import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import make_classification
from sklearn.linear_model import Perceptron
from sklearn.neural_network import MLPClassifier
from sklearn.model_selection import train_test_split, learning_curve
from sklearn.metrics import accuracy_score

# Наполнение искусственными данными
rs = np.random.RandomState(42)
X, y = make_classification(n_samples=500, n_features=2, n_redundant=0, n_informative=2, random_state=rs,
                         n_clusters_per_class=1)

# Обучающие и тестовые наборы данных
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=rs)

# Список моделей для обучения
models = [
   ('Персептрон', Perceptron()),
   ('MLP (10 нейронов)', MLPClassifier(hidden_layer_sizes=(10,), alpha=0.01, random_state=rs)),
   ('MLP (100 нейронов)', MLPClassifier(hidden_layer_sizes=(100,), alpha=0.01, random_state=rs))
]

fig, axs = plt.subplots(1, len(models), figsize=(12, 4))

# Визуализация графиков
for i, (name, model) in enumerate(models):
   model.fit(X_train, y_train)
   y_pred = model.predict(X_test)
   accuracy = accuracy_score(y_test, y_pred)

   # Построение кривых обуч ения
   train_sizes, train_scores, valid_scores = learning_curve(
       model, X, y, train_sizes=[50, 80, 110], cv=5)
   axs[i].plot(train_sizes, train_scores.mean(axis=1), 'o-', color="r",
               label="Оценка обучения")
   axs[i].plot(train_sizes, valid_scores.mean(axis=1), 'o-', color="g",
               label="Оценка кросс-валидации")
   axs[i].set_title(f'{name} (Точность: {accuracy:.2f})')
   axs[i].set_xlabel("Training examples")
   axs[i].set_ylabel("Score")
   axs[i].legend(loc="best")
   axs[i].grid()

plt.show()