Automatic Differentiation
 
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pareto_lpdf.hpp
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1#ifndef STAN_MATH_OPENCL_PRIM_PARETO_LPDF_HPP
2#define STAN_MATH_OPENCL_PRIM_PARETO_LPDF_HPP
3#ifdef STAN_OPENCL
4
12
13namespace stan {
14namespace math {
15
33template <
34 bool propto, typename T_y_cl, typename T_scale_cl, typename T_shape_cl,
36 T_shape_cl>* = nullptr,
37 require_any_not_stan_scalar_t<T_y_cl, T_scale_cl, T_shape_cl>* = nullptr>
39 const T_y_cl& y, const T_scale_cl& y_min, const T_shape_cl& alpha) {
40 static constexpr const char* function = "pareto_lpdf(OpenCL)";
42 using std::isfinite;
43 using std::isnan;
44
45 check_consistent_sizes(function, "Random variable", y, "Scale parameter",
46 y_min, "Shape parameter", alpha);
47 const size_t N = max_size(y, y_min, alpha);
48 if (N == 0) {
49 return 0.0;
50 }
52 return 0.0;
53 }
54
55 const auto& y_col = as_column_vector_or_scalar(y);
56 const auto& y_min_col = as_column_vector_or_scalar(y_min);
57 const auto& alpha_col = as_column_vector_or_scalar(alpha);
58
59 const auto& y_val = value_of(y_col);
60 const auto& y_min_val = value_of(y_min_col);
61 const auto& alpha_val = value_of(alpha_col);
62
63 auto check_y_not_nan
64 = check_cl(function, "Random variable", y_val, "not NaN");
65 auto y_not_nan = !isnan(y_val);
66 auto check_y_min_positive_finite
67 = check_cl(function, "Scale parameter", y_min_val, "positive finite");
68 auto y_min_positive_finite = 0 < y_min_val && isfinite(y_min_val);
69 auto check_alpha_positive_finite
70 = check_cl(function, "Shape parameter", alpha_val, "positive finite");
71 auto alpha_positive_finite = 0 < alpha_val && isfinite(alpha_val);
72
73 auto y_less_than_y_min = colwise_max(cast<char>(y_val < y_min_val));
74 auto log_y = log(y_val);
75 auto inv_y = elt_divide(1.0, y_val);
76 auto log_y_min = log(y_min_val);
77 auto logp1 = static_select<include_summand<propto, T_shape_cl>::value>(
78 log(alpha_val), constant(0.0, N, 1));
79 auto logp2
80 = static_select<include_summand<propto, T_y_cl, T_shape_cl>::value>(
81 logp1 - elt_multiply(alpha_val, log_y) - log_y, logp1);
82 auto logp_expr = colwise_sum(
84 logp2 + elt_multiply(alpha_val, log_y_min), logp2));
85
86 auto y_deriv = -(elt_multiply(alpha_val, inv_y) + inv_y);
87 auto y_min_deriv = elt_divide(alpha_val, y_min_val);
88 auto alpha_deriv = elt_divide(1.0, alpha_val) + log_y_min - log_y;
89
90 matrix_cl<char> y_less_than_y_min_cl;
91 matrix_cl<double> logp_cl;
92 matrix_cl<double> y_min_deriv_cl;
93 matrix_cl<double> y_deriv_cl;
94 matrix_cl<double> alpha_deriv_cl;
95
96 results(check_y_not_nan, check_y_min_positive_finite,
97 check_alpha_positive_finite, y_less_than_y_min_cl, logp_cl,
98 y_deriv_cl, y_min_deriv_cl, alpha_deriv_cl)
99 = expressions(y_not_nan, y_min_positive_finite, alpha_positive_finite,
100 y_less_than_y_min, logp_expr,
104
105 if (from_matrix_cl(y_less_than_y_min_cl).any()) {
106 return LOG_ZERO;
107 }
108
109 T_partials_return logp = sum(from_matrix_cl(logp_cl));
110
111 auto ops_partials = make_partials_propagator(y_col, y_min_col, alpha_col);
112
114 partials<0>(ops_partials) = std::move(y_deriv_cl);
115 }
117 partials<1>(ops_partials) = std::move(y_min_deriv_cl);
118 }
120 partials<2>(ops_partials) = std::move(alpha_deriv_cl);
121 }
122 return ops_partials.build(logp);
123}
124
125} // namespace math
126} // namespace stan
127#endif
128#endif
Represents an arithmetic matrix on the OpenCL device.
Definition matrix_cl.hpp:47
elt_multiply_< as_operation_cl_t< T_a >, as_operation_cl_t< T_b > > elt_multiply(T_a &&a, T_b &&b)
isfinite_< as_operation_cl_t< T > > isfinite(T &&a)
auto check_cl(const char *function, const char *var_name, T &&y, const char *must_be)
Constructs a check on opencl matrix or expression.
Definition check_cl.hpp:219
results_cl< T_results... > results(T_results &&... results)
Deduces types for constructing results_cl object.
auto as_column_vector_or_scalar(T &&a)
as_column_vector_or_scalar of a kernel generator expression.
elt_divide_< as_operation_cl_t< T_a >, as_operation_cl_t< T_b > > elt_divide(T_a &&a, T_b &&b)
auto constant(const T a, int rows, int cols)
Matrix of repeated values in kernel generator expressions.
Definition constant.hpp:130
auto colwise_max(T &&a)
Column wise max - reduction of a kernel generator expression.
calc_if_< true, as_operation_cl_t< T > > calc_if(T &&a)
Definition calc_if.hpp:121
auto colwise_sum(T &&a)
Column wise sum - reduction of a kernel generator expression.
expressions_cl< T_expressions... > expressions(T_expressions &&... expressions)
Deduces types for constructing expressions_cl object.
return_type_t< T_y_cl, T_scale_cl, T_shape_cl > pareto_lpdf(const T_y_cl &y, const T_scale_cl &y_min, const T_shape_cl &alpha)
The log of the Cauchy density for the specified scalar(s) given the specified location parameter(s) a...
auto from_matrix_cl(const T &src)
Copies the source matrix that is stored on the OpenCL device to the destination Eigen matrix.
Definition copy.hpp:61
require_all_t< is_prim_or_rev_kernel_expression< std::decay_t< Types > >... > require_all_prim_or_rev_kernel_expression_t
Require type satisfies is_prim_or_rev_kernel_expression.
typename return_type< Ts... >::type return_type_t
Convenience type for the return type of the specified template parameters.
static constexpr double LOG_ZERO
The natural logarithm of 0, .
Definition constants.hpp:68
size_t max_size(const T1 &x1, const Ts &... xs)
Calculate the size of the largest input.
Definition max_size.hpp:19
constexpr bool any(T x)
Return true if any values in the input are true.
Definition any.hpp:21
T value_of(const fvar< T > &v)
Return the value of the specified variable.
Definition value_of.hpp:18
fvar< T > log(const fvar< T > &x)
Definition log.hpp:15
T1 static_select(T1 &&a, T2 &&b)
Returns one of the arguments that can be of different type, depending on the compile time condition.
void check_consistent_sizes(const char *)
Trivial no input case, this function is a no-op.
fvar< T > sum(const std::vector< fvar< T > > &m)
Return the sum of the entries of the specified standard vector.
Definition sum.hpp:22
auto make_partials_propagator(Ops &&... ops)
Construct an partials_propagator.
typename partials_return_type< Args... >::type partials_return_t
The lgamma implementation in stan-math is based on either the reentrant safe lgamma_r implementation ...
Definition fvar.hpp:9
bool isnan(const stan::math::var &a)
Checks if the given number is NaN.
Definition std_isnan.hpp:18
Metaprogramming struct to detect whether a given type is constant in the mathematical sense (not the ...
Template metaprogram to calculate whether a summand needs to be included in a proportional (log) prob...