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реферат на тему: Классификация сейсмических сигналов на основе нейросетевых технологий
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(char *fmt, ...)
{ FILE *file;
va_list argptr;
if (DEBUG == Yes)
{ if ((file = fopen(DebugFile,"a+"))==NULL)
{ fprintf(stderr, "\nCannot open DEBUG file.\n");
exit(1);
}
va_start(argptr, fmt);
vfprintf(file, fmt, argptr);
va_end(argptr);
fclose (file);
}
}
void Report (char *fmt, ...)
{ FILE *file;
va_list argptr;
if ((file = fopen(ReportFile,"a+"))==NULL)
{ fprintf(stderr, "Cannot open REPORT file.\n");
exit(1);
}
va_start(argptr, fmt);
vfprintf(file, fmt, argptr);
vprintf(fmt,argptr);
va_end(argptr);
fclose (file);
}
*/
/*
* ^ReadPattern
*/
void ReadPattern (PAT *input, char *name, int Len)
{ int i=0, j=0, id, TmpNp=0, TmpNd=0, Flag=0;
char *buf1="NumOfPattern:";
char *buf2="PatternDimens:";
char str[40],str1[10];
PAT Ptr;
FILE *DataFile;
float tmp;
Debug ("\nReadPattern(%s,%d) - started",name,Len);
Ptr.A =(float*) malloc (NDATA * sizeof(float));
if ((DataFile = fopen(name,"r")) == NULL )
{ Debug("\nCan't read the data file (%s)",name);
fclose(DataFile);
exit (1);
}
if ((strcmp(name,TestVector)) == 0) /* if read TestVector, then read */
Flag = 1; /* only ID and A[i]. (NO Target) */
fscanf(DataFile,"%s %s",str,str1);
if ((strcmp(str,buf1))==0)
TmpNp = atoi (str1);
Debug("\nNumOfPattern = %d",TmpNp);
fscanf(DataFile,"%s %s",str,str1);
if ((strcmp(str,buf2))==0)
TmpNd = atoi (str1);
Debug("\nPatternDimens = %d",TmpNd);
if (TmpNp != Len)
Debug ("\n\tWARNING! - NumOfPattern NOT EQUAL Param (%d != %d)",TmpNp,Len);
if (TmpNd != NDATA)
Debug ("\n\tWARNING! - PatternDimens NOT EQUAL NDATA (%d != %d)",TmpNd,NDATA);
for (i = 0; i < Len; i++)
{fscanf(DataFile,"%d",&id);
Ptr.ID = id;
for (j=0; j < NDATA; j++)
{ fscanf (DataFile,"%f",&tmp);
Ptr.A[j]=tmp;
}
if ( Flag )
tmp = -1;
else
fscanf(DataFile,"%f",&tmp);
Ptr.Target = tmp;
input[i]=patcpy(input[i],Ptr);
}
fclose(DataFile);
}
/*
* ^LocPatMemory - locate memory for (PAT *)
*/
PAT* LocPatMemory(int num)
{ int i;
PAT *src;
src = (PAT *) malloc (num * sizeof(PAT));
for (i=0; i< num; i++)
{src[i].ID = -1;
src[i].A = (float*) malloc (NDATA * sizeof(float));
src[i].Target = -1.0;
}
return (src);
}
void FreePatMemory( PAT* src, int num )
{ int i;
for (i=0;i
/*
* Copies pattern src to dest.
* Return dest.
*/
PAT patcpy (PAT dest, PAT src)
{ int i;
dest.ID = src.ID;
for (i=0;i x/32768
* -> x from [0,1]
*/
float RavnRaspr(float A, float B)
{float x;
x = (B-A)*rand()/(RAND_MAX+1.0) + A;
return x;
}
float NormRaspr(float A,float B)
{ float mat_ogidanie=A, Sigma=B;
float Sumx=0.0, x;
int i;
for (i=0;i<12;i++)
Sumx = Sumx + RavnRaspr(0,1); /* from R[0,1] -> N[a,sigma]*/
x = Sigma*(Sumx-6) + mat_ogidanie;
return x;
}
int Init_W ( void )
{ int i,j;
float A, B;
time_t t,t1;
t = time(NULL);
t1=t;
/* restart random generator*/
while (t==t1)
srand((unsigned) time(&t));
if (InitFunc == Random)
{ A = -Constant;
B = Constant;
Debug ("\nInit_W () --- Start (%ld))",t);
Debug ("\n InitFunc=Random[%4.2f,%4.2f]",A,B);
for(i=0; i<=NDATA; i++)
for(j=0; j
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= Alfa;
B = Sigma;
Debug ("\nInit_W () --- Start (%ld))",t);
Debug ("\n InitFunc=Gauss[%4.2f,%4.2f]",A,B);
for(i=0; i<=NDATA; i++)
for(j=0; j
/* LearnFunc */
int LearnFunc (void)
{ int i, j, n, K, NumErr=0;
int num=0;
float err_cur=0.0, Res=0;
time_t tim;
float ep[NMAXPAT];
GL_Error=1.0;
time(&tim);
Debug ("\nLearnFunc () --- Started");
Debug ("\n eta = %4.2f",eta);
Debug ("\n LearnTolerance = %4.2f",LearnTolerance);
Init_PromW();
do
{ num++;
err_cur = 0.0;
NumErr = 0;
for (n = 0; n < NWORK; n++)
{ K = Cur_Number[n];
Res=Forward(Work[K]);
ep[n]=fabs(Res-Work[K].Target);
if (ep[n] > LearnTolerance)
{ NumErr++;
Init_PromW();
Update_Last(K, Res);
Update_Prom1(K);
Prom_to_W();
}
err_cur = err_cur + (ep[n]*ep[n]);
}
err_cur=0.5*(err_cur/NWORK);
result = Reset(err_cur, num, NumErr);
if ((num % NumOut)==0)
Debug("\nStep :%d NumErr :%d Error:%6.4f",num,NumErr,err_cur);
} while (result == CONT || result == RESTART);
Debug("\nStep :%d NumErr :%d Error:%6.4f",num,NumErr,err_cur);
return num;
}
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