Package org.apache.mahout.common

Examples of org.apache.mahout.common.TimingStatistics.newCall()

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    Writable one = new IntWritable(0);
    Writable vec = new VectorWritable();
    TimingStatistics stats = new TimingStatistics();
    for (int l = 0; l < loop; l++) {
      for (int i = 0; i < numVectors; i++) {
        TimingStatistics.Call call = stats.newCall();
        reader.next(one, vec);
        call.end();
      }
    }
    reader.close();
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    reader = new SequenceFile.Reader(fs,
      new Path("/tmp/randsparse-vector"), conf);
    stats = new TimingStatistics();
    for (int l = 0; l < loop; l++) {
      for (int i = 0; i < numVectors; i++) {
        TimingStatistics.Call call = stats.newCall();
        reader.next(one, vec);
        call.end();
      }
    }
    reader.close();
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    reader = new SequenceFile.Reader(fs,
      new Path("/tmp/seqsparse-vector"), conf);
    stats = new TimingStatistics();
    for (int l = 0; l < loop; l++) {
      for (int i = 0; i < numVectors; i++) {
        TimingStatistics.Call call = stats.newCall();
        reader.next(one, vec);
        call.end();
      }
    }
    reader.close();
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  public void dotBenchmark() {
    double result = 0;
    TimingStatistics stats = new TimingStatistics();
    for (int l = 0; l < loop; l++) {
      for (int i = 0; i < numVectors; i++) {
        TimingStatistics.Call call = stats.newCall();
        result += vectors[0][i].dot(vectors[0][(i + 1) % numVectors]);
        call.end();
      }
    }
    // print result to prevent hotspot from eliminating deadcode
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    printStats(stats, "DotProduct", "DenseVector", "sum = " + result + ' ');
    result = 0;
    stats = new TimingStatistics();
    for (int l = 0; l < loop; l++) {
      for (int i = 0; i < numVectors; i++) {
        TimingStatistics.Call call = stats.newCall();
        result += vectors[1][i].dot(vectors[1][(i + 1) % numVectors]);
        call.end();
      }
    }
    // print result to prevent hotspot from eliminating deadcode
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    printStats(stats, "DotProduct", "RandSparseVector", "sum = " + result + ' ');
    result = 0;
    stats = new TimingStatistics();
    for (int l = 0; l < loop; l++) {
      for (int i = 0; i < numVectors; i++) {
        TimingStatistics.Call call = stats.newCall();
        result += vectors[2][i].dot(vectors[2][(i + 1) % numVectors]);
        call.end();
      }
    }
    // print result to prevent hotspot from eliminating deadcode
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          Map<String,List<String>> document = new NGrams(line, Integer.parseInt(params.get("gramSize")))
              .generateNGrams();
          for (Map.Entry<String,List<String>> stringListEntry : document.entrySet()) {
            String correctLabel = stringListEntry.getKey();
            List<String> strings = stringListEntry.getValue();
            TimingStatistics.Call call = operationStats.newCall();
            TimingStatistics.Call outercall = totalStatistics.newCall();
            ClassifierResult classifiedLabel = classifier.classifyDocument(strings.toArray(new String[strings
                .size()]), params.get("defaultCat"));
            call.end();
            outercall.end();
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  public TimingStatistics benchmark(BenchmarkFn function) {
    TimingStatistics stats = new TimingStatistics();
    boolean result = false;
    while (true) {
      int i = R.nextInt(BUCKET_SIZE);
      TimingStatistics.Call call = stats.newCall(leadTimeUsec);
      result = result ^ function.apply(i);
      if (call.end(maxTimeUsec)) {
        break;
      }
    }
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  public TimingStatistics benchmarkD(BenchmarkFnD function) {
    TimingStatistics stats = new TimingStatistics();
    double result = 0;
    while (true) {
      int i = R.nextInt(BUCKET_SIZE);
      TimingStatistics.Call call = stats.newCall(leadTimeUsec);
      result += function.apply(i);
      if (call.end(maxTimeUsec)) {
        break;
      }
    }
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    }

    long distanceCalculations = 0;
    TimingStatistics stats = new TimingStatistics();
    for (int l = 0; l < mark.loop; l++) {
      TimingStatistics.Call call = stats.newCall(mark.leadTimeUsec);
      for (int i = 0; i < mark.numVectors; i++) {
        Vector vector = mark.vectors[1][mark.vIndex(i)];
        double minDistance = Double.MAX_VALUE;
        for (int k = 0; k < mark.numClusters; k++) {
          double distance = measure.distance(vector, mark.clusters[k]);
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