FIX: Leaving all channels in cov

Addresses #402. MNE C code left all channels in the cov matrix, including bads. This code allows that to happen. 

However, note that regularization removes any bad channels. I tried coding it so that it would keep them, but that broke the beamforming code (which presumably assumed that regularization returned a "cleaned" covariance). In any case, the changes I made to `minimum_norm/inverse.py` should make it so a covariance with all data channels (including bads) will work, as well as a covariance with data restricted to only good channels. I think this is the sanest solution for now...