Stan Math Library  2.14.0
reverse mode automatic differentiation
operator_addition.hpp
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1 #ifndef STAN_MATH_REV_CORE_OPERATOR_ADDITION_HPP
2 #define STAN_MATH_REV_CORE_OPERATOR_ADDITION_HPP
3 
8 #include <limits>
9 
10 namespace stan {
11  namespace math {
12 
13  namespace {
14  class add_vv_vari : public op_vv_vari {
15  public:
16  add_vv_vari(vari* avi, vari* bvi) :
17  op_vv_vari(avi->val_ + bvi->val_, avi, bvi) {
18  }
19  void chain() {
20  if (unlikely(is_nan(avi_->val_)
21  || is_nan(bvi_->val_))) {
22  avi_->adj_ = std::numeric_limits<double>::quiet_NaN();
23  bvi_->adj_ = std::numeric_limits<double>::quiet_NaN();
24  } else {
25  avi_->adj_ += adj_;
26  bvi_->adj_ += adj_;
27  }
28  }
29  };
30 
31  class add_vd_vari : public op_vd_vari {
32  public:
33  add_vd_vari(vari* avi, double b) :
34  op_vd_vari(avi->val_ + b, avi, b) {
35  }
36  void chain() {
37  if (unlikely(is_nan(avi_->val_)
38  || is_nan(bd_)))
39  avi_->adj_ = std::numeric_limits<double>::quiet_NaN();
40  else
41  avi_->adj_ += adj_;
42  }
43  };
44  }
45 
84  inline var operator+(const var& a, const var& b) {
85  return var(new add_vv_vari(a.vi_, b.vi_));
86  }
87 
99  inline var operator+(const var& a, double b) {
100  if (b == 0.0)
101  return a;
102  return var(new add_vd_vari(a.vi_, b));
103  }
104 
116  inline var operator+(double a, const var& b) {
117  if (a == 0.0)
118  return b;
119  return var(new add_vd_vari(b.vi_, a)); // by symmetry
120  }
121 
122  }
123 }
124 #endif
Independent (input) and dependent (output) variables for gradients.
Definition: var.hpp:30
#define unlikely(x)
Definition: likely.hpp:9
vari * vi_
Pointer to the implementation of this variable.
Definition: var.hpp:42
fvar< T > operator+(const fvar< T > &x1, const fvar< T > &x2)
int is_nan(const fvar< T > &x)
Returns 1 if the input&#39;s value is NaN and 0 otherwise.
Definition: is_nan.hpp:21

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