Data.Colour.SRGB:transferFunction from colour-2.3.3

Average Accuracy: 100.0% → 100.0%

Time: 2.8s

Precision: binary64

Cost: 6720

Math

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

↓

\[\mathsf{fma}\left(x, y, y - x\right) \]

FPCore

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

↓

(FPCore (x y) :precision binary64 (fma x y (- y x)))

C

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

↓

double code(double x, double y) {
	return fma(x, y, (y - x));
}

Julia

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

↓

function code(x, y)
	return fma(x, y, Float64(y - x))
end

Wolfram

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

↓

code[x_, y_] := N[(x * y + N[(y - x), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 100.0%

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

2. Simplified 100.0%

   \[\leadsto \color{blue}{\mathsf{fma}\left(x, y, y - x\right)} \]
   Proof

   [Start] 100.0	\[ \left(x + 1\right) \cdot y - x \]
   sub-neg [=>] 100.0	\[ \color{blue}{\left(x + 1\right) \cdot y + \left(-x\right)} \]
   *-commutative [=>] 100.0	\[ \color{blue}{y \cdot \left(x + 1\right)} + \left(-x\right) \]
   distribute-lft-in [=>] 100.0	\[ \color{blue}{\left(y \cdot x + y \cdot 1\right)} + \left(-x\right) \]
   associate-+l+ [=>] 100.0	\[ \color{blue}{y \cdot x + \left(y \cdot 1 + \left(-x\right)\right)} \]
   *-commutative [=>] 100.0	\[ \color{blue}{x \cdot y} + \left(y \cdot 1 + \left(-x\right)\right) \]
   *-lft-identity [<=] 100.0	\[ x \cdot \color{blue}{\left(1 \cdot y\right)} + \left(y \cdot 1 + \left(-x\right)\right) \]
   *-commutative [<=] 100.0	\[ x \cdot \color{blue}{\left(y \cdot 1\right)} + \left(y \cdot 1 + \left(-x\right)\right) \]
   fma-def [=>] 100.0	\[ \color{blue}{\mathsf{fma}\left(x, y \cdot 1, y \cdot 1 + \left(-x\right)\right)} \]
   *-commutative [=>] 100.0	\[ \mathsf{fma}\left(x, \color{blue}{1 \cdot y}, y \cdot 1 + \left(-x\right)\right) \]
   *-lft-identity [=>] 100.0	\[ \mathsf{fma}\left(x, \color{blue}{y}, y \cdot 1 + \left(-x\right)\right) \]
   sub-neg [<=] 100.0	\[ \mathsf{fma}\left(x, y, \color{blue}{y \cdot 1 - x}\right) \]
   *-commutative [=>] 100.0	\[ \mathsf{fma}\left(x, y, \color{blue}{1 \cdot y} - x\right) \]
   *-lft-identity [=>] 100.0	\[ \mathsf{fma}\left(x, y, \color{blue}{y} - x\right) \]

3. Final simplification 100.0%

   \[\leadsto \mathsf{fma}\left(x, y, y - x\right) \]

Alternatives

Alternative 1
Accuracy	70.8%
Cost	656

\[\begin{array}{l} \mathbf{if}\;y \leq -5 \cdot 10^{-25}:\\ \;\;\;\;y\\ \mathbf{elif}\;y \leq 3.3 \cdot 10^{-40}:\\ \;\;\;\;-x\\ \mathbf{elif}\;y \leq 1.15 \cdot 10^{-31}:\\ \;\;\;\;y\\ \mathbf{elif}\;y \leq 4.4 \cdot 10^{-13}:\\ \;\;\;\;-x\\ \mathbf{else}:\\ \;\;\;\;y\\ \end{array} \]

Alternative 2
Accuracy	98.7%
Cost	585

\[\begin{array}{l} \mathbf{if}\;y \leq -7.6 \lor \neg \left(y \leq 0.00092\right):\\ \;\;\;\;y \cdot \left(x + 1\right)\\ \mathbf{else}:\\ \;\;\;\;y - x\\ \end{array} \]

Alternative 3
Accuracy	98.7%
Cost	584

\[\begin{array}{l} \mathbf{if}\;y \leq -7.6:\\ \;\;\;\;y + x \cdot y\\ \mathbf{elif}\;y \leq 0.00092:\\ \;\;\;\;y - x\\ \mathbf{else}:\\ \;\;\;\;y \cdot \left(x + 1\right)\\ \end{array} \]

Alternative 4
Accuracy	84.7%
Cost	456

\[\begin{array}{l} \mathbf{if}\;x \leq 54000000000:\\ \;\;\;\;y - x\\ \mathbf{elif}\;x \leq 1.16 \cdot 10^{+78}:\\ \;\;\;\;x \cdot y\\ \mathbf{else}:\\ \;\;\;\;y - x\\ \end{array} \]

Alternative 5
Accuracy	100.0%
Cost	448

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

Alternative 6
Accuracy	43.5%
Cost	64

\[y \]

Reproduce

herbie shell --seed 2023129 
(FPCore (x y)
  :name "Data.Colour.SRGB:transferFunction from colour-2.3.3"
  :precision binary64
  (- (* (+ x 1.0) y) x))