Add QN creation and sampling

Author: ReubenJ

Project.toml

@@ -4,8 +4,12 @@ authors = ["Reuben Gardos Reid <[email protected]>"]
 version = "0.1.0"
 
 [deps]
+AbstractTrees = "1520ce14-60c1-5f80-bbc7-55ef81b5835c"
 DynamicalSystems = "61744808-ddfa-5f27-97ff-6e42cc95d634"
 FileIO = "5789e2e9-d7fb-5bc7-8068-2c6fae9b9549"
+HerbCore = "2b23ba43-8213-43cb-b5ea-38c12b45bd45"
+HerbGrammar = "4ef9e186-2fe5-4b24-8de7-9f7291f24af7"
+HerbSearch = "3008d8e8-f9aa-438a-92ed-26e9c7b4829f"
 MetaGraphsNext = "fa8bd995-216d-47f1-8a91-f3b68fbeb377"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 SoleLogics = "b002da8f-3cb3-4d91-bbe3-2953433912b5"

src/GraphDynamicalSystems.jl

@@ -4,10 +4,12 @@ using SoleLogics
 using MetaGraphsNext
 using DynamicalSystems
 
-include("BooleanNetworks.jl")
-
+include("boolean_networks.jl")
 export BooleanNetworks
 
+include("qualitative_networks.jl")
+export QualitativeNetworks
+
 # """
 #     gds(bn::MetaGraph)
 

src/BooleanNetworks.jl src/boolean_networks.jlsrc/BooleanNetworks.jl renamed to src/boolean_networks.jl

@@ -0,0 +1,96 @@
+module QualitativeNetworks
+
+using AbstractTrees: Leaves
+using HerbCore: AbstractGrammar, AbstractRuleNode, RuleNode, get_rule
+using HerbGrammar: @csgrammar, add_rule!
+using HerbSearch
+using MetaGraphsNext: MetaGraph, SimpleDiGraph, add_edge!
+
+const base_qn_grammar = @csgrammar begin
+    Vals = Pos() | Neg()
+    Val =
+        (Val + Val) |
+        (Val - Val) |
+        Val / Val |
+        Avg(Vals) |
+        Min(Val, Val) |
+        Max(Val, Val) |
+        Ceil(Val) |
+        Floor(Val)
+end
+
+const default_qn_constants = [2]
+
+function build_qn_grammar(
+    entity_names::AbstractVector{Symbol},
+    constants::AbstractVector{Int},
+)
+    g = deepcopy(base_qn_grammar)
+
+    for e in entity_names
+        add_rule!(g, :(Val = $e))
+    end
+
+    for c in constants
+        add_rule!(g, :(Val = $c))
+    end
+
+    return g
+end
+
+function update_functions_to_network(
+    update_functions::AbstractDict{Symbol,<:AbstractRuleNode},
+    grammar::AbstractGrammar,
+)
+    network = MetaGraph(
+        SimpleDiGraph();
+        label_type = Symbol,
+        vertex_data_type = AbstractRuleNode,
+        graph_data = grammar,
+    )
+
+    for (e, f) in update_functions
+        network[e] = f
+    end
+
+    for (e1, f) in update_functions
+        leaves = get_rule.(collect(Leaves(f)))
+        input_variables = grammar.rules[leaves]
+        for e2 in input_variables
+            add_edge!(network, e1, e2)
+        end
+    end
+
+    return network
+end
+
+function sample_qualitative_network(entities::AbstractVector{Symbol}, max_eq_depth::Int)
+    g = build_qn_grammar(entities, default_qn_constants)
+    update_fns = Dict([e => rand(RuleNode, g, :Val, max_eq_depth) for e in entities])
+    graph = update_functions_to_network(update_fns, g)
+
+    return graph
+end
+
+function sample_qualitative_network(size::Int, max_eq_depth::Int)
+    entities = [Symbol(e) for e = 1:size]
+    sample_qualitative_network(entities, max_eq_depth)
+end
+
+mutable struct QualitativeNetwork
+    graph::MetaGraph
+    state::AbstractVector{Int}
+end
+
+const QN = QualitativeNetwork
+
+function qn_step!(model::QN) end
+
+extract_state(model::QN) = model.state
+extract_parameters(model::QN) = model.graph
+
+function reset_model!(model::QN, u, _)
+    model.state .= u
+end
+
+end

src/qualitative_networks.jl

@@ -1,5 +1,6 @@
 [deps]
 DynamicalSystems = "61744808-ddfa-5f27-97ff-6e42cc95d634"
+Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6"
 MetaGraphsNext = "fa8bd995-216d-47f1-8a91-f3b68fbeb377"
 SoleLogics = "b002da8f-3cb3-4d91-bbe3-2953433912b5"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"

test/Project.toml

@@ -0,0 +1,18 @@
+using Graphs: ne, nv
+
+@testset "QN Grammar Creation" begin
+    entities = [:a, :b, :c]
+    constants = [i for i = 1:10]
+    g = QualitativeNetworks.build_qn_grammar(entities, constants)
+
+    @test issubset(Set(entities), Set(g.rules))
+    @test issubset(Set(constants), Set(g.rules))
+end
+
+@testset "QN Sampling" begin
+    size = 3
+    max_eq_depth = 3
+    qn = QualitativeNetworks.sample_qualitative_network(size, max_eq_depth)
+    @test nv(qn.graph) == size
+    @test ne(qn.graph) > 0
+end