Coursera—machine learning(Andrew Ng)第七周编程作业

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 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

dataset3Params.m

function [C, sigma] = dataset3Params(X, y, Xval, yval)%DATASET3PARAMS 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] = DATASET3PARAMS(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))%steps = [0.01, 0.03, 0.1, 0.3, 1, 3, 10, 30];minError = Inf;minC = Inf;minSigma = Inf;for i = 1 : length(steps)    for j = 1 : length(steps)        currentC = steps(i);        currentSigma = steps(j);        model = svmTrain(X, y, currentC, @(x1, x2) gaussianKernel(x1, x2, currentSigma));        predictions = svmPredict(model, Xval);        error = mean(double(predictions ~= yval));        if (error < minError)            minError = error;            minC = currentC;            minSigma = currentSigma;        end    endendC = minC;sigma = minSigma;% =========================================================================end

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 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 i = 1 : length(vocabList)    if (strcmp(vocabList(i), str))        word_indices = [word_indices, i];        break;    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

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 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) = 1;end% =========================================================================    end

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