code: def _cac(
      I, L, bidirectional=True, excl_factor=5, custom_iac=None, seed=0
  ):
      """
      Compute the corrected arc curve (CAC)
  
      Parameters
      ----------
      I : numpy.ndarray
          The matrix profile indices for the time series of interest
  
      L : int
          The subsequence length that is set roughly to be one period length.
          This is likely to be the same value as the window size, `m`, used
          to compute the matrix profile and matrix profile index but it can
          be different since this is only used to manage edge effects
          and has no bearing on any of the IAC or CAC core calculations.
  
      bidirectional : bool, default True
          Flag for normalizing the arc curve with a bidirectional (`True`) or
          1-dimensional (`False`) idealized arc curve. If a `custom_iac` is
          specified then this flag is ignored.
  
      excl_factor : int, default 5
          The multiplying factor for the first and last regime exclusion zones
  
      custom_iac : numpy.ndarray, default None
          A custom idealized arc curve (IAC) that will used for correcting the
          arc curve
  
      seed : int, default 0
          NumPy random seed used in sampling the `iac` beta distribution. Set this
          to your desired value for reproducibility purposes. The default value is
          set to `0`.
  
      Returns
      -------
      output : numpy.ndarray
          A corrected arc curve (CAC)
  
      Notes
      -----
      DOI: 10.1109/ICDM.2017.21 <https://www.cs.ucr.edu/~eamonn/Segmentation_ICDM.pdf>`__
  
      See Table I
  
      This is the implementation for the corrected arc curve (CAC).
      """
      k = I.shape[0]
      AC = _nnmark(I)
      CAC = np.zeros(k, dtype=np.float64)
  
      if custom_iac is None:
          IAC = _iac(k, bidirectional, seed=seed)
      else:
          IAC = custom_iac
      IAC[IAC == 0.0] = 10**-10  # Avoid divide by zero
      CAC[:] = AC / IAC
      CAC[CAC > 1.0] = 1.0  # Equivalent to min
  
      if excl_factor > 0:
          CAC[: L * excl_factor] = 1.0
          CAC[-L * excl_factor :] = 1.0
  
      return CAC
  
file_path: stumpy/stumpy/floss.py
imports: import copy
  import numpy
  import scipy.stats
  from .None import config, core
label_desc: Use image node_id 3 for calling a global function with example usage: _cac(I, L, bidirectional, excl_factor, custom_iac, seed) and returns: CAC