From:	"Javier Calleja" <dismuntel AT apdo DOT com>
To:	"djgpp" <djgpp AT delorie DOT com>
Subject:	=?iso-8859-1?Q?Why_don=B4t_I_have_enough_virtual_memory=3F?=
Date:	Mon, 6 Jul 1998 17:57:34 +0200
Message-ID:	<01bda8f6$cab62dc0$0100a8c0@dismuntel.ctv.es>
MIME-Version:	1.0

This is a multi-part message in MIME format.

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Hi world:

  When I execute my program it crashes. A message about that I don't have
enough virtual memory appears. But the go32-v2 says that I have

  DPMI memory available 64711 Kb
  DPMI swap space available 130389 Kb.

  I am programing an aplication which do the training of a neural network.
I donīt know if you have knowledge about this topic, but it's basically a
system that find the parameter of a non-linear function. In this case, the
function predicts the weigh of an object.

   In this program (that I send you attached with this e-mail) I initialize
the network in the first moment, and I begin the training of the network in
the  'red' object function called 'entrenar'.

  This function is a 'for' loop which have the 'it' parameter (number of
'iterations') that is the number of times that the network must be trained
(the 'for' loop limit).

   The programm do well when the number of iterations is low (it=5000), but
when it's high the programm crahses in a certain iteration and the message
'not enough virtual memory' appears in the screen.

    I donīt understand why, because in this function I donīt use the 'new'
operator and I donīt allocate any memory. I make the objects and allocate
the memory before I execute the
training function. But the error message appears in this function.

    Please, could you help me?

    I send you the programm source (red.cc) and the file with the training
dates (datos3.dat) ant the executable (red.exe). You only must execute the
'.bat' programm, which execute the programm with the correct parameters, or
use the programm with the define 'PRUEBA' set on. You can configure the
number
of iterations if you modify the '/it:xxx' parameter of the bat. The programm
will create a file with the results of the training.

  I execute the programm under WIN95, but not a DOS window. I break the
start up with F8 and only load the DOS or reinitialize in DOS mode.

   I am looking forward from you. Thank you in anticipation.



                              Javier Calleja



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------=_NextPart_000_002C_01BDA907.8E3EFDC0
Content-Type: application/octet-stream;
	name="RED.CC"
Content-Transfer-Encoding: quoted-printable
Content-Disposition: attachment;
	filename="RED.CC"

		  //PROGRAMA REDES NEURONALES

//#define PRUEBA
#include <unistd.h>
#include <stdio.h>
#include <math>
#include <string.h>
#include <stdlib.h>

#include <io.h>
#include <new>
#include <values.h>
#include <time.h>
#include <crt0.h>
    =20

//#include <except.h>

#define ALEA 20000
#define ALEA_MED (MAXINT >>1)
#define CTE1  1.716		//FUNCION DE ACTIVACIO: CTE1*TANH(CTE2*V)
#define CTE2  0.667
#define CTE3  1.144     //CTE3=3DCTE1*CTE2



								  //DESCRIPCION DE LOS DISTINTOS OBJETOS Y FUNCIONES QUE
								  //COMPONEN LA RED


	template <class tipo>
	class coef {			  //CLASE DEFINIDA POR NUMERO DE
		 tipo *v;           //COEFICIENTES PASADOS Y LOS COEFICIENTES
		 int num;
	  public:
		 void asignar_int(char *);
		 void asignar_float(char *);
		 void ini(tipo);
		 void ini(int,tipo *);
		 tipo retornar (int)const;
		 int retornar ()const;
		 coef();
		 ~coef();
	};
								  //DEL S.O. SE LA PASAN LOS PARAMETROS
	template <class tipo>  //ENTEROS
	void coef<tipo>::asignar_int(char *str){
	  int i;
	  char *ptr=3Dstr-1;

	  num=3D0;
	  while(ptr=3Dstrchr(ptr+1,',')) num++;
	  try{v=3Dnew tipo[++num];}
	  catch (bad_alloc){
		 printf("\nERROR: MEMORIA INSUFICIENTE");
		 exit(-1);
	  }
	  strtok(str, ":");
	  for(i=3D0;i<num;i++)
		 v[i]=3Datoi(strtok(NULL, ","));
	  return;
	}

	template <class tipo>  //PARAMETROS REALES
	void coef<tipo>::asignar_float(char *str){
	  int i;
	  char *ptr=3Dstr-1;

	  num=3D0;
	  while(ptr=3Dstrchr(ptr+1,',')) num++;
	  try{v=3Dnew tipo[++num];}
	  catch (bad_alloc){
		 printf("\nERROR: MEMORIA INSUFICIENTE");
		 exit(-1);
	  }
	  strtok(str, ":");
	  for(i=3D0;i<num;i++)
		 v[i]=3Datof(strtok(NULL, ","));
	  return;
	}

	template <class tipo>  //INICIALIZADORES DE LOS ATRIBUTOS DE LA CLASE
	void coef<tipo>::ini(tipo valor){
	  try{v=3Dnew tipo(valor);}
	  catch (bad_alloc){
		 printf("\nERROR: MEMORIA INSUFICIENTE");
		 exit(-1);
	  }
	  num=3D1;
	  return;
	}

	template <class tipo>
	void coef<tipo>::ini(int ent,tipo *valor){
	  int i;

	  try{v=3Dnew tipo[num=3Dent];}
	  catch (bad_alloc){
		 printf("\nERROR: MEMORIA INSUFICIENTE");
		 exit(-1);
	  }
	  for(i=3D0;i<num;i++)
		  v[i]=3Dvalor[i];
	  return;
	}

	template <class tipo>
	inline int coef<tipo>::retornar()const{    //PROPORCIONA NUMERO DE =
ELEMENTOS
	  return num;                              //ALMACENADOS EN COEF
	}
															 //PROPORCIONA VALOR DEL
															 //COEFICIENTE ALMACENADO EN LA
	template <class tipo>							 //POSICION 'ORD' DEL VECTOR COEF
	inline tipo coef<tipo>::retornar(int ord)const{
	  return v[ord];
	}

	template <class tipo>                      //CONSTRUCTOR Y DESTRUCTOR
	coef<tipo>::coef(){
		v=3DNULL;
	}

	template <class tipo>
	coef<tipo>::~coef(){
	  delete[] v;
	}


	class datos{                       //VECTORES DE VALIDACION Y =
ENTRENAMIENTO
	 protected:
	  float **i_ent,**o_ent,**i_val,**o_val;
	  int dim_e,dim_v;
	 public:
		void pon_dim(int,int);
		datos();
	  ~datos();
	};
												  //DEFINEN N=BA PATRONES
	void datos::pon_dim(int ent1,int ent2){
		dim_e=3Dent1;
		dim_v=3Dent2;
		return;
	}

	datos::datos(){                    //CONSTRUCTOR Y DESTRUCTOR DATOS
		i_ent=3Do_ent=3Di_val=3Do_val=3DNULL;
	}

	datos::~datos(){
	  int i;

	  for(i=3D0;i<dim_e;i++){
		 delete []i_ent[i];
		 delete []o_ent[i];
	  }
	  for(i=3D0;i<dim_v;i++){
		 delete []i_val[i];
		 delete []o_val[i];
	  }
	  delete []i_ent;
	  delete []o_ent;
	  delete []i_val;
	  delete []o_val;
	}

	class ficheros{                //FICHEROS DE ENTRADA Y SALIDA
	 protected:
	  FILE *in,*out;
          char nom_out[60],nom_in[60];
	 public:
	  int salvar(float);
	  int salvar(float*,int);
	  int salvar(int);
	  int salvar(int*,int);
	  long int tamanyo();
	  ficheros(char*,char*);
	  ~ficheros();
	};
											 //DEVUELVE TAMANYO DEL FICHERO
	long int ficheros::tamanyo(){
//		 return  (filelength(fileno(in))/sizeof(float));
            long a=3Dfilelength(fileno(in))/sizeof(float);
		 return  (a);

	}
											 //SOBRECARGAMOS FUNCION SALVAR PARA PODER
											 //GUARDAR INT Y FLOAT
	int ficheros::salvar(float valor){
          if ((out =3D fopen(nom_out, "ab"))=3D=3D NULL)
	   fprintf(stderr, "ERROR: apertura de fichero escritura.\n");

	  if(fwrite(&valor,sizeof(float),1,out)=3D=3D1)
          {
     	   fclose(out);
            return 1;
          }
	  else return 0;
	}

	int ficheros::salvar(float *ptr,int num){
          if ((out =3D fopen(nom_out, "ab"))=3D=3D NULL)
	   fprintf(stderr, "ERROR: apertura de fichero escritura.\n");

	  if(num=3D=3Dfwrite(ptr,sizeof(float),num,out))
          {
     	   fclose(out);
            return num;
          }
	  else return 0;
	}

	int ficheros::salvar(int valor){
          if ((out =3D fopen(nom_out, "ab"))=3D=3D NULL)
	   fprintf(stderr, "ERROR: apertura de fichero escritura.\n");

	  if(fwrite(&valor,sizeof(int),1,out)=3D=3D1)
          {
     	   fclose(out);
   	   return 1;
          }
	  else return 0;
	}

	int ficheros::salvar(int *ptr,int num){
          if ((out =3D fopen(nom_out, "ab"))=3D=3D NULL)
	   fprintf(stderr, "ERROR: apertura de fichero escritura.\n");

	  if(num=3D=3Dfwrite(ptr,sizeof(int),num,out))
          {
     	   fclose(out);
   	   return num;
          }
	  else return 0;
	}
											 //CONSTRUCTOR  Y DESTRUCTOR FICHEROS
	ficheros::ficheros(char *nom1,char *nom2){
          strcpy(nom_in,nom1);
          strcpy(nom_out,nom2);
	  if ((in =3D fopen(nom_in, "rb"))=3D=3D NULL)
		 fprintf(stderr, "ERROR: apertura de fichero lectura.\n");

	}

	ficheros::~ficheros(){
	}



	class neurona{                      //NEURONA
	  float *vec_peso,sesgo,sal,delta,**entrada,*act,*ant_act;
	  int dim_ant,dim_post,neu;         //NUMERO DE PESOS DE LA NEURONA
													//Y DE NEURONAS PROXIMA CAPA;
													//IDENTIFICADOR NEURONA
	  neurona **backward;               //PUNTEROS A NEURONA DE LA PROXIMA =
CAPA
	 public:
	  void act_p_vec();                 //ACTUALIZA VECTOR DE ADAPTACION
	  void act_vec();                   //ACTUALIZA VECTOR DE ADAPTACION
	  void act_peso(float,float,float);	//ACTUALIZA LOS PESOS
	  void asig_backward(int,neurona*); //ASIGNAMOS VALORES A LOS ATRIBUTOS
	  void asig_entrada(int,float*);
	  void asig_entrada(float*);
	  void calc_error(float );          //CALCULO DEL ERROR EN LA NEURONA =
DE
													//SALIDA
	  void calc_delta();					   //CALCULO DELTA
	  void calc_p_salida();             //CALCULO SALIDA NEURONAS 1=AACAPA
	  void calc_salida();               //CALCULO SALIDA
	  int clasif(float)const;  			//DEVUELVE 1 SI EL PATRON ESTA
													//BIEN CLASIFICADO
	  void creo_vec();                  //CREA VECTOR ADAPTACION, MOMENTO Y =
PESO
	  float dame_delta()const;          //PROPORCIONA VALOR DE DELTA
	  float dame_delta(int)const; 		//PROPORCIONA VALOR DE PONDERADO DELTA
													//A NEURONA DE LA CAPA ANTERIOR
	  void guardo_pes(float *)const;
	  void ini_p_capa(int,int,int);     //INICIALIZAMOS NEURONA CAPA =
ENTRADA
	  void ini_i_capa(int,int,int);     //INICIALIZAMOS NEURONA CAPA =
INTERMEDIA
	  void ini_s_capa(int,int);         //INICIALIZAMOS NEURONA CAPA SALIDA
													//INICIALIZAMOS ATRIBUTOS
	  void ini_backward();					//INICIALIZAMOS VECTORES DE ENTRADA
	  void ini_entrada();               //Y PUNTEROS A CAPA SIGUIENTE
	  void ini_peso(float);
	  void p_delta();				   		//CALCULO DELTA NEURONA DE SALIDA
	  void poda(float);						//PODA EL VECTOR DE PESOS
	  void res_vec();                   //RESETEA VECTOR DE ADAPTACION
	  float * salida();                 //PUNTERO A LA SALIDA
	  neurona();                        //CONSTRUCTOR Y DESTRUCTOR
	  ~neurona();
	};

	//ACTUALIZA VECTOR DE ADAPTACION neuronas 1=AA CAPA
	void neurona::act_p_vec(){
	  int i;
													  //ACTUALIZAMOS PESOS
	  for(i=3D0;i<dim_ant;i++) act[i]+=3D*((*entrada)+i)*delta;
	  act[i]-=3Ddelta;                      //ACTUALIZAMOS SESGO
	  return;
	}

	//ACTUALIZA VECTOR DE PESOS
	void neurona::act_peso(float coef1,float coef2,float coef3){
	  int i;

	  for(i=3D0;i<dim_ant;i++){
		 if(vec_peso[i]){       //SI EL PESO NO HA SIDO PODADO
										//CALCULAMOS LA ACTUALIZACION DE PESO
										//Y LA GUARDAMOS
			ant_act[i]=3Dcoef1*act[i]+coef2*ant_act[i]-coef3*vec_peso[i];
										//ACTUALIZAMOS PESO
			vec_peso[i]+=3Dant_act[i];
		 }
	  }
										//CALCULAMOS LA ACTUALIZACION DE SESGO
										//Y LA GUARDAMOS
	  ant_act[i]=3Dcoef1*act[i]+coef2*ant_act[i]-coef3*sesgo;
										//ACTUALIZAMOS SESGO
	  sesgo+=3Dant_act[i];
	  return;
	}

	//ACTUALIZA  VECTOR DE ADAPTACION
	void neurona::act_vec(){
	  int i;
										//ACTUALIZAMOS PESOS
	  for(i=3D0;i<dim_ant;i++)act[i]+=3D*entrada[i]*delta*CTE1;
	  act[i]-=3Ddelta;           //ACTUALIZAMOS SESGO
	  return;
	}

	//INICIALIZAMOS ENTRADA Y CONEXIONES A NEURONA CAPA SIGUIENTE
	void neurona::asig_backward(int num,neurona *ptr){
	  backward[num]=3Dptr;
	  return;
	}

	//ASIGNAMOS ENTRADAS NEURONAS PRIMERA CAPA
	void neurona::asig_entrada(float *ptr){
	  try{entrada=3Dnew float *(ptr);}
	  catch (bad_alloc){
		 printf("\nERROR: MEMORIA INSUFICIENTE");
		 exit(-1);
	  }
	  return;
	}

	//ASIGNAMOS ENTRADAS RESTO DE NEURONAS
	void neurona::asig_entrada(int num,float *ptr){
	  entrada[num]=3Dptr;
	  return;
	}

	//PROPORCIONA DELTA
	float neurona::dame_delta()const{
	  return delta;
	}

	//PROPORCIONA DELTA PONDERADO
	inline float neurona::dame_delta(int num)const{
	  return (delta*vec_peso[num]);
	}

	//CALCULO DEL ERROR NEURONA DE SALIDA
	inline void neurona::calc_error(float ideal){
	  delta=3Dideal-CTE1*sal;
	  return;
	}

	//CALCULAMOS DELTA DE LA NEURONA
	void neurona::calc_delta(){
	  int i;
	  float res=3D0;

	  for(i=3D0;i<dim_post;i++)
		 res+=3Dbackward[i]->dame_delta(neu);
	  delta=3DCTE3*(1-(float)pow((double)sal,2))*res;
	  return;
	}

	//CALCULAMOS P SALIDA
	void neurona::calc_p_salida(){
	  int i;
	  float res=3D0;

	  for(i=3D0;i<dim_ant;i++)
		 res+=3D*((*entrada)+i)*vec_peso[i];
			 sal=3Dtanh(CTE2*(res-sesgo));
	  return;
	}

	//CALCULAMOS SALIDA
	void neurona::calc_salida(){
	  int i;
	  float res=3D0;

	  for(i=3D0;i<dim_ant;i++)
		 res+=3D*entrada[i]*vec_peso[i];
	  sal=3Dtanh(CTE2*(CTE1*res-sesgo));
	  return;
	}

	//CLASIFICAMOS PATRONES VALIDACION
	inline int neurona::clasif(float valor)const{
	  int clas=3D(valor>0)&&(sal>0)||(valor<0)&&(sal<0);
	  return clas;

	}

	//CREAMOS PESOS, VECTOR DE ADAPTACION Y MOMENTO
	void neurona::creo_vec(){
	  try{act=3Dnew float[dim_ant+1];
	  ant_act=3Dnew float[dim_ant+1];
	  vec_peso=3Dnew float[dim_ant];}
	  catch (bad_alloc){
		 printf("\nERROR: MEMORIA INSUFICIENTE");
		 exit(-1);
	  }
	  return;
	}

	//ALMACENAMOS PESOS QUE MAXIMIZAN NUMERO ACIERTOS
	void neurona::guardo_pes(float *dir) const{
	  int i;
	  for(i=3D0;i<dim_ant;i++)
		 dir[i]=3Dvec_peso[i];
	  dir[i]=3Dsesgo;
	  return;
	}

	//INICIALIZAMOS PUNTEROS A NEURONAS CAPA SIGUIENTE
	void neurona::ini_backward(){
	  try{backward=3Dnew neurona *[dim_post];}
	  catch (bad_alloc){
		 printf("\nERROR: MEMORIA INSUFICIENTE");
		 exit(-1);
	  }
	  return;
	}

	//INICIALIZAMOS PUNTEROS A ENTRADAS
	void neurona::ini_entrada(){
	  try{entrada=3Dnew float* [dim_ant];}
	  catch (bad_alloc){
		 printf("\nERROR: MEMORIA INSUFICIENTE");
		 exit(-1);
	  }
	  return;
	}

	//INICIALIZAMOS PESOS
	void neurona::ini_peso(float rango){
	  int i;

	  for(i=3D0;i<dim_ant;i++){
		 vec_peso[i]=3D(rango*(random()-(ALEA_MED)))/((float)ALEA_MED);
		 act[i]=3Dant_act[i]=3D0;
	  }
	  sesgo=3D(rango*(random()-ALEA_MED))/((float)ALEA_MED);
	  act[i]=3Dant_act[i]=3D0;
	  return;
	}

	//INICIALIZAMOS NEURONA 1=AA CAPA
	void neurona::ini_p_capa(int ant,int post,int pos){
		 dim_ant=3Dant;
		 dim_post=3Dpost;
		 neu=3Dpos;
		 ini_backward();
		 creo_vec();
		 return;
	}

	//INICIALIZAMOS NEURONA CAPAS INTERMEDIAS
	void neurona::ini_i_capa(int ant,int post,int pos){
	  dim_ant=3Dant;
	  dim_post=3Dpost;
	  neu=3Dpos;
	  creo_vec();
	  ini_entrada();
	  ini_backward();
	  return;
	}

  //INICIALIZAMOS NEURONA CAPAS SALIDA
	void neurona::ini_s_capa(int ant,int pos){
	  dim_ant=3Dant;
	  neu=3Dpos;
	  creo_vec();
	  ini_entrada();
	  return;
	}

	//CALCULO DE LA DELTA DE LA NEURONA DE SALIDA
	inline void neurona::p_delta(){
	  delta*=3DCTE3*(1-(float)pow((double)sal,2));
	  return;
	}

	//PODA DEL VECTOR DE PESOS
	void neurona::poda(float coef){
	  int i;

	  for(i=3D0;i<dim_ant;i++)
		 if((float)fabs((double)vec_peso[i])<coef) vec_peso[i]=3D0;
	  return;
	}

	//RESETEAMOS VECTOR DE ADAPTACION
	void neurona::res_vec(){
	  int i,tam=3Ddim_ant+1;

	  for(i=3D0;i<tam;i++) act[i]=3D0;
	  return;
	}

	//PUNTERO A LA SALIDA
	float * neurona::salida(){
	  return &sal;
	}

	//CONSTRUCTOR Y DESTRUCTOR
	neurona::neurona(){
	  vec_peso=3Dact=3Dant_act=3DNULL;
	  entrada=3DNULL;
	  backward=3DNULL;
	  dim_post=3D0;
	}

	neurona::~neurona(){
	  delete []entrada;
	  delete []vec_peso;
	  if(backward!=3DNULL)
		 delete []backward;
	}


	class capa{                             //CAPA
	  neurona *vec_neu;
	  int dim;
	 public:
	  int dimension()const;
	  inline neurona * dir_neu(int)const;   //PROPORCIONA DIRECCION DE LA =
NEURONA
	  void ini_capa(int);
	  int num_acier(float*);
	  capa();                               //CONSTRUCTOR Y DESTRUCTOR
	  ~capa();
	};

	int capa::dimension()const{        //DEVUELVE LA DIMENSION DE LA CAPA
	  return dim;
	}

	neurona * capa::dir_neu(int i) const{
	  return vec_neu+i;                //PROPORCIONA DIRECCION DE NEURONA I
	}

	void capa::ini_capa(int ent){      //INICIALIZAR CAPA CON 'DIM' =
NEURONAS
	  try{vec_neu=3Dnew neurona[dim=3Dent];}
	  catch (bad_alloc){
		 printf("\nERROR: MEMORIA INSUFICIENTE");
		 exit(-1);
	  }
     return;
	}

	int capa::num_acier(float *ptr){
	  int i=3D0,res=3D1;
												  //RECORREMOS NEURONAS CAPA SALIDA
	  while((i<dim)&&(res!=3D0))         //DE LA CLASIFICACION SEA CORRECTO
		 res*=3Dvec_neu[i++].clasif(*(ptr+i));
	  return res;
	}

	capa::capa(){                      //CONSTRUCTOR Y DESTRUCTOR
	  vec_neu=3DNULL;
	}

	capa::~capa(){
		delete []vec_neu;
	}

												  //RED NEURONAL

	class red:public datos{
	  protected:
	  coef<int> est;                   //PARAMETROS ESTRUCTURA RED
	  capa *nivel;                     //ESTRUCTURA DE LA RED COMUN PARA =
TODOS LOS
												  //EXPERIMENTOS
	  float u,al,                      //COEFICIENTES CTE. ADAPTACION Y =
MOMENTO
			  la,w,*errores,*min_pesos;  //ERROR;COEFICIENTE PODA,LIMITE PODA
												  //VECTOR CINTENIENDO ERRORES Y PESOS
												  //QUE MAXIMIZAN NUMERO ACIERTOS
	  int num1,num2,it,*aciertos;

	 public:                           //INICIALIZAR PARAMETROS DE LA RED
	  void ini_red(float,float,float,float);
	  void entrenar();
	  red();
	  ~red();
	};

	//INICIALIZAR PARAMETROS DE LA RED
	void red::ini_red(float num1,float num2,float num3,float num4){
	  u=3Dnum1;
	  al=3Dnum2;
	  la=3Dnum3;
	  w=3Dnum4;
	  return;
	}

	//ENTRENAMIENTO DE LA RED
	void red::entrenar(){
	  int =
i,j,k,l,tam=3Dest.retornar()-1,neu_p_capa=3Dest.retornar(1),max_aci=3D0;
	  float rang=3D(2.4/est.retornar(0)),*punt;

	  srandom(time(NULL));
	  for(i=3D0;i<tam;i++)                      //INICIALIZAMOS PESOS
		 for(j=3D0;j<est.retornar(i+1);j++)
			nivel[i].dir_neu(j)->ini_peso(rang);

	  for(i=3D0;i<it;i++){
/*             if(i=3D=3D110)
                 printf("\n%i",i);*/

               	 errores[i]=3D0;
		 for(j=3D0;j<num1;j++){                  //ENTRENAMIENTO
			for(l=3D0;l<neu_p_capa;l++){          //ASIGNAMOS PATRON COMO ENTRADA
			  nivel[0].dir_neu(l)->asig_entrada(i_ent[j]);
			  nivel[0].dir_neu(l)->calc_p_salida();
			}                                   //CALCULO SALIDA NEURONAS 1=AA =
CAPA

			for(l=3D1;l<tam;l++)                  //CALCULO SALIDA NEURONAS
			  for(k=3D0;k<est.retornar(l+1);k++)
				 nivel[l].dir_neu(k)->calc_salida();

			for(k=3D0;k<est.retornar(tam);k++){   //CALCULO EL ERROR DE CADA =
NEURONA
			  nivel[tam-1].dir_neu(k)->calc_error(o_ent[j][k]);
			  =
errores[i]+=3D(float)pow((double)nivel[tam-1].dir_neu(k)->dame_delta(),
											 2);
			  nivel[tam-1].dir_neu(k)->p_delta();  //CALCULO DELTA NEURONAS
			}                                      //CAPA SALIDA

			for(k=3D0;k<est.retornar(tam);k++)       //SE ACTUALIZAN VECTORES DE
			  nivel[tam-1].dir_neu(k)->act_vec();  //ADAPTACION NEURONAS CAPA
																//SALIDA
			for(l=3Dtam-2;l<0;l--)
			  for(k=3D0;k<est.retornar(l+1);k++){    //PARA EL RESTO DE NEURONAS
				 nivel[l].dir_neu(k)->calc_delta(); //CALCULAMOS DELTA
				 nivel[l].dir_neu(k)->act_vec();    //GUARDAMOS RESULTADOS
			}

			for(k=3D0;k<est.retornar(l+1);k++){
			  nivel[0].dir_neu(k)->calc_delta();   //CALCULAMOS DELTA
			  nivel[0].dir_neu(k)->act_p_vec();    //GUARDAMOS RESULTADOS
			}                                      //PARA FUTURA ACTUALIZACION
		 }

		 for(l=3D0;l<tam;l++)
			for(k=3D0;k<est.retornar(l+1);k++){      //ACTUALIZACION DE PESOS
			  nivel[l].dir_neu(k)->act_peso(u,al,la);
			  nivel[l].dir_neu(k)->res_vec();      //RESETEAMOS VECTOR DE =
'ACT_VEC'
		 }


		 if(!((i+1)%5))
			for(l=3D0;l<tam;l++)
			  for(k=3D0;k<est.retornar(l+1);k++)
				 nivel[l].dir_neu(k)->poda(w);        //PODAMOS CADA 5 EPOCAS

		 aciertos[i]=3D0;
		 for(j=3D0;j<num2;j++){                     //VALIDACION
																//ASIGNAMOS A NEURONAS CAPA DE
			for(l=3D0;l<neu_p_capa;l++){             //ENTRADA LA ENTRADA
			  nivel[0].dir_neu(l)->asig_entrada(i_val[j]);
			  nivel[0].dir_neu(l)->calc_p_salida();
			}

			for(l=3D1;l<tam;l++)                      //CALCULAMOS LA SALIDA DE =
CADA
			  for(k=3D0;k<est.retornar(l+1);k++)      //NEURONA
				 nivel[l].dir_neu(k)->calc_salida();
																 //CONTAMOS N=BA ACIERTOS POR
			aciertos[i]+=3Dnivel[tam-1].num_acier(o_val[j]);
		 }

																 //SI NUMERO DE ACIERTOS ES
																 //MAXIMO, GUARDAMOS PESOS
		 if(max_aci<=3Daciertos[i]){
			max_aci=3Daciertos[i];
			punt=3Dmin_pesos;
			for(l=3D0;l<tam;l++){
			  for(k=3D0;k<est.retornar(l+1);k++)
				 nivel[l].dir_neu(k)->guardo_pes(punt+k*(est.retornar(l)+1));
			  punt+=3Dest.retornar(l+1)*(est.retornar(l)+1);
			}
		 }

	  }                                          //FIN ENTRENAMIENTO
	  return;
	}

	red::red(){                                  //CONSTRUCTOR Y DESTRUCTOR
	  nivel=3DNULL;
	  aciertos=3DNULL;
	  errores=3DNULL;
	  min_pesos=3DNULL;
	}

	red::~red(){
	  delete []nivel;
	  delete []errores;
	  delete []aciertos;
	  delete []min_pesos;
	}

										 //EXPERIMENTO CON DISTINTAS REDES

	class experimento: public ficheros,public red{
	  coef<float>  *vec;        //DISTINTOS COEFICIENTES PARA LOS DISTINTOS
										 //EXPERIMENTOS
	  int num_pesos;            //NUMERO DE PESOS QUE TENDRA LA RED
	 public:
	  int clasif(char *,char*);   //DESCIFRA LOS PARAMETROS PASADOR CON =
'ARGV'
	  int comp_datos();           //COMPROBAR DIMENSIONES CON NUMERO DE =
PATRONES
	  void ejecutar();            //EJECUTA EXPERIMENTO
	  void ini_capas();           //INICIALIZAR CAPAS
	  void int_datos();           //INTRODUCE LOS PATRONES EN DATOS
	  experimento(int,char **);
	  ~experimento();
	};
											//CONSTRUCTOR CONFIGURACION

	int experimento::clasif(char *str1,char *str2){
		  return !strncmp(strlwr(str1),str2,5);
	}

											//COMPROBACION DE CORRECION DE DATOS
	int experimento::comp_datos(){
	  long int total=3D(long =
int)(est.retornar(0)+est.retornar(est.retornar()-1))*
	                 (long int)(num1+num2);

	  if(total=3D=3Dtamanyo())
		 return 1;
	  else
		 return 0;
	}
											//EJECUCION DEL EXPERIMENTO
	void experimento::ejecutar(){
	  int i,j,k,l;

	  if(!comp_datos()){
		 fprintf(stderr,"ERROR: datos introducidos.\n");
		 exit(1);
	  }
	  else{
		 ini_capas();					//INICIALIZACION ESTRUCTURA DE LA RED
		 int_datos();
											//GUARDAMOS N=BA REDES NEURONALES ENTRENADAS
											//N=BA ITERACIONES POR RED Y ESTRUCTURA
		 if(!salvar(est.retornar())){
			printf("ERROR GUARDANDO DATOS");
			exit(1);
		 }

		 for(i=3D0;i<est.retornar();i++)
			if(!salvar(est.retornar(i))){
			  printf("ERROR GUARDANDO DATOS");
			  exit(1);
			}

		 if(!(salvar(it)*salvar(vec[0].retornar()*vec[1].retornar()*
				vec[2].retornar()*vec[3].retornar()))){
			printf("ERROR GUARDANDO DATOS");
			exit(1);
		 }
											//PARA CADA CONJUNTO DE PARAMETROS
		 for(i=3D0;i<vec[0].retornar();i++)
			for(j=3D0;j<vec[1].retornar();j++)
			  for(k=3D0;k<vec[2].retornar();k++)
				 for(l=3D0;l<vec[3].retornar();l++){
					ini_red(vec[0].retornar(i),vec[1].retornar(j),
							  vec[2].retornar(k),vec[3].retornar(l));
					entrenar();

					if(!(salvar(vec[0].retornar(i))*
                    salvar(vec[1].retornar(j))*
                    salvar(vec[2].retornar(k))*
                    salvar(vec[3].retornar(l))*
                    salvar(min_pesos,num_pesos)*
                    salvar(aciertos,it)*
						  salvar(errores,it)))
                      {
					         printf("\ERROR GUARDANDO DATOS");
             			   exit(1);
                      }
		 }
	  }
	  return;
	}

	//INTRODUCCION DE LOS PATRONES EN DATOS
	void experimento::int_datos(){
		int =
i,dim_in=3Dest.retornar(0),dim_out=3Dest.retornar(est.retornar()-1);
		try{											 //RESERVAMOS MEMORIA PARA ALMACENAR
		  i_ent=3Dnew float *[num1];           //PATRONES
		  o_ent=3Dnew float *[num1];
		  i_val=3Dnew float *[num2];
		  o_val=3Dnew float *[num2];


		  for(i=3D0;i<num1;i++){               //GUARDAMOS PATRONES DE =
ENTRENAMIENTO
			 i_ent[i]=3Dnew float[dim_in];
			 o_ent[i]=3Dnew float[dim_out];
			 fread(i_ent[i],sizeof(float),dim_in,in);
			 fread(o_ent[i],sizeof(float),dim_out,in);
		  }
		  for(i=3D0;i<num2;i++){               //GUARDAMOS PATRONES VALIDACION
			 i_val[i]=3Dnew float[dim_in];
			 o_val[i]=3Dnew float[dim_out];
			 fread(i_val[i],sizeof(float),dim_in,in);
			 fread(o_val[i],sizeof(float),dim_out,in);
		  }
               	  fclose(in);
		}
		catch (bad_alloc){
		 printf("\nERROR: MEMORIA INSUFICIENTE");
		 exit(-1);
	  }
		return;
	}

	experimento::experimento(int num,char *msg[]):ficheros(msg[1],msg[2]){
	  int i,p[3]=3D{20,2,1};
	  try{vec=3Dnew coef<float>[4];}
	  catch (bad_alloc){
		 printf("\nERROR: MEMORIA INSUFICIENTE");
		 exit(-1);
	  }
	  num_pesos=3D0;
	  num1=3D1595;
	  num2=3D285;
	  it=3D10;
	  vec[0].ini(1.5e-4);
	  vec[1].ini(0.4);
	  vec[2].ini(0.0);
	  vec[3].ini(0.0);
	  est.ini(3,p);

	  for(i=3D3;i<num;i++){					//CONFIGURACION DEL EXPERIMENTO
		 if(clasif(msg[i],"/ent:"))      //NUMERO PATRONES ENTRENAMIENTO EN EL
			num1=3Datoi(msg[i]+5);          //FICHERO DE ENTRADA
		 if(clasif(msg[i],"/val:"))      //NUMERO PATRONES VALIDACION EN EL
			num2=3Datoi(msg[i]+5);          //FICHERO DE ENTRADA
		 if(clasif(msg[i],"/est:"))      //ESTRUCTURA RED NEURONAL
			est.asignar_int(msg[i]);
		 if(clasif(msg[i],"/adp:"))      //CTE. ADAPTACION
			vec[0].asignar_float(msg[i]);
		 if(clasif(msg[i],"/mom:"))      //MOMENTO
			vec[1].asignar_float(msg[i]);
		 if(clasif(msg[i],"/lan:"))      //CTE. TERMINO DE PODA
			vec[2].asignar_float(msg[i]);
		 if(clasif(msg[i],"/pod:"))      //UMBRAL DE PODA
			vec[3].asignar_float(msg[i]);
		 if(clasif(msg[i],"/itr:"))      //NUMERO DE ITERACIONES
			it=3Datoi(msg[i]+5);
	  }
	  try{                              //CREAMOS VECTOR DE ERRORES Y =
ACIERTOS
		 errores=3Dnew float[it];
		 aciertos=3Dnew int[it];
													//CALCULAMOS MEMORIA PARA ALMACENAR
		 for (i=3D1;i<est.retornar();i++)  //PESOS
			num_pesos+=3Dest.retornar(i)*(est.retornar(i-1)+1);
		 min_pesos=3Dnew float[num_pesos];
	  }
	  catch (bad_alloc){
		 printf("\nERROR: MEMORIA INSUFICIENTE");
		 exit(-1);
	  }

	  pon_dim(num1,num2);               //INFORMAMOS A CAPA DIMENSION DE =
MATRIZ
	}                                   //DE PATRONES

	experimento::~experimento(){        //DESTRUCTOR
	  delete[] vec;
	}

								 //INICIALIZAR CAPAS
	void experimento::ini_capas(){
	  int i=3D0,j,k,tam=3Dest.retornar()-1;
								 //CREAMOS LAS DISTINTAS CAPAS DE LA RED
	  try{nivel=3Dnew capa[tam];}
	  catch (bad_alloc){
		 printf("\nERROR: MEMORIA INSUFICIENTE");
		 exit(-1);
	  }
	  for(j=3D0;j<tam;j++)  //INICIALIZAMOS LAS CAPAS
		 nivel[j].ini_capa(est.retornar(j+1));
								 //INICIALIZAMOS NEURONAS DE LA PRIMERA CAPA

	  for(j=3D0;j<est.retornar(i+1);j++)
		 nivel[i].dir_neu(j)->ini_p_capa(est.retornar(i),est.retornar(i+2),j);
								 //INICIALIZAMOS NEURONAS DE LAS CAPAS INTERMEDIAS
	  for(i=3D1;i<tam-1;i++)
		 for(j=3D0;j<est.retornar(i+1);j++)
			nivel[i].dir_neu(j)->ini_i_capa(est.retornar(i),est.retornar(i+2),j);

								 //INICIALIZAMOS NEURONAS CAPA DE SALIDA
	  for(j=3D0;j<est.retornar(i+1);j++)
		 nivel[tam-1].dir_neu(j)->ini_s_capa(est.retornar(i),j);
								 //ASIGNAMOS PUNTEROS A NEURONAS DE CAPA SIGUIENTE
	  for(i=3D0;i<tam-1;i++)
		 for(j=3D0;j<est.retornar(i+1);j++)
			for(k=3D0;k<est.retornar(i+2);k++)
			  nivel[i].dir_neu(j)->asig_backward(k,nivel[i+1].dir_neu(k));

								 //ASIGNAMOS PUNTEROS A SALIDA DE NEURONAS CAPA ANTERIOR
	  for(i=3D1;i<tam;i++)
		 for(j=3D0;j<est.retornar(i+1);j++)
			for(k=3D0;k<est.retornar(i);k++)
			  =
nivel[i].dir_neu(j)->asig_entrada(k,nivel[i-1].dir_neu(k)->salida());
	  return;
	}


    void my_new_handler()
    {
       throw bad_alloc();
    }
								 //CUERPO DEL PROGRAMA
  void main(int argc,char **argv){
  int i;
  #ifdef PRUEBA

    char *msg[60];
    msg[0]=3D"red10";
    msg[1]=3D"d:\\djgpp\\bin\\datos3.dat";
    msg[2]=3D"red136.dat";
    msg[3]=3D"/itr:15000";
    msg[4]=3D"/est:20,15,3";
    msg[5]=3D"/adp:1e-4";
    set_new_handler(my_new_handler);
    experimento exp(6,msg);
  #else
    experimento exp(argc,argv);
  #endif



	 exp.ejecutar();
	 return;
  }



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Content-Type: application/octet-stream;
	name="execute.bat"
Content-Transfer-Encoding: 7bit
Content-Disposition: attachment;
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red datos3.dat red136.dat /adp:1e-4 /mom:4e-1 /itr:15000 /est:20,15,3


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