python_arima

说明：  1) 使用凝聚型层次聚类算法（即最小生成树算法）对所有数据点进行聚类，最后聚成3类。相异度定义方法可选择single linkage、complete linkage、average linkage或者average group linkage中任意一种。 2) 使用C-Means算法对所有数据点进行聚类。C=3。 任务2（必做）： 使用高斯混合模型（GMM）聚类算法对所有数据点进行聚类。C=3。并请给出得到的混合模型参数（包括比例??、均值??和协方差Σ）。 任务3（全做）： 1) 参考数据文件第三列的类标签，使用聚类有效性评价的外部方法Normalized Mutual Information指标，分别计算任务1和任务2聚类结果的有效性。 2) 使用聚类有效性评价的内部方法Xie-Beni指标，分别计算任务1和任务2聚类结果的有效性。(The main results are as follows: 1) the condensed hierarchical clustering algorithm (that is, the minimum spanning tree algorithm) is used to cluster all the data points, and finally it is grouped into three categories. Any of the single linkage,complete linkage,average linkage or average group linkage methods can be selected for the definition of dissimilarity. 2) using C-Means algorithm to cluster all data points. C = 3.)

说明：  用python实现二维和三维数据的k-均值聚类算法(Implementation of k-means clustering algorithm for 2D and 3D data with python)

说明：  相机自标定有完整的例子halcon提供了一种自标定的算子，它可以在不用标定板的情况下，标定出相机内参（无焦距），相对于多幅标定无法获取相机的外参(There are complete examples of camera self calibration. Halcon provides a self calibration operator, which can calibrate the camera's internal parameters (without focal length) without using a calibration board. Compared with multi frame calibration, the camera's external parameters cannot be obtained)

【实例简介】

说明：  Python程序调用jieba库与词云提取平凡的世界词云(Extracting ordinary world word cloud with Jieba library and word cloud)

说明：  在1024进行邀请码探测，检验是否可用。(to catch 1024 num,and then check it)

说明：  用django实现用户管理系统的后台包括前端展示，主要功能包括从前端页面直接对数据库进行增删改查，还有登录，身份验证，导入导出excel，增加装饰器实现页面保护，分页等功能，可直接用于向内部展示。(The background of user management system with Django includes front-end display. The main functions include adding, deleting, modifying and checking the database directly from the front-end page, login, authentication, importing and exporting excel, adding decorator to realize page protection, paging and other functions, which can be directly used for internal display.)

vgg深度学习，图像识别，用于图像的分类，在python上运行(vgg deep learning, image recognition, used for image classification, running on Python)

说明：  Ironpython客户端的初始化文件，可实现多种功能(Initial file for Ironpython client for a variety of functions)

说明：  使用鸢尾花数据集，预测鸢尾花种类。并随机选取100个样本作为训练数据，剩下50个作为测试数据。设置输入层四个神经元，接收输入的四个向量数据。设置一层隐含层，使用sigmoi激活函数，此问题为三分类问题，输出层激活函数使用sigmoi函数，神经元个数设置为3个，本类别输出1，其余类别输出0。(Iris, using data sets to predict Iris species. 100 samples were randomly selected as training data, and the remaining 50 samples were used as test data. Four neurons in the input layer are set to receive the four vector data. Set a hidden layer and use the sigmoi activation function. This problem is a three classification problem. The output layer activation function uses the sigmoi function. The number of neurons is set to 3. This category outputs 1 and the other categories output 0.)