单神经元算法框架（线性）

单神经元算法思想：

A.初始化学习参数

B.计算结果Y1，与Y比较：Y-Y1=y1

C.用学习率正向修正（赋正值）学习参数，回到B重新计算y2

                   判断y=y2-y1，如果小于：

                          判断步数是否为零，是则return        

                          否则：记录步数（step++），执行C 

                  否则：

                          反向修正（赋负值）学习率

                          判断步数是否为零，是则return

                          否则：记录步数（step--），执行B

判断Y=Xn

对[X，Y]的学习样本，从样本中求最佳学习参数：

//
//  Compare.hpp
//   Neuron_Algorithm
//
//  Created by 韩立国 on 2021/10/28.
//

#ifndef Compare_hpp
#define Compare_hpp

#include 
#include 
using namespace std;


extern int i;
extern float result;
extern float Amount_change,Amount_change_change;
extern float Compare(int sample_x[], int sample_y[], float *Learning_param, float *ratioq);
extern int step; //步数，控制搜索方向；
extern bool flag_D; //控制游标方向；

#endif 

//
//  Compare.cpp
//   Neuron_Algorithm
//
//  Created by 韩立国 on 2021/10/28.
//

#include 
#include "Compare.hpp"
#include 
#include 
using namespace std;
bool flag_D=true;
float Compare(int sample_x[],int sample_y[],float *Learning_param,float *ratioq)
{
    //float result=0;
    //float Amount_change,Amount_change_change=0;
    if(flag_D==true)
    {
    step++;
    result=(*Learning_param)* sample_x[i];
    Amount_change= abs(result-sample_y[i]);
    result=((*Learning_param)+ (*ratioq) )* sample_x[i];
    Amount_change_change =abs(result-sample_y[i]);
    cout<<"result is n"<1&&step>200&&step<-200) flag_D=false;
    return  Amount_change_change/Amount_change;
    }else{
        step--;
        result=(*Learning_param)* sample_x[i];
        Amount_change= abs(result-sample_y[i]);
        result=((*Learning_param)-(*ratioq) )* sample_x[i];
        Amount_change_change =abs(result-sample_y[i]);
        cout<<"result is n"< 
//
//  Single_neuron_algorithm_param.hpp
//   Neuron_Algorithm
//
//  Created by 韩立国 on 2021/10/28.
//

#ifndef Single_neuron_algorithm_param_hpp
#define Single_neuron_algorithm_param_hpp

#include 
#include "Compare.hpp"



extern int sample_x[]; //样本用例数据；
extern int sample_y[]; //样本用例数据；
extern int n;         //样本数据对数；
extern float Learning_param;  //初始化用学习参数；
extern float ratio;     // 初始化学习率；

extern float  Single_neuron_algorithm_param (int sample_x[], int sample_y[], int n, float *Learning_param, float *ratio);

#endif 
 
//
//  Single_neuron_algorithm_param.cpp
//   Neuron_Algorithm
//
//  Created by 韩立国 on 2021/10/28.
//

#include 
#include "Compare.hpp"
#include "Single_neuron_algorithm_param.hpp"
using namespace std;

float  Single_neuron_algorithm_param (int sample_x[], int sample_y[], int n, float *Learning_param, float *ratioq)
{
int i;
float  direction, param[n],Learning_param_result=0;
for(i=0; i<=n; i++)
{
    while ((direction=Compare(&sample_x[i], &sample_y[i], Learning_param, ratioq))!=0&&step<200&&step>-200)
        {
            cout<<"direction is n"< 
//
//  Neuron audit.hpp
//   Neuron_Algorithm
//
//  Created by 韩立国 on 2021/10/28.
//

#ifndef Neuron_audit_hpp
#define Neuron_audit_hpp

#include 
#include "Compare.hpp"
#include "Single_neuron_algorithm_param.hpp"


extern float data_x[]; //实测数据；
extern float data_y[]; //预测数据；
extern int *Nn;        //实测数据对数；
extern float *Learning_param_result; //最优学习参数；
extern float Neuron_audit(float data_x[],float data_y[],int *Nn,float *Learning_param_result);

#endif 
 
//
//  Neuron audit.cpp
//   Neuron_Algorithm
//
//  Created by 韩立国 on 2021/10/28.
//

#include 
#include "Compare.hpp"
#include "Single_neuron_algorithm_param.hpp"
#include "Neuron audit.hpp"


float Neuron_audit(float data_x[],float data_y[],int *n,float *Learning_param_result)
{
    int i ;
    for (i=0;i<=*n;i++)
    {
        data_y[i]=(*Learning_param_result)*data_x[i];
    }
    return 0;
}
 
//
//  main.cpp
//   Neuron_Algorithm
//
//  Created by 韩立国 on 2021/10/28.
//


#include 
#include 
#include "Compare.hpp"
#include "Single_neuron_algorithm_param.hpp"
#include "Neuron audit.hpp"

using namespace std;
int sample_x[]={12,21,31,41,51,61,71,81,1,91,221};
int sample_y[]={12,43,303,2,45,585,64,77,85,808,1242};
int n=11;
float Learning_param=7;
float ratioq =5;
int i=0;
float result=0;
float Amount_change=0;
float Amount_change_change=0;
int step=11;

int main(int argc, const char * argv[]) {
    float s=0;
    cout << "Hello, Welcome to single neuron algorithm!n";
    cout << "Please enter a test sample!n";
    s=Single_neuron_algorithm_param(sample_x,sample_y,n,&Learning_param,&ratioq);
    cout <<"resaltn"< 
					
										


					

						
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