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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.
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Prod.py kernel should take a list of kernels rather than two arguments for kernels.
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transformations.py should have limits on what is fed into exp() particularly for the negative log logistic.
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Load in a model with mlp kernel, plot it, change a parameter, plot it again. It doesn't update the plot.
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Tests for kernels which work directly on the kernel implementation (not through GP).
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Should stationary covariances have their own kernpart type?
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Where do we declare default kernel parameters. In constructors.py or in the definition file for the kernel?
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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.
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When we print a model should we also include information such as number of inputs and number of outputs?
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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.
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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:
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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).
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Can we comment up some of the list incomprehensions in hierarchical.py??
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Need to tidy up classification.py,
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many examples include help that doesn't apply
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(it is suggested that you can try different approximation types)
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