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nins_ t1_iwlhcbk wrote

So it seems like you have 8 * 5 * 2 = 80 features per training sample if you want to use all 8 sides to predict your binary label. Is this representation causing problems?

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snairgit OP t1_iwlnn2v wrote

Ya true, but it's a bit more than that. I've added an explanation to another comment, adding that below.

My doubt is this: for a row, how to put the velocity and time together for a single feature. For eg: row 1 column 1 would be - 1st out of 8 side -> 1st out of 5 values -> velocity, distance. Here how do I represent (velocity, distance)? Do I keep them as a tuple or as separate adjacent features or any other format? This is exactly where I'm stuck.

My confusion/question is on what could be the right way to put these values together. Do I directly stack them as 80 features like you mentioned or do I adopt another format such that the 2 values (velocity, time) are bundled together like a tuple or a dict or something else? Thanks.

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visarga t1_iwlxe03 wrote

About representing your features - I would not feed float values directly to a neural net. I think you either need to discretise the values or to embed them like absolute positional embeddings in transformers. Or try using a SIREN on your float values directly.

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snairgit OP t1_iwnzie7 wrote

Okay, thanks. I'll keep that in mind and I'll look into it.

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