update

Author: GiggleLiu

test/Project.toml

@@ -3,7 +3,9 @@ CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 GenericTensorNetworks = "3521c873-ad32-4bb4-b63d-f4f178f42b49"
 KaHyPar = "2a6221f6-aa48-11e9-3542-2d9e0ef01880"
+LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 OMEinsum = "ebe7aa44-baf0-506c-a96f-8464559b3922"
 Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"

test/sampling.jl

@@ -67,20 +67,20 @@ end
 @testset "sample MPS" begin
     n = 4
     chi = 3
+    Random.seed!(140)
     mps = random_matrix_product_state(n, chi)
-    Random.seed!(134)
     num_samples = 10000
-    # samples = map(1:num_samples) do i
-    #     sample(mps, 1; queryvars=vcat(mps.mars...)).samples[:,1]
-    # end
+    samples = map(1:num_samples) do i
+        sample(mps, 1; queryvars=vcat(mps.mars...)).samples[:,1]
+    end
     samples = sample(mps, num_samples; queryvars=vcat(mps.mars...))
     indices = map(samples) do sample
         sum(i->sample[i] * 2^(i-1), 1:n) + 1
     end
     distribution = map(1:2^n) do i
         count(j->j==i, indices) / num_samples
     end
-    probs = normalize!(real.(vec(DynamicEinCode(ixs, collect(1:4))(mps.tensors...))), 1)
+    probs = normalize!(real.(vec(DynamicEinCode(OMEinsum.getixsv(mps.code), collect(1:4))(mps.tensors...))), 1)
     negative_loglikelyhood(probs, samples) = -sum(log.(probs[samples]))/length(samples)
     entropy(probs) = -sum(probs .* log.(probs))
     @show negative_loglikelyhood(probs, indices), entropy(probs)