BayesianLinearRegression

sorting different kind of arrays

点云曲面建模，基于距离函数，运用移动最小二乘法，拟合曲面，提取等值面，用于散乱点云的快速三角网格构建(Point cloud surface modeling, based on the distance function, the use of moving least squares method, fitting surface, isosurface extraction for fast scattered point cloud build triangular mesh)

大气参数计算，含密度、湿度、粘性、热导率等(-Air parameters, density, humidity, viscosity, thermal conductivity)

说明：  该算法用于现代武器的弹道曲线中，使一种用的很广的先进算法(the algorithm used modern weapons ballistic curve, a widely used in the advanced algorithm)

说明：  同伦连续算法，很好得用来求解非线性方程组(Homotopy continuation method, may very well be used to solve nonlinear equations)

最小二乘估值的SVD分解计算方法，本程序可将最小二乘估值问题转化为超定方程组的问题处理，且可用奇异值分解的方法计算最小二乘问题。(Least Squares Estimates of the SVD decomposition method, the valuation process can be transformed into squares overdetermined equations deal with the problem, and can be calculated singular value decomposition least squares problem.)

谐波检测的RELAX算法，优于FFT,能够对幅值，频率，相位进行有效估计(an algorithm for estimation of sinusoid parameter)

wolfe程序模块的C++算法，可以重复使用,对于解决一维搜索是很好的..(C++ algorithm wolfe program modules can be reused for solving one-dimensional search is good ..)

说明：  数值分析 显示算法 非线弹性 滞回曲线(Numerical analysis showed that the algorithm the linear elastic hysteresis curve)

支持向量机和BP神经网络都可以用来做非线性回归拟合，但它们的原理是不相同的，支持向量机基于结构风险最小化理论，普遍认为其泛化能力要比神经网络的强。大量仿真证实，支持向量机的泛化能力强于BP网络，而且能避免神经网络的固有缺陷——训练结果不稳定。本源码可以用于线性回归、非线性回归、非线性函数拟合、数据建模、预测、分类等多种应用场合(Support vector machines and BP neural network can be used for non-linear regression fit, but they is not the same principle, support vector machine based on structural risk minimization theory, generally considered its generalization ability than the neural network of strong . Inherent defect- a large number of simulation confirmed that generalization ability of support vector machine is stronger than the BP network, but also to avoid neural network training results unstable. The source code can be used for linear regression, nonlinear regression, nonlinear function fitting a variety of applications, data modeling, forecasting, classification)