Update main.py

shadaev_anton_lab_1/main.py

@@ -3,7 +3,7 @@ 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
@@ -16,7 +16,7 @@ X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_
 
 # Список моделей для обучения
 models = [
-   ('Персептрон', Perceptron()),
+  ('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))
 ]
@@ -29,17 +29,9 @@ for i, (name, model) in enumerate(models):
   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()
+  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()