Kriging 插值程序，實(shí)現(xiàn)二維三維數(shù)據(jù)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.
上傳時(shí)間： 2016-03-27
上傳用戶(hù)：縹緲
法國(guó)cromda編寫(xiě)的新版本MATRICE 2（矩陣和矢量運(yùn)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
標(biāo)簽： MATRICE cromda 法國(guó) 編寫(xiě)
上傳時(shí)間： 2014-01-23
上傳用戶(hù)：sy_jiadeyi
單片機(jī)的lcd驅(qū)動(dòng)，測(cè)試過(guò)，matrix graphic display module driver
標(biāo)簽： lcd 單片機(jī) 驅(qū)動(dòng)
上傳時(shí)間： 2016-04-03
上傳用戶(hù)：qunquan
著名IDAutomation公司的JAVA條碼控件源碼，支持Code 128, Code 39, Postnet, ITF, UPC, EAN, GS1, Intelligent Mail, Data matrix & PDF417
標(biāo)簽： IDAutomation JAVA 條碼 控件
上傳時(shí)間： 2014-01-15
上傳用戶(hù)：zl5712176
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.
標(biāo)簽： CENTRES KMEANS OPTIONS cluster
上傳時(shí)間： 2014-01-07
上傳用戶(hù)：zhouli
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.
標(biāo)簽： Probabilistic Components Principal Analysis
上傳時(shí)間： 2016-04-28
上傳用戶(hù)：qb1993225
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.
標(biāo)簽： Simulator simulator variables EKF-SLAM
上傳時(shí)間： 2016-05-02
上傳用戶(hù)：lunshaomo
功能：在默認(rèn)有唯一解的情況下，求出一次方程組的解 使用說(shuō)明：1.在方程計(jì)算器所在目錄下新建一個(gè)文本文檔并另存為“matrix”（注意文件名的大小寫(xiě)和拼寫(xiě)） 格式： 第一行是未知數(shù)個(gè)數(shù)n 后面每行n+1個(gè)用空格隔開(kāi)的數(shù)，表示一個(gè)方程 （e.g） 2x+3y-z=6 就輸入 2 3 -1 6 2.保存之后雙擊運(yùn)行“方程計(jì)算器” 3.程序會(huì)在目錄下生成名為“answer”的文本文檔，打開(kāi)就是答案了。
標(biāo)簽：
上傳時(shí)間： 2016-05-02
上傳用戶(hù)：xg262122
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).
標(biāo)簽： LDPC introduction simulation software
上傳時(shí)間： 2014-01-14
上傳用戶(hù)：大融融rr
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).
標(biāo)簽： LDPC introduction simulation software
上傳時(shí)間： 2014-12-05
上傳用戶(hù)：change0329
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