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Lab2/backend/geneticsAlgorithm.py

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+#arr-points - массив пар точек x и y
+
+import random
+
+# Определение функции Розенброка
+def rosenbrock(x, y):
+ return (1 - x) ** 2 + 100 * (y - x ** 2) ** 2
+
+# Создание начальной популяции
+def create_population(population_size):
+ return [(random.uniform(-5, 5), random.uniform(-5, 5)) for _ in range(population_size)]
+
+# Вычисление пригодности (fitness) для каждой особи в популяции
+def compute_fitness(population,rosenbrock):
+ fitness_scores = []
+ for ind in population:
+ x, y = ind
+ fitness_scores.append(rosenbrock(x, y))
+ return fitness_scores
+
+# Селекция (выбор лучших особей)
+def select_best_individuals(population, fitness_scores, num_parents):
+ selected_indices = sorted(range(len(fitness_scores)), key=lambda i: fitness_scores[i])[:num_parents]
+ return [population[i] for i in selected_indices]
+
+# Скрещивание (кроссовер)
+def crossover(parents, offspring_size):
+ offspring = []
+ while len(offspring) < offspring_size:
+ parent1, parent2 = random.sample(parents, 2)
+ crossover_point = random.randint(1, len(parent1) - 1)
+ child = parent1[:crossover_point] + parent2[crossover_point:]
+ offspring.append(child)
+ return offspring
+
+# Мутация
+def mutate(offspring):
+ mutated_offspring = []
+ for child in offspring:
+ x, y = child
+ if random.random() < 0.1: # Вероятность мутации
+ x += random.uniform(-0.5, 0.5)
+ y += random.uniform(-0.5, 0.5)
+ mutated_offspring.append((x, y))
+ return mutated_offspring
+
+# Генетический алгоритм
+def genetic_algorithm(population_size, num_generations,rosenbrock):
+ population = create_population(population_size)
+ arr_points = []
+ for generation in range(num_generations):
+ fitness_scores = compute_fitness(population,rosenbrock)
+ parents = select_best_individuals(population, fitness_scores, population_size // 2)
+ offspring = crossover(parents, population_size - len(parents))
+ mutated_offspring = mutate(offspring)
+ population = parents + mutated_offspring
+ best_fitness = min(fitness_scores)
+ #print(f"Поколение {generation + 1}: Лучшее значение функции Розенброка = {best_fitness}")
+ arr_points.append(population[fitness_scores.index(min(fitness_scores))])
+
+
+ best_solution = population[fitness_scores.index(min(fitness_scores))]
+ return best_solution, min(fitness_scores),arr_points
+
+# Запуск генетического алгоритма
+#population_size = 50
+#num_generations = 100
+#best_solution, best_fitness,arr_points = genetic_algorithm(population_size, num_generations,rosenbrock)
+#print("Оптимальное значение функции Розенброка:", best_fitness)
+#print("Оптимальные значения переменных:")
+#print("x =", best_solution[0])
+#print("y =", best_solution[1])
+#print(arr_points)

Lab2/backend/simplexMethod.py

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+import numpy as np
+
+def get_index(func, elem):
+ for i in range(len(func)):
+ if func[i] == elem:
+ return i
+ return 0
+
+
+def simplex_method(x1, x2,f):
+ triangle = []
+ x0 = float(x1)
+ z0 = float(x2)
+ e = 5
+ alpha = 2
+ points = [[x0 - alpha / 2, z0 - 0.29 * alpha],
+ [x0 + alpha / 2, z0 - 0.29 * alpha],
+ [x0, z0 + 0.58 * alpha]]
+ func = [f(points[0][0], points[0][1]),
+ f(points[1][0], points[1][1]),
+ f(points[2][0], points[2][1])]
+ triangle.append(list(points))
+ x_min = x0
+ z_min = z0
+ y_min = f(x0, z0)
+ flag = 0
+ x_max = x0
+ z_max = z0
+ while abs(f(x_max, z_max) - min(func)) > e:
+ if flag:
+ flag = 0
+ x0, z0 = points[get_index(func, min(func))]
+ x0 += points[get_index(func, max(func))][0]
+ z0 += points[get_index(func, max(func))][1]
+ x0 /= 2
+ z0 /= 2
+ points.remove(points[get_index(func, max(func))])
+ func.remove(max(func))
+ x1, z1 = points[get_index(func, min(func))]
+ x1 += points[get_index(func, max(func))][0]
+ z1 += points[get_index(func, max(func))][1]
+ x1 /= 2
+ z1 /= 2
+ points.remove(points[get_index(func, max(func))])
+ func.remove(max(func))
+ func.append(f(x0, z0))
+ points.append([x0, z0])
+ func.append(f(x1, z1))
+ points.append([x1, z1])
+ else:
+ x_max, z_max = points[get_index(func, max(func))]
+ points.remove(points[get_index(func, max(func))])
+ func.remove(max(func))
+ x0 = 0 - x_max
+ z0 = 0 - z_max
+ for value in points:
+ x0 += value[0]
+ z0 += value[1]
+ if f(x0, z0) > max(func):
+ func.append(f(x_max, z_max))
+ points.append([x_max, z_max])
+ flag = 1
+ else:
+ func.append(f(x0, z0))
+ points.append([x0, z0])
+
+ if f(x_min, z_min) > min(func):
+ x_min, z_min = points[get_index(func, min(func))]
+ y_min = min(func)
+
+ triangle.append(list(points))
+
+ x_min = round(x_min, 2)
+ z_min = round(z_min, 2)
+ y_min = round(y_min, 2)
+ return triangle, x_min, y_min, z_min
+
+def get_points(start_x,start_y,function):
+ triangle, x_min, z_min,y_min = simplex_method(start_x,start_y,function)
+ points = []
+ for tr in triangle:
+ if min(tr)[1] >y_min:
+ point = [tr[0][0], tr[0][1], min(tr)[1]]
+ points.append(point)
+ else:
+ points.append([x_min, y_min,z_min])
+ break
+ return points
+
+#points = get_points(10,2,f)
+#print(points)

Lab2/frontshow/placement_elements.py

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+import tkinter as tk
+from tkinter import ttk
+ttk = tk
+class createLabel:
+
+ @staticmethod
+ def placement_label(root,text,row,column,padx,rowspan,columnspan,pady):
+ label = ttk.Label(root, text=text)
+ label.grid(row=row, column=column, padx=padx, rowspan=rowspan, columnspan=columnspan, pady=pady)
+ return label

Lab2/global_variable.py

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+START = -5
+END = 5
+STEP = 0.5
+
+SPEED = 100
+SIZE_POINT = 50
+
+current_function = None
+x_data = []
+y_data = []
+
+scatter_points = []
+
+canvas_3d = None
+canvas_3d_widget = None
+
+textReachGradientPoint = None
+textReachQuadPoint = None
+textReachGeneraticPoints = None
+