WIP: add option for automated regularization to mne.cov.compute_covariance

Todo:

- [x] show basic sensor examples / update existing examples
- [x] consolidate API including default values
- [x] deal with projs
Well that's what I did, the 'fro' test failed

> On 09 Dec 2014, at 23:45, Alexandre Gramfort <notifications@github.com> wrote:
> 
> In mne/cov.py:
> 
> > +
> > +        for i, epochs_t in enumerate(epochs):
> > +
> > +            tslice = _get_tslice(epochs_t, tmin, tmax)
> > +            for e in epochs_t:
> > +                e = e[picks_meeg][:, tslice]
> > +                if not keep_sample_mean:
> > +                    data_mean[i] += e
> > +                data += np.dot(e, e.T)
> > +                n_samples[i] += e.shape[1]
> > +                n_epochs[i] += 1
> > +
> > +        n_samples_tot = int(np.sum(n_samples))
> > +        if keep_sample_mean:
> > +            data /= n_samples_tot
> > +        else:
> why you need to do is:
> 
> compute the data_mean for all type of epochs. Subtract the mean of each type of epochs to each epoch in this Epochs. Then concatenate the data from all epochs to run cv on it.
> 
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