Converting list to numpy array when calling the fitness function

Changing coordinate from tuple to list

src/genetic_algo.py

 import random
 import math
 import json
-
+import numpy as np
 def calculate_distance(ind):
     x1, y1 = ind[0]
     x2, y2 = ind[1]
@@ -29,11 +29,10 @@ def generate_random_coordinates(start_point, side_length):
     Random coordinates of four points that are inside a square-box that is defined by the input parameters
 
     """
+    def one_coordinate():
+        return [random.uniform(start_point[0], start_point[0]+side_length), random.uniform(start_point[1], start_point[1]+side_length)]
 
-    return [(random.randint(start_point[0], start_point[0]+side_length), random.randint(start_point[1], start_point[1]+side_length)), 
-        (random.randint(start_point[0], start_point[0]+side_length), random.randint(start_point[1], start_point[1]+side_length)),
-        (random.randint(start_point[0], start_point[0]+side_length), random.randint(start_point[1], start_point[1]+side_length)),
-        (random.randint(start_point[0], start_point[0]+side_length), random.randint(start_point[1], start_point[1]+side_length))]
+    return [one_coordinate(),one_coordinate(),one_coordinate(),one_coordinate()]
 
 class Individual():
     """Indivual of four points
@@ -93,7 +92,7 @@ class Population():
         self.fit_func = fit_func
         self.population = [Individual(coordinates=generate_random_coordinates(self.__start_point, self.__side_length)) for _ in range(self.__size)]
         for i in self.population:
-            i.set_fitness_score(self.fit_func(i.coordinates))
+            i.set_fitness_score(self.fit_func(np.array(i.coordinates)))
 
     def get_size(self):
         return self.__size
@@ -213,11 +212,11 @@ class Genetic_algorithm():
                 dad = top_individuals[i+1]
                 coordinate1 = [dad.coordinates[0], dad.coordinates[1], mom.coordinates[2], mom.coordinates[3]]
                 child1 = Individual(coordinates=coordinate1) 
-                child1.set_fitness_score(self.fit_func(coordinate1))
+                child1.set_fitness_score(self.fit_func(np.array(coordinate1)))
                 children.append(child1)
                 coordinate2 = [mom.coordinates[0], mom.coordinates[1], dad.coordinates[2], dad.coordinates[3]]
                 child2 = Individual(coordinates=coordinate2) 
-                child2.set_fitness_score(self.fit_func(coordinate2))
+                child2.set_fitness_score(self.fit_func(np.array(coordinate2)))
                 children.append(child2)
         return children
 
@@ -244,7 +243,7 @@ random.randint(start_point[1], start_point[1]+side_length)),
             gene_index=random.choice([0,1,2,3])
             c.coordinates[gene_index] =(random.randint(self.__start_point[0], self.__start_point[0]+self.__side_length),random.randint(self.__start_point[1], self.__start_point[1]+self.__side_length)) 
 
-            c.set_fitness_score(self.fit_func(c.coordinates))
+            c.set_fitness_score(self.fit_func(np.array(c.coordinates)))
         #print(f'mutated children : ', [i.fitness_score for i in population.population])