资源列表
sparse-subspace-clustering
- 关于稀疏子空间聚类的算法程序实现，及对应的论文，matlab源码实现。-On sparse subspace clustering algorithm procedures, and the corresponding paper, matlab source code.
Datasets
- 一些复杂网络的数据集，包括karate，dolphins，football等，分别为n*2和n*n矩阵形式，还有对数据集进行转换的代码-datasets of complex networks
HestonCallQuad
- Heston期权定价模型的蒙托卡罗模拟。调用fun函数即可运行-Generalized wave equation parameters of Heston option pricing model Moment Estimation
optimal_interpolation
- 利用fortran语言实现最优插值算法，使用于大气海洋资料融合分析-Using FORTRAN language to realize the optimal interpolation algorithm, used in Marine atmosphere data integration analysis
density-peaks-clustering
- 这是密度峰聚类的简易代码，比原作者给出的代码要简洁迅速。并提供RI指数检测。用matlab编程的代码- 这是密度峰聚类的简易代码，比原作者给出的代码要简洁迅速。并提供RI指数检测。用matlab编程的代码 This is a simple density peak clustering code, compared to the original author of the code to be concise and rapid. And provides RI index de
simpleMKL
- 多核学习之simpleMKL工具箱，里面包含了simpleMKL算法和MKL-wrapper算法，其中原始工具箱中MKL-wrapper不能运行，此处已进行了修正。-Multi-core learning simpleMKL toolbox, containing the simpleMKL algorithm and MKL- wrapper algorithm, the original toolbox MKL- wrapper can not run, has been revised.
TurbulentWindGenerator
- 三维风场模拟.利用Kaimal spectrum结合FFT进行风场模拟，生成风速时程得进行必要参数的定义。-3D Turbulent Wind Generation。 Generation of three-dimensional turbulent wind fields, by employing a Kaimal spectrum and IEC-based coherence function.
Spark快速大数据分析
- 利用Python和Spark进行海量数据的实时分析，解决商业方案(Using Python and Spark to do real-time analysis of mass data, and to solve business solutions)
R
- 金融数据的 R 分析，包括线性模型，时序分析，波动率分析，VaR 风险分析等。(An Introduction to Analysis of Financial Data with R)
CnkiSpider-master
- CNKI爬虫代码，可以用来爬取知网论文题录信息(CNKI crawler code can be used to take up the National Bibliographic Information)
机器学习与R语言（machine_learning）
- 机器学习与R语言（machine_learning）源代码下载，内含R语言实现机器学习算法和应用案例(Machine learning and R language (machine_learning) source code download, containing R language to implement machine learning algorithms and application cases)
局部线性嵌入
- 利用局部线性嵌入算法将高维数据映射到低维空间中，达到降维效果；(The local linear embedding algorithm is used to map the high dimensional data into the low dimensional space to achieve the effect of reducing the dimension.)