fix bugs :: fix names

cluster_ga/cluster.py

@@ -3,11 +3,11 @@
 import matplotlib.pyplot as plt
 from sklearn import datasets
 import random
-from .genetic import genetic 
+from .genetic import GeneticClustering 
 
 
-class Cluster:
-    def __init__(self, X: np.array, y: np.array, size_population: int = 200, goal: float = 0.7, iters: int = 200) -> None:
+class cluster:
+    def __init__(self, X: np.array, y: np.array, size_population: int = 200, goal: float = 0.9, iters: int = 200) -> None:
         self.X = X
         self.y = y
         self.size_population = size_population
@@ -23,7 +23,7 @@ def __init__(self, X: np.array, y: np.array, size_population: int = 200, goal: f
     def fit(self) -> None:
         for _ in range(self.size_population):
             chromosome = self._create_random_chromosome()
-            self.population.append(genetic(chromosome, self.X, self.genom))
+            self.population.append(GeneticClustering(chromosome, self.X, self.genom))
 
         self.counter = 1
 
@@ -51,7 +51,7 @@ def fit(self) -> None:
 
             # Apply mutation to the new population
             for index in range(self.size_population):
-                self.population[index] = genetic(self.population[index].mutation(), self.X)
+                self.population[index] = GeneticClustering(self.population[index].mutate(), self.X, self.genom)
 
             # Update fitness scores
             self.fitness.append(self.population[0].fitness_scores)

cluster_ga/genetic.py

@@ -7,6 +7,7 @@ def __init__(self, chromosome: dict, points: np.array, list_class: list) -> None
         self.chromosome = chromosome
         self.points = points
         self.list_class = list_class
+        self.fitness_scores = self.get_fitness()
 
     def get_fitness(self):
         labels = np.array(list(self.chromosome.values()))