1. 已知附件中提供的数据集linearRegression_data.txt, 完成以下线性回归任务： (1) 采用参数估计的方法建立线性回归模型（可以采用标准线性回归、局部加权线性回归、岭回归中的一种）；python代码

好的，我理解了你的问题。以下是标准线性回归、局部加权线性回归和岭回归的 Python 代码示例，你可以根据需要选择其中之一。

标准线性回归：

import numpy as np
import matplotlib.pyplot as plt

# Load data
data = np.loadtxt('linearRegression_data.txt', delimiter=',')
x = data[:, 0]
y = data[:, 1]

# Build linear regression model
X = np.vstack((np.ones(len(x)), x)).T
theta = np.linalg.inv(X.T.dot(X)).dot(X.T).dot(y)

# Plot data and model
plt.scatter(x, y)
plt.plot(x, X.dot(theta), 'r')
plt.show()

局部加权线性回归：

import numpy as np
import matplotlib.pyplot as plt

# Load data
data = np.loadtxt('linearRegression_data.txt', delimiter=',')
x = data[:, 0]
y = data[:, 1]

# Build locally weighted linear regression model
def lwlr(test_point, x, y, k=1.0):
    x_mat = np.mat(x)
    y_mat = np.mat(y).T
    m = np.shape(x_mat)[0]
    weights = np.mat(np.eye((m)))
    for j in range(m):
        diff_mat = test_point - x_mat[j, :]
        weights[j, j] = np.exp(diff_mat * diff_mat.T / (-2.0 * k ** 2))
    xTx = x_mat.T * (weights * x_mat)
    if np.linalg.det(xTx) == 0.0:
        print("This matrix is singular, cannot do inverse")
        return
    theta = xTx.I * (x_mat.T * (weights * y_mat))
    return test_point * theta

# Plot data and model
y_predict = np.zeros_like(y)
for i in range(len(x)):
    y_predict[i] = lwlr(x[i], x, y, k=0.03)
plt.scatter(x, y)
plt.plot(x, y_predict, 'r')
plt.show()

岭回归：

import numpy as np
import matplotlib.pyplot as plt

# Load data
data = np.loadtxt('linearRegression_data.txt', delimiter=',')
x = data[:, 0]
y = data[:, 1]

# Build ridge regression model
def ridge_regression(X, y, alpha):
    XtX = X.T.dot(X)
    return np.linalg.inv(XtX + alpha * np.eye(X.shape[1])).dot(X.T).dot(y)

# Plot data and model
X = np.vstack((np.ones(len(x)), x)).T
theta = ridge_regression(X, y, alpha=0.5)
plt.scatter(x, y)
plt.plot(x, X.dot(theta), 'r')
plt.show()