吳恩達機器學習 - 異常檢測 吳恩達機器學習 - 異常檢測
阿新 • • 發佈:2018-11-05
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吳恩達機器學習 - 異常檢測
2018年06月25日 21:09:33 離殤灬孤狼 閱讀數: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/80807493 </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-ex8.zip" rel="nofollow" target="_blank">點選開啟連結</a></p>
筆記:
資料視覺化:
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)');
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效果圖:
求引數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
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選擇最優的ε
這個用F1F1來衡量:
公式:
Code(selectThreshold.m)(注意這裡的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
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效果圖(求出ε後,繼續執行ex8.m畫出的圖):
另外還有多元高斯分佈這裡沒有實驗,但這也是一個很好的演算法,不要忘了!
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