add cli option for init search

lib/sasacore/exhaustSearch.ml

@@ -115,7 +115,17 @@ let (bnb : out_channel -> bool -> 'v ss ->
     )
   in
   let pot0 = pot st0 in
-  pot_init := pot0;  
+  pot_init := pot0;
+(*  let t0 = 1000. in
+  let lambda = 0.99 in
+  let sa_temp pt =
+    if args.init_search_sa then
+      pt *. lambda
+    else
+      min_float
+  in
+  let sa_reset
+  *)
   let pb =
     { 
       init = { st=marshall_ss st0; d=0; pot=pot0 ; cpt=0; path=[]; cost=pot0 }, Q.empty, ();

lib/sasacore/sasArg.ml

-(* Time-stamp: <modified the 08/11/2021 (at 10:53) by Erwan Jahier> *)
+(* Time-stamp: <modified the 14/11/2021 (at 18:34) by Erwan Jahier> *)
 
 
 type t = {
@@ -18,6 +18,7 @@ type t = {
   mutable output_algos: bool;
   mutable gen_register: bool;
   mutable init_search_max_trials: int option;
+  mutable init_search_sa: bool;
   
   mutable _args : (string * Arg.spec * string) list;
   mutable _user_man  : (string * string list) list; 
@@ -52,6 +53,7 @@ let (make_args : unit -> t) =
       output_algos = false;
       gen_register = false;
       init_search_max_trials = None;
+      init_search_sa = false;
       _args = [];        
       _user_man  = [];   
       _hidden_man  = []; 
@@ -156,6 +158,9 @@ let (mkoptab : string array -> t -> unit) =
       ["Use local search algorithms to find an initial configuration that pessimize ";
        "the step number. The argument is the maximum number of trials to do the search. ";
        "Require the state_to_nums Algo.to_register field to be defined."]  ~arg:" <int>";
+    mkopt args  ["--init-search-sa";"-issa"]
+      (Arg.Int(fun i -> args.init_search_sa <- true; args.init_search_max_trials <- Some i))
+      ["ditto + simulated annealing. XXX experimental"]  ~arg:" <int>";
 
     mkopt args  ["--cores-nb";"-cn"]
       (Arg.Int(fun i -> args.cores_nb <- i))

lib/sasacore/sasArg.mli

-(* Time-stamp: <modified the 19/10/2021 (at 15:11) by Erwan Jahier> *)
+(* Time-stamp: <modified the 14/11/2021 (at 18:34) by Erwan Jahier> *)
 
 type t = {
   mutable topo: string;
@@ -17,6 +17,7 @@ type t = {
   mutable output_algos: bool;
   mutable gen_register: bool;
   mutable init_search_max_trials: int option;
+  mutable init_search_sa: bool;
 
   mutable _args : (string * Arg.spec * string) list;
   mutable _user_man  : (string * string list) list; 

lib/sasacore/worstInit.ml

-(* Time-stamp: <modified the 08/11/2021 (at 11:48) by Erwan Jahier> *)
+(* Time-stamp: <modified the 17/11/2021 (at 12:06) by Erwan Jahier> *)
 
 open Register
 
@@ -7,13 +7,16 @@ type point = value Array.t
 
 let debug = false
 
-
-let deltaf=1.0 (* something else ?? *)
-let mutate_value = function
+type distance = Far | Close
+let mutate_value distance = function
   | F (minf,f,maxf) ->
+    let deltaf = match distance with
+      | Far -> (maxf-.minf) 
+      | Close -> (maxf-.minf) /. 100.
+    in
     let nf =
-    if minf > f -. deltaf then
-      minf+.(Random.float (2.0 *. deltaf))
+      if minf > f -. deltaf then
+        minf+.(Random.float (2.0 *. deltaf))
     else if maxf < f +. deltaf then
       maxf -. (Random.float (2.0 *. deltaf))
     else
@@ -22,37 +25,42 @@ let mutate_value = function
     F(minf, nf, maxf)
 
   | I (mini,i,maxi) ->
-    let ni = if i=mini then i+1 else if i=maxi then i-1 else
-      if Random.bool () then i+1 else i-1
+    let deltai = match distance with
+      | Far -> (maxi - mini) / 2
+      | Close -> ((maxi - mini) / 100)
+    in
+    let deltai = 1+Random.int (max 1 deltai) in
+    let ni = if i=mini then i+deltai else if i=maxi then i-deltai else
+      if Random.bool () then i+deltai else i-deltai
     in
     I (mini,ni,maxi)
-  | B b -> B (not b)
-                                
+  |  B b -> B (not b)
                                 
-let one_dim_succ n =
+let one_dim_succ d n =
   let p = Array.copy n in
   let j = Random.int (Array.length p) in
-  p.(j) <- mutate_value p.(j);
+  p.(j) <- mutate_value d p.(j);
   p
 
 
-let ran_dim_succ n =
+let ran_dim_succ d n =
   let p = Array.copy n in
   for j=0 to Array.length p - 1 do
-    if Random.bool () then p.(j) <- mutate_value p.(j)
+    if Random.bool () then p.(j) <- mutate_value d p.(j)
   done;
   p
 
-let all_dim_succ n =
+let all_dim_succ d n =
   let p = Array.copy n in
   for j=0 to Array.length p - 1 do
-    p.(j) <- mutate_value p.(j)
+    p.(j) <- mutate_value d p.(j)
   done;
   p
     
       
-(* let tf = float_of_int  *)
-
+let tf = float_of_int
+let ti = int_of_float
+    
 let (choose : int -> point -> (point -> point) -> point list) =
   fun n p heuristic ->
   (*choose n successors of p using heuristic *)  
@@ -163,7 +171,8 @@ let point2str p =
 let (fchc : out_channel -> ('v SimuState.t -> int) -> 'v SimuState.t -> int
         -> 'v SimuState.t) =
   fun log run ss_init dmax ->
-  let cpt = ref 0 in  
+  let cpt = ref 0 in
+  let step_cpt = ref 1 in
   let cost p = run (point_to_ss p ss_init) in
   let pinit = ss_to_point ss_init in
   let percent_done = ref 0 in  
@@ -171,11 +180,27 @@ let (fchc : out_channel -> ('v SimuState.t -> int) -> 'v SimuState.t -> int
     { 
       init = ({ st = pinit ; d = 0 ; cost = cost pinit ; cpt = 0 }, Q.empty, ());
       succ = (fun n ->
-          let m = min 50 (max 1 (dmax / 10)) in
-          let m = min m (max 1 (dmax - !cpt - 2)) in
-          let pl = (choose m n.st one_dim_succ) @
-                   (choose 1 n.st ran_dim_succ) @
-                   (choose 1 n.st all_dim_succ)
+          (* let beam_size = min 50 (max 1 (dmax / 10)) in *)
+          (* let beam_size = min m (max 1 (dmax - !cpt - 2)) in *)
+          (* let beam_size = max 1 (dmax / (10 * !step_cpt)) in (* 10% of the points to simulate. *)
+
+          let beam_size_part0 = beam_size / 6 in
+          let beam_size_part1 = max 1 beam_size_part0 in
+
+             *)
+          let beam_size = max 50 ss_init.sasarg.cores_nb in
+          let percent_close = ((tf !cpt) /. (tf dmax)) ** 2.0 in
+          let percent_far = 1.0 -. percent_close in
+          
+          let far_nb = max 1 (ti ((tf beam_size) *. percent_far) / 6) in
+          let close_nb = max 1 (ti ((tf beam_size) *. percent_close) / 6) in
+          incr step_cpt;
+          let pl = (choose far_nb n.st (one_dim_succ Close)) @
+                   (choose far_nb n.st (ran_dim_succ Close)) @
+                   (choose far_nb n.st (all_dim_succ Close)) @
+                   (choose close_nb n.st (one_dim_succ Far)) @
+                   (choose close_nb n.st (ran_dim_succ Far)) @
+                   (choose close_nb n.st (all_dim_succ Far))
           in
           let new_cpt, res =
             map_local_fold

salut/src/dune

+;; Time-stamp: <modified the 21/11/2021 (at 17:50) by Erwan Jahier>
+
+(executable
+ (name dot2lus)
+ (flags -noassert)
+  (link_flags (-linkall))
+ (libraries ocamlgraph lutils sasacore algo)
+)
+
+(install
+ (section bin)
+ (package sasa)
+ (files (dot2lus.exe as salut))
+; (files sasaRun.cmxa)
+)
+