代码搜索：Matrix
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www.eeworm.com/read/149968/12329173
m pfc.m
% Face recognition by Santiago Serrano
%人脸识别代码
clear all
close all
clc
% number of images on your training set.
%训练集数目
M=10;
%Chosen std and mean.
%It can be any number that it is close to t
www.eeworm.com/read/149968/12329178
asv pfc.asv
% Face recognition by Santiago Serrano
%人脸识别代码
clear all
close all
clc
% number of images on your training set.
%训练集数目
M=10;
%Chosen std and mean.
%It can be any number that it is close to t
www.eeworm.com/read/251528/12339445
m kf_loop.m
%KF_LOOP Performs the prediction and update steps of the Kalman filter
% for a set of measurements.
%
% Syntax:
% [MM,PP] = KF_LOOP(X,P,H,R,Y,A,Q)
%
% In:
% X - Nx1 initial estimate f
www.eeworm.com/read/251528/12339469
m etf_smooth1.m
%ETF_SMOOTH1 Smoother based on two extended Kalman filters
%
% Syntax:
% [M,P] = ETF_SMOOTH1(M,P,Y,A,Q,ia,W,aparam,H,R,h,V,hparam,same_p_a,same_p_h)
%
% In:
% M - NxK matrix of K mean estimates f
www.eeworm.com/read/251528/12339496
m schol.m
%SCHOL Cholesky factorization for positive semidefinite matrices
%
% Syntax:
% [L,def] = schol(A)
%
% In:
% A - Symmetric pos.semi.def matrix to be factorized
%
% Out:
% L - Lower triangular
www.eeworm.com/read/149739/12352799
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/149739/12353564
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/149739/12353581
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/149739/12353703
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/149739/12354045
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