作业6 线性 岭回归 多项式

numerical_analysis/6/README.md

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+
+![image-20210117045857685](https://public.veypi.com/img/screenshot/20210117045857.png)

numerical_analysis/6/main.py

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+import math
+import matplotlib.pyplot as plt
+import numpy as np
+import random
+from sklearn.linear_model import LinearRegression, Ridge, RidgeCV
+from sklearn.preprocessing import PolynomialFeatures
+
+
+def f1(x, e):
+    return math.exp(-x) * math.sin(x) \
+           + random.normalvariate(0, e ** 2)
+
+
+def solve1(x, y):
+    return LinearRegression().fit(x, y)
+
+
+def solve2(x, y):
+    poly = PolynomialFeatures(degree=4)
+    X_poly = poly.fit_transform(x)
+    poly.fit(X_poly, y)
+    return poly
+
+
+def solve3(x, y):
+    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
+    model.fit(x, y)
+    return model
+
+
+if __name__ == '__main__':
+    x = np.linspace(0, 10, 100)
+    data = [f1(i, 1) for i in x]
+    y1 = []
+    model = solve3(x.reshape((-1, 1)), data)
+    for i in x:
+        y1.append(model.predict([[i]])[0])
+    print(model.predict([[1]]))
+    plt.plot(x, data, label='0')
+    plt.plot(x, y1, label='1')
+    plt.legend()
+    plt.show()