Example usage for com.google.common.io Closeables close

List of usage examples for com.google.common.io Closeables close

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Introduction

In this page you can find the example usage for com.google.common.io Closeables close.

Prototype

public static void close(@Nullable Closeable closeable, boolean swallowIOException) throws IOException

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Document

Closes a Closeable , with control over whether an IOException may be thrown.

Usage

From source file:org.apache.mahout.classifier.sgd.PrintResourceOrFile.java

public static void main(String[] args) throws Exception {
    Preconditions.checkArgument(args.length == 1, "Must have a single argument that names a file or resource.");
    BufferedReader in = TrainLogistic.open(args[0]);
    try {//from ww  w  . j a v  a2  s  . co  m
        String line;
        while ((line = in.readLine()) != null) {
            System.out.println(line);
        }
    } finally {
        Closeables.close(in, true);
    }
}

From source file:com.google.android.stardroid.data.AsciiToBinaryProtoWriter.java

public static void main(String[] args) throws IOException {
    if (args.length != 1 || !args[0].endsWith(".ascii")) {
        System.out.println("Usage: AsciiToBinaryProtoWriter <inputprefix>.ascii");
        System.exit(1);//from   w ww  .j ava  2s  .  c  o  m
    }

    FileReader in = null;
    FileOutputStream out = null;

    try {
        in = new FileReader(args[0]);
        AstronomicalSourcesProto.Builder builder = AstronomicalSourcesProto.newBuilder();
        TextFormat.merge(in, builder);

        out = new FileOutputStream(args[0].substring(0, args[0].length() - 6) + ".binary");

        AstronomicalSourcesProto sources = builder.build();
        System.out.println("Source count " + sources.getSourceCount());
        sources.writeTo(out);
    } finally {
        Closeables.closeQuietly(in);
        Closeables.close(out, false);
    }
}

From source file:org.apache.mahout.cf.taste.example.kddcup.ToCSV.java

public static void main(String[] args) throws Exception {

    File inputFile = new File(args[0]);
    File outputFile = new File(args[1]);
    int columnsToOutput = 4;
    if (args.length >= 3) {
        columnsToOutput = Integer.parseInt(args[2]);
    }//from  ww w  .  j a  v  a  2 s  .c  o m

    OutputStream outStream = new GZIPOutputStream(new FileOutputStream(outputFile));
    Writer outWriter = new BufferedWriter(new OutputStreamWriter(outStream, Charsets.UTF_8));

    try {
        for (Pair<PreferenceArray, long[]> user : new DataFileIterable(inputFile)) {
            PreferenceArray prefs = user.getFirst();
            long[] timestamps = user.getSecond();
            for (int i = 0; i < prefs.length(); i++) {
                outWriter.write(String.valueOf(prefs.getUserID(i)));
                outWriter.write(',');
                outWriter.write(String.valueOf(prefs.getItemID(i)));
                if (columnsToOutput > 2) {
                    outWriter.write(',');
                    outWriter.write(String.valueOf(prefs.getValue(i)));
                }
                if (columnsToOutput > 3) {
                    outWriter.write(',');
                    outWriter.write(String.valueOf(timestamps[i]));
                }
                outWriter.write('\n');
            }
        }
    } finally {
        Closeables.close(outWriter, false);
    }
}

From source file:org.apache.mahout.clustering.display.DisplaySpectralKMeans.java

public static void main(String[] args) throws Exception {
    System.out.println("hello");
    DistanceMeasure measure = new ManhattanDistanceMeasure();
    Path samples = new Path(SAMPLES);
    Path output = new Path(OUTPUT);
    Path tempDir = new Path(TEMP);
    Configuration conf = new Configuration();
    conf.set("mapred.job.tracker", "ajila-server-01:54311");
    conf.set("fs.default.name", "hdfs://ajila-server-01:54310");
    HadoopUtil.delete(conf, samples);//from w ww .  java  2 s.c  o m
    HadoopUtil.delete(conf, output);

    RandomUtils.useTestSeed();
    DisplayClustering.generateSamples();
    writeSampleData(samples);
    Path affinities = new Path(output, AFFINITIES);
    FileSystem fs = FileSystem.get(output.toUri(), conf);
    if (!fs.exists(output)) {
        fs.mkdirs(output);
    }
    Writer writer = null;
    try {
        writer = Files.newWriter(new File(affinities.toString()), Charsets.UTF_8);
        for (int i = 0; i < SAMPLE_DATA.size(); i++) {
            for (int j = 0; j < SAMPLE_DATA.size(); j++) {
                writer.write(i + "," + j + ','
                        + measure.distance(SAMPLE_DATA.get(i).get(), SAMPLE_DATA.get(j).get()) + '\n');
            }
        }
    } finally {
        Closeables.close(writer, false);
    }
    int maxIter = 10;
    double convergenceDelta = 0.001;
    SpectralKMeansDriver.run(new Configuration(), affinities, output, SAMPLE_DATA.size(), 3, measure,
            convergenceDelta, maxIter, tempDir, false);
    new DisplaySpectralKMeans();
}

From source file:com.google.android.stardroid.data.AsciiProtoRewriter.java

public static void main(String[] args) throws IOException {
    if (args.length != 1 || !args[0].endsWith("_R.ascii")) {
        System.out.println("Usage: AsciiToBinaryProtoWriter <inputprefix>_R.ascii");
        System.exit(1);//from  w ww  .  ja va  2 s .co  m
    }

    String inputFile = args[0];
    String outputFile = args[0].replaceAll("_R.ascii", ".ascii");

    BufferedReader in = null;
    PrintWriter out = null;
    try {
        in = new BufferedReader(new FileReader(inputFile));
        out = new PrintWriter(new FileWriter(outputFile));

        String s;
        while ((s = in.readLine()) != null) {
            if (s.contains("REMOVE")) {
                try {
                    s = s.replaceAll("REMOVE_", "");
                    String[] tokens = s.split("\\s+");
                    for (String token : tokens) {
                        if (token.contains("R.string")) {
                            int value = getString(token);
                            s = s.replaceAll(token, "" + value);
                        }
                    }
                } catch (MissingStringException m) {
                    System.out.println("Warning: " + m.getMessage() + ".  Skipping...");
                    continue;
                }
            }
            out.println(s);
        }
    } finally {
        Closeables.closeQuietly(in);
        Closeables.close(out, false);
    }
}

From source file:org.apache.mahout.cf.taste.example.kddcup.track1.Track1Runner.java

public static void main(String[] args) throws Exception {

    File dataFileDirectory = new File(args[0]);
    if (!dataFileDirectory.exists() || !dataFileDirectory.isDirectory()) {
        throw new IllegalArgumentException("Bad data file directory: " + dataFileDirectory);
    }/*w ww  .  j av  a  2  s.c o m*/

    long start = System.currentTimeMillis();

    KDDCupDataModel model = new KDDCupDataModel(KDDCupDataModel.getTrainingFile(dataFileDirectory));
    Track1Recommender recommender = new Track1Recommender(model);

    long end = System.currentTimeMillis();
    log.info("Loaded model in {}s", (end - start) / 1000);
    start = end;

    Collection<Track1Callable> callables = Lists.newArrayList();
    for (Pair<PreferenceArray, long[]> tests : new DataFileIterable(
            KDDCupDataModel.getTestFile(dataFileDirectory))) {
        PreferenceArray userTest = tests.getFirst();
        callables.add(new Track1Callable(recommender, userTest));
    }

    int cores = Runtime.getRuntime().availableProcessors();
    log.info("Running on {} cores", cores);
    ExecutorService executor = Executors.newFixedThreadPool(cores);
    List<Future<byte[]>> results = executor.invokeAll(callables);
    executor.shutdown();

    end = System.currentTimeMillis();
    log.info("Ran recommendations in {}s", (end - start) / 1000);
    start = end;

    OutputStream out = new BufferedOutputStream(new FileOutputStream(new File(args[1])));
    try {
        for (Future<byte[]> result : results) {
            for (byte estimate : result.get()) {
                out.write(estimate);
            }
        }
    } finally {
        Closeables.close(out, false);
    }

    end = System.currentTimeMillis();
    log.info("Wrote output in {}s", (end - start) / 1000);
}

From source file:org.apache.mahout.classifier.sequencelearning.hmm.RandomSequenceGenerator.java

public static void main(String[] args) throws IOException {
    DefaultOptionBuilder optionBuilder = new DefaultOptionBuilder();
    ArgumentBuilder argumentBuilder = new ArgumentBuilder();

    Option outputOption = optionBuilder.withLongName("output")
            .withDescription("Output file with sequence of observed states").withShortName("o")
            .withArgument(argumentBuilder.withMaximum(1).withMinimum(1).withName("path").create())
            .withRequired(false).create();

    Option modelOption = optionBuilder.withLongName("model").withDescription("Path to serialized HMM model")
            .withShortName("m")
            .withArgument(argumentBuilder.withMaximum(1).withMinimum(1).withName("path").create())
            .withRequired(true).create();

    Option lengthOption = optionBuilder.withLongName("length").withDescription("Length of generated sequence")
            .withShortName("l")
            .withArgument(argumentBuilder.withMaximum(1).withMinimum(1).withName("number").create())
            .withRequired(true).create();

    Group optionGroup = new GroupBuilder().withOption(outputOption).withOption(modelOption)
            .withOption(lengthOption).withName("Options").create();

    try {/*www. ja  va 2  s  . c  o  m*/
        Parser parser = new Parser();
        parser.setGroup(optionGroup);
        CommandLine commandLine = parser.parse(args);

        String output = (String) commandLine.getValue(outputOption);

        String modelPath = (String) commandLine.getValue(modelOption);

        int length = Integer.parseInt((String) commandLine.getValue(lengthOption));

        //reading serialized HMM
        DataInputStream modelStream = new DataInputStream(new FileInputStream(modelPath));
        HmmModel model;
        try {
            model = LossyHmmSerializer.deserialize(modelStream);
        } finally {
            Closeables.close(modelStream, true);
        }

        //generating observations
        int[] observations = HmmEvaluator.predict(model, length, System.currentTimeMillis());

        //writing output
        PrintWriter writer = new PrintWriter(
                new OutputStreamWriter(new FileOutputStream(output), Charsets.UTF_8), true);
        try {
            for (int observation : observations) {
                writer.print(observation);
                writer.print(' ');
            }
        } finally {
            Closeables.close(writer, false);
        }
    } catch (OptionException e) {
        CommandLineUtil.printHelp(optionGroup);
    }
}

From source file:org.apache.mahout.classifier.sequencelearning.hmm.ViterbiEvaluator.java

public static void main(String[] args) throws IOException {
    DefaultOptionBuilder optionBuilder = new DefaultOptionBuilder();
    ArgumentBuilder argumentBuilder = new ArgumentBuilder();

    Option inputOption = DefaultOptionCreator.inputOption().create();

    Option outputOption = DefaultOptionCreator.outputOption().create();

    Option modelOption = optionBuilder.withLongName("model").withDescription("Path to serialized HMM model")
            .withShortName("m")
            .withArgument(argumentBuilder.withMaximum(1).withMinimum(1).withName("path").create())
            .withRequired(true).create();

    Option likelihoodOption = optionBuilder.withLongName("likelihood")
            .withDescription("Compute likelihood of observed sequence").withShortName("l").withRequired(false)
            .create();/*w  w  w .j  a va  2s  .c  o m*/

    Group optionGroup = new GroupBuilder().withOption(inputOption).withOption(outputOption)
            .withOption(modelOption).withOption(likelihoodOption).withName("Options").create();

    try {
        Parser parser = new Parser();
        parser.setGroup(optionGroup);
        CommandLine commandLine = parser.parse(args);

        String input = (String) commandLine.getValue(inputOption);
        String output = (String) commandLine.getValue(outputOption);

        String modelPath = (String) commandLine.getValue(modelOption);

        boolean computeLikelihood = commandLine.hasOption(likelihoodOption);

        //reading serialized HMM
        DataInputStream modelStream = new DataInputStream(new FileInputStream(modelPath));
        HmmModel model;
        try {
            model = LossyHmmSerializer.deserialize(modelStream);
        } finally {
            Closeables.close(modelStream, true);
        }

        //reading observations
        List<Integer> observations = Lists.newArrayList();
        Scanner scanner = new Scanner(new FileInputStream(input), "UTF-8");
        try {
            while (scanner.hasNextInt()) {
                observations.add(scanner.nextInt());
            }
        } finally {
            scanner.close();
        }

        int[] observationsArray = new int[observations.size()];
        for (int i = 0; i < observations.size(); ++i) {
            observationsArray[i] = observations.get(i);
        }

        //decoding
        int[] hiddenStates = HmmEvaluator.decode(model, observationsArray, true);

        //writing output
        PrintWriter writer = new PrintWriter(
                new OutputStreamWriter(new FileOutputStream(output), Charsets.UTF_8), true);
        try {
            for (int hiddenState : hiddenStates) {
                writer.print(hiddenState);
                writer.print(' ');
            }
        } finally {
            Closeables.close(writer, false);
        }

        if (computeLikelihood) {
            System.out.println("Likelihood: " + HmmEvaluator.modelLikelihood(model, observationsArray, true));
        }
    } catch (OptionException e) {
        CommandLineUtil.printHelp(optionGroup);
    }
}

From source file:SimpleCsvExamples.java

public static void main(String[] args) throws IOException {
    FeatureVectorEncoder[] encoder = new FeatureVectorEncoder[FIELDS];
    for (int i = 0; i < FIELDS; i++) {
        encoder[i] = new ConstantValueEncoder("v" + 1);
    }/*from   w ww.j a v  a 2  s .  c om*/

    OnlineSummarizer[] s = new OnlineSummarizer[FIELDS];
    for (int i = 0; i < FIELDS; i++) {
        s[i] = new OnlineSummarizer();
    }
    long t0 = System.currentTimeMillis();
    Vector v = new DenseVector(1000);
    if ("--generate".equals(args[0])) {
        PrintWriter out = new PrintWriter(
                new OutputStreamWriter(new FileOutputStream(new File(args[2])), Charsets.UTF_8));
        try {
            int n = Integer.parseInt(args[1]);
            for (int i = 0; i < n; i++) {
                Line x = Line.generate();
                out.println(x);
            }
        } finally {
            Closeables.close(out, false);
        }
    } else if ("--parse".equals(args[0])) {
        BufferedReader in = Files.newReader(new File(args[1]), Charsets.UTF_8);
        double total = 0;
        try {
            String line = in.readLine();
            while (line != null) {
                v.assign(0);
                Line x = new Line(line);
                for (int i = 0; i < FIELDS; i++) {
                    double z = x.getDouble(i);
                    total += z;
                    //s[i].add(x.getDouble(i));
                    encoder[i].addToVector(x.get(i), v);
                }
                line = in.readLine();
            }
        } finally {
            Closeables.close(in, true);
        }
        //      String separator = "";
        //      for (int i = 0; i < FIELDS; i++) {
        //        System.out.printf("%s%.3f", separator, s[i].getMean());
        //        separator = ",";
        //      }
        System.out.println("total: " + total);
    } else if ("--fast".equals(args[0])) {
        FastLineReader in = new FastLineReader(new FileInputStream(args[1]));
        double total = 0;
        try {
            FastLine line = in.read();
            while (line != null) {
                v.assign(0);
                for (int i = 0; i < FIELDS; i++) {
                    double z = line.getDouble(i);
                    total += z;
                    //s[i].add(z);
                    encoder[i].addToVector((byte[]) null, z, v);
                }
                line = in.read();
            }
        } finally {
            Closeables.close(in, true);
        }
        //      String separator = "";
        //      for (int i = 0; i < FIELDS; i++) {
        //        System.out.printf("%s%.3f", separator, s[i].getMean());
        //        separator = ",";
        //      }
        System.out.println("total: " + total);
    }
    System.out.printf("\nElapsed time = %.3f%n", (System.currentTimeMillis() - t0) / 1000.0);
}

From source file:org.apache.mahout.cf.taste.example.kddcup.track2.Track2Runner.java

public static void main(String[] args) throws Exception {

    File dataFileDirectory = new File(args[0]);
    if (!dataFileDirectory.exists() || !dataFileDirectory.isDirectory()) {
        throw new IllegalArgumentException("Bad data file directory: " + dataFileDirectory);
    }//w w  w. j a  v  a 2 s.  c  o  m

    long start = System.currentTimeMillis();

    KDDCupDataModel model = new KDDCupDataModel(KDDCupDataModel.getTrainingFile(dataFileDirectory));
    Track2Recommender recommender = new Track2Recommender(model, dataFileDirectory);

    long end = System.currentTimeMillis();
    log.info("Loaded model in {}s", (end - start) / 1000);
    start = end;

    Collection<Track2Callable> callables = Lists.newArrayList();
    for (Pair<PreferenceArray, long[]> tests : new DataFileIterable(
            KDDCupDataModel.getTestFile(dataFileDirectory))) {
        PreferenceArray userTest = tests.getFirst();
        callables.add(new Track2Callable(recommender, userTest));
    }

    int cores = Runtime.getRuntime().availableProcessors();
    log.info("Running on {} cores", cores);
    ExecutorService executor = Executors.newFixedThreadPool(cores);
    List<Future<UserResult>> futures = executor.invokeAll(callables);
    executor.shutdown();

    end = System.currentTimeMillis();
    log.info("Ran recommendations in {}s", (end - start) / 1000);
    start = end;

    OutputStream out = new BufferedOutputStream(new FileOutputStream(new File(args[1])));
    try {
        long lastUserID = Long.MIN_VALUE;
        for (Future<UserResult> future : futures) {
            UserResult result = future.get();
            long userID = result.getUserID();
            if (userID <= lastUserID) {
                throw new IllegalStateException();
            }
            lastUserID = userID;
            out.write(result.getResultBytes());
        }
    } finally {
        Closeables.close(out, false);
    }

    end = System.currentTimeMillis();
    log.info("Wrote output in {}s", (end - start) / 1000);
}