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/367655/9738564
m knn.m
function [C,P]=knn(d, Cp, K)
%KNN K-Nearest Neighbor classifier using an arbitrary distance matrix
%
% [C,P]=knn(d, Cp, [K])
%
% Input and output arguments ([]'s are optional):
% d (matrix)
www.eeworm.com/read/415663/11059491
m lle_pd.m
% LLE ALGORITHM with pairwise distances (using K nearest neighbors)
%
% [Y] = lle_pd(X,K,dmax)
%
% X = pairwise distances as N x N matrix (N = #points)
% K = number of neighbors
% dmax = max embedding
www.eeworm.com/read/133537/14036727
install
If you don't have it already, get a recent version of the Perl programming
language. Then go to your nearest CPAN mirror and get the Net::FTP module.
Net::FTP is in Graham Barr's libnet bundle, which
www.eeworm.com/read/235928/14041693
m knn.m
function [C,P]=knn(d, Cp, K)
%KNN K-Nearest Neighbor classifier using an arbitrary distance matrix
%
% [C,P]=knn(d, Cp, [K])
%
% Input and output arguments ([]'s are optional):
% d (matrix)
www.eeworm.com/read/362013/10023708
cpp rhopathdists2.cpp
#include "mex.h"
#include
#include
static const char* const copyright =
"Calculate squared rho-path distances of a set of points to a subset on a nearest neighbor graph.\n"
www.eeworm.com/read/361798/10035454
m nene.m
function [s,V] = nene(z,rho,theta)
% function NENE.M
% Nearest neighbor approximation algorithm
% [s,V] = nene(z,rho,theta)
% z = M by N matrix with samples in frequency domain
% rho, theta = radius
www.eeworm.com/read/314681/13561798
m contents.m
Spatial Statistics Toolbox 2.0
Kelley Pace, www.spatial-statistics.com, 1/15/03
Two dimensional weight matrices
fdelw2 - spatial contiguity weight matrix
fneighbors2 - nearest neighbors weig
www.eeworm.com/read/101082/6245503
3m floor.3m
.TH floor 3m RISC
.SH Name
floor, ffloor, fabs, ceil, ceil, trunc, ftrunc, fmod, rint \- floor, absolute value, ceiling, truncation, floating point remainder and round-to-nearest functions
.SH Syntax
www.eeworm.com/read/479088/6699334
m nene.m
function [s,V] = nene(z,rho,theta)
% function NENE.M
% Nearest neighbor approximation algorithm
% [s,V] = nene(z,rho,theta)
% z = M by N matrix with samples in frequency domain
% rho, theta = radius
www.eeworm.com/read/15921/597561
c kdtree.c
/*
Functions and structures for maintaining a k-d tree database of image
features.
For more information, refer to:
Beis, J. S. and Lowe, D. G. Shape indexing using approximate
nearest-neighb