mftraining.cpp

一ＯＣＲ的相关资料。．希望对研究ＯＣＲ的朋友有所帮助．

}	// WriteMergedTrainingSamples

/*--------------------------------------------------------------------------*/
void WriteMicrofeat(
    char	*Directory,
	LIST	ClassList)

{
	FILE		*File;
	char		Filename[MAXNAMESIZE];
	MERGE_CLASS MergeClass;

	strcpy (Filename, "");
	if (Directory != NULL)
	{
		strcat (Filename, Directory);
		strcat (Filename, "/");
	}
	strcat (Filename, "Microfeat");
	File = Efopen (Filename, "w");
	printf ("\nWriting Merged %s ...", Filename);
	iterate(ClassList)
	{
		MergeClass = (MERGE_CLASS) first (ClassList);
		WriteProtos(File, MergeClass);
		WriteConfigs(File, MergeClass->Class);
	}
	fclose (File);
} // WriteMicrofeat

/*---------------------------------------------------------------------------*/
void WriteProtos(
	FILE* File,
	MERGE_CLASS MergeClass)
{
	float Values[3];
	int i;
	PROTO Proto;

	fprintf(File, "%c\n", NameToChar(MergeClass->Label));
	fprintf(File, "%d\n", NumProtosIn(MergeClass->Class));
	for(i=0; i < NumProtosIn(MergeClass->Class); i++)
	{
		Proto = ProtoIn(MergeClass->Class,i);
		fprintf(File, "\t%8.4f %8.4f %8.4f %8.4f ", ProtoX(Proto), ProtoY(Proto),
			ProtoLength(Proto), ProtoAngle(Proto));
		Values[0] = ProtoX(Proto);
		Values[1] = ProtoY(Proto);
		Values[2] = ProtoAngle(Proto);
		Normalize(Values);
		fprintf(File, "%8.4f %8.4f %8.4f\n", Values[0], Values[1], Values[2]);
	}
} // WriteProtos

/*----------------------------------------------------------------------------*/
void WriteConfigs(
	FILE* File,
	CLASS_TYPE Class)
{
	BIT_VECTOR Config;
	int i, j, WordsPerConfig;

	WordsPerConfig = WordsInVectorOfSize(NumProtosIn(Class));
	fprintf(File, "%d %d\n", NumConfigsIn(Class),WordsPerConfig);
	for(i=0; i < NumConfigsIn(Class); i++)
	{
		Config = ConfigIn(Class,i);
		for(j=0; j < WordsPerConfig; j++)
			fprintf(File, "%08x ", Config[j]);
		fprintf(File, "\n");
	}
	fprintf(File, "\n");
} // WriteConfigs

/*---------------------------------------------------------------------------*/
void FreeTrainingSamples (
     LIST	CharList)

/*
**	Parameters:
**		FontList	list of all fonts in document
**	Globals: none
**	Operation:
**		This routine deallocates all of the space allocated to
**		the specified list of training samples.
**	Return: none
**	Exceptions: none
**	History: Fri Aug 18 17:44:27 1989, DSJ, Created.
*/

{
	LABELEDLIST	CharSample;
	FEATURE_SET	FeatureSet;
	LIST		FeatureList;


	printf ("\nFreeTrainingSamples...");
	iterate (CharList) 		/* iterate thru all of the fonts */
	{
		CharSample = (LABELEDLIST) first (CharList);
		FeatureList = CharSample->List;
		iterate (FeatureList)	/* iterate thru all of the classes */
		{
			FeatureSet = (FEATURE_SET) first (FeatureList);
			FreeFeatureSet (FeatureSet);
		}
		FreeLabeledList (CharSample);
	}
	destroy (CharList);

}	/* FreeTrainingSamples */

/*-----------------------------------------------------------------------------*/
void FreeLabeledClassList (
     LIST	ClassList)

/*
**	Parameters:
**		FontList	list of all fonts in document
**	Globals: none
**	Operation:
**		This routine deallocates all of the space allocated to
**		the specified list of training samples.
**	Return: none
**	Exceptions: none
**	History: Fri Aug 18 17:44:27 1989, DSJ, Created.
*/

{
	MERGE_CLASS	MergeClass;

	iterate (ClassList) 		/* iterate thru all of the fonts */
	{
		MergeClass = (MERGE_CLASS) first (ClassList);
		free (MergeClass->Label);
		FreeClass(MergeClass->Class);
		free (MergeClass);
	}
	destroy (ClassList);

}	/* FreeLabeledClassList */

/*---------------------------------------------------------------------------*/
void FreeLabeledList (
     LABELEDLIST	LabeledList)

/*
**	Parameters:
**		LabeledList	labeled list to be freed
**	Globals: none
**	Operation:
**		This routine deallocates all of the memory consumed by
**		a labeled list.  It does not free any memory which may be
**		consumed by the items in the list.
**	Return: none
**	Exceptions: none
**	History: Fri Aug 18 17:52:45 1989, DSJ, Created.
*/

{
	destroy (LabeledList->List);
	free (LabeledList->Label);
	free (LabeledList);

}	/* FreeLabeledList */

/*---------------------------------------------------------------------------*/
CLUSTERER *SetUpForClustering(
     LABELEDLIST	CharSample)

/*
**	Parameters:
**		CharSample: LABELEDLIST that holds all the feature information for a
**		given character.
**	Globals:
**		None
**	Operation:
**		This routine reads samples from a LABELEDLIST and enters
**		those samples into a clusterer data structure.  This
**		data structure is then returned to the caller.
**	Return:
**		Pointer to new clusterer data structure.
**	Exceptions:
**		None
**	History:
**		8/16/89, DSJ, Created.
*/

{
	UINT16	N;
	int		i, j;
	FLOAT32	*Sample = NULL;
	CLUSTERER	*Clusterer;
	INT32		CharID;
	LIST FeatureList = NULL;
	FEATURE_SET FeatureSet = NULL;
	FEATURE_DESC FeatureDesc = NULL;
//	PARAM_DESC* ParamDesc;

	FeatureDesc = DefinitionOf(ShortNameToFeatureType(PROGRAM_FEATURE_TYPE));
	N = FeatureDesc->NumParams;
//	ParamDesc = ConvertToPARAMDESC(FeatureDesc->ParamDesc, N);
	Clusterer = MakeClusterer(N,FeatureDesc->ParamDesc);
//	free(ParamDesc);

	FeatureList = CharSample->List;
	CharID = 0;
	iterate(FeatureList)
	{
		if (CharID >= MaxNumSamples) break;

		FeatureSet = (FEATURE_SET) first (FeatureList);
		for (i=0; i < FeatureSet->MaxNumFeatures; i++)
		{
			if (Sample == NULL)
				Sample = (FLOAT32 *)Emalloc(N * sizeof(FLOAT32));
			for (j=0; j < N; j++)
				if (RoundingAccuracy != 0.0)
					Sample[j] = round(FeatureSet->Features[i]->Params[j], RoundingAccuracy);
				else
					Sample[j] = FeatureSet->Features[i]->Params[j];
				MakeSample (Clusterer, Sample, CharID);
		}
		CharID++;
	}
	if ( Sample != NULL ) free( Sample );
	return( Clusterer );

}	/* SetUpForClustering */

/*------------------------------------------------------------------------*/
LIST RemoveInsignificantProtos(
	LIST ProtoList,
	BOOL8 KeepSigProtos,
	BOOL8 KeepInsigProtos,
	int N)

{
	LIST NewProtoList = NIL;
	LIST pProtoList;
	PROTOTYPE* Proto;
	PROTOTYPE* NewProto;
	int i;

	pProtoList = ProtoList;
	iterate(pProtoList)
	{
		Proto = (PROTOTYPE *) first (pProtoList);
		if ((Proto->Significant && KeepSigProtos) ||
			(!Proto->Significant && KeepInsigProtos))
		{
			NewProto = (PROTOTYPE *)Emalloc(sizeof(PROTOTYPE));

			NewProto->Mean = (FLOAT32 *)Emalloc(N * sizeof(FLOAT32));
			NewProto->Significant = Proto->Significant;
			NewProto->Style = Proto->Style;
			NewProto->NumSamples = Proto->NumSamples;
			NewProto->Cluster = NULL;
			NewProto->Distrib = NULL;

			for (i=0; i < N; i++)
				NewProto->Mean[i] = Proto->Mean[i];
			if (Proto->Variance.Elliptical != NULL)
			{
				NewProto->Variance.Elliptical = (FLOAT32 *)Emalloc(N * sizeof(FLOAT32));
				for (i=0; i < N; i++)
					NewProto->Variance.Elliptical[i] = Proto->Variance.Elliptical[i];
			}
			else
				NewProto->Variance.Elliptical = NULL;
			//---------------------------------------------
			if (Proto->Magnitude.Elliptical != NULL)
			{
				NewProto->Magnitude.Elliptical = (FLOAT32 *)Emalloc(N * sizeof(FLOAT32));
				for (i=0; i < N; i++)
					NewProto->Magnitude.Elliptical[i] = Proto->Magnitude.Elliptical[i];
			}
			else
				NewProto->Magnitude.Elliptical = NULL;
			//------------------------------------------------
			if (Proto->Weight.Elliptical != NULL)
			{
				NewProto->Weight.Elliptical = (FLOAT32 *)Emalloc(N * sizeof(FLOAT32));
				for (i=0; i < N; i++)
					NewProto->Weight.Elliptical[i] = Proto->Weight.Elliptical[i];
			}
			else
				NewProto->Weight.Elliptical = NULL;

			NewProto->TotalMagnitude = Proto->TotalMagnitude;
			NewProto->LogMagnitude = Proto->LogMagnitude;
			NewProtoList = push_last(NewProtoList, NewProto);
		}
	}
	//FreeProtoList (ProtoList);
	return (NewProtoList);
}	/* RemoveInsignificantProtos */
/*-----------------------------------------------------------------------------*/
void CleanUpUnusedData(
	LIST ProtoList)
{
	PROTOTYPE* Prototype;

	iterate(ProtoList)
	{
		Prototype = (PROTOTYPE *) first (ProtoList);
		if(Prototype->Variance.Elliptical != NULL)
		{
			memfree(Prototype->Variance.Elliptical);
			Prototype->Variance.Elliptical = NULL;
		}
		if(Prototype->Magnitude.Elliptical != NULL)
		{
			memfree(Prototype->Magnitude.Elliptical);
			Prototype->Magnitude.Elliptical = NULL;
		}
		if(Prototype->Weight.Elliptical != NULL)
		{
			memfree(Prototype->Weight.Elliptical);
			Prototype->Weight.Elliptical = NULL;
		}
	}
}

/*--------------------------------------------------------------------------*/
void Normalize (
   float  *Values)
{
	register float Slope;
	register float Intercept;
	register float Normalizer;

	Slope      = tan (Values [2] * 2 * PI);
	Intercept  = Values [1] - Slope * Values [0];
	Normalizer = 1 / sqrt (Slope * Slope + 1.0);

	Values [0] = Slope * Normalizer;
	Values [1] = - Normalizer;
	Values [2] = Intercept * Normalizer;
} // Normalize

/** SetUpForFloat2Int **************************************************/
void SetUpForFloat2Int(
	LIST LabeledClassList)
{
	MERGE_CLASS	MergeClass;
	CLASS_TYPE		Class;
	int				NumProtos;
	int				NumConfigs;
	int				NumWords;
	int				i, j;
	float			Values[3];
	PROTO			NewProto;
	PROTO			OldProto;
	BIT_VECTOR		NewConfig;
	BIT_VECTOR		OldConfig;

	printf("Float2Int ...");

	iterate(LabeledClassList)
	{
		MergeClass = (MERGE_CLASS) first (LabeledClassList);
		Class = &TrainingData[NameToChar(MergeClass->Label)];
		NumProtos = NumProtosIn(MergeClass->Class);
		NumConfigs = NumConfigsIn(MergeClass->Class);

		NumProtosIn(Class) = NumProtos;
		Class->MaxNumProtos = NumProtos;
		Class->Prototypes = (PROTO) Emalloc (sizeof(PROTO_STRUCT) * NumProtos);
		for(i=0; i < NumProtos; i++)
		{
			NewProto = ProtoIn(Class, i);
			OldProto = ProtoIn(MergeClass->Class, i);
			Values[0] = ProtoX(OldProto);
			Values[1] = ProtoY(OldProto);
			Values[2] = ProtoAngle(OldProto);
			Normalize(Values);
			ProtoX(NewProto) = ProtoX(OldProto);
			ProtoY(NewProto) = ProtoY(OldProto);
			ProtoLength(NewProto) = ProtoLength(OldProto);
			ProtoAngle(NewProto) = ProtoAngle(OldProto);
			CoefficientA(NewProto) = Values[0];
			CoefficientB(NewProto) = Values[1];
			CoefficientC(NewProto) = Values[2];
		}

		NumConfigsIn(Class) = NumConfigs;
		Class->MaxNumConfigs = NumConfigs;
		Class->Configurations = (BIT_VECTOR*) Emalloc (sizeof(BIT_VECTOR) * NumConfigs);
		NumWords = WordsInVectorOfSize(NumProtos);
		for(i=0; i < NumConfigs; i++)
		{
			NewConfig = NewBitVector(NumProtos);
			OldConfig = ConfigIn(MergeClass->Class, i);
			for(j=0; j < NumWords; j++)
				NewConfig[j] = OldConfig[j];
			ConfigIn(Class, i) = NewConfig;
		}
	}
} // SetUpForFloat2Int