actcap

用于abaqus软件的单元删除，是应用在abaqus软件后处理时，删除已经失效的单元(Unit for abaqus software delete application abaqus software processing, the unit has failed to delete)

应用HOG和adbost进行人体检测试过了，效果还不错，推荐给大家！！！(Application for human testing HOG and adbost tried, the results were good, recommend it to everyone! ! !)

meep是由mit的研究组开发的用于模拟电磁场问题的开源软件包。meep4py是meep的python接口(python wrapper of the well-kown opensource FDTD package: meep, developed by research group in MIT)

说明：  Python调用新浪、网易、腾讯的股票Tick数据接口(Python calls stock tick data interface of sina, Netease and Tencent)

Python developer Guides!

基于强化学习算法Sarsa实现的小案例。(A small case based on the enhanced learning algorithm Sarsa.)

说明：  使用unet对图像进行分割的源码，里面有训练集，可以根据自己的需要更换训练数据。(Use the source code of the image segmentation using UNET, which has a training set, you can change the training data according to your own needs.)

张正友OpenCv相机标定程序，是用vs2015写的，需要配置OpenCv环境。(Zhang Zhengyou OpenCv camera calibration program, written in vs2015, needs to configure the OpenCv environment.)

通过opencv进行码本背景模型，进而对视频运动目标进行检测

说明：  用N来表示训练用例（样本）的个数，M表示特征数目。 输入特征数目m，用于确定决策树上一个节点的决策结果；其中m应远小于M。 从N个训练用例（样本）中以有放回抽样的方式，取样N次，形成一个训练集（即bootstrap取样），并用未抽到的用例（样本）作预测，评估其误差。 对于每一个节点，随机选择m个特征，决策树上每个节点的决定都是基于这些特征确定的。根据这m个特征，计算其最佳的分裂方式。 每棵树都会完整成长而不会剪枝，这有可能在建完一棵正常树状分类器后会被采用）。(N is used to represent the number of training cases (samples), and M is used to represent the number of features. The number of input features m is used to determine the decision result of a node in the decision tree, where m should be far less than m. From N training cases (samples), n times are sampled in the way of put back sampling to form a training set (i.e. bootstrap sampling), and the unselected cases (samples) are used to predict and evaluate the error. For each node, m features are randomly selected, and the decision of each node in the decision tree is determined based on these features. According to these m characteristics, the best splitting mode is calculated. Each tree will grow completely without pruning, which may be adopted after building a normal tree classifier).)