contin.cpp

计算机人工智能方面的决策树方法 c4.5

/*************************************************************************/
/*                                                                       */
/*      Evaluation of a test on a continuous valued attribute            */
/*      -----------------------------------------------------            */
/*                                                                       */
/*************************************************************************/


#include "buildex.h"
#include "extern.h"


double
*SplitGain,     /* SplitGain[i] = gain with att value of item i as threshold */
*SplitInfo;     /* SplitInfo[i] = potential info ditto */

extern FILE *fpScreen;

/*************************************************************************/
/*                                                                       */
/*  Continuous attributes are treated as if they have possible values    */
/*      0 (unknown), 1 (less than cut), 2(greater than cut)              */
/*  This routine finds the best cut for items Fp through Lp and sets     */
/*  Info[], Gain[] and Bar[]                                             */
/*                                                                       */
/*************************************************************************/


void    EvalContinuousAtt(Attribute Att, ItemNo Fp, ItemNo Lp)
/*  -----------------  */ 
{ 
    ItemNo i, BestI, Xp, Tries=0;
    ItemCount Items, KnownItems, LowItems, MinSplit;
    ClassNo c;
    double AvGain=0, Val, BestVal, BaseInfo;
	
	
    Verbosity(2) 
	{
		fprintf(fpScreen,"\tAtt %s", AttName[Att]);
		printf("\tAtt %s", AttName[Att]);
	}
    Verbosity(3) 
	{
		fprintf(fpScreen,"\n");
		printf("\n");
	}
	
    ResetFreq(2);
	
    /*  Omit and count unknown values */
	
    Items = CountItems(Fp, Lp);
    Xp = Fp;
    ForEach(i, Fp, Lp)
    {
		if ( CVal(Item[i],Att) == Unknown )
		{
			Freq[ 0 ][ Class(Item[i]) ] += Weight[i];
			Swap(Xp, i);
			Xp++;
		}
    }
	
    ValFreq[0] = 0;
    ForEach(c, 0, MaxClass)
    {
		ValFreq[0] += Freq[0][c];
    }
	
    KnownItems = Items - ValFreq[0];
    UnknownRate[Att] = 1.0 - KnownItems / Items;
	
    /*  Special case when very few known values  */
	
    if ( KnownItems < 2 * MINOBJS )
    {
		Verbosity(2)
		{
			fprintf(fpScreen,"\tinsufficient cases with known values\n");
			printf("\tinsufficient cases with known values\n");
		}
		
		Gain[Att] = -Epsilon;
		Info[Att] = 0.0;
		return;
    }
	
    Quicksort(Xp, Lp, Att, Swap);
	
    /*  Count base values and determine base information  */
	
    ForEach(i, Xp, Lp)
    {
		Freq[ 2 ][ Class(Item[i]) ] += Weight[i];
		SplitGain[i] = -Epsilon;
		SplitInfo[i] = 0;
    }
	
    BaseInfo = TotalInfo(Freq[2], 0, MaxClass) / KnownItems;
	
    /*  Try possible cuts between items i and i+1, and determine the
	information and gain of the split in each case.  We have to be wary
	of splitting a small number of items off one end, as we can always
	split off a single item, but this has little predictive power.  */
	
    MinSplit = 0.10 * KnownItems / (MaxClass + 1);
    if ( MinSplit <= MINOBJS ) MinSplit = MINOBJS;
    else
		if ( MinSplit > 25 ) MinSplit = 25;
		
		LowItems = 0;
		ForEach(i, Xp, Lp - 1)
		{
			c = Class(Item[i]);
			LowItems   += Weight[i];
			Freq[1][c] += Weight[i];
			Freq[2][c] -= Weight[i];
			
			if ( LowItems < MinSplit ) continue;
			else
				if ( LowItems > KnownItems - MinSplit ) break;
				
				if ( CVal(Item[i],Att) < CVal(Item[i+1],Att) - 1E-5 )
				{
					ValFreq[1] = LowItems;
					ValFreq[2] = KnownItems - LowItems;
					SplitGain[i] = ComputeGain(BaseInfo, UnknownRate[Att], 2, KnownItems);
					SplitInfo[i] = TotalInfo(ValFreq, 0, 2) / Items;
					AvGain += SplitGain[i];
					Tries++;
					
					Verbosity(3)
					{   fprintf(fpScreen,"\t\tCut at %.3f  (gain %.3f, val %.3f):",
					( CVal(Item[i],Att) + CVal(Item[i+1],Att) ) / 2,
					SplitGain[i],
					Worth(SplitInfo[i], SplitGain[i], Epsilon));
					PrintDistribution(Att, 2, true);
					printf("\t\tCut at %.3f  (gain %.3f, val %.3f):",
						( CVal(Item[i],Att) + CVal(Item[i+1],Att) ) / 2,
						SplitGain[i],
						Worth(SplitInfo[i], SplitGain[i], Epsilon));
					PrintDistribution(Att, 2, true);
					}
				}
		}
		
		/*  Find the best attribute according to the given criterion  */
		
		BestVal = -Epsilon;
		BestI   = None;
		AvGain  = ( Tries ? AvGain / Tries : 1E6 );
		ForEach(i, Xp, Lp - 1)
		{
			Val = Worth(SplitInfo[i], SplitGain[i], AvGain);
			if ( SplitGain[i] >= 0 && Val >= BestVal )
			{
				BestI   = i;
				BestVal = Val;
			}
		}
		
		/*  If a test on the attribute is able to make a gain,
		set the best break point, gain and information  */ 
		
		if ( BestI == None )
		{
			Gain[Att] = -Epsilon;
			Info[Att] = 0.0;
			
			Verbosity(2) 
			{
				fprintf(fpScreen,"\tno gain\n");
				printf("\tno gain\n");
			}	
		}
		else
		{
			Bar[Att]  = (CVal(Item[BestI],Att) + CVal(Item[BestI+1],Att)) / 2;
			Gain[Att] = SplitGain[BestI];
			Info[Att] = SplitInfo[BestI];
			
			Verbosity(2)
			{
				fprintf(fpScreen,"\tcut=%.3f, inf %.3f, gain %.3f\n",
				Bar[Att], Info[Att], Gain[Att]);
				printf("\tcut=%.3f, inf %.3f, gain %.3f\n",
				Bar[Att], Info[Att], Gain[Att]);
			}
		}
} 



/*************************************************************************/
/*                                                                       */
/*  Change a leaf into a test on a continuous attribute                  */
/*                                                                       */
/*************************************************************************/


void ContinTest(Tree Node, Attribute Att)
/*  ----------  */
{
    double Thresh;
    
	
    Sprout(Node, 2);
	
    Thresh = GreatestValueBelow(Att, Bar[Att]);
	
    Node->NodeType      = ThreshContin;
    Node->Tested        = Att;
    Node->Cut           =
		Node->Lower         =
		Node->Upper         = Thresh;
    Node->Errors        = 0;
}



/*************************************************************************/
/*                                                                       */
/*  Return the greatest value of attribute Att below threshold t         */
/*                                                                       */
/*************************************************************************/


double GreatestValueBelow(Attribute Att, double t)
/*    ------------------  */
{
    ItemNo i;
    double v, Best;
	
    Best = -1E20;
	
    ForEach(i, 0, MaxItem)
    {
		v = CVal(Item[i], Att);
		if ( v != Unknown && v <= t && v > Best ) Best = v;
    }

    return Best;
}