Andrew Ng coursera上的《机器学习》ex6

按照课程所给的ex6的文档要求，ex6要求完成以下几个计算过程的代码编写：

一、gaussianKernel.m

要求是求两个变量之间的相似性。

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 vectorsx1 = x1(:); x2 = x2(:);% You need to return the following variables correctly.sim = 0;% ====================== YOUR CODE HERE ======================% Instructions: Fill in this function to return the similarity between x1%               and x2 computed using a Gaussian kernel with bandwidth%               sigma%%sim = exp(-sum((x1-x2).^2)/(2*sigma^2));% =============================================================end

根据ex6文档中给出的公式就可以写出相应的代码。

二、dataset3Params.m

要求找出最优的参数C和α。

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;sigma = 0.3;% ====================== 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))%vec = [0.01 0.03 0.1 0.3 1 3 10 30]';C = 0.01;sigma = 0.01;model= svmTrain(X, y, C, @(x1, x2) gaussianKernel(x1, x2, sigma)); predictions = svmPredict(model,Xval);meanMin = mean(double(predictions ~= yval));C_optimal = C;sigma_optimal = sigma;for i = 1:length(vec)    for j = 1:length(vec)        C = vec(i);        sigma = vec(j);        model= svmTrain(X, y, C, @(x1, x2) gaussianKernel(x1, x2, sigma));         predictions = svmPredict(model,Xval);        if(meanMin >= mean(double(predictions ~= yval)))            meanMin = mean(double(predictions ~= yval));            C_optimal = C;            sigma_optimal = sigma;        end    endendC = C_optimal;sigma = sigma_optimal;% =========================================================================end

采用的是两层循环遍历C和α的所有可能的值，最终求出最优值。

三、processEmail.m

任务是找出相应的单词以及它在单词表中的索引。

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 VocabularyvocabList = getVocabList();% Init return valueword_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 caseemail_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 spaceemail_contents = regexprep(email_contents, '<[^<>]+>', ' ');% Handle Numbers% Look for one or more characters between 0-9email_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 middleemail_contents = regexprep(email_contents, '[^\s]+@[^\s]+', 'emailaddr');% Handle $ signemail_contents = regexprep(email_contents, '[$]+', 'dollar');% ========================== Tokenize Email ===========================% Output the email to screen as wellfprintf('\n==== Processed Email ====\n\n');% Process filel = 0;while ~isempty(email_contents)    % Tokenize and also get rid of any punctuation    [str, email_contents] = ...       strtok(email_contents, ...              [' @$/#.-:&*+=[]?!(){},''">_<;%' char(10) char(13)]);    % Remove any non alphanumeric characters    str = 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 short    if length(str) < 1       continue;    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.    %for idx = 1:1899    if(strcmp(str, vocabList{idx}) == 1)        word_indices = [word_indices ; idx];    endend    % =============================================================    % Print to screen, ensuring that the output lines are not too long    if (l + length(str) + 1) > 78        fprintf('\n');        l = 0;    end    fprintf('%s ', str);    l = l + length(str) + 1;end% Print footerfprintf('\n\n=========================\n');end

作业只要求找出相应的索引。

四、emailFeatures.m

要求是将邮件中出现的关键字提取出来，然后和单词表进行匹配，如果单词表里面的单词出现在邮件当中的话，就将其标记为1，否则为0，因此该操作结束之后返回一个N维的数组。

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 dictionaryn = 1899;% You need to return the following variables correctly.x = zeros(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 ..];%%for i = 1:length(word_indices)    x(word_indices(i)) = 1;end% ========================================================================= end

具体的实例可以查看代码注释中给出的例子。