Example paper - Solution needed

 
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//Program to illustrate the perceptron training rule

#include <iostream>
#include <cstdlib>
#include <ctime>
#include <cmath>

using namespace std;

int
main()
{
      float w [4]; //array to hold perceptron weights
      float t, o;
      const double eta = 1;
      int i;
      int count = 0;
      bool correct = 0;

      //define and initialize training data
      int train [4] [4] =              {  0, 1, 0, 0,
                                          1, 0, 0, 1,
                                          0, 0, 1, 0,
                                          1, 1, 1, 1 };

      int x0, x1, x2, x3; //will hold the inputs
      float weightsum, deltaw1, deltaw2, deltaw3;

      //initialize random number generator
      srand((unsigned)(time(0)));
      rand();

      //set bias
      x0 = 1;
      //w[0] = fabs((float)(rand())/(32767/2)-1);
      w[0] = -1;
      for ( i = 1; i < 4; ++i)
	//  w = (float)(rand())/(32767/2) - 1;
	w = 0;
 
      cout << "INITIAL WEIGHTS" << endl
             << "---------------" << endl;
      cout << "w0 = " << w[0] << endl;
      cout << "w1 = " << w[1] << endl;
      cout << "w2 = " << w[2] << endl;
      cout << "w3 = " << w[3] << endl;
     

      //implement perceptron training rule as long as training examples are
      //classified incorrectly
      while(!correct)
      {
            correct = 1;
            count++;
            for (i = 0; i < 4; ++i)
            {
                  x1 = train[0]; //input for x1
                  x2 = train[1]; //input for x2
		  x3 = train[2];
                  //find weighted sum of inputs and threshold values
                  weightsum = x0 * w[0] + x1 * w[1] + x2 * w[2] + x3 * w[3];
                  //determine output of perceptron
                  if (weightsum > 0) o = 1;
                  else o = 0;
                  cout << "training set no " << i + 1;
		  cout << " o = " << o;
                  //determine true ouput
                  t = train[3];
		  cout << " d = " << t << endl;
                  //if the ouput is incorrect adjust the weights.
                  if (o != t)
                  {
                        deltaw1 = eta * (t - o) * x1;
                        w[1] = w[1] + deltaw1;
                        deltaw2 = eta * (t - o) * x2;
                        w[2] = w[2] + deltaw2;
			deltaw3 = eta * (t - o) * x3;
                        w[3] = w[3] + deltaw3;
                        correct = 0;
			cout << endl << endl;
			cout << "NEW WEIGHTS" << endl
			     << "-------------" << endl;
			cout << "w0 = " << w[0] << endl;
			cout << "w1 = " << w[1] << endl;
			cout << "w2 = " << w[2] << endl;
			cout << "w3 = " << w[3] << endl;

                  }
            }
      }

      //OUTPUT
      cout << endl << endl;
      cout << "FINAL WEIGHTS" << endl
             << "-------------" << endl;
      cout << "w0 = " << w[0] << endl;
      cout << "w1 = " << w[1] << endl;
      cout << "w2 = " << w[2] << endl;
      cout << "w3 = " << w[3] << endl;
      cout << "The delta rules were invoked " << count << " times"<<endl;
      return 0;
}

INITIAL WEIGHTS
---------------
w0 = -1
w1 = 0
w2 = 0
w3 = 0
training set no 1 o = 0 d = 0
training set no 2 o = 0 d = 1


NEW WEIGHTS
-------------
w0 = -1
w1 = 1
w2 = 0
w3 = 0
training set no 3 o = 0 d = 0
training set no 4 o = 0 d = 1


NEW WEIGHTS
-------------
w0 = -1
w1 = 2
w2 = 1
w3 = 1
training set no 1 o = 0 d = 0
training set no 2 o = 1 d = 1
training set no 3 o = 0 d = 0
training set no 4 o = 1 d = 1


FINAL WEIGHTS
-------------
w0 = -1
w1 = 2
w2 = 1
w3 = 1
The delta rules were invoked 2 times

 
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