【毕设进行时-工业大数据，数据挖掘】用C++对数据进行整改，修缮一下！
正文之前
因为数据差距实在太大，从10-10000都有，要是全搞决策树我估计我是啥都不用搞了，看着电脑卡死就ok！所以特地将连续的数据转化为连续的数据！看看是不是会生成新的、更好地决策树！正文 废话不多说！直接丢代码！不然真是难受的一批！写了好一会儿才搞定的！
#include
#include
#include
using namespace std;
int main()
{
int count=0;
float attr[34];
ifstream in("/Users/zhangzhaobo/Documents/Graduation-Design/Mydata.txt");
ofstream out("/Users/zhangzhaobo/Documents/Graduation-Design/Data/New_Data.txt");
string line[34];
for (int i = 0;
i < 34;
++i)
{
in>>line[i];
}
out<<"Diff_X"<<"\t"<<"Diff_Y"<<"\t";
for (int i = 4;
i < 8;
++i)
{
out<>attr[i];
}
float X_dis=attr[1]-attr[0];
float Y_dis=attr[3]-attr[2];
float Luminosity_dis=attr[9]-attr[8];
float TypeOfSteel=attr[11];
out<
正文
改善之后的属性为：
Diff_XDiff_YPixels_AreasX_Perimeter Y_Perimeter Sum_of_LuminosityDiff_Luminosity Length_of_ConveyerTypeouOfSteelSteel_Plate_ThicknessEdges_Index Empty_Index Square_IndexOutside_X_Index Edges_X_IndexEdges_Y_IndexOutside_Global_IndexLogOfAreasLog_X_Index Log_Y_Index Orientation_IndexLuminosity_IndexSigmoidOfAreasFault
844267 1744242203216871800.04980.24150.18180.00470.4706112.42650.90311.64350.8182-0.2913 0.5822128
629108 1030113973916871800.76470.37930.20690.00360.6 0.966712.03340.77821.46240.7931-0.1756 0.2984128
为此还特地写了个C++的程序来观察！
#include
#include
#include
using namespace std;
int main()
{
string line[72];
int count=0;
for (int i = 0;
i < 72;
++i)
{
cin>>line[i];
}
for (int i = 0;
i < 24;
++i)
{
cout<<"[->"< "< "<
【【毕设进行时-工业大数据，数据挖掘】用C++对数据进行整改，修缮一下！】最后整出来还蛮好看！
[->0: Diff_X --> 8 --> 6
[->1: Diff_Y --> 44 --> 29
[->2: Pixels_Areas --> 267 --> 108
[->3: X_Perimeter --> 17 --> 10
[->4: Y_Perimeter --> 44 --> 30
[->5: Sum_of_Luminosity --> 24220 --> 11397
[->6: Diff_Luminosity --> 32 --> 39
[->7: Length_of_Conveyer --> 1687 --> 1687
[->8: TypeouOfSteel --> 1 --> 1
[->9: Steel_Plate_Thickness --> 80 --> 80
[->10: Edges_Index --> 0.0498 --> 0.7647
[->11: Empty_Index --> 0.2415 --> 0.3793
[->12: Square_Index --> 0.1818 --> 0.2069
[->13: Outside_X_Index --> 0.0047 --> 0.0036
[->14: Edges_X_Index --> 0.4706 --> 0.6
[->15: Edges_Y_Index --> 1 --> 0.9667
[->16: Outside_Global_Index --> 1 --> 1
[->17: LogOfAreas --> 2.4265 --> 2.0334
[->18: Log_X_Index --> 0.9031 --> 0.7782
[->19: Log_Y_Index --> 1.6435 --> 1.4624
[->20: Orientation_Index --> 0.8182 --> 0.7931
[->21: Luminosity_Index --> -0.2913 --> -0.1756
[->22: SigmoidOfAreas --> 0.5822 --> 0.2984
[->23: Fault --> 128 --> 128
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