WOA

用于信号特征提取，提取信号的小波包能量谱(Extracting the wavelet packet energy spectrum of the signal)

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.)

主要利用R语言进行广义加法模型，进行回归预测(This paper mainly uses R language to carry on the generalized additive model, and carries on the regression forecast)

用EM算法求解高斯混合模型并可视化，数据是男女生的身高分布，前提是初始化男女生身高各自的均值和方差和比例，然后由EM算法求解，男女生身高的均值方差，以拟合数据。(The EM algorithm is used to solve the Gauss mixture model and visualize. The data is the height distribution of male and female. The premise is to initialize the mean, variance and proportion of the male and female height, then the mean variance of the height of male and female is solved by the EM algorithm, so as to fit the data.)

说明：  从数据库获取车辆在一段时间内的所有行驶记录的相关数据，确定所需数据为GPS经纬度坐标和驾驶时长等，QB模型采用MDF的思想,其基本思想为:通过平均直接翻转距离函数定义两条轨迹之间的距离，两条轨迹需要具有相同的经纬度点数，具有相同点数的轨迹最大的优点是对轨迹距离成对计算，且相同轨迹之间具有更高的分辨率，对于轨迹聚类的结果有一定的优化。(Retrieved from the database cars all over a period of time, record the related data, determine the required data for the GPS latitude and longitude coordinates, and the driving time, QB model by adopting the idea of MDF, its basic idea is: flip directly by the average distance function definition of the distance between two trajectories, two tracks will have the same latitude and longitude points, and has the biggest advantages of the same points of trajectory track distance calculation in pairs, and has higher resolution, between the same trajectory for trajectory clustering results have certain optimization.)

hosvd 迭代分解,很好用，是一个硕士论文里的代码(terative HOSVD algorithm to decompose tensor and find its Singular factors in each mode.)

算法分析中的贪心算法的实现,并有完整的测试数据()

GWR能够实现地理加权回归计算，里面有GWR4操作说明，虽然是英文，但有图片介绍，很容易理解，是GWR模型入门的好工具，适合经济学、数据挖掘等人员使用(GWR can realize geographically weighted regression calculation, including GWR4 operation instructions. Although it is in English, it has pictures to introduce, and is easy to understand. It is a good tool for GWR model entry. It is suitable for personnel such as economics and data mining.)

在linux 下C语言实现最近邻聚类算法，工程已经使用(near K neighbor cluster)

Clustering the data with Density-Based Scan Algorithm with Noise (DBSCAN)