Add vocab.txt listing words which appears in spam over 100 times
Add visualizeBoundary.m which plots a non-linear decision boundary learned by the SVM
Add visializeBoundaryLinear.m which lots a linear decision boundary learned by the SVM
Add svmTrain which trains an SVM classifier using a simplified version of the SMO algorithm
Add svmPredict.m which returns a vector of predictions using a trained SVM model
Add spamTrain.mat which is a spam training set
Add spamTest.mat which is a spam test set
Add spamSample1.txt which is sample spam 1
Add spamSample2.txt which is sample spam 2
Add emailSample1.txt which is sample email 1
Add emailSample2.txt which is sample email 2
Add ex6data1.mat which is example dataset 1
Add ex6data2.mat which is example dataset 2
Add ex6data3.mat which is example dataset 3
Add plotData.m which plots the data points X and y into a new figure
Add ex6.m which is Octave script for the first half of the exercise
Add ex6_spam.m which is Octave script for the second half of the exercise
Add readFile.m which reads a file and returns its entire contents
Add porterStemmer.m which implements the Porter Stemming algorithm to stem a string
Add getVocabList.m which reads the fixed vocabulary list in vocab.txt and returns a cell array of the words