def naivebayesPY(X, Y): """ naivebayesPY(X, Y) returns [pos,neg] Computation of P(Y) Input: X : n input vectors of d dimensions (nxd) Y : n labels (-1 or +1) (nx1) Output: pos: probability p(y=1) neg: probability p(y=-1) """ # add one positive and negative example to avoid division by zero ("plus-one smoothing") Y = np.concatenate([Y, [-1,1]]) n = len(Y) # YOUR CODE HERE raise NotImplementedError()