ex6_spam.m

clear ; close all; clc



fprintf('\nPreprocessing sample email (emailSample1.txt)\n');


file_contents = readFile('emailSample1.txt');
word_indices  = processEmail(file_contents);

% Print Stats
fprintf('Word Indices: \n');
fprintf(' %d', word_indices);
fprintf('\n\n');

fprintf('Program paused. Press enter to continue.\n');
pause;


fprintf('\nExtracting features from sample email (emailSample1.txt)\n');

file_contents = readFile('emailSample1.txt');
word_indices  = processEmail(file_contents);
features      = emailFeatures(word_indices);


fprintf('Length of feature vector: %d\n', length(features));
fprintf('Number of non-zero entries: %d\n', sum(features > 0));

fprintf('Program paused. Press enter to continue.\n');
pause;

load('spamTrain.mat');

fprintf('\nTraining Linear SVM (Spam Classification)\n')
fprintf('(this may take 1 to 2 minutes) ...\n')

C = 0.1;
model = svmTrain(X, y, C, @linearKernel);

p = svmPredict(model, X);

fprintf('Training Accuracy: %f\n', mean(double(p == y)) * 100);

load('spamTest.mat');

fprintf('\nEvaluating the trained Linear SVM on a test set ...\n')

p = svmPredict(model, Xtest);

fprintf('Test Accuracy: %f\n', mean(double(p == ytest)) * 100);
pause;



[weight, idx] = sort(model.w, 'descend');
vocabList = getVocabList();

fprintf('\nTop predictors of spam: \n');
for i = 1:15
    fprintf(' %-15s (%f) \n', vocabList{idx(i)}, weight(i));
end

fprintf('\n\n');
fprintf('\nProgram paused. Press enter to continue.\n');
pause;


filename = 'spamSample1.txt';

% Read and predict
file_contents = readFile(filename);
word_indices  = processEmail(file_contents);
x             = emailFeatures(word_indices);
p = svmPredict(model, x);

fprintf('\nProcessed %s\n\nSpam Classification: %d\n', filename, p);
fprintf('(1 indicates spam, 0 indicates not spam)\n\n');