Numeric.SpecFunctions:invIncompleteBetaWorker from math-functions-0.1.5.2, F

Percentage Accurate: 77.9% → 99.8%

Time: 12.5s

Alternatives: 10

Speedup: 9.9×

Specification

Math

\[\begin{array}{l} \\ \frac{e^{x \cdot \log \left(\frac{x}{x + y}\right)}}{x} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (/ (exp (* x (log (/ x (+ x y))))) x))

C

double code(double x, double y) {
	return exp((x * log((x / (x + y))))) / x;
}

Fortran

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

Java

public static double code(double x, double y) {
	return Math.exp((x * Math.log((x / (x + y))))) / x;
}

Python

def code(x, y):
	return math.exp((x * math.log((x / (x + y))))) / x

Julia

function code(x, y)
	return Float64(exp(Float64(x * log(Float64(x / Float64(x + y))))) / x)
end

MATLAB

function tmp = code(x, y)
	tmp = exp((x * log((x / (x + y))))) / x;
end

Wolfram

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

Initial Program: 77.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{e^{x \cdot \log \left(\frac{x}{x + y}\right)}}{x} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (/ (exp (* x (log (/ x (+ x y))))) x))

C

double code(double x, double y) {
	return exp((x * log((x / (x + y))))) / x;
}

Fortran

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

Java

public static double code(double x, double y) {
	return Math.exp((x * Math.log((x / (x + y))))) / x;
}

Python

def code(x, y):
	return math.exp((x * math.log((x / (x + y))))) / x

Julia

function code(x, y)
	return Float64(exp(Float64(x * log(Float64(x / Float64(x + y))))) / x)
end

MATLAB

function tmp = code(x, y)
	tmp = exp((x * log((x / (x + y))))) / x;
end

Wolfram

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

Alternative 1: 99.8% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -5 \cdot 10^{+32} \lor \neg \left(x \leq 1\right):\\ \;\;\;\;\frac{e^{-y}}{x}\\ \mathbf{else}:\\ \;\;\;\;\frac{{\left(e^{x}\right)}^{\log \left(\frac{x}{x + y}\right)}}{x}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (or (<= x -5e+32) (not (<= x 1.0)))
   (/ (exp (- y)) x)
   (/ (pow (exp x) (log (/ x (+ x y)))) x)))

C

double code(double x, double y) {
	double tmp;
	if ((x <= -5e+32) || !(x <= 1.0)) {
		tmp = exp(-y) / x;
	} else {
		tmp = pow(exp(x), log((x / (x + 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 <= (-5d+32)) .or. (.not. (x <= 1.0d0))) then
        tmp = exp(-y) / x
    else
        tmp = (exp(x) ** log((x / (x + y)))) / x
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if ((x <= -5e+32) || !(x <= 1.0)) {
		tmp = Math.exp(-y) / x;
	} else {
		tmp = Math.pow(Math.exp(x), Math.log((x / (x + y)))) / x;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (x <= -5e+32) or not (x <= 1.0):
		tmp = math.exp(-y) / x
	else:
		tmp = math.pow(math.exp(x), math.log((x / (x + y)))) / x
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if ((x <= -5e+32) || !(x <= 1.0))
		tmp = Float64(exp(Float64(-y)) / x);
	else
		tmp = Float64((exp(x) ^ log(Float64(x / Float64(x + y)))) / x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if ((x <= -5e+32) || ~((x <= 1.0)))
		tmp = exp(-y) / x;
	else
		tmp = (exp(x) ^ log((x / (x + y)))) / x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[Or[LessEqual[x, -5e+32], N[Not[LessEqual[x, 1.0]], $MachinePrecision]], N[(N[Exp[(-y)], $MachinePrecision] / x), $MachinePrecision], N[(N[Power[N[Exp[x], $MachinePrecision], N[Log[N[(x / N[(x + y), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision] / x), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -4.9999999999999997e32 or 1 < x

   1. Initial program 72.7%

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

      1. *-commutative 72.7%

         \[\leadsto \frac{e^{\color{blue}{\log \left(\frac{x}{x + y}\right) \cdot x}}}{x} \]

      2. exp-to-pow 72.7%

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

   3. Simplified 72.7%

      \[\leadsto \color{blue}{\frac{{\left(\frac{x}{x + y}\right)}^{x}}{x}} \]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 99.9%

      \[\leadsto \frac{\color{blue}{e^{-1 \cdot y}}}{x} \]
   6. Step-by-step derivation

      1. mul-1-neg 99.9%

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

   7. Simplified 99.9%

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

   if -4.9999999999999997e32 < x < 1

   1. Initial program 75.7%

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

      1. exp-prod 99.7%

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

   3. Simplified 99.7%

      \[\leadsto \color{blue}{\frac{{\left(e^{x}\right)}^{\log \left(\frac{x}{x + y}\right)}}{x}} \]
   4. Add Preprocessing
3. Recombined 2 regimes into one program.

4. Final simplification 99.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -5 \cdot 10^{+32} \lor \neg \left(x \leq 1\right):\\ \;\;\;\;\frac{e^{-y}}{x}\\ \mathbf{else}:\\ \;\;\;\;\frac{{\left(e^{x}\right)}^{\log \left(\frac{x}{x + y}\right)}}{x}\\ \end{array} \]
5. Add Preprocessing

Alternative 2: 98.8% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -9 \cdot 10^{+17} \lor \neg \left(x \leq 0.0305\right):\\ \;\;\;\;\frac{e^{-y}}{x}\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{x}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (or (<= x -9e+17) (not (<= x 0.0305))) (/ (exp (- y)) x) (/ 1.0 x)))

C

double code(double x, double y) {
	double tmp;
	if ((x <= -9e+17) || !(x <= 0.0305)) {
		tmp = exp(-y) / x;
	} else {
		tmp = 1.0 / 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 <= (-9d+17)) .or. (.not. (x <= 0.0305d0))) then
        tmp = exp(-y) / x
    else
        tmp = 1.0d0 / x
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if ((x <= -9e+17) || !(x <= 0.0305)) {
		tmp = Math.exp(-y) / x;
	} else {
		tmp = 1.0 / x;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (x <= -9e+17) or not (x <= 0.0305):
		tmp = math.exp(-y) / x
	else:
		tmp = 1.0 / x
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if ((x <= -9e+17) || !(x <= 0.0305))
		tmp = Float64(exp(Float64(-y)) / x);
	else
		tmp = Float64(1.0 / x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if ((x <= -9e+17) || ~((x <= 0.0305)))
		tmp = exp(-y) / x;
	else
		tmp = 1.0 / x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[Or[LessEqual[x, -9e+17], N[Not[LessEqual[x, 0.0305]], $MachinePrecision]], N[(N[Exp[(-y)], $MachinePrecision] / x), $MachinePrecision], N[(1.0 / x), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -9e17 or 0.030499999999999999 < x

   1. Initial program 73.6%

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

      1. *-commutative 73.6%

         \[\leadsto \frac{e^{\color{blue}{\log \left(\frac{x}{x + y}\right) \cdot x}}}{x} \]

      2. exp-to-pow 73.6%

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

   3. Simplified 73.6%

      \[\leadsto \color{blue}{\frac{{\left(\frac{x}{x + y}\right)}^{x}}{x}} \]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 99.9%

      \[\leadsto \frac{\color{blue}{e^{-1 \cdot y}}}{x} \]
   6. Step-by-step derivation

      1. mul-1-neg 99.9%

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

   7. Simplified 99.9%

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

   if -9e17 < x < 0.030499999999999999

   1. Initial program 74.5%

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

      1. exp-prod 99.7%

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

   3. Simplified 99.7%

      \[\leadsto \color{blue}{\frac{{\left(e^{x}\right)}^{\log \left(\frac{x}{x + y}\right)}}{x}} \]
   4. Add Preprocessing

   5. Taylor expanded in x around 0 98.3%

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

4. Final simplification 99.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -9 \cdot 10^{+17} \lor \neg \left(x \leq 0.0305\right):\\ \;\;\;\;\frac{e^{-y}}{x}\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{x}\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 81.6% accurate, 5.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{1}{x} \cdot 0.5\\ \mathbf{if}\;x \leq -9 \cdot 10^{+17}:\\ \;\;\;\;\frac{1 + y \cdot \left(y \cdot \left(0.5 + \left(y \cdot -0.16666666666666666 + t\_0\right)\right) + -1\right)}{x}\\ \mathbf{elif}\;x \leq 580000:\\ \;\;\;\;\frac{1}{x}\\ \mathbf{else}:\\ \;\;\;\;\frac{1 + y \cdot \left(y \cdot \left(0.5 + \left(t\_0 + y \cdot \left(0.5 \cdot \frac{-1}{x} - 0.16666666666666666\right)\right)\right) + -1\right)}{x}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (* (/ 1.0 x) 0.5)))
   (if (<= x -9e+17)
     (/
      (+ 1.0 (* y (+ (* y (+ 0.5 (+ (* y -0.16666666666666666) t_0))) -1.0)))
      x)
     (if (<= x 580000.0)
       (/ 1.0 x)
       (/
        (+
         1.0
         (*
          y
          (+
           (*
            y
            (+ 0.5 (+ t_0 (* y (- (* 0.5 (/ -1.0 x)) 0.16666666666666666)))))
           -1.0)))
        x)))))

C

double code(double x, double y) {
	double t_0 = (1.0 / x) * 0.5;
	double tmp;
	if (x <= -9e+17) {
		tmp = (1.0 + (y * ((y * (0.5 + ((y * -0.16666666666666666) + t_0))) + -1.0))) / x;
	} else if (x <= 580000.0) {
		tmp = 1.0 / x;
	} else {
		tmp = (1.0 + (y * ((y * (0.5 + (t_0 + (y * ((0.5 * (-1.0 / x)) - 0.16666666666666666))))) + -1.0))) / x;
	}
	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 = (1.0d0 / x) * 0.5d0
    if (x <= (-9d+17)) then
        tmp = (1.0d0 + (y * ((y * (0.5d0 + ((y * (-0.16666666666666666d0)) + t_0))) + (-1.0d0)))) / x
    else if (x <= 580000.0d0) then
        tmp = 1.0d0 / x
    else
        tmp = (1.0d0 + (y * ((y * (0.5d0 + (t_0 + (y * ((0.5d0 * ((-1.0d0) / x)) - 0.16666666666666666d0))))) + (-1.0d0)))) / x
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double t_0 = (1.0 / x) * 0.5;
	double tmp;
	if (x <= -9e+17) {
		tmp = (1.0 + (y * ((y * (0.5 + ((y * -0.16666666666666666) + t_0))) + -1.0))) / x;
	} else if (x <= 580000.0) {
		tmp = 1.0 / x;
	} else {
		tmp = (1.0 + (y * ((y * (0.5 + (t_0 + (y * ((0.5 * (-1.0 / x)) - 0.16666666666666666))))) + -1.0))) / x;
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = (1.0 / x) * 0.5
	tmp = 0
	if x <= -9e+17:
		tmp = (1.0 + (y * ((y * (0.5 + ((y * -0.16666666666666666) + t_0))) + -1.0))) / x
	elif x <= 580000.0:
		tmp = 1.0 / x
	else:
		tmp = (1.0 + (y * ((y * (0.5 + (t_0 + (y * ((0.5 * (-1.0 / x)) - 0.16666666666666666))))) + -1.0))) / x
	return tmp

Julia

function code(x, y)
	t_0 = Float64(Float64(1.0 / x) * 0.5)
	tmp = 0.0
	if (x <= -9e+17)
		tmp = Float64(Float64(1.0 + Float64(y * Float64(Float64(y * Float64(0.5 + Float64(Float64(y * -0.16666666666666666) + t_0))) + -1.0))) / x);
	elseif (x <= 580000.0)
		tmp = Float64(1.0 / x);
	else
		tmp = Float64(Float64(1.0 + Float64(y * Float64(Float64(y * Float64(0.5 + Float64(t_0 + Float64(y * Float64(Float64(0.5 * Float64(-1.0 / x)) - 0.16666666666666666))))) + -1.0))) / x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = (1.0 / x) * 0.5;
	tmp = 0.0;
	if (x <= -9e+17)
		tmp = (1.0 + (y * ((y * (0.5 + ((y * -0.16666666666666666) + t_0))) + -1.0))) / x;
	elseif (x <= 580000.0)
		tmp = 1.0 / x;
	else
		tmp = (1.0 + (y * ((y * (0.5 + (t_0 + (y * ((0.5 * (-1.0 / x)) - 0.16666666666666666))))) + -1.0))) / x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(N[(1.0 / x), $MachinePrecision] * 0.5), $MachinePrecision]}, If[LessEqual[x, -9e+17], N[(N[(1.0 + N[(y * N[(N[(y * N[(0.5 + N[(N[(y * -0.16666666666666666), $MachinePrecision] + t$95$0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + -1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / x), $MachinePrecision], If[LessEqual[x, 580000.0], N[(1.0 / x), $MachinePrecision], N[(N[(1.0 + N[(y * N[(N[(y * N[(0.5 + N[(t$95$0 + N[(y * N[(N[(0.5 * N[(-1.0 / x), $MachinePrecision]), $MachinePrecision] - 0.16666666666666666), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + -1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / x), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if x < -9e17

   1. Initial program 67.4%

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

      1. *-commutative 67.4%

         \[\leadsto \frac{e^{\color{blue}{\log \left(\frac{x}{x + y}\right) \cdot x}}}{x} \]

      2. exp-to-pow 67.4%

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

   3. Simplified 67.4%

      \[\leadsto \color{blue}{\frac{{\left(\frac{x}{x + y}\right)}^{x}}{x}} \]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 66.7%

      \[\leadsto \frac{\color{blue}{e^{-1 \cdot y} + 0.5 \cdot \frac{e^{-1 \cdot y} \cdot \left(-1 \cdot {y}^{2} + 2 \cdot {y}^{2}\right)}{x}}}{x} \]
   6. Step-by-step derivation

      1. mul-1-neg 66.7%

         \[\leadsto \frac{e^{\color{blue}{-y}} + 0.5 \cdot \frac{e^{-1 \cdot y} \cdot \left(-1 \cdot {y}^{2} + 2 \cdot {y}^{2}\right)}{x}}{x} \]

      2. associate-/l* 66.7%

         \[\leadsto \frac{e^{-y} + 0.5 \cdot \color{blue}{\left(e^{-1 \cdot y} \cdot \frac{-1 \cdot {y}^{2} + 2 \cdot {y}^{2}}{x}\right)}}{x} \]

      3. mul-1-neg 66.7%

         \[\leadsto \frac{e^{-y} + 0.5 \cdot \left(e^{\color{blue}{-y}} \cdot \frac{-1 \cdot {y}^{2} + 2 \cdot {y}^{2}}{x}\right)}{x} \]

      4. distribute-rgt-out 66.7%

         \[\leadsto \frac{e^{-y} + 0.5 \cdot \left(e^{-y} \cdot \frac{\color{blue}{{y}^{2} \cdot \left(-1 + 2\right)}}{x}\right)}{x} \]

      5. metadata-eval 66.7%

         \[\leadsto \frac{e^{-y} + 0.5 \cdot \left(e^{-y} \cdot \frac{{y}^{2} \cdot \color{blue}{1}}{x}\right)}{x} \]

   7. Simplified 66.7%

      \[\leadsto \frac{\color{blue}{e^{-y} + 0.5 \cdot \left(e^{-y} \cdot \frac{{y}^{2} \cdot 1}{x}\right)}}{x} \]

   8. Taylor expanded in y around 0 73.0%

      \[\leadsto \frac{e^{-y} + 0.5 \cdot \color{blue}{\frac{{y}^{2}}{x}}}{x} \]

   9. Taylor expanded in y around 0 70.9%

      \[\leadsto \frac{\color{blue}{1 + y \cdot \left(y \cdot \left(0.5 + \left(-0.16666666666666666 \cdot y + 0.5 \cdot \frac{1}{x}\right)\right) - 1\right)}}{x} \]

   if -9e17 < x < 5.8e5

   1. Initial program 74.7%

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

      1. exp-prod 99.7%

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

   3. Simplified 99.7%

      \[\leadsto \color{blue}{\frac{{\left(e^{x}\right)}^{\log \left(\frac{x}{x + y}\right)}}{x}} \]
   4. Add Preprocessing

   5. Taylor expanded in x around 0 97.4%

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

   if 5.8e5 < x

   1. Initial program 77.7%

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

      1. *-commutative 77.7%

         \[\leadsto \frac{e^{\color{blue}{\log \left(\frac{x}{x + y}\right) \cdot x}}}{x} \]

      2. exp-to-pow 77.7%

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

   3. Simplified 77.7%

      \[\leadsto \color{blue}{\frac{{\left(\frac{x}{x + y}\right)}^{x}}{x}} \]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 84.4%

      \[\leadsto \frac{\color{blue}{e^{-1 \cdot y} + 0.5 \cdot \frac{e^{-1 \cdot y} \cdot \left(-1 \cdot {y}^{2} + 2 \cdot {y}^{2}\right)}{x}}}{x} \]
   6. Step-by-step derivation

      1. mul-1-neg 84.4%

         \[\leadsto \frac{e^{\color{blue}{-y}} + 0.5 \cdot \frac{e^{-1 \cdot y} \cdot \left(-1 \cdot {y}^{2} + 2 \cdot {y}^{2}\right)}{x}}{x} \]

      2. associate-/l* 84.4%

         \[\leadsto \frac{e^{-y} + 0.5 \cdot \color{blue}{\left(e^{-1 \cdot y} \cdot \frac{-1 \cdot {y}^{2} + 2 \cdot {y}^{2}}{x}\right)}}{x} \]

      3. mul-1-neg 84.4%

         \[\leadsto \frac{e^{-y} + 0.5 \cdot \left(e^{\color{blue}{-y}} \cdot \frac{-1 \cdot {y}^{2} + 2 \cdot {y}^{2}}{x}\right)}{x} \]

      4. distribute-rgt-out 88.9%

         \[\leadsto \frac{e^{-y} + 0.5 \cdot \left(e^{-y} \cdot \frac{\color{blue}{{y}^{2} \cdot \left(-1 + 2\right)}}{x}\right)}{x} \]

      5. metadata-eval 88.9%

         \[\leadsto \frac{e^{-y} + 0.5 \cdot \left(e^{-y} \cdot \frac{{y}^{2} \cdot \color{blue}{1}}{x}\right)}{x} \]

   7. Simplified 88.9%

      \[\leadsto \frac{\color{blue}{e^{-y} + 0.5 \cdot \left(e^{-y} \cdot \frac{{y}^{2} \cdot 1}{x}\right)}}{x} \]

   8. Taylor expanded in y around 0 66.6%

      \[\leadsto \frac{\color{blue}{1 + y \cdot \left(y \cdot \left(0.5 + \left(-1 \cdot \left(y \cdot \left(0.16666666666666666 + 0.5 \cdot \frac{1}{x}\right)\right) + 0.5 \cdot \frac{1}{x}\right)\right) - 1\right)}}{x} \]
3. Recombined 3 regimes into one program.

4. Final simplification 80.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -9 \cdot 10^{+17}:\\ \;\;\;\;\frac{1 + y \cdot \left(y \cdot \left(0.5 + \left(y \cdot -0.16666666666666666 + \frac{1}{x} \cdot 0.5\right)\right) + -1\right)}{x}\\ \mathbf{elif}\;x \leq 580000:\\ \;\;\;\;\frac{1}{x}\\ \mathbf{else}:\\ \;\;\;\;\frac{1 + y \cdot \left(y \cdot \left(0.5 + \left(\frac{1}{x} \cdot 0.5 + y \cdot \left(0.5 \cdot \frac{-1}{x} - 0.16666666666666666\right)\right)\right) + -1\right)}{x}\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 81.6% accurate, 6.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -9 \cdot 10^{+17} \lor \neg \left(x \leq 245000\right):\\ \;\;\;\;\frac{1 + y \cdot \left(y \cdot \left(0.5 + \left(y \cdot -0.16666666666666666 + \frac{1}{x} \cdot 0.5\right)\right) + -1\right)}{x}\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{x}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (or (<= x -9e+17) (not (<= x 245000.0)))
   (/
    (+
     1.0
     (*
      y
      (+ (* y (+ 0.5 (+ (* y -0.16666666666666666) (* (/ 1.0 x) 0.5)))) -1.0)))
    x)
   (/ 1.0 x)))

C

double code(double x, double y) {
	double tmp;
	if ((x <= -9e+17) || !(x <= 245000.0)) {
		tmp = (1.0 + (y * ((y * (0.5 + ((y * -0.16666666666666666) + ((1.0 / x) * 0.5)))) + -1.0))) / x;
	} else {
		tmp = 1.0 / 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 <= (-9d+17)) .or. (.not. (x <= 245000.0d0))) then
        tmp = (1.0d0 + (y * ((y * (0.5d0 + ((y * (-0.16666666666666666d0)) + ((1.0d0 / x) * 0.5d0)))) + (-1.0d0)))) / x
    else
        tmp = 1.0d0 / x
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if ((x <= -9e+17) || !(x <= 245000.0)) {
		tmp = (1.0 + (y * ((y * (0.5 + ((y * -0.16666666666666666) + ((1.0 / x) * 0.5)))) + -1.0))) / x;
	} else {
		tmp = 1.0 / x;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (x <= -9e+17) or not (x <= 245000.0):
		tmp = (1.0 + (y * ((y * (0.5 + ((y * -0.16666666666666666) + ((1.0 / x) * 0.5)))) + -1.0))) / x
	else:
		tmp = 1.0 / x
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if ((x <= -9e+17) || !(x <= 245000.0))
		tmp = Float64(Float64(1.0 + Float64(y * Float64(Float64(y * Float64(0.5 + Float64(Float64(y * -0.16666666666666666) + Float64(Float64(1.0 / x) * 0.5)))) + -1.0))) / x);
	else
		tmp = Float64(1.0 / x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if ((x <= -9e+17) || ~((x <= 245000.0)))
		tmp = (1.0 + (y * ((y * (0.5 + ((y * -0.16666666666666666) + ((1.0 / x) * 0.5)))) + -1.0))) / x;
	else
		tmp = 1.0 / x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[Or[LessEqual[x, -9e+17], N[Not[LessEqual[x, 245000.0]], $MachinePrecision]], N[(N[(1.0 + N[(y * N[(N[(y * N[(0.5 + N[(N[(y * -0.16666666666666666), $MachinePrecision] + N[(N[(1.0 / x), $MachinePrecision] * 0.5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + -1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / x), $MachinePrecision], N[(1.0 / x), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -9e17 or 245000 < x

   1. Initial program 73.5%

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

      1. *-commutative 73.5%

         \[\leadsto \frac{e^{\color{blue}{\log \left(\frac{x}{x + y}\right) \cdot x}}}{x} \]

      2. exp-to-pow 73.5%

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

   3. Simplified 73.5%

      \[\leadsto \color{blue}{\frac{{\left(\frac{x}{x + y}\right)}^{x}}{x}} \]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 77.1%

      \[\leadsto \frac{\color{blue}{e^{-1 \cdot y} + 0.5 \cdot \frac{e^{-1 \cdot y} \cdot \left(-1 \cdot {y}^{2} + 2 \cdot {y}^{2}\right)}{x}}}{x} \]
   6. Step-by-step derivation

      1. mul-1-neg 77.1%

         \[\leadsto \frac{e^{\color{blue}{-y}} + 0.5 \cdot \frac{e^{-1 \cdot y} \cdot \left(-1 \cdot {y}^{2} + 2 \cdot {y}^{2}\right)}{x}}{x} \]

      2. associate-/l* 77.1%

         \[\leadsto \frac{e^{-y} + 0.5 \cdot \color{blue}{\left(e^{-1 \cdot y} \cdot \frac{-1 \cdot {y}^{2} + 2 \cdot {y}^{2}}{x}\right)}}{x} \]

      3. mul-1-neg 77.1%

         \[\leadsto \frac{e^{-y} + 0.5 \cdot \left(e^{\color{blue}{-y}} \cdot \frac{-1 \cdot {y}^{2} + 2 \cdot {y}^{2}}{x}\right)}{x} \]

      4. distribute-rgt-out 79.7%

         \[\leadsto \frac{e^{-y} + 0.5 \cdot \left(e^{-y} \cdot \frac{\color{blue}{{y}^{2} \cdot \left(-1 + 2\right)}}{x}\right)}{x} \]

      5. metadata-eval 79.7%

         \[\leadsto \frac{e^{-y} + 0.5 \cdot \left(e^{-y} \cdot \frac{{y}^{2} \cdot \color{blue}{1}}{x}\right)}{x} \]

   7. Simplified 79.7%

      \[\leadsto \frac{\color{blue}{e^{-y} + 0.5 \cdot \left(e^{-y} \cdot \frac{{y}^{2} \cdot 1}{x}\right)}}{x} \]

   8. Taylor expanded in y around 0 76.8%

      \[\leadsto \frac{e^{-y} + 0.5 \cdot \color{blue}{\frac{{y}^{2}}{x}}}{x} \]

   9. Taylor expanded in y around 0 68.3%

      \[\leadsto \frac{\color{blue}{1 + y \cdot \left(y \cdot \left(0.5 + \left(-0.16666666666666666 \cdot y + 0.5 \cdot \frac{1}{x}\right)\right) - 1\right)}}{x} \]

   if -9e17 < x < 245000

   1. Initial program 74.7%

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

      1. exp-prod 99.7%

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

   3. Simplified 99.7%

      \[\leadsto \color{blue}{\frac{{\left(e^{x}\right)}^{\log \left(\frac{x}{x + y}\right)}}{x}} \]
   4. Add Preprocessing

   5. Taylor expanded in x around 0 97.4%

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

4. Final simplification 80.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -9 \cdot 10^{+17} \lor \neg \left(x \leq 245000\right):\\ \;\;\;\;\frac{1 + y \cdot \left(y \cdot \left(0.5 + \left(y \cdot -0.16666666666666666 + \frac{1}{x} \cdot 0.5\right)\right) + -1\right)}{x}\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{x}\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 81.5% accurate, 7.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -9 \cdot 10^{+17}:\\ \;\;\;\;\frac{1 + y \cdot \left(\frac{0.5 \cdot \left(x \cdot y\right)}{x} + -1\right)}{x}\\ \mathbf{elif}\;x \leq 400000:\\ \;\;\;\;\frac{1}{x}\\ \mathbf{else}:\\ \;\;\;\;\frac{1 + y \cdot \left(\frac{0.5 \cdot \left(y + x \cdot y\right)}{x} + -1\right)}{x}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= x -9e+17)
   (/ (+ 1.0 (* y (+ (/ (* 0.5 (* x y)) x) -1.0))) x)
   (if (<= x 400000.0)
     (/ 1.0 x)
     (/ (+ 1.0 (* y (+ (/ (* 0.5 (+ y (* x y))) x) -1.0))) x))))

C

double code(double x, double y) {
	double tmp;
	if (x <= -9e+17) {
		tmp = (1.0 + (y * (((0.5 * (x * y)) / x) + -1.0))) / x;
	} else if (x <= 400000.0) {
		tmp = 1.0 / x;
	} else {
		tmp = (1.0 + (y * (((0.5 * (y + (x * y))) / x) + -1.0))) / 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 <= (-9d+17)) then
        tmp = (1.0d0 + (y * (((0.5d0 * (x * y)) / x) + (-1.0d0)))) / x
    else if (x <= 400000.0d0) then
        tmp = 1.0d0 / x
    else
        tmp = (1.0d0 + (y * (((0.5d0 * (y + (x * y))) / x) + (-1.0d0)))) / x
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (x <= -9e+17) {
		tmp = (1.0 + (y * (((0.5 * (x * y)) / x) + -1.0))) / x;
	} else if (x <= 400000.0) {
		tmp = 1.0 / x;
	} else {
		tmp = (1.0 + (y * (((0.5 * (y + (x * y))) / x) + -1.0))) / x;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if x <= -9e+17:
		tmp = (1.0 + (y * (((0.5 * (x * y)) / x) + -1.0))) / x
	elif x <= 400000.0:
		tmp = 1.0 / x
	else:
		tmp = (1.0 + (y * (((0.5 * (y + (x * y))) / x) + -1.0))) / x
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (x <= -9e+17)
		tmp = Float64(Float64(1.0 + Float64(y * Float64(Float64(Float64(0.5 * Float64(x * y)) / x) + -1.0))) / x);
	elseif (x <= 400000.0)
		tmp = Float64(1.0 / x);
	else
		tmp = Float64(Float64(1.0 + Float64(y * Float64(Float64(Float64(0.5 * Float64(y + Float64(x * y))) / x) + -1.0))) / x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (x <= -9e+17)
		tmp = (1.0 + (y * (((0.5 * (x * y)) / x) + -1.0))) / x;
	elseif (x <= 400000.0)
		tmp = 1.0 / x;
	else
		tmp = (1.0 + (y * (((0.5 * (y + (x * y))) / x) + -1.0))) / x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[x, -9e+17], N[(N[(1.0 + N[(y * N[(N[(N[(0.5 * N[(x * y), $MachinePrecision]), $MachinePrecision] / x), $MachinePrecision] + -1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / x), $MachinePrecision], If[LessEqual[x, 400000.0], N[(1.0 / x), $MachinePrecision], N[(N[(1.0 + N[(y * N[(N[(N[(0.5 * N[(y + N[(x * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / x), $MachinePrecision] + -1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / x), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < -9e17

   1. Initial program 67.4%

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

      1. exp-prod 67.4%

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

   3. Simplified 67.4%

      \[\leadsto \color{blue}{\frac{{\left(e^{x}\right)}^{\log \left(\frac{x}{x + y}\right)}}{x}} \]
   4. Add Preprocessing

   5. Taylor expanded in y around 0 66.4%

      \[\leadsto \frac{\color{blue}{1 + y \cdot \left(y \cdot \left(0.5 + 0.5 \cdot \frac{1}{x}\right) - 1\right)}}{x} \]

   6. Taylor expanded in x around 0 67.9%

      \[\leadsto \frac{1 + y \cdot \left(\color{blue}{\frac{0.5 \cdot y + 0.5 \cdot \left(x \cdot y\right)}{x}} - 1\right)}{x} \]
   7. Step-by-step derivation

      1. distribute-lft-out 67.9%

         \[\leadsto \frac{1 + y \cdot \left(\frac{\color{blue}{0.5 \cdot \left(y + x \cdot y\right)}}{x} - 1\right)}{x} \]

   8. Simplified 67.9%

      \[\leadsto \frac{1 + y \cdot \left(\color{blue}{\frac{0.5 \cdot \left(y + x \cdot y\right)}{x}} - 1\right)}{x} \]

   9. Taylor expanded in x around inf 67.9%

      \[\leadsto \frac{1 + y \cdot \left(\frac{0.5 \cdot \color{blue}{\left(x \cdot y\right)}}{x} - 1\right)}{x} \]
   10. Step-by-step derivation

       1. *-commutative 67.9%

          \[\leadsto \frac{1 + y \cdot \left(\frac{0.5 \cdot \color{blue}{\left(y \cdot x\right)}}{x} - 1\right)}{x} \]

   11. Simplified 67.9%

       \[\leadsto \frac{1 + y \cdot \left(\frac{0.5 \cdot \color{blue}{\left(y \cdot x\right)}}{x} - 1\right)}{x} \]

   if -9e17 < x < 4e5

   1. Initial program 74.7%

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

      1. exp-prod 99.7%

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

   3. Simplified 99.7%

      \[\leadsto \color{blue}{\frac{{\left(e^{x}\right)}^{\log \left(\frac{x}{x + y}\right)}}{x}} \]
   4. Add Preprocessing

   5. Taylor expanded in x around 0 97.4%

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

   if 4e5 < x

   1. Initial program 77.7%

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

      1. exp-prod 77.1%

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

   3. Simplified 77.1%

      \[\leadsto \color{blue}{\frac{{\left(e^{x}\right)}^{\log \left(\frac{x}{x + y}\right)}}{x}} \]
   4. Add Preprocessing

   5. Taylor expanded in y around 0 64.2%

      \[\leadsto \frac{\color{blue}{1 + y \cdot \left(y \cdot \left(0.5 + 0.5 \cdot \frac{1}{x}\right) - 1\right)}}{x} \]

   6. Taylor expanded in x around 0 66.2%

      \[\leadsto \frac{1 + y \cdot \left(\color{blue}{\frac{0.5 \cdot y + 0.5 \cdot \left(x \cdot y\right)}{x}} - 1\right)}{x} \]
   7. Step-by-step derivation

      1. distribute-lft-out 66.2%

         \[\leadsto \frac{1 + y \cdot \left(\frac{\color{blue}{0.5 \cdot \left(y + x \cdot y\right)}}{x} - 1\right)}{x} \]

   8. Simplified 66.2%

      \[\leadsto \frac{1 + y \cdot \left(\color{blue}{\frac{0.5 \cdot \left(y + x \cdot y\right)}{x}} - 1\right)}{x} \]
3. Recombined 3 regimes into one program.

4. Final simplification 79.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -9 \cdot 10^{+17}:\\ \;\;\;\;\frac{1 + y \cdot \left(\frac{0.5 \cdot \left(x \cdot y\right)}{x} + -1\right)}{x}\\ \mathbf{elif}\;x \leq 400000:\\ \;\;\;\;\frac{1}{x}\\ \mathbf{else}:\\ \;\;\;\;\frac{1 + y \cdot \left(\frac{0.5 \cdot \left(y + x \cdot y\right)}{x} + -1\right)}{x}\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 81.5% accurate, 8.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -9 \cdot 10^{+17} \lor \neg \left(x \leq 220000\right):\\ \;\;\;\;\frac{1 + y \cdot \left(\frac{0.5 \cdot \left(x \cdot y\right)}{x} + -1\right)}{x}\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{x}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (or (<= x -9e+17) (not (<= x 220000.0)))
   (/ (+ 1.0 (* y (+ (/ (* 0.5 (* x y)) x) -1.0))) x)
   (/ 1.0 x)))

C

double code(double x, double y) {
	double tmp;
	if ((x <= -9e+17) || !(x <= 220000.0)) {
		tmp = (1.0 + (y * (((0.5 * (x * y)) / x) + -1.0))) / x;
	} else {
		tmp = 1.0 / 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 <= (-9d+17)) .or. (.not. (x <= 220000.0d0))) then
        tmp = (1.0d0 + (y * (((0.5d0 * (x * y)) / x) + (-1.0d0)))) / x
    else
        tmp = 1.0d0 / x
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if ((x <= -9e+17) || !(x <= 220000.0)) {
		tmp = (1.0 + (y * (((0.5 * (x * y)) / x) + -1.0))) / x;
	} else {
		tmp = 1.0 / x;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (x <= -9e+17) or not (x <= 220000.0):
		tmp = (1.0 + (y * (((0.5 * (x * y)) / x) + -1.0))) / x
	else:
		tmp = 1.0 / x
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if ((x <= -9e+17) || !(x <= 220000.0))
		tmp = Float64(Float64(1.0 + Float64(y * Float64(Float64(Float64(0.5 * Float64(x * y)) / x) + -1.0))) / x);
	else
		tmp = Float64(1.0 / x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if ((x <= -9e+17) || ~((x <= 220000.0)))
		tmp = (1.0 + (y * (((0.5 * (x * y)) / x) + -1.0))) / x;
	else
		tmp = 1.0 / x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[Or[LessEqual[x, -9e+17], N[Not[LessEqual[x, 220000.0]], $MachinePrecision]], N[(N[(1.0 + N[(y * N[(N[(N[(0.5 * N[(x * y), $MachinePrecision]), $MachinePrecision] / x), $MachinePrecision] + -1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / x), $MachinePrecision], N[(1.0 / x), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -9e17 or 2.2e5 < x

   1. Initial program 73.5%

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

      1. exp-prod 73.1%

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

   3. Simplified 73.1%

      \[\leadsto \color{blue}{\frac{{\left(e^{x}\right)}^{\log \left(\frac{x}{x + y}\right)}}{x}} \]
   4. Add Preprocessing

   5. Taylor expanded in y around 0 65.1%

      \[\leadsto \frac{\color{blue}{1 + y \cdot \left(y \cdot \left(0.5 + 0.5 \cdot \frac{1}{x}\right) - 1\right)}}{x} \]

   6. Taylor expanded in x around 0 66.9%

      \[\leadsto \frac{1 + y \cdot \left(\color{blue}{\frac{0.5 \cdot y + 0.5 \cdot \left(x \cdot y\right)}{x}} - 1\right)}{x} \]
   7. Step-by-step derivation

      1. distribute-lft-out 66.9%

         \[\leadsto \frac{1 + y \cdot \left(\frac{\color{blue}{0.5 \cdot \left(y + x \cdot y\right)}}{x} - 1\right)}{x} \]

   8. Simplified 66.9%

      \[\leadsto \frac{1 + y \cdot \left(\color{blue}{\frac{0.5 \cdot \left(y + x \cdot y\right)}{x}} - 1\right)}{x} \]

   9. Taylor expanded in x around inf 66.9%

      \[\leadsto \frac{1 + y \cdot \left(\frac{0.5 \cdot \color{blue}{\left(x \cdot y\right)}}{x} - 1\right)}{x} \]
   10. Step-by-step derivation

       1. *-commutative 66.9%

          \[\leadsto \frac{1 + y \cdot \left(\frac{0.5 \cdot \color{blue}{\left(y \cdot x\right)}}{x} - 1\right)}{x} \]

   11. Simplified 66.9%

       \[\leadsto \frac{1 + y \cdot \left(\frac{0.5 \cdot \color{blue}{\left(y \cdot x\right)}}{x} - 1\right)}{x} \]

   if -9e17 < x < 2.2e5

   1. Initial program 74.7%

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

      1. exp-prod 99.7%

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

   3. Simplified 99.7%

      \[\leadsto \color{blue}{\frac{{\left(e^{x}\right)}^{\log \left(\frac{x}{x + y}\right)}}{x}} \]
   4. Add Preprocessing

   5. Taylor expanded in x around 0 97.4%

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

4. Final simplification 79.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -9 \cdot 10^{+17} \lor \neg \left(x \leq 220000\right):\\ \;\;\;\;\frac{1 + y \cdot \left(\frac{0.5 \cdot \left(x \cdot y\right)}{x} + -1\right)}{x}\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{x}\\ \end{array} \]
5. Add Preprocessing

Alternative 7: 79.4% accurate, 9.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := y \cdot \left(y \cdot 0.5 + -1\right)\\ \mathbf{if}\;x \leq -9 \cdot 10^{+17}:\\ \;\;\;\;\frac{1 + t\_0}{x}\\ \mathbf{elif}\;x \leq 250000:\\ \;\;\;\;\frac{1}{x}\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{x} + \frac{t\_0}{x}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (* y (+ (* y 0.5) -1.0))))
   (if (<= x -9e+17)
     (/ (+ 1.0 t_0) x)
     (if (<= x 250000.0) (/ 1.0 x) (+ (/ 1.0 x) (/ t_0 x))))))

C

double code(double x, double y) {
	double t_0 = y * ((y * 0.5) + -1.0);
	double tmp;
	if (x <= -9e+17) {
		tmp = (1.0 + t_0) / x;
	} else if (x <= 250000.0) {
		tmp = 1.0 / x;
	} else {
		tmp = (1.0 / x) + (t_0 / x);
	}
	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 * ((y * 0.5d0) + (-1.0d0))
    if (x <= (-9d+17)) then
        tmp = (1.0d0 + t_0) / x
    else if (x <= 250000.0d0) then
        tmp = 1.0d0 / x
    else
        tmp = (1.0d0 / x) + (t_0 / x)
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double t_0 = y * ((y * 0.5) + -1.0);
	double tmp;
	if (x <= -9e+17) {
		tmp = (1.0 + t_0) / x;
	} else if (x <= 250000.0) {
		tmp = 1.0 / x;
	} else {
		tmp = (1.0 / x) + (t_0 / x);
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = y * ((y * 0.5) + -1.0)
	tmp = 0
	if x <= -9e+17:
		tmp = (1.0 + t_0) / x
	elif x <= 250000.0:
		tmp = 1.0 / x
	else:
		tmp = (1.0 / x) + (t_0 / x)
	return tmp

Julia

function code(x, y)
	t_0 = Float64(y * Float64(Float64(y * 0.5) + -1.0))
	tmp = 0.0
	if (x <= -9e+17)
		tmp = Float64(Float64(1.0 + t_0) / x);
	elseif (x <= 250000.0)
		tmp = Float64(1.0 / x);
	else
		tmp = Float64(Float64(1.0 / x) + Float64(t_0 / x));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = y * ((y * 0.5) + -1.0);
	tmp = 0.0;
	if (x <= -9e+17)
		tmp = (1.0 + t_0) / x;
	elseif (x <= 250000.0)
		tmp = 1.0 / x;
	else
		tmp = (1.0 / x) + (t_0 / x);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(y * N[(N[(y * 0.5), $MachinePrecision] + -1.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -9e+17], N[(N[(1.0 + t$95$0), $MachinePrecision] / x), $MachinePrecision], If[LessEqual[x, 250000.0], N[(1.0 / x), $MachinePrecision], N[(N[(1.0 / x), $MachinePrecision] + N[(t$95$0 / x), $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if x < -9e17

   1. Initial program 67.4%

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

      1. exp-prod 67.4%

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

   3. Simplified 67.4%

      \[\leadsto \color{blue}{\frac{{\left(e^{x}\right)}^{\log \left(\frac{x}{x + y}\right)}}{x}} \]
   4. Add Preprocessing

   5. Taylor expanded in y around 0 66.4%

      \[\leadsto \frac{\color{blue}{1 + y \cdot \left(y \cdot \left(0.5 + 0.5 \cdot \frac{1}{x}\right) - 1\right)}}{x} \]

   6. Taylor expanded in x around inf 66.4%

      \[\leadsto \frac{1 + y \cdot \left(\color{blue}{0.5 \cdot y} - 1\right)}{x} \]
   7. Step-by-step derivation

      1. *-commutative 66.4%

         \[\leadsto \frac{1 + y \cdot \left(\color{blue}{y \cdot 0.5} - 1\right)}{x} \]

   8. Simplified 66.4%

      \[\leadsto \frac{1 + y \cdot \left(\color{blue}{y \cdot 0.5} - 1\right)}{x} \]

   if -9e17 < x < 2.5e5

   1. Initial program 74.7%

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

      1. exp-prod 99.7%

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

   3. Simplified 99.7%

      \[\leadsto \color{blue}{\frac{{\left(e^{x}\right)}^{\log \left(\frac{x}{x + y}\right)}}{x}} \]
   4. Add Preprocessing

   5. Taylor expanded in x around 0 97.4%

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

   if 2.5e5 < x

   1. Initial program 77.7%

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

      1. *-commutative 77.7%

         \[\leadsto \frac{e^{\color{blue}{\log \left(\frac{x}{x + y}\right) \cdot x}}}{x} \]

      2. exp-to-pow 77.7%

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

   3. Simplified 77.7%

      \[\leadsto \color{blue}{\frac{{\left(\frac{x}{x + y}\right)}^{x}}{x}} \]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 99.9%

      \[\leadsto \frac{\color{blue}{e^{-1 \cdot y}}}{x} \]
   6. Step-by-step derivation

      1. mul-1-neg 99.9%

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

   7. Simplified 99.9%

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

   8. Taylor expanded in y around 0 60.0%

      \[\leadsto \color{blue}{y \cdot \left(0.5 \cdot \frac{y}{x} - \frac{1}{x}\right) + \frac{1}{x}} \]

   9. Taylor expanded in x around 0 64.2%

      \[\leadsto \color{blue}{\frac{y \cdot \left(0.5 \cdot y - 1\right)}{x}} + \frac{1}{x} \]
3. Recombined 3 regimes into one program.

4. Final simplification 78.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -9 \cdot 10^{+17}:\\ \;\;\;\;\frac{1 + y \cdot \left(y \cdot 0.5 + -1\right)}{x}\\ \mathbf{elif}\;x \leq 250000:\\ \;\;\;\;\frac{1}{x}\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{x} + \frac{y \cdot \left(y \cdot 0.5 + -1\right)}{x}\\ \end{array} \]
5. Add Preprocessing

Alternative 8: 79.4% accurate, 9.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -9 \cdot 10^{+17} \lor \neg \left(x \leq 400000\right):\\ \;\;\;\;\frac{1 + y \cdot \left(y \cdot 0.5 + -1\right)}{x}\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{x}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (or (<= x -9e+17) (not (<= x 400000.0)))
   (/ (+ 1.0 (* y (+ (* y 0.5) -1.0))) x)
   (/ 1.0 x)))

C

double code(double x, double y) {
	double tmp;
	if ((x <= -9e+17) || !(x <= 400000.0)) {
		tmp = (1.0 + (y * ((y * 0.5) + -1.0))) / x;
	} else {
		tmp = 1.0 / 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 <= (-9d+17)) .or. (.not. (x <= 400000.0d0))) then
        tmp = (1.0d0 + (y * ((y * 0.5d0) + (-1.0d0)))) / x
    else
        tmp = 1.0d0 / x
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if ((x <= -9e+17) || !(x <= 400000.0)) {
		tmp = (1.0 + (y * ((y * 0.5) + -1.0))) / x;
	} else {
		tmp = 1.0 / x;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (x <= -9e+17) or not (x <= 400000.0):
		tmp = (1.0 + (y * ((y * 0.5) + -1.0))) / x
	else:
		tmp = 1.0 / x
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if ((x <= -9e+17) || !(x <= 400000.0))
		tmp = Float64(Float64(1.0 + Float64(y * Float64(Float64(y * 0.5) + -1.0))) / x);
	else
		tmp = Float64(1.0 / x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if ((x <= -9e+17) || ~((x <= 400000.0)))
		tmp = (1.0 + (y * ((y * 0.5) + -1.0))) / x;
	else
		tmp = 1.0 / x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[Or[LessEqual[x, -9e+17], N[Not[LessEqual[x, 400000.0]], $MachinePrecision]], N[(N[(1.0 + N[(y * N[(N[(y * 0.5), $MachinePrecision] + -1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / x), $MachinePrecision], N[(1.0 / x), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -9e17 or 4e5 < x

   1. Initial program 73.5%

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

      1. exp-prod 73.1%

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

   3. Simplified 73.1%

      \[\leadsto \color{blue}{\frac{{\left(e^{x}\right)}^{\log \left(\frac{x}{x + y}\right)}}{x}} \]
   4. Add Preprocessing

   5. Taylor expanded in y around 0 65.1%

      \[\leadsto \frac{\color{blue}{1 + y \cdot \left(y \cdot \left(0.5 + 0.5 \cdot \frac{1}{x}\right) - 1\right)}}{x} \]

   6. Taylor expanded in x around inf 65.1%

      \[\leadsto \frac{1 + y \cdot \left(\color{blue}{0.5 \cdot y} - 1\right)}{x} \]
   7. Step-by-step derivation

      1. *-commutative 65.1%

         \[\leadsto \frac{1 + y \cdot \left(\color{blue}{y \cdot 0.5} - 1\right)}{x} \]

   8. Simplified 65.1%

      \[\leadsto \frac{1 + y \cdot \left(\color{blue}{y \cdot 0.5} - 1\right)}{x} \]

   if -9e17 < x < 4e5

   1. Initial program 74.7%

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

      1. exp-prod 99.7%

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

   3. Simplified 99.7%

      \[\leadsto \color{blue}{\frac{{\left(e^{x}\right)}^{\log \left(\frac{x}{x + y}\right)}}{x}} \]
   4. Add Preprocessing

   5. Taylor expanded in x around 0 97.4%

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

4. Final simplification 78.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -9 \cdot 10^{+17} \lor \neg \left(x \leq 400000\right):\\ \;\;\;\;\frac{1 + y \cdot \left(y \cdot 0.5 + -1\right)}{x}\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{x}\\ \end{array} \]
5. Add Preprocessing

Alternative 9: 76.3% accurate, 13.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -9 \cdot 10^{+17}:\\ \;\;\;\;\frac{1}{x} + y \cdot \left(y \cdot \frac{0.5}{x}\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{x}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= x -9e+17) (+ (/ 1.0 x) (* y (* y (/ 0.5 x)))) (/ 1.0 x)))

C

double code(double x, double y) {
	double tmp;
	if (x <= -9e+17) {
		tmp = (1.0 / x) + (y * (y * (0.5 / x)));
	} else {
		tmp = 1.0 / 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 <= (-9d+17)) then
        tmp = (1.0d0 / x) + (y * (y * (0.5d0 / x)))
    else
        tmp = 1.0d0 / x
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (x <= -9e+17) {
		tmp = (1.0 / x) + (y * (y * (0.5 / x)));
	} else {
		tmp = 1.0 / x;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if x <= -9e+17:
		tmp = (1.0 / x) + (y * (y * (0.5 / x)))
	else:
		tmp = 1.0 / x
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (x <= -9e+17)
		tmp = Float64(Float64(1.0 / x) + Float64(y * Float64(y * Float64(0.5 / x))));
	else
		tmp = Float64(1.0 / x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (x <= -9e+17)
		tmp = (1.0 / x) + (y * (y * (0.5 / x)));
	else
		tmp = 1.0 / x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[x, -9e+17], N[(N[(1.0 / x), $MachinePrecision] + N[(y * N[(y * N[(0.5 / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(1.0 / x), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -9e17

   1. Initial program 67.4%

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

      1. *-commutative 67.4%

         \[\leadsto \frac{e^{\color{blue}{\log \left(\frac{x}{x + y}\right) \cdot x}}}{x} \]

      2. exp-to-pow 67.4%

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

   3. Simplified 67.4%

      \[\leadsto \color{blue}{\frac{{\left(\frac{x}{x + y}\right)}^{x}}{x}} \]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 100.0%

      \[\leadsto \frac{\color{blue}{e^{-1 \cdot y}}}{x} \]
   6. Step-by-step derivation

      1. mul-1-neg 100.0%

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

   7. Simplified 100.0%

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

   8. Taylor expanded in y around 0 57.4%

      \[\leadsto \color{blue}{y \cdot \left(0.5 \cdot \frac{y}{x} - \frac{1}{x}\right) + \frac{1}{x}} \]

   9. Taylor expanded in y around inf 56.8%

      \[\leadsto y \cdot \color{blue}{\left(0.5 \cdot \frac{y}{x}\right)} + \frac{1}{x} \]
   10. Step-by-step derivation

       1. *-commutative 56.8%

          \[\leadsto y \cdot \color{blue}{\left(\frac{y}{x} \cdot 0.5\right)} + \frac{1}{x} \]

       2. associate-*l/ 56.8%

          \[\leadsto y \cdot \color{blue}{\frac{y \cdot 0.5}{x}} + \frac{1}{x} \]

       3. associate-*r/ 56.8%

          \[\leadsto y \cdot \color{blue}{\left(y \cdot \frac{0.5}{x}\right)} + \frac{1}{x} \]

   11. Simplified 56.8%

       \[\leadsto y \cdot \color{blue}{\left(y \cdot \frac{0.5}{x}\right)} + \frac{1}{x} \]

   if -9e17 < x

   1. Initial program 76.1%

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

      1. exp-prod 89.2%

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

   3. Simplified 89.2%

      \[\leadsto \color{blue}{\frac{{\left(e^{x}\right)}^{\log \left(\frac{x}{x + y}\right)}}{x}} \]
   4. Add Preprocessing

   5. Taylor expanded in x around 0 79.6%

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

4. Final simplification 74.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -9 \cdot 10^{+17}:\\ \;\;\;\;\frac{1}{x} + y \cdot \left(y \cdot \frac{0.5}{x}\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{x}\\ \end{array} \]
5. Add Preprocessing

Alternative 10: 74.4% accurate, 69.7× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 74.0%

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

   1. exp-prod 83.8%

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

3. Simplified 83.8%

   \[\leadsto \color{blue}{\frac{{\left(e^{x}\right)}^{\log \left(\frac{x}{x + y}\right)}}{x}} \]
4. Add Preprocessing

5. Taylor expanded in x around 0 72.4%

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

6. Final simplification 72.4%

   \[\leadsto \frac{1}{x} \]
7. Add Preprocessing

Developer target: 77.4% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{e^{\frac{-1}{y}}}{x}\\ t_1 := \frac{{\left(\frac{x}{y + x}\right)}^{x}}{x}\\ \mathbf{if}\;y < -3.7311844206647956 \cdot 10^{+94}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;y < 2.817959242728288 \cdot 10^{+37}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;y < 2.347387415166998 \cdot 10^{+178}:\\ \;\;\;\;\log \left(e^{t\_1}\right)\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (/ (exp (/ -1.0 y)) x)) (t_1 (/ (pow (/ x (+ y x)) x) x)))
   (if (< y -3.7311844206647956e+94)
     t_0
     (if (< y 2.817959242728288e+37)
       t_1
       (if (< y 2.347387415166998e+178) (log (exp t_1)) t_0)))))

C

double code(double x, double y) {
	double t_0 = exp((-1.0 / y)) / x;
	double t_1 = pow((x / (y + x)), x) / x;
	double tmp;
	if (y < -3.7311844206647956e+94) {
		tmp = t_0;
	} else if (y < 2.817959242728288e+37) {
		tmp = t_1;
	} else if (y < 2.347387415166998e+178) {
		tmp = log(exp(t_1));
	} 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) :: t_1
    real(8) :: tmp
    t_0 = exp(((-1.0d0) / y)) / x
    t_1 = ((x / (y + x)) ** x) / x
    if (y < (-3.7311844206647956d+94)) then
        tmp = t_0
    else if (y < 2.817959242728288d+37) then
        tmp = t_1
    else if (y < 2.347387415166998d+178) then
        tmp = log(exp(t_1))
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double t_0 = Math.exp((-1.0 / y)) / x;
	double t_1 = Math.pow((x / (y + x)), x) / x;
	double tmp;
	if (y < -3.7311844206647956e+94) {
		tmp = t_0;
	} else if (y < 2.817959242728288e+37) {
		tmp = t_1;
	} else if (y < 2.347387415166998e+178) {
		tmp = Math.log(Math.exp(t_1));
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = math.exp((-1.0 / y)) / x
	t_1 = math.pow((x / (y + x)), x) / x
	tmp = 0
	if y < -3.7311844206647956e+94:
		tmp = t_0
	elif y < 2.817959242728288e+37:
		tmp = t_1
	elif y < 2.347387415166998e+178:
		tmp = math.log(math.exp(t_1))
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y)
	t_0 = Float64(exp(Float64(-1.0 / y)) / x)
	t_1 = Float64((Float64(x / Float64(y + x)) ^ x) / x)
	tmp = 0.0
	if (y < -3.7311844206647956e+94)
		tmp = t_0;
	elseif (y < 2.817959242728288e+37)
		tmp = t_1;
	elseif (y < 2.347387415166998e+178)
		tmp = log(exp(t_1));
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = exp((-1.0 / y)) / x;
	t_1 = ((x / (y + x)) ^ x) / x;
	tmp = 0.0;
	if (y < -3.7311844206647956e+94)
		tmp = t_0;
	elseif (y < 2.817959242728288e+37)
		tmp = t_1;
	elseif (y < 2.347387415166998e+178)
		tmp = log(exp(t_1));
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(N[Exp[N[(-1.0 / y), $MachinePrecision]], $MachinePrecision] / x), $MachinePrecision]}, Block[{t$95$1 = N[(N[Power[N[(x / N[(y + x), $MachinePrecision]), $MachinePrecision], x], $MachinePrecision] / x), $MachinePrecision]}, If[Less[y, -3.7311844206647956e+94], t$95$0, If[Less[y, 2.817959242728288e+37], t$95$1, If[Less[y, 2.347387415166998e+178], N[Log[N[Exp[t$95$1], $MachinePrecision]], $MachinePrecision], t$95$0]]]]]

Reproduce

herbie shell --seed 2024077 
(FPCore (x y)
  :name "Numeric.SpecFunctions:invIncompleteBetaWorker from math-functions-0.1.5.2, F"
  :precision binary64

  :alt
  (if (< y -3.7311844206647956e+94) (/ (exp (/ -1.0 y)) x) (if (< y 2.817959242728288e+37) (/ (pow (/ x (+ y x)) x) x) (if (< y 2.347387415166998e+178) (log (exp (/ (pow (/ x (+ y x)) x) x))) (/ (exp (/ -1.0 y)) x))))

  (/ (exp (* x (log (/ x (+ x y))))) x))