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]>
JuliaDynamics · Dec 14, 2020 · d7c5f3f · d7c5f3f
1 parent 1d45308
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diff --git a/Project.toml b/Project.toml
@@ -1,7 +1,7 @@
 name = "RecurrenceAnalysis"
 uuid = "639c3291-70d9-5ea2-8c5b-839eba1ee399"
 repo = "https://github.com/JuliaDynamics/RecurrenceAnalysis.jl.git"
-version = "1.3.2"
+version = "1.4.0"
 
 [deps]
 DelayEmbeddings = "5732040d-69e3-5649-938a-b6b4f237613f"
@@ -15,7 +15,7 @@ Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 UnicodePlots = "b8865327-cd53-5732-bb35-84acbb429228"
 
 [compat]
-DelayEmbeddings = "0.1,0.2,0.3,1"
+DelayEmbeddings = "1"
 Distances = "0.8, 0.9, 0.10"
 StaticArrays = "0.8,0.9,0.10,0.11,0.12, 1.0"
 UnicodePlots = "0.3,1"

diff --git a/src/RecurrenceAnalysis.jl b/src/RecurrenceAnalysis.jl
@@ -23,7 +23,7 @@ end
 
 
 export RecurrenceMatrix, CrossRecurrenceMatrix, JointRecurrenceMatrix,
-       AbstractRecurrenceMatrix
+       AbstractRecurrenceMatrix, WithinRange, NeighborNumber, FAN
 
 export embed,
        reconstruct,

diff --git a/src/deprecate.jl b/src/deprecate.jl
@@ -1,22 +1,23 @@
-function RecurrenceMatrix(x::AbstractMatrix, ε; kwargs...)
-    @warn "`RecurrenceMatrix(x::AbstractMatrix, ε; kwargs...)` is deprecated, use `RecurrenceMatrix(Dataset(x), ε; kwargs...)`"
-    RecurrenceMatrix(Dataset(x), ε; kwargs...)
-end
-
-for call in (:CrossRecurrenceMatrix, :JointRecurrenceMatrix)
-    @eval function ($call)(x::AbstractMatrix, y, ε; kwargs...)
-        @warn string("`", $call, "(x::AbstractMatrix, y, ε; kwargs...)` is deprecated, use `", $call, "(Dataset(x), y, ε; kwargs...)`")
-        ($call)(Dataset(x), y, ε; kwargs...)
-    end
-
-    @eval function ($call)(x, y::AbstractMatrix, ε; kwargs...)
-        @warn string("`", $call, "(x, y::AbstractMatrix, ε; kwargs...)` is deprecated, use `", $call, "(x, Dataset(y), ε; kwargs...)`")
-        ($call)(x, Dataset(y), ε; kwargs...)
+for T in (:WithinRange, :NeighborNumber)
+    @eval function RecurrenceMatrix{$T}(x::AbstractMatrix, ε; kwargs...)
+        @warn string("`RecurrenceMatrix{", $T, "}(x::AbstractMatrix, ε; kwargs...)` is deprecated, use `RecurrenceMatrix{", $T, "}(Dataset(x), ε; kwargs...)`")
+        RecurrenceMatrix{$T}(Dataset(x), ε; kwargs...)
     end
 
-    @eval function ($call)(x::AbstractMatrix, y::AbstractMatrix, ε; kwargs...)
-        @warn string("`", $call, "(x::AbstractMatrix, y::AbstractMatrix, ε; kwargs...)` is deprecated, use `", $call, "(Dataset(x), Dataset(y), ε; kwargs...)`")
-        ($call)(Dataset(x), Dataset(y), ε; kwargs...)
+    for call in (:CrossRecurrenceMatrix, :JointRecurrenceMatrix)
+        @eval function $call{$T}(x::AbstractMatrix, y, ε; kwargs...)
+            @warn string("`", $call, "{", $T, "}(x::AbstractMatrix, y, ε; kwargs...)` is deprecated, use `", $call, "{", $T, "}(Dataset(x), y, ε; kwargs...)`")
+            $call{$T}(Dataset(x), y, ε; kwargs...)
+        end
+
+        @eval function $call{$T}(x, y::AbstractMatrix, ε; kwargs...)
+            @warn string("`", $call, "{", $T, "}(x, y::AbstractMatrix, ε; kwargs...)` is deprecated, use `", $call, "{", $T, "}(x, Dataset(y), ε; kwargs...)`")
+            $call{$T}(x, Dataset(y), ε; kwargs...)
+        end
+
+        @eval function $call{$T}(x::AbstractMatrix, y::AbstractMatrix, ε; kwargs...)
+            @warn string("`", $call, "{", $T, "}(x::AbstractMatrix, y::AbstractMatrix, ε; kwargs...)` is deprecated, use `", $call, "{", $T, "}(Dataset(x), Dataset(y), ε; kwargs...)`")
+            $call{$T}(Dataset(x), Dataset(y), ε; kwargs...)
+        end
     end
 end
-
diff --git a/src/distance_matrix.jl b/src/distance_matrix.jl
@@ -7,7 +7,7 @@
 Create a matrix with the distances between each pair of points of the
 time series `x` and `y` using `metric`.
 
-The time series `x` and `y` can be `Dataset`s or vectors or matrices with data points
+The time series `x` and `y` can be `AbstractDataset`s or vectors or matrices with data points
 in rows.
 The data point dimensions (or number of columns) must be the same for `x` and `y`.
 The returned value is a `n×m` matrix, with `n` being the length (or number of rows)
@@ -34,7 +34,7 @@ distancematrix(x, y, metric::String, parallel = size(x)[1] > 500) = distancematr
 
 const MAXDIM = 9
 function distancematrix(x::Tx, y::Ty, metric::Metric=DEFAULT_METRIC, parallel = size(x)[1] > 500) where
-         {Tx<:Union{AbstractMatrix, Dataset}} where {Ty<:Union{AbstractMatrix, Dataset}}
+         {Tx<:Union{AbstractMatrix, AbstractDataset}} where {Ty<:Union{AbstractMatrix, AbstractDataset}}
     sx, sy = size(x), size(y)
     @assert sx[2] == sy[2] """
         The dimensions of the data points in `x` and `y` must be equal!
@@ -54,8 +54,8 @@ end
 _distancematrix(x::AbstractMatrix, y::AbstractMatrix, metric::Metric, ::Val{false}) = pairwise(metric, x', y', dims=2)
 
 # First we define the serial versions of the functions.
-# Core function for Datasets
-function _distancematrix(x::Dataset{S,Tx}, y::Dataset{S,Ty},
+# Core function for AbstractDatasets
+function _distancematrix(x::AbstractDataset{S,Tx}, y::AbstractDataset{S,Ty},
     metric::Metric, ::Val{false}) where {S, Tx, Ty}
 
     x = x.data
@@ -71,7 +71,7 @@ end
 
 # Now, we define the parallel versions.
 
-function _distancematrix(x::Dataset{S,Tx}, y::Dataset{S,Ty},
+function _distancematrix(x::AbstractDataset{S,Tx}, y::AbstractDataset{S,Ty},
     metric::Metric, ::Val{true}) where {S, Tx, Ty}
 
     x = x.data
@@ -118,7 +118,7 @@ function _distancematrix(x::Vector{T}, metric::Metric, ::Val{false}) where T
 
 end
 
-function _distancematrix(x::Dataset{S, T}, metric::Metric, ::Val{false}) where T where S
+function _distancematrix(x::AbstractDataset{S, T}, metric::Metric, ::Val{false}) where T where S
     d = zeros(T, length(x), length(x))
 
     for j in 1:length(x)
@@ -175,7 +175,7 @@ function _distancematrix(x::Vector{T}, metric::Metric, ::Val{true}) where T
 
 end
 
-function _distancematrix(x::Dataset{S, T}, metric::Metric, ::Val{true}) where T where S
+function _distancematrix(x::AbstractDataset{S, T}, metric::Metric, ::Val{true}) where T where S
     d = zeros(T, length(x), length(x))
 
     Threads.@threads for k in partition_indices(length(x))