siftrules.c

这是一个决策树实现的算法

	{
	    SumCover++;
	    if( ThisClass != FocusClass ) FPos++;
	}
	else
	if ( ThisClass == FocusClass )
	{
	    FNeg++;
	}
    }

    ForEach(r, 1, NRules)
    {
	if ( Rule[r].Rhs == FocusClass )
	{
	    Right[r] = Wrong[r] = 0;

	    if ( RuleIn[r] )
	    {
		RuleBits += Rule[r].Bits;
		Selected++;
	    }

	    RuleMatch = Match[r];

	    ForEach(i, 0, MaxItem)
	    {
		if ( RuleMatch[i] &&
		   ( ! (Times = Covered[i]) || Times == 1 && RuleIn[r] ) )
		{
		    if ( Class(Item[i]) == FocusClass )
		    {
			Right[r]++;
		    }
		    else
		    {
			Wrong[r]++;
		    }
		}
	    }
	}
    }

    RuleBits -= LogFact[Selected];	/* allow for reordering of rules */

    BaseBits = CodeWeight * RuleBits + ExceptionBits(SumCover, FPos, FNeg);

    /*  From the Right and Wrong of each rule, calculate its value  */

    Verbosity(1)
    {
        printf("\t");
    	InCount = -1;
    }

    ForEach(r, 1, NRules)
    {
	if ( Rule[r].Rhs == FocusClass )
	{
	    if ( RuleIn[r] )
	    {
		NewBits = ExceptionBits(SumCover-Right[r]-Wrong[r],
					FPos-Wrong[r], FNeg+Right[r]) +
			  CodeWeight *
			      (RuleBits - Rule[r].Bits + LogItemNo[Selected]);
		Value[r] = NewBits - BaseBits;
	    }
	    else
	    {
		NewBits = ExceptionBits(SumCover+Right[r]+Wrong[r],
					FPos+Wrong[r], FNeg-Right[r]) +
			  CodeWeight *
			      (RuleBits + Rule[r].Bits - LogItemNo[Selected+1]);
		Value[r] = BaseBits - NewBits;
	    }

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

    Verbosity(1)
    {
	printf("\n\t\t%d rules, %d firings: F+=%d, F-=%d, %.1f bits (rules=%.1f)\n",
		Selected, SumCover, FPos, FNeg, BaseBits, RuleBits);
    }

    if ( BaseBits < SubsetValue )
    {
	SubsetValue = BaseBits;
	memcpy(Subset, RuleIn, NRules+1);
    }
}



/*************************************************************************/
/*								         */
/*  Add rule r to the set of included rules and increase the number of	 */
/*  rules covering each of the items that fire the rule		         */
/*								         */
/*************************************************************************/


    AddRule(r)
/*  -------  */
    RuleNo r;
{
    ItemNo i;

    RuleIn[r] = true;

    ForEach(i, 0, MaxItem)
    {
	if ( Match[r][i] )
	{
	    Covered[i]++;
	}
    }

    Verbosity(1) printf("%5d+  %6.1f", r, Value[r]);
}



/*************************************************************************/
/*								         */
/*  Delete rule r from the included rules and decrease the number of	 */
/*  rules covering each of the items covered by the rule	         */
/*								         */
/*************************************************************************/


    DeleteRule(r)
/*  ----------  */
    RuleNo r;
{
    ItemNo i;

    RuleIn[r] = false;

    ForEach(i, 0, MaxItem)
    {
	if ( Match[r][i] )
	{
	    Covered[i]--;
	}
    }

    Verbosity(1) printf("%5d-  %6.1f", r, -Value[r]);
}



/*************************************************************************/
/*								         */
/*  Make an index of included rules in RuleIndex.  Select first those    */
/*  classes whose rules have the fewest false positives.  Within a	 */
/*  class, put rules with higher accuracy ahead.			 */
/*								         */
/*************************************************************************/


    MakeIndex()
/*  ---------  */
{
    ClassNo c, BestC, Pass;
    RuleNo r, BestR, NewNRules = 0;
    ItemNo i;
    Boolean *Included;

    Included = (Boolean *) calloc(MaxClass+1, sizeof(Boolean));
    RuleIndex = (RuleNo *) calloc(NRules+1, sizeof(RuleNo));

    Verbosity(1) printf("\nFalsePos  Class\n");

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

    /*  Select the best class to put next  */

    ForEach(Pass, 0, MaxClass)
    {
	ForEach(c, 0, MaxClass)
	{
	    if ( Included[c] ) continue;

	    FalsePos[c] = 0;

	    ForEach(i, 0, MaxItem)
	    {
		if ( Covered[i] || Class(Item[i]) == c ) continue;

		ForEach(r, 1, NRules)
		{
		    if ( Rule[r].Rhs == c && RuleIn[r] && Match[r][i] )
		    {
			FalsePos[c]++;
			break;
		    }
		}
	    }
	}

	BestC = -1;
	ForEach(c, 0, MaxClass)
	{
	    if ( ! Included[c] &&
	         ( BestC < 0 || FalsePos[c] < FalsePos[BestC] ) )
	    {
		BestC = c;
	    }
	}
	Included[BestC] = true;

	Verbosity(1)
	    printf("%5d     %s\n", FalsePos[BestC], ClassName[BestC]);

	/*  Now grab the rules for this class  */

	do
	{
	    BestR = 0;

	    /*  Find the best rule to put next  */

	    ForEach(r, 1, NRules)
	    {
		if ( RuleIn[r] && Rule[r].Rhs == BestC &&
		     ( ! BestR || Rule[r].Error < Rule[BestR].Error ) )
		{
		    BestR = r;
		}
	    }

	    if ( BestR )
	    {
		RuleIndex[++NewNRules] = BestR;
		RuleIn[BestR] = false;

		ForEach(i, 0, MaxItem)
		{
		    Covered[i] |= Match[BestR][i];
		}
	    }
	} while ( BestR );
    }

    NRules = NewNRules;
    free(Included);
}



/*************************************************************************/
/*								         */
/*  Find the default class as the one with most items not covered by	 */
/*  any rule.  Resolve ties in favour of more frequent classes.		 */
/*  (Note: Covered has been set by MakeIndex.)				 */
/*								         */
/*************************************************************************/


    FindDefault()
/*  -----------  */
{
    ClassNo c;
    ItemNo i;

    /*  Determine uncovered items  */

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

    ForEach(i, 0, MaxItem)
    {
	if ( ! Covered[i] )
	{
	    NoRule[Class(Item[i])]++;
	}
    }

    Verbosity(1)
    {
	printf("\nItems: Uncovered   Class\n");
	ForEach(c, 0, MaxClass)
	{
	    printf("%5d %7d      %s\n", ClassFreq[c], NoRule[c], ClassName[c]);
	}
	printf("\n");
    }

    DefaultClass = 0;
    ForEach(c, 1, MaxClass)
    {
	if ( NoRule[c] > NoRule[DefaultClass] ||
	     NoRule[c] == NoRule[DefaultClass] &&
	     ClassFreq[c] > ClassFreq[DefaultClass] )
	{
	    DefaultClass = c;
	}
    }
}



/*************************************************************************/
/*								         */
/*  Given a rule and a case, determine the strength with which we can    */
/*  conclude that the case belongs to the class specified by the rule's  */
/*  right-hand side.						         */
/*								         */
/*  If the case doesn't satisfy all the conditions of the rule,		 */
/*  then this is 0.						         */
/*								         */
/*************************************************************************/


float Strength(ThisRule, Case)
/*    --------  */
    PR ThisRule;
    Description Case;
{
    short d;
    Boolean Satisfies();

    if ( ThisRule.Error > 0.7 ) return 0.0;

    ForEach(d, 1, ThisRule.Size)
    {
	if ( ! Satisfies(Case, ThisRule.Lhs[d]) )
	{
	    return 0.0;
	}
    }

    return ( 1 - ThisRule.Error );
}



/*************************************************************************/
/*									 */
/*  Determine the number of bits to encode exceptions.  Unlike the	 */
/*  version in the book, this uses an approximate encoding that 	 */
/*  penalizes unbalanced numbers of false positives and false negatives  */
/*  as described in my paper at 1995 International Machine Learning      */
/*  Conference (published by Morgan Kaufmann).				 */
/*									 */
/*************************************************************************/


float Biased(N, E, ExpE)
/*    ------  */
    int N, E;
    float ExpE;
{
    float Rate;

    if ( ExpE <= 1E-6 )
    {
	return ( E == 0 ? 0.0 : 1E6 );
    }
    else
    if ( ExpE >= N-1E-6 )
    {
	return ( E == N ? 0.0 : 1E6 );
    }

    Rate = ExpE / N;
    return -E * Log(Rate) - (N-E) * Log(1-Rate);
}



float ExceptionBits(Fires, FP, FN)
/*    -------------  */
    int Fires, FP, FN;
{
    if ( Fires > 0.5 * (MaxItem+1) )
    {
	return Log(MaxItem+1)
		+ Biased(Fires, FP, 0.5 * (FP+FN))
		+ Biased(MaxItem+1-Fires, FN, (float) FN);
    }
    else
    {
	return Log(MaxItem+1)
		+ Biased(Fires, FP, (float) FP)
		+ Biased(MaxItem+1-Fires, FN, 0.5 * (FP+FN));
    }
}



/*************************************************************************/
/*									 */
/*  Find encoding lengths for all rules					 */
/*									 */
/*************************************************************************/


    FindRuleCodes()
/*  -------------  */
{
    RuleNo r;
    short d, NCond;
    float Bits, CondBits();

    ForEach(r, 1, NRules)
    {
	NCond = Rule[r].Size;
	Bits = 0;

	ForEach(d, 1, NCond)
	{
	    Bits += CondBits(Rule[r].Lhs[d]);
	}

	/*  Must encode the number of conditions, but credit the total
	    encoding by the ways conditions can be reordered  */

	Rule[r].Bits = Bits + LogItemNo[NCond] - LogFact[NCond];
    }
}



/*************************************************************************/
/*									 */
/*  Determine the number of bits required to encode a condition		 */
/*									 */
/*************************************************************************/


float CondBits(C)
/*    --------  */
    Condition C;
{
    Test t;
    Attribute a;

    t = C->CondTest;
    a = t->Tested;

    switch ( t->NodeType )
    {
	case BrDiscr:		/* test of discrete attribute */
	case ThreshContin:	/* test of continuous attribute */

	    return AttTestBits/REDUNDANCY + BranchBits[a];

	case BrSubset:		/* subset test on discrete attribute  */

	    return AttTestBits/REDUNDANCY + MaxAttVal[a];
    } 
}