代码搜索：Matrices
找到约 3,616 项符合「Matrices」的源代码
代码结果 3,616
www.eeworm.com/read/314653/13562513
m nbayesc.m
%NBAYESC Bayes Classifier for given normal densities
%
% W = NBAYESC(U,G)
%
% INPUT
% U Dataset of means of classes
% G Covariance matrices (optional; default: identity matrices)
%
% OUTP
www.eeworm.com/read/314653/13562523
m meancov.m
%MEANCOV Estimation of the means and covariances from multiclass data
%
% [U,G] = MEANCOV(A,N)
%
% INPUT
% A Dataset
% N Normalization to use for calculating covariances: by M, the number
%
www.eeworm.com/read/309287/13675192
readme
LAPACK++ v. 2.x
http://www.sourceforge.net/projects/lapackpp
Download page: http://sourceforge.net/project/showfiles.php?group_id=99696
LAPACK++ is a library for high performance linear alg
www.eeworm.com/read/307145/13727630
readme
Chapter 16 - Graphs
The files in this directory make up a simple but versatile library for
manipulating graphs. Algorithms are provided for topological sorting,
finding minimum spanning trees and
www.eeworm.com/read/305190/13777289
m blociobi.m
function Z = blociobi(U,Y,i,l,m)
%BLOCIOBI Assembles Y_i (U_i) for use in the Bilinear Identification Toolbox.
%
% BLOCIOBI(U,Y,i,l,m) returns Y_{i-1|0} (U_{i-1|0} if U==Y and l==m), where U and Y
www.eeworm.com/read/170690/6326711
m dist2.m
function n2 = dist2(x, c)
%DIST2 Calculates squared distance between two sets of points.
%
% Description
% D = DIST2(X, C) takes two matrices of vectors and calculates the
% squared Euclidean distance
www.eeworm.com/read/493294/6399912
m gauss.m
%GAUSS Generation of a multivariate Gaussian dataset
%
% A = GAUSS(N,U,G,LABTYPE)
%
% INPUT
% N Array of number of objects to generate for each class
% U Dataset with means, labels a
www.eeworm.com/read/493294/6400246
m nbayesc.m
%NBAYESC Bayes Classifier for given normal densities
%
% W = NBAYESC(U,G)
%
% INPUT
% U Dataset of means of classes
% G Covariance matrices (optional; default: identity matrices)
%
% OUTP
www.eeworm.com/read/493294/6400264
m meancov.m
%MEANCOV Estimation of the means and covariances from multiclass data
%
% [U,G] = MEANCOV(A,N)
%
% INPUT
% A Dataset
% N Normalization to use for calculating covariances: by M, the number
%
www.eeworm.com/read/493401/6402358
c matmult.c
/* matmult.c
* Test program to do matrix multiplication on large arrays.
*
* Intended to stress virtual memory system.
*
* Ideally, we could read the matrices off of the file system,
*