loss_vectorized.py

def loss(X, Y, W):
    delta = 1

    scores = np.dot(W, X.T)

    rows = np.arange(Y.shape[0])
    margins = np.maximum(0, scores - scores[Y, rows] + delta)

    # Find indices that need to be set to zero.
    # Case when j == y in loss_i_v.
    subtract_Y_indices = np.zeros(shape=margins.shape, dtype=bool)
    subtract_Y_indices[Y, rows] = True
    # Set proper indices to 0.
    margins[subtract_Y_indices] = 0

    return np.sum(margins)