代码搜索：Nearest
找到约 1,596 项符合「Nearest」的源代码
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www.eeworm.com/read/159921/10587754
m knnclass.m
% KNNCLASS k-Nearest Neighbours classifier.
% [tst_labels] = knnclass(tst_data,trn_data,trn_labels, k)
%
% Input:
% tst_data [dim x n_tst] data to be classified.
% trn_data [dim x n_trn] training da
www.eeworm.com/read/421949/10676406
m knnclass.m
% KNNCLASS k-Nearest Neighbours classifier.
% [tst_labels] = knnclass(tst_data,trn_data,trn_labels, k)
%
% Input:
% tst_data [dim x n_tst] data to be classified.
% trn_data [dim x n_trn] training da
www.eeworm.com/read/322475/13379429
m knn.m
function [class]=knn(Pr,Tr,Pt,kN)
% Usage: [Cmat,C_rate]=knn(Pr,Tr,Pt,Tt,kN)
% kNN- k-Nearest Neighbor Classifier
% copyright 1993-1996 by Yu Hen Hu
% Last revision 2/12/03 by Marco F. Duarte
% Pr: t
www.eeworm.com/read/230811/14273536
m p9search.m
% Initial search for nearest effective refractive index.
logr9=zeros(1,200); effind9=logr9;
mn=effind8(2);
m=1; % sets initial value of mode counting parameter
for k=1:200; effind8(1)=(refrmax8-
www.eeworm.com/read/229007/14355754
m knn.m
function [class]=knn(Pr,Tr,Pt,kN)
% Usage: [Cmat,C_rate]=knn(Pr,Tr,Pt,Tt,kN)
% kNN- k-Nearest Neighbor Classifier
% copyright 1993-1996 by Yu Hen Hu
% Last revision 2/12/03 by Marco F. Duarte
% Pr: t
www.eeworm.com/read/430506/1929379
m knnclass.m
% KNNCLASS k-Nearest Neighbours classifier.
% [tst_labels] = knnclass(tst_data,trn_data,trn_labels, k)
%
% Input:
% tst_data [dim x n_tst] data to be classified.
% trn_data [dim x n_trn] training da
www.eeworm.com/read/373460/2761813
m knnclass.m
% KNNCLASS k-Nearest Neighbours classifier.
% [tst_labels] = knnclass(tst_data,trn_data,trn_labels, k)
%
% Input:
% tst_data [dim x n_tst] data to be classified.
% trn_data [dim x n_trn] training da
www.eeworm.com/read/393368/8293858
m chongdiebaoliu.m
function y=chongdiebaoliu(h,x,N)
M=length(h);
x_l=length(x);
if M>x_l||M==x_l||N
www.eeworm.com/read/367442/9747782
m knnclass.m
% KNNCLASS k-Nearest Neighbours classifier.
% [tst_labels] = knnclass(tst_data,trn_data,trn_labels, k)
%
% Input:
% tst_data [dim x n_tst] data to be classified.
% trn_data [dim x n_trn] training da
www.eeworm.com/read/429878/8784188
htm knn.htm
Netlab Reference Manual knn
knn
Purpose
Creates a K-nearest-neighbour classifier.
Synopsis
net = knn(nin, nout