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.
On Fri, Dec 14, 2012 at 5:34 PM, Gael Varoquaux <
[email protected]> wrote:
> On Fri, Dec 14, 2012 at 05:32:26PM +0100, federico vaggi wrote:
> > What I wanted to know is:
>
> > 1- Is there a way to efficiently calculate the covariance matrix given
> this
> > data which isn't than manually calculating all NxN weights?
>
> If X is you matrix, (n_samples x n_features), np.dot(X.T, X) is the
> sample covariance matrix, as long as X is centered (remove the mean in
> the sample direction).
>
> G
>
>
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