--- a/dataset3Params.m
+++ b/dataset3Params.m
@@ -1,34 +1,33 @@
 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.
-%
+  ##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;
+  t = [10.^[-2:1], 3*10.^[-2:1]];
+
+  n = length (t);
+
+  best = zeros (n);
 
-% ====================== 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))
-%
+  for i=1:n
+    for j=1:n
+      C = t(i);
+      sigma = t(j);
+      model = svmTrain (X, y, C, 
+                        @(x1, x2) gaussianKernel (x1, x2, sigma),
+                        1e-3, 20);
+      pred = svmPredict (model, Xval);
+      best(i,j) = mean (yval != pred);
+    endfor
+  endfor
 
-
-
-
-
+  [~, Idx] = min (best(:));
+  [i, j] = ind2sub ([n,n], idx);
+  C = t(i);
+  sigma = t(j);
 
-
-% =========================================================================
-
-end
+endfunction

--- a/emailFeatures.m
+++ b/emailFeatures.m
@@ -1,61 +1,15 @@
 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);
+  ##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. 
 
-% ====================== 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 ..];
-%
-%
+  ## Total number of words in the dictionary
+  n = 1899;
 
-
-
-
-
-
+  ## You need to return the following variables correctly.
+  x = zeros(n, 1);
+  
+  x(word_indices) = 1;
 
-
-% =========================================================================
-    
-
-end
+endfunction

--- a/gaussianKernel.m
+++ b/gaussianKernel.m
@@ -1,26 +1,10 @@
 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;
+  ##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
 
-% ====================== YOUR CODE HERE ======================
-% Instructions: Fill in this function to return the similarity between x1
-%               and x2 computed using a Gaussian kernel with bandwidth
-%               sigma
-%
-%
-
+  ## Ensure that x1 and x2 are column vectors
+  x1 = x1(:); x2 = x2(:);
 
-
-
-
-
-% =============================================================
-    
-end
+  sim = exp ( -sumsq (x1-x2)/(2*sigma^2));
+endfunction
\ No newline at end of file

--- a/processEmail.m
+++ b/processEmail.m
@@ -1,125 +1,100 @@
 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. 
-%
+  ##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();
+  ## Load Vocabulary
+  vocabList = getVocabList();
 
-% Init return value
-word_indices = [];
+  ## Init return value
+  word_indices = [];
 
-% ========================== Preprocess Email ===========================
+  ## ========================== Preprocess Email ===========================
 
-% Find the Headers ( \n\n and remove )
-% Uncomment the following lines if you are working with raw emails with the
-% full headers
+  ## 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);
+  ## hdrstart = strfind(email_contents, ([char(10) char(10)]));
+  ## email_contents = email_contents(hdrstart(1):end);
 
-% Lower case
-email_contents = lower(email_contents);
+  ## 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, '<[^<>]+>', ' ');
+  ## 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 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 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 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');
+  ## Handle $ sign
+  email_contents = regexprep(email_contents, '[$]+', 'dollar');
 
 
-% ========================== Tokenize Email ===========================
+  ## ========================== Tokenize Email ===========================
 
-% Output the email to screen as well
-fprintf('\n==== Processed Email ====\n\n');
+  ## Output the email to screen as well
+  fprintf('\n==== Processed Email ====\n\n');
 
-% Process file
-l = 0;
+  ## Process file
+  l = 0;
 
-while ~isempty(email_contents)
+  while ~isempty(email_contents)
 
-    % Tokenize and also get rid of any punctuation
-    [str, email_contents] = ...
-       strtok(email_contents, ...
-              [' @$/#.-:&*+=[]?!(){},''">_<;%' char(10) char(13)]);
-   
-    % Remove any non alphanumeric characters
+    ## Tokenize and also get rid of any punctuation
+    [str, email_contents] = \
+        strtok(email_contents, \
+               [" @$/#.-:&*+=[]?!(){},'\">_<;%" 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)
+    ## Stem the word 
+    ## (the porterStemmer sometimes has issues, so we use a try catch block)
     try str = porterStemmer(strtrim(str)); 
     catch str = ''; continue;
-    end;
+    end_try_catch;
 
-    % Skip the word if it is too short
+    ## Skip the word if it is too short
     if length(str) < 1
-       continue;
-    end
+      continue;
+    endif
+    
+    ## Convert the vocabulary list
+    for i = 1:numel (vocabList)
+      if strcmp (vocabList{i}, str)
+        word_indices(end+1) = i;
+        break;
+      endif
+    endfor
 
-    % 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.
-    %
-
-
+    ## Print to screen, ensuring that the output lines are not too long
+    if (l + length(str) + 1) > 78
+      fprintf('\n');
+      l = 0;
+    endif
+    fprintf("%s ", str);
+    l = l + length(str) + 1;
+    
+  endwhile
 
-
-
-
-
-
-
-
-    % =============================================================
-
-
-    % 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 footer
-fprintf('\n\n=========================\n');
-
-end
+  ## Print footer
+  fprintf("\n\n=========================\n");
+  
+endfunction
+          
\ No newline at end of file