set_thresh.cpp

实现决策树分类训练试验。 源自c4.5

/*************************************************************************/
/*									 */
/*	Soften thresholds for continuous attributes			 */
/*	-------------------------------------------			 */
/*									 */
/*************************************************************************/
#include "stdafx.h"
#include "MyBase.h"

#ifdef _DEBUG
#define new DEBUG_NEW
#undef THIS_FILE
static char THIS_FILE[] = __FILE__;
#endif


extern FILE *fLog;
Boolean *LHSErr,	/*  Does a misclassification occur with this value of an att  */
	*RHSErr;	/*  if the below or above threshold branches are taken  */

ItemNo	*ThreshErrs;	/*  ThreshErrs[i] is the no. of misclassifications if thresh is i  */

float	*CVals;		/*  All values of a continuous attribute  */


/*************************************************************************/
/*									 */
/*  Soften all thresholds for continuous attributes in tree T		 */
/*									 */
/*************************************************************************/
void SoftenThresh(Tree T)
{
    CVals = (float *) calloc(MaxItem+1, sizeof(float));
    LHSErr = (Boolean *) calloc(MaxItem+1, sizeof(Boolean));
    RHSErr = (Boolean *) calloc(MaxItem+1, sizeof(Boolean));
    ThreshErrs = (ItemNo *) calloc(MaxItem+1, sizeof(ItemNo));

    InitialiseWeights();

    ScanTree(T, 0, MaxItem);

    delete ThreshErrs;
    delete RHSErr;
    delete LHSErr;
    delete CVals;
}



/*************************************************************************/
/*								  	 */
/*  Calculate upper and lower bounds for each test on a continuous	 */
/*  attribute in tree T, using data items from Fp to Lp			 */
/*								  	 */
/*************************************************************************/
void ScanTree(Tree T, ItemNo Fp, ItemNo Lp)
{
    short v;
    float Val, Se, Limit, Lower, Upper;
    ItemNo i, Kp, Ep, LastI, Errors, BaseErrors;
    ClassNo CaseClass, Class1, Class2;
    Boolean LeftThresh=false;
    Description CaseDesc;
    Attribute Att;

    /*  Stop when get to a leaf  */

    if ( ! T->NodeType ) return;

    /*  Group the unknowns together  */

    Kp = Group(0, Fp, Lp, T);

    /*  Soften a threshold for a continuous attribute  */

    Att = T->Tested;

    if ( T->NodeType == ThreshContin )
    {
		fprintf(fLog,"\nTest %s <> %g\n", AttName[Att], T->Cut);

		Quicksort(Kp+1, Lp, Att, 0);

		ForEach(i, Kp+1, Lp)
		{
			/*  See how this item would be classified if its
			value were on each side of the threshold  */

			CaseDesc = Item[i];
			CaseClass = Class(CaseDesc);
			Val = CVal(CaseDesc, Att);
			
			Class1 = Category(CaseDesc, T->Branch[1]);
			Class2 = Category(CaseDesc, T->Branch[2]);

			CVals[i] = Val;
			LHSErr[i] = (Class1 != CaseClass ? 1 : 0);
			RHSErr[i] = (Class2 != CaseClass ? 1 : 0);
		}

		/*  Set Errors to total errors if take above thresh branch,
			and BaseErrors to errors if threshold has original value  */

		Errors = BaseErrors = 0;
		ForEach(i, Kp+1, Lp)
		{
			Errors += RHSErr[i];

			if ( Below(CVals[i], T->Cut) )
			{
				BaseErrors += LHSErr[i];
			}
			else
			{
				BaseErrors += RHSErr[i];
			}
		}

		/*  Calculate standard deviation of the number of errors  */

		Se = sqrt( (BaseErrors+0.5) * (Lp-Kp-BaseErrors+0.5) / (Lp-Kp+1) );
		Limit = BaseErrors + Se;

		Verbosity(1)
		{
			fprintf(fLog,"\t\t\tBase errors %d, items %d, se=%.1f\n",
			   BaseErrors, Lp-Kp, Se);
			fprintf(fLog,"\n\tVal <=   Errors\t\t+Errors\n");
			fprintf(fLog,"\t         %6d\n", Errors);
		}

		/*  Set ThreshErrs[i] to the no. of errors if the threshold were i  */

		ForEach(i, Kp+1, Lp)
		{
			ThreshErrs[i] = Errors = Errors + LHSErr[i] - RHSErr[i];

			if ( i == Lp || CVals[i] != CVals[i+1] )
			{
				Verbosity(1)
					fprintf(fLog,"\t%6g   %6d\t\t%7d\n",
					CVals[i], Errors, Errors - BaseErrors);
			}
		}

		/*  Choose Lower and Upper so that if threshold were set to
			either, the number of items misclassified would be one
			standard deviation above BaseErrors  */

		LastI = Kp+1;
		Lower = Min(T->Cut, CVals[LastI]);
		Upper = Max(T->Cut, CVals[Lp]);
		while ( CVals[LastI+1] == CVals[LastI] ) LastI++;

		while ( LastI < Lp )
		{
			i = LastI + 1;
			while ( i < Lp && CVals[i+1] == CVals[i] ) i++;

			if ( ! LeftThresh && ThreshErrs[LastI] > Limit &&
			 ThreshErrs[i] <= Limit && Below(CVals[i], T->Cut) )
			{
				Lower = CVals[i] -
					(CVals[i] - CVals[LastI]) * (Limit - ThreshErrs[i]) /
					(ThreshErrs[LastI] - ThreshErrs[i]);
				LeftThresh = true;
			}
			else if ( ThreshErrs[LastI] <= Limit && ThreshErrs[i] > Limit &&
				! Below(CVals[i], T->Cut) )
			{
				Upper = CVals[LastI] + 
					(CVals[i] - CVals[LastI]) * (Limit - ThreshErrs[LastI]) /
					(ThreshErrs[i] - ThreshErrs[LastI]);
				if ( Upper < T->Cut ) Upper = T->Cut;
			}

			LastI = i;
		}

		T->Lower = Lower;
		T->Upper = Upper;

		Verbosity(1) fprintf(fLog,"\n");

		fprintf(fLog,"\tLower = %g, Upper = %g\n", T->Lower, T->Upper);
    }
	
	/*  Recursively scan each branch  */
	
	ForEach(v, 1, T->Forks)
	{
		Ep = Group(v, Kp+1, Lp, T);

		if ( Kp < Ep )
		{
			ScanTree(T->Branch[v], Kp+1, Ep);
			Kp = Ep;
		}
    }
}