That did the trick - thanks a bunch!
On Thu, Jun 7, 2012 at 4:12 PM, Alexandre Gramfort <
[email protected]> wrote:
> hi Federico,
>
> lasso = Lasso(fit_intercept=False)
> A_ = lasso.fit(X.T, B.T).coef_
>
> should do the trick If I followed correctly. I just transposed your
> problem.
>
> Alex
>
> On Thu, Jun 7, 2012 at 4:05 PM, federico vaggi <[email protected]>
> wrote:
> > Hello,
> >
> > I have a fairly simple model I am trying to solve using Lasso:
> >
> > AX + B = 0
> >
> > Where:
> >
> > A is an [nxn] matrix
> > X is an [nxm] matrix
> > and B is an [nxm] matrix
> >
> > And I am attempting to solve for A, given X and B.
> >
> > Using a pseudo-inverse, this is quite easy to solve - but I am not really
> > sure how to solve this using Lasso or any of the other linear solvers.
> Most
> > of the solvers available in sklearn assume that you are trying to solve a
> > problem of type:
> >
> > Xw - y
> >
> > Given X and y.
> >
> > Is there a way to move the problem around (ideally, a method that is
> > somewhat numerically robust) so that I can use the existing solvers?
> >
> > The second issue - once I've obtained the parameters from the regression,
> > what's the correct way to calculate the covariance matrix to obtain an
> > estimate of the parameters? Does the choice of linear model I've made to
> > estimate the parameters influence the proper methodology I should use to
> > estimate the covariance matrix?
> >
> > Thank you very much,
> >
> > Federico
> >
> >
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