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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,65 +1,155 @@ | ||
| export PepsTensor | ||
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| mutable struct PepsTensor | ||
| nbrs::Dict{String, Int} | ||
| left::AbstractArray | ||
| right::AbstractArray | ||
| up::AbstractArray | ||
| down::AbstractArray | ||
| loc::AbstractArray | ||
| tensor::AbstractArray | ||
|
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| function PepsTensor(fg::MetaDiGraph, v::Int) | ||
| pc = new() | ||
| pc.nbrs = Dict() | ||
| pc.loc = get_prop(fg, v, :local_exp) | ||
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| outgoing = outneighbors(fg, v) | ||
| incoming = inneighbors(fg, v) | ||
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| for u ∈ outgoing | ||
| e = SimpleEdge(v, u) | ||
| if get_prop(fg, e, :orientation) == "horizontal" | ||
| pc.right = first(get_prop(fg, e, :decomposition)) | ||
| push!(pc.nbrs, "h_out" => u) | ||
| else | ||
| pc.down = first(get_prop(fg, e, :decomposition)) | ||
| push!(pc.nbrs, "v_out" => u) | ||
| end | ||
| end | ||
| export NetworkGraph, PepsNetwork | ||
| export generate_tensor, MPO | ||
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| for u ∈ incoming | ||
| e = SimpleEdge(u, v) | ||
| if get_prop(fg, e, :orientation) == "horizontal" | ||
| pc.left = last(get_prop(fg, e, :decomposition)) | ||
| push!(pc.nbrs, "h_in" => u) | ||
| else | ||
| pc.up = last(get_prop(fg, e, :decomposition)) | ||
| push!(pc.nbrs, "v_in" => u) | ||
| end | ||
| end | ||
|
|
||
| # open boundary conditions | ||
| if !isdefined(pc, :left) | ||
| pc.left = ones(1, size(pc.right, 1)) | ||
| mutable struct NetworkGraph | ||
| factor_graph::MetaDiGraph | ||
| nbrs::Dict | ||
| β::Number | ||
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| function NetworkGraph(factor_graph::MetaDiGraph, nbrs::Dict, β::Number) | ||
| ng = new(factor_graph, nbrs, β) | ||
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| count = 0 | ||
| for v ∈ vertices(ng.factor_graph), w ∈ ng.nbrs[v] | ||
| if has_edge(ng.factor_graph, v, w) count += 1 end | ||
| end | ||
|
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||
| if !isdefined(pc, :right) | ||
| pc.right = ones(size(pc.left, 2), 1) | ||
| mc = ne(ng.factor_graph) | ||
| if count < mc | ||
| error("Error: $(count) < $(mc)") | ||
| end | ||
| ng | ||
| end | ||
| end | ||
|
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| function generate_tensor(ng::NetworkGraph, v::Int) | ||
| fg = ng.factor_graph | ||
| loc_exp = exp.(-ng.β .* get_prop(fg, v, :loc_en)) | ||
|
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| if !isdefined(pc, :up) | ||
| pc.up = ones(1, size(pc.down, 1)) | ||
| dim = [] | ||
| @cast tensor[_, i] := loc_exp[i] | ||
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| for w ∈ ng.nbrs[v] | ||
| if has_edge(fg, w, v) | ||
| _, _, pv = get_prop(fg, w, v, :split) | ||
| pv = pv' | ||
| elseif has_edge(fg, v, w) | ||
| pv, _, _ = get_prop(fg, v, w, :split) | ||
| else | ||
| pv = ones(length(loc_exp), 1) | ||
| end | ||
|
|
||
| if !isdefined(pc, :down) | ||
| pc.down = ones(size(pc.up, 2), 1) | ||
| @cast tensor[(c, γ), σ] |= tensor[c, σ] * pv[σ, γ] | ||
| push!(dim, size(pv, 2)) | ||
| end | ||
|
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| reshape(tensor, dim..., :) | ||
| end | ||
|
|
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| function generate_tensor(ng::NetworkGraph, v::Int, w::Int) | ||
| fg = ng.factor_graph | ||
| if has_edge(fg, w, v) | ||
| _, e, _ = get_prop(fg, w, v, :split) | ||
| tensor = exp.(-ng.β .* e') | ||
| elseif has_edge(fg, v, w) | ||
| _, e, _ = get_prop(fg, v, w, :split) | ||
| tensor = exp.(-ng.β .* e) | ||
| else | ||
| tensor = ones(1, 1) | ||
| end | ||
| tensor | ||
| end | ||
|
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||
| mutable struct PepsNetwork | ||
| size::NTuple{2, Int} | ||
| map::Dict | ||
| network_graph::NetworkGraph | ||
| origin::Symbol | ||
| i_max::Int | ||
| j_max::Int | ||
|
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| function PepsNetwork(m::Int, n::Int, fg::MetaDiGraph, β::Number, origin::Symbol) | ||
| pn = new((m, n)) | ||
| pn.map, pn.i_max, pn.j_max = LinearIndices(m, n, origin) | ||
|
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| nbrs = Dict() | ||
| for i ∈ 1:pn.i_max, j ∈ 1:pn.j_max | ||
| push!(nbrs, | ||
| pn.map[i, j] => (pn.map[i, j-1], pn.map[i-1, j], | ||
| pn.map[i, j+1], pn.map[i+1, j])) | ||
| end | ||
| pn.network_graph = NetworkGraph(fg, nbrs, β) | ||
| pn | ||
| end | ||
| end | ||
|
|
||
| generate_tensor(pn::PepsNetwork, m::NTuple{2,Int}) = generate_tensor(pn.network_graph, pn.map[m]) | ||
| generate_tensor(pn::PepsNetwork, m::NTuple{2,Int}, n::NTuple{2,Int}) = generate_tensor(pn.network_graph, pn.map[m], pn.map[n]) | ||
|
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| function MPO(::Type{T}, Ψ::PEPSRow, σ::Vector{State}) where {T <: Number} | ||
| n = length(Ψ) | ||
| ϕ = MPO(T, n) | ||
| for i=1:n | ||
| k = σ[n] | ||
| A = Ψ[i] | ||
| @cast B[l, u, r, d] |= A[l, u, r, d, $k] | ||
|
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| ϕ[i] = B | ||
| end | ||
| ϕ | ||
| end | ||
| MPO(ψ::PEPSRow, σ::Vector{State}) = MPO(Float64, ψ, σ) | ||
|
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| function MPO(::Type{T}, Ψ::PEPSRow) where {T <: Number} | ||
| n = length(Ψ) | ||
| ϕ = MPO(T, n) | ||
| for i=1:n | ||
| A = Ψ[i] | ||
| @reduce B[l, u, r, d] |= sum(σ) A[l, u, r, d, σ] | ||
| ϕ[i] = B | ||
| end | ||
| ϕ | ||
| end | ||
| MPO(ψ::PEPSRow) = MPO(Float64, ψ) | ||
|
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||
| @cast pc.tensor[l, r, u, d, σ] |= pc.loc[σ] * pc.left[l, σ] * pc.right[σ, r] * pc.up[u, σ] * pc.down[σ, d] | ||
| function PEPSRow(::Type{T}, peps::PepsNetwork, i::Int) where {T <: Number} | ||
| n = peps.j_max | ||
| ψ = PEPSRow(T, n) | ||
|
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||
| pc | ||
| for j ∈ 1:n | ||
| ψ[j] = generate_tensor(peps, (i, j)) | ||
| end | ||
|
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| for j ∈ 1:n-1 | ||
| ten = generate_tensor(peps, (i, j), (i, j+1)) | ||
| A = ψ[j] | ||
| @tensor B[l, u, r, d, σ] := A[l, u, r̃, d, σ] * ten[r̃, r] | ||
| ψ[j] = B | ||
| end | ||
| ψ | ||
| end | ||
| PEPSRow(peps::PepsNetwork, i::Int) = PEPSRow(Float64, peps, i) | ||
|
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| function MPO(::Type{T}, peps::PepsNetwork, i::Int, k::Int) where {T <: Number} | ||
| n = peps.j_max | ||
|
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| ψ = MPO(T, n) | ||
| fg = peps.network_graph.factor_graph | ||
|
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| for j ∈ 1:n | ||
| v, w = peps.map[i, j], peps.map[k, j] | ||
|
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| Base.size(A::PepsTensor) = size(A.tensor) | ||
| if has_edge(fg, v, w) | ||
| _, en, _ = get_prop(fg, v, w, :split) | ||
| elseif has_edge(fg, w, v) | ||
| _, en, _ = get_prop(fg, w, v, :split) | ||
| else | ||
| en = ones(1, 1) | ||
| end | ||
|
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| @cast A[u, _, d, _] := en[u, d] | ||
| ψ[j] = A | ||
| end | ||
| ψ | ||
| end | ||
| MPO(peps::PepsNetwork, i::Int, k::Int) = MPO(Float64, peps, i, k) |
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