代码搜索：gaussian
找到约 7,040 项符合「gaussian」的源代码
代码结果 7,040
www.eeworm.com/read/161043/10459201
m gaussian_sinc.m
% gaussian_sinc.m - Gauusian windowed sinc waveform
% written by Nadav Levanon on 4 June 2003
% single bit shape
nn=201; nn2=(nn-1)/2; nn22=nn2/2;
small=.00000001;
nnn=-nn2:nn2;
arg_bit=small+4*pi
www.eeworm.com/read/424281/10468813
tex rand-gaussian.tex
www.eeworm.com/read/351998/10588974
m gmm_to_gaussian.m
function [x,P,wn] = gmm_to_gaussian(g)
% Convert gmm to a single Gaussian (ie, compute first two moments of mixture).
[g, wn] = gmm_normalise(g); % gmm must be normalised for weighted sums to be
www.eeworm.com/read/351998/10589082
m kernel_to_gaussian.m
function [x,P,wn] = kernel_to_gaussian(g)
% Convert kernels to a single Gaussian (ie, compute first two moments).
[g, wn] = kernel_normalise(g); % must be normalised for weighted sums to be meani
www.eeworm.com/read/349646/10808458
m gaussian_prob.m
function p = gaussian_prob(x, m, C, use_log)
% GAUSSIAN_PROB Evaluate a multivariate Gaussian density.
% p = gaussian_prob(X, m, C)
% p(i) = N(X(:,i), m, C) where C = covariance matrix and each COL
www.eeworm.com/read/349646/10808465
m sample_gaussian.m
function M = sample_gaussian(mu, Sigma, N)
% SAMPLE_GAUSSIAN Draw N random row vectors from a Gaussian distribution
% samples = sample_gaussian(mean, cov, N)
if nargin==2
N = 1;
end
% If Y
www.eeworm.com/read/349646/10808486
m gaussian_sample.m
function x = gsamp(mu, covar, nsamp)
%GSAMP Sample from a Gaussian distribution.
%
% Description
%
% X = GSAMP(MU, COVAR, NSAMP) generates a sample of size NSAMP from a
% D-dimensional Gaussian
www.eeworm.com/read/349646/10808517
m marginalize_gaussian.m
function [muX, SXX] = marginalize_gaussian(mu, Sigma, X, Y, ns)
% MARGINALIZE_GAUSSIAN Compute Pr(X) from Pr(X,Y) where X and Y are jointly Gaussian.
% [muX, SXX] = marginalize_gaussian(mu, Sigma, X
www.eeworm.com/read/349646/10809009
m gaussian_prob.m
function p = gaussian_prob(x, m, C, use_log)
% GAUSSIAN_PROB Evaluate a multivariate Gaussian density.
% p = gaussian_prob(X, m, C)
% p(i) = N(X(:,i), m, C) where C = covariance matrix and each COL
www.eeworm.com/read/349646/10809025
m sample_gaussian.m
function M = sample_gaussian(mu, Sigma, N)
% SAMPLE_GAUSSIAN Draw N random row vectors from a Gaussian distribution
% samples = sample_gaussian(mean, cov, N)
if nargin==2
N = 1;
end
% If Y