Data.Colour.CIE:cieLABView from colour-2.3.3, C

Average Accuracy: 100.0% → 100.0%

Time: 4.5s

Precision: binary64

Cost: 320

Math

\[200 \cdot \left(x - y\right) \]

↓

\[200 \cdot \left(x - y\right) \]

FPCore

(FPCore (x y) :precision binary64 (* 200.0 (- x y)))

↓

(FPCore (x y) :precision binary64 (* 200.0 (- x y)))

C

double code(double x, double y) {
	return 200.0 * (x - y);
}

↓

double code(double x, double y) {
	return 200.0 * (x - y);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = 200.0d0 * (x - y)
end function

↓

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = 200.0d0 * (x - y)
end function

Java

public static double code(double x, double y) {
	return 200.0 * (x - y);
}

↓

public static double code(double x, double y) {
	return 200.0 * (x - y);
}

Python

def code(x, y):
	return 200.0 * (x - y)

↓

def code(x, y):
	return 200.0 * (x - y)

Julia

function code(x, y)
	return Float64(200.0 * Float64(x - y))
end

↓

function code(x, y)
	return Float64(200.0 * Float64(x - y))
end

MATLAB

function tmp = code(x, y)
	tmp = 200.0 * (x - y);
end

↓

function tmp = code(x, y)
	tmp = 200.0 * (x - y);
end

Wolfram

code[x_, y_] := N[(200.0 * N[(x - y), $MachinePrecision]), $MachinePrecision]

↓

code[x_, y_] := N[(200.0 * N[(x - y), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[200 \cdot \left(x - y\right) \]

2. Final simplification 100.0%

   \[\leadsto 200 \cdot \left(x - y\right) \]

Alternatives

Alternative 1
Accuracy	74.9%
Cost	456

\[\begin{array}{l} \mathbf{if}\;x \leq -1.7 \cdot 10^{+44}:\\ \;\;\;\;200 \cdot x\\ \mathbf{elif}\;x \leq 1.4 \cdot 10^{-7}:\\ \;\;\;\;y \cdot -200\\ \mathbf{else}:\\ \;\;\;\;200 \cdot x\\ \end{array} \]

Alternative 2
Accuracy	50.6%
Cost	192

\[y \cdot -200 \]

Reproduce

herbie shell --seed 2023137 
(FPCore (x y)
  :name "Data.Colour.CIE:cieLABView from colour-2.3.3, C"
  :precision binary64
  (* 200.0 (- x y)))