原

吴恩达机器学习 - 异常检测

2018年06月25日 21:09:33 离殇灬孤狼 阅读数：69 </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/80807493 </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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数据可视化：

load('ex8data1.mat'); % Visualize the example dataset plot(X(:, 1), X(:, 2), 'bx'); axis([0 30 0 30]); xlabel('Latency (ms)'); ylabel('Throughput (mb/s)'); 1234567

效果图：

求参数μ和σ2σ2：

公式：

Code（estimateGaussian.m）：

function [mu sigma2] = estimateGaussian(X) %ESTIMATEGAUSSIAN This function estimates the parameters of a %Gaussian distribution using the data in X % [mu sigma2] = estimateGaussian(X), % The input X is the dataset with each n-dimensional data point in one row % The output is an n-dimensional vector mu, the mean of the data set % and the variances sigma^2, an n x 1 vector % % Useful variables [m, n] = size(X); % You should return these values correctly mu = zeros(n, 1); sigma2 = zeros(n, 1); % ====================== YOUR CODE HERE ====================== % Instructions: Compute the mean of the data and the variances % In particular, mu(i) should contain the mean of % the data for the i-th feature and sigma2(i) % should contain variance of the i-th feature. % mu = mean(X)'; sigma2 = (sum(bsxfun(@minus,X,mu').^2)./m)'; % ============================================================= end 12345678910111213141516171819202122232425262728

选择最优的ε

这个用F1F1来衡量：

公式：

Code（selectThreshold.m）（注意这里的true position,false position,false negative的求法）：

function [bestEpsilon bestF1] = selectThreshold(yval, pval) %SELECTTHRESHOLD Find the best threshold (epsilon) to use for selecting %outliers % [bestEpsilon bestF1] = SELECTTHRESHOLD(yval, pval) finds the best % threshold to use for selecting outliers based on the results from a % validation set (pval) and the ground truth (yval). % bestEpsilon = 0; bestF1 = 0; F1 = 0; stepsize = (max(pval) - min(pval)) / 1000; for epsilon = min(pval):stepsize:max(pval) % ====================== YOUR CODE HERE ====================== % Instructions: Compute the F1 score of choosing epsilon as the % threshold and place the value in F1. The code at the % end of the loop will compare the F1 score for this % choice of epsilon and set it to be the best epsilon if % it is better than the current choice of epsilon. % % Note: You can use predictions = (pval < epsilon) to get a binary vector % of 0's and 1's of the outlier predictions predictions = pval < epsilon; tp = sum((predictions == 1) & (yval == 1)); %true positive fp = sum((predictions == 1) & (yval == 0)); %false positive fn = sum((predictions == 0) & (yval == 1)); %false negative prec = tp/(tp+fp); %precision rec = tp/(tp+fn); %recall F1 = 2*prec*rec/(prec+rec); % ============================================================= if F1 > bestF1 bestF1 = F1; bestEpsilon = epsilon; end end end 123456789101112131415161718192021222324252627282930313233343536373839404142

效果图（求出ε后，继续运行ex8.m画出的图）：

另外还有多元高斯分布这里没有实验，但这也是一个很好的算法，不要忘了！

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