代码搜索：Matrices
找到约 3,616 项符合「Matrices」的源代码
代码结果 3,616
www.eeworm.com/read/143706/12849671
m dist2.m
function n2 = dist2(x, c)
%DIST2 Calculates squared distance between two sets of points.
%
% Description
% D = DIST2(X, C) takes two matrices of vectors and calculates the
% squared Euclidean distance
www.eeworm.com/read/140851/13059033
m dist2.m
function n2 = dist2(x, c)
%DIST2 Calculates squared distance between two sets of points.
%
% Description
% D = DIST2(X, C) takes two matrices of vectors and calculates the
% squared Euclidean dis
www.eeworm.com/read/326313/13148671
m uminus.m
function Q = uminus(P)
% UMINUS -- unary minus for matrix polynomials
%
% Q = - P
% Q = uminus(P)
%
% This routine is not meant to be called by the user. It is called by
% Matla
www.eeworm.com/read/138798/13212084
m dist2.m
function n2 = dist2(x, c)
%DIST2 Calculates squared distance between two sets of points.
%
% Description
% D = DIST2(X, C) takes two matrices of vectors and calculates the
% squared Euclidean dis
www.eeworm.com/read/137160/13341837
m gauss.m
%GAUSS Generation of a multivariate Gaussian dataset
%
% A = GAUSS(N,U,G,LABTYPE)
%
% INPUT
% N Array of number of objects to generate for each class
% U Dataset with means, labels a
www.eeworm.com/read/137160/13342256
m nbayesc.m
%NBAYESC Bayes Classifier for given normal densities
%
% W = NBAYESC(U,G)
%
% INPUT
% U Dataset of means of classes
% G Covariance matrices (optional; default: identity matrices)
%
% OUTP
www.eeworm.com/read/137160/13342274
m meancov.m
%MEANCOV Estimation of the means and covariances from multiclass data
%
% [U,G] = MEANCOV(A,N)
%
% INPUT
% A Dataset
% N Normalization to use for calculating covariances: by M, the number
%
www.eeworm.com/read/316047/13531172
m fnorm.m
function y=fnorm(w,f,p)
%FNORM Norms of MVFR matrix.
% FNORM(W,F,p) applies NORM(Fm,p) to each component matrix
% Fm of the MVFR matrix, F. The results are returned as
% a column vector of len
www.eeworm.com/read/316047/13531174
m ftrn.m
function fout=ftrn(w,f)
%FTRN Complex conjugate transpose of MVFR matrix.
% FTRN(W,F) returns an MVFR matrix whose component
% matrices are the complex conjugate transposes
% o
www.eeworm.com/read/314653/13562223
m gauss.m
%GAUSS Generation of a multivariate Gaussian dataset
%
% A = GAUSS(N,U,G,LABTYPE)
%
% INPUT
% N Array of number of objects to generate for each class
% U Dataset with means, labels a