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#ifndef PYTHONIC_NUMPY_MEDIAN_HPP
#define PYTHONIC_NUMPY_MEDIAN_HPP
#include "pythonic/include/numpy/median.hpp"
#include "pythonic/utils/functor.hpp"
#include "pythonic/types/ndarray.hpp"
#include "pythonic/numpy/asarray.hpp"
#include "pythonic/numpy/sort.hpp"
#include <algorithm>
#include <memory>
PYTHONIC_NS_BEGIN
namespace numpy
{
namespace
{
template <class T_out, class T, class pS>
typename std::enable_if<std::tuple_size<pS>::value != 1, void>::type
_median(T_out *out, types::ndarray<T, pS> const &tmp, long axis)
{
auto tmp_shape = sutils::getshape(tmp);
const long step =
std::accumulate(tmp_shape.begin() + axis, tmp_shape.end(), 1L,
std::multiplies<long>());
long const buffer_size = tmp_shape[axis];
std::unique_ptr<T[]> buffer{new T[buffer_size]};
const long stepper = step / tmp_shape[axis];
const long n = tmp.flat_size() / tmp_shape[axis] * step;
long ith = 0, nth = 0;
for (long i = 0; i < n; i += step) {
T *buffer_iter = buffer.get();
T const *iter = tmp.buffer + ith;
T const *iend = iter + step;
while (iter != iend) {
*buffer_iter++ = *iter;
iter += stepper;
}
if (buffer_size % 2 == 1) {
std::nth_element(buffer.get(), buffer.get() + buffer_size / 2,
buffer_iter, ndarray::comparator<T>{});
*out++ = buffer[buffer_size / 2];
} else {
std::nth_element(buffer.get(), buffer.get() + buffer_size / 2,
buffer_iter, ndarray::comparator<T>{});
auto t0 = buffer[buffer_size / 2];
std::nth_element(buffer.get(), buffer.get() + buffer_size / 2 - 1,
buffer.get() + buffer_size / 2,
ndarray::comparator<T>{});
auto t1 = buffer[buffer_size / 2 - 1];
*out++ = (t0 + t1) / double(2);
}
ith += step;
if (ith - nth == tmp.flat_size()) {
++nth;
ith = nth;
}
}
}
}
template <class T, class pS>
decltype(std::declval<T>() + 1.) median(types::ndarray<T, pS> const &arr,
types::none_type)
{
size_t n = arr.flat_size();
std::unique_ptr<T[]> tmp{new T[n]};
std::copy(arr.buffer, arr.buffer + n, tmp.get());
std::nth_element(tmp.get(), tmp.get() + n / 2, tmp.get() + n,
ndarray::comparator<T>{});
T t0 = tmp[n / 2];
if (n % 2 == 1) {
return t0;
} else {
std::nth_element(tmp.get(), tmp.get() + n / 2 - 1, tmp.get() + n / 2,
ndarray::comparator<T>{});
T t1 = tmp[n / 2 - 1];
return (t0 + t1) / 2.;
}
}
template <class T, class pS>
typename std::enable_if<
std::tuple_size<pS>::value != 1,
types::ndarray<decltype(std::declval<T>() + 1.),
types::array<long, std::tuple_size<pS>::value - 1>>>::type
median(types::ndarray<T, pS> const &arr, long axis)
{
constexpr auto N = std::tuple_size<pS>::value;
if (axis < 0)
axis += N;
types::array<long, std::tuple_size<pS>::value - 1> shp;
auto stmp = sutils::getshape(arr);
auto next = std::copy(stmp.begin(), stmp.begin() + axis, shp.begin());
std::copy(stmp.begin() + axis + 1, stmp.end(), next);
types::ndarray<decltype(std::declval<T>() + 1.),
types::array<long, std::tuple_size<pS>::value - 1>>
out(shp, types::none_type{});
_median(out.buffer, arr, axis);
return out;
}
template <class T, class pS>
typename std::enable_if<std::tuple_size<pS>::value == 1,
decltype(std::declval<T>() + 1.)>::type
median(types::ndarray<T, pS> const &arr, long axis)
{
if (axis != 0)
throw types::ValueError("axis out of bounds");
return median(arr);
}
NUMPY_EXPR_TO_NDARRAY0_IMPL(median);
}
PYTHONIC_NS_END
#endif
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