代码搜索：Nearest

找到约 1,596 项符合「Nearest」的源代码

代码结果 1,596
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h fast_float_math_hack.h

# ifdef __ICL /* only Intel C compiler has fmath ??? */ #include /* Nearest integer, absolute value, etc. */ #define ceil ceilf #define fabs fabsf #define floor floorf
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www.eeworm.com/read/293183/8310822

m knnm.m

%KNNM Estimate K-Nearest Neighbour densities % % W = KNNM(A,KNN) % % D = B*W % % For each of the classes in the dataset A a NN density % is estimated. The result is stored as a K*C mapping in W, in wh
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www.eeworm.com/read/266128/11238913

txt fknn.txt

function [predicted,memberships, numhits] = fknn(data, labels, test, ... testlabels, k_values, info, fuzzy) % FKNN Fuzzy k-nearest neighbor classification algorithm. % Y = FKNN(DATA, LABELS,
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www.eeworm.com/read/203890/15349760

m at.m

function [idx] = at(y,xs) % [idx] = at(y,xs) Return elements of y nearest to xs % AT is the inverse of the indexing paren operator. Ie, % % xs=y(idx) idx=at(y,xs) % % AT can for instance be used
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www.eeworm.com/read/367675/2833770

txt 40.txt

发信人: GzLi (笑梨), 信区: DataMining 标 题: [合集]请教有关于k-nearest neighbor的问题？？？ 发信站: 南京大学小百合站 (Sat Sep 21 13:23:02 2002), 站内信件 waden (waden) 于Mon Sep 16 22:44:39 2002提到： 小弟做了个k-nearest neighbor的分类器，用的数
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www.eeworm.com/read/367675/2838149

txt 62.txt

发信人: GzLi (笑梨), 信区: DataMining 标 题: [合集]请教有关于k-nearest neighbor的问题？？？ 发信站: 南京大学小百合站 (Sat Sep 21 13:23:02 2002), 站内信件 waden (waden) 于Mon Sep 16 22:44:39 2002提到： 小弟做了个k-nearest neighbor的分类器，用的数
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www.eeworm.com/read/389962/8490880

m snn.m

% SNN - Creates forecasts of a time series on t+1 using multivariate nearest neighbor algorithm. % % REQUIRES MREGRESS.M FILE available at http://www.mathworks.com/matlabcentral/fileexchange/l
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www.eeworm.com/read/289487/8548706

m svmclassnpa.m

function [xsup,alpha,b,pos]=svmclassnpa(x,y,C,kernel,kerneloption,verbose); % USAGE % [xsup,alpha,b,pos]=svmclassnpa(x,y,C,kernel,kerneloption,verbose); % % % Main ROUTINE For Nearest P
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www.eeworm.com/read/289334/8558640

m knnclass.m

function y = knnclass(X,model) % KNNCLASS k-Nearest Neighbours classifier. % % Synopsis: % y = knnclass(X,model) % % Description: % The input feature vectors X are classified using the K-NN % rule
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www.eeworm.com/read/386048/8769828

m lle_roweis.m

% LLE ALGORITHM (using K nearest neighbors) % % [Y] = lle(X,K,dmax) % % X = data as D x N matrix (D = dimensionality, N = #points) % K = number of neighbors % dmax = max embedding dimensionality
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