Stan Math Library  2.11.0
reverse mode automatic differentiation
decouple_ode_states.hpp
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1 #ifndef STAN_MATH_REV_ARR_FUN_DECOUPLE_ODE_STATES_HPP
2 #define STAN_MATH_REV_ARR_FUN_DECOUPLE_ODE_STATES_HPP
3 
4 #include <stan/math/rev/core.hpp>
7 #include <vector>
8 
9 namespace stan {
10 
11  namespace math {
12 
34  template <typename T_initial, typename T_param>
35  inline
36  std::vector<std::vector<typename stan::return_type<T_initial,
37  T_param>::type> >
38  decouple_ode_states(const std::vector<std::vector<double> >& y,
39  const std::vector<T_initial>& y0,
40  const std::vector<T_param>& theta) {
41  using std::vector;
42  using stan::math::var;
44 
45  vector<typename stan::return_type<T_initial, T_param>::type> vars;
46  typedef stan::is_var<T_initial> initial_var;
47  typedef stan::is_var<T_param> param_var;
48 
49  const size_t N = y0.size();
50  const size_t M = theta.size();
51  const size_t S = (initial_var::value ? N : 0)
52  + (param_var::value ? M : 0);
53 
54  vars.reserve(S);
55  if (initial_var::value)
56  vars.insert(vars.end(), y0.begin(), y0.end());
57  if (param_var::value)
58  vars.insert(vars.end(), theta.begin(), theta.end());
59 
60  vector<var> temp_vars(N);
61  vector<double> temp_gradients(S);
62  vector<vector<var> > y_return(y.size());
63 
64  for (size_t i = 0; i < y.size(); ++i) {
65  for (size_t j = 0; j < N; ++j) {
66  for (size_t k = 0; k < S; ++k) {
67  temp_gradients[k] = y[i][N + N * k + j];
68  }
69  temp_vars[j] = precomputed_gradients(y[i][j],
70  vars, temp_gradients);
71  }
72  y_return[i] = temp_vars;
73  }
74  return y_return;
75  }
76 
86  template <>
87  inline
88  std::vector<std::vector<double> >
89  decouple_ode_states(const std::vector<std::vector<double> >& y,
90  const std::vector<double>& y0,
91  const std::vector<double>& theta) {
92  return y;
93  }
94 
95  }
96 }
97 #endif
var precomputed_gradients(const double value, const std::vector< var > &operands, const std::vector< double > &gradients)
This function returns a var for an expression that has the specified value, vector of operands...
Metaprogram to calculate the base scalar return type resulting from promoting all the scalar types of...
Definition: return_type.hpp:19
Independent (input) and dependent (output) variables for gradients.
Definition: var.hpp:31
std::vector< std::vector< typename stan::return_type< T_initial, T_param >::type > > decouple_ode_states(const std::vector< std::vector< double > > &y, const std::vector< T_initial > &y0, const std::vector< T_param > &theta)
Takes sensitivity output from integrators and returns results in precomputed_gradients format...

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