The module LSQ is for unconstrained linear least-squares fitting. It is
based upon Applied Statistics algorithm AS 274 (see comments at the start
of the module). A planar-rotation algorithm is used to update the QR-
factorization. This makes it suitable for updating regressions as more
data become available. The module contains a test for singularities which
is simpler and quicker than calculating the singular-value decomposition.
An important feature of the algorithm is that it does not square the condition
number. The matrix X X is not formed. Hence it is suitable for ill-
conditioned problems, such as fitting polynomials.
By taking advantage of the MODULE facility, it has been possible to remove
many of the arguments to routines. Apart from the new function VARPRD,
and a back-substitution routine BKSUB2 which it calls, the routines behave
as in AS 274.
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Form1.cs是應用聚類算法DBSCAN (Density-Based Spatical Clustering of Application with Noise)的示例，可以通過兩個參數EPS和MinPts調節聚類。DBSCAN.cs是全部算法的實現文件，聚類算法的進一步信息請參考“數據挖掘”或者相關書籍。聚類示例數據來自于sxdb.mdb，一個Access數據庫。
