GPy/GPy/notes.txt

Prod.py kernel should take a list of kernels rather than two arguments for kernels.
transformations.py should have limits on what is fed into exp() particularly for the negative log logistic. 

Load in a model with mlp kernel, plot it, change a parameter, plot it again. It doesn't update the plot.

Tests for kernels which work directly on the kernel implementation (not through GP).

Should stationary covariances have their own kernpart type?

Where do we declare default kernel parameters. In constructors.py or in the definition file for the kernel?

When printing to stdout, can we check that our approach is also working nicely for the ipython notebook? I like the way our optimization ticks over, but at the moment this doesn't seem to work in the ipython notebook, it would be nice if it did.

When we print a model should we also include information such as number of inputs and number of outputs?

Let's not use N for giving the number of data in the model. When it pops up as a help tip it's not as clear as num_samples or num_data. Prefer the second, but oddly I've been using first.

Loving the fact that the * has been overloaded on the kernels (oddly never thought to check this before). Although naming can be a bit confusing. Can we think how to deal with the names in a clearer way when we use a kernel like this one:
kern = GPy.kern.rbf(30)*(GPy.kern.mlp(30)+GPy.kern.poly(30, degree=5)) + GPy.kern.bias(30). There seems to be some tieing of parameters going on ... should there be? (you can try it as the kernel for the robot wireless model).

Can we comment up some of the list incomprehensions in hierarchical.py??

Need to tidy up classification.py, 
many examples include help that doesn't apply 
(it is suggested that you can try different approximation types)

Shall we overload the ** operator to have tensor products?

People aren't filling the doc strings in as they go *everyone* needs to get in the habit of this (and modifying them as they edit, or correcting them when there is a problem).