Update main.py

shadaev_anton_lab_1/main.py

@@ -3,43 +3,35 @@ 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.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)
+                       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()),
+  ('Перцептрон', Perceptron()),
-   ('MLP (10 нейронов)', MLPClassifier(hidden_layer_sizes=(10,), alpha=0.01, random_state=rs)),
+  ('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))
+  ('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)
+  model.fit(X_train, y_train)
-   y_pred = model.predict(X_test)
+  y_pred = model.predict(X_test)
-   accuracy = accuracy_score(y_test, y_pred)
+  accuracy = accuracy_score(y_test, y_pred)
 
-   # Построение кривых обуч ения
+  axs[i].scatter(X_test[:, 0], X_test[:, 1], c=y_pred, cmap=plt.cm.Paired)
-   train_sizes, train_scores, valid_scores = learning_curve(
+  axs[i].set_title(f'{name} (Accuracy: {accuracy:.2f})')
-       model, X, y, train_sizes=[50, 80, 110], cv=5)
+  axs[i].set_xlabel("Размер обучающего набора")
-   axs[i].plot(train_sizes, train_scores.mean(axis=1), 'o-', color="r",
+  axs[i].set_ylabel("Средняя точность модели")
-               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()