正文之前
昨天好不容易心气爆发,肝了一会儿的毕业设计,也算是初步走上了正轨,很是期待后面完成之后我自己回首会是怎样一般场景了!不过昨天那个对半划分数据集,一半作为训练集,一半作为验证集的划分方式效果实在是令人不敢恭维。。。我是死了心这么分了。到时候毕设答辩的时候拿这个去说简直就是丢死人了。
正文
我这次采用的是以前的那一拨钢板的数据集,清洗数据后剩下六个属性,分类也分为六类。这就构成了这个1940条记录的数据集。但是因为数据的分类过于集中。很是让人烦恼!
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诸君请看,前面的这些异常分类都是0,也就是对应的故障列表的第一项:
String[] Fault = new String[]{"Pastry","Z_Scratch","K_Scatch","Stains","Dirtiness","Bumps","Other_Faults"};
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mysql> select min(id) from steelplate where Fault=1;
+---------+
| min(id) |
+---------+
| 158 |
+---------+
1 row in set (0.01 sec)
分类为1 的最小的记录编号都已经到了158了。所以我选择交叉获取数据的方式分配训练集和数据集。即第一条到训练集,第二条到验证集。这样下来尚且算是分配均匀了。
不过各位看看最终的实验效果就知道。。我这个分配均匀到底有多坑了!
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准确率0.51。。。。堪堪过半!!简直无敌自容了好吗?!!!!所以今天我准备修改训练集和验证集 的比例,调成4:1左右的话会不会好一点呢?肯定会的好吗?这时候训练集有1552,测试集388。而且继续采用交叉办法,肯定能有较好的效果了!
不过,我还没开始呢。。。所以先放0.51的代码。然后下面再来一篇放另外的吧!!
//读取测试集和验证集的方法
public Object[][] readTrainData() {
int columnCount=0;
try {
mysql.Connect();
Statement statement=mysql.getStatement();
String GETCOLUMN="select max(id) from steelplate";
String getDataQuery="";
Object[][] DataTrain;
ResultSet answer = statement.executeQuery(GETCOLUMN);
if(answer.next())
columnCount = answer.getInt(1);
DataTrain = new Object[columnCount/2][7];
for (int i = 0;i<columnCount/2;++i) {
getDataQuery = getSelectQuery(Name,"steelplate",i*2);
ResultSet select_ok;
select_ok = statement.executeQuery(getDataQuery);
select_ok.next();
for (int j = 0; j<7;++j){
DataTrain[i][j]=select_ok.getObject((String) Name[j]);
}
}
statement.close();
mysql.Dis_Connect();
return DataTrain;
} catch (SQLException e) {
e.printStackTrace();
} catch (Exception e) {
e.printStackTrace();
}
return new Object[1][1];
}
public Object[][] readTestData() {
int columnCount=0;
try {
mysql.Connect();
Statement statement=mysql.getStatement();
String GETCOLUMN="select max(id) from steelplate";
Object[][] DataTest;
ResultSet answer = statement.executeQuery(GETCOLUMN);
if(answer.next())
columnCount = answer.getInt(1);
DataTest = new Object[columnCount/2][7];
for (int i = 0 ;i<columnCount/2-1;++i) {
String getDataQuery = getSelectQuery(Name,"steelplate",i*2+1);
ResultSet select_ok;
select_ok = statement.executeQuery(getDataQuery);
select_ok.next();
for (int j = 0; j<7;++j){
DataTest[i][j]=select_ok.getObject((String) Name[j]);
}
}
statement.close();
mysql.Dis_Connect();
return DataTest;
} catch (SQLException e) {
e.printStackTrace();
} catch (Exception e) {
e.printStackTrace();
}
return new Object[1][1];
}
下面是读取验证集并且计算正确率的函数
else if(command.toLowerCase().equals("autotest")){
if (TData.isEmpty()){
jl12.setText(Space+"Please Open the Test File to load the Data!");
return;
}
else {
for (int i=0;i<TData.size();++i) {
Object[] test = TData.get(i).split(" ");
String res="";
res=TestData.TestData(tree, Test_Names,test,res);
if (res.contains(":")){
String Fault = res.substring(res.indexOf(":")+1);
Fault = Fault.trim();
String Fa = FaultMap.get(Fault);
if(Fa.equals((String) test[test.length-1])){
RightCount++;
}
else {
FaultCount++;
}
}
else {
FaultCount++;
}
}
System.out.println(RightCount+" "+FaultCount);
jl12.setText(Space+"准确率: "+((float)RightCount/(float)(RightCount+FaultCount)));
RightCount = 0;
FaultCount = 0;
}
}
另外还有一个故障编号Map
public Map<String,String> FaultMap = new HashMap<String,String>();
FaultMap.put("Pastry","0");
FaultMap.put("Z_Scratch","1");
FaultMap.put("K_Scatch","2");
FaultMap.put("Stains","3");
FaultMap.put("Dirtiness","4");
FaultMap.put("Bumps","5");
FaultMap.put("Other_Faults","6");
正文之后
溜了溜了。。。我室友借我车去吃饭,结果半路上跟外卖小哥来了段相爱相杀,求问这种时候我室友还垫付了200+的医药费,万一外卖小哥纠缠报警的话会吃亏不?外卖小哥的车技一向是。。。生死时速的!
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