MRG: 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
No just backwards compat --- allow to compute empirical cov without sklearn

> On 27 Dec 2014, at 09:35, mainakjas <notifications@github.com> wrote:
> 
> In mne/cov.py:
> 
> > +
> > +    covs = list()
> > +    for this_method, data in cov_data.items():
> > +        cov = Covariance(None)
> > +        cov.update(kind=1, diag=False, dim=len(data['data']), names=ch_names,
> > +                   data=data.pop('data'), projs=projs, bads=info['bads'],
> > +                   nfree=n_samples_tot, eig=None, eigvec=None)
> > +
> > +        logger.info('Number of samples used : %d' % n_samples_tot)
> > +        logger.info('[done]')
> > +
> > +        # add extra info
> > +        cov.update(method=this_method, **data)
> > +        covs.append(cov)
> > +
> > +    if ok_sklearn:
> ah, ok. So, there are cases where sklearn is helpful but not necessary?
> 
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