"fake" Poisson sampling with alias sampling

CovidAlertVaccinationModel/Manifest.toml

@@ -206,6 +206,12 @@ version = "0.1.1"
 deps = ["Mmap"]
 uuid = "8bb1440f-4735-579b-a4ab-409b98df4dab"
 
+[[Dictionaries]]
+deps = ["Indexing", "Random"]
+git-tree-sha1 = "12e8b690de74848e9f41853817a94900a9e13bff"
+uuid = "85a47980-9c8c-11e8-2b9f-f7ca1fa99fb4"
+version = "0.3.8"
+
 [[Distributed]]
 deps = ["Random", "Serialization", "Sockets"]
 uuid = "8ba89e20-285c-5b6f-9357-94700520ee1b"
@@ -383,6 +389,11 @@ git-tree-sha1 = "5aa65f6204e1b68dfef024421e61fbcacecfbfa6"
 uuid = "c65182e5-40f4-518f-8165-175b85689199"
 version = "1.1.0"
 
+[[Indexing]]
+git-tree-sha1 = "ce1566720fd6b19ff3411404d4b977acd4814f9f"
+uuid = "313cdc1a-70c2-5d6a-ae34-0150d3930a38"
+version = "1.1.1"
+
 [[Inflate]]
 git-tree-sha1 = "f5fc07d4e706b84f72d54eedcc1c13d92fb0871c"
 uuid = "d25df0c9-e2be-5dd7-82c8-3ad0b3e990b9"

CovidAlertVaccinationModel/Project.toml

@@ -10,6 +10,7 @@ DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
 Dates = "ade2ca70-3891-5945-98fb-dc099432e06a"
 DelimitedFiles = "8bb1440f-4735-579b-a4ab-409b98df4dab"
+Dictionaries = "85a47980-9c8c-11e8-2b9f-f7ca1fa99fb4"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 ImportAll = "c65182e5-40f4-518f-8165-175b85689199"
 Intervals = "d8418881-c3e1-53bb-8760-2df7ec849ed5"

CovidAlertVaccinationModel/src/ABM/contact_vectors.jl

@@ -33,4 +33,4 @@ function generate_contact_vectors!(ij_dist,ji_dist,i_to_j_contacts::Vector{T}, j
         end
     end
     return nothing
-end
+end
\ No newline at end of file

CovidAlertVaccinationModel/src/ABM/mixing_graphs.jl

@@ -3,9 +3,7 @@ struct GraphEdge
     a::Int
     b::Int
 end
-
 function Base.hash(e::GraphEdge)
-    # display(e)
     return hash(minmax(e.a,e.b))
 end
 function Base.isequal(e1::GraphEdge,e2::GraphEdge)
@@ -16,7 +14,7 @@ function sample_mixing_graph!(mixing_graph,population_demographics)
     for i in 1:size(mixing_edges.contact_array)[1], j in 1:i  #diagonal
             demo_i = Int(population_demographics[i])
             demo_j = Int(population_demographics[j])
-            rand!(RNG, mixing_edges.weights_distribution_matrix[demo_i,demo_j],mixing_edges.sample_cache[i,j])
+            rand!(RNG, mixing_edges.sampler_matrix[demo_i,demo_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]
@@ -32,19 +30,12 @@ struct MixingEdges{M}
     contact_array::Matrix{Tuple{Vector{Int},Vector{Int}}}
     sample_cache::Matrix{Vector{Int}}
     weights_dict::Dictionary{GraphEdge,UInt8}
-    weights_distribution_matrix::M
+    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)
         weights_dict = Dictionary{GraphEdge,UInt8}()
         new{typeof(sampler_matrix)}(total_edges,contact_array,sample_cache,weights_dict,sampler_matrix)
     end
-    function MixingEdges(total_edges,contact_array,weights_distribution_matrix::Matrix{M}) where M<:UnivariateDistribution
-        sample_cache = map(v-> Vector{Int}(undef,length(v[1])),contact_array)
-        weights_dict = Dictionary{GraphEdge,UInt8}()
-        sampler_matrix =  map(m -> Distributions.PoissonCountSampler.(m),weights_distribution_matrix)
-        new{typeof(sampler_matrix)}(total_edges,contact_array,sample_cache,weights_dict,sampler_matrix)
-    end
-    
 end
 
 function create_mixing_edges(demographic_index_vectors,mixing_matrix,weights_distribution_matrix)
@@ -59,7 +50,6 @@ function create_mixing_edges(demographic_index_vectors,mixing_matrix,weights_dis
         stubs_i = Vector{Int}(undef,m)
         stubs_j = Vector{Int}(undef,m)
         if m>0
-            # @assert m == sum(num_degrees_ji)
             sample!(RNG,demographic_index_vectors[i],Weights(num_degrees_ij./m),stubs_i)
             sample!(RNG,demographic_index_vectors[j],Weights(num_degrees_ji./m),stubs_j)
             tot += m
@@ -125,7 +115,7 @@ function remake!(time_dep_mixing_graph,demographic_index_vectors,mixing_matrix)
     for weighted_graph in time_dep_mixing_graph.resampled_graphs
         empty!.(weighted_graph.g.fadjlist) #empty all the vector edgelists
         weighted_graph.g.ne = 0
-        weighted_graph.mixing_edges = create_mixing_edges(demographic_index_vectors,mixing_matrix,weighted_graph.mixing_edges.weights_distribution_matrix)
+        weighted_graph.mixing_edges = create_mixing_edges(demographic_index_vectors,mixing_matrix,weighted_graph.mixing_edges.sampler_matrix)
         graph_from_mixing_edges(weighted_graph.g,weighted_graph.mixing_edges)
     end
 end

CovidAlertVaccinationModel/src/data.jl

@@ -108,6 +108,9 @@ function load_contact_time_distributions()
         ws = "ws",
         rest = "rest"
     )
+    function make_sampler(λ) #our samples can only be in 0:143 so we can fake a poisson distribution by explicitly sampling
+        return Distributions.DiscreteNonParametricSampler(0:durmax,[pdf(Poisson(λ),x) for x in 0:durmax])
+    end
     contact_distributions_tuple = map(fnames) do fname
         dat = deserialize(joinpath(PACKAGE_FOLDER,"intervals_model_output","simulation_output","$fname.dat"))
         return map(p -> Poisson(mode(p.particles)), as_symmetric_matrix(dat[distkey].P))