Code for Anomaly Detection

Estimating parameters for a Gaussian

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); % 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=var(X); end

Selecting the threshold, ε

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) % 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 % you can find out how many values in this vector are 0 by using: sum(v == 0). predictions=(pval<epsilon); tp=sum((yval==1)&(predictions==1)); fp=sum((yval==0)&(predictions==1)); fn=sum((yval==1)&(predictions==0)); prec=tp/(tp+fp); rec=tp/(tp+fn); F1=2*prec*rec/(prec+rec); if F1 > bestF1 bestF1 = F1; bestEpsilon = epsilon; end end end