[fix] error handling

template/.gitignore

@@ -1,2 +1,3 @@
 .vscode/
 __pycache__/
+.ipynb_checkpoints/

template/Dockerfile

@@ -4,4 +4,5 @@ ADD ./ /tsp
 
 WORKDIR /tsp
 
-CMD ["/bin/bash", "-c", "pip install -r requirements.txt && python main.py; mkdir -p /output && cp -r output/* /output"]
+RUN pip install -r requirements.txt 
+CMD ["/bin/bash", "-c", "python main.py; mkdir -p /output && cp -r output/* /output"]

template/main.py

@@ -7,5 +7,4 @@ from utils.tsp import TSP_Bench_ALL
 from model.my_model import MyModel
 
 if __name__ == "__main__":
-    model = MyModel()
-    TSP_Bench_ALL("./data", model)
+    TSP_Bench_ALL("./data", MyModel)

template/model/base_model.py

@@ -23,6 +23,8 @@ class Model:
         """
         Total distance of the current solution path.
         """
+        if(len(solution) != self.N):
+            return math.inf
         cur_fit = 0
         for i in range(self.N):
             cur_fit += self.dist(solution[i % self.N], solution[(i + 1) % self.N])

template/model/my_model.py

@@ -1,22 +1,27 @@
 import math
 import random
 from model.base_model import Model
+import numpy as np
 
 class MyModel(Model):
     def __init__(self):
         super().__init__()
 
-    def init(self, coords):
+    def init(self, nodes):
         """
         Put your initialization here.
         """
-        super().init(coords)
-        self.log("Nothing to initialize in your model now")
+        super().init(nodes)
 
-    def fit(self, max_it=1000, visualize=False):
+    def fit(self, max_it):
         """
         Put your iteration process here.
         """
-        self.log("Nothing happens in your model now")
+        random_solutions = []
+        for i in range(0, max_it):
+            solution = np.random.permutation(self.N).tolist()
+            random_solutions.append(solution)
+            self.fitness_list.append(self.fitness(solution))
 
-        return self.best_solution, self.fitness_list
+        self.best_solution = random_solutions[self.fitness_list.index(min(self.fitness_list))]
+        return self.best_solution, self.fitness_list

template/requirements.txt

@@ -0,0 +1 @@
+numpy

template/utils/tsp.py

@@ -19,6 +19,8 @@ def dist(node_0, node_1, coords):
 
 def fitness(solution, coords):
     N = len(coords)
+    if(len(solution) != N):
+        return math.inf
     cur_fit = 0
     for i in range(len(solution)):
         cur_fit += dist(solution[i % N], solution[(i + 1) % N], coords)
@@ -64,6 +66,7 @@ def TSP(tsp_file, model, *args, max_it=1000, timeout=60):
 
     nodes = load_data(tsp_file)
 
+    model = model()
     # Set timeout
     signal.signal(signal.SIGALRM, timeout_handler)
     signal.alarm(timeout)
@@ -80,27 +83,32 @@ def TSP(tsp_file, model, *args, max_it=1000, timeout=60):
             log("Timeout -3")
             with open('output/' + os.path.splitext(os.path.basename(tsp_file))[0] + '.txt', "w") as outfile:
                 outfile.write("-3")
-            best_solution = [-1] * len(nodes)
+        elif (len(best_solution) == 0):
+            print(exec)
+            log("No Answer -1")
+            with open('output/' + os.path.splitext(os.path.basename(tsp_file))[0] + '.txt', "w") as outfile:
+                outfile.write("-1")
         else:
             print(exc)
             log("Unkown -4")
             with open('output/' + os.path.splitext(os.path.basename(tsp_file))[0] + '.txt', "w") as outfile:
                 outfile.write("-4")
 
+        best_solution = [-1] * len(nodes)
+        return best_solution, fitness_list
+    
     signal.alarm(0)
 
     # Collect results
     if(len(fitness_list) > 0):
         log("[Node] " + str(len(best_solution)) + ", [Best] " + str(fitness_list[fitness_list.index(min(fitness_list))]))
 
-    if (len(best_solution) == 0):
-        log("No Answer -1")
-        with open('output/' + os.path.splitext(os.path.basename(tsp_file))[0] + '.txt', "w") as outfile:
-            outfile.write("-1")
-    elif (len(best_solution) != len(nodes)):
+    if (len(best_solution) != len(nodes)):
+        print(len(best_solution))
         log("Invalid -2")
         with open('output/' + os.path.splitext(os.path.basename(tsp_file))[0] + '.txt', "w") as outfile:
             outfile.write("-2")
+        best_solution = [-1] * len(nodes)
     else:
         # log("Writing the best solution to file" )
         with open('output/' + os.path.splitext(os.path.basename(tsp_file))[0] + '.txt', "w") as outfile: