def _setup_residuals(self): self._residuals = ci.sqrt(self._weightings_vectorized) * \ ci.veccat([ \ self._discretization.optimization_variables["V"], self._discretization.optimization_variables["EPS_U"], ])
def _setup_residuals(self): self._residuals = ci.sqrt(self._weightings_vectorized) * \ ci.veccat([ \ self._discretization.optimization_variables["EPS_U"], self._discretization.optimization_variables["V"], ])
def standard_deviations(self): try: return ci.sqrt([abs(var) for var \ in ci.diag(self.covariance_matrix)]) except AttributeError: raise AttributeError(''' Standard deviations for the estimated parameters not yet computed. Run compute_covariance_matrix() to do so. ''')
def _print_experimental_properties(self, covariance_matrix): np.set_printoptions(linewidth = 200, \ formatter={'float': lambda x: format(x, ' 10.8e')}) print("\nParameters p_i:") for k, pk in enumerate(self._pdata): print(" p_{0:<3} = {1} +/- {2}".format( \ k, pk, ci.sqrt(abs(ci.diag(covariance_matrix)[k])))) print("\nCovariance matrix for this setup:") print(np.atleast_2d(covariance_matrix))
def standard_deviations(self): try: variances = [] for k in range(ci.diag(self.covariance_matrix).numel()): variances.append(abs(ci.diag(self.covariance_matrix)[k])) standard_deviations = ci.sqrt(variances) return standard_deviations # return ci.sqrt([abs(var) for var \ # in ci.diag(self.covariance_matrix)]) except AttributeError: raise AttributeError(''' Standard deviations for the estimated parameters not yet computed. Run compute_covariance_matrix() to do so. ''')