simplify marginals()

src/abstract_tensor_train.jl

@@ -77,23 +77,16 @@ Base.isapprox(A::T, B::T; kw...) where {T<:AbstractTensorTrain} = isapprox(A.ten
 
 function accumulate_M(A::AbstractTensorTrain)
     L = length(A)
-    M = [zeros(0, 0) for _ in 1:L, _ in 1:L]
-
-    # initial condition
-    for t in 1:L-1
-        range_aᵗ⁺¹ = axes(A[t+1], 1)
-        Mᵗᵗ⁺¹ = [float((a == c)) for a in range_aᵗ⁺¹, c in range_aᵗ⁺¹]
-        M[t, t+1] = Mᵗᵗ⁺¹
-    end
 
-    for t in 1:L-1
-        Mᵗᵘ⁻¹ = M[t, t+1]
-        for u in t+2:L
-            Aᵘ⁻¹ = _reshape1(A[u-1])
-            @tullio Mᵗᵘ[aᵗ⁺¹, aᵘ] := Mᵗᵘ⁻¹[aᵗ⁺¹, aᵘ⁻¹] * Aᵘ⁻¹[aᵘ⁻¹, aᵘ, x]
-            M[t, u] = Mᵗᵘ
-            Mᵗᵘ⁻¹, Mᵗᵘ = Mᵗᵘ, Mᵗᵘ⁻¹
+    M = fill(zeros(0, 0), L, L)
+
+    for u in 2:L
+        Au = trace(A[u-1])
+        for t in 1:u-2
+            M[t, u] = M[t, u-1] * Au
         end
+        # initial condition
+        M[u-1, u] = Matrix(I, size(A[u],1), size(A[u],1))
     end
 
     return M
@@ -126,25 +119,15 @@ Compute the marginal distributions ``p(x^l)`` at each site
 function marginals(A::AbstractTensorTrain{F,N};
     l = accumulate_L(A), r = accumulate_R(A)) where {F<:Real,N}
 
-    A¹ = _reshape1(A[begin]); r² = r[2]
-    @tullio p¹[x] := A¹[a¹,a²,x] * r²[a²,a¹]
-    p¹ ./= sum(p¹)
-    p¹ = reshape(p¹, size(A[begin])[3:end])
-
-    Aᴸ = _reshape1(A[end]); lᴸ⁻¹ = l[end-1]
-    @tullio pᴸ[x] := lᴸ⁻¹[a¹,aᴸ] * Aᴸ[aᴸ,a¹,x]
-    pᴸ ./= sum(pᴸ)
-    pᴸ = reshape(pᴸ, size(A[end])[3:end])
-
-    p = map(2:length(A)-1) do t 
+    map(eachindex(A)) do t 
         Aᵗ = _reshape1(A[t])
-        rl = r[t+1] * l[t-1]
-        @tullio pᵗ[x] := rl[aᵗ⁺¹,aᵗ] * Aᵗ[aᵗ,aᵗ⁺¹,x]  
+        RL = t == length(A) ? l[end-1] : 
+            t == 1 ? r[begin+1] :
+            r[t+1]*l[t-1]
+        @tullio pᵗ[x] := RL[aᵗ⁺¹,aᵗ] * Aᵗ[aᵗ,aᵗ⁺¹,x]  
         pᵗ ./= sum(pᵗ)
         reshape(pᵗ, size(A[t])[3:end])
     end
-
-    return append!([p¹], p, [pᴸ])
 end
 
 """
@@ -170,9 +153,15 @@ function twovar_marginals(A::AbstractTensorTrain{F,N};
             rᵘ⁺¹ = u == length(A) ? Matrix(I, d, d) : r[u+1]
             Aᵘ = _reshape1(A[u])
             Mᵗᵘ = M[t, u]
-            @tullio bᵗᵘ[xᵗ, xᵘ] :=
-                lᵗ⁻¹[a¹,aᵗ] * Aᵗ[aᵗ, aᵗ⁺¹, xᵗ] * Mᵗᵘ[aᵗ⁺¹, aᵘ] * 
-                Aᵘ[aᵘ, aᵘ⁺¹, xᵘ] * rᵘ⁺¹[aᵘ⁺¹,a¹]
+            rl = rᵘ⁺¹ * lᵗ⁻¹
+            @tullio rlAt[aᵘ⁺¹, aᵗ⁺¹, xᵗ] := rl[aᵘ⁺¹,aᵗ] * Aᵗ[aᵗ, aᵗ⁺¹, xᵗ]
+            @tullio rlAtMtu[aᵘ⁺¹,xᵗ,aᵘ] := rlAt[aᵘ⁺¹, aᵗ⁺¹, xᵗ] * Mᵗᵘ[aᵗ⁺¹, aᵘ]
+            @tullio bᵗᵘ[xᵗ, xᵘ] := rlAtMtu[aᵘ⁺¹,xᵗ,aᵘ] * Aᵘ[aᵘ, aᵘ⁺¹, xᵘ]
+
+            #@tullio bᵗᵘ[xᵗ, xᵘ] :=
+            #lᵗ⁻¹[a¹,aᵗ] * Aᵗ[aᵗ, aᵗ⁺¹, xᵗ] * Mᵗᵘ[aᵗ⁺¹, aᵘ] * 
+            #Aᵘ[aᵘ, aᵘ⁺¹, xᵘ] * rᵘ⁺¹[aᵘ⁺¹,a¹]
+
             bᵗᵘ ./= sum(bᵗᵘ)
             b[t,u] = reshape(bᵗᵘ, qs)
         end