▍1. 潮流计算(3机9节点)
本程序应用python编程，用到3机9节点算例，数据也给出，可以实现潮流计算。(This program uses python programming, uses 3 machines and 9 nodes to calculate the example, and the data is also given, which can realize the load flow calculation.)
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▍2. zuniniudunfa
无约束优化问题的阻尼牛顿法python代码(Damped Newton Python code for unconstrained optimization problems)
▍3. vumat案例
说明： vumat案例,可以参考这个vumat子程序自行学习vumat的编写(hi. it is a file for VUMAT. it may help you to learn about it.)
▍4. 通过模型决定系数完成支持向量机参数优化 SVR_fitting
说明： 通过模型决定系数完成支持向量机参数优化，并建立基于大数据的支持向量机模型实现回归拟合(The parameters of SVM are optimized by the coefficient determined by the model, and the regression fitting is realized by the support vector machine model based on big data)
▍5. Test for KNN
说明： 基于欧拉距离的KNN分类器，可自行调节map和预测点的位置。运行时首先显示分布散点图，之后输出预测值(Here is a KNN classifier based on Euler distance with self-adjusting map and prediction point positions. The distribution scatter will plot displayed map at first, then show the predicted value.)
▍6. PolynomialCurveFitting
说明： 基于python的多项式曲线拟合，本程序以1 3 5 9次为例子。(Based on the polynomial curve fitting of python, this program takes 1 3 5 9 times as an example.)
▍7. GA-DF2
说明： 利用GA遗传算法解决欺骗函数最优问题，具体问题描述如下，如有问题请与我联系(The deceptive functions are a family of functions in which there exists low-order building blocks that do not combine to form the higher-order building blocks. Here, a deceptive problem that consists of 25 copies of the order-4 fully deceptive function DF2 is constructed for this paper. DF2 can be described as follows: f(0000)=28 f(0001)=26 f(0010)=24 f(0011)=18 f(0100)=22 f(0101)=6 f(0110)=14 f(0111)=0 f(1000)=20 f(1001)=12 f(1010)=10 f(1011)=2 f(1100)=8 f(1101)=4 f(1110)=6 f(1111)=30 This problem has a maximal function value of 750.)
▍9. LM_method
说明： LM莱文博格马夸特优化算法，python语言(this is a file of LM algorithm which was wroten by python language)
▍10. correlation
说明： 相关性矩阵源代码，用于计算已知数据的相关性系数(Relevance matrix source code, used to calculate the correlation coefficient of known data)
▍11. 一种优化的神经网络数字预失真方法
说明： 基于神经网络的数字预失真技术，即智能DPD(Digital Predistortion Technology Based on Neural Network)
▍13. python-Machine-learning-master
一个机器学习的python文件，里面拥有各种机器学习方法，可以供大家参考(A Python file for machine learning, which has various machine learning methods, can be used for your reference.)
▍14. python-Machine-learning-master
说明： 一个机器学习的python文件，里面拥有各种机器学习方法，可以供大家参考(A Python file for machine learning, which has various machine learning methods, can be used for your reference.)
▍15. ADAM
ADAM （Adaptive Moment Estimation）是另外一种自适应学习率算法，它结合动量梯度 下降法，在不同参数方向上采用不同学习率，保留前几次迭代的梯度，能够很好 的适应于稀疏数据。(ADAM (Adaptive Moment Estimation) is another adaptive learning rate algorithm, which combines momentum gradient. The descent method, which uses different learning rates in different parameter directions and retains the gradients of previous iterations, is very good. It is suitable for sparse data.)
▍16. ADAM
说明： ADAM （Adaptive Moment Estimation）是另外一种自适应学习率算法，它结合动量梯度 下降法，在不同参数方向上采用不同学习率，保留前几次迭代的梯度，能够很好 的适应于稀疏数据。(ADAM (Adaptive Moment Estimation) is another adaptive learning rate algorithm, which combines momentum gradient. The descent method, which uses different learning rates in different parameter directions and retains the gradients of previous iterations, is very good. It is suitable for sparse data.)
▍19. Python for Data Analysis, 2nd Edition
Python for Data Analysis DATA WRANGLING WITH PANDAS, NUMPY, AND IPYTHON
▍20. smote
smote过采样方法对不平衡数据集进行人工数据合成(smote oversampling and classification)
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