On Mon, Jul 19, 2010 at 9:02 PM, Keith Goodman <kwgoodman@gmail.com> wrote:
On Mon, Jul 19, 2010 at 5:50 PM, Charles R Harris <charlesr.harris@gmail.com> wrote:
Hi All,
I'm thinking about adding some functionality to lstsq because I find myself doing the same fixes over and over. List follows.
Add weights so data points can be weighted. Use column scaling so condition numbers make more sense. Compute covariance approximation?
Unfortunately, the last will require using svd since there no linear least squares routines in LAPACK that also compute the covariance, at least
On Mon, Jul 19, 2010 at 6:53 PM, Joshua Holbrook <josh.holbrook@gmail.com> wrote: that
google knows about.
Thoughts?
Maybe make 2 functions--one which implements 1 and 2, and another which implements 3? I think weights is an excellent idea!
I'd like a lstsq that did less, like not calculate the sum of squared residuals. That's useful in tight loops. So I also think having two lstsq makes sense. One as basic as possible; one with bells. How does scipy's lstsq fit into all this?
I think the computation of the residues is cheap in lstsq. The algolrithm used starts by reducing the design matrix to bidiagonal from and reduces the rhs at the same time. In other words an mxn problem becomes a (n+1)xn problem. That's why the summed square of residuals is available but not the individual residuals, after the reduction there is only one residual and its square it the residue. Chuck