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1 | 1 | module StatisticalTraits
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2 | 2 |
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3 |
| -using ScientificTypes |
| 3 | +using ScientificTypesBase |
4 | 4 | import Base.instances
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5 | 5 |
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6 | 6 | ## CONSTANTS
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@@ -125,7 +125,7 @@ snakecase(s::Symbol) = Symbol(snakecase(string(s)))
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125 | 125 | ## TRAITS
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126 | 126 |
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127 | 127 | # The following can return any scientific type, that is, any type
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128 |
| -# defined in the package ScientificTypes.jl, and any ordinary type |
| 128 | +# defined in the package ScientificTypesBase.jl, and any ordinary type |
129 | 129 | # that functions as a scientific type (eg, `Missing`). Here "target"
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130 | 130 | # is a synonym for "labels", as in supervised learning; "input" is a
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131 | 131 | # synonym for "features":
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@@ -207,11 +207,11 @@ traits that are meaninful for the object.
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207 | 207 | *Note on overloading.* This method can be overloaded directly, as in
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208 | 208 | `info(X::SomeAbstractType) = ...` or, using `info(X,
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209 | 209 | ::Val{:some_trait}) = ...` where `:some_trait` is a key of
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210 |
| -`ScientificTypes.TRAIT_FUNCTION_GIVEN_NAME` (such as `:is_measure` |
| 210 | +`ScientificTypesBase.TRAIT_FUNCTION_GIVEN_NAME` (such as `:is_measure` |
211 | 211 | with value `is_measure`).
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212 | 212 |
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213 | 213 | """
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214 |
| -info(X) = info(X, Val(ScientificTypes.trait(X))) |
| 214 | +info(X) = info(X, Val(ScientificTypesBase.trait(X))) |
215 | 215 | info(X, ::Val{:other}) = NamedTuple()
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216 | 216 |
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217 | 217 | end # module
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