Phylogeny Clustering: private API

PortfolioOptimisers.HClE_HCl — Type

const HClE_HCl = Union{<:ClustersEstimator{<:Any, <:Any,
                                           <:AbstractHierarchicalClusteringAlgorithm,
                                           <:Any},
                       <:Clusters{<:Clustering.Hclust, <:Any, <:Any, <:Any},
                       <:NetworkClustersEstimator{<:Any,
                                              <:AbstractHierarchicalClusteringAlgorithm}}

Alias for a hierarchical clustering estimator or result.

Matches either a ClustersEstimator parameterised with a hierarchical clustering algorithm, or a Clusters result wrapping a Clustering.Hclust. Used internally for dispatch in hierarchical clustering workflows.

Related

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PortfolioOptimisers._clusterise — Method

_clusterise(
    alg::HClustAlgorithm,
    onc::AbstractOptimalNumberClustersEstimator,
    S::AbstractMatrix{<:Union{var"#s136", var"#s53"} where {var"#s136"<:Number, var"#s53"<:AbstractJuMPScalar}},
    D::AbstractMatrix{<:Union{var"#s136", var"#s53"} where {var"#s136"<:Number, var"#s53"<:AbstractJuMPScalar}},
    P::AbstractMatrix{<:Union{var"#s136", var"#s53"} where {var"#s136"<:Number, var"#s53"<:AbstractJuMPScalar}};
    branchorder
) -> Clusters{Clustering.Hclust{T}, var"#s185", var"#s1851", <:AbstractMatrix{var"#s137"}, <:Integer} where {T<:Real, var"#s137"<:(Union{var"#s136", var"#s53"} where {var"#s136"<:Number, var"#s53"<:AbstractJuMPScalar}), var"#s185"<:AbstractMatrix{var"#s137"}, var"#s137"<:(Union{var"#s136", var"#s53"} where {var"#s136"<:Number, var"#s53"<:AbstractJuMPScalar}), var"#s1851"<:AbstractMatrix{var"#s137"}, var"#s137"<:(Union{var"#s136", var"#s53"} where {var"#s136"<:Number, var"#s53"<:AbstractJuMPScalar})}

Internal dispatch helper for constructing a Clusters result within a network-based clustering workflow.

Selects the appropriate clustering routine based on alg, determines the optimal number of clusters, and returns a Clusters result encapsulating all relevant outputs.

Algorithm

Cluster P, through the branch that alg selects, giving res.
- HClustAlgorithm: Clustering.hclust under alg.linkage and branchorder.
- DBHT: DBHTs over P and S, whose last returned value is the clustering.
- AbstractNonHierarchicalClusteringAlgorithm: optimal_number_clusters, which answers with the clustering and the count together.
Select the number of clusters k with optimal_number_clusters over onc, res and P. The third branch holds k from step 1 already.
Assemble the Clusters result from res, S, D, P and k.

Arguments

alg: Clustering algorithm.
- alg::HClustAlgorithm: Applies hierarchical clustering via Clustering.hclust on the pseudo-distance matrix P.
- alg::DBHT: Applies Direct Bubble Hierarchical Tree clustering via DBHTs on P and S.
- alg::AbstractNonHierarchicalClusteringAlgorithm: Applies non-hierarchical clustering via optimal_number_clusters on P.
onc: Optimal number of clusters estimator.
S: Similarity matrix.
D: Distance matrix.
P::MatNum: Symmetric pseudo-distance matrix derived from the network or similarity structure.
branchorder: Branch ordering strategy for hierarchical clustering.

Returns

clr::Clusters: Clustering result containing the clustering object, similarity matrix, distance matrix, pseudo-distance matrix, and optimal number of clusters.

Related

source