我最近发现“拓扑数据分析”(TDA) 是一种可视化大型数据集的独特方式。这是一篇斯坦福大学的论文，最后有示例输出 https://research.math.osu.edu/tgda/mapperPBG.pdf .
我想产生类似的结果，但很难在网络上找到可运行的代码，您可以在其中安装包、加载示例数据，然后执行几行代码(例如 http://scikit-learn.org/ 示例)。我的语言偏好是 Python，但也可以使用 R。
有没有人能够使用 TDA，如果有，关于如何启动和运行代码的任何建议？
最佳答案
有一个新的 r 包出来了:
TDA: Statistical Tools for Topological Data Analysis
该软件包提供了持久同源性统计分析和密度聚类的工具。
可以在这里找到写得很好的小插图:Introduction to the R package TDA
摘要
We present a short tutorial and introduction to using the R package TDA, which provides some tools for Topological Data Analysis. In particular, it includes implementations of functions that, given some data, provide topological information about the underlying space, such as the distance function, the distance to a measure, the kNN density estimator, the kernel density estimator, and the kernel distance. The salient topological features of the sublevel sets (or superlevel sets) of these functions can be quantified with persistent homology. We provide an R interface for the efficient algorithms of the C++ libraries GUDHI, Dionysus and PHAT, including a function for the persistent homology of the Rips filtration, and one for the persistent homology of sublevel sets (or superlevel sets) of arbitrary functions evaluated over a grid of points. The significance of the features in the resulting persistence diagrams can be analyzed with functions that implement the methods discussed in Fasy, Lecci, Rinaldo, Wasserman, Balakrishnan, and Singh (2014), Chazal, Fasy, Lecci, Rinaldo, and Wasserman (2014c) and Chazal, Fasy, Lecci, Michel, Rinaldo, and Wasserman (2014a). The R package TDA also includes the implementation of an algorithm for density clustering, which allows us to identify the spatial organization of the probability mass associated to a density function and visualize it by means of a dendrogram, the cluster tree.
关于python - 拓扑数据分析 - 从哪里开始，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/25161533/