siftrules.c

这是一个决策树实现的算法

/*************************************************************************/
/*									 */
/*	Process sets of rules						 */
/*	---------------------					         */
/*								         */
/*************************************************************************/


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


ItemNo	*ClassFreq,	/* ClassFreq[c]	= no. items of class c  */
	*Covered,	/* Covered[i]	= no. included rules that cover item i */
	*FalsePos,	/* FalsePos[c]	= no. false positives from rules
					  selected for class c */
	*NoRule,	/* NoRule[c]	= no. items covered by no selected rule */

	*Right,		/* Right[r]	= no. correct rule firings */
	*Wrong;		/* Wrong[r]	= no. incorrect rule firings */

float	*Value,		/* Value[r]	= advantage attributable to rule r or
					  realisable if rule r included */
	SubsetValue,	/* value of best class subset so far */
	CodeWeight;	/* multiplying factor for rule encodings */

Boolean	*RuleIn,	/* RuleIn[r]	= true if rule r included */
	*Subset,	/* best class subset so far */
	**Match;	/* Match[r][i]	= true if rule r fires on item i */

RuleNo	*ClassRules;	/* list of all rules for current target class */

ClassNo	FocusClass;



/*************************************************************************/
/*									 */
/*  Construct an ordered subset (indexed by RuleIndex) of the current	 */
/*  set of rules							 */
/*									 */
/*************************************************************************/


    ConstructRuleset()
/*  ----------------  */
{
    RuleNo r, OldNRules = NRules;

    /*  Allocate tables  */

    Right = (ItemNo *) calloc(NRules+1, sizeof(ItemNo));
    Wrong = (ItemNo *) calloc(NRules+1, sizeof(ItemNo));

    Value = (float *) calloc(NRules+1, sizeof(float));

    RuleIn = (Boolean *) calloc(NRules+1, sizeof(Boolean));
    Subset = (Boolean *) malloc((NRules+1) * sizeof(Boolean));

    ClassRules = (RuleNo *) malloc((NRules+1) * sizeof(RuleNo));

    ClassFreq = (ItemNo *) calloc(MaxClass+1, sizeof(ItemNo));

    Covered = (ItemNo *) calloc(MaxItem+1, sizeof(ItemNo));

    Match = (Boolean **) calloc(NRules+1, sizeof(Boolean *));

    FalsePos = (ItemNo *) calloc(MaxClass+1, sizeof(ItemNo));

    NoRule = (ItemNo *) calloc(MaxClass+1, sizeof(ItemNo));

    ForEach(r, 1, NRules)
    {
	Match[r] = (Boolean *) calloc(MaxItem+1, sizeof(Boolean));
    }

    /*  Cover each class, then order the classes to give an index of rules  */

    InitialiseTables();

    FindRuleCodes();
    CodeWeight = 0.5;

    ForEach(FocusClass, 0, MaxClass)
    {
	CoverClass();
    }

    MakeIndex();
    FindDefault();

    /*  Clear  */

    free(Value);
    free(RuleIn);
    free(ClassRules);
    free(Subset);
    free(Covered);
    free(FalsePos);
    free(NoRule);
    ForEach(r, 1, OldNRules)
    {
	free(Match[r]);
    }
    free(Match);
}



/*************************************************************************/
/*									 */
/*		Initialise all tables used in sifting			 */
/*									 */
/*************************************************************************/


    InitialiseTables()
/*  ----------------  */
{
    ItemNo i;
    RuleNo r;
    ClassNo c;
    float Strength();

    ForEach(r, 1, NRules)
    {
	RuleIn[r] = false;
	Rule[r].Used = Rule[r].Incorrect = 0;
    }

    ForEach(c, 0, MaxClass)
    {
	ClassFreq[c] = 0;
    }

    ForEach(i, 0, MaxItem)
    {
	ClassFreq[Class(Item[i])]++;

	ForEach(r, 1, NRules)
	{
	    Match[r][i] = Strength(Rule[r], Item[i]) > 0.1;

	    if ( Match[r][i] )
	    {
		Rule[r].Used++;
		if ( Class(Item[i]) != Rule[r].Rhs ) Rule[r].Incorrect++;
	    }
	}
    }
}



/*************************************************************************/
/*								         */
/*	Select a subset of the rules for class FocusClass	         */
/*								         */
/*************************************************************************/


    CoverClass()
/*  ----------  */
{
    RuleNo r, RuleCount=0;
    ItemNo i;

    Verbosity(1)
	printf("\nClass %s\n-----\nAction  Change  Value",
		ClassName[FocusClass]);

    ForEach(i, 0, MaxItem)
    {
	Covered[i] = 0;
    }

    ForEach(r, 1, NRules)
    {
	if ( Rule[r].Rhs == FocusClass )
	{
	    RuleCount++;
	    ClassRules[RuleCount] = r;
	}
    }

    if ( ! RuleCount )
    {
	return;
    }

    SubsetValue = 1E10;

    if ( RuleCount <= 10 )
    {
	AllCombinations(RuleCount);
    }
    else
    if ( SIMANNEAL )
    {
	SimAnneal(RuleCount);
    }
    else
    {
	SpotSearch(RuleCount);
    }

    memcpy(RuleIn, Subset, NRules+1);
    Verbosity(1) printf("\n\tBest value %.1f\n", SubsetValue);
}


 
/*************************************************************************/
/*									 */
/*    Try all combinations of rules to find best value			 */
/*									 */
/*************************************************************************/


    AllCombinations(NR)
/*  ---------------  */
    RuleNo NR;
{
    RuleNo r;

    if ( ! NR )
    {
	CalculateValue();
    }
    else
    {
	r = ClassRules[NR];

	AllCombinations(NR-1);

	AddRule(r);
	AllCombinations(NR-1);

	DeleteRule(r);
	Verbosity(1) printf("\n");
    }
}



/*************************************************************************/
/*									 */
/*  Find a good subset by simulated annealing				 */
/*									 */
/*************************************************************************/


    SimAnneal(RuleCount)
/*  ---------  */
    RuleNo RuleCount;
{
    RuleNo r, OutCount;
    short ri, Tries;
    float Temp, Delta;
    Boolean Changed;

    /*  Keep dropping and adding rules until can't improve  */

    for ( Temp = 1000 ; Temp > 0.001 ; Temp *= 0.95 )
    {
	CalculateValue();

	Verbosity(2)
	{
	    OutCount = 0;

	    ForEach(ri, 1, RuleCount)
	    {
		r = ClassRules[ri];

		if ( ! RuleIn[r] )
		{
		    if ( ! (OutCount++ % 3) ) printf("\n\t\t");
		    printf("%d<%d|%d=%.1f> ", r, Right[r], Wrong[r], Value[r]);
		}
	    }

	    printf("\n\n");
	}

	Changed = false;

	for ( Tries = 100 ; ! Changed && Tries > 0 ; Tries-- )
	{
	    /*  Choose a rule to add or delete  */

	    ri = RuleCount * Random + 1;

	    r = ClassRules[ri];

	    Delta = ( RuleIn[r] ? -Value[r] : Value[r] );

	    if ( Delta > 0 || Random < exp(Delta / Temp) )
	    {
		if ( RuleIn[r] )
		{
		    DeleteRule(r);
		}
		else
		{
		    AddRule(r);
		}
		
		Changed = true;
	    }
	}

	if ( ! Changed ) break;
    }

    /*  Try to improve best subset so far by hill-climbing  */

    Verbosity(1) printf("Polish: ");
    memcpy(RuleIn, Subset, NRules+1);
    HillClimb(RuleCount);
}



/*************************************************************************/
/*									 */
/*  Find a good subset by repeated greedy search			 */
/*									 */
/*************************************************************************/


    SpotSearch(RuleCount)
/*  ----------  */
    RuleNo RuleCount;
{
    RuleNo r;
    short ri, Trial;
    float ProbIn;

    ForEach(Trial, 0, 10)
    {
	Verbosity(1) printf("\n    Trial %d:", Trial);

	/*  Add rules randomly to the initial subset  */

	ProbIn = Trial / 10.0;
	ForEach(ri, 1, RuleCount)
	{
	    r = ClassRules[ri];
	    RuleIn[r] = Random < ProbIn;
	}

	HillClimb(RuleCount);
    }
}



/*************************************************************************/
/*									 */
/*  Improve a subset of rules by adding and deleting rules		 */
/*									 */
/*************************************************************************/


    HillClimb(RuleCount)
/*  ---------  */
    RuleNo RuleCount;
{
    RuleNo r, Bestr;
    short ri, OutCount;
    ItemNo i;
    float Delta, BestDelta;

    ForEach(i, 0, MaxItem)
    {
	Covered[i] = 0;
    }

    ForEach(ri, 1, RuleCount)
    {
	r = ClassRules[ri];
	if ( RuleIn[r] )
	{
	    ForEach(i, 0, MaxItem)
	    {
		if ( Match[r][i] )
		{
		    Covered[i]++;
		}
	    }
	}
    }
    
    /*  Add or drop rule with greatest reduction in coding cost  */

    while ( true )
    {
	CalculateValue();

	Verbosity(2)
	{
	    OutCount = 0;

	    ForEach(ri, 1, RuleCount)
	    {
		r = ClassRules[ri];

		if ( ! RuleIn[r] )
		{
		    if ( ! (OutCount++ % 3) ) printf("\n\t\t");
		    printf("%d<%d|%d=%.1f> ", r, Right[r], Wrong[r], Value[r]);
		}
	    }

	    printf("\n\n");
	}

	Bestr = BestDelta = 0;
	ForEach(ri, 1, RuleCount)
	{
	    r = ClassRules[ri];
	    Delta = ( RuleIn[r] ? -Value[r] : Value[r] );
	    if ( Delta > BestDelta )
	    {
		Bestr = r;
		BestDelta = Delta;
	    }
	}
	if ( ! Bestr ) break;

	if ( RuleIn[Bestr] )
	{
	    DeleteRule(Bestr);
	}
	else
	{
	    AddRule(Bestr);
	}
    }
}



/*************************************************************************/
/*								         */
/*  Find the number of correct and incorrect rule firings for rules      */
/*  for class FocusClass and hence determine the Value of the rules.     */
/*  If best so far, remember.						 */
/*								         */
/*************************************************************************/


    CalculateValue()
/*  --------------  */
{
    RuleNo r, Selected=0, InCount;
    ItemNo i, Times, FPos=0, FNeg=0, SumCover=0;
    float BaseBits, RuleBits=0, NewBits, ExceptionBits();
    ClassNo ThisClass;
    Boolean *RuleMatch;

    ForEach(i, 0, MaxItem)
    {
	ThisClass = Class(Item[i]);

	if ( Covered[i] )