class_docstr: Bitmap Algorithm.
  This method breaks time series into chunks and uses the frequency of similar chunks
  to determine anomaly scores.
  The ideas are from this paper:
  Assumption-Free Anomaly Detection in Time Series(http://alumni.cs.ucr.edu/~ratana/SSDBM05.pdf).
class_docstr_tok: ["Bitmap","Algorithm",".","This","method","breaks","time","series","into","chunks","and","uses","the","frequency","of","similar","chunks","to","determine","anomaly","scores",".","The","ideas","are","from","this","paper",":","Assumption-Free","Anomaly","Detection","in","Time","Series","(","http",":","\/\/alumni.cs.ucr.edu\/~ratana\/SSDBM05.pdf",")","."]
docstr: Compute anomaly scores for the time series by sliding both lagging window and future window.
docstr_tok: ["Compute","anomaly","scores","for","the","time","series","by","sliding","both","lagging","window","and","future","window","."]
code: def _set_scores(self):
      """
      Compute anomaly scores for the time series by sliding both lagging window and future window.
      """
      anom_scores = {}
      self._generate_SAX()
      self._construct_all_SAX_chunk_dict()
      length = self.time_series_length
      lws = self.lag_window_size
      fws = self.future_window_size
  
      for i, timestamp in enumerate(self.time_series.timestamps):
          if i < lws or i > length - fws:
              anom_scores[timestamp] = 0
          else:
              anom_scores[
                  timestamp
              ] = self._compute_anom_score_between_two_windows(i)
      self.anom_scores = TimeSeries(self._denoise_scores(anom_scores))
  
file_path: luminol/src/luminol/algorithms/anomaly_detector_algorithms/bitmap_detector.py
imports: from collections import defaultdict
  from copy import copy
  import math
  from luminol import exceptions
  from luminol.algorithms.anomaly_detector_algorithms import AnomalyDetectorAlgorithm
  from luminol.modules.time_series import TimeSeries
  from luminol.constants import DEFAULT_BITMAP_PRECISION, DEFAULT_BITMAP_CHUNK_SIZE, DEFAULT_BITMAP_LAGGING_WINDOW_SIZE_PCT, DEFAULT_BITMAP_LEADING_WINDOW_SIZE_PCT, DEFAULT_BITMAP_MINIMAL_POINTS_IN_WINDOWS, DEFAULT_BITMAP_MAXIMAL_POINTS_IN_WINDOWS
label_desc: Use image node_id 8 for calling the BitmapDetector obj's underlying member method code with example usage: obj._set_scores() without return types