}
template<typename _InputIterator, typename _OutputIterator,
- typename _UnaryOperation>
+ typename _Tp>
_OutputIterator
- __transform(_InputIterator __first, _InputIterator __last,
- _OutputIterator __result, _UnaryOperation __unary_op)
+ __normalize(_InputIterator __first, _InputIterator __last,
+ _OutputIterator __result, const _Tp& __factor)
{
for (; __first != __last; ++__first, ++__result)
- *__result = __unary_op(*__first);
+ *__result = *__first / __factor;
return __result;
}
const double __sum = std::accumulate(_M_prob.begin(),
_M_prob.end(), 0.0);
// Now normalize the probabilites.
- __detail::__transform(_M_prob.begin(), _M_prob.end(), _M_prob.begin(),
- std::bind2nd(std::divides<double>(), __sum));
+ __detail::__normalize(_M_prob.begin(), _M_prob.end(), _M_prob.begin(),
+ __sum);
// Accumulate partial sums.
_M_cp.reserve(_M_prob.size());
std::partial_sum(_M_prob.begin(), _M_prob.end(),
const double __sum = std::accumulate(_M_den.begin(),
_M_den.end(), 0.0);
- __detail::__transform(_M_den.begin(), _M_den.end(), _M_den.begin(),
- std::bind2nd(std::divides<double>(), __sum));
+ __detail::__normalize(_M_den.begin(), _M_den.end(), _M_den.begin(),
+ __sum);
_M_cp.reserve(_M_den.size());
std::partial_sum(_M_den.begin(), _M_den.end(),
}
// Now normalize the densities...
- __detail::__transform(_M_den.begin(), _M_den.end(), _M_den.begin(),
- std::bind2nd(std::divides<double>(), __sum));
+ __detail::__normalize(_M_den.begin(), _M_den.end(), _M_den.begin(),
+ __sum);
// ... and partial sums...
- __detail::__transform(_M_cp.begin(), _M_cp.end(), _M_cp.begin(),
- std::bind2nd(std::divides<double>(), __sum));
+ __detail::__normalize(_M_cp.begin(), _M_cp.end(), _M_cp.begin(), __sum);
// ... and slopes.
- __detail::__transform(_M_m.begin(), _M_m.end(), _M_m.begin(),
- std::bind2nd(std::divides<double>(), __sum));
+ __detail::__normalize(_M_m.begin(), _M_m.end(), _M_m.begin(), __sum);
+
// Make sure the last cumulative probablility is one.
_M_cp[_M_cp.size() - 1] = 1.0;
}