IIS_2023_1/shadaev_anton_lab_1/main.py

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
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 = [
  ('Перцептрон', 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)

  axs[i].scatter(X_test[:, 0], X_test[:, 1], c=y_pred, cmap=plt.cm.Paired)
  axs[i].set_title(f'{name} (Accuracy: {accuracy:.2f})')
  axs[i].set_xlabel("Размер обучающего набора")
  axs[i].set_ylabel("Средняя точность модели")

plt.show()