Beckmann Distribution sample, tan2theta, alphax != alphay, u1 <= 0.5

Average Error: 12.5 → 0.6

Time: 14.6s

Precision: binary32

Cost: 17284

\[\left(\left(\left(\left(0.0001 \leq alphax \land alphax \leq 1\right) \land \left(0.0001 \leq alphay \land alphay \leq 1\right)\right) \land \left(2.328306437 \cdot 10^{-10} \leq u0 \land u0 \leq 1\right)\right) \land \left(0 \leq cos2phi \land cos2phi \leq 1\right)\right) \land 0 \leq sin2phi\]

Math

\[\frac{-\log \left(1 - u0\right)}{\frac{cos2phi}{alphax \cdot alphax} + \frac{sin2phi}{alphay \cdot alphay}} \]

↓

\[\begin{array}{l} t_0 := \frac{cos2phi}{alphax \cdot alphax} + \frac{sin2phi}{alphay \cdot alphay}\\ t_1 := \log \left(1 - u0\right)\\ \mathbf{if}\;1 - u0 \leq 0.9599999785423279:\\ \;\;\;\;\frac{-\left(t_1 \cdot \frac{1}{\left(t_1 \cdot \frac{1}{t_1}\right) \cdot t_1}\right) \cdot t_1}{t_0}\\ \mathbf{else}:\\ \;\;\;\;\frac{-\left(-1 \cdot u0 + \left(-0.3333333333333333 \cdot {u0}^{3} + \left(-0.5 \cdot {u0}^{2} + -0.25 \cdot {u0}^{4}\right)\right)\right)}{t_0}\\ \end{array} \]

FPCore

(FPCore (alphax alphay u0 cos2phi sin2phi)
 :precision binary32
 (/
  (- (log (- 1.0 u0)))
  (+ (/ cos2phi (* alphax alphax)) (/ sin2phi (* alphay alphay)))))

↓

(FPCore (alphax alphay u0 cos2phi sin2phi)
 :precision binary32
 (let* ((t_0 (+ (/ cos2phi (* alphax alphax)) (/ sin2phi (* alphay alphay))))
        (t_1 (log (- 1.0 u0))))
   (if (<= (- 1.0 u0) 0.9599999785423279)
     (/ (- (* (* t_1 (/ 1.0 (* (* t_1 (/ 1.0 t_1)) t_1))) t_1)) t_0)
     (/
      (-
       (+
        (* -1.0 u0)
        (+
         (* -0.3333333333333333 (pow u0 3.0))
         (+ (* -0.5 (pow u0 2.0)) (* -0.25 (pow u0 4.0))))))
      t_0))))

C

float code(float alphax, float alphay, float u0, float cos2phi, float sin2phi) {
	return -logf((1.0f - u0)) / ((cos2phi / (alphax * alphax)) + (sin2phi / (alphay * alphay)));
}

↓

float code(float alphax, float alphay, float u0, float cos2phi, float sin2phi) {
	float t_0 = (cos2phi / (alphax * alphax)) + (sin2phi / (alphay * alphay));
	float t_1 = logf((1.0f - u0));
	float tmp;
	if ((1.0f - u0) <= 0.9599999785423279f) {
		tmp = -((t_1 * (1.0f / ((t_1 * (1.0f / t_1)) * t_1))) * t_1) / t_0;
	} else {
		tmp = -((-1.0f * u0) + ((-0.3333333333333333f * powf(u0, 3.0f)) + ((-0.5f * powf(u0, 2.0f)) + (-0.25f * powf(u0, 4.0f))))) / t_0;
	}
	return tmp;
}

Fortran

real(4) function code(alphax, alphay, u0, cos2phi, sin2phi)
    real(4), intent (in) :: alphax
    real(4), intent (in) :: alphay
    real(4), intent (in) :: u0
    real(4), intent (in) :: cos2phi
    real(4), intent (in) :: sin2phi
    code = -log((1.0e0 - u0)) / ((cos2phi / (alphax * alphax)) + (sin2phi / (alphay * alphay)))
end function

↓

real(4) function code(alphax, alphay, u0, cos2phi, sin2phi)
    real(4), intent (in) :: alphax
    real(4), intent (in) :: alphay
    real(4), intent (in) :: u0
    real(4), intent (in) :: cos2phi
    real(4), intent (in) :: sin2phi
    real(4) :: t_0
    real(4) :: t_1
    real(4) :: tmp
    t_0 = (cos2phi / (alphax * alphax)) + (sin2phi / (alphay * alphay))
    t_1 = log((1.0e0 - u0))
    if ((1.0e0 - u0) <= 0.9599999785423279e0) then
        tmp = -((t_1 * (1.0e0 / ((t_1 * (1.0e0 / t_1)) * t_1))) * t_1) / t_0
    else
        tmp = -(((-1.0e0) * u0) + (((-0.3333333333333333e0) * (u0 ** 3.0e0)) + (((-0.5e0) * (u0 ** 2.0e0)) + ((-0.25e0) * (u0 ** 4.0e0))))) / t_0
    end if
    code = tmp
end function

Julia

function code(alphax, alphay, u0, cos2phi, sin2phi)
	return Float32(Float32(-log(Float32(Float32(1.0) - u0))) / Float32(Float32(cos2phi / Float32(alphax * alphax)) + Float32(sin2phi / Float32(alphay * alphay))))
end

↓

function code(alphax, alphay, u0, cos2phi, sin2phi)
	t_0 = Float32(Float32(cos2phi / Float32(alphax * alphax)) + Float32(sin2phi / Float32(alphay * alphay)))
	t_1 = log(Float32(Float32(1.0) - u0))
	tmp = Float32(0.0)
	if (Float32(Float32(1.0) - u0) <= Float32(0.9599999785423279))
		tmp = Float32(Float32(-Float32(Float32(t_1 * Float32(Float32(1.0) / Float32(Float32(t_1 * Float32(Float32(1.0) / t_1)) * t_1))) * t_1)) / t_0);
	else
		tmp = Float32(Float32(-Float32(Float32(Float32(-1.0) * u0) + Float32(Float32(Float32(-0.3333333333333333) * (u0 ^ Float32(3.0))) + Float32(Float32(Float32(-0.5) * (u0 ^ Float32(2.0))) + Float32(Float32(-0.25) * (u0 ^ Float32(4.0))))))) / t_0);
	end
	return tmp
end

MATLAB

function tmp = code(alphax, alphay, u0, cos2phi, sin2phi)
	tmp = -log((single(1.0) - u0)) / ((cos2phi / (alphax * alphax)) + (sin2phi / (alphay * alphay)));
end

↓

function tmp_2 = code(alphax, alphay, u0, cos2phi, sin2phi)
	t_0 = (cos2phi / (alphax * alphax)) + (sin2phi / (alphay * alphay));
	t_1 = log((single(1.0) - u0));
	tmp = single(0.0);
	if ((single(1.0) - u0) <= single(0.9599999785423279))
		tmp = -((t_1 * (single(1.0) / ((t_1 * (single(1.0) / t_1)) * t_1))) * t_1) / t_0;
	else
		tmp = -((single(-1.0) * u0) + ((single(-0.3333333333333333) * (u0 ^ single(3.0))) + ((single(-0.5) * (u0 ^ single(2.0))) + (single(-0.25) * (u0 ^ single(4.0)))))) / t_0;
	end
	tmp_2 = tmp;
end

Derivation

1. Split input into 2 regimes

2. if (-.f32 1 u0) < 0.959999979

   1. Initial program 1.6

      \[\frac{-\log \left(1 - u0\right)}{\frac{cos2phi}{alphax \cdot alphax} + \frac{sin2phi}{alphay \cdot alphay}} \]

   2. Applied egg-rr 1.6

      \[\leadsto \frac{-\color{blue}{\left(\log \left(1 - u0\right) \cdot \frac{1}{\log \left(1 - u0\right)}\right) \cdot \log \left(1 - u0\right)}}{\frac{cos2phi}{alphax \cdot alphax} + \frac{sin2phi}{alphay \cdot alphay}} \]

   3. Applied egg-rr 1.6

      \[\leadsto \frac{-\left(\log \left(1 - u0\right) \cdot \frac{1}{\color{blue}{\left(\log \left(1 - u0\right) \cdot \frac{1}{\log \left(1 - u0\right)}\right) \cdot \log \left(1 - u0\right)}}\right) \cdot \log \left(1 - u0\right)}{\frac{cos2phi}{alphax \cdot alphax} + \frac{sin2phi}{alphay \cdot alphay}} \]

   if 0.959999979 < (-.f32 1 u0)

   1. Initial program 14.3

      \[\frac{-\log \left(1 - u0\right)}{\frac{cos2phi}{alphax \cdot alphax} + \frac{sin2phi}{alphay \cdot alphay}} \]

   2. Taylor expanded in u0 around 0 0.5

      \[\leadsto \frac{-\color{blue}{\left(-1 \cdot u0 + \left(-0.5 \cdot {u0}^{2} + \left(-0.3333333333333333 \cdot {u0}^{3} + -0.25 \cdot {u0}^{4}\right)\right)\right)}}{\frac{cos2phi}{alphax \cdot alphax} + \frac{sin2phi}{alphay \cdot alphay}} \]

   3. Simplified 0.5

      \[\leadsto \frac{-\color{blue}{\left(-1 \cdot u0 + \left(-0.3333333333333333 \cdot {u0}^{3} + \left(-0.5 \cdot {u0}^{2} + -0.25 \cdot {u0}^{4}\right)\right)\right)}}{\frac{cos2phi}{alphax \cdot alphax} + \frac{sin2phi}{alphay \cdot alphay}} \]
      Proof

      [Start] 0.5	\[ \frac{-\left(-1 \cdot u0 + \left(-0.5 \cdot {u0}^{2} + \left(-0.3333333333333333 \cdot {u0}^{3} + -0.25 \cdot {u0}^{4}\right)\right)\right)}{\frac{cos2phi}{alphax \cdot alphax} + \frac{sin2phi}{alphay \cdot alphay}} \]
      rational_best_oopsla_all_46_json_45_simplify-82 [=>] 0.5	\[ \frac{-\left(-1 \cdot u0 + \color{blue}{\left(-0.3333333333333333 \cdot {u0}^{3} + \left(-0.5 \cdot {u0}^{2} + -0.25 \cdot {u0}^{4}\right)\right)}\right)}{\frac{cos2phi}{alphax \cdot alphax} + \frac{sin2phi}{alphay \cdot alphay}} \]

3. Recombined 2 regimes into one program.

4. Final simplification 0.6

   \[\leadsto \begin{array}{l} \mathbf{if}\;1 - u0 \leq 0.9599999785423279:\\ \;\;\;\;\frac{-\left(\log \left(1 - u0\right) \cdot \frac{1}{\left(\log \left(1 - u0\right) \cdot \frac{1}{\log \left(1 - u0\right)}\right) \cdot \log \left(1 - u0\right)}\right) \cdot \log \left(1 - u0\right)}{\frac{cos2phi}{alphax \cdot alphax} + \frac{sin2phi}{alphay \cdot alphay}}\\ \mathbf{else}:\\ \;\;\;\;\frac{-\left(-1 \cdot u0 + \left(-0.3333333333333333 \cdot {u0}^{3} + \left(-0.5 \cdot {u0}^{2} + -0.25 \cdot {u0}^{4}\right)\right)\right)}{\frac{cos2phi}{alphax \cdot alphax} + \frac{sin2phi}{alphay \cdot alphay}}\\ \end{array} \]

Alternatives

Alternative 1
Error	0.6
Cost	10724

\[\begin{array}{l} t_0 := \frac{cos2phi}{alphax \cdot alphax}\\ t_1 := \frac{sin2phi}{alphay \cdot alphay}\\ \mathbf{if}\;1 - u0 \leq 0.9599999785423279:\\ \;\;\;\;\frac{-\log \left(1 - u0\right)}{t_0 \cdot \left(t_0 \cdot \frac{1}{t_0}\right) + t_1}\\ \mathbf{else}:\\ \;\;\;\;\frac{-\left(-1 \cdot u0 + \left(-0.3333333333333333 \cdot {u0}^{3} + \left(-0.5 \cdot {u0}^{2} + -0.25 \cdot {u0}^{4}\right)\right)\right)}{t_0 + t_1}\\ \end{array} \]

Alternative 2
Error	0.6
Cost	10628

\[\begin{array}{l} t_0 := \frac{cos2phi}{alphax \cdot alphax}\\ t_1 := \frac{sin2phi}{alphay \cdot alphay}\\ \mathbf{if}\;1 - u0 \leq 0.9599999785423279:\\ \;\;\;\;\frac{-\log \left(1 - u0\right)}{t_0 \cdot \left(t_0 \cdot \frac{1}{t_0}\right) + t_1}\\ \mathbf{else}:\\ \;\;\;\;\frac{u0 + \left(0.5 \cdot {u0}^{2} + \left(0.3333333333333333 \cdot {u0}^{3} + 0.25 \cdot {u0}^{4}\right)\right)}{t_0 + t_1}\\ \end{array} \]

Alternative 3
Error	1.2
Cost	7524

\[\begin{array}{l} t_0 := \frac{cos2phi}{alphax \cdot alphax}\\ t_1 := -\log \left(1 - u0\right)\\ t_2 := \frac{sin2phi}{alphay \cdot alphay}\\ \mathbf{if}\;t_1 \leq 0.002199999988079071:\\ \;\;\;\;\frac{-\left(\left(-u0\right) + -0.5 \cdot {u0}^{2}\right)}{t_0 + t_2}\\ \mathbf{else}:\\ \;\;\;\;\frac{t_1}{t_0 \cdot \left(t_0 \cdot \frac{1}{t_0}\right) + t_2}\\ \end{array} \]

Alternative 4
Error	0.8
Cost	7332

\[\begin{array}{l} t_0 := \frac{cos2phi}{alphax \cdot alphax}\\ t_1 := \frac{sin2phi}{alphay \cdot alphay}\\ \mathbf{if}\;1 - u0 \leq 0.9879999756813049:\\ \;\;\;\;\frac{-\log \left(1 - u0\right)}{t_0 \cdot \left(t_0 \cdot \frac{1}{t_0}\right) + t_1}\\ \mathbf{else}:\\ \;\;\;\;\frac{-\left(\left(-u0\right) + \left(-0.5 \cdot {u0}^{2} + -0.3333333333333333 \cdot {u0}^{3}\right)\right)}{t_0 + t_1}\\ \end{array} \]

Alternative 5
Error	0.8
Cost	7268

\[\begin{array}{l} t_0 := \frac{cos2phi}{alphax \cdot alphax}\\ t_1 := \frac{sin2phi}{alphay \cdot alphay}\\ \mathbf{if}\;1 - u0 \leq 0.9879999756813049:\\ \;\;\;\;\frac{-\log \left(1 - u0\right)}{t_0 \cdot \left(t_0 \cdot \frac{1}{t_0}\right) + t_1}\\ \mathbf{else}:\\ \;\;\;\;\frac{\left(u0 + 0.5 \cdot {u0}^{2}\right) + 0.3333333333333333 \cdot {u0}^{3}}{t_0 + t_1}\\ \end{array} \]

Alternative 6
Error	1.2
Cost	7204

\[\begin{array}{l} t_0 := \frac{cos2phi}{alphax \cdot alphax} + \frac{sin2phi}{alphay \cdot alphay}\\ t_1 := -\log \left(1 - u0\right)\\ \mathbf{if}\;t_1 \leq 0.002199999988079071:\\ \;\;\;\;\frac{-\left(\left(-u0\right) + -0.5 \cdot {u0}^{2}\right)}{t_0}\\ \mathbf{else}:\\ \;\;\;\;\frac{t_1}{t_0}\\ \end{array} \]

Alternative 7
Error	3.1
Cost	7076

\[\begin{array}{l} t_0 := -\log \left(1 - u0\right)\\ t_1 := \frac{cos2phi}{alphax \cdot alphax} + \frac{sin2phi}{alphay \cdot alphay}\\ \mathbf{if}\;t_0 \leq 0.00013000000035390258:\\ \;\;\;\;\frac{u0}{t_1}\\ \mathbf{else}:\\ \;\;\;\;\frac{t_0}{t_1}\\ \end{array} \]

Alternative 8
Error	7.7
Cost	416

\[\frac{u0}{\frac{cos2phi}{alphax \cdot alphax} + \frac{sin2phi}{alphay \cdot alphay}} \]

Reproduce

herbie shell --seed 2023090 
(FPCore (alphax alphay u0 cos2phi sin2phi)
  :name "Beckmann Distribution sample, tan2theta, alphax != alphay, u1 <= 0.5"
  :precision binary32
  :pre (and (and (and (and (and (<= 0.0001 alphax) (<= alphax 1.0)) (and (<= 0.0001 alphay) (<= alphay 1.0))) (and (<= 2.328306437e-10 u0) (<= u0 1.0))) (and (<= 0.0 cos2phi) (<= cos2phi 1.0))) (<= 0.0 sin2phi))
  (/ (- (log (- 1.0 u0))) (+ (/ cos2phi (* alphax alphax)) (/ sin2phi (* alphay alphay)))))