The WH class is an implementation of the Widrow-Hoff. The Widrow-Hoff (WH) algorithm, often called Least Mean Square (LMS), is an online-algorithm. The WH can be interpreted as a gradient descent procedure on the error space. In other words, the

实现对离散数据的最小二乘法拟合，得到多项式表达式-Least squares fit of the discrete data obtained polynomial expression

近代平差，包括：序贯平差、最小二乘配置、抗差估计、卡尔曼滤波、粗差探测、参数加权平差、相关抗差估计-Modern adjustment, including: sequential adjustment, least square configuration, robust estimation, Kalman filtering, gross error detection, parameter weighting adjustment, related to robust estimatio

利用已知点在不同坐标系下的坐标值，根据最小二乘法的原则，求出七参数，实现不同空间直角坐标系的转换。(According to the principle of the least square method, seven parameters are obtained by using the coordinate values of known points in different coordinate systems to realize the transformation of dif