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prai-reproducibility

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在为《模式识别与人工智能》(Pattern Recognition and Artificial Intelligence, PR&AI) 准备可复现性与实验可重复材料时调用。覆盖固定随机种子、锁定软硬件环境与依赖版本、固定数据集划分与预处理、预训练权重与大模型版本的记录、复现包(脚本/配置/环境文件)的组织、大数据与权重的稳定托管、以及可复现自查冒烟测试。用于让本刊模式识别/机器学习论文的实验结果能被外审与后续读者重复，降低"结果无法复现"的信任风险；本刊是否设强制复现要求以官网当期须知为准(待核实)。

Pattern-Recognition-and-Artificial-Intelligence-Skills/skills/prai-reproducibility/SKILL.md

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# 《模式识别与人工智能》可复现性与实验可重复

本技能帮助为《模式识别与人工智能》(Pattern Recognition and Artificial Intelligence, PR&AI) 论文
建立可复现的实验证据。模式识别/机器学习结果易受随机性、环境与数据划分影响，可复现性直接关系外审
对结果的信任。事实核验日期 2026-07-09，见 `resources/official-source-map.md`；本刊是否设**强制**
代码/数据可用性或复现要求 **待核实**，以官网当期须知为准。

## 一、随机性控制

- 固定所有随机源的**种子**：框架(PyTorch/NumPy/Python)、数据打乱、初始化、增强。
- 记录是否使用确定性算子（如 cudnn.deterministic），并说明其对速度的影响。
- 报告**多次运行**的均值±标准差（与 `prai-experiments` 一致），而非单次幸运结果。

## 二、环境与依赖锁定
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