hopsy.Mixture#
- class hopsy.Mixture(self, components, weights=[1, ..., 1])#
The
Mixture
is a weighted sum of \(n\) components, so its unnormalized density is given as\[f(x) = \sum_{i=1}^n w_i f_i(x)\]The components may be arbitrary python objects implementing the methods as required in a
hopsy.PyModel
- Parameters:
components (list[object]) – The Mixture’s model components.
weights (list[float]) – Component weights. If none are given, they will be assumed to be all 1.
Attributes
components
A list of component weights, which has to match the number of components.
weights
A list of model components, where every components is supposed to be a Python object implementing hopsy.Model or being wrapped inside hopsy.PyModel.
Methods
compute_expected_fisher_information
(self, x)deprecated:: 1.4
compute_log_likelihood_gradient
(self, x)deprecated:: 1.4
compute_negative_log_likelihood
(self, x)deprecated:: 1.4
log_curvature
(self x)This method is not implemented, as there exists no closed-form solution to computing the log curvature (typically defined as the expected Fisher information) of a general mixture model.
log_density
(self, x)Computes the log probability density of the model components
log_gradient
(self, x)Computes the gradient of the logarithm of the weighted sum of the probability density functions of the model components