91 lines
3.9 KiB
Python
91 lines
3.9 KiB
Python
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# 6 вариант
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# Данные: make_classification (n_samples=500, n_features=2,
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# n_redundant=0, n_informative=2, random_state=rs, n_clusters_per_class=1)
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# Модели:
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# · Линейная регрессия
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# · Полиномиальная регрессия (со степенью 4)
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# · Гребневая полиномиальная регрессия (со степенью 4, alpha = 1.0)
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import numpy as np
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import matplotlib.pyplot as plt
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from matplotlib.colors import ListedColormap
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from sklearn.model_selection import train_test_split
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from sklearn.linear_model import LogisticRegression, LinearRegression
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from sklearn.preprocessing import PolynomialFeatures, StandardScaler
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from sklearn.pipeline import make_pipeline
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from sklearn.datasets import make_moons, make_circles, make_classification
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from sklearn.neural_network import MLPClassifier
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from sklearn.linear_model import Ridge
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# Задаем параметры генерации данных
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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 = 42
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n_clusters_per_class = 1
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def main():
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# Генерация данных
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X, y = make_classification(n_samples=n_samples,
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n_features=n_features,
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n_redundant=n_redundant,
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n_informative=n_informative,
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random_state=random_state,
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n_clusters_per_class=n_clusters_per_class)
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# Тестовая и обучающая выборки
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.65, random_state=random_state)
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# Создание моделей
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linear_regression(X_train, X_test, y_train, y_test)
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polin_regression_4(X_train, X_test, y_train, y_test)
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gr_polin_regression_4(X_train, X_test, y_train, y_test)
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# Создание линейной регрессии
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def linear_regression(X_train, X_test, y_train, y_test):
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linear_regression = LogisticRegression()
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linear_regression.fit(X_train, y_train)
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linear_regression_score = linear_regression.score(X_train, y_train)
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linear_pred = linear_regression.predict(X_test)
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plt.scatter(X_test[:, 0], X_test[:, 1], c=linear_pred, cmap="bwr")
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plt.title("Линейная регрессия")
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plt.xlabel("Признак 1")
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plt.ylabel("Признак 2")
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plt.show()
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print("Линейная регрессия: {}".format(linear_regression_score))
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#Создание полиномиальной регрессии со степенью 4
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def polin_regression_4(X_train, X_test, y_train, y_test):
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poly_regression = make_pipeline(PolynomialFeatures(degree=4), LogisticRegression())
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poly_regression.fit(X_train, y_train)
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polynomial_regression_score = poly_regression.score(X_train, y_train)
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poly_pred = poly_regression.predict(X_test)
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plt.scatter(X_test[:, 0], X_test[:, 1], c=poly_pred, cmap="bwr")
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plt.title("Полиномиальная регрессия")
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plt.xlabel("Признак 1")
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plt.ylabel("Признак 2")
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plt.show()
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print("Полиномиальная регрессия: {}".format(polynomial_regression_score))
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#Создание гребневой полиномиальной регрессии со степенью 4 и alpha = 1.0
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def gr_polin_regression_4(X_train, X_test, y_train, y_test):
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ridge_regression = make_pipeline(PolynomialFeatures(degree=4), Ridge(alpha=1.0))
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ridge_regression.fit(X_train, y_train)
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ridge_regression_score = ridge_regression.score(X_train, y_train)
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ridge_pred = ridge_regression.predict(X_test)
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plt.scatter(X_test[:, 0], X_test[:, 1], c=ridge_pred, cmap="bwr")
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plt.title("Гребневая полиномиальная регрессия")
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plt.xlabel("Признак 1")
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plt.ylabel("Признак 2")
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plt.show()
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print("Гребневая полиномиальная регрессия: {}".format(ridge_regression_score))
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main()
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