guzhujushi
5319589dfd
审查并精简 ChatGPT 的建库建议后落地的第一版骨架，只定接口不写实现。 - 建议审查：14 个包 → 7 个；约 250 个函数 → 约 40 个；5 个 Result 类 → 1 个 （docs/01-suggestion-review.md） - docs/：架构、路线图（含永久不做清单）、环境、Git 工作流、函数卡片模板 - src/mcm_toolkit/：core / data / stats / models / solve / decision / viz 共 23 个模块，均为签名 + docstring + NotImplementedError - tests/：9 个文件，其中 test_api_surface.py 已通过（20 passed） - examples/ problems/ notebooks/ figures/ tables/ 骨架 - pyproject.toml（核心依赖 4 个 + 6 组 extras）、environment.yml - scripts/：setup-venv.ps1（ASCII + LF）、setup-conda.ps1、doctor.ps1 - 环境：.venv（system-site-packages）与 conda 环境 mcm-toolkit 均已建好， 数值栈版本与 base 完全对齐（numpy 2.4.6 / scipy 1.18.1 / pandas 3.0.6，MKL 后端）
86 lines
2.8 KiB
Python
86 lines
2.8 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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pytestmark = pytest.mark.skip(reason="未实现：见 docs/03-roadmap.md L0/L1")
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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 abs(r.data - 1 / 3) < 0.01
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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(
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lambda z: z > 0,
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lambda rng, **kw: rng.standard_normal(),
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n=5000,
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random_state=0,
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)
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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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args = (lambda z: z > 0, lambda rng, **kw: rng.standard_normal())
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a = monte_carlo_probability(*args, n=1000, random_state=9)
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b = monte_carlo_probability(*args, n=1000, random_state=9)
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assert a.metrics["probability"] == b.metrics["probability"]
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def test_markov_steady_state_two_state_chain():
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"""P=[[0.9,0.1],[0.2,0.8]] 的平稳分布是 [2/3, 1/3]。"""
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from mcm_toolkit.solve import markov_steady_state
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r = markov_steady_state(np.array([[0.9, 0.1], [0.2, 0.8]]))
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assert np.allclose(r.data, [2 / 3, 1 / 3], atol=1e-6)
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def test_knapsack_dp_known_optimum():
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from mcm_toolkit.solve import knapsack_dp
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r = knapsack_dp(weights=[1, 3, 4, 5], values=[1, 4, 5, 7], capacity=7)
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assert np.isclose(r.metrics["total_value"], 9.0)
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assert np.isclose(r.metrics["total_weight"], 7.0)
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def test_linear_program_basic():
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from mcm_toolkit.solve import linear_program
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r = linear_program(c=[1, 2], A_ub=[[1, 1]], b_ub=[4], bounds=[(0, None)] * 2)
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assert r.success
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assert np.isclose(r.metrics["objective"], 4.0)
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def test_mm1_metrics_closed_form():
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from mcm_toolkit.solve import mm1_metrics
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r = mm1_metrics(arrival_rate=2, service_rate=3)
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assert np.isclose(r.metrics["rho"], 2 / 3, rtol=1e-6)
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assert np.isclose(r.metrics["L"], 2.0, rtol=1e-6)
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def test_mm1_rejects_unstable_system():
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"""λ >= μ 时系统不稳定，应 success=False 而不是返回 inf。"""
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from mcm_toolkit.solve import mm1_metrics
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assert mm1_metrics(arrival_rate=3, service_rate=3).success is False
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def test_simulated_annealing_finds_quadratic_minimum():
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from mcm_toolkit.solve import simulated_annealing
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f = lambda v: (v[0] - 3.0) ** 2 + (v[1] + 1.0) ** 2 # noqa: E731
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r = simulated_annealing(f, x0=[0.0, 0.0], n_iter=5000, random_state=0)
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assert np.allclose(r.params["x"], [3.0, -1.0], atol=0.1)
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