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Pack phil -- prolog/phil.pl
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This module provides algorithms for learning the structure and the parameters of Hierachical Probabilic Logic Programs (HPLP) from data. Structure learning is done by predicates invention and parameter learning by gradient descent (Backpropagation) or Expectation maximization.

- Arnaud Nguembang Fadja and Fabrizio Riguzzi
 test_hplp(:P:probabilistic_program, +TestFolds:list_of_atoms, -LL:float, -AUCROC:float, -ROC:dict, -AUCPR:float, -PR:dict) is det
The predicate takes as input in P a probabilistic program, tests P on the folds indicated in TestFolds and returns the log likelihood of the test examples in LL, the area under the Receiver Operating Characteristic curve in AUCROC, a dict containing the points of the ROC curve in ROC, the area under the Precision Recall curve in AUCPR and a dict containing the points of the PR curve in PR
 induce_hplp_par(:TrainFolds:list_of_atoms, -P:probabilistic_program) is det
The predicate learns the parameters of the program stored in the in/1 fact of the input file using the folds indicated in TrainFolds for training. It returns in P the input program with the updated parameters.
 inference_hplp(:Query:atom, +Model:constant, -Prob:probabity, -Circuit:Term)
Performs inference and returns both the probability and the arithmetic circuit of atom in Prob and Circuit respectively.
 sample_hplp(+N, List:list, -Sampled:list, -Rest:list) is det
Samples N elements from List and returns them in Sampled. The rest of List is returned in Rest If List contains less than N elements, Sampled is List and Rest is [].
 set_hplp(:Parameter:atom, +Value:term) is det
The predicate sets the value of a parameter For a list of parameters see https://github.com/friguzzi/cplint/blob/master/doc/manual.pdf or http://ds.ing.unife.it/~friguzzi/software/cplint-swi/manual.html

Undocumented predicates

The following predicates are exported, but not or incorrectly documented.

 induce_hplp(Arg1, Arg2)
 inference_hplp(Arg1, Arg2, Arg3)