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

Percentage Accurate: 87.9% → 99.8%

Time: 8.0s

Alternatives: 7

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: 87.9% 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: 99.8% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{x + y}{1 - \frac{y}{z}}\\ \mathbf{if}\;t\_0 \leq -1 \cdot 10^{-291} \lor \neg \left(t\_0 \leq 0\right):\\ \;\;\;\;t\_0\\ \mathbf{else}:\\ \;\;\;\;z \cdot \left(-1 - \frac{x}{y}\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (/ (+ x y) (- 1.0 (/ y z)))))
   (if (or (<= t_0 -1e-291) (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-291) || !(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-291)) .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-291) || !(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-291) 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-291) || !(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-291) || ~((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-291], 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 1 (/.f64 y z))) < -9.99999999999999962e-292 or -0.0 < (/.f64 (+.f64 x y) (-.f64 1 (/.f64 y z)))

   1. Initial program 99.9%

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

   if -9.99999999999999962e-292 < (/.f64 (+.f64 x y) (-.f64 1 (/.f64 y z))) < -0.0

   1. Initial program 6.1%

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

   3. Taylor expanded in y around inf 6.1%

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

      1. neg-mul-1 6.1%

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

      2. distribute-neg-frac2 6.1%

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

   5. Simplified 6.1%

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

   6. Taylor expanded in x around 0 99.9%

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

      1. mul-1-neg 99.9%

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

      2. unsub-neg 99.9%

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

      3. mul-1-neg 99.9%

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

      4. *-commutative 99.9%

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

      5. associate-/l* 99.9%

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

   8. Simplified 99.9%

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

   9. Taylor expanded in z around 0 99.9%

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

       1. *-commutative 99.9%

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

       2. associate-*l* 99.9%

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

       3. +-commutative 99.9%

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

       4. distribute-rgt1-in 99.9%

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

       5. *-commutative 99.9%

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

       6. mul-1-neg 99.9%

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

       7. sub-neg 99.9%

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

   11. 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%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{x + y}{1 - \frac{y}{z}} \leq -1 \cdot 10^{-291} \lor \neg \left(\frac{x + y}{1 - \frac{y}{z}} \leq 0\right):\\ \;\;\;\;\frac{x + y}{1 - \frac{y}{z}}\\ \mathbf{else}:\\ \;\;\;\;z \cdot \left(-1 - \frac{x}{y}\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 2: 71.7% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 1 - \frac{y}{z}\\ \mathbf{if}\;z \leq -1.05 \cdot 10^{+16}:\\ \;\;\;\;x + y\\ \mathbf{elif}\;z \leq -1.6 \cdot 10^{-20}:\\ \;\;\;\;\frac{y}{t\_0}\\ \mathbf{elif}\;z \leq -1.15 \cdot 10^{-128}:\\ \;\;\;\;\frac{x}{t\_0}\\ \mathbf{elif}\;z \leq 3 \cdot 10^{+47}:\\ \;\;\;\;z \cdot \left(-1 - \frac{x}{y}\right)\\ \mathbf{else}:\\ \;\;\;\;x + y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (- 1.0 (/ y z))))
   (if (<= z -1.05e+16)
     (+ x y)
     (if (<= z -1.6e-20)
       (/ y t_0)
       (if (<= z -1.15e-128)
         (/ x t_0)
         (if (<= z 3e+47) (* z (- -1.0 (/ x y))) (+ x y)))))))

C

double code(double x, double y, double z) {
	double t_0 = 1.0 - (y / z);
	double tmp;
	if (z <= -1.05e+16) {
		tmp = x + y;
	} else if (z <= -1.6e-20) {
		tmp = y / t_0;
	} else if (z <= -1.15e-128) {
		tmp = x / t_0;
	} else if (z <= 3e+47) {
		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) :: t_0
    real(8) :: tmp
    t_0 = 1.0d0 - (y / z)
    if (z <= (-1.05d+16)) then
        tmp = x + y
    else if (z <= (-1.6d-20)) then
        tmp = y / t_0
    else if (z <= (-1.15d-128)) then
        tmp = x / t_0
    else if (z <= 3d+47) 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 t_0 = 1.0 - (y / z);
	double tmp;
	if (z <= -1.05e+16) {
		tmp = x + y;
	} else if (z <= -1.6e-20) {
		tmp = y / t_0;
	} else if (z <= -1.15e-128) {
		tmp = x / t_0;
	} else if (z <= 3e+47) {
		tmp = z * (-1.0 - (x / y));
	} else {
		tmp = x + y;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = 1.0 - (y / z)
	tmp = 0
	if z <= -1.05e+16:
		tmp = x + y
	elif z <= -1.6e-20:
		tmp = y / t_0
	elif z <= -1.15e-128:
		tmp = x / t_0
	elif z <= 3e+47:
		tmp = z * (-1.0 - (x / y))
	else:
		tmp = x + y
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(1.0 - Float64(y / z))
	tmp = 0.0
	if (z <= -1.05e+16)
		tmp = Float64(x + y);
	elseif (z <= -1.6e-20)
		tmp = Float64(y / t_0);
	elseif (z <= -1.15e-128)
		tmp = Float64(x / t_0);
	elseif (z <= 3e+47)
		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)
	t_0 = 1.0 - (y / z);
	tmp = 0.0;
	if (z <= -1.05e+16)
		tmp = x + y;
	elseif (z <= -1.6e-20)
		tmp = y / t_0;
	elseif (z <= -1.15e-128)
		tmp = x / t_0;
	elseif (z <= 3e+47)
		tmp = z * (-1.0 - (x / y));
	else
		tmp = 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[z, -1.05e+16], N[(x + y), $MachinePrecision], If[LessEqual[z, -1.6e-20], N[(y / t$95$0), $MachinePrecision], If[LessEqual[z, -1.15e-128], N[(x / t$95$0), $MachinePrecision], If[LessEqual[z, 3e+47], N[(z * N[(-1.0 - N[(x / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + y), $MachinePrecision]]]]]]

Derivation

1. Split input into 4 regimes

2. if z < -1.05e16 or 3.0000000000000001e47 < z

   1. Initial program 99.9%

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

   3. Taylor expanded in z around inf 88.8%

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

      1. +-commutative 88.8%

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

   5. Simplified 88.8%

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

   if -1.05e16 < z < -1.59999999999999985e-20

   1. Initial program 99.9%

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

   3. Taylor expanded in x around 0 99.9%

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

   if -1.59999999999999985e-20 < z < -1.15e-128

   1. Initial program 95.3%

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

   3. Taylor expanded in x around inf 60.8%

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

   if -1.15e-128 < z < 3.0000000000000001e47

   1. Initial program 71.7%

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

   3. Taylor expanded in y around inf 48.0%

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

      1. neg-mul-1 48.0%

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

      2. distribute-neg-frac2 48.0%

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

   5. Simplified 48.0%

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

   6. Taylor expanded in x around 0 72.4%

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

      1. mul-1-neg 72.4%

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

      2. unsub-neg 72.4%

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

      3. mul-1-neg 72.4%

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

      4. *-commutative 72.4%

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

      5. associate-/l* 72.2%

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

   8. Simplified 72.2%

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

   9. Taylor expanded in z around 0 72.2%

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

       1. *-commutative 72.2%

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

       2. associate-*l* 72.2%

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

       3. +-commutative 72.2%

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

       4. distribute-rgt1-in 72.2%

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

       5. *-commutative 72.2%

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

       6. mul-1-neg 72.2%

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

       7. sub-neg 72.2%

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

   11. Simplified 72.2%

       \[\leadsto \color{blue}{z \cdot \left(-1 - \frac{x}{y}\right)} \]
3. Recombined 4 regimes into one program.

4. Final simplification 80.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -1.05 \cdot 10^{+16}:\\ \;\;\;\;x + y\\ \mathbf{elif}\;z \leq -1.6 \cdot 10^{-20}:\\ \;\;\;\;\frac{y}{1 - \frac{y}{z}}\\ \mathbf{elif}\;z \leq -1.15 \cdot 10^{-128}:\\ \;\;\;\;\frac{x}{1 - \frac{y}{z}}\\ \mathbf{elif}\;z \leq 3 \cdot 10^{+47}:\\ \;\;\;\;z \cdot \left(-1 - \frac{x}{y}\right)\\ \mathbf{else}:\\ \;\;\;\;x + y\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 70.8% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -1.4 \cdot 10^{+16}:\\ \;\;\;\;x + y\\ \mathbf{elif}\;z \leq -5 \cdot 10^{-24}:\\ \;\;\;\;\frac{y}{1 - \frac{y}{z}}\\ \mathbf{elif}\;z \leq -3.4 \cdot 10^{-170}:\\ \;\;\;\;x \cdot \frac{-1}{\frac{y}{z} + -1}\\ \mathbf{elif}\;z \leq 1.8 \cdot 10^{+47}:\\ \;\;\;\;z \cdot \left(-1 - \frac{x}{y}\right)\\ \mathbf{else}:\\ \;\;\;\;x + y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= z -1.4e+16)
   (+ x y)
   (if (<= z -5e-24)
     (/ y (- 1.0 (/ y z)))
     (if (<= z -3.4e-170)
       (* x (/ -1.0 (+ (/ y z) -1.0)))
       (if (<= z 1.8e+47) (* z (- -1.0 (/ x y))) (+ x y))))))

C

double code(double x, double y, double z) {
	double tmp;
	if (z <= -1.4e+16) {
		tmp = x + y;
	} else if (z <= -5e-24) {
		tmp = y / (1.0 - (y / z));
	} else if (z <= -3.4e-170) {
		tmp = x * (-1.0 / ((y / z) + -1.0));
	} else if (z <= 1.8e+47) {
		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 (z <= (-1.4d+16)) then
        tmp = x + y
    else if (z <= (-5d-24)) then
        tmp = y / (1.0d0 - (y / z))
    else if (z <= (-3.4d-170)) then
        tmp = x * ((-1.0d0) / ((y / z) + (-1.0d0)))
    else if (z <= 1.8d+47) 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 (z <= -1.4e+16) {
		tmp = x + y;
	} else if (z <= -5e-24) {
		tmp = y / (1.0 - (y / z));
	} else if (z <= -3.4e-170) {
		tmp = x * (-1.0 / ((y / z) + -1.0));
	} else if (z <= 1.8e+47) {
		tmp = z * (-1.0 - (x / y));
	} else {
		tmp = x + y;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if z <= -1.4e+16:
		tmp = x + y
	elif z <= -5e-24:
		tmp = y / (1.0 - (y / z))
	elif z <= -3.4e-170:
		tmp = x * (-1.0 / ((y / z) + -1.0))
	elif z <= 1.8e+47:
		tmp = z * (-1.0 - (x / y))
	else:
		tmp = x + y
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (z <= -1.4e+16)
		tmp = Float64(x + y);
	elseif (z <= -5e-24)
		tmp = Float64(y / Float64(1.0 - Float64(y / z)));
	elseif (z <= -3.4e-170)
		tmp = Float64(x * Float64(-1.0 / Float64(Float64(y / z) + -1.0)));
	elseif (z <= 1.8e+47)
		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 (z <= -1.4e+16)
		tmp = x + y;
	elseif (z <= -5e-24)
		tmp = y / (1.0 - (y / z));
	elseif (z <= -3.4e-170)
		tmp = x * (-1.0 / ((y / z) + -1.0));
	elseif (z <= 1.8e+47)
		tmp = z * (-1.0 - (x / y));
	else
		tmp = x + y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[z, -1.4e+16], N[(x + y), $MachinePrecision], If[LessEqual[z, -5e-24], N[(y / N[(1.0 - N[(y / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, -3.4e-170], N[(x * N[(-1.0 / N[(N[(y / z), $MachinePrecision] + -1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 1.8e+47], N[(z * N[(-1.0 - N[(x / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + y), $MachinePrecision]]]]]

Derivation

1. Split input into 4 regimes

2. if z < -1.4e16 or 1.80000000000000004e47 < z

   1. Initial program 99.9%

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

   3. Taylor expanded in z around inf 88.8%

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

      1. +-commutative 88.8%

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

   5. Simplified 88.8%

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

   if -1.4e16 < z < -4.9999999999999998e-24

   1. Initial program 99.9%

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

   3. Taylor expanded in x around 0 99.9%

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

   if -4.9999999999999998e-24 < z < -3.40000000000000013e-170

   1. Initial program 95.8%

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

   3. Taylor expanded in x around inf 61.6%

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

      1. *-un-lft-identity 61.6%

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

      2. associate-*l/ 61.7%

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

      3. frac-2neg 61.7%

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

      4. metadata-eval 61.7%

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

      5. sub-neg 61.7%

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

      6. distribute-frac-neg2 61.7%

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

      7. distribute-neg-in 61.7%

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

      8. metadata-eval 61.7%

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

      9. distribute-frac-neg2 61.7%

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

      10. remove-double-neg 61.7%

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

   5. Applied egg-rr 61.7%

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

   if -3.40000000000000013e-170 < z < 1.80000000000000004e47

   1. Initial program 70.8%

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

   3. Taylor expanded in y around inf 47.4%

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

      1. neg-mul-1 47.4%

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

      2. distribute-neg-frac2 47.4%

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

   5. Simplified 47.4%

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

   6. Taylor expanded in x around 0 72.5%

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

      1. mul-1-neg 72.5%

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

      2. unsub-neg 72.5%

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

      3. mul-1-neg 72.5%

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

      4. *-commutative 72.5%

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

      5. associate-/l* 72.3%

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

   8. Simplified 72.3%

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

   9. Taylor expanded in z around 0 72.3%

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

       1. *-commutative 72.3%

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

       2. associate-*l* 72.3%

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

       3. +-commutative 72.3%

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

       4. distribute-rgt1-in 72.3%

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

       5. *-commutative 72.3%

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

       6. mul-1-neg 72.3%

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

       7. sub-neg 72.3%

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

   11. Simplified 72.3%

       \[\leadsto \color{blue}{z \cdot \left(-1 - \frac{x}{y}\right)} \]
3. Recombined 4 regimes into one program.

4. Final simplification 80.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -1.4 \cdot 10^{+16}:\\ \;\;\;\;x + y\\ \mathbf{elif}\;z \leq -5 \cdot 10^{-24}:\\ \;\;\;\;\frac{y}{1 - \frac{y}{z}}\\ \mathbf{elif}\;z \leq -3.4 \cdot 10^{-170}:\\ \;\;\;\;x \cdot \frac{-1}{\frac{y}{z} + -1}\\ \mathbf{elif}\;z \leq 1.8 \cdot 10^{+47}:\\ \;\;\;\;z \cdot \left(-1 - \frac{x}{y}\right)\\ \mathbf{else}:\\ \;\;\;\;x + y\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 71.4% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -4.8 \cdot 10^{+17} \lor \neg \left(z \leq 1.1 \cdot 10^{+49}\right):\\ \;\;\;\;x + y\\ \mathbf{else}:\\ \;\;\;\;z \cdot \left(-1 - \frac{x}{y}\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= z -4.8e+17) (not (<= z 1.1e+49))) (+ x y) (* z (- -1.0 (/ x y)))))

C

double code(double x, double y, double z) {
	double tmp;
	if ((z <= -4.8e+17) || !(z <= 1.1e+49)) {
		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) :: tmp
    if ((z <= (-4.8d+17)) .or. (.not. (z <= 1.1d+49))) 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 tmp;
	if ((z <= -4.8e+17) || !(z <= 1.1e+49)) {
		tmp = x + y;
	} else {
		tmp = z * (-1.0 - (x / y));
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (z <= -4.8e+17) or not (z <= 1.1e+49):
		tmp = x + y
	else:
		tmp = z * (-1.0 - (x / y))
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((z <= -4.8e+17) || !(z <= 1.1e+49))
		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)
	tmp = 0.0;
	if ((z <= -4.8e+17) || ~((z <= 1.1e+49)))
		tmp = x + y;
	else
		tmp = z * (-1.0 - (x / y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[z, -4.8e+17], N[Not[LessEqual[z, 1.1e+49]], $MachinePrecision]], N[(x + y), $MachinePrecision], N[(z * N[(-1.0 - N[(x / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if z < -4.8e17 or 1.1e49 < z

   1. Initial program 99.9%

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

   3. Taylor expanded in z around inf 88.8%

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

      1. +-commutative 88.8%

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

   5. Simplified 88.8%

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

   if -4.8e17 < z < 1.1e49

   1. Initial program 77.9%

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

   3. Taylor expanded in y around inf 49.5%

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

      1. neg-mul-1 49.5%

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

      2. distribute-neg-frac2 49.5%

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

   5. Simplified 49.5%

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

   6. Taylor expanded in x around 0 68.6%

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

      1. mul-1-neg 68.6%

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

      2. unsub-neg 68.6%

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

      3. mul-1-neg 68.6%

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

      4. *-commutative 68.6%

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

      5. associate-/l* 68.4%

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

   8. Simplified 68.4%

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

   9. Taylor expanded in z around 0 68.4%

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

       1. *-commutative 68.4%

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

       2. associate-*l* 68.4%

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

       3. +-commutative 68.4%

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

       4. distribute-rgt1-in 68.4%

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

       5. *-commutative 68.4%

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

       6. mul-1-neg 68.4%

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

       7. sub-neg 68.4%

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

   11. Simplified 68.4%

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

4. Final simplification 78.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -4.8 \cdot 10^{+17} \lor \neg \left(z \leq 1.1 \cdot 10^{+49}\right):\\ \;\;\;\;x + y\\ \mathbf{else}:\\ \;\;\;\;z \cdot \left(-1 - \frac{x}{y}\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 67.0% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -8.5 \cdot 10^{+153} \lor \neg \left(y \leq 3.4 \cdot 10^{+99}\right):\\ \;\;\;\;-z\\ \mathbf{else}:\\ \;\;\;\;x + y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= y -8.5e+153) (not (<= y 3.4e+99))) (- z) (+ x y)))

C

double code(double x, double y, double z) {
	double tmp;
	if ((y <= -8.5e+153) || !(y <= 3.4e+99)) {
		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 <= (-8.5d+153)) .or. (.not. (y <= 3.4d+99))) 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 <= -8.5e+153) || !(y <= 3.4e+99)) {
		tmp = -z;
	} else {
		tmp = x + y;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (y <= -8.5e+153) or not (y <= 3.4e+99):
		tmp = -z
	else:
		tmp = x + y
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((y <= -8.5e+153) || !(y <= 3.4e+99))
		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 <= -8.5e+153) || ~((y <= 3.4e+99)))
		tmp = -z;
	else
		tmp = x + y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[y, -8.5e+153], N[Not[LessEqual[y, 3.4e+99]], $MachinePrecision]], (-z), N[(x + y), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -8.49999999999999935e153 or 3.39999999999999984e99 < y

   1. Initial program 64.5%

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

   3. Taylor expanded in y around inf 73.3%

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

      1. mul-1-neg 73.3%

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

   5. Simplified 73.3%

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

   if -8.49999999999999935e153 < y < 3.39999999999999984e99

   1. Initial program 96.5%

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

   3. Taylor expanded in z around inf 71.9%

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

      1. +-commutative 71.9%

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

   5. Simplified 71.9%

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

4. Final simplification 72.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -8.5 \cdot 10^{+153} \lor \neg \left(y \leq 3.4 \cdot 10^{+99}\right):\\ \;\;\;\;-z\\ \mathbf{else}:\\ \;\;\;\;x + y\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 58.9% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -350000000 \lor \neg \left(y \leq 1.9 \cdot 10^{+19}\right):\\ \;\;\;\;-z\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= y -350000000.0) (not (<= y 1.9e+19))) (- z) x))

C

double code(double x, double y, double z) {
	double tmp;
	if ((y <= -350000000.0) || !(y <= 1.9e+19)) {
		tmp = -z;
	} 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 ((y <= (-350000000.0d0)) .or. (.not. (y <= 1.9d+19))) then
        tmp = -z
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((y <= -350000000.0) || !(y <= 1.9e+19)) {
		tmp = -z;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (y <= -350000000.0) or not (y <= 1.9e+19):
		tmp = -z
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((y <= -350000000.0) || !(y <= 1.9e+19))
		tmp = Float64(-z);
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((y <= -350000000.0) || ~((y <= 1.9e+19)))
		tmp = -z;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[y, -350000000.0], N[Not[LessEqual[y, 1.9e+19]], $MachinePrecision]], (-z), x]

Derivation

1. Split input into 2 regimes

2. if y < -3.5e8 or 1.9e19 < y

   1. Initial program 75.2%

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

   3. Taylor expanded in y around inf 55.4%

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

      1. mul-1-neg 55.4%

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

   5. Simplified 55.4%

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

   if -3.5e8 < y < 1.9e19

   1. Initial program 99.9%

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

   3. Taylor expanded in y around 0 61.4%

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

4. Final simplification 58.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -350000000 \lor \neg \left(y \leq 1.9 \cdot 10^{+19}\right):\\ \;\;\;\;-z\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]
5. Add Preprocessing

Alternative 7: 34.9% 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 88.5%

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

3. Taylor expanded in y around 0 37.0%

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

4. Final simplification 37.0%

   \[\leadsto x \]
5. Add Preprocessing

Developer target: 93.6% 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 2024044 
(FPCore (x y z)
  :name "Graphics.Rendering.Chart.Backend.Diagrams:calcFontMetrics from Chart-diagrams-1.5.1, A"
  :precision binary64

  :herbie-target
  (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))))