#include?
#include?
#include?
#include?
#include?“Binary_Decision_Tree.cpp“

bool?first=true;
using?namespace?std;
const?int?totalTrees=10;

double?line（double?error）{
	return?0.5-error;
}
void?copyArray（int?*sourceint?*destint?length）{
	for（int?i=0;i		dest[i]=source[i];
}

void?normalize（double?*arrayint?length）{
	double?sum=0;
	for（int?i=0;i		sum+=array[i];
	for（int?i=0;i		array[i]=array[i]/sum;
}

class?AdaBoost{
	public:
		int?rowscolsnumTrees;
		int?total;
		DecisionTree?**treeSet;
		double?*weightSamples;
		double?*weightTrees;
	public:
		int?sign（double?x）;
		AdaBoost（int?rint?cint?numTrees）;
		void?train（int?**featureint?*category）;
		int?predict（int?*sample）;
		void?fillSamples（int?**featureint?*categoryint?**samplesint?*types）;
};
int?AdaBoost::sign（double?x）{
	if（x>0）
		return?1;
	else?if（x==0）
		return?0;
	else
		return?-1;
}
AdaBoost::AdaBoost（int?rint?cint?num）{
	rows=r;
	cols=c;
	numTrees=num;
	total=10*rows;
	
	weightSamples=new?double[rows];
	setValue（weightSamples（double）1/rowsrows）;
	
	weightTrees=new?double[numTrees];?
	setValue（weightTrees（double）1/numTreesnumTrees）;
	
	treeSet=new?DecisionTree*[numTrees];
	for（int?i=0;i		treeSet[i]=new?DecisionTree（10*rowscols）;
}
void?AdaBoost::train（int?**featureint?*category）{
	int?**samples=new?int*[total];
	for（int?i=0;i		samples[i]=new?int[cols];
	
	int?*types=new?int[total];
	int?judge[rows];
	
	for（int?i=0;i		fillSamples（featurecategorysamplestypes）;
		treeSet[i]->train（samplestypes）;
		
		double?error=0;
		setValue（judge-1rows）;//correct?jugment

		for（int?j=0;j			int?cat=treeSet[i]->predict（feature[j]）;
			if（cat!=category[j]）{
				judge[j]=1;//wrong?jugment
				error+=weightSamples[j];
			}
		}

		weightTrees[i]=line（error）;
		
		if（weightTrees[i]>5）{
			cout<<“weight:“<			cout<<“error:“<		}
			
		if（i+1			for（int?j=0;j				double?exponent=weightTrees[i]*judge[j];
				weightSamples[j]=weightSamples[j]*exp（exponent）;
			}
			normalize（weightSamplesrows）;
		}
	}
	normalize（weightTreesnumTrees）;
}
void?AdaBoost::fillSamples（int?**featureint?*categoryint?**samplesint?*types）{
	int?tag=0pre=0;
	for（int?i=0;i		tag+=weightSamples[i]*total;
		for（int?j=pre;j			copyArray（feature[i]samples[j]cols）;
			types[j]=category[i];
		}
		pre=tag;
	}

	if（tag		for（int?j=tag;j			int?random=rand（）%rows;
			copyArray（feature[random]samples[j]cols）;
			types[j]=category[random];
		}
}
int?AdaBoost::predict（int?*sample）{
	double?sum=0;
	for（int?i=0;i		sum+=weightTrees[i]*treeSet[i]->predict（sample）;
	return?（int）（sum+0.5）;
}
int?main（）{
	srand（（unsigned）time（NULL））;
	int?rows=1000cols=8;
	AdaBoost

?屬性????????????大小?????日期????時間???名稱
-----------?---------??----------?-----??----
?????文件????????4545??2015-09-24?22:05??AdaBoost_Demo.cpp
?????文件????????7853??2015-09-24?17:38??Binary_Decision_Tree.cpp