From 95ebc1c5e9b29b4debf7dde392e516ac394717a9 Mon Sep 17 00:00:00 2001 From: Neil Lawrence Date: Mon, 19 Aug 2013 07:38:35 +0200 Subject: [PATCH] Added notes file for issues raised while looking through code, some are things I need to raise on github, others need some informal discussion, but for the moment thought to put them informally here, given flakiness of internet connection. --- GPy/notes.txt | 25 +++++++++++++++++++++++++ 1 file changed, 25 insertions(+) create mode 100644 GPy/notes.txt diff --git a/GPy/notes.txt b/GPy/notes.txt new file mode 100644 index 00000000..011dc432 --- /dev/null +++ b/GPy/notes.txt @@ -0,0 +1,25 @@ +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)