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/299984/7140032
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/299984/7140368
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/299984/7140376
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/299984/7140534
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/299984/7140596
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/299984/7140733
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/165343/7143771
m lu2.m
function [lu,indx] = lu2(A)
% function [lu,indx] = lu2(A)
%
% Compute the lu factorization of a binary matrix A
%
% lu = matrix contining L and U factors
% indx = index of pivot permutations
%
% If A
www.eeworm.com/read/165343/7143787
h pmatlib.h
/******************************
*
* matrix library using pointers
*
* This library implements matrix arithmetic
*
* Storage is based on the array of pointers idea for
* flexible (and fast) indexing.
www.eeworm.com/read/165343/7143914
m gaussj2.m
function [success,Ainv,Ac,pidx,Asyst] = gaussj2(A)
% function [success,Ainv,Ac,pidx,Asyst] = gaussj2(A)
%
% Do Gaussian elimination over GF(2) on the A matrix
%
% success = 0 if matrix is singular
%