diff --git a/.github/workflows/CI.yml b/.github/workflows/CI.yml index 1902114..6cf9d84 100644 --- a/.github/workflows/CI.yml +++ b/.github/workflows/CI.yml @@ -1,48 +1,41 @@ name: CI on: - - push - - pull_request + pull_request: + branches: + - master + push: jobs: test: - name: Julia ${{ matrix.version }} - ${{ matrix.os }} - ${{ matrix.arch }} - runs-on: ${{ matrix.os }} + name: Julia ${{ matrix.version }} + runs-on: [self-hosted,titan,gpu] strategy: fail-fast: false matrix: version: - '1.9' - '1.10' - os: - - ubuntu-latest - - macOS-latest - arch: - - x64 steps: - - uses: actions/checkout@v2 - - uses: julia-actions/setup-julia@v1 + - uses: actions/checkout@v4 + - uses: julia-actions/setup-julia@v2 with: version: ${{ matrix.version }} - arch: ${{ matrix.arch }} - uses: julia-actions/julia-buildpkg@latest - uses: julia-actions/julia-runtest@latest env: JULIA_NUM_THREADS: 4 - uses: julia-actions/julia-processcoverage@v1 - # - uses: codecov/codecov-action@v1 - # with: - # file: lcov.info - uses: coverallsapp/github-action@master with: github-token: ${{ secrets.GITHUB_TOKEN }} path-to-lcov: lcov.info - flag-name: job-${{ matrix.version }}-${{ matrix.os }}-${{ matrix.arch }} parallel: true - + flag-name: run-${{ matrix.version }} finish: needs: test - runs-on: ubuntu-latest + runs-on: [self-hosted,titan] steps: - - uses: coverallsapp/github-action@master + - name: Close parallel build + uses: coverallsapp/github-action@v1 with: - github-token: ${{ secrets.GITHUB_TOKEN }} parallel-finished: true + carryforward: "run-1.9,run-1.10" \ No newline at end of file diff --git a/Project.toml b/Project.toml index e39277a..249db32 100644 --- a/Project.toml +++ b/Project.toml @@ -1,22 +1,34 @@ name = "SpinGlassNetworks" uuid = "b7f6bd3e-55dc-4da6-96a9-ef9dbec6ac19" -authors = ["Krzysztof Domino ", "Anna Maria Dziubyna ", "Bartłomiej Gardas ", "Konrad Jałowiecki ", "Łukasz Pawela ", "Marek M. Rams "] -version = "0.3.2" +authors = ["Anna Maria Dziubyna ", "Tomasz Śmierzchalski ", "Bartłomiej Gardas ", "Konrad Jałowiecki ", "Łukasz Pawela ", "Marek M. Rams "] +version = "1.0.0" [deps] CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b" +CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba" DocStringExtensions = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae" Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6" +HDF5 = "f67ccb44-e63f-5c2f-98bd-6dc0ccc4ba2f" +JLD2 = "033835bb-8acc-5ee8-8aae-3f567f8a3819" LabelledGraphs = "605abd48-4d17-4660-b914-d4df33194460" LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e" +MKL = "33e6dc65-8f57-5167-99aa-e5a354878fb2" MetaGraphs = "626554b9-1ddb-594c-aa3c-2596fe9399a5" +SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf" +SpinGlassTensors = "7584fc6a-5a23-4eeb-8277-827aab0146ea" [compat] -CSV = "0.8 - 0.10" -DocStringExtensions = "0.8" -Graphs = "1.9" -LabelledGraphs = "^0.4.4" -MetaGraphs = "0.7.2" +CSV = "0.8" +CUDA = "4.4" +DocStringExtensions = "0.9" +Graphs = "1.9.0" +HDF5 = "0.17" +JLD2 = "0.4" +LabelledGraphs = "0.4.4" +MKL = "0.4.2" +MetaGraphs = "0.7" +SparseArrays = "1.9" +SpinGlassTensors = "1" julia = "1.9, 1.10" [extras] diff --git a/README.md b/README.md index c690b9a..324843c 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,5 @@ [![Coverage Status](https://coveralls.io/repos/github/iitis/SpinGlassNetworks.jl/badge.svg?branch=master)](https://coveralls.io/github/iitis/SpinGlassNetworks.jl?branch=master) # SpinGlassNetworks.jl +Part of SpinGlassPEPS package. It constitutes the basis for the preparation of tensors and operations on them. + +We don't expect the user to interact with this package, as it is more of a "back-end" type. Nevertheless, we provide API references should the need arise. \ No newline at end of file diff --git a/docs/make.jl b/docs/make.jl index fbb6101..ae35025 100644 --- a/docs/make.jl +++ b/docs/make.jl @@ -1,21 +1,23 @@ using Documenter, SpinGlassNetworks _pages = [ - "Introduction" => "index.md", "User guide" => "userguide.md", - "API Reference" => "api.md" + "Ising graph" => "ising.md", + "Lattice geometries" => "lattice.md", + "Clustered hamiltonian" => "clh.md", + "Local dimensional reduction" => "bp.md", + "API Reference for auxiliary functions" => "api.md", ] # ============================ -format = Documenter.HTML(edit_link = "master", - prettyurls = get(ENV, "CI", nothing) == "true", -) +format = + Documenter.HTML(edit_link = "master", prettyurls = get(ENV, "CI", nothing) == "true") # format = Documenter.LaTeX(platform="none") makedocs( - sitename="SpinGlassNetworks.jl", + sitename = "SpinGlassNetworks.jl", modules = [SpinGlassNetworks], pages = _pages, - format = format - ) \ No newline at end of file + format = format, +) diff --git a/docs/src/api.md b/docs/src/api.md index 8a1c80a..cf987b7 100644 --- a/docs/src/api.md +++ b/docs/src/api.md @@ -1,36 +1,66 @@ # Library ---- - ```@meta CurrentModule = SpinGlassNetworks ``` ## Ising Graphs - ```@docs -ising_graph -biases -couplings -cluster +inter_cluster_edges prune -rank_vec +couplings ``` -## Factor Graphs +## Clustered Hamiltonian +```@docs +split_into_clusters +decode_clustered_hamiltonian_state +rank_reveal +energy +energy_2site +cluster_size +bond_energy +exact_cond_prob +truncate_clustered_hamiltonian +``` +## Belief propagation ```@docs -factor_graph -decode_factor_graph_state +local_energy +interaction_energy +get_neighbors +MergedEnergy +update_message +merge_vertices_cl_h +projector +SparseCSC ``` -## Auxiliary Functions +## Projectors +```@docs +PoolOfProjectors +get_projector! +add_projector! +empty! +``` +## Spectrum ```@docs -brute_force -energy -full_spectrum +Spectrum +matrix_to_integers gibbs_tensor -rank_reveal -super_square_lattice +brute_force +``` + +## Truncate +```@docs +truncate_clustered_hamiltonian_1site_BP +truncate_clustered_hamiltonian_2site_energy +select_numstate_best +``` + +## Auxiliary Functions +```@docs +zephyr_to_linear +load_openGM ``` \ No newline at end of file diff --git a/docs/src/bp.md b/docs/src/bp.md new file mode 100644 index 0000000..918f826 --- /dev/null +++ b/docs/src/bp.md @@ -0,0 +1,9 @@ +## Belief propagation + +Local dimensional reduction can be achieved by selectively choosing states in the clustered Hamiltonian that have the lowest local energy in the cluster. This approach aims to reduce the dimensionality of the problem by focusing on the most relevant and energetically favorable states. It can be done by truncation based on energy or truncation based on Loopy Belief Propagation. + +```@docs +clustered_hamiltonian_2site +belief_propagation +truncate_clustered_hamiltonian_2site_BP +``` \ No newline at end of file diff --git a/docs/src/clh.md b/docs/src/clh.md new file mode 100644 index 0000000..c3ba34a --- /dev/null +++ b/docs/src/clh.md @@ -0,0 +1,24 @@ +# Introduction +A clustered Hamiltonian is a graphical representation that allows for a convenient and intuitive way to describe the structure of a network. + +The concept of a clustered Hamiltonian within `SpinGlassNetworks.jl` introduces a mechanism for organizing spins into desired geometries, facilitating a structured approach to modeling complex spin systems. Analogous to a standard factor graph, the clustered Hamiltonian involves nodes that represent tensors within the underlying network. The edges connecting these nodes in the clustered Hamiltonian correspond to the indices shared between the respective tensors in the tensor network. + +```@docs +clustered_hamiltonian +``` + +## Simple example + +```@example +using SpinGlassNetworks + +# Prepare simple instance +instance = "$(@__DIR__)/../../src/instances/square_diagonal/5x5/diagonal.txt" +ig = ising_graph(instance) + +# Create clustered Hamiltonian +cl_h = clustered_hamiltonian( + ig, + cluster_assignment_rule = super_square_lattice((5,5,4)) +) +``` \ No newline at end of file diff --git a/docs/src/images/annealing.png b/docs/src/images/annealing.png new file mode 100644 index 0000000..6ca60bb Binary files /dev/null and b/docs/src/images/annealing.png differ diff --git a/docs/src/images/bb.png b/docs/src/images/bb.png new file mode 100644 index 0000000..32b0edd Binary files /dev/null and b/docs/src/images/bb.png differ diff --git a/docs/src/images/branch_bound.png b/docs/src/images/branch_bound.png new file mode 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b/docs/src/ising.md new file mode 100644 index 0000000..f2bec58 --- /dev/null +++ b/docs/src/ising.md @@ -0,0 +1,25 @@ +# Ising model + +The Ising model is a mathematical model used to describe the behavior of interacting particles, such as atoms or molecules, in a magnetic field. In the Ising model, each particle is represented as a binary variable $s_i$ that can take on the values of either +1 or -1. The total energy of the system is given by the Hamiltonian: + +$H = \sum_{(i,j) \in \mathcal{E}} J_{ij} s_i s_j + \sum_{i} h_i s_i$ + +where $J_{ij}$ is the coupling constant between particles $i$ and $j$, $h_i$ is the external magnetic field at particle $i$, and the sum is taken over all pairs of particles and all particles in the system $\mathcal{E}$, respectively. + +In `SpinGlassPEPS.jl` package, an Ising graph can be created using the command `ising_graph`. +```@docs +ising_graph +``` + +## Simple example +In this simple example below we show how to create Ising graph of a instance given as txt file in a format (i, j, Jij). The resulting graph has vertices corresponding to positions of spins in the system and edges defining coupling strength between spins. Each vertex contains information about local field. + +```@example +using SpinGlassNetworks +# Create Ising instance +instance = "$(@__DIR__)/../../src/instances/square_diagonal/5x5/diagonal.txt" +ig = ising_graph(instance) + +# View graph properties +@show biases(ig), couplings(ig) +``` \ No newline at end of file diff --git a/docs/src/lattice.md b/docs/src/lattice.md new file mode 100644 index 0000000..05f4821 --- /dev/null +++ b/docs/src/lattice.md @@ -0,0 +1,105 @@ +# Lattice geometries +The Ising graph allowed for loading instances directly from a file and translating them into a graph. The next step towards constructing the tensor network is to build a lattice, based on which we will transform the Ising graph into a clustered Hamiltonian. +Within the `SpinGlassNetworks.jl` package, users have the flexibility to construct three types of lattice geometries, each tailored to specific needs. + +## Super square lattice +The `super_square_lattice` geometry represents a square lattice with nearest neighbors interactions (horizontal and vertical interactions between unit cells) and next nearest neighbor interactions (diagonal interactions). Unit cells depicted on the schematic picture below as red ellipses can consist of multiple spins. +This geometry allows for an exploration of spin interactions beyond the traditional square lattice framework. +```@raw html + +``` + +In `SpinGlassPEPS.jl` solver, a grid of this type can be loaded using the command `super_square_lattice`. + +```@docs +super_square_lattice +``` + +Below you find simple example of usage `super_square_latttice` function. + +```@example +using SpinGlassEngine, SpinGlassNetworks, LabelledGraphs + +instance = "$(@__DIR__)/../../src/instances/square_diagonal/5x5/diagonal.txt" +ig = ising_graph(instance) + +m = 5 +n = 5 +t = 4 + +cl_h = clustered_hamiltonian( + ig, + cluster_assignment_rule = super_square_lattice((m, n, t)) +) + +println("Number of nodes in oryginal instance: ", length(LabelledGraphs.vertices(ig)), "\n", " Number of nodes in clustered Hamiltonian: ", length(LabelledGraphs.vertices(cl_h))) +``` + +## Pegasus graphs +The Pegasus graph is a type of graph architecture used in quantum computing systems, particularly in the quantum annealing machines developed by D-Wave Systems. It is designed to provide a grid of qubits with specific connectivity patterns optimized for solving certain optimization problems. Futer details can be found [here](https://docs.dwavesys.com/docs/latest/c_gs_4.html#pegasus-graph). +```@raw html + +``` + +In `SpinGlassPEPS.jl` solver, a grid of this type can be loaded using the command `pegasus_lattice`. + +```@docs +pegasus_lattice +``` + +Below you find simple example of usage `pegasus_latttice` function. + +```@example +using SpinGlassEngine, SpinGlassNetworks, LabelledGraphs + +# load Chimera instance and create Ising graph +instance = "$(@__DIR__)/../../src/instances/pegasus_random/P4/RAU/001_sg.txt" +ig = ising_graph(instance) + +# Loaded instance is pegasus graph +m = 3 +n = 3 +t = 3 + +cl_h = clustered_hamiltonian( + ig, + cluster_assignment_rule = pegasus_lattice((m, n, t)) +) + +println("Number of nodes in original instance: ", length(LabelledGraphs.vertices(ig)), "\n", " Number of nodes in clustered Hamiltonian: ", length(LabelledGraphs.vertices(cl_h))/2) +``` + + +## Zephyr graphs +The Zephyr graph is a type of graph architecture used in quantum computing systems, particularly in the quantum annealing machines developed by D-Wave Systems. Futer details can be found [here](https://docs.dwavesys.com/docs/latest/c_gs_4.html#zephyr-graph). +```@raw html + +``` + +In `SpinGlassPEPS.jl` solver, a grid of this type can be loaded using the command `zephyr_lattice`. + +```@docs +zephyr_lattice +``` + +Below you find simple example of usage `zephyr_latttice` function. + +```@example +using SpinGlassEngine, SpinGlassNetworks, LabelledGraphs + +# load Chimera instance and create Ising graph +instance = "$(@__DIR__)/../../src/instances/zephyr_random/Z3/RAU/001_sg.txt" +ig = ising_graph(instance) + +# Loaded instance is zephyr graph +m = 6 +n = 6 +t = 4 + +cl_h = clustered_hamiltonian( + ig, + cluster_assignment_rule = zephyr_lattice((m, n, t)) +) + +println("Number of nodes in oryginal instance: ", length(LabelledGraphs.vertices(ig))) +``` \ No newline at end of file diff --git a/docs/src/userguide.md b/docs/src/userguide.md index 20f33b5..cb92310 100644 --- a/docs/src/userguide.md +++ b/docs/src/userguide.md @@ -1,122 +1,5 @@ # Introduction -This section provides examples of how to use the 'ising_graph' function to generate an Ising model graph and the 'factor_graph' function to convert the Ising graph into a factor graph. -# Ising Graph -The Ising model is a mathematical model used to describe the behavior of interacting particles, such as atoms or molecules, in a magnetic field. In the Ising model, each particle is represented as a binary variable $s_i$ that can take on the values of either +1 or -1. The total energy of the system is given by the Hamiltonian: +A [Julia](http://julialang.org) package for building and interacting with Ising spin glass models in context of tensor networks. Part of [SpinGlassPEPS](https://github.com/euro-hpc-pl/SpinGlassPEPS.jl) package. -$$H = \sum_{(i,j) \in \mathcal{E}} J_{ij} s_i s_j + \sum_{i} h_i s_i$$ - -where $J_{ij}$ is the coupling constant between particles $i$ and $j$, $h_i$ is the external magnetic field at particle $i$, and the sum is taken over all pairs of particles and all particles in the system $\mathcal{E}$, respectively. - - -## simple examle -```@example -using SpinGlassNetworks -# Create Ising instance -instance = Dict((1, 1) => 1.0, (2, 2) => 0.5, (3, 3) => -0.25, -(1, 2) => -1.0, (2, 3) => 1.0) - -# Generate Ising graph -ig = ising_graph(instance) - -# View graph properties -@show biases(ig), couplings(ig) - -``` - -## Generalisation - -The 'ising_graph' function is quite general function. One can easly convert instance into another hamiltonian convention, ex. -```math -H = - \sum_{(i,j) \in \mathcal{E}} J_{ij} s_i s_j - \mu \sum_{i} h_i s_i -``` -This conversion is directed by `sgn` parameter of `ising_graph`. It is wort doing, because then all `energy` functions will work as one intended. -```@example -using SpinGlassNetworks, LabelledGraphs - -# lets define simple Ising system -instance = Dict((1,1) => 1.0, (2, 2) => -1.0, (1, 2) => -1.25) -ig = ising_graph(instance) - -# use above convention -μ = 0.25 -sgn = -1 - -# absorb μ into h and construct ising graph and -instance2 = Dict((1,1) => instance[(1,1)]*μ, (2, 2) => instance[(2,2)]*μ, (1, 2) => -1.25) - -ig2 = ising_graph(instance2, sgn=sgn) - -# select arbitrary state -σ = [1, 1] - -println("energy without conversion: ", energy(σ, ig), -" energy after conversion: ", energy(σ, ig2)) -``` - -'ising_graph' also allows for building other spin system, not only spin-``\frac{1}{2}`` system. For example one may build chain where sites allows for 3 spin values. - -```@example -using SpinGlassNetworks - -# Create spin chain instance -instance = Dict((1, 1) => 1.0, (2, 2) => 0.5, (3, 3) => -0.25, -(1, 2) => -1.0, (2, 3) => 1.0) - -rank_override = Dict(1 => 3, 2 => 3, 3 => 3) - -sg = ising_graph(instance, rank_override=rank_override) - -# see ranks of each site -rank_vec(sg) -``` - -# factor graph - -A factor graph is a graphical representation that allows for a convenient and intuitive way to describe the structure of a tensor network. - -A factor graph consists of two types of nodes: variable nodes and factor nodes. Variable nodes represent the tensors in the tensor network, and factor nodes represent the operations that are applied to those tensors. - -Each variable node in the factor graph corresponds to a tensor in the tensor network, and each factor node corresponds to a tensor contraction or operation that is applied to one or more of those tensors. The edges between the nodes in the factor graph represent the indices that are shared between the corresponding tensors in the tensor network. - -## Simple example - -```@example -using SpinGlassNetworks - -# Prepare simple instance -instance = Dict((1, 1) => 1.0, (2, 2) => 0.5, (3, 3) => -0.25, -(1, 2) => -1.0, (2, 3) => 1.0) -ig = ising_graph(instance) - -# Create factor graph. -fg = factor_graph( - ig, - cluster_assignment_rule = super_square_lattice((3, 1, 1)) -) -``` - -## Chimera graphs -The Chimera graph is a type of graph architecture used in quantum computing systems, particularly in the quantum annealing machines developed by D-Wave Systems. It is a two-dimensional lattice of unit cells, each consisting of a bipartite graph of $K_{4,4}$ complete bipartite subgraphs. Futer details can be found [here](https://docs.dwavesys.com/docs/latest/c_gs_4.html#chimera-graph). - - -```@example -using SpinGlassEngine, SpinGlassNetworks, LabelledGraphs - -# load Chimera instance and create Ising graph -instance = "$(@__DIR__)/../../src/instances/chimera_droplets/128power/001.txt" -ig = ising_graph(instance) - -# Loaded instance is 4x4x8 chimera graph -m = 4 -n = 4 -t = 8 - -fg = fg = factor_graph( - ig, - cluster_assignment_rule = super_square_lattice((m, n, t)) -) - -println("Number of nodes in oryginal instance: ", length(LabelledGraphs.vertices(ig)), "\n", - " Number of nodes in factor graph: ", length(LabelledGraphs.vertices(fg))) -``` \ No newline at end of file +The contents of our package are illustrated through comprehensive examples, showcasing the practical utilization of key functions. Specifically, the `ising_graph` function is highlighted, demonstrating its capacity to generate Ising model graphs — a fundamental step in modeling spin systems. Additionally, the `clustered_hamiltonian` function is presented as a tool for converting Ising graphs into clustered Hamiltonians. The package delves into various lattice geometries, providing insights into constructing diverse structures such as the `super_square_lattice`, `pegasus_lattice`, and `zephyr_lattice`. Moreover, the documentation outlines methods for local dimensional reduction, shedding light on techniques to streamline computations and enhance the efficiency of spin system simulations. diff --git a/examples/bp.jl b/examples/bp.jl new file mode 100644 index 0000000..b580145 --- /dev/null +++ b/examples/bp.jl @@ -0,0 +1,69 @@ +using SpinGlassNetworks +using LabelledGraphs +using Graphs +using MetaGraphs +using Logging + +""" +Instance below looks like this: + +1 -- 2 -- 3 +| +4 -- 5 -- 6 +| +7 -- 8 -- 9 +""" +function create_larger_example_clustered_hamiltonian_tree() + instance = Dict( + (1, 1) => 0.5, + (2, 2) => 0.25, + (3, 3) => 0.3, + (4, 4) => 0.1, + (5, 5) => -0.1, + (6, 6) => 0.1, + (7, 7) => 0.0, + (8, 8) => 0.1, + (9, 9) => 0.01, + (1, 2) => -1.0, + (2, 3) => 1.0, + (1, 4) => 1.0, + (4, 5) => 1.0, + (5, 6) => 1.0, + (4, 7) => 1.0, + (7, 8) => 1.0, + (8, 9) => 1.0, + ) + + ig = ising_graph(instance) + + assignment_rule = Dict( + 1 => (1, 1, 1), + 2 => (1, 2, 1), + 3 => (1, 3, 1), + 4 => (2, 1, 1), + 5 => (2, 2, 1), + 6 => (2, 3, 1), + 7 => (3, 1, 1), + 8 => (3, 2, 1), + 9 => (3, 3, 1), + ) + + cl_h = clustered_hamiltonian( + ig, + Dict{NTuple{3,Int},Int}(), + spectrum = full_spectrum, + cluster_assignment_rule = assignment_rule, + ) + + ig, cl_h +end + +ig, cl_h = create_larger_example_clustered_hamiltonian_tree() +beta = 0.1 +iter = 0 +beliefs = belief_propagation(cl_h, beta; iter = iter) + +for v in vertices(cl_h) + en = get_prop(cl_h, v, :spectrum).energies + println("vertex ", v, " energy = ", en .- minimum(en), " bp = ", beliefs[v]) +end diff --git a/examples/penguin-small.h5 b/examples/penguin-small.h5 new file mode 100644 index 0000000..4b72c88 Binary files /dev/null and b/examples/penguin-small.h5 differ diff --git a/examples/temp.jl b/examples/temp.jl new file mode 100644 index 0000000..78d325b --- /dev/null +++ b/examples/temp.jl @@ -0,0 +1,138 @@ +using HDF5 +using Graphs +using LinearAlgebra +using LabelledGraphs +using MetaGraphs +using SpinGlassNetworks + + +function load_openGM(fname::String, Nx::Integer, Ny::Integer) + file = h5open(fname, "r") + + file_keys = collect(keys(read(file))) + data = read(file[file_keys[1]]) + H = collect(Int64, data["header"]) + F = Array{Int64}(data["factors"]) + J = Array{Int64}(data["function-id-16000"]["indices"]) + V = Array{Real}(data["function-id-16000"]["values"]) + N = Array{Int64}(data["numbers-of-states"]) + + F = reverse(F) + factors = Dict() + + while length(F) > 0 + f1 = pop!(F) + z1 = pop!(F) + nn = pop!(F) + n = [] + + for _ = 1:nn + tt = pop!(F) + ny, nx = divrem(tt, Nx) + push!(n, ny, nx) + end + + if length(n) == 4 + if abs(n[1] - n[3]) + abs(n[2] - n[4]) != 1 + throw(Exception("Not nearest neighbour")) + end + end + + if length(n) == 2 + if (n[1] >= Ny) || (n[2] >= Nx) + throw(Exception("Wrong size")) + end + end + + factors[tuple(n...)] = f1 + + if z1 != 0 + throw(Exception("Something wrong with the expected convention.")) + end + end + + J = reverse(J) + functions = Dict() + ii = -1 + lower = 0 + + while length(J) > 0 + ii += 1 + nn = pop!(J) + n = [] + + for _ = 1:nn + push!(n, pop!(J)) + end + + upper = lower + prod(n) + functions[ii] = reshape(V[lower+1:upper], reverse(n)...)' + + lower = upper + end + + result = Dict( + "fun" => functions, + "fac" => factors, + "N" => reshape(N, (Ny, Nx)), + "Nx" => Nx, + "Ny" => Ny, + ) + result +end + +function clustered_hamiltonian(fname::String, Nx::Integer = 240, Ny::Integer = 320) + loaded_rmf = load_openGM(fname, Nx, Ny) + functions = loaded_rmf["fun"] + factors = loaded_rmf["fac"] + N = loaded_rmf["N"] + + clusters = super_square_lattice((Nx, Ny, 1)) + cl_h = LabelledGraph{MetaDiGraph}(sort(collect(values(clusters)))) + for v ∈ cl_h.labels + x, y = v + sp = Spectrum( + Vector{Real}(undef, 1), + Array{Vector{Int}}(undef, 1, 1), + Vector{Int}(undef, 1), + ) + set_props!(cl_h, v, Dict(:cluster => v, :spectrum => sp)) + end + for (index, value) in factors + if length(index) == 2 + y, x = index + Eng = sum(functions[value]) + set_props!(cl_h, (x + 1, y + 1), Dict(:eng => Eng)) + elseif length(index) == 4 + y1, x1, y2, x2 = index + add_edge!(cl_h, (x1 + 1, y1 + 1), (x2 + 1, y2 + 1)) + Eng = sum(functions[value], dims = 2) + set_props!( + cl_h, + (x1 + 1, y1 + 1), + (x2 + 1, y2 + 1), + Dict( + :outer_edges => ((x1 + 1, y1 + 1), (x2 + 1, y2 + 1)), + :eng => Eng, + :pl => I, + :pr => I, + ), + ) + else + throw( + ErrorException( + "Something is wrong with factor index, it has length $(length(index))", + ), + ) + end + end + + cl_h +end + + +x, y = 240, 320 +filename = "/home/tsmierzchalski/.julia/dev/SpinGlassNetworks/examples/penguin-small.h5" + + +cf = clustered_hamiltonian(filename, x, y) diff --git a/src/SpinGlassNetworks.jl b/src/SpinGlassNetworks.jl index 5e64f60..05c2ea7 100644 --- a/src/SpinGlassNetworks.jl +++ b/src/SpinGlassNetworks.jl @@ -1,99 +1,22 @@ module SpinGlassNetworks using LabelledGraphs using Graphs -using MetaGraphs # TODO: remove that +using MetaGraphs # TODO: to be replaced by MetaGraphsNext using CSV using DocStringExtensions -using LinearAlgebra +using LinearAlgebra, MKL using Base.Cartesian - +using SparseArrays +using CUDA, CUDA.CUSPARSE +using SpinGlassTensors import Base.Prehashed -export unique_neighbors - - -unique_neighbors(ig::LabelledGraph, i::Int) = filter(j -> j > i, neighbors(ig, i)) - -@generated function unique_dims(A::AbstractArray{T,N}, dim::Integer) where {T,N} - quote - 1 <= dim <= $N || return copy(A) - hashes = zeros(UInt, axes(A, dim)) - - # Compute hash for each row - k = 0 - @nloops $N i A d -> ( - if d == dim - k = i_d - end - ) begin - @inbounds hashes[k] = hash(hashes[k], hash((@nref $N A i))) - end - - # Collect index of first row for each hash - uniquerow = similar(Array{Int}, axes(A, dim)) - firstrow = Dict{Prehashed,Int}() - for k in axes(A, dim) - uniquerow[k] = get!(firstrow, Prehashed(hashes[k]), k) - end - uniquerows = collect(values(firstrow)) - - # Check for collisions - collided = falses(axes(A, dim)) - @inbounds begin - @nloops $N i A d -> ( - if d == dim - k = i_d - j_d = uniquerow[k] - else - j_d = i_d - end - ) begin - if (@nref $N A j) != (@nref $N A i) - collided[k] = true - end - end - end - - if any(collided) - nowcollided = similar(BitArray, axes(A, dim)) - while any(collided) - # Collect index of first row for each collided hash - empty!(firstrow) - for j in axes(A, dim) - collided[j] || continue - uniquerow[j] = get!(firstrow, Prehashed(hashes[j]), j) - end - for v ∈ values(firstrow) - push!(uniquerows, v) - end - - # Check for collisions - fill!(nowcollided, false) - @nloops $N i A d -> begin - if d == dim - k = i_d - j_d = uniquerow[k] - (!collided[k] || j_d == k) && continue - else - j_d = i_d - end - end begin - if (@nref $N A j) != (@nref $N A i) - nowcollided[k] = true - end - end - (collided, nowcollided) = (nowcollided, collided) - end - end - - (@nref $N A d -> d == dim ? sort!(uniquerows) : (axes(A, d))), - indexin(uniquerow, uniquerows) - end -end -include("states.jl") include("ising.jl") include("spectrum.jl") include("lattice.jl") -include("factor.jl") +include("clustered_hamiltonian.jl") +include("bp.jl") +include("truncate.jl") +include("utils.jl") end # module diff --git a/src/bp.jl b/src/bp.jl new file mode 100644 index 0000000..1ef02ef --- /dev/null +++ b/src/bp.jl @@ -0,0 +1,507 @@ + +export belief_propagation, + clustered_hamiltonian_2site, + projector, + get_neighbors, + MergedEnergy, + update_message, + merge_vertices_cl_h, + local_energy, + interaction_energy, + SparseCSC + +""" +$(TYPEDSIGNATURES) +Perform loopy belief propagation on a given clustered Hamiltonian. + +# Arguments: +- `cl_h::LabelledGraph{S, T}`: The clustered Hamiltonian represented as a labelled graph. +- `beta::Real`: The inverse temperature parameter for the belief propagation algorithm. +- `tol::Real (optional, default=1e-6)`: The convergence tolerance. The algorithm stops when the message updates between iterations are smaller than this value. +- `iter::Int (optional, default=1)`: The maximum number of iterations to perform. + +# Returns: +- `beliefs::Dict`: A dictionary where keys are vertices of clustered hamiltonian, and values are the +resulting beliefs after belief propagation. + +The function implements loopy belief propagation on the given clustered hamiltonian `cl_h` to calculate beliefs for each vertex. +Belief propagation is an iterative algorithm that computes beliefs by passing messages between vertices and edges of the clustered hamiltonian. +The algorithm continues until convergence or until the specified maximum number of iterations is reached. +The beliefs are computed based on the inverse temperature parameter `beta`, which controls the influence of energy values on the beliefs. +""" +function belief_propagation( + cl_h::LabelledGraph{S,T}, + beta::Real; + tol = 1e-6, + iter = 1, +) where {S,T} + messages_ve = Dict() + messages_ev = Dict() + + # Initialize messages with uniform probabilities + for v in vertices(cl_h) + for (n, pv, _) in get_neighbors(cl_h, v) + push!(messages_ev, (n, v) => ones(maximum(pv))) + end + end + + # Perform message passing until convergence + converged = false + iteration = 0 + while !converged && iteration < iter # Set an appropriate number of iterations and convergence threshold + iteration += 1 + old_messages_ev = deepcopy(messages_ev) + for v in vertices(cl_h) + #update messages from vertex to edge + node_messages = Dict() + for (n1, pv1, _) ∈ get_neighbors(cl_h, v) + node_messages[n1, v] = messages_ev[n1, v][pv1] + end + for (n1, pv1, _) ∈ get_neighbors(cl_h, v) + E_local = get_prop(cl_h, v, :spectrum).energies + temp = exp.(-(E_local .- minimum(E_local)) * beta) + for (n2, pv2, _) in get_neighbors(cl_h, v) + if n1 == n2 + continue + end + temp .*= node_messages[n2, v] # messages_ev[n2, v][pv2] + end + temp ./= sum(temp) + messages_ve[v, n1] = SparseCSC(eltype(temp), pv1) * temp + end + end + + #update messages from edge to vertex + for v in vertices(cl_h) + for (n, _, en) ∈ get_neighbors(cl_h, v) + messages_ev[n, v] = update_message(en, messages_ve[n, v], beta) + end + end + + # Check convergence + converged = all([ + all(abs.(old_messages_ev[v] .- messages_ev[v]) .< tol) for + v in keys(messages_ev) + ]) + end + + beliefs = Dict() + for v in vertices(cl_h) + E_local = get_prop(cl_h, v, :spectrum).energies + beliefs[v] = exp.(-E_local * beta) + for (n, pv, _) ∈ get_neighbors(cl_h, v) + beliefs[v] .*= messages_ev[n, v][pv] + end + beliefs[v] = -log.(beliefs[v]) ./ beta + beliefs[v] = beliefs[v] .- minimum(beliefs[v]) + end + + beliefs +end + +""" +$(TYPEDSIGNATURES) +Returns the neighbors of a given vertex in a clustered Hamiltonian. + +# Arguments: +- `cl_h::LabelledGraph{S, T}`: The clustered Hamiltonian represented as a labeled graph. +- `vertex::NTuple`: The vertex for which neighbors are to be retrieved. + +# Returns: +- `neighbors::Vector{Tuple}`: A vector of tuples representing the neighbors of the specified vertex. +Each tuple contains the following information: +- `dst_node::T`: The neighboring vertex. +- `pv::Matrix`: The projector associated with the edge connecting the vertex and its neighbor. +- `en::Real`: The energy associated with the edge connecting the vertex and its neighbor. + +This function retrieves the neighbors of a given vertex in a clustered Hamiltonian graph. +It iterates through the edges of the graph and identifies edges connected to the specified vertex. +For each neighboring edge, it extracts and returns the neighboring vertex, the associated projector, and the energy. +""" +function get_neighbors(cl_h::LabelledGraph{S,T}, vertex::NTuple) where {S,T} + neighbors = [] + for edge in edges(cl_h) + src_node, dst_node = src(edge), dst(edge) + if src_node == vertex + en = get_prop(cl_h, src_node, dst_node, :en) + idx_pv = get_prop(cl_h, src_node, dst_node, :ipl) + pv = get_projector!(get_prop(cl_h, :pool_of_projectors), idx_pv, :CPU) + push!(neighbors, (dst_node, pv, en)) + elseif dst_node == vertex + en = get_prop(cl_h, src_node, dst_node, :en)' + idx_pv = get_prop(cl_h, src_node, dst_node, :ipr) + pv = get_projector!(get_prop(cl_h, :pool_of_projectors), idx_pv, :CPU) + push!(neighbors, (src_node, pv, en)) + end + end + return neighbors +end + +""" +$(TYPEDSIGNATURES) + +A custom Julia struct representing energy values in a merged format for use in specific calculations. + +# Fields: +- `e11::AbstractMatrix{T}` +- `e12::AbstractMatrix{T}` +- `e21::AbstractMatrix{T}` +- `e22::AbstractMatrix{T}` + +The `MergedEnergy` struct is used to represent energy values that are organized in a merged format. +This format is often utilized in certain computational tasks, where energy values are categorized based on combinations of left and right factors. + +Each field of the `MergedEnergy` struct stores energy values as an `AbstractMatrix{T}` of type `T`, +where `T` is a subtype of the `Real` abstract type. +The specific organization and interpretation of these energy values depend on the context in which this struct is used. +""" +struct MergedEnergy{T<:Real} + e11::AbstractMatrix{T} + e12::AbstractMatrix{T} + e21::AbstractMatrix{T} + e22::AbstractMatrix{T} +end + +Base.adjoint(s::MergedEnergy) = MergedEnergy(s.e11', s.e21', s.e12', s.e22') + +""" +$(TYPEDSIGNATURES) + +Update a message using energy values and temperature. + +# Arguments: +- `E_bond::AbstractArray`: An array of energy values associated with a bond or interaction. +- `message::Vector`: The input message vector to be updated. +- `beta::Real`: The temperature parameter controlling the influence of energy values. + +# Returns: +- `updated_message::Vector`: The updated message vector after applying the energy-based update. + +This function takes energy values `E_bond` associated with a bond or interaction, an input message vector `message`, +and a temperature parameter `beta`. It updates the message by first adjusting the energy values relative to their minimum value, +exponentiating them with a negative sign and scaling by `beta`, and then multiplying them element-wise with the input message. + +The result is an updated message that reflects the influence of energy values and temperature. +""" +function update_message(E_bond::AbstractArray, message::Vector, beta::Real) + E_bond = E_bond .- minimum(E_bond) + exp.(-beta * E_bond) * message +end + +""" +$(TYPEDSIGNATURES) + +Update a message using energy values and temperature in a merged energy format. + +# Arguments: +- `E_bond::MergedEnergy`: An instance of the `MergedEnergy` type representing energy values for the bond or interaction. +- `message::Vector`: The input message vector to be updated. +- `beta::Real`: The temperature parameter controlling the influence of energy values. + +# Returns: +- `updated_message::Vector`: The updated message vector after applying the energy-based update. + +This function takes energy values `E_bond` in a merged energy format, an input message vector `message`, +and a temperature parameter `beta`. It updates the message based on the energy values and temperature using a specified algorithm. + +The `MergedEnergy` type represents energy values in a merged format, and the function processes these values +accordingly to update the message vector. +""" +function update_message(E_bond::MergedEnergy, message::Vector, beta::Real) + e11, e12, e21, e22 = E_bond.e11, E_bond.e12, E_bond.e21, E_bond.e22 + # equivalent to + # @cast E[(l1, l2), (r1, r2)] := e11[l1, r1] + e21[l2, r1] + e12[l1, r2] + e22[l2, r2] + # exp.(-beta * E) * message + + e11 = exp.(-beta .* (e11 .- minimum(e11))) + e12 = exp.(-beta .* (e12 .- minimum(e12))) + e21 = exp.(-beta .* (e21 .- minimum(e21))) + e22 = exp.(-beta .* (e22 .- minimum(e22))) + sl1, sl2, sr1, sr2 = size(e11, 1), size(e21, 1), size(e21, 2), size(e22, 2) + + if sl1 * sl2 * sr1 * sr2 < max(sr1 * sr2 * min(sl1, sl2), sl1 * sl2 * min(sr1, sr2)) + R = reshape(e11, sl1, 1, sr1, 1) .* reshape(e21, 1, sl2, sr1, 1) + R = R .* reshape(e12, sl1, 1, 1, sr2) + R = R .* reshape(e22, 1, sl2, 1, sr2) + R = reshape(R, sl1 * sl2, sr1 * sr2) * message + elseif sl1 <= sl2 && sr1 <= sr2 + R = reshape(e12, sl1, 1, sr2) .* reshape(message, 1, sr1, sr2) + R = reshape(reshape(R, sl1 * sr1, sr2) * e22', sl1, sr1, sl2) # [l1, r1, l2] + R .*= reshape(e11, sl1, sr1, 1) # [l1, r1, l2] .* [l1, r1, :] + R .*= reshape(e21', 1, sr1, sl2) # [l1, r1, l2] .* [:, r1, l2] + R = reshape(sum(R, dims = 2), sl1 * sl2) + elseif sl1 <= sl2 && sr2 <= sr1 + R = reshape(e11', sr1, sl1, 1) .* reshape(message, sr1, 1, sr2) + R = reshape(e21 * reshape(R, sr1, sl1 * sr2), sl2, sl1, sr2) + R .*= reshape(e12, 1, sl1, sr2) # [l2, l1, r2] .* [:, l1, r2] + R .*= reshape(e22, sl2, 1, sr2) # [l2, l1, r2] .* [l2, :, r2] + R = reshape(reshape(sum(R, dims = 3), sl2, sl1)', sl1 * sl2) + elseif sl2 <= sl1 && sr1 <= sr2 + R = reshape(e22, sl2, 1, sr2) .* reshape(message, 1, sr1, sr2) + R = reshape(reshape(R, sl2 * sr1, sr2) * e12', sl2, sr1, sl1) # [l2, r1, l1] + R .*= reshape(e11', 1, sr1, sl1) # [l2, r1, l1] .* [:, r1, l1] + R .*= reshape(e21, sl2, sr1, 1) # [l2, r1, l1] .* [l2, r1, :] + R = reshape(reshape(sum(R, dims = 2), sl2, sl1)', sl1 * sl2) + else # sl2 <= sl1 && sr2 <= sr1 + R = reshape(e21', sr1, sl2, 1) .* reshape(message, sr1, 1, sr2) + R = reshape(e11 * reshape(R, sr1, sl2 * sr2), sl1, sl2, sr2) + R .*= reshape(e12, sl1, 1, sr2) # [l1, l2, r2] .* [l1, :, r2] + R .*= reshape(e22, 1, sl2, sr2) # [l1, l2, r2] .* [:, l2, r2] + R = reshape(sum(R, dims = 3), sl1 * sl2) + end + R +end + +""" +$(TYPEDSIGNATURES) + +Constructs a clustered Hamiltonian for a given clustered Hamiltonian with a 2-site cluster approximation used in Pegasus graph. + +# Arguments: +- `cl_h::LabelledGraph{S, T}`: The clustered Hamiltonian represented as a labelled graph. +- `beta::Real`: The inverse temperature parameter for the 2-site cluster Hamiltonian construction. + +# Returns: +- `new_cl_h::LabelledGraph{MetaDiGraph}`: A new labelled graph representing the 2-site cluster Hamiltonian. + +This function constructs a clustered Hamiltonian `cl_h` by applying a 2-site cluster approximation. +It combines and merges vertices and edges of the original graph to create a simplified representation of the Hamiltonian. + +The resulting `new_cl_h` graph represents the 2-site cluster Hamiltonian with simplified interactions between clusters. +The energy values, projectors, and spectra associated with the new vertices and edges are computed based on +the provided temperature parameter `beta`. +""" +function clustered_hamiltonian_2site(cl_h::LabelledGraph{S,T}, beta::Real) where {S,T} + + unified_vertices = unique([vertex[1:2] for vertex in vertices(cl_h)]) + new_cl_h = LabelledGraph{MetaDiGraph}(unified_vertices) + new_lp = PoolOfProjectors{Int}() + + vertx = Set() + for v in vertices(cl_h) + i, j, _ = v + if (i, j) ∈ vertx + continue + end + E1 = local_energy(cl_h, (i, j, 1)) + E2 = local_energy(cl_h, (i, j, 2)) + E = energy_2site(cl_h, i, j) .+ reshape(E1, :, 1) .+ reshape(E2, 1, :) + sp = Spectrum(reshape(E, :), [], []) + set_props!(new_cl_h, (i, j), Dict(:spectrum => sp)) + push!(vertx, (i, j)) + end + + edge_states = Set() + for e ∈ edges(cl_h) + if e in edge_states + continue + end + v, w = src(e), dst(e) + v1, v2, _ = v + w1, w2, _ = w + + if (v1, v2) == (w1, w2) + continue + end + + add_edge!(new_cl_h, (v1, v2), (w1, w2)) + + E, pl, pr = merge_vertices_cl_h(cl_h, beta, v, w) + ipl = add_projector!(new_lp, pl) + ipr = add_projector!(new_lp, pr) + set_props!(new_cl_h, (v1, v2), (w1, w2), Dict(:ipl => ipl, :en => E, :ipr => ipr)) + push!(edge_states, sort([(v1, v2), (w1, w2)])) + end + set_props!(new_cl_h, Dict(:pool_of_projectors => new_lp)) + new_cl_h +end + +""" +$(TYPEDSIGNATURES) + +Merge two vertices in a clustered Hamiltonian to create a single merged vertex. + +# Arguments: +- `cl_h::LabelledGraph{S, T}`: The clustered Hamiltonian represented as a labeled graph. +- `β::Real`: The temperature parameter controlling the influence of energy values. +- `node1::NTuple{3, Int64}`: The coordinates of the first vertex to merge. +- `node2::NTuple{3, Int64}`: The coordinates of the second vertex to merge. + +# Returns: +- `merged_energy::MergedEnergy`: An instance of the `MergedEnergy` type representing the merged energy values. +- `pl::AbstractVector`: The merged left projector. +- `pr::AbstractVector`: The merged right projector. + +This function merges two vertices in a clustered Hamiltonian graph `cl_h` to create a single merged vertex. +The merging process combines projectors and energy values associated with the original vertices based on +the provided temperature parameter `β`. + +The merged energy values, left projector `pl`, and right projector `pr` are computed based on the interactions +between the original vertices and their respective projectors. +""" +function merge_vertices_cl_h( + cl_h::LabelledGraph{S,T}, + β::Real, + node1::NTuple{3,Int64}, + node2::NTuple{3,Int64}, +) where {S,T} + i1, j1, _ = node1 + i2, j2, _ = node2 + + p21l = projector(cl_h, (i1, j1, 2), (i2, j2, 1)) + p22l = projector(cl_h, (i1, j1, 2), (i2, j2, 2)) + p12l = projector(cl_h, (i1, j1, 1), (i2, j2, 2)) + p11l = projector(cl_h, (i1, j1, 1), (i2, j2, 1)) + + p1l, (p11l, p12l) = fuse_projectors((p11l, p12l)) + p2l, (p21l, p22l) = fuse_projectors((p21l, p22l)) + + p11r = projector(cl_h, (i2, j2, 1), (i1, j1, 1)) + p21r = projector(cl_h, (i2, j2, 1), (i1, j1, 2)) + p12r = projector(cl_h, (i2, j2, 2), (i1, j1, 1)) + p22r = projector(cl_h, (i2, j2, 2), (i1, j1, 2)) + + p1r, (p11r, p21r) = fuse_projectors((p11r, p21r)) + p2r, (p12r, p22r) = fuse_projectors((p12r, p22r)) + + pl = outer_projector(p1l, p2l) + pr = outer_projector(p1r, p2r) + + e11 = interaction_energy(cl_h, (i1, j1, 1), (i2, j2, 1)) + e12 = interaction_energy(cl_h, (i1, j1, 1), (i2, j2, 2)) + e21 = interaction_energy(cl_h, (i1, j1, 2), (i2, j2, 1)) + e22 = interaction_energy(cl_h, (i1, j1, 2), (i2, j2, 2)) + + e11 = e11[p11l, p11r] + e21 = e21[p21l, p21r] + e12 = e12[p12l, p12r] + e22 = e22[p22l, p22r] + + MergedEnergy(e11, e12, e21, e22), pl, pr +end + +""" +$(TYPEDSIGNATURES) + +Get the local energy associated with a vertex in a clustered Hamiltonian. + +# Arguments: +- `cl_h::LabelledGraph{S, T}`: The clustered Hamiltonian represented as a labeled graph. +- `v::NTuple{3, Int64}`: The coordinates of the vertex for which the local energy is requested. + +# Returns: +- `local_energy::AbstractVector`: An abstract vector containing the local energy values associated with the specified vertex. + +This function retrieves the local energy values associated with a given vertex `v` in a clustered Hamiltonian graph `cl_h`. +If the vertex exists in the graph and has associated energy values, it returns those values; otherwise, it returns a vector of zeros. + +The local energy values are typically obtained from the spectrum associated with the vertex. +""" +function local_energy(cl_h::LabelledGraph{S,T}, v::NTuple{3,Int64}) where {S,T} + has_vertex(cl_h, v) ? get_prop(cl_h, v, :spectrum).energies : zeros(1) +end + +""" +$(TYPEDSIGNATURES) + +Get the interaction energy between two vertices in a clustered Hamiltonian. + +# Arguments: +- `cl_h::LabelledGraph{S, T}`: The clustered Hamiltonian represented as a labeled graph. +- `v::NTuple{3, Int64}`: The coordinates of the first vertex. +- `w::NTuple{3, Int64}`: The coordinates of the second vertex. + +# Returns: +- `interaction_energy::AbstractMatrix`: An abstract matrix containing the interaction energy values between the specified vertices. + +This function retrieves the interaction energy values between two vertices, `v` and `w`, in a clustered Hamiltonian graph `cl_h`. +If there is a directed edge from `w` to `v`, it returns the corresponding energy values; +if there is a directed edge from `v` to `w`, it returns the transpose of the energy values; +otherwise, it returns a matrix of zeros. +The interaction energy values represent the energy associated with the interaction or connection between the two vertices. +""" +function interaction_energy( + cl_h::LabelledGraph{S,T}, + v::NTuple{3,Int64}, + w::NTuple{3,Int64}, +) where {S,T} + if has_edge(cl_h, w, v) + get_prop(cl_h, w, v, :en)' + elseif has_edge(cl_h, v, w) + get_prop(cl_h, v, w, :en) + else + zeros(1, 1) + end +end + +""" +$(TYPEDSIGNATURES) + +Get the projector associated with an edge between two vertices in a clustered Hamiltonian. + +# Arguments: +- `cl_h::LabelledGraph{S, T}`: The clustered Hamiltonian represented as a labeled graph. +- `v::NTuple{N, Int64}`: The coordinates of one of the two vertices connected by the edge. +- `w::NTuple{N, Int64}`: The coordinates of the other vertex connected by the edge. + +# Returns: +- `p::AbstractVector`: An abstract vector representing the projector associated with the specified edge. + +This function retrieves the projector associated with an edge between two vertices, `v` and `w`, +in a clustered Hamiltonian graph `cl_h`. +If there is a directed edge from `w` to `v`, it returns the index of right projector (`:ipr`); +if there is a directed edge from `v` to `w`, it returns the index of left projector (`:ipl`). +If no edge exists between the vertices, it returns a vector of ones. +""" +function projector( + cl_h::LabelledGraph{S,T}, + v::NTuple{N,Int64}, + w::NTuple{N,Int64}, +) where {S,T,N} + if has_edge(cl_h, w, v) + idx_p = get_prop(cl_h, w, v, :ipr) + p = get_projector!(get_prop(cl_h, :pool_of_projectors), idx_p, :CPU) + elseif has_edge(cl_h, v, w) + idx_p = get_prop(cl_h, v, w, :ipl) + p = get_projector!(get_prop(cl_h, :pool_of_projectors), idx_p, :CPU) + else + p = ones( + Int, + v ∈ vertices(cl_h) ? length(get_prop(cl_h, v, :spectrum).energies) : 1, + ) + end +end + +function fuse_projectors(projectors::NTuple{N,K}) where {N,K} + fused, transitions_matrix = rank_reveal(hcat(projectors...), :PE) + transitions = Tuple(Array(t) for t ∈ eachcol(transitions_matrix)) + fused, transitions +end + +function outer_projector(p1::Array{T,1}, p2::Array{T,1}) where {T<:Number} + reshape(reshape(p1, :, 1) .+ maximum(p1) .* reshape(p2 .- 1, 1, :), :) +end + +""" +$(TYPEDSIGNATURES) + +Create a sparse column-compressed (CSC) matrix with specified column indices and values. + +# Arguments: +- `::Type{R}`: The element type of the sparse matrix (e.g., `Float64`, `Int64`). +- `p::Vector{Int64}`: A vector of column indices for the non-zero values. + +# Returns: +- `sparse_matrix::SparseMatrixCSC{R}`: A sparse column-compressed matrix with non-zero values at specified columns. + +This constructor function creates a sparse column-compressed (CSC) matrix of element type `R` based on the provided +column indices `p` and values. The resulting matrix has non-zero values at the specified column indices, while all other elements are zero. +The `SparseCSC` constructor is useful for creating sparse matrices with specific column indices and values efficiently. +""" +function SparseCSC(::Type{R}, p::Vector{Int64}) where {R<:Real} + n = length(p) + mp = maximum(p) + cn = collect(1:n) + co = ones(R, n) + sparse(p, cn, co, mp, n) +end diff --git a/src/clustered_hamiltonian.jl b/src/clustered_hamiltonian.jl new file mode 100644 index 0000000..f5c3f78 --- /dev/null +++ b/src/clustered_hamiltonian.jl @@ -0,0 +1,535 @@ +export clustered_hamiltonian, + rank_reveal, + split_into_clusters, + decode_clustered_hamiltonian_state, + energy, + energy_2site, + cluster_size, + truncate_clustered_hamiltonian, + exact_cond_prob, + bond_energy, + cluster_size + +""" +$(TYPEDSIGNATURES) + +Group spins into clusters based on an assignment rule, mapping clustered Hamiltonian coordinates to groups of spins in the Ising graph. +Dict(clustered Hamiltonian coordinates -> group of spins in Ising graph) + +# Arguments: +- `ig::LabelledGraph{G, L}`: The Ising graph represented as a labeled graph. +- `assignment_rule`: A mapping that assigns Ising graph vertices to clusters based on clustered Hamiltonian coordinates. + +# Returns: +- `clusters::Dict{L, Vertex}`: A dictionary mapping cluster identifiers to representative vertices in the Ising graph. + +This function groups spins in the Ising graph into clusters based on an assignment rule. +The assignment rule defines how clustered Hamiltonian coordinates correspond to clusters of spins in the Ising graph. +Each cluster is represented by a vertex from the Ising graph. + +The `split_into_clusters` function is useful for organizing and analyzing spins in complex spin systems, particularly in the context of clustered Hamiltonian. + +""" +function split_into_clusters(ig::LabelledGraph{G,L}, assignment_rule) where {G,L} + cluster_id_to_verts = Dict(i => L[] for i in values(assignment_rule)) + for v in vertices(ig) + push!(cluster_id_to_verts[assignment_rule[v]], v) + end + Dict(i => first(cluster(ig, verts)) for (i, verts) ∈ cluster_id_to_verts) +end + +""" +$(TYPEDSIGNATURES) + +Create a clustered Hamiltonian. + +This function constructs a clustered Hamiltonian from an Ising graph by introducing a natural order in clustered Hamiltonian coordinates. + +# Arguments: +- `ig::IsingGraph`: The Ising graph representing the spin system. +- `num_states_cl::Int`: The number of states per cluster taken into account when calculating the spectrum. In every cluster the number of states is constant. +- `spectrum::Function`: A function for calculating the spectrum of the clustered Hamiltonian. It can be `full_spectrum` or `brute_force`. +- `cluster_assignment_rule::Dict{Int, L}`: A dictionary specifying the assignment rule that maps Ising graph vertices to clusters. It can be `super_square_lattice`, `pegasus_lattice` or `zephyr_lattice`. + +# Returns: +- `cl_h::LabelledGraph{S, T}`: The clustered Hamiltonian represented as a labelled graph. + +The `clustered_hamiltonian` function takes an Ising graph (`ig`) as input and constructs a clustered Hamiltonian by +introducing a natural order in clustered Hamiltonian coordinates. +It allows you to specify the number of states per cluster, a spectrum calculation function, +and a cluster assignment rule, which maps Ising graph vertices to clusters. +""" +function clustered_hamiltonian( + ig::IsingGraph, + num_states_cl::Int; + spectrum::Function = full_spectrum, + cluster_assignment_rule::Dict{Int,L}, # e.g. square lattice +) where {L} + ns = Dict(i => num_states_cl for i ∈ Set(values(cluster_assignment_rule))) + clustered_hamiltonian( + ig, + ns, + spectrum = spectrum, + cluster_assignment_rule = cluster_assignment_rule, + ) +end + +""" +$(TYPEDSIGNATURES) + +Create a clustered Hamiltonian. + +This function constructs a clustered Hamiltonian from an Ising graph by introducing a natural order in clustered Hamiltonian coordinates. + +# Arguments: +- `ig::IsingGraph`: The Ising graph representing the spin system. +- `num_states_cl::Dict{T, Int}`: A dictionary specifying the number of states per cluster for different clusters. Number of states are considered when calculating the spectrum. +- `spectrum::Function`: A function for calculating the spectrum of the clustered Hamiltonian. It can be `full_spectrum` or `brute_force`. +- `cluster_assignment_rule::Dict{Int, T}`: A dictionary specifying the assignment rule that maps Ising graph vertices to clusters. It can be `super_square_lattice`, `pegasus_lattice` or `zephyr_lattice`. + +# Returns: +- `cl_h::LabelledGraph{MetaDiGraph}`: The clustered Hamiltonian represented as a labelled graph. + +The `clustered_hamiltonian` function takes an Ising graph (`ig`) as input and constructs a clustered Hamiltonian +by introducing a natural order in clustered Hamiltonian coordinates. It allows you to specify the number of +states per cluster which can vary for different clusters, a spectrum calculation function, +and a cluster assignment rule, which maps Ising graph vertices to clusters. + +""" +function clustered_hamiltonian( + ig::IsingGraph, + num_states_cl::Dict{T,Int}; + spectrum::Function = full_spectrum, + cluster_assignment_rule::Dict{Int,T}, +) where {T} + cl_h = LabelledGraph{MetaDiGraph}(sort(unique(values(cluster_assignment_rule)))) + + lp = PoolOfProjectors{Int}() + + for (v, cl) ∈ split_into_clusters(ig, cluster_assignment_rule) + sp = spectrum(cl, num_states = get(num_states_cl, v, basis_size(cl))) + set_props!(cl_h, v, Dict(:cluster => cl, :spectrum => sp)) + end + + for (i, v) ∈ enumerate(vertices(cl_h)), w ∈ vertices(cl_h)[i+1:end] + cl1, cl2 = get_prop(cl_h, v, :cluster), get_prop(cl_h, w, :cluster) + outer_edges, J = inter_cluster_edges(ig, cl1, cl2) + + if !isempty(outer_edges) + ind1 = any(i -> i != 0, J, dims = 2) + ind2 = any(i -> i != 0, J, dims = 1) + ind1 = reshape(ind1, length(ind1)) + ind2 = reshape(ind2, length(ind2)) + JJ = J[ind1, ind2] + + states_v = get_prop(cl_h, v, :spectrum).states + states_w = get_prop(cl_h, w, :spectrum).states + + pl, unique_states_v = rank_reveal([s[ind1] for s ∈ states_v], :PE) + pr, unique_states_w = rank_reveal([s[ind2] for s ∈ states_w], :PE) + en = inter_cluster_energy(unique_states_v, JJ, unique_states_w) + ipl = add_projector!(lp, pl) + ipr = add_projector!(lp, pr) + + add_edge!(cl_h, v, w) + set_props!( + cl_h, + v, + w, + Dict(:outer_edges => outer_edges, :ipl => ipl, :en => en, :ipr => ipr), + ) + end + end + set_props!(cl_h, Dict(:pool_of_projectors => lp)) + cl_h +end + +""" +$(TYPEDSIGNATURES) + +Create a clustered Hamiltonian with optional cluster sizes. + +This function constructs a clustered Hamiltonian from an Ising graph by introducing a natural order in clustered Hamiltonian coordinates. + +# Arguments: +- `ig::IsingGraph`: The Ising graph representing the spin system. +- `spectrum::Function`: A function for calculating the spectrum of the clustered Hamiltonian. It can be `full_spectrum` or `brute_force`. Default is `full_spectrum`. +- `cluster_assignment_rule::Dict{Int, T}`: A dictionary specifying the assignment rule that maps Ising graph vertices to clusters. It can be `super_square_lattice`, `pegasus_lattice` or `zephyr_lattice`. + +# Returns: +- `cl_h::LabelledGraph{MetaDiGraph}`: The clustered Hamiltonian represented as a labelled graph. + +The `clustered_hamiltonian` function takes an Ising graph (`ig`) as input and constructs a clustered Hamiltonian +by introducing a natural order in clustered Hamiltonian coordinates. +You can optionally specify a spectrum calculation function and a cluster assignment rule, which maps Ising graph vertices to clusters. +This version of `clustered_hamiltonian` function does not truncate states in the cluster while calculating the spectrum. +If you want to specify custom cluster sizes, use the alternative version of this function by +passing a `Dict{T, Int}` containing the number of states per cluster as `num_states_cl`. +""" +function clustered_hamiltonian( + ig::IsingGraph; + spectrum::Function = full_spectrum, + cluster_assignment_rule::Dict{Int,T}, +) where {T} + clustered_hamiltonian( + ig, + Dict{T,Int}(), + spectrum = spectrum, + cluster_assignment_rule = cluster_assignment_rule, + ) +end + +# """ +# $(TYPEDSIGNATURES) + +# Reveal ranks and energies in a specified order. + +# This function calculates and reveals the ranks and energies of a set of states in either the +# 'PE' (Projector Energy) or 'EP' (Energy Projector) order. + +# # Arguments: +# - `energy`: The energy values of states. +# - `order::Symbol`: The order in which to reveal the ranks and energies. +# It can be either `:PE` for 'Projector Energy)' order (default) or `:EP` for 'Energy Projector' order. + +# # Returns: +# - If `order` is `:PE`, the function returns a tuple `(P, E)` where: +# - `P`: A permutation matrix representing projectors. +# - `E`: An array of energy values. +# - If `order` is `:EP`, the function returns a tuple `(E, P)` where: +# - `E`: An array of energy values. +# - `P`: A permutation matrix representing projectors. +# """ +# function rank_reveal(energy, order=:PE) #TODO: add type +# @assert order ∈ (:PE, :EP) +# dim = order == :PE ? 1 : 2 +# E, idx = unique_dims(energy, dim) +# P = identity.(idx) +# order == :PE ? (P, E) : (E, P) +# end + +""" +$(TYPEDSIGNATURES) + +TODO: check the order consistency over external packages. + +Decode a clustered Hamiltonian state into Ising graph spin values. + +This function decodes a state from a clustered Hamiltonian into Ising graph spin values and +returns a dictionary mapping each Ising graph vertex to its corresponding spin value. + +# Arguments: +- `cl_h::LabelledGraph{S, T}`: The clustered Hamiltonian represented as a labeled graph. +- `state::Vector{Int}`: The state to be decoded, represented as an array of state indices for each vertex in the clustered Hamiltonian. + +# Returns: +- `spin_values::Dict{Int, Int}`: A dictionary mapping each Ising graph vertex to its corresponding spin value. + +This function assumes that the state has the same order as the vertices in the clustered Hamiltonian. +It decodes the state consistently based on the cluster assignments and spectra of the clustered Hamiltonian. +""" +function decode_clustered_hamiltonian_state( + cl_h::LabelledGraph{S,T}, + state::Vector{Int}, +) where {S,T} + ret = Dict{Int,Int}() + for (i, vert) ∈ zip(state, vertices(cl_h)) + spins = get_prop(cl_h, vert, :cluster).labels + states = get_prop(cl_h, vert, :spectrum).states + if length(states) > 0 + curr_state = states[i] + merge!(ret, Dict(k => v for (k, v) ∈ zip(spins, curr_state))) + end + end + ret +end + +""" +$(TYPEDSIGNATURES) + +Calculate the energy of a clustered Hamiltonian state. + +This function calculates the energy of a given state in a clustered Hamiltonian. +The state is represented as a dictionary mapping each Ising graph vertex to its corresponding spin value. + +# Arguments: +- `cl_h::LabelledGraph{S, T}`: The clustered Hamiltonian represented as a labeled graph. +- `σ::Dict{T, Int}`: A dictionary mapping Ising graph vertices to their spin values. + +# Returns: +- `en_cl_h::Float64`: The energy of the state in the clustered Hamiltonian. + +This function computes the energy by summing the energies associated with individual +clusters and the interaction energies between clusters. +It takes into account the cluster spectra and projectors stored in the clustered Hamiltonian. +""" +function energy(cl_h::LabelledGraph{S,T}, σ::Dict{T,Int}) where {S,T} + en_cl_h = 0.0 + for v ∈ vertices(cl_h) + en_cl_h += get_prop(cl_h, v, :spectrum).energies[σ[v]] + end + for edge ∈ edges(cl_h) + idx_pl = get_prop(cl_h, edge, :ipl) + pl = get_projector!(get_prop(cl_h, :pool_of_projectors), idx_pl, :CPU) + idx_pr = get_prop(cl_h, edge, :ipr) + pr = get_projector!(get_prop(cl_h, :pool_of_projectors), idx_pr, :CPU) + en = get_prop(cl_h, edge, :en) + en_cl_h += en[pl[σ[src(edge)]], pr[σ[dst(edge)]]] + end + en_cl_h +end + +""" +$(TYPEDSIGNATURES) + +Calculate the interaction energy between two nodes in a clustered Hamiltonian. + +This function computes the interaction energy between two specified nodes in a clustered Hamiltonian, represented as a labeled graph. + +# Arguments: +- `cl_h::LabelledGraph{S, T}`: The clustered Hamiltonian represented as a labeled graph. +- `i::Int`: The index of the first site. +- `j::Int`: The index of the second site. + +# Returns: +- `int_eng::AbstractMatrix{T}`: The interaction energy matrix between the specified sites. + +The function checks if there is an interaction edge between the two sites (i, j) in both directions (i -> j and j -> i). +If such edges exist, it retrieves the interaction energy matrix, projectors, and calculates the interaction energy. +If no interaction edge is found, it returns a zero matrix. +""" +function energy_2site(cl_h::LabelledGraph{S,T}, i::Int, j::Int) where {S,T} + # matrix of interaction energies between two nodes + if has_edge(cl_h, (i, j, 1), (i, j, 2)) + en12 = copy(get_prop(cl_h, (i, j, 1), (i, j, 2), :en)) + idx_pl = get_prop(cl_h, (i, j, 1), (i, j, 2), :ipl) + pl = copy(get_projector!(get_prop(cl_h, :pool_of_projectors), idx_pl, :CPU)) + idx_pr = get_prop(cl_h, (i, j, 1), (i, j, 2), :ipr) + pr = copy(get_projector!(get_prop(cl_h, :pool_of_projectors), idx_pr, :CPU)) + int_eng = en12[pl, pr] + elseif has_edge(cl_h, (i, j, 2), (i, j, 1)) + en21 = copy(get_prop(cl_h, (i, j, 2), (i, j, 1), :en)) + idx_pl = get_prop(cl_h, (i, j, 2), (i, j, 1), :ipl) + pl = copy(get_projector!(get_prop(cl_h, :pool_of_projectors), idx_pl, :CPU)) + idx_pr = get_prop(cl_h, (i, j, 2), (i, j, 1), :ipr) + pr = copy(get_projector!(get_prop(cl_h, :pool_of_projectors), idx_pr, :CPU)) + int_eng = en21[pl, pr]' + else + int_eng = zeros(1, 1) + end + int_eng +end + +""" +$(TYPEDSIGNATURES) + +Calculate the bond energy between two clusters in a clustered Hamiltonian. + +This function computes the bond energy between two specified clusters (cluster nodes) in a clustered Hamiltonian, represented as a labeled graph. + +# Arguments: +- `cl_h::LabelledGraph{S, T}`: The clustered Hamiltonian represented as a labeled graph. +- `cl_h_u::NTuple{N, Int64}`: The coordinates of the first cluster. +- `cl_h_v::NTuple{N, Int64}`: The coordinates of the second cluster. +- `σ::Int`: Index for which the bond energy is calculated. + +# Returns: +- `energies::AbstractVector{T}`: The bond energy vector between the two clusters for the specified index. + +The function checks if there is an edge between the two clusters (u -> v and v -> u). +If such edges exist, it retrieves the bond energy matrix and projectors and calculates the bond energy. +If no bond edge is found, it returns a zero vector. +""" +function bond_energy( + cl_h::LabelledGraph{S,T}, + cl_h_u::NTuple{N,Int64}, + cl_h_v::NTuple{N,Int64}, + σ::Int, +) where {S,T,N} + if has_edge(cl_h, cl_h_u, cl_h_v) + ipu, en, ipv = get_prop.(Ref(cl_h), Ref(cl_h_u), Ref(cl_h_v), (:ipl, :en, :ipr)) + pu = get_projector!(get_prop(cl_h, :pool_of_projectors), ipu, :CPU) + pv = get_projector!(get_prop(cl_h, :pool_of_projectors), ipv, :CPU) + @inbounds energies = en[pu, pv[σ]] + elseif has_edge(cl_h, cl_h_v, cl_h_u) + ipv, en, ipu = get_prop.(Ref(cl_h), Ref(cl_h_v), Ref(cl_h_u), (:ipl, :en, :ipr)) + pu = get_projector!(get_prop(cl_h, :pool_of_projectors), ipu, :CPU) + pv = get_projector!(get_prop(cl_h, :pool_of_projectors), ipv, :CPU) + @inbounds energies = en[pv[σ], pu] + else + energies = zeros(cluster_size(cl_h, cl_h_u)) + end +end + +""" +$(TYPEDSIGNATURES) + +Get the size of a cluster in a clustered Hamiltonian. + +This function returns the size (number of states) of a cluster in a clustered Hamiltonian, represented as a labeled graph. + +# Arguments: +- `clustered_hamiltonian::LabelledGraph{S, T}`: The clustered Hamiltonian represented as a labeled graph. +- `vertex::T`: The vertex (cluster) for which the size is to be determined. + +# Returns: +- `size::Int`: The number of states in the specified cluster. + +The function retrieves the spectrum associated with the specified cluster and returns the length of the energy vector in that spectrum. +""" +function cluster_size(clustered_hamiltonian::LabelledGraph{S,T}, vertex::T) where {S,T} + length(get_prop(clustered_hamiltonian, vertex, :spectrum).energies) +end + +""" +$(TYPEDSIGNATURES) + +Calculate the exact conditional probability of a target state in a clustered Hamiltonian. + +This function computes the exact conditional probability of a specified target state in a clustered Hamiltonian, represented as a labelled graph. + +# Arguments: +- `clustered_hamiltonian::LabelledGraph{S, T}`: The clustered Hamiltonian represented as a labeled graph. +- `beta`: The inverse temperature parameter. +- `target_state::Dict`: A dictionary specifying the target state as a mapping of cluster vertices to Ising spin values. + +# Returns: +- `prob::Float64`: The exact conditional probability of the target state. + +The function generates all possible states for the clusters in the clustered Hamiltonian, +calculates their energies, and computes the probability distribution based on the given inverse temperature parameter. +It then calculates the conditional probability of the specified target state by summing the probabilities of states that match the target state. +""" +function exact_cond_prob( + clustered_hamiltonian::LabelledGraph{S,T}, + beta, + target_state::Dict, +) where {S,T} + # TODO: Not going to work without PoolOfProjectors + ver = vertices(clustered_hamiltonian) + rank = cluster_size.(Ref(clustered_hamiltonian), ver) + states = [Dict(ver .=> σ) for σ ∈ Iterators.product([1:r for r ∈ rank]...)] + energies = SpinGlassNetworks.energy.(Ref(clustered_hamiltonian), states) + prob = exp.(-beta .* energies) + prob ./= sum(prob) + sum(prob[findall([all(s[k] == v for (k, v) ∈ target_state) for s ∈ states])]) +end + +""" +$(TYPEDSIGNATURES) + +Truncate a clustered Hamiltonian based on specified states. + +This function truncates a given clustered Hamiltonian by selecting a subset of states for each cluster based on the provided `states` dictionary. +The resulting truncated Hamiltonian contains only the selected states for each cluster. + +# Arguments: +- `cl_h::LabelledGraph{S, T}`: The clustered Hamiltonian represented as a labeled graph. +- `states::Dict`: A dictionary specifying the states to be retained for each cluster. + +# Returns: +- `new_cl_h::LabelledGraph{MetaDiGraph}`: The truncated clustered Hamiltonian with reduced states. + +The function creates a new clustered Hamiltonian `new_cl_h` with the same structure as the input `cl_h`. +It then updates the spectrum of each cluster in `new_cl_h` by selecting the specified states from the original spectrum. +Additionally, it updates the interactions and projectors between clusters based on the retained states. +The resulting `new_cl_h` represents a truncated version of the original Hamiltonian. +""" +function truncate_clustered_hamiltonian(cl_h::LabelledGraph{S,T}, states::Dict) where {S,T} + + new_cl_h = LabelledGraph{MetaDiGraph}(vertices(cl_h)) + new_lp = PoolOfProjectors{Int}() + + for v ∈ vertices(new_cl_h) + cl = get_prop(cl_h, v, :cluster) + sp = get_prop(cl_h, v, :spectrum) + if sp.states == Vector{Int64}[] + sp = Spectrum(sp.energies[states[v]], sp.states, [1]) + else + sp = Spectrum(sp.energies[states[v]], sp.states[states[v]]) + end + set_props!(new_cl_h, v, Dict(:cluster => cl, :spectrum => sp)) + end + + for e ∈ edges(cl_h) + v, w = src(e), dst(e) + add_edge!(new_cl_h, v, w) + outer_edges = get_prop(cl_h, v, w, :outer_edges) + ipl = get_prop(cl_h, v, w, :ipl) + pl = get_projector!(get_prop(cl_h, :pool_of_projectors), ipl, :CPU) + ipr = get_prop(cl_h, v, w, :ipr) + pr = get_projector!(get_prop(cl_h, :pool_of_projectors), ipr, :CPU) + en = get_prop(cl_h, v, w, :en) + pl = pl[states[v]] + pr = pr[states[w]] + pl_transition, pl_unique = rank_reveal(pl, :PE) + pr_transition, pr_unique = rank_reveal(pr, :PE) + en = en[pl_unique, pr_unique] + ipl = add_projector!(new_lp, pl_transition) + ipr = add_projector!(new_lp, pr_transition) + set_props!( + new_cl_h, + v, + w, + Dict(:outer_edges => outer_edges, :ipl => ipl, :en => en, :ipr => ipr), + ) + end + set_props!(new_cl_h, Dict(:pool_of_projectors => new_lp)) + + new_cl_h +end + +function clustered_hamiltonian( + fname::String, + Nx::Union{Integer,Nothing} = nothing, + Ny::Union{Integer,Nothing} = nothing, +) + + loaded_rmf = load_openGM(fname, Nx, Ny) + functions = loaded_rmf["fun"] + factors = loaded_rmf["fac"] + N = loaded_rmf["N"] + + X, Y = loaded_rmf["Nx"], loaded_rmf["Ny"] + + clusters = super_square_lattice((X, Y, 1)) + cl_h = LabelledGraph{MetaDiGraph}(sort(collect(values(clusters)))) + lp = PoolOfProjectors{Int}() + for v ∈ cl_h.labels + set_props!(cl_h, v, Dict(:cluster => v)) + end + for (index, value) in factors + if length(index) == 2 + y, x = index + Eng = functions[value]' + sp = Spectrum(collect(Eng), Vector{Vector{Int}}[], zeros(Int, N[y+1, x+1])) + set_props!(cl_h, (x + 1, y + 1), Dict(:spectrum => sp)) + elseif length(index) == 4 + y1, x1, y2, x2 = index + add_edge!(cl_h, (x1 + 1, y1 + 1), (x2 + 1, y2 + 1)) + Eng = functions[value] + ipl = add_projector!(lp, collect(1:N[y1+1, x1+1])) + ipr = add_projector!(lp, collect(1:N[y2+1, x2+1])) + set_props!( + cl_h, + (x1 + 1, y1 + 1), + (x2 + 1, y2 + 1), + Dict( + :outer_edges => ((x1 + 1, y1 + 1), (x2 + 1, y2 + 1)), + :en => Eng, + :ipl => ipl, + :ipr => ipr, + ), + ) + else + throw( + ErrorException( + "Something is wrong with factor index, it has length $(length(index))", + ), + ) + end + end + + set_props!(cl_h, Dict(:pool_of_projectors => lp, :Nx => X, :Ny => Y)) + cl_h +end diff --git a/src/factor.jl b/src/factor.jl deleted file mode 100644 index c52bf24..0000000 --- a/src/factor.jl +++ /dev/null @@ -1,148 +0,0 @@ -export factor_graph, rank_reveal, projectors, split_into_clusters, decode_factor_graph_state - - -function split_into_clusters(ig::LabelledGraph{S,T}, assignment_rule) where {S,T} - cluster_id_to_verts = Dict(i => T[] for i in values(assignment_rule)) - - for v in vertices(ig) - push!(cluster_id_to_verts[assignment_rule[v]], v) - end - - Dict(i => first(cluster(ig, verts)) for (i, verts) ∈ cluster_id_to_verts) -end - - -function factor_graph( - ig::IsingGraph, - num_states_cl::Int; - spectrum::Function = full_spectrum, - cluster_assignment_rule::Dict{Int,T}, # e.g. square lattice -) where {T} - ns = Dict(i => num_states_cl for i ∈ Set(values(cluster_assignment_rule))) - factor_graph( - ig, - ns, - spectrum = spectrum, - cluster_assignment_rule = cluster_assignment_rule, - ) -end - -""" - factor_graph( - ig::IsingGraph, - num_states_cl::Dict; - spectrum::Function = full_spectrum, - cluster_assignment_rule::Dict) where {T} - -Constructs a factor graph representation of the given `IsingGraph`. - -# Arguments -- `ig::IsingGraph`: The Ising graph to convert to a factor graph. -- `num_states_cl::Dict` : A dictionary mapping each cluster to the number of states it can take on. If given empty dictionary - function will try to interfere number of states for each cluster. Can be also given as `Int`, then each cluster will have - specified number of states. If ommited completly, function will behave as if an empy directory was passed. -- `spectrum::Function`: A function that computes the spectrum (i.e., list of energies of all possible states) of - a given cluster. The default is `full_spectrum`, which computes the spectrum exactly. -- `cluster_assignment_rule::Dict`: A dictionary that assigns each vertex of the Ising graph to a cluster. - -# Output -A `LabelledGraph` that represents the factor graph of the Ising graph. -""" - function factor_graph( - ig::IsingGraph, - num_states_cl::Dict{T,Int}; - spectrum::Function = full_spectrum, - cluster_assignment_rule::Dict{Int,T}, # e.g. square lattice -) where {T} - L = maximum(values(cluster_assignment_rule)) - fg = LabelledGraph{MetaDiGraph}(sort(unique(values(cluster_assignment_rule)))) - - for (v, cl) ∈ split_into_clusters(ig, cluster_assignment_rule) - sp = spectrum(cl, num_states = get(num_states_cl, v, basis_size(cl))) - set_props!(fg, v, Dict(:cluster => cl, :spectrum => sp)) - end - - for (i, v) ∈ enumerate(vertices(fg)), w ∈ vertices(fg)[i+1:end] - cl1, cl2 = get_prop(fg, v, :cluster), get_prop(fg, w, :cluster) - - outer_edges, J = inter_cluster_edges(ig, cl1, cl2) - - if !isempty(outer_edges) - en = inter_cluster_energy( - get_prop(fg, v, :spectrum).states, - J, - get_prop(fg, w, :spectrum).states, - ) - pl, en = rank_reveal(en, :PE) - en, pr = rank_reveal(en, :EP) - add_edge!(fg, v, w) - set_props!( - fg, - v, - w, - Dict(:outer_edges => outer_edges, :pl => pl, :en => en, :pr => pr), - ) - end - end - fg -end - - -function factor_graph( - ig::IsingGraph; - spectrum::Function = full_spectrum, - cluster_assignment_rule::Dict{Int,T}, -) where {T} - factor_graph( - ig, - Dict{T,Int}(), - spectrum = spectrum, - cluster_assignment_rule = cluster_assignment_rule, - ) -end - -""" - rank_reveal(energy, order = :PE) - -calculate the rank of the matrix represented by `energy`, and returns a tuple of two matrices `P` and `E`, -where `P` is a binary matrix that reveals the rank of `energy`, and `E` is the set of non-zero singular -values of `energy` in decreasing order. Argument `order=:PE` specifies the order of the output matrices. -The default is `:PE`, which means that the `P` matrix is returned first, followed by the `E` matrix. -If `order` is set to `:EP`, the order is reversed. -""" -function rank_reveal(energy, order = :PE) - @assert order ∈ (:PE, :EP) - dim = order == :PE ? 1 : 2 - - E, idx = unique_dims(energy, dim) - - if order == :PE - P = zeros(size(energy, 1), size(E, 1)) - else - P = zeros(size(E, 2), size(energy, 2)) - end - - for (i, elements) ∈ enumerate(eachslice(P, dims = dim)) - elements[idx[i]] = 1 - end - - order == :PE ? (P, E) : (E, P) -end - -""" - decode_factor_graph_state(fg, state::Vector{Int}) - -Convert factor graphs clusters states into state of the original Ising system. -""" -function decode_factor_graph_state(fg, state::Vector{Int}) - ret = Dict{Int,Int}() - for (i, vert) ∈ zip(state, vertices(fg)) - spins = get_prop(fg, vert, :cluster).labels - states = get_prop(fg, vert, :spectrum).states - if length(states) > 0 - curr_state = states[i] - merge!(ret, Dict(k => v for (k, v) ∈ zip(spins, curr_state))) - end - end - ret -end diff --git a/src/ising.jl b/src/ising.jl index 17fb1f4..7042138 100644 --- a/src/ising.jl +++ b/src/ising.jl @@ -1,62 +1,65 @@ using LabelledGraphs export ising_graph, - rank_vec, cluster, rank, nodes, basis_size, biases, couplings, IsingGraph, prune + rank_vec, + cluster, + rank, + nodes, + basis_size, + biases, + couplings, + IsingGraph, + prune, + inter_cluster_edges const Instance = Union{String,Dict} +const IsingGraph{T} = LabelledGraph{MetaGraph{Int,T}} - -unique_nodes(ising_tuples) = +function unique_nodes(ising_tuples) sort(collect(Set(Iterators.flatten((i, j) for (i, j, _) ∈ ising_tuples)))) - - - -const IsingGraph = LabelledGraph{MetaGraph{Int64,Float64},Int64} +end """ ising_graph(instance, sgn::Number = 1.0, rank_override::Dict{Int,Int} = Dict{Int,Int}()) -Create an Ising model graph from an input `instance``. +Create an Ising graph from interaction data. -# Arguments -- `instance`: Instance of the Ising spin glass system. Can be given eiter as dictionary or a CSV file. -- `sgn::Number`: decides the used convention for the energy sum -- `rank_override`: Dict{Int,Int} +This function creates an Ising graph (LabelledGraph) from interaction data provided in the form of an `inst` argument. +The Ising graph represents a system of spins, where each spin is associated with a vertex, +and interactions between spins are represented as edges with corresponding weights. -# Output -A `LabelledGraph` that represents inputed Ising instance. +# Arguments: +- `::Type{T}`: The type of the edge weights, typically `Float64` or `Float32`. +- `inst::Instance`: Interaction data, which can be either a file path to a CSV file or a collection of triples `(i, j, J)` representing interactions between spins, where `i` and `j` are spin indices, and `J` is the interaction strength. +- `scale::Real`: The scale factor establishes the convention in the Hamiltonian (default is 1). +- `rank_override::Dict`: A dictionary specifying the rank (number of states) for each vertex. If not provided, a default rank of 2 is used for all vertices. -# Example -```@example -instance = Dict((1, 1) => 0.5, (2, 2) => 0.75, (3, 3) => -0.25, (1, 2) => -1.0, (2, 3) => 1.0) +# Returns: +- `ig::IsingGraph{T}`: The Ising graph (LabelledGraph) representing the spin system. -ig = ising_graph(instance) -``` +The function reads interaction data and constructs an Ising graph `ig`. +It assigns interaction strengths to edges between spins and optionally scales them by the `scale` factor. 'Scale' option allows for the change of convention in the Hamiltonian. +The `rank_override` dictionary can be used to specify the rank (number of states) for individual vertices, allowing customization of the Ising model. +Convention: H = scale * sum_{i, j} (J_{ij} * s_i * s_j + J_{ii} * s_i) """ function ising_graph( - instance::Instance; - sgn::Number = 1.0, - rank_override::Dict{Int,Int} = Dict{Int,Int}(), -) - # load the Ising instance - if instance isa String - ising = CSV.File(instance, types = [Int, Int, Float64], header = 0, comment = "#") + ::Type{T}, + inst::Instance; + scale::Real = 1, + rank_override::Dict = Dict{Int,Int}(), +) where {T} + if inst isa String + ising = CSV.File(inst, types = [Int, Int, T], header = 0, comment = "#") else - ising = [(i, j, J) for ((i, j), J) ∈ instance] + ising = [(i, j, T(J)) for ((i, j), J) ∈ inst] end + ig = IsingGraph{T}(unique_nodes(ising)) - nodes = unique_nodes(ising) - L = length(nodes) - nodes_to_vertices = Dict(w => i for (i, w) ∈ enumerate(nodes)) - - ig = LabelledGraph{MetaGraph}(nodes) - - set_prop!.(Ref(ig), vertices(ig), :h, 0) + set_prop!.(Ref(ig), vertices(ig), :h, zero(T)) foreach(v -> set_prop!(ig, v, :rank, get(rank_override, v, 2)), vertices(ig)) for (i, j, v) ∈ ising - v *= sgn - + v *= T(scale) if i == j set_prop!(ig, i, :h, v) else @@ -64,55 +67,76 @@ function ising_graph( set_prop!(ig, i, j, :J, v) end end - - - set_prop!(ig, :rank, Dict{Int,Int}(v => get(rank_override, v, 2) for v in vertices(ig))) - + set_prop!(ig, :rank, Dict(v => get(rank_override, v, 2) for v in vertices(ig))) ig end -""" - rank_vec(ising_graph) +function ising_graph(inst::Instance; scale::Real = 1, rank_override::Dict = Dict{Int,Int}()) + ising_graph(Float64, inst; scale = scale, rank_override = rank_override) +end +Base.eltype(ig::IsingGraph{T}) where {T} = T -Return vector of 'ranks' of nodes. In this context rank tels us how many spins values node can have. -""" rank_vec(ig::IsingGraph) = Int[get_prop((ig), v, :rank) for v ∈ vertices(ig)] -basis_size(ig::IsingGraph) = prod(prod(rank_vec(ig))) +basis_size(ig::IsingGraph) = prod(rank_vec(ig)) +biases(ig::IsingGraph) = get_prop.(Ref(ig), vertices(ig), :h) """ - biases(ising_graph) +$(TYPEDSIGNATURES) -Return vector of biases of ising model defined by given 'ising_graph'. -""" -biases(ig::IsingGraph) = get_prop.(Ref(ig), vertices(ig), :h) +Return the coupling strengths between vertices of an Ising graph. -""" - couplings(ising_graph) +This function computes and returns the coupling strengths (interaction energies) between pairs of vertices in an Ising graph `ig`. +The coupling strengths are represented as a matrix, where each element `(i, j)` corresponds to the interaction energy between vertex `i` and vertex `j`. + +# Arguments: +- `ig::IsingGraph{T}`: The Ising graph representing a system of spins with associated interaction strengths. + +# Returns: +- `J::Matrix{T}`: A matrix of coupling strengths between vertices of the Ising graph. -Return couplings of ising model defined by given 'ising_graph'. -The couplings are presented as an uper-triangular matrix +The function iterates over the edges of the Ising graph and extracts the interaction strengths associated with each edge, populating the `J` matrix accordingly. """ -function couplings(ig::IsingGraph) - J = zeros(nv(ig), nv(ig)) - for edge in edges(ig) - i, j = ig.reverse_label_map[src(edge)], ig.reverse_label_map[dst(edge)] - J[i, j] = get_prop(ig, edge, :J) +function couplings(ig::IsingGraph{T}) where {T} + J = zeros(T, nv(ig), nv(ig)) + for edge ∈ edges(ig) + i = ig.reverse_label_map[src(edge)] + j = ig.reverse_label_map[dst(edge)] + @inbounds J[i, j] = get_prop(ig, edge, :J) end J end +cluster(ig::IsingGraph, verts) = induced_subgraph(ig, collect(verts)) """ - cluster(ig::IsingGraph, verts) +$(TYPEDSIGNATURES) -Return cluster specified by `verts` -""" -cluster(ig::IsingGraph, verts) = induced_subgraph(ig, collect(verts)) +Return the dense adjacency matrix between clusters of vertices in an Ising graph. + +This function computes and returns the dense adjacency matrix `J` between clusters of vertices represented by two +Ising graphs, `cl1` and `cl2`, within the context of the larger Ising graph `ig`. +The adjacency matrix represents the interaction strengths between clusters of vertices, +where each element `(i, j)` corresponds to the interaction strength between cluster `i` in `cl1` and cluster `j` in `cl2`. + +# Arguments: +- `ig::IsingGraph{T}`: The Ising graph representing a system of spins with associated interaction strengths. +- `cl1::IsingGraph{T}`: The first Ising graph representing one cluster of vertices. +- `cl2::IsingGraph{T}`: The second Ising graph representing another cluster of vertices. + +# Returns: +- `outer_edges::Vector{LabelledEdge}`: A vector of labeled edges representing the interactions between clusters. +- `J::Matrix{T}`: A dense adjacency matrix representing interaction strengths between clusters. -function inter_cluster_edges(ig::IsingGraph, cl1::IsingGraph, cl2::IsingGraph) +The function first identifies the outer edges that connect vertices between the two clusters in the context of the larger Ising graph `ig`. +It then computes the interaction strengths associated with these outer edges and populates the dense adjacency matrix `J` accordingly. +""" +function inter_cluster_edges( + ig::IsingGraph{T}, + cl1::IsingGraph{T}, + cl2::IsingGraph{T}, +) where {T} outer_edges = [LabelledEdge(i, j) for i ∈ vertices(cl1), j ∈ vertices(cl2) if has_edge(ig, i, j)] - - J = zeros(nv(cl1), nv(cl2)) + J = zeros(T, nv(cl1), nv(cl2)) for e ∈ outer_edges i, j = cl1.reverse_label_map[src(e)], cl2.reverse_label_map[dst(e)] @inbounds J[i, j] = get_prop(ig, e, :J) @@ -120,23 +144,34 @@ function inter_cluster_edges(ig::IsingGraph, cl1::IsingGraph, cl2::IsingGraph) outer_edges, J end - """ - prune(ising_graph) +$(TYPEDSIGNATURES) + +Used only in MPS_search, would be obsolete if MPS_search uses QMps. +Remove non-existing spins from an Ising graph. -Return copy of 'ising_graph' with removed isolated vertices. +This function removes non-existing spins from the given Ising graph `ig`. +Non-existing spins are those that have zero degree (no connections to other spins) and also have an external +magnetic field (`h`) that is not approximately equal to zero within the specified tolerance `atol`. + +# Arguments: +- `ig::IsingGraph`: The Ising graph to be pruned. +- `atol::Real`: The tolerance for considering the external magnetic field as zero. The default value is `1e-14`. + +# Returns: +- `pruned_graph::IsingGraph`: A new Ising graph with non-existing spins removed. + +The function returns a pruned version of the input Ising graph, where non-existing spins and their associated properties are removed. """ -function prune(ig::IsingGraph) +function prune(ig::IsingGraph; atol::Real = 1e-14) to_keep = vcat( findall(!iszero, degree(ig)), findall( x -> - iszero(degree(ig, x)) && - !isapprox(get_prop(ig, x, :h), 0, atol = 1e-14), + iszero(degree(ig, x)) && !isapprox(get_prop(ig, x, :h), 0, atol = atol), vertices(ig), ), ) - gg = ig[ig.labels[to_keep]] labels = collect(vertices(gg.inner_graph)) reverse_label_map = Dict(i => i for i = 1:nv(gg.inner_graph)) diff --git a/src/lattice.jl b/src/lattice.jl index 29a4211..1b90ff3 100644 --- a/src/lattice.jl +++ b/src/lattice.jl @@ -1,21 +1,297 @@ -export super_square_lattice +export super_square_lattice, + pegasus_lattice, + pegasus_lattice_masoud, + pegasus_lattice_tomek, + j_function, + zephyr_lattice, + zephyr_lattice_5tuple, + zephyr_lattice_5tuple_rotated, + periodic_lattice +""" +$(TYPEDSIGNATURES) + +Create a mapping from Ising graph coordinates to a super square lattice arrangement. +Variable number of Ising graph -> cluster hamiltonian coordinate system + +This function generates a mapping that relates Ising graph coordinates to a super square lattice arrangement. +The super square lattice is defined by the size of five dimensions: `(m, um, n, un, t)`, +where m is the number of columns, n is the number of rows and t denotes the number of spins stored in the cluster. + +# Arguments: +- `size::NTuple{5, Int}`: A tuple specifying the size of the super square lattice in five dimensions: `(m, um, n, un, t)`. + +# Returns: +- `coord_map::Dict`: A dictionary that maps Ising graph coordinates to the corresponding lattice coordinates. + +The `size` tuple represents the dimensions of the super square lattice. The function creates a dictionary where +ising graph coordinates are associated with their corresponding lattice coordinates. +""" function super_square_lattice(size::NTuple{5,Int}) m, um, n, un, t = size old = LinearIndices((1:t, 1:un, 1:n, 1:um, 1:m)) - Dict(old[k, uj, j, ui, i] => (i, j) for i = 1:m, ui = 1:um, j = 1:n, uj = 1:un, k = 1:t) end """ - super_square_lattice(size::NTuple{3,Int}) +$(TYPEDSIGNATURES) -Create cluster assignment rule for Chimera architecture. The input is asumed to have form `(m, n, t)` where `m` is number of -columns, `n` number of rows and `t` is the size of the shore within each Chimera tile -([details](https://docs.ocean.dwavesys.com/en/stable/docs_dnx/reference/generated/dwave_networkx.chimera_graph.html)). -If `t=1` then this assigment rule can be used for square lattice, where every site will form its own unit cell. +Create a mapping from Ising graph coordinates to a simplified super square lattice arrangement. + +This function generates a mapping that relates Ising graph coordinates to a simplified super square lattice arrangement. +The simplified super square lattice is defined by the size of three dimensions: `(m, n, t)`, where m is the number of columns, +n is the number of rows and t denotes the number of spins stored in the cluster. + +# Arguments: +- `size::NTuple{3, Int}`: A tuple specifying the size of the simplified super square lattice in three dimensions: `(m, n, t)`, where `m` is number of columns, `n` number of rows and `t` denotes numberr of spins in cluster. + +# Returns: +- `coord_map::Dict`: A dictionary that maps Ising graph coordinates to the corresponding lattice coordinates. + +The `size` tuple represents the dimensions of the simplified super square lattice. +The function internally adds the required dimensions `(1, 1)` to make it compatible with the `super_square_lattice` function, which deals with five dimensions. """ function super_square_lattice(size::NTuple{3,Int}) m, n, t = size super_square_lattice((m, 1, n, 1, t)) end + +pegasus_lattice(size::NTuple{2,Int}) = pegasus_lattice((size[1], size[2], 3)) + +""" +$(TYPEDSIGNATURES) + +Create a mapping from Ising graph coordinates to Pegasus lattice coordinates. + +This function generates a mapping that relates Ising graph coordinates to Pegasus lattice coordinates +based on the specified size of the Pegasus lattice in three dimensions: `(m, n, t)`. + +# Arguments: +- `size::NTuple{3, Int}`: A tuple specifying the size of the Pegasus lattice in three dimensions: `(m, n, t)`, where `m` is number of columns, `n` number of rows and `t` denotes number of spins in cluster. Convention: `t` is already divided by 8, so `t`=3 for Pegasus lattice. + +# Returns: +- `coord_map::Dict`: A dictionary that maps Ising graph coordinates to the corresponding Pegasus lattice coordinates. + +The `pegasus_lattice` allows you to build the graph relevant for D-Wave Pegasus architecture. +""" +function pegasus_lattice(size::NTuple{3,Int}) + m, n, t = size + old = LinearIndices((1:8*t, 1:n, 1:m)) + map = Dict( + old[k, j, i] => (i, j, 1) for i = 1:m, j = 1:n, + k ∈ (p * 8 + q for p ∈ 0:t-1, q ∈ 1:4) + ) + for i = 1:m, j = 1:n, k ∈ (p * 8 + q for p ∈ 0:t-1, q ∈ 5:8) + push!(map, old[k, j, i] => (i, j, 2)) + end + map +end + +# TODO masoud / tomek should be removed from function names +function pegasus_lattice_alternative(size::NTuple{3,Int}) + m, n, t = size + old = LinearIndices((1:8*t, 1:n, 1:m)) + map = Dict( + old[k, j, i] => (i, j, 2) for i = 1:m, j = 1:n, + k ∈ (p * 8 + q for p ∈ 0:t-1, q ∈ 1:4) + ) + for i = 1:m, j = 1:n, k ∈ (p * 8 + q for p ∈ 0:t-1, q ∈ 5:8) + push!(map, old[k, j, i] => (i, j, 1)) + end + map +end + +function pegasus_lattice_old_numering(size::NTuple{3,Int}) + m, n, t = size + old = LinearIndices((1:8*t, 1:n, 1:m)) + map = Dict( + old[k, j, i] => (i, n - j + 1, 2) for i = 1:m, j = 1:n, + k ∈ (p * 8 + q for p ∈ 0:t-1, q ∈ 1:4) + ) + for i = 1:m, j = 1:n, k ∈ (p * 8 + q for p ∈ 0:t-1, q ∈ 5:8) + push!(map, old[k, j, i] => (i, n - j + 1, 1)) + end + map +end + +function zephyr_lattice_z1(size::NTuple{3,Int}) + m, n, t = size # t is identical to dwave (Tile parameter for the Zephyr lattice) + map = Dict{Int,NTuple{3,Int}}() + for i = 1:2*n, j ∈ 1:2*m + for p in p_func(i, j, t, n, m) + push!( + map, + (i - 1) * (2 * n * t) + + (j - 1) * (2 * m * t) + + p * n + + (i - 1) * (j % 2) + + 1 => (i, j, 1), + ) + end + + for q ∈ q_func(i, j, t, n, m) + push!( + map, + 2 * t * (2 * n + 1) + + (i - 1) * (2 * n * t) + + (j % 2) * (2 * m * t) + + q * m + + (j - 1) * (i - 1) + + 1 => (i, j, 2), + ) + end + end + map +end + +function j_function(i::Int, n::Int) + i ∈ collect(1:n) && return collect((n+1-i):(n+i)) + collect((i-n):(3*n+1-i)) +end + +zephyr_lattice(size::NTuple{2,Int}) = zephyr_lattice((size[1], size[2], 4)) + +""" +$(TYPEDSIGNATURES) + +Create a mapping from Ising graph coordinates to Zephyr lattice coordinates. + +This function generates a mapping that relates Ising graph coordinates to Zephyr lattice +coordinates based on the specified size of the Zephyr lattice in three dimensions: `(m, n, t)`. + +# Arguments: +- `size::NTuple{3, Int}`: A tuple specifying the size of the Zephyr lattice in three dimensions: `(m, n, t)`, where `m` is double number of columns, `n` double number of rows and `t` denotes number of spins in cluster. Convention: `t` is already divided by 4, so `t`=4 for Zephyr lattice. E.g. to create 3x3x16 Zephyr lattice, you should use `m`=6, `n`=6, `t`=4. + +# Returns: +- `coord_map::Dict`: A dictionary that maps Ising graph coordinates to the corresponding Zephyr lattice coordinates. + +The `zephyr_lattice` allows you to build the graph relevant for D-Wave Zephyr architecture. +""" +function zephyr_lattice(size::NTuple{3,Int}) + m, n, t = size + zephyr_lattice_5tuple_rotated( + m + 1, + n + 1, + zephyr_lattice_5tuple((Int(m / 2), Int(n / 2), t)), + ) +end + +function zephyr_lattice_5tuple(size::NTuple{3,Int}) + m, n, t = size # t is identical to dwave (Tile parameter for the Zephyr lattice) + map = Dict{Int,NTuple{3,Int}}() + # ( u, w, k, ζ, z) + for u in 0 + for w ∈ 0:2:2*m, k ∈ 0:t-1, ζ ∈ 0:1, (i, z) ∈ enumerate(0:n-1) + push!(map, zephyr_to_linear(m, t, (u, w, k, ζ, z)) => (2 * i, w + 1, 1)) + end + for w ∈ 1:2:2*m, k ∈ 0:t-1, ζ ∈ 0:1, z ∈ 0:n-1 + push!( + map, + zephyr_to_linear(m, t, (u, w, k, ζ, z)) => (2 * z + 2 * ζ + 1, w + 1, 1), + ) + end + end + + for u in 1 + for w ∈ 0:2:2*m, k ∈ 0:t-1, ζ ∈ 0:1, (i, z) ∈ enumerate(0:n-1) + push!(map, zephyr_to_linear(m, t, (u, w, k, ζ, z)) => (w + 1, 2 * i, 2)) + end + for w ∈ 1:2:2*m, k ∈ 0:t-1, ζ ∈ 0:1, z ∈ 0:n-1 + push!( + map, + zephyr_to_linear(m, t, (u, w, k, ζ, z)) => (w + 1, 2 * z + 2 * ζ + u, 2), + ) + end + end + map +end + +function rotate(m::Int, n::Int) + new_dict = Dict{NTuple{3,Int},NTuple{3,Int}}() + for (k, j) ∈ enumerate(1:2:m) + for (l, i) ∈ enumerate(n-1:-2:1) + push!(new_dict, (i, j, 1) => (i / 2 + k - 1, l + k - 1, 1)) + push!(new_dict, (i, j, 2) => (i / 2 + k - 1, l + k - 1, 2)) + end + end + + for (k, j) ∈ enumerate(2:2:m) + for (l, i) ∈ enumerate(n:-2:1) + push!(new_dict, (i, j, 1) => ((i - 1) / 2 + k, l + k - 1, 1)) + push!(new_dict, (i, j, 2) => ((i - 1) / 2 + k, l + k - 1, 2)) + end + end + new_dict +end + +function empty_clusters(m::Int, n::Int) + p = (m - 1) / 2 + count, ii = 0, [] + for (i, j) ∈ enumerate(1:p-1) + count += i + push!(ii, i) + end + (count, reverse(ii)) +end + +function zephyr_lattice_5tuple_rotated(m::Int, n::Int, map::Dict{Int,NTuple{3,Int}}) + rotated_map = rotate(m, n) + new_map = Dict{Int,NTuple{3,Int}}() + (empty, ii) = empty_clusters(m, n) + for k in keys(map) + push!(new_map, k => rotated_map[map[k]]) + end + + empty_vertices = empty_indexing(m, n) + for (k, l) ∈ enumerate(empty_vertices) + push!(new_map, -k => l) + end + new_map +end + +function empty_indexing(m::Int, n::Int) + (empty, ii) = empty_clusters(m, n) + p = Int((m - 1) / 2) + empty_vertices = [] + for (k, l) ∈ enumerate(ii) + for i ∈ 1:l + push!(empty_vertices, (k, i, 1)) + push!(empty_vertices, (k, i, 2)) + push!(empty_vertices, (k, i + m - p + k - 1, 1)) + push!(empty_vertices, (k, i + m - p + k - 1, 2)) + end + end + for (k, l) ∈ enumerate(reverse(ii)) + for i ∈ 1:l + push!(empty_vertices, (k + m - p, i, 1)) + push!(empty_vertices, (k + m - p, i, 2)) + push!(empty_vertices, (k + m - p, ii[k] + m - p + k - i, 1)) + push!(empty_vertices, (k + m - p, ii[k] + m - p + k - i, 2)) + end + end + empty_vertices +end + +function periodic_lattice(size::NTuple{3,Int}) + mm, nn, tt = size + m, n = 2 * mm, 2 * nn + map = super_square_lattice((m, n, 1)) + new_map = Dict{Int,NTuple{2,Int}}() + for (key, val) ∈ map + i, j = val + if i <= m / 2 + if j <= m / 2 + push!(new_map, key => (i, j)) + elseif j > m / 2 + push!(new_map, key => (i, m - j + 1)) + end + elseif i > m / 2 + if j <= m / 2 + push!(new_map, key => (m - i + 1, j)) + elseif j > m / 2 + push!(new_map, key => (m - i + 1, m - j + 1)) + end + end + end + new_map +end diff --git a/src/spectrum.jl b/src/spectrum.jl index 5a7bed6..a0a1470 100644 --- a/src/spectrum.jl +++ b/src/spectrum.jl @@ -1,105 +1,212 @@ -export all_states, local_basis -export gibbs_tensor -export brute_force, full_spectrum, energy -export Spectrum +export all_states, + local_basis, + gibbs_tensor, + brute_force, + full_spectrum, + Spectrum, + idx, + local_basis, + energy, + matrix_to_integers + +@inline idx(σ::Int) = (σ == -1) ? 1 : σ + 1 +@inline local_basis(d::Int) = union(-1, 1:d-1) +all_states(rank::Union{Vector,NTuple}) = Iterators.product(local_basis.(rank)...) + +const State = Vector{Int} """ - gibbs_tensor(ig::IsingGraph, β::Float64 = 1.0) +$(TYPEDSIGNATURES) -Calculates Gibbs state of a classical Ising Hamiltonian +A `Spectrum` represents the energy spectrum of a system. -# Details +A `Spectrum` consists of energy levels, their corresponding states, and integer representations of the states. -Calculates matrix elements (probabilities) of \$\\rho\$ -```math -\\bra{\\sigma}\\rho\\ket{\\sigma} -``` -for all possible configurations \$\\sigma\$. +# Fields: +- `energies::Vector{<:Real}`: An array of energy levels. +- `states::AbstractArray{State}`: An array of states. +- `states_int::Vector{Int}`: An array of integer representations of states. + +# Constructors: +- `Spectrum(energies, states, states_int)`: Creates a `Spectrum` object with the specified energy levels, states, and integer representations. +- `Spectrum(energies, states)`: Creates a `Spectrum` object with the specified energy levels and states, automatically generating integer representations. """ -function gibbs_tensor(ig::IsingGraph, β::Float64 = 1.0) - states = collect.(all_states(rank_vec(ig))) - ρ = exp.(-β .* energy.(states, Ref(ig))) - ρ ./ sum(ρ) +struct Spectrum + energies::Vector{<:Real} + states::AbstractArray{State} + states_int::Vector{Int} + function Spectrum(energies, states, states_int) + new(energies, states, states_int) + end + function Spectrum(energies, states) + states_int = matrix_to_integers(states) + new(energies, states, states_int) + end end - """ - energy(σ::Vector, ig::IsingGraph) +$(TYPEDSIGNATURES) -Calculate the Ising energy -```math -E = \\sum_{} σ_i J_{ij} σ_j + \\sum_j h_i σ_j. -``` -""" -energy(σ::Vector, ig::IsingGraph)= energy(σ, couplings(ig)) + energy(σ, biases(ig)) +Converts a matrix of binary vectors to their integer representations. -""" - energy(σ::Vector, h::Vector) = dot(h, σ) +This function takes a matrix of binary vectors, where each row represents a binary vector, and converts them into their corresponding integer representations. -Caltulate linear part of Ising energy -```math -\\sum_j h_i σ_j -``` -""" -energy(σ::Vector, h::Vector) = dot(h, σ) +# Arguments: +- `matrix::Vector{Vector{T}}`: A matrix of binary vectors. +# Returns: +- `Vector{Int}`: An array of integer representations of the binary vectors. """ - energy(σ::Vector, J::Matrix, η::Vector = σ) +function matrix_to_integers(matrix::Vector{Vector{T}}) where {T} + nrows = length(matrix[1]) + multipliers = 2 .^ collect(0:nrows-1) + div.((hcat(matrix...)' .+ 1), 2) * multipliers +end -Calculate quadratic part of Ising energy -```math -\\sum_{} σ_i J_{ij} σ_j -``` """ -energy(σ::Vector, J::Matrix, η::Vector = σ) = dot(σ, J, η) - + energy(σ::Vector, ig::IsingGraph) + +Calculates the energy of a state in an Ising graph. + +This function calculates the energy of a given state in the context of an Ising graph. +The energy is computed based on the interactions between spins and their associated biases. + +# Arguments: +- `σ::AbstractArray{State}`: An array representing the state of spins in the Ising graph. +- `ig::IsingGraph`: The Ising graph defining the interactions and biases. +# Returns: +- `Vector{Float64}`: An array of energy values for each state. +""" +function energy(σ::AbstractArray{State}, ig::IsingGraph) + J, h = couplings(ig), biases(ig) + dot.(σ, Ref(J), σ) + dot.(Ref(h), σ) +end """ - brute_force(ig::IsingGraph; sorted = true, num_states::Int = 1) +$(TYPEDSIGNATURES) + +Calculates the energy of a state in an Ising graph. -Return the low energy spectrum +This function computes the energy of a given state in the context of an Ising graph. +The energy is calculated based on the interactions between spins and their associated biases. -# Details +# Arguments: +- `ig::IsingGraph{T}`: The Ising graph defining the interactions and biases. +- `ig_state::Dict{Int, Int}`: A dictionary mapping spin indices to their corresponding states. -Calculates \$k\$ lowest energy states -together with the coresponding energies -of a classical Ising Hamiltonian +# Returns: +- `T`: The energy of the state in the Ising graph. """ -function brute_force(ig::IsingGraph; sorted = true, num_states::Int = 1) - if nv(ig) == 0 - return Spectrum(zeros(1), []) +function energy(ig::IsingGraph{T}, ig_state::Dict{Int,Int}) where {T} + en = zero(T) + for (i, σ) ∈ ig_state + en += get_prop(ig, i, :h) * σ + for (j, η) ∈ ig_state + if has_edge(ig, i, j) + en += T(1 / 2) * σ * get_prop(ig, i, j, :J) * η + elseif has_edge(ig, j, i) + en += T(1 / 2) * σ * get_prop(ig, j, i, :J) * η + end + end end - ig_rank = rank_vec(ig) - num_states = min(num_states, prod(ig_rank)) + en +end - σ = collect.(all_states(ig_rank)) - energies = energy.(σ, Ref(ig)) - if sorted - perm = partialsortperm(vec(energies), 1:num_states) - return Spectrum(energies[perm], σ[perm]) - else - return Spectrum(energies[1:num_states], σ[1:num_states]) +""" +$(TYPEDSIGNATURES) + +Generates the energy spectrum for an Ising graph. + +This function computes the energy spectrum (energies and corresponding states) for a given Ising graph. +The energy spectrum represents all possible energy levels and their associated states in the Ising graph. + +# Arguments: +- `ig::IsingGraph{T}`: The Ising graph for which the energy spectrum is generated. + +# Returns: +- `Spectrum`: An instance of the `Spectrum` type containing the energy levels and states. +""" +function Spectrum(ig::IsingGraph{T}) where {T} + L = nv(ig) + N = 2^L + energies = zeros(T, N) + states = Vector{State}(undef, N) + + J, h = couplings(ig), biases(ig) + Threads.@threads for i = 0:N-1 + σ = 2 .* digits(i, base = 2, pad = L) .- 1 + @inbounds energies[i+1] = dot(σ, J, σ) + dot(h, σ) + @inbounds states[i+1] = σ end + Spectrum(energies, states) +end + +""" +$(TYPEDSIGNATURES) + +Computes the Gibbs tensor for an Ising graph at a given inverse temperature. + +This function calculates the Gibbs tensor for an Ising graph at a specified inverse temperature (β). +The Gibbs tensor represents the conditional probabilities of states given the inverse temperature and the Ising graph. + +# Arguments: +- `ig::IsingGraph{T}`: The Ising graph for which the Gibbs tensor is computed. +- `β::T (optional)`: The inverse temperature parameter. Default is 1. + +# Returns: +- `Matrix{T}`: A matrix representing the Gibbs tensor with conditional probabilities. +""" +function gibbs_tensor(ig::IsingGraph{T}, β::T = 1) where {T} + σ = collect.(all_states(rank_vec(ig))) + ρ = exp.(-β .* energy(σ, ig)) + ρ ./ sum(ρ) end +function brute_force(ig::IsingGraph, s::Symbol = :CPU; num_states::Int = 1) + brute_force(ig, Val(s); num_states) +end """ - full_spectrum(ig::IsingGraph; num_states::Int = 1) +$(TYPEDSIGNATURES) -Calculates full energy spectrum via the brute force method. Returns `num_states` energy -states together with the coresponding energies +TODO only one of brute_force and full_spectrum should remain + +Performs brute-force calculation of the lowest-energy states and their energies for an Ising graph. + +This function exhaustively computes the lowest-energy states and their corresponding energies for an Ising graph. +The calculation is done using brute-force enumeration, making it feasible only for small Ising graphs. + +# Arguments: +- `ig::IsingGraph{T}`: The Ising graph for which the lowest-energy states are computed. +- `::Val{:CPU}`: A value indicating that the computation is performed on the CPU. +- `num_states::Int (optional)`: The maximum number of lowest-energy states to calculate. Default is 1. + +# Returns: +- `Spectrum`: A `Spectrum` object containing the lowest-energy states and their energies. """ -full_spectrum(ig::IsingGraph; num_states::Int = 1) = - brute_force(ig, sorted = false, num_states = num_states) +function brute_force(ig::IsingGraph{T}, ::Val{:CPU}; num_states::Int = 1) where {T} + L = nv(ig) + L == 0 && return Spectrum(zeros(T, 1), Vector{Vector{Int}}[], zeros(T, 1)) + sp = Spectrum(ig) + num_states = min(num_states, prod(rank_vec(ig))) + idx = partialsortperm(vec(sp.energies), 1:num_states) + Spectrum(sp.energies[idx], sp.states[idx]) +end -struct Spectrum - energies::Vector{Float64} - states::Vector{Vector{Int}} +function full_spectrum(ig::IsingGraph{T}; num_states::Int = 1) where {T} + nv(ig) == 0 && return Spectrum(zeros(T, 1), Vector{Vector{Int}}[], zeros(T, 1)) + ig_rank = rank_vec(ig) + num_states = min(num_states, prod(ig_rank)) + σ = collect.(all_states(ig_rank)) + energies = energy(σ, ig) + Spectrum(energies[begin:num_states], σ[begin:num_states]) end -# Please don't make the below another energy method. -# There is already so much mess going on :) -function inter_cluster_energy(cl1_states, J::Matrix, cl2_states) +function inter_cluster_energy( + cl1_states::Vector{State}, + J::Matrix{<:Real}, + cl2_states::Vector{State}, +) hcat(collect.(cl1_states)...)' * J * hcat(collect.(cl2_states)...) end diff --git a/src/states.jl b/src/states.jl deleted file mode 100644 index 8feeafc..0000000 --- a/src/states.jl +++ /dev/null @@ -1,10 +0,0 @@ -export idx -export local_basis - -idx(σ::Int) = (σ == -1) ? 1 : σ + 1 -local_basis(d::Int) = union(-1, 1:d-1) - -function all_states(rank::Union{Vector,NTuple}) - basis = [local_basis(r) for r ∈ rank] - Iterators.product(basis...) -end diff --git a/src/truncate.jl b/src/truncate.jl new file mode 100644 index 0000000..5870c97 --- /dev/null +++ b/src/truncate.jl @@ -0,0 +1,201 @@ +export truncate_clustered_hamiltonian_2site_energy, + truncate_clustered_hamiltonian_1site_BP, + truncate_clustered_hamiltonian_2site_BP, + select_numstate_best + +""" +$(TYPEDSIGNATURES) + +Truncates a clustered Hamiltonian using belief propagation (BP) for a single site cluster. + +This function employs belief propagation (BP) to approximate the most probable states and energies for a clustered Hamiltonian +associated with a single-site cluster. It then truncates the clustered Hamiltonian based on the most probable states. + +# Arguments: +- `cl_h::LabelledGraph{S, T}`: The clustered Hamiltonian represented as a labeled graph. +- `num_states::Int`: The maximum number of most probable states to keep. +- `beta::Real (optional)`: The inverse temperature parameter for the BP algorithm. Default is 1.0. +- `tol::Real (optional)`: The tolerance value for convergence in BP. Default is 1e-10. +- `iter::Int (optional)`: The maximum number of BP iterations. Default is 1. + +# Returns: +- `LabelledGraph{S, T}`: A truncated clustered Hamiltonian. +""" +function truncate_clustered_hamiltonian_1site_BP( + cl_h::LabelledGraph{S,T}, + num_states::Int; + beta = 1.0, + tol = 1e-10, + iter = 1, +) where {S,T} + states = Dict() + beliefs = belief_propagation(cl_h, beta; tol = tol, iter = iter) + for node in vertices(cl_h) + indices = partialsortperm(beliefs[node], 1:min(num_states, length(beliefs[node]))) + push!(states, node => indices) + end + truncate_clustered_hamiltonian(cl_h, states) +end + +""" +$(TYPEDSIGNATURES) + +Truncate a clustered Hamiltonian based on 2-site energy states. + +This function truncates a clustered Hamiltonian by considering 2-site energy states and selecting the most probable states +to keep. It computes the energies for all 2-site combinations and selects the states that maximize the probability. + +# Arguments: +- `cl_h::LabelledGraph{S, T}`: The clustered Hamiltonian represented as a labeled graph. +- `num_states::Int`: The maximum number of most probable states to keep. + +# Returns: +- `LabelledGraph{S, T}`: A truncated clustered Hamiltonian. +""" +function truncate_clustered_hamiltonian_2site_energy( + cl_h::LabelledGraph{S,T}, + num_states::Int, +) where {S,T} + # TODO: name to be clean to make it consistent with square2 and squarestar2 + states = Dict() + for node in vertices(cl_h) + if node in keys(states) + continue + end + i, j, _ = node + E1 = copy(get_prop(cl_h, (i, j, 1), :spectrum).energies) + E2 = copy(get_prop(cl_h, (i, j, 2), :spectrum).energies) + E = energy_2site(cl_h, i, j) .+ reshape(E1, :, 1) .+ reshape(E2, 1, :) + sx, sy = size(E) + E = reshape(E, sx * sy) + ind1, ind2 = select_numstate_best(E, sx, num_states) + push!(states, (i, j, 1) => ind1) + push!(states, (i, j, 2) => ind2) + end + truncate_clustered_hamiltonian(cl_h, states) +end + +function load_file(filename) + if isfile(filename) + try + load_object(string(filename)) + catch e + return nothing + end + else + return nothing + end +end + +""" +$(TYPEDSIGNATURES) + +Truncate a clustered Hamiltonian based on 2-site belief propagation states. + +This function truncates a clustered Hamiltonian by considering 2-site belief propagation states and selecting the most probable states +to keep. It computes the beliefs for all 2-site combinations and selects the states that maximize the probability. + +# Arguments: +- `cl_h::LabelledGraph{S, T}`: The clustered Hamiltonian represented as a labelled graph. +- `beliefs::Dict`: A dictionary containing belief values for 2-site interactions. +- `num_states::Int`: The maximum number of most probable states to keep. +- `beta::Real (optional)`: The inverse temperature parameter (default is 1.0). + +# Returns: +- `LabelledGraph{S, T}`: A truncated clustered Hamiltonian. +""" +function truncate_clustered_hamiltonian_2site_BP( + cl_h::LabelledGraph{S,T}, + beliefs::Dict, + num_states::Int, + result_folder::String = "results_folder", + inst::String = "inst"; + beta = 1.0, +) where {S,T} + states = Dict() + + saved_states = load_file(joinpath(result_folder, "$(inst).jld2")) + for node in vertices(cl_h) + if node in keys(states) + continue + end + i, j, _ = node + sx = + has_vertex(cl_h, (i, j, 1)) ? + length(get_prop(cl_h, (i, j, 1), :spectrum).energies) : 1 + E = beliefs[(i, j)] + ind1, ind2 = select_numstate_best(E, sx, num_states) + push!(states, (i, j, 1) => ind1) + push!(states, (i, j, 2) => ind2) + end + path = joinpath(result_folder, "$(inst).jld2") + save_object(string(path), states) + truncate_clustered_hamiltonian(cl_h, states) +end + +""" +$(TYPEDSIGNATURES) + +Select a specified number of best states based on energy. + +This function selects a specified number of best states from a list of energies based on energy values in two nodes of clustered hamiltonian. +It fine-tunes the selection to ensure that the resulting states have the expected number. + +# Arguments: +- `E::Vector{Real}`: A vector of energy values. +- `sx::Int`: The size of the clustered Hamiltonian for one of the nodes. +- `num_states::Int`: The desired number of states to select. + +# Returns: +- `Tuple{Vector{Int}, Vector{Int}}`: A tuple containing two vectors of indices, `ind1` and `ind2`, +which represent the selected states for two nodes of a clustered Hamiltonian. +""" +function select_numstate_best(E, sx, num_states) + low, high = 1, min(num_states, length(E)) + + while true + guess = div(low + high, 2) + ind = partialsortperm(E, 1:guess) + ind1 = mod.(ind .- 1, sx) .+ 1 + ind2 = div.(ind .- 1, sx) .+ 1 + ind1 = sort([Set(ind1)...]) + ind2 = sort([Set(ind2)...]) + if high - low <= 1 + return ind1, ind2 + end + if length(ind1) * length(ind2) > num_states + high = guess + else + low = guess + end + end +end + +function truncate_clustered_hamiltonian( + cl_h, + β, + cs, + result_folder, + inst; + tol = 1e-6, + iter = iter, +) + states = Dict() + saved_states = load_file(joinpath(result_folder, "$(inst).jld2")) + if isnothing(saved_states) + new_cl_h = clustered_hamiltonian_2site(cl_h, β) + beliefs = belief_propagation(new_cl_h, β; tol = 1e-6, iter = iter) + cl_h = truncate_clustered_hamiltonian_2site_BP( + cl_h, + beliefs, + cs, + result_folder, + inst; + beta = β, + ) + else + states = saved_states + cl_h = truncate_clustered_hamiltonian(cl_h, states) + end + cl_h +end diff --git a/src/utils.jl b/src/utils.jl new file mode 100644 index 0000000..1a82549 --- /dev/null +++ b/src/utils.jl @@ -0,0 +1,200 @@ +export zephyr_to_linear, unique_neighbors, load_openGM + +import Base.Prehashed +using HDF5 + + +""" +$(TYPEDSIGNATURES) + +Rewriten from Dwave-networkx +m - Grid parameter for the Zephyr lattice. +t - Tile parameter for the Zephyr lattice; must be even. +""" +function zephyr_to_linear(m::Int, t::Int, q::NTuple{5,Int}) + M = 2 * m + 1 + u, w, k, j, z = q + (((u * M + w) * t + k) * 2 + j) * m + z + 1 +end + +unique_neighbors(ig::LabelledGraph, i::Int) = filter(j -> j > i, neighbors(ig, i)) + +# @generated function unique_dims(A::AbstractArray{T,N}, dim::Integer) where {T,N} +# quote +# 1 <= dim <= $N || return copy(A) +# hashes = zeros(UInt, axes(A, dim)) + +# # Compute hash for each row +# k = 0 +# @nloops $N i A d->(if d == dim; k = i_d; end) begin +# @inbounds hashes[k] = hash(hashes[k], hash((@nref $N A i))) +# end + +# # Collect index of first row for each hash +# uniquerow = similar(Array{Int}, axes(A, dim)) +# firstrow = Dict{Prehashed,Int}() +# for k = axes(A, dim) +# uniquerow[k] = get!(firstrow, Prehashed(hashes[k]), k) +# end +# uniquerows = collect(values(firstrow)) + +# # Check for collisions +# collided = falses(axes(A, dim)) +# @inbounds begin +# @nloops $N i A d->(if d == dim +# k = i_d +# j_d = uniquerow[k] +# else +# j_d = i_d +# end) begin +# if (@nref $N A j) != (@nref $N A i) +# collided[k] = true +# end +# end +# end + +# if any(collided) +# nowcollided = similar(BitArray, axes(A, dim)) +# while any(collided) +# # Collect index of first row for each collided hash +# empty!(firstrow) +# for j = axes(A, dim) +# collided[j] || continue +# uniquerow[j] = get!(firstrow, Prehashed(hashes[j]), j) +# end +# for v ∈ values(firstrow) +# push!(uniquerows, v) +# end + +# # Check for collisions +# fill!(nowcollided, false) +# @nloops $N i A d->begin +# if d == dim +# k = i_d +# j_d = uniquerow[k] +# (!collided[k] || j_d == k) && continue +# else +# j_d = i_d +# end +# end begin +# if (@nref $N A j) != (@nref $N A i) +# nowcollided[k] = true +# end +# end +# (collided, nowcollided) = (nowcollided, collided) +# end +# end + +# (@nref $N A d->d == dim ? sort!(uniquerows) : (axes(A, d))), indexin(uniquerow, uniquerows) +# end +# end + +""" +$(TYPEDSIGNATURES) +Loads some factored graphs written in openGM format. Assumes rectangular lattice. + +Args: + file_name (str): a path to file with factor graph in openGM format. + ints Nx, Ny: it is assumed that graph if forming an :math:N_x \times N_y lattice with + nearest-neighbour interactions only. + +Returns: + dictionary with factors and funcitons defining the energy functional. +""" +function load_openGM( + fname::String, + Nx::Union{Integer,Nothing} = nothing, + Ny::Union{Integer,Nothing} = nothing, +) + file = h5open(fname, "r") + + file_keys = collect(keys(read(file))) + data = read(file[file_keys[1]]) + H = collect(Int64, data["header"]) + F = Array{Int64}(data["factors"]) + J = Array{Int64}(data["function-id-16000"]["indices"]) + V = Array{Real}(data["function-id-16000"]["values"]) + N = Array{Int64}(data["numbers-of-states"]) + + if isnothing(Nx) || isnothing(Ny) + filename, _ = splitext(basename(fname)) + Nx, Ny = benchmark_names[filename] + end + + F = reverse(F) + factors = Dict() + + while length(F) > 0 + f1 = pop!(F) + z1 = pop!(F) + nn = pop!(F) + n = [] + + for _ = 1:nn + tt = pop!(F) + ny, nx = divrem(tt, Nx) + push!(n, ny, nx) + end + if length(n) == 4 + if abs(n[1] - n[3]) + abs(n[2] - n[4]) ∉ [1, 2] || ( + abs(n[1] - n[3]) + abs(n[2] - n[4]) == 2 && + (abs(n[1] - n[3]) == 2 || abs(n[2] - n[4] == 2)) + ) + throw(ErrorException("Not nearest neighbour or diagonal neighbors")) + end + end + + if length(n) == 2 + if (n[1] >= Ny) || (n[2] >= Nx) + throw(ErrorException("Wrong size")) + end + end + + factors[tuple(n...)] = f1 + + if z1 != 0 + throw(ErrorException("Something wrong with the expected convention.")) + end + end + + J = reverse(J) + functions = Dict() + ii = -1 + lower = 0 + + while length(J) > 0 + ii += 1 + nn = pop!(J) + n = [] + + for _ = 1:nn + push!(n, pop!(J)) + end + + upper = lower + prod(n) + functions[ii] = reshape(V[lower+1:upper], reverse(n)...)' + + lower = upper + end + + result = Dict( + "fun" => functions, + "fac" => factors, + "N" => reshape(N, (Ny, Nx)), + "Nx" => Nx, + "Ny" => Ny, + ) + result +end + +benchmark_names = Dict( + "penguin-small" => (240, 320), + "palm-small" => (240, 360), + "clownfish-small" => (240, 360), + "crops-small" => (240, 360), + "pfau-small" => (240, 320), + "lake-small" => (240, 360), + "snail" => (240, 320), + "fourcolors" => (240, 320), + "strawberry-glass-2-small" => (320, 240), +) diff --git a/test/bp_1site.jl b/test/bp_1site.jl new file mode 100644 index 0000000..c7f18d0 --- /dev/null +++ b/test/bp_1site.jl @@ -0,0 +1,163 @@ +""" +Instance below looks like this: + +1 -- 2 +| +3 -- 4 + +""" +function create_larger_example_clustered_hamiltonian_tree_basic() + instance = Dict( + (1, 1) => -0.50, + (2, 2) => 0.25, + (3, 3) => -0.30, + (4, 4) => 0.10, + (1, 2) => -0.23, + (1, 3) => 1.10, + (3, 4) => 0.71, + ) + + ig = ising_graph(instance) + + assignment_rule = Dict(1 => (1, 1, 1), 2 => (1, 2, 1), 3 => (2, 1, 1), 4 => (2, 2, 2)) + + cl_h = clustered_hamiltonian( + ig, + Dict{NTuple{3,Int},Int}(), + spectrum = full_spectrum, + cluster_assignment_rule = assignment_rule, + ) + + ig, cl_h +end + +""" +Instance below looks like this: + +1 -- 2 -- 3 +| +4 -- 5 -- 6 +| +7 -- 8 -- 9 +""" +function create_larger_example_clustered_hamiltonian_tree() + instance = Dict( + (1, 1) => 0.53, + (2, 2) => -0.25, + (3, 3) => 0.30, + (4, 4) => -0.10, + (5, 5) => -0.10, + (6, 6) => 0.10, + (8, 8) => 0.10, + (9, 9) => 0.01, + (1, 2) => -1.00, + (2, 3) => 1.00, + (1, 4) => 0.33, + (4, 5) => 0.76, + (5, 6) => -0.45, + (4, 7) => -0.28, + (7, 8) => 0.36, + (8, 9) => -1.07, + ) + + ig = ising_graph(instance) + + assignment_rule = Dict( + 1 => (1, 1, 1), + 2 => (1, 2, 1), + 3 => (1, 3, 1), + 4 => (2, 1, 1), + 5 => (2, 2, 1), + 6 => (2, 3, 1), + 7 => (3, 1, 1), + 8 => (3, 2, 1), + 9 => (3, 3, 1), + ) + + cl_h = clustered_hamiltonian( + ig, + Dict{NTuple{3,Int},Int}(), + spectrum = full_spectrum, + cluster_assignment_rule = assignment_rule, + ) + + ig, cl_h +end + +""" +Instance below looks like this: + +123 -- 45 -- 6 +| | +7 8 + +""" +function create_larger_example_clustered_hamiltonian_tree_pathological() + instance = Dict( + (1, 1) => 0.52, + (2, 2) => 0.25, + (3, 3) => -0.31, + (4, 4) => 0.17, + (5, 5) => -0.12, + (6, 6) => 0.13, + (7, 7) => 0.00, + (8, 8) => 0.43, + (1, 2) => -1.01, + (1, 3) => 1.00, + (3, 4) => 0.97, + (3, 5) => -0.98, + (5, 6) => 1.00, + (2, 7) => 0.53, + (3, 7) => 1.06, + (5, 8) => -0.64, + ) + + ig = ising_graph(instance) + + assignment_rule = Dict( + 1 => (1, 1), + 2 => (1, 1), + 3 => (1, 1), + 4 => (1, 2), + 5 => (1, 2), + 6 => (1, 3), + 7 => (2, 1), + 8 => (2, 2), + ) + + cl_h = clustered_hamiltonian( + ig, + Dict{NTuple{2,Int},Int}(), + spectrum = full_spectrum, + cluster_assignment_rule = assignment_rule, + ) + + ig, cl_h +end + +@testset "Belief propagation" begin + for (ig, cl_h) ∈ [ + create_larger_example_clustered_hamiltonian_tree_basic(), + create_larger_example_clustered_hamiltonian_tree(), + create_larger_example_clustered_hamiltonian_tree_pathological(), + ] + for beta ∈ [0.5, 1] + iter = 16 + beliefs = belief_propagation(cl_h, beta; iter = iter) + exact_marginal = Dict() + for k in keys(beliefs) + push!( + exact_marginal, + k => [ + exact_cond_prob(cl_h, beta, Dict(k => a)) for + a = 1:length(beliefs[k]) + ], + ) + end + for v in keys(beliefs) + temp = -log.(exact_marginal[v]) ./ beta + @test beliefs[v] ≈ temp .- minimum(temp) + end + end + end +end diff --git a/test/bp_2site.jl b/test/bp_2site.jl new file mode 100644 index 0000000..5878444 --- /dev/null +++ b/test/bp_2site.jl @@ -0,0 +1,212 @@ +""" +Instance below looks like this: + +12 -- 34 +| +56 -- 78 + +""" +function create_larger_example_clustered_hamiltonian_tree_2site() + instance = Dict( + (1, 1) => 0.50, + (2, 2) => -0.25, + (3, 3) => 0.30, + (4, 4) => -0.10, + (5, 5) => 0.10, + (6, 6) => 0.20, + (7, 7) => -0.40, + (8, 8) => 0.50, + (1, 2) => -0.01, + (1, 3) => 1.00, + (3, 4) => 1.10, + (2, 5) => 0.77, + (6, 7) => -1.43, + (6, 8) => 0.21, + (6, 2) => 1.00, + ) + + ig = ising_graph(instance) + + assignment_rule1 = Dict( + 1 => (1, 1), + 2 => (1, 1), + 3 => (1, 2), + 4 => (1, 2), + 5 => (2, 1), + 6 => (2, 1), + 7 => (2, 2), + 8 => (2, 2), + ) + + assignment_rule2 = Dict( + 1 => (1, 1, 1), + 2 => (1, 1, 2), + 3 => (1, 2, 1), + 4 => (1, 2, 2), + 5 => (2, 1, 1), + 6 => (2, 1, 2), + 7 => (2, 2, 1), + 8 => (2, 2, 2), + ) + + cl_h1 = clustered_hamiltonian( + ig, + Dict{NTuple{2,Int},Int}(), + spectrum = full_spectrum, + cluster_assignment_rule = assignment_rule1, + ) + + cl_h2 = clustered_hamiltonian( + ig, + Dict{NTuple{3,Int},Int}(), + spectrum = full_spectrum, + cluster_assignment_rule = assignment_rule2, + ) + + ig, cl_h1, cl_h2 +end + +""" +Instance below looks like this: + +12 -- 345 -- 6 + | + 789 + | + 10 + +""" +function create_larger_example_clustered_hamiltonian_tree_2site_pathological() + instance = Dict( + (1, 1) => -0.50, + (2, 2) => 0.25, + (3, 3) => 0.31, + (4, 4) => 0.77, + (5, 5) => -0.15, + (6, 6) => 0.21, + (7, 7) => -0.10, + (8, 8) => 0.13, + (9, 9) => 0.01, + (10, 10) => 0.5, + (1, 2) => -0.01, + (1, 3) => 0.79, + (2, 5) => -0.93, + (5, 6) => 0.81, + (5, 9) => -0.17, + (3, 4) => 0.71, + (4, 6) => -0.43, + (5, 8) => 0.75, + (7, 10) => -1.03, + (8, 9) => 0.65, + (9, 10) => -0.57, + ) + + ig = ising_graph(instance) + + assignment_rule1 = Dict( + 1 => (1, 1), + 2 => (1, 1), + 3 => (1, 2), + 4 => (1, 2), + 5 => (1, 2), + 6 => (1, 3), + 7 => (2, 2), + 8 => (2, 2), + 9 => (2, 2), + 10 => (3, 2), + ) + + assignment_rule2 = Dict( + 1 => (1, 1, 1), + 2 => (1, 1, 2), + 3 => (1, 2, 1), + 4 => (1, 2, 1), + 5 => (1, 2, 2), + 6 => (1, 3, 2), + 7 => (2, 2, 1), + 8 => (2, 2, 1), + 9 => (2, 2, 2), + 10 => (3, 2, 2), + ) + + cl_h1 = clustered_hamiltonian( + ig, + Dict{NTuple{2,Int},Int}(), + spectrum = full_spectrum, + cluster_assignment_rule = assignment_rule1, + ) + + cl_h2 = clustered_hamiltonian( + ig, + Dict{NTuple{3,Int},Int}(), + spectrum = full_spectrum, + cluster_assignment_rule = assignment_rule2, + ) + + ig, cl_h1, cl_h2 +end + + +@testset "Belief propagation 2site" begin + + for (ig, cl_h1, cl_h2) ∈ [ + create_larger_example_clustered_hamiltonian_tree_2site(), + create_larger_example_clustered_hamiltonian_tree_2site_pathological(), + ] + for beta ∈ [0.6, 1.1] + tol = 1e-12 + iter = 16 + num_states = 10 + + new_cl_h1 = clustered_hamiltonian_2site(cl_h2, beta) + + @test vertices(new_cl_h1) == vertices(cl_h1) + @test edges(new_cl_h1) == edges(cl_h1) + for e ∈ vertices(new_cl_h1) + @test get_prop(new_cl_h1, e, :spectrum).energies ≈ + get_prop(cl_h1, e, :spectrum).energies + end + for e ∈ edges(new_cl_h1) + E = get_prop(new_cl_h1, src(e), dst(e), :en) + # @cast E[(l1, l2), (r1, r2)] := + # E.e11[l1, r1] + E.e21[l2, r1] + E.e12[l1, r2] + E.e22[l2, r2] + a11 = reshape(CuArray(E.e11), size(E.e11, 1), :, size(E.e11, 2)) + a21 = reshape(CuArray(E.e21), :, size(E.e21, 1), size(E.e21, 2)) + a12 = reshape(CuArray(E.e12), size(E.e12, 1), 1, 1, size(E.e12, 2)) + a22 = reshape(CuArray(E.e22), 1, size(E.e22, 1), 1, size(E.e22, 2)) + E = @__dot__(a11 + a21 + a12 + a22) + E = reshape(E, size(E, 1) * size(E, 2), size(E, 3) * size(E, 4)) + @test Array(E) == get_prop(cl_h1, src(e), dst(e), :en) + end + for e ∈ edges(new_cl_h1) + il1 = get_prop(new_cl_h1, src(e), dst(e), :ipl) + il2 = get_prop(cl_h1, src(e), dst(e), :ipl) + ir1 = get_prop(new_cl_h1, src(e), dst(e), :ipr) + ir2 = get_prop(cl_h1, src(e), dst(e), :ipr) + + pl1 = get_projector!(get_prop(new_cl_h1, :pool_of_projectors), il1, :CPU) + pl2 = get_projector!(get_prop(cl_h1, :pool_of_projectors), il2, :CPU) + pr1 = get_projector!(get_prop(new_cl_h1, :pool_of_projectors), ir1, :CPU) + pr2 = get_projector!(get_prop(cl_h1, :pool_of_projectors), ir2, :CPU) + @test pl1 == pl2 + @test pr1 == pr2 + end + + beliefs = belief_propagation(new_cl_h1, beta; iter = iter, tol = tol) + + exact_marginal = Dict() + for k in keys(beliefs) + temp = + -1 / beta .* + log.([ + exact_cond_prob(cl_h1, beta, Dict(k => a)) for + a = 1:length(beliefs[k]) + ]) + push!(exact_marginal, k => temp .- minimum(temp)) + end + for v in keys(beliefs) + @test beliefs[v] ≈ exact_marginal[v] + end + end + end +end diff --git a/test/clustered_hamiltonian.jl b/test/clustered_hamiltonian.jl new file mode 100644 index 0000000..5bf9d25 --- /dev/null +++ b/test/clustered_hamiltonian.jl @@ -0,0 +1,294 @@ +using MetaGraphs +using Graphs +using CSV + +enum(vec) = Dict(v => i for (i, v) ∈ enumerate(vec)) + +@testset "Lattice graph" begin + m = 4 + n = 4 + t = 4 + L = 128 + + instance = "$(@__DIR__)/instances/chimera_droplets/$(L)power/001.txt" + + for T ∈ [Float16, Float32, Float64] + ig = ising_graph(T, instance) + + cl_h = clustered_hamiltonian( + ig, + 2, + cluster_assignment_rule = super_square_lattice((m, n, 2 * t)), + ) + + @test collect(vertices(cl_h)) == [(i, j) for i ∈ 1:m for j ∈ 1:n] + + clv = [] + cle = [] + rank = rank_vec(ig) + + for v ∈ vertices(cl_h) + cl = get_prop(cl_h, v, :cluster) + push!(clv, vertices(cl)) + push!(cle, collect(edges(cl))) + + @test rank_vec(cl) == get_prop.(Ref(ig), vertices(cl), :rank) + end + + # Check if graph is factored correctly + @test isempty(intersect(clv...)) + @test isempty(intersect(cle...)) + end +end + +@testset "Factor graph builds on pathological instance" begin + m = 3 + n = 4 + t = 3 + L = n * m * t + + instance = "$(@__DIR__)/instances/pathological/test_$(m)_$(n)_$(t).txt" + + ising = CSV.File(instance, types = [Int, Int, Float64], header = 0, comment = "#") + + couplings = Dict((i, j) => v for (i, j, v) ∈ ising) + + cedges = Dict( + ((1, 1), (1, 2)) => [(1, 4), (1, 5), (1, 6)], + ((1, 1), (2, 1)) => [(1, 13)], + ((1, 2), (1, 3)) => [(4, 7), (5, 7), (6, 8), (6, 9)], + ((1, 2), (2, 2)) => [(6, 16), (6, 18), (5, 16)], + ((2, 1), (2, 2)) => [(13, 16), (13, 18)], + ((2, 2), (3, 2)) => [(18, 28)], + ((3, 2), (3, 3)) => [ + (28, 31), + (28, 32), + (28, 33), + (29, 31), + (29, 32), + (29, 33), + (30, 31), + (30, 32), + (30, 33), + ], + ) + + cells = Dict( + (1, 1) => [1], + (1, 2) => [4, 5, 6], + (1, 3) => [7, 8, 9], + (1, 4) => [], + (2, 1) => [13], + (2, 2) => [16, 18], + (2, 3) => [], + (2, 4) => [], + (3, 1) => [], + (3, 2) => [28, 29, 30], + (3, 3) => [31, 32, 33], + (3, 4) => [], + ) + + d = 2 + rank = Dict(c => fill(d, length(idx)) for (c, idx) ∈ cells if !isempty(idx)) + bond_dimensions = [2, 2, 8, 4, 2, 2, 8] + + for T ∈ [Float16, Float32, Float64] + ig = ising_graph(T, instance) + @test eltype(ig) == T + + cl_h = clustered_hamiltonian( + ig, + spectrum = full_spectrum, + cluster_assignment_rule = super_square_lattice((m, n, t)), + ) + + for (bd, e) in zip(bond_dimensions, edges(cl_h)) + ipl, en, ipr = + get_prop(cl_h, e, :ipl), get_prop(cl_h, e, :en), get_prop(cl_h, e, :ipr) + pl = get_projector!(get_prop(cl_h, :pool_of_projectors), ipl, :CPU) + pr = get_projector!(get_prop(cl_h, :pool_of_projectors), ipr, :CPU) + + @test minimum(size(en)) == bd + @test maximum(pl) == size(en, 1) + @test maximum(pr) == size(en, 2) + end + + for ((i, j), cedge) ∈ cedges + ipl, en, ipr = get_prop(cl_h, i, j, :ipl), + get_prop(cl_h, i, j, :en), + get_prop(cl_h, i, j, :ipr) + pl = get_projector!(get_prop(cl_h, :pool_of_projectors), ipl, :CPU) + pr = get_projector!(get_prop(cl_h, :pool_of_projectors), ipr, :CPU) + base_i = all_states(rank[i]) + base_j = all_states(rank[j]) + + idx_i = enum(cells[i]) + idx_j = enum(cells[j]) + + # Change it to test if energy is calculated using passed 'energy' function + energy = zeros(T, prod(rank[i]), prod(rank[j])) + + for (ii, σ) ∈ enumerate(base_i), (jj, η) ∈ enumerate(base_j) + eij = zero(T) + for (k, l) ∈ values(cedge) + kk = enum(cells[i])[k] + ll = enum(cells[j])[l] + s = σ[idx_i[k]] + r = η[idx_j[l]] + J = couplings[k, l] + eij += s * J * r + end + energy[ii, jj] = eij + end + @test eltype(energy) == T == eltype(en) + @test energy ≈ en[pl, pr] + end + @testset "each cluster comprises expected cells" begin + for v ∈ vertices(cl_h) + cl = get_prop(cl_h, v, :cluster) + @test issetequal(vertices(cl), cells[v]) + end + end + @testset "each edge comprises expected bunch of edges from source Ising graph" begin + for e ∈ edges(cl_h) + outer_edges = get_prop(cl_h, e, :outer_edges) + @test issetequal( + cedges[(src(e), dst(e))], + [(src(oe), dst(oe)) for oe ∈ outer_edges], + ) + end + end + end +end + +function create_example_clustered_hamiltonian(::Type{T}) where {T} + J12 = -1 + h1 = 1 / 2 + h2 = 0.75 + + D = Dict((1, 2) => J12, (1, 1) => h1, (2, 2) => h2) + ig = ising_graph(T, D) + + clustered_hamiltonian( + ig, + Dict((1, 1) => 2, (1, 2) => 2), + spectrum = full_spectrum, + cluster_assignment_rule = Dict(1 => (1, 1), 2 => (1, 2)), + ) +end + +cl_h_state_to_spin = + [([1, 1], [-1, -1]), ([1, 2], [-1, 1]), ([2, 1], [1, -1]), ([2, 2], [1, 1])] + +@testset "Decoding solution gives correct spin assignment" begin + + for T ∈ [Float16, Float32, Float64] + cl_h = create_example_clustered_hamiltonian(T) + @test all(eltype(get_prop(cl_h, e, :en)) == T for e ∈ edges(cl_h)) + for (state, spin_values) ∈ cl_h_state_to_spin + d = decode_clustered_hamiltonian_state(cl_h, state) + states = collect(values(d))[collect(keys(d))] + @test states == spin_values + end + end +end + +""" +Instance below looks like this: + +1 -- 2 -- 3 +| | | +4 -- 5 -- 6 +| | | +7 -- 8 -- 9 + +And we group the following spins together: [1, 2, 4, 5], [3, 6], [7, 8], [9]. +""" +function create_larger_example_clustered_hamiltonian() + instance = Dict( + (1, 1) => 0.5, + (2, 2) => 0.25, + (3, 3) => 0.3, + (4, 4) => 0.1, + (5, 5) => 0.0, + (6, 6) => -2.0, + (7, 7) => -1.0, + (8, 8) => 2.0, + (9, 9) => 3.1, + (1, 2) => -1.0, + (2, 3) => 1.0, + (4, 5) => 0.5, + (5, 6) => -0.3, + (7, 8) => 0.1, + (8, 9) => 2.2, + (1, 4) => -1.7, + (4, 7) => 1.2, + (2, 5) => 0.2, + (5, 8) => 0.3, + (3, 6) => 1.1, + (6, 9) => 0.7, + ) + ig = ising_graph(instance) + + assignment_rule = Dict( + 1 => (1, 1), + 2 => (1, 1), + 4 => (1, 1), + 5 => (1, 1), + 3 => (1, 2), + 6 => (1, 2), + 7 => (2, 1), + 8 => (2, 1), + 9 => (2, 2), + ) + + cl_h = clustered_hamiltonian( + ig, + Dict{NTuple{2,Int},Int}(), + spectrum = full_spectrum, + cluster_assignment_rule = assignment_rule, + ) + + ig, cl_h +end + +function clustered_hamiltonian_energy(cl_h, state) + # This is highly inefficient, but simple, which makes it suitable for testing. + # If such a function is needed elsewhere, we need to implement it properly. + total_en = 0 + + # Collect local terms from each cluster + for (s, v) ∈ zip(state, vertices(cl_h)) + total_en += get_prop(cl_h, v, :spectrum).energies[s] + end + + # Collect inter-cluster terms + for edge ∈ edges(cl_h) + i, j = cl_h.reverse_label_map[src(edge)], cl_h.reverse_label_map[dst(edge)] + ipl, en, ipr = get_prop(cl_h, edge, :ipl), + get_prop(cl_h, edge, :en), + get_prop(cl_h, edge, :ipr) + pl = get_projector!(get_prop(cl_h, :pool_of_projectors), ipl, :CPU) + pr = get_projector!(get_prop(cl_h, :pool_of_projectors), ipr, :CPU) + edge_energy = en[pl, pr] + total_en += edge_energy[state[i], state[j]] + end + total_en +end + +@testset "Decoding solution gives spins configuration with corresponding energies" begin + ig, cl_h = create_larger_example_clustered_hamiltonian() + + # Corresponding bases sizes for each cluster are 16, 4, 4, 2. + all_states = [[i, j, k, l] for i ∈ 1:16 for j ∈ 1:4 for k ∈ 1:4 for l ∈ 1:2] + + for state ∈ all_states + d = decode_clustered_hamiltonian_state(cl_h, state) + spins = zeros(length(d)) + for (k, v) ∈ d + spins[k] = v + end + σ = [Int.(spins)] + @test clustered_hamiltonian_energy(cl_h, state) ≈ energy(σ, ig)[] + end +end diff --git a/test/factor.jl b/test/factor.jl deleted file mode 100644 index 958ab1f..0000000 --- a/test/factor.jl +++ /dev/null @@ -1,316 +0,0 @@ -using MetaGraphs -using Graphs -using CSV - -enum(vec) = Dict(v => i for (i, v) ∈ enumerate(vec)) - -@testset "Lattice graph" begin - m = 4 - n = 4 - t = 4 - L = 128 - - instance = "$(@__DIR__)/instances/chimera_droplets/$(L)power/001.txt" - - ig = ising_graph(instance) - - fg = factor_graph(ig, 2, cluster_assignment_rule = super_square_lattice((m, n, 2 * t))) - - @test collect(vertices(fg)) == [(i, j) for i ∈ 1:m for j ∈ 1:n] - - clv = [] - cle = [] - rank = rank_vec(ig) - - for v ∈ vertices(fg) - cl = get_prop(fg, v, :cluster) - push!(clv, vertices(cl)) - push!(cle, collect(edges(cl))) - - @test rank_vec(cl) == get_prop.(Ref(ig), vertices(cl), :rank) - end - - # Check if graph is factored correctly - @test isempty(intersect(clv...)) - @test isempty(intersect(cle...)) -end - -@testset "Factor graph builds on pathological instance" begin - m = 3 - n = 4 - t = 3 - L = n * m * t - - instance = "$(@__DIR__)/instances/pathological/test_$(m)_$(n)_$(t).txt" - - ising = CSV.File(instance, types = [Int, Int, Float64], header = 0, comment = "#") - - couplings = Dict((i, j) => v for (i, j, v) ∈ ising) - - cedges = Dict( - ((1, 1), (1, 2)) => [(1, 4), (1, 5), (1, 6)], - ((1, 1), (2, 1)) => [(1, 13)], - ((1, 2), (1, 3)) => [(4, 7), (5, 7), (6, 8), (6, 9)], - ((1, 2), (2, 2)) => [(6, 16), (6, 18), (5, 16)], - ((2, 1), (2, 2)) => [(13, 16), (13, 18)], - ((2, 2), (3, 2)) => [(18, 28)], - ((3, 2), (3, 3)) => [ - (28, 31), - (28, 32), - (28, 33), - (29, 31), - (29, 32), - (29, 33), - (30, 31), - (30, 32), - (30, 33), - ], - ) - - cells = Dict( - (1, 1) => [1], - (1, 2) => [4, 5, 6], - (1, 3) => [7, 8, 9], - (1, 4) => [], - (2, 1) => [13], - (2, 2) => [16, 18], - (2, 3) => [], - (2, 4) => [], - (3, 1) => [], - (3, 2) => [28, 29, 30], - (3, 3) => [31, 32, 33], - (3, 4) => [], - ) - - d = 2 - rank = Dict(c => fill(d, length(idx)) for (c, idx) ∈ cells if !isempty(idx)) - - bond_dimensions = [2, 2, 8, 4, 2, 2, 8] - - fg = factor_graph( - ising_graph(instance), - spectrum = full_spectrum, - cluster_assignment_rule = super_square_lattice((m, n, t)), - ) - - for (bd, e) in zip(bond_dimensions, edges(fg)) - pl, en, pr = get_prop(fg, e, :pl), get_prop(fg, e, :en), get_prop(fg, e, :pr) - @test minimum(size(en)) == bd - end - - for ((i, j), cedge) ∈ cedges - pl, en, pr = - get_prop(fg, i, j, :pl), get_prop(fg, i, j, :en), get_prop(fg, i, j, :pr) - base_i = all_states(rank[i]) - base_j = all_states(rank[j]) - - idx_i = enum(cells[i]) - idx_j = enum(cells[j]) - - # Change it to test if energy is calculated using passed 'energy' function - energy = zeros(prod(rank[i]), prod(rank[j])) - - for (ii, σ) ∈ enumerate(base_i) - for (jj, η) ∈ enumerate(base_j) - eij = 0.0 - for (k, l) ∈ values(cedge) - kk, ll = enum(cells[i])[k], enum(cells[j])[l] - s, r = σ[idx_i[k]], η[idx_j[l]] - J = couplings[k, l] - eij += s * J * r - end - energy[ii, jj] = eij - end - end - @test energy ≈ pl * (en * pr) - end - - @testset "each cluster comprises expected cells" begin - for v ∈ vertices(fg) - cl = get_prop(fg, v, :cluster) - - @test issetequal(vertices(cl), cells[v]) - end - end - - @testset "each edge comprises expected bunch of edges from source Ising graph" begin - for e ∈ edges(fg) - outer_edges = get_prop(fg, e, :outer_edges) - - @test issetequal( - cedges[(src(e), dst(e))], - [(src(oe), dst(oe)) for oe ∈ outer_edges], - ) - end - end -end - - -@testset "Rank reveal correctly decomposes energy row-wise" begin - energy = [[1 2 3]; [0 -1 0]; [1 2 3]] - P, E = rank_reveal(energy, :PE) - @test size(P) == (3, 2) - @test size(E) == (2, 3) - @test P * E ≈ energy -end - -@testset "Rank reveal correctly decomposes energy column-wise" begin - energy = [[1, 2, 3] [0, -1, 1] [1, 2, 3]] - E, P = rank_reveal(energy, :EP) - @test size(P) == (2, 3) - @test size(E) == (3, 2) - @test E * P ≈ energy -end - -@testset "Rank reveal correctly decomposes energy into projector, energy, projector" begin - #energy = [[1 2 3]; [0 -1 0]; [1 2 3]] - energy = [ - [1.0 -0.5 1.5 0.0 0.0 -1.5 0.5 -1.0] - [0.0 0.5 0.5 1.0 -1.0 -0.5 -0.5 0.0] - [0.5 0.0 1.0 0.5 -0.5 -1.0 0.0 -0.5] - [-0.5 1.0 0.0 1.5 -1.5 0.0 -1.0 0.5] - [0.5 -1.0 0.0 -1.5 1.5 0.0 1.0 -0.5] - [-0.5 0.0 -1.0 -0.5 0.5 1.0 0.0 0.5] - [0.0 -0.5 -0.5 -1.0 1.0 0.5 0.5 0.0] - [-1.0 0.5 -1.5 0.0 0.0 1.5 -0.5 1.0] - ] - Pl, E_old = rank_reveal(energy, :PE) - @test size(Pl) == (8, 8) - @test size(E_old) == (8, 8) - @test Pl * E_old ≈ energy - - E, Pr = rank_reveal(E_old, :EP) - @test size(Pr) == (8, 8) - @test size(E) == (8, 8) - @test E * Pr ≈ E_old - @test Pl * E * Pr ≈ energy -end - - -function create_example_factor_graph() - J12 = -1.0 - h1 = 0.5 - h2 = 0.75 - - - D = Dict((1, 2) => J12, (1, 1) => h1, (2, 2) => h2) - ig = ising_graph(D) - - factor_graph( - ig, - Dict((1, 1) => 2, (1, 2) => 2), - spectrum = full_spectrum, - cluster_assignment_rule = Dict(1 => (1, 1), 2 => (1, 2)), - ) -end - -const fg_state_to_spin = - [([1, 1], [-1, -1]), ([1, 2], [-1, 1]), ([2, 1], [1, -1]), ([2, 2], [1, 1])] - -@testset "Decoding solution gives correct spin assignment" begin - fg = create_example_factor_graph() - for (state, spin_values) ∈ fg_state_to_spin - d = decode_factor_graph_state(fg, state) - states = collect(values(d))[collect(keys(d))] - @test states == spin_values - end -end - -""" -Instance below looks like this: - -1 -- 2 -- 3 -| | | -4 -- 5 -- 6 -| | | -7 -- 8 -- 9 - -And we group the following spins together: [1, 2, 4, 5], [3, 6], [7, 8], [9]. -""" -function create_larger_example_factor_graph() - instance = Dict( - (1, 1) => 0.5, - (2, 2) => 0.25, - (3, 3) => 0.3, - (4, 4) => 0.1, - (5, 5) => 0.0, - (6, 6) => -2.0, - (7, 7) => -1.0, - (8, 8) => 2.0, - (9, 9) => 3.1, - (1, 2) => -1.0, - (2, 3) => 1.0, - (4, 5) => 0.5, - (5, 6) => -0.3, - (7, 8) => 0.1, - (8, 9) => 2.2, - (1, 4) => -1.7, - (4, 7) => 1.2, - (2, 5) => 0.2, - (5, 8) => 0.3, - (3, 6) => 1.1, - (6, 9) => 0.7, - ) - - ig = ising_graph(instance) - - assignment_rule = Dict( - 1 => (1, 1), - 2 => (1, 1), - 4 => (1, 1), - 5 => (1, 1), - 3 => (1, 2), - 6 => (1, 2), - 7 => (2, 1), - 8 => (2, 1), - 9 => (2, 2), - ) - - fg = factor_graph( - ig, - Dict{NTuple{2,Int},Int}(), - spectrum = full_spectrum, - cluster_assignment_rule = assignment_rule, - ) - - ig, fg -end - -function factor_graph_energy(fg, state) - # This is highly inefficient, but simple, which makes it suitable for testing. - # If such a function is needed elsewhere, we need to implement it properly. - total_en = 0.0 - - # Collect local terms from each cluster - for (s, v) ∈ zip(state, vertices(fg)) - total_en += get_prop(fg, v, :spectrum).energies[s] - end - - # Collect inter-cluster terms - for edge ∈ edges(fg) - i, j = fg.reverse_label_map[src(edge)], fg.reverse_label_map[dst(edge)] - pl, en, pr = - get_prop(fg, edge, :pl), get_prop(fg, edge, :en), get_prop(fg, edge, :pr) - edge_energy = pl * en * pr - total_en += edge_energy[state[i], state[j]] - end - - total_en -end - - -@testset "Decoding solution gives spins configuration with corresponding energies" begin - ig, fg = create_larger_example_factor_graph() - - # Corresponding bases sizes for each cluster are 16, 4, 4, 2. - all_states = [[i, j, k, l] for i ∈ 1:16 for j ∈ 1:4 for k ∈ 1:4 for l ∈ 1:2] - - for state ∈ all_states - d = decode_factor_graph_state(fg, state) - spins = zeros(length(d)) - for (k, v) ∈ d - spins[k] = v - end - @test factor_graph_energy(fg, state) ≈ energy(spins, ig) - end -end diff --git a/test/instances/pathological/cross_3_4_dd.txt b/test/instances/pathological/cross_3_4_dd.txt new file mode 100644 index 0000000..914e665 --- /dev/null +++ b/test/instances/pathological/cross_3_4_dd.txt @@ -0,0 +1,95 @@ +# i j Jij +1 2 0.034238 +1 3 0.750061 +1 4 0.589578 +2 3 0.429516 +2 17 0.189869 +3 14 -0.928508 +4 5 -0.870021 +4 14 -0.397741 +5 6 0.951815 +5 9 -0.443220 +5 15 -0.157941 +6 8 0.590863 +6 9 -0.251337 +6 16 0.574823 +7 16 0.080566 +7 24 0.865862 +8 11 0.822161 +9 12 -0.641085 +9 19 -0.984943 +11 12 0.114662 +11 21 0.921200 +12 22 -0.380927 +13 14 0.144119 +13 17 -0.277623 +14 15 -0.190409 +14 16 0.254528 +14 28 -0.248506 +15 18 0.278264 +15 26 0.533558 +15 30 -0.687650 +16 17 -0.188157 +16 18 -0.622391 +16 19 0.866969 +16 20 0.684857 +16 21 -0.643313 +17 18 0.240160 +17 19 -0.891268 +17 20 0.737441 +17 21 -0.493823 +17 32 -0.399723 +18 19 -0.293740 +18 20 -0.830321 +18 21 -0.900943 +18 26 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(k, l) ∈ values(cedges[i, j]) - for m ∈ 1:length(config[k]) - s = config[k][m] - r = config[l][m] - J = couplings[k, l] - if k == l - eng[m] += dot(s, J) - else - eng[m] += dot(s, J, r) - end - end - end - end - eng -end - -function _energy(ig::LabelledGraph, config::Array) - s = size(config, 1) - eng = zeros(s) - for i ∈ 1:s - eng[i] = energy(config[i, :], ig) - end - eng -end - @testset "Ising graph cannot be created" begin - @testset "if input instance contains duplicate edges" begin - @test_throws ArgumentError ising_graph( - Dict((1, 1) => 2.0, (1, 2) => 0.5, (2, 1) => -1.0), - ) + for T ∈ [Float16, Float32, Float64] + @test_throws ArgumentError ising_graph( + T, + Dict((1, 1) => 2.0, (1, 2) => 0.5, (2, 1) => -1.0), + ) + end end end - -for (instance, source) ∈ ( - ("$(@__DIR__)/instances/example.txt", "file"), - ( - Dict((1, 1) => 0.1, (2, 2) => 0.5, (1, 4) => -2.0, (4, 2) => 1.0, (1, 2) => -0.3), - "array", - ), -) - @testset "Ising graph created from $(source)" begin - expected_num_vertices = 3 - expected_biases = [0.1, 0.5, 0.0] - expected_couplings = Dict( - LabelledEdge(1, 2) => -0.3, - LabelledEdge(1, 4) => -2.0, - LabelledEdge(2, 4) => 1.0, - ) - expected_J_matrix = [ - [0 -0.3 -2.0] - [0 0 1.0] - [0 0 0] - ] - - ig = ising_graph(instance) - - @testset "contains the same number vertices as original instance" begin - @test nv(ig) == expected_num_vertices - end - - @testset "has collection of edges comprising all interactions from instance" begin - # This test uses the fact that edges iterates in the lex ordering. - @test collect(edges(ig)) == - [LabelledEdge(e...) for e in [(1, 2), (1, 4), (2, 4)]] - end - - @testset "stores biases as property of vertices" begin - @test biases(ig) == expected_biases - end - - @testset "stores couplings both as property of edges and its own property" begin - @test couplings(ig) == expected_J_matrix - end - - @testset "has default rank stored for each active vertex" begin - @test get_prop(ig, :rank) == Dict(1 => 2, 2 => 2, 4 => 2) +for T ∈ [Float16, Float32, Float64] + for (instance, source) ∈ ( + ("$(@__DIR__)/instances/example.txt", "file"), + ( + Dict{Tuple{Int,Int},T}( + (1, 1) => 0.1, + (2, 2) => 0.5, + (1, 4) => -2.0, + (4, 2) => 1.0, + (1, 2) => -0.3, + ), + "array", + ), + ) + @testset "Ising graph created from $(source)" begin + expected_num_vertices = 3 + expected_biases = [T(1 / 10), T(1 / 2), T(0)] + expected_couplings = Dict( + LabelledEdge(1, 2) => -T(3 / 10), + LabelledEdge(1, 4) => -T(2), + LabelledEdge(2, 4) => T(1), + ) + expected_J_matrix = + [[T(0) -T(3 / 10) -T(2)]; [T(0) T(0) T(1)]; [T(0) T(0) T(0)]] + + ig = ising_graph(T, instance) + @test eltype(ig) == T + @testset "contains the same number vertices as original instance" begin + @test nv(ig) == expected_num_vertices + end + @testset "has collection of edges comprising all interactions from instance" begin + # This test uses the fact that edges iterates in the lex ordering. + @test collect(edges(ig)) == + [LabelledEdge(e...) for e ∈ [(1, 2), (1, 4), (2, 4)]] + end + @testset "stores biases as property of vertices" begin + @test biases(ig) == expected_biases + end + @testset "stores couplings both as property of edges and its own property" begin + @test couplings(ig) == expected_J_matrix + end + @testset "has default rank stored for each active vertex" begin + @test get_prop(ig, :rank) == Dict(1 => 2, 2 => 2, 4 => 2) + end end end end - @testset "Ising graph created with additional parameters" begin expected_biases = [-0.1, -0.5, 0.0] expected_couplings = Dict(Edge(1, 2) => 0.3, Edge(1, 3) => 2.0, Edge(2, 3) => -1.0) + expected_couplings = Dict(Edge(1, 2) => 0.3, Edge(1, 3) => 2.0, Edge(2, 3) => -1.0) expected_J_matrix = [ [0 0.3 2.0] [0 0 -1.0] @@ -100,7 +73,7 @@ end ig = ising_graph( "$(@__DIR__)/instances/example.txt", - sgn = -1, + scale = -1, rank_override = Dict(1 => 3, 4 => 4), ) @@ -116,22 +89,18 @@ end end end - -@testset "Ising" begin +@testset "Ising model is correct" begin L = 4 N = L^2 instance = "$(@__DIR__)/instances/$(N)_001.txt" - ig = ising_graph(instance) @test nv(ig) == N - for i ∈ 1:N @test has_vertex(ig, i) end A = adjacency_matrix(ig) - B = zeros(Int, N, N) for i ∈ 1:N nbrs = SpinGlassNetworks.unique_neighbors(ig, i) @@ -139,56 +108,12 @@ end B[i, j] = 1 end end - @test B + B' == A - @testset "Naive brute force for +/-1" begin - k = 2^N - - sp = brute_force(ig, num_states = k) - - s = 5 - - @test sp.energies ≈ energy.(sp.states, Ref(ig)) - - β = rand(Float64) - ρ = gibbs_tensor(ig, β) - - @test size(ρ) == Tuple(fill(2, N)) - - r = exp.(-β .* sp.energies) - R = r ./ sum(r) - - @test sum(R) ≈ 1 - @test sum(ρ) ≈ 1 - - @test [ρ[idx.(σ)...] for σ ∈ sp.states] ≈ R - end - - @testset "Naive brute force for general spins" begin - L = 4 - instance = "$(@__DIR__)/instances/$(L)_001.txt" - - ig = ising_graph(instance) - - set_prop!(ig, :rank, [3, 2, 5, 4]) - rank = get_prop(ig, :rank) - - all = prod(rank) - sp = brute_force(ig, num_states = all) - - β = rand(Float64) - ρ = exp.(-β .* sp.energies) - - ϱ = ρ ./ sum(ρ) - ϱ̃ = gibbs_tensor(ig, β) - - @test [ϱ̃[idx.(σ)...] for σ ∈ sp.states] ≈ ϱ - end - @testset "Reading from Dict" begin instance_dict = Dict() ising = CSV.File(instance, types = [Int, Int, Float64], header = 0, comment = "#") + ising = CSV.File(instance, types = [Int, Int, Float64], header = 0, comment = "#") for (i, j, v) ∈ ising push!(instance_dict, (i, j) => v) @@ -198,7 +123,6 @@ end ig_dict = ising_graph(instance_dict) @test nv(ig_dict) == nv(ig) - @test collect(edges(ig)) == collect(edges(ig_dict)) end @@ -210,19 +134,16 @@ end β = 1 instance = "$(@__DIR__)/instances/pathological/test_$(m)_$(n)_$(t).txt" - ising = CSV.File(instance, types = [Int, Int, Float64], header = 0, comment = "#") ig = ising_graph(instance) conf = [ - -1 1 1 -1 -1 -1 1 1 1 -1 1 1 -1 1 -1 1 - -1 1 1 -1 -1 -1 1 1 1 -1 1 1 -1 1 -1 -1 - -1 1 1 -1 -1 1 1 1 1 -1 1 1 -1 1 -1 1 - -1 1 1 -1 -1 1 1 1 1 -1 1 1 -1 1 -1 -1 + [-1, 1, 1, -1, -1, -1, 1, 1, 1, -1, 1, 1, -1, 1, -1, 1], + [-1, 1, 1, -1, -1, -1, 1, 1, 1, -1, 1, 1, -1, 1, -1, -1], + [-1, 1, 1, -1, -1, 1, 1, 1, 1, -1, 1, 1, -1, 1, -1, 1], + [-1, 1, 1, -1, -1, 1, 1, 1, 1, -1, 1, 1, -1, 1, -1, -1], ] - - eng = _energy(ig, conf) - + eng = energy(conf, ig) couplings = Dict() for (i, j, v) ∈ ising push!(couplings, (i, j) => v) @@ -231,12 +152,9 @@ end cedges = Dict() push!(cedges, (1, 2) => [(1, 4), (1, 5), (1, 6)]) push!(cedges, (1, 5) => [(1, 13)]) - push!(cedges, (2, 3) => [(4, 7), (5, 7), (6, 8), (6, 9)]) push!(cedges, (2, 6) => [(6, 16), (6, 18), (5, 16)]) - push!(cedges, (5, 6) => [(13, 16), (13, 18)]) - push!(cedges, (6, 10) => [(18, 28)]) push!( cedges, @@ -323,16 +241,13 @@ end -15.4, -15.4, ] - for i ∈ 1:num_config @test exact_energy[i] == low_energies[i] == eng[i] end - end end @testset "Pruning" begin - @testset "No vertices of degree zero" begin instance = Dict( (1, 1) => 0.1, @@ -341,7 +256,6 @@ end (4, 2) => 1.0, (1, 2) => -0.3, ) - ig = ising_graph(instance) ng = prune(ig) @test nv(ig) == nv(ng) @@ -349,7 +263,6 @@ end @testset "All vertices of degree zero with no local fields" begin instance = Dict((1, 1) => 0.0, (2, 2) => 0.0) - ig = ising_graph(instance) ng = prune(ig) @test nv(ng) == 0 @@ -357,7 +270,6 @@ end @testset "All vertices of degree zero, but with local fields" begin instance = Dict((1, 1) => 0.1, (2, 2) => 0.5) - ig = ising_graph(instance) ng = prune(ig) @test nv(ng) == 2 @@ -372,10 +284,8 @@ end (1, 2) => -0.3, (5, 5) => 0.1, ) - ig = ising_graph(instance) ng = prune(ig) - @test nv(ng) == nv(ig) @test vertices(ng) == collect(1:nv(ng)) end @@ -389,10 +299,8 @@ end (1, 2) => -0.3, (5, 5) => 0.0, ) - ig = ising_graph(instance) ng = prune(ig) - @test nv(ng) == nv(ig) - 1 @test vertices(ng) == collect(1:nv(ng)) end diff --git a/test/runtests.jl b/test/runtests.jl index e6d9866..c968233 100644 --- a/test/runtests.jl +++ b/test/runtests.jl @@ -1,13 +1,40 @@ using SpinGlassNetworks +using SpinGlassTensors using LabelledGraphs using Graphs using MetaGraphs using Logging using Test +using CUDA -Base.:(==)(e1::LabelledEdge, e2::LabelledEdge) = src(e1) == src(e2) && dst(e1) == dst(e2) -my_tests = ["ising.jl", "factor.jl"] +function _energy(config::Dict, couplings::Dict, cedges::Dict, n::Int) + eng = zeros(1, n) + for (i, j) ∈ keys(cedges) + for (k, l) ∈ values(cedges[i, j]) + for m ∈ 1:length(config[k]) + s = config[k][m] + r = config[l][m] + J = couplings[k, l] + if k == l + eng[m] += dot(s, J) + else + eng[m] += dot(s, J, r) + end + end + end + end + eng +end + +my_tests = [ + "ising.jl", + "clustered_hamiltonian.jl", + "bp_1site.jl", + "bp_2site.jl", + "utils.jl", + "spectrum.jl", +] for my_test ∈ my_tests include(my_test) diff --git a/test/spectrum.jl b/test/spectrum.jl new file mode 100644 index 0000000..cf7148a --- /dev/null +++ b/test/spectrum.jl @@ -0,0 +1,50 @@ +using CSV +using LinearAlgebra +using LabelledGraphs + +@testset "Spectrum and related properties of the Ising model are correct" begin + L = 4 + N = L^2 + instance = "$(@__DIR__)/instances/$(N)_001.txt" + ig = ising_graph(instance) + + @testset "Naive brute force for +/-1" begin + k = 2^N + sp = brute_force(ig, num_states = k) + + β = rand(Float64) + ρ = gibbs_tensor(ig, β) + + r = exp.(-β .* sp.energies) + R = r ./ sum(r) + + @test size(ρ) == Tuple(fill(2, N)) + @test sum(R) ≈ sum(ρ) ≈ 1 + @test sp.energies ≈ energy(sp.states, ig) + @test [ρ[idx.(σ)...] for σ ∈ sp.states] ≈ R + + for (i, state) in enumerate(sp.states) + state_dict = Dict(i => s for (i, s) ∈ enumerate(state)) + energy(ig, state_dict) ≈ sp.energies[i] + end + end + + @testset "Naive brute force for general spins" begin + L = 4 + ig = ising_graph("$(@__DIR__)/instances/$(L)_001.txt") + + set_prop!(ig, :rank, [3, 2, 5, 4]) + rank = get_prop(ig, :rank) + + all = prod(rank) + sp = full_spectrum(ig, num_states = all) + + β = rand(Float64) + ρ = exp.(-β .* sp.energies) + + ϱ = ρ ./ sum(ρ) + ϱ̃ = gibbs_tensor(ig, β) + + @test [ϱ̃[idx.(σ)...] for σ ∈ sp.states] ≈ ϱ + end +end diff --git a/test/utils.jl b/test/utils.jl new file mode 100644 index 0000000..c822e8a --- /dev/null +++ b/test/utils.jl @@ -0,0 +1,26 @@ +instance_dir = "$(@__DIR__)/instances/pegasus/" +instances = ["P2"] #, "P4", "P8", "P16"] + +@testset verbose = true "Renumerated instances generate correct clustered Hamiltonian" begin + size = [2, 4, 8, 16] + + @testset "$instance" for (i, instance) ∈ enumerate(instances) + instance = instance * ".txt" + s = size[i] - 1 + m, n, t = s, s, 24 + max_cl_states = 2 + + ig = ising_graph(joinpath(instance_dir, instance)) + cl_h = clustered_hamiltonian( + ig, + max_cl_states, + spectrum = brute_force, + cluster_assignment_rule = super_square_lattice((m, n, t)), + ) + @test nv(cl_h) == s^2 + + if s > 1 + @test all(has_edge(cl_h, (l, k), (l + 1, k - 1)) for l ∈ 1:s-1, k ∈ 2:s) + end + end +end