Automatic Differentiation
 
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neg_binomial_2_lpmf.hpp
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1#ifndef STAN_MATH_OPENCL_PRIM_NEG_BINOMIAL_2_LPMF_HPP
2#define STAN_MATH_OPENCL_PRIM_NEG_BINOMIAL_2_LPMF_HPP
3#ifdef STAN_OPENCL
4
12
13namespace stan {
14namespace math {
15
34template <bool propto, typename T_n_cl, typename T_location_cl,
35 typename T_precision_cl,
37 T_n_cl, T_location_cl, T_precision_cl>* = nullptr,
38 require_any_not_stan_scalar_t<T_n_cl, T_location_cl,
39 T_precision_cl>* = nullptr>
41 const T_n_cl& n, const T_location_cl& mu, const T_precision_cl& phi) {
42 static constexpr const char* function = "neg_binomial_2_lpmf(OpenCL)";
43 using T_partials_return
45 using std::isfinite;
46 using std::isnan;
47
48 check_consistent_sizes(function, "Failures variable", n, "Location parameter",
49 mu, "Precision parameter", phi);
50 const size_t N = max_size(n, mu, phi);
51 if (N == 0) {
52 return 0.0;
53 }
54 if constexpr (!include_summand<propto, T_n_cl, T_location_cl,
55 T_precision_cl>::value) {
56 return 0.0;
57 }
58
59 const auto& mu_col = as_column_vector_or_scalar(mu);
60 const auto& phi_col = as_column_vector_or_scalar(phi);
61
62 const auto& mu_val = value_of(mu_col);
63 const auto& phi_val = value_of(phi_col);
64
65 auto check_n_nonnegative
66 = check_cl(function, "Failures variable", n, "nonnegative");
67 auto n_nonnegative = n >= 0;
68 auto check_mu_positive_finite
69 = check_cl(function, "Log location parameter", mu_val, "positive finite");
70 auto mu_positive_finite = 0 < mu_val && isfinite(mu_val);
71 auto check_phi_positive_finite
72 = check_cl(function, "Precision parameter", phi_val, "positive finite");
73 auto phi_positive_finite = 0 < phi_val && isfinite(phi_val);
74
75 auto log_phi = log(phi_val);
76 auto mu_plus_phi = mu_val + phi_val;
77 auto log_mu_plus_phi = log(mu_plus_phi);
78 auto n_plus_phi = n + phi_val;
79
80 auto logp1 = -elt_multiply(phi_val, log1p(elt_divide(mu_val, phi_val)))
81 - elt_multiply(n, log_mu_plus_phi);
82 auto logp2 = static_select<include_summand<propto, T_precision_cl>::value>(
83 logp1 + binomial_coefficient_log(n_plus_phi - 1, n), logp1);
84 auto logp_expr = colwise_sum(
86 logp2 + lmultiply(n, mu_val), logp2));
87
88 auto mu_deriv = elt_divide(n, mu_val) - elt_divide(n + phi_val, mu_plus_phi);
89 auto log_term
90 = select(mu_val < phi_val, log1p(-elt_divide(mu_val, mu_plus_phi)),
91 log_phi - log_mu_plus_phi);
92 auto phi_deriv = elt_divide(mu_val - n, mu_plus_phi) + log_term
93 - digamma(phi_val) + digamma(n_plus_phi);
94
95 matrix_cl<double> logp_cl;
96 matrix_cl<double> mu_deriv_cl;
97 matrix_cl<double> phi_deriv_cl;
98
99 results(check_n_nonnegative, check_mu_positive_finite,
100 check_phi_positive_finite, logp_cl, mu_deriv_cl, phi_deriv_cl)
101 = expressions(n_nonnegative, mu_positive_finite, phi_positive_finite,
102 logp_expr, calc_if<is_autodiff_v<T_location_cl>>(mu_deriv),
103 calc_if<is_autodiff_v<T_precision_cl>>(phi_deriv));
104
105 T_partials_return logp = sum(from_matrix_cl(logp_cl));
106
107 auto ops_partials = make_partials_propagator(mu_col, phi_col);
108
109 if constexpr (is_autodiff_v<T_location_cl>) {
110 partials<0>(ops_partials) = std::move(mu_deriv_cl);
111 }
112 if constexpr (is_autodiff_v<T_precision_cl>) {
113 partials<1>(ops_partials) = std::move(phi_deriv_cl);
114 }
115 return ops_partials.build(logp);
116}
117
118} // namespace math
119} // namespace stan
120#endif
121#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)
select_< as_operation_cl_t< T_condition >, as_operation_cl_t< T_then >, as_operation_cl_t< T_else > > select(T_condition &&condition, T_then &&then, T_else &&els)
Selection operation on kernel generator expressions.
Definition select.hpp:148
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_location_cl, T_precision_cl > neg_binomial_2_lpmf(const T_n_cl &n, const T_location_cl &mu, const T_precision_cl &phi)
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
fvar< T > log(const fvar< T > &x)
Definition log.hpp:18
fvar< T > lmultiply(const fvar< T > &x1, const fvar< T > &x2)
Definition lmultiply.hpp:14
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
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
Template metaprogram to calculate whether a summand needs to be included in a proportional (log) prob...