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/314681/13561800
m fts.m
function dm=fts(z, ms, rho, nnmat)
%
%This function computes the spatial lag of the variables z based on the index matrix nnmat.
%It gives a weighting directly related to the parameter rho.
%
%IN
www.eeworm.com/read/314681/13561803
m fdet_chebyshev_seq2.m
function [lowerbounds, chebyshevest, upperbounds, alphafine]=fdet_chebyshev_seq2(d, alphafine)
%
%[lowerbounds, chebyshevest, upperbounds, alphafine]=fdet_chebyshev_seq2(d, alphafine)
%
%%This fun
www.eeworm.com/read/314653/13562270
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/314653/13562550
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/314653/13562557
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/314653/13562606
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/314653/13562728
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/314385/13568726
m thornton.m
function [x,U,D] = thornton(xin,Phi,Uin,Din,Gin,Q)
%
% function [x,U,D] = thornton(xin,Phi,Uin,Din,Gin,Q)
%
% M. S. Grewal, L. R. Weill and A. P. Andrews
% Global Positioning Systems, Inertial Na
www.eeworm.com/read/313963/13577701
m ss_tbl31.m
Gold sequences for the table of problem 3.1 of Spread Spectrum Chapter
Note that the sequences are the columns of the below matrix not the rows,
so we have to take the transpose of the following m
www.eeworm.com/read/313956/13577983
m ss_tbl31.m
Gold sequences for the table of problem 3.1 of Spread Spectrum Chapter
Note that the sequences are the columns of the below matrix not the rows,
so we have to take the transpose of the following m