[Chapter 5 - SVM]: Linear SVM with Batch gradient descent

Hi,

In Geron's Github for chapter 5 he includes a Linear SVC implementation with batch gradient descent. If possible, could someone confirm my intuition on the following code:

t = y * 2 - 1 # -1 if t==0, +1 if t==1
X_t = X * t
self.Js=[]

# Training
for epoch in range(self.n_epochs):
support_vectors_idx = (X_t.dot(w) + t * b < 1).ravel()
X_t_sv = X_t[support_vectors_idx]
t_sv = t[support_vectors_idx]

J = 1/2 * np.sum(w * w) + self.C * (np.sum(1 - X_t_sv.dot(w)) - b * np.sum(t_sv))
self.Js.append(J)

In particular I am looking for a better understanding of :

X_t = X * t

I understand that the cost function for linear SVM is being written as:

J = 1/2 * np.sum(w * w) + self.C * (np.sum(1 - X_t_sv.dot(w)) - b * np.sum(t_sv))

as opposed to:

J = 1/2 * np.sum(w * w) + self.C * (np.sum(max(0,1-t(i)(X.dot(w)) + b)

Is the idea that once the last term is multiplied out you get:

J = 1/2 * p.sum(w * w) + self.C * (np.sum(1 - (t(i)*X.dot(w)) - t(i)*b)

where max(0,1) goes to 1 for support vectors and then the reason in the code you work out t_sv and X_sv is because the 2nd term in the cost function is only concerned with the support vectors. Hence,

J = 1/2 * np.sum(w * w) + self.C * (np.sum(1 - X_t_sv.dot(w)) - b * np.sum(t_sv)) is just the cost function being written such that the 2nd term has the following:

- X_t_sv the X*t values for the support vectors only
- b * np.sum(t_sv) is the bias term subtracted for only those support vectors

Any clarification would be greatly appreciated!

(see screenshot below for code from Githib)