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/393604/8273510
h types.h
/*
* types.h
* Copyright (c) Inst. of Machine Intelligence at Nankai University
*/
#ifndef _TYPES_H
#define _TYPES_H
typedef unsigned int uint32;
typedef unsigned int pte_t;
typedef un
www.eeworm.com/read/411674/11233812
m rbfpreimg3.m
function x = rbfpreimg3(model,nn)
% RBFPREIMG3 RBF pre-image problem by Kwok-Tsang's algorithm.
%
% Synopsis:
% x = rbfpreimg3(model)
% x = rbfpreimg3(model,nn)
%
% Description:
% x = rbfpreimg3(mo
www.eeworm.com/read/147681/12539823
m ellipse_phi.m
function phi = ellipse_phi (X, a, b, phi, myeps);
%ELLIPSE_PHI Compute nearest points to ellipse
%
% phi = ellipse_phi (X, a, b, phi{}, myeps{sqrt(myeps)});
% compute angles for nearest points on e
www.eeworm.com/read/200886/15420865
m mztocoord.m
function coord = mzToCoord(mz,LOW,numDigits)
if nargin==1
numDigits=2;
LOW=400;
end
%% round mz to nearest 0.5
mzr=round(mz*numDigits)/numDigits;
coord=(mzr-LOW)*numDigits+1; %CORRECT
retur
www.eeworm.com/read/108818/15574998
c ll_distance.c
/*--------------------------------------------------------------------------*/
/* mwcommand
name = {ll_distance};
version={"1.1"};
author={"Frederic Guichard, Lionel Moisan"};
function={"Compute signe
www.eeworm.com/read/191902/8417125
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/289743/8529943
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/289334/8558633
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/286662/8751731
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/428849/8834640
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