Examples of org.apache.mahout.common.TimingStatistics$Call

org.apache.mahout.common.TimingStatistics

    }
    call.end();
  }


  public void incrementalCreateBenchmark() {
    TimingStatistics stats = new TimingStatistics();
    for (int l = 0; l < loop; l++) {
      for (int i = 0; i < numVectors; i++) {
        vectors[0][i] = new DenseVector(cardinality);
        buildVectorIncrementally(stats, i, vectors[0][i], false);
      }
    }
    printStats(stats, "Create (incrementally)", "DenseVector");


    stats = new TimingStatistics();
    for (int l = 0; l < loop; l++) {
      for (int i = 0; i < numVectors; i++) {
        vectors[1][i] = new RandomAccessSparseVector(cardinality);
        buildVectorIncrementally(stats, i, vectors[1][i], false);
      }
    }
    printStats(stats, "Create (incrementally)", "RandSparseVector");


    stats = new TimingStatistics();
    for (int l = 0; l < loop; l++) {
      for (int i = 0; i < numVectors; i++) {
        vectors[2][i] = new SequentialAccessSparseVector(cardinality);
        buildVectorIncrementally(stats, i, vectors[2][i], false);
      }

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    }
    printStats(stats, "Create (incrementally)", "SeqSparseVector");
  }
  
  public void cloneBenchmark() {
    TimingStatistics stats = new TimingStatistics();
    for (int l = 0; l < loop; l++) {
      for (int i = 0; i < numVectors; i++) {
        TimingStatistics.Call call = stats.newCall();
        vectors[0][i] = vectors[0][i].clone();
        call.end();
      }
    }
    printStats(stats, "Clone", "DenseVector");
    
    stats = new TimingStatistics();
    for (int l = 0; l < loop; l++) {
      for (int i = 0; i < numVectors; i++) {
        TimingStatistics.Call call = stats.newCall();
        vectors[1][i] = vectors[1][i].clone();
        call.end();
      }
    }
    printStats(stats, "Clone", "RandSparseVector");
    
    stats = new TimingStatistics();
    for (int l = 0; l < loop; l++) {
      for (int i = 0; i < numVectors; i++) {
        TimingStatistics.Call call = stats.newCall();
        vectors[2][i] = vectors[2][i].clone();
        call.end();
      }
    }
    printStats(stats, "Clone", "SeqSparseVector");

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      new Path("/tmp/dense-vector"), IntWritable.class, VectorWritable.class);


    Writable one = new IntWritable(0);
    VectorWritable 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();
        vec.set(vectors[0][i]);
        writer.append(one, vec);
        call.end();
      }
    }
    writer.close();
    printStats(stats, "Serialize", "DenseVector");
    
    writer = new SequenceFile.Writer(fs, conf,
      new Path("/tmp/randsparse-vector"), IntWritable.class, VectorWritable.class);
    stats = new TimingStatistics();
    for (int l = 0; l < loop; l++) {
      for (int i = 0; i < numVectors; i++) {
        TimingStatistics.Call call = stats.newCall();
        vec.set(vectors[1][i]);
        writer.append(one, vec);
        call.end();
      }
    }
    writer.close();
    printStats(stats, "Serialize", "RandSparseVector");
    
    writer = new SequenceFile.Writer(fs, conf,
      new Path("/tmp/seqsparse-vector"), IntWritable.class, VectorWritable.class);
    stats = new TimingStatistics();
    for (int l = 0; l < loop; l++) {
      for (int i = 0; i < numVectors; i++) {
        TimingStatistics.Call call = stats.newCall();
        vec.set(vectors[2][i]);
        writer.append(one, vec);
        call.end();
      }
    }

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    SequenceFile.Reader reader = new SequenceFile.Reader(fs,
      new Path("/tmp/dense-vector"), conf);


    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();
    printStats(stats, "Deserialize", "DenseVector");
    
    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();
    printStats(stats, "Deserialize", "RandSparseVector");
    
    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
    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
    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
    printStats(stats, "DotProduct", "SeqSparseVector", "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[0][i].dot(vectors[1][(i + 1) % numVectors]);
        call.end();
      }
    }
    // print result to prevent hotspot from eliminating deadcode
    printStats(stats, "DotProduct", "Dense.fn(Rand)", "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[0][i].dot(vectors[2][(i + 1) % numVectors]);
        call.end();
      }
    }
    // print result to prevent hotspot from eliminating deadcode
    printStats(stats, "DotProduct", "Dense.fn(Seq)", "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[0][(i + 1) % numVectors]);
        call.end();
      }
    }
    // print result to prevent hotspot from eliminating deadcode
    printStats(stats, "DotProduct", "Rand.fn(Dense)", "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[2][(i + 1) % numVectors]);
        call.end();
      }
    }
    // print result to prevent hotspot from eliminating deadcode
    printStats(stats, "DotProduct", "Rand.fn(Seq)", "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[0][(i + 1) % numVectors]);
        call.end();
      }
    }
    // print result to prevent hotspot from eliminating deadcode
    printStats(stats, "DotProduct", "Seq.fn(Dense)", "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[1][(i + 1) % numVectors]);
        call.end();
      }
    }
    // print result to prevent hotspot from eliminating deadcode

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      throw new IllegalArgumentException("Unrecognized dataSource type: " + params.get("dataSource"));
    }
    ClassifierContext classifier = new ClassifierContext(algorithm, datastore);
    classifier.initialize();
    ResultAnalyzer resultAnalyzer = new ResultAnalyzer(classifier.getLabels(), params.get("defaultCat"));
    TimingStatistics totalStatistics = new TimingStatistics();
    if (subdirs != null) {
      
      for (File file : subdirs) {
        if (verbose) {
          log.info("--------------");
          log.info("Testing: {}", file);
        }
        TimingStatistics operationStats = new TimingStatistics();
        
        long lineNum = 0;
        for (String line : new FileLineIterable(new File(file.getPath()), Charset.forName(params
            .get("encoding")), false)) {
          
          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();
            boolean correct = resultAnalyzer.addInstance(correctLabel, classifiedLabel);
            if (verbose) {
              // We have one document per line
              log.info("Line Number: {} Line(30): {} Expected Label: {} Classified Label: {} Correct: {}",
                new Object[] {lineNum, line.length() > 30 ? line.substring(0, 30) : line, correctLabel,
                              classifiedLabel.getLabel(), correct,});
            }
            // log.info("{} {}", correctLabel, classifiedLabel);
            
          }
          lineNum++;
        }
        /*
         * log.info("{}\t{}\t{}/{}", new Object[] {correctLabel,
         * resultAnalyzer.getConfusionMatrix().getAccuracy(correctLabel),
         * resultAnalyzer.getConfusionMatrix().getCorrect(correctLabel),
         * resultAnalyzer.getConfusionMatrix().getTotal(correctLabel)});
         */
        log.info("Classified instances from {}", file.getName());
        if (verbose) {
          log.info("Performance stats {}", operationStats.toString());
        }
      }
      
    }
    if (verbose) {

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    }
    return call.end(maxTimeUsec);
  }


  public void incrementalCreateBenchmark() {
    TimingStatistics stats = new TimingStatistics();
    for (int i = 0; i < loop; i++) {
      vectors[0][vIndex(i)] = new DenseVector(cardinality);
      if (buildVectorIncrementally(stats, vIndex(i), vectors[0][vIndex(i)], false)) {
        break;
      }
    }
    printStats(stats, CREATE_INCREMENTALLY, DENSE_VECTOR);


    stats = new TimingStatistics();
    for (int i = 0; i < loop; i++) {
      vectors[1][vIndex(i)] = new RandomAccessSparseVector(cardinality);
      if (buildVectorIncrementally(stats, vIndex(i), vectors[1][vIndex(i)], false)) {
        break;
      }
    }
    printStats(stats, CREATE_INCREMENTALLY, RAND_SPARSE_VECTOR);


    stats = new TimingStatistics();
    for (int i = 0; i < loop; i++) {
      vectors[2][vIndex(i)] = new SequentialAccessSparseVector(cardinality);
      if (buildVectorIncrementally(stats, vIndex(i), vectors[2][vIndex(i)], false)) {
        break;
      }
    }
    printStats(stats, CREATE_INCREMENTALLY, SEQ_SPARSE_VECTOR);


    if (numClusters > 0) {
      stats = new TimingStatistics();
      for (int i = 0; i < loop; i++) {
        clusters[cIndex(i)] = new RandomAccessSparseVector(cardinality);
        if (buildVectorIncrementally(stats, vIndex(i), clusters[cIndex(i)], false)) {
          break;
        }

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  private static boolean randBool() {
    return R.nextBoolean();
  }


  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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    }
    return stats;
  }


  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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        clusterDistances.setQuick(i, j, distance);
      }
    }


    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]);
          distanceCalculations++;
          if (distance < minDistance) {
            minDistance = distance;
          }
        }
      }
      if (call.end(mark.maxTimeUsec)) {
        break;
      }
    }
    mark.printStats(stats, measure.getClass().getName(), "Closest C w/o Elkan's trick", "distanceCalculations = "
        + distanceCalculations);


    distanceCalculations = 0;
    stats = new TimingStatistics();
    Random rand = RandomUtils.getRandom();
    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)];
        int closestCentroid = rand.nextInt(mark.numClusters);
        double dist = measure.distance(vector, mark.clusters[closestCentroid]);
        distanceCalculations++;

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