A3引擎注册机

IEEE33节点配电系统的最优潮流程序，基于matpower(Optimal power flow of 33 node distribution system)

深度信念网络工具箱，用python写的，包含了受限波尔兹曼机的程序(deep belief network toolbox, including Restricted Boltzmann Machines, python edition)

vc环境下对pro/engineer 进行二次开发的小例子。示例演示了对话框的开发方法。-vc environment on the pro/engineer to carry out the secondary development of small examples. Examples demonstrate the development of methods of dialog.

缠论线段指标，MT4，金融，外汇,mql4(BoundTheoryLine.mq4 finance)

图像显示窗口，可以打开图片，放大缩小图片，拖动图片。加载相机图片(Image display window, you can open the picture, zoom in and zoom out the picture, drag the picture. Loading Camera Pictures)

MX88V44 LCD monitor开发液晶显示源程序-MX88V44 LCD monitor development LCD source

Jboss_as_7_官方_配置手册高清pdf版(Jboss_as_7_ official definition pdf version _ Configuration Guide)

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).)

设计一个一元多项式加法器：两个多项式相加，输出多项式并计算-One dollar design a polynomial adder: the sum of two polynomials, and calculate the output polynomial

Arduino的計時器的樣板可應用在其餘測試中例如應用在時脈開發或shield的使用上這提供了一個不錯的樣板範例