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吴恩达机器学习 - 推荐系统

2018年06月25日 22:26:51 离殇灬孤狼 阅读数：187 </div> <div class="operating"> </div> </div> </div> </div> <article> <div id="article_content" class="article_content clearfix csdn-tracking-statistics" data-pid="blog" data-mod="popu_307" data-dsm="post" style="height: 2070px; overflow: hidden;"> <div class="article-copyright"> 版权声明：如果感觉写的不错，转载标明出处链接哦~blog.csdn.net/wyg1997 https://blog.csdn.net/wyg1997/article/details/80808388 </div> <div class="markdown_views"> <!-- flowchart 箭头图标 勿删 --> <svg xmlns="http://www.w3.org/2000/svg" style="display: none;"><path stroke-linecap="round" d="M5,0 0,2.5 5,5z" id="raphael-marker-block" style="-webkit-tap-highlight-color: rgba(0, 0, 0, 0);"></path></svg> <p>题目链接：<a href="https://s3.amazonaws.com/spark-public/ml/exercises/on-demand/machine-learning-ex8.zip" rel="nofollow" target="_blank">点击打开链接</a></p>

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笔记：

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每个算法最重要的莫过于代价函数了：

公式：

求代价：

求梯度：

Code（cofiCostFunc.m）：

function [J, grad] = cofiCostFunc(params, Y, R, num_users, num_movies, ... num_features, lambda) %COFICOSTFUNC Collaborative filtering cost function % [J, grad] = COFICOSTFUNC(params, Y, R, num_users, num_movies, ... % num_features, lambda) returns the cost and gradient for the % collaborative filtering problem. % % Unfold the U and W matrices from params X = reshape(params(1:num_movies*num_features), num_movies, num_features); Theta = reshape(params(num_movies*num_features+1:end), ... num_users, num_features); % You need to return the following values correctly J = 0; X_grad = zeros(size(X)); Theta_grad = zeros(size(Theta)); % ====================== YOUR CODE HERE ====================== % Instructions: Compute the cost function and gradient for collaborative % filtering. Concretely, you should first implement the cost % function (without regularization) and make sure it is % matches our costs. After that, you should implement the % gradient and use the checkCostFunction routine to check % that the gradient is correct. Finally, you should implement % regularization. % % Notes: X - num_movies x num_features matrix of movie features % Theta - num_users x num_features matrix of user features % Y - num_movies x num_users matrix of user ratings of movies % R - num_movies x num_users matrix, where R(i, j) = 1 if the % i-th movie was rated by the j-th user % % You should set the following variables correctly: % % X_grad - num_movies x num_features matrix, containing the % partial derivatives w.r.t. to each element of X % Theta_grad - num_users x num_features matrix, containing the % partial derivatives w.r.t. to each element of Theta % %求代价 J = sum(sum((R.*(X*Theta')-Y).^2))/2.0 + ... lambda/2.0*sum(sum(Theta.^2)) + lambda/2.0*sum(sum(X.^2)); %求梯度（这一步的向量化计算不好理解，调试看维度才知道谁乘谁） X_grad = (R.*(X*Theta')-Y)*Theta + lambda.*X; Theta_grad = (R.*(X*Theta')-Y)'*X + lambda.*Theta; % ============================================================= grad = [X_grad(:); Theta_grad(:)]; end 12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455

最后给了一个推荐电影的例子，用笔记上的流程就行啦。

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