Python3 use fmin_l_bfgs_b to get MLE of Negative Binomial

MLE_nbinom.py

from scipy.optimize import fmin_l_bfgs_b

""" Get reasonable initial values """
# estimates from fitdistr function in R
m = np.mean(X)
v = np.var(X)
size = (m ** 2) / (v - m) if v > m else 1
# convert mu/size parametrization to prob/size
p0 = size / ((size + m) if size + m != 0 else 1)
r0 = size
initial_params = np.array([r0, p0])

""" Obtain MLE of parameters """
optimized_result = fmin_l_bfgs_b(log_likelihood,
                                 x0=initial_params,
                                 args=(X_raw,),
                                 approx_grad=True,
                                 bounds=[(infinitesimal, None), (infinitesimal, 1)])

MLE_params = {'size': optimized_result[0][0], 'prob': optimized_result[0][1]}