E.V.E
v2023.02.15
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◆ manhattan

auto eve::manhattan = functor<manhattan_t>
inlineconstexpr

Example

// revision 0
#include <eve/module/core.hpp>
#include <iostream>
#include <iomanip>
int main()
{
eve::wide wf0{0.01, 1.01, 2.01, 3.01, -1.01, -2.01, -3.01, -4.01};
eve::wide wf1{0.01, -4.01, 1.01, -1.01, 2.01, -2.01, 3.01, -3.01};
eve::wide wi0{0, 1, 2, 3, -1, -2, -3, -4};
eve::wide wi1{0, -4, 1, -1, 2, -2, 3, -3};
eve::wide wu0{0u, 1u, 2u, 3u, 4u, 5u, 6u, 7u};
eve::wide wu1{7u, 6u, 5u, 4u, 3u, 2u, 1u, 0u};
std::cout << "<- wf0 = " << wf0 << "\n";
std::cout << "<- wf1 = " << wf1 << "\n";
std::cout << "<- wi0 = " << wi0 << "\n";
std::cout << "<- wi1 = " << wi1 << "\n";
std::cout << "<- wu0 = " << wu0 << "\n";
std::cout << "<- wu1 = " << wu1 << "\n";
std::cout << "-> manhattan(wf0, wf1) = " << eve::manhattan(wf0, wf1) << "\n";
std::cout << "-> manhattan[ignore_last(2)](wf0, wf1) = " << eve::manhattan[eve::ignore_last(2)](wf0, wf1) << "\n";
std::cout << "-> manhattan[wf0 != 0](wf0, wf1) = " << eve::manhattan[wf0 != 0](wf0, wf1) << "\n";
std::cout << "-> manhattan[pedantic](wf0, wf1) = " << eve::manhattan[eve::pedantic](wf0, wf1) << "\n";
std::cout << "-> manhattan(wu0, wu1) = " << eve::manhattan(wu0, wu1) << "\n";
std::cout << "-> manhattan(wi0, wi1) = " << eve::manhattan(wi0, wi1) << "\n";
std::cout << std::hexfloat;
std::cout << "-> manhattan[lower][strict](wf0, wf1) = " << eve::manhattan[eve::lower][eve::strict](wf0, wf1) << "\n";
std::cout << "-> manhattan[lower](wf0, wf1) = " << eve::manhattan[eve::lower](wf0, wf1) << "\n";
std::cout << "-> manhattan(wf0, wf1) = " << eve::manhattan(wf0, wf1) << "\n";
std::cout << "-> manhattan[upper](wf0, wf1) = " << eve::manhattan[eve::upper](wf0, wf1) << "\n";
std::cout << "-> manhattan[upper][strict](wf0, wf1) = " << eve::manhattan[eve::upper][eve::strict](wf0, wf1) << "\n";
auto eps_2 = eve::eps(eve::as<float>())/2;
auto tup = kumi::tuple{1.0f, eps_2, eps_2, eps_2};
std::cout << "-> manhattan[kahan](tup) = " << eve::manhattan[eve::kahan](tup) << "\n";
float o = 1.0f;
float i = eve::inf(eve::as(o));
float n = eve::nan(eve::as(o));
float m = (eve::valmax(eve::as(o))/3)*2;
std::cout << "<- o = " << o << "\n";
std::cout << "-> i = " << i << "\n";
std::cout << "-> n = " << n << "\n";
std::cout << "-> manhattan(i, o, -i) = " << eve::manhattan(i, o, -i) << "\n";
std::cout << "-> manhattan(i, o, n) = " << eve::manhattan(i, o, n) << "\n";
std::cout << "-> manhattan[pedantic](i, o, -i) = " << eve::manhattan[eve::pedantic](i, o, -i) << "\n";
std::cout << "-> manhattan[pedantic](i, o, n) = " << eve::manhattan[eve::pedantic](i, o, n) << "\n";
std::cout << "-> manhattan[pedantic](o, o, n) = " << eve::manhattan[eve::pedantic](o, o, n) << "\n";
std::cout << "-> manhattan[pedantic](o, n, o) = " << eve::manhattan[eve::pedantic](o, n, o) << "\n";
std::cout << "-> manhattan[pedantic](n, o, o) = " << eve::manhattan[eve::pedantic](n, o, o) << "\n";
std::cout << "-> manhattan (o, o, n) = " << eve::manhattan(o, o, n) << "\n";
std::cout << "-> manhattan (o, n, o) = " << eve::manhattan(o, n, o) << "\n";
std::cout << "-> manhattan (n, o, o) = " << eve::manhattan(n, o, o) << "\n";
std::cout << "-> manhattan(n, n, n) = " << eve::manhattan(n, n, n) << "\n";
std::cout << "-> manhattan(m, o, o) = " << eve::manhattan(m, o, o)<< "\n";
std::cout << "-> manhattan(m, m, n) = " << eve::manhattan(m, m, n)<< "\n";
std::cout << "-> manhattan(i, n) = " << eve::manhattan(i, n) << "\n";
std::cout << "-> manhattan[pedantic](m, m, n) = " << eve::manhattan[eve::pedantic](m, m, n)<< "\n";
std::cout << "-> manhattan[pedantic](i, n) = " << eve::manhattan[eve::pedantic](i, n) << "\n";
}
constexpr auto manhattan
tuple_callable object computing the manhattan norm ( ) of its arguments.
Definition manhattan.hpp:100
constexpr auto nan
Computes the IEEE quiet NaN constant.
Definition nan.hpp:67
constexpr auto eps
Computes a constant to the machine epsilon.
Definition eps.hpp:74
constexpr auto valmax
Computes the greatest representable value.
Definition valmax.hpp:67
constexpr auto inf
Computes the infinity ieee value.
Definition inf.hpp:67
constexpr auto strict
Turns the guarantee of lower or upper into a strict inequality.
Definition core.hpp:105
constexpr auto lower
Guarantees a result no greater than the exact mathematical one.
Definition core.hpp:103
constexpr auto upper
Guarantees a result no smaller than the exact mathematical one.
Definition core.hpp:102
constexpr auto pedantic
Follows the corner cases of the corresponding standard function.
Definition core.hpp:91
Lightweight type-wrapper.
Definition as.hpp:29
Conditional expression ignoring the k last lanes from a eve::simd_value.
Definition conditional.hpp:361
Wrapper for SIMD registers.
Definition wide.hpp:94

Header file

#include <eve/module/core.hpp>

Callable Signatures

namespace eve
{
// Regular overloads
constexpr auto manhattan(value auto x, value auto ... xs) noexcept; // 1
constexpr auto manhattan(eve::non_empty_product_type auto const& tup) noexcept; // 2
// Lanes masking
constexpr auto manhattan[conditional_expr auto c](/*any of the above overloads*/) noexcept; // 3
constexpr auto manhattan[logical_value auto m](/*any of the above overloads*/) noexcept; // 3
// Semantic options
constexpr auto manhattan[saturated](/*any of the above overloads*/) noexcept; // 4
constexpr auto manhattan[pedantic](/*any of the above overloads*/) noexcept; // 5
constexpr auto manhattan[kahan](/*any of the above overloads*/) noexcept; // 6
}
Specifies that a type is a Conditional Expression.
Definition conditional.hpp:28
The concept logical_value<T> is satisfied if and only if T satisfies eve::value and the element type ...
Definition value.hpp:134
The concept value<T> is satisfied if and only if T satisfies either eve::scalar_value or eve::simd_va...
Definition value.hpp:34
constexpr auto saturated
Keeps the result inside the range of its type instead of wrapping or overflowing.
Definition core.hpp:104
EVE Main Namespace.
Definition abi.hpp:19

Parameters

Return value

  1. The value of the sum of the absolute value of the arguments is returned.
  2. equivalent to the call on the elements of the tuple.
  3. The operation is performed conditionally
  4. internally uses saturated options.
  5. returns \(\infty\) as soon as after disabling possible Nan parameters the result is \(\infty\).
  6. uses kahan like compensated algorithm for better accuracy.

External references