refactor: rm ported code

_OLD/rand/linfilter.jl

@@ -1,41 +1,3 @@
-"""
-    linearfilter(N::BinaryNetwork, α::Vector{Float64}=[0.25, 0.25, 0.25, 0.25])
-
-Given a network `N` compute the linear filter scores according to Stock et al.
-(2017). High scores for negative interactions indicate potential false negative
-or missing interactions. Though this it returned as a probabilistic network,
-score do not necessary convey a probabilistic interpretation.
-
-The values of α give the *relative* weight of, in order, the measured
-interaction value, the out-degree of the species, the in-degree of the species,
-and of network connectance. The default parameterization is to have all four at
-equal importance.
-
-#### References
-
-Stock, M., Pahikkala, T., Airola, A., Waegeman, W., Baets, B.D., 2018. Algebraic
-Shortcuts for Leave-One-Out Cross-Validation in Supervised Network Inference.
-bioRxiv 242321. https://doi.org/10.1101/242321
-
-Stock, M., Poisot, T., Waegeman, W., Baets, B.D., 2017. Linear filtering reveals
-false negatives in species interaction data. Scientific Reports 7, 45908.
-https://doi.org/10.1038/srep45908
-"""
-function linearfilter(N::T; α::Vector{Float64}=fill(1.0, 4)) where {T <: BinaryNetwork}
-    @assert length(α) == 4
-    @assert all(α .≥ 0.0)
-    α = α ./ sum(α) # This ensures that α sums to 1.0
-
-    # Get the new weights
-    W = α[1]N.edges .+ α[2]mean(N.edges; dims=2) .+ α[3]mean(N.edges; dims=1) .+ α[4]mean(N.edges)
-
-    # Prepare a return object
-    return_type = T <: AbstractBipartiteNetwork ? BipartiteProbabilisticNetwork : UnipartiteProbabilisticNetwork
-    F = return_type(W, EcologicalNetworks._species_objects(N)...)
-
-    return F
-end
-
 """
     linearfilterzoo(N::BinaryNetwork, α::Vector{Float64}=[0.25, 0.25, 0.25, 0.25])