server/algorithm/Fit.h

#pragma 

#include <vector>
using namespace std;

class Fit{
public:
	std::vector<double> factor;	//拟合后的方程系数
	double ssr;		//回归平方和
	double sse;		//剩余平方和
	double rmse;	//均方根误差
	std::vector<double> fitedYs;///<存放拟合后的y值，在拟合时可设置为不保存节省内存
public:
	Fit():ssr(0),sse(0),rmse(0){factor.resize(2,0);}

public:
	template<typename T>  
	bool linearFit(const std::vector<typename T>& x, const std::vector<typename T>& y,bool isSaveFitYs=false)  
	{  
		return linearFit(&x[0],&y[0],getSeriesLength(x,y),isSaveFitYs);  
	}  
	template<typename T>  
	bool linearFit(const T* x, const T* y,size_t length,bool isSaveFitYs=false)  
	{  
		factor.resize(2,0);  
		typename T t1 = 0, t2 = 0, t3 = 0, t4 = 0;
		for(size_t i = 0; i < length; ++i)  
		{  
			t1 += x[i]*x[i];  
			t2 += x[i];  
			t3 += x[i]*y[i];  
			t4 += y[i];  
		}  
		factor[1] = (t3*length - t2*t4) / (t1*length - t2*t2);  
		factor[0] = (t1*t4 - t2*t3) / (t1*length - t2*t2);  
		//////////////////////////////////////////////////////////////////////////  
		//计算误差  
		calcError(x,y,length,this->ssr,this->sse,this->rmse,isSaveFitYs);  
		return true;  
	}

	/// \brief 多项式拟合，拟合y=a0+a1*x+a2*x^2+……+apoly_n*x^poly_n  
	/// \param x 观察值的x  
	/// \param y 观察值的y  
	/// \param poly_n 期望拟合的阶数，若poly_n=2，则y=a0+a1*x+a2*x^2  
	/// \param isSaveFitYs 拟合后的数据是否保存，默认是  
	///   
	template<typename T>  
	void polyfit(const std::vector<typename T>& x  
		,const std::vector<typename T>& y  
		,int poly_n  
		,bool isSaveFitYs=true)  
	{  
		polyfit(&x[0],&y[0],getSeriesLength(x,y),poly_n,isSaveFitYs);  
	}  

	template<typename T>  
	void polyfit(const T* x,const T* y,size_t length,int poly_n,bool isSaveFitYs=true)  
	{  
		factor.resize(poly_n+1,0);  
		int i = 0;
		size_t j = 0;  
		std::vector<double> tempx(length,1.0);  
		std::vector<double> tempy(y,y+length);  
		std::vector<double> sumxx(poly_n*2+1);  
		std::vector<double> ata((poly_n+1)*(poly_n+1));  
		std::vector<double> sumxy(poly_n+1);  
		for (i = 0;i < 2*poly_n + 1;i++){  
			for (sumxx[i]=0,j=0;j<length;j++)  
			{  
				sumxx[i]+=tempx[j];  
				tempx[j]*=x[j];  
			}  
		}  
		for (i = 0;i < poly_n + 1;i++){  
			for (sumxy[i] = 0,j = 0;j < length;j++)  
			{  
				sumxy[i]+=tempy[j];  
				tempy[j]*=x[j];  
			}  
		}  
		for (i = 0;i < poly_n + 1;i++)  
			for (j = 0;j < poly_n + 1;j++)  
				ata[i*(poly_n+1)+j]=sumxx[i+j];  
		gauss_solve(poly_n+1,ata,factor,sumxy);  
		//计算拟合后的数据并计算误差  
		fitedYs.reserve(length);  
		calcError(&x[0],&y[0],length,this->ssr,this->sse,this->rmse,isSaveFitYs);  
	}  

	void getFactor(std::vector<double>& factor){factor = this->factor;} 

	/// \brief 获取拟合方程对应的y值，前提是拟合时设置isSaveFitYs为true  
	///  
	void getFitedYs(std::vector<double>& fitedYs){fitedYs = this->fitedYs;}  

	/// \brief 根据x获取拟合方程的y值  
	/// \return 返回x对应的y值  
	///  
	template<typename T>  
	double getY(const T x) const  
	{  
		double ans(0);  
		for (size_t i = 0;i < factor.size();++i)  
		{  
			ans += factor[i]*pow((double)x,(int)i);  
		}  

		return ans;  
	}  

	double getSlope(){return factor[1];}  
	///   
	/// \brief 获取截距  
	/// \return 截距值  
	///  
	double getIntercept(){return factor[0];}  
	///   
	/// \brief 剩余平方和  
	/// \return 剩余平方和  
	///  
	double getSSE(){return sse;}  
	///   
	/// \brief 回归平方和  
	/// \return 回归平方和  
	///  
	double getSSR(){return ssr;}  
	///   
	/// \brief 均方根误差  
	/// \return 均方根误差  
	///  
	double getRMSE(){return rmse;}  
	///   
	/// \brief 确定系数，系数是0~1之间的数，是数理上判定拟合优度的一个量  
	/// \return 确定系数  
	///  
	double getR_square(){return 1-(sse/(ssr+sse));}  

	template<typename T>  
	size_t getSeriesLength(const std::vector<typename T>& x  
		,const std::vector<typename T>& y)  
	{  
		return (x.size() > y.size() ? y.size() : x.size());  
	}  

	/// \brief 计算均值  
	/// \return 均值  
	///  
	template <typename T>  
	static T Mean(const std::vector<T>& v)  
	{  
		return Mean(&v[0],v.size());  
	}  
	template <typename T>  
	static T Mean(const T* v,size_t length)  
	{  
		T total(0);  
		for (size_t i=0;i<length;++i)  
		{  
			total += v[i];  
		}  
		return (total / length);  
	}  
	///   
	/// \brief 获取拟合方程系数的个数  
	/// \return 拟合方程系数的个数  
	///  
	size_t getFactorSize(){return factor.size();}  
	///   
	/// \brief 根据阶次获取拟合方程的系数，  
	/// 如getFactor(2),就是获取y=a0+a1*x+a2*x^2+……+apoly_n*x^poly_n中a2的值  
	/// \return 拟合方程的系数  
	///  
	double getFactor(size_t i){return factor.at(i);}  

private:
	template<typename T>  
	void calcError(const T* x  
		,const T* y  
		,size_t length  
		,double& r_ssr  
		,double& r_sse  
		,double& r_rmse  
		,bool isSaveFitYs=true  
		)  
	{  
		T mean_y = Mean<T>(y,length);  
		T yi(0);  
		fitedYs.reserve(length);  
		for (size_t i = 0; i < length; ++i)  
		{  
			yi = getY(x[i]);  
			r_ssr += ((yi-mean_y)*(yi-mean_y));//计算回归平方和  
			r_sse += ((yi-y[i])*(yi-y[i]));//残差平方和  
			if (isSaveFitYs)  
			{  
				fitedYs.push_back(double(yi));  
			}  
		}  
		r_rmse = sqrt(r_sse/(double(length)));  
	}  

	template<typename T>  
	void gauss_solve(int n  
		,std::vector<typename T>& A  
		,std::vector<typename T>& x  
		,std::vector<typename T>& b)  
	{  
		gauss_solve(n,&A[0],&x[0],&b[0]);     
	}  

	template<typename T>  
	void gauss_solve(int n  
		,T* A  
		,T* x  
		,T* b)  
	{  
		int i,j,k,r;  
		double max;  
		for (k=0;k<n-1;k++)  
		{  
			max=fabs(A[k*n+k]); /*find maxmum*/  
			r=k;  
			for (i=k+1;i<n-1;i++){  
				if (max<fabs(A[i*n+i]))  
				{  
					max=fabs(A[i*n+i]);  
					r=i;  
				}  
			}  
			if (r!=k){  
				for (i=0;i<n;i++)         /*change array:A[k]&A[r] */  
				{  
					max=A[k*n+i];  
					A[k*n+i]=A[r*n+i];  
					A[r*n+i]=max;  
				}  
			}  
			max=b[k];                    /*change array:b[k]&b[r]     */  
			b[k]=b[r];  
			b[r]=max;  
			for (i=k+1;i<n;i++)  
			{  
				for (j=k+1;j<n;j++)  
					A[i*n+j]-=A[i*n+k]*A[k*n+j]/A[k*n+k];  
				b[i]-=A[i*n+k]*b[k]/A[k*n+k];  
			}  
		}   

		for (i=n-1;i>=0;x[i]/=A[i*n+i],i--)  
			for (j=i+1,x[i]=b[i];j<n;j++)  
				x[i]-=A[i*n+j]*x[j];  
	}  
};