代码搜索：multivariate
找到约 564 项符合「multivariate」的源代码
代码结果 564
www.eeworm.com/read/250980/12372069
m multivariate_gauss.m
function s= multivariate_gauss(x,P,n)
%function s= multivariate_gauss(x,P,n)
%
% INPUTS:
% (x, P) mean vector and covariance matrix
% obtain n samples
% OUTPUT:
% sample set
%
% Random
www.eeworm.com/read/131588/14136165
m multivariate_splines.m
function D = Multivariate_Splines(train_features, train_targets, params, region)
% Classify using multivariate adaptive regression splines
% Inputs:
% features - Train features
% targets -
www.eeworm.com/read/129915/14217607
m multivariate_splines.m
function D = Multivariate_Splines(train_features, train_targets, params, region)
% Classify using multivariate adaptive regression splines
% Inputs:
% features - Train features
% targets -
www.eeworm.com/read/216806/14991728
m multivariate_gauss.m
function s= multivariate_gauss(x,P,n)
%function s= multivariate_gauss(x,P,n)
%
% INPUTS:
% (x, P) mean vector and covariance matrix
% obtain n samples
% OUTPUT:
% sample set
%
% Random sample f
www.eeworm.com/read/273525/4209996
hlp q_multivariate.hlp
{smcl}
{* 04apr2005}{...}
{bf:Stata 9 Multivariate Statistics Reference Manual Datasets}
{hline}
{p 4 4 2}
Datasets used in the Stata Documentation were selected to demonstrate
the use of Stata
www.eeworm.com/read/386597/2570101
m multivariate_splines.m
function test_targets = Multivariate_Splines(train_patterns, train_targets, test_patterns, params)
% Classify using multivariate adaptive regression splines
% Inputs:
% train_patterns - Train pa
www.eeworm.com/read/474600/6813415
m multivariate_splines.m
function test_targets = Multivariate_Splines(train_patterns, train_targets, test_patterns, params)
% Classify using multivariate adaptive regression splines
% Inputs:
% train_patterns - Train pa
www.eeworm.com/read/393504/8281434
m multivariate_gauss.m
function s= multivariate_gauss(x,P,n)
%function s= multivariate_gauss(x,P,n)
%
% INPUTS:
% (x, P) mean vector and covariance matrix
% obtain n samples
% OUTPUT:
% sample set
%
% Random sample f
www.eeworm.com/read/415311/11077030
m multivariate_splines.m
function D = Multivariate_Splines(train_features, train_targets, params, region)
% Classify using multivariate adaptive regression splines
% Inputs:
% features - Train features
% targets -
www.eeworm.com/read/487127/6515507
m multivariate_gauss_noise.m
function s= multivariate_gauss_noise(x,P,n)
len= length(x);
S= chol(P)';
X = randn(len,n);
s = S*X + x*ones(1,n);