# is a library for the Python programming language, adding support for large,
# multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.
import numpy as np
#  Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. Matplotlib makes easy things easy and hard things possible.
import matplotlib.pyplot as plt
import networks
from utils import create_digit_image, add_noise

# Создание датасета
def create_dataset(num_samples=1000, noise_level=0.1):
    images = []
    labels = []
    for _ in range(num_samples):
        digit = np.random.randint(0, 10)
        image = create_digit_image(digit)
        noisy_image = add_noise(image, noise_level)
        images.append(noisy_image.flatten())
        labels.append(digit)
    return np.array(images), np.array(labels)

# Создание тренировочного и тестового наборов данных
train_images, train_labels = create_dataset(num_samples=1000, noise_level=0.01)
test_images, test_labels = create_dataset(num_samples=200, noise_level=0.01)

# # Визуализация нескольких примеров (цифры)
# fig, axes = plt.subplots(1, 4, figsize=(10, 3))
# for i, ax in enumerate(axes):
#     digit = np.random.randint(0, 10)
#     image = create_digit_image(digit)
#     ax.imshow(image, cmap='gray')
#     ax.set_title(f'Label: {digit}')
#     ax.axis('off')
# plt.show()

# # Визуализация нескольких примеров (данные обучения)
# fig, axes = plt.subplots(1, 5, figsize=(10, 3))
# for i, ax in enumerate(axes):
#     ax.imshow(train_images[i].reshape(5, 3), cmap='gray')
#     ax.set_title(f'Label: {train_labels[i]}')
#     ax.axis('off')
# plt.show()
#
# # Визуализация нескольких примеров (данные проверки)
# fig, axes = plt.subplots(1, 5, figsize=(10, 3))
# for i, ax in enumerate(axes):
#     ax.imshow(test_images[i].reshape(5, 3), cmap='gray')
#     ax.set_title(f'Label: {test_labels[i]}')
#     ax.axis('off')
# plt.show()

def run():
    # Инициализация модели
    input_size = 5 * 3
    hidden_size = 64
    output_size = 10
    model = networks.SimpleNeuralNetwork(input_size, hidden_size, output_size) # SimpleNeuralNetwork

    # Обучение модели
    model.train(train_images, train_labels, learning_rate=0.01, epochs=5000)

    # Оценка модели
    model.evaluate(train_images[0], train_labels[0])

    # Построение графиков
    model.plot_metrics()

    # Предсказание на тестовых данных
    predictions = model.predict(test_images)
    accuracy = np.mean(predictions == test_labels)
    print(f'Test accuracy: {accuracy}')

    # Визуализация нескольких примеров
    fig, axes = plt.subplots(1, 5, figsize=(10, 3))
    for i, ax in enumerate(axes):
        ax.imshow(test_images[i].reshape(5, 3), cmap='gray')
        ax.set_title(f'True: {test_labels[i]}, Pred: {predictions[i]}')
        ax.axis('off')
    plt.show()