# HG changeset patch # User blanchet # Date 1403782582 -7200 # Node ID b75438e23925ed27d2364d7a2db51cfbaa81c4b7 # Parent cb6667e7cbc1c9bf90afbc07dd22653eba69ebd9 renamed experimental learning engines diff -r cb6667e7cbc1 -r b75438e23925 src/HOL/Tools/Sledgehammer/sledgehammer_mash.ML --- a/src/HOL/Tools/Sledgehammer/sledgehammer_mash.ML Thu Jun 26 13:36:13 2014 +0200 +++ b/src/HOL/Tools/Sledgehammer/sledgehammer_mash.ML Thu Jun 26 13:36:22 2014 +0200 @@ -38,9 +38,9 @@ datatype mash_engine = MaSh_Py | MaSh_SML_kNN - | MaSh_SML_kNN_Cpp + | MaSh_SML_kNN_Ext | MaSh_SML_NB - | MaSh_SML_NB_Cpp + | MaSh_SML_NB_Ext val is_mash_enabled : unit -> bool val the_mash_engine : unit -> mash_engine @@ -144,9 +144,9 @@ datatype mash_engine = MaSh_Py | MaSh_SML_kNN -| MaSh_SML_kNN_Cpp +| MaSh_SML_kNN_Ext | MaSh_SML_NB -| MaSh_SML_NB_Cpp +| MaSh_SML_NB_Ext fun mash_engine () = let val flag1 = Options.default_string @{system_option MaSh} in @@ -155,9 +155,9 @@ | "py" => SOME MaSh_Py | "sml" => SOME MaSh_SML_NB | "sml_knn" => SOME MaSh_SML_kNN - | "sml_knn_cpp" => SOME MaSh_SML_kNN_Cpp + | "sml_knn_ext" => SOME MaSh_SML_kNN_Ext | "sml_nb" => SOME MaSh_SML_NB - | "sml_nb_cpp" => SOME MaSh_SML_NB_Cpp + | "sml_nb_ext" => SOME MaSh_SML_NB_Ext | _ => NONE) end @@ -574,7 +574,7 @@ end (* experimental *) -fun c_plus_plus_tool tool max_suggs learns cfeats = +fun experimental_external_tool tool max_suggs learns cfeats = let val ser = string_of_int (serial ()) (* poor person's attempt at thread-safety *) val ocs = TextIO.openOut ("adv_syms" ^ ser) @@ -608,9 +608,9 @@ forkexec max_suggs) end -val k_nearest_neighbors_cpp = - c_plus_plus_tool ("newknn/knn" ^ " " ^ string_of_int number_of_nearest_neighbors) -val naive_bayes_cpp = c_plus_plus_tool "predict/nbayes" +val k_nearest_neighbors_ext = + experimental_external_tool ("newknn/knn" ^ " " ^ string_of_int number_of_nearest_neighbors) +val naive_bayes_ext = experimental_external_tool "predict/nbayes" fun reorder_learns (num_facts, fact_tab) learns0 = let @@ -624,10 +624,10 @@ fun query ctxt engine (fact_xtab as (num_facts, fact_tab)) (num_feats, feat_tab) visible_facts max_suggs learns0 conj_feats = - if engine = MaSh_SML_kNN_Cpp then - k_nearest_neighbors_cpp max_suggs learns0 conj_feats - else if engine = MaSh_SML_NB_Cpp then - naive_bayes_cpp max_suggs learns0 conj_feats + if engine = MaSh_SML_kNN_Ext then + k_nearest_neighbors_ext max_suggs learns0 conj_feats + else if engine = MaSh_SML_NB_Ext then + naive_bayes_ext max_suggs learns0 conj_feats else let val learns = reorder_learns fact_xtab learns0