代码搜索：Matrix
找到约 10,000 项符合「Matrix」的源代码
代码结果 10,000
www.eeworm.com/read/461381/7228409
m makebasis.m
function A = makebasis(X)
% A = makebase(X)
%
% Function that creates the "faces space", i. e. the
% basis of the space created throught the eigenvectors
% of the covariance matrix of the populat
www.eeworm.com/read/460815/7240410
cpp 000.cpp
/**********************************************************************\
* 指派问题的匈牙利算法 *
*
www.eeworm.com/read/460435/7250507
m iscolumn.m
%ISCOLUMN Checks whether the argument is a column array
%
% [OK,Y] = ISCOLUMN(X)
%
% INPUT
% X Array: an array of entities such as numbers, strings or cells
%
% OUTPUT
% OK 1 if X is a column
www.eeworm.com/read/460435/7250843
m ldc.m
%LDC Linear Bayes Normal Classifier (BayesNormal_1)
%
% [W.R,S,M] = LDC(A,R,S,M)
% W = A*LDC([],R,S,M);
%
% INPUT
% A Dataset
% R,S Regularization parameters, 0
www.eeworm.com/read/460435/7250851
m fisherm.m
%FISHERM Optimal discrimination linear mapping (Fisher mapping, LDA)
%
% W = FISHERM(A,N,ALF)
%
% INPUT
% A Dataset
% N Number of dimensions to map to, N < C, where C is the number of classes
www.eeworm.com/read/460435/7251010
m distm.m
%DISTM Compute square Euclidean distance matrix
%
% D = DISTM(A,B)
%
% INPUT
% A,B Datasets or matrices; B is optional, default B = A
%
% OUTPUT
% D Square Euclidean distance dataset or
www.eeworm.com/read/460435/7251072
m setcost.m
%SETCOST Reset classification cost matrix of mapping
%
% W = SETCOST(W,COST,LABLIST)
%
% The classification cost matrix of the dataset W is reset to COST.
% W has to be a trained classifier. CO
www.eeworm.com/read/460435/7251210
m covm.m
%COVM Compute covariance matrix for large datasets
%
% C = COVM(A)
%
% Similar to C = COV(A) this routine computes the covariance matrix
% for the datavectors stored in the rows of A. No large int
www.eeworm.com/read/460264/7254624
c hmath.c
/* ----------------------------------------------------------- */
/* */
/* ___ */
/*
www.eeworm.com/read/459616/7270549
out allpairs.out
Enter number of edges of 5 vertex weighted digraph
enter edge 1
enter edge 2
enter edge 3
enter edge 4
enter edge 5
enter edge 6
enter edge 7
The weighted digraph is
0 4 2 0 8
0 0 0 4 5
0 0 0 1 0
0