ideally, this will graph random restarts as needed

a2/tsp_local.py

@@ -1,6 +1,7 @@
 import sys
 import copy
 import math
+import time
 from heapq import heappush, heappop
 from random import shuffle, sample, randint, SystemRandom
 import matplotlib.pyplot as plt
@@ -158,6 +159,7 @@ def tenzing_norgay(cities):
 # returns the hill climbing solution w/ x random restarts for a given input
 # if you want x restarts, call with x + 1
 def reinhold_messner(cities, x):
+    start = time.process_time()
     curcost = length(cities)
     curpath = copy.deepcopy(cities)
     bestcost = length(cities)
@@ -178,7 +180,8 @@ def reinhold_messner(cities, x):
             curcost = length(curpath)
             x -= 1
         
-    return(bestpath, bestcost)
+    end = time.process_time()
+    return(bestpath, bestcost, end - start)
 
 
 # returns true or false randomly, based on the distribution e^(delta / temp)
@@ -357,7 +360,7 @@ def sir_edmund_hillary_graph():
         plt.close()
 
 
-# function to generate PDF results for basic hill climbing
+# function to generate PDF results for basic hill climbing w/ tabu and sideways moves
 def tenzing_norgay_graph():
     plt.ioff()
     plt.switch_backend('agg')
@@ -459,10 +462,74 @@ def tenzing_norgay_graph():
         pdf.savefig()
         plt.close()
         
+        
+# function to generate PDF results for basic hill climbing w/ random restarts
+def reinhold_messner_graph():
+    plt.ioff()
+    plt.switch_backend('agg')
+    solution_time = []
+    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)
+            
+            # num of repeats
+            for k in range(1, 16):
+                ktimes = []
+                prob_times.append(ktimes)
+                kscores = []
+                prob_scores.append(kscores)
+            
+                runtime = 0
+                qual = 0
+                # run problem 100 times
+                for j in range(0, 100):
+                    # path, cost, runtime returned
+                    result = solver(filepath, 3, 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_restarts.pdf") as pdf:
+        figure, axes = plt.subplots(3, 2, True)
+        figure.suptitle("Hill Climbing - Restarts")
+        
+        for i in range(0, 3):
+            for j in range(0, 2):
+                axes[i][j].plot(range(1, 16), solution_times[i][j], color = 'b')
+                axes[i][j].set_xlabel("Problem Instance w/ " + str(i + 14) + " cities")
+                axes[i][j].set_ylabel("Time Taken", color = 'b')
+                axis_twin = axes[i].twinx()
+                axis_twin.plot(range(1, 16), 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()
+    #tenzing_norgay_graph()
+    reinhold_messner_graph()
 
 main()