumes RandomPartitioner, standard columns and UTF8 encoded row keys * * Does not require a cassandra.yaml file or system tables. * * @param args command lines arguments//www. ja v a 2 s . co m * * @throws java.io.IOException on failure to open/read/write files or output streams */ public static void main(String[] args) throws IOException, ParseException { String usage = String.format("Usage: %s [-l] <sstable> [<sstable> ...]%n", CountTombstones.class.getName()); final Options options = new Options(); options.addOption("l", "legend", false, "Include column name explanation"); options.addOption("p", "partitioner", true, "The partitioner used by database"); CommandLineParser parser = new BasicParser(); CommandLine cmd = parser.parse(options, args); if (cmd.getArgs().length < 1) { System.err.println("You must supply at least one sstable"); System.err.println(usage); System.exit(1); } // Fake DatabaseDescriptor settings so we don't have to load cassandra.yaml etc Config.setClientMode(true); String partitionerName = String.format("org.apache.cassandra.dht.%s", options.hasOption("p") ? options.getOption("p") : "RandomPartitioner"); try { Class<?> clazz = Class.forName(partitionerName); IPartitioner partitioner = (IPartitioner) clazz.newInstance(); DatabaseDescriptor.setPartitioner(partitioner); } catch (Exception e) { throw new RuntimeException("Can't instantiate partitioner " + partitionerName); } PrintStream out = System.out; for (String arg : cmd.getArgs()) { String ssTableFileName = new File(arg).getAbsolutePath(); Descriptor descriptor = Descriptor.fromFilename(ssTableFileName); run(descriptor, cmd, out); } System.exit(0); }
From source file:com.github.zerkseez.codegen.wrappergenerator.Main.java
public static void main(final String[] args) throws Exception { final Options options = new Options(); options.addOption(Option.builder().longOpt("outputDirectory").hasArg().required().build()); options.addOption(Option.builder().longOpt("classMappings").hasArgs().required().build()); final CommandLineParser parser = new DefaultParser(); try {//from w w w . j av a2 s . c o m final CommandLine line = parser.parse(options, args); final String outputDirectory = line.getOptionValue("outputDirectory"); final String[] classMappings = line.getOptionValues("classMappings"); for (String classMapping : classMappings) { final String[] tokens = classMapping.split(":"); if (tokens.length != 2) { throw new IllegalArgumentException( String.format("Invalid class mapping format \"%s\"", classMapping)); } final Class<?> wrappeeClass = Class.forName(tokens[0]); final String fullWrapperClassName = tokens[1]; final int indexOfLastDot = fullWrapperClassName.lastIndexOf('.'); final String wrapperPackageName = (indexOfLastDot == -1) ? "" : fullWrapperClassName.substring(0, indexOfLastDot); final String simpleWrapperClassName = (indexOfLastDot == -1) ? fullWrapperClassName : fullWrapperClassName.substring(indexOfLastDot + 1); System.out.println(String.format("Generating wrapper class for %s...", wrappeeClass)); final WrapperGenerator generator = new WrapperGenerator(wrappeeClass, wrapperPackageName, simpleWrapperClassName); generator.writeTo(outputDirectory, true); } System.out.println("Done"); } catch (MissingOptionException e) { final HelpFormatter formatter = new HelpFormatter(); formatter.printHelp(String.format("java -cp CLASSPATH %s", Main.class.getName()), options); } }
From source file:examples.cnn.cifar.Cifar10Classification.java
public static void main(String[] args) { CifarReader.downloadAndExtract();/*from ww w .j av a 2s.co m*/ int numLabels = 10; SparkConf conf = new SparkConf(); conf.setMaster(String.format("local[%d]", NUM_CORES)); conf.setAppName("Cifar-10 CNN Classification"); conf.set(SparkDl4jMultiLayer.AVERAGE_EACH_ITERATION, String.valueOf(true)); try (JavaSparkContext sc = new JavaSparkContext(conf)) { NetworkTrainer trainer = new NetworkTrainer.Builder().model(ModelLibrary.net2) .networkToSparkNetwork(net -> new SparkDl4jMultiLayer(sc, net)).numLabels(numLabels) .cores(NUM_CORES).build(); JavaPairRDD<String, PortableDataStream> files = sc.binaryFiles("data/cifar-10-batches-bin"); JavaRDD<double[]> imagesTrain = files .filter(f -> ArrayUtils.contains(CifarReader.TRAIN_DATA_FILES, extractFileName.apply(f._1))) .flatMap(f -> CifarReader.rawDouble(f._2.open())); JavaRDD<double[]> imagesTest = files .filter(f -> CifarReader.TEST_DATA_FILE.equals(extractFileName.apply(f._1))) .flatMap(f -> CifarReader.rawDouble(f._2.open())); JavaRDD<DataSet> testDataset = imagesTest.map(i -> { INDArray label = FeatureUtil.toOutcomeVector(Double.valueOf(i[0]).intValue(), numLabels); double[] arr = Arrays.stream(ArrayUtils.remove(i, 0)).boxed().map(normalize2) .mapToDouble(Double::doubleValue).toArray(); INDArray features = Nd4j.create(arr, new int[] { 1, arr.length }); return new DataSet(features, label); }).cache(); log.info("Number of test images {}", testDataset.count()); JavaPairRDD<INDArray, double[]> labelsWithDataTrain = imagesTrain.mapToPair(i -> { INDArray label = FeatureUtil.toOutcomeVector(Double.valueOf(i[0]).intValue(), numLabels); double[] arr = Arrays.stream(ArrayUtils.remove(i, 0)).boxed().map(normalize2) .mapToDouble(Double::doubleValue).toArray(); return new Tuple2<>(label, arr); }); JavaRDD<DataSet> flipped = labelsWithDataTrain.map(t -> { double[] arr = t._2; int idx = 0; double[] farr = new double[arr.length]; for (int i = 0; i < arr.length; i += trainer.getWidth()) { double[] temp = Arrays.copyOfRange(arr, i, i + trainer.getWidth()); ArrayUtils.reverse(temp); for (int j = 0; j < trainer.getHeight(); ++j) { farr[idx++] = temp[j]; } } INDArray features = Nd4j.create(farr, new int[] { 1, farr.length }); return new DataSet(features, t._1); }); JavaRDD<DataSet> trainDataset = labelsWithDataTrain.map(t -> { INDArray features = Nd4j.create(t._2, new int[] { 1, t._2.length }); return new DataSet(features, t._1); }).union(flipped).cache(); log.info("Number of train images {}", trainDataset.count()); trainer.train(trainDataset, testDataset); } }