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

Average Accuracy: 100.0% → 100.0%

Time: 6.0s

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	61.8%
Cost	980

\[\begin{array}{l} t_0 := \frac{y}{x} + -1\\ \mathbf{if}\;x \leq -1.05 \cdot 10^{+45}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;x \leq -6.8 \cdot 10^{-284}:\\ \;\;\;\;1 - \frac{x}{y}\\ \mathbf{elif}\;x \leq 5.8 \cdot 10^{-257}:\\ \;\;\;\;y \cdot -0.5\\ \mathbf{elif}\;x \leq 19000000:\\ \;\;\;\;1\\ \mathbf{elif}\;x \leq 2.05 \cdot 10^{+155}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;x \leq 2.3 \cdot 10^{+168}:\\ \;\;\;\;1\\ \mathbf{else}:\\ \;\;\;\;-1\\ \end{array} \]

Alternative 2
Accuracy	72.2%
Cost	980

\[\begin{array}{l} t_0 := \frac{x}{2 - x}\\ t_1 := \frac{y}{y + -2}\\ \mathbf{if}\;x \leq -8.5 \cdot 10^{+42}:\\ \;\;\;\;\frac{y}{x} + -1\\ \mathbf{elif}\;x \leq -1.6 \cdot 10^{-134}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq -2.7 \cdot 10^{-171}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;x \leq 350000:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq 10^{+149}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;x \leq 2.3 \cdot 10^{+168}:\\ \;\;\;\;1\\ \mathbf{else}:\\ \;\;\;\;-1\\ \end{array} \]

Alternative 3
Accuracy	61.4%
Cost	856

\[\begin{array}{l} \mathbf{if}\;x \leq -5 \cdot 10^{+42}:\\ \;\;\;\;-1\\ \mathbf{elif}\;x \leq -1.4 \cdot 10^{-284}:\\ \;\;\;\;1\\ \mathbf{elif}\;x \leq 4.8 \cdot 10^{-262}:\\ \;\;\;\;y \cdot -0.5\\ \mathbf{elif}\;x \leq 8500000:\\ \;\;\;\;1\\ \mathbf{elif}\;x \leq 2.05 \cdot 10^{+155}:\\ \;\;\;\;-1\\ \mathbf{elif}\;x \leq 6.4 \cdot 10^{+168}:\\ \;\;\;\;1\\ \mathbf{else}:\\ \;\;\;\;-1\\ \end{array} \]

Alternative 4
Accuracy	61.5%
Cost	856

\[\begin{array}{l} \mathbf{if}\;x \leq -6.5 \cdot 10^{+43}:\\ \;\;\;\;-1\\ \mathbf{elif}\;x \leq -2.7 \cdot 10^{-283}:\\ \;\;\;\;1 - \frac{x}{y}\\ \mathbf{elif}\;x \leq 2.3 \cdot 10^{-256}:\\ \;\;\;\;y \cdot -0.5\\ \mathbf{elif}\;x \leq 18500000:\\ \;\;\;\;1\\ \mathbf{elif}\;x \leq 2 \cdot 10^{+155}:\\ \;\;\;\;-1\\ \mathbf{elif}\;x \leq 2.3 \cdot 10^{+168}:\\ \;\;\;\;1\\ \mathbf{else}:\\ \;\;\;\;-1\\ \end{array} \]

Alternative 5
Accuracy	72.0%
Cost	716

\[\begin{array}{l} t_0 := 1 - \frac{x}{y}\\ \mathbf{if}\;y \leq -2.95 \cdot 10^{+23}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;y \leq 9.5 \cdot 10^{-128}:\\ \;\;\;\;\frac{x}{2 - x}\\ \mathbf{elif}\;y \leq 20:\\ \;\;\;\;y \cdot -0.5\\ \mathbf{else}:\\ \;\;\;\;t_0\\ \end{array} \]

Alternative 6
Accuracy	61.4%
Cost	592

\[\begin{array}{l} \mathbf{if}\;x \leq -2 \cdot 10^{+50}:\\ \;\;\;\;-1\\ \mathbf{elif}\;x \leq 108:\\ \;\;\;\;1\\ \mathbf{elif}\;x \leq 2.05 \cdot 10^{+155}:\\ \;\;\;\;-1\\ \mathbf{elif}\;x \leq 2.3 \cdot 10^{+168}:\\ \;\;\;\;1\\ \mathbf{else}:\\ \;\;\;\;-1\\ \end{array} \]

Alternative 7
Accuracy	38.1%
Cost	64

\[-1 \]

Reproduce

herbie shell --seed 2023138 
(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))))