def _calcSkipRecords(numIngested, windowSize, learningPeriod):
    """Return the value of skipRecords for passing to estimateAnomalyLikelihoods

    If `windowSize` is very large (bigger than the amount of data) then this
    could just return `learningPeriod`. But when some values have fallen out of
    the historical sliding window of anomaly records, then we have to take those
    into account as well so we return the `learningPeriod` minus the number
    shifted out.

    :param numIngested - (int) number of data points that have been added to the
      sliding window of historical data points.
    :param windowSize - (int) size of sliding window of historical data points.
    :param learningPeriod - (int) the number of iterations required for the
      algorithm to learn the basic patterns in the dataset and for the anomaly
      score to 'settle down'.
    """
    numShiftedOut = max(0, numIngested - windowSize)
    return min(numIngested, max(0, learningPeriod - numShiftedOut))
def _calcSkipRecords(numIngested, windowSize, learningPeriod):
    """Return the value of skipRecords for passing to estimateAnomalyLikelihoods

    If `windowSize` is very large (bigger than the amount of data) then this
    could just return `learningPeriod`. But when some values have fallen out of
    the historical sliding window of anomaly records, then we have to take those
    into account as well so we return the `learningPeriod` minus the number
    shifted out.

    :param numIngested - (int) number of data points that have been added to the
      sliding window of historical data points.
    :param windowSize - (int) size of sliding window of historical data points.
    :param learningPeriod - (int) the number of iterations required for the
      algorithm to learn the basic patterns in the dataset and for the anomaly
      score to 'settle down'.
    """
    numShiftedOut = max(0, numIngested - windowSize)
    return min(numIngested, max(0, learningPeriod - numShiftedOut))
def _calcSkipRecords ( numIngested , windowSize , learningPeriod ) : numShiftedOut = max ( 0 , numIngested - windowSize ) return min ( numIngested , max ( 0 , learningPeriod - numShiftedOut ) )
Return the value of skipRecords for passing to estimateAnomalyLikelihoods

    If `windowSize` is very large (bigger than the amount of data) then this
    could just return `learningPeriod`. But when some values have fallen out of
    the historical sliding window of anomaly records, then we have to take those
    into account as well so we return the `learningPeriod` minus the number
    shifted out.

    :param numIngested - (int) number of data points that have been added to the
      sliding window of historical data points.
    :param windowSize - (int) size of sliding window of historical data points.
    :param learningPeriod - (int) the number of iterations required for the
      algorithm to learn the basic patterns in the dataset and for the anomaly
      score to 'settle down'.
Return the value of skipRecords for passing to estimateAnomalyLikelihoods