C++ (Cpp) tansig 예제들

예제 #1

파일: util_win.c 프로젝트: cran/brnn

SEXP predictions_nn(SEXP X, SEXP n, SEXP p, SEXP theta, SEXP neurons,SEXP yhat, SEXP reqCores)
{
   int i,j,k;
   double sum,z;
   int rows, columns, nneurons;
   double *pX, *ptheta, *pyhat;
   
   SEXP list;

   rows=INTEGER_VALUE(n);
   columns=INTEGER_VALUE(p);
   nneurons=INTEGER_VALUE(neurons);

   PROTECT(X=AS_NUMERIC(X));
   pX=NUMERIC_POINTER(X);

   PROTECT(theta=AS_NUMERIC(theta));
   ptheta=NUMERIC_POINTER(theta);


   PROTECT(yhat=AS_NUMERIC(yhat));
   pyhat=NUMERIC_POINTER(yhat);
   
   for(i=0;i<rows;i++)
   {
      		sum=0;
      		for(k=0;k<nneurons;k++)
      		{
	 		z=0;
	 		for(j=0;j<columns;j++)
	 		{
	    			z+=pX[i+(j*rows)]*ptheta[(columns+2)*k+j+2];
	 		}
	 		z+=ptheta[(columns+2)*k+1];      
	 		sum+=ptheta[(columns+2)*k]*tansig(z);
      		}
      		pyhat[i]=sum;
   }
   
   PROTECT(list=allocVector(VECSXP,1));
   SET_VECTOR_ELT(list,0,yhat);

   UNPROTECT(4);

   return(list);
}

예제 #2

파일: Calcq.c 프로젝트: minikh/NeuroNet

//*************************************************** NeuroNet ***************************************************
//Нейронная сеть 3-10-1
//3 входа, 10 нейронов в скрытом (первом) слое, один нейрон в выходном (втором) слое  
float NeuroNet( float Nst, float Ps, float Pd)
{
float IN[3], sum, LW_1[10], LW_2;
int i,j;

IN[0]= Nst;
//Ограничения по оборотам
if (IN[0] <13500) IN[0]=13500;
if (IN[0] >22500) IN[0]=22500;

//Переводим из кгс/см2 в атм
IN[1]= Ps / 0.010197 / 101.325;
//Ограничения по входному давлению
if (IN[1] <30) IN[1]=30;
if (IN[1] >55) IN[1]=55;

//Переводим из кгс/см2 в атм
IN[2]= Pd / 0.010197 / 101.325;
//Ограничения по выходному давлению
if (IN[2] <40) IN[2]=40;
if (IN[2] >160) IN[2]=160;


//Первый слой
for (j= 0; j<10; j++)
	{
	sum= 0;
	for (i= 0; i < 3; i++)
		sum= sum + IN[i] * W1[j][i];

	sum= sum + off_1[j];
	//выход i-го нейрона первого слоя
	LW_1[j]= tansig(sum);
	}  //for j:= 1 to 10 do

sum= 0;
//Второй слой
for (i= 0; i<10; i++) 
	sum= sum + LW_1[i] * W2[i];

//выход нейрона второго слоя	
LW_2= sum + off_2;
return LW_2;
}

예제 #3

파일: util_unix.c 프로젝트: cran/brnn

//This function will calculate the Jocobian for the errors
SEXP jacobian_(SEXP X, SEXP n, SEXP p, SEXP theta, SEXP neurons,SEXP J, SEXP reqCores)
{
   int i,j,k;
   double z,dtansig;
   int useCores, haveCores;
   double *pX;
   double *ptheta;
   double *pJ;
   int rows, columns, nneurons;

   SEXP list;

   rows=INTEGER_VALUE(n);
   columns=INTEGER_VALUE(p);
   nneurons=INTEGER_VALUE(neurons);
  
   PROTECT(X=AS_NUMERIC(X));
   pX=NUMERIC_POINTER(X);
   
   PROTECT(theta=AS_NUMERIC(theta));
   ptheta=NUMERIC_POINTER(theta);
   
   PROTECT(J=AS_NUMERIC(J));
   pJ=NUMERIC_POINTER(J);
   
   /*
   Set the number of threads
   */

   #ifdef _OPENMP
     //R_CStackLimit=(uintptr_t)-1;
     useCores=INTEGER_VALUE(reqCores);
     haveCores=omp_get_num_procs();
     if(useCores<=0 || useCores>haveCores) useCores=haveCores;
     omp_set_num_threads(useCores); 
   #endif

   #pragma omp parallel private(j,k,z,dtansig) 
   {
        #pragma omp for schedule(static)
   	for(i=0; i<rows; i++)
   	{
                //Rprintf("i=%d\n",i);
     		for(k=0; k<nneurons; k++)
     		{
	  		z=0;
	  		for(j=0;j<columns;j++)
	  		{
	      			z+=pX[i+(j*rows)]*ptheta[(columns+2)*k+j+2]; 
	  		}
	  		z+=ptheta[(columns+2)*k+1];
	  		dtansig=pow(sech(z),2.0);
	  
	  		/*
	  		 Derivative with respect to the weight
	  		*/
	  		pJ[i+(((columns+2)*k)*rows)]=-tansig(z);
	 
	  		/*
	  		Derivative with respect to the bias
	 		*/
	 
	 		pJ[i+(((columns+2)*k+1)*rows)]=-ptheta[(columns+2)*k]*dtansig;

	 		/*
	  		 Derivate with respect to the betas
	  		*/
	 		for(j=0; j<columns;j++)
	 		{
	     			pJ[i+(((columns+2)*k+j+2)*rows)]=-ptheta[(columns+2)*k]*dtansig*pX[i+(j*rows)];
	 		}
     		}
   	}
   }
  
   PROTECT(list=allocVector(VECSXP,1));
   SET_VECTOR_ELT(list,0,J);
   
   UNPROTECT(4);
   
   return(list);
}

예제 #4

파일: util_unix.c 프로젝트: cran/brnn

SEXP predictions_nn(SEXP X, SEXP n, SEXP p, SEXP theta, SEXP neurons,SEXP yhat, SEXP reqCores)
{
   int i,j,k;
   double sum,z;
   int rows, columns, nneurons;
   int useCores, haveCores;
   double *pX, *ptheta, *pyhat;
   
   SEXP list;

   rows=INTEGER_VALUE(n);
   columns=INTEGER_VALUE(p);
   nneurons=INTEGER_VALUE(neurons);

   PROTECT(X=AS_NUMERIC(X));
   pX=NUMERIC_POINTER(X);

   PROTECT(theta=AS_NUMERIC(theta));
   ptheta=NUMERIC_POINTER(theta);


   PROTECT(yhat=AS_NUMERIC(yhat));
   pyhat=NUMERIC_POINTER(yhat);

   /*
   Set the number of threads
   */
   
   #ifdef _OPENMP     
     //R_CStackLimit=(uintptr_t)-1;
     useCores=INTEGER_VALUE(reqCores);
     haveCores=omp_get_num_procs();
     if(useCores<=0 || useCores>haveCores) useCores=haveCores;
     omp_set_num_threads(useCores);   
   #endif

   #pragma omp parallel private(j,k,z,sum) 
   {
        #pragma omp for schedule(static)
   	for(i=0;i<rows;i++)
   	{
      		sum=0;
      		for(k=0;k<nneurons;k++)
      		{
	 		z=0;
	 		for(j=0;j<columns;j++)
	 		{
	    			z+=pX[i+(j*rows)]*ptheta[(columns+2)*k+j+2];
	 		}
	 		z+=ptheta[(columns+2)*k+1];      
	 		sum+=ptheta[(columns+2)*k]*tansig(z);
      		}
      		pyhat[i]=sum;
   	}
   }

   PROTECT(list=allocVector(VECSXP,1));
   SET_VECTOR_ELT(list,0,yhat);

   UNPROTECT(4);

   return(list);
}

예제 #5

파일: util_win.c 프로젝트: cran/brnn

//This function will calculate the Jocobian for the errors
SEXP jacobian_(SEXP X, SEXP n, SEXP p, SEXP theta, SEXP neurons,SEXP J, SEXP reqCores)
{
   int i,j,k;
   double z,dtansig;
   double *pX;
   double *ptheta;
   double *pJ;
   int rows, columns, nneurons;

   SEXP list;

   rows=INTEGER_VALUE(n);
   columns=INTEGER_VALUE(p);
   nneurons=INTEGER_VALUE(neurons);
  
   PROTECT(X=AS_NUMERIC(X));
   pX=NUMERIC_POINTER(X);
   
   PROTECT(theta=AS_NUMERIC(theta));
   ptheta=NUMERIC_POINTER(theta);
   
   PROTECT(J=AS_NUMERIC(J));
   pJ=NUMERIC_POINTER(J);
   
  for(i=0; i<rows; i++)
  {
                //Rprintf("i=%d\n",i);
     		for(k=0; k<nneurons; k++)
     		{
	  		z=0;
	  		for(j=0;j<columns;j++)
	  		{
	      			z+=pX[i+(j*rows)]*ptheta[(columns+2)*k+j+2]; 
	  		}
	  		z+=ptheta[(columns+2)*k+1];
	  		dtansig=pow(sech(z),2.0);
	  
	  		/*
	  		 Derivative with respect to the weight
	  		*/
	  		pJ[i+(((columns+2)*k)*rows)]=-tansig(z);
	 
	  		/*
	  		Derivative with respect to the bias
	 		*/
	 
	 		pJ[i+(((columns+2)*k+1)*rows)]=-ptheta[(columns+2)*k]*dtansig;

	 		/*
	  		 Derivate with respect to the betas
	  		*/
	 		for(j=0; j<columns;j++)
	 		{
	     			pJ[i+(((columns+2)*k+j+2)*rows)]=-ptheta[(columns+2)*k]*dtansig*pX[i+(j*rows)];
	 		}
     		}
  }
  
  PROTECT(list=allocVector(VECSXP,1));
  SET_VECTOR_ELT(list,0,J);
  
  UNPROTECT(4);
   
  return(list);
}