import java.io.BufferedReader;import java.io.File;import java.io.FileReader;import java.io.IOException;public class LinearRegression {    /*     * 训练数据示例：     *   x0        x1        x2        y         1.0       1.0       2.0       7.2         1.0       2.0       1.0       4.9         1.0       3.0       0.0       2.6         1.0       4.0       1.0       6.3         1.0       5.0      -1.0       1.0         1.0       6.0       0.0       4.7         1.0       7.0      -2.0      -0.6         注意！！！！x1，x2，y三列是用户实际输入的数据，x0是为了推导出来的公式统一，特地补上的一列。        x0,x1,x2是“特征”，y是结果                h(x) = theta0 * x0 + theta1* x1 + theta2 * x2                theta0,theta1,theta2 是想要训练出来的参数                 此程序采用“梯度下降法”             *      */    private double [][] trainData;//训练数据，一行一个数据，每一行最后一个数据为 y    private int row;//训练数据  行数    private int column;//训练数据 列数        private double [] theta;//参数theta        private double alpha;//训练步长    private int iteration;//迭代次数        public LinearRegression(String fileName)    {           int rowoffile=getRowNumber(fileName);//获取输入训练数据文本的   行数        int columnoffile = getColumnNumber(fileName);//获取输入训练数据文本的   列数                trainData = new double[rowoffile][columnoffile+1];//这里需要注意，为什么要+1，因为为了使得公式整齐，我们加了一个特征x0，x0恒等于1        this.row=rowoffile;        this.column=columnoffile+1;                this.alpha = 0.001;//步长默认为0.001        this.iteration=100000;//迭代次数默认为 100000                theta = new double [column-1];//h(x)=theta0 * x0 + theta1* x1 + theta2 * x2 + .......        initialize_theta();                loadTrainDataFromFile(fileName,rowoffile,columnoffile);    }    public LinearRegression(String fileName,double alpha,int iteration)    {           int rowoffile=getRowNumber(fileName);//获取输入训练数据文本的   行数        int columnoffile = getColumnNumber(fileName);//获取输入训练数据文本的   列数                trainData = new double[rowoffile][columnoffile+1];//这里需要注意，为什么要+1，因为为了使得公式整齐，我们加了一个特征x0，x0恒等于1        this.row=rowoffile;        this.column=columnoffile+1;                this.alpha = alpha;        this.iteration=iteration;                theta = new double [column-1];//h(x)=theta0 * x0 + theta1* x1 + theta2 * x2 + .......        initialize_theta();                loadTrainDataFromFile(fileName,rowoffile,columnoffile);    }            private int getRowNumber(String fileName)    {        int count =0;        File file = new File(fileName);        BufferedReader reader = null;        try {            reader = new BufferedReader(new FileReader(file));            while ( reader.readLine() != null)                 count++;            reader.close();        } catch (IOException e) {            e.printStackTrace();        } finally {            if (reader != null) {                try {                    reader.close();                } catch (IOException e1) {                }            }        }        return count;            }        private int getColumnNumber(String fileName)    {        int count =0;        File file = new File(fileName);        BufferedReader reader = null;        try {            reader = new BufferedReader(new FileReader(file));            String tempString = reader.readLine();            if(tempString!=null)                count = tempString.split(" ").length;            reader.close();        } catch (IOException e) {            e.printStackTrace();        } finally {            if (reader != null) {                try {                    reader.close();                } catch (IOException e1) {                }            }        }        return count;    }        private void initialize_theta()//将theta各个参数全部初始化为1.0    {        for(int i=0;i<theta.length;i++)            theta[i]=1.0;    }        public void trainTheta()    {        int iteration = this.iteration;        while( (iteration--)>0 )        {                //对每个theta i 求 偏导数            double [] partial_derivative = compute_partial_derivative();//偏导数                //更新每个theta            for(int i =0; i< theta.length;i++)                theta[i]-= alpha * partial_derivative[i];        }    }        private double [] compute_partial_derivative()    {        double [] partial_derivative = new double[theta.length];        for(int j =0;j<theta.length;j++)//遍历，对每个theta求偏导数        {            partial_derivative[j]= compute_partial_derivative_for_theta(j);//对 theta i 求 偏导        }        return partial_derivative;    }    private double compute_partial_derivative_for_theta(int j)    {        double sum=0.0;        for(int i=0;i<row;i++)//遍历 每一行数据        {            sum+=h_theta_x_i_minus_y_i_times_x_j_i(i,j);        }        return sum/row;    }    private double h_theta_x_i_minus_y_i_times_x_j_i(int i,int j)    {        double[] oneRow = getRow(i);//取一行数据，前面是feature，最后一个是y        double result = 0.0;                for(int k=0;k< (oneRow.length-1);k++)            result+=theta[k]*oneRow[k];        result-=oneRow[oneRow.length-1];        result*=oneRow[j];        return result;    }    private double [] getRow(int i)//从训练数据中取出第i行，i=0，1，2，。。。，（row-1）    {        return trainData[i];    }            private void loadTrainDataFromFile(String fileName,int row, int column)    {           for(int i=0;i< row;i++)//trainData的第一列全部置为1.0（feature x0）            trainData[i][0]=1.0;                File file = new File(fileName);        BufferedReader reader = null;        try {            reader = new BufferedReader(new FileReader(file));            String tempString = null;            int counter = 0;            while ( (counter<row) && (tempString = reader.readLine()) != null) {                String [] tempData = tempString.split(" ");                for(int i=0;i<column;i++)                    trainData[counter][i+1]=Double.parseDouble(tempData[i]);                counter++;            }            reader.close();        } catch (IOException e) {            e.printStackTrace();        } finally {            if (reader != null) {                try {                    reader.close();                } catch (IOException e1) {                }            }        }    }        public void printTrainData()    {        System.out.println("Train Data:\n");        for(int i=0;i<column-1;i++)            System.out.printf("%10s","x"+i+" ");        System.out.printf("%10s","y"+" \n");        for(int i=0;i<row;i++)        {            for(int j=0;j<column;j++)            {                System.out.printf("%10s",trainData[i][j]+" ");            }            System.out.println();        }        System.out.println();    }        public void printTheta()    {        for(double a:theta)            System.out.print(a+" ");    }}

public class TestLinearRegression {    public static void main(String[] args) {        // TODO Auto-generated method stub         LinearRegression m = new LinearRegression("trainData",0.001,1000000);         m.printTrainData();         m.trainTheta();         m.printTheta();    }}