Data.Colour.RGB:hslsv from colour-2.3.3, C

Average Accuracy: 100.0% → 100.0%

Time: 6.1s

Precision: binary64

Cost: 576

Math

\[\frac{x - y}{2 - \left(x + y\right)} \]

↓

\[\frac{x - y}{2 - \left(x + y\right)} \]

FPCore

(FPCore (x y) :precision binary64 (/ (- x y) (- 2.0 (+ x y))))

↓

(FPCore (x y) :precision binary64 (/ (- x y) (- 2.0 (+ x y))))

C

double code(double x, double y) {
	return (x - y) / (2.0 - (x + y));
}

↓

double code(double x, double y) {
	return (x - y) / (2.0 - (x + y));
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (x - y) / (2.0d0 - (x + y))
end function

↓

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (x - y) / (2.0d0 - (x + y))
end function

Java

public static double code(double x, double y) {
	return (x - y) / (2.0 - (x + y));
}

↓

public static double code(double x, double y) {
	return (x - y) / (2.0 - (x + y));
}

Python

def code(x, y):
	return (x - y) / (2.0 - (x + y))

↓

def code(x, y):
	return (x - y) / (2.0 - (x + y))

Julia

function code(x, y)
	return Float64(Float64(x - y) / Float64(2.0 - Float64(x + y)))
end

↓

function code(x, y)
	return Float64(Float64(x - y) / Float64(2.0 - Float64(x + y)))
end

MATLAB

function tmp = code(x, y)
	tmp = (x - y) / (2.0 - (x + y));
end

↓

function tmp = code(x, y)
	tmp = (x - y) / (2.0 - (x + y));
end

Wolfram

code[x_, y_] := N[(N[(x - y), $MachinePrecision] / N[(2.0 - N[(x + y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

↓

code[x_, y_] := N[(N[(x - y), $MachinePrecision] / N[(2.0 - N[(x + y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Target

Original	100.0%
Target	100.0%
Herbie	100.0%

\[\frac{x}{2 - \left(x + y\right)} - \frac{y}{2 - \left(x + y\right)} \]

Derivation

1. Initial program 100.0%

   \[\frac{x - y}{2 - \left(x + y\right)} \]

2. Final simplification 100.0%

   \[\leadsto \frac{x - y}{2 - \left(x + y\right)} \]

Alternatives

Alternative 1
Accuracy	59.9%
Cost	1120

\[\begin{array}{l} \mathbf{if}\;y \leq -7 \cdot 10^{+44}:\\ \;\;\;\;1\\ \mathbf{elif}\;y \leq -5.5 \cdot 10^{-256}:\\ \;\;\;\;-1\\ \mathbf{elif}\;y \leq 1.05 \cdot 10^{-274}:\\ \;\;\;\;x \cdot 0.5\\ \mathbf{elif}\;y \leq 4.5 \cdot 10^{-215}:\\ \;\;\;\;-1\\ \mathbf{elif}\;y \leq 2 \cdot 10^{-153}:\\ \;\;\;\;y \cdot -0.5\\ \mathbf{elif}\;y \leq 9.5 \cdot 10^{-101}:\\ \;\;\;\;-1\\ \mathbf{elif}\;y \leq 1.38 \cdot 10^{-74}:\\ \;\;\;\;y \cdot -0.5\\ \mathbf{elif}\;y \leq 5 \cdot 10^{+54}:\\ \;\;\;\;-1\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \]

Alternative 2
Accuracy	71.6%
Cost	1112

\[\begin{array}{l} t_0 := \frac{x}{2 - x}\\ \mathbf{if}\;y \leq -9.5 \cdot 10^{+44}:\\ \;\;\;\;1\\ \mathbf{elif}\;y \leq 7 \cdot 10^{-189}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;y \leq 1.12 \cdot 10^{-153}:\\ \;\;\;\;y \cdot -0.5\\ \mathbf{elif}\;y \leq 1.65 \cdot 10^{-90}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;y \leq 8.4 \cdot 10^{+14}:\\ \;\;\;\;\frac{y}{y + -2}\\ \mathbf{elif}\;y \leq 1.9 \cdot 10^{+66}:\\ \;\;\;\;t_0\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \]

Alternative 3
Accuracy	60.3%
Cost	856

\[\begin{array}{l} \mathbf{if}\;y \leq -1.4 \cdot 10^{+45}:\\ \;\;\;\;1\\ \mathbf{elif}\;y \leq 3.6 \cdot 10^{-216}:\\ \;\;\;\;-1\\ \mathbf{elif}\;y \leq 2.8 \cdot 10^{-152}:\\ \;\;\;\;y \cdot -0.5\\ \mathbf{elif}\;y \leq 4.8 \cdot 10^{-100}:\\ \;\;\;\;-1\\ \mathbf{elif}\;y \leq 1.4 \cdot 10^{-74}:\\ \;\;\;\;y \cdot -0.5\\ \mathbf{elif}\;y \leq 1.02 \cdot 10^{+56}:\\ \;\;\;\;-1\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \]

Alternative 4
Accuracy	71.9%
Cost	856

\[\begin{array}{l} t_0 := \frac{x}{2 - x}\\ \mathbf{if}\;y \leq -6.6 \cdot 10^{+44}:\\ \;\;\;\;1\\ \mathbf{elif}\;y \leq 7 \cdot 10^{-189}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;y \leq 1.12 \cdot 10^{-153}:\\ \;\;\;\;y \cdot -0.5\\ \mathbf{elif}\;y \leq 5.6 \cdot 10^{-92}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;y \leq 1.52 \cdot 10^{-74}:\\ \;\;\;\;y \cdot -0.5\\ \mathbf{elif}\;y \leq 4.3 \cdot 10^{+54}:\\ \;\;\;\;-1\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \]

Alternative 5
Accuracy	61.8%
Cost	328

\[\begin{array}{l} \mathbf{if}\;y \leq -3.25 \cdot 10^{+44}:\\ \;\;\;\;1\\ \mathbf{elif}\;y \leq 1.95 \cdot 10^{+55}:\\ \;\;\;\;-1\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \]

Alternative 6
Accuracy	37.7%
Cost	64

\[-1 \]

Reproduce

herbie shell --seed 2023133 
(FPCore (x y)
  :name "Data.Colour.RGB:hslsv from colour-2.3.3, C"
  :precision binary64

  :herbie-target
  (- (/ x (- 2.0 (+ x y))) (/ y (- 2.0 (+ x y))))

  (/ (- x y) (- 2.0 (+ x y))))