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/253950/12173449
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/339665/12211383
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/151030/12238684
txt readme.txt
///////////////////////////////////////////////////////////////////////////////
//
// Copyright (C) 2001 Oh-Wook Kwon, all rights reserved. ohwook@yahoo.com
//
// Easy Matrix
www.eeworm.com/read/150905/12248309
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/150905/12249128
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/150905/12249170
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/150905/12249997
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/149739/12352683
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/149739/12353491
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/149739/12353504
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
%