Merge branch 'simulated_annealing' of gitlab.ensta.fr:ggrzeczkowicz/sho

sho/algo.py

@@ -35,7 +35,26 @@ def greedy(func, init, neighb, again):
         i += 1
     return best_val, best_sol
 
-# TODO add a simulated-annealing template.
+
+def simulated_annealing(func, init, neighb, again):
+    """Iterative randomized simulated-annealing template."""
+    best_sol = init()
+    best_val = func(best_sol)
+    val,sol = best_val,best_sol
+    i = 1
+    T = 1
+    while again(i, best_val, best_sol):
+        sol = neighb(best_sol)
+        val = func(sol)
+        # Use >= and not >, so as to avoid random walk on plateus.
+        if val >= best_val:
+            best_val = val
+            best_sol = sol
+        elif np.random.random() < np.exp(-(best_val-val)/T):
+            best_val = val
+            best_sol = sol
+        i += 1
+    return best_val, best_sol
 
 # TODO add a population-based stochastic heuristic template.
 

snp.py

@@ -32,7 +32,7 @@ if __name__=="__main__":
     can.add_argument("-s", "--seed", metavar="VAL", default=None, type=int,
             help="Random pseudo-generator seed (none for current epoch)")
 
-    solvers = ["num_greedy","bit_greedy"]
+    solvers = ["num_greedy","bit_greedy","num_simulated_annealing","bit_simulated_annealing"]
     can.add_argument("-m", "--solver", metavar="NAME", choices=solvers, default="num_greedy",
             help="Solver to use, among: "+", ".join(solvers))
 
@@ -121,6 +121,38 @@ if __name__=="__main__":
             )
         sensors = bit.to_sensors(sol)
 
+    elif the.solver == "num_simulated_annealing":
+        val,sol = algo.simulated_annealing(
+                make.func(num.cover_sum,
+                    domain_width = the.domain_width,
+                    sensor_range = the.sensor_range,
+                    dim = d * the.nb_sensors),
+                make.init(num.rand,
+                    dim = d * the.nb_sensors,
+                    scale = the.domain_width),
+                make.neig(num.neighb_square,
+                    scale = the.variation_scale,
+                    domain_width = the.domain_width),
+                iters
+            )
+        sensors = num.to_sensors(sol)
+
+    elif the.solver == "bit_simulated_annealing":
+        val,sol = algo.simulated_annealing(
+                make.func(bit.cover_sum,
+                    domain_width = the.domain_width,
+                    sensor_range = the.sensor_range,
+                    dim = d * the.nb_sensors),
+                make.init(bit.rand,
+                    domain_width = the.domain_width,
+                    nb_sensors = the.nb_sensors),
+                make.neig(bit.neighb_square,
+                    scale = the.variation_scale,
+                    domain_width = the.domain_width),
+                iters
+            )
+        sensors = bit.to_sensors(sol)
+
     # Fancy output.
     print("\n{} : {}".format(val,sensors))