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Reuse intermediate computations in distributions part 2 #1752

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Reuse intermediate computations in distributions part 2 #1752

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41af3de
Reuse intermediate computations in chi_square and [scaled_]inv_chi_sq…
mcol Feb 28, 2020
0abd9f1
Reuse intermediate computations in gamma and inv_gamma
mcol Feb 28, 2020
e851f2c
Move size_zero checks after arguments checks and further cleanups
mcol Mar 2, 2020
cae9b46
Merge commit 'f0341173706be97c95f5b76af06f27a0fa597791' into HEAD
yashikno Mar 2, 2020
0680496
[Jenkins] auto-formatting by clang-format version 6.0.0 (tags/google/…
stan-buildbot Mar 2, 2020
b597f07
Merge branch 'develop' into cleanup/1230-better-intermediates-prob-p2
mcol Mar 6, 2020
6cd5d46
Remove duplicated using statements introduced by a bad merge
mcol Mar 6, 2020
356de66
Merge remote-tracking branch 'origin' into cleanup/1230-better-interm…
bbbales2 Jul 19, 2020
60a574d
Add using std::double to gamma cdf (Issue #1763)
bbbales2 Jul 19, 2020
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38 changes: 19 additions & 19 deletions stan/math/prim/prob/chi_square_cdf.hpp
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Original file line number Diff line number Diff line change
Expand Up @@ -50,6 +50,7 @@ return_type_t<T_y, T_dof> chi_square_cdf(const T_y& y, const T_dof& nu) {

scalar_seq_view<T_y> y_vec(y);
scalar_seq_view<T_dof> nu_vec(nu);
size_t size_nu = stan::math::size(nu);
size_t N = max_size(y, nu);

operands_and_partials<T_y, T_dof> ops_partials(y, nu);
Expand All @@ -65,16 +66,17 @@ return_type_t<T_y, T_dof> chi_square_cdf(const T_y& y, const T_dof& nu) {
using std::exp;
using std::pow;

VectorBuilder<!is_constant_all<T_y, T_dof>::value, T_partials_return, T_dof>
tgamma_vec(size_nu);
VectorBuilder<!is_constant_all<T_dof>::value, T_partials_return, T_dof>
gamma_vec(size(nu));
VectorBuilder<!is_constant_all<T_dof>::value, T_partials_return, T_dof>
digamma_vec(size(nu));

if (!is_constant_all<T_dof>::value) {
for (size_t i = 0; i < stan::math::size(nu); i++) {
const T_partials_return alpha_dbl = value_of(nu_vec[i]) * 0.5;
gamma_vec[i] = tgamma(alpha_dbl);
digamma_vec[i] = digamma(alpha_dbl);
digamma_vec(size_nu);
if (!is_constant_all<T_y, T_dof>::value) {
for (size_t i = 0; i < size_nu; i++) {
const T_partials_return half_nu_dbl = 0.5 * value_of(nu_vec[i]);
tgamma_vec[i] = tgamma(half_nu_dbl);
if (!is_constant_all<T_dof>::value) {
digamma_vec[i] = digamma(half_nu_dbl);
}
}
}

Expand All @@ -85,23 +87,21 @@ return_type_t<T_y, T_dof> chi_square_cdf(const T_y& y, const T_dof& nu) {
continue;
}

const T_partials_return y_dbl = value_of(y_vec[n]);
const T_partials_return alpha_dbl = value_of(nu_vec[n]) * 0.5;
const T_partials_return beta_dbl = 0.5;

const T_partials_return Pn = gamma_p(alpha_dbl, beta_dbl * y_dbl);
const T_partials_return half_nu_dbl = 0.5 * value_of(nu_vec[n]);
const T_partials_return half_y_dbl = 0.5 * value_of(y_vec[n]);
const T_partials_return Pn = gamma_p(half_nu_dbl, half_y_dbl);

cdf *= Pn;

if (!is_constant_all<T_y>::value) {
ops_partials.edge1_.partials_[n] += beta_dbl * exp(-beta_dbl * y_dbl)
* pow(beta_dbl * y_dbl, alpha_dbl - 1)
/ tgamma(alpha_dbl) / Pn;
ops_partials.edge1_.partials_[n] += 0.5 * exp(-half_y_dbl)
* pow(half_y_dbl, half_nu_dbl - 1)
/ (tgamma_vec[n] * Pn);
}
if (!is_constant_all<T_dof>::value) {
ops_partials.edge2_.partials_[n]
-= 0.5
* grad_reg_inc_gamma(alpha_dbl, beta_dbl * y_dbl, gamma_vec[n],
* grad_reg_inc_gamma(half_nu_dbl, half_y_dbl, tgamma_vec[n],
digamma_vec[n])
/ Pn;
}
Expand All @@ -113,7 +113,7 @@ return_type_t<T_y, T_dof> chi_square_cdf(const T_y& y, const T_dof& nu) {
}
}
if (!is_constant_all<T_dof>::value) {
for (size_t n = 0; n < stan::math::size(nu); ++n) {
for (size_t n = 0; n < size_nu; ++n) {
ops_partials.edge2_.partials_[n] *= cdf;
}
}
Expand Down
49 changes: 24 additions & 25 deletions stan/math/prim/prob/chi_square_lccdf.hpp
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Original file line number Diff line number Diff line change
Expand Up @@ -51,59 +51,58 @@ return_type_t<T_y, T_dof> chi_square_lccdf(const T_y& y, const T_dof& nu) {

scalar_seq_view<T_y> y_vec(y);
scalar_seq_view<T_dof> nu_vec(nu);
size_t size_nu = stan::math::size(nu);
size_t N = max_size(y, nu);

operands_and_partials<T_y, T_dof> ops_partials(y, nu);

// Explicit return for extreme values
// The gradients are technically ill-defined, but treated as zero
for (size_t i = 0; i < stan::math::size(y); i++) {
if (value_of(y_vec[i]) == 0) {
const T_partials_return y_dbl = value_of(y_vec[i]);
if (y_dbl == 0) {
return ops_partials.build(0.0);
}
if (y_dbl == INFTY) {
return ops_partials.build(NEGATIVE_INFTY);
}
}

using std::exp;
using std::log;
using std::pow;

VectorBuilder<true, T_partials_return, T_dof> half_nu(size_nu);
VectorBuilder<!is_constant_all<T_y, T_dof>::value, T_partials_return, T_dof>
tgamma_vec(size_nu);
VectorBuilder<!is_constant_all<T_dof>::value, T_partials_return, T_dof>
gamma_vec(size(nu));
VectorBuilder<!is_constant_all<T_dof>::value, T_partials_return, T_dof>
digamma_vec(size(nu));

if (!is_constant_all<T_dof>::value) {
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for (size_t i = 0; i < stan::math::size(nu); i++) {
const T_partials_return alpha_dbl = value_of(nu_vec[i]) * 0.5;
gamma_vec[i] = tgamma(alpha_dbl);
digamma_vec[i] = digamma(alpha_dbl);
digamma_vec(size_nu);
for (size_t i = 0; i < size_nu; i++) {
const T_partials_return half_nu_dbl = 0.5 * value_of(nu_vec[i]);
half_nu[i] = half_nu_dbl;
if (!is_constant_all<T_y, T_dof>::value) {
tgamma_vec[i] = tgamma(half_nu[i]);
}
if (!is_constant_all<T_dof>::value) {
digamma_vec[i] = digamma(half_nu_dbl);
}
}

for (size_t n = 0; n < N; n++) {
// Explicit results for extreme values
// The gradients are technically ill-defined, but treated as zero
if (value_of(y_vec[n]) == INFTY) {
return ops_partials.build(negative_infinity());
}

const T_partials_return y_dbl = value_of(y_vec[n]);
const T_partials_return alpha_dbl = value_of(nu_vec[n]) * 0.5;
const T_partials_return beta_dbl = 0.5;

const T_partials_return Pn = gamma_q(alpha_dbl, beta_dbl * y_dbl);
const T_partials_return half_y_dbl = 0.5 * value_of(y_vec[n]);
const T_partials_return Pn = gamma_q(half_nu[n], half_y_dbl);

ccdf_log += log(Pn);

if (!is_constant_all<T_y>::value) {
ops_partials.edge1_.partials_[n] -= beta_dbl * exp(-beta_dbl * y_dbl)
* pow(beta_dbl * y_dbl, alpha_dbl - 1)
/ tgamma(alpha_dbl) / Pn;
ops_partials.edge1_.partials_[n] -= 0.5 * exp(-half_y_dbl)
* pow(half_y_dbl, half_nu[n] - 1)
/ (tgamma_vec[n] * Pn);
}
if (!is_constant_all<T_dof>::value) {
ops_partials.edge2_.partials_[n]
+= 0.5
* grad_reg_inc_gamma(alpha_dbl, beta_dbl * y_dbl, gamma_vec[n],
* grad_reg_inc_gamma(half_nu[n], half_y_dbl, tgamma_vec[n],
digamma_vec[n])
/ Pn;
}
Expand Down
51 changes: 25 additions & 26 deletions stan/math/prim/prob/chi_square_lcdf.hpp
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Original file line number Diff line number Diff line change
Expand Up @@ -51,59 +51,58 @@ return_type_t<T_y, T_dof> chi_square_lcdf(const T_y& y, const T_dof& nu) {

scalar_seq_view<T_y> y_vec(y);
scalar_seq_view<T_dof> nu_vec(nu);
size_t size_nu = stan::math::size(nu);
size_t N = max_size(y, nu);

operands_and_partials<T_y, T_dof> ops_partials(y, nu);

// Explicit return for extreme values
// The gradients are technically ill-defined, but treated as zero
for (size_t i = 0; i < stan::math::size(y); i++) {
if (value_of(y_vec[i]) == 0) {
return ops_partials.build(negative_infinity());
const T_partials_return y_dbl = value_of(y_vec[i]);
if (y_dbl == 0) {
return ops_partials.build(NEGATIVE_INFTY);
}
if (y_dbl == INFTY) {
return ops_partials.build(0.0);
}
}

using std::exp;
using std::log;
using std::pow;

VectorBuilder<true, T_partials_return, T_dof> half_nu(size_nu);
VectorBuilder<!is_constant_all<T_y, T_dof>::value, T_partials_return, T_dof>
tgamma_vec(size_nu);
VectorBuilder<!is_constant_all<T_dof>::value, T_partials_return, T_dof>
gamma_vec(size(nu));
VectorBuilder<!is_constant_all<T_dof>::value, T_partials_return, T_dof>
digamma_vec(size(nu));

if (!is_constant_all<T_dof>::value) {
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for (size_t i = 0; i < stan::math::size(nu); i++) {
const T_partials_return alpha_dbl = value_of(nu_vec[i]) * 0.5;
gamma_vec[i] = tgamma(alpha_dbl);
digamma_vec[i] = digamma(alpha_dbl);
digamma_vec(size_nu);
for (size_t i = 0; i < size_nu; i++) {
const T_partials_return half_nu_dbl = 0.5 * value_of(nu_vec[i]);
half_nu[i] = half_nu_dbl;
if (!is_constant_all<T_y, T_dof>::value) {
tgamma_vec[i] = tgamma(half_nu[i]);
}
if (!is_constant_all<T_dof>::value) {
digamma_vec[i] = digamma(half_nu_dbl);
}
}

for (size_t n = 0; n < N; n++) {
// Explicit results for extreme values
// The gradients are technically ill-defined, but treated as zero
if (value_of(y_vec[n]) == INFTY) {
return ops_partials.build(0.0);
}

const T_partials_return y_dbl = value_of(y_vec[n]);
const T_partials_return alpha_dbl = value_of(nu_vec[n]) * 0.5;
const T_partials_return beta_dbl = 0.5;

const T_partials_return Pn = gamma_p(alpha_dbl, beta_dbl * y_dbl);
const T_partials_return half_y_dbl = 0.5 * value_of(y_vec[n]);
const T_partials_return Pn = gamma_p(half_nu[n], half_y_dbl);

cdf_log += log(Pn);

if (!is_constant_all<T_y>::value) {
ops_partials.edge1_.partials_[n] += beta_dbl * exp(-beta_dbl * y_dbl)
* pow(beta_dbl * y_dbl, alpha_dbl - 1)
/ tgamma(alpha_dbl) / Pn;
ops_partials.edge1_.partials_[n] += 0.5 * exp(-half_y_dbl)
* pow(half_y_dbl, half_nu[n] - 1)
/ (tgamma_vec[n] * Pn);
}
if (!is_constant_all<T_dof>::value) {
ops_partials.edge2_.partials_[n]
-= 0.5
* grad_reg_inc_gamma(alpha_dbl, beta_dbl * y_dbl, gamma_vec[n],
* grad_reg_inc_gamma(half_nu[n], half_y_dbl, tgamma_vec[n],
digamma_vec[n])
/ Pn;
}
Expand Down
18 changes: 0 additions & 18 deletions stan/math/prim/prob/chi_square_log.hpp
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Original file line number Diff line number Diff line change
Expand Up @@ -8,25 +8,7 @@ namespace stan {
namespace math {

/** \ingroup prob_dists
* The log of a chi-squared density for y with the specified
* degrees of freedom parameter.
* The degrees of freedom prarameter must be greater than 0.
* y must be greater than or equal to 0.
*
\f{eqnarray*}{
y &\sim& \chi^2_\nu \\
\log (p (y \, |\, \nu)) &=& \log \left( \frac{2^{-\nu / 2}}{\Gamma (\nu / 2)}
y^{\nu / 2 - 1} \exp^{- y / 2} \right) \\
&=& - \frac{\nu}{2} \log(2) - \log (\Gamma (\nu / 2)) + (\frac{\nu}{2} - 1)
\log(y) - \frac{y}{2} \\ & & \mathrm{ where } \; y \ge 0 \f}
*
* @deprecated use <code>chi_square_lpdf</code>
* @param y A scalar variable.
* @param nu Degrees of freedom.
* @throw std::domain_error if nu is not greater than or equal to 0
* @throw std::domain_error if y is not greater than or equal to 0.
* @tparam T_y Type of scalar.
* @tparam T_dof Type of degrees of freedom.
*/
template <bool propto, typename T_y, typename T_dof>
return_type_t<T_y, T_dof> chi_square_log(const T_y& y, const T_dof& nu) {
Expand Down
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