Create simulated annealing.py

Author: Anwarul-Azim

simulated annealing.py

@@ -0,0 +1,59 @@
+"""
+
+ simulated annealing search on one-dimensional objective function to find the better local minima 
+ 
+"""
+from numpy import asarray
+from numpy import exp
+from numpy.random import randn
+from numpy.random import rand
+from numpy.random import seed
+
+# 7 degree polynomial objective function 
+def objective(p):
+    # x and u for shifting the a templet function 
+    x = p + 22 
+    y = 0
+    return (x**5)-(7* x**3)+(2* x**2)-(x**4)+(6*x)+(.005 * x**7) + y
+
+# simulated annealing algorithm
+def simulated_annealing(objective, bounds, n_iterations, step_size, temp):
+    best = bounds[:,0] + randn(len(bounds)) + (bounds[:,1] - bounds[:,0])
+    best_eval = objective(best)
+    curr, curr_eval = best, best_eval
+    
+    for i in range(n_iterations):
+        candidate = curr + randn(len(bounds)) * step_size
+        #candidate = best + randn() * step_size
+        candidate_eval = objective(candidate)
+        
+        if(candidate_eval < best_eval):
+            best, best_eval = candidate, candidate_eval
+            print(i, best, best_eval)
+        diff = candidate_eval - curr_eval
+        t = float(temp/(i+1))
+        metropolis = exp(-diff/t)
+        if (diff<0 or metropolis < randn()):
+            curr,curr_eval = candidate, candidate_eval        
+                
+    return [ best, best_eval]
+
+	
+	
+
+
+seed(2)
+# define range for input **most important hyperparameter
+bounds = asarray([[-30, -21.0]])
+# define the total iterations
+n_iterations = 2000
+# define the maximum step size
+step_size = .01
+# initial temperature ** another hyperparameter 
+temp = 100
+
+# perform the simulated annealing search
+best, score = simulated_annealing(objective, bounds, n_iterations, step_size, temp)
+print('Complete!')
+print('f(%s) = %f' % (best, score))
+