- Add comments in example.cpp, add documentation. - Write documentation. - Improve error handling. - http://www.boost.org/development/requirements.html#Design_and_Programming OPTIMIZATION: - BipartiteGraph::isConnected should be optimized using boost::graph - Can the FactorGraph constructors be optimized further? - Cache second-order neighborhoods (delta's) in BipGraph? - Replace VarSets by small_set if appropriate, in order to minimize the use of findVar(). - A DAIAlg should not inherit from a FactorGraph/RegionGraph, but should store a reference to it IDEAS: - Use "gcc -MM" to generate dependencies for targets. - Use a PropertySet as output of a DAIAlg, instead of functions like maxDiff and Iterations(). - A FactorGraph and a RegionGraph are often equipped with additional properties for nodes and edges. The code to initialize those is often quite similar; maybe this can be abstracted to classes like ExtFactorGraph and ExtRegionGraph (Ext stands for Extended), e.g. template class ExtFactorGraph : public FactorGraph { public: std::vector nodeProps; std::vector > edgeProps; // blabla } A disadvantage of this approach may be worse cachability. A problem is if there are nog properties for nodes (type 1), nodes (type 2) or edges. Maybe this can be solved using specializations, or using variadac template arguments or something similar? Idea: you could define a "class Empty {}", and add some code that checks for the typeid, comparing it with Empty, and doing something special in that case (e.g., not allocating memory). The main disadvantage of this approach seems to be that it leads to even more entanglement. - Instead of polymorphism by inheritance, use polymorphism by template parameterization. For example, the real reason you introduced the complicated inheritance scheme was for functions like Factor calcMarginal( const InferenceAlgorithm & obj, const VarSet & ns, bool reInit ); Now instead, you could use a template function: template Factor calcMarginal( const InferenceAlgorithm &obj, const VarSet &ns, bool reInit ); This would assume that InferenceAlgorithm supports certain methods, like clone(), grm(), ...... Ideally, you would use concepts to define different classes of inference algorithms with different capabilities, for example the ability to calculate logZ, the ability to calculate marginals, the ability to calculate bounds, the ability to calculate MAP states, etcetera. Then, use traits classes in order to be able to query the capabilities of the model. For example, you would be able to query whether the inference algorithm supports calculation of logZ. Unfortunately, this is compile-time polymorphism, whereas tests/test needs runtime polymorphism. - If you ever do a rewrite, make sure that the graphical properties are not entangled with the probabilistic properties. E.g., a factor graph really should be represented as a bipartite graph, with a separate array of variable labels and dimensions, and a seperate array of (pointers to) factor tables. In this way, each factor could be implemented differently, e.g., we could have some sparse factors, some noisy-OR factors, some dense factors, some arbitrary precision factors, etc. Or we could make more use of templates to have a more generic factor graph. Maybe in the end, HasA relations are better than IsA relations... Also, the current setup is stupid: I wrote a new function that works on FactorGraphs, and I had to write boiler plate code for it in graphicalmodel.h and in regiongraph.h (which is stupid). - Use Boost::uBLAS framework to deal with matrices, especially, with 2D sparse matrices. See http://www.boost.org/libs/numeric/ublas/doc/matrix_sparse.htm and tests/errorbounds/errorbounds3 - Introduce naming scheme: all Vars and VarSets should be named v_..., e.g. v_i instead of i all Factors should be named f_..., e.g. f_I instead of I all indices should be named _..., e.g. _k instead of k all iterators should be named i_, e.g. i_i is an iterator to i all const_iterators should be named ci_, e.g. ci_i is an iterator to i - Improve weightedgraph or use Boost::Graph - Iterations and maxDiff are only interesting for iterative inference algorithms. Yet, tests/test wants to know these values in a generic way. Maybe we have to think of some way (e.g. using a Properties object) to return these values from run(). Then we can simply look whether a InferenceAlgorithm supports these fields. What other results could we return? Time. MaxDiff during initialization for LC methods. Or maybe we could use some traits mechanism which we can ask whether the object has _iterations and _maxdiff variables. - Think about whether the cavity initialization belongs to init() or to run(). - Fix LCLin. - setFactor and setFactors should only change Probs, not Factors. - Simplify Properties framework: each Property should be a std::string. Each DAIAlg should have its own _properties struct and handle conversion. - Forwardport the Bart patch - Another design question that needs to be answered: should a BP object own a FactorGraph, or only store a reference to a FactorGraph (which can optimize memory), or should it be a FactorGraph with additional variables and functions? Probably, the first or second option would be the best. Since FactorGraphs are assumed to be rather static, it would make sense to store *references* to FactorGraphs inside AIAlgs. Or maybe some smart_ptr? It would make sense to prevent unnecessary copying of FactorGraphs. Also, general marginalization functions now copy a complete object. Instead, it would make more sense that they construct a new object without copying the FactorGraph. Or they can be made simply methods of the general InfAlg class. DIFFICULT - What to do in case of NANs? - Bug: TreeEP::logZ() seems to be wrong (?). - Kees' trick for preventing NANs in GBP updates: if quantity smaller than epsilon, replace by epsilon. OPTIONAL - Define a better fileformat for .fg files (maybe using XML)? - Find a good multi-platform build system (GNU autotool? Boost JAM?) - Another design question that needs to be answered: should a BP object own a FactorGraph, or only store a reference to a FactorGraph (which can optimize memory), or should it be a FactorGraph with additional variables and functions? Probably, the first or second option would be the best. Since FactorGraphs are assumed to be rather static, it would make sense to store *references* to FactorGraphs inside AIAlgs. Or maybe some smart_ptr? It would make sense to prevent unnecessary copying of FactorGraphs. Also, general marginalization functions now copy a complete object. Instead, it would make more sense that they construct a new object without copying the FactorGraph. Or they can be made simply methods of the general InfAlg class. - Forwardport the Bart/Max patch. - Alternative implementation of undo factor changes: the only things that have to be undone are setting a factor to all ones and setting a factor to a Kronecker delta. This could also be implemented in the factor itself, which could maintain its state (one/delta/full) and act accordingly. - Think about the _fac2ind variable: a better implementation of this feature is probably possible. - Optimize GBP with precalculated indices. - Optimize all indices as follows: keep a cache of all indices that have been computed (use a hash). Then, instead of computing on the fly, use the precomputed ones. - Variable elimination should be implemented generically using a function object that tells you which variable to delete. - Improve general support for graphs and trees. - Add support for sparse potentials. - Optimize BP_SEQMAX (it should use a better data structure than a vector for the residuals).