modified solver to accept parameters to run any algo w/ any params

a2/tsp_local.py

@@ -4,6 +4,7 @@ import math
 from heapq import heappush, heappop
 from secrets import randbelow
 from random import shuffle, sample
+import matplotlib.pyplot as plt
 
 distances = []
 n = 0
@@ -96,9 +97,11 @@ def random_child(cities):
 def sir_edmund_hillary(cities):
     curcost = length(cities)
     curpath = copy.deepcopy(cities)
+    steps = 0
     
     # keep trying things til we can no longer try things
     while(1):
+        steps += 1
         child = best_child(curpath, curcost)
         if (child[1] < curcost):
             curcost = child[1]
@@ -106,7 +109,7 @@ def sir_edmund_hillary(cities):
             continue
         break
         
-    return(curpath, curcost)
+    return(curpath, curcost, steps)
 
 
 # retuns a tuple of the indices (ordered) in a list of cities
@@ -202,8 +205,11 @@ def beck_weathers(cities, opt):
     return(curpath, curcost)
 
 
-# solves the inputted TSP problem
-def solver(problem):
+# solves the inputted TSP problem (filepath), using algorithm algo
+# 1 = hill climbing, 2 = tabu/sideways allowed, 3 = random restarts, 4 = annealing
+# x represents the optional parameters for 3 and 4
+# specifically, the # of restarts and the annealing schedule, respectively
+def solver(problem, algo, x):
     # import map
     prob = problem
     with open(prob) as file:
@@ -226,21 +232,24 @@ def solver(problem):
     
     # add in goal state
     cities.append(cities[0])
-
-    #print(solve(frontier, cities))
-    print(cities)
-    #print(randbelow(n - 1) + 1)
-    print("#####")
     input = rand_order(cities)
-    print(input)
-    print("#####")
-    #result = reinhold_messner(input, 20)
-    result = beck_weathers(input, 1)
-    print(result[0])
-    print(result[1])
     
+    if (algo == 1):
+        result = sir_edmund_hillary(cities)
+    elif (algo == 2):
+        result = tenzing_norgay(cities)
+    elif (algo == 3):
+        result = reinhold_messner(cities, x)
+    else:
+        result = beck_weathers(cities, x)
     
+    return result
+    
+
+# main function to get aggregate data and graph stuff
 def main():
-    solver("instance_10.txt")
+    plt.ioff()
+    plt.switch_backend('agg')
+    solver("instance_10.txt", 1, 0)
 
 main()