Automatic Differentiation
 
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neg_binomial_lpmf.hpp
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1#ifndef STAN_MATH_OPENCL_PRIM_NEG_BINOMIAL_LPMF_HPP
2#define STAN_MATH_OPENCL_PRIM_NEG_BINOMIAL_LPMF_HPP
3#ifdef STAN_OPENCL
4
12
13namespace stan {
14namespace math {
15
34template <bool propto, typename T_n_cl, typename T_shape_cl,
35 typename T_inv_scale_cl,
37 T_n_cl, T_shape_cl, T_inv_scale_cl>* = nullptr,
38 require_any_not_stan_scalar_t<T_n_cl, T_shape_cl,
39 T_inv_scale_cl>* = nullptr>
41 const T_n_cl& n, const T_shape_cl& alpha, const T_inv_scale_cl& beta) {
42 static constexpr const char* function = "neg_binomial_lpmf(OpenCL)";
43 using T_partials_return
45 using std::isfinite;
46 using std::isnan;
47
48 check_consistent_sizes(function, "Failures variable", n, "Shape parameter",
49 alpha, "Inverse scale parameter", beta);
50 const size_t N = max_size(n, alpha, beta);
51 if (N == 0) {
52 return 0.0;
53 }
55 return 0.0;
56 }
57
58 const auto& alpha_col = as_column_vector_or_scalar(alpha);
59 const auto& beta_col = as_column_vector_or_scalar(beta);
60
61 const auto& alpha_val = value_of(alpha_col);
62 const auto& beta_val = value_of(beta_col);
63
64 auto check_n_nonnegative
65 = check_cl(function, "Failures variable", n, "nonnegative");
66 auto n_nonnegative = n >= 0;
67 auto check_alpha_positive_finite
68 = check_cl(function, "Shape parameter", alpha_val, "positive finite");
69 auto alpha_positive_finite = 0 < alpha_val && isfinite(alpha_val);
70 auto check_beta_positive_finite = check_cl(
71 function, "Inverse scale parameter", beta_val, "positive finite");
72 auto beta_positive_finite = 0 < beta_val && isfinite(beta_val);
73
74 auto digamma_alpha = digamma(alpha_val);
75 auto log1p_inv_beta = log1p(elt_divide(1.0, beta_val));
76 auto log1p_beta = log1p(beta_val);
77 auto lambda_m_alpha_over_1p_beta
78 = elt_divide(alpha_val, beta_val) - elt_divide(alpha_val, 1.0 + beta_val);
79
80 auto logp1
81 = -elt_multiply(alpha_val, log1p_inv_beta) - elt_multiply(n, log1p_beta);
82 auto logp_expr
84 logp1
85 + binomial_coefficient_log(n + alpha_val - 1.0, alpha_val - 1.0),
86 logp1));
87
88 auto alpha_deriv = digamma(alpha_val + n) - digamma_alpha - log1p_inv_beta;
89 auto beta_deriv = lambda_m_alpha_over_1p_beta - elt_divide(n, beta_val + 1.0);
90
91 matrix_cl<double> logp_cl;
92 matrix_cl<double> alpha_deriv_cl;
93 matrix_cl<double> beta_deriv_cl;
94
95 results(check_n_nonnegative, check_alpha_positive_finite,
96 check_beta_positive_finite, logp_cl, alpha_deriv_cl, beta_deriv_cl)
97 = expressions(n_nonnegative, alpha_positive_finite, beta_positive_finite,
98 logp_expr,
101
102 T_partials_return logp = sum(from_matrix_cl(logp_cl));
103
104 auto ops_partials = make_partials_propagator(alpha_col, beta_col);
105
107 partials<0>(ops_partials) = std::move(alpha_deriv_cl);
108 }
110 partials<1>(ops_partials) = std::move(beta_deriv_cl);
111 }
112 return ops_partials.build(logp);
113}
114
115} // namespace math
116} // namespace stan
117#endif
118#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.
binomial_coefficient_log_< as_operation_cl_t< T1 >, as_operation_cl_t< T2 > > binomial_coefficient_log(T1 &&a, T2 &&b)
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)
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_n_cl, T_shape_cl, T_inv_scale_cl > neg_binomial_lpmf(const T_n_cl &n, const T_shape_cl &alpha, const T_inv_scale_cl &beta)
The log of the negative binomial density for the specified scalars given the specified mean(s) and de...
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.
require_any_not_t< is_stan_scalar< std::decay_t< Types > >... > require_any_not_stan_scalar_t
Require at least one of the types do not satisfy is_stan_scalar.
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
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 > log1p(const fvar< T > &x)
Definition log1p.hpp:12
auto sum(const std::vector< T > &m)
Return the sum of the entries of the specified standard vector.
Definition sum.hpp:23
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.
fvar< T > digamma(const fvar< T > &x)
Return the derivative of the log gamma function at the specified argument.
Definition digamma.hpp:23
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 ...
Template metaprogram to calculate whether a summand needs to be included in a proportional (log) prob...