in theory, graphing annealing. decided on a schedule too

a2/tsp_local.py

@@ -190,18 +190,20 @@ def bad_weather(delta, temp):
 
 
 # decrements temp by the option specified by opt: 1 is linear, 2 is logarithmic, 3 is exponential
-def colder(temp, opt):
+def colder(temp, opt, count):
     if (opt == 1):
-        return temp - 25
+        return temp - 15
     if (opt == 2):
         return temp - (math.log(temp) + 2)
     else:
-        return temp - math.exp(-0.001 * temp)
+        return 500000 * math.exp(-0.0005 * count) - 1
 
 
 # returns the hill climbing solution using simulated annealing w/ schedule opt
 def beck_weathers(cities, opt):
-    temp = 1000
+    start = time.process_time()
+    temp = 500000
+    count = 1
     curcost = length(cities)
     curpath = copy.deepcopy(cities)
     
@@ -212,9 +214,11 @@ def beck_weathers(cities, opt):
         if (delta > 0 or bad_weather(delta, temp)):
             curcost = child[1]
             curpath = child[0]
-        temp = colder(temp, opt)
-        
-    return(curpath, curcost)
+        temp = colder(temp, opt, count)
+        count += 1
+    
+    end = time.process_time()
+    return(curpath, curcost, end - start)
 
 
 # solves the inputted TSP problem (filepath), using algorithm algo
@@ -524,12 +528,75 @@ def reinhold_messner_graph():
         pdf.savefig()
         plt.close()
         
+        
+# function to generate PDF results for basic hill climbing w/ annealing
+def beck_weathers_graph():
+    plt.ioff()
+    plt.switch_backend('agg')
+    solution_times = []
+    solution_scores = []
+    
+    # num of cities
+    for i in range(14, 17):
+        times = []
+        solution_times.append(times)
+        scores = []
+        solution_scores.append(scores)
+        
+        # num of instances
+        for j in range(1, 3):
+            filepath = "tsp_problems/" + str(i) + "/instance_" + str(j) + ".txt"
+            prob_times = []
+            times.append(prob_times)
+            prob_scores = []
+            scores.append(prob_scores)
+            
+            # diff schedules
+            for k in range(1, 4):
+                ktimes = []
+                prob_times.append(ktimes)
+                kscores = []
+                prob_scores.append(kscores)
+            
+                runtime = 0
+                qual = 0
+                # run problem 100 times
+                for num in range(0, 100):
+                    # path, cost, runtime returned
+                    result = solver(filepath, 4, k)
+                    runtime += result[2]
+                    qual += (result[1] / neos[i][j - 1])
+            
+                ktimes.append(runtime / 100.0)
+                kscores.append(qual / 100.0)
+            
+    with PdfPages("hill_climbing_annealing.pdf") as pdf:
+        figure, axes = plt.subplots(3, 2, True)
+        figure.suptitle("Hill Climbing - Annealing")
+        
+        for i in range(0, 3):
+            for j in range(0, 2):
+                axes[i][j].plot(range(1, 4), solution_times[i][j], color = 'b')
+                axes[i][j].set_title("Problem Instance w/ " + str(i + 14) + " cities")
+                axes[i][j].set_xlabel("Annealing Schedule " + str(j + 1))
+                axes[i][j].set_ylabel("Time Taken", color = 'b')
+                axis_twin = axes[i][j].twinx()
+                axis_twin.plot(range(1, 4), solution_scores[i][j], color = 'r')
+                axis_twin.set_ylabel("Solution Quality", color = 'r')
+                #axes[i].legend()
+                #axis_twin.legend()
+
+        plt.tight_layout()
+        plt.subplots_adjust(top=0.92)
+        pdf.savefig()
+        plt.close()
 
 
 # main function that will call needed graphing function(s)
 def main():
     #sir_edmund_hillary_graph()
     #tenzing_norgay_graph()
-    reinhold_messner_graph()
+    #reinhold_messner_graph()
+    beck_weathers_graph()
 
 main()