I didn't know I could do simply average covariances. That works.
On the second point though:
The (n_samples, n_features) matrix is the adjacency matrix of a network.
By calculating the covariance matrix, I was trying to find the covariance
between all possible edges of the network - and thus I was expecting the
resulting covariance to be of size (n_samples * n_features, n_samples,
n_features). I have a hard time figuring out what the (n_samples,
n_features) matrix actually represents..
Federico
On Fri, Dec 14, 2012 at 5:52 PM, Gael Varoquaux <
[email protected]> wrote:
> On Fri, Dec 14, 2012 at 05:51:08PM +0100, federico vaggi wrote:
> > In this case, X is:
>
> > (n_samples, n_features, M_bootstrapped_iterations):
>
> > So I thought I could get a better estimate by taking each M-length
> vector and
> > calculating the covariance against every other vector, which would
> result in a
> > (n_samples * n_features, n_samples* n_features) sized matrix, not a
> (n_samples,
> > n_features) sized matrix.
>
> Compute an (n_features, n_features) covariance matrix for each
> bootsrapped iteration and average them.
>
> G
>
>
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