Kriging 插值程序，實現二維三維數據插值
Makefile_hp700
Makefile_ibm6000
kriging.mdf
kriging.c
outboard.c
inv_m.tar.gz The inverse Matrix source codes which got from Netlib.
f2c.tar.gz The head file and library which are used to inverse Matrix source codes.
krig.net The example dx file.
data1.dx The input example.
法國cromda編寫的新版本MATRICE 2（矩陣和矢量運算單元）。
// ----------------------------------------------------------
// 12-01-02 : MODIFIED Matrice to Matrice2 (Delphi 6)
// All routines now operate on rectangular Matrix, except (InvMat and SysLin)
// No more need to use the InitMat procedure (suppressed) :
// - the routines detect automaticaly the dimensions of Matrix and vector
// - error code MatDimNul is generated if zero lines or column in Matrix and vector (See DimensionMatrice and DimensionVecteur)
// - error code MatMauvDim is generated if the dimensions of Matrix/vector don t allow valid result
// -
// The result Matrix is dimensioned automaticaly
KMEANS Trains a k means cluster model.CENTRES = KMEANS(CENTRES, DATA, OPTIONS) uses the batch K-means
algorithm to set the centres of a cluster model. The Matrix DATA
represents the data which is being clustered, with each row
corresponding to a vector. The sum of squares error function is used.
The point at which a local minimum is achieved is returned as
CENTRES.
Probabilistic Principal Components Analysis. [VAR, U, LAMBDA] = PPCA(X, PPCA_DIM) computes the principal
％ component subspace U of dimension PPCA_DIM using a centred covariance
Matrix X. The variable VAR contains the off-subspace variance (which
is assumed to be spherical), while the vector LAMBDA contains the
variances of each of the principal components. This is computed
using the eigenvalue and eigenvector decomposition of X.
EKF-SLAM Simulator
This version of the simulator uses global variables for
all large objects, such as the state covariance Matrix.
While bad programming practice, it is a necessary evil
for MatLab efficiency, as MatLab has no facility to avoid
gratuitous memory allocation and copying when passing
(and modifying) variables between functions. With this
concession, effort has been made to keep the code as
clean and modular as possible.
This LDPC software is intended as an introduction to LDPC codes computer based simulation. The pseudo-random irregular low density parity check Matrix is based on Radford M. Neal’s programs collection, which can be found in [1]. While Neal’s collection is well documented, in my opinion, C source codes are still overwhelming, especially if you are not knowledgeable in C language. My software is written for MATLAB, which is more readable than C. You may also want to refer to another MATLAB based LDPC source codes in [2], which has different flavor of code-writing style (in fact Arun has error in his log-likelihood decoder).
This LDPC software is intended as an introduction to LDPC codes computer based simulation. The pseudo-random irregular low density parity check Matrix is based on Radford M. Neal’s programs collection, which can be found in [1]. While Neal’s collection is well documented, in my opinion, C source codes are still overwhelming, especially if you are not knowledgeable in C language. My software is written for MATLAB, which is more readable than C. You may also want to refer to another MATLAB based LDPC source codes in [2], which has different flavor of code-writing style (in fact Arun has error in his log-likelihood decoder).
