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Question

我正在尝试运行此代码并返回给我

  

    

nil的未定义方法`image'：NilClass

  

但语法和代码似乎很好。

索引：

 - @recipes.each_slice(4) do |recipes|
  .row
    - recipes.each do |recipe|
      .col-md-3
         %h4.modal-title= recipe.title
         .modal-body
            = render :partial =>'show', :locals => {:recipe => @recipe}

_show：

.main_content
  #recipe_top.row
    .col-md-4
      = image_tag @recipe.image.url(:medium), class:"recipe_image"

Answer 1

将您的代码更改为：

<div class="row text-center">

由于您没有实例变量= render :partial =>'show', :locals => { :recipe => recipe } ，但在行@recipe中定义了本地变量recipe。

Answer 2

试试这个， 只需在传入区域设置时替换实例变量@recipe，因为您已经对其进行了处理并获取了本地变量配方，因此请在您的语言环境中传递它，然后在您的显示页面中调用此方法。

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 values
    m = length(y); % number of training examples
    n = length(theta); %number of parameters (features)

    % You need to return the following variables correctly 
    J = 0;
    grad = zeros(size(theta));

    % ====================== 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

    % ----------------------1. Compute the cost-------------------
    %hypothesis
    h = sigmoid(X * theta);

    for i = 1 : m
        % The cost for the ith term before regularization
        J = J - ( y(i) * log(h(i)) )   -  ( (1 - y(i)) * log(1 - h(i)) );

        % Adding regularization term
        for j = 2 : n
            J = J + (lambda / (2*m) ) * ( theta(j) )^2;
        end            
    end
    J = J/m; 

    % ----------------------2. Compute the gradients-------------------

    %not regularizing theta[0] i.e. theta(1) in matlab

    j = 1;

    for i = 1 : m
        grad(j) = grad(j) + ( h(i) - y(i) ) * X(i,j);
    end

    for j = 2 : n    
        for i = 1 : m
            grad(j) = grad(j) + ( h(i) - y(i) ) * X(i,j) + lambda * theta(j);
        end    
    end

    grad = (1/m) * grad;

    % =============================================================
end

Answer 3

您在代码中有错误的含义：

而不是:recipe => @recipe 使用:recipe => recipe，然后在show partial中使用recipe代替@recipe

或

索引中的

定义@recipe = recipe并在show中使用@recipe。