代码搜索：Matrix
找到约 10,000 项符合「Matrix」的源代码
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www.eeworm.com/read/450798/7476651
c 逆矩阵.c
#define N 5 /*[注]：修改6为你所要的矩阵阶数*/
#include "stdio.h"
#include "conio.h"
/*js()函数用于计算行列式,通过递归算法实现*/
int js(s,n)
int s[][N],n;
{int z,j,k,r,total=0;
int b[N][N];/*b[N][N]用于存放，在矩阵s[
www.eeworm.com/read/450608/7480142
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/450608/7480422
m fisherm.m
%FISHERM Optimal discrimination linear mapping (Fisher mapping)
%
% 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/450608/7480429
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/450608/7480478
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/450608/7480596
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/450539/7482441
3 libdmtx.3
.\" $Id: libdmtx.3,v 1.4 2006/10/15 22:08:14 mblaughton Exp $
.\"
.\" Man page for the libdmtx project.
.\"
.\" $ groff -man -T ascii libdmtx.3
.\"
.TH LIBDMTX 3 "October 15, 2006"
.SH NAME
libdmtx \-
www.eeworm.com/read/450507/7482928
c 逆矩阵.c
#define N 5 /*[注]：修改6为你所要的矩阵阶数*/
#include "stdio.h"
#include "conio.h"
/*js()函数用于计算行列式,通过递归算法实现*/
int js(s,n)
int s[][N],n;
{int z,j,k,r,total=0;
int b[N][N];/*b[N][N]用于存放，在矩阵s[
www.eeworm.com/read/449694/7497938
c 逆矩阵.c
#define N 5 /*[注]：修改6为你所要的矩阵阶数*/
#include "stdio.h"
#include "conio.h"
/*js()函数用于计算行列式,通过递归算法实现*/
int js(s,n)
int s[][N],n;
{int z,j,k,r,total=0;
int b[N][N];/*b[N][N]用于存放，在矩阵s[
www.eeworm.com/read/449504/7502210
m multilogit.m
function results = multilogit(y,x,beta0,maxit,tol);
% PURPOSE: implements multinomial logistic regression
% Pr(y_i=j) = exp(x_i'beta_j)/sum_l[exp(x_i'beta_l)]
% where:
% i = 1,2,...,nobs