distances_svm.py

def distances(self, w, with_lagrange=True):
    distances = self.y * (np.dot(self.X, w)) - 1

    # get distance from the current decision boundary
    # by considering 1 width of margin

    if with_lagrange:  # if lagrange multiplier considered
        # if distance is more than 0
        # sample is not on the support vector
        # Lagrange multiplier will be 0
        distances[distances > 0] = 0

    return distances