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)


def f2(x, y, e):
    return math.exp(-x ** 2 - y ** 2) * math.sin(x * y) + random.normalvariate(0, e)


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()