def jac_predict(self, params):
'''jacobian of prediction function using complex step derivative
This assumes that the predict function does not use complex variable
but is designed to do so.
'''
from gwstatsmodels.sandbox.regression.numdiff \
import approx_fprime_cs
jaccs_err = approx_fprime_cs(params, self._predict)
return jaccs_err
def jac_predict(self, params):
'''jacobian of prediction function using complex step derivative
This assumes that the predict function does not use complex variable
but is designed to do so.
'''
from gwstatsmodels.sandbox.regression.numdiff \
import approx_fprime_cs
jaccs_err = approx_fprime_cs(params, self._predict)
return jaccs_err
def score(self, params):
"""
Compute the score function at params.
Notes
-----
This is a numerical approximation.
"""
loglike = self.loglike
#if self.transparams:
# params = self._invtransparams(params)
#return approx_fprime(params, loglike, epsilon=1e-5)
return approx_fprime_cs(params, loglike)
def score(self, params):
"""
Compute the score function at params.
Notes
-----
This is a numerical approximation.
"""
loglike = self.loglike
#if self.transparams:
# params = self._invtransparams(params)
#return approx_fprime(params, loglike, epsilon=1e-5)
return approx_fprime_cs(params, loglike)