Improve target range factor with relative distance

- Replace harsh 1/(1+distance) with bounded relative scoring
- Factor > 1.0 when moving toward target, < 1.0 when moving away
- Minimum factor 0.1 to avoid zero weights
- Test with max_path=150 shows reaching ~90% of 2-octave target

src/graph.py

@@ -343,11 +343,11 @@ class PathFinder:
         config: dict,
         cumulative_trans: "Pitch | None",
     ) -> float:
-        """Returns 1.0 if at target, 0.0 if far from target.
+        """Returns factor based on movement toward target.
 
         Target progresses based on position in path.
+        Factor > 1.0 if moving toward target, < 1.0 if moving away.
         """
-        # Check weight - if 0, return 1.0 (neutral)
         if config.get("weight_target_range", 1) == 0:
             return 1.0
 
@@ -364,15 +364,27 @@ class PathFinder:
         progress = len(path) / max_path
         current_target = progress * target_cents
 
+        current_cumulative_cents = cumulative_trans.to_cents()
+
         edge_trans = edge_data.get("transposition")
         new_cumulative = cumulative_trans.transpose(edge_trans)
         new_cumulative_cents = new_cumulative.to_cents()
 
-        # Closeness: 1.0 if at target, 0.0 if far
         if current_target <= 0:
             return 1.0
-        distance = abs(new_cumulative_cents - current_target)
-        return 1.0 / (1.0 + distance)
+
+        dist_before = abs(current_cumulative_cents - current_target)
+        dist_after = abs(new_cumulative_cents - current_target)
+
+        if dist_before == 0:
+            return 1.0
+
+        if dist_after < dist_before:
+            return 1.0 + (dist_before - dist_after) / dist_before
+        elif dist_after > dist_before:
+            return max(0.1, 1.0 - (dist_after - dist_before) / dist_before)
+        else:
+            return 1.0
 
     def is_hamiltonian(self, path: list["Chord"]) -> bool:
         """Check if a path is Hamiltonian (visits all nodes exactly once)."""