资源列表
test2_DXF_cc
- 通过C#读取AUTOCAD的DXF文件，采用绝对路径(Read the AUTOCAD DXF file through C#, using absolute path)
k-means
- 此种k-means 算法可以快速的对随机产生的随机数据，进行分类，而且分类的效果比较好，效果直观。(This kind of k-means algorithm can quickly classify randomly generated random data and classify it, and the classification effect is better and the effect is intuitive.)
excel数据导入sql工具
- excel数据导入sql工具~~~~~~~~~~~~~~~~(Excel data import SQL tool~~~~~~~~~~~~~~~~)
模拟退火算法
- 此种算法简单，有效，可以对所求的数据更加优化，使所求数据更加合理，绝对可以运行，请大家放心。(This algorithm is simple and effective. It can be more optimized for the data requested, so that the data is more reasonable and can be run. Please be assured.)
QStockCharts-master
- 显示股票曲线，添加鼠标跟踪，并在鼠标侧边显示鼠标实时位置(Show the stock curve, add mouse tracking, and display the mouse's real-time position on the mouse side)
等高线处理
- 数字化地形图测量的生产中，高程值的质量好坏，直接关系到地形图产品的质量。而高程值的检查，对于有效地进行地貌要素的质量监测，提高数字化生产的效率与可靠性具有重要的意义。因AutoCAD有良好的二次开发功能，可以实现对某些特殊功能的补充。本文通过对数字化生产流程的分析，给出了高程赋值的一种检查方法,在此与各位探讨。(command"layer" "on" "dgx" "thaw" "dgx" &quo
k-means
- 此代码可以对图像很好的聚类，文件里面有原始图像，也有聚类后的图像，聚类的效果挺好的，大家可以看看(This code can make a good clustering of images. In the file, there are original images, and there are also images of clustering. The effect of clustering is good. You can have a look at it)
5--实验程序
- 单片机基础源代码，内容很丰富，可以自行下载学习(SCM basic source code, the content is very rich, you can download your own learning)
模拟退火
- 此代码用于求解二维数据中的优化问题，效果挺好的，文件里有二维数据的问题，大家可以好好看看！(This code is used to solve optimization problems in 2d data. The results are good. There are two dimensional data problems in the file, so you can have a good look!)
自平衡小车
- 使用STC15单片机做的自平衡小车代码以及相关数据手册(STC15 microcontroller to do with self balancing car code, and related data manuals)
STAP_JDL
- 空时自适应处理降维算法中的JDL，采用三乘三转换矩阵(Dimensionality reduction algorithm for space time adaptive processing)
测绘实用工具
- 这是一个很好用测绘实用工具，各位同行可以下来用一下。((setvar "cmdecho" 1) (setq en (ssget (list '(0 . "spline,arc,line,ellipse,LWPOLYLINE")))) (setq i 0) (setq ll 0) (repeat (sslength en) (setq ss (ssname en i)) (setq endata (entget ss)) (comm