基于改进粒子群优化支持向量机的数据分类预测-非线性权重递减、

Python求解线性规划问题_两阶段法实现的单纯形法，包括.py和.ipynb两种格式，用Jupyter Notebook打开.ipynb或者用Python软件打开.py都可成功运行，压缩包中包括测试数据，代码可输出唯一解，无穷多解，无界解，无解四种情况。

线性拟合结果中各参数的含义 A: Intercept value and its standard error. 截距值及它的标准误差 B: Slope value and its standard error. 斜率值及它的标准误差 R: Correlation coefficient. 相关系数 p: value - Probability (that R is zero). R=0的概率 N: Number of data points. 数据点个数 SD: Standard deviation of the fit. 拟合的标准偏差

LIBLINEAR is a simple package for solving large-scale regularized linear classification and regression. It currently supports - L2-regularized logistic regression/L2-loss support vector classification/L1-loss support vector classification - L1-regularized L2-loss support vector classification/L1-regularized logistic regression - L2-regularized L2-loss support vector regression/L1-loss support vector regression. When to use LIBLINEAR but not LIBSVM ==================================== There are some large data for which with/without nonlinear mappings gives similar performances. Without using kernels, one can efficiently train a much larger set via linear classification/regression. These data usually have a large number of features. Document classification is an example. Warning: While generally liblinear is very fast, its default solver may be slow under certain situations (e.g., data not scaled or C is large). See Appendix B of our SVM guide about how to handle such cases. http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf Warning: If you are a beginner and your data sets are not large, you should consider LIBSVM first. LIBSVM page: http://www.csie.ntu.edu.tw/~cjlin/libsvm

非线性拟合软件，无需赋初值，包含各种求解方式

以齿轮系统动力学和非线性动力学理论为基础,针对齿轮系统时变啮合刚度和齿侧间隙耦合作用的具体特点,建立了齿轮系统非线性模型,并用数值积分和数值仿真方法对其在某些参数域中进行了非线性振动研究

raw10原始图像，拜尔模式rggb

本例的能实现0~360度的任意角度线性渐变双色矩形填充。 用两种RGB颜色对矩形进行线性渐变填充时，需要合适的算法计算各点的颜色，这样才不会出现中间过渡色，或者出现渐变填充不完整。而在增加从任意角度进行渐变后，情况似乎变得更加复杂。详情请看http://blog.csdn.net/yaoyuanyylyy/article/details/52069571

BM算法求线性综合解和DES加密是用C++写的，DSA签名使用java写的。

这个方法可以解出权重所有的解 可以代替梯度下降方法（也是暴力算法） 可以通过数据逐一筛选出数据通用解，或许是走向人工智能，神经网络（权重层）可解释性