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feat: implement Hungarian algorithm for optimal instance pairing in reconciler
- Replaced the previous greedy matching approach with the Hungarian (Kuhn-Munkres) algorithm for improved accuracy in instance pairing, especially in ambiguous groups with multiple same-named instances. - Introduced a new Hungarian.lua module to encapsulate the algorithm's logic. - Updated the reconciler to utilize the new optimal assignment function, enhancing overall matching performance. - Documented changes in the CHANGELOG for clarity on the new implementation.
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Joe
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7 changed files with 234 additions and 287 deletions
103
plugin/src/Hungarian.lua
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103
plugin/src/Hungarian.lua
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@ -0,0 +1,103 @@
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--!strict
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--!native
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--!optimize 2
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--[[
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Minimum-cost bipartite matching via the Hungarian (Kuhn-Munkres) algorithm.
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Given a 1-indexed cost matrix cost[i][j] for `rows` row-agents and `cols`
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column-agents, returns an array of {row, col} pairs representing the
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optimal assignment that minimizes total cost. Handles rectangular matrices
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by padding to square with `padCost`.
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Complexity: O(N^3) where N = max(rows, cols).
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]]
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local function minCostAssignment(cost: { { number } }, rows: number, cols: number, padCost: number): { { number } }
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if rows == 0 or cols == 0 then
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return {}
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end
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local n = math.max(rows, cols)
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local S = n + 1
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local INF = math.huge
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local c: { { number } } = table.create(n)
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for i = 1, n do
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local row = table.create(n, padCost)
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for j = 1, n do
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if i <= rows and j <= cols then
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row[j] = cost[i][j]
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end
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end
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c[i] = row
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end
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local u = table.create(S, 0)
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local v = table.create(S, 0)
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local p = table.create(S, 0)
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local way = table.create(S, 0)
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for i = 1, n do
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p[S] = i
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local j0 = S
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local minV = table.create(S, INF)
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local used = table.create(S, false)
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while true do
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used[j0] = true
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local i0 = p[j0]
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local delta = INF
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local j1 = S
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for j = 1, n do
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if used[j] then
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continue
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end
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local cur = c[i0][j] - u[i0] - v[j]
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if cur < minV[j] then
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minV[j] = cur
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way[j] = j0
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end
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if minV[j] < delta then
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delta = minV[j]
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j1 = j
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end
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end
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u[p[S]] += delta
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v[S] -= delta
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for j = 1, n do
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if used[j] then
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u[p[j]] += delta
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v[j] -= delta
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else
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minV[j] -= delta
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end
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end
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j0 = j1
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if p[j0] == 0 then
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break
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end
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end
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while true do
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local j1 = way[j0]
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p[j0] = p[j1]
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j0 = j1
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if j0 == S then
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break
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end
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end
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end
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local result: { { number } } = {}
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for j = 1, n do
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if p[j] ~= 0 and p[j] <= rows and j <= cols then
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table.insert(result, { p[j], j })
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end
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end
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return result
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end
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return minCostAssignment
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@ -23,6 +23,7 @@ local Packages = script.Parent.Parent.Parent.Packages
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local RbxDom = require(Packages.RbxDom)
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local trueEquals = require(script.Parent.trueEquals)
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local minCostAssignment = require(script.Parent.Parent.Hungarian)
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local UNMATCHED_PENALTY = 10000
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local MAX_SCORING_DEPTH = 3
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@ -326,104 +327,6 @@ local function countOwnDiffs(vCache: VCache, sCache: SCache, classKeys: ClassCom
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return cost
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end
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-- ================================================================
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-- Optimal assignment via Hungarian (Kuhn-Munkres) algorithm
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-- ================================================================
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local function minCostAssignment(cost: { { number } }, rows: number, cols: number): { { number } }
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if rows == 0 or cols == 0 then
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return {}
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end
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local n = math.max(rows, cols)
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local big = UNMATCHED_PENALTY * 2
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local S = n + 1 -- sentinel column (replaces 0-index from the Rust version)
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local INF = math.huge
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local c: { { number } } = table.create(n)
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for i = 1, n do
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local row = table.create(n, big)
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for j = 1, n do
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if i <= rows and j <= cols then
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row[j] = cost[i][j]
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end
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end
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c[i] = row
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end
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-- u[1..n] = row potentials, u[S] unused but allocated for uniform indexing
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local u = table.create(S, 0)
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-- v[1..n] = col potentials, v[S] = sentinel potential
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local v = table.create(S, 0)
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-- p[j] = row assigned to column j (0 = unassigned), p[S] = sentinel
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local p = table.create(S, 0)
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-- way[j] = previous column in augmenting path
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local way = table.create(S, 0)
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for i = 1, n do
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p[S] = i
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local j0 = S
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local minV = table.create(S, INF)
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local used = table.create(S, false)
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while true do
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used[j0] = true
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local i0 = p[j0]
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local delta = INF
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local j1 = S
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for j = 1, n do
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if used[j] then
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continue
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end
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local cur = c[i0][j] - u[i0] - v[j]
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if cur < minV[j] then
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minV[j] = cur
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way[j] = j0
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end
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if minV[j] < delta then
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delta = minV[j]
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j1 = j
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end
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end
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-- Update potentials: sentinel + real columns
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u[p[S]] += delta
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v[S] -= delta
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for j = 1, n do
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if used[j] then
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u[p[j]] += delta
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v[j] -= delta
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else
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minV[j] -= delta
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end
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end
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j0 = j1
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if p[j0] == 0 then
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break
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end
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end
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while true do
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local j1 = way[j0]
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p[j0] = p[j1]
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j0 = j1
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if j0 == S then
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break
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end
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end
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end
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local result: { { number } } = {}
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for j = 1, n do
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if p[j] ~= 0 and p[j] <= rows and j <= cols then
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table.insert(result, { p[j], j })
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end
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end
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return result
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end
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-- ================================================================
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-- Core: 2-function mutual recursion
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-- ================================================================
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@ -666,7 +569,7 @@ matchChildren = function(
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end
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-- Optimal assignment via the Hungarian algorithm.
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local assignment = minCostAssignment(costMatrix, m, n)
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local assignment = minCostAssignment(costMatrix, m, n, UNMATCHED_PENALTY * 2)
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for _, pair in ipairs(assignment) do
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local vi = vIndices[pair[1]]
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local si = sIndices[pair[2]]
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