gnu.trove.iterator.TDoubleIterator Java Examples

private ContinuousQueryDispatcher(Function<Retriever, RetrievalTask> taskFactory,
    TObjectDoubleMap<Retriever> retrieverWeights,
    RetrieverInitializer initializer) {
  this.taskFactory = taskFactory;
  this.initializer = initializer;
  this.retrieverWeights = retrieverWeights;

  double weightSum = 0d;
  TDoubleIterator i = retrieverWeights.valueCollection().iterator();
  while (i.hasNext()) {
    weightSum += i.next();
  }
  this.retrieverWeightSum = weightSum;
  LOGGER.trace("Initialized continuous query dispatcher with retrievers {}", retrieverWeights);
}
 

/** See {@link gnu.trove.list.array.TDoubleArrayList#iterator()} */
public TDoubleIterator iterator() {
  return delegate.iterator();
}
 

/**
 * A slight more complex test. This calls estimateAnomalyLikelihoods
 * to estimate the distribution on fake data, followed by several calls
 * to updateAnomalyLikelihoods.
 */
@Test
public void testUpdateAnomalyLikelihoods() {
    
    //----------------------------------------
    // Step 1. Generate an initial estimate using fake distribution of anomaly scores.
    List<Sample> data1 = generateSampleData(0.2, 0.2, 0.2, 0.2).subList(0, 1000);
    AnomalyLikelihoodMetrics metrics1 = an.estimateAnomalyLikelihoods(data1, 5, 0);
    
    //----------------------------------------
    // Step 2. Generate some new data with a higher average anomaly
    // score. Using the estimator from step 1, to compute likelihoods. Now we
    // should see a lot more anomalies.
    List<Sample> data2 = generateSampleData(0.6, 0.2, 0.2, 0.2).subList(0, 300);
    AnomalyLikelihoodMetrics metrics2 = an.updateAnomalyLikelihoods(data2, metrics1.getParams());
    assertEquals(metrics2.getLikelihoods().length, data2.size());
    assertEquals(metrics2.getAvgRecordList().size(), data2.size());
    assertTrue(an.isValidEstimatorParams(metrics2.getParams()));
    
    // The new running total should be different
    assertFalse(metrics1.getAvgRecordList().total == metrics2.getAvgRecordList().total);
    
    // We should have many more samples where likelihood is < 0.01, but not all
    Condition<Double> cond = new Condition.Adapter<Double>() {
        public boolean eval(double d) { return d < 0.01; }
    };
    int conditionCount = ArrayUtils.where(metrics2.getLikelihoods(), cond).length;
    assertTrue(conditionCount >= 25);
    assertTrue(conditionCount <= 250);
    
    //----------------------------------------
    // Step 3. Generate some new data with the expected average anomaly score. We
    // should see fewer anomalies than in Step 2.
    List<Sample> data3 = generateSampleData(0.2, 0.2, 0.2, 0.2).subList(0, 1000);
    AnomalyLikelihoodMetrics metrics3 = an.updateAnomalyLikelihoods(data3, metrics2.getParams());
    assertEquals(metrics3.getLikelihoods().length, data3.size());
    assertEquals(metrics3.getAvgRecordList().size(), data3.size());
    assertTrue(an.isValidEstimatorParams(metrics3.getParams()));
    
    // The new running total should be different
    assertFalse(metrics1.getAvgRecordList().total == metrics3.getAvgRecordList().total);
    assertFalse(metrics2.getAvgRecordList().total == metrics3.getAvgRecordList().total);
    
    // We should have a small number of samples where likelihood is < 0.02
    conditionCount = ArrayUtils.where(metrics3.getLikelihoods(), cond).length;
    assertTrue(conditionCount >= 1);
    assertTrue(conditionCount <= 100);
    
    //------------------------------------------
    // Step 4. Validate that sending data incrementally is the same as sending
    // in one batch
    List<Sample> allData = new ArrayList<>();
    allData.addAll(data1);
    allData.addAll(data2);
    allData.addAll(data3);
    AveragedAnomalyRecordList recordList = an.anomalyScoreMovingAverage(allData, 5);
    
    double[] historicalValuesAll = new double[recordList.historicalValues.size()];
    int i = 0;
    for(TDoubleIterator it = recordList.historicalValues.iterator();it.hasNext();) {
        historicalValuesAll[i++] = it.next();
    }
    assertEquals(ArrayUtils.sum(historicalValuesAll), ArrayUtils.sum(
        metrics3.getParams().movingAverage().getSlidingWindow().toArray()), 0);
    
    assertEquals(recordList.total, metrics3.getParams().movingAverage().getTotal(), 0);
}