somtrain.cpp

自組織映射網路(SOM)

#include <stdlib.h>
#include <stdio.h>
#include "somneuro.h"

main()
{
	char filename[128];
	int key=1;
	int nb_cycle;
	int i,j,k,n1,n2,n3,N;

	cout<<"Input filename of training data for SOM clustering : ";
	cin>>filename;
	ifstream in1(filename);
//  cout<<"Output filename of category map : ";
//  cin>>filename;
//  ofstream out1(filename);
//	ifstream in1("test.dat");
   	ofstream out1("sommap.dat");
	ofstream out2("somwgt.dat");
	if(!in1){cout<<"Cannot open file! Any key to terminate...";cin>>key;exit(1);}
	cout<<"Input the numeber of cycles:";
	cin>>nb_cycle;
	in1>>n1>>n2>>n3;
        //in1>>n1>>n3;
	if(n2>1)
	{
		cout<<"The number of observed output can't larger than 1! Any key to terminate...";
      		getchar();exit(1);
	}
	f2D Input;
	f1D Output;
	Input.Initialize(n3,n1);
	Output.Initialize(n3);
	for(i=0;i<n3;i++)
	{
		for(j=0;j<n1;j++)in1>>Input.m[i][j];
		in1>>Output.m[i];
	}
  	cout<<" Enter Size of 2-dimensional output Kohonom layer(Size*Size= # of category): ";
	cin>>N;
	SOMNeuroNet SOM;
	SOM.LoadTrainingData(N, Input, Output);

	float convergence,err;
	k=0;
	while(key==1)
	{
		for(i=0;i<nb_cycle;i++)
      		{
      		convergence = SOM.LearningFromBatchData();
         	cout<<i<<endl;
      		}
		cout<<"k="<<k<<" , convergence coefficient="<<convergence<<endl;
   		err=SOM.CalculCategoryMap();
		cout<<"Mis-classification error"<<err<<endl<<endl;
		cout<<"encore? (1/0) ";cin>>key;
		k++;
	}
	SOM.SaveCategoryMap(out1);
	SOM.Save(out2);
	cout<<"any key to terminate...";
   	getchar();
}