TDA-tutorial:一组使用python Gudhi库以及流行的机器学习和数据科学库来实践TDA的jupyter笔记本

上传者: 42109732 | 上传时间: 2022-05-06 15:25:01 | 文件大小: 24.43MB | 文件类型: ZIP
使用Gudhi库进行拓扑数据分析的教程 拓扑数据分析(TDA)是一个新兴且发展Swift的领域,它提供了一组新的拓扑和几何工具来推断可能复杂数据的相关特征。 在这里,我们使用Python Gudhi库以及流行的机器学习库和数据科学库为TDA的实践提出了一套笔记本。 例如,请参阅了解有关数据科学的TDA的介绍。 笔记本的完整列表也可以在此页的末尾找到。 安装Python Gudhi库 请参阅或者如果您有conda,则可以进行。 TDA分析管道 01-单纯形树和简单组合 TDA通常旨在从网络中的点云中提取拓扑特征。 或在一般指标空间中。 通过研究点云的拓扑,我们实际上意味着研究以点云为中心的球的并集的拓扑,也称为偏移。 但是,非离散集(例如偏移量)以及连续的数学形状(如曲线,曲面和更一般的流形)不能轻易地编码为有限的离散结构。 因此,在计算几何中使用形来近似这样的形状。 单纯形复数是一组,它

文件下载

资源详情

[{"title":"( 58 个子文件 24.43MB ) TDA-tutorial:一组使用python Gudhi库以及流行的机器学习和数据科学库来实践TDA的jupyter笔记本","children":[{"title":"TDA-tutorial-master","children":[{"title":"Tuto-GUDHI-simplicial-complexes-from-distance-matrix.ipynb <span style='color:#111;'> 50.56KB </span>","children":null,"spread":false},{"title":"Tuto-GUDHI-optimization.ipynb <span style='color:#111;'> 182.19KB </span>","children":null,"spread":false},{"title":"Tuto-GUDHI-Expected-persistence-diagrams.ipynb <span style='color:#111;'> 46.54KB </span>","children":null,"spread":false},{"title":"Tuto-GUDHI-Barycenters-of-persistence-diagrams.ipynb <span style='color:#111;'> 92.61KB </span>","children":null,"spread":false},{"title":"README.Rmd <span style='color:#111;'> 10.12KB </span>","children":null,"spread":false},{"title":"Images","children":[{"title":"nappe_distance_avec_bruit.png <span style='color:#111;'> 31.03KB </span>","children":null,"spread":false},{"title":"MatchingDiag.png <span style='color:#111;'> 274.90KB </span>","children":null,"spread":false},{"title":"symbole_infini.png <span style='color:#111;'> 7.35KB </span>","children":null,"spread":false},{"title":"sous_niveau_kPDTM_cov2.png <span style='color:#111;'> 68.03KB </span>","children":null,"spread":false},{"title":"CodeCogsEqnRp.gif <span style='color:#111;'> 204B </span>","children":null,"spread":false},{"title":"nappe_distance_sans_bruit.png <span style='color:#111;'> 54.46KB </span>","children":null,"spread":false},{"title":"persistence.png <span style='color:#111;'> 612.41KB </span>","children":null,"spread":false},{"title":"pers.png <span style='color:#111;'> 596.97KB </span>","children":null,"spread":false},{"title":"nappe_dtm_avec_bruit.png <span style='color:#111;'> 25.33KB </span>","children":null,"spread":false},{"title":"sous_niveau_kPDTM2.png <span style='color:#111;'> 45.99KB </span>","children":null,"spread":false},{"title":"sublevf.png <span style='color:#111;'> 12.50KB </span>","children":null,"spread":false},{"title":"Pers14.PNG <span style='color:#111;'> 14.21KB </span>","children":null,"spread":false}],"spread":false},{"title":".binder","children":[{"title":"requirements.txt <span style='color:#111;'> 114B </span>","children":null,"spread":false}],"spread":true},{"title":"Tuto-GUDHI-kPDTM-kPLM.ipynb <span style='color:#111;'> 310.87KB </span>","children":null,"spread":false},{"title":".github","children":[{"title":"workflows","children":[{"title":"test_notebooks.yml <span style='color:#111;'> 761B </span>","children":null,"spread":false}],"spread":true}],"spread":true},{"title":"Tuto-GUDHI-representations.ipynb <span style='color:#111;'> 150.57KB </span>","children":null,"spread":false},{"title":"Tuto-GUDHI-simplex-Trees.ipynb <span style='color:#111;'> 14.17KB </span>","children":null,"spread":false},{"title":"persistence_statistics.py <span style='color:#111;'> 3.21KB </span>","children":null,"spread":false},{"title":"LICENSE <span style='color:#111;'> 1.05KB </span>","children":null,"spread":false},{"title":"Tuto-GUDHI-ConfRegions-PersDiag-datapoints.ipynb <span style='color:#111;'> 90.07KB </span>","children":null,"spread":false},{"title":"Tuto-GUDHI-extended-persistence.ipynb <span style='color:#111;'> 428.23KB </span>","children":null,"spread":false},{"title":"Tuto-GUDHI-cubical-complexes.ipynb <span style='color:#111;'> 66.20KB </span>","children":null,"spread":false},{"title":"Tuto-GUDHI-alpha-complex-visualization.ipynb <span style='color:#111;'> 3.60MB </span>","children":null,"spread":false},{"title":".gitignore <span style='color:#111;'> 41B </span>","children":null,"spread":false},{"title":"Tuto-GUDHI-persistence-diagrams.ipynb <span style='color:#111;'> 68.42KB </span>","children":null,"spread":false},{"title":"DTM_filtrations.py <span style='color:#111;'> 10.55KB </span>","children":null,"spread":false},{"title":"Tuto-GUDHI-simplicial-complexes-from-data-points.ipynb <span style='color:#111;'> 61.02KB </span>","children":null,"spread":false},{"title":"README.md <span style='color:#111;'> 11.27KB </span>","children":null,"spread":false},{"title":"Tuto-GUDHI-PyTorch-optimization.ipynb <span style='color:#111;'> 270.51KB </span>","children":null,"spread":false},{"title":"datasets","children":[{"title":"trefoil_dist <span style='color:#111;'> 7.63MB </span>","children":null,"spread":false},{"title":"human.off <span style='color:#111;'> 556.31KB </span>","children":null,"spread":false},{"title":"Corr_ProteinBinding","children":[{"title":"4mbp.corr_7.txt <span style='color:#111;'> 2.09MB </span>","children":null,"spread":false},{"title":"1ez9.corr_1.txt <span style='color:#111;'> 2.09MB </span>","children":null,"spread":false},{"title":"1omp.corr_7.txt <span style='color:#111;'> 2.09MB </span>","children":null,"spread":false},{"title":"1lls.corr_6.txt <span style='color:#111;'> 2.09MB </span>","children":null,"spread":false},{"title":"1mpd.corr_4.txt <span style='color:#111;'> 2.09MB </span>","children":null,"spread":false},{"title":"1fqd.corr_3.txt <span style='color:#111;'> 2.09MB </span>","children":null,"spread":false},{"title":"1fqb.corr_3.txt <span style='color:#111;'> 2.09MB </span>","children":null,"spread":false},{"title":"1fqc.corr_2.txt <span style='color:#111;'> 2.09MB </span>","children":null,"spread":false},{"title":"1anf.corr_1.txt <span style='color:#111;'> 2.09MB </span>","children":null,"spread":false},{"title":"1fqa.corr_2.txt <span style='color:#111;'> 2.09MB </span>","children":null,"spread":false},{"title":"1jw4.corr_4.txt <span style='color:#111;'> 2.09MB </span>","children":null,"spread":false},{"title":"1jw5.corr_5.txt <span style='color:#111;'> 2.09MB </span>","children":null,"spread":false},{"title":"3hpi.corr_5.txt <span style='color:#111;'> 2.09MB </span>","children":null,"spread":false},{"title":"3mbp.corr_6.txt <span style='color:#111;'> 2.09MB </span>","children":null,"spread":false}],"spread":false},{"title":"mnist_test.csv <span style='color:#111;'> 17.44MB </span>","children":null,"spread":false},{"title":"NoisyTrefoil180.txt <span style='color:#111;'> 8.79KB </span>","children":null,"spread":false},{"title":"crater_tuto <span style='color:#111;'> 117.91KB </span>","children":null,"spread":false},{"title":"ElongatedTorus.txt <span style='color:#111;'> 97.66KB </span>","children":null,"spread":false},{"title":"human.txt <span style='color:#111;'> 437.73KB </span>","children":null,"spread":false},{"title":"tore3D_1307.off <span style='color:#111;'> 36.43KB </span>","children":null,"spread":false},{"title":"data_acc <span style='color:#111;'> 1.39MB </span>","children":null,"spread":false}],"spread":false},{"title":"Tuto-GUDHI-DTM-filtrations.ipynb <span style='color:#111;'> 468.33KB </span>","children":null,"spread":false}],"spread":false}],"spread":true}]

评论信息

免责申明

【只为小站】的资源来自网友分享,仅供学习研究,请务必在下载后24小时内给予删除,不得用于其他任何用途,否则后果自负。基于互联网的特殊性,【只为小站】 无法对用户传输的作品、信息、内容的权属或合法性、合规性、真实性、科学性、完整权、有效性等进行实质审查;无论 【只为小站】 经营者是否已进行审查,用户均应自行承担因其传输的作品、信息、内容而可能或已经产生的侵权或权属纠纷等法律责任。
本站所有资源不代表本站的观点或立场,基于网友分享,根据中国法律《信息网络传播权保护条例》第二十二条之规定,若资源存在侵权或相关问题请联系本站客服人员,zhiweidada#qq.com,请把#换成@,本站将给予最大的支持与配合,做到及时反馈和处理。关于更多版权及免责申明参见 版权及免责申明