代码搜索：clusters
找到约 1,502 项符合「clusters」的源代码
代码结果 1,502
www.eeworm.com/read/227886/14406929
m n_clusters.m
el=0.0013*10^-12; es=10*10^-12; Ee=50*10^-9; Ebf=5*10^-9;
n=1000; l=4000; Nf=10000; M=100;
[m]=[1:1:10];
d=150;
f1=el*(d^4)-Ebf.*m-(2*m-1)*Ee;
k=((n/(2*3.14))^(0.5))*((es./f1).^(0.5))*M;
plot(m,
www.eeworm.com/read/304962/3782171
edg clusters2.edg
47
-20.3046 30.4569 -784.264 109.137
-20.3046 30.4569 -20.3046 30.4569
-20.3046 30.4569 662.437 365.482
812.183 152.284 -20.3046 30.4569
824.873 53.2995 -20.3046 30.4569
819.797 -119.289 -20.3046 30.4
www.eeworm.com/read/418440/2088815
cpp analyzer_clusters.cpp
#include "analyzer_clusters.h"
void analyze_clusters(string resultFile,
colorsLayer & results,
map & numNodes)
{
// Legge simulazione per simulazione.
// Ad ogni simula
www.eeworm.com/read/418440/2088830
h analyzer_clusters.h
#ifndef _ANALYZER_CLUSTERS_H_
#define _ANALYZER_CLUSTERS_H_
#include
#include
#include
#include
#include "gzstream.h"
#include "graph.h"
#include "utility.h"
www.eeworm.com/read/399441/2360678
eps 4clusters.eps
%!PS-Adobe-2.0 EPSF-2.0
%%Creator: nam
%%DocumentFonts: Helvetica
%%BoundingBox: 36 123 526 618
%%EndComments
gsave 36 36 translate
/namdict 20 dict def
namdict begin
/ft {/Helvetica findfont exch sca
www.eeworm.com/read/399441/2360682
sc-4clusters
$node_(0) set X_ 209.74571818465
$node_(0) set Y_ 448.116095193461
$node_(0) set Z_ 0.0
$node_(1) set X_ 74.3426639333249
$node_(1) set Y_ 432.608467972983
$node_(1) set Z_ 0.0
$node_(2) set X_
www.eeworm.com/read/396844/2407731
m kmeans_clusters.m
function [centers,clusters,errors,ind] = kmeans_clusters(sD, n_max, c_max, verbose)
% KMEANS_CLUSTERS Clustering with k-means with different values for k.
%
% [c, p, err, ind] = kmeans_clusters(sD, [
www.eeworm.com/read/294611/8216452
m kmeans_clusters.m
function [centers,clusters,errors,ind] = kmeans_clusters(sD, n_max, c_max, verbose)
% KMEANS_CLUSTERS Clustering with k-means with different values for k.
%
% [c, p, err, ind] = kmeans_clusters(sD, [
www.eeworm.com/read/367875/9724781
m kmeans_clusters.m
function [centers,clusters,errors,ind] = kmeans_clusters(sD, n_max, c_max, verbose)
% KMEANS_CLUSTERS Clustering with k-means with different values for k.
%
% [c, p, err, ind] = kmeans_clusters(sD, [
www.eeworm.com/read/367442/9747868