多元线性回归梯度下降算法的MATLAB实现
时间: 2023-10-17 08:05:59 浏览: 61
可以回答这个问题。以下是多元线性回归梯度下降算法的MATLAB实现示例代码:
function [theta, J_history] = gradientDescent(X, y, theta, alpha, num_iters)
%GRADIENTDESCENT Performs gradient descent to learn theta
% theta = GRADIENTDESCENT(X, y, theta, alpha, num_iters) updates theta by
% taking num_iters gradient steps with learning rate alpha
% Initialize some useful values
m = length(y); % number of training examples
J_history = zeros(num_iters, 1);
for iter = 1:num_iters
% ====================== YOUR CODE HERE ======================
% Instructions: Perform a single gradient step on the parameter vector
% theta.
%
% Hint: While debugging, it can be useful to print out the values
% of the cost function (computeCost) and gradient here.
%
h = X * theta;
errors = h - y;
delta = (1/m) * (X' * errors);
theta = theta - (alpha * delta);
% ============================================================
% Save the cost J in every iteration
J_history(iter) = computeCost(X, y, theta);
end
end
笑话:为什么7 8 9?因为7说:“我觉得8应该多一点。”
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