I want to do a least squares fit of a line in 3 or 4-dimensional space to 16 data points. I looked at the manual, it seems gsl provides least squares linear fits only for onedimensional stuff. The classic way would be the principal component analysis (PCA), again gsl does not provide this. PCA can be done by estimating the covariance matrix and getting thet eigenvector for the biggest eigenvalue. gsl seems to provide functions to get the eigenvalues and vectors, it even sorts them for me. It might be a bit inefficient to calculate them all when I need only the biggest, but that shouldn't be much of a problem. However gsl seems to provide estimation of covariance only in one dimension, so I would have to implement estimation of the covariance matrix myself. Is this correct? Will performance be okay for such small data sets (16 data points, in 3 or 4 dimensions) or is gsl optimized too much towards "bigger" problems?
Philipp _______________________________________________ Help-gsl mailing list [email protected] http://lists.gnu.org/mailman/listinfo/help-gsl
