running both to generate graphs, although sudoku still does seem to be really slow for algorithm #1. might still be broken

a1/sudoku.py

@@ -146,7 +146,16 @@ def solve(working, domains, unassigned, count):
         if (count >= 10000):
           print("took too long")
           return False
-        result = solve(working, newdomains, copy.deepcopy(unassigned), count)
+
+        # check for invalidated nodes (empty domains)
+        flag = True
+        result = False
+        for i in range(0, 9):
+          for j in range(0, 9):
+            if (not newdomains[i][j]):
+              flag = False
+        
+        if (flag): result = solve(working, newdomains, copy.deepcopy(unassigned), count)
         if (result):
             return result
         else:
@@ -285,7 +294,7 @@ def solveh(working, domains, unassigned, count):
         newdomains = infer(domains, working, index[0], index[1], val)
         for i in range(0, 9):
             for j in range(0, 9):
-                if (not domains[i][j]):
+                if (not newdomains[i][j]):
                     flag = False
         
         count += 1

a1/tsp.py

@@ -114,7 +114,7 @@ def main():
     plt.switch_backend('agg')
     averages = []
     
-    for i in range(1, 14):
+    for i in range(1, 17):
         average = 0
         for j in range(1, 11):
             filepath = "tsp_problems/" + str(i) + "/instance_" + str(j) + ".txt"
@@ -123,7 +123,7 @@ def main():
         averages.append(average / 10.0)
     
     figure, axes = plt.subplots(1, 1, True)
-    axes.plot(range(1, 14), averages, label='TSP Solver (Heuristic)')
+    axes.plot(range(1, 17), averages, label='TSP Solver (Heuristic)')
     axes.legend()
     plt.xlabel("Number of Cities")
     plt.ylabel("Average Number of Nodes Generated in 10 Runs")