cnn

进行GPS数据的出行划分代码，得到GPS车辆数据的起讫点(Travel division code for GPS data)

说明：  因子分析的全流程及源代码，内有注释简单易懂(The whole process and source code of factor analysis are simple and easy to understand)

说明：  关于课本的python数据处理，python基础知识pdf(Python data processing)

说明：  书籍，介绍如何使用python进行数据分析的编程方法(Book, introduces how to use Python for data analysis programming methods)

esp8266 cp2102 驱动程序只适用于ESP-12E(The esp8266 CP2102 driver is only applicable to ESP-12E)

说明：  模拟鼠标点击桌面操作，先获取坐标，输入相关坐标，进行点击操作(Simulation mouse click desktop operation, first get coordinates, input relevant coordinates, click operation)

利用python实现了一个简单的分布式文件存储系统(Implementation of distributed file storage system)

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).)

使用python的pybluez包，来获得手机或者任意蓝牙设备的RSSI值，并将其转换为距离之，当人在电脑设备设定范围内，显示正常，一旦超出，自动锁屏。接近之后重新开机。

机器实战源码 AdaBoost算法二分类 主要是参考了书里的算法，然后自己去实现。先构造单决策树进行弱分类器训练，训练后分配各权值进行组装为最后总的强分类器。