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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def initialTrendSmoothingFactors(self, timeSeries):
 """ Calculate the initial Trend smoothing Factor b0. Explanation: http://en.wikipedia.org/wiki/Exponential_smoothing#Triple_exponential_smoothing :return: Returns the initial trend smoothing factor b0 """


        result = 0.0
        seasonLength = self.get_parameter("seasonLength")
        k = min(len(timeSeries) - seasonLength, seasonLength) #In case of only one full season, use average trend of the months that we have twice
        for i in xrange(0, k):
            result += (timeSeries[seasonLength + i][1] - timeSeries[i][1]) / seasonLength
        return result / k