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Pack ccprism -- prolog/ccprism/learn.pl
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 converge(+C:convergence, +L:pred(-learner), -LL:list(float), +P1:sw_params, -P2:sw_params) is det
Use L to create a predicate to do one step of learning, and then iterate this until convergence, starting from P1 and ending with P2. History of objective function values is returned in LL. Convergence C is of type:
convergence ---> abs(float); rel(float).
 learn(+Method:learn_method, +Stats:stats_method, +ITemp:number, +G:graph, -U:learner) is det
 learn(+Method:learn_method, +Stats:stats_method, +G:graph, -U:learner) is det
Get update predicate for several EM-based parameter learning methods. learn/4 invokes learn/5 with ITemp=1.0.
learn_method ---> ml; map(sw_params); vb(sw_params).
stats_method ---> io(scaling); vit.
scaling      ---> lin; log.
learner == pred(-float, +sw_params, -sw_params).