吳恩達機器學習-多變數線性迴歸 吳恩達機器學習 - 多變數線性迴歸
阿新 • • 發佈：2018-11-10
原
這些效果圖展示了α值和梯度下降速度的關係
吳恩達機器學習 - 多變數線性迴歸
2018年06月18日 17:50:26 離殤灬孤狼 閱讀數：84 收起<div class="tags-box space"> <span class="label">個人分類：</span> <a class="tag-link" href="https://blog.csdn.net/wyg1997/article/category/7742222" target="_blank">吳恩達機器學習 </a> </div> </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"> <div class="article-copyright"> 版權宣告：如果感覺寫的不錯，轉載標明出處連結哦~blog.csdn.net/wyg1997 https://blog.csdn.net/wyg1997/article/details/80725396 </div> <div class="markdown_views"> <!-- flowchart 箭頭圖示 勿刪 --> <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>題目連結：<a href="https://s3.amazonaws.com/spark-public/ml/exercises/on-demand/machine-learning-ex1.zip" rel="nofollow" target="_blank">點選開啟連結</a></p>
先附上筆記
然後是程式的執行流程：
首先是對資料的特徵縮放，使用均值歸一化的方式：
featureNormalize.m
function [X_norm, mu, sigma] = featureNormalize(X)
%FEATURENORMALIZE Normalizes the features in X
% FEATURENORMALIZE(X) returns a normalized version of X where
% the mean value of each feature is
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然後是計算代價函式（和單變數的差別不大）
computeCostMulti.m
function J = computeCostMulti(X, y, theta)
%COMPUTECOSTMULTI Compute cost for linear regression with multiple variables
% J = COMPUTECOSTMULTI(X, y, theta) computes the cost of using theta as the
% parameter for linear regression to fit the data points in X and y
% Initialize some useful values
m = length(y); % number of training examples
% You need to return the following variables correctly
J = 0;
% ====================== YOUR CODE HERE ======================
% Instructions: Compute the cost of a particular choice of theta
% You should set J to the cost.
temp = X*theta-y;
J = temp'*temp/2.0/m;
% =========================================================================
end
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使用梯度下降法求出θ
gradientDescentMulti.m
function [theta, J_history] = gradientDescentMulti(X, y, theta, alpha, num_iters)
%GRADIENTDESCENTMULTI Performs gradient descent to learn theta
% theta = GRADIENTDESCENTMULTI(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 (computeCostMulti) and gradient here.
%
theta = theta - alpha/m*X'*(X*theta - y);
% ============================================================
% Save the cost J in every iteration
J_history(iter) = computeCostMulti(X, y, theta);
end
end
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這些效果圖展示了α值和梯度下降速度的關係
(通過調整ex1_multi.m中的α值)
最後一個取3的時候J開始上升，所以α可以取1，可以更快的計算出結果
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