feat(solve): random_walk 落地；修默认采样器真 bug（integers 不收 float）
random_walk 由用户本人写；Codex 改一处真 bug + 工程侧。 - 真 bug：默认采样器 rng.integers(0, 2, size=size, dtype=float) 被 numpy 拒掉 （Unsupported dtype dtype(float64) for integers），默认路径 100% success=False、 data=None，连 docstring 的 Examples 都跑不通。改成 integers(size=size) * 2 - 1， 由后面的 np.asarray(..., dtype=float) 转 float；实测 (5,101)、步长恒 ±1、同种子可复现 - 工程：ruff W293 x2；docstring 补全（n_steps/n_paths/start/random_state + Returns / Raises / Notes，写清 step_sampler 收二维 size=(n_paths, n_steps)） - tests：random_walk 首次有测试，新增 6 条（形状 + start、步长恒 ±1、可复现、自定义 高斯采样器、3 类非法入参、docstring Example） - monte_carlo_integral：用户第二轮已收 ndarray + 把三行提出 if/else（决策 52） - 验证：205 收集 / 174 passed / 31 skipped / 0 xfail，ruff 0，pyright 0 - docs：STATUS 决策 53 + random_walk 4 条待办；CHANGELOG；易错点 24/25，第 18 条复发 +1
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@@ -286,10 +286,13 @@ def monte_carlo_integral(
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>>> abs(r.metrics["mean"] - 1 / 3) < 0.01 # ∫₀¹ x² dx = 1/3
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True
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"""
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if not isinstance(bounds, (list, tuple)) or len(bounds) == 0:
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raise ValueError("bounds 必须是非空的列表或元组")
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if not isinstance(bounds, np.ndarray):
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if not isinstance(bounds, (list, tuple)) or len(bounds) == 0:
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raise ValueError("bounds 必须是非空的列表或元组")
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bounds_arr = np.asarray(bounds)
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bounds_arr = np.asarray(bounds)
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else:
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bounds_arr = bounds.copy()
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if bounds_arr.ndim != 2 or bounds_arr.shape[1] != 2:
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raise ValueError(f"bounds 必须形如 [(lo, hi), ...]，当前形状为 {bounds_arr.shape}，请检查是否漏了括号")
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@@ -320,15 +323,12 @@ def monte_carlo_integral(
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random_state=random_state,
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)
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result.name = "monte_carlo_integral"
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result.params["bounds"] = bounds
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result.params["dimension"] = d
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if result.success:
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result.name = "monte_carlo_integral"
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result.params["bounds"] = bounds
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result.params["dimension"] = d
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result.message = f"蒙特卡洛多重积分成功：维度 {d}，体积 {volume:.4f}，模拟 {n} 次。"
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else:
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result.name = "monte_carlo_integral"
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result.params["bounds"] = bounds
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result.params["dimension"] = d
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result.message = f"蒙特卡洛多重积分失败：维度 {d}，体积 {volume:.4f}，模拟 {n} 次。{result.message}"
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return result
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@@ -341,25 +341,91 @@ def random_walk(
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start: float = 0.0,
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random_state: int | None = None,
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) -> Any:
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"""随机游走轨迹。
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"""随机游走轨迹：一次抽完整批增量，再 `cumsum` 成路径。
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Parameters
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----------
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n_steps:
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每条路径的步数，正整数。`data` 的列数是 `n_steps + 1`（多出的那一列是起点）。
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n_paths:
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路径条数，正整数，默认 1 条。
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step_sampler:
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`f(rng, size) -> 增量`，默认为 ±1 等概率（对称随机游走）。
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`f(rng, size) -> 一批增量`。批量大小由本函数以 `size=(n_paths, n_steps)` 传入，
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必须返回形状 `(n_paths, n_steps)` 的数组（契约同 `monte_carlo` 的 `sampler`，只是这里是二维）。
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默认 ±1 等概率（对称随机游走）。
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start:
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起点，写进每条路径的第 0 列。
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random_state:
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随机种子。`None` 时跟随 `core.set_seed` 的全局流，见 `core.random.get_rng`。
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Returns
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-------
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`Result`：`data` 形状 `(n_paths, n_steps + 1)`。
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`Result`：`data` 形状 `(n_paths, n_steps + 1)`，第 0 列恒为 `start`，之后是逐列累加出来的位置；
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`metrics={"n_steps", "n_paths"}`。
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Raises
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------
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ValueError:
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`n_steps` / `n_paths` 不是正整数。
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TypeError:
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`start` 不是数值。
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Notes
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-----
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随机游走是"平方根扩散"的原型：位置的标准差随步数按 `√t` 增长，所以典型图形是 `±√t` 的喇叭口。
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本函数只负责 `cumsum`；**建模差异全在 `step_sampler` 里** —— 换成 `rng.normal(0, 1, size)` 就是高斯游走，
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换成带正漂移的增量就是有趋势的游走。
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Examples
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--------
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>>> import numpy as np
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>>> r = random_walk(n_steps=100, n_paths=5, random_state=0)
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>>> r.data.shape
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(5, 101)
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>>> bool(np.all(np.abs(np.diff(r.data, axis=1)) == 1)) # 默认步长恒为 1
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True
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"""
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# TODO(L1)
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raise NotImplementedError
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if isinstance(n_steps, bool) or not isinstance(n_steps, (int, np.integer)) or n_steps <= 0:
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raise ValueError(f"n_steps 必须是正整数，你传入了 {n_steps}")
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if isinstance(n_paths, bool) or not isinstance(n_paths, (int, np.integer)) or n_paths <= 0:
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raise ValueError(f"n_paths 必须是正整数，你传入了 {n_paths}")
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if not isinstance(start, (int, float, np.number)):
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raise TypeError(f"start 必须是数值，你传入了 {type(start).__name__}")
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rng = get_rng(random_state)
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if step_sampler is None:
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def _default_step_sampler(rng, size):
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return rng.integers(0, 2, size=size) * 2 - 1
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step_sampler = _default_step_sampler
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try:
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steps = np.asarray(step_sampler(rng, size=(n_paths, n_steps)), dtype=float)
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cumsum = np.cumsum(steps, axis=1)
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paths = np.zeros((n_paths, n_steps + 1), dtype=float)
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paths[:, 0] = start
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paths[:, 1:] = start + cumsum
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return Result(
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name="random_walk",
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params={"n_steps": n_steps, "n_paths": n_paths, "start": start},
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metrics={"n_steps": n_steps, "n_paths": n_paths},
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data=paths,
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success=True,
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message=f"随机游走模拟成功：生成了 {n_paths} 条路径，每条路径 {n_steps} 步。"
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)
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except Exception as e:
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return Result(
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name="random_walk",
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params={"n_steps": n_steps, "n_paths": n_paths, "start": start},
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metrics={},
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data=None,
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success=False,
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message=f"随机游走模拟失败：{type(e).__name__} - {str(e)}"
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)
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def markov_simulate(
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