def get_log_like(self): """ Return the value of the log-likelihood with the current values for the parameters """ expectation = self._get_total_expectation() if self._is_poisson: # Poisson log-likelihood return np.sum( poisson_log_likelihood_ideal_bkg(self._y, np.zeros_like(self._y), expectation)) else: # Chi squared chi2_ = half_chi2(self._y, self._yerr, expectation) assert np.all(np.isfinite(chi2_)) return np.sum(chi2_) * (-1)
def _chi2_like(y, yerr, expectation): chi2_ = half_chi2(y, yerr, expectation) assert np.all(np.isfinite(chi2_)) return np.sum(chi2_) * (-1)
def get_current_value(self): chi2_ = half_chi2(self._spectrum_plugin.current_observed_counts, self._spectrum_plugin.current_observed_count_errors, self._spectrum_plugin.get_model()) assert np.all(np.isfinite(chi2_)) return np.sum(chi2_) * (-1), None
def get_current_value(self): chi2_ = half_chi2(self._spectrum_plugin.current_observed_counts, self._spectrum_plugin.current_observed_count_errors, self._spectrum_plugin.get_model()) assert np.all(np.isfinite(chi2_)) return np.sum(chi2_) * (-1), None
def get_current_value(self, precalc_fluxes: Optional[np.array]=None): model_counts = self._spectrum_plugin.get_model(precalc_fluxes=precalc_fluxes) chi2_ = half_chi2( self._spectrum_plugin.current_observed_counts, self._spectrum_plugin.current_observed_count_errors, model_counts, ) assert np.all(np.isfinite(chi2_)) return nb_sum(chi2_) * (-1), None
def get_log_like(self): """ Return the value of the log-likelihood with the current values for the parameters """ expectation = self._get_total_expectation() if self._is_poisson: # Poisson log-likelihood return np.sum(poisson_log_likelihood_ideal_bkg(self._y, np.zeros_like(self._y), expectation)) else: # Chi squared chi2_ = half_chi2(self._y, self._yerr, expectation) assert np.all(np.isfinite(chi2_)) return np.sum(chi2_) * (-1)