Automatic Differentiation
 
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square.hpp
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1#ifndef STAN_MATH_PRIM_FUN_SQUARE_HPP
2#define STAN_MATH_PRIM_FUN_SQUARE_HPP
3
8#include <cmath>
9
10namespace stan {
11namespace math {
12
26inline double square(double x) { return std::pow(x, 2); }
27
35struct square_fun {
36 template <typename T>
37 static inline auto fun(const T& x) {
38 return square(x);
39 }
40};
41
49template <
50 typename Container, require_not_stan_scalar_t<Container>* = nullptr,
54inline auto square(const Container& x) {
56}
57
66template <typename Container,
68inline auto square(const Container& x) {
70 x, [](const auto& v) { return v.array().square(); });
71}
72
73} // namespace math
74} // namespace stan
75
76#endif
require_not_t< container_type_check_base< is_container, scalar_type_t, TypeCheck, Check... > > require_not_container_st
Require type does not satisfy is_container.
require_t< container_type_check_base< is_container, scalar_type_t, TypeCheck, Check... > > require_container_st
Require type satisfies is_container.
require_not_t< is_nonscalar_prim_or_rev_kernel_expression< std::decay_t< T > > > require_not_nonscalar_prim_or_rev_kernel_expression_t
Require type does not satisfy is_nonscalar_prim_or_rev_kernel_expression.
require_not_t< is_stan_scalar< std::decay_t< T > > > require_not_stan_scalar_t
Require type does not satisfy is_stan_scalar.
require_not_t< is_var_matrix< std::decay_t< T > > > require_not_var_matrix_t
Require type does not satisfy is_var_matrix.
fvar< T > square(const fvar< T > &x)
Definition square.hpp:12
The lgamma implementation in stan-math is based on either the reentrant safe lgamma_r implementation ...
Base template class for vectorization of unary scalar functions defined by a template class F to a sc...
static auto fun(const T &x)
Definition square.hpp:37
Structure to wrap square() so that it can be vectorized.
Definition square.hpp:35