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/293183/8310265
m nmc.m
%NMC Nearest Mean Classifier
%
% W = nmc(A)
%
% Computation of the nearest mean classifier between the classes in
% the dataset A.
%
% See also datasets, mappings, nmsc, ldc, fisherc, qdc, udc
www.eeworm.com/read/277578/10624259
c events.c
/* include */
#include "events.h"
#include
/* namespace */
using namespace std;
/******************************************************
* global variables
***********************
www.eeworm.com/read/174450/9587225
cpp testtex2dvstexrect.cpp
// TestTex2DvsTexRect.cpp: implementation of the TestTex2DvsTexRect class.
//
//////////////////////////////////////////////////////////////////////
#include "stdafx.h"
#include "shadow.h"
#inc
www.eeworm.com/read/284357/8938248
m knn1.m
function [eachClass, ensembleClass, nearestSampleIndex, knnmat] = ...
knn(sampledata, testdata, k)
% KNN K-nearest neighbor rule for classification
% Usage:
% [EACH_CLASS, ENSEMBLE_CLASS, NEAREST
www.eeworm.com/read/360710/10080941
m knn.m
function [eachClass, ensembleClass, nearestSampleIndex, knnmat] = ...
knn(sampledata, testdata, k)
% KNN K-nearest neighbor rule for classification
% Usage:
% [EACH_CLASS, ENSEMBLE_CLASS, NEAREST
www.eeworm.com/read/148901/12415753
cpp knn.cpp
//====
// knn.cpp
// - k nearest neighbours (the classic case-based classifier)
// - returns the most likely category of a target according to
// its k nearest neighbours whose categories are kn
www.eeworm.com/read/386050/8769559
m knnm.m
%KNNM K-Nearest Neighbour based density estimate
%
% W = KNNM(A,KNN)
%
% D = B*W
%
% INPUT
% A Dataset
% KNN Number of nearest neighbours
%
% OUTPUT
% W Density estimate
%
% DESC
www.eeworm.com/read/299984/7140717
m knnm.m
%KNNM K-Nearest Neighbour based density estimate
%
% W = KNNM(A,KNN)
%
% D = B*W
%
% INPUT
% A Dataset
% KNN Number of nearest neighbours
%
% OUTPUT
% W Density estimate
%
% DESC
www.eeworm.com/read/460435/7251193
m knnm.m
%KNNM K-Nearest Neighbour based density estimate
%
% W = KNNM(A,KNN)
%
% D = B*W
%
% INPUT
% A Dataset
% KNN Number of nearest neighbours
%
% OUTPUT
% W Density estimate
%
% DESC
www.eeworm.com/read/450608/7480585
m knnm.m
%KNNM K-Nearest Neighbour based density estimate
%
% W = KNNM(A,KNN)
%
% D = B*W
%
% INPUT
% A Dataset
% KNN Number of nearest neighbours
%
% OUTPUT
% W Density estimate
%
% DESC