粗糙集

用于数据自编码，非线性压缩，降维的一种方法；与PCA不同，pca为线性降维方式(Data auto encoding, nonlinear compression)

说明：  利用python进行数据分析，英文书籍，从pandas库的数据分析工具开始利用高性能工具对数据进行加载、清理、转换、合并以及重塑；利用matpIotlib创建散点图以及静态或交互式的可视化结果；利用pandas的groupby功能对数据集进行切片、切块和汇总操作；处理各种各样的时间序列数据。(Starting from the data analysis tools of pandas database, high performance tools are used to load, clean, transform, merge and remodel data; scatter plots and static or interactive visualization results are created by matpIotlib; data sets are sliced, sliced and aggregated by pandas group by function; and various operations are processed. Time series data.)

说明：  Python数据预处理示例，包括数据清洗、数据整合、数据变换等操作。(Python data preprocessing examples, including data cleaning, data integration, data transformation and other operations.)

强大的计算电磁场本征函数与本征模的程序,matlab版本(A powerful Program for calculating the Eigenfunction and Eigenmode of electromagnetic Field)

动画演示多种排序算法,包括冒泡排序,选择排序,插入排序,快速排序等,()

说明：  波形设计算法，阵列信号处理等相关知识的介绍仿真等(Introduction and Simulation of waveform design algorithm, array signal processing and other related knowledge)

说明：  实现了无监督AP聚类 其优势在于不用知道聚类个数(The advantage of unsupervised AP clustering is that the number of clusters is not known)

时间序列法，通过过去数据来建立相应模型来预测未来数据(Time series, using past data to establish corresponding models to predict future data)

说明：  分类器的性能比较与调优: 使用scikit-learn 包中的tree，贝叶斯，knn，对数据进行模型训练，尽量了解其原理及运用。 使用不同分析三种分类器在实验中的性能比较，分析它们的特点。 本实验采用的数据集为house与segment。(Performance comparison and optimization of classifiers: We use tree, Bayesian and KNN in scikit-learnpackage to train the data model and try to understand its principle and application. The performances of three classifiers are compared and their characteristics are analyzed. The data set used in this experiment is house and segment.)

说明：  多种方法实现分位数回归,有完整原理解释，直接可用。(Multiple methods for quantile regression)