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124 lines
3.7 KiB
Matlab
124 lines
3.7 KiB
Matlab
%% Machine Learning Online Class
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% Exercise 8 | Anomaly Detection and Collaborative Filtering
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%
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% Instructions
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% ------------
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%
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% This file contains code that helps you get started on the
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% exercise. You will need to complete the following functions:
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%
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% estimateGaussian.m
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% selectThreshold.m
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% cofiCostFunc.m
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%
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% For this exercise, you will not need to change any code in this file,
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% or any other files other than those mentioned above.
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%
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%% Initialization
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clear ; close all; clc
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%% ================== Part 1: Load Example Dataset ===================
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% We start this exercise by using a small dataset that is easy to
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% visualize.
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%
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% Our example case consists of 2 network server statistics across
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% several machines: the latency and throughput of each machine.
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% This exercise will help us find possibly faulty (or very fast) machines.
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%
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fprintf('Visualizing example dataset for outlier detection.\n\n');
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% The following command loads the dataset. You should now have the
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% variables X, Xval, yval in your environment
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load('ex8data1.mat');
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% Visualize the example dataset
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plot(X(:, 1), X(:, 2), 'bx');
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axis([0 30 0 30]);
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xlabel('Latency (ms)');
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ylabel('Throughput (mb/s)');
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fprintf('Program paused. Press enter to continue.\n');
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pause
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%% ================== Part 2: Estimate the dataset statistics ===================
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% For this exercise, we assume a Gaussian distribution for the dataset.
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%
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% We first estimate the parameters of our assumed Gaussian distribution,
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% then compute the probabilities for each of the points and then visualize
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% both the overall distribution and where each of the points falls in
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% terms of that distribution.
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%
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fprintf('Visualizing Gaussian fit.\n\n');
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% Estimate my and sigma2
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[mu sigma2] = estimateGaussian(X);
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% Returns the density of the multivariate normal at each data point (row)
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% of X
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p = multivariateGaussian(X, mu, sigma2);
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% Visualize the fit
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visualizeFit(X, mu, sigma2);
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xlabel('Latency (ms)');
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ylabel('Throughput (mb/s)');
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fprintf('Program paused. Press enter to continue.\n');
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pause;
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%% ================== Part 3: Find Outliers ===================
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% Now you will find a good epsilon threshold using a cross-validation set
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% probabilities given the estimated Gaussian distribution
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%
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pval = multivariateGaussian(Xval, mu, sigma2);
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[epsilon F1] = selectThreshold(yval, pval);
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fprintf('Best epsilon found using cross-validation: %e\n', epsilon);
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fprintf('Best F1 on Cross Validation Set: %f\n', F1);
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fprintf(' (you should see a value epsilon of about 8.99e-05)\n\n');
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% Find the outliers in the training set and plot the
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outliers = find(p < epsilon);
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% Draw a red circle around those outliers
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hold on
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plot(X(outliers, 1), X(outliers, 2), 'ro', 'LineWidth', 2, 'MarkerSize', 10);
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hold off
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fprintf('Program paused. Press enter to continue.\n');
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pause;
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%% ================== Part 4: Multidimensional Outliers ===================
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% We will now use the code from the previous part and apply it to a
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% harder problem in which more features describe each datapoint and only
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% some features indicate whether a point is an outlier.
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%
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% Loads the second dataset. You should now have the
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% variables X, Xval, yval in your environment
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load('ex8data2.mat');
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% Apply the same steps to the larger dataset
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[mu sigma2] = estimateGaussian(X);
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% Training set
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p = multivariateGaussian(X, mu, sigma2);
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% Cross-validation set
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pval = multivariateGaussian(Xval, mu, sigma2);
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% Find the best threshold
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[epsilon F1] = selectThreshold(yval, pval);
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fprintf('Best epsilon found using cross-validation: %e\n', epsilon);
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fprintf('Best F1 on Cross Validation Set: %f\n', F1);
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fprintf('# Outliers found: %d\n', sum(p < epsilon));
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fprintf(' (you should see a value epsilon of about 1.38e-18)\n\n');
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pause
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