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diff --git a/CovidAlertVaccinationModel/src/ABM/mixing_graphs.jl b/CovidAlertVaccinationModel/src/ABM/mixing_graphs.jl
index 501a7ef1d9d4bbeffd4b532dd85c1efb7774f71b..652e5f842e05915b4b8fb5cf34bff50e0dfeeda7 100644
--- a/CovidAlertVaccinationModel/src/ABM/mixing_graphs.jl
+++ b/CovidAlertVaccinationModel/src/ABM/mixing_graphs.jl
@@ -10,7 +10,6 @@ struct GraphEdge
b::Int
end
-
"""
Define a hash on GraphEdge such that ``hash(a,b) = hash(b,a)`` (hash is commutative).
@@ -21,12 +20,13 @@ function Base.hash(e::GraphEdge)
end
"""
-Define symmetric (?) edge equality, matches the hash function.
+Define symmetric edge equality, matches the hash function.
"""
function Base.isequal(e1::GraphEdge,e2::GraphEdge)
return isequal(minmax(e1.a,e1.b),minmax(e2.a,e2.b))
end
+
"""
sample_mixing_graph!(mixing_graph::Graph)
@@ -36,13 +36,11 @@ Resample all the weights in `mixing_graph`
function sample_mixing_graph!(mixing_graph)
mixing_edges = mixing_graph.mixing_edges
for i in 1:size(mixing_edges.contact_array)[1], j in 1:i #diagonal
- rand!(RNG, mixing_edges.sampler_matrix[i,j],mixing_edges.sample_cache[i,j])
- for k in 1:length(mixing_edges.contact_array[i,j][1])
- kth_node_i = mixing_edges.contact_array[i,j][1][k]
- kth_node_j = mixing_edges.contact_array[i,j][2][k]
- edge_weight_k = mixing_edges.sample_cache[i,j][k]
- set!(mixing_edges.weights_dict,GraphEdge(kth_node_i,kth_node_j), edge_weight_k)
- end
+ rand!(RNG, mixing_edges.sampler_matrix[i,j],mixing_edges.sample_cache[i,j])
+ for k in 1:length(mixing_edges.contact_array[i,j])
+ edge_weight_k = mixing_edges.sample_cache[i,j][k]
+ set!(mixing_edges.weights_dict, mixing_edges.contact_array[i,j][k], edge_weight_k)
+ end
end
end
@@ -73,12 +71,12 @@ This is the matrix of distributions, from which the edge weights are sampled. Sp
"""
struct MixingEdges{M}
total_edges::Int
- contact_array::Matrix{Tuple{Vector{Int},Vector{Int}}}
+ contact_array::Matrix{Vector{GraphEdge}}
sample_cache::Matrix{Vector{Int}}
weights_dict::Dictionary{GraphEdge,UInt8}
sampler_matrix::M
function MixingEdges(total_edges,contact_array,sampler_matrix::Matrix{M}) where M<:Sampleable{Univariate, Discrete}
- sample_cache = map(v-> Vector{Int}(undef,length(v[1])),contact_array)
+ sample_cache = map(v-> Vector{Int}(undef,length(v)),contact_array)
weights_dict = Dictionary{GraphEdge,UInt8}()
new{typeof(sampler_matrix)}(total_edges,contact_array,sample_cache,weights_dict,sampler_matrix)
end
@@ -88,7 +86,7 @@ end
This function constructs the `MixingEdges` type for rest and WS graphs. Calls `generate_contact_vectors!(..)` to get the degree distributions for a bipartite graph for each pair of demographics, and then adds edges to MixingEdges.contact_array with the Chung-Lu approach.
"""
function create_mixing_edges(demographic_index_vectors,mixing_matrix,weights_distribution_matrix)
- contact_array =[(Vector{Int}(),Vector{Int}()) for i in 1:length(demographic_index_vectors),j in 1:length(demographic_index_vectors)]
+ contact_array =[Vector{GraphEdge}() for i in 1:length(demographic_index_vectors),j in 1:length(demographic_index_vectors)]
tot = 0
for i in 1:size(mixing_matrix)[1], j in 1:i #diagonal
num_degrees_ij = zeros(Int,length(demographic_index_vectors[i]))
@@ -103,7 +101,8 @@ function create_mixing_edges(demographic_index_vectors,mixing_matrix,weights_dis
sample!(RNG,demographic_index_vectors[j],Weights(num_degrees_ji./m),stubs_j)
tot += m
end
- contact_array[j,i] = (stubs_i,stubs_j)
+
+ contact_array[j,i] = GraphEdge.(stubs_i,stubs_j)
end
return MixingEdges(tot,contact_array,weights_distribution_matrix)
end
@@ -113,14 +112,13 @@ end
This function constructs the `MixingEdges` type for the home graphs. Simply adds edges to MixingEdges from the graph `g`, since we already create that in `generate_population`.
"""
function create_mixing_edges(g::SimpleGraph,demographics,demographic_index_vectors,weights_distribution_matrix)
- contact_array = [(Vector{Int}(),Vector{Int}()) for i in 1:length(demographic_index_vectors),j in 1:length(demographic_index_vectors)]
- for e in edges(g)
+ contact_array = [sizehint!(Vector{GraphEdge}(),ne(g)) for i in 1:length(demographic_index_vectors),j in 1:length(demographic_index_vectors)]
+ for e in edges(g)
i = src(e)
j = dst(e)
- push!(contact_array[Int(demographics[j]),Int(demographics[i])][1],i)
- push!(contact_array[Int(demographics[j]),Int(demographics[i])][2],j)
+ push!(contact_array[Int(demographics[j]),Int(demographics[i])], GraphEdge(i,j))
end
- sample_cache = map(v-> Vector{Int}(undef,length(v[1])),contact_array)
+ sample_cache = map(v-> Vector{Int}(undef,length(v)),contact_array)
return MixingEdges(ne(g),contact_array,weights_distribution_matrix)
end
@@ -202,8 +200,9 @@ Add the edges defined by MixingEdges to the actual graph G. Another big bottlene
"""
function graph_from_mixing_edges(g,mixing_edges)
for i in 1:size(mixing_edges.contact_array)[1], j in 1:i #diagonal
- for k in 1:length(mixing_edges.contact_array[i,j][1])
- add_edge!(g,mixing_edges.contact_array[i,j][1][k],mixing_edges.contact_array[i,j][2][k])
+ for k in 1:length(mixing_edges.contact_array[i,j])
+ e = mixing_edges.contact_array[i,j][k]
+ add_edge!(g,e.a,e.b)
end
end
end
@@ -227,7 +226,7 @@ mutable struct WeightedGraph{G,M}
function WeightedGraph(g::SimpleGraph,demographics,demographic_index_vectors,weights_distribution_matrix)
mixing_edges = create_mixing_edges(g, demographics, demographic_index_vectors,weights_distribution_matrix)
weights_dict = Dict{GraphEdge,UInt8}()
- sample_cache = map(v-> Vector{Int}(undef,length(v[1])),mixing_edges.contact_array)
+ sample_cache = map(v-> Vector{Int}(undef,length(v)),mixing_edges.contact_array)
return new{typeof(g),typeof(mixing_edges)}(
g,
mixing_edges,