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K线包含处理
缠论K线包含处理dll，顶分型和底分型的判断，无笔和线段的编写(Free good stock index source code, although this version is not strong DLL version, but it is also very accurate)
- 2020-12-14 00:39:15下载
- 积分：1
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FF
说明： 利用AUTOLISP进行CAD二次开发，里面画了盘式法兰的两个视图，适合新手(Using AUTOLISP to redevelop CAD, two views of disc flange are drawn, which is suitable for beginners.)
- 2019-08-18 16:16:11下载
- 积分：1
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基于PHP和MySQL开发。这是一个入门级编程。好的参考…
developed based on PHP & mysql. It s an entry level programming. Good for reference
- 2022-07-11 21:55:02下载
- 积分：1
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Dual_Clutch_Transmission_Simulink
DCT控制原理，离合器模型，入门了解的好帮手(DCT control principle, clutch model, access to a good understanding of the helper)
- 2020-11-30 10:19:27下载
- 积分：1
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程序
说明： 数模中求解偏微分方程时，在已知数据的基础上进行线性插值拟合。利用差分法求解该模型，先用网格划分区域，对 区域内部节点做泰勒展开。对偏导数进行离散化处理。(tthe solution to the PDE problem in mathematical modeling)
- 2019-06-22 18:21:45下载
- 积分：1
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This program for hamming code calculation
This program for hamming code calculation
- 2023-06-06 17:40:03下载
- 积分：1
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computer_design
该程序能够实现计算器程序设计，采用C++编程，来自C++程序设计原理与实践。(The program can achieve calculator program design, using C++ programming, program design from C++ Principles and Practice)
- 2014-02-19 14:34:59下载
- 积分：1
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Well as resection of the double, after extracting a text document with a code
很好的双像后方交会，解压后是一个文本文档，内有代码-Well as resection of the double, after extracting a text document with a code
- 2022-03-24 11:06:16下载
- 积分：1
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OSS_SDK_DOTNET_2_3_0
说明： 阿里云oss源码sdk，oss文件存储对象存储sdk(Aliyun OSS source code sdk, OSS file storage object storage SDK)
- 2020-06-21 23:00:02下载
- 积分：1
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随机森林
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).)
- 2021-01-28 13:47:33下载
- 积分：1
