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Message: [QUOTE="Brandon, post: 3610046"] John: Yes, that is what I meant, my apologies. Due to my current level of numbskullery, when I start to see things like tuples as keys, the apparent ease of this evaporates in front of my eyes! I know that I need more practice, though, and it will come. You are very correct about the Laplace adjustment. However, a more precise statement of my overall problem would involve training and testing which utilizes bigram probabilities derived in part from the Laplace adjustment; as I understand the workflow that I should follow, I can't allow myself to be constrained only to bigrams that actually exist in training or my overall probability when I run through testing will be thrown off to 0 as soon as a test bigram that doesn't exist in training is encountered. Hence my desire to find all possible bigrams in train (having taken steps to ensure proper set relations between train and test). The best way I can currently see to do this is with my current two-dictionary "caper", and by iterating over foo, not bar :) And yes, I know it seems silly to wish for that document with the use- cases, but personally speaking, even if the thing is rather lengthy, I would probably pick up better techniques for general knowledge by reading through it and seeing the examples. I actually think that there would be a good market (if only in mindshare) for a thorough examination of the power of lists, nested lists, and dictionaries (with glorious examples) - something that might appeal to a lot of non-full time programmers who need to script a lot but want to be efficient about it, yet don't want to deal with a tutorial that unnecessarily covers all the aspects of Python. My $0.027 (having gone up due to the commodities markets). Thanks again for the input, I do appreciate it! Brandon [/QUOTE]

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