guzhujushi
dd9bd638b8
用户实现的第一版 markov_simulate 算法本体经探针实测正确（终点频率 [0.657, 0.343] 对照平稳分布 [2/3, 1/3]，同种子可复现），本次只动工程侧，未改任何算法行： - ruff 8 条全清（I001 导入排序 + 5 处 W293 / 2 处 W291） - docstring 从一行补全为 Parameters / Returns / Raises / Notes / Examples - 文案：马尔科夫→马尔可夫（3 处）、initail→initial、半角逗号→全角、paths[:,0]→paths[:, 0] - arr, kind = as_array(...) 的 kind 全程未用 → arr, _ - tests/test_solve.py 新增 9 条 markov_simulate 测试（此前 0 条） - STATUS.md 新增决策 56（initial 语义待用户拍板，A/B/C 三选项）与 5 条待办 - CHANGELOG 与 docs/dev/personal-pitfalls.md 易错点 27（入口分流后两条分支口径矛盾） 校验：pytest 188 passed / 31 skipped（219 收集）；ruff 0；pyright 0
564 lines
21 KiB
Python
564 lines
21 KiB
Python
"""solve 包测试。"""
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from __future__ import annotations
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import numpy as np
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import pytest
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_NOT_IMPLEMENTED = "未实现：见 docs/03-roadmap.md L0/L1"
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def _normal_batch(rng, size, **kwargs):
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"""`monte_carlo` 的 sampler 契约：收下实现传来的 `size`，一次抽够。"""
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return rng.normal(0, 1, size)
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def test_monte_carlo_integral_converges_to_analytic_value():
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"""∫₀¹ x² dx = 1/3。n=2e5 时误差应 < 0.01。"""
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from mcm_toolkit.solve import monte_carlo_integral
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r = monte_carlo_integral(lambda X: X[:, 0] ** 2, bounds=[(0, 1)], n=200_000, random_state=0)
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assert r.success
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assert abs(r.metrics["mean"] - 1 / 3) < 0.01 # 体积必须是正的
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assert len(r.data) == 200_000 # data 是 n 个样本各自的贡献
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def test_monte_carlo_integral_odd_dimension_sign():
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"""∫∫∫ x dV over [0,1]³ = 1/2 —— 奇数维最容易暴露"体积符号写反"。"""
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from mcm_toolkit.solve import monte_carlo_integral
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r = monte_carlo_integral(lambda X: X[:, 0], bounds=[(0, 1)] * 3, n=200_000, random_state=0)
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assert abs(r.metrics["mean"] - 0.5) < 0.01
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def test_monte_carlo_integral_signed_bounds():
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"""∫₀² x dx = 2 —— 边界带宽度时，体积算错和符号写反都会现形。"""
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from mcm_toolkit.solve import monte_carlo_integral
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r = monte_carlo_integral(lambda X: X[:, 0], bounds=[(0, 2)], n=200_000, random_state=0)
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assert abs(r.metrics["mean"] - 2.0) < 0.02
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def test_monte_carlo_integral_2d_and_params():
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from mcm_toolkit.solve import monte_carlo_integral
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bounds = [(0, 1), (0, 1)]
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r = monte_carlo_integral(lambda X: X[:, 0] * X[:, 1], bounds=bounds, n=200_000, random_state=0)
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assert abs(r.metrics["mean"] - 0.25) < 0.01
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assert r.name == "monte_carlo_integral"
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assert r.params["dimension"] == 2
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assert r.params["bounds"] == bounds
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def test_monte_carlo_integral_rejects_bad_bounds():
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"""四类非法 bounds 都要在入口抛 ValueError：区间反了 / 漏括号 / 空 / 参差不齐。"""
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from mcm_toolkit.solve import monte_carlo_integral
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for bad in ([(1, 0)], [0, 1], [], [(0, 1), (0, 2, 3)]):
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with pytest.raises(ValueError):
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monte_carlo_integral(lambda X: X[:, 0], bounds=bad, n=100, random_state=0)
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def test_monte_carlo_integral_failure_message_stays_failure():
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"""失败时 message 不能被包装层改写成"成功"（易错点 22）。"""
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from mcm_toolkit.solve import monte_carlo_integral
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r = monte_carlo_integral(lambda X: 1.0, bounds=[(0, 1)], n=100, random_state=0)
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assert r.success is False
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assert "成功" not in r.message
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assert "失败" in r.message
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assert r.params["dimension"] == 1 # 失败也要留下追溯信息
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def test_monte_carlo_integral_accepts_numpy_integer_n():
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"""§4.7：`np.int64` 不是 `int` 的子类，注解与运行期都要收（易错点 1）。"""
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from mcm_toolkit.solve import monte_carlo_integral
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r = monte_carlo_integral(lambda X: X[:, 0], bounds=[(0, 1)], n=np.int64(1000), random_state=0)
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assert r.success
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assert len(r.data) == 1000
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def test_monte_carlo_probability_of_standard_normal():
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"""P(Z > 0) = 0.5，n=5000 时误差应 < 0.05。"""
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from mcm_toolkit.solve import monte_carlo_probability
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r = monte_carlo_probability(lambda z: z > 0, _normal_batch, n=5000, random_state=0)
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assert r.success
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assert abs(r.metrics["probability"] - 0.5) < 0.05
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def test_monte_carlo_is_reproducible():
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from mcm_toolkit.solve import monte_carlo_probability
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a = monte_carlo_probability(lambda z: z > 0, _normal_batch, n=1000, random_state=9)
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b = monte_carlo_probability(lambda z: z > 0, _normal_batch, n=1000, random_state=9)
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assert a.metrics["probability"] == b.metrics["probability"]
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def test_monte_carlo_returns_n_results_and_documented_metrics():
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from mcm_toolkit.solve import monte_carlo
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r = monte_carlo(_normal_batch, lambda x: x**2, n=2000, random_state=0)
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assert r.success
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assert r.data.shape == (2000,)
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assert set(r.metrics) == {"mean", "std", "median", "p05", "p95", "ci_low", "ci_high"}
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def test_monte_carlo_calls_simulator_only_once():
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"""`simulator` 只被调用一次 —— 这是"必须向量化"的可执行版本。"""
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from mcm_toolkit.solve import monte_carlo
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counter = {"calls": 0}
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def simulator(params):
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counter["calls"] += 1
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return params
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r = monte_carlo(_normal_batch, simulator, n=1000, random_state=0)
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assert r.success
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assert counter["calls"] == 1
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def test_monte_carlo_data_is_reproducible_with_same_seed():
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from mcm_toolkit.solve import monte_carlo
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a = monte_carlo(_normal_batch, lambda x: x, n=2000, random_state=11)
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b = monte_carlo(_normal_batch, lambda x: x, n=2000, random_state=11)
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assert np.array_equal(a.data, b.data)
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def test_monte_carlo_follows_set_seed_when_random_state_omitted():
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from mcm_toolkit.core import set_seed
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from mcm_toolkit.solve import monte_carlo
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set_seed(5)
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a = monte_carlo(_normal_batch, lambda x: x, n=500)
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set_seed(5)
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b = monte_carlo(_normal_batch, lambda x: x, n=500)
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assert np.array_equal(a.data, b.data)
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def test_monte_carlo_estimates_known_expectation():
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"""E[X²] = 1（X ~ N(0, 1)）；n = 2e5 时误差应 < 0.02。"""
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from mcm_toolkit.solve import monte_carlo
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r = monte_carlo(_normal_batch, lambda x: x**2, n=200_000, random_state=0)
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assert abs(r.metrics["mean"] - 1.0) < 0.02
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def test_monte_carlo_docstring_example_runs():
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"""docstring 的 Examples 必须真的能跑通（此前那一版是坏的）。"""
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from mcm_toolkit.solve import monte_carlo
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draw = lambda rng, size, **k: rng.normal(0, 1, size) # noqa: E731
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r = monte_carlo(draw, lambda x: x**2, n=10000, random_state=0)
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assert r.success
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assert abs(r.metrics["mean"] - 1.0) < 0.1
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def test_monte_carlo_accepts_numpy_integer_n():
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"""§4.7：`np.int64` 不是 `int` 的子类，不能漏。"""
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from mcm_toolkit.solve import monte_carlo
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r = monte_carlo(_normal_batch, lambda x: x, n=np.int64(100), random_state=0)
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assert r.success
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assert r.data.shape == (100,)
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def test_monte_carlo_counts_boolean_outcomes():
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"""布尔结果按 0/1 统计 —— 事件概率是 MC 最常见的用法。"""
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from mcm_toolkit.solve import monte_carlo
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r = monte_carlo(
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lambda rng, size, **k: rng.normal(0, 1, size) > 0,
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lambda x: x,
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n=5000,
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random_state=0,
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)
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assert r.success
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assert r.data.dtype == float
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assert abs(r.metrics["mean"] - 0.5) < 0.05
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def test_monte_carlo_rejects_nan_results():
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"""结果里出现 NaN 就判失败，不静默给出一列 nan 指标。"""
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from mcm_toolkit.solve import monte_carlo
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r = monte_carlo(
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lambda rng, size, **k: np.r_[np.zeros(size - 1), np.nan],
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lambda x: x,
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n=10,
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random_state=0,
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)
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assert not r.success
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assert "NaN" in r.message
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def test_monte_carlo_rejects_bad_arguments():
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from mcm_toolkit.solve import monte_carlo
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with pytest.raises(TypeError):
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monte_carlo(123, lambda x: x) # type: ignore[arg-type] # 就是要传错
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with pytest.raises(TypeError):
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monte_carlo(_normal_batch, 123) # type: ignore[arg-type] # 就是要传错
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with pytest.raises(TypeError):
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monte_carlo(_normal_batch, lambda x: x, n=True)
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with pytest.raises(TypeError):
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monte_carlo(_normal_batch, lambda x: x, n=10.0) # type: ignore[arg-type] # 就是要传错
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with pytest.raises(ValueError):
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monte_carlo(_normal_batch, lambda x: x, n=0)
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def test_monte_carlo_probability_returns_documented_metrics():
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from mcm_toolkit.solve import monte_carlo_probability
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r = monte_carlo_probability(lambda z: z > 0, _normal_batch, n=20000, random_state=0)
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assert r.success
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assert set(r.metrics) == {"probability", "ci_low", "ci_high", "std_error"}
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assert r.data.shape == (20000,)
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assert 0.0 <= r.metrics["ci_low"] <= r.metrics["probability"]
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assert r.metrics["probability"] <= r.metrics["ci_high"] <= 1.0
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def test_monte_carlo_probability_std_error_matches_binomial_formula():
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"""`std_error` 就是比例的标准误 √(p(1-p)/n)。"""
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from mcm_toolkit.solve import monte_carlo_probability
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n = 20000
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r = monte_carlo_probability(lambda z: z > 0, _normal_batch, n=n, random_state=0)
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p = r.metrics["probability"]
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assert np.isclose(r.metrics["std_error"], np.sqrt(p * (1 - p) / n))
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def test_monte_carlo_probability_ci_narrows_with_n():
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"""区间宽度随 n 增大而收窄（O(1/√n)）。"""
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from mcm_toolkit.solve import monte_carlo_probability
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def width(n):
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r = monte_carlo_probability(lambda z: z > 0, _normal_batch, n=n, random_state=0)
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return r.metrics["ci_high"] - r.metrics["ci_low"]
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assert width(100_000) < width(1_000)
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def test_monte_carlo_probability_higher_confidence_is_wider():
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from mcm_toolkit.solve import monte_carlo_probability
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def width(confidence):
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r = monte_carlo_probability(
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lambda z: z > 0, _normal_batch, n=5000, random_state=0, confidence=confidence
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)
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return r.metrics["ci_high"] - r.metrics["ci_low"]
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assert width(0.99) > width(0.90)
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def test_monte_carlo_probability_ci_matches_statsmodels_wilson():
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"""区间必须对得上独立实现（statsmodels 的 wilson）—— `^` 写成异或那次就靠它拦住。"""
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from statsmodels.stats.proportion import proportion_confint
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from mcm_toolkit.solve import monte_carlo_probability
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n = 5000
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r = monte_carlo_probability(lambda z: z > 0, _normal_batch, n=n, random_state=0)
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count = int(r.data.sum())
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lo, hi = proportion_confint(count, n, alpha=0.05, method="wilson")
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assert np.isclose(r.metrics["ci_low"], lo)
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assert np.isclose(r.metrics["ci_high"], hi)
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def test_monte_carlo_probability_rejects_bad_confidence():
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from mcm_toolkit.solve import monte_carlo_probability
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for bad in (0.0, 1.0, -0.5, 1.5):
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with pytest.raises(ValueError):
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monte_carlo_probability(lambda z: z > 0, _normal_batch, n=100, confidence=bad)
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def test_random_walk_shape_and_start():
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"""`data` 形状 (n_paths, n_steps + 1)，第 0 列是起点。"""
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from mcm_toolkit.solve import random_walk
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r = random_walk(n_steps=100, n_paths=5, start=2.5, random_state=0)
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assert r.success
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assert r.data.shape == (5, 101)
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assert np.all(r.data[:, 0] == 2.5)
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def test_random_walk_default_steps_are_plus_minus_one():
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"""默认是对称随机游走：每一步都恰好是 ±1。"""
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from mcm_toolkit.solve import random_walk
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r = random_walk(n_steps=200, n_paths=3, random_state=0)
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assert set(np.unique(np.diff(r.data, axis=1))) == {-1.0, 1.0}
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def test_random_walk_is_reproducible():
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from mcm_toolkit.solve import random_walk
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a = random_walk(n_steps=50, n_paths=4, random_state=7)
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b = random_walk(n_steps=50, n_paths=4, random_state=7)
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assert np.array_equal(a.data, b.data)
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def test_random_walk_accepts_custom_step_sampler():
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"""换成高斯增量就是带漂移的高斯游走 —— 建模差异全在 step_sampler 里。"""
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from mcm_toolkit.solve import random_walk
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r = random_walk(
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n_steps=100,
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n_paths=3,
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step_sampler=lambda rng, size: rng.normal(0.5, 1.0, size),
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random_state=0,
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)
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assert r.success
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assert r.data.shape == (3, 101)
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assert abs(r.data[:, -1].mean() - 50.0) < 20.0 # 100 步 × 漂移 0.5
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def test_random_walk_rejects_bad_arguments():
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"""§4.7 的分工：类型不对抛 TypeError，取值不对（<= 0）抛 ValueError。"""
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from mcm_toolkit.solve import random_walk
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with pytest.raises(TypeError):
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random_walk(n_steps=2.5) # type: ignore[arg-type] # 就是要传错
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with pytest.raises(TypeError):
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random_walk(n_steps=True)
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with pytest.raises(TypeError):
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random_walk(n_steps=10, n_paths=1.5) # type: ignore[arg-type] # 就是要传错
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with pytest.raises(ValueError):
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random_walk(n_steps=0)
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with pytest.raises(ValueError):
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random_walk(n_steps=10, n_paths=0)
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with pytest.raises(TypeError):
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random_walk(n_steps=10, start="a") # type: ignore[arg-type] # 就是要传错
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def test_random_walk_rejects_2d_step_sampler():
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"""`step_sampler` 必须返回**一维** —— 二维返回（含方阵）一律判失败（易错点 25 的根治）。"""
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from mcm_toolkit.solve import random_walk
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def transposed(rng, size):
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return rng.normal(0, 1, (4, 3)) # 无论要多少，一律给二维
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r = random_walk(n_steps=4, n_paths=3, step_sampler=transposed, random_state=0)
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assert r.success is False # 失败也要返回 Result，不能抛给调用方
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assert "长度" in r.message
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assert r.params["n_paths"] == 3 # 失败也要留下追溯信息
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assert r.data is None
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# 旧方案在 n_paths == n_steps 时对"转置"完全无感；现在同样拦住
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assert random_walk(n_steps=3, n_paths=3, step_sampler=transposed, random_state=0).success is False
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def test_random_walk_step_sampler_gets_flat_size_and_is_row_major():
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"""契约：`size` 是整数 `n_paths * n_steps`，一维增量按**行主序**切成 n_paths 行。"""
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from mcm_toolkit.solve import random_walk
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seen = {}
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def sampler(rng, size):
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seen["size"] = size
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return np.arange(1, size + 1, dtype=float) # 1, 2, 3, 4, 5, 6
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r = random_walk(n_steps=3, n_paths=2, step_sampler=sampler, random_state=0)
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assert seen["size"] == 6 # 整数，不是 (n_paths, n_steps)
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assert r.data.tolist() == [[0, 1, 3, 6], [0, 4, 9, 15]] # cumsum(1,2,3) / cumsum(4,5,6)
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def test_random_walk_rejects_nan_steps():
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"""增量含 NaN 判失败 —— 与 monte_carlo 的 NaN 口径（决策 48）一致。"""
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from mcm_toolkit.solve import random_walk
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r = random_walk(
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n_steps=4,
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n_paths=2,
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step_sampler=lambda rng, size: np.full(size, np.nan),
|
||
random_state=0,
|
||
)
|
||
assert r.success is False
|
||
assert "NaN" in r.message
|
||
|
||
|
||
def test_random_walk_accepts_numpy_integer():
|
||
"""§4.7：`np.int64` 不是 `int` 的子类，注解与运行期都要收（易错点 1）。"""
|
||
from mcm_toolkit.solve import random_walk
|
||
|
||
r = random_walk(n_steps=np.int64(20), n_paths=np.int64(3), random_state=0)
|
||
assert r.success
|
||
assert r.data.shape == (3, 21)
|
||
|
||
|
||
def test_random_walk_metrics_are_terminal_positions():
|
||
"""终点位置的标准差按 √t 扩散（t = n_steps）—— 随机游走的核心性质。"""
|
||
from mcm_toolkit.solve import random_walk
|
||
|
||
n_steps = 100
|
||
r = random_walk(n_steps=n_steps, n_paths=2000, start=5.0, random_state=0)
|
||
assert abs(r.metrics["mean_terminal"] - 5.0) < 5.0 # 对称游走，期望还在起点附近
|
||
assert abs(r.metrics["std_terminal"] - np.sqrt(n_steps)) < 5.0 # √t 扩散
|
||
|
||
|
||
def test_random_walk_docstring_example_runs():
|
||
from mcm_toolkit.solve import random_walk
|
||
|
||
r = random_walk(n_steps=100, n_paths=5, random_state=0)
|
||
assert r.data.shape == (5, 101)
|
||
assert np.all(np.abs(np.diff(r.data, axis=1)) == 1)
|
||
|
||
def test_markov_simulate_shape_start_and_states():
|
||
"""`data` 形状 (n_paths, steps + 1)，第 0 列是初始状态，取值只能是合法状态编号。"""
|
||
from mcm_toolkit.solve import markov_simulate
|
||
|
||
P = np.array([[0.9, 0.1], [0.2, 0.8]])
|
||
r = markov_simulate(P, initial=0, steps=100, n_paths=5, random_state=0)
|
||
assert r.success
|
||
assert r.data.shape == (5, 101)
|
||
assert np.all(r.data[:, 0] == 0)
|
||
assert set(np.unique(r.data)) <= {0, 1}
|
||
assert np.issubdtype(r.data.dtype, np.integer)
|
||
|
||
|
||
def test_markov_simulate_absorbing_states_never_move():
|
||
"""单位转移矩阵：状态自锁，整条路径恒等于初始状态（确定性自检）。"""
|
||
from mcm_toolkit.solve import markov_simulate
|
||
|
||
r = markov_simulate(np.eye(2), initial=1, steps=30, n_paths=4, random_state=0)
|
||
assert r.success
|
||
assert np.all(r.data == 1)
|
||
assert r.metrics["mean_terminal"] == 1.0
|
||
assert r.metrics["std_terminal"] == 0.0
|
||
|
||
|
||
def test_markov_simulate_is_reproducible():
|
||
from mcm_toolkit.solve import markov_simulate
|
||
|
||
P = np.array([[0.7, 0.3], [0.4, 0.6]])
|
||
a = markov_simulate(P, initial=0, steps=40, n_paths=6, random_state=11)
|
||
b = markov_simulate(P, initial=0, steps=40, n_paths=6, random_state=11)
|
||
c = markov_simulate(P, initial=0, steps=40, n_paths=6, random_state=12)
|
||
assert np.array_equal(a.data, b.data)
|
||
assert not np.array_equal(a.data, c.data)
|
||
|
||
|
||
def test_markov_simulate_terminal_frequency_matches_stationary():
|
||
"""终点频率收敛到平稳分布 [2/3, 1/3] —— 马尔可夫链模拟的核心性质。"""
|
||
from mcm_toolkit.solve import markov_simulate
|
||
|
||
P = np.array([[0.9, 0.1], [0.2, 0.8]])
|
||
r = markov_simulate(P, initial=0, steps=200, n_paths=20000, random_state=0)
|
||
freq = np.bincount(r.data[:, -1], minlength=2) / r.data.shape[0]
|
||
assert np.allclose(freq, [2 / 3, 1 / 3], atol=0.02)
|
||
|
||
|
||
def test_markov_simulate_metrics_are_terminal_states():
|
||
from mcm_toolkit.solve import markov_simulate
|
||
|
||
P = np.array([[0.9, 0.1], [0.2, 0.8]])
|
||
r = markov_simulate(P, initial=0, steps=50, n_paths=500, random_state=3)
|
||
assert r.metrics["mean_terminal"] == float(np.mean(r.data[:, -1]))
|
||
assert r.metrics["std_terminal"] == float(np.std(r.data[:, -1]))
|
||
|
||
|
||
def test_markov_simulate_rejects_bad_transition():
|
||
"""非方阵、行和不为 1 都要在入口直接报错，不能悄悄模拟。"""
|
||
from mcm_toolkit.solve import markov_simulate
|
||
|
||
with pytest.raises(ValueError):
|
||
markov_simulate(np.array([[0.5, 0.5, 0.0], [0.2, 0.8, 0.0]]), initial=0)
|
||
with pytest.raises(ValueError):
|
||
markov_simulate(np.array([[0.5, 0.4], [0.2, 0.8]]), initial=0)
|
||
|
||
|
||
def test_markov_simulate_rejects_bad_arguments():
|
||
"""§4.7：步数 / 路径数的类型与正负都要卡住（bool 先判，易错点 1）。"""
|
||
from mcm_toolkit.solve import markov_simulate
|
||
|
||
P = np.array([[0.9, 0.1], [0.2, 0.8]])
|
||
with pytest.raises(ValueError):
|
||
markov_simulate(P, initial=0, steps=0)
|
||
with pytest.raises(ValueError):
|
||
markov_simulate(P, initial=0, n_paths=0)
|
||
with pytest.raises(TypeError):
|
||
markov_simulate(P, initial=0, steps=1.5) # type: ignore[arg-type] # 就是要传错
|
||
with pytest.raises(TypeError):
|
||
markov_simulate(P, initial=0, n_paths=1.5) # type: ignore[arg-type] # 就是要传错
|
||
with pytest.raises(TypeError):
|
||
markov_simulate(P, initial=0, steps=True) # type: ignore[arg-type] # 就是要传错
|
||
with pytest.raises(TypeError):
|
||
markov_simulate(P, initial=0, n_paths=True) # type: ignore[arg-type] # 就是要传错
|
||
|
||
|
||
def test_markov_simulate_accepts_numpy_integer():
|
||
from mcm_toolkit.solve import markov_simulate
|
||
|
||
P = np.array([[0.9, 0.1], [0.2, 0.8]])
|
||
r = markov_simulate(P, initial=0, steps=np.int64(10), n_paths=np.int64(2), random_state=0)
|
||
assert r.success
|
||
assert r.data.shape == (2, 11)
|
||
|
||
|
||
def test_markov_simulate_docstring_example_runs():
|
||
from mcm_toolkit.solve import markov_simulate
|
||
|
||
P = np.array([[0.9, 0.1], [0.2, 0.8]])
|
||
r = markov_simulate(P, initial=0, steps=50, n_paths=4, random_state=0)
|
||
assert r.data.shape == (4, 51)
|
||
assert np.all(r.data[:, 0] == 0)
|
||
|
||
|
||
|
||
|
||
@pytest.mark.skip(reason=_NOT_IMPLEMENTED)
|
||
def test_markov_steady_state_two_state_chain():
|
||
"""P=[[0.9,0.1],[0.2,0.8]] 的平稳分布是 [2/3, 1/3]。"""
|
||
from mcm_toolkit.solve import markov_steady_state
|
||
|
||
r = markov_steady_state(np.array([[0.9, 0.1], [0.2, 0.8]]))
|
||
assert np.allclose(r.data, [2 / 3, 1 / 3], atol=1e-6)
|
||
|
||
|
||
@pytest.mark.skip(reason=_NOT_IMPLEMENTED)
|
||
def test_knapsack_dp_known_optimum():
|
||
from mcm_toolkit.solve import knapsack_dp
|
||
|
||
r = knapsack_dp(weights=[1, 3, 4, 5], values=[1, 4, 5, 7], capacity=7)
|
||
assert np.isclose(r.metrics["total_value"], 9.0)
|
||
assert np.isclose(r.metrics["total_weight"], 7.0)
|
||
|
||
|
||
@pytest.mark.skip(reason=_NOT_IMPLEMENTED)
|
||
def test_linear_program_basic():
|
||
from mcm_toolkit.solve import linear_program
|
||
|
||
r = linear_program(c=[1, 2], A_ub=[[1, 1]], b_ub=[4], bounds=[(0, None)] * 2)
|
||
assert r.success
|
||
assert np.isclose(r.metrics["objective"], 4.0)
|
||
|
||
|
||
@pytest.mark.skip(reason=_NOT_IMPLEMENTED)
|
||
def test_mm1_metrics_closed_form():
|
||
from mcm_toolkit.solve import mm1_metrics
|
||
|
||
r = mm1_metrics(arrival_rate=2, service_rate=3)
|
||
assert np.isclose(r.metrics["rho"], 2 / 3, rtol=1e-6)
|
||
assert np.isclose(r.metrics["L"], 2.0, rtol=1e-6)
|
||
|
||
|
||
@pytest.mark.skip(reason=_NOT_IMPLEMENTED)
|
||
def test_mm1_rejects_unstable_system():
|
||
"""λ >= μ 时系统不稳定，应 success=False 而不是返回 inf。"""
|
||
from mcm_toolkit.solve import mm1_metrics
|
||
|
||
assert mm1_metrics(arrival_rate=3, service_rate=3).success is False
|
||
|
||
|
||
@pytest.mark.skip(reason=_NOT_IMPLEMENTED)
|
||
def test_simulated_annealing_finds_quadratic_minimum():
|
||
from mcm_toolkit.solve import simulated_annealing
|
||
|
||
f = lambda v: (v[0] - 3.0) ** 2 + (v[1] + 1.0) ** 2 # noqa: E731
|
||
r = simulated_annealing(f, x0=[0.0, 0.0], n_iter=5000, random_state=0)
|
||
assert np.allclose(r.params["x"], [3.0, -1.0], atol=0.1)
|