C++ Neural Networks and Fuzzy Logic by Valluru B. Rao M&T Books, IDG Books Worldwide, Inc. ISBN: 1558515526   Pub Date: 06/01/95

Let’s look at the implementation file in Listing 3.2.

Listing 3.2 fuzzfier.cpp

// fuzzfier.cpp      V. Rao, H. Rao
// program to fuzzify data

#include <iostream.h>
#include <stdlib.h>
#include <time.h>
#include <string.h>
#include <fuzzfier.h>

void category::setname(char *n)
{
strcpy(name,n);
}

char * category::getname()
{
return name;
}

void category::setval(float &h, float &m, float &l)
{
highval=h;
midval=m;
lowval=l;
}

float category::getlowval()
{
return lowval;
}

float category::getmidval()
{
return midval;
}

float category::gethighval()
{
return highval;
}

float category::getshare(const float & input)
{
// this member function returns the relative membership
// of an input in a category, with a maximum of 1.0

float output;
float midlow, highmid;

midlow=midval-lowval;
highmid=highval-midval;

// if outside the range, then output=0
if ((input <= lowval) || (input >= highval))
     output=0;

else
     {

     if (input > midval)

          output=(highval-input)/highmid;

     else

     if (input==midval)

          output=1.0;

     else

          output=(input-lowval)/midlow;

     }
return output;

}

int randomnum(int maxval)
{
// random number generator
// will return an integer up to maxval

srand ((unsigned)time(NULL));
return rand() % maxval;
}

void main()
{
// a fuzzifier program that takes category information:
// lowval, midval and highval and category name
// and fuzzifies an input based on
// the total number of categories and the membership
// in each category

int i=0,j=0,numcat=0,randnum;
float l,m,h, inval=1.0;

char input[30]="               ";
category * ptr[10];
float relprob[10];
float total=0, runtotal=0;

//input the category information; terminate with `done';

while (1)

     {

     cout << "\nPlease type in a category name, e.g. Cool\n";
     cout << "Enter one word without spaces\n";
     cout << "When you are done, type `done' :\n\n";

     ptr[i]= new category;
     cin >> input;

     if ((input[0]=='d' && input[1]=='o' &&
             input[2]=='n' && input[3]=='e')) break;

     ptr[i]->setname(input);

     cout << "\nType in the lowval, midval and highval\n";
     cout << "for each category, separated by spaces\n";
     cout << " e.g. 1.0 3.0 5.0 :\n\n";

     cin >> l >> m >> h;
     ptr[i]->setval(h,m,l);

     i++;

     }

numcat=i; // number of categories

// Categories set up: Now input the data to fuzzify
cout <<"\n\n";
cout << "===================================\n";
cout << "==Fuzzifier is ready for data==\n";
cout << "===================================\n";

while (1)

     {
     cout << "\ninput a data value, type 0 to terminate\n";

     cin >> inval;

     if (inval == 0) break;

     // calculate relative probabilities of
     //   input being in each category

     total=0;
     for (j=0;j<numcat;j++)
          {
          relprob[j]=100*ptr[j]->getshare(inval);
          total+=relprob[j];
          }

     if (total==0)
          {
          cout << "data out of range\n";
          exit(1);
          }

     randnum=randomnum((int)total);

     j=0;
     runtotal=relprob[0];

     while ((runtotal<randnum)&&(j<numcat))
          {
          j++;
          runtotal += relprob[j];
          }

     cout << "\nOutput fuzzy category is ==> " <<
          ptr[j]->getname()<<"<== \n";

          cout <<"category\t"<<"membership\n";
          cout <<"---------------\n";

     for (j=0;j<numcat;j++)
          {
          cout << ptr[j]->getname()<<"\t\t"<<
               (relprob[j]/total) <<"\n";
          }

     }

cout << "\n\nAll done. Have a fuzzy day !\n";

}

This program first sets up all the categories you define. These could be for the example we choose or any example you can think of. After the categories are defined, you can start entering data to be fuzzified. As you enter data you see the probability aspect come into play. If you enter the same value twice, you may end up with different categories! You will see sample output shortly, but first a technical note on how the weighted probabilities are set up. The best way to explain it is with an example. Suppose that you have defined three categories, A, B, and C. Suppose that category A has a relative membership of 0.8, category B of 0.4, and category C of 0.2. In the program, these numbers are first multiplied by 100, so you end up with A=80, B=40, and C=20. Now these are stored in a vector with an index j initialized to point to the first category. Let’s say that these three numbers represent three adjacent number bins that are joined together. Now pick a random number to index into the bin that has its maximum value of (80+40+20). If the number is 100, then it is greater than 80 and less than (80+40), you end up in the second bin that represents B. Does this scheme give you weighted probabilities? Yes it does, since the size of the bin (given a uniform distribution of random indexes into it) determines the probability of falling into the bin. Therefore, the probability of falling into bin A is 80/(80+40+20).