给出用梯度下降法优化Huber回归的损失函数的MATLAB代码

以下是用梯度下降法优化Huber回归的损失函数的MATLAB代码：

% 设置数据集
x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10];
y = [1.1, 1.9, 3.2, 4.1, 5.2, 6.1, 6.9, 8.1, 9.2, 10.0];

% 初始化参数
theta = [0; 0];
alpha = 0.01;
iterations = 1000;
delta = 1;

% 定义损失函数
h = @(x) theta(1) + theta(2)*x;
loss = @(x, y) delta^2 * sum(abs(y - h(x)).*(abs(y - h(x)) <= delta)/2) + delta * sum((abs(y - h(x)) - delta/2).*(abs(y - h(x)) > delta)/2);

% 梯度下降
m = length(y);
for i = 1:iterations
    theta = theta - alpha/m * [sum((h(x) - y).*(abs(y - h(x)) <= delta)); sum((h(x) - y).*x.*(abs(y - h(x)) <= delta))];
end

% 输出结果
fprintf('theta0 = %f\ntheta1 = %f\n', theta(1), theta(2));
fprintf('Loss = %f\n', loss(x, y));
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