Moving course1 to course1 subdir.
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machine_learning/course1/mlclass-ex6-008/mlclass-ex6/emailFeatures.m
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machine_learning/course1/mlclass-ex6-008/mlclass-ex6/emailFeatures.m
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function x = emailFeatures(word_indices)
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%EMAILFEATURES takes in a word_indices vector and produces a feature vector
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%from the word indices
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% x = EMAILFEATURES(word_indices) takes in a word_indices vector and
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% produces a feature vector from the word indices.
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% Total number of words in the dictionary
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n = 1899;
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% You need to return the following variables correctly.
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x = zeros(n, 1);
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% ====================== YOUR CODE HERE ======================
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% Instructions: Fill in this function to return a feature vector for the
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% given email (word_indices). To help make it easier to
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% process the emails, we have have already pre-processed each
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% email and converted each word in the email into an index in
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% a fixed dictionary (of 1899 words). The variable
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% word_indices contains the list of indices of the words
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% which occur in one email.
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%
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% Concretely, if an email has the text:
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%
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% The quick brown fox jumped over the lazy dog.
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%
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% Then, the word_indices vector for this text might look
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% like:
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%
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% 60 100 33 44 10 53 60 58 5
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%
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% where, we have mapped each word onto a number, for example:
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%
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% the -- 60
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% quick -- 100
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% ...
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%
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% (note: the above numbers are just an example and are not the
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% actual mappings).
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%
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% Your task is take one such word_indices vector and construct
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% a binary feature vector that indicates whether a particular
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% word occurs in the email. That is, x(i) = 1 when word i
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% is present in the email. Concretely, if the word 'the' (say,
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% index 60) appears in the email, then x(60) = 1. The feature
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% vector should look like:
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%
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% x = [ 0 0 0 0 1 0 0 0 ... 0 0 0 0 1 ... 0 0 0 1 0 ..];
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%
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%
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for i=1:length(word_indices)
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x(word_indices(i)) |= 1;
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end
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% =========================================================================
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end
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