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

Percentage Accurate: 100.0% → 100.0%

Time: 11.6s

Alternatives: 11

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, 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. Add Preprocessing
3. Add Preprocessing

Alternative 2: 86.3% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{x - y}{2 - y}\\ \mathbf{if}\;y \leq -3 \cdot 10^{+106}:\\ \;\;\;\;\frac{y - x}{y}\\ \mathbf{elif}\;y \leq -9.6 \cdot 10^{+83}:\\ \;\;\;\;\frac{x}{\left(-y\right) - x}\\ \mathbf{elif}\;y \leq -9 \cdot 10^{-12}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;y \leq 7.8 \cdot 10^{+26}:\\ \;\;\;\;\frac{x - y}{2 - x}\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (/ (- x y) (- 2.0 y))))
   (if (<= y -3e+106)
     (/ (- y x) y)
     (if (<= y -9.6e+83)
       (/ x (- (- y) x))
       (if (<= y -9e-12) t_0 (if (<= y 7.8e+26) (/ (- x y) (- 2.0 x)) t_0))))))

C

double code(double x, double y) {
	double t_0 = (x - y) / (2.0 - y);
	double tmp;
	if (y <= -3e+106) {
		tmp = (y - x) / y;
	} else if (y <= -9.6e+83) {
		tmp = x / (-y - x);
	} else if (y <= -9e-12) {
		tmp = t_0;
	} else if (y <= 7.8e+26) {
		tmp = (x - y) / (2.0 - x);
	} else {
		tmp = t_0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: t_0
    real(8) :: tmp
    t_0 = (x - y) / (2.0d0 - y)
    if (y <= (-3d+106)) then
        tmp = (y - x) / y
    else if (y <= (-9.6d+83)) then
        tmp = x / (-y - x)
    else if (y <= (-9d-12)) then
        tmp = t_0
    else if (y <= 7.8d+26) then
        tmp = (x - y) / (2.0d0 - x)
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double t_0 = (x - y) / (2.0 - y);
	double tmp;
	if (y <= -3e+106) {
		tmp = (y - x) / y;
	} else if (y <= -9.6e+83) {
		tmp = x / (-y - x);
	} else if (y <= -9e-12) {
		tmp = t_0;
	} else if (y <= 7.8e+26) {
		tmp = (x - y) / (2.0 - x);
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = (x - y) / (2.0 - y)
	tmp = 0
	if y <= -3e+106:
		tmp = (y - x) / y
	elif y <= -9.6e+83:
		tmp = x / (-y - x)
	elif y <= -9e-12:
		tmp = t_0
	elif y <= 7.8e+26:
		tmp = (x - y) / (2.0 - x)
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y)
	t_0 = Float64(Float64(x - y) / Float64(2.0 - y))
	tmp = 0.0
	if (y <= -3e+106)
		tmp = Float64(Float64(y - x) / y);
	elseif (y <= -9.6e+83)
		tmp = Float64(x / Float64(Float64(-y) - x));
	elseif (y <= -9e-12)
		tmp = t_0;
	elseif (y <= 7.8e+26)
		tmp = Float64(Float64(x - y) / Float64(2.0 - x));
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = (x - y) / (2.0 - y);
	tmp = 0.0;
	if (y <= -3e+106)
		tmp = (y - x) / y;
	elseif (y <= -9.6e+83)
		tmp = x / (-y - x);
	elseif (y <= -9e-12)
		tmp = t_0;
	elseif (y <= 7.8e+26)
		tmp = (x - y) / (2.0 - x);
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(N[(x - y), $MachinePrecision] / N[(2.0 - y), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -3e+106], N[(N[(y - x), $MachinePrecision] / y), $MachinePrecision], If[LessEqual[y, -9.6e+83], N[(x / N[((-y) - x), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, -9e-12], t$95$0, If[LessEqual[y, 7.8e+26], N[(N[(x - y), $MachinePrecision] / N[(2.0 - x), $MachinePrecision]), $MachinePrecision], t$95$0]]]]]

Derivation

1. Split input into 4 regimes

2. if y < -3.0000000000000001e106

   1. Initial program 100.0%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in x around 0 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in y around inf 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]

   12. Applied egg-rr 0

       \[\leadsto expr\]

   if -3.0000000000000001e106 < y < -9.59999999999999965e83

   1. Initial program 100.0%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in y around inf 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]

   if -9.59999999999999965e83 < y < -8.99999999999999962e-12 or 7.8e26 < y

   1. Initial program 99.9%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in x around 0 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if -8.99999999999999962e-12 < y < 7.8e26

   1. Initial program 100.0%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in y around 0 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]
3. Recombined 4 regimes into one program.
4. Add Preprocessing

Alternative 3: 86.1% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{y - x}{y}\\ \mathbf{if}\;y \leq -3.05 \cdot 10^{+106}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;y \leq -1.05 \cdot 10^{+84}:\\ \;\;\;\;\frac{x}{\left(-y\right) - x}\\ \mathbf{elif}\;y \leq -9 \cdot 10^{-12}:\\ \;\;\;\;\frac{y}{y + -2}\\ \mathbf{elif}\;y \leq 1.35 \cdot 10^{+35}:\\ \;\;\;\;\frac{x - y}{2 - x}\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (/ (- y x) y)))
   (if (<= y -3.05e+106)
     t_0
     (if (<= y -1.05e+84)
       (/ x (- (- y) x))
       (if (<= y -9e-12)
         (/ y (+ y -2.0))
         (if (<= y 1.35e+35) (/ (- x y) (- 2.0 x)) t_0))))))

C

double code(double x, double y) {
	double t_0 = (y - x) / y;
	double tmp;
	if (y <= -3.05e+106) {
		tmp = t_0;
	} else if (y <= -1.05e+84) {
		tmp = x / (-y - x);
	} else if (y <= -9e-12) {
		tmp = y / (y + -2.0);
	} else if (y <= 1.35e+35) {
		tmp = (x - y) / (2.0 - x);
	} else {
		tmp = t_0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: t_0
    real(8) :: tmp
    t_0 = (y - x) / y
    if (y <= (-3.05d+106)) then
        tmp = t_0
    else if (y <= (-1.05d+84)) then
        tmp = x / (-y - x)
    else if (y <= (-9d-12)) then
        tmp = y / (y + (-2.0d0))
    else if (y <= 1.35d+35) then
        tmp = (x - y) / (2.0d0 - x)
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double t_0 = (y - x) / y;
	double tmp;
	if (y <= -3.05e+106) {
		tmp = t_0;
	} else if (y <= -1.05e+84) {
		tmp = x / (-y - x);
	} else if (y <= -9e-12) {
		tmp = y / (y + -2.0);
	} else if (y <= 1.35e+35) {
		tmp = (x - y) / (2.0 - x);
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = (y - x) / y
	tmp = 0
	if y <= -3.05e+106:
		tmp = t_0
	elif y <= -1.05e+84:
		tmp = x / (-y - x)
	elif y <= -9e-12:
		tmp = y / (y + -2.0)
	elif y <= 1.35e+35:
		tmp = (x - y) / (2.0 - x)
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y)
	t_0 = Float64(Float64(y - x) / y)
	tmp = 0.0
	if (y <= -3.05e+106)
		tmp = t_0;
	elseif (y <= -1.05e+84)
		tmp = Float64(x / Float64(Float64(-y) - x));
	elseif (y <= -9e-12)
		tmp = Float64(y / Float64(y + -2.0));
	elseif (y <= 1.35e+35)
		tmp = Float64(Float64(x - y) / Float64(2.0 - x));
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = (y - x) / y;
	tmp = 0.0;
	if (y <= -3.05e+106)
		tmp = t_0;
	elseif (y <= -1.05e+84)
		tmp = x / (-y - x);
	elseif (y <= -9e-12)
		tmp = y / (y + -2.0);
	elseif (y <= 1.35e+35)
		tmp = (x - y) / (2.0 - x);
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(N[(y - x), $MachinePrecision] / y), $MachinePrecision]}, If[LessEqual[y, -3.05e+106], t$95$0, If[LessEqual[y, -1.05e+84], N[(x / N[((-y) - x), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, -9e-12], N[(y / N[(y + -2.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 1.35e+35], N[(N[(x - y), $MachinePrecision] / N[(2.0 - x), $MachinePrecision]), $MachinePrecision], t$95$0]]]]]

Derivation

1. Split input into 4 regimes

2. if y < -3.05e106 or 1.35000000000000001e35 < y

   1. Initial program 100.0%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in x around 0 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in y around inf 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]

   12. Applied egg-rr 0

       \[\leadsto expr\]

   if -3.05e106 < y < -1.05000000000000009e84

   1. Initial program 100.0%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in y around inf 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]

   if -1.05000000000000009e84 < y < -8.99999999999999962e-12

   1. Initial program 99.7%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in x around 0 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if -8.99999999999999962e-12 < y < 1.35000000000000001e35

   1. Initial program 100.0%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in y around 0 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]
3. Recombined 4 regimes into one program.
4. Add Preprocessing

Alternative 4: 60.6% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -3.8 \cdot 10^{+108}:\\ \;\;\;\;1\\ \mathbf{elif}\;y \leq -5.8 \cdot 10^{+83}:\\ \;\;\;\;-1 + \frac{y}{x}\\ \mathbf{elif}\;y \leq -2:\\ \;\;\;\;1\\ \mathbf{elif}\;y \leq -3 \cdot 10^{-131}:\\ \;\;\;\;y \cdot -0.5\\ \mathbf{elif}\;y \leq 4.2 \cdot 10^{+29}:\\ \;\;\;\;-1\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y -3.8e+108)
   1.0
   (if (<= y -5.8e+83)
     (+ -1.0 (/ y x))
     (if (<= y -2.0)
       1.0
       (if (<= y -3e-131) (* y -0.5) (if (<= y 4.2e+29) -1.0 1.0))))))

C

double code(double x, double y) {
	double tmp;
	if (y <= -3.8e+108) {
		tmp = 1.0;
	} else if (y <= -5.8e+83) {
		tmp = -1.0 + (y / x);
	} else if (y <= -2.0) {
		tmp = 1.0;
	} else if (y <= -3e-131) {
		tmp = y * -0.5;
	} else if (y <= 4.2e+29) {
		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 (y <= (-3.8d+108)) then
        tmp = 1.0d0
    else if (y <= (-5.8d+83)) then
        tmp = (-1.0d0) + (y / x)
    else if (y <= (-2.0d0)) then
        tmp = 1.0d0
    else if (y <= (-3d-131)) then
        tmp = y * (-0.5d0)
    else if (y <= 4.2d+29) 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 (y <= -3.8e+108) {
		tmp = 1.0;
	} else if (y <= -5.8e+83) {
		tmp = -1.0 + (y / x);
	} else if (y <= -2.0) {
		tmp = 1.0;
	} else if (y <= -3e-131) {
		tmp = y * -0.5;
	} else if (y <= 4.2e+29) {
		tmp = -1.0;
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= -3.8e+108:
		tmp = 1.0
	elif y <= -5.8e+83:
		tmp = -1.0 + (y / x)
	elif y <= -2.0:
		tmp = 1.0
	elif y <= -3e-131:
		tmp = y * -0.5
	elif y <= 4.2e+29:
		tmp = -1.0
	else:
		tmp = 1.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= -3.8e+108)
		tmp = 1.0;
	elseif (y <= -5.8e+83)
		tmp = Float64(-1.0 + Float64(y / x));
	elseif (y <= -2.0)
		tmp = 1.0;
	elseif (y <= -3e-131)
		tmp = Float64(y * -0.5);
	elseif (y <= 4.2e+29)
		tmp = -1.0;
	else
		tmp = 1.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= -3.8e+108)
		tmp = 1.0;
	elseif (y <= -5.8e+83)
		tmp = -1.0 + (y / x);
	elseif (y <= -2.0)
		tmp = 1.0;
	elseif (y <= -3e-131)
		tmp = y * -0.5;
	elseif (y <= 4.2e+29)
		tmp = -1.0;
	else
		tmp = 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, -3.8e+108], 1.0, If[LessEqual[y, -5.8e+83], N[(-1.0 + N[(y / x), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, -2.0], 1.0, If[LessEqual[y, -3e-131], N[(y * -0.5), $MachinePrecision], If[LessEqual[y, 4.2e+29], -1.0, 1.0]]]]]

Derivation

1. Split input into 4 regimes

2. if y < -3.80000000000000008e108 or -5.79999999999999999e83 < y < -2 or 4.2000000000000003e29 < y

   1. Initial program 99.9%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in y around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if -3.80000000000000008e108 < y < -5.79999999999999999e83

   1. Initial program 100.0%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in y around inf 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]

   11. Taylor expanded in x around inf 0

       \[\leadsto expr\]

   13. Simplified 0

       \[\leadsto expr\]

   if -2 < y < -2.99999999999999996e-131

   1. Initial program 99.9%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in x around 0 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in y around 0 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]

   11. Taylor expanded in x around 0 0

       \[\leadsto expr\]

   13. Simplified 0

       \[\leadsto expr\]

   if -2.99999999999999996e-131 < y < 4.2000000000000003e29

   1. Initial program 100.0%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]
3. Recombined 4 regimes into one program.
4. Add Preprocessing

Alternative 5: 60.6% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1 \cdot 10^{+107}:\\ \;\;\;\;1\\ \mathbf{elif}\;y \leq -3 \cdot 10^{+83}:\\ \;\;\;\;-1\\ \mathbf{elif}\;y \leq -2:\\ \;\;\;\;1\\ \mathbf{elif}\;y \leq -6.8 \cdot 10^{-133}:\\ \;\;\;\;y \cdot -0.5\\ \mathbf{elif}\;y \leq 2 \cdot 10^{+27}:\\ \;\;\;\;-1\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y -1e+107)
   1.0
   (if (<= y -3e+83)
     -1.0
     (if (<= y -2.0)
       1.0
       (if (<= y -6.8e-133) (* y -0.5) (if (<= y 2e+27) -1.0 1.0))))))

C

double code(double x, double y) {
	double tmp;
	if (y <= -1e+107) {
		tmp = 1.0;
	} else if (y <= -3e+83) {
		tmp = -1.0;
	} else if (y <= -2.0) {
		tmp = 1.0;
	} else if (y <= -6.8e-133) {
		tmp = y * -0.5;
	} else if (y <= 2e+27) {
		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 (y <= (-1d+107)) then
        tmp = 1.0d0
    else if (y <= (-3d+83)) then
        tmp = -1.0d0
    else if (y <= (-2.0d0)) then
        tmp = 1.0d0
    else if (y <= (-6.8d-133)) then
        tmp = y * (-0.5d0)
    else if (y <= 2d+27) 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 (y <= -1e+107) {
		tmp = 1.0;
	} else if (y <= -3e+83) {
		tmp = -1.0;
	} else if (y <= -2.0) {
		tmp = 1.0;
	} else if (y <= -6.8e-133) {
		tmp = y * -0.5;
	} else if (y <= 2e+27) {
		tmp = -1.0;
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= -1e+107:
		tmp = 1.0
	elif y <= -3e+83:
		tmp = -1.0
	elif y <= -2.0:
		tmp = 1.0
	elif y <= -6.8e-133:
		tmp = y * -0.5
	elif y <= 2e+27:
		tmp = -1.0
	else:
		tmp = 1.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= -1e+107)
		tmp = 1.0;
	elseif (y <= -3e+83)
		tmp = -1.0;
	elseif (y <= -2.0)
		tmp = 1.0;
	elseif (y <= -6.8e-133)
		tmp = Float64(y * -0.5);
	elseif (y <= 2e+27)
		tmp = -1.0;
	else
		tmp = 1.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= -1e+107)
		tmp = 1.0;
	elseif (y <= -3e+83)
		tmp = -1.0;
	elseif (y <= -2.0)
		tmp = 1.0;
	elseif (y <= -6.8e-133)
		tmp = y * -0.5;
	elseif (y <= 2e+27)
		tmp = -1.0;
	else
		tmp = 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, -1e+107], 1.0, If[LessEqual[y, -3e+83], -1.0, If[LessEqual[y, -2.0], 1.0, If[LessEqual[y, -6.8e-133], N[(y * -0.5), $MachinePrecision], If[LessEqual[y, 2e+27], -1.0, 1.0]]]]]

Derivation

1. Split input into 3 regimes

2. if y < -9.9999999999999997e106 or -3e83 < y < -2 or 2e27 < y

   1. Initial program 99.9%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in y around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if -9.9999999999999997e106 < y < -3e83 or -6.80000000000000012e-133 < y < 2e27

   1. Initial program 100.0%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if -2 < y < -6.80000000000000012e-133

   1. Initial program 99.9%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in x around 0 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in y around 0 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]

   11. Taylor expanded in x around 0 0

       \[\leadsto expr\]

   13. Simplified 0

       \[\leadsto expr\]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 6: 73.7% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -7.2 \cdot 10^{+111}:\\ \;\;\;\;1\\ \mathbf{elif}\;y \leq -6.6 \cdot 10^{+81}:\\ \;\;\;\;-1 + \frac{y}{x}\\ \mathbf{elif}\;y \leq -9 \cdot 10^{-12}:\\ \;\;\;\;1\\ \mathbf{elif}\;y \leq 2.1 \cdot 10^{+37}:\\ \;\;\;\;\frac{x}{2 - x}\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y -7.2e+111)
   1.0
   (if (<= y -6.6e+81)
     (+ -1.0 (/ y x))
     (if (<= y -9e-12) 1.0 (if (<= y 2.1e+37) (/ x (- 2.0 x)) 1.0)))))

C

double code(double x, double y) {
	double tmp;
	if (y <= -7.2e+111) {
		tmp = 1.0;
	} else if (y <= -6.6e+81) {
		tmp = -1.0 + (y / x);
	} else if (y <= -9e-12) {
		tmp = 1.0;
	} else if (y <= 2.1e+37) {
		tmp = x / (2.0 - 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 (y <= (-7.2d+111)) then
        tmp = 1.0d0
    else if (y <= (-6.6d+81)) then
        tmp = (-1.0d0) + (y / x)
    else if (y <= (-9d-12)) then
        tmp = 1.0d0
    else if (y <= 2.1d+37) then
        tmp = x / (2.0d0 - x)
    else
        tmp = 1.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (y <= -7.2e+111) {
		tmp = 1.0;
	} else if (y <= -6.6e+81) {
		tmp = -1.0 + (y / x);
	} else if (y <= -9e-12) {
		tmp = 1.0;
	} else if (y <= 2.1e+37) {
		tmp = x / (2.0 - x);
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= -7.2e+111:
		tmp = 1.0
	elif y <= -6.6e+81:
		tmp = -1.0 + (y / x)
	elif y <= -9e-12:
		tmp = 1.0
	elif y <= 2.1e+37:
		tmp = x / (2.0 - x)
	else:
		tmp = 1.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= -7.2e+111)
		tmp = 1.0;
	elseif (y <= -6.6e+81)
		tmp = Float64(-1.0 + Float64(y / x));
	elseif (y <= -9e-12)
		tmp = 1.0;
	elseif (y <= 2.1e+37)
		tmp = Float64(x / Float64(2.0 - x));
	else
		tmp = 1.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= -7.2e+111)
		tmp = 1.0;
	elseif (y <= -6.6e+81)
		tmp = -1.0 + (y / x);
	elseif (y <= -9e-12)
		tmp = 1.0;
	elseif (y <= 2.1e+37)
		tmp = x / (2.0 - x);
	else
		tmp = 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, -7.2e+111], 1.0, If[LessEqual[y, -6.6e+81], N[(-1.0 + N[(y / x), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, -9e-12], 1.0, If[LessEqual[y, 2.1e+37], N[(x / N[(2.0 - x), $MachinePrecision]), $MachinePrecision], 1.0]]]]

Derivation

1. Split input into 3 regimes

2. if y < -7.2000000000000004e111 or -6.6e81 < y < -8.99999999999999962e-12 or 2.1000000000000001e37 < y

   1. Initial program 99.9%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in y around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if -7.2000000000000004e111 < y < -6.6e81

   1. Initial program 100.0%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in y around inf 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]

   11. Taylor expanded in x around inf 0

       \[\leadsto expr\]

   13. Simplified 0

       \[\leadsto expr\]

   if -8.99999999999999962e-12 < y < 2.1000000000000001e37

   1. Initial program 100.0%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in y around 0 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 7: 61.8% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -3 \cdot 10^{+106}:\\ \;\;\;\;1\\ \mathbf{elif}\;y \leq -5.8 \cdot 10^{+83}:\\ \;\;\;\;-1\\ \mathbf{elif}\;y \leq -9 \cdot 10^{-12}:\\ \;\;\;\;1\\ \mathbf{elif}\;y \leq 10^{+37}:\\ \;\;\;\;-1\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y -3e+106)
   1.0
   (if (<= y -5.8e+83)
     -1.0
     (if (<= y -9e-12) 1.0 (if (<= y 1e+37) -1.0 1.0)))))

C

double code(double x, double y) {
	double tmp;
	if (y <= -3e+106) {
		tmp = 1.0;
	} else if (y <= -5.8e+83) {
		tmp = -1.0;
	} else if (y <= -9e-12) {
		tmp = 1.0;
	} else if (y <= 1e+37) {
		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 (y <= (-3d+106)) then
        tmp = 1.0d0
    else if (y <= (-5.8d+83)) then
        tmp = -1.0d0
    else if (y <= (-9d-12)) then
        tmp = 1.0d0
    else if (y <= 1d+37) 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 (y <= -3e+106) {
		tmp = 1.0;
	} else if (y <= -5.8e+83) {
		tmp = -1.0;
	} else if (y <= -9e-12) {
		tmp = 1.0;
	} else if (y <= 1e+37) {
		tmp = -1.0;
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= -3e+106:
		tmp = 1.0
	elif y <= -5.8e+83:
		tmp = -1.0
	elif y <= -9e-12:
		tmp = 1.0
	elif y <= 1e+37:
		tmp = -1.0
	else:
		tmp = 1.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= -3e+106)
		tmp = 1.0;
	elseif (y <= -5.8e+83)
		tmp = -1.0;
	elseif (y <= -9e-12)
		tmp = 1.0;
	elseif (y <= 1e+37)
		tmp = -1.0;
	else
		tmp = 1.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= -3e+106)
		tmp = 1.0;
	elseif (y <= -5.8e+83)
		tmp = -1.0;
	elseif (y <= -9e-12)
		tmp = 1.0;
	elseif (y <= 1e+37)
		tmp = -1.0;
	else
		tmp = 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, -3e+106], 1.0, If[LessEqual[y, -5.8e+83], -1.0, If[LessEqual[y, -9e-12], 1.0, If[LessEqual[y, 1e+37], -1.0, 1.0]]]]

Derivation

1. Split input into 2 regimes

2. if y < -3.0000000000000001e106 or -5.79999999999999999e83 < y < -8.99999999999999962e-12 or 9.99999999999999954e36 < y

   1. Initial program 99.9%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in y around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if -3.0000000000000001e106 < y < -5.79999999999999999e83 or -8.99999999999999962e-12 < y < 9.99999999999999954e36

   1. Initial program 100.0%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 8: 75.0% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.52 \cdot 10^{+29}:\\ \;\;\;\;\frac{x}{\left(-y\right) - x}\\ \mathbf{elif}\;x \leq 700:\\ \;\;\;\;\frac{y}{y + -2}\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{\left(2 - y\right) - x}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= x -1.52e+29)
   (/ x (- (- y) x))
   (if (<= x 700.0) (/ y (+ y -2.0)) (/ x (- (- 2.0 y) x)))))

C

double code(double x, double y) {
	double tmp;
	if (x <= -1.52e+29) {
		tmp = x / (-y - x);
	} else if (x <= 700.0) {
		tmp = y / (y + -2.0);
	} else {
		tmp = x / ((2.0 - y) - x);
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (x <= (-1.52d+29)) then
        tmp = x / (-y - x)
    else if (x <= 700.0d0) then
        tmp = y / (y + (-2.0d0))
    else
        tmp = x / ((2.0d0 - y) - x)
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (x <= -1.52e+29) {
		tmp = x / (-y - x);
	} else if (x <= 700.0) {
		tmp = y / (y + -2.0);
	} else {
		tmp = x / ((2.0 - y) - x);
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if x <= -1.52e+29:
		tmp = x / (-y - x)
	elif x <= 700.0:
		tmp = y / (y + -2.0)
	else:
		tmp = x / ((2.0 - y) - x)
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (x <= -1.52e+29)
		tmp = Float64(x / Float64(Float64(-y) - x));
	elseif (x <= 700.0)
		tmp = Float64(y / Float64(y + -2.0));
	else
		tmp = Float64(x / Float64(Float64(2.0 - y) - x));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (x <= -1.52e+29)
		tmp = x / (-y - x);
	elseif (x <= 700.0)
		tmp = y / (y + -2.0);
	else
		tmp = x / ((2.0 - y) - x);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[x, -1.52e+29], N[(x / N[((-y) - x), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 700.0], N[(y / N[(y + -2.0), $MachinePrecision]), $MachinePrecision], N[(x / N[(N[(2.0 - y), $MachinePrecision] - x), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < -1.52e29

   1. Initial program 99.9%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in y around inf 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]

   if -1.52e29 < x < 700

   1. Initial program 100.0%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in x around 0 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if 700 < x

   1. Initial program 100.0%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 9: 74.8% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.7 \cdot 10^{+31}:\\ \;\;\;\;\frac{x}{\left(-y\right) - x}\\ \mathbf{elif}\;x \leq 880:\\ \;\;\;\;\frac{y}{y + -2}\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{2 - x}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= x -1.7e+31)
   (/ x (- (- y) x))
   (if (<= x 880.0) (/ y (+ y -2.0)) (/ x (- 2.0 x)))))

C

double code(double x, double y) {
	double tmp;
	if (x <= -1.7e+31) {
		tmp = x / (-y - x);
	} else if (x <= 880.0) {
		tmp = y / (y + -2.0);
	} else {
		tmp = x / (2.0 - x);
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (x <= (-1.7d+31)) then
        tmp = x / (-y - x)
    else if (x <= 880.0d0) then
        tmp = y / (y + (-2.0d0))
    else
        tmp = x / (2.0d0 - x)
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (x <= -1.7e+31) {
		tmp = x / (-y - x);
	} else if (x <= 880.0) {
		tmp = y / (y + -2.0);
	} else {
		tmp = x / (2.0 - x);
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if x <= -1.7e+31:
		tmp = x / (-y - x)
	elif x <= 880.0:
		tmp = y / (y + -2.0)
	else:
		tmp = x / (2.0 - x)
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (x <= -1.7e+31)
		tmp = Float64(x / Float64(Float64(-y) - x));
	elseif (x <= 880.0)
		tmp = Float64(y / Float64(y + -2.0));
	else
		tmp = Float64(x / Float64(2.0 - x));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (x <= -1.7e+31)
		tmp = x / (-y - x);
	elseif (x <= 880.0)
		tmp = y / (y + -2.0);
	else
		tmp = x / (2.0 - x);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[x, -1.7e+31], N[(x / N[((-y) - x), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 880.0], N[(y / N[(y + -2.0), $MachinePrecision]), $MachinePrecision], N[(x / N[(2.0 - x), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < -1.6999999999999999e31

   1. Initial program 99.9%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in y around inf 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]

   if -1.6999999999999999e31 < x < 880

   1. Initial program 100.0%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in x around 0 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if 880 < x

   1. Initial program 100.0%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in y around 0 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 10: 74.7% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.75 \cdot 10^{+31}:\\ \;\;\;\;-1 + \frac{y}{x}\\ \mathbf{elif}\;x \leq 820:\\ \;\;\;\;\frac{y}{y + -2}\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{2 - x}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= x -1.75e+31)
   (+ -1.0 (/ y x))
   (if (<= x 820.0) (/ y (+ y -2.0)) (/ x (- 2.0 x)))))

C

double code(double x, double y) {
	double tmp;
	if (x <= -1.75e+31) {
		tmp = -1.0 + (y / x);
	} else if (x <= 820.0) {
		tmp = y / (y + -2.0);
	} else {
		tmp = x / (2.0 - x);
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (x <= (-1.75d+31)) then
        tmp = (-1.0d0) + (y / x)
    else if (x <= 820.0d0) then
        tmp = y / (y + (-2.0d0))
    else
        tmp = x / (2.0d0 - x)
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (x <= -1.75e+31) {
		tmp = -1.0 + (y / x);
	} else if (x <= 820.0) {
		tmp = y / (y + -2.0);
	} else {
		tmp = x / (2.0 - x);
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if x <= -1.75e+31:
		tmp = -1.0 + (y / x)
	elif x <= 820.0:
		tmp = y / (y + -2.0)
	else:
		tmp = x / (2.0 - x)
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (x <= -1.75e+31)
		tmp = Float64(-1.0 + Float64(y / x));
	elseif (x <= 820.0)
		tmp = Float64(y / Float64(y + -2.0));
	else
		tmp = Float64(x / Float64(2.0 - x));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (x <= -1.75e+31)
		tmp = -1.0 + (y / x);
	elseif (x <= 820.0)
		tmp = y / (y + -2.0);
	else
		tmp = x / (2.0 - x);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[x, -1.75e+31], N[(-1.0 + N[(y / x), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 820.0], N[(y / N[(y + -2.0), $MachinePrecision]), $MachinePrecision], N[(x / N[(2.0 - x), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < -1.75e31

   1. Initial program 99.9%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in y around inf 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]

   11. Taylor expanded in x around inf 0

       \[\leadsto expr\]

   13. Simplified 0

       \[\leadsto expr\]

   if -1.75e31 < x < 820

   1. Initial program 100.0%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in x around 0 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if 820 < x

   1. Initial program 100.0%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in y around 0 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 11: 38.3% 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%

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

3. Simplified 0

   \[\leadsto expr\]
4. Add Preprocessing

5. Taylor expanded in x around inf 0

   \[\leadsto expr\]

7. Simplified 0

   \[\leadsto expr\]
8. Add Preprocessing

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 2024110 
(FPCore (x y)
  :name "Data.Colour.RGB:hslsv from colour-2.3.3, C"
  :precision binary64

  :alt
  (- (/ x (- 2.0 (+ x y))) (/ y (- 2.0 (+ x y))))

  (/ (- x y) (- 2.0 (+ x y))))