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/338327/7109294
m nearest_point.m
%skyhawk#flyinghawk
%计算最后一个相点的最近相点的位置及最短距离
function [idx,min_d,idx1,min_d1]=nearest_point(m,whlsj,whlsl,P)
%参数说明：
%输入：m - 嵌入维数， whlsj － 待分析数据， whlsl - 待分析的数据个数， P - 平均循环周期
% idx - 最后一个相点的最近相
www.eeworm.com/read/219035/7147280
m nearest_ind.m
function out = nearest_ind(v,x)
[dummy, out] = min(abs(v-x));
www.eeworm.com/read/444270/7615418
m nearest_neighbor.m
function index = nearest_neighbor(x,vectors,maxdist,varargin)
% function index = nearest_neigbor(x,vectors,maxdist,pointdist*,pointlimit*,k*)
% x is a row vector
% pointdist (optional) - vector of
www.eeworm.com/read/444270/7615420
m nearest_mahal.m
function index = nearest_mahal(x,mu,sigma)
% function index = nearest_mahal(x,mu,sigma)
% x is a vector
% mu(i,:) is the mean of the ith Gaussian
% sigma(:,:,i) is the covariance of the ith Gaussi
www.eeworm.com/read/440070/7694218
c false_nearest.c
/*Author: Rainer Hegger. Last modified: Sep 3, 1999 */
#include
#include
#include
#include
#include
#include "routines/tsa.h"
#define WID_STR "Dete
www.eeworm.com/read/440070/7694836
html false_nearest.html
false_nearest
Description of the program: false_nearest
This program looks for the near
www.eeworm.com/read/399996/7816599
m nearest_neighbor.m
function test_targets = Nearest_Neighbor(train_patterns, train_targets, test_patterns, Knn)
% Classify using the Nearest neighbor algorithm
% Inputs:
% train_patterns - Train patterns
% train_t
www.eeworm.com/read/299891/7826247
txt nearest.txt.txt
csdn ....... :(
class CShortPath
{
private:
class Edge
{
private:
int dest; //the operation of the other vertex of edge
int weight;//quan zhi
Edge *link;
};
class Vertex
{
www.eeworm.com/read/397106/8067534
m nearest_neighbor.m
function D = Nearest_Neighbor(train_features, train_targets, Knn, region)
% Classify using the Nearest neighbor algorithm
% Inputs:
% features - Train features
% targets - Train targets
% Knn - Num
www.eeworm.com/read/397099/8068738
m nearest_neighbor.m
function test_targets = Nearest_Neighbor(train_patterns, train_targets, test_patterns, Knn)
% Classify using the Nearest neighbor algorithm
% Inputs:
% train_patterns - Train patterns
% train_t