fit_predict_svm.py

def fit(self):
    for i in range(self.num_of_epochs):
        L, dw = self.get_cost_grads(self.X, self.w, self.y)
        self.w = self.w - self.lr * dw
        if i % 1000 == 0:
            print(i, ' | ', L)


def predict(self, X):
    X = np.column_stack((np.ones(len(X)), X))
    return np.sign(X @ self.w)