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Andrew Ng机器学习week7(Support Vector Machines)编程习题
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;% ====================== 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;% i*j means every condition of different C and Sigma.
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;endend
endC = 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 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.%for i = 1:length(vocabList)if(strcmp(vocabList(i), str))word_indices = [word_indices; i]break;end
end % =============================================================% 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
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);% ====================== 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
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