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Define explicit abstract numerical types, bump
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AP6YC committed Jul 1, 2021
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2 changes: 1 addition & 1 deletion Project.toml
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Expand Up @@ -2,7 +2,7 @@ name = "AdaptiveResonance"
uuid = "3d72adc0-63d3-4141-bf9b-84450dd0395b"
authors = ["Sasha Petrenko"]
description = "A Julia package for Adaptive Resonance Theory (ART) algorithms."
version = "0.2.4"
version = "0.3.0"

[deps]
ClusterValidityIndices = "2fefd308-f647-4698-a2f0-e90cfcbca9ad"
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@AP6YC AP6YC commented on 444b5bb Jul 1, 2021

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Release notes:

This version defines several internal type aliases to standardize accepted numerical types throughout the package (inspired by StatsBase.jl).
Explicitly defining types has the benefit of improving compilation/run time, and generalizing these types takes a step towards letting this package run on 32-bit systems.
These internal abstract types are:

  • RealArray: any-dimensional array of real (floating point or integer) numbers.
  • RealVector: 1-D array of real numbers.
  • RealMatrix: 2-D array of real numbers.
  • IntegerArray: any-dimensional array of integer numbers.
  • IntegerVector: 1-D array of integers.
  • IntegerMatrix: 2-D array of integers.
  • RealFP: floating point numbers (32- or 64-bit, depending on the system).

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Registration pull request created: JuliaRegistries/General/40040

After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.

This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:

git tag -a v0.3.0 -m "<description of version>" 444b5bb599fe5658eeaa28298be311b6216c1db4
git push origin v0.3.0

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