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

Percentage Accurate: 88.1% → 99.8%

Time: 6.3s

Alternatives: 10

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: 88.1% 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

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{x + y}{1 - \frac{y}{z}}\\ \mathbf{if}\;t_0 \leq -5 \cdot 10^{-299} \lor \neg \left(t_0 \leq 0\right):\\ \;\;\;\;t_0\\ \mathbf{else}:\\ \;\;\;\;\frac{z \cdot \left(\left(-y\right) - x\right)}{y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (/ (+ x y) (- 1.0 (/ y z)))))
   (if (or (<= t_0 -5e-299) (not (<= t_0 0.0))) t_0 (/ (* z (- (- y) 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 <= -5e-299) || !(t_0 <= 0.0)) {
		tmp = t_0;
	} else {
		tmp = (z * (-y - 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 <= (-5d-299)) .or. (.not. (t_0 <= 0.0d0))) then
        tmp = t_0
    else
        tmp = (z * (-y - 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 <= -5e-299) || !(t_0 <= 0.0)) {
		tmp = t_0;
	} else {
		tmp = (z * (-y - x)) / y;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = (x + y) / (1.0 - (y / z))
	tmp = 0
	if (t_0 <= -5e-299) or not (t_0 <= 0.0):
		tmp = t_0
	else:
		tmp = (z * (-y - 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 <= -5e-299) || !(t_0 <= 0.0))
		tmp = t_0;
	else
		tmp = Float64(Float64(z * Float64(Float64(-y) - 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 <= -5e-299) || ~((t_0 <= 0.0)))
		tmp = t_0;
	else
		tmp = (z * (-y - 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, -5e-299], N[Not[LessEqual[t$95$0, 0.0]], $MachinePrecision]], t$95$0, N[(N[(z * N[((-y) - x), $MachinePrecision]), $MachinePrecision] / y), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (+.f64 x y) (-.f64 1 (/.f64 y z))) < -4.99999999999999956e-299 or 0.0 < (/.f64 (+.f64 x y) (-.f64 1 (/.f64 y z)))

   1. Initial program 99.9%

      \[\frac{x + y}{1 - \frac{y}{z}} \]

   if -4.99999999999999956e-299 < (/.f64 (+.f64 x y) (-.f64 1 (/.f64 y z))) < 0.0

   1. Initial program 6.3%

      \[\frac{x + y}{1 - \frac{y}{z}} \]

   2. Taylor expanded in z around 0 99.9%

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

4. Final simplification 99.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{x + y}{1 - \frac{y}{z}} \leq -5 \cdot 10^{-299} \lor \neg \left(\frac{x + y}{1 - \frac{y}{z}} \leq 0\right):\\ \;\;\;\;\frac{x + y}{1 - \frac{y}{z}}\\ \mathbf{else}:\\ \;\;\;\;\frac{z \cdot \left(\left(-y\right) - x\right)}{y}\\ \end{array} \]

Alternative 2: 66.1% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.9 \cdot 10^{+147}:\\ \;\;\;\;-z\\ \mathbf{elif}\;y \leq -1 \cdot 10^{+102}:\\ \;\;\;\;x + y\\ \mathbf{elif}\;y \leq -1.15 \cdot 10^{+48}:\\ \;\;\;\;z \cdot \left(1 - \frac{x}{y}\right)\\ \mathbf{elif}\;y \leq -8.5 \cdot 10^{+41}:\\ \;\;\;\;y \cdot \left(1 + \frac{y}{z}\right)\\ \mathbf{elif}\;y \leq 1.1 \cdot 10^{+120}:\\ \;\;\;\;\frac{x}{1 - \frac{y}{z}}\\ \mathbf{else}:\\ \;\;\;\;-z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y -1.9e+147)
   (- z)
   (if (<= y -1e+102)
     (+ x y)
     (if (<= y -1.15e+48)
       (* z (- 1.0 (/ x y)))
       (if (<= y -8.5e+41)
         (* y (+ 1.0 (/ y z)))
         (if (<= y 1.1e+120) (/ x (- 1.0 (/ y z))) (- z)))))))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= -1.9e+147) {
		tmp = -z;
	} else if (y <= -1e+102) {
		tmp = x + y;
	} else if (y <= -1.15e+48) {
		tmp = z * (1.0 - (x / y));
	} else if (y <= -8.5e+41) {
		tmp = y * (1.0 + (y / z));
	} else if (y <= 1.1e+120) {
		tmp = x / (1.0 - (y / z));
	} 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 <= (-1.9d+147)) then
        tmp = -z
    else if (y <= (-1d+102)) then
        tmp = x + y
    else if (y <= (-1.15d+48)) then
        tmp = z * (1.0d0 - (x / y))
    else if (y <= (-8.5d+41)) then
        tmp = y * (1.0d0 + (y / z))
    else if (y <= 1.1d+120) then
        tmp = x / (1.0d0 - (y / z))
    else
        tmp = -z
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (y <= -1.9e+147) {
		tmp = -z;
	} else if (y <= -1e+102) {
		tmp = x + y;
	} else if (y <= -1.15e+48) {
		tmp = z * (1.0 - (x / y));
	} else if (y <= -8.5e+41) {
		tmp = y * (1.0 + (y / z));
	} else if (y <= 1.1e+120) {
		tmp = x / (1.0 - (y / z));
	} else {
		tmp = -z;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if y <= -1.9e+147:
		tmp = -z
	elif y <= -1e+102:
		tmp = x + y
	elif y <= -1.15e+48:
		tmp = z * (1.0 - (x / y))
	elif y <= -8.5e+41:
		tmp = y * (1.0 + (y / z))
	elif y <= 1.1e+120:
		tmp = x / (1.0 - (y / z))
	else:
		tmp = -z
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= -1.9e+147)
		tmp = Float64(-z);
	elseif (y <= -1e+102)
		tmp = Float64(x + y);
	elseif (y <= -1.15e+48)
		tmp = Float64(z * Float64(1.0 - Float64(x / y)));
	elseif (y <= -8.5e+41)
		tmp = Float64(y * Float64(1.0 + Float64(y / z)));
	elseif (y <= 1.1e+120)
		tmp = Float64(x / Float64(1.0 - Float64(y / z)));
	else
		tmp = Float64(-z);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (y <= -1.9e+147)
		tmp = -z;
	elseif (y <= -1e+102)
		tmp = x + y;
	elseif (y <= -1.15e+48)
		tmp = z * (1.0 - (x / y));
	elseif (y <= -8.5e+41)
		tmp = y * (1.0 + (y / z));
	elseif (y <= 1.1e+120)
		tmp = x / (1.0 - (y / z));
	else
		tmp = -z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, -1.9e+147], (-z), If[LessEqual[y, -1e+102], N[(x + y), $MachinePrecision], If[LessEqual[y, -1.15e+48], N[(z * N[(1.0 - N[(x / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, -8.5e+41], N[(y * N[(1.0 + N[(y / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 1.1e+120], N[(x / N[(1.0 - N[(y / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], (-z)]]]]]

Derivation

1. Split input into 5 regimes

2. if y < -1.89999999999999985e147 or 1.1000000000000001e120 < y

   1. Initial program 69.0%

      \[\frac{x + y}{1 - \frac{y}{z}} \]

   2. Taylor expanded in y around inf 75.0%

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

      1. mul-1-neg 75.0%

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

   4. Simplified 75.0%

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

   if -1.89999999999999985e147 < y < -9.99999999999999977e101

   1. Initial program 99.9%

      \[\frac{x + y}{1 - \frac{y}{z}} \]

   2. Taylor expanded in z around inf 61.7%

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

      1. +-commutative 61.7%

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

   4. Simplified 61.7%

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

   if -9.99999999999999977e101 < y < -1.15e48

   1. Initial program 71.3%

      \[\frac{x + y}{1 - \frac{y}{z}} \]

   2. Taylor expanded in z around 0 80.5%

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

      1. mul-1-neg 80.5%

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

      2. associate-/l* 80.4%

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

      3. associate-/r/ 57.3%

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

      4. distribute-rgt-neg-in 57.3%

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

      5. +-commutative 57.3%

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

      6. distribute-neg-in 57.3%

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

      7. sub-neg 57.3%

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

   4. Simplified 57.3%

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

      1. sub-neg 57.3%

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

      2. distribute-lft-in 57.3%

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

      3. add-sqr-sqrt 57.3%

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

      4. sqrt-unprod 57.3%

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

      5. sqr-neg 57.3%

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

      6. sqrt-unprod 0.0%

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

      7. add-sqr-sqrt 39.8%

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

   6. Applied egg-rr 39.8%

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

      1. distribute-lft-out 39.8%

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

      2. sub-neg 39.8%

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

      3. associate-*l/ 53.6%

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

      4. associate-*r/ 53.7%

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

      5. div-sub 53.7%

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

      6. *-inverses 53.7%

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

   8. Simplified 53.7%

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

   if -1.15e48 < y < -8.49999999999999938e41

   1. Initial program 76.9%

      \[\frac{x + y}{1 - \frac{y}{z}} \]

   2. Taylor expanded in z around inf 75.4%

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

      1. associate-+r+ 75.4%

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

      2. *-lft-identity 75.4%

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

      3. associate-/l* 75.4%

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

      4. associate-/r/ 75.4%

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

      5. distribute-rgt-in 75.4%

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

      6. +-commutative 75.4%

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

   4. Simplified 75.4%

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

   5. Taylor expanded in x around 0 75.4%

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

   if -8.49999999999999938e41 < y < 1.1000000000000001e120

   1. Initial program 98.8%

      \[\frac{x + y}{1 - \frac{y}{z}} \]

   2. Taylor expanded in x around inf 73.6%

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

4. Final simplification 72.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1.9 \cdot 10^{+147}:\\ \;\;\;\;-z\\ \mathbf{elif}\;y \leq -1 \cdot 10^{+102}:\\ \;\;\;\;x + y\\ \mathbf{elif}\;y \leq -1.15 \cdot 10^{+48}:\\ \;\;\;\;z \cdot \left(1 - \frac{x}{y}\right)\\ \mathbf{elif}\;y \leq -8.5 \cdot 10^{+41}:\\ \;\;\;\;y \cdot \left(1 + \frac{y}{z}\right)\\ \mathbf{elif}\;y \leq 1.1 \cdot 10^{+120}:\\ \;\;\;\;\frac{x}{1 - \frac{y}{z}}\\ \mathbf{else}:\\ \;\;\;\;-z\\ \end{array} \]

Alternative 3: 73.8% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{-z}{\frac{y}{x + y}}\\ \mathbf{if}\;y \leq -1.8 \cdot 10^{+22}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;y \leq 2.25 \cdot 10^{-70}:\\ \;\;\;\;\frac{x}{1 - \frac{y}{z}}\\ \mathbf{elif}\;y \leq 4 \cdot 10^{+15}:\\ \;\;\;\;\left(x + y\right) \cdot \left(-\frac{z}{y}\right)\\ \mathbf{elif}\;y \leq 7.5 \cdot 10^{+56}:\\ \;\;\;\;x + y\\ \mathbf{else}:\\ \;\;\;\;t_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (/ (- z) (/ y (+ x y)))))
   (if (<= y -1.8e+22)
     t_0
     (if (<= y 2.25e-70)
       (/ x (- 1.0 (/ y z)))
       (if (<= y 4e+15)
         (* (+ x y) (- (/ z y)))
         (if (<= y 7.5e+56) (+ x y) t_0))))))

C

double code(double x, double y, double z) {
	double t_0 = -z / (y / (x + y));
	double tmp;
	if (y <= -1.8e+22) {
		tmp = t_0;
	} else if (y <= 2.25e-70) {
		tmp = x / (1.0 - (y / z));
	} else if (y <= 4e+15) {
		tmp = (x + y) * -(z / y);
	} else if (y <= 7.5e+56) {
		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 / (y / (x + y))
    if (y <= (-1.8d+22)) then
        tmp = t_0
    else if (y <= 2.25d-70) then
        tmp = x / (1.0d0 - (y / z))
    else if (y <= 4d+15) then
        tmp = (x + y) * -(z / y)
    else if (y <= 7.5d+56) 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 / (y / (x + y));
	double tmp;
	if (y <= -1.8e+22) {
		tmp = t_0;
	} else if (y <= 2.25e-70) {
		tmp = x / (1.0 - (y / z));
	} else if (y <= 4e+15) {
		tmp = (x + y) * -(z / y);
	} else if (y <= 7.5e+56) {
		tmp = x + y;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = -z / (y / (x + y))
	tmp = 0
	if y <= -1.8e+22:
		tmp = t_0
	elif y <= 2.25e-70:
		tmp = x / (1.0 - (y / z))
	elif y <= 4e+15:
		tmp = (x + y) * -(z / y)
	elif y <= 7.5e+56:
		tmp = x + y
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(Float64(-z) / Float64(y / Float64(x + y)))
	tmp = 0.0
	if (y <= -1.8e+22)
		tmp = t_0;
	elseif (y <= 2.25e-70)
		tmp = Float64(x / Float64(1.0 - Float64(y / z)));
	elseif (y <= 4e+15)
		tmp = Float64(Float64(x + y) * Float64(-Float64(z / y)));
	elseif (y <= 7.5e+56)
		tmp = Float64(x + y);
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = -z / (y / (x + y));
	tmp = 0.0;
	if (y <= -1.8e+22)
		tmp = t_0;
	elseif (y <= 2.25e-70)
		tmp = x / (1.0 - (y / z));
	elseif (y <= 4e+15)
		tmp = (x + y) * -(z / y);
	elseif (y <= 7.5e+56)
		tmp = x + y;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[((-z) / N[(y / N[(x + y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -1.8e+22], t$95$0, If[LessEqual[y, 2.25e-70], N[(x / N[(1.0 - N[(y / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 4e+15], N[(N[(x + y), $MachinePrecision] * (-N[(z / y), $MachinePrecision])), $MachinePrecision], If[LessEqual[y, 7.5e+56], N[(x + y), $MachinePrecision], t$95$0]]]]]

Derivation

1. Split input into 4 regimes

2. if y < -1.8e22 or 7.4999999999999999e56 < y

   1. Initial program 76.8%

      \[\frac{x + y}{1 - \frac{y}{z}} \]

   2. Taylor expanded in z around 0 60.4%

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

      1. mul-1-neg 60.4%

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

      2. associate-/l* 78.1%

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

      3. distribute-neg-frac 78.1%

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

      4. +-commutative 78.1%

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

   4. Simplified 78.1%

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

   if -1.8e22 < y < 2.25000000000000011e-70

   1. Initial program 99.9%

      \[\frac{x + y}{1 - \frac{y}{z}} \]

   2. Taylor expanded in x around inf 82.4%

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

   if 2.25000000000000011e-70 < y < 4e15

   1. Initial program 99.8%

      \[\frac{x + y}{1 - \frac{y}{z}} \]

   2. Taylor expanded in z around 0 66.9%

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

      1. mul-1-neg 66.9%

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

      2. associate-/l* 70.7%

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

      3. associate-/r/ 70.7%

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

      4. distribute-rgt-neg-in 70.7%

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

      5. +-commutative 70.7%

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

      6. distribute-neg-in 70.7%

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

      7. sub-neg 70.7%

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

   4. Simplified 70.7%

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

   if 4e15 < y < 7.4999999999999999e56

   1. Initial program 84.9%

      \[\frac{x + y}{1 - \frac{y}{z}} \]

   2. Taylor expanded in z around inf 75.8%

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

      1. +-commutative 75.8%

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

   4. Simplified 75.8%

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

4. Final simplification 79.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1.8 \cdot 10^{+22}:\\ \;\;\;\;\frac{-z}{\frac{y}{x + y}}\\ \mathbf{elif}\;y \leq 2.25 \cdot 10^{-70}:\\ \;\;\;\;\frac{x}{1 - \frac{y}{z}}\\ \mathbf{elif}\;y \leq 4 \cdot 10^{+15}:\\ \;\;\;\;\left(x + y\right) \cdot \left(-\frac{z}{y}\right)\\ \mathbf{elif}\;y \leq 7.5 \cdot 10^{+56}:\\ \;\;\;\;x + y\\ \mathbf{else}:\\ \;\;\;\;\frac{-z}{\frac{y}{x + y}}\\ \end{array} \]

Alternative 4: 67.2% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.9 \cdot 10^{+147}:\\ \;\;\;\;-z\\ \mathbf{elif}\;y \leq -2 \cdot 10^{+94}:\\ \;\;\;\;x + y\\ \mathbf{elif}\;y \leq -4.2 \cdot 10^{+49}:\\ \;\;\;\;-z \cdot \frac{x}{y}\\ \mathbf{elif}\;y \leq -2.4 \cdot 10^{+47}:\\ \;\;\;\;y \cdot \left(1 + \frac{y}{z}\right)\\ \mathbf{elif}\;y \leq 1.02 \cdot 10^{+115}:\\ \;\;\;\;x + y\\ \mathbf{else}:\\ \;\;\;\;-z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y -1.9e+147)
   (- z)
   (if (<= y -2e+94)
     (+ x y)
     (if (<= y -4.2e+49)
       (- (* z (/ x y)))
       (if (<= y -2.4e+47)
         (* y (+ 1.0 (/ y z)))
         (if (<= y 1.02e+115) (+ x y) (- z)))))))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= -1.9e+147) {
		tmp = -z;
	} else if (y <= -2e+94) {
		tmp = x + y;
	} else if (y <= -4.2e+49) {
		tmp = -(z * (x / y));
	} else if (y <= -2.4e+47) {
		tmp = y * (1.0 + (y / z));
	} else if (y <= 1.02e+115) {
		tmp = x + y;
	} 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 <= (-1.9d+147)) then
        tmp = -z
    else if (y <= (-2d+94)) then
        tmp = x + y
    else if (y <= (-4.2d+49)) then
        tmp = -(z * (x / y))
    else if (y <= (-2.4d+47)) then
        tmp = y * (1.0d0 + (y / z))
    else if (y <= 1.02d+115) then
        tmp = x + y
    else
        tmp = -z
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (y <= -1.9e+147) {
		tmp = -z;
	} else if (y <= -2e+94) {
		tmp = x + y;
	} else if (y <= -4.2e+49) {
		tmp = -(z * (x / y));
	} else if (y <= -2.4e+47) {
		tmp = y * (1.0 + (y / z));
	} else if (y <= 1.02e+115) {
		tmp = x + y;
	} else {
		tmp = -z;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if y <= -1.9e+147:
		tmp = -z
	elif y <= -2e+94:
		tmp = x + y
	elif y <= -4.2e+49:
		tmp = -(z * (x / y))
	elif y <= -2.4e+47:
		tmp = y * (1.0 + (y / z))
	elif y <= 1.02e+115:
		tmp = x + y
	else:
		tmp = -z
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= -1.9e+147)
		tmp = Float64(-z);
	elseif (y <= -2e+94)
		tmp = Float64(x + y);
	elseif (y <= -4.2e+49)
		tmp = Float64(-Float64(z * Float64(x / y)));
	elseif (y <= -2.4e+47)
		tmp = Float64(y * Float64(1.0 + Float64(y / z)));
	elseif (y <= 1.02e+115)
		tmp = Float64(x + y);
	else
		tmp = Float64(-z);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (y <= -1.9e+147)
		tmp = -z;
	elseif (y <= -2e+94)
		tmp = x + y;
	elseif (y <= -4.2e+49)
		tmp = -(z * (x / y));
	elseif (y <= -2.4e+47)
		tmp = y * (1.0 + (y / z));
	elseif (y <= 1.02e+115)
		tmp = x + y;
	else
		tmp = -z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, -1.9e+147], (-z), If[LessEqual[y, -2e+94], N[(x + y), $MachinePrecision], If[LessEqual[y, -4.2e+49], (-N[(z * N[(x / y), $MachinePrecision]), $MachinePrecision]), If[LessEqual[y, -2.4e+47], N[(y * N[(1.0 + N[(y / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 1.02e+115], N[(x + y), $MachinePrecision], (-z)]]]]]

Derivation

1. Split input into 4 regimes

2. if y < -1.89999999999999985e147 or 1.02e115 < y

   1. Initial program 70.6%

      \[\frac{x + y}{1 - \frac{y}{z}} \]

   2. Taylor expanded in y around inf 72.7%

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

      1. mul-1-neg 72.7%

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

   4. Simplified 72.7%

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

   if -1.89999999999999985e147 < y < -2e94 or -2.40000000000000019e47 < y < 1.02e115

   1. Initial program 98.3%

      \[\frac{x + y}{1 - \frac{y}{z}} \]

   2. Taylor expanded in z around inf 69.0%

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

      1. +-commutative 69.0%

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

   4. Simplified 69.0%

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

   if -2e94 < y < -4.20000000000000022e49

   1. Initial program 71.3%

      \[\frac{x + y}{1 - \frac{y}{z}} \]

   2. Taylor expanded in x around inf 33.3%

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

   3. Taylor expanded in y around inf 52.6%

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

      1. associate-*r/ 38.7%

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

      2. associate-*l* 38.7%

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

      3. neg-mul-1 38.7%

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

   5. Simplified 38.7%

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

   6. Taylor expanded in x around 0 52.6%

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

      1. neg-mul-1 52.6%

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

      2. associate-*l/ 52.6%

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

      3. *-commutative 52.6%

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

      4. distribute-rgt-neg-in 52.6%

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

      5. distribute-neg-frac 52.6%

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

   8. Simplified 52.6%

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

   if -4.20000000000000022e49 < y < -2.40000000000000019e47

   1. Initial program 99.2%

      \[\frac{x + y}{1 - \frac{y}{z}} \]

   2. Taylor expanded in z around inf 100.0%

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

      1. associate-+r+ 100.0%

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

      2. *-lft-identity 100.0%

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

      3. associate-/l* 100.0%

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

      4. associate-/r/ 100.0%

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

      5. distribute-rgt-in 100.0%

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

      6. +-commutative 100.0%

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

   4. Simplified 100.0%

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

   5. Taylor expanded in x around 0 100.0%

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

4. Final simplification 69.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1.9 \cdot 10^{+147}:\\ \;\;\;\;-z\\ \mathbf{elif}\;y \leq -2 \cdot 10^{+94}:\\ \;\;\;\;x + y\\ \mathbf{elif}\;y \leq -4.2 \cdot 10^{+49}:\\ \;\;\;\;-z \cdot \frac{x}{y}\\ \mathbf{elif}\;y \leq -2.4 \cdot 10^{+47}:\\ \;\;\;\;y \cdot \left(1 + \frac{y}{z}\right)\\ \mathbf{elif}\;y \leq 1.02 \cdot 10^{+115}:\\ \;\;\;\;x + y\\ \mathbf{else}:\\ \;\;\;\;-z\\ \end{array} \]

Alternative 5: 67.2% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -2.15 \cdot 10^{+147}:\\ \;\;\;\;-z\\ \mathbf{elif}\;y \leq -1.05 \cdot 10^{+95}:\\ \;\;\;\;x + y\\ \mathbf{elif}\;y \leq -3.1 \cdot 10^{+49}:\\ \;\;\;\;z \cdot \left(1 - \frac{x}{y}\right)\\ \mathbf{elif}\;y \leq -2.4 \cdot 10^{+47}:\\ \;\;\;\;y \cdot \left(1 + \frac{y}{z}\right)\\ \mathbf{elif}\;y \leq 1.95 \cdot 10^{+108}:\\ \;\;\;\;x + y\\ \mathbf{else}:\\ \;\;\;\;-z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y -2.15e+147)
   (- z)
   (if (<= y -1.05e+95)
     (+ x y)
     (if (<= y -3.1e+49)
       (* z (- 1.0 (/ x y)))
       (if (<= y -2.4e+47)
         (* y (+ 1.0 (/ y z)))
         (if (<= y 1.95e+108) (+ x y) (- z)))))))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= -2.15e+147) {
		tmp = -z;
	} else if (y <= -1.05e+95) {
		tmp = x + y;
	} else if (y <= -3.1e+49) {
		tmp = z * (1.0 - (x / y));
	} else if (y <= -2.4e+47) {
		tmp = y * (1.0 + (y / z));
	} else if (y <= 1.95e+108) {
		tmp = x + y;
	} 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 <= (-2.15d+147)) then
        tmp = -z
    else if (y <= (-1.05d+95)) then
        tmp = x + y
    else if (y <= (-3.1d+49)) then
        tmp = z * (1.0d0 - (x / y))
    else if (y <= (-2.4d+47)) then
        tmp = y * (1.0d0 + (y / z))
    else if (y <= 1.95d+108) then
        tmp = x + y
    else
        tmp = -z
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (y <= -2.15e+147) {
		tmp = -z;
	} else if (y <= -1.05e+95) {
		tmp = x + y;
	} else if (y <= -3.1e+49) {
		tmp = z * (1.0 - (x / y));
	} else if (y <= -2.4e+47) {
		tmp = y * (1.0 + (y / z));
	} else if (y <= 1.95e+108) {
		tmp = x + y;
	} else {
		tmp = -z;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if y <= -2.15e+147:
		tmp = -z
	elif y <= -1.05e+95:
		tmp = x + y
	elif y <= -3.1e+49:
		tmp = z * (1.0 - (x / y))
	elif y <= -2.4e+47:
		tmp = y * (1.0 + (y / z))
	elif y <= 1.95e+108:
		tmp = x + y
	else:
		tmp = -z
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= -2.15e+147)
		tmp = Float64(-z);
	elseif (y <= -1.05e+95)
		tmp = Float64(x + y);
	elseif (y <= -3.1e+49)
		tmp = Float64(z * Float64(1.0 - Float64(x / y)));
	elseif (y <= -2.4e+47)
		tmp = Float64(y * Float64(1.0 + Float64(y / z)));
	elseif (y <= 1.95e+108)
		tmp = Float64(x + y);
	else
		tmp = Float64(-z);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (y <= -2.15e+147)
		tmp = -z;
	elseif (y <= -1.05e+95)
		tmp = x + y;
	elseif (y <= -3.1e+49)
		tmp = z * (1.0 - (x / y));
	elseif (y <= -2.4e+47)
		tmp = y * (1.0 + (y / z));
	elseif (y <= 1.95e+108)
		tmp = x + y;
	else
		tmp = -z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, -2.15e+147], (-z), If[LessEqual[y, -1.05e+95], N[(x + y), $MachinePrecision], If[LessEqual[y, -3.1e+49], N[(z * N[(1.0 - N[(x / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, -2.4e+47], N[(y * N[(1.0 + N[(y / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 1.95e+108], N[(x + y), $MachinePrecision], (-z)]]]]]

Derivation

1. Split input into 4 regimes

2. if y < -2.1499999999999999e147 or 1.94999999999999992e108 < y

   1. Initial program 70.6%

      \[\frac{x + y}{1 - \frac{y}{z}} \]

   2. Taylor expanded in y around inf 72.7%

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

      1. mul-1-neg 72.7%

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

   4. Simplified 72.7%

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

   if -2.1499999999999999e147 < y < -1.05e95 or -2.40000000000000019e47 < y < 1.94999999999999992e108

   1. Initial program 98.3%

      \[\frac{x + y}{1 - \frac{y}{z}} \]

   2. Taylor expanded in z around inf 69.0%

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

      1. +-commutative 69.0%

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

   4. Simplified 69.0%

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

   if -1.05e95 < y < -3.09999999999999992e49

   1. Initial program 71.3%

      \[\frac{x + y}{1 - \frac{y}{z}} \]

   2. Taylor expanded in z around 0 80.5%

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

      1. mul-1-neg 80.5%

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

      2. associate-/l* 80.4%

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

      3. associate-/r/ 57.3%

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

      4. distribute-rgt-neg-in 57.3%

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

      5. +-commutative 57.3%

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

      6. distribute-neg-in 57.3%

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

      7. sub-neg 57.3%

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

   4. Simplified 57.3%

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

      1. sub-neg 57.3%

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

      2. distribute-lft-in 57.3%

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

      3. add-sqr-sqrt 57.3%

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

      4. sqrt-unprod 57.3%

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

      5. sqr-neg 57.3%

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

      6. sqrt-unprod 0.0%

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

      7. add-sqr-sqrt 39.8%

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

   6. Applied egg-rr 39.8%

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

      1. distribute-lft-out 39.8%

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

      2. sub-neg 39.8%

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

      3. associate-*l/ 53.6%

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

      4. associate-*r/ 53.7%

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

      5. div-sub 53.7%

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

      6. *-inverses 53.7%

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

   8. Simplified 53.7%

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

   if -3.09999999999999992e49 < y < -2.40000000000000019e47

   1. Initial program 99.2%

      \[\frac{x + y}{1 - \frac{y}{z}} \]

   2. Taylor expanded in z around inf 100.0%

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

      1. associate-+r+ 100.0%

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

      2. *-lft-identity 100.0%

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

      3. associate-/l* 100.0%

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

      4. associate-/r/ 100.0%

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

      5. distribute-rgt-in 100.0%

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

      6. +-commutative 100.0%

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

   4. Simplified 100.0%

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

   5. Taylor expanded in x around 0 100.0%

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

4. Final simplification 69.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -2.15 \cdot 10^{+147}:\\ \;\;\;\;-z\\ \mathbf{elif}\;y \leq -1.05 \cdot 10^{+95}:\\ \;\;\;\;x + y\\ \mathbf{elif}\;y \leq -3.1 \cdot 10^{+49}:\\ \;\;\;\;z \cdot \left(1 - \frac{x}{y}\right)\\ \mathbf{elif}\;y \leq -2.4 \cdot 10^{+47}:\\ \;\;\;\;y \cdot \left(1 + \frac{y}{z}\right)\\ \mathbf{elif}\;y \leq 1.95 \cdot 10^{+108}:\\ \;\;\;\;x + y\\ \mathbf{else}:\\ \;\;\;\;-z\\ \end{array} \]

Alternative 6: 63.4% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 1 - \frac{y}{z}\\ t_1 := \frac{x}{t_0}\\ \mathbf{if}\;x \leq -4.2 \cdot 10^{-63}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq 7.5 \cdot 10^{-146}:\\ \;\;\;\;\frac{y}{t_0}\\ \mathbf{elif}\;x \leq 8 \cdot 10^{-81}:\\ \;\;\;\;x + y\\ \mathbf{elif}\;x \leq 2.2 \cdot 10^{+133}:\\ \;\;\;\;-z\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (- 1.0 (/ y z))) (t_1 (/ x t_0)))
   (if (<= x -4.2e-63)
     t_1
     (if (<= x 7.5e-146)
       (/ y t_0)
       (if (<= x 8e-81) (+ x y) (if (<= x 2.2e+133) (- z) t_1))))))

C

double code(double x, double y, double z) {
	double t_0 = 1.0 - (y / z);
	double t_1 = x / t_0;
	double tmp;
	if (x <= -4.2e-63) {
		tmp = t_1;
	} else if (x <= 7.5e-146) {
		tmp = y / t_0;
	} else if (x <= 8e-81) {
		tmp = x + y;
	} else if (x <= 2.2e+133) {
		tmp = -z;
	} 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 = x / t_0
    if (x <= (-4.2d-63)) then
        tmp = t_1
    else if (x <= 7.5d-146) then
        tmp = y / t_0
    else if (x <= 8d-81) then
        tmp = x + y
    else if (x <= 2.2d+133) then
        tmp = -z
    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 = x / t_0;
	double tmp;
	if (x <= -4.2e-63) {
		tmp = t_1;
	} else if (x <= 7.5e-146) {
		tmp = y / t_0;
	} else if (x <= 8e-81) {
		tmp = x + y;
	} else if (x <= 2.2e+133) {
		tmp = -z;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = 1.0 - (y / z)
	t_1 = x / t_0
	tmp = 0
	if x <= -4.2e-63:
		tmp = t_1
	elif x <= 7.5e-146:
		tmp = y / t_0
	elif x <= 8e-81:
		tmp = x + y
	elif x <= 2.2e+133:
		tmp = -z
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(1.0 - Float64(y / z))
	t_1 = Float64(x / t_0)
	tmp = 0.0
	if (x <= -4.2e-63)
		tmp = t_1;
	elseif (x <= 7.5e-146)
		tmp = Float64(y / t_0);
	elseif (x <= 8e-81)
		tmp = Float64(x + y);
	elseif (x <= 2.2e+133)
		tmp = Float64(-z);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = 1.0 - (y / z);
	t_1 = x / t_0;
	tmp = 0.0;
	if (x <= -4.2e-63)
		tmp = t_1;
	elseif (x <= 7.5e-146)
		tmp = y / t_0;
	elseif (x <= 8e-81)
		tmp = x + y;
	elseif (x <= 2.2e+133)
		tmp = -z;
	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[(x / t$95$0), $MachinePrecision]}, If[LessEqual[x, -4.2e-63], t$95$1, If[LessEqual[x, 7.5e-146], N[(y / t$95$0), $MachinePrecision], If[LessEqual[x, 8e-81], N[(x + y), $MachinePrecision], If[LessEqual[x, 2.2e+133], (-z), t$95$1]]]]]]

Derivation

1. Split input into 4 regimes

2. if x < -4.2e-63 or 2.2e133 < x

   1. Initial program 91.3%

      \[\frac{x + y}{1 - \frac{y}{z}} \]

   2. Taylor expanded in x around inf 79.1%

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

   if -4.2e-63 < x < 7.49999999999999981e-146

   1. Initial program 86.0%

      \[\frac{x + y}{1 - \frac{y}{z}} \]

   2. Taylor expanded in x around 0 68.5%

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

   if 7.49999999999999981e-146 < x < 7.9999999999999997e-81

   1. Initial program 90.4%

      \[\frac{x + y}{1 - \frac{y}{z}} \]

   2. Taylor expanded in z around inf 77.8%

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

      1. +-commutative 77.8%

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

   4. Simplified 77.8%

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

   if 7.9999999999999997e-81 < x < 2.2e133

   1. Initial program 85.1%

      \[\frac{x + y}{1 - \frac{y}{z}} \]

   2. Taylor expanded in y around inf 58.0%

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

      1. mul-1-neg 58.0%

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

   4. Simplified 58.0%

      \[\leadsto \color{blue}{-z} \]
3. Recombined 4 regimes into one program.

4. Final simplification 72.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -4.2 \cdot 10^{-63}:\\ \;\;\;\;\frac{x}{1 - \frac{y}{z}}\\ \mathbf{elif}\;x \leq 7.5 \cdot 10^{-146}:\\ \;\;\;\;\frac{y}{1 - \frac{y}{z}}\\ \mathbf{elif}\;x \leq 8 \cdot 10^{-81}:\\ \;\;\;\;x + y\\ \mathbf{elif}\;x \leq 2.2 \cdot 10^{+133}:\\ \;\;\;\;-z\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{1 - \frac{y}{z}}\\ \end{array} \]

Alternative 7: 67.9% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -2.15 \cdot 10^{+147} \lor \neg \left(y \leq 10^{+105}\right):\\ \;\;\;\;-z\\ \mathbf{else}:\\ \;\;\;\;x + y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= y -2.15e+147) (not (<= y 1e+105))) (- z) (+ x y)))

C

double code(double x, double y, double z) {
	double tmp;
	if ((y <= -2.15e+147) || !(y <= 1e+105)) {
		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 <= (-2.15d+147)) .or. (.not. (y <= 1d+105))) 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 <= -2.15e+147) || !(y <= 1e+105)) {
		tmp = -z;
	} else {
		tmp = x + y;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (y <= -2.15e+147) or not (y <= 1e+105):
		tmp = -z
	else:
		tmp = x + y
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((y <= -2.15e+147) || !(y <= 1e+105))
		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 <= -2.15e+147) || ~((y <= 1e+105)))
		tmp = -z;
	else
		tmp = x + y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[y, -2.15e+147], N[Not[LessEqual[y, 1e+105]], $MachinePrecision]], (-z), N[(x + y), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -2.1499999999999999e147 or 9.9999999999999994e104 < y

   1. Initial program 70.6%

      \[\frac{x + y}{1 - \frac{y}{z}} \]

   2. Taylor expanded in y around inf 72.7%

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

      1. mul-1-neg 72.7%

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

   4. Simplified 72.7%

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

   if -2.1499999999999999e147 < y < 9.9999999999999994e104

   1. Initial program 96.7%

      \[\frac{x + y}{1 - \frac{y}{z}} \]

   2. Taylor expanded in z around inf 66.1%

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

      1. +-commutative 66.1%

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

   4. Simplified 66.1%

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

4. Final simplification 68.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -2.15 \cdot 10^{+147} \lor \neg \left(y \leq 10^{+105}\right):\\ \;\;\;\;-z\\ \mathbf{else}:\\ \;\;\;\;x + y\\ \end{array} \]

Alternative 8: 58.5% accurate, 1.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -5200000 \lor \neg \left(y \leq 2.8 \cdot 10^{-76}\right):\\ \;\;\;\;-z\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= y -5200000.0) (not (<= y 2.8e-76))) (- z) x))

C

double code(double x, double y, double z) {
	double tmp;
	if ((y <= -5200000.0) || !(y <= 2.8e-76)) {
		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 <= (-5200000.0d0)) .or. (.not. (y <= 2.8d-76))) 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 <= -5200000.0) || !(y <= 2.8e-76)) {
		tmp = -z;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (y <= -5200000.0) or not (y <= 2.8e-76):
		tmp = -z
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((y <= -5200000.0) || !(y <= 2.8e-76))
		tmp = Float64(-z);
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((y <= -5200000.0) || ~((y <= 2.8e-76)))
		tmp = -z;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[y, -5200000.0], N[Not[LessEqual[y, 2.8e-76]], $MachinePrecision]], (-z), x]

Derivation

1. Split input into 2 regimes

2. if y < -5.2e6 or 2.8000000000000001e-76 < y

   1. Initial program 81.0%

      \[\frac{x + y}{1 - \frac{y}{z}} \]

   2. Taylor expanded in y around inf 52.4%

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

      1. mul-1-neg 52.4%

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

   4. Simplified 52.4%

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

   if -5.2e6 < y < 2.8000000000000001e-76

   1. Initial program 99.9%

      \[\frac{x + y}{1 - \frac{y}{z}} \]

   2. Taylor expanded in y around 0 67.9%

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

4. Final simplification 58.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -5200000 \lor \neg \left(y \leq 2.8 \cdot 10^{-76}\right):\\ \;\;\;\;-z\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]

Alternative 9: 39.4% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -3.8 \cdot 10^{-259}:\\ \;\;\;\;x\\ \mathbf{elif}\;x \leq 2.7 \cdot 10^{-184}:\\ \;\;\;\;y\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= x -3.8e-259) x (if (<= x 2.7e-184) y x)))

C

double code(double x, double y, double z) {
	double tmp;
	if (x <= -3.8e-259) {
		tmp = x;
	} else if (x <= 2.7e-184) {
		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 <= (-3.8d-259)) then
        tmp = x
    else if (x <= 2.7d-184) 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 <= -3.8e-259) {
		tmp = x;
	} else if (x <= 2.7e-184) {
		tmp = y;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if x <= -3.8e-259:
		tmp = x
	elif x <= 2.7e-184:
		tmp = y
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (x <= -3.8e-259)
		tmp = x;
	elseif (x <= 2.7e-184)
		tmp = y;
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (x <= -3.8e-259)
		tmp = x;
	elseif (x <= 2.7e-184)
		tmp = y;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[x, -3.8e-259], x, If[LessEqual[x, 2.7e-184], y, x]]

Derivation

1. Split input into 2 regimes

2. if x < -3.8e-259 or 2.7000000000000001e-184 < x

   1. Initial program 89.4%

      \[\frac{x + y}{1 - \frac{y}{z}} \]

   2. Taylor expanded in y around 0 41.5%

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

   if -3.8e-259 < x < 2.7000000000000001e-184

   1. Initial program 84.2%

      \[\frac{x + y}{1 - \frac{y}{z}} \]

   2. Taylor expanded in x around 0 78.3%

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

   3. Taylor expanded in y around 0 44.5%

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

4. Final simplification 42.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -3.8 \cdot 10^{-259}:\\ \;\;\;\;x\\ \mathbf{elif}\;x \leq 2.7 \cdot 10^{-184}:\\ \;\;\;\;y\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]

Alternative 10: 35.1% 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 88.6%

   \[\frac{x + y}{1 - \frac{y}{z}} \]

2. Taylor expanded in y around 0 36.1%

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

3. Final simplification 36.1%

   \[\leadsto x \]

Developer target: 93.9% accurate, 0.7× 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 2023310 
(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))))