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www.eeworm.com/read/146984/12596419
m knnclassification.m
function result = knnclassification(testsamplesX,samplesX, samplesY, Knn,type)
% Classify using the Nearest neighbor algorithm
% Inputs:
% samplesX - Train samples
% samplesY - Train labe
www.eeworm.com/read/334348/12609041
txt 使用说明.txt
使用说明：
系统要求：WIN9X/ME/NT/2000 VC++6.0 且安装了VC ACTIVEX控件（在VC6安装时选上）
免费
简介：在VC++6.0中用MSComm控件编程，可以实现串口接收数据和发送数据，数据分别显示在接收框和发送框中。
如何建立工程：建立新文件夹，将文档用WINZIP解压后，双击 Scommtest.dsw 即可在VC6.0中打开工程文件。 ...
www.eeworm.com/read/146625/12630085
cpp 040320131.cpp
#include
#include
#include
#include
#include
using namespace std;
double **dic;//dic[i][j]存放第i个圆与第j个圆得圆心
double *r;//存放n个圆的半径
double best;
te
www.eeworm.com/read/300299/13921360
txt guotaoout.txt
初始种群：
1 4.4545
2 -2.9329
3 -0.9309
4 -6.0561
5 -3.8338
6 2.2122
7 0.1502
8 -9.6396
9 -9.3393
10 -7.4775
11 7.2372
12 -6.2362
13 7.5776
14 -8.6987
1
www.eeworm.com/read/133541/14036090
out t02.out
Thu Aug 24 16:55:32 1995
Linear inequalities :
Domains :
0.00
www.eeworm.com/read/133278/14049545
asv main.asv
function main(t)
common;
l = length(t);
t = [t -100 -100 -100];
i = 1;
result = [];
while (abs(t(i)) < 100)
[y, yl] = finddata(t(i)*1000+t(i+1)*100+t(i+2)*10+t(i+3), 'list4.txt
www.eeworm.com/read/133278/14049580
m main.m
function main(t)
l = length(t);
t = [t -10000 -10000 -10000];
i = 1;
result = [];
while (abs(t(i)) < 100)
[y, yl] = finddata(t(i)*1000+t(i+1)*100+t(i+2)*10+t(i+3), 'list4.txt');
www.eeworm.com/read/202201/15389777
cpp f0507.cpp
//=====================================
// f0507.cpp
// 函数指针数组
//=====================================
#include
using namespace std;
//-------------------------------------
typedef vo
www.eeworm.com/read/202201/15389789
cpp f0508.cpp
//=====================================
// f0508.cpp
// 函数指针向量
//=====================================
#include
#include
using namespace std;
//-------------------------------
www.eeworm.com/read/200524/15431222
m badest.m
% 求出群体中最小得适应值及其个体
%遗传算法子程序
%Name: badest.m
function [badestindividual,badestfit]=best(pop,fitvalue)
[px,py]=size(pop);
badestindividual=pop(1,:);
badestfit=fitvalue(1);
for i=1:px;
if fi