plfit/plfit.py
def discrete_likelihood(data, xmin, alpha):
    """
    Equation B.8 in Clauset

    Given a data set, an xmin value, and an alpha "scaling parameter", computes
    the log-likelihood (the value to be maximized)
    """
    if not scipyOK:
        raise ImportError("Can't import scipy.  Need scipy for zeta function.")
    from scipy.special import zeta as zeta

    zz = data[data>=xmin]
    nn = len(zz)

    sum_log_data = np.log(zz).sum()

    zeta = zeta(alpha, xmin)

    L_of_alpha = -1*nn*log(zeta) - alpha * sum_log_data

    return L_of_alpha
def discrete_likelihood(data, xmin, alpha):
    if not scipyOK:
        raise ImportError("Can't import scipy.  Need scipy for zeta function.")
    from scipy.special import zeta as zeta

    zz = data[data>=xmin]
    nn = len(zz)

    sum_log_data = np.log(zz).sum()

    zeta = zeta(alpha, xmin)

    L_of_alpha = -1*nn*log(zeta) - alpha * sum_log_data

    return L_of_alpha
def discrete_likelihood ( data , xmin , alpha ) : if not scipyOK : raise ImportError ( "Can't import scipy.  Need scipy for zeta function." ) from scipy . special import zeta as zeta zz = data [ data >= xmin ] nn = len ( zz ) sum_log_data = np . log ( zz ) . sum ( ) zeta = zeta ( alpha , xmin ) L_of_alpha = - 1 * nn * log ( zeta ) - alpha * sum_log_data return L_of_alpha
Equation B.8 in Clauset

    Given a data set, an xmin value, and an alpha "scaling parameter", computes
    the log-likelihood (the value to be maximized)
Equation B . 8 in Clauset