Updated structure of lecture plan and file names (needs to be double checked)

exercises/02450Toolbox_Matlab/Scripts/ex1_6_2.m

-%% exercise 3.1.2
+%% exercise 1.6.2
 cdir = fileparts(mfilename('fullpath')); 
 [A, D] = tmg(fullfile(cdir,'../Data/textDocs.txt'));
 X = full(A)';

exercises/02450Toolbox_Matlab/Scripts/ex1_6_3.m

-%% exercise 3.1.3
cdir = fileparts(mfilename('fullpath')); 
TMGOpts.stoplist = fullfile(cdir,'../Data/stopWords.txt');
[A, D] = tmg(fullfile(cdir,'../Data/textDocs.txt'), TMGOpts);
X = full(A)';
attributeNames = cellstr(D);

%% Display the result
display(attributeNames);
display(X);
+%% exercise 1.6.3
\ No newline at end of file
+cdir = fileparts(mfilename('fullpath')); 
+TMGOpts.stoplist = fullfile(cdir,'../Data/stopWords.txt');
+[A, D] = tmg(fullfile(cdir,'../Data/textDocs.txt'), TMGOpts);
+X = full(A)';
+attributeNames = cellstr(D);
+
+%% Display the result
+display(attributeNames);
+display(X);

exercises/02450Toolbox_Matlab/Scripts/ex1_6_4.m

-%% exercise 3.1.4
cdir = fileparts(mfilename('fullpath')); 
TMGOpts.stoplist = '../Data/stopWords.txt';
TMGOpts.stemming = 1;
[A, D] = tmg(fullfile(cdir,'../Data/textDocs.txt'), TMGOpts);
X = full(A)';
attributeNames = cellstr(D);

%% Display the result
display(attributeNames);
display(X);
+%% exercise 1.6.4
\ No newline at end of file
+cdir = fileparts(mfilename('fullpath')); 
+TMGOpts.stoplist = '../Data/stopWords.txt';
+TMGOpts.stemming = 1;
+[A, D] = tmg(fullfile(cdir,'../Data/textDocs.txt'), TMGOpts);
+X = full(A)';
+attributeNames = cellstr(D);
+
+%% Display the result
+display(attributeNames);
+display(X);

exercises/02450Toolbox_Matlab/Scripts/ex1_6_5.m

-%% exercise 3.1.5
+%% exercise 1.6.5
 
 % Query vector
 q = [0; 0; 0; 0; 0; 0; 0; 1; 0; 0; 0; 0; 1; 1; 0; 0; 0]';

exercises/02450Toolbox_Matlab/Scripts/ex2_1_1.m

-% exercise 3.2.1
+% exercise 2.1.1
 
 x = [-0.68; -2.11; 2.39; 0.26; 1.46; 1.33; 1.03; -0.41; -0.33; 0.47];
 

exercises/02450Toolbox_Matlab/Scripts/ex2_2_1.m

-% exercise 3.3.1
+% exercise 2.2.1
 
 % Image to use as query
 i = 1;

exercises/02450Toolbox_Matlab/Scripts/ex2_2_2.m

-% exercise 3.3.2
+% exercise 2.2.2
 
 % Generate two data objects with M random attributes
 M = 5;

exercises/02450Toolbox_Matlab/Scripts/ex2_3_1.m

-% exercise 4.2.1
+% exercise 2.3.1
 
 % Disable xlsread warning
 warning('off', 'MATLAB:xlsread:ActiveX'); 

exercises/02450Toolbox_Matlab/Scripts/ex2_3_2.m

-% exercise 4.2.2
+% exercise 2.3.2
 
 mfig('Histogram for attributes'); clf;
 for m = 1:M

exercises/02450Toolbox_Matlab/Scripts/ex2_3_3.m

-% exercise 4.2.3
+% exercise 2.3.3
 
 %% Boxplot of each attribute
 mfig('Boxplot'); clf;

exercises/02450Toolbox_Matlab/Scripts/ex2_3_4.m

-% exercise 4.2.4
+% exercise 2.3.4
 
 %% Boxplot of each attribute for each class
 mfig('Boxplot per class'); clf;

exercises/02450Toolbox_Matlab/Scripts/ex2_3_5.m

-% exercise 4.2.5
+% exercise 2.3.5
 
 mfig('Matrix of scatter plots'); clf;
 for m1 = 1:M

exercises/02450Toolbox_Matlab/Scripts/ex2_3_6.m

-% exercise 4.2.6
+% exercise 2.3.6
 
 ind = [1 2 3]; % Indices of the variables to plot
 mfig('3D scatter plot'); clf; hold all;

exercises/02450Toolbox_Matlab/Scripts/ex2_3_7.m

-% exercise 4.2.7
+% exercise 2.3.7
 
 mfig('Data matrix (standardized)'); clf;
 imagesc(zscore(X));

exercises/02450Toolbox_Matlab/Scripts/ex2_4_1.m

-% exercise 4.3.1
+% exercise 2.4.1
 
 % Load the data
 cdir = fileparts(mfilename('fullpath')); 

exercises/02450Toolbox_Matlab/Scripts/ex2_4_2.m

-% exercise 4.3.2
+% exercise 2.4.2
 
 % Load Matlab data file and extract variables of interest
 mat_data = load('../Data/wine.mat')

exercises/02450Toolbox_Matlab/Scripts/ex3_1_1.m

-%% exercise 2.1.1
+%% exercise 3.1.1
 % Load the data into Matlab
 cdir = fileparts(mfilename('fullpath')); 
 [NUMERIC, TXT, RAW] = xlsread(fullfile(cdir,'../Data/nanonose.xls'));

exercises/02450Toolbox_Matlab/Scripts/ex3_1_2.m

-%% exercise 2.1.2
+%% exercise 3.1.2
 % Data attributes to be plotted
 i = 1;
 j = 2;

exercises/02450Toolbox_Matlab/Scripts/ex3_1_3.m

 
-%% exercise 2.1.3
+%% exercise 3.1.3
 
 % Subtract the mean from the data
 Y = bsxfun(@minus, X, mean(X));

exercises/02450Toolbox_Matlab/Scripts/ex3_1_4.m

-%% exercise 2.1.4
+%% exercise 3.1.4
 
 % Index of the principal components
 i = 1;