def score(self, AB_mask):
"""
Return the gradient of the loglike at AB_mask.
Parameters
----------
AB_mask : unknown values of A and B matrix concatenated
Notes
-----
Return numerical gradient
"""
loglike = self.loglike
return approx_fprime(AB_mask, loglike, epsilon=1e-8)
def score(self, AB_mask):
"""
Return the gradient of the loglike at AB_mask.
Parameters
----------
AB_mask : unknown values of A and B matrix concatenated
Notes
-----
Return numerical gradient
"""
loglike = self.loglike
return approx_fprime(AB_mask, loglike, epsilon=1e-8)
def score(self, params):
"""
Return the gradient of the loglikelihood at params.
Parameters
----------
params : array-like
The parameter values at which to evaluate the score function.
Notes
-----
Returns numerical gradient.
"""
loglike = self.loglike
return approx_fprime(params, loglike, epsilon=1e-8)
def score(self, params):
"""
Return the gradient of the loglikelihood at params.
Parameters
----------
params : array-like
The parameter values at which to evaluate the score function.
Notes
-----
Returns numerical gradient.
"""
loglike = self.loglike
#NOTE: always calculate at out of bounds params for estimation
#TODO: allow for user-specified epsilon?
return approx_fprime(params, loglike, epsilon=1e-8)
def score(self, params):
"""
Return the gradient of the loglikelihood at params.
Parameters
----------
params : array-like
The parameter values at which to evaluate the score function.
Notes
-----
Returns numerical gradient.
"""
loglike = self.loglike
#NOTE: always calculate at out of bounds params for estimation
#TODO: allow for user-specified epsilon?
return approx_fprime(params, loglike, epsilon=1e-8)