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).
The CoinUtils project is a collection of open-source utilities developed and used by a variety of other projects in the COIN-OR repository. The project includes classes for storing and manipulating sparse matrices and vectors, performing matrix factorization, parsing input files in standard formats, building representations of mathematical programs, comparing floating point numbers with a tolerance, performing simple presolve operations, and warm starting algorithms for mathematical programs, among others.
This version of the book is a DRAFT! The chapters are mostly complete, but not carefully edited. Some of the debugging sections are not done, and not all chapters have exercises.
If you have high-level comments about the organization of the book or the topics covered, please send me email at feedback{at}thinkpython{dot}com. It is probably too early for detailed comments like spelling errors.
此文件是引文原版的matlab programming
它包括以下內容
Creating and Concatenating Matrices
Accessing Elements of a Matrix
Getting Information About a Matrix
。。。。。
總之包括所有matlab的使用方法
ofdm信道特性
Channel transmission simulator
Channel transmission simulator
％
％ inputs:
％ sig2 - noise variance
％ Mt - number of Tx antennas
％ Mr - number of Rx antennas
％ x - vector of complex input symbols (for MIMO, this is a matrix, where each column
％ is the value of the antenna outputs at a single time instance)
％ H - frequency selective channel - represented in block-Toeplitz form for MIMO transmission
％ N - number of symbols transmitted in OFDM frame
％
％ outputs:
％ y - vector of channel outputs (matrix for MIMO again, just like x matrix)
％ create noise vector sequence (each row is a different antenna, each column is a
％ different time index) note: noise is spatially and temporally white
