fix configuration and add a new example

Author: GiggleLiu

Project.toml

@@ -1,7 +1,7 @@
 name = "GraphTensorNetworks"
 uuid = "0978c8c2-34f6-49c7-9826-ea2cc20dabd2"
 authors = ["GiggleLiu <cacate0129@gmail.com> and contributors"]
-version = "0.2.0"
+version = "0.2.1"
 
 [deps]
 CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"

examples/unitdisk_graph.jl

@@ -0,0 +1,43 @@
+# # Solving independent set for a weighted unit disk graph
+using GraphTensorNetworks, Graphs
+
+# This example shows how to check the MIS degeneracy (another way of saying counting) of a weight unit disk graph.
+# Let us construct a unit disk graph by specifying the locations of nodes, the unit disk radius is 1.5,
+# which means two vertices within distance 1.5 are connected.
+# For each vertex, we assign a weight to it.
+locations = [(6,-3),(1, -1), (0,0), (6,-2), (1,1), (2,0),
+     (2,2), (2,-2), (3,1), (3,-2), (4,1), (4,-1),
+     (5, 1), (5, -1), (6,0), (6,-1), (7,-1), (7, -4),
+     (8, -2), (8, -4), (9,-3)]
+
+
+weights = [0.7439015222121296, 0.722970984162338,
+     0.9502792990276312, 0.6617352332568173, 
+     0.8592866066961992, 1.0, 1.0, 1.0, 1.0, 
+     1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 
+     1.0, 1.0, 1.0, 1.0, 1.0]
+
+graph = unit_disk_graph(locations, 1.5)
+
+show_graph(graph; locs=locations)
+
+# ## Find the best configurations
+# We can easily check the MIS degeneracy by computing the [`CountingMax`](@ref) property.
+counting_mapped = solve(IndependentSet(graph; weights=weights), CountingMax())[]
+
+# The MIS degeneracy is the second field, which is 3.
+# We can compute the [`ConfigsMax`](@ref) to enumerate all configurations with maximum independent set size.
+
+configs_mapped = solve(IndependentSet(graph; weights=weights), ConfigsMax())[]
+
+# * Solution 1
+show_graph(graph; locs=locations, vertex_colors=
+          [iszero(configs_mapped.c[1][i]) ? "white" : "red" for i=1:nv(graph)])
+
+# * Solution 2
+show_graph(graph; locs=locations, vertex_colors=
+          [iszero(configs_mapped.c[2][i]) ? "white" : "red" for i=1:nv(graph)])
+
+# * Solution 3
+show_graph(graph; locs=locations, vertex_colors=
+          [iszero(configs_mapped.c[3][i]) ? "white" : "red" for i=1:nv(graph)])

src/bounding.jl

@@ -24,7 +24,8 @@ function backward_tropical(mode, ixs, @nospecialize(xs::Tuple), iy, @nospecializ
         niy = ixs[i]
         if mode isa AllConfigs
             mask = zeros(Bool, size(xs[i]))
-            mask .= inv.(einsum(EinCode(nixs, niy), nxs, size_dict)) .<= xs[i] .* Tropical(largest_k(mode)-1)
+            # here, we set a threshold `1e-15` to avoid round off errors.
+            mask .= inv.(einsum(EinCode(nixs, niy), nxs, size_dict)) .<= xs[i] .* Tropical(largest_k(mode)-1+1e-15)
             push!(masks, mask)
         elseif mode isa SingleConfig
             A = zeros(eltype(xs[i]), size(xs[i]))
@@ -43,7 +44,7 @@ function onehotmask(A::AbstractArray{T}, X::AbstractArray{T}) where T
     mask = falses(size(A)...)
     found = false
     @inbounds for j=1:length(A)
-        if X[j] == inv(A[j]) && !found
+        if X[j] ≈ inv(A[j]) && !found
             mask[j] = true
             found = true
         else

src/configurations.jl

@@ -17,7 +17,8 @@ function best_solutions(gp::GraphProblem; all=false, usecuda=false)
         ymask = CuArray(ymask)
     end
     if all
-        xs = generate_tensors(fx_solutions(gp, CountingTropical{Int64}, all), gp)
+        # we use `Float64` types because we want to support weighted graphs.
+        xs = generate_tensors(fx_solutions(gp, CountingTropical{Float64,Float64}, all), gp)
         return bounding_contract(AllConfigs{1}(), gp.code, xst, ymask, xs)
     else
         @assert ndims(ymask) == 0

src/networks/IndependentSet.jl

@@ -16,8 +16,8 @@ end
 
 function IndependentSet(g::SimpleGraph; weights=UnWeighted(), openvertices=(), optimizer=GreedyMethod(), simplifier=nothing)
     @assert weights isa UnWeighted || length(weights) == nv(g)
-    rawcode = EinCode(([[i] for i in Graphs.vertices(g)]..., # labels for vertex tensors
-                       [[minmax(e.src,e.dst)...] for e in Graphs.edges(g)]...), collect(Int, openvertices))  # labels for edge tensors
+    rawcode = EinCode([[[i] for i in Graphs.vertices(g)]..., # labels for vertex tensors
+                       [[minmax(e.src,e.dst)...] for e in Graphs.edges(g)]...], collect(Int, openvertices))  # labels for edge tensors
     code = _optimize_code(rawcode, uniformsize(rawcode, 2), optimizer, simplifier)
     IndependentSet(code, nv(g), weights)
 end