Stan Math Library  2.14.0
reverse mode automatic differentiation
grad_F32.hpp
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1 #ifndef STAN_MATH_PRIM_SCAL_FUN_GRAD_F32_HPP
2 #define STAN_MATH_PRIM_SCAL_FUN_GRAD_F32_HPP
3 
4 #include <cmath>
5 
6 namespace stan {
7  namespace math {
8 
9  template<typename T>
10  void grad_F32(T* g, T a, T b, T c, T d, T e, T z, T precision = 1e-6) {
11  using std::log;
12  using std::fabs;
13  using std::exp;
14 
15  T gOld[6];
16 
17  for (T *p = g; p != g + 6; ++p) *p = 0;
18  for (T *p = gOld; p != gOld + 6; ++p) *p = 0;
19 
20  T tOld = 1;
21  T tNew = 0;
22 
23  T logT = 0;
24 
25  T logZ = log(z);
26 
27  int k = 0;
28 
29  while (fabs(tNew) > precision || k == 0) {
30  T C = (a + k) / (d + k);
31  C *= (b + k) / (e + k);
32  C *= (c + k) / (1 + k);
33 
34  // If a, b, or c is a negative integer then the series terminates
35  // after a finite number of interations
36  if (C == 0) break;
37 
38  logT += (C > 0 ? 1 : -1) * log(fabs(C)) + logZ;
39 
40  tNew = exp(logT);
41 
42  gOld[0] = tNew * (gOld[0] / tOld + 1.0 / (a + k));
43  gOld[1] = tNew * (gOld[1] / tOld + 1.0 / (b + k));
44  gOld[2] = tNew * (gOld[2] / tOld + 1.0 / (c + k));
45 
46  gOld[3] = tNew * (gOld[3] / tOld - 1.0 / (d + k));
47  gOld[4] = tNew * (gOld[4] / tOld - 1.0 / (e + k));
48 
49  gOld[5] = tNew * (gOld[5] / tOld + 1.0 / z);
50 
51  for (int i = 0; i < 6; ++i) g[i] += gOld[i];
52 
53  tOld = tNew;
54 
55  ++k;
56  }
57  }
58 
59  }
60 }
61 #endif
fvar< T > fabs(const fvar< T > &x)
Definition: fabs.hpp:15
fvar< T > log(const fvar< T > &x)
Definition: log.hpp:14
fvar< T > exp(const fvar< T > &x)
Definition: exp.hpp:10
double e()
Return the base of the natural logarithm.
Definition: constants.hpp:94
void grad_F32(T *g, T a, T b, T c, T d, T e, T z, T precision=1e-6)
Definition: grad_F32.hpp:10

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