CAS Best Practices

Specific Domains Machine Learning
78

But subtypes should not override the defaults (or “fallbacks”) of their supertype “willy nilly”. There should be some kind of consistency with the fallback. e.g.:

julia> typeof(1:4)
UnitRange{Int64}

julia> typeof(1:4) <: AbstractArray
true

julia> (1:4) .+ 5
6:9

julia> collect(1:4) .+ 5
4-element Vector{Int64}:
 6
 7
 8
 9

julia> collect((1:4) .+ 5) == collect(1:4) .+ 5  # consistency
true

If broadcasted addition on UnitRange{Int64} were defined in such a way that the “# consistency” property did not hold, that would be bad for generic programming!

Does that badness apply in this CAS setting? What consistency should exist across rules that are defined on types that have a subtype relationship among them? I am not sure. That’s what the question is.

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