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

Percentage Accurate: 100.0% → 100.0%

Time: 2.7s

Alternatives: 6

Speedup: 1.0×

Specification

Math

\[\begin{array}{l} \\ \left(1 - x\right) - y \end{array} \]

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(1 - x\right) - y \end{array} \]

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(1 - x\right) - y \end{array} \]

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 100.0%

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

2. Final simplification 100.0%

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

Alternative 2: 50.5% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq 3.2 \cdot 10^{-179}:\\ \;\;\;\;-x\\ \mathbf{elif}\;y \leq 1:\\ \;\;\;\;1\\ \mathbf{else}:\\ \;\;\;\;-y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y 3.2e-179) (- x) (if (<= y 1.0) 1.0 (- y))))

C

double code(double x, double y) {
	double tmp;
	if (y <= 3.2e-179) {
		tmp = -x;
	} else if (y <= 1.0) {
		tmp = 1.0;
	} else {
		tmp = -y;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (y <= 3.2d-179) then
        tmp = -x
    else if (y <= 1.0d0) then
        tmp = 1.0d0
    else
        tmp = -y
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (y <= 3.2e-179) {
		tmp = -x;
	} else if (y <= 1.0) {
		tmp = 1.0;
	} else {
		tmp = -y;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= 3.2e-179:
		tmp = -x
	elif y <= 1.0:
		tmp = 1.0
	else:
		tmp = -y
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= 3.2e-179)
		tmp = Float64(-x);
	elseif (y <= 1.0)
		tmp = 1.0;
	else
		tmp = Float64(-y);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= 3.2e-179)
		tmp = -x;
	elseif (y <= 1.0)
		tmp = 1.0;
	else
		tmp = -y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, 3.2e-179], (-x), If[LessEqual[y, 1.0], 1.0, (-y)]]

Derivation

1. Split input into 3 regimes

2. if y < 3.2000000000000001e-179

   1. Initial program 100.0%

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

   2. Taylor expanded in x around inf 45.7%

      \[\leadsto \color{blue}{-1 \cdot x} \]
   3. Step-by-step derivation

      1. neg-mul-1 45.7%

         \[\leadsto \color{blue}{-x} \]

   4. Simplified 45.7%

      \[\leadsto \color{blue}{-x} \]

   if 3.2000000000000001e-179 < y < 1

   1. Initial program 100.0%

      \[\left(1 - x\right) - y \]
   2. Step-by-step derivation

      1. associate--l- 100.0%

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

      2. flip3-- 49.2%

         \[\leadsto \color{blue}{\frac{{1}^{3} - {\left(x + y\right)}^{3}}{1 \cdot 1 + \left(\left(x + y\right) \cdot \left(x + y\right) + 1 \cdot \left(x + y\right)\right)}} \]

      3. metadata-eval 49.2%

         \[\leadsto \frac{\color{blue}{1} - {\left(x + y\right)}^{3}}{1 \cdot 1 + \left(\left(x + y\right) \cdot \left(x + y\right) + 1 \cdot \left(x + y\right)\right)} \]

      4. metadata-eval 49.2%

         \[\leadsto \frac{1 - {\left(x + y\right)}^{3}}{\color{blue}{1} + \left(\left(x + y\right) \cdot \left(x + y\right) + 1 \cdot \left(x + y\right)\right)} \]

      5. *-un-lft-identity 49.2%

         \[\leadsto \frac{1 - {\left(x + y\right)}^{3}}{1 + \left(\left(x + y\right) \cdot \left(x + y\right) + \color{blue}{\left(x + y\right)}\right)} \]

   3. Applied egg-rr 49.2%

      \[\leadsto \color{blue}{\frac{1 - {\left(x + y\right)}^{3}}{1 + \left(\left(x + y\right) \cdot \left(x + y\right) + \left(x + y\right)\right)}} \]
   4. Step-by-step derivation

      1. +-commutative 49.2%

         \[\leadsto \frac{1 - {\color{blue}{\left(y + x\right)}}^{3}}{1 + \left(\left(x + y\right) \cdot \left(x + y\right) + \left(x + y\right)\right)} \]

      2. distribute-lft1-in 49.2%

         \[\leadsto \frac{1 - {\left(y + x\right)}^{3}}{1 + \color{blue}{\left(\left(x + y\right) + 1\right) \cdot \left(x + y\right)}} \]

      3. +-commutative 49.2%

         \[\leadsto \frac{1 - {\left(y + x\right)}^{3}}{1 + \color{blue}{\left(1 + \left(x + y\right)\right)} \cdot \left(x + y\right)} \]

      4. associate-+r+ 49.2%

         \[\leadsto \frac{1 - {\left(y + x\right)}^{3}}{1 + \color{blue}{\left(\left(1 + x\right) + y\right)} \cdot \left(x + y\right)} \]

      5. +-commutative 49.2%

         \[\leadsto \frac{1 - {\left(y + x\right)}^{3}}{1 + \left(\left(1 + x\right) + y\right) \cdot \color{blue}{\left(y + x\right)}} \]

   5. Simplified 49.2%

      \[\leadsto \color{blue}{\frac{1 - {\left(y + x\right)}^{3}}{1 + \left(\left(1 + x\right) + y\right) \cdot \left(y + x\right)}} \]

   6. Taylor expanded in x around 0 41.7%

      \[\leadsto \color{blue}{\frac{1 - {y}^{3}}{1 + y \cdot \left(1 + y\right)}} \]

   7. Taylor expanded in y around 0 36.6%

      \[\leadsto \color{blue}{1} \]

   if 1 < y

   1. Initial program 100.0%

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

   2. Taylor expanded in y around inf 69.4%

      \[\leadsto \color{blue}{-1 \cdot y} \]
   3. Step-by-step derivation

      1. neg-mul-1 69.4%

         \[\leadsto \color{blue}{-y} \]

   4. Simplified 69.4%

      \[\leadsto \color{blue}{-y} \]
3. Recombined 3 regimes into one program.

4. Final simplification 50.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq 3.2 \cdot 10^{-179}:\\ \;\;\;\;-x\\ \mathbf{elif}\;y \leq 1:\\ \;\;\;\;1\\ \mathbf{else}:\\ \;\;\;\;-y\\ \end{array} \]

Alternative 3: 75.6% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq 2.5 \cdot 10^{+36}:\\ \;\;\;\;1 - x\\ \mathbf{else}:\\ \;\;\;\;-y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (if (<= y 2.5e+36) (- 1.0 x) (- y)))

C

double code(double x, double y) {
	double tmp;
	if (y <= 2.5e+36) {
		tmp = 1.0 - x;
	} else {
		tmp = -y;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (y <= 2.5d+36) then
        tmp = 1.0d0 - x
    else
        tmp = -y
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (y <= 2.5e+36) {
		tmp = 1.0 - x;
	} else {
		tmp = -y;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= 2.5e+36:
		tmp = 1.0 - x
	else:
		tmp = -y
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= 2.5e+36)
		tmp = Float64(1.0 - x);
	else
		tmp = Float64(-y);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= 2.5e+36)
		tmp = 1.0 - x;
	else
		tmp = -y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, 2.5e+36], N[(1.0 - x), $MachinePrecision], (-y)]

Derivation

1. Split input into 2 regimes

2. if y < 2.49999999999999988e36

   1. Initial program 100.0%

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

   2. Taylor expanded in y around 0 74.7%

      \[\leadsto \color{blue}{1 - x} \]

   if 2.49999999999999988e36 < y

   1. Initial program 100.0%

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

   2. Taylor expanded in y around inf 75.6%

      \[\leadsto \color{blue}{-1 \cdot y} \]
   3. Step-by-step derivation

      1. neg-mul-1 75.6%

         \[\leadsto \color{blue}{-y} \]

   4. Simplified 75.6%

      \[\leadsto \color{blue}{-y} \]
3. Recombined 2 regimes into one program.

4. Final simplification 74.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq 2.5 \cdot 10^{+36}:\\ \;\;\;\;1 - x\\ \mathbf{else}:\\ \;\;\;\;-y\\ \end{array} \]

Alternative 4: 75.6% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq 1.7 \cdot 10^{-7}:\\ \;\;\;\;1 - x\\ \mathbf{else}:\\ \;\;\;\;1 - y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (if (<= y 1.7e-7) (- 1.0 x) (- 1.0 y)))

C

double code(double x, double y) {
	double tmp;
	if (y <= 1.7e-7) {
		tmp = 1.0 - x;
	} else {
		tmp = 1.0 - y;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (y <= 1.7d-7) then
        tmp = 1.0d0 - x
    else
        tmp = 1.0d0 - y
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (y <= 1.7e-7) {
		tmp = 1.0 - x;
	} else {
		tmp = 1.0 - y;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= 1.7e-7:
		tmp = 1.0 - x
	else:
		tmp = 1.0 - y
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= 1.7e-7)
		tmp = Float64(1.0 - x);
	else
		tmp = Float64(1.0 - y);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= 1.7e-7)
		tmp = 1.0 - x;
	else
		tmp = 1.0 - y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, 1.7e-7], N[(1.0 - x), $MachinePrecision], N[(1.0 - y), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < 1.69999999999999987e-7

   1. Initial program 100.0%

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

   2. Taylor expanded in y around 0 75.6%

      \[\leadsto \color{blue}{1 - x} \]

   if 1.69999999999999987e-7 < y

   1. Initial program 100.0%

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

   2. Taylor expanded in x around 0 71.9%

      \[\leadsto \color{blue}{1 - y} \]
3. Recombined 2 regimes into one program.

4. Final simplification 74.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq 1.7 \cdot 10^{-7}:\\ \;\;\;\;1 - x\\ \mathbf{else}:\\ \;\;\;\;1 - y\\ \end{array} \]

Alternative 5: 43.3% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1:\\ \;\;\;\;-x\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (if (<= x -1.0) (- x) 1.0))

C

double code(double x, double y) {
	double tmp;
	if (x <= -1.0) {
		tmp = -x;
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (x <= (-1.0d0)) then
        tmp = -x
    else
        tmp = 1.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (x <= -1.0) {
		tmp = -x;
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if x <= -1.0:
		tmp = -x
	else:
		tmp = 1.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (x <= -1.0)
		tmp = Float64(-x);
	else
		tmp = 1.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (x <= -1.0)
		tmp = -x;
	else
		tmp = 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[x, -1.0], (-x), 1.0]

Derivation

1. Split input into 2 regimes

2. if x < -1

   1. Initial program 100.0%

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

   2. Taylor expanded in x around inf 71.1%

      \[\leadsto \color{blue}{-1 \cdot x} \]
   3. Step-by-step derivation

      1. neg-mul-1 71.1%

         \[\leadsto \color{blue}{-x} \]

   4. Simplified 71.1%

      \[\leadsto \color{blue}{-x} \]

   if -1 < x

   1. Initial program 100.0%

      \[\left(1 - x\right) - y \]
   2. Step-by-step derivation

      1. associate--l- 100.0%

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

      2. flip3-- 45.2%

         \[\leadsto \color{blue}{\frac{{1}^{3} - {\left(x + y\right)}^{3}}{1 \cdot 1 + \left(\left(x + y\right) \cdot \left(x + y\right) + 1 \cdot \left(x + y\right)\right)}} \]

      3. metadata-eval 45.2%

         \[\leadsto \frac{\color{blue}{1} - {\left(x + y\right)}^{3}}{1 \cdot 1 + \left(\left(x + y\right) \cdot \left(x + y\right) + 1 \cdot \left(x + y\right)\right)} \]

      4. metadata-eval 45.2%

         \[\leadsto \frac{1 - {\left(x + y\right)}^{3}}{\color{blue}{1} + \left(\left(x + y\right) \cdot \left(x + y\right) + 1 \cdot \left(x + y\right)\right)} \]

      5. *-un-lft-identity 45.2%

         \[\leadsto \frac{1 - {\left(x + y\right)}^{3}}{1 + \left(\left(x + y\right) \cdot \left(x + y\right) + \color{blue}{\left(x + y\right)}\right)} \]

   3. Applied egg-rr 45.2%

      \[\leadsto \color{blue}{\frac{1 - {\left(x + y\right)}^{3}}{1 + \left(\left(x + y\right) \cdot \left(x + y\right) + \left(x + y\right)\right)}} \]
   4. Step-by-step derivation

      1. +-commutative 45.2%

         \[\leadsto \frac{1 - {\color{blue}{\left(y + x\right)}}^{3}}{1 + \left(\left(x + y\right) \cdot \left(x + y\right) + \left(x + y\right)\right)} \]

      2. distribute-lft1-in 45.2%

         \[\leadsto \frac{1 - {\left(y + x\right)}^{3}}{1 + \color{blue}{\left(\left(x + y\right) + 1\right) \cdot \left(x + y\right)}} \]

      3. +-commutative 45.2%

         \[\leadsto \frac{1 - {\left(y + x\right)}^{3}}{1 + \color{blue}{\left(1 + \left(x + y\right)\right)} \cdot \left(x + y\right)} \]

      4. associate-+r+ 45.2%

         \[\leadsto \frac{1 - {\left(y + x\right)}^{3}}{1 + \color{blue}{\left(\left(1 + x\right) + y\right)} \cdot \left(x + y\right)} \]

      5. +-commutative 45.2%

         \[\leadsto \frac{1 - {\left(y + x\right)}^{3}}{1 + \left(\left(1 + x\right) + y\right) \cdot \color{blue}{\left(y + x\right)}} \]

   5. Simplified 45.2%

      \[\leadsto \color{blue}{\frac{1 - {\left(y + x\right)}^{3}}{1 + \left(\left(1 + x\right) + y\right) \cdot \left(y + x\right)}} \]

   6. Taylor expanded in x around 0 37.8%

      \[\leadsto \color{blue}{\frac{1 - {y}^{3}}{1 + y \cdot \left(1 + y\right)}} \]

   7. Taylor expanded in y around 0 28.3%

      \[\leadsto \color{blue}{1} \]
3. Recombined 2 regimes into one program.

4. Final simplification 39.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -1:\\ \;\;\;\;-x\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \]

Alternative 6: 26.1% accurate, 5.0× speedup

Math

\[\begin{array}{l} \\ 1 \end{array} \]

FPCore

(FPCore (x y) :precision binary64 1.0)

C

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

Fortran

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

Java

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

Python

def code(x, y):
	return 1.0

Julia

function code(x, y)
	return 1.0
end

MATLAB

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

Wolfram

code[x_, y_] := 1.0

Derivation

1. Initial program 100.0%

   \[\left(1 - x\right) - y \]
2. Step-by-step derivation

   1. associate--l- 100.0%

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

   2. flip3-- 38.4%

      \[\leadsto \color{blue}{\frac{{1}^{3} - {\left(x + y\right)}^{3}}{1 \cdot 1 + \left(\left(x + y\right) \cdot \left(x + y\right) + 1 \cdot \left(x + y\right)\right)}} \]

   3. metadata-eval 38.4%

      \[\leadsto \frac{\color{blue}{1} - {\left(x + y\right)}^{3}}{1 \cdot 1 + \left(\left(x + y\right) \cdot \left(x + y\right) + 1 \cdot \left(x + y\right)\right)} \]

   4. metadata-eval 38.4%

      \[\leadsto \frac{1 - {\left(x + y\right)}^{3}}{\color{blue}{1} + \left(\left(x + y\right) \cdot \left(x + y\right) + 1 \cdot \left(x + y\right)\right)} \]

   5. *-un-lft-identity 38.4%

      \[\leadsto \frac{1 - {\left(x + y\right)}^{3}}{1 + \left(\left(x + y\right) \cdot \left(x + y\right) + \color{blue}{\left(x + y\right)}\right)} \]

3. Applied egg-rr 38.4%

   \[\leadsto \color{blue}{\frac{1 - {\left(x + y\right)}^{3}}{1 + \left(\left(x + y\right) \cdot \left(x + y\right) + \left(x + y\right)\right)}} \]
4. Step-by-step derivation

   1. +-commutative 38.4%

      \[\leadsto \frac{1 - {\color{blue}{\left(y + x\right)}}^{3}}{1 + \left(\left(x + y\right) \cdot \left(x + y\right) + \left(x + y\right)\right)} \]

   2. distribute-lft1-in 38.4%

      \[\leadsto \frac{1 - {\left(y + x\right)}^{3}}{1 + \color{blue}{\left(\left(x + y\right) + 1\right) \cdot \left(x + y\right)}} \]

   3. +-commutative 38.4%

      \[\leadsto \frac{1 - {\left(y + x\right)}^{3}}{1 + \color{blue}{\left(1 + \left(x + y\right)\right)} \cdot \left(x + y\right)} \]

   4. associate-+r+ 38.4%

      \[\leadsto \frac{1 - {\left(y + x\right)}^{3}}{1 + \color{blue}{\left(\left(1 + x\right) + y\right)} \cdot \left(x + y\right)} \]

   5. +-commutative 38.4%

      \[\leadsto \frac{1 - {\left(y + x\right)}^{3}}{1 + \left(\left(1 + x\right) + y\right) \cdot \color{blue}{\left(y + x\right)}} \]

5. Simplified 38.4%

   \[\leadsto \color{blue}{\frac{1 - {\left(y + x\right)}^{3}}{1 + \left(\left(1 + x\right) + y\right) \cdot \left(y + x\right)}} \]

6. Taylor expanded in x around 0 28.9%

   \[\leadsto \color{blue}{\frac{1 - {y}^{3}}{1 + y \cdot \left(1 + y\right)}} \]

7. Taylor expanded in y around 0 21.8%

   \[\leadsto \color{blue}{1} \]

8. Final simplification 21.8%

   \[\leadsto 1 \]

Reproduce

herbie shell --seed 2023230 
(FPCore (x y)
  :name "Data.Colour.CIE.Chromaticity:chromaCoords from colour-2.3.3"
  :precision binary64
  (- (- 1.0 x) y))