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int NumOut = 250; /* Number of itteration, after which show result in debugfile. */ int Num_Iter=10;/* The parameters requred in the procecc of */ float Percent=0.25; /* dinamic lerning with change 'eta' */

float LearnTolerance = 0.10; float TestTolerance = 0.5;

float MAX_ERR=0.00001; /* min error */ float eta = 1.0; /* learning coefficient*/ float MIN_ETA=0.000001;

float **Array_MinMax; int *Cur_Number;

float W1[NMXINP][NMXUNIT]; float W2[NMXUNIT];

float PromW1[NMXINP][NMXUNIT]; float PromW2[NMXUNIT];

float PromW1_OLD[NMXINP][NMXUNIT]; float PromW2_OLD[NMXUNIT];

float Err1[NMXUNIT]; float Err2; float OLD_ERROR; float GL_Error=0.0;

float Out1[NMXUNIT]; float Out2;

char NetStr[20]="Auto"; /* String with pattern of Net Structure*/

int Type = TYPE_THREE; /* Enter the mode of work of programm */

int InitFunc = Random; /* Random [=10] weigth will RandomDistribution
Gauss [=20] - ... GaussianDistributon */ float Constant = 1; /* RandomDistribution [-Constant,Constant]*/

float Alfa = 0; /* GaussianDistribution [Alfa,Sigma]*/ float Sigma = 1; /* ... */ int Widrow = No; /* Nguyen-Widrow initialization start weigth*/

int Loop = 1; /* Number repeat of Learning cycle */

char *PatternFile; /* File with input patterns*/ char *TestVector; char *ReportFile="report.txt"; /* name of report file */ char *NetworkFile; /* Name of input NetConfig file */ char *ResNetFname; /* Name of output NetConfig file */

int DEBUG = Yes; /* if 'Yes' then debug info in the DebugFile
*/ char *DebugFile="Logfile.log"; /* Name of the debug file*/

int NumberVector = 0; /* Number of TEST vector */ int Shuffle = Yes; /* Flag - shuffle the input vectors*/ int Scaling = Yes; /* Scaling input vector */ int MaxLearnCycles = 1999; /* Max number of learning iteration */

FILE *Dfp; /* Debug file pointer */
FILE *Rfp; /* Report file pointer*/

typedef struct Pattern { int ID; /* ID number this vector in all set of pattern */ float *A; /* pattern (vector) A={a[0],a[1],...,a[NDATA]} */ float Target; /* class which this vector is present*/
} PAT;

PAT *Input;
PAT *Work;
PAT *Test;

/* lines in defaults file are in the form "NAME=value" */ typedef struct Default { char *name; /* name of the default */ char *value; /* value of the default */
} DEF;

/* structure of statistics info about one test vector */ typedef struct Statistic { int ID; /* Primery number from input file */ float Target; float TotalRes; /* Total propability */ int Flag; /* Flag = 1, if vector was error and = 0 in over case */ float *result; /* Result of testing vector on current iteration */ int *TmpFlag; /* analog 'Flag' on current itteration */ int *NumIter; /* Number iteration of learning on which

Learning cycle STOPED */ int **NumLE; /* Error vectors after cycle of learning was test*/
} STAT;

/* structure of the some result of learning cycle */ typedef struct ResLearning { int NumIter; int LearnError[NMAXPAT+1]; /* A[0]-count of error,

A[1]-ID1,

A[2]-ID2,...

A[NMAXRL]-ID?.*/
} RL;

/* function prototypes */

void OnlyTestVector(void); void TestAfterLearn (void); void CheckOneVector ( void ); void CrossValidation ( void );

DEF **defbuild(char *filename);
DEF *defread(FILE *fp);
FILE *defopen (char *filename); char *defvalue(DEF **deflist, const char *name); int defclose(FILE *fp); void defdestroy(DEF **, int); void getvalues(void);

void Debug (char *fmt, ...); void Report (char *fmt, ...);

void Widrow_Init(void); int Init_W( void ); float RavnRaspr(float A, float B); float NormRaspr(float B,float A);

void ShufflePat(int *INP,int Koll_El);

float F_Act(float x); float Forward (PAT src); int LearnFunc (void); int Reset (float ResErr, int Cnt, int N_Err); void Update_Last (int n, float Total_Out); void Update_Prom1 (int n); void Prom_to_W (void); void Update_All_W (int num, float err_cur ); void Init_PromW(void); void Prom_to_OLD(void); int CheckVector(float Res, PAT src); int *TestLearn(int *src);

RL FurtherLearning(int NumIteration, float StartLearnTolerans, float EndLearnTolerans,

RL src);

STAT *definestat (PAT src);
STAT **DefineAllStat (PAT *src,int Num); void FillStatForm (STAT *st, int iteration, float res, RL lr); void FillSimpleStatForm (STAT *st, float res); void destroystat ( STAT *st, int param); void DestroyAllStat (STAT **st, int Num); void PrintStatHeader(void); void printstat(STAT *st); void PrintStatLearn(RL src); void PrintTestStat(STAT **st, int len); void PrintErrorStat (STAT **st,int Len);

int DefineNetStructure (char *ptr); void getStructure(char buf[20]);

PAT patcpy (PAT dest, PAT src);
PAT* LocPatMemory(int num); void ReadPattern (PAT *input, char *name,int Len); void FreePatMemory(PAT* src, int num); void ShowPattern (char *fname, PAT *src, int len); void ShowVector(char *fname,PAT src); float getPatTarget (float res);

PAT* DataOrder (PAT* src,int Len, int Ubit, PAT* dest, PAT* test); void FindMinMax (PAT *src,int Dimens, int Num_elem, float **Out_Array); void ConvX_AB_01(PAT src);

int *DefineCN (int len); int getPosition (int Num, int *src, int Len); void DestroyCN (int *src); void ShowCurN (int LEN);

float **LocateMemAMM(void); void FreeAMM (float **src);

void WriteHeaderNet(char *fname, float **src); void WriteNet (char *fname,int It); void ReadHeaderNet(char *fname, float **src); int ReadNet (char *fname, int It);
FILE *OpenFile(char *name); int CloseFile(FILE *fp);

/* End of common file */

6. Файл автоматической компиляции программы под Unix -“Makefile”.
CC= cc
LIBS= -lm

OBJ= nvclass.o

nvclass: $(OBJ)

$(CC) -o nvclass $(LIBS) $(OBJ)

nvclass.o: nvclass.c

7. Основной модуль - “nvclass.с”
/*
* Neuron Classificator ver 1.0
*/

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