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--- a/src/modules/glm/samplers/BinaryFactory.h +++ b/src/modules/glm/samplers/BinaryFactory.h @@ -7,17 +7,44 @@ class BinaryGLM; + /** + * @short Base class for Binary GLM factories + */ class BinaryFactory : public GLMFactory { bool _gibbs; public: BinaryFactory(std::string const &name, bool gibbs); + /** + * Checks that the outcome variable is a Bernoulli random + * variable or a binomial variable with size fixed to 1, and + * the link function is logit or probit. Linear models with + * identity link are also permitted. + */ bool checkOutcome(StochasticNode const *snode, LinkNode const *lnode) const; + /** + * Returns a newly allocated object of class BinaryGLM, unless + * we have a pure linear model with normal outcome variables. + * In this case, an object of class Linear is returned, as + * the overhead of the BinaryGLM class is not required. + * + * This function is called by GLMFactory#makeSampler + */ GLMMethod *newMethod(GraphView const *view, std::vector<GraphView const *> const &sub_views, unsigned int chain) const; + /** + * If the factory was constructed with parameter "gibbs" set + * to true, then snode must be scalar. Otherwise, snode must + * be unbounded. + * + * @param snode Candidate node for sampling by BinaryGLM + */ bool canSample(StochasticNode const *snode) const; + /** + * Returns a newly allocated BinaryGLM object. + */ virtual BinaryGLM *newBinary(GraphView const *view, std::vector<GraphView const *> const &sub_views,