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

Percentage Accurate: 100.0% → 100.0%

Time: 7.2s

Alternatives: 7

Speedup: 1.0×

Specification

Math

\[\begin{array}{l} \\ \frac{x - y}{2 - \left(x + y\right)} \end{array} \]

FPCore

(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));
}

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

Java

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))

Julia

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

Wolfram

code[x_, y_] := N[(N[(x - y), $MachinePrecision] / N[(2.0 - N[(x + y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Initial Program: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{x - y}{2 - \left(x + y\right)} \end{array} \]

FPCore

(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));
}

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

Java

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))

Julia

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

Wolfram

code[x_, y_] := N[(N[(x - y), $MachinePrecision] / N[(2.0 - N[(x + y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Alternative 1: 100.0% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 2 - \left(x + y\right)\\ \frac{x}{t_0} - \frac{y}{t_0} \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (- 2.0 (+ x y)))) (- (/ x t_0) (/ y t_0))))

C

double code(double x, double y) {
	double t_0 = 2.0 - (x + y);
	return (x / t_0) - (y / t_0);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: t_0
    t_0 = 2.0d0 - (x + y)
    code = (x / t_0) - (y / t_0)
end function

Java

public static double code(double x, double y) {
	double t_0 = 2.0 - (x + y);
	return (x / t_0) - (y / t_0);
}

Python

def code(x, y):
	t_0 = 2.0 - (x + y)
	return (x / t_0) - (y / t_0)

Julia

function code(x, y)
	t_0 = Float64(2.0 - Float64(x + y))
	return Float64(Float64(x / t_0) - Float64(y / t_0))
end

MATLAB

function tmp = code(x, y)
	t_0 = 2.0 - (x + y);
	tmp = (x / t_0) - (y / t_0);
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(2.0 - N[(x + y), $MachinePrecision]), $MachinePrecision]}, N[(N[(x / t$95$0), $MachinePrecision] - N[(y / t$95$0), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Initial program 100.0%

   \[\frac{x - y}{2 - \left(x + y\right)} \]
2. Step-by-step derivation

   1. div-sub 100.0%

      \[\leadsto \color{blue}{\frac{x}{2 - \left(x + y\right)} - \frac{y}{2 - \left(x + y\right)}} \]

3. Applied egg-rr 100.0%

   \[\leadsto \color{blue}{\frac{x}{2 - \left(x + y\right)} - \frac{y}{2 - \left(x + y\right)}} \]

4. Final simplification 100.0%

   \[\leadsto \frac{x}{2 - \left(x + y\right)} - \frac{y}{2 - \left(x + y\right)} \]

Alternative 2: 73.7% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -3.2 \cdot 10^{+19}:\\ \;\;\;\;-1\\ \mathbf{elif}\;x \leq 1.22 \cdot 10^{-76}:\\ \;\;\;\;\frac{-y}{2 - y}\\ \mathbf{elif}\;x \leq 8.6 \cdot 10^{-35}:\\ \;\;\;\;\frac{x}{2 - x}\\ \mathbf{elif}\;x \leq 6.5 \cdot 10^{+24}:\\ \;\;\;\;1\\ \mathbf{else}:\\ \;\;\;\;-1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= x -3.2e+19)
   -1.0
   (if (<= x 1.22e-76)
     (/ (- y) (- 2.0 y))
     (if (<= x 8.6e-35) (/ x (- 2.0 x)) (if (<= x 6.5e+24) 1.0 -1.0)))))

C

double code(double x, double y) {
	double tmp;
	if (x <= -3.2e+19) {
		tmp = -1.0;
	} else if (x <= 1.22e-76) {
		tmp = -y / (2.0 - y);
	} else if (x <= 8.6e-35) {
		tmp = x / (2.0 - x);
	} else if (x <= 6.5e+24) {
		tmp = 1.0;
	} 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 <= (-3.2d+19)) then
        tmp = -1.0d0
    else if (x <= 1.22d-76) then
        tmp = -y / (2.0d0 - y)
    else if (x <= 8.6d-35) then
        tmp = x / (2.0d0 - x)
    else if (x <= 6.5d+24) then
        tmp = 1.0d0
    else
        tmp = -1.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (x <= -3.2e+19) {
		tmp = -1.0;
	} else if (x <= 1.22e-76) {
		tmp = -y / (2.0 - y);
	} else if (x <= 8.6e-35) {
		tmp = x / (2.0 - x);
	} else if (x <= 6.5e+24) {
		tmp = 1.0;
	} else {
		tmp = -1.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if x <= -3.2e+19:
		tmp = -1.0
	elif x <= 1.22e-76:
		tmp = -y / (2.0 - y)
	elif x <= 8.6e-35:
		tmp = x / (2.0 - x)
	elif x <= 6.5e+24:
		tmp = 1.0
	else:
		tmp = -1.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (x <= -3.2e+19)
		tmp = -1.0;
	elseif (x <= 1.22e-76)
		tmp = Float64(Float64(-y) / Float64(2.0 - y));
	elseif (x <= 8.6e-35)
		tmp = Float64(x / Float64(2.0 - x));
	elseif (x <= 6.5e+24)
		tmp = 1.0;
	else
		tmp = -1.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (x <= -3.2e+19)
		tmp = -1.0;
	elseif (x <= 1.22e-76)
		tmp = -y / (2.0 - y);
	elseif (x <= 8.6e-35)
		tmp = x / (2.0 - x);
	elseif (x <= 6.5e+24)
		tmp = 1.0;
	else
		tmp = -1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[x, -3.2e+19], -1.0, If[LessEqual[x, 1.22e-76], N[((-y) / N[(2.0 - y), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 8.6e-35], N[(x / N[(2.0 - x), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 6.5e+24], 1.0, -1.0]]]]

Derivation

1. Split input into 4 regimes

2. if x < -3.2e19 or 6.4999999999999996e24 < x

   1. Initial program 100.0%

      \[\frac{x - y}{2 - \left(x + y\right)} \]

   2. Taylor expanded in x around inf 81.8%

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

   if -3.2e19 < x < 1.22e-76

   1. Initial program 100.0%

      \[\frac{x - y}{2 - \left(x + y\right)} \]

   2. Taylor expanded in x around 0 84.9%

      \[\leadsto \color{blue}{-1 \cdot \frac{y}{2 - y}} \]
   3. Step-by-step derivation

      1. mul-1-neg 84.9%

         \[\leadsto \color{blue}{-\frac{y}{2 - y}} \]

      2. distribute-neg-frac 84.9%

         \[\leadsto \color{blue}{\frac{-y}{2 - y}} \]

   4. Simplified 84.9%

      \[\leadsto \color{blue}{\frac{-y}{2 - y}} \]

   if 1.22e-76 < x < 8.6000000000000004e-35

   1. Initial program 100.0%

      \[\frac{x - y}{2 - \left(x + y\right)} \]

   2. Taylor expanded in y around 0 89.7%

      \[\leadsto \color{blue}{\frac{x}{2 - x}} \]

   if 8.6000000000000004e-35 < x < 6.4999999999999996e24

   1. Initial program 100.0%

      \[\frac{x - y}{2 - \left(x + y\right)} \]

   2. Taylor expanded in y around inf 80.1%

      \[\leadsto \color{blue}{1} \]
3. Recombined 4 regimes into one program.

4. Final simplification 83.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -3.2 \cdot 10^{+19}:\\ \;\;\;\;-1\\ \mathbf{elif}\;x \leq 1.22 \cdot 10^{-76}:\\ \;\;\;\;\frac{-y}{2 - y}\\ \mathbf{elif}\;x \leq 8.6 \cdot 10^{-35}:\\ \;\;\;\;\frac{x}{2 - x}\\ \mathbf{elif}\;x \leq 6.5 \cdot 10^{+24}:\\ \;\;\;\;1\\ \mathbf{else}:\\ \;\;\;\;-1\\ \end{array} \]

Alternative 3: 61.2% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -5 \cdot 10^{+21}:\\ \;\;\;\;-1\\ \mathbf{elif}\;x \leq 3 \cdot 10^{-87}:\\ \;\;\;\;1\\ \mathbf{elif}\;x \leq 1.3 \cdot 10^{-30}:\\ \;\;\;\;x \cdot 0.5\\ \mathbf{elif}\;x \leq 5 \cdot 10^{+24}:\\ \;\;\;\;1\\ \mathbf{else}:\\ \;\;\;\;-1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= x -5e+21)
   -1.0
   (if (<= x 3e-87)
     1.0
     (if (<= x 1.3e-30) (* x 0.5) (if (<= x 5e+24) 1.0 -1.0)))))

C

double code(double x, double y) {
	double tmp;
	if (x <= -5e+21) {
		tmp = -1.0;
	} else if (x <= 3e-87) {
		tmp = 1.0;
	} else if (x <= 1.3e-30) {
		tmp = x * 0.5;
	} else if (x <= 5e+24) {
		tmp = 1.0;
	} 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 <= (-5d+21)) then
        tmp = -1.0d0
    else if (x <= 3d-87) then
        tmp = 1.0d0
    else if (x <= 1.3d-30) then
        tmp = x * 0.5d0
    else if (x <= 5d+24) then
        tmp = 1.0d0
    else
        tmp = -1.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (x <= -5e+21) {
		tmp = -1.0;
	} else if (x <= 3e-87) {
		tmp = 1.0;
	} else if (x <= 1.3e-30) {
		tmp = x * 0.5;
	} else if (x <= 5e+24) {
		tmp = 1.0;
	} else {
		tmp = -1.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if x <= -5e+21:
		tmp = -1.0
	elif x <= 3e-87:
		tmp = 1.0
	elif x <= 1.3e-30:
		tmp = x * 0.5
	elif x <= 5e+24:
		tmp = 1.0
	else:
		tmp = -1.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (x <= -5e+21)
		tmp = -1.0;
	elseif (x <= 3e-87)
		tmp = 1.0;
	elseif (x <= 1.3e-30)
		tmp = Float64(x * 0.5);
	elseif (x <= 5e+24)
		tmp = 1.0;
	else
		tmp = -1.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (x <= -5e+21)
		tmp = -1.0;
	elseif (x <= 3e-87)
		tmp = 1.0;
	elseif (x <= 1.3e-30)
		tmp = x * 0.5;
	elseif (x <= 5e+24)
		tmp = 1.0;
	else
		tmp = -1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[x, -5e+21], -1.0, If[LessEqual[x, 3e-87], 1.0, If[LessEqual[x, 1.3e-30], N[(x * 0.5), $MachinePrecision], If[LessEqual[x, 5e+24], 1.0, -1.0]]]]

Derivation

1. Split input into 3 regimes

2. if x < -5e21 or 5.00000000000000045e24 < x

   1. Initial program 100.0%

      \[\frac{x - y}{2 - \left(x + y\right)} \]

   2. Taylor expanded in x around inf 81.8%

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

   if -5e21 < x < 3.00000000000000016e-87 or 1.29999999999999993e-30 < x < 5.00000000000000045e24

   1. Initial program 100.0%

      \[\frac{x - y}{2 - \left(x + y\right)} \]

   2. Taylor expanded in y around inf 62.1%

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

   if 3.00000000000000016e-87 < x < 1.29999999999999993e-30

   1. Initial program 100.0%

      \[\frac{x - y}{2 - \left(x + y\right)} \]

   2. Taylor expanded in y around 0 80.9%

      \[\leadsto \color{blue}{\frac{x}{2 - x}} \]

   3. Taylor expanded in x around 0 80.9%

      \[\leadsto \color{blue}{0.5 \cdot x} \]
   4. Step-by-step derivation

      1. *-commutative 80.9%

         \[\leadsto \color{blue}{x \cdot 0.5} \]

   5. Simplified 80.9%

      \[\leadsto \color{blue}{x \cdot 0.5} \]
3. Recombined 3 regimes into one program.

4. Final simplification 71.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -5 \cdot 10^{+21}:\\ \;\;\;\;-1\\ \mathbf{elif}\;x \leq 3 \cdot 10^{-87}:\\ \;\;\;\;1\\ \mathbf{elif}\;x \leq 1.3 \cdot 10^{-30}:\\ \;\;\;\;x \cdot 0.5\\ \mathbf{elif}\;x \leq 5 \cdot 10^{+24}:\\ \;\;\;\;1\\ \mathbf{else}:\\ \;\;\;\;-1\\ \end{array} \]

Alternative 4: 74.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -7.2 \cdot 10^{+35}:\\ \;\;\;\;1\\ \mathbf{elif}\;y \leq 10000:\\ \;\;\;\;\frac{x}{2 - x}\\ \mathbf{else}:\\ \;\;\;\;1 + \frac{2}{y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y -7.2e+35)
   1.0
   (if (<= y 10000.0) (/ x (- 2.0 x)) (+ 1.0 (/ 2.0 y)))))

C

double code(double x, double y) {
	double tmp;
	if (y <= -7.2e+35) {
		tmp = 1.0;
	} else if (y <= 10000.0) {
		tmp = x / (2.0 - x);
	} else {
		tmp = 1.0 + (2.0 / 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 <= (-7.2d+35)) then
        tmp = 1.0d0
    else if (y <= 10000.0d0) then
        tmp = x / (2.0d0 - x)
    else
        tmp = 1.0d0 + (2.0d0 / y)
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (y <= -7.2e+35) {
		tmp = 1.0;
	} else if (y <= 10000.0) {
		tmp = x / (2.0 - x);
	} else {
		tmp = 1.0 + (2.0 / y);
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= -7.2e+35:
		tmp = 1.0
	elif y <= 10000.0:
		tmp = x / (2.0 - x)
	else:
		tmp = 1.0 + (2.0 / y)
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= -7.2e+35)
		tmp = 1.0;
	elseif (y <= 10000.0)
		tmp = Float64(x / Float64(2.0 - x));
	else
		tmp = Float64(1.0 + Float64(2.0 / y));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= -7.2e+35)
		tmp = 1.0;
	elseif (y <= 10000.0)
		tmp = x / (2.0 - x);
	else
		tmp = 1.0 + (2.0 / y);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, -7.2e+35], 1.0, If[LessEqual[y, 10000.0], N[(x / N[(2.0 - x), $MachinePrecision]), $MachinePrecision], N[(1.0 + N[(2.0 / y), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if y < -7.2000000000000001e35

   1. Initial program 100.0%

      \[\frac{x - y}{2 - \left(x + y\right)} \]

   2. Taylor expanded in y around inf 77.2%

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

   if -7.2000000000000001e35 < y < 1e4

   1. Initial program 100.0%

      \[\frac{x - y}{2 - \left(x + y\right)} \]

   2. Taylor expanded in y around 0 71.5%

      \[\leadsto \color{blue}{\frac{x}{2 - x}} \]

   if 1e4 < y

   1. Initial program 100.0%

      \[\frac{x - y}{2 - \left(x + y\right)} \]

   2. Taylor expanded in x around 0 76.2%

      \[\leadsto \color{blue}{-1 \cdot \frac{y}{2 - y}} \]
   3. Step-by-step derivation

      1. mul-1-neg 76.2%

         \[\leadsto \color{blue}{-\frac{y}{2 - y}} \]

      2. distribute-neg-frac 76.2%

         \[\leadsto \color{blue}{\frac{-y}{2 - y}} \]

   4. Simplified 76.2%

      \[\leadsto \color{blue}{\frac{-y}{2 - y}} \]

   5. Taylor expanded in y around inf 75.5%

      \[\leadsto \color{blue}{1 + 2 \cdot \frac{1}{y}} \]
   6. Step-by-step derivation

      1. associate-*r/ 75.5%

         \[\leadsto 1 + \color{blue}{\frac{2 \cdot 1}{y}} \]

      2. metadata-eval 75.5%

         \[\leadsto 1 + \frac{\color{blue}{2}}{y} \]

   7. Simplified 75.5%

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

4. Final simplification 74.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -7.2 \cdot 10^{+35}:\\ \;\;\;\;1\\ \mathbf{elif}\;y \leq 10000:\\ \;\;\;\;\frac{x}{2 - x}\\ \mathbf{else}:\\ \;\;\;\;1 + \frac{2}{y}\\ \end{array} \]

Alternative 5: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{x - y}{2 - \left(x + y\right)} \end{array} \]

FPCore

(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));
}

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

Java

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))

Julia

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

Wolfram

code[x_, y_] := N[(N[(x - y), $MachinePrecision] / N[(2.0 - N[(x + y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[\frac{x - y}{2 - \left(x + y\right)} \]

2. Final simplification 100.0%

   \[\leadsto \frac{x - y}{2 - \left(x + y\right)} \]

Alternative 6: 61.4% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -5 \cdot 10^{+23}:\\ \;\;\;\;-1\\ \mathbf{elif}\;x \leq 1.95 \cdot 10^{+25}:\\ \;\;\;\;1\\ \mathbf{else}:\\ \;\;\;\;-1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= x -5e+23) -1.0 (if (<= x 1.95e+25) 1.0 -1.0)))

C

double code(double x, double y) {
	double tmp;
	if (x <= -5e+23) {
		tmp = -1.0;
	} else if (x <= 1.95e+25) {
		tmp = 1.0;
	} 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 <= (-5d+23)) then
        tmp = -1.0d0
    else if (x <= 1.95d+25) then
        tmp = 1.0d0
    else
        tmp = -1.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (x <= -5e+23) {
		tmp = -1.0;
	} else if (x <= 1.95e+25) {
		tmp = 1.0;
	} else {
		tmp = -1.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if x <= -5e+23:
		tmp = -1.0
	elif x <= 1.95e+25:
		tmp = 1.0
	else:
		tmp = -1.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (x <= -5e+23)
		tmp = -1.0;
	elseif (x <= 1.95e+25)
		tmp = 1.0;
	else
		tmp = -1.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (x <= -5e+23)
		tmp = -1.0;
	elseif (x <= 1.95e+25)
		tmp = 1.0;
	else
		tmp = -1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[x, -5e+23], -1.0, If[LessEqual[x, 1.95e+25], 1.0, -1.0]]

Derivation

1. Split input into 2 regimes

2. if x < -4.9999999999999999e23 or 1.9500000000000001e25 < x

   1. Initial program 100.0%

      \[\frac{x - y}{2 - \left(x + y\right)} \]

   2. Taylor expanded in x around inf 81.8%

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

   if -4.9999999999999999e23 < x < 1.9500000000000001e25

   1. Initial program 100.0%

      \[\frac{x - y}{2 - \left(x + y\right)} \]

   2. Taylor expanded in y around inf 58.5%

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

4. Final simplification 68.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -5 \cdot 10^{+23}:\\ \;\;\;\;-1\\ \mathbf{elif}\;x \leq 1.95 \cdot 10^{+25}:\\ \;\;\;\;1\\ \mathbf{else}:\\ \;\;\;\;-1\\ \end{array} \]

Alternative 7: 37.0% accurate, 9.0× 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%

   \[\frac{x - y}{2 - \left(x + y\right)} \]

2. Taylor expanded in x around inf 36.4%

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

3. Final simplification 36.4%

   \[\leadsto -1 \]

Developer target: 100.0% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 2 - \left(x + y\right)\\ \frac{x}{t_0} - \frac{y}{t_0} \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (- 2.0 (+ x y)))) (- (/ x t_0) (/ y t_0))))

C

double code(double x, double y) {
	double t_0 = 2.0 - (x + y);
	return (x / t_0) - (y / t_0);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: t_0
    t_0 = 2.0d0 - (x + y)
    code = (x / t_0) - (y / t_0)
end function

Java

public static double code(double x, double y) {
	double t_0 = 2.0 - (x + y);
	return (x / t_0) - (y / t_0);
}

Python

def code(x, y):
	t_0 = 2.0 - (x + y)
	return (x / t_0) - (y / t_0)

Julia

function code(x, y)
	t_0 = Float64(2.0 - Float64(x + y))
	return Float64(Float64(x / t_0) - Float64(y / t_0))
end

MATLAB

function tmp = code(x, y)
	t_0 = 2.0 - (x + y);
	tmp = (x / t_0) - (y / t_0);
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(2.0 - N[(x + y), $MachinePrecision]), $MachinePrecision]}, N[(N[(x / t$95$0), $MachinePrecision] - N[(y / t$95$0), $MachinePrecision]), $MachinePrecision]]

Reproduce

herbie shell --seed 2023319 
(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))))