SOM网络聚类完整示例（利用python和java）
下面是几个城市的GDP等信息，根据这些信息，写一个SOM网络，使之对下面城市进行聚类。并且，将结果画在一个二维平面上。
//表1中，X。为人均GDP(元)；X2为工业总产值(亿元)；X。为社会消费品零售总额(亿元)；x。为批发零售贸易总额(亿元)；x。为地区货运总量(万吨)，表1中数据来自2002年城市统计年鉴。
//城市 X1 X2 X3 Xa X5
北京 27527 2738.30 1494.83 3055.63 30500
青岛 29682 1212.02 182.80 598.06 29068
天津 22073 2663.56 782.33 1465.65 28151
烟台 21017 298.73 92.71 227.39 8178
石家庄 25584 467.42 156.02 763.46 12415
郑州 17330 261.80 215.63 402.98 7373
唐山 19387 338.67 95.73 199.69 14522
武汉 17882 1020.84 685.82 1452 16244
太原 13919 304.13 141.94 155.22 15170
长沙 26327 241.76 269.93 369.83 7550
呼和浩特 13738 82.23 69.27 108.12 2415
衡阳 12386 61.53 63.95 72.65 3004
沈阳 21736 729.04 590.26 1752.4 15156
广州 42828 2446.97 1166.10 3214.19 24500
大连 34659 1003.56 431.83 728.08 19736
深圳 152099 3079.63 609.26 801.06 5167
长春 24799 900.26 309.75 173.99 10346
油头 19414 192.93 112.96 280.84 1443
哈尔滨 20737 402.73 360.38 762.94 8814
湛江 15290 228.45 99.08 149.16 5524
上海 40788 6935.57 1531.89 3921.2 49499
南宁 17715 109.39 142.08 264.32 3371
南京 26697 1579.21 401.20 1253.73 14120
柳州 17598 256.76 68.93 159.44 3397
徐州 19727 295.73 108.17 187.39 7124
海口 24782 100.13 81.03 142.54 2018
连云港 17869 112.18 47.94 134.89 4096
成都 22956 412.23 400.56 754.07 23724
杭州 31784 1615.63 373.28 1788.29 15841
重庆 9778 870.82 389.60 823.72 29470
宁波 46471 751.58 167.70 529.68 11182
贵阳 13176 207.95 108.93 285.27 4885
温州 29781 381.93 233.44 272.84 6292
昆明 24554 303.78 227.44 428.64 12084
合肥 19770 330.14 140.14 328.98 2903
西安 16002 449.14 323.37 558.27 7728
福州 33570 379.51 209.72 613.24 7280
兰州 16629 354.30 163.97 374.9 5401
厦门 42039 803.29 186.55 620.47 2547
西宁 7261 38.00 48.95 91.14 1837
南昌 19923 238.82 14.09 348.21 3246
银川 12779 77.74 41.22 53.16 1573
济南 25642 616.97 323.08 462.39 13057
乌鲁木齐 19793 251.19 129.05 277.8 9283
首先，利用python对这些数据进行处理，具体过程如下：
1，读入文件
2，使用1000,100,10,1三个数字分别替换x1列的数值，判断的标准为中位数和两个四分位数。
3，代替x2列的数值
4，替换x3列的数值
5，替换Xa列的数值
6，替换X5列的数值
最终得到的结果：
excel排序算分段值截图：
应该可以用四分位数直接可以得到这些数字，这里操作的稍微的麻烦了一点。
聚类的java的代码：
package com.cgjr.som; import org.neuroph.core.Neuron;
import org.neuroph.core.data.DataSet;
import org.neuroph.core.data.DataSetRow;
import org.neuroph.nnet.Kohonen; public class AreaClutering { public static double[][] data = {
{ 1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1}, //北京
{ 0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,1,0,0,0,0,1}, //青岛
{ 0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1},//天津
{ 0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,1,0},//烟台
{ 0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,1,0},//石家庄
{ 0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,1,0,0},//郑州
{ 0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,1,0},//唐山
{ 0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1}, //武汉
{ 0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,1,0,0,0,0,0,0,1}, //太原
{ 0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,1,0,0,0,1,0,0}, //长沙
{ 0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0}, //呼和浩特
{ 0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0}, //衡阳
{ 0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,1,0}, //沈阳
{ 0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1}, //广州
{ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,1,0,0,0,0,1}, //大连
{ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,1,0,0}, //深圳
{ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,1,0}, //长春
{ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,1,0,0,1,0,0,0}, //油头
{ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0}, //哈尔滨
{ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,1,0,0}, //湛江
{ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1}, //上海
{ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,1,0,0,1,0,0,0}, //南宁
{ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,1,0}, //南京
{ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0,0,0,1,0,0,0,1,0,0,0}, //柳州
{ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,1,0,0,0,0,1,0,0}, //徐州
{ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0}, //海口
{ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,1,0,0}, //连云港
{ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,1}, //成都
{ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,1}, //杭州
{ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,1}, //重庆
{ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,1,0,0,0,0,1,0,0,0,1,0}, //宁波
{ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0}, //贵阳
{ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,0,1,0,0,0,1,0,0}, //温州
{ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,1,0}, //昆明
{ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,0,1,0,0,0}, //合肥
{ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,1,0,0},//西安
{ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,0,0,1,0,0,1,0,0}, //福州
{ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,0}, //兰州
{ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,0,0,1,0,1,0,0,0}, //厦门
{ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0}, //西宁
{ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0}, //南昌
{ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0}, //银川
{ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0}, //济南
{ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,1,0}, //乌鲁木齐 }; public static String[] dataKey={"北京","青岛","天津","烟台","石家庄","郑州","唐山","武汉","太原","长沙","呼和浩特",
"衡阳","沈阳","广州","大连","深圳","长春","油头","哈尔滨","湛江","上海","南宁",
"南京","柳州","徐州","海口","连云港","成都","杭州","重庆","宁波","贵阳","温州",
"昆明","合肥","西安","福州","兰州","厦门","西宁","南昌","银川","济南","乌鲁木齐"}; public static void main(String[] args) {
ResultFrame frame = new ResultFrame();
Kohonen som = new Kohonen(64, 100);
DataSet ds = new DataSet(64);
for (double[] row : data) {
ds.addRow(new DataSetRow(row));
} som.learn(ds); for (int i=0;i<data.length;i++) {
som.setInput(data[i]);
som.calculate();
int winnerIndex=getWinnerIndex(som);
int x=getRowFromIndex(winnerIndex);
int y=getColFromIndex(winnerIndex);
System.out.println(dataKey[i]+" "+x+" "+y );
frame.addElementString(new ResultFrame.ElementString(dataKey[i], x, y));
}
frame.showMe();
} // get unit with closetst weight vector
private static int getWinnerIndex(Kohonen neuralNetwork) {
Neuron winner = new Neuron();
double minOutput = 100;
int winnerIndex=-1;
Neuron[] neurons=neuralNetwork.getLayerAt(1).getNeurons();
for (int i=0;i<neurons.length;i++) {
double out = neurons[i].getOutput();
if (out < minOutput) {
minOutput = out;
winnerIndex = i;
} // if
} // while
return winnerIndex;
} /**
* 10行10列中的位置
* @param index
* @return
*/
private static int getRowFromIndex(int index){
return index/10+1;
}
private static int getColFromIndex(int index){
return index%10+1;
} }
package com.cgjr.som; import java.awt.Font;
import java.awt.Graphics;
import java.awt.Graphics2D;
import java.util.ArrayList;
import java.util.List; import javax.swing.JFrame;
import javax.swing.JPanel; public class ResultFrame extends JFrame {
private List<ElementString> elements=new ArrayList<ElementString>(); public ResultFrame() {
} private void init() {
setTitle("训练结果");
setSize(800, 800);
DrawPanel panel = new DrawPanel();
add(panel);
} public void showMe(){
if(elements.size()==0)throw new RuntimeException("elements is empty");
init();
normalCood();
setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);
setVisible(true);
} public void addElementString(ElementString str){
elements.add(str);
} public void normalCood(){
float minX=Float.MAX_VALUE,maxX=0,minY=Float.MAX_VALUE,maxY=0;
for(ElementString es:elements){
if(es.x>maxX)maxX=es.x;
if(es.y>maxY)maxY=es.y;
if(es.x<minX)minX=es.x;
if(es.y<minY)minY=es.y;
}
for(ElementString es:elements){
es.x=(es.x-minX)/(maxX-minX)*700+20;
es.y=(es.y-minY)/(maxY-minY)*700+20;
}
} public static void main(String[] args) {
ResultFrame frame = new ResultFrame();
frame.showMe();
} class DrawPanel extends JPanel {
public void paintComponent(Graphics g) {
super.paintComponent(g);
Graphics2D g2 = (Graphics2D) g;//将Graphics对象转换为Graphics2D对象
g2.setFont(new Font("TimesRoman", Font.PLAIN, 20));
for(ElementString es:elements){
g2.drawString(es.text, es.x, es.y);
}
}
} public static class ElementString{
private String text;
private float x;
private float y; public ElementString(String text, float x, float y) {
super();
this.text = text;
this.x = x;
this.y = y;
}
public String getText() {
return text;
}
public void setText(String text) {
this.text = text;
}
public float getX() {
return x;
}
public void setX(float x) {
this.x = x;
}
public float getY() {
return y;
}
public void setY(float y) {
this.y = y;
}
}
}
运行效果的截图：
SOM网络聚类完整示例（利用python和java）的更多相关文章
- 智能客服 利用python运行java代码
因为需要在linux中用python来进行分析,顾需要利用python来运行java中语音转文字和文字转语音代码 在python中运行java代码需要利用jpype
- Python中利用原始套接字进行网络编程的示例
Python中利用原始套接字进行网络编程的示例 在实验中需要自己构造单独的HTTP数据报文,而使用SOCK_STREAM进行发送数据包,需要进行完整的TCP交互. 因此想使用原始套接字进行编程,直接构 ...
- 如何利用Python网络爬虫抓取微信朋友圈的动态（上）
今天小编给大家分享一下如何利用Python网络爬虫抓取微信朋友圈的动态信息,实际上如果单独的去爬取朋友圈的话,难度会非常大,因为微信没有提供向网易云音乐这样的API接口,所以很容易找不到门.不过不要慌 ...
- 如何利用Python网络爬虫爬取微信朋友圈动态--附代码（下）
前天给大家分享了如何利用Python网络爬虫爬取微信朋友圈数据的上篇(理论篇),今天给大家分享一下代码实现(实战篇),接着上篇往下继续深入. 一.代码实现 1.修改Scrapy项目中的items.py ...
- 【转】利用python的KMeans和PCA包实现聚类算法
转自:https://www.cnblogs.com/yjd_hycf_space/p/7094005.html 题目: 通过给出的驾驶员行为数据(trip.csv),对驾驶员不同时段的驾驶类型进行聚 ...
- 利用python的KMeans和PCA包实现聚类算法
题目: 通过给出的驾驶员行为数据(trip.csv),对驾驶员不同时段的驾驶类型进行聚类,聚成普通驾驶类型,激进类型和超冷静型3类 . 利用Python的scikit-learn包中的Kmeans算法 ...
- 利用Python网络爬虫爬取学校官网十条标题
利用Python网络爬虫爬取学校官网十条标题 案例代码: # __author : "J" # date : 2018-03-06 # 导入需要用到的库文件 import urll ...
- Python 利用Python编写简单网络爬虫实例3
利用Python编写简单网络爬虫实例3 by:授客 QQ:1033553122 实验环境 python版本:3.3.5(2.7下报错 实验目的 获取目标网站“http://bbs.51testing. ...
- Python 利用Python编写简单网络爬虫实例2
利用Python编写简单网络爬虫实例2 by:授客 QQ:1033553122 实验环境 python版本:3.3.5(2.7下报错 实验目的 获取目标网站“http://www.51testing. ...
随机推荐
- Linux学习之sudo命令
在学习Linux用户管理时,我们不得不需要了解一个命令,那就是sudo.sudo的作用是切换身份,以其他身份来执行命令. 那么为什么在Linux系统中我们需要来切换身份呢?原因有以下几个方面 1.养成 ...
- Sublime Text前端开发环境配置
Sublime Text是前端开发不可不说的编辑器,本文以Sublime Text 3为例讲解一下如何搭建前端的开发环境. 下载Sublime Text 3 下载地址 #==> Sublime ...
- Apache Mina入门实例
一.mina是啥 ApacheMINA是一个网络应用程序框架,用来帮助用户简单地开发高性能和高可扩展性的网络应用程序.它提供了一个通过Java NIO在不同的传输例如TCP/IP和UDP/IP上抽象的 ...
- Oracle ASM数据库故障数据恢复过程
一.故障描述 ASM磁盘组掉线 ,ASM实例不能mount.ASM磁盘组有4个500G的磁盘组成,数据库和ASM软件为10.2.0.1,急需恢复oracle数据库.二.故障分析 分析组成ASM磁盘 ...
- 性能测试培训: 监控CPU之python
性能测试培训: 监控CPU之python 作为一名测试开发工程师,开发脚本是为了测试服务的,我们在手里没有性能监控工具的情况下,我们会自己来进行开发脚本完成监控任务.下面是python监控cpu '' ...
- 性能调优之MYSQL高并发优化
性能调优之MYSQL高并发优化 一.数据库结构的设计 如果不能设计一个合理的数据库模型,不仅会增加客户端和服务器段程序的编程和维护的难度,而且将会影响系统实际运行的性能.所以,在一个系统开始实施之 ...
- PAT 1047
1049. Counting Ones (30) The task is simple: given any positive integer N, you are supposed to count ...
- 将linux的HOME目录下的文件夹名字改回英文
为了使用起来方便,装了Ubuntu中文版,自然在home文件里用户目录的“桌面”.“图片”.“视频”.“音乐”……都是中文的.很多时候都喜欢在桌面上放一些要操作的文件,linux里命令行操作又多,难免 ...
- 封装Web Uploader 上传插件、My97DatePicker、百度 编辑器 的使用 (ASP.NET MVC)
Web Uploader: WebUploader是由Baidu WebFE(FEX)团队开发的一个简单的以HTML5为主,FLASH为辅的现代文件上传组件.在现代的浏览器里面能充分发挥HTML5的优 ...
- libev事件库使用笔记
源码下载地址:http://dist.schmorp.de/libev/ libev是一个高性能的事件循环库,比libevent库的性能要好. 安装: tar -zxf libev-4.15.tar. ...