统计学习中感知机的C++代码

感知机是古老的统计学习方法，主要应用于二类线性可分数据，策略是在给定的超平面上对误差点进行纠正，从而保证所有的点都是正确可分的。

用到的方法是随机梯度下降法，由于是线性可分的，可保证最终在有限步内收敛。具体可参考李航的《统计学习方法》

#include<iostream>

#include<algorithm>

#include<vector>

#include<fstream>

using namespace std;

typedef vector<double> feature;

typedef int label;

class PercepMachine

{

private:

    vector<feature> dataset;

    vector<label> labelset;

    double learningrate;

    double vector_multi (const feature &x, const feature &y)

    {

        double sum = 0.0;

        for (int i = 0; i != x.size(); ++i)

        {

            sum += x[i] * y[i];

        }

        return sum;

    }

    feature vector_multi(double x, const feature &y)

    {

        feature temp;

        for (int i = 0; i != y.size(); ++i)

        {

            temp.push_back(x*y[i]);

        }

        return temp;

    }

    feature vector_add(const feature &x, const feature &y)

    {

        feature temp(0);

        for (int i = 0; i != x.size(); ++i)

        {

            temp.push_back(x[i] + y[i]);

        }

        return temp;

    }

public:

    feature w;

    double b;

    PercepMachine(vector<feature> &traindata, vector<label> &trainlabel, feature &startw, double startb, double rate) :dataset(traindata), labelset(trainlabel), w(startw), b(startb), learningrate(rate){}

    void calculate_percep();

};

void PercepMachine::calculate_percep()

{

    vector<int> flag(dataset.size(), 1);

    while (find(flag.begin(), flag.end(), 1) != flag.end())

    {

        for (int i = 0; i != dataset.size(); ++i)

        {

            double multi = vector_multi(dataset[i], w);

            if ((multi + b)*labelset[i] <= 0)//有误分类点

            {

                flag[i] = 1;

                w = vector_add(w, vector_multi(learningrate*labelset[i], dataset[i]));

                b = b + learningrate*labelset[i];

            }

            else

            {

                flag[i] = 0;

            }

        }

    }

}

int main()

{

    ifstream  fin("data.txt");

    if (!fin)

    {

        cout << "can not open the file data.txt" << endl;

        exit(1);

    }

    /* input the dataSet 假设是平面数据，存储在txt文件中3列多行，最后一列存储类别信息1或-1*/

    int feature_dimension = 2;

    vector<feature> traindata;

    vector<label> trainlabel;

    while (!fin.eof())

    {

        feature temp_data;

        double temp;

        for (int i = 0; i < feature_dimension; ++i)

        {

            fin >> temp;

            temp_data.push_back(temp);

        }

        traindata.push_back(temp_data);

        label mylabel;

        fin >> mylabel;

        trainlabel.push_back(mylabel);

    }

    feature startw(2,1);

    double startb = 1.0;

    double rate = 0.5;

    PercepMachine permachine(traindata, trainlabel, startw, startb, rate);

    permachine.calculate_percep();

    cout << "w=" << "("<<permachine.w[0] << " " << permachine.w[1]<<")" << endl;

    cout << "b=" << permachine.b << endl;

    return 0;

}