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

Percentage Accurate: 100.0% → 100.0%

Time: 5.7s

Alternatives: 7

Speedup: 1.0×

Specification

Math

\[\begin{array}{l} \\ \frac{x - y}{x + y} \end{array} \]

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{x - y}{x + y} \end{array} \]

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{x - y}{x + y} \end{array} \]

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 100.0%

   \[\frac{x - y}{x + y} \]
2. Add Preprocessing
3. Add Preprocessing

Alternative 2: 74.5% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.45 \lor \neg \left(x \leq 1.25 \cdot 10^{+26}\right):\\ \;\;\;\;1 - \frac{y}{x}\\ \mathbf{else}:\\ \;\;\;\;\frac{y}{\left(-x\right) - y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (or (<= x -1.45) (not (<= x 1.25e+26)))
   (- 1.0 (/ y x))
   (/ y (- (- x) y))))

C

double code(double x, double y) {
	double tmp;
	if ((x <= -1.45) || !(x <= 1.25e+26)) {
		tmp = 1.0 - (y / x);
	} else {
		tmp = y / (-x - y);
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if ((x <= (-1.45d0)) .or. (.not. (x <= 1.25d+26))) then
        tmp = 1.0d0 - (y / x)
    else
        tmp = y / (-x - y)
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if ((x <= -1.45) || !(x <= 1.25e+26)) {
		tmp = 1.0 - (y / x);
	} else {
		tmp = y / (-x - y);
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (x <= -1.45) or not (x <= 1.25e+26):
		tmp = 1.0 - (y / x)
	else:
		tmp = y / (-x - y)
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if ((x <= -1.45) || !(x <= 1.25e+26))
		tmp = Float64(1.0 - Float64(y / x));
	else
		tmp = Float64(y / Float64(Float64(-x) - y));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if ((x <= -1.45) || ~((x <= 1.25e+26)))
		tmp = 1.0 - (y / x);
	else
		tmp = y / (-x - y);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[Or[LessEqual[x, -1.45], N[Not[LessEqual[x, 1.25e+26]], $MachinePrecision]], N[(1.0 - N[(y / x), $MachinePrecision]), $MachinePrecision], N[(y / N[((-x) - y), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -1.44999999999999996 or 1.25e26 < x

   1. Initial program 100.0%

      \[\frac{x - y}{x + y} \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf 82.3%

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

   4. Taylor expanded in x around 0 82.3%

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

      1. neg-mul-1 82.3%

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

      2. sub-neg 82.3%

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

      3. div-sub 82.3%

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

      4. *-rgt-identity 82.3%

         \[\leadsto \frac{\color{blue}{x \cdot 1}}{x} - \frac{y}{x} \]

      5. associate-*r/ 82.2%

         \[\leadsto \color{blue}{x \cdot \frac{1}{x}} - \frac{y}{x} \]

      6. rgt-mult-inverse 82.3%

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

   6. Simplified 82.3%

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

   if -1.44999999999999996 < x < 1.25e26

   1. Initial program 100.0%

      \[\frac{x - y}{x + y} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0 77.4%

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

      1. neg-mul-1 77.4%

         \[\leadsto \frac{\color{blue}{-y}}{x + y} \]

   5. Simplified 77.4%

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

4. Final simplification 79.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -1.45 \lor \neg \left(x \leq 1.25 \cdot 10^{+26}\right):\\ \;\;\;\;1 - \frac{y}{x}\\ \mathbf{else}:\\ \;\;\;\;\frac{y}{\left(-x\right) - y}\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 74.6% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -12:\\ \;\;\;\;1 + -2 \cdot \frac{y}{x}\\ \mathbf{elif}\;x \leq 3.7 \cdot 10^{+25}:\\ \;\;\;\;\frac{y}{\left(-x\right) - y}\\ \mathbf{else}:\\ \;\;\;\;1 - \frac{y}{x}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= x -12.0)
   (+ 1.0 (* -2.0 (/ y x)))
   (if (<= x 3.7e+25) (/ y (- (- x) y)) (- 1.0 (/ y x)))))

C

double code(double x, double y) {
	double tmp;
	if (x <= -12.0) {
		tmp = 1.0 + (-2.0 * (y / x));
	} else if (x <= 3.7e+25) {
		tmp = y / (-x - y);
	} else {
		tmp = 1.0 - (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 <= (-12.0d0)) then
        tmp = 1.0d0 + ((-2.0d0) * (y / x))
    else if (x <= 3.7d+25) then
        tmp = y / (-x - y)
    else
        tmp = 1.0d0 - (y / x)
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (x <= -12.0) {
		tmp = 1.0 + (-2.0 * (y / x));
	} else if (x <= 3.7e+25) {
		tmp = y / (-x - y);
	} else {
		tmp = 1.0 - (y / x);
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if x <= -12.0:
		tmp = 1.0 + (-2.0 * (y / x))
	elif x <= 3.7e+25:
		tmp = y / (-x - y)
	else:
		tmp = 1.0 - (y / x)
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (x <= -12.0)
		tmp = Float64(1.0 + Float64(-2.0 * Float64(y / x)));
	elseif (x <= 3.7e+25)
		tmp = Float64(y / Float64(Float64(-x) - y));
	else
		tmp = Float64(1.0 - Float64(y / x));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (x <= -12.0)
		tmp = 1.0 + (-2.0 * (y / x));
	elseif (x <= 3.7e+25)
		tmp = y / (-x - y);
	else
		tmp = 1.0 - (y / x);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[x, -12.0], N[(1.0 + N[(-2.0 * N[(y / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 3.7e+25], N[(y / N[((-x) - y), $MachinePrecision]), $MachinePrecision], N[(1.0 - N[(y / x), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < -12

   1. Initial program 99.9%

      \[\frac{x - y}{x + y} \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0 79.3%

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

   if -12 < x < 3.6999999999999999e25

   1. Initial program 100.0%

      \[\frac{x - y}{x + y} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0 77.4%

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

      1. neg-mul-1 77.4%

         \[\leadsto \frac{\color{blue}{-y}}{x + y} \]

   5. Simplified 77.4%

      \[\leadsto \frac{\color{blue}{-y}}{x + y} \]

   if 3.6999999999999999e25 < x

   1. Initial program 100.0%

      \[\frac{x - y}{x + y} \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf 87.5%

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

   4. Taylor expanded in x around 0 87.5%

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

      1. neg-mul-1 87.5%

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

      2. sub-neg 87.5%

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

      3. div-sub 87.5%

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

      4. *-rgt-identity 87.5%

         \[\leadsto \frac{\color{blue}{x \cdot 1}}{x} - \frac{y}{x} \]

      5. associate-*r/ 87.3%

         \[\leadsto \color{blue}{x \cdot \frac{1}{x}} - \frac{y}{x} \]

      6. rgt-mult-inverse 87.5%

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

   6. Simplified 87.5%

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

4. Final simplification 80.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -12:\\ \;\;\;\;1 + -2 \cdot \frac{y}{x}\\ \mathbf{elif}\;x \leq 3.7 \cdot 10^{+25}:\\ \;\;\;\;\frac{y}{\left(-x\right) - y}\\ \mathbf{else}:\\ \;\;\;\;1 - \frac{y}{x}\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 74.4% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -2.9 \lor \neg \left(x \leq 7 \cdot 10^{+24}\right):\\ \;\;\;\;1 - \frac{y}{x}\\ \mathbf{else}:\\ \;\;\;\;\frac{x - y}{y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (or (<= x -2.9) (not (<= x 7e+24))) (- 1.0 (/ y x)) (/ (- x y) y)))

C

double code(double x, double y) {
	double tmp;
	if ((x <= -2.9) || !(x <= 7e+24)) {
		tmp = 1.0 - (y / x);
	} else {
		tmp = (x - y) / y;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if ((x <= (-2.9d0)) .or. (.not. (x <= 7d+24))) then
        tmp = 1.0d0 - (y / x)
    else
        tmp = (x - y) / y
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if ((x <= -2.9) || !(x <= 7e+24)) {
		tmp = 1.0 - (y / x);
	} else {
		tmp = (x - y) / y;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (x <= -2.9) or not (x <= 7e+24):
		tmp = 1.0 - (y / x)
	else:
		tmp = (x - y) / y
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if ((x <= -2.9) || !(x <= 7e+24))
		tmp = Float64(1.0 - Float64(y / x));
	else
		tmp = Float64(Float64(x - y) / y);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if ((x <= -2.9) || ~((x <= 7e+24)))
		tmp = 1.0 - (y / x);
	else
		tmp = (x - y) / y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[Or[LessEqual[x, -2.9], N[Not[LessEqual[x, 7e+24]], $MachinePrecision]], N[(1.0 - N[(y / x), $MachinePrecision]), $MachinePrecision], N[(N[(x - y), $MachinePrecision] / y), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -2.89999999999999991 or 7.0000000000000004e24 < x

   1. Initial program 100.0%

      \[\frac{x - y}{x + y} \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf 82.3%

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

   4. Taylor expanded in x around 0 82.3%

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

      1. neg-mul-1 82.3%

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

      2. sub-neg 82.3%

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

      3. div-sub 82.3%

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

      4. *-rgt-identity 82.3%

         \[\leadsto \frac{\color{blue}{x \cdot 1}}{x} - \frac{y}{x} \]

      5. associate-*r/ 82.2%

         \[\leadsto \color{blue}{x \cdot \frac{1}{x}} - \frac{y}{x} \]

      6. rgt-mult-inverse 82.3%

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

   6. Simplified 82.3%

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

   if -2.89999999999999991 < x < 7.0000000000000004e24

   1. Initial program 100.0%

      \[\frac{x - y}{x + y} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0 77.3%

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

4. Final simplification 79.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -2.9 \lor \neg \left(x \leq 7 \cdot 10^{+24}\right):\\ \;\;\;\;1 - \frac{y}{x}\\ \mathbf{else}:\\ \;\;\;\;\frac{x - y}{y}\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 74.1% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -0.72 \lor \neg \left(x \leq 2.8 \cdot 10^{+24}\right):\\ \;\;\;\;1 - \frac{y}{x}\\ \mathbf{else}:\\ \;\;\;\;-1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (or (<= x -0.72) (not (<= x 2.8e+24))) (- 1.0 (/ y x)) -1.0))

C

double code(double x, double y) {
	double tmp;
	if ((x <= -0.72) || !(x <= 2.8e+24)) {
		tmp = 1.0 - (y / 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 <= (-0.72d0)) .or. (.not. (x <= 2.8d+24))) then
        tmp = 1.0d0 - (y / x)
    else
        tmp = -1.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if ((x <= -0.72) || !(x <= 2.8e+24)) {
		tmp = 1.0 - (y / x);
	} else {
		tmp = -1.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (x <= -0.72) or not (x <= 2.8e+24):
		tmp = 1.0 - (y / x)
	else:
		tmp = -1.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if ((x <= -0.72) || !(x <= 2.8e+24))
		tmp = Float64(1.0 - Float64(y / x));
	else
		tmp = -1.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if ((x <= -0.72) || ~((x <= 2.8e+24)))
		tmp = 1.0 - (y / x);
	else
		tmp = -1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[Or[LessEqual[x, -0.72], N[Not[LessEqual[x, 2.8e+24]], $MachinePrecision]], N[(1.0 - N[(y / x), $MachinePrecision]), $MachinePrecision], -1.0]

Derivation

1. Split input into 2 regimes

2. if x < -0.71999999999999997 or 2.8000000000000002e24 < x

   1. Initial program 100.0%

      \[\frac{x - y}{x + y} \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf 82.3%

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

   4. Taylor expanded in x around 0 82.3%

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

      1. neg-mul-1 82.3%

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

      2. sub-neg 82.3%

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

      3. div-sub 82.3%

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

      4. *-rgt-identity 82.3%

         \[\leadsto \frac{\color{blue}{x \cdot 1}}{x} - \frac{y}{x} \]

      5. associate-*r/ 82.2%

         \[\leadsto \color{blue}{x \cdot \frac{1}{x}} - \frac{y}{x} \]

      6. rgt-mult-inverse 82.3%

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

   6. Simplified 82.3%

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

   if -0.71999999999999997 < x < 2.8000000000000002e24

   1. Initial program 100.0%

      \[\frac{x - y}{x + y} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0 76.7%

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

4. Final simplification 79.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -0.72 \lor \neg \left(x \leq 2.8 \cdot 10^{+24}\right):\\ \;\;\;\;1 - \frac{y}{x}\\ \mathbf{else}:\\ \;\;\;\;-1\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 73.8% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1000:\\ \;\;\;\;1\\ \mathbf{elif}\;x \leq 2 \cdot 10^{+24}:\\ \;\;\;\;-1\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= x -1000.0) 1.0 (if (<= x 2e+24) -1.0 1.0)))

C

double code(double x, double y) {
	double tmp;
	if (x <= -1000.0) {
		tmp = 1.0;
	} else if (x <= 2e+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 <= (-1000.0d0)) then
        tmp = 1.0d0
    else if (x <= 2d+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 <= -1000.0) {
		tmp = 1.0;
	} else if (x <= 2e+24) {
		tmp = -1.0;
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if x <= -1000.0:
		tmp = 1.0
	elif x <= 2e+24:
		tmp = -1.0
	else:
		tmp = 1.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (x <= -1000.0)
		tmp = 1.0;
	elseif (x <= 2e+24)
		tmp = -1.0;
	else
		tmp = 1.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (x <= -1000.0)
		tmp = 1.0;
	elseif (x <= 2e+24)
		tmp = -1.0;
	else
		tmp = 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[x, -1000.0], 1.0, If[LessEqual[x, 2e+24], -1.0, 1.0]]

Derivation

1. Split input into 2 regimes

2. if x < -1e3 or 2e24 < x

   1. Initial program 100.0%

      \[\frac{x - y}{x + y} \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf 81.9%

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

   if -1e3 < x < 2e24

   1. Initial program 100.0%

      \[\frac{x - y}{x + y} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0 76.7%

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

Alternative 7: 50.5% accurate, 7.0× 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%

   \[\frac{x - y}{x + y} \]
2. Add Preprocessing

3. Taylor expanded in x around 0 48.6%

   \[\leadsto \color{blue}{-1} \]
4. Add Preprocessing

Developer Target 1: 100.0% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \frac{x}{x + y} - \frac{y}{x + y} \end{array} \]

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Reproduce

herbie shell --seed 2024114 
(FPCore (x y)
  :name "Data.Colour.RGB:hslsv from colour-2.3.3, D"
  :precision binary64

  :alt
  (! :herbie-platform default (- (/ x (+ x y)) (/ y (+ x y))))

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