Automatic Differentiation
 
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uniform_lcdf.hpp
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1#ifndef STAN_MATH_OPENCL_PRIM_UNIFORM_LCDF_HPP
2#define STAN_MATH_OPENCL_PRIM_UNIFORM_LCDF_HPP
3#ifdef STAN_OPENCL
4
12
13namespace stan {
14namespace math {
15
29template <typename T_y_cl, typename T_low_cl, typename T_high_cl,
31 T_high_cl>* = nullptr,
32 require_any_not_stan_scalar_t<T_y_cl, T_low_cl, T_high_cl>* = nullptr>
34 const T_low_cl& alpha,
35 const T_high_cl& beta) {
36 static constexpr const char* function = "uniform_lcdf(OpenCL)";
37 using T_partials_return = partials_return_t<T_y_cl, T_low_cl, T_high_cl>;
38 using std::isfinite;
39 using std::isnan;
40
41 check_consistent_sizes(function, "Random variable", y, "Location parameter",
42 alpha, "Scale parameter", beta);
43 const size_t N = max_size(y, alpha, beta);
44 if (N == 0) {
45 return 0.0;
46 }
47
48 const auto& y_col = as_column_vector_or_scalar(y);
49 const auto& alpha_col = as_column_vector_or_scalar(alpha);
50 const auto& beta_col = as_column_vector_or_scalar(beta);
51
52 const auto& y_val = value_of(y_col);
53 const auto& alpha_val = value_of(alpha_col);
54 const auto& beta_val = value_of(beta_col);
55
56 auto check_y_not_nan
57 = check_cl(function, "Random variable", y_val, "not NaN");
58 auto y_not_nan_expr = !isnan(y_val);
59 auto check_alpha_finite
60 = check_cl(function, "Lower bound parameter", alpha_val, "finite");
61 auto alpha_finite_expr = isfinite(alpha_val);
62 auto check_beta_finite
63 = check_cl(function, "Upper bound parameter", beta_val, "finite");
64 auto beta_finite_expr = isfinite(beta_val);
65 auto b_minus_a = beta_val - alpha_val;
66 auto check_diff_positive = check_cl(
67 function, "Difference between upper and lower bound parameters", beta_val,
68 "positive");
69 auto diff_positive_expr = b_minus_a > 0.0;
70
71 auto any_y_out_of_bounds
72 = colwise_max(cast<char>(y_val < alpha_val || y_val > beta_val));
73 auto y_minus_alpha = y_val - alpha_val;
74 auto cdf_n = elt_divide(y_minus_alpha, b_minus_a);
75 auto lcdf_expr = colwise_sum(log(cdf_n));
76
77 auto y_deriv = elt_divide(1.0, y_minus_alpha);
78 auto low_deriv
79 = elt_divide(y_val - beta_val, elt_multiply(b_minus_a, y_minus_alpha));
80 auto high_deriv = elt_divide(-1.0, b_minus_a);
81
82 matrix_cl<char> any_y_out_of_bounds_cl;
83 matrix_cl<double> lcdf_cl;
84 matrix_cl<double> alpha_deriv_cl;
85 matrix_cl<double> y_deriv_cl;
86 matrix_cl<double> beta_deriv_cl;
87
88 results(check_y_not_nan, check_alpha_finite, check_beta_finite,
89 check_diff_positive, any_y_out_of_bounds_cl, lcdf_cl, y_deriv_cl,
90 alpha_deriv_cl, beta_deriv_cl)
91 = expressions(y_not_nan_expr, alpha_finite_expr, beta_finite_expr,
92 diff_positive_expr, any_y_out_of_bounds, lcdf_expr,
96
97 if (from_matrix_cl(any_y_out_of_bounds_cl).maxCoeff()) {
98 return -INFINITY;
99 }
100
101 T_partials_return lcdf = from_matrix_cl(lcdf_cl).sum();
102
103 auto ops_partials = make_partials_propagator(y_col, alpha_col, beta_col);
104
106 partials<0>(ops_partials) = std::move(y_deriv_cl);
107 }
109 partials<1>(ops_partials) = std::move(alpha_deriv_cl);
110 }
112 partials<2>(ops_partials) = std::move(beta_deriv_cl);
113 }
114 return ops_partials.build(lcdf);
115}
116
117} // namespace math
118} // namespace stan
119#endif
120#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 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_low_cl, T_high_cl > uniform_lcdf(const T_y_cl &y, const T_low_cl &alpha, const T_high_cl &beta)
Returns the log uniform cumulative distribution function for the given location, and scale.
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.
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
void check_consistent_sizes(const char *)
Trivial no input case, this function is a no-op.
int64_t max_size(const T1 &x1, const Ts &... xs)
Calculate the size of the largest input.
Definition max_size.hpp:20
fvar< T > beta(const fvar< T > &x1, const fvar< T > &x2)
Return fvar with the beta function applied to the specified arguments and its gradient.
Definition beta.hpp:51
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 ...
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 ...