unit1.~cpp

人工神经网络在模式识别

/**
 *Title: AIR_BPNN_Learn-C++
 *Description: Neural Network Learner
 *Copyright: Copyleft (c) 2002 (See gpl.txt for details)
 *Company: www.air-robot.net
 *Author M. T. Li ([e-mail])
 *Version 1.0 ; 2000.10.24 ; M. T. Li ; Rewriting from AIR_BPNN_Learn-C
 *Version 1.1 ; 2000.11.24 ; M. T. Li ; debug
 *Version 1.2 ; 2000.11.25 ; M. T. Li ; Parameter output
 *Version 1.3 ; 2000.11.25 ; M. T. Li ; Optimization
 *Version 1.4 ; 2001.04.06 ; M. T. Li ; Input array
 *Version 2.0 ; 2001.04.07 ; M. T. Li ; Show the sample
 *Version 2.1 ; 2001.04.08 ; M. T. Li ; Sample editor
 *Version 2.2 ; 2002.07.28 ; M. T. Li ; Distribute by GPL
 *Version 2.3 ; 2002.11.24 ; M. T. Li ; English version
 */

//---------------------------------------------------------------------------

#include <vcl.h>
#pragma hdrstop

#include "Unit1.h"
#include "Unit2.h"

//---------------------------------------------------------------------------
#pragma package(smart_init)
#pragma resource "*.dfm"
TForm1 *Form1;
//---------------------------------------------------------------------------
__fastcall TForm1::TForm1(TComponent* Owner)
        : TForm(Owner)
{
}
//---------------------------------------------------------------------------

void __fastcall TForm1::FormCreate(TObject *Sender)
{
    StringGrid1->ColCount=2;
    StringGrid1->RowCount=3;
    StringGrid1->Cells[0][0]="Hidden Layer";
    StringGrid1->Cells[1][0]="Node Number";
    StringGrid1->Cells[0][1]="Layer[1]";
    StringGrid1->Cells[1][1]="10";
    StringGrid1->Cells[0][2]="Layer[2]";
    StringGrid1->Cells[1][2]="10";

    Memo1->Clear();
}
//---------------------------------------------------------------------------

void __fastcall TForm1::Edit2Change(TObject *Sender)
{
    AnsiString asTemp;
    if(Edit2->Text!="")
    {
        StringGrid1->RowCount=Edit2->Text.ToInt()+1;

        if((Edit2->Text.ToInt()+1)>1)
        {
            for(int i=0;i<Edit2->Text.ToInt();i++)
            {
                asTemp="Layer[";
                asTemp+=(i+1);
                asTemp+="]";
                StringGrid1->Cells[0][i+1]=asTemp;
                StringGrid1->Cells[1][i+1]="10";
            }
        }
    }
}
//---------------------------------------------------------------------------
DWORD CALLBACK ThreadFunc1(void* P)
{

        int iLearnTimes=Form1->Edit7->Text.ToInt();
        int iRefreshRate=Form1->Edit10->Text.ToInt();
        for(Form1->iLearnedTimes=0;Form1->iLearnedTimes<iLearnTimes;Form1->iLearnedTimes++)
        {
            //To learn once
            Form1->BPNNO1.Learn();

            //Show the result
            if(Form1->BPNNO1.inLearnedTimes%iRefreshRate==0)
            {
                Form1->Edit8->Text=AnsiString(Form1->BPNNO1.inLearnedTimes);
                Form1->Edit8->Update();

                Form1->Edit9->Text=AnsiString(Form1->BPNNO1.dnMse);
                Form1->Edit9->Update();

                Form1->Series1->AddXY(double(Form1->BPNNO1.inLearnedTimes),Form1->BPNNO1.dnMse,"",clRed);
            }
        }


        //To save the network
        AnsiString asTemp;
        asTemp="*Save neural network data to file =";
        Form1->Memo1->Lines->Add(asTemp);
        asTemp=Form1->SaveDialog1->FileName;
        Form1->Memo1->Lines->Add(asTemp);
        Form1->BPNNO1.DumpNeuralNetwork(Form1->SaveDialog1->FileName);

        //To save the training result
        asTemp="*Save training result to file =";
        Form1->Memo1->Lines->Add(asTemp);
        asTemp=Form1->SaveDialog2->FileName;
        Form1->Memo1->Lines->Add(asTemp);
        Form1->BPNNO1.DumpParameter(Form1->SaveDialog2->FileName);

        //To show that training complete
        MessageBox(Form1->Handle,"Training Complete!","AIR-robot BPNN-C++-Learn", MB_OK);

        return 1;
}
void __fastcall TForm1::Button1Click(TObject *Sender)
{

    AnsiString asTemp;


    Form1->OpenDialog1->Title="Open training sample from file";


    Form1->SaveDialog1->FileName="NeuralNetwork.txt";
    Form1->SaveDialog1->Title="Save neural network data to file";


    Form1->SaveDialog2->FileName="TrainResult.txt";
    Form1->SaveDialog2->Title="Save training result to file";

    if(Form1->OpenDialog1->Execute()&&Form1->SaveDialog1->Execute()&&Form1->SaveDialog2->Execute())
    {



        Button4->Enabled=true;
        Button5->Enabled=true;
        
        Form1->Edit11->Text=Form1->OpenDialog1->FileName;
        Form1->Edit12->Text=Form1->SaveDialog1->FileName;
        Form1->Edit13->Text=Form1->SaveDialog2->FileName;

        ifstream fin;    //Input file stream

        asTemp=OpenDialog1->FileName;
        fin.open(asTemp.c_str());

        Form1->iInputLayerNNNum=Form1->Edit1->Text.ToInt();
        Form1->iHidLayerNum=Form1->Edit2->Text.ToInt();
        Form1->iOutputLayerNNNum=Form1->Edit3->Text.ToInt();
        Form1->iTrainSampleSetNum=Form1->Edit4->Text.ToInt();

        
        Form1->M2DTrainInputSample.FreeDouble();
        Form1->M2DTrainOutputSample.FreeDouble();
        Form1->M2DTrainInputSample.AllocateDouble(Form1->iTrainSampleSetNum,Form1->iInputLayerNNNum);
        Form1->M2DTrainOutputSample.AllocateDouble(Form1->iTrainSampleSetNum,Form1->iOutputLayerNNNum);

        int i,j;
        double dTemp;
        for(i=0;i<Form1->iTrainSampleSetNum;i++)
        {

            for(j=0;j<Form1->iInputLayerNNNum;j++)
            {
                fin>>dTemp;
                Form1->M2DTrainInputSample.dValue[i][j]=dTemp;
            }

            for(j=0;j<Form1->iOutputLayerNNNum;j++)
            {
                fin>>dTemp;
                Form1->M2DTrainOutputSample.dValue[i][j]=dTemp;
            }
        }


        fin.close();

        //To show the training sample
        Form2->StringGrid1->ColCount=Form1->iInputLayerNNNum+Form1->iOutputLayerNNNum+1;
        Form2->StringGrid1->RowCount=Form1->iTrainSampleSetNum+1;
        for(i=0;i<Form1->iInputLayerNNNum;i++)
        {
            Form2->StringGrid1->Cells[i+1][0]=AnsiString("InputLayer")+AnsiString(i);
        }
        for(i=0;i<Form1->iOutputLayerNNNum;i++)
        {
            Form2->StringGrid1->Cells[Form1->iInputLayerNNNum+i+1][0]=AnsiString("OutputLayer")+AnsiString(i);
        }
        for(i=0;i<Form1->iTrainSampleSetNum;i++)
        {
            Form2->StringGrid1->Cells[0][i+1]=AnsiString("Sample")+AnsiString(i);
        }

        for(i=0;i<Form1->iTrainSampleSetNum;i++)
        {          
            for(j=0;j<Form1->iInputLayerNNNum;j++)
            {
                Form2->StringGrid1->Cells[j+1][i+1]=Form1->M2DTrainInputSample.dValue[i][j];
            }
            for(j=0;j<Form1->iOutputLayerNNNum;j++)
            {
                Form2->StringGrid1->Cells[Form1->iInputLayerNNNum+j+1][i+1]=Form1->M2DTrainOutputSample.dValue[i][j];
            }
        }

        Form2->ShowModal();


    }

}
//---------------------------------------------------------------------------
void __fastcall TForm1::Button2Click(TObject *Sender)
{
    ShellExecute(Handle,"open","http://www.air-robot.net",NULL,NULL,SW_SHOWDEFAULT);        
}
//---------------------------------------------------------------------------

void __fastcall TForm1::Button3Click(TObject *Sender)
{
    ShellExecute(Handle,"open","mailto:[e-mail]",NULL,NULL,SW_SHOWDEFAULT);        
}
//---------------------------------------------------------------------------

void __fastcall TForm1::Button4Click(TObject *Sender)
{
    Series1->Clear();
    Button1->Enabled=false;
    Form1->Button4->Enabled=false;

    int i,j;
    double dTemp;

    AnsiString asTemp;


    asTemp="*Open training sample from file:";
    Form1->Memo1->Lines->Add(asTemp);
    asTemp=Form1->OpenDialog1->FileName;
    Form1->Memo1->Lines->Add(asTemp);



    //Step 2. Input the number of hidden layer
    Form1->BPNNO1.InputHidLayerNum(iHidLayerNum);

    //Step 3. Input the neural node number of input layer
    Form1->BPNNO1.InputLayerNNNum(0,Form1->iInputLayerNNNum);
    if(Form1->iHidLayerNum!=0)
    {
            for(i=0;i<Form1->iHidLayerNum;i++)
            {
                Form1->BPNNO1.InputLayerNNNum(i+1,Form1->StringGrid1->Cells[1][i+1].ToInt());  //Input the neural node number of hidden layer
            }
    }
    Form1->BPNNO1.InputLayerNNNum(Form1->iHidLayerNum+1,Form1->iOutputLayerNNNum);   //Input the neural node number of output layer

    //Step 4. Input the training sample number
    Form1->BPNNO1.InputTrainSampleSetNum(Form1->iTrainSampleSetNum);

    //Step 5. Input the sample of input
    Form1->BPNNO1.InputTrainInputSample(Form1->M2DTrainInputSample);

    //Step 6. Input the sample of output
    Form1->BPNNO1.OutputTrainInputSample(Form1->M2DTrainOutputSample);

    //Step 7. Set up the learning rate
    Form1->BPNNO1.dnLearnRate=Form1->Edit5->Text.ToDouble();

    //Step 8. Set up the inertia parameter
    BPNNO1.dnInertia=Form1->Edit6->Text.ToDouble();

    //Step 9. Create the neural network
    Form1->BPNNO1.CreateNeuralNetwork();

    //Step 10. Start to learn