Change training data and copy model parameters

[Taco de Wolff]

• After training, be able to change the training data
• Be able to save just the model parameters (not data) or copy to a new model

[Taco de Wolff]

Is it possible to change the training data after training a model? Some models don't allow this (OpperArchambeau and Hensman, which keep q_mu and q_var parameters that have the same length as the input data), and I'm unsure if it makes sense.

Other than that, kernel parameters can now be copied using model.copy_parameters(other_model), see eefbd8c

[Taco de Wolff]

It's best to copy model parameters instead of changing the training data, closing