System.Random.MWC.Distributions:gamma from mwc-random-0.13.3.2

Percentage Accurate: 99.9% → 99.9%

Time: 12.0s

Precision: binary64

Cost: 13376

Math

\[x \cdot 0.5 + y \cdot \left(\left(1 - z\right) + \log z\right) \]

↓

\[\mathsf{fma}\left(\left(1 - z\right) + \log z, y, x \cdot 0.5\right) \]

FPCore

(FPCore (x y z) :precision binary64 (+ (* x 0.5) (* y (+ (- 1.0 z) (log z)))))

↓

(FPCore (x y z) :precision binary64 (fma (+ (- 1.0 z) (log z)) y (* x 0.5)))

C

double code(double x, double y, double z) {
	return (x * 0.5) + (y * ((1.0 - z) + log(z)));
}

↓

double code(double x, double y, double z) {
	return fma(((1.0 - z) + log(z)), y, (x * 0.5));
}

Julia

function code(x, y, z)
	return Float64(Float64(x * 0.5) + Float64(y * Float64(Float64(1.0 - z) + log(z))))
end

↓

function code(x, y, z)
	return fma(Float64(Float64(1.0 - z) + log(z)), y, Float64(x * 0.5))
end

Wolfram

code[x_, y_, z_] := N[(N[(x * 0.5), $MachinePrecision] + N[(y * N[(N[(1.0 - z), $MachinePrecision] + N[Log[z], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

↓

code[x_, y_, z_] := N[(N[(N[(1.0 - z), $MachinePrecision] + N[Log[z], $MachinePrecision]), $MachinePrecision] * y + N[(x * 0.5), $MachinePrecision]), $MachinePrecision]

Target

Original	99.9%
Target	99.8%
Herbie	99.9%

\[\left(y + 0.5 \cdot x\right) - y \cdot \left(z - \log z\right) \]

Derivation

1. Initial program 99.9%

   \[x \cdot 0.5 + y \cdot \left(\left(1 - z\right) + \log z\right) \]

2. Applied egg-rr 99.9%

   \[\leadsto \color{blue}{\mathsf{fma}\left(\left(1 - z\right) + \log z, y, x \cdot 0.5\right)} \]
   Step-by-step derivation

   [Start] 99.9	\[ x \cdot 0.5 + y \cdot \left(\left(1 - z\right) + \log z\right) \]
   +-commutative [=>] 99.9	\[ \color{blue}{y \cdot \left(\left(1 - z\right) + \log z\right) + x \cdot 0.5} \]
   *-commutative [=>] 99.9	\[ \color{blue}{\left(\left(1 - z\right) + \log z\right) \cdot y} + x \cdot 0.5 \]
   fma-def [=>] 99.9	\[ \color{blue}{\mathsf{fma}\left(\left(1 - z\right) + \log z, y, x \cdot 0.5\right)} \]

3. Final simplification 99.9%

   \[\leadsto \mathsf{fma}\left(\left(1 - z\right) + \log z, y, x \cdot 0.5\right) \]

Alternatives

Alternative 1
Accuracy	85.3%
Cost	7368

\[\begin{array}{l} \mathbf{if}\;x \cdot 0.5 \leq -5 \cdot 10^{+22}:\\ \;\;\;\;x \cdot 0.5 - z \cdot y\\ \mathbf{elif}\;x \cdot 0.5 \leq 2 \cdot 10^{-15}:\\ \;\;\;\;y \cdot \left(\left(1 + \log z\right) - z\right)\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(-z, y, x \cdot 0.5\right)\\ \end{array} \]

Alternative 2
Accuracy	98.7%
Cost	7108

\[\begin{array}{l} \mathbf{if}\;z \leq 0.28:\\ \;\;\;\;x \cdot 0.5 + \left(y + \log z \cdot y\right)\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(-z, y, x \cdot 0.5\right)\\ \end{array} \]

Alternative 3
Accuracy	99.9%
Cost	7104

\[x \cdot 0.5 + \left(\left(1 - z\right) + \log z\right) \cdot y \]

Alternative 4
Accuracy	75.4%
Cost	6916

\[\begin{array}{l} \mathbf{if}\;z \leq 1.08 \cdot 10^{-150}:\\ \;\;\;\;y + \log z \cdot y\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(-z, y, x \cdot 0.5\right)\\ \end{array} \]

Alternative 5
Accuracy	75.4%
Cost	6852

\[\begin{array}{l} \mathbf{if}\;z \leq 1.25 \cdot 10^{-149}:\\ \;\;\;\;y + \log z \cdot y\\ \mathbf{else}:\\ \;\;\;\;x \cdot 0.5 - z \cdot y\\ \end{array} \]

Alternative 6
Accuracy	75.4%
Cost	448

\[x \cdot 0.5 - z \cdot y \]

Alternative 7
Accuracy	61.0%
Cost	388

\[\begin{array}{l} \mathbf{if}\;z \leq 2.4 \cdot 10^{+25}:\\ \;\;\;\;x \cdot 0.5\\ \mathbf{else}:\\ \;\;\;\;-z \cdot y\\ \end{array} \]

Alternative 8
Accuracy	40.1%
Cost	192

\[x \cdot 0.5 \]

Alternative 9
Accuracy	1.9%
Cost	64

\[y \]

Reproduce

herbie shell --seed 2023159 
(FPCore (x y z)
  :name "System.Random.MWC.Distributions:gamma from mwc-random-0.13.3.2"
  :precision binary64

  :herbie-target
  (- (+ y (* 0.5 x)) (* y (- z (log z))))

  (+ (* x 0.5) (* y (+ (- 1.0 z) (log z)))))