Кластеризация групп входящих пакетов с помощью нейронных сетей конкурирующего типа
Курсовой проект - Компьютеры, программирование
Другие курсовые по предмету Компьютеры, программирование
;
Str:=;
While(ch<> ) do
begin
Str:=Str+Ch;
Read(F,Ch);
end;
MyList.Add(Str);
if Str=YES then LocalH[i]:=1 else LocalH[i]:=0;
Read(F,Ch);
Read(F,Ch);
Str:=;
While(ch<> ) do
begin
Str:=Str+Ch;
Read(F,Ch);
end;
MyList.Add(Str);
if Str=YES then Frag[i]:=1 else Frag[i]:=0;
Read(F,Size[i]);
MyList.Add(IntToStr(Size[i]));
Read(F,Proc[i]);
MyList.Add(IntToStr(Proc[i]));
Read(F,Ch);
Read(F,Ch);
Read(F,Ch);
Str:=;
While(ch<> ) do
begin
Str:=Str+Ch;
Read(F,Ch);
end;
MyList.Add(Str);
if Str=YES then Active[i]:=1 else Active[i]:=0;
StringGrid1.Rows[j]:=MyList;
Read(F,Ch);
if Ch=H then HACK[i]:=1 else HACK[i]:=0;
//Memo4.Lines.Add(**************);
end;
//Memo4.Lines.Add(IntToStr(Q));
CloseFile(F);
SetLength(Temp,10);
SetLength(Mass,(Q-1)*11);
SetLength(SHack,Q-1);
for i:=0 to Q-12 do
begin
Mass[12*i]:=0;
for j:=0 to 9 do Mass[12*i]:=Mass[12*i]+LocalH[i+j];
Mass[12*i+1]:=0;
for j:=0 to 9 do Mass[12*i+1]:=Mass[12*i+1]+Frag[i+j];
Mass[12*i+2]:=0;
Mass[12*i+3]:=0;
for j:=0 to 9 do
if Prot[i+j]=TCP then Mass[12*i+2]:=Mass[12*i+2]+1;
for j:=0 to 9 do
if Prot[i+j]=UDP then Mass[12*i+3]:=Mass[12*i+3]+1;
Sum:=1;
s1:=0;
for j:=0 to 9 do
begin
Str:=Host[i+j];
p:=0;
for k:=0 to 9 do
begin
if Str=Host[i+k] then
begin
p:=p+1;
s2:=k;
end;
end;
if p>Sum then
begin
Sum:=p;
s1:=s2;
end;
end;
Mass[12*i+4]:=Sum;
Mass[12*i+5]:=LocalH[i+s1];
Sum:=0;
for j:=0 to 9 do Sum:=Sum+Proc[i+j];
Mass[12*i+6]:=Sum/10;
Mass[12*i+7]:=Proc[i+9]-Proc[i];
Sum:=0;
for j:=0 to 9 do Sum:=Sum+Size[i+j];
Mass[12*i+8]:=Sum/10;
Sum:=0;
for j:=0 to 9 do
if (Size[i+j]>=0.8*Mass[12*i+8])and
(Size[i+j]<=1.2*Mass[12*i+8]) then Sum:=Sum+1;
Mass[12*i+9]:=Sum;
Sum:=0;
for j:=0 to 9 do Sum:=Sum+Active[i+j];
Mass[12*i+10]:=Sum;
for j:=0 to 9 do Temp[j]:=Host[i+j];
for j:=0 to 8 do
begin
Str:=Temp[j];
for k:=0 to 9 do
if k<>j then
if Str=Temp[k] then Temp[k]:=;
end;
Sum:=0;
for j:=0 to 9 do
if Temp[j]<> then Sum:=Sum+1;
Mass[12*i+11]:=Sum;
Sum:=0;
for j:=0 to 9 do Sum:=Sum+HACK[i+j];
SHAck[i]:=Sum;
end;
end;
end;
procedure TForm1.InitializationMap;
var i,j,p : integer;
begin
for i:=0 to H*W-1 do
begin
p:=Random(Q-13);
for j:=0 to 10 do
KMap.Neurons[i].MassWeight[j+1]:=Mass[12*p+j];
end;
end;
procedure TForm1.Button3Click(Sender: TObject);
var i,j,p,k,m : integer;
Quant,Winner : integer;
Part : TExtArray;
SMax,SMin : Extended;
begin
InitializationMap;
for i:=0 to Image1.Height-1 do
for j:=0 to Image1.Width-1 do
Image1.Picture.Bitmap.Canvas.Pixels[i,j]:=RGB(150,150,150);
Quant:=StrToInt(Edit3.Text);
Part:=TExtArray.Init(12);
SMax:=0.7;
SMin:=0.0001;
ProgressBar1.Max:=Quant;
ProgressBar1.Position:=0;
for i:=0 to Quant-1 do
begin
KMap.SigmaInit(10*(1-i/Quant)+0.1);
p:=Random(Q-12);
for j:=0 to 11 do Part.Value[j]:=Mass[12*p+j];
KMap.GetInputValues(@Part);
KMap.Excitement;
Case RadioGroup1.ItemIndex of
0:
begin
Winner:=KMap.Classic;
if CheckBox1.Checked then KMap.LearningNeib(Winner,SMax-(SMax-SMin)*i/Quant)
else KMap.Learning(Winner,SMax-(SMax-SMin)*i/Quant);
end;
1:
begin
Winner:=KMAp.TheWinnerTakesItAll;
if CheckBox1.Checked then KMap.LearningNeib(Winner,SMax-(SMax-SMin)*i/Quant)
else KMap.Learning(Winner,SMax-(SMax-SMin)*i/Quant)
end;
2:
begin
KMap.NeuralGaz(SMax-(SMax-SMin)*i/Quant);
end;
end;
ProgressBar1.StepBy(1);
end;
ProgressBar1.Position:=0;
for i:=0 to KMap.QNeurons-1 do
KMap.Neurons[i].MassWeight[0]:=0;
for i:=0 to Q-12 do
begin
for j:=0 to 11 do Part.Value[j]:=Mass[12*i+j];
KMap.GetInputValues(@Part);
KMap.Excitement;
Winner:=KMap.Classic;
KMap.Neurons[Winner].MassWeight[0]:=1;
//Memo4.Lines.Add(IntToStr(Winner));
if SHack[i]>=8 then
begin
for m:=0 to W1-1 do
for k:=0 to H1-1 do
Image1.Picture.Bitmap.Canvas.Pixels[(Winner div W)*W1+m,(Winner mod W)*H1+k]:=RGB(255,0,0);
end
else if SHack[i]=7 then
begin
for m:=0 to W1-1 do
for k:=0 to H1-1 do
Image1.Picture.Bitmap.Canvas.Pixels[(Winner div W)*W1+m,(Winner mod W)*H1+k]:=RGB(255,40,40);
end
else if SHack[i]=6 then
begin
for m:=0 to W1-1 do
for k:=0 to H1-1 do
Image1.Picture.Bitmap.Canvas.Pixels[(Winner div W)*W1+m,(Winner mod W)*H1+k]:=RGB(225,80,80);
end
else if SHack[i]=5 then
begin
for m:=0 to W1-1 do
for k:=0 to H1-1 do
Image1.Picture.Bitmap.Canvas.Pixels[(Winner div W)*W1+m,(Winner mod W)*H1+k]:=RGB(225,120,120);
end
else if SHack[i]=4 then
begin
for m:=0 to W1-1 do
for k:=0 to H1-1 do
Image1.Picture.Bitmap.Canvas.Pixels[(Winner div W)*W1+m,(Winner mod W)*H1+k]:=RGB(225,160,160);
end
else if SHack[i]=3 then
begin
for m:=0 to W1-1 do
for k:=0 to H1-1 do
Image1.Picture.Bitmap.Canvas.Pixels[(Winner div W)*W1+m,(Winner mod W)*H1+k]:=RGB(225,200,200);
end
else
begin
for m:=0 to W1-1 do
for k:=0 to H1-1 do
Image1.Picture.Bitmap.Canvas.Pixels[(Winner div W)*W1+m,(Winner mod W)*H1+k]:=RGB(225,225,225);
end;
//Image2.Picture.Bitmap.Canvas.
//.Pixels[j,i]:=RGB(
end;
for i:=0 to KMap.QNeurons-1 do
begin
if KMap.Neurons[i].MassWeight[0]=1 then
begin
Memo3.Lines.Add( +IntToStr(i));
for j:=0 to KMap.QInputs-1 do
Memo3.Lines.Add(FloatToStr(KMap.Neurons[i].MassWeight[j]));
end;
end;
end;