Graphics.Rendering.Chart.Backend.Diagrams:calcFontMetrics from Chart-diagrams-1.5.1, A

Percentage Accurate: 88.3% → 99.6%

Time: 7.0s

Alternatives: 9

Speedup: 0.3×

Specification

Math

\[\begin{array}{l} \\ \frac{x + y}{1 - \frac{y}{z}} \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (/ (+ x y) (- 1.0 (/ y z))))

C

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

Fortran

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

Java

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

Python

def code(x, y, z):
	return (x + y) / (1.0 - (y / z))

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 88.3% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{x + y}{1 - \frac{y}{z}} \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (/ (+ x y) (- 1.0 (/ y z))))

C

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

Fortran

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

Java

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

Python

def code(x, y, z):
	return (x + y) / (1.0 - (y / z))

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 99.6% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{x + y}{1 - \frac{y}{z}}\\ \mathbf{if}\;t\_0 \leq -1 \cdot 10^{-261} \lor \neg \left(t\_0 \leq 0\right):\\ \;\;\;\;t\_0\\ \mathbf{else}:\\ \;\;\;\;z \cdot \left(-1 - \frac{x}{y}\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (/ (+ x y) (- 1.0 (/ y z)))))
   (if (or (<= t_0 -1e-261) (not (<= t_0 0.0))) t_0 (* z (- -1.0 (/ x y))))))

C

double code(double x, double y, double z) {
	double t_0 = (x + y) / (1.0 - (y / z));
	double tmp;
	if ((t_0 <= -1e-261) || !(t_0 <= 0.0)) {
		tmp = t_0;
	} else {
		tmp = z * (-1.0 - (x / y));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: tmp
    t_0 = (x + y) / (1.0d0 - (y / z))
    if ((t_0 <= (-1d-261)) .or. (.not. (t_0 <= 0.0d0))) then
        tmp = t_0
    else
        tmp = z * ((-1.0d0) - (x / y))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = (x + y) / (1.0 - (y / z));
	double tmp;
	if ((t_0 <= -1e-261) || !(t_0 <= 0.0)) {
		tmp = t_0;
	} else {
		tmp = z * (-1.0 - (x / y));
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = (x + y) / (1.0 - (y / z))
	tmp = 0
	if (t_0 <= -1e-261) or not (t_0 <= 0.0):
		tmp = t_0
	else:
		tmp = z * (-1.0 - (x / y))
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(Float64(x + y) / Float64(1.0 - Float64(y / z)))
	tmp = 0.0
	if ((t_0 <= -1e-261) || !(t_0 <= 0.0))
		tmp = t_0;
	else
		tmp = Float64(z * Float64(-1.0 - Float64(x / y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = (x + y) / (1.0 - (y / z));
	tmp = 0.0;
	if ((t_0 <= -1e-261) || ~((t_0 <= 0.0)))
		tmp = t_0;
	else
		tmp = z * (-1.0 - (x / y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(x + y), $MachinePrecision] / N[(1.0 - N[(y / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[Or[LessEqual[t$95$0, -1e-261], N[Not[LessEqual[t$95$0, 0.0]], $MachinePrecision]], t$95$0, N[(z * N[(-1.0 - N[(x / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (+.f64 x y) (-.f64 #s(literal 1 binary64) (/.f64 y z))) < -9.99999999999999984e-262 or 0.0 < (/.f64 (+.f64 x y) (-.f64 #s(literal 1 binary64) (/.f64 y z)))

   1. Initial program 99.9%

      \[\frac{x + y}{1 - \frac{y}{z}} \]
   2. Add Preprocessing

   if -9.99999999999999984e-262 < (/.f64 (+.f64 x y) (-.f64 #s(literal 1 binary64) (/.f64 y z))) < 0.0

   1. Initial program 16.8%

      \[\frac{x + y}{1 - \frac{y}{z}} \]
   2. Add Preprocessing

   3. Taylor expanded in z around 0 96.1%

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

      1. mul-1-neg 96.1%

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

      2. associate-/l* 99.9%

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

      3. distribute-rgt-neg-in 99.9%

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

      4. distribute-neg-frac2 99.9%

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

      5. +-commutative 99.9%

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

   5. Simplified 99.9%

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

   6. Taylor expanded in z around 0 96.1%

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

      1. associate-*r/ 99.9%

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

      2. associate-*r* 99.9%

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

      3. mul-1-neg 99.9%

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

      4. *-lft-identity 99.9%

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

      5. associate-*l/ 99.7%

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

      6. distribute-rgt-in 99.7%

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

      7. rgt-mult-inverse 99.9%

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

      8. associate-*r/ 99.9%

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

      9. *-rgt-identity 99.9%

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

      10. +-commutative 99.9%

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

      11. distribute-rgt-in 99.8%

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

      12. *-lft-identity 99.8%

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

      13. mul-1-neg 99.8%

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

      14. distribute-rgt-neg-in 99.8%

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

      15. distribute-lft-neg-in 99.8%

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

      16. mul-1-neg 99.8%

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

      17. distribute-rgt-in 99.9%

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

      18. mul-1-neg 99.9%

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

      19. unsub-neg 99.9%

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

   8. Simplified 99.9%

      \[\leadsto \color{blue}{z \cdot \left(-1 - \frac{x}{y}\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 99.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{x + y}{1 - \frac{y}{z}} \leq -1 \cdot 10^{-261} \lor \neg \left(\frac{x + y}{1 - \frac{y}{z}} \leq 0\right):\\ \;\;\;\;\frac{x + y}{1 - \frac{y}{z}}\\ \mathbf{else}:\\ \;\;\;\;z \cdot \left(-1 - \frac{x}{y}\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 2: 73.1% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := z \cdot \left(-1 - \frac{x}{y}\right)\\ \mathbf{if}\;y \leq -1.2 \cdot 10^{+71}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;y \leq -1 \cdot 10^{-71}:\\ \;\;\;\;x + y\\ \mathbf{elif}\;y \leq -1.35 \cdot 10^{-210}:\\ \;\;\;\;\frac{x}{1 - \frac{y}{z}}\\ \mathbf{elif}\;y \leq 1350 \lor \neg \left(y \leq 3.6 \cdot 10^{+59}\right) \land y \leq 4 \cdot 10^{+116}:\\ \;\;\;\;x + y\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* z (- -1.0 (/ x y)))))
   (if (<= y -1.2e+71)
     t_0
     (if (<= y -1e-71)
       (+ x y)
       (if (<= y -1.35e-210)
         (/ x (- 1.0 (/ y z)))
         (if (or (<= y 1350.0) (and (not (<= y 3.6e+59)) (<= y 4e+116)))
           (+ x y)
           t_0))))))

C

double code(double x, double y, double z) {
	double t_0 = z * (-1.0 - (x / y));
	double tmp;
	if (y <= -1.2e+71) {
		tmp = t_0;
	} else if (y <= -1e-71) {
		tmp = x + y;
	} else if (y <= -1.35e-210) {
		tmp = x / (1.0 - (y / z));
	} else if ((y <= 1350.0) || (!(y <= 3.6e+59) && (y <= 4e+116))) {
		tmp = x + y;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: tmp
    t_0 = z * ((-1.0d0) - (x / y))
    if (y <= (-1.2d+71)) then
        tmp = t_0
    else if (y <= (-1d-71)) then
        tmp = x + y
    else if (y <= (-1.35d-210)) then
        tmp = x / (1.0d0 - (y / z))
    else if ((y <= 1350.0d0) .or. (.not. (y <= 3.6d+59)) .and. (y <= 4d+116)) then
        tmp = x + y
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = z * (-1.0 - (x / y));
	double tmp;
	if (y <= -1.2e+71) {
		tmp = t_0;
	} else if (y <= -1e-71) {
		tmp = x + y;
	} else if (y <= -1.35e-210) {
		tmp = x / (1.0 - (y / z));
	} else if ((y <= 1350.0) || (!(y <= 3.6e+59) && (y <= 4e+116))) {
		tmp = x + y;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = z * (-1.0 - (x / y))
	tmp = 0
	if y <= -1.2e+71:
		tmp = t_0
	elif y <= -1e-71:
		tmp = x + y
	elif y <= -1.35e-210:
		tmp = x / (1.0 - (y / z))
	elif (y <= 1350.0) or (not (y <= 3.6e+59) and (y <= 4e+116)):
		tmp = x + y
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(z * Float64(-1.0 - Float64(x / y)))
	tmp = 0.0
	if (y <= -1.2e+71)
		tmp = t_0;
	elseif (y <= -1e-71)
		tmp = Float64(x + y);
	elseif (y <= -1.35e-210)
		tmp = Float64(x / Float64(1.0 - Float64(y / z)));
	elseif ((y <= 1350.0) || (!(y <= 3.6e+59) && (y <= 4e+116)))
		tmp = Float64(x + y);
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = z * (-1.0 - (x / y));
	tmp = 0.0;
	if (y <= -1.2e+71)
		tmp = t_0;
	elseif (y <= -1e-71)
		tmp = x + y;
	elseif (y <= -1.35e-210)
		tmp = x / (1.0 - (y / z));
	elseif ((y <= 1350.0) || (~((y <= 3.6e+59)) && (y <= 4e+116)))
		tmp = x + y;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(z * N[(-1.0 - N[(x / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -1.2e+71], t$95$0, If[LessEqual[y, -1e-71], N[(x + y), $MachinePrecision], If[LessEqual[y, -1.35e-210], N[(x / N[(1.0 - N[(y / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[Or[LessEqual[y, 1350.0], And[N[Not[LessEqual[y, 3.6e+59]], $MachinePrecision], LessEqual[y, 4e+116]]], N[(x + y), $MachinePrecision], t$95$0]]]]]

Derivation

1. Split input into 3 regimes

2. if y < -1.1999999999999999e71 or 1350 < y < 3.5999999999999999e59 or 4.00000000000000006e116 < y

   1. Initial program 78.1%

      \[\frac{x + y}{1 - \frac{y}{z}} \]
   2. Add Preprocessing

   3. Taylor expanded in z around 0 58.3%

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

      1. mul-1-neg 58.3%

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

      2. associate-/l* 78.5%

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

      3. distribute-rgt-neg-in 78.5%

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

      4. distribute-neg-frac2 78.5%

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

      5. +-commutative 78.5%

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

   5. Simplified 78.5%

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

   6. Taylor expanded in z around 0 58.3%

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

      1. associate-*r/ 78.5%

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

      2. associate-*r* 78.5%

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

      3. mul-1-neg 78.5%

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

      4. *-lft-identity 78.5%

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

      5. associate-*l/ 78.4%

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

      6. distribute-rgt-in 78.4%

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

      7. rgt-mult-inverse 78.5%

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

      8. associate-*r/ 78.5%

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

      9. *-rgt-identity 78.5%

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

      10. +-commutative 78.5%

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

      11. distribute-rgt-in 78.5%

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

      12. *-lft-identity 78.5%

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

      13. mul-1-neg 78.5%

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

      14. distribute-rgt-neg-in 78.5%

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

      15. distribute-lft-neg-in 78.5%

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

      16. mul-1-neg 78.5%

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

      17. distribute-rgt-in 78.5%

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

      18. mul-1-neg 78.5%

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

      19. unsub-neg 78.5%

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

   8. Simplified 78.5%

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

   if -1.1999999999999999e71 < y < -9.9999999999999992e-72 or -1.34999999999999996e-210 < y < 1350 or 3.5999999999999999e59 < y < 4.00000000000000006e116

   1. Initial program 99.2%

      \[\frac{x + y}{1 - \frac{y}{z}} \]
   2. Add Preprocessing

   3. Taylor expanded in z around inf 79.7%

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

      1. +-commutative 79.7%

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

   5. Simplified 79.7%

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

   if -9.9999999999999992e-72 < y < -1.34999999999999996e-210

   1. Initial program 99.8%

      \[\frac{x + y}{1 - \frac{y}{z}} \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf 77.4%

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

4. Final simplification 79.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1.2 \cdot 10^{+71}:\\ \;\;\;\;z \cdot \left(-1 - \frac{x}{y}\right)\\ \mathbf{elif}\;y \leq -1 \cdot 10^{-71}:\\ \;\;\;\;x + y\\ \mathbf{elif}\;y \leq -1.35 \cdot 10^{-210}:\\ \;\;\;\;\frac{x}{1 - \frac{y}{z}}\\ \mathbf{elif}\;y \leq 1350 \lor \neg \left(y \leq 3.6 \cdot 10^{+59}\right) \land y \leq 4 \cdot 10^{+116}:\\ \;\;\;\;x + y\\ \mathbf{else}:\\ \;\;\;\;z \cdot \left(-1 - \frac{x}{y}\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 73.2% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 1 - \frac{y}{z}\\ t_1 := z \cdot \left(-1 - \frac{x}{y}\right)\\ \mathbf{if}\;y \leq -6.4 \cdot 10^{+93}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;y \leq -7.5 \cdot 10^{-42}:\\ \;\;\;\;\frac{y}{t\_0}\\ \mathbf{elif}\;y \leq -3.5 \cdot 10^{-211}:\\ \;\;\;\;\frac{x}{t\_0}\\ \mathbf{elif}\;y \leq 36000 \lor \neg \left(y \leq 1.5 \cdot 10^{+59}\right) \land y \leq 4.2 \cdot 10^{+116}:\\ \;\;\;\;x + y\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (- 1.0 (/ y z))) (t_1 (* z (- -1.0 (/ x y)))))
   (if (<= y -6.4e+93)
     t_1
     (if (<= y -7.5e-42)
       (/ y t_0)
       (if (<= y -3.5e-211)
         (/ x t_0)
         (if (or (<= y 36000.0) (and (not (<= y 1.5e+59)) (<= y 4.2e+116)))
           (+ x y)
           t_1))))))

C

double code(double x, double y, double z) {
	double t_0 = 1.0 - (y / z);
	double t_1 = z * (-1.0 - (x / y));
	double tmp;
	if (y <= -6.4e+93) {
		tmp = t_1;
	} else if (y <= -7.5e-42) {
		tmp = y / t_0;
	} else if (y <= -3.5e-211) {
		tmp = x / t_0;
	} else if ((y <= 36000.0) || (!(y <= 1.5e+59) && (y <= 4.2e+116))) {
		tmp = x + y;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: tmp
    t_0 = 1.0d0 - (y / z)
    t_1 = z * ((-1.0d0) - (x / y))
    if (y <= (-6.4d+93)) then
        tmp = t_1
    else if (y <= (-7.5d-42)) then
        tmp = y / t_0
    else if (y <= (-3.5d-211)) then
        tmp = x / t_0
    else if ((y <= 36000.0d0) .or. (.not. (y <= 1.5d+59)) .and. (y <= 4.2d+116)) then
        tmp = x + y
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = 1.0 - (y / z);
	double t_1 = z * (-1.0 - (x / y));
	double tmp;
	if (y <= -6.4e+93) {
		tmp = t_1;
	} else if (y <= -7.5e-42) {
		tmp = y / t_0;
	} else if (y <= -3.5e-211) {
		tmp = x / t_0;
	} else if ((y <= 36000.0) || (!(y <= 1.5e+59) && (y <= 4.2e+116))) {
		tmp = x + y;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = 1.0 - (y / z)
	t_1 = z * (-1.0 - (x / y))
	tmp = 0
	if y <= -6.4e+93:
		tmp = t_1
	elif y <= -7.5e-42:
		tmp = y / t_0
	elif y <= -3.5e-211:
		tmp = x / t_0
	elif (y <= 36000.0) or (not (y <= 1.5e+59) and (y <= 4.2e+116)):
		tmp = x + y
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(1.0 - Float64(y / z))
	t_1 = Float64(z * Float64(-1.0 - Float64(x / y)))
	tmp = 0.0
	if (y <= -6.4e+93)
		tmp = t_1;
	elseif (y <= -7.5e-42)
		tmp = Float64(y / t_0);
	elseif (y <= -3.5e-211)
		tmp = Float64(x / t_0);
	elseif ((y <= 36000.0) || (!(y <= 1.5e+59) && (y <= 4.2e+116)))
		tmp = Float64(x + y);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = 1.0 - (y / z);
	t_1 = z * (-1.0 - (x / y));
	tmp = 0.0;
	if (y <= -6.4e+93)
		tmp = t_1;
	elseif (y <= -7.5e-42)
		tmp = y / t_0;
	elseif (y <= -3.5e-211)
		tmp = x / t_0;
	elseif ((y <= 36000.0) || (~((y <= 1.5e+59)) && (y <= 4.2e+116)))
		tmp = x + y;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(1.0 - N[(y / z), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(z * N[(-1.0 - N[(x / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -6.4e+93], t$95$1, If[LessEqual[y, -7.5e-42], N[(y / t$95$0), $MachinePrecision], If[LessEqual[y, -3.5e-211], N[(x / t$95$0), $MachinePrecision], If[Or[LessEqual[y, 36000.0], And[N[Not[LessEqual[y, 1.5e+59]], $MachinePrecision], LessEqual[y, 4.2e+116]]], N[(x + y), $MachinePrecision], t$95$1]]]]]]

Derivation

1. Split input into 4 regimes

2. if y < -6.4000000000000003e93 or 36000 < y < 1.5e59 or 4.2000000000000002e116 < y

   1. Initial program 77.4%

      \[\frac{x + y}{1 - \frac{y}{z}} \]
   2. Add Preprocessing

   3. Taylor expanded in z around 0 57.3%

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

      1. mul-1-neg 57.3%

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

      2. associate-/l* 79.0%

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

      3. distribute-rgt-neg-in 79.0%

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

      4. distribute-neg-frac2 79.0%

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

      5. +-commutative 79.0%

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

   5. Simplified 79.0%

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

   6. Taylor expanded in z around 0 57.3%

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

      1. associate-*r/ 79.0%

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

      2. associate-*r* 79.0%

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

      3. mul-1-neg 79.0%

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

      4. *-lft-identity 79.0%

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

      5. associate-*l/ 78.9%

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

      6. distribute-rgt-in 78.9%

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

      7. rgt-mult-inverse 79.0%

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

      8. associate-*r/ 79.0%

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

      9. *-rgt-identity 79.0%

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

      10. +-commutative 79.0%

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

      11. distribute-rgt-in 79.0%

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

      12. *-lft-identity 79.0%

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

      13. mul-1-neg 79.0%

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

      14. distribute-rgt-neg-in 79.0%

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

      15. distribute-lft-neg-in 79.0%

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

      16. mul-1-neg 79.0%

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

      17. distribute-rgt-in 79.0%

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

      18. mul-1-neg 79.0%

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

      19. unsub-neg 79.0%

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

   8. Simplified 79.0%

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

   if -6.4000000000000003e93 < y < -7.49999999999999972e-42

   1. Initial program 97.5%

      \[\frac{x + y}{1 - \frac{y}{z}} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0 71.0%

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

   if -7.49999999999999972e-42 < y < -3.5e-211

   1. Initial program 99.9%

      \[\frac{x + y}{1 - \frac{y}{z}} \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf 75.9%

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

   if -3.5e-211 < y < 36000 or 1.5e59 < y < 4.2000000000000002e116

   1. Initial program 98.9%

      \[\frac{x + y}{1 - \frac{y}{z}} \]
   2. Add Preprocessing

   3. Taylor expanded in z around inf 84.1%

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

      1. +-commutative 84.1%

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

   5. Simplified 84.1%

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

4. Final simplification 79.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -6.4 \cdot 10^{+93}:\\ \;\;\;\;z \cdot \left(-1 - \frac{x}{y}\right)\\ \mathbf{elif}\;y \leq -7.5 \cdot 10^{-42}:\\ \;\;\;\;\frac{y}{1 - \frac{y}{z}}\\ \mathbf{elif}\;y \leq -3.5 \cdot 10^{-211}:\\ \;\;\;\;\frac{x}{1 - \frac{y}{z}}\\ \mathbf{elif}\;y \leq 36000 \lor \neg \left(y \leq 1.5 \cdot 10^{+59}\right) \land y \leq 4.2 \cdot 10^{+116}:\\ \;\;\;\;x + y\\ \mathbf{else}:\\ \;\;\;\;z \cdot \left(-1 - \frac{x}{y}\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 72.9% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 1 - \frac{y}{z}\\ \mathbf{if}\;y \leq -5.8 \cdot 10^{+93}:\\ \;\;\;\;z \cdot \frac{x + y}{-y}\\ \mathbf{elif}\;y \leq -1.8 \cdot 10^{-41}:\\ \;\;\;\;\frac{y}{t\_0}\\ \mathbf{elif}\;y \leq -3.1 \cdot 10^{-212}:\\ \;\;\;\;\frac{x}{t\_0}\\ \mathbf{elif}\;y \leq 225 \lor \neg \left(y \leq 2.5 \cdot 10^{+56}\right) \land y \leq 8 \cdot 10^{+120}:\\ \;\;\;\;x + y\\ \mathbf{else}:\\ \;\;\;\;z \cdot \left(-1 - \frac{x}{y}\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (- 1.0 (/ y z))))
   (if (<= y -5.8e+93)
     (* z (/ (+ x y) (- y)))
     (if (<= y -1.8e-41)
       (/ y t_0)
       (if (<= y -3.1e-212)
         (/ x t_0)
         (if (or (<= y 225.0) (and (not (<= y 2.5e+56)) (<= y 8e+120)))
           (+ x y)
           (* z (- -1.0 (/ x y)))))))))

C

double code(double x, double y, double z) {
	double t_0 = 1.0 - (y / z);
	double tmp;
	if (y <= -5.8e+93) {
		tmp = z * ((x + y) / -y);
	} else if (y <= -1.8e-41) {
		tmp = y / t_0;
	} else if (y <= -3.1e-212) {
		tmp = x / t_0;
	} else if ((y <= 225.0) || (!(y <= 2.5e+56) && (y <= 8e+120))) {
		tmp = x + y;
	} else {
		tmp = z * (-1.0 - (x / y));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: tmp
    t_0 = 1.0d0 - (y / z)
    if (y <= (-5.8d+93)) then
        tmp = z * ((x + y) / -y)
    else if (y <= (-1.8d-41)) then
        tmp = y / t_0
    else if (y <= (-3.1d-212)) then
        tmp = x / t_0
    else if ((y <= 225.0d0) .or. (.not. (y <= 2.5d+56)) .and. (y <= 8d+120)) then
        tmp = x + y
    else
        tmp = z * ((-1.0d0) - (x / y))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = 1.0 - (y / z);
	double tmp;
	if (y <= -5.8e+93) {
		tmp = z * ((x + y) / -y);
	} else if (y <= -1.8e-41) {
		tmp = y / t_0;
	} else if (y <= -3.1e-212) {
		tmp = x / t_0;
	} else if ((y <= 225.0) || (!(y <= 2.5e+56) && (y <= 8e+120))) {
		tmp = x + y;
	} else {
		tmp = z * (-1.0 - (x / y));
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = 1.0 - (y / z)
	tmp = 0
	if y <= -5.8e+93:
		tmp = z * ((x + y) / -y)
	elif y <= -1.8e-41:
		tmp = y / t_0
	elif y <= -3.1e-212:
		tmp = x / t_0
	elif (y <= 225.0) or (not (y <= 2.5e+56) and (y <= 8e+120)):
		tmp = x + y
	else:
		tmp = z * (-1.0 - (x / y))
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(1.0 - Float64(y / z))
	tmp = 0.0
	if (y <= -5.8e+93)
		tmp = Float64(z * Float64(Float64(x + y) / Float64(-y)));
	elseif (y <= -1.8e-41)
		tmp = Float64(y / t_0);
	elseif (y <= -3.1e-212)
		tmp = Float64(x / t_0);
	elseif ((y <= 225.0) || (!(y <= 2.5e+56) && (y <= 8e+120)))
		tmp = Float64(x + y);
	else
		tmp = Float64(z * Float64(-1.0 - Float64(x / y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = 1.0 - (y / z);
	tmp = 0.0;
	if (y <= -5.8e+93)
		tmp = z * ((x + y) / -y);
	elseif (y <= -1.8e-41)
		tmp = y / t_0;
	elseif (y <= -3.1e-212)
		tmp = x / t_0;
	elseif ((y <= 225.0) || (~((y <= 2.5e+56)) && (y <= 8e+120)))
		tmp = x + y;
	else
		tmp = z * (-1.0 - (x / y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(1.0 - N[(y / z), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -5.8e+93], N[(z * N[(N[(x + y), $MachinePrecision] / (-y)), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, -1.8e-41], N[(y / t$95$0), $MachinePrecision], If[LessEqual[y, -3.1e-212], N[(x / t$95$0), $MachinePrecision], If[Or[LessEqual[y, 225.0], And[N[Not[LessEqual[y, 2.5e+56]], $MachinePrecision], LessEqual[y, 8e+120]]], N[(x + y), $MachinePrecision], N[(z * N[(-1.0 - N[(x / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

1. Split input into 5 regimes

2. if y < -5.7999999999999997e93

   1. Initial program 80.6%

      \[\frac{x + y}{1 - \frac{y}{z}} \]
   2. Add Preprocessing

   3. Taylor expanded in z around 0 54.2%

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

      1. mul-1-neg 54.2%

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

      2. associate-/l* 79.3%

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

      3. distribute-rgt-neg-in 79.3%

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

      4. distribute-neg-frac2 79.3%

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

      5. +-commutative 79.3%

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

   5. Simplified 79.3%

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

   if -5.7999999999999997e93 < y < -1.8e-41

   1. Initial program 97.5%

      \[\frac{x + y}{1 - \frac{y}{z}} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0 71.0%

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

   if -1.8e-41 < y < -3.10000000000000006e-212

   1. Initial program 99.9%

      \[\frac{x + y}{1 - \frac{y}{z}} \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf 75.9%

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

   if -3.10000000000000006e-212 < y < 225 or 2.50000000000000012e56 < y < 7.9999999999999998e120

   1. Initial program 98.9%

      \[\frac{x + y}{1 - \frac{y}{z}} \]
   2. Add Preprocessing

   3. Taylor expanded in z around inf 84.1%

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

      1. +-commutative 84.1%

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

   5. Simplified 84.1%

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

   if 225 < y < 2.50000000000000012e56 or 7.9999999999999998e120 < y

   1. Initial program 73.8%

      \[\frac{x + y}{1 - \frac{y}{z}} \]
   2. Add Preprocessing

   3. Taylor expanded in z around 0 60.8%

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

      1. mul-1-neg 60.8%

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

      2. associate-/l* 78.7%

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

      3. distribute-rgt-neg-in 78.7%

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

      4. distribute-neg-frac2 78.7%

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

      5. +-commutative 78.7%

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

   5. Simplified 78.7%

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

   6. Taylor expanded in z around 0 60.8%

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

      1. associate-*r/ 78.7%

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

      2. associate-*r* 78.7%

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

      3. mul-1-neg 78.7%

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

      4. *-lft-identity 78.7%

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

      5. associate-*l/ 78.7%

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

      6. distribute-rgt-in 78.7%

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

      7. rgt-mult-inverse 78.8%

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

      8. associate-*r/ 78.7%

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

      9. *-rgt-identity 78.7%

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

      10. +-commutative 78.7%

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

      11. distribute-rgt-in 78.7%

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

      12. *-lft-identity 78.7%

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

      13. mul-1-neg 78.7%

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

      14. distribute-rgt-neg-in 78.7%

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

      15. distribute-lft-neg-in 78.7%

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

      16. mul-1-neg 78.7%

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

      17. distribute-rgt-in 78.7%

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

      18. mul-1-neg 78.7%

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

      19. unsub-neg 78.7%

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

   8. Simplified 78.7%

      \[\leadsto \color{blue}{z \cdot \left(-1 - \frac{x}{y}\right)} \]
3. Recombined 5 regimes into one program.

4. Final simplification 79.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -5.8 \cdot 10^{+93}:\\ \;\;\;\;z \cdot \frac{x + y}{-y}\\ \mathbf{elif}\;y \leq -1.8 \cdot 10^{-41}:\\ \;\;\;\;\frac{y}{1 - \frac{y}{z}}\\ \mathbf{elif}\;y \leq -3.1 \cdot 10^{-212}:\\ \;\;\;\;\frac{x}{1 - \frac{y}{z}}\\ \mathbf{elif}\;y \leq 225 \lor \neg \left(y \leq 2.5 \cdot 10^{+56}\right) \land y \leq 8 \cdot 10^{+120}:\\ \;\;\;\;x + y\\ \mathbf{else}:\\ \;\;\;\;z \cdot \left(-1 - \frac{x}{y}\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 73.1% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -7.2 \cdot 10^{+71} \lor \neg \left(y \leq 15500\right) \land \left(y \leq 3.6 \cdot 10^{+58} \lor \neg \left(y \leq 2.9 \cdot 10^{+119}\right)\right):\\ \;\;\;\;z \cdot \left(-1 - \frac{x}{y}\right)\\ \mathbf{else}:\\ \;\;\;\;x + y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= y -7.2e+71)
         (and (not (<= y 15500.0)) (or (<= y 3.6e+58) (not (<= y 2.9e+119)))))
   (* z (- -1.0 (/ x y)))
   (+ x y)))

C

double code(double x, double y, double z) {
	double tmp;
	if ((y <= -7.2e+71) || (!(y <= 15500.0) && ((y <= 3.6e+58) || !(y <= 2.9e+119)))) {
		tmp = z * (-1.0 - (x / y));
	} else {
		tmp = x + y;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if ((y <= (-7.2d+71)) .or. (.not. (y <= 15500.0d0)) .and. (y <= 3.6d+58) .or. (.not. (y <= 2.9d+119))) then
        tmp = z * ((-1.0d0) - (x / y))
    else
        tmp = x + y
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((y <= -7.2e+71) || (!(y <= 15500.0) && ((y <= 3.6e+58) || !(y <= 2.9e+119)))) {
		tmp = z * (-1.0 - (x / y));
	} else {
		tmp = x + y;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (y <= -7.2e+71) or (not (y <= 15500.0) and ((y <= 3.6e+58) or not (y <= 2.9e+119))):
		tmp = z * (-1.0 - (x / y))
	else:
		tmp = x + y
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((y <= -7.2e+71) || (!(y <= 15500.0) && ((y <= 3.6e+58) || !(y <= 2.9e+119))))
		tmp = Float64(z * Float64(-1.0 - Float64(x / y)));
	else
		tmp = Float64(x + y);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((y <= -7.2e+71) || (~((y <= 15500.0)) && ((y <= 3.6e+58) || ~((y <= 2.9e+119)))))
		tmp = z * (-1.0 - (x / y));
	else
		tmp = x + y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[y, -7.2e+71], And[N[Not[LessEqual[y, 15500.0]], $MachinePrecision], Or[LessEqual[y, 3.6e+58], N[Not[LessEqual[y, 2.9e+119]], $MachinePrecision]]]], N[(z * N[(-1.0 - N[(x / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + y), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -7.1999999999999999e71 or 15500 < y < 3.59999999999999996e58 or 2.90000000000000007e119 < y

   1. Initial program 78.1%

      \[\frac{x + y}{1 - \frac{y}{z}} \]
   2. Add Preprocessing

   3. Taylor expanded in z around 0 58.3%

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

      1. mul-1-neg 58.3%

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

      2. associate-/l* 78.5%

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

      3. distribute-rgt-neg-in 78.5%

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

      4. distribute-neg-frac2 78.5%

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

      5. +-commutative 78.5%

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

   5. Simplified 78.5%

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

   6. Taylor expanded in z around 0 58.3%

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

      1. associate-*r/ 78.5%

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

      2. associate-*r* 78.5%

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

      3. mul-1-neg 78.5%

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

      4. *-lft-identity 78.5%

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

      5. associate-*l/ 78.4%

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

      6. distribute-rgt-in 78.4%

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

      7. rgt-mult-inverse 78.5%

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

      8. associate-*r/ 78.5%

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

      9. *-rgt-identity 78.5%

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

      10. +-commutative 78.5%

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

      11. distribute-rgt-in 78.5%

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

      12. *-lft-identity 78.5%

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

      13. mul-1-neg 78.5%

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

      14. distribute-rgt-neg-in 78.5%

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

      15. distribute-lft-neg-in 78.5%

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

      16. mul-1-neg 78.5%

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

      17. distribute-rgt-in 78.5%

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

      18. mul-1-neg 78.5%

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

      19. unsub-neg 78.5%

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

   8. Simplified 78.5%

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

   if -7.1999999999999999e71 < y < 15500 or 3.59999999999999996e58 < y < 2.90000000000000007e119

   1. Initial program 99.3%

      \[\frac{x + y}{1 - \frac{y}{z}} \]
   2. Add Preprocessing

   3. Taylor expanded in z around inf 76.7%

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

      1. +-commutative 76.7%

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

   5. Simplified 76.7%

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

4. Final simplification 77.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -7.2 \cdot 10^{+71} \lor \neg \left(y \leq 15500\right) \land \left(y \leq 3.6 \cdot 10^{+58} \lor \neg \left(y \leq 2.9 \cdot 10^{+119}\right)\right):\\ \;\;\;\;z \cdot \left(-1 - \frac{x}{y}\right)\\ \mathbf{else}:\\ \;\;\;\;x + y\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 58.3% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -2 \cdot 10^{+67}:\\ \;\;\;\;-z\\ \mathbf{elif}\;y \leq -1.75 \cdot 10^{-40}:\\ \;\;\;\;y\\ \mathbf{elif}\;y \leq 10000:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;-z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y -2e+67) (- z) (if (<= y -1.75e-40) y (if (<= y 10000.0) x (- z)))))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= -2e+67) {
		tmp = -z;
	} else if (y <= -1.75e-40) {
		tmp = y;
	} else if (y <= 10000.0) {
		tmp = x;
	} else {
		tmp = -z;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (y <= (-2d+67)) then
        tmp = -z
    else if (y <= (-1.75d-40)) then
        tmp = y
    else if (y <= 10000.0d0) then
        tmp = x
    else
        tmp = -z
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (y <= -2e+67) {
		tmp = -z;
	} else if (y <= -1.75e-40) {
		tmp = y;
	} else if (y <= 10000.0) {
		tmp = x;
	} else {
		tmp = -z;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if y <= -2e+67:
		tmp = -z
	elif y <= -1.75e-40:
		tmp = y
	elif y <= 10000.0:
		tmp = x
	else:
		tmp = -z
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= -2e+67)
		tmp = Float64(-z);
	elseif (y <= -1.75e-40)
		tmp = y;
	elseif (y <= 10000.0)
		tmp = x;
	else
		tmp = Float64(-z);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (y <= -2e+67)
		tmp = -z;
	elseif (y <= -1.75e-40)
		tmp = y;
	elseif (y <= 10000.0)
		tmp = x;
	else
		tmp = -z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, -2e+67], (-z), If[LessEqual[y, -1.75e-40], y, If[LessEqual[y, 10000.0], x, (-z)]]]

Derivation

1. Split input into 3 regimes

2. if y < -1.99999999999999997e67 or 1e4 < y

   1. Initial program 80.3%

      \[\frac{x + y}{1 - \frac{y}{z}} \]
   2. Add Preprocessing

   3. Taylor expanded in y around inf 56.0%

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

      1. mul-1-neg 56.0%

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

   5. Simplified 56.0%

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

   if -1.99999999999999997e67 < y < -1.7500000000000001e-40

   1. Initial program 99.8%

      \[\frac{x + y}{1 - \frac{y}{z}} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0 73.0%

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

   4. Taylor expanded in y around 0 47.4%

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

   if -1.7500000000000001e-40 < y < 1e4

   1. Initial program 99.9%

      \[\frac{x + y}{1 - \frac{y}{z}} \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0 66.6%

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

4. Final simplification 59.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -2 \cdot 10^{+67}:\\ \;\;\;\;-z\\ \mathbf{elif}\;y \leq -1.75 \cdot 10^{-40}:\\ \;\;\;\;y\\ \mathbf{elif}\;y \leq 10000:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;-z\\ \end{array} \]
5. Add Preprocessing

Alternative 7: 67.2% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -2.6 \cdot 10^{+74} \lor \neg \left(y \leq 5.8 \cdot 10^{+139}\right):\\ \;\;\;\;-z\\ \mathbf{else}:\\ \;\;\;\;x + y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= y -2.6e+74) (not (<= y 5.8e+139))) (- z) (+ x y)))

C

double code(double x, double y, double z) {
	double tmp;
	if ((y <= -2.6e+74) || !(y <= 5.8e+139)) {
		tmp = -z;
	} else {
		tmp = x + y;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if ((y <= (-2.6d+74)) .or. (.not. (y <= 5.8d+139))) then
        tmp = -z
    else
        tmp = x + y
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((y <= -2.6e+74) || !(y <= 5.8e+139)) {
		tmp = -z;
	} else {
		tmp = x + y;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (y <= -2.6e+74) or not (y <= 5.8e+139):
		tmp = -z
	else:
		tmp = x + y
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((y <= -2.6e+74) || !(y <= 5.8e+139))
		tmp = Float64(-z);
	else
		tmp = Float64(x + y);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((y <= -2.6e+74) || ~((y <= 5.8e+139)))
		tmp = -z;
	else
		tmp = x + y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[y, -2.6e+74], N[Not[LessEqual[y, 5.8e+139]], $MachinePrecision]], (-z), N[(x + y), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -2.6000000000000001e74 or 5.7999999999999998e139 < y

   1. Initial program 76.1%

      \[\frac{x + y}{1 - \frac{y}{z}} \]
   2. Add Preprocessing

   3. Taylor expanded in y around inf 65.9%

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

      1. mul-1-neg 65.9%

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

   5. Simplified 65.9%

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

   if -2.6000000000000001e74 < y < 5.7999999999999998e139

   1. Initial program 98.3%

      \[\frac{x + y}{1 - \frac{y}{z}} \]
   2. Add Preprocessing

   3. Taylor expanded in z around inf 72.7%

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

      1. +-commutative 72.7%

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

   5. Simplified 72.7%

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

4. Final simplification 70.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -2.6 \cdot 10^{+74} \lor \neg \left(y \leq 5.8 \cdot 10^{+139}\right):\\ \;\;\;\;-z\\ \mathbf{else}:\\ \;\;\;\;x + y\\ \end{array} \]
5. Add Preprocessing

Alternative 8: 41.0% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -3.3 \cdot 10^{-134}:\\ \;\;\;\;x\\ \mathbf{elif}\;x \leq 2.3 \cdot 10^{-68}:\\ \;\;\;\;y\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= x -3.3e-134) x (if (<= x 2.3e-68) y x)))

C

double code(double x, double y, double z) {
	double tmp;
	if (x <= -3.3e-134) {
		tmp = x;
	} else if (x <= 2.3e-68) {
		tmp = y;
	} else {
		tmp = x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (x <= (-3.3d-134)) then
        tmp = x
    else if (x <= 2.3d-68) then
        tmp = y
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (x <= -3.3e-134) {
		tmp = x;
	} else if (x <= 2.3e-68) {
		tmp = y;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if x <= -3.3e-134:
		tmp = x
	elif x <= 2.3e-68:
		tmp = y
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (x <= -3.3e-134)
		tmp = x;
	elseif (x <= 2.3e-68)
		tmp = y;
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (x <= -3.3e-134)
		tmp = x;
	elseif (x <= 2.3e-68)
		tmp = y;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[x, -3.3e-134], x, If[LessEqual[x, 2.3e-68], y, x]]

Derivation

1. Split input into 2 regimes

2. if x < -3.30000000000000019e-134 or 2.29999999999999997e-68 < x

   1. Initial program 90.2%

      \[\frac{x + y}{1 - \frac{y}{z}} \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0 49.1%

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

   if -3.30000000000000019e-134 < x < 2.29999999999999997e-68

   1. Initial program 92.8%

      \[\frac{x + y}{1 - \frac{y}{z}} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0 78.6%

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

   4. Taylor expanded in y around 0 38.5%

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

4. Final simplification 45.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -3.3 \cdot 10^{-134}:\\ \;\;\;\;x\\ \mathbf{elif}\;x \leq 2.3 \cdot 10^{-68}:\\ \;\;\;\;y\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]
5. Add Preprocessing

Alternative 9: 34.8% accurate, 9.0× speedup

Math

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

FPCore

(FPCore (x y z) :precision binary64 x)

C

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

Fortran

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

Java

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

Python

def code(x, y, z):
	return x

Julia

function code(x, y, z)
	return x
end

MATLAB

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

Wolfram

code[x_, y_, z_] := x

Derivation

1. Initial program 91.1%

   \[\frac{x + y}{1 - \frac{y}{z}} \]
2. Add Preprocessing

3. Taylor expanded in y around 0 36.5%

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

4. Final simplification 36.5%

   \[\leadsto x \]
5. Add Preprocessing

Developer target: 93.9% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{y + x}{-y} \cdot z\\ \mathbf{if}\;y < -3.7429310762689856 \cdot 10^{+171}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;y < 3.5534662456086734 \cdot 10^{+168}:\\ \;\;\;\;\frac{x + y}{1 - \frac{y}{z}}\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* (/ (+ y x) (- y)) z)))
   (if (< y -3.7429310762689856e+171)
     t_0
     (if (< y 3.5534662456086734e+168) (/ (+ x y) (- 1.0 (/ y z))) t_0))))

C

double code(double x, double y, double z) {
	double t_0 = ((y + x) / -y) * z;
	double tmp;
	if (y < -3.7429310762689856e+171) {
		tmp = t_0;
	} else if (y < 3.5534662456086734e+168) {
		tmp = (x + y) / (1.0 - (y / z));
	} else {
		tmp = t_0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: tmp
    t_0 = ((y + x) / -y) * z
    if (y < (-3.7429310762689856d+171)) then
        tmp = t_0
    else if (y < 3.5534662456086734d+168) then
        tmp = (x + y) / (1.0d0 - (y / z))
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = ((y + x) / -y) * z;
	double tmp;
	if (y < -3.7429310762689856e+171) {
		tmp = t_0;
	} else if (y < 3.5534662456086734e+168) {
		tmp = (x + y) / (1.0 - (y / z));
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = ((y + x) / -y) * z
	tmp = 0
	if y < -3.7429310762689856e+171:
		tmp = t_0
	elif y < 3.5534662456086734e+168:
		tmp = (x + y) / (1.0 - (y / z))
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(Float64(Float64(y + x) / Float64(-y)) * z)
	tmp = 0.0
	if (y < -3.7429310762689856e+171)
		tmp = t_0;
	elseif (y < 3.5534662456086734e+168)
		tmp = Float64(Float64(x + y) / Float64(1.0 - Float64(y / z)));
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = ((y + x) / -y) * z;
	tmp = 0.0;
	if (y < -3.7429310762689856e+171)
		tmp = t_0;
	elseif (y < 3.5534662456086734e+168)
		tmp = (x + y) / (1.0 - (y / z));
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(N[(y + x), $MachinePrecision] / (-y)), $MachinePrecision] * z), $MachinePrecision]}, If[Less[y, -3.7429310762689856e+171], t$95$0, If[Less[y, 3.5534662456086734e+168], N[(N[(x + y), $MachinePrecision] / N[(1.0 - N[(y / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$0]]]

Reproduce

herbie shell --seed 2024067 
(FPCore (x y z)
  :name "Graphics.Rendering.Chart.Backend.Diagrams:calcFontMetrics from Chart-diagrams-1.5.1, A"
  :precision binary64

  :alt
  (if (< y -3.7429310762689856e+171) (* (/ (+ y x) (- y)) z) (if (< y 3.5534662456086734e+168) (/ (+ x y) (- 1.0 (/ y z))) (* (/ (+ y x) (- y)) z)))

  (/ (+ x y) (- 1.0 (/ y z))))