class_docstr: Exponential Moving Average.
  This method uses a data point's deviation from the exponential moving average of a lagging window
  to determine its anomaly score.
docstr: Compute anomaly scores for the time series.
  Currently uses a lagging window covering all the data points before.
code: def _set_scores(self):
      """
      Compute anomaly scores for the time series.
      Currently uses a lagging window covering all the data points before.
      """
      if self.use_lag_window:
          self._compute_anom_data_using_window()
      self._compute_anom_data_decay_all()
  
file_path: luminol/src/luminol/algorithms/anomaly_detector_algorithms/exp_avg_detector.py
imports: import numpy
  from luminol import utils
  from luminol.algorithms.anomaly_detector_algorithms import AnomalyDetectorAlgorithm
  from luminol.modules.time_series import TimeSeries
  from luminol.constants import DEFAULT_EMA_SMOOTHING_FACTOR, DEFAULT_EMA_WINDOW_SIZE_PCT
label_desc: Use image node_id 5 for calling the ExpAvgDetector obj's underlying member method code with example usage: obj._set_scores() without return types