Util.py 文件源码

def norm_cdf(x, mu=0, sigma=1):
        """

        Parameters
        ----------
        x :
        mu : float
            distribution's mean
        sigma : float
            distribution's standard deviation

        Returns
        -------
        float
            pdf or cdf value, depending on input flag ``f``

        Notes
        -----
        http://stackoverflow.com/questions/809362/how-to-calculate-cumulative-normal-distribution-in-python

        Examples
        --------
        Compares total absolute error for 100 values

        >>> from scipy.stats import norm
        >>> sum( [abs(Util.norm_cdf(x) - norm.cdf(x)) for x in range(100)])
        3.3306690738754696e-16
        """

        y = 0.5 * (1 - math.erf(-(x - mu)/(sigma * math.sqrt(2.0))))
        if y > 1: y = 1
        return y