火炬指标 PyTorch的模型评估指标 火炬指标作为自定义库，以提供Pytorch共同ML评价指标，类似于tf.keras.metrics 。 如，Pytorch没有用于模型评估指标的内置库torch.metrics 。 这类似于的指标库。 用法 pip install --upgrade torch-metrics from torch_metrics import Accuracy ## define metric ## metric = Accuracy ( from_logits = False ) y_pred = torch . tensor ([ 1 , 2 , 3 , 4 ]) y_true = torch . tensor ([ 0 , 2 , 3 , 4 ]) print ( metric ( y_pred , y_true )) ## define metri

《软件架构指标：案例研究提升架构质量》是一本关于软件架构指标的书籍，旨在帮助开发者提高软件架构质量。本书由Christian Ciceri, Dave Farley, Neal Ford, Andrew Harmel-Law, Michael Keeling, Carola Lilienthal, João Rosa, Alexander von Zitzewitz, Rene Weiss和Eoin Woods共同撰写。书中介绍了一系列案例研究，以帮助读者理解和应用软件架构指标来改善其架构质量。其中涉及的指标包括性能、可伸缩性、安全性、可维护性和可测试性等方面。通过仔细研究这些案例，读者可以学到如何选择和应用合适的指标，以及如何评估和改善软件架构。这本书是软件开发者和架构师的必备工具书，具有重要的实用价值。

matlab光照模型代码ImageJ-光度学立体工具 西北大学/芝加哥艺术学院艺术科学研究中心（NU-ACCESS）通常使用这里描述的工具来测量艺术品和特别是涂漆表面的表面形状。 这些脚本是用Python编写的，可以在ImageJ中使用，并且基于即将发表的论文中详细描述的算法：Salvant等。 等人，“金属肥皂的艺术–保护与研究”以及与美国西北航空的“计算摄影实验室”在上所做的早期工作中的“通过紫外线诱导的荧光进行光度学立体检测，以检测佐治亚·奥基夫的画上的突起”。 我们使用ImageJ（Fiji）是因为它具有出色的用户界面，它是开放源代码，并提供了一组功能强大的图像处理工具的访问权限，这些图像处理工具可以很容易地适用于使用此处介绍的方法或许多方法提取表面梯度。与文化遗产中的计算机成像有关的其他问题。 脚本入门 下载的最新版本，并将脚本放入plugins文件夹中。 启动斐济或“刷新菜单”（如果已运行）。 可以在插件下拉菜单中访问脚本。 先决条件 要充分利用这些工具，您还需要安装另外两个ImageJ插件： 和 每个脚本做什么？ 光度立体：此脚本是主要的工作工具。 它使用最小二乘法求解一

Nonparametric methods

An Overview of Distance Metric Learning (by Liu Yang)，特别经典的度量学习综述论文，英文文档。

Geometric Folding Algorithms MIT 几何折叠算法配套教材

Bayesian methods are increasingly becoming attractive to researchers in many fields. Econometrics, however, is a field in which Bayesian methods have had relatively less influence. A key reason for this absence is the lack of a suitable advanced undergraduate or graduate level textbook. Existing Bayesian books are either out-dated, and hence do not cover the computational advances that have revolutionized the field of Bayesian econometrics since the late 1980s, or do not provide the broad coverage necessary for the student interested in empirical work applying Bayesian methods. For instance, Arnold Zellner’s seminal Bayesian econometrics book (Zellner, 1971) was published in 1971. Dale Poirier’s influential book (Poirier, 1995) focuses on the methodology and statistical theory underlying Bayesian and frequentist methods, but does not discuss models used by applied economists beyond regression. Other important Bayesian books, such as Bauwens, Lubrano and Richard (1999), deal only with particular areas of econometrics (e.g. time series models). In writing this book, my aim has been to fill the gap in the existing set of Bayesian textbooks, and create a Bayesian counterpart to the many popular non-Bayesian econometric textbooks now available (e.g. Greene, 1995). That is, my aim has been to write a book that covers a wide range of models and prepares the student to undertake applied work using Bayesian methods. This book is intended to be accessible to students with no prior training in econometrics, and only a single course in mathematics (e.g. basic calculus). Students will find a previous undergraduate course in probability and statistics useful; however Appendix B offers a brief introduction to these topics for those without the prerequisite background. Throughout the book, I have tried to keep the level of mathematical sophistication reasonably low. In contrast to other Bayesian and comparable frequentist textbooks, I have included more computer-related material. Modern Bayesian econometrics relies heavily on the computer, and developing some basic programming skills is essential for the applied Bayesian. The required level of computer programming skills is not that high, but I expect that this aspect of Bayesian econometrics might be most unfamiliar to the student brought up in the world of spreadsheets and click-and-press computer packages. Accordingly, in addition to discussing computation in detail in the book itself, the website associated with the book contains MATLAB programs for performing Bayesian analysis in a wide variety of models. In general, the focus of the book is on application rather than theory. Hence, I expect that the applied economist interested in using Bayesian methods will find it more useful than the theoretical econometrician.

PyTextGCN 对TextGCN的重新实现。 此实现使用Cython进行文本到图形的转换，因此速度相当快。 图形和GCN基于库。 要求 该项目的构建具有： 的Python 3.8.5 Cython 0.29.21 CUDA 10.2（GPU支持可选） scikit学习0.23.2 pytorch 1.7.0 火炬几何1.6.3 海湾合作委员会9.3.0 nltk 3.5 scipy 1.5.2 至少Text2Graph模块也应该与这些库的其他版本一起使用。 安装 cython编译可以从项目的根目录执行： cd textgcn/lib/clib && python setup.py build_ext --inplace 用法 要从称为X的字符串列表（每个字符串包含一个文档的文本）中计算出图形，请创建名为y的标签列表以及测试索引test_idx的列表，只需运行：

实现光照特效插件

派尔蒙 PyLMNN是用于在纯python中进行度量学习的算法的实现。 此实现紧密遵循在找到的Kilian Weinberger的原始MATLAB代码。 此版本解决了无限制的优化问题，并使用L-BFGS作为后端优化器找到了线性变换。 该软件包还可以使用出色的软件包通过贝叶斯优化找到LMNN的最佳超参数。 安装 该代码是在Ubuntu 16.04下的python 3.5中开发的，并且也在Ubuntu 18.04和python 3.6下进行了测试。 您可以使用以下命令克隆存储库： git clone https://github.com/johny-c/pylmnn.git 或通过pip安装它： pip3 install pylmnn 依存关系 numpy的> = 1.11.2 scipy> = 0.18.1 scikit_learn> = 0.18.1 可选依赖项 如果要使用超参