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Compressed Table Representation #19
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Interesting. This representation has the side effect that input features can also represent distributions. Question: how would the squashed out feature represent the following
? Also, what feature in |
I am not sure I get every thing either. And the squashing thing is not discussed in opencog/matrix. It just encourages to add more values to the pair. And hopefully we can exploit that to compress redundancies. // the out puts EvaluationLink (count={2, 1}, value={T,F}) Predicate "|identifier|" ListLink Schema "r1" Predicate "out" // the inputs EvaluationLink (count=3, value=F) Predicate "|identifier|" ListLink Schema "r1" Predicate "f2" EvaluationLink (count=3, value=1.5) Predicate "|identifier|" ListLink Schema "r1" Schema "f1" It is a bit naive and if it is going to work it is going to requires a lot of cleaning. |
I see... I wonder about the following
which would even be closer to CTable representation. Not sure how well it fits with the |
OverView
Data representation has been discussed a number of times opencog#12, opencog#14. This will merely conceder the idea of compressed table, In order to avoid re-execution of a program over redundant inputs.
Loose Hypothesis
To store a matrix in atomspace 'opencog/matrix' uses a mechanism that looks like Dictionary Of Keys(DOK) representation for sparse matrix. And provides sets of elementary operations to access the stored Atoms as a matrix giving an abstraction of the portion of the atomspace as a matrix.
For example a table might be stored in DOK as:
And For a typical dataset
We can use the atomese bellow to represent the redundant two rows(row 1 & 2).
The next thing we need is a matrix-API that can read the above atomese as a matrix.
An example API [based on opencog/matrix] would contain:-
And guys forgive me if I got it all wrong!
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