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hkraemer
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FAN method (#102)
* added FAN-threshold method * parallel_false implementation done * debugged fan-methods; set up tests * minor improvements * made requested changes; resolve_scale type-stable * Parametrized types of AbstractRecurrenceMatrix for FAN * FAN by columns, not rows * exclude LOI in FAN for RP with single time series make `_computescale` type stable * allow non-square CrossRecurrenceMatrix{FAN} * avoid errors in @windowed RecurrenceMatrix{FAN} etc. * adapt deprecate.jl and tests to parametric types * generalize dispatch on AbstractDataset * redefine FixedRange > WithinRange and FixedAmount > NeighborNumber Co-authored-by: K. Hauke Kraemer <[email protected]>
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‎Project.toml‎

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@@ -1,7 +1,7 @@
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name = "RecurrenceAnalysis"
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uuid = "639c3291-70d9-5ea2-8c5b-839eba1ee399"
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repo = "https://github.com/JuliaDynamics/RecurrenceAnalysis.jl.git"
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version = "1.3.2"
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version = "1.4.0"
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[deps]
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DelayEmbeddings = "5732040d-69e3-5649-938a-b6b4f237613f"
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UnicodePlots = "b8865327-cd53-5732-bb35-84acbb429228"
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[compat]
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DelayEmbeddings = "0.1,0.2,0.3,1"
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DelayEmbeddings = "1"
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Distances = "0.8, 0.9, 0.10"
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StaticArrays = "0.8,0.9,0.10,0.11,0.12, 1.0"
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UnicodePlots = "0.3,1"

‎src/RecurrenceAnalysis.jl‎

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@@ -23,7 +23,7 @@ end
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export RecurrenceMatrix, CrossRecurrenceMatrix, JointRecurrenceMatrix,
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AbstractRecurrenceMatrix
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AbstractRecurrenceMatrix, WithinRange, NeighborNumber, FAN
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export embed,
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reconstruct,

‎src/deprecate.jl‎

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@@ -1,22 +1,23 @@
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function RecurrenceMatrix(x::AbstractMatrix, ε; kwargs...)
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@warn "`RecurrenceMatrix(x::AbstractMatrix, ε; kwargs...)` is deprecated, use `RecurrenceMatrix(Dataset(x), ε; kwargs...)`"
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RecurrenceMatrix(Dataset(x), ε; kwargs...)
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end
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for call in (:CrossRecurrenceMatrix, :JointRecurrenceMatrix)
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@eval function ($call)(x::AbstractMatrix, y, ε; kwargs...)
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@warn string("`", $call, "(x::AbstractMatrix, y, ε; kwargs...)` is deprecated, use `", $call, "(Dataset(x), y, ε; kwargs...)`")
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($call)(Dataset(x), y, ε; kwargs...)
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end
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@eval function ($call)(x, y::AbstractMatrix, ε; kwargs...)
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@warn string("`", $call, "(x, y::AbstractMatrix, ε; kwargs...)` is deprecated, use `", $call, "(x, Dataset(y), ε; kwargs...)`")
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($call)(x, Dataset(y), ε; kwargs...)
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for T in (:WithinRange, :NeighborNumber)
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@eval function RecurrenceMatrix{$T}(x::AbstractMatrix, ε; kwargs...)
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@warn string("`RecurrenceMatrix{", $T, "}(x::AbstractMatrix, ε; kwargs...)` is deprecated, use `RecurrenceMatrix{", $T, "}(Dataset(x), ε; kwargs...)`")
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RecurrenceMatrix{$T}(Dataset(x), ε; kwargs...)
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end
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@eval function ($call)(x::AbstractMatrix, y::AbstractMatrix, ε; kwargs...)
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@warn string("`", $call, "(x::AbstractMatrix, y::AbstractMatrix, ε; kwargs...)` is deprecated, use `", $call, "(Dataset(x), Dataset(y), ε; kwargs...)`")
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($call)(Dataset(x), Dataset(y), ε; kwargs...)
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for call in (:CrossRecurrenceMatrix, :JointRecurrenceMatrix)
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@eval function $call{$T}(x::AbstractMatrix, y, ε; kwargs...)
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@warn string("`", $call, "{", $T, "}(x::AbstractMatrix, y, ε; kwargs...)` is deprecated, use `", $call, "{", $T, "}(Dataset(x), y, ε; kwargs...)`")
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$call{$T}(Dataset(x), y, ε; kwargs...)
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end
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@eval function $call{$T}(x, y::AbstractMatrix, ε; kwargs...)
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@warn string("`", $call, "{", $T, "}(x, y::AbstractMatrix, ε; kwargs...)` is deprecated, use `", $call, "{", $T, "}(x, Dataset(y), ε; kwargs...)`")
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$call{$T}(x, Dataset(y), ε; kwargs...)
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end
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@eval function $call{$T}(x::AbstractMatrix, y::AbstractMatrix, ε; kwargs...)
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@warn string("`", $call, "{", $T, "}(x::AbstractMatrix, y::AbstractMatrix, ε; kwargs...)` is deprecated, use `", $call, "{", $T, "}(Dataset(x), Dataset(y), ε; kwargs...)`")
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$call{$T}(Dataset(x), Dataset(y), ε; kwargs...)
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end
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end
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end
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‎src/distance_matrix.jl‎

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@@ -7,7 +7,7 @@
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Create a matrix with the distances between each pair of points of the
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time series `x` and `y` using `metric`.
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The time series `x` and `y` can be `Dataset`s or vectors or matrices with data points
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The time series `x` and `y` can be `AbstractDataset`s or vectors or matrices with data points
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in rows.
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The data point dimensions (or number of columns) must be the same for `x` and `y`.
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The returned value is a `n×m` matrix, with `n` being the length (or number of rows)
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const MAXDIM = 9
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function distancematrix(x::Tx, y::Ty, metric::Metric=DEFAULT_METRIC, parallel = size(x)[1] > 500) where
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{Tx<:Union{AbstractMatrix, Dataset}} where {Ty<:Union{AbstractMatrix, Dataset}}
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{Tx<:Union{AbstractMatrix, AbstractDataset}} where {Ty<:Union{AbstractMatrix, AbstractDataset}}
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sx, sy = size(x), size(y)
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@assert sx[2] == sy[2] """
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The dimensions of the data points in `x` and `y` must be equal!
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_distancematrix(x::AbstractMatrix, y::AbstractMatrix, metric::Metric, ::Val{false}) = pairwise(metric, x', y', dims=2)
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# First we define the serial versions of the functions.
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# Core function for Datasets
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function _distancematrix(x::Dataset{S,Tx}, y::Dataset{S,Ty},
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# Core function for AbstractDatasets
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function _distancematrix(x::AbstractDataset{S,Tx}, y::AbstractDataset{S,Ty},
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metric::Metric, ::Val{false}) where {S, Tx, Ty}
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x = x.data
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# Now, we define the parallel versions.
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function _distancematrix(x::Dataset{S,Tx}, y::Dataset{S,Ty},
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function _distancematrix(x::AbstractDataset{S,Tx}, y::AbstractDataset{S,Ty},
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metric::Metric, ::Val{true}) where {S, Tx, Ty}
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x = x.data
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end
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function _distancematrix(x::Dataset{S, T}, metric::Metric, ::Val{false}) where T where S
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function _distancematrix(x::AbstractDataset{S, T}, metric::Metric, ::Val{false}) where T where S
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d = zeros(T, length(x), length(x))
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for j in 1:length(x)
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end
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function _distancematrix(x::Dataset{S, T}, metric::Metric, ::Val{true}) where T where S
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function _distancematrix(x::AbstractDataset{S, T}, metric::Metric, ::Val{true}) where T where S
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d = zeros(T, length(x), length(x))
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Threads.@threads for k in partition_indices(length(x))

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