st-thresh.c

用c++实现的决策树算法

/*************************************************************************/
/*									 */
/*	Soften thresholds for continuous attributes			 */
/*	-------------------------------------------			 */
/*									 */
/*************************************************************************/


#include "defns.i"
#include "types.i"
#include "extern.i"

#include <malloc.h>

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  */


#define	Below(v,t)	(v <= t + 1E-6)

void    InitialiseWeights();
void     ScanTree(Tree T,ItemNo Fp,ItemNo Lp);
ItemNo Group(DiscrValue V,ItemNo Fp,ItemNo Lp,Tree TestNode);
void    Quicksort(ItemNo Fp,ItemNo Lp,Attribute Att, void (*Exchange)());
void Swap(ItemNo a,ItemNo b);
ClassNo Category(Description CaseDesc,Tree DecisionTree) ;

/*************************************************************************/
/*									 */
/*  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);

    cfree(ThreshErrs);
    cfree(RHSErr);
    cfree(LHSErr);
    cfree(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 )
    {
		printf("\nTest %s <> %g\n", AttName[Att], T->Cut);
		
		Quicksort(Kp+1, Lp, Att, Swap);
		
		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 = (float) sqrt( (BaseErrors+0.5) * (Lp-Kp-BaseErrors+0.5) / (Lp-Kp+1) );
		Limit = BaseErrors + Se;
		
		Verbosity(1)
		{
			printf("\t\t\tBase errors %d, items %d, se=%.1f\n",
				BaseErrors, Lp-Kp, Se);
			printf("\n\tVal <=   Errors\t\t+Errors\n");
			printf("\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)
					printf("\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) printf("\n");
		
		printf("\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;
		}
    }
}