training/commontraining.cpp

// Copyright 2008 Google Inc. All Rights Reserved.
// Author: scharron@google.com (Samuel Charron)
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
// http://www.apache.org/licenses/LICENSE-2.0
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

#include "commontraining.h"

#include "oldlist.h"
#include "globals.h"
#include "mf.h"
#include "clusttool.h"
#include "cluster.h"
#include "mergenf.h"
#include "tessopt.h"
#include "featdefs.h"
#include "efio.h"
#include "emalloc.h"
#include "tprintf.h"
#include "freelist.h"
#include "unicity_table.h"

#include <math.h>

#define round(x,frag)(floor(x/frag+.5)*frag)

// Global Variables.
char	*Directory = NULL;

const char *InputUnicharsetFile = NULL;
const char *OutputUnicharsetFile = NULL;

const char *InputFontInfoFile = NULL;

// globals used to control what information is saved in the output file
BOOL8		ShowSignificantProtos = TRUE;
BOOL8		ShowInsignificantProtos = FALSE;

FLOAT32 RoundingAccuracy = 0.0f;

char CTFontName[MAXNAMESIZE];

const char* test_ch = "";

// The unicharset used during training
UNICHARSET unicharset_training;

/*---------------------------------------------------------------------------*/
void ParseArguments(int argc, char **argv)
/*
**	Parameters:
**		argc	number of command line arguments to parse
**		argv	command line arguments
**	Globals:
**		ShowSignificantProtos	flag controlling proto display
**		ShowInsignificantProtos	flag controlling proto display
**		Config			current clustering parameters
**		tessoptarg, tessoptind		defined by tessopt sys call
**		Argc, Argv		global copies of argc and argv
**	Operation:
**		This routine parses the command line arguments that were
**		passed to the program.  The legal arguments are:
**			-d		"turn off display of samples"
**			-p		"turn off significant protos"
**			-n		"turn off insignificant proto"
**			-S [ spherical | elliptical | mixed | automatic ]
**			-M MinSamples	"min samples per prototype (%)"
**			-B MaxIllegal	"max illegal chars per cluster (%)"
**			-I Independence	"0 to 1"
**			-C Confidence	"1e-200 to 1.0"
**			-D Directory
**			-R RoundingAccuracy
**			-U InputUnicharsetFile
**			-O OutputUnicharsetFile

**	Return: none
**	Exceptions: Illegal options terminate the program.
**	History: 7/24/89, DSJ, Created.
*/

{
	int		Option;
	int		ParametersRead;
	BOOL8		Error;

        Error = FALSE;
	while (( Option = tessopt( argc, argv, "F:O:U:R:D:C:I:M:B:S:n:p" )) != EOF )
	{
		switch ( Option )
		{
		case 'n':
                        sscanf(tessoptarg,"%d", &ParametersRead);
			ShowInsignificantProtos = ParametersRead;
			break;
		case 'p':
                        sscanf(tessoptarg,"%d", &ParametersRead);
			ShowSignificantProtos = ParametersRead;
			break;
		case 'C':
			ParametersRead = sscanf( tessoptarg, "%lf", &(Config.Confidence) );
			if ( ParametersRead != 1 ) Error = TRUE;
			else if ( Config.Confidence > 1 ) Config.Confidence = 1;
			else if ( Config.Confidence < 0 ) Config.Confidence = 0;
			break;
		case 'I':
			ParametersRead = sscanf( tessoptarg, "%f", &(Config.Independence) );
			if ( ParametersRead != 1 ) Error = TRUE;
			else if ( Config.Independence > 1 ) Config.Independence = 1;
			else if ( Config.Independence < 0 ) Config.Independence = 0;
			break;
		case 'M':
			ParametersRead = sscanf( tessoptarg, "%f", &(Config.MinSamples) );
			if ( ParametersRead != 1 ) Error = TRUE;
			else if ( Config.MinSamples > 1 ) Config.MinSamples = 1;
			else if ( Config.MinSamples < 0 ) Config.MinSamples = 0;
			break;
		case 'B':
			ParametersRead = sscanf( tessoptarg, "%f", &(Config.MaxIllegal) );
			if ( ParametersRead != 1 ) Error = TRUE;
			else if ( Config.MaxIllegal > 1 ) Config.MaxIllegal = 1;
			else if ( Config.MaxIllegal < 0 ) Config.MaxIllegal = 0;
			break;
		case 'R':
			ParametersRead = sscanf( tessoptarg, "%f", &RoundingAccuracy );
			if ( ParametersRead != 1 ) Error = TRUE;
			else if ( RoundingAccuracy > 0.01f ) RoundingAccuracy = 0.01f;
			else if ( RoundingAccuracy < 0.0f ) RoundingAccuracy = 0.0f;
			break;
		case 'S':
			switch ( tessoptarg[0] )
			{
			case 's': Config.ProtoStyle = spherical; break;
			case 'e': Config.ProtoStyle = elliptical; break;
			case 'm': Config.ProtoStyle = mixed; break;
			case 'a': Config.ProtoStyle = automatic; break;
			default: Error = TRUE;
			}
			break;
		case 'D':
			Directory = tessoptarg;
			break;
                case 'U':
                        InputUnicharsetFile = tessoptarg;
                        break;
                case 'O':
                        OutputUnicharsetFile = tessoptarg;
                        break;
                case 'F':
                        InputFontInfoFile = tessoptarg;
                        break;
		case '?':
			Error = TRUE;
			break;
		}
		if ( Error )
		{
			fprintf (stderr, "usage: %s [-d] [-p] [-n]\n", argv[0] );
			fprintf (stderr, "\t[-S ProtoStyle]\n");
			fprintf (stderr, "\t[-M MinSamples] [-B MaxBad] [-I Independence]\n");
			fprintf (stderr, "\t[-C Confidence] [-D Directory]\n");
			fprintf (stderr, "\t[-U InputUnicharsetFile] [-O OutputUnicharsetFile]\n");
                        fprintf (stderr, "\t[-F FontInfoFile]\n");
                        fprintf (stderr, "\t[ TrainingPage ... ]\n");
			exit (2);
		}
	}
}	// ParseArguments

/*---------------------------------------------------------------------------*/
char *GetNextFilename (int Argc, char** Argv)
/*
**	Parameters: none
**	Globals:
**		tessoptind			defined by tessopt sys call
**	Operation:
**		This routine returns the next command line argument.  If
**		there are no remaining command line arguments, it returns
**		NULL.  This routine should only be called after all option
**		arguments have been parsed and removed with ParseArguments.
**	Return: Next command line argument or NULL.
**	Exceptions: none
**	History: Fri Aug 18 09:34:12 1989, DSJ, Created.
*/

{
	if (tessoptind < Argc)
		return (Argv [tessoptind++]);
	else
		return (NULL);

}	/* GetNextFilename */



/*---------------------------------------------------------------------------*/
LABELEDLIST FindList (
     LIST	List,
     char	*Label)

/*
**	Parameters:
**		List		list to search
**		Label		label to search for
**	Globals: none
**	Operation:
**		This routine searches thru a list of labeled lists to find
**		a list with the specified label.  If a matching labeled list
**		cannot be found, NULL is returned.
**	Return: Labeled list with the specified Label or NULL.
**	Exceptions: none
**	History: Fri Aug 18 15:57:41 1989, DSJ, Created.
*/

{
	LABELEDLIST	LabeledList;

	iterate (List)
    {
		LabeledList = (LABELEDLIST) first_node (List);
		if (strcmp (LabeledList->Label, Label) == 0)
			return (LabeledList);
    }
	return (NULL);

}	/* FindList */

/*---------------------------------------------------------------------------*/
LABELEDLIST NewLabeledList (
     const char	*Label)

/*
**	Parameters:
**		Label	label for new list
**	Globals: none
**	Operation:
**		This routine allocates a new, empty labeled list and gives
**		it the specified label.
**	Return: New, empty labeled list.
**	Exceptions: none
**	History: Fri Aug 18 16:08:46 1989, DSJ, Created.
*/

{
	LABELEDLIST	LabeledList;

	LabeledList = (LABELEDLIST) Emalloc (sizeof (LABELEDLISTNODE));
	LabeledList->Label = (char*)Emalloc (strlen (Label)+1);
	strcpy (LabeledList->Label, Label);
	LabeledList->List = NIL;
    LabeledList->SampleCount = 0;
	return (LabeledList);

}	/* NewLabeledList */

/*---------------------------------------------------------------------------*/
void WriteTrainingSamples (
     char	*Directory,
     LIST	CharList,
     const char* program_feature_type)

/*
**	Parameters:
**		Directory	directory to place sample files into
**		FontList	list of fonts used in the training samples
**	Operation:
**		This routine writes the specified samples into files which
**		are organized according to the font name and character name
**		of the samples.
**	Return: none
**	Exceptions: none
**	History: Fri Aug 18 16:17:06 1989, DSJ, Created.
*/

{
	LABELEDLIST	CharSample;
	FEATURE_SET	FeatureSet;
	LIST		FeatureList;
	FILE		*File;
	char		Filename[MAXNAMESIZE];
	int		NumSamples;

	iterate (CharList)		// iterate thru all of the fonts
	{
		CharSample = (LABELEDLIST) first_node (CharList);

		// construct the full pathname for the current samples file
		strcpy (Filename, "");
		if (Directory != NULL)
		{
			strcat (Filename, Directory);
			strcat (Filename, "/");
		}
		strcat (Filename, CTFontName);
		strcat (Filename, "/");
		strcat (Filename, CharSample->Label);
		strcat (Filename, ".");
		strcat (Filename, program_feature_type);
		printf ("\nWriting %s ...", Filename);

		/* if file does not exist, create a new one with an appropriate
		header; otherwise append samples to the existing file */
		File = fopen (Filename, "r");
		if (File == NULL)
		{
			File = Efopen (Filename, "w");
      WriteOldParamDesc(
          File,
          FeatureDefs.FeatureDesc[ShortNameToFeatureType(
              program_feature_type)]);
		}
		else
		{
			fclose (File);
			File = Efopen (Filename, "a");
		}

		// append samples onto the file
		FeatureList = CharSample->List;
		NumSamples = 0;
		iterate (FeatureList)
		{
			FeatureSet = (FEATURE_SET) first_node (FeatureList);
			WriteFeatureSet (File, FeatureSet);
			NumSamples++;
		}
		fclose (File);
	}
}	/* WriteTrainingSamples */

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

}	/* FreeTrainingSamples */

/*---------------------------------------------------------------------------*/
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,
     const char* program_feature_type)

/*
**	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 =
    FeatureDefs.FeatureDesc[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)
	{
		FeatureSet = (FEATURE_SET) first_node (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 != 0.0f)
					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 */

/*------------------------------------------------------------------------*/
void MergeInsignificantProtos(LIST ProtoList, const char* label,
                              CLUSTERER	*Clusterer, CLUSTERCONFIG *Config) {
  PROTOTYPE	*Prototype;
  bool debug = strcmp(test_ch, label) == 0;

  LIST pProtoList = ProtoList;
  iterate(pProtoList) {
    Prototype = (PROTOTYPE *) first_node (pProtoList);
    if (Prototype->Significant || Prototype->Merged)
      continue;
    FLOAT32 best_dist = 0.125;
    PROTOTYPE* best_match = NULL;
    // Find the nearest alive prototype.
    LIST list_it = ProtoList;
    iterate(list_it) {
      PROTOTYPE* test_p = (PROTOTYPE *) first_node (list_it);
      if (test_p != Prototype && !test_p->Merged) {
        FLOAT32 dist = ComputeDistance(Clusterer->SampleSize,
                                       Clusterer->ParamDesc,
                                       Prototype->Mean, test_p->Mean);
        if (dist < best_dist) {
          best_match = test_p;
          best_dist = dist;
        }
      }
    }
    if (best_match != NULL && !best_match->Significant) {
      if (debug)
         tprintf("Merging red clusters (%d+%d) at %g,%g and %g,%g\n",
                 best_match->NumSamples, Prototype->NumSamples,
                 best_match->Mean[0], best_match->Mean[1],
                 Prototype->Mean[0], Prototype->Mean[1]);
      best_match->NumSamples = MergeClusters(Clusterer->SampleSize,
                                             Clusterer->ParamDesc,
                                             best_match->NumSamples,
                                             Prototype->NumSamples,
                                             best_match->Mean,
                                             best_match->Mean, Prototype->Mean);
      Prototype->NumSamples = 0;
      Prototype->Merged = 1;
    } else if (best_match != NULL) {
      if (debug)
        tprintf("Red proto at %g,%g matched a green one at %g,%g\n",
                Prototype->Mean[0], Prototype->Mean[1],
                best_match->Mean[0], best_match->Mean[1]);
      Prototype->Merged = 1;
    }
  }
  // Mark significant those that now have enough samples.
  int min_samples = (inT32) (Config->MinSamples * Clusterer->NumChar);
  pProtoList = ProtoList;
  iterate(pProtoList) {
    Prototype = (PROTOTYPE *) first_node (pProtoList);
    // Process insignificant protos that do not match a green one
    if (!Prototype->Significant && Prototype->NumSamples >= min_samples &&
        !Prototype->Merged) {
      if (debug)
        tprintf("Red proto at %g,%g becoming green\n",
                Prototype->Mean[0], Prototype->Mean[1]);
      Prototype->Significant = true;
    }
  }
}	/* MergeInsignificantProtos */

/*-----------------------------------------------------------------------------*/
void CleanUpUnusedData(
	LIST ProtoList)
{
	PROTOTYPE* Prototype;

	iterate(ProtoList)
	{
		Prototype = (PROTOTYPE *) first_node (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;
		}
	}
}

/*------------------------------------------------------------------------*/
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_node (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 */

/*----------------------------------------------------------------------------*/
MERGE_CLASS FindClass (
     LIST	List,
     char	*Label)
{
	MERGE_CLASS	MergeClass;

	iterate (List)
    {
		MergeClass = (MERGE_CLASS) first_node (List);
		if (strcmp (MergeClass->Label, Label) == 0)
			return (MergeClass);
    }
	return (NULL);

}	/* FindClass */

/*---------------------------------------------------------------------------*/
MERGE_CLASS NewLabeledClass (
     char	*Label)
{
	MERGE_CLASS	MergeClass;

	MergeClass = new MERGE_CLASS_NODE;
	MergeClass->Label = (char*)Emalloc (strlen (Label)+1);
	strcpy (MergeClass->Label, Label);
	MergeClass->Class = NewClass (MAX_NUM_PROTOS, MAX_NUM_CONFIGS);
	return (MergeClass);

}	/* NewLabeledClass */

/*-----------------------------------------------------------------------------*/
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_node (ClassList);
		free (MergeClass->Label);
		FreeClass(MergeClass->Class);
		delete MergeClass;
	}
	destroy (ClassList);

}	/* FreeLabeledClassList */

/** 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 ...\n");

	iterate(LabeledClassList)
	{
                UnicityTableEqEq<int>   font_set;
		MergeClass = (MERGE_CLASS) first_node (LabeledClassList);
		Class = &TrainingData[unicharset_training.unichar_to_id(
                                          MergeClass->Label)];
    NumProtos = MergeClass->Class->NumProtos;
    NumConfigs = MergeClass->Class->NumConfigs;
                font_set.move(&MergeClass->Class->font_set);
    Class->NumProtos = 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] = OldProto->X;
      Values[1] = OldProto->Y;
      Values[2] = OldProto->Angle;
			Normalize(Values);
      NewProto->X = OldProto->X;
      NewProto->Y = OldProto->Y;
      NewProto->Length = OldProto->Length;
      NewProto->Angle = OldProto->Angle;
      NewProto->A = Values[0];
      NewProto->B = Values[1];
      NewProto->C = Values[2];
		}

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

/*--------------------------------------------------------------------------*/
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

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

{
	LABELEDLIST	CharSample;

	iterate (CharList) 		/* iterate thru all of the fonts */
	{
		CharSample = (LABELEDLIST) first_node (CharList);
		FreeLabeledList (CharSample);
	}
	destroy (CharList);

}	// FreeNormProtoList

/*---------------------------------------------------------------------------*/
void AddToNormProtosList(
	LIST* NormProtoList,
	LIST ProtoList,
	char* CharName)
{
	PROTOTYPE* Proto;
	LABELEDLIST LabeledProtoList;

	LabeledProtoList = NewLabeledList(CharName);
	iterate(ProtoList)
	{
		Proto = (PROTOTYPE *) first_node (ProtoList);
		LabeledProtoList->List = push(LabeledProtoList->List, Proto);
	}
	*NormProtoList = push(*NormProtoList, LabeledProtoList);
}

/*---------------------------------------------------------------------------*/
int NumberOfProtos(
	LIST ProtoList,
    BOOL8	CountSigProtos,
    BOOL8	CountInsigProtos)
{
	int N = 0;
	PROTOTYPE	*Proto;

	iterate(ProtoList)
	{
		Proto = (PROTOTYPE *) first_node ( ProtoList );
		if (( Proto->Significant && CountSigProtos )	||
			( ! Proto->Significant && CountInsigProtos ) )
			N++;
	}
	return(N);
}