www.pudn.com > TextClassify.rar > Classifier.h

// Classifier.h: interface for the CClassifier class. 
// 
////////////////////////////////////////////////////////////////////// 
 
#if !defined(AFX_CLASSIFIER_H__FA4DB8D8_AC36_44A8_884B_0D715575B7A1__INCLUDED_) 
#define AFX_CLASSIFIER_H__FA4DB8D8_AC36_44A8_884B_0D715575B7A1__INCLUDED_ 
 
#if _MSC_VER > 1000 
#pragma once 
#endif // _MSC_VER > 1000 
 
#include "CatalogList.h" 
#include "ClassifierParam.h" 
#include "WordList.h" 
#include "Compute_Param.h" 
#include "Compute_Prompt.h" 
#include "Compute_Result.h" 
#include "svm.h" 
#include "BAYES.h" 
 
struct sSortType 
{ 
	char        word[40]; 
	double		dWeight; 
	CWordNode	*pclsWordNode; 
}; 
 
struct DocWeight 
{ 
	double dWeight; 
	long   lDocID; 
}; 
 
struct DocCatalog 
{ 
	CDocNode * pDocNode; 
	short      nCataID; 
}; 
 
class CClassifier  
{ 
public: 
	CClassifier(); 
	virtual ~CClassifier(); 
public: //训练时需要用到的公有成员方法 
	//参数bGenDic=false 层次分类时使用, nType决定分类模型的类别 
	bool Train(int nType=0,bool bFlag=true); 
	void TrainSVM(); 
	void TrainBAYES(); 
	bool GenDic(); 
	void InitTrain(); 
	bool WriteModel(CString strFileName, int nType=0); 
	void Evaluate(CString strPath); 
	void CopyFile(char *pFileName, char *pSource, char *pTarget, char *pCatalog); 
	long SaveResults(CCatalogList &cataList, CString strFileName, CStringArray *aryType=NULL); 
private: //训练时需要用到的私有成员方法 
	void Sort(struct sSortType *,int); 
	void QuickSort(struct sSortType *,int,int); 
	void GenSortBuf(CWordList& wordList,sSortType *psSortBuf,int nCatalog); 
	void GenModel(); 
	void FeatherSelection(CWordList& dstWordList); 
	void FeatherWeight(CWordList& wordList); 
public: //分类时需要用到的公有成员方法 
	bool OpenModel(CString strFileName); 
	void Prepare(); 
	bool Classify(); 
	bool ClassifySmart(); 
	bool KNNClassify(char *, CDocNode &, bool bFile=true, int nCmpType=0); 
	long KNNClassify(CCatalogList&,int nCmpType=0); 
	short KNNCategory(char *,CDocNode &,bool bFile=true, int nCmpType=0); 
	short KNNCategory(char *pPath, bool bFile=true, int nCmpType=0); 
	long SVMClassify(CCatalogList& cataList); 
	bool SVMClassify(char *pPath, CDocNode &docNode, bool bFile=true); 
	short SVMCategory(char *pPath, CDocNode &docNode, bool bFile=true); 
	short SVMCategory(char *file, bool bFile=true); 
	void SVMClassifyVectorFile(CString strFileName); 
	short GetCategory(char *file, bool bFile=true); 
	short GetCategory(char *path, CDocNode &docNode, bool bFile=true); 
	bool Classify(char *path, CDocNode &docNode, bool bFile=true); 
	long Classify(CCatalogList& cataList); 
	short SingleCategory(CDocNode &docNode); 
	bool MultiCategory(CDocNode &docNode, CArray& aryResult, double dThreshold); 
private: //分类时需要用到的私有成员方法 
	void ComputeWeight(bool bMult=false); 
	void ComputeSimRatio(CDocNode &, int nCmpType=0); 
	void Sort(DocWeight *,int); 
	void QuickSort(DocWeight*, int, int); 
private:  //分类时需要用到的私有成员变量 
	//指向训练文本集的指针,用来加快读取速度 
	DocCatalog *m_pDocs; 
	//暂时保存当前测试文档和训练文档中每一篇文档的相似度 
	DocWeight *m_pSimilarityRatio; 
	//保存当前测试文档与每类的概率 
	DocWeight *m_pProbability; 
	//训练文档的个数 
	long m_lDocNum; 
	//训练文档的类别数 
	short m_nClassNum; 
public: 
	void ComputePro(CDocNode &docNode); 
	bool BAYESClassify(char *, CDocNode &, bool bFile=true); 
	short BAYESCategory(char *pPath, CDocNode &, bool bFile=true); 
	long BAYESClassify(CCatalogList& cataList); 
 
	//分类模型文件头标识符 
	int n_Type; 
	static const DWORD dwModelFileID; 
	CClassifierParam  m_paramClassifier; 
	CWordList         m_lstTrainWordList; 
	//训练时需要用到的,用来保存在没有进行特征选择之前训练集中所有的特征 
	CWordList         m_lstWordList; 
	CCatalogList      m_lstTrainCatalogList; 
	CCatalogList      m_lstTestCatalogList; 
	CSVM              m_theSVM; 
	CBAYES			  m_theBAYES; 
}; 
 
extern CClassifier theClassifier; 
#endif // !defined(AFX_CLASSIFIER_H__FA4DB8D8_AC36_44A8_884B_0D715575B7A1__INCLUDED_)