Refactor DCA factor: rename to DCA Voice Movement and DCA Hamiltonian
- Rename _factor_dca to _factor_dca_voice_movement (tracks voice pitch changes) - Rename _factor_hamiltonian to _factor_dca_hamiltonian (tracks node visit counts) - Update CLI: --weight-dca -> --weight-dca-voice-movement - Update CLI: --weight-hamiltonian -> --weight-dca-hamiltonian - Remove hamiltonian coverage from analysis (no longer tracking)
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@ -195,28 +195,17 @@ def format_analysis(metrics: dict) -> str:
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f"Percentage: {metrics['contrary_motion_percent']:.1f}%",
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f"Avg score: {metrics['contrary_motion_score']:.2f}",
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"",
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"--- DCA (Voice Stay) ---",
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"--- DCA Voice Movement ---",
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f"Avg stay count: {metrics['dca_avg_voice_stay']:.2f} steps",
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f"Max stay count: {metrics['dca_max_voice_stay']} steps",
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"",
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"--- Hamiltonian ---",
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f"Unique nodes: {metrics['hamiltonian_unique_nodes']}",
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"--- Target Range ---",
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f"Target: {metrics['target_octaves']} octaves ({metrics['target_cents']:.0f} cents)",
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f"Start: {metrics['target_start_cents']:.0f} cents",
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f"End: {metrics['target_end_cents']:.0f} cents",
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f"Achieved: {metrics['target_actual_cents']:.0f} cents ({metrics['target_percent']:.1f}%)",
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]
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if metrics["hamiltonian_coverage"] is not None:
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lines.append(f"Coverage: {metrics['hamiltonian_coverage']:.1f}%")
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lines.extend(
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[
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"",
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"--- Target Range ---",
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f"Target: {metrics['target_octaves']} octaves ({metrics['target_cents']:.0f} cents)",
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f"Start: {metrics['target_start_cents']:.0f} cents",
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f"End: {metrics['target_end_cents']:.0f} cents",
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f"Achieved: {metrics['target_actual_cents']:.0f} cents ({metrics['target_percent']:.1f}%)",
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]
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)
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return "\n".join(lines)
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57
src/graph.py
57
src/graph.py
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@ -44,6 +44,11 @@ class PathFinder:
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graph_path = [graph_node]
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# Track how long since each node was last visited (for DCA Hamiltonian)
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node_visit_counts = {node: 0 for node in self.graph.nodes()}
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# Mark start node as just visited
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node_visit_counts[graph_node] = 0
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from .pitch import Pitch
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dims = output_chord.dims
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@ -55,6 +60,10 @@ class PathFinder:
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voice_stay_count = [0] * num_voices
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for _ in range(max_length):
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# Increment all node visit counts
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for node in node_visit_counts:
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node_visit_counts[node] += 1
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out_edges = list(self.graph.out_edges(graph_node, data=True))
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if not out_edges:
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@ -68,6 +77,7 @@ class PathFinder:
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tuple(voice_stay_count),
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graph_path,
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cumulative_trans,
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node_visit_counts,
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)
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edge = choices(out_edges, weights=weights)[0]
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@ -103,6 +113,10 @@ class PathFinder:
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graph_node = next_graph_node
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graph_path.append(graph_node)
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# Reset visit count for visited node
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if next_graph_node in node_visit_counts:
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node_visit_counts[next_graph_node] = 0
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path.append(output_chord)
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last_graph_nodes = last_graph_nodes + (graph_node,)
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if len(last_graph_nodes) > 2:
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@ -164,6 +178,7 @@ class PathFinder:
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voice_stay_count: tuple[int, ...] | None = None,
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graph_path: list["Chord"] | None = None,
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cumulative_trans: "Pitch | None" = None,
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node_visit_counts: dict | None = None,
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) -> list[float]:
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"""Calculate weights for edges based on configuration.
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@ -201,10 +216,12 @@ class PathFinder:
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melodic_values.append(self._factor_melodic_threshold(edge_data, config))
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contrary_values.append(self._factor_contrary_motion(edge_data, config))
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hamiltonian_values.append(
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self._factor_hamiltonian(edge, graph_path, config)
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self._factor_dca_hamiltonian(edge, node_visit_counts, config)
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)
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dca_values.append(
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self._factor_dca(edge, path, voice_stay_count, config, cumulative_trans)
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self._factor_dca_voice_movement(
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edge, path, voice_stay_count, config, cumulative_trans
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)
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)
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target_values.append(
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self._factor_target_range(edge, path, config, cumulative_trans)
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@ -248,9 +265,9 @@ class PathFinder:
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if contrary_norm:
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w += contrary_norm[i] * config.get("weight_contrary_motion", 0)
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if hamiltonian_norm:
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w += hamiltonian_norm[i] * config.get("weight_hamiltonian", 1)
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w += hamiltonian_norm[i] * config.get("weight_dca_hamiltonian", 1)
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if dca_norm:
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w += dca_norm[i] * config.get("weight_dca", 1)
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w += dca_norm[i] * config.get("weight_dca_voice_movement", 1)
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if target_norm:
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w += target_norm[i] * config.get("weight_target_range", 1)
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@ -321,23 +338,33 @@ class PathFinder:
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distance = abs(num_up - ideal_up)
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return max(0.0, 1.0 - (distance / ideal_up))
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def _factor_hamiltonian(
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self, edge: tuple, graph_path: list | None, config: dict
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def _factor_dca_hamiltonian(
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self, edge: tuple, node_visit_counts: dict | None, config: dict
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) -> float:
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"""Returns 1.0 if destination not visited, lower if already visited."""
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# Check weight - if 0, return 1.0 (neutral)
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if config.get("weight_hamiltonian", 1) == 0:
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"""Returns probability based on how long since node was last visited.
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DCA Hamiltonian: longer since visited = higher probability.
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Similar to DCA voice movement but for graph nodes.
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"""
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if config.get("weight_dca_hamiltonian", 1) == 0:
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return 1.0
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if not config.get("hamiltonian", False):
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if node_visit_counts is None:
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return 1.0
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destination = edge[1]
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if graph_path and destination in graph_path:
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return 0.1 # penalize revisiting
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return 1.0
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if destination not in node_visit_counts:
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return 1.0
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def _factor_dca(
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visit_count = node_visit_counts[destination]
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total_counts = sum(node_visit_counts.values())
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if total_counts == 0:
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return 1.0
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return visit_count / total_counts
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def _factor_dca_voice_movement(
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self,
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edge: tuple,
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path: list,
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@ -352,7 +379,7 @@ class PathFinder:
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Higher probability = more likely to choose edge where long-staying voices change.
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"""
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if config.get("weight_dca", 1) == 0:
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if config.get("weight_dca_voice_movement", 1) == 0:
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return 1.0
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if voice_stay_count is None or len(path) == 0:
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12
src/io.py
12
src/io.py
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@ -303,16 +303,16 @@ def main():
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help="Weight for contrary motion factor (0=disabled, default: 0)",
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)
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parser.add_argument(
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"--weight-hamiltonian",
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"--weight-dca-hamiltonian",
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type=float,
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default=1,
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help="Weight for Hamiltonian factor (0=disabled, default: 1)",
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help="Weight for DCA Hamiltonian factor - favors long-unvisited nodes (0=disabled, default: 1)",
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)
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parser.add_argument(
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"--weight-dca",
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"--weight-dca-voice-movement",
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type=float,
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default=1,
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help="Weight for DCA factor - favors edges where voices stay (0=disabled, default: 1)",
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help="Weight for DCA voice movement factor - favors voices that stay long to change (0=disabled, default: 1)",
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)
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parser.add_argument(
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"--weight-target-range",
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@ -435,8 +435,8 @@ def main():
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# Soft factor weights
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weights_config["weight_melodic"] = args.weight_melodic
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weights_config["weight_contrary_motion"] = args.weight_contrary_motion
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weights_config["weight_hamiltonian"] = args.weight_hamiltonian
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weights_config["weight_dca"] = args.weight_dca
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weights_config["weight_dca_hamiltonian"] = args.weight_dca_hamiltonian
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weights_config["weight_dca_voice_movement"] = args.weight_dca_voice_movement
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# Target range
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if args.target_range > 0:
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