55 lines
2.0 KiB
Python
55 lines
2.0 KiB
Python
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import matplotlib.pyplot as plt
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import numpy as np
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from sklearn.datasets import make_classification
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from sklearn.model_selection import train_test_split
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from sklearn.linear_model import Perceptron
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from sklearn.neural_network import MLPClassifier
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from sklearn.metrics import accuracy_score
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# Установите random_state, чтобы результаты были воспроизводимыми
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rs = 42
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# Генерация данных
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X, y = make_classification(
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n_samples=500, n_features=2, n_redundant=0, n_informative=2,
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random_state=rs, n_clusters_per_class=1
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)
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# Разделение данных на обучающий и тестовый наборы
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=rs)
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# Создание моделей
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models = [
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('Perceptron', Perceptron(random_state=rs)),
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('MLP (10 neurons)', MLPClassifier(hidden_layer_sizes=(10,), alpha=0.01, random_state=rs)),
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('MLP (100 neurons)', MLPClassifier(hidden_layer_sizes=(100,), alpha=0.01, random_state=rs))
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]
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# Обучение и оценка моделей
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results = {}
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plt.figure(figsize=(15, 5))
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for i, (name, model) in enumerate(models, 1):
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plt.subplot(1, 3, i)
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model.fit(X_train, y_train)
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y_pred = model.predict(X_test)
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accuracy = accuracy_score(y_test, y_pred)
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results[name] = accuracy
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# Разбиение точек на классы
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plt.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=plt.cm.Paired, edgecolors='k')
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# Построение границы решения для каждой модели
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h = .02 # Шаг сетки
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x_min, x_max = X_test[:, 0].min() - 1, X_test[:, 0].max() + 1
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y_min, y_max = X_test[:, 1].min() - 1, X_test[:, 1].max() + 1
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xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
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Z = model.predict(np.c_[xx.ravel(), yy.ravel()])
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Z = Z.reshape(xx.shape)
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plt.contourf(xx, yy, Z, cmap=plt.cm.Paired, alpha=0.8)
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plt.title(f'{name}\nAccuracy: {accuracy:.2f}')
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plt.show()
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