代码搜索:Multivariate Analysis

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www.eeworm.com/read/204311/15341635

asv analysis.asv

% Analysis disp(' '), disp('------------------------------------------------------------') disp('Preparing Analysis') figure(1), clf if (input_type == 1) & (test_input_type == 1) subplot(221), s
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m analysis.m

% Analysis disp(' '), disp('------------------------------------------------------------') disp('Preparing Analysis') figure(1), clf if (input_type == 1) & (test_input_type == 1) subplot(221), s
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h analysis.h

/***************************************** * 分析处理所需使用的结构 * 周国祥 2001/08/18 */ #define DDINT 0 #define DDDOUB 1 extern char audit_title[40]; typedef union { int intnum; double doublen
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c analysis.c

#include "c.h" //#define DOPRINTF #ifndef DOPRINTF #define PrintRegister(a) #define PrintTemporary(a) #endif //#include extern void qsort(void *, size_t, size_t, int (*)(const void
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m gauss_rnd.m

%GAUSS_RND Multivariate Gaussian random variables % % Syntax: % X = GAUSS_RND(M,S,N) % % In: % M - Dx1 mean of distibution or K values as DxK matrix. % S - DxD covariance matrix % N -
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m gauss_rnd.m

%GAUSS_RND Multivariate Gaussian random variables % % Syntax: % X = GAUSS_RND(M,S,N) % % In: % M - Dx1 mean of distibution or K values as DxK matrix. % S - DxD covariance matrix % N -
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m gauss_rnd.m

%GAUSS_RND Multivariate Gaussian random variables % % Syntax: % X = GAUSS_RND(M,S,N) % % In: % M - Dx1 mean of distibution or K values as DxK matrix. % S - DxD covariance matrix % N -
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m gauss_rnd.m

%GAUSS_RND Multivariate Gaussian random variables % % Syntax: % X = GAUSS_RND(M,S,N) % % In: % M - Dx1 mean of distibution or K values as DxK matrix. % S - DxD covariance matrix % N -
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m gauss_rnd.m

%GAUSS_RND Multivariate Gaussian random variables % % Syntax: % X = GAUSS_RND(M,S,N) % % In: % M - Dx1 mean of distibution or K values as DxK matrix. % S - DxD covariance matrix % N -
www.eeworm.com/read/407295/11422471

m gauss_rnd.m

%GAUSS_RND Multivariate Gaussian random variables % % Syntax: % X = GAUSS_RND(M,S,N) % % In: % M - Dx1 mean of distibution or K values as DxK matrix. % S - DxD covariance matrix % N -