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Feature request: Imperative interface #34
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Hi @rickardnorlander thank you for the feedback. I'd like to try and get a better understanding of your use case. It is the intention of the (current) API in this library that the It would be really helpful to see some code to be honest. Maybe you can show me the pattern you've had to use, and a counter example of how you'd like it to work? |
Something like this maybe
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i haven't forgotten about this! I'm travelling at the moment, so haven't had a chance to look at it properly |
By the way @rickardnorlander, do you have any of this code on GitHub? |
It's for a lossless image compressor that I didn't make public yet because I haven't able to achieve the kind of compression I want (I beat png by a bit, but lose to modern ones like webp). Anyway I was able to hack together an interface for myself so I'm not blocked by this. |
I'd prefer you weren't just unblocked, but that you had a solution that was nice. I was going to suggest creating an iterator adaptor, or something along those lines, which folded your low-level symbols into the 'intermediate' symbols |
For me it would be more convenient to use an interface where I push probabilities into the coder, rather than the coder pulling them out of my model. I have a few different predictors, and they can take varying number of arguments of different types. With an imperative interface it would straightforward, I first encode which predictor to use and push it into the coder, then I push the arguments one by one. But with the callback pattern it becomes quite awkward. I have to do multiple passes and have one root model that interprets some symbols as a predictor id and others as argument and then route it correctly.
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