"bad params" error when initialising a model with certain sampling plans

[Nate28]

_I've recently tried initialising a pyKriging object with a 2D sampling plan of 100 points (generated using the package's optimallhc method). However, a "bad params" error was raised by the model's updatePsi method during training despite it having worked just fine for most other sampling plans (for example, a 74 point optimal LHC produced a good model, but failed with a 75 point LHC).

Am I missing something fundamental to the mathematics here, or is this an idiosyncrasy of the code itself?

Thanks very much for developing pyKriging by the way - it's a brilliant toolbox!_

EDIT:

I managed to track down the issue - it turns out it was the mathematics after all. It seems that when the model initially calls its "updatePsi" method, it runs a Cholesky decomposition using NumPy's linalg.cholesky function. If this method is not supplied with a definite positive matrix as its input, it fails, resulting in an overall failure to build the pyKriging object.

Have you come up against this issue in the past? If so, do you know of any ways to generate an optimal LHS while ensuring that Psi will be positive definite when processed by pyKriging?