Data.Colour.CIE.Chromaticity:chromaCoords from colour-2.3.3

Average Accuracy: 100.0% → 100.0%

Time: 1.7s

Precision: binary64

Cost: 320

Math

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

↓

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

FPCore

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

↓

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

C

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

↓

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

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (1.0d0 - x) - y
end function

↓

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = 1.0d0 - (x + y)
end function

Java

public static double code(double x, double y) {
	return (1.0 - x) - y;
}

↓

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

Python

def code(x, y):
	return (1.0 - x) - y

↓

def code(x, y):
	return 1.0 - (x + y)

Julia

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

↓

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

MATLAB

function tmp = code(x, y)
	tmp = (1.0 - x) - y;
end

↓

function tmp = code(x, y)
	tmp = 1.0 - (x + y);
end

Wolfram

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

↓

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

Derivation

1. Initial program 100.0%

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

2. Simplified 100.0%

   \[\leadsto \color{blue}{1 - \left(x + y\right)} \]
   Proof

   [Start] 100.0	\[ \left(1 - x\right) - y \]
   associate--l- [=>] 100.0	\[ \color{blue}{1 - \left(x + y\right)} \]

3. Final simplification 100.0%

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

Reproduce

herbie shell --seed 2023147 
(FPCore (x y)
  :name "Data.Colour.CIE.Chromaticity:chromaCoords from colour-2.3.3"
  :precision binary64
  (- (- 1.0 x) y))