gen_alg_new

import random
import scipy.io
import matplotlib.pyplot as plt
import numpy as np

classroom_width = 7
classroom_length = 5

num_app = 5

population_size = 12

generations = 10

mutation_rate = 0.1


def calculate_signal_strength(ap_x, ap_y, point_x, point_y):
    distance = np.sqrt((ap_x - point_x)**2 + (ap_y - point_y)**2)
    return 1/(distance+1)

def visualize_initial_and_final(classroom_width, classroom_length, initial_population, final_population):
    plt.figure(figsize=(15,6))

    for i, (x, y) in enumerate(initial_population):
        plt.text(x, y, str(i+1), color='black', fontsize=8, ha='center', va='center')
    plt.scatter(*zip(*initial_population), color="red", marker="o", label="Начална позиция на точките за дотъп до WI-FI")

    for i, (x,y) in enumerate(final_population):
        plt.text(x, y, str(i+1), color='black', fontsize=8, ha='center', va='center')
    plt.scatter(*zip(*final_population), color="blue", marker="o", label="Финална позиция на точките за дотъп до WI-FI")

    plt.xlabel("Ширина на учебна зала (М)")
    plt.ylabel("Дължина на учебна зала (М)")
    plt.title("Начало и крайно разпределение на точките за достъп")
    plt.legend()
    plt.show()

def visualize_dynamic_changes(classroom_width, classroom_length, population, fitness_scores):
    for generation in range(generations):
        plt.figure(figsize=(15,6))

        plt.subplot(1, 2, 1)
        ap_placements = population[generation]

        x = np.linspace(0, classroom_width, 100)
        y = np.linspace(0, classroom_length, 100)
        X, Y = np.meshgrid(x, y)
        Z = np.zeros_like(X)
        for ap_x, ap_y in ap_placements:
            Z += calculate_signal_strength(ap_x, ap_y, X, Y)
        plt.pcolormesh(X, Y, Z, shading='auto', cmap='viridis')
        plt.colorbar(label="СИла на сигнала")
        for i, (x, y) in enumerate(ap_placements):
            plt.text(x, y, str(i+1), color="black", fontsize=8, ha="center", va="center")
        plt.scatter(*zip(*ap_placements), color="red", marker="o", label="Разпределение на точките за достъп")
        plt.xlabel("Ширина на учебна зала (м)")
        plt.ylabel("Дължина на учебната зала (м)")
        plt.title(f'Сила на Wi-Fi сигнала и разпределение на точките за достъп (Поколение {generation + 1})')
        plt.legend()




        plt.subplot(1, 2, 2)
        plt.plot(range(1, generation + 2), fitness_scores[:generation + 1], marker = 'o')
        plt.xlabel("Поколение")
        plt.ylabel("Резултат от фитнеса")
        plt.title("Резултат от фитнеса за различните поколения")

        plt.tight_layout()
        plt.show()


initial_population = [(random.uniform(0, classroom_width), random.uniform(0, classroom_length)) for _ in range(population_size)]
population = [initial_population.copy()]

best_solution = None
best_fitness = float('-inf')
all_fitness_scores = []


for generation in range(generations):
    fitness_scores = []
    for ap_x, ap_y in population[generation]:
        coverage = 0
        for point_x in range(classroom_width):
            for point_y in range(classroom_length):
                coverage += calculate_signal_strength(ap_x, ap_y, point_x, point_y)
        fitness_scores.append(coverage)

    if(max(fitness_scores)) > best_fitness:
        best_solution = population[generation][fitness_scores.index(max(fitness_scores))]
        best_fitness = max(fitness_scores)

    all_fitness_scores.append(max(fitness_scores))
    selected_parents = random.choices(population[generation], weights=fitness_scores, k=population_size)

    offspring = []
    for _ in range(population_size):
        parent1, parent2 = random.sample(selected_parents,2)
        crossover_x = random.uniform(parent1[0], parent2[0])
        crossover_y = random.uniform(parent1[1], parent2[1])
        offspring.append((crossover_x, crossover_y))

    for i in range(len(offspring)):
        if random.random() < mutation_rate:
            offspring[i] = (random.uniform(0, classroom_width), random.uniform(0, classroom_length))
    population.append(offspring)


#visualize_initial_and_final(classroom_width, classroom_length, initial_population, population[-1])

#visualize_dynamic_changes(classroom_width, classroom_length, population, all_fitness_scores)


print(f"Най-доброто разпределение на точките за дотъп: {best_solution}")
print(f"Най-доброто покритие: {best_fitness}")

#Траектория на популация
trajectory_scores = []
for generation in range(generations + 1):
    distances = [np.linalg.norm(np.array(point) - np.array(best_solution)) for point in population[generation]]
    trajectory_scores.append(np.mean(distances))

plt.figure(figsize=(10,6))
plt.plot(range(generation + 1), trajectory_scores, marker="o")
plt.xlabel("Поколение")
plt.ylabel("Средно разстояние до най-добро решение")
plt.title('Траектория на популация')
plt.show()


# Графика на сходство
plt.figure()
plt.plot(range(1, generations + 1), all_fitness_scores, marker="o")
plt.xlabel('Поколение')
plt.ylabel('Най-добър фитнес резултат')
plt.title("Графика на сходство")
plt.show()