fix bugs and names

cluster_ga/cluster.py

@@ -1,43 +1,46 @@
 import numpy as np
 import pandas as pd
+import matplotlib.pyplot as plt
 from sklearn import datasets
 import random
-from .genetic import genetic_iris 
+from .genetic import genetic 
+
 
 class cluster:
-  def __init__(self, x, y,  size_population = 50, goal = 0.9, repeat = 100):
+  def __init__(self, x : np.array, y : np.array,  size_population : int, goal : float, iters  : int) -> None:
     self.x = x
+    self.y = y
+    self.n_samples = x.shape[0]
     self.genom = list(np.unique(y))
     self.size_population = size_population
     self.goal = goal
-    self.repeat = repeat
+    self.iters = iters
+
     self.population = []
     self.fitness = []
 
 
-  def fit(self):
+  def fit(self) -> None:
     for _ in range(self.size_population):
-      chromosome = self._creat_random_chromosome()
+      chromosome = self._CreatRandomChromosome()
       self.population.append(genetic(chromosome, self.x))
+
+
     self.counter = 1
 
     while True :
-      if  self.counter == self.repeat:
+      if  self.counter == self.iters and (self.goal <= self.population[0].fitness_scores and  self.population[0].fitness_scores <= 1):
         break
+
       self.population =  sorted(self.population, key=lambda chromosome : chromosome.fitness_scores, reverse = True)
-
-
-      if self.goal <= self.population[0].fitness_scores <= 1:
-        break
-
       new_generation = list()
 
       size_best_people = int((10 * self.size_population) / 100)
-      new_generation.extend(self.population[: size_best_people])
+      new_generation.extend(self.population[ : size_best_people])
 
       for _ in range(int((90 * self.size_population) / 100)):
-        parent1 = random.choice(self.population[:50])
-        parent2  = random.choice(self.population[:50])
+        parent1 = random.choice(self.population[ : 50])
+        parent2  = random.choice(self.population[ : 50])
         child = parent1.genrate(parent2)
         new_generation.append(child)
 
@@ -51,15 +54,13 @@ def fit(self):
       self.counter += 1
       self.show()
 
-  def _creat_random_chromosome(self):
-    chromosome = dict()
-    for index in range(self.x.shape[0]):
-      chromosome[index] = random.choice(self.genom)
+  def _CreatRandomChromosome(self) -> dict:
+    chromosome = {index: random.choice(self.genom) for index in range(self.n_samples)}
     return chromosome
 
-  def show(self):
+  def show(self) -> None:
     print(f"loop is  : {self.counter}")
-    print(f"generation: {self.population[0]} \tFitness: {self.population[0].fitness_scores}")
+    print(f"===== >> generation: {self.population[0]} \tFitness: {self.population[0].fitness_scores}")
 
-  def show_plot(self):
+  def show_plot(self) -> None:
     plt.plot(self.fitness)