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我是 JAX 的新手。我在下面有这段代码，其中“特征矩阵”作为数组，“目标向量”作为数组。但我不希望程序读取这些数据数组。这些数组已经存在于代码中。我想修改代码，以便它读取我已导入的波士顿房价数据集。有人可以告诉我需要对此代码进行哪些更改，以便使线性回归起作用吗？

import jax.numpy as np
from jax import grad, jit

from sklearn.datasets import load_boston
import sklearn.linear_model as sk

boston = load_boston()
X = np.array(boston.data)
y = np.array(boston.target)

def J(X, w, b, y):
    """Cost function for a linear regression. A forward pass of our model.

    Args:
        X: a features matrix.
        w: weights (a column vector).
        b: a bias.
        y: a target vector.

    Returns:
        scalar: a cost of this solution.    
    """
    y_hat = X.dot(w) + b # Predict values.
    return ((y_hat - y)**2).mean() # Return cost.

# A features matrix.
X = np.array([
                 [4., 7.],
                 [1., 8.],
                 [-5., -6.],
                 [3., -1.],
                 [0., 9.]
             ])

# A target column vector.
y = np.array([
                 [37.],
                 [24.],
                 [-34.], 
                 [16.],
                 [21.]
             ])

learning_rate = 0.01

w = np.zeros((2, 1))
b = 0.

%timeit grad(J, argnums=1)(X, w, b, y)

%timeit grad(J, argnums=2)(X, w, b, y)

for i in range(100):
    w -= learning_rate * grad(J, argnums=1)(X, w, b, y)
    b -= learning_rate * grad(J, argnums=2)(X, w, b, y)
    
    if i % 10 == 0:
        print(J(X, w, b, y))
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