一、ex6.m

%% Machine Learning Online Class % Exercise 6 | Support Vector Machines % % Instructions % ------------ % % This file contains code that helps you get started on the % exercise. You will need to complete the following functions: % % gaussianKernel.m % dataset3Params.m % processEmail.m % emailFeatures.m % % For this exercise, you will not need to change any code in this file, % or any other files other than those mentioned above. %%% Initialization clear ; close all; clc%% =============== Part 1: Loading and Visualizing Data ================ % We start the exercise by first loading and visualizing the dataset. % The following code will load the dataset into your environment and plot % the data. %fprintf('Loading and Visualizing Data ...\n')% Load from ex6data1: % You will have X, y in your environment load('ex6data1.mat');% Plot training data plotData(X, y);fprintf('Program paused. Press enter to continue.\n'); pause;%% ==================== Part 2: Training Linear SVM ==================== % The following code will train a linear SVM on the dataset and plot the % decision boundary learned. %% Load from ex6data1: % You will have X, y in your environment load('ex6data1.mat');fprintf('\nTraining Linear SVM ...\n')% You should try to change the C value below and see how the decision % boundary varies (e.g., try C = 1000) C = 1; model = svmTrain(X, y, C, @linearKernel, 1e-3, 20); visualizeBoundaryLinear(X, y, model);fprintf('Program paused. Press enter to continue.\n'); pause;%% =============== Part 3: Implementing Gaussian Kernel =============== % You will now implement the Gaussian kernel to use % with the SVM. You should complete the code in gaussianKernel.m % fprintf('\nEvaluating the Gaussian Kernel ...\n')x1 = [1 2 1]; x2 = [0 4 -1]; sigma = 2; sim = gaussianKernel(x1, x2, sigma);fprintf(['Gaussian Kernel between x1 = [1; 2; 1], x2 = [0; 4; -1], sigma = 0.5 :' ...'\n\t%f\n(this value should be about 0.324652)\n'], sim);fprintf('Program paused. Press enter to continue.\n'); pause;%% =============== Part 4: Visualizing Dataset 2 ================ % The following code will load the next dataset into your environment and % plot the data. %fprintf('Loading and Visualizing Data ...\n')% Load from ex6data2: % You will have X, y in your environment load('ex6data2.mat');% Plot training data plotData(X, y);fprintf('Program paused. Press enter to continue.\n'); pause;%% ========== Part 5: Training SVM with RBF Kernel (Dataset 2) ========== % After you have implemented the kernel, we can now use it to train the % SVM classifier. % fprintf('\nTraining SVM with RBF Kernel (this may take 1 to 2 minutes) ...\n');% Load from ex6data2: % You will have X, y in your environment load('ex6data2.mat');% SVM Parameters C = 1; sigma = 0.1;% We set the tolerance and max_passes lower here so that the code will run % faster. However, in practice, you will want to run the training to % convergence. model= svmTrain(X, y, C, @(x1, x2) gaussianKernel(x1, x2, sigma)); visualizeBoundary(X, y, model);fprintf('Program paused. Press enter to continue.\n'); pause;%% =============== Part 6: Visualizing Dataset 3 ================ % The following code will load the next dataset into your environment and % plot the data. %fprintf('Loading and Visualizing Data ...\n')% Load from ex6data3: % You will have X, y in your environment load('ex6data3.mat');% Plot training data plotData(X, y);fprintf('Program paused. Press enter to continue.\n'); pause;%% ========== Part 7: Training SVM with RBF Kernel (Dataset 3) ==========% This is a different dataset that you can use to experiment with. Try % different values of C and sigma here. % % Load from ex6data3: % You will have X, y in your environment load('ex6data3.mat');% Try different SVM Parameters here [C, sigma] = dataset3Params(X, y, Xval, yval);% Train the SVM model= svmTrain(X, y, C, @(x1, x2) gaussianKernel(x1, x2, sigma)); visualizeBoundary(X, y, model);fprintf('Program paused. Press enter to continue.\n'); pause;

二、gaussianKernel.m

function sim = gaussianKernel(x1, x2, sigma) %RBFKERNEL returns a radial basis function kernel between x1 and x2 % sim = gaussianKernel(x1, x2) returns a gaussian kernel between x1 and x2 % and returns the value in sim% Ensure that x1 and x2 are column vectors x1 = x1(:); x2 = x2(:);% You need to return the following variables correctly. sim = 0; % 1*1% ====================== YOUR CODE HERE ====================== % Instructions: Fill in this function to return the similarity between x1 % and x2 computed using a Gaussian kernel with bandwidth % sigma % %square_diff = sum((x1 - x2) .^ 2); sim = exp(-square_diff / 2 /(sigma^2));% =============================================================end

三、dataset3Params.m

function [C, sigma] = dataset3Params(X, y, Xval, yval) %EX6PARAMS returns your choice of C and sigma for Part 3 of the exercise %where you select the optimal (C, sigma) learning parameters to use for SVM %with RBF kernel % [C, sigma] = EX6PARAMS(X, y, Xval, yval) returns your choice of C and % sigma. You should complete this function to return the optimal C and % sigma based on a cross-validation set. %% You need to return the following variables correctly. C = 1; % 1*1 sigma = 0.3; % 1*1% ====================== YOUR CODE HERE ====================== % Instructions: Fill in this function to return the optimal C and sigma % learning parameters found using the cross validation set. % You can use svmPredict to predict the labels on the cross % validation set. For example, % predictions = svmPredict(model, Xval); % will return the predictions on the cross validation set. % % Note: You can compute the prediction error using % mean(double(predictions ~= yval)) %set_values = [0.01, 0.03, 0.1, 0.3, 1, 3, 10, 30]; results = []; long = numel(set_values); for i = 1:long for j = 1:longC_temp = set_values(i); sigma_temp = set_values(j);model = svmTrain(X, y, C_temp, @(x1, x2) gaussianKernel(x1, x2, sigma_temp)); predictions = svmPredict(model, Xval);pre_error = mean(double(predictions ~= yval));results = [results; C_temp, sigma_temp, pre_error]; end end[smallest_error, idx] = min(results(:, 3)); C = results(idx, 1); sigma = results(idx, 2); % =========================================================================end

四、processEmail.m

function word_indices = processEmail(email_contents) %PROCESSEMAIL preprocesses a the body of an email and %returns a list of word_indices % word_indices = PROCESSEMAIL(email_contents) preprocesses % the body of an email and returns a list of indices of the % words contained in the email. %% Load Vocabulary vocabList = getVocabList();% Init return value word_indices = [];% ========================== Preprocess Email ===========================% Find the Headers ( \n\n and remove ) % Uncomment the following lines if you are working with raw emails with the % full headers% hdrstart = strfind(email_contents, ([char(10) char(10)])); % email_contents = email_contents(hdrstart(1):end);% Lower case email_contents = lower(email_contents);% Strip all HTML % Looks for any expression that starts with < and ends with > and replace % and does not have any < or > in the tag it with a space email_contents = regexprep(email_contents, '<[^<>]+>', ' ');% Handle Numbers % Look for one or more characters between 0-9 email_contents = regexprep(email_contents, '[0-9]+', 'number');% Handle URLS % Look for strings starting with http:// or https:// email_contents = regexprep(email_contents, ...'(http|https)://[^\s]*', 'httpaddr');% Handle Email Addresses % Look for strings with @ in the middle email_contents = regexprep(email_contents, '[^\s]+@[^\s]+', 'emailaddr');% Handle $ sign email_contents = regexprep(email_contents, '[$]+', 'dollar');% ========================== Tokenize Email ===========================% Output the email to screen as well fprintf('\n==== Processed Email ====\n\n');% Process file l = 0;while ~isempty(email_contents)% Tokenize and also get rid of any punctuation[str, email_contents] = ...strtok(email_contents, ...[' @$/#.-:&*+=[]?!(){},''">_<;%' char(10) char(13)]);% Remove any non alphanumeric charactersstr = regexprep(str, '[^a-zA-Z0-9]', '');% Stem the word % (the porterStemmer sometimes has issues, so we use a try catch block)try str = porterStemmer(strtrim(str)); catch str = ''; continue;end;% Skip the word if it is too shortif length(str) < 1continue;end% Look up the word in the dictionary and add to word_indices if% found% ====================== YOUR CODE HERE ======================% Instructions: Fill in this function to add the index of str to% word_indices if it is in the vocabulary. At this point% of the code, you have a stemmed word from the email in% the variable str. You should look up str in the% vocabulary list (vocabList). If a match exists, you% should add the index of the word to the word_indices% vector. Concretely, if str = 'action', then you should% look up the vocabulary list to find where in vocabList% 'action' appears. For example, if vocabList{18} =% 'action', then, you should add 18 to the word_indices % vector (e.g., word_indices = [word_indices ; 18]; ).% % Note: vocabList{idx} returns a the word with index idx in the% vocabulary list.% % Note: You can use strcmp(str1, str2) to compare two strings (str1 and% str2). It will return 1 only if the two strings are equivalent.%%%%%%%%%%%%%%%%%%%%%% NOT CORRECT %%%%%%%%%%%%%%%%%%%%% %str2 = str(:); %long_dic = numel(vocabList2); %long_email = numel(str2);%for i = 1:long_email %for j = 1:long_dic %if 1 == strcmp(str2(i), vocabList2(j)) %word_indices = [word_indices ; j]; %break; %end % if-end %end %end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%CORRECT word_indices = [word_indices, find(ismember(vocabList, str))];% =============================================================% Print to screen, ensuring that the output lines are not too longif (l + length(str) + 1) > 78fprintf('\n');l = 0;endfprintf('%s ', str);l = l + length(str) + 1;end% Print footer fprintf('\n\n=========================\n');end

五、emailFeatures.m

function x = emailFeatures(word_indices) %EMAILFEATURES takes in a word_indices vector and produces a feature vector %from the word indices % x = EMAILFEATURES(word_indices) takes in a word_indices vector and % produces a feature vector from the word indices. % Total number of words in the dictionary n = 1899;% You need to return the following variables correctly. x = zeros(n, 1); % n*1% ====================== YOUR CODE HERE ====================== % Instructions: Fill in this function to return a feature vector for the % given email (word_indices). To help make it easier to % process the emails, we have have already pre-processed each % email and converted each word in the email into an index in % a fixed dictionary (of 1899 words). The variable % word_indices contains the list of indices of the words % which occur in one email. % % Concretely, if an email has the text: % % The quick brown fox jumped over the lazy dog. % % Then, the word_indices vector for this text might look % like: % % 60 100 33 44 10 53 60 58 5 % % where, we have mapped each word onto a number, for example: % % the -- 60 % quick -- 100 % ... % % (note: the above numbers are just an example and are not the % actual mappings). % % Your task is take one such word_indices vector and construct % a binary feature vector that indicates whether a particular % word occurs in the email. That is, x(i) = 1 when word i % is present in the email. Concretely, if the word 'the' (say, % index 60) appears in the email, then x(60) = 1. The feature % vector should look like: % % x = [ 0 0 0 0 1 0 0 0 ... 0 0 0 0 1 ... 0 0 0 1 0 ..]; % %x([word_indices]) = 1;% =========================================================================end

六、submit results

總結

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