baseline

2026-09-12 16:58:25 +08:00
parent 1fa2264a17
commit 892451dca9
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+"""问题二同案配对对比：最优 / 两个基线 / 网格参考，用同一评估器打分。"""
+
+from __future__ import annotations
+
+import math
+
+from mcm26b.config import ProblemConfig
+from mcm26b.planner import _worst_posterior_radius
+
+from project.common import (Method, RESULTS_DIR, markdown_table, mean, ratio,
+                            write_json, write_text)
+from project.q2 import baseline, cases, optimal, reference
+from project.q2.region import first_bearing_region
+
+METHODS: tuple[Method, ...] = (optimal.METHOD, baseline.PERP, baseline.MEC,
+                               reference.METHOD)
+EVAL_WITNESS_SPACING_M = 60.0
+
+
+def _evaluate(case: dict, config: ProblemConfig) -> list[dict]:
+    region = first_bearing_region(case, config)
+    station = (case["station"][0], case["station"][1])
+    initial_radius = region.mec()[1]
+    half_angle = (config.field.bearing_error_half_deg
+                  + config.search.bearing_rounding_margin_deg)
+    witnesses = region.witnesses(EVAL_WITNESS_SPACING_M)
+
+    plans = [optimal.candidate(case, config),
+             baseline.candidate_perp(case, config),
+             baseline.candidate_mec(case, config),
+             reference.candidate(case, config)]
+    rows = []
+    for plan in plans:
+        q = (plan["position"][0], plan["position"][1])
+        guarantee_distance = max(math.dist(q, vertex) for vertex in region.polygon)
+        worst = _worst_posterior_radius(
+            region, q, witnesses, half_angle,
+            config.field.near_radius_m, config.search.polygon_sides)
+        move_cost = math.dist(station, q) / config.cost.speed_mps
+        rows.append({
+            "case_id": case["id"],
+            "method": plan["method"],
+            "initial_radius_m": round(initial_radius, 4),
+            "q": [round(q[0], 2), round(q[1], 2)],
+            "guaranteed_reception": bool(guarantee_distance
+                                         <= config.field.r_eff_min_m + 1e-6),
+            "guarantee_distance_m": round(guarantee_distance, 3),
+            "worst_posterior_radius_m": round(worst, 4),
+            "improvement_m": round(initial_radius - worst, 4),
+            "final_ratio": round(worst / initial_radius, 4) if initial_radius > 0 else None,
+            "move_cost_s": round(move_cost, 4),
+            "reason": plan["reason"],
+        })
+    return rows
+
+
+def _aggregate(rows: list[dict], labels: list[str]) -> dict:
+    summary: dict[str, dict] = {}
+    initial = mean(r["initial_radius_m"] for r in rows) if rows else 0.0
+    for label in labels:
+        part = [r for r in rows if r["method"] == label]
+        if not part:
+            continue
+        summary[label] = {
+            "cases": len(part),
+            "guaranteed_rate": ratio(sum(r["guaranteed_reception"] for r in part),
+                                     len(part)),
+            "worst_posterior_mean_m": mean(r["worst_posterior_radius_m"] for r in part),
+            "worst_posterior_median_m": sorted(
+                r["worst_posterior_radius_m"] for r in part)[len(part) // 2],
+            "improvement_mean_m": mean(r["improvement_m"] for r in part),
+            "final_ratio_mean": mean(r["final_ratio"] for r in part
+                                     if r["final_ratio"] is not None),
+            "move_cost_mean_s": mean(r["move_cost_s"] for r in part),
+        }
+    summary["_initial_radius_mean_m"] = initial
+    return summary
+
+
+def _render(rows: list[dict], aggregate: dict, labels: list[str]) -> str:
+    headers = ["算例", "方法", "初始 ρ(m)", "第二点 q", "保证接收",
+               "q 到 K₁ 最远(m)", "最坏后验 ρ'(m)", "收缩(m)", "移动(s)"]
+    body = []
+    order = {label: index for index, label in enumerate(labels)}
+    for row in sorted(rows, key=lambda r: (r["case_id"], order.get(r["method"], 99))):
+        body.append([
+            row["case_id"], row["method"], _f(row["initial_radius_m"]),
+            f"({row['q'][0]:.0f},{row['q'][1]:.0f})",
+            "是" if row["guaranteed_reception"] else "否",
+            _f(row["guarantee_distance_m"]), _f(row["worst_posterior_radius_m"]),
+            _f(row["improvement_m"]), _f(row["move_cost_s"]),
+        ])
+    text = ["# 问题二：第二检测点选择对比", "",
+            "初始 ρ 为首次示向度候选集 K₁ 的最小包围半径；最坏后验按"
+            "目标边界采样 × 读数区间端点统一评估。", "",
+            markdown_table(headers, body), "", "## 逐方法汇总", ""]
+    agg_headers = ["方法", "保证接收率", "最坏后验均值(m)", "中位(m)",
+                   "平均收缩(m)", "终值比 ρ'/ρ", "移动均值(s)"]
+    agg_rows = []
+    for label in labels:
+        s = aggregate.get(label)
+        if not s:
+            continue
+        agg_rows.append([label, f"{s['guaranteed_rate']*100:.1f}%",
+                         _f(s["worst_posterior_mean_m"]),
+                         _f(s["worst_posterior_median_m"]),
+                         _f(s["improvement_mean_m"]),
+                         _f(s["final_ratio_mean"], 3),
+                         _f(s["move_cost_mean_s"])])
+    text.append(markdown_table(agg_headers, agg_rows))
+    text.append("")
+    text.append(f"初始 ρ 均值：{aggregate['_initial_radius_mean_m']:.2f} m")
+    return "\n".join(text) + "\n"
+
+
+def _f(value, digits: int = 2) -> str:
+    if value is None:
+        return "—"
+    if isinstance(value, bool):
+        return "是" if value else "否"
+    if isinstance(value, float):
+        return f"{value:.{digits}f}"
+    return str(value)
+
+
+def run(quick: bool = False) -> dict:
+    config = ProblemConfig()
+    case_list = cases.random_cases(8 if quick else 30, config=config)
+    rows = [row for case in case_list for row in _evaluate(case, config)]
+    labels = [m.key for m in METHODS]
+    aggregate = _aggregate(rows, labels)
+    payload = {"question": 2, "methods": [m.__dict__ for m in METHODS],
+               "cases": case_list, "rows": rows, "aggregate": aggregate}
+    write_json(RESULTS_DIR / "q2.json", payload)
+    write_text(RESULTS_DIR / "q2.md", _render(rows, aggregate, labels))
+    return payload