47 lines
1.1 KiB
Python
47 lines
1.1 KiB
Python
import math
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import matplotlib.pyplot as plt
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import numpy as np
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import random
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from sklearn.linear_model import LinearRegression, Ridge, RidgeCV
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from sklearn.preprocessing import PolynomialFeatures
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def f1(x, e):
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return math.exp(-x) * math.sin(x) \
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+ random.normalvariate(0, e)
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def f2(x, y, e):
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return math.exp(-x ** 2 - y ** 2) * math.sin(x * y) + random.normalvariate(0, e)
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def solve1(x, y):
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return LinearRegression().fit(x, y)
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def solve2(x, y):
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poly = PolynomialFeatures(degree=4)
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X_poly = poly.fit_transform(x)
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poly.fit(X_poly, y)
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return poly
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def solve3(x, y):
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model = RidgeCV(alphas=[0.1, 1.0, 10.0]) # 通过RidgeCV可以设置多个参数值，算法使用交叉验证获取最佳参数值
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model.fit(x, y)
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return model
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if __name__ == '__main__':
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x = np.linspace(0, 10, 100)
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data = [f1(i, 1) for i in x]
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y1 = []
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model = solve3(x.reshape((-1, 1)), data)
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for i in x:
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y1.append(model.predict([[i]])[0])
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print(model.predict([[1]]))
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plt.plot(x, data, label='0')
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plt.plot(x, y1, label='1')
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plt.legend()
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plt.show()
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