> On Mon, Jul 26, 2010 at 10:05 PM, Alan G Isaac<ais...@american.edu> wrote: >> But I am still confused about the use case. >> What is the scalar- (or 1d-array-) returning procedure >> invoked before taking the determinant?
On 7/27/2010 8:51 AM, Skipper Seabold wrote: > Recently I ran into this trying to make the log-likelihood of a > multivariate and univariate autoregressive process use the same > function. One has log(sigma_scalar) and one calls for > logdet(Sigma_matrix). I also ran in to again yesterday working on the > Kalman filter, depending on the process being modeled and how the user > writes a function if the needed coefficient arrays depend on > parameters. To be more general, I have to put in atleast_2d, even > though these checks are really in slogdet. OK, I see. Two comments, without going over the code. 1. It seems the problem really arises earlier, when computing the residuals. I suppose the single equation code produces a 1d array, while the multi-equation code must produce a 2d array of residuals. This seems like the better place to fix things if you want general handling: make sure the residuals are always 2d. 2. If you don't want to do this, you could always branch on the LinAlgError. Cheers, Alan Isaac _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
