代码搜索：Nearest
找到约 1,596 项符合「Nearest」的源代码
代码结果 1,596
www.eeworm.com/read/186878/8898336
m test.m
%function t = EvalFGT()
%EVALFGT Evaluate the Fast Gauss Transform.
% EVALFGT evaluates the fast Gauss transform method.
% Supported methods include:
%
% 'nearest' (default) nearest
www.eeworm.com/read/282683/9074178
c find_nn.c
#include "mex.h"
#include
void mexFunction(
int nlhs, mxArray *plhs[],
int nrhs, const mxArray *prhs[]
)
{
/* Declare variables. */
int *
www.eeworm.com/read/177129/9468804
m interactive_learning.m
function D = Interactive_Learning(train_features, train_targets, params, region);
% Classify using nearest neighbors and interactive learning
% Inputs:
% features- Train features
% targets - Tr
www.eeworm.com/read/372113/9521128
m interactive_learning.m
function test_targets = Interactive_Learning(train_patterns, train_targets, test_patterns, params)
% Classify using nearest neighbors and interactive learning
% Inputs:
% train_patterns - Train
www.eeworm.com/read/362246/10010115
m knnrule.m
function model=knnrule(data,K)
% KNNRULE Creates K-nearest neighbours classifier.
%
% Synopsis:
% model=knnrule(data)
% model=knnrule(data,K)
%
% Description:
% It creates model of the K-nearest ne
www.eeworm.com/read/362013/10023686
m calcnndists.m
% (C) A. Zien and O. Chapelle, MPI for biol. Cybernetics, Germany
function [ NN, D2 ] = calcNnDists( X, D2full, nofNn, annEps );
assert( nargin == 4 );
if( nofNn == 0 | annEps < 0 )
% --- obtain f
www.eeworm.com/read/362008/10023827
m interactive_learning.m
function test_targets = Interactive_Learning(train_patterns, train_targets, test_patterns, params)
% Classify using nearest neighbors and interactive learning
% Inputs:
% train_patterns - Train
www.eeworm.com/read/359177/10162368
m ch5example14prg1.m
% ch5example14prg1.m
clear;
I=imread('colorbar.tif');% 或用 autumn.tif，sydney.JPG等图像文件
figure(1);imshow(I);
I=double(I);
[m,n,p]=size(I);
I=-(0.75-0.125)./(255).*I+0.75; % 换算为0.125到0.75电平
R=I(:,
www.eeworm.com/read/357874/10199081
m interactive_learning.m
function test_targets = Interactive_Learning(train_patterns, train_targets, test_patterns, params)
% Classify using nearest neighbors and interactive learning
% Inputs:
% train_patterns - Train
www.eeworm.com/read/280595/10311889
m knnrule.m
function model=knnrule(data,K)
% KNNRULE Creates K-nearest neighbours classifier.
%
% Synopsis:
% model=knnrule(data)
% model=knnrule(data,K)
%
% Description:
% It creates model of the K-nearest ne