[new] Implement container

model/anneal_model.py

@@ -1,19 +1,24 @@
 import math
 import random
-import matplotlib.pyplot as plt
 from model.base_model import Model
 
 class SimAnneal(Model):
-    def __init__(self, coords, T=-1, alpha=-1, stopping_T=-1):
-        super().__init__(coords)
+    def __init__(self, T=-1, alpha=-1, stopping_T=-1):
+        super().__init__()
 
         self.iteration = 0
 
-        self.T = math.sqrt(self.N) if T == -1 else T
-        self.T_save = self.T  # save inital T to reset if batch annealing is used
+        self.T = T
         self.alpha = 0.995 if alpha == -1 else alpha
         self.stopping_temperature = 1e-8 if stopping_T == -1 else stopping_T
 
+    def init(self, coords):
+        super().init(coords)
+
+        if (self.T == -1):
+            self.T = math.sqrt(self.N) 
+        self.T_save = self.T  # save inital T to reset if batch annealing is used
+
     def initial_solution(self):
         """
         Greedy algorithm to get an initial solution (closest-neighbour).

model/base_model.py

@@ -2,15 +2,16 @@ import math
 
 class Model:
 
-    def __init__(self, coords):
+    def __init__(self):
+        self.best_solution = []
+        self.best_fitness = float("Inf")
+        self.fitness_list = []
+
+    def init(self, coords):
         self.coords = coords
         self.N = len(coords)
         self.nodes = [i for i in range(self.N)]
 
-        self.best_solution = None
-        self.best_fitness = float("Inf")
-        self.fitness_list = []
-
     def dist(self, node_0, node_1):
         """
         Euclidean distance between two nodes.

model/my_model.py

@@ -3,11 +3,14 @@ import random
 from model.base_model import Model
 
 class MyModel(Model):
-    def __init__(self, coords):
-        super().__init__(coords)
+    def __init__(self):
+        super().__init__()
+
+    def init(self, coords):
         """
         Put your initialization here.
         """
+        super().init(coords)
         self.log("Nothing to initialize in your model now")
 
     def fit(self, max_it=1000, visualize=False):