101 lines
3.9 KiB
Python
101 lines
3.9 KiB
Python
# 12 вариант
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# Данные: make_classification (n_samples=500, n_features=2, n_redundant=0,
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# n_informative=2, random_state=rs, n_clusters_per_class=1)
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# Модели:
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# -- Линейную регрессию
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# -- Персептрон
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# -- Гребневую полиномиальную регрессию (со степенью 4, alpha = 1.0)
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import numpy as np
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from sklearn.datasets import make_classification
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from sklearn.linear_model import LinearRegression, Perceptron, Ridge
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from sklearn.model_selection import train_test_split
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from sklearn.pipeline import make_pipeline
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from sklearn.preprocessing import PolynomialFeatures
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from matplotlib import pyplot as plt
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from matplotlib.colors import ListedColormap
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cm_bright_1 = ListedColormap(['#7FFFD4', '#00FFFF'])
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cm_bright_2 = ListedColormap(['#FF69B4', '#FF1493'])
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def main():
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X, y = make_classification(
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n_samples=500,
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n_features=2,
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n_redundant=0,
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n_informative=2,
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random_state=0,
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n_clusters_per_class=1)
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rng = np.random.RandomState(2)
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X += 2 * rng.uniform(size=X.shape)
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=10, random_state=40)
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# модели на основе сгенерированных данных
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my_linear_regression(X_train, X_test, y_train, y_test)
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my_perceptron(X_train, X_test, y_train, y_test)
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my_poly_ridge(X_train, X_test, y_train, y_test)
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# Линейная регрессия
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def my_linear_regression(X_train, X_test, y_train, y_test):
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lin_reg_model = LinearRegression() # создание модели регрессии
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lin_reg_model.fit(X_train, y_train) # обучение
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y_pred = lin_reg_model.predict(X_test) # предсказание по тестовым даннным
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# вывод в консоль
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print()
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print('===> Линейная регрессия <===')
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print('Оценка точности: ', lin_reg_model.score(X_train, y_train))
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# вывод в график
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plt.title('Линейная регрессия')
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plt.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright_1)
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plt.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright_2, alpha=0.8)
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plt.plot(X_test, y_pred, color='red', linewidth=1)
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plt.savefig('1_linear_regression.png')
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plt.show()
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# Персептрон
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def my_perceptron(X_train, X_test, y_train, y_test):
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perceptron_model = Perceptron()
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perceptron_model.fit(X_train, y_train)
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y_pred = perceptron_model.predict(X_test)
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# вывод в консоль
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print()
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print('===> Персептрон <===')
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print('Оценка точности: ', perceptron_model.score(X_train, y_train))
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# вывод в график
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plt.title('Персептрон')
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plt.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright_1)
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plt.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright_2, alpha=0.8)
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plt.plot(X_test, y_pred, color='red', linewidth=1)
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plt.savefig('2_perceptron.png')
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plt.show()
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# Гребневая полиномиальная регрессия (степень=4, alpha=1.0)
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def my_poly_ridge(X_train, X_test, y_train, y_test):
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poly_rige_model = make_pipeline(PolynomialFeatures(degree=4), Ridge(alpha=1.0))
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poly_rige_model.fit(X_train, y_train)
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y_pred = poly_rige_model.predict(X_test)
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# вывод в консоль
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print()
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print('===> Гребневая полиномиальная регрессия <===')
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print('Оценка точности: ', poly_rige_model.score(X_train, y_train))
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# вывод в график
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plt.title('Гребневая полиномиальная регрессия')
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plt.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright_1)
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plt.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright_2, alpha=0.8)
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plt.plot(X_test, y_pred, color='red', linewidth=1)
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plt.savefig('3_poly_ridge.png')
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plt.show()
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main() |