def AIC(model):
"""Calculates the Akaike information criterion
AIC = 2 k - 2 ln(L)
where
L is the maximum likelihood function value,
k is the number of free parameters.
"""
# maybe should not have any Offset components?
# more than a single pixel, needs iterating, don't do that for now
if model.axes_manager.navigation_size:
raise NavigationSizeError(model.axes_manager.navigation_size, 0)
model._set_p0() # correctly set the parameters (numbers / values)
lnL = model._poisson_likelihood_function(
model.p0, model.axis.axis[model.channel_switches])
k = len(model.p0) + 1 # +1 for the variance
return 2 * k - 2 * lnL
def BIC(model):
"""Calculates the Bayesian information criterion
BIC = -2 * ln(L) + k * ln(n)
where
L is the maximum likelihood function,
k is the number of free parameters, and
n is the number of data points (observations) / sample size.
"""
# maybe should not have any Offset components?
# more than a single pixel, needs iterating, don't do that for now
if model.axes_manager.navigation_size:
raise NavigationSizeError(model.axes_manager.navigation_size, 0)
model._set_p0() # correctly set the parameters (numbers / values)
lnL = model._poisson_likelihood_function(
model.p0, model.axis.axis[model.channel_switches])
n = model.axes_manager.signal_size
k = len(model.p0) + 1
return k * np.log(n) - 2. * lnL