c++13.cpp

#include<bits/stdc++.h> // header file for all c++ libraries
using namespace std;   // stdout library for printing values 
bool custom_sort(double a, double b) /* this custom sort function is defined to 
                                     sort on basis of min absolute value or error*/
{
    double  a1=abs(a-0);
    double  b1=abs(b-0);
    return a1<b1;
}
int main()
{
/*Intialization Phase*/
double x1[] = {2.7810836, 1.465489372, 3.396561688, 1.38807019, 3.06407232,7.627531214,5.332441248,6.922596716,8.675418651 ,7.673756466};                          
double x2[] = {2.550537003,2.362125076,4.400293529,1.850220317,3.005305973,2.759262235,2.088626775,1.77106367,-0.2420686549,3.508563011};
double y[] = {0, 0, 0, 0, 0, 1, 1, 1, 1, 1};
 
vector<double>error; // for storing the error values
double err;    // for calculating error on each stage
double b0 = 0; // initializing b0
double b1 = 0; // initializing b1
double b2=  0; // initializing b2
double alpha = 0.01; // initializing our learning rate
double  e = 2.71828
  
/*Training Phase*/
for (int i = 0; i < 40; i ++) { //Since there are 10 values in our dataset and we want to run for 4 epochs so total for loop run 40 times
    int idx = i % 10;   //for accessing index after every epoch
    double p = -(b0 + b1 * x1[idx]+ b2* x2[idx]); //making the prediction
    double pred  = 1/(1+ pow(e,p)); //calculating final prediction applying sigmoid
    err = y[idx]-pred;  //calculating the error
    b0 = b0 - alpha * err*pred *(1-pred)* 1.0;   //updating b0
    b1 = b1 + alpha * err * pred*(1-pred) *x1[idx];//updating b1
    b2 = b2 + alpha * err * pred*(1-pred) * x2[idx];//updating b2
    cout<<"B0="<<b0<<" "<<"B1="<<b1<<" "<<"B2="<<b2<<" error="<<err<<endl;// printing values after every step
    error.push_back(err);
    }
sort(error.begin(),error.end(),custom_sort);//custom sort based on absolute error difference
cout<<"Final Values are: "<<"B0="<<b0<<" "<<"B1="<<b1<<" "<<"B2="<<b2<<" error="<<error[0];
  
/*Testing Phase*/
double test1,test2; //enter test x1 and x2
cin>>test1>>test2;
double pred=b0+b1*test1+b2*test2; //make prediction
cout<<"The value predicted by the model= "<<pred<<endl;
if(pred>0.5)
pred=1;
else
pred=0;
cout<<"The class predicted by the model= "<<pred;
}