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Update cross references and increment patch version (#392)
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@kahaaga
kahaaga committed Jan 17, 2024
1 parent 89c61a8 commit 550c444
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2 changes: 1 addition & 1 deletion Project.toml
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Expand Up @@ -2,7 +2,7 @@ name = "ComplexityMeasures"
uuid = "ab4b797d-85ee-42ba-b621-05d793b346a2"
authors = "Kristian Agasøster Haaga <kahaaga@gmail.com>, George Datseries <datseris.george@gmail.com>"
repo = "https://github.com/juliadynamics/ComplexityMeasures.jl.git"
version = "3.4.0"
version = "3.4.1"

[deps]
Combinatorics = "861a8166-3701-5b0c-9a16-15d98fcdc6aa"
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1 change: 1 addition & 0 deletions src/core/complexity.jl
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Expand Up @@ -24,6 +24,7 @@ any subtype of [`ComplexityEstimator`](@ref):
- [`MissingDispersionPatterns`](@ref).
- [`ReverseDispersion`](@ref).
- [`SampleEntropy`](@ref).
- [`BubbleEntropy`](@ref).
- [`StatisticalComplexity`](@ref).
"""
function complexity(c::ComplexityEstimator, x)
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2 changes: 2 additions & 0 deletions src/core/encodings.jl
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Expand Up @@ -16,6 +16,8 @@ Current available encodings are:
- [`RelativeMeanEncoding`](@ref).
- [`RelativeFirstDifferenceEncoding`](@ref).
- [`UniqueElementsEncoding`](@ref).
- [`BubbleSortSwapsEncoding`](@ref).
- [`PairDistanceEncoding`](@ref).
- [`CombinationEncoding`](@ref), which can combine any of the above encodings.
"""
abstract type Encoding end
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32 changes: 17 additions & 15 deletions src/core/outcome_spaces.jl
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Expand Up @@ -24,21 +24,23 @@ discretize/encode the data directly.
## Implementations
| Outcome space | Principle | Input data | Counting-compatible |
| :-------------------------------------- | :--------------------------- | :------------------------ | :------------------ |
| [`UniqueElements`](@ref) | Count of unique elements | `Any` | ✔ |
| [`ValueBinning`](@ref) | Binning (histogram) | `Vector`, `StateSpaceSet` | ✔ |
| [`OrdinalPatterns`](@ref) | Ordinal patterns | `Vector`, `StateSpaceSet` | ✔ |
| [`SpatialOrdinalPatterns`](@ref) | Ordinal patterns in space | `Array` | ✔ |
| [`Dispersion`](@ref) | Dispersion patterns | `Vector` | ✔ |
| [`SpatialDispersion`](@ref) | Dispersion patterns in space | `Array` | ✔ |
| [`CosineSimilarityBinning`](@ref) | Cosine similarity | `Vector` | ✔ |
| [`TransferOperator`](@ref) | Binning (transfer operator) | `Vector`, `StateSpaceSet` | ✖ |
| [`NaiveKernel`](@ref) | Kernel density estimation | `StateSpaceSet` | ✖ |
| [`WeightedOrdinalPatterns`](@ref) | Ordinal patterns | `Vector`, `StateSpaceSet` | ✖ |
| [`AmplitudeAwareOrdinalPatterns`](@ref) | Ordinal patterns | `Vector`, `StateSpaceSet` | ✖ |
| [`WaveletOverlap`](@ref) | Wavelet transform | `Vector` | ✖ |
| [`PowerSpectrum`](@ref) | Fourier transform | `Vector` | ✖ |
| Outcome space | Principle | Input data | Counting-compatible |
| :-------------------------------------- | :-------------------------------- | :------------------------ | :------------------ |
| [`UniqueElements`](@ref) | Count of unique elements | `Any` | ✔ |
| [`ValueBinning`](@ref) | Binning (histogram) | `Vector`, `StateSpaceSet` | ✔ |
| [`OrdinalPatterns`](@ref) | Ordinal patterns | `Vector`, `StateSpaceSet` | ✔ |
| [`SpatialOrdinalPatterns`](@ref) | Ordinal patterns in space | `Array` | ✔ |
| [`Dispersion`](@ref) | Dispersion patterns | `Vector` | ✔ |
| [`SpatialDispersion`](@ref) | Dispersion patterns in space | `Array` | ✔ |
| [`CosineSimilarityBinning`](@ref) | Cosine similarity | `Vector` | ✔ |
| [`BubbleSortSwaps`](@ref) | Swap counts when sorting | `Vector` | ✔ |
| [`SequentialPairDistances`](@ref) | Sequential state vector distances | `Vector`, `StateSpaceSet` | ✔ |
| [`TransferOperator`](@ref) | Binning (transfer operator) | `Vector`, `StateSpaceSet` | ✖ |
| [`NaiveKernel`](@ref) | Kernel density estimation | `StateSpaceSet` | ✖ |
| [`WeightedOrdinalPatterns`](@ref) | Ordinal patterns | `Vector`, `StateSpaceSet` | ✖ |
| [`AmplitudeAwareOrdinalPatterns`](@ref) | Ordinal patterns | `Vector`, `StateSpaceSet` | ✖ |
| [`WaveletOverlap`](@ref) | Wavelet transform | `Vector` | ✖ |
| [`PowerSpectrum`](@ref) | Fourier transform | `Vector` | ✖ |
In the column "input data" it is assumed that the `eltype` of the input is `<: Real`.
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Registration pull request created: JuliaRegistries/General/99016

Tip: Release Notes

Did you know you can add release notes too? Just add markdown formatted text underneath the comment after the text
"Release notes:" and it will be added to the registry PR, and if TagBot is installed it will also be added to the
release that TagBot creates. i.e.

@JuliaRegistrator register

Release notes:

## Breaking changes

- blah

To add them here just re-invoke and the PR will be updated.

Tagging

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 v3.4.1 -m "<description of version>" 550c444651053ee3fe1a09fbe511ece195783baf
git push origin v3.4.1

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