Hi all, I am trying to calculate a Hessian. I am using numdifftools for this ( https://pypi.python.org/pypi/Numdifftools).
My question is, is it possible to make it using pure numpy?. The actual code is like this: *import numdifftools as nd* *import numpy as np* *def log_likelihood(params):* * sum1 = 0; sum2 = 0* * mu = params[0]; sigma = params[1]; xi = params[2]* * for z in data:* * x = 1 + xi * ((z-mu)/sigma)* * sum1 += np.log(x)* * sum2 += x**(-1.0/xi)* * return -((-len(data) * np.log(sigma)) - (1 + 1/xi)*sum1 - sum2) # negated so we can use 'minimum'* *kk = nd.Hessian(log_likelihood)* Thanks in advance.
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