This simplifies Bayes nets quite a bit. Also created a Conditional base class, derived classes ConditionalGaussian and SymbolicConditional
Finally, some changes were needed because I moved some headers to .cpp
The main renaming that happened is
BayesChain -> BayesNet
ChordalBayesNet -> GaussianBayesNet == BayesNet<ConditionalGaussian>
SymbolicBayesChain -> SymbolicBayesNet == BayesNet<SymbolicConditional>
* Factors are now templated on the configuration type. Factor Graphs are now templated on the factor type and configuration type.
* LinearFactor is a factor on an FGConfig.
* LinearFactorGraph uses LinearFactor and FGConfig.
* NonLinearFactor is still templated on Config.
* NonLinearFactorGraph uses NonLinearFactors, but is still templated on Config.
* Tests and VSLAMFactor have been updated to reflect those changes.
(1) FactorGraph and NonlinearOptimizer now no longer have a .cpp file, but a -inl.h file as in [http://google-styleguide.googlecode.com/svn/trunk/cppguide.xml Google's C++ Style Guide]. This means if you expect to instantiate one of the functions in a cpp file, you have to include the -inl.h file.
(1) getOrdering is now in FactorGraph, and the non-linear version does *not* take a config anymore.
Long version: I made this change because colamd works on the graph structure alone, and should not depend on the type of graph. Instead, because getOrdering happened to implemented in LinearFactorGraph first, the non-linear version converted to a linear factor graph (at the cost of an unnecessary linearization), and then threw all that away to call colamd. To implement this in a key-neutral way (a hidden agenda), i had to modify the keys_ type to a list, so a lot of changes resulted from that.
Chris Beall
Alex Cunningham
Frank Dellaert
Manohar Paluri
Kai Ni
Richard Roberts