MachineLearning_LogisticRegression使用matlab编程遇到问题

- **[cost, grad] = costFunctionReg(initial_theta, X, y, lambda);**调用costFunctionReg函数总是出现A(I)=X:X must have the same size as I.costFunctionReg.m:function [J, grad] = costFunctionReg(theta, X, y, lambda)%COSTFUNCTIONREG Compute cost and gradient for logistic regression with regularization%   J = COSTFUNCTIONREG(theta, X, y, lambda) computes the cost of using%   theta as the parameter for regularized logistic regression and the%   gradient of the cost w.r.t. to the parameters. % Initialize some useful valuesm = length(y) % number of training examples% You need to return the following variables correctly h = sigmoid(X*theta);J = -(log(h')*y+(log(ones(1,m)-h')*(ones(m,1)-y)))/m ... + lambda/(2*m)*(sum(theta(2:end).^2));       %attention:we abadon theta0grad=zeros(size(X,2),1);                                    %要先对grad进行定义，否则会出现矩阵规格不一赋值出错grad(2:end) = X(:,2:end)'*(h-y)/m + (lambda/m)*theta(2:end);grad(1) = ((h-y)'*X(:,1))'/m ;                %attention!!!% ====================== YOUR CODE HERE ======================% Instructions: Compute the cost of a particular choice of theta.%               You should set J to the cost.%               Compute the partial derivatives and set grad to the partial%               derivatives of the cost w.r.t. each parameter in theta% ============================================================