convolve_filter<T>::convolve_filter(const univector<T>& data, size_t block_size)
: fft(2 * next_poweroftwo(block_size)), size(data.size()), block_size(next_poweroftwo(block_size)),
temp(fft.temp_size),
segments((data.size() + next_poweroftwo(block_size) - 1) / next_poweroftwo(block_size)),
ir_segments((data.size() + next_poweroftwo(block_size) - 1) / next_poweroftwo(block_size)),
input_position(0), position(0)
{
set_data(data);
}
univector<T> correlate(const univector_ref<const T>& src1, const univector_ref<const T>& src2)
{
const size_t size = next_poweroftwo(src1.size() + src2.size() - 1);
univector<complex<T>> src1padded = src1;
univector<complex<T>> src2padded = reverse(src2);
src1padded.resize(size, 0);
src2padded.resize(size, 0);
dft_plan_ptr<T> dft = dft_cache::instance().get(ctype_t<T>(), size);
univector<u8> temp(dft->temp_size);
dft->execute(src1padded, src1padded, temp);
dft->execute(src2padded, src2padded, temp);
src1padded = src1padded * src2padded;
dft->execute(src1padded, src1padded, temp, true);
const T invsize = reciprocal<T>(size);
return truncate(real(src1padded), src1.size() + src2.size() - 1) * invsize;
}
constexpr size_t alignment()
{
return const_min(size_t(platform<>::native_vector_alignment), next_poweroftwo(sizeof(T) * N));
}
convolve_filter<T>::convolve_filter(size_t size, size_t block_size)
: fft(2 * next_poweroftwo(block_size)), size(size), block_size(block_size), temp(fft.temp_size),
segments((size + block_size - 1) / block_size)
{
}
CMT_INLINE T horizontal_impl(const vec<T, N>& value, ReduceFn&& reduce)
{
const T initial = reduce(initialvalue<T>());
return horizontal_impl(widen<next_poweroftwo(N)>(value, initial), std::forward<ReduceFn>(reduce));
}
