原文地址
How to build a simple neural network in 9 lines of Python code

这个项目是我们c艹老师叫我们用c艹重写的，给出了模板程序，要求通过eigen库进行改进使得循环尽可能少
下面给出个人实现
其中值得一提的是，对于求一个行向量的各个数的倒数时，采用的是化为对角阵后求逆再化为行向量的方式
而两个行向量的按位向乘采用了化为对角阵后进行矩阵乘法的方式

#include <iostream>
#include <Dense>
using Eigen::MatrixXd;
using namespace std;
MatrixXd weights(1, 3), input(4, 3), output(1, 4), errors(1, 4), guess(1, 4), problem(3, 1), adjust(1, 3);



double GetRand() //随机生成(-1, 1)的数
{
    return 2.0 * rand() / RAND_MAX - 1.0;
}

int main() {
    srand((unsigned int)time(0));
    /*
    input << 0, 0, 1,
        1, 1, 1,
        1, 0, 1,
        0, 1, 1;
    */
        input << 1, 1, 1,
            0, 0, 1,
            1, 1, 1,
            0, 0, 1;

   // output << 0, 1, 1, 0;
    output << 1, 0, 1, 0;
  //  problem << 0, 0, 0;
    problem << 1, 0, 0;
    for (int i = 0; i < 3; i++)
        weights(0, i) = GetRand();
    for (int k = 0; k < 10000; k++) {
        MatrixXd x(1, 4), a(1, 4);
        a << 1, 1, 1, 1;
        x = (input * weights.transpose());
        x = -x;
        guess = ((x.array().exp().matrix().transpose() + a).asDiagonal()).inverse().diagonal();
        errors = output - guess;
        adjust << 0, 0, 0;
        a << 1, 1, 1, 1;
        adjust = (input.transpose() * (((a - guess) * guess.asDiagonal()) * errors.asDiagonal()).transpose()).transpose();
        weights += adjust;
    }
    double y;
    MatrixXd x(1, 1);
    x = weights * problem;
    y = 1 / (1 + exp(-x(0, 0)));
    cout << "guess [1,0,0] is " << y << endl;
    return 0;
}