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

Average Error: 0.0 → 0.0

Time: 1.7s

Precision: binary64

Cost: 320

Math

\[500 \cdot \left(x - y\right) \]

↓

\[500 \cdot \left(x - y\right) \]

FPCore

(FPCore (x y) :precision binary64 (* 500.0 (- x y)))

↓

(FPCore (x y) :precision binary64 (* 500.0 (- x y)))

C

double code(double x, double y) {
	return 500.0 * (x - y);
}

↓

double code(double x, double y) {
	return 500.0 * (x - y);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = 500.0d0 * (x - y)
end function

↓

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = 500.0d0 * (x - y)
end function

Java

public static double code(double x, double y) {
	return 500.0 * (x - y);
}

↓

public static double code(double x, double y) {
	return 500.0 * (x - y);
}

Python

def code(x, y):
	return 500.0 * (x - y)

↓

def code(x, y):
	return 500.0 * (x - y)

Julia

function code(x, y)
	return Float64(500.0 * Float64(x - y))
end

↓

function code(x, y)
	return Float64(500.0 * Float64(x - y))
end

MATLAB

function tmp = code(x, y)
	tmp = 500.0 * (x - y);
end

↓

function tmp = code(x, y)
	tmp = 500.0 * (x - y);
end

Wolfram

code[x_, y_] := N[(500.0 * N[(x - y), $MachinePrecision]), $MachinePrecision]

↓

code[x_, y_] := N[(500.0 * N[(x - y), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 0.0

   \[500 \cdot \left(x - y\right) \]

2. Final simplification 0.0

   \[\leadsto 500 \cdot \left(x - y\right) \]

Alternatives

Alternative 1
Error	17.3
Cost	456

\[\begin{array}{l} \mathbf{if}\;y \leq -1.95 \cdot 10^{+74}:\\ \;\;\;\;y \cdot -500\\ \mathbf{elif}\;y \leq 7 \cdot 10^{+94}:\\ \;\;\;\;500 \cdot x\\ \mathbf{else}:\\ \;\;\;\;y \cdot -500\\ \end{array} \]

Alternative 2
Error	32.0
Cost	192

\[y \cdot -500 \]

Reproduce

herbie shell --seed 2023016 
(FPCore (x y)
  :name "Data.Colour.CIE:cieLABView from colour-2.3.3, B"
  :precision binary64
  (* 500.0 (- x y)))