plfit/plfit.py
def discrete_alpha_mle(data, xmin):
    """
    Equation B.17 of Clauset et al 2009

    The Maximum Likelihood Estimator of the "scaling parameter" alpha in the
    discrete case is similar to that in the continuous case
    """
    # boolean indices of positive data
    gexmin = (data>=xmin)
    nn = gexmin.sum()
    if nn < 2:
        return 0
    xx = data[gexmin]
    alpha = 1.0 + float(nn) * (sum(log(xx/(float(xmin)-0.5))))**-1
    return alpha
def discrete_alpha_mle(data, xmin):
    """
    Equation B.17 of Clauset et al 2009

    The Maximum Likelihood Estimator of the "scaling parameter" alpha in the
    discrete case is similar to that in the continuous case
    """
    # boolean indices of positive data
    gexmin = (data>=xmin)
    nn = gexmin.sum()
    if nn < 2:
        return 0
    xx = data[gexmin]
    alpha = 1.0 + float(nn) * (sum(log(xx/(float(xmin)-0.5))))**-1
    return alpha
def discrete_alpha_mle ( data , xmin ) : # boolean indices of positive data gexmin = ( data >= xmin ) nn = gexmin . sum ( ) if nn < 2 : return 0 xx = data [ gexmin ] alpha = 1.0 + float ( nn ) * ( sum ( log ( xx / ( float ( xmin ) - 0.5 ) ) ) ) ** - 1 return alpha
Equation B.17 of Clauset et al 2009

    The Maximum Likelihood Estimator of the "scaling parameter" alpha in the
    discrete case is similar to that in the continuous case
Equation B . 17 of Clauset et al 2009