代码搜索：Distance
找到约 8,736 项符合「Distance」的源代码
代码结果 8,736
www.eeworm.com/read/351998/10588903
m gmm_distance_bhattacharyya.m
function [Bc,Bd] = gmm_distance_bhattacharyya(g1, g2, N)
% Monte Carlo approximation of Bhattacharyya distance.
%
% Note: MC integration of the form: integral of p(x)f(x) dx
% require that p(x)
www.eeworm.com/read/351998/10588936
m gmm_distance_kld.m
function K = gmm_distance_KLD(g1, g2, N)
if nargin == 3
K = gmm_KLD_montecarlo(g1, g2, N);
else
K = gmm_KLD_unscented(g1, g2);
end
%
%
function K = gmm_KLD_montecarlo(g1, g2, N)
www.eeworm.com/read/351998/10588940
m kernel_distance_kld.m
function K = kernel_distance_KLD(g1, g2, N)
s = kernel_samples(g1, N);
w1 = kernel_evaluate(g1, s);
w2 = kernel_evaluate(g2, s);
%K = sum(log(w1) - log(w2)) / N;
ii = find(w1 ~= 0 & w2 ~= 0
www.eeworm.com/read/351998/10589039
m kernel_distance_bhattacharyya.m
function [Bc,Bd] = kernel_distance_bhattacharyya(g1, g2, N)
s = kernel_samples(g1, N); % use g1 as proposal
w1 = kernel_evaluate(g1, s);
w2 = kernel_evaluate(g2, s);
Bc = sum(sqrt(w1.*w2)./w1
www.eeworm.com/read/351998/10589060
m gmm_distance_bayes.m
function D = gmm_distance_bayes(g1, g2)
% Normalising constant from Gmm multiplication
D = 0;
for i=1:size(g1.x,2)
for j=1:size(g2.x,2)
wij = gauss_likelihood(g1.x(:,i)-g2.x(:,j),
www.eeworm.com/read/351998/10589098
m kernel_distance_bayes.m
function D = kernel_distance_bayes(g1, g2)
%
% Evidence of hypothesis, or Bayes normaliser, or probability of observation p(z|Z)
dim = size(g1.x, 1);
S = g1.P + g2.P;
Sc = chol(S)';
denom =
www.eeworm.com/read/420999/10761686
bak distance_uv2.bak
### uVision2 Project, (C) Keil Software
### Do not modify !
Target (Target 1), 0x0000 // Tools: 'MCS-51'
Group (Source Group 1)
File 1,1, 0x0
Options 1,0,0 /
www.eeworm.com/read/467873/7003117
m l2_distance.m
function d = l2_distance(X,Y)
% d = l2_distance(X)
% Compute pairwise l2 distances between column vectors in X
%
% Warning -- this implementation suffers from cancellation error.
%
% Author: Dav
www.eeworm.com/read/445660/7592214
txt points to the distance segment.txt
// distance to a segment
// construct a line perpendicular to the segment
// if the two end points of the line is on the same side of this line
// return min distance to the end points
// return d
www.eeworm.com/read/444599/7611034
m l2_distance.m
function d = L2_distance(a,b,df)
% --- L2_distance function
% Written by Roland Bunschoten, University of Amsterdam, 1999
if (size(a,1) == 1)
a = [a; zeros(1,size(a,2))];
b = [b; zeros(1,s