simplify DFA decoder

src/jvmMain/kotlin/ai/hypergraph/kaliningraph/automata/JFSA.kt

@@ -50,13 +50,16 @@ fun JAutomaton<String, Double>.toDot(processed: MutableSet<Any> = mutableSetOf()
  * previous n-1 transitions, i.e., q' ~ argmax_{q'} P(q' | q_{t-1}, ..., q_{t-n+1})
  */
 
-data class FSATrajectory(val traj: List<Σᐩ?>, val lastState: BState, val score: Double, val id: Int = traj.hashCode()) {
+data class FSATrajectory(val traj: List<Σᐩ?>, val lastState: BState,
+                         val score: Double, val id: Int = traj.hashCode()): Comparable<FSATrajectory> {
   val isComplete: Boolean = lastState.isAccept
   val tokens by lazy { traj.reversed().filterNotNull() }
+  val lenNormedScore = score / traj.size
   fun append(tok: Σᐩ?, state: BState, score: Double) =
     FSATrajectory(listOf(tok) + traj, state, score, id * 31 + tok.hashCode())
   override fun toString() = tokens.joinToString(" ")
-  override fun equals(other: Any?): Boolean = other is FSATrajectory && id == other.id
+  override fun equals(other: Any?): Boolean = other is FSATrajectory && lenNormedScore == other.lenNormedScore
+  override fun compareTo(other: FSATrajectory): Int = lenNormedScore.compareTo(other.lenNormedScore)
 }
 
 fun BAutomaton.min(): BAutomaton = minimize(this)
@@ -105,8 +108,8 @@ fun BAutomaton.decodeDFA(
   beamWidth: Long = 1_000_000L, // Maximum number of trajectories to keep at each step
 ): List<Σᐩ> {
   val startTime = TimeSource.Monotonic.markNow()
-  val fullTrajectories = PriorityBlockingQueue<FSATrajectory>(10000, compareBy { it.score / it.traj.size }) // Max-heap for full trajectories
-  val beam = PriorityQueue<FSATrajectory>(compareBy { it.score / it.traj.size }) // Beam for partial trajectories
+  val fullTrajectories = PriorityBlockingQueue<FSATrajectory>(10000) // Max-heap for full trajectories
+  val beam = PriorityQueue<FSATrajectory>() // Beam for partial trajectories
 
   beam.add(FSATrajectory(List(mc.memory) { null }, initialState, 0.0))
 
@@ -127,17 +130,16 @@ fun BAutomaton.decodeDFA(
         if (traj.isComplete) {
           fullTrajectories.add(traj)
           callback(traj.toString())
-          if (traj.lastState.transitions.isNotEmpty()) listOf(traj to traj.score) else emptyList()
-        } else { listOf(traj to traj.score) }
+          if (traj.lastState.transitions.isNotEmpty()) listOf(traj) else emptyList()
+        } else { listOf(traj) }
       }.stream()
-    }.sorted(compareBy { it.second / it.first.traj.size })
-      .limit(beamWidth).map { it.first }.toList()
+    }.sorted().limit(beamWidth).toList()
 
     beam.clear()
     beam.addAll(nextBeam)
   }
 
-  val deduped = fullTrajectories.map { it.toString() }.distinct().toList()
+  val deduped = fullTrajectories.distinct().map { it.toString() }.toList()
 
   println("Took ${startTime.elapsedNow()} to decode ${deduped.size} trajectories, with ${beam.size} in queue")
   return deduped