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[Jenkins] auto-formatting by clang-format version 10.0.0-4ubuntu1
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Lines changed: 7 additions & 10 deletions

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stan/math/prim/prob/wiener5_lpdf.hpp

Lines changed: 5 additions & 6 deletions
Original file line numberDiff line numberDiff line change
@@ -532,8 +532,7 @@ inline auto wiener5_grad_w(const T_y& y, const T_a& a, const T_v& v,
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// - 0.5 log(1 + sv^2 y)
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// + [-v^2 y + 2 a v (1-w) + a^2 (1-w)^2 sv^2]
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// / [2 (1 + sv^2 y)].
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const auto pref_grad_w
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= (-a * v - square(a) * sv_sqr * q) / one_plus_svsqr_y;
535+
const auto pref_grad_w = (-a * v - square(a) * sv_sqr * q) / one_plus_svsqr_y;
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538537
// Use the density branch decision. The derivative is the derivative of
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// the same scalar value, so do not let the w-gradient path switch to a
@@ -546,13 +545,13 @@ inline auto wiener5_grad_w(const T_y& y, const T_a& a, const T_v& v,
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const auto n_small_grad
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= wiener5_n_terms_small_t<false, GradientCalc::ON>(y, a, w, log_error);
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const auto n_large_grad
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= wiener5_gradient_large_reaction_time_terms<GradientCalc::ON>(
550-
y, a, w, log_error);
548+
= wiener5_gradient_large_reaction_time_terms<GradientCalc::ON>(y, a, w,
549+
log_error);
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552551
const int n_small = static_cast<int>(
553-
fmax(value_of_rec(n_small_density), value_of_rec(n_small_grad)));
552+
fmax(value_of_rec(n_small_density), value_of_rec(n_small_grad)));
554553
const int n_large = static_cast<int>(
555-
fmax(value_of_rec(n_large_density), value_of_rec(n_large_grad)));
554+
fmax(value_of_rec(n_large_density), value_of_rec(n_large_grad)));
556555

557556
ret_t series_grad_w = 0.0;
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test/unit/math/mix/prob/wiener_lpdf_ad_test.cpp

Lines changed: 2 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -24,8 +24,7 @@ TEST(MathMixProbWienerLpdf, fiveParamZeroSvWGradientExpectAd) {
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2525
TEST(MathMixProbWienerLpdf, fullParamWGradientExpectAd) {
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auto f = [](const auto& w) {
27-
return stan::math::wiener_lpdf(6.0, 10.0, 0.01, w, -3.0, 0.2, 0.1,
28-
0.0);
27+
return stan::math::wiener_lpdf(6.0, 10.0, 0.01, w, -3.0, 0.2, 0.1, 0.0);
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};
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stan::test::ad_tolerances tols;
@@ -67,8 +66,7 @@ TEST(MathMixProbWienerLpdf, existingFullRowsWGradientExpectAd) {
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SCOPED_TRACE(c.name);
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auto f = [c](const auto& w) {
70-
return stan::math::wiener_lpdf(c.y, c.a, c.t0, w, c.v, c.sv, c.sw,
71-
c.st0);
69+
return stan::math::wiener_lpdf(c.y, c.a, c.t0, w, c.v, c.sv, c.sw, c.st0);
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};
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// The row sweep is intended to check the reverse-mode w adjoint against

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