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)