Practical Neural Network Recipies in C++

376

Chapter

20

I

int lo, mid, hi ;

double p ;

if (observed > x[n-1])

return 100. * (1.0 - rprob[n-1]) ;

if (observed <= x[0])

return 0. ;

lo = 0 ; //At all times we keep x[lo] < observed

hi = n-1 ; // and x[hi] >= observed

for (;;) { // This loop cuts interval in half each time

mid = (lo + hi) / 2 ; // Center of interval

if (mid == lo) // Happens when lo and hi adjacent

break ; // So then we are done

if (x[mid] < observed) // Replace appropriate intval end with mid

lo = mid ;

else

hi = mid ;

}

p = (observed - x[hi-1]) / (x[hi] - x[hi-1]) * (rprob[hi] - rprob[hi-1]) ;

p += rprob[hi-1] ;

return 100. * (1.0-p) ;

Bayesian Methods

The method fo