smo算法的c++实现

#include <iostream>
#include <fstream>
#include <sstream>
#include <string>
#include <vector>
#include <cstring>
#include <algorithm>
#include <cmath>
#include <random>
#include <Eigen/Dense>
#include <time.h>

using namespace std;
using namespace Eigen;

class svm
{
public:
	EIGEN_MAKE_ALIGNED_OPERATOR_NEW    //use the Eigen 
	svm(const string &filename)
	{
		dataInit(filename);
	}

	void print()
	{
		smoP();
		caclWs();
		cout<<w<<endl;		

	}

	void test()
	{

		double errCount=0.0;
		cout<<w<<endl;
		for (int i=0;i<trainDataNum;i++)
		{
			if((trainDataMat.row(i)*w+b<0&&trainLabelMat[i]>0)||
				(trainDataMat.row(i)*w+b>0&&trainLabelMat[i]<0))
				errCount+=1.0;
		}
		cout<<errCount<<endl;
		cout<<"error rate is: "<<errCount/trainDataNum<<endl;
	}


private:

	void dataInit(const string &filename)
	{
		ifstream file(filename);
		string line;
		while(getline(file,line))
		{
			istringstream record(line);
			vector<double> data;
			double temp;
			while(record>>temp)
				data.push_back(temp);
			trainLabelVec.push_back(temp);
			data.pop_back();
			trainDataVec.push_back(data);
		}
		trainDataNum=trainDataVec.size();
		trainFeatureNum=trainDataVec[0].size();

		trainDataMat.resize(trainDataNum,trainFeatureNum);
		trainLabelMat.resize(trainDataNum);
		alphas.resize(trainDataNum);
		eCache.resize(trainDataNum,2);
		w.resize(trainFeatureNum);

		for(int i=0;i<trainDataNum;i++)
		for(int j=0;j<trainFeatureNum;j++)
			trainDataMat(i,j)=trainDataVec[i][j];

		for(int i=0;i<trainDataNum;i++)
		{
			trainLabelMat[i]=trainLabelVec[i];
			alphas[i]=0;
			eCache(i,0)=0;
			eCache(i,1)=0;
		}
			

	}
	vector<vector<double>> trainDataVec;
	vector<double> trainLabelVec;
	int trainDataNum;
	int trainFeatureNum;
	Eigen::MatrixXd trainDataMat;
	Eigen::VectorXd trainLabelMat;
	double C=0.6;
	double toler=0.001;
	double b=0;
	Eigen::VectorXd alphas;
	Eigen::MatrixXd eCache;
	Eigen::VectorXd w;
	
	double calcEk(int k)
	{
		Eigen::VectorXd alpMulLab(trainDataNum);
		for(int i=0;i<trainDataNum;i++)
			alpMulLab(i)=alphas(i)*trainLabelMat(i);
		double fXk=alpMulLab.transpose()*
					(trainDataMat*(trainDataMat.row(k).transpose()))+b;
		return fXk-trainLabelMat(k);
	}

	int selectJrand(int i,int m)
	{
		int j=i;
		std::default_random_engine generator(rand()%m);
		std::uniform_int_distribution<int> dis(0,m);
		while(j==i)
			j=dis(generator);
		return j;
	}


	int selectJ(int i,double Ei,double &Ej)
	{
		int maxK=-1;
		double maxDeltaE=0;
		Ej=0;
		eCache(i,0)=1;
		eCache(i,1)=Ei;
		int flag=0;
		for(int k=0;k<trainDataNum;k++)
		{
			if(eCache(k,0)==1)
			{
				if(k==i)
					continue;
				else
				{
					flag=1;
					double Ek=calcEk(k);
					double deltaE=abs(Ei-Ej);
					if(deltaE>maxDeltaE)
					{
						maxK=k;
						maxDeltaE=deltaE;
						Ej=Ek;
					}
				}
			}
		}

		if(flag)
			return maxK;
		else
		{
			int j=selectJrand(i,trainDataNum);
			Ej=calcEk(j);
			return j;
		}
	}

	//对alpha剪辑
	double cliAlpha(double aj,double H,double L)
	{
		if (aj>H)
			aj=H;
		if (L>aj)
			aj=L;
		return aj;
	}

	int innerL(int i)
	{
		double Ei=calcEk(i);
		if(((trainLabelMat[i]*Ei<-toler)&&(alphas[i]<C))||
		   ((trainLabelMat[i]*Ei>toler)&&(alphas[i]>0)))
		{
			double Ej;
			int j=selectJ(i,Ei,Ej);
			double alphaIold=alphas[i];
			double alphaJold=alphas[j];
			double L,H;
			if(trainLabelMat[i]!=trainLabelMat[j])
			{
				L=max(0.0,alphas[j]-alphas[i]);
				H=min(C,C+alphas[j]-alphas[i]);
			}
			else
			{
				L=max(0.0,alphas[j]+alphas[i]-C);
				H=min(C,alphas[j]+alphas[i]);
			}
			if(L==H)
				return 0;
			double eta=2.0*trainDataMat.row(i).dot(trainDataMat.row(j))-
						trainDataMat.row(i).dot(trainDataMat.row(i))-
						trainDataMat.row(j).dot(trainDataMat.row(j));
			if(eta>=0)
				return 0;
			alphas[j]-=trainLabelMat[j]*(Ei-Ej)/eta;
			alphas[j]=cliAlpha(alphas[j],H,L);
			updataEk(j);

			if(abs(alphas[j]-alphaJold)<0.00001)
				return 0;

			alphas[i]+=trainLabelMat[j]*trainLabelMat[i]*(alphaJold-alphas[j]);
			updataEk(i);

			double b1=b-Ei-trainLabelMat[i]*(alphas[i]-alphaIold)*
						(trainDataMat.row(i).dot(trainDataMat.row(i)))-
						trainLabelMat[j]*(alphas[j]-alphaJold)*
						(trainDataMat.row(i).dot(trainDataMat.row(j)));
			double b2=b-Ej-trainLabelMat[i]*(alphas[i]-alphaIold)*
						(trainDataMat.row(i).dot(trainDataMat.row(j)))-
						trainLabelMat[i]*(alphas[j]-alphaJold)*
						(trainDataMat.row(j).dot(trainDataMat.row(j)));
			if (0<alphas[i]&&C>alphas[i])
						b=b1;
			else if(0<alphas[j]&&alphas[j]<C)
						b=b2;
			else
					b=(b1+b2)/2.0;
			return 1;
		}
		else
			return 0;
	}

	void smoP()
	{
		int iter=0;
		int alphaPairsChanged=0;
		int entireSet=1;
		while((iter<40)&&((alphaPairsChanged>0)||(entireSet)))
		{
			alphaPairsChanged=0;
			if(entireSet) //go over all
			{
				for(int i=0;i<trainDataNum;i++)
					alphaPairsChanged+=innerL(i);
				iter+=1;
			}
			else	//go over non-bound (railed) alphas
			{
				for( int i=0;i<trainDataNum;i++)
				{
					if(alphas[i]>0&&alphas[i]<C)
					{
						alphaPairsChanged+=innerL(i);
					}
				}
				iter+=1;
			}
			if(entireSet)
				entireSet=0;
			else
			{
				if(alphaPairsChanged==0)
					entireSet=1;
			}
		}
	}

	void updataEk(int k)
	{
		double Ek=calcEk(k);
		eCache(k,0)=1;
		eCache(k,1)=Ek;
	}

	void smoSimple()
	{
		int iter=0;
		while (iter<40)
		{
			int alphaPairsChanged=0;
			for(int i=0;i<trainDataNum;i++)
			{
				double Ei=calcEk(i);
				if(((trainLabelMat[i]*Ei<-toler)&&(alphas[i]<C))||
					((trainLabelMat[i]*Ei>toler)&&(alphas[i]>0)))
				{
					int j=selectJrand(i,trainDataNum);
					double Ej=calcEk(j);
					double alphaIold=alphas[i];
					double alphaJold=alphas[j];
					double L,H;
					if(trainLabelMat[i]!=trainLabelMat[j])
					{
						L=max(0.0,alphas[j]-alphas[i]);
						H=min(C,C+alphas[j]-alphas[i]);
					}
					else
					{
						L=max(0.0,alphas[j]+alphas[i]-C);
						H=min(C,alphas[j]+alphas[i]);
					}
					if (L==H)
					{
						cout<<"L==H"<<endl;
						continue;
					}
					double eta=2.0*trainDataMat.row(i).dot(trainDataMat.row(j))-
						trainDataMat.row(i).dot(trainDataMat.row(i))-
						trainDataMat.row(j).dot(trainDataMat.row(j));
					if (eta>=0)
					{
						cout<<"eta>=0";
						continue;
					}
					alphas[j]-=trainLabelMat[j]*(Ei-Ej)/eta;
					alphas[j]=cliAlpha(alphas[j],H,L);
					if(abs(alphas[j]-alphaJold)<0.00001)
					{
						cout<<"j not moving enough"<<endl;
						continue;
					}
					alphas[i]+=trainLabelMat[j]*trainLabelMat[i]*(alphaJold-alphas[j]);
					double b1=b-Ei-trainLabelMat[i]*(alphas[i]-alphaIold)*
						(trainDataMat.row(i).dot(trainDataMat.row(i)))-
						trainLabelMat[j]*(alphas[j]-alphaJold)*
						(trainDataMat.row(i).dot(trainDataMat.row(j)));
					double b2=b-Ej-trainLabelMat[i]*(alphas[i]-alphaIold)*
						(trainDataMat.row(i).dot(trainDataMat.row(j)))-
						trainLabelMat[i]*(alphas[j]-alphaJold)*
						(trainDataMat.row(j).dot(trainDataMat.row(j)));
					if (0<alphas[i]&&C>alphas[i])
						b=b1;
					else if(0<alphas[j]&&alphas[j]<C)
						b=b2;
					else
						b=(b1+b2)/2.0;
					alphaPairsChanged+=1;
					cout<<"iter: "<<iter<<"pairs changed: "<<alphaPairsChanged;
				}
				if(alphaPairsChanged==0)
					iter+=1;
				else
					iter=0;
			}
		}
	}


	void caclWs()
	{
		Eigen::VectorXd alpMulLab(trainDataNum);
		for(int i=0;i<trainDataNum;i++)
			alpMulLab(i)=alphas(i)*trainLabelMat(i);

		for(int i=0;i<trainFeatureNum;i++)
			w[i]=alpMulLab.transpose()*(trainDataMat.col(i));
	}


};


int main()
{

	svm svm("testSet.txt");
	svm.print();
	svm.test();
}