class_docstr: Default detector.
  Not configurable.
docstr: Set anomaly scores using a weighted sum.
code: def _set_scores(self):
      """
      Set anomaly scores using a weighted sum.
      """
      anom_scores_ema = self.exp_avg_detector.run()
      anom_scores_deri = self.derivative_detector.run()
      anom_scores = {}
      for timestamp in anom_scores_ema.timestamps:
          # Compute a weighted anomaly score.
          anom_scores[timestamp] = max(
              anom_scores_ema[timestamp],
              anom_scores_ema[timestamp] * DEFAULT_DETECTOR_EMA_WEIGHT
              + anom_scores_deri[timestamp]
              * (1 - DEFAULT_DETECTOR_EMA_WEIGHT),
          )
          # If ema score is significant enough, take the bigger one of the weighted score and deri score.
          if (
              anom_scores_ema[timestamp]
              > DEFAULT_DETECTOR_EMA_SIGNIFICANT
          ):
              anom_scores[timestamp] = max(
                  anom_scores[timestamp], anom_scores_deri[timestamp]
              )
      self.anom_scores = TimeSeries(self._denoise_scores(anom_scores))
  
file_path: luminol/src/luminol/algorithms/anomaly_detector_algorithms/default_detector.py
imports: from luminol.algorithms.anomaly_detector_algorithms.exp_avg_detector import ExpAvgDetector
  from luminol.algorithms.anomaly_detector_algorithms.derivative_detector import DerivativeDetector
  from luminol.algorithms.anomaly_detector_algorithms import AnomalyDetectorAlgorithm
  from luminol.modules.time_series import TimeSeries
  from luminol.constants import DEFAULT_DETECTOR_EMA_WEIGHT, DEFAULT_DETECTOR_EMA_SIGNIFICANT
label_desc: Use image node_id 2 for calling the DefaultDetector obj's underlying member method code with example usage: obj._set_scores() without return types