GraphicalModelLearning.jl
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Default behavior when parsing CSV
How should we deal with float vs int data types?
Are we talking about the default behavior of the learning algorithm or the parser for the data structure? If it is the earlier we can imagine having a different default behavior depending on the algo. (Inverse Ising can convert to bool or int, gaussian methods can convert to float, etc...).
The core issue here is when a user reads discrete data into Julia as floats, instead of int or bool.
You propose a good solution. If the user passes a floating point samples into a discrete model solver, we can first try to convert the floating point data to discrete data and throw a warning if it works and an error if it does not work.