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 scikits.statsmodels.sandbox.regression.numdiff import approx_fprime_cs

        jaccs_err = approx_fprime_cs(params, self._predict)
        return jaccs_err
Exemple #2
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    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 scikits.statsmodels.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)
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    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)