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

Percentage Accurate: 88.3% → 99.8%

Time: 7.3s

Alternatives: 13

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: 99.8% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{x + y}{1 - \frac{y}{z}}\\ \mathbf{if}\;t_0 \leq -4 \cdot 10^{-275} \lor \neg \left(t_0 \leq 0\right):\\ \;\;\;\;t_0\\ \mathbf{else}:\\ \;\;\;\;z \cdot \frac{\left(-x\right) - y}{y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (/ (+ x y) (- 1.0 (/ y z)))))
   (if (or (<= t_0 -4e-275) (not (<= t_0 0.0))) t_0 (* z (/ (- (- x) y) y)))))

C

double code(double x, double y, double z) {
	double t_0 = (x + y) / (1.0 - (y / z));
	double tmp;
	if ((t_0 <= -4e-275) || !(t_0 <= 0.0)) {
		tmp = t_0;
	} else {
		tmp = z * ((-x - y) / 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 <= (-4d-275)) .or. (.not. (t_0 <= 0.0d0))) then
        tmp = t_0
    else
        tmp = z * ((-x - y) / 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 <= -4e-275) || !(t_0 <= 0.0)) {
		tmp = t_0;
	} else {
		tmp = z * ((-x - y) / y);
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = (x + y) / (1.0 - (y / z))
	tmp = 0
	if (t_0 <= -4e-275) or not (t_0 <= 0.0):
		tmp = t_0
	else:
		tmp = z * ((-x - y) / 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 <= -4e-275) || !(t_0 <= 0.0))
		tmp = t_0;
	else
		tmp = Float64(z * Float64(Float64(Float64(-x) - y) / 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 <= -4e-275) || ~((t_0 <= 0.0)))
		tmp = t_0;
	else
		tmp = z * ((-x - y) / 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, -4e-275], N[Not[LessEqual[t$95$0, 0.0]], $MachinePrecision]], t$95$0, N[(z * N[(N[((-x) - y), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (+.f64 x y) (-.f64 1 (/.f64 y z))) < -3.99999999999999974e-275 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 -3.99999999999999974e-275 < (/.f64 (+.f64 x y) (-.f64 1 (/.f64 y z))) < -0.0

   1. Initial program 6.0%

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

   2. Taylor expanded in y around inf 6.0%

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

      1. neg-mul-1 6.0%

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

      2. distribute-neg-frac 6.0%

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

   4. Simplified 6.0%

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

      1. frac-2neg 6.0%

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

      2. remove-double-neg 6.0%

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

      3. associate-/r/ 100.0%

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

      4. +-commutative 100.0%

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

   6. Applied egg-rr 100.0%

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

4. Final simplification 99.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{x + y}{1 - \frac{y}{z}} \leq -4 \cdot 10^{-275} \lor \neg \left(\frac{x + y}{1 - \frac{y}{z}} \leq 0\right):\\ \;\;\;\;\frac{x + y}{1 - \frac{y}{z}}\\ \mathbf{else}:\\ \;\;\;\;z \cdot \frac{\left(-x\right) - y}{y}\\ \end{array} \]

Alternative 2: 69.5% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left(x + y\right) \cdot \frac{-z}{y}\\ \mathbf{if}\;y \leq -1.02 \cdot 10^{+138}:\\ \;\;\;\;-z\\ \mathbf{elif}\;y \leq -1.3 \cdot 10^{-45}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;y \leq 5.5 \cdot 10^{+46}:\\ \;\;\;\;x + y\\ \mathbf{elif}\;y \leq 9 \cdot 10^{+156}:\\ \;\;\;\;t_0\\ \mathbf{else}:\\ \;\;\;\;-z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* (+ x y) (/ (- z) y))))
   (if (<= y -1.02e+138)
     (- z)
     (if (<= y -1.3e-45)
       t_0
       (if (<= y 5.5e+46) (+ x y) (if (<= y 9e+156) t_0 (- z)))))))

C

double code(double x, double y, double z) {
	double t_0 = (x + y) * (-z / y);
	double tmp;
	if (y <= -1.02e+138) {
		tmp = -z;
	} else if (y <= -1.3e-45) {
		tmp = t_0;
	} else if (y <= 5.5e+46) {
		tmp = x + y;
	} else if (y <= 9e+156) {
		tmp = t_0;
	} 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) :: t_0
    real(8) :: tmp
    t_0 = (x + y) * (-z / y)
    if (y <= (-1.02d+138)) then
        tmp = -z
    else if (y <= (-1.3d-45)) then
        tmp = t_0
    else if (y <= 5.5d+46) then
        tmp = x + y
    else if (y <= 9d+156) then
        tmp = t_0
    else
        tmp = -z
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = (x + y) * (-z / y);
	double tmp;
	if (y <= -1.02e+138) {
		tmp = -z;
	} else if (y <= -1.3e-45) {
		tmp = t_0;
	} else if (y <= 5.5e+46) {
		tmp = x + y;
	} else if (y <= 9e+156) {
		tmp = t_0;
	} else {
		tmp = -z;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = (x + y) * (-z / y)
	tmp = 0
	if y <= -1.02e+138:
		tmp = -z
	elif y <= -1.3e-45:
		tmp = t_0
	elif y <= 5.5e+46:
		tmp = x + y
	elif y <= 9e+156:
		tmp = t_0
	else:
		tmp = -z
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(Float64(x + y) * Float64(Float64(-z) / y))
	tmp = 0.0
	if (y <= -1.02e+138)
		tmp = Float64(-z);
	elseif (y <= -1.3e-45)
		tmp = t_0;
	elseif (y <= 5.5e+46)
		tmp = Float64(x + y);
	elseif (y <= 9e+156)
		tmp = t_0;
	else
		tmp = Float64(-z);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = (x + y) * (-z / y);
	tmp = 0.0;
	if (y <= -1.02e+138)
		tmp = -z;
	elseif (y <= -1.3e-45)
		tmp = t_0;
	elseif (y <= 5.5e+46)
		tmp = x + y;
	elseif (y <= 9e+156)
		tmp = t_0;
	else
		tmp = -z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(x + y), $MachinePrecision] * N[((-z) / y), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -1.02e+138], (-z), If[LessEqual[y, -1.3e-45], t$95$0, If[LessEqual[y, 5.5e+46], N[(x + y), $MachinePrecision], If[LessEqual[y, 9e+156], t$95$0, (-z)]]]]]

Derivation

1. Split input into 3 regimes

2. if y < -1.02e138 or 9.00000000000000061e156 < y

   1. Initial program 63.7%

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

   2. Taylor expanded in y around inf 84.8%

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

      1. mul-1-neg 84.8%

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

   4. Simplified 84.8%

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

   if -1.02e138 < y < -1.29999999999999993e-45 or 5.4999999999999998e46 < y < 9.00000000000000061e156

   1. Initial program 88.1%

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

   2. Taylor expanded in z around 0 75.7%

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

      1. mul-1-neg 75.7%

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

      2. associate-/l* 74.1%

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

      3. associate-/r/ 66.4%

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

      4. distribute-rgt-neg-in 66.4%

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

      5. distribute-neg-in 66.4%

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

      6. unsub-neg 66.4%

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

   4. Simplified 66.4%

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

   if -1.29999999999999993e-45 < y < 5.4999999999999998e46

   1. Initial program 99.9%

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

   2. Taylor expanded in z around inf 77.4%

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

      1. +-commutative 77.4%

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

   4. Simplified 77.4%

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

4. Final simplification 76.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1.02 \cdot 10^{+138}:\\ \;\;\;\;-z\\ \mathbf{elif}\;y \leq -1.3 \cdot 10^{-45}:\\ \;\;\;\;\left(x + y\right) \cdot \frac{-z}{y}\\ \mathbf{elif}\;y \leq 5.5 \cdot 10^{+46}:\\ \;\;\;\;x + y\\ \mathbf{elif}\;y \leq 9 \cdot 10^{+156}:\\ \;\;\;\;\left(x + y\right) \cdot \frac{-z}{y}\\ \mathbf{else}:\\ \;\;\;\;-z\\ \end{array} \]

Alternative 3: 69.8% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 1 - \frac{y}{z}\\ \mathbf{if}\;y \leq -1.08 \cdot 10^{+139}:\\ \;\;\;\;-z\\ \mathbf{elif}\;y \leq -1.15 \cdot 10^{-14}:\\ \;\;\;\;\frac{y}{t_0}\\ \mathbf{elif}\;y \leq -1.65 \cdot 10^{-65}:\\ \;\;\;\;\frac{x}{t_0}\\ \mathbf{elif}\;y \leq 1.2 \cdot 10^{+104}:\\ \;\;\;\;x + y\\ \mathbf{else}:\\ \;\;\;\;-z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (- 1.0 (/ y z))))
   (if (<= y -1.08e+139)
     (- z)
     (if (<= y -1.15e-14)
       (/ y t_0)
       (if (<= y -1.65e-65) (/ x t_0) (if (<= y 1.2e+104) (+ x y) (- z)))))))

C

double code(double x, double y, double z) {
	double t_0 = 1.0 - (y / z);
	double tmp;
	if (y <= -1.08e+139) {
		tmp = -z;
	} else if (y <= -1.15e-14) {
		tmp = y / t_0;
	} else if (y <= -1.65e-65) {
		tmp = x / t_0;
	} else if (y <= 1.2e+104) {
		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) :: t_0
    real(8) :: tmp
    t_0 = 1.0d0 - (y / z)
    if (y <= (-1.08d+139)) then
        tmp = -z
    else if (y <= (-1.15d-14)) then
        tmp = y / t_0
    else if (y <= (-1.65d-65)) then
        tmp = x / t_0
    else if (y <= 1.2d+104) 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 t_0 = 1.0 - (y / z);
	double tmp;
	if (y <= -1.08e+139) {
		tmp = -z;
	} else if (y <= -1.15e-14) {
		tmp = y / t_0;
	} else if (y <= -1.65e-65) {
		tmp = x / t_0;
	} else if (y <= 1.2e+104) {
		tmp = x + y;
	} else {
		tmp = -z;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = 1.0 - (y / z)
	tmp = 0
	if y <= -1.08e+139:
		tmp = -z
	elif y <= -1.15e-14:
		tmp = y / t_0
	elif y <= -1.65e-65:
		tmp = x / t_0
	elif y <= 1.2e+104:
		tmp = x + y
	else:
		tmp = -z
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(1.0 - Float64(y / z))
	tmp = 0.0
	if (y <= -1.08e+139)
		tmp = Float64(-z);
	elseif (y <= -1.15e-14)
		tmp = Float64(y / t_0);
	elseif (y <= -1.65e-65)
		tmp = Float64(x / t_0);
	elseif (y <= 1.2e+104)
		tmp = Float64(x + y);
	else
		tmp = Float64(-z);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = 1.0 - (y / z);
	tmp = 0.0;
	if (y <= -1.08e+139)
		tmp = -z;
	elseif (y <= -1.15e-14)
		tmp = y / t_0;
	elseif (y <= -1.65e-65)
		tmp = x / t_0;
	elseif (y <= 1.2e+104)
		tmp = x + y;
	else
		tmp = -z;
	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, -1.08e+139], (-z), If[LessEqual[y, -1.15e-14], N[(y / t$95$0), $MachinePrecision], If[LessEqual[y, -1.65e-65], N[(x / t$95$0), $MachinePrecision], If[LessEqual[y, 1.2e+104], N[(x + y), $MachinePrecision], (-z)]]]]]

Derivation

1. Split input into 4 regimes

2. if y < -1.08000000000000004e139 or 1.2e104 < y

   1. Initial program 67.3%

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

   2. Taylor expanded in y around inf 79.1%

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

      1. mul-1-neg 79.1%

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

   4. Simplified 79.1%

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

   if -1.08000000000000004e139 < y < -1.14999999999999999e-14

   1. Initial program 86.0%

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

   2. Taylor expanded in x around 0 61.0%

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

   if -1.14999999999999999e-14 < y < -1.6500000000000001e-65

   1. Initial program 99.6%

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

   2. Taylor expanded in x around inf 77.1%

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

   if -1.6500000000000001e-65 < y < 1.2e104

   1. Initial program 98.6%

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

   2. Taylor expanded in z around inf 74.2%

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

      1. +-commutative 74.2%

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

   4. Simplified 74.2%

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

4. Final simplification 74.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1.08 \cdot 10^{+139}:\\ \;\;\;\;-z\\ \mathbf{elif}\;y \leq -1.15 \cdot 10^{-14}:\\ \;\;\;\;\frac{y}{1 - \frac{y}{z}}\\ \mathbf{elif}\;y \leq -1.65 \cdot 10^{-65}:\\ \;\;\;\;\frac{x}{1 - \frac{y}{z}}\\ \mathbf{elif}\;y \leq 1.2 \cdot 10^{+104}:\\ \;\;\;\;x + y\\ \mathbf{else}:\\ \;\;\;\;-z\\ \end{array} \]

Alternative 4: 56.1% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -6.5 \cdot 10^{-32}:\\ \;\;\;\;-z\\ \mathbf{elif}\;y \leq -8 \cdot 10^{-137}:\\ \;\;\;\;y\\ \mathbf{elif}\;y \leq 2.05 \cdot 10^{-103}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 4 \cdot 10^{-19}:\\ \;\;\;\;y\\ \mathbf{elif}\;y \leq 2.1 \cdot 10^{+44}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;-z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y -6.5e-32)
   (- z)
   (if (<= y -8e-137)
     y
     (if (<= y 2.05e-103) x (if (<= y 4e-19) y (if (<= y 2.1e+44) x (- z)))))))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= -6.5e-32) {
		tmp = -z;
	} else if (y <= -8e-137) {
		tmp = y;
	} else if (y <= 2.05e-103) {
		tmp = x;
	} else if (y <= 4e-19) {
		tmp = y;
	} else if (y <= 2.1e+44) {
		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 <= (-6.5d-32)) then
        tmp = -z
    else if (y <= (-8d-137)) then
        tmp = y
    else if (y <= 2.05d-103) then
        tmp = x
    else if (y <= 4d-19) then
        tmp = y
    else if (y <= 2.1d+44) 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 <= -6.5e-32) {
		tmp = -z;
	} else if (y <= -8e-137) {
		tmp = y;
	} else if (y <= 2.05e-103) {
		tmp = x;
	} else if (y <= 4e-19) {
		tmp = y;
	} else if (y <= 2.1e+44) {
		tmp = x;
	} else {
		tmp = -z;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if y <= -6.5e-32:
		tmp = -z
	elif y <= -8e-137:
		tmp = y
	elif y <= 2.05e-103:
		tmp = x
	elif y <= 4e-19:
		tmp = y
	elif y <= 2.1e+44:
		tmp = x
	else:
		tmp = -z
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= -6.5e-32)
		tmp = Float64(-z);
	elseif (y <= -8e-137)
		tmp = y;
	elseif (y <= 2.05e-103)
		tmp = x;
	elseif (y <= 4e-19)
		tmp = y;
	elseif (y <= 2.1e+44)
		tmp = x;
	else
		tmp = Float64(-z);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (y <= -6.5e-32)
		tmp = -z;
	elseif (y <= -8e-137)
		tmp = y;
	elseif (y <= 2.05e-103)
		tmp = x;
	elseif (y <= 4e-19)
		tmp = y;
	elseif (y <= 2.1e+44)
		tmp = x;
	else
		tmp = -z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, -6.5e-32], (-z), If[LessEqual[y, -8e-137], y, If[LessEqual[y, 2.05e-103], x, If[LessEqual[y, 4e-19], y, If[LessEqual[y, 2.1e+44], x, (-z)]]]]]

Derivation

1. Split input into 3 regimes

2. if y < -6.49999999999999988e-32 or 2.09999999999999987e44 < y

   1. Initial program 75.2%

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

   2. Taylor expanded in y around inf 63.9%

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

      1. mul-1-neg 63.9%

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

   4. Simplified 63.9%

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

   if -6.49999999999999988e-32 < y < -7.99999999999999982e-137 or 2.04999999999999998e-103 < y < 3.9999999999999999e-19

   1. Initial program 99.9%

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

   2. Taylor expanded in x around 0 60.5%

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

   3. Taylor expanded in y around 0 45.3%

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

   if -7.99999999999999982e-137 < y < 2.04999999999999998e-103 or 3.9999999999999999e-19 < y < 2.09999999999999987e44

   1. Initial program 99.9%

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

   2. Taylor expanded in y around 0 63.9%

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

4. Final simplification 61.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -6.5 \cdot 10^{-32}:\\ \;\;\;\;-z\\ \mathbf{elif}\;y \leq -8 \cdot 10^{-137}:\\ \;\;\;\;y\\ \mathbf{elif}\;y \leq 2.05 \cdot 10^{-103}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 4 \cdot 10^{-19}:\\ \;\;\;\;y\\ \mathbf{elif}\;y \leq 2.1 \cdot 10^{+44}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;-z\\ \end{array} \]

Alternative 5: 74.4% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.2 \cdot 10^{-42} \lor \neg \left(y \leq 1.9 \cdot 10^{+49}\right):\\ \;\;\;\;z \cdot \frac{\left(-x\right) - y}{y}\\ \mathbf{else}:\\ \;\;\;\;x + y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= y -1.2e-42) (not (<= y 1.9e+49)))
   (* z (/ (- (- x) y) y))
   (+ x y)))

C

double code(double x, double y, double z) {
	double tmp;
	if ((y <= -1.2e-42) || !(y <= 1.9e+49)) {
		tmp = z * ((-x - y) / 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 <= (-1.2d-42)) .or. (.not. (y <= 1.9d+49))) then
        tmp = z * ((-x - y) / 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 <= -1.2e-42) || !(y <= 1.9e+49)) {
		tmp = z * ((-x - y) / y);
	} else {
		tmp = x + y;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (y <= -1.2e-42) or not (y <= 1.9e+49):
		tmp = z * ((-x - y) / y)
	else:
		tmp = x + y
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((y <= -1.2e-42) || !(y <= 1.9e+49))
		tmp = Float64(z * Float64(Float64(Float64(-x) - y) / y));
	else
		tmp = Float64(x + y);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((y <= -1.2e-42) || ~((y <= 1.9e+49)))
		tmp = z * ((-x - y) / y);
	else
		tmp = x + y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[y, -1.2e-42], N[Not[LessEqual[y, 1.9e+49]], $MachinePrecision]], N[(z * N[(N[((-x) - y), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision], N[(x + y), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -1.20000000000000001e-42 or 1.8999999999999999e49 < y

   1. Initial program 75.6%

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

   2. Taylor expanded in y around inf 57.8%

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

      1. neg-mul-1 57.8%

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

      2. distribute-neg-frac 57.8%

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

   4. Simplified 57.8%

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

      1. frac-2neg 57.8%

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

      2. remove-double-neg 57.8%

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

      3. associate-/r/ 81.3%

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

      4. +-commutative 81.3%

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

   6. Applied egg-rr 81.3%

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

   if -1.20000000000000001e-42 < y < 1.8999999999999999e49

   1. Initial program 99.9%

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

   2. Taylor expanded in z around inf 76.8%

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

      1. +-commutative 76.8%

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

   4. Simplified 76.8%

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

4. Final simplification 79.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1.2 \cdot 10^{-42} \lor \neg \left(y \leq 1.9 \cdot 10^{+49}\right):\\ \;\;\;\;z \cdot \frac{\left(-x\right) - y}{y}\\ \mathbf{else}:\\ \;\;\;\;x + y\\ \end{array} \]

Alternative 6: 73.4% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.65 \cdot 10^{-39}:\\ \;\;\;\;z \cdot \frac{\left(-x\right) - y}{y}\\ \mathbf{elif}\;y \leq 1.9 \cdot 10^{+44}:\\ \;\;\;\;x + y\\ \mathbf{else}:\\ \;\;\;\;\left(-z\right) - \frac{x}{\frac{y}{z}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y -1.65e-39)
   (* z (/ (- (- x) y) y))
   (if (<= y 1.9e+44) (+ x y) (- (- z) (/ x (/ y z))))))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= -1.65e-39) {
		tmp = z * ((-x - y) / y);
	} else if (y <= 1.9e+44) {
		tmp = x + y;
	} else {
		tmp = -z - (x / (y / 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 <= (-1.65d-39)) then
        tmp = z * ((-x - y) / y)
    else if (y <= 1.9d+44) then
        tmp = x + y
    else
        tmp = -z - (x / (y / z))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (y <= -1.65e-39) {
		tmp = z * ((-x - y) / y);
	} else if (y <= 1.9e+44) {
		tmp = x + y;
	} else {
		tmp = -z - (x / (y / z));
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if y <= -1.65e-39:
		tmp = z * ((-x - y) / y)
	elif y <= 1.9e+44:
		tmp = x + y
	else:
		tmp = -z - (x / (y / z))
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= -1.65e-39)
		tmp = Float64(z * Float64(Float64(Float64(-x) - y) / y));
	elseif (y <= 1.9e+44)
		tmp = Float64(x + y);
	else
		tmp = Float64(Float64(-z) - Float64(x / Float64(y / z)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (y <= -1.65e-39)
		tmp = z * ((-x - y) / y);
	elseif (y <= 1.9e+44)
		tmp = x + y;
	else
		tmp = -z - (x / (y / z));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, -1.65e-39], N[(z * N[(N[((-x) - y), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 1.9e+44], N[(x + y), $MachinePrecision], N[((-z) - N[(x / N[(y / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if y < -1.64999999999999992e-39

   1. Initial program 78.1%

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

   2. Taylor expanded in y around inf 63.1%

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

      1. neg-mul-1 63.1%

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

      2. distribute-neg-frac 63.1%

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

   4. Simplified 63.1%

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

      1. frac-2neg 63.1%

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

      2. remove-double-neg 63.1%

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

      3. associate-/r/ 83.8%

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

      4. +-commutative 83.8%

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

   6. Applied egg-rr 83.8%

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

   if -1.64999999999999992e-39 < y < 1.9000000000000001e44

   1. Initial program 99.9%

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

   2. Taylor expanded in z around inf 76.8%

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

      1. +-commutative 76.8%

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

   4. Simplified 76.8%

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

   if 1.9000000000000001e44 < y

   1. Initial program 71.8%

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

   2. Taylor expanded in y around inf 49.4%

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

      1. neg-mul-1 49.4%

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

      2. distribute-neg-frac 49.4%

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

   4. Simplified 49.4%

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

   5. Taylor expanded in x around 0 77.0%

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

      1. mul-1-neg 77.0%

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

      2. unsub-neg 77.0%

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

      3. neg-mul-1 77.0%

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

      4. associate-/l* 77.5%

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

   7. Simplified 77.5%

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

4. Final simplification 79.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1.65 \cdot 10^{-39}:\\ \;\;\;\;z \cdot \frac{\left(-x\right) - y}{y}\\ \mathbf{elif}\;y \leq 1.9 \cdot 10^{+44}:\\ \;\;\;\;x + y\\ \mathbf{else}:\\ \;\;\;\;\left(-z\right) - \frac{x}{\frac{y}{z}}\\ \end{array} \]

Alternative 7: 73.4% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -3.7 \cdot 10^{-41}:\\ \;\;\;\;\frac{-z}{\frac{y}{x + y}}\\ \mathbf{elif}\;y \leq 2.5 \cdot 10^{+44}:\\ \;\;\;\;x + y\\ \mathbf{else}:\\ \;\;\;\;\left(-z\right) - \frac{x}{\frac{y}{z}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y -3.7e-41)
   (/ (- z) (/ y (+ x y)))
   (if (<= y 2.5e+44) (+ x y) (- (- z) (/ x (/ y z))))))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= -3.7e-41) {
		tmp = -z / (y / (x + y));
	} else if (y <= 2.5e+44) {
		tmp = x + y;
	} else {
		tmp = -z - (x / (y / 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 <= (-3.7d-41)) then
        tmp = -z / (y / (x + y))
    else if (y <= 2.5d+44) then
        tmp = x + y
    else
        tmp = -z - (x / (y / z))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (y <= -3.7e-41) {
		tmp = -z / (y / (x + y));
	} else if (y <= 2.5e+44) {
		tmp = x + y;
	} else {
		tmp = -z - (x / (y / z));
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if y <= -3.7e-41:
		tmp = -z / (y / (x + y))
	elif y <= 2.5e+44:
		tmp = x + y
	else:
		tmp = -z - (x / (y / z))
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= -3.7e-41)
		tmp = Float64(Float64(-z) / Float64(y / Float64(x + y)));
	elseif (y <= 2.5e+44)
		tmp = Float64(x + y);
	else
		tmp = Float64(Float64(-z) - Float64(x / Float64(y / z)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (y <= -3.7e-41)
		tmp = -z / (y / (x + y));
	elseif (y <= 2.5e+44)
		tmp = x + y;
	else
		tmp = -z - (x / (y / z));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, -3.7e-41], N[((-z) / N[(y / N[(x + y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 2.5e+44], N[(x + y), $MachinePrecision], N[((-z) - N[(x / N[(y / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if y < -3.7000000000000002e-41

   1. Initial program 78.1%

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

   2. Taylor expanded in z around 0 76.5%

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

      1. mul-1-neg 76.5%

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

      2. associate-/l* 84.8%

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

      3. distribute-neg-frac 84.8%

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

      4. +-commutative 84.8%

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

   4. Simplified 84.8%

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

   if -3.7000000000000002e-41 < y < 2.4999999999999998e44

   1. Initial program 99.9%

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

   2. Taylor expanded in z around inf 76.8%

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

      1. +-commutative 76.8%

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

   4. Simplified 76.8%

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

   if 2.4999999999999998e44 < y

   1. Initial program 71.8%

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

   2. Taylor expanded in y around inf 49.4%

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

      1. neg-mul-1 49.4%

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

      2. distribute-neg-frac 49.4%

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

   4. Simplified 49.4%

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

   5. Taylor expanded in x around 0 77.0%

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

      1. mul-1-neg 77.0%

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

      2. unsub-neg 77.0%

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

      3. neg-mul-1 77.0%

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

      4. associate-/l* 77.5%

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

   7. Simplified 77.5%

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

4. Final simplification 79.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -3.7 \cdot 10^{-41}:\\ \;\;\;\;\frac{-z}{\frac{y}{x + y}}\\ \mathbf{elif}\;y \leq 2.5 \cdot 10^{+44}:\\ \;\;\;\;x + y\\ \mathbf{else}:\\ \;\;\;\;\left(-z\right) - \frac{x}{\frac{y}{z}}\\ \end{array} \]

Alternative 8: 67.6% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.6 \cdot 10^{-15}:\\ \;\;\;\;-z\\ \mathbf{elif}\;y \leq -1 \cdot 10^{-68}:\\ \;\;\;\;\frac{x}{1 - \frac{y}{z}}\\ \mathbf{elif}\;y \leq 1.2 \cdot 10^{+104}:\\ \;\;\;\;x + y\\ \mathbf{else}:\\ \;\;\;\;-z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y -1.6e-15)
   (- z)
   (if (<= y -1e-68)
     (/ x (- 1.0 (/ y z)))
     (if (<= y 1.2e+104) (+ x y) (- z)))))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= -1.6e-15) {
		tmp = -z;
	} else if (y <= -1e-68) {
		tmp = x / (1.0 - (y / z));
	} else if (y <= 1.2e+104) {
		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 <= (-1.6d-15)) then
        tmp = -z
    else if (y <= (-1d-68)) then
        tmp = x / (1.0d0 - (y / z))
    else if (y <= 1.2d+104) 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 <= -1.6e-15) {
		tmp = -z;
	} else if (y <= -1e-68) {
		tmp = x / (1.0 - (y / z));
	} else if (y <= 1.2e+104) {
		tmp = x + y;
	} else {
		tmp = -z;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if y <= -1.6e-15:
		tmp = -z
	elif y <= -1e-68:
		tmp = x / (1.0 - (y / z))
	elif y <= 1.2e+104:
		tmp = x + y
	else:
		tmp = -z
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= -1.6e-15)
		tmp = Float64(-z);
	elseif (y <= -1e-68)
		tmp = Float64(x / Float64(1.0 - Float64(y / z)));
	elseif (y <= 1.2e+104)
		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 <= -1.6e-15)
		tmp = -z;
	elseif (y <= -1e-68)
		tmp = x / (1.0 - (y / z));
	elseif (y <= 1.2e+104)
		tmp = x + y;
	else
		tmp = -z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, -1.6e-15], (-z), If[LessEqual[y, -1e-68], N[(x / N[(1.0 - N[(y / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 1.2e+104], N[(x + y), $MachinePrecision], (-z)]]]

Derivation

1. Split input into 3 regimes

2. if y < -1.6e-15 or 1.2e104 < y

   1. Initial program 73.1%

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

   2. Taylor expanded in y around inf 69.7%

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

      1. mul-1-neg 69.7%

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

   4. Simplified 69.7%

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

   if -1.6e-15 < y < -1.00000000000000007e-68

   1. Initial program 99.6%

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

   2. Taylor expanded in x around inf 77.1%

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

   if -1.00000000000000007e-68 < y < 1.2e104

   1. Initial program 98.6%

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

   2. Taylor expanded in z around inf 74.2%

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

      1. +-commutative 74.2%

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

   4. Simplified 74.2%

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

4. Final simplification 72.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1.6 \cdot 10^{-15}:\\ \;\;\;\;-z\\ \mathbf{elif}\;y \leq -1 \cdot 10^{-68}:\\ \;\;\;\;\frac{x}{1 - \frac{y}{z}}\\ \mathbf{elif}\;y \leq 1.2 \cdot 10^{+104}:\\ \;\;\;\;x + y\\ \mathbf{else}:\\ \;\;\;\;-z\\ \end{array} \]

Alternative 9: 66.7% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -4.5 \cdot 10^{-16}:\\ \;\;\;\;-z\\ \mathbf{elif}\;y \leq -1.15 \cdot 10^{-44}:\\ \;\;\;\;\frac{-x}{\frac{y}{z}}\\ \mathbf{elif}\;y \leq 3 \cdot 10^{+104}:\\ \;\;\;\;x + y\\ \mathbf{else}:\\ \;\;\;\;-z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y -4.5e-16)
   (- z)
   (if (<= y -1.15e-44) (/ (- x) (/ y z)) (if (<= y 3e+104) (+ x y) (- z)))))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= -4.5e-16) {
		tmp = -z;
	} else if (y <= -1.15e-44) {
		tmp = -x / (y / z);
	} else if (y <= 3e+104) {
		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 <= (-4.5d-16)) then
        tmp = -z
    else if (y <= (-1.15d-44)) then
        tmp = -x / (y / z)
    else if (y <= 3d+104) 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 <= -4.5e-16) {
		tmp = -z;
	} else if (y <= -1.15e-44) {
		tmp = -x / (y / z);
	} else if (y <= 3e+104) {
		tmp = x + y;
	} else {
		tmp = -z;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if y <= -4.5e-16:
		tmp = -z
	elif y <= -1.15e-44:
		tmp = -x / (y / z)
	elif y <= 3e+104:
		tmp = x + y
	else:
		tmp = -z
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= -4.5e-16)
		tmp = Float64(-z);
	elseif (y <= -1.15e-44)
		tmp = Float64(Float64(-x) / Float64(y / z));
	elseif (y <= 3e+104)
		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 <= -4.5e-16)
		tmp = -z;
	elseif (y <= -1.15e-44)
		tmp = -x / (y / z);
	elseif (y <= 3e+104)
		tmp = x + y;
	else
		tmp = -z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, -4.5e-16], (-z), If[LessEqual[y, -1.15e-44], N[((-x) / N[(y / z), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 3e+104], N[(x + y), $MachinePrecision], (-z)]]]

Derivation

1. Split input into 3 regimes

2. if y < -4.5000000000000002e-16 or 2.99999999999999969e104 < y

   1. Initial program 73.1%

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

   2. Taylor expanded in y around inf 69.7%

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

      1. mul-1-neg 69.7%

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

   4. Simplified 69.7%

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

   if -4.5000000000000002e-16 < y < -1.14999999999999999e-44

   1. Initial program 99.5%

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

   2. Taylor expanded in y around inf 89.0%

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

      1. neg-mul-1 89.0%

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

      2. distribute-neg-frac 89.0%

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

   4. Simplified 89.0%

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

   5. Taylor expanded in x around inf 72.2%

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

      1. mul-1-neg 72.2%

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

      2. associate-/l* 72.1%

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

      3. distribute-neg-frac 72.1%

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

   7. Simplified 72.1%

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

   if -1.14999999999999999e-44 < y < 2.99999999999999969e104

   1. Initial program 98.6%

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

   2. Taylor expanded in z around inf 73.9%

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

      1. +-commutative 73.9%

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

   4. Simplified 73.9%

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

4. Final simplification 72.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -4.5 \cdot 10^{-16}:\\ \;\;\;\;-z\\ \mathbf{elif}\;y \leq -1.15 \cdot 10^{-44}:\\ \;\;\;\;\frac{-x}{\frac{y}{z}}\\ \mathbf{elif}\;y \leq 3 \cdot 10^{+104}:\\ \;\;\;\;x + y\\ \mathbf{else}:\\ \;\;\;\;-z\\ \end{array} \]

Alternative 10: 66.6% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.1 \cdot 10^{-14}:\\ \;\;\;\;-z\\ \mathbf{elif}\;y \leq -1.1 \cdot 10^{-46}:\\ \;\;\;\;\frac{x \cdot \left(-z\right)}{y}\\ \mathbf{elif}\;y \leq 8 \cdot 10^{+104}:\\ \;\;\;\;x + y\\ \mathbf{else}:\\ \;\;\;\;-z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y -1.1e-14)
   (- z)
   (if (<= y -1.1e-46) (/ (* x (- z)) y) (if (<= y 8e+104) (+ x y) (- z)))))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= -1.1e-14) {
		tmp = -z;
	} else if (y <= -1.1e-46) {
		tmp = (x * -z) / y;
	} else if (y <= 8e+104) {
		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 <= (-1.1d-14)) then
        tmp = -z
    else if (y <= (-1.1d-46)) then
        tmp = (x * -z) / y
    else if (y <= 8d+104) 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 <= -1.1e-14) {
		tmp = -z;
	} else if (y <= -1.1e-46) {
		tmp = (x * -z) / y;
	} else if (y <= 8e+104) {
		tmp = x + y;
	} else {
		tmp = -z;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if y <= -1.1e-14:
		tmp = -z
	elif y <= -1.1e-46:
		tmp = (x * -z) / y
	elif y <= 8e+104:
		tmp = x + y
	else:
		tmp = -z
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= -1.1e-14)
		tmp = Float64(-z);
	elseif (y <= -1.1e-46)
		tmp = Float64(Float64(x * Float64(-z)) / y);
	elseif (y <= 8e+104)
		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 <= -1.1e-14)
		tmp = -z;
	elseif (y <= -1.1e-46)
		tmp = (x * -z) / y;
	elseif (y <= 8e+104)
		tmp = x + y;
	else
		tmp = -z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, -1.1e-14], (-z), If[LessEqual[y, -1.1e-46], N[(N[(x * (-z)), $MachinePrecision] / y), $MachinePrecision], If[LessEqual[y, 8e+104], N[(x + y), $MachinePrecision], (-z)]]]

Derivation

1. Split input into 3 regimes

2. if y < -1.1e-14 or 8e104 < y

   1. Initial program 73.1%

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

   2. Taylor expanded in y around inf 69.7%

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

      1. mul-1-neg 69.7%

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

   4. Simplified 69.7%

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

   if -1.1e-14 < y < -1.1e-46

   1. Initial program 99.5%

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

   2. Taylor expanded in y around inf 89.0%

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

      1. neg-mul-1 89.0%

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

      2. distribute-neg-frac 89.0%

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

   4. Simplified 89.0%

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

   5. Taylor expanded in x around inf 72.2%

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

      1. associate-*r/ 72.2%

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

      2. associate-*r* 72.2%

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

      3. neg-mul-1 72.2%

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

   7. Simplified 72.2%

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

   if -1.1e-46 < y < 8e104

   1. Initial program 98.6%

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

   2. Taylor expanded in z around inf 73.9%

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

      1. +-commutative 73.9%

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

   4. Simplified 73.9%

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

4. Final simplification 72.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1.1 \cdot 10^{-14}:\\ \;\;\;\;-z\\ \mathbf{elif}\;y \leq -1.1 \cdot 10^{-46}:\\ \;\;\;\;\frac{x \cdot \left(-z\right)}{y}\\ \mathbf{elif}\;y \leq 8 \cdot 10^{+104}:\\ \;\;\;\;x + y\\ \mathbf{else}:\\ \;\;\;\;-z\\ \end{array} \]

Alternative 11: 67.2% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -3.6 \cdot 10^{-25} \lor \neg \left(y \leq 1.3 \cdot 10^{+104}\right):\\ \;\;\;\;-z\\ \mathbf{else}:\\ \;\;\;\;x + y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= y -3.6e-25) (not (<= y 1.3e+104))) (- z) (+ x y)))

C

double code(double x, double y, double z) {
	double tmp;
	if ((y <= -3.6e-25) || !(y <= 1.3e+104)) {
		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 <= (-3.6d-25)) .or. (.not. (y <= 1.3d+104))) 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 <= -3.6e-25) || !(y <= 1.3e+104)) {
		tmp = -z;
	} else {
		tmp = x + y;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (y <= -3.6e-25) or not (y <= 1.3e+104):
		tmp = -z
	else:
		tmp = x + y
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((y <= -3.6e-25) || !(y <= 1.3e+104))
		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 <= -3.6e-25) || ~((y <= 1.3e+104)))
		tmp = -z;
	else
		tmp = x + y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[y, -3.6e-25], N[Not[LessEqual[y, 1.3e+104]], $MachinePrecision]], (-z), N[(x + y), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -3.5999999999999999e-25 or 1.3e104 < y

   1. Initial program 74.3%

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

   2. Taylor expanded in y around inf 67.7%

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

      1. mul-1-neg 67.7%

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

   4. Simplified 67.7%

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

   if -3.5999999999999999e-25 < y < 1.3e104

   1. Initial program 98.6%

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

   2. Taylor expanded in z around inf 72.6%

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

      1. +-commutative 72.6%

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

   4. Simplified 72.6%

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

4. Final simplification 70.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -3.6 \cdot 10^{-25} \lor \neg \left(y \leq 1.3 \cdot 10^{+104}\right):\\ \;\;\;\;-z\\ \mathbf{else}:\\ \;\;\;\;x + y\\ \end{array} \]

Alternative 12: 41.5% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -2.05 \cdot 10^{-162}:\\ \;\;\;\;x\\ \mathbf{elif}\;x \leq 4.3 \cdot 10^{-117}:\\ \;\;\;\;y\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= x -2.05e-162) x (if (<= x 4.3e-117) y x)))

C

double code(double x, double y, double z) {
	double tmp;
	if (x <= -2.05e-162) {
		tmp = x;
	} else if (x <= 4.3e-117) {
		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 <= (-2.05d-162)) then
        tmp = x
    else if (x <= 4.3d-117) 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 <= -2.05e-162) {
		tmp = x;
	} else if (x <= 4.3e-117) {
		tmp = y;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if x <= -2.05e-162:
		tmp = x
	elif x <= 4.3e-117:
		tmp = y
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (x <= -2.05e-162)
		tmp = x;
	elseif (x <= 4.3e-117)
		tmp = y;
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (x <= -2.05e-162)
		tmp = x;
	elseif (x <= 4.3e-117)
		tmp = y;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[x, -2.05e-162], x, If[LessEqual[x, 4.3e-117], y, x]]

Derivation

1. Split input into 2 regimes

2. if x < -2.0500000000000001e-162 or 4.3e-117 < x

   1. Initial program 86.4%

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

   2. Taylor expanded in y around 0 36.7%

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

   if -2.0500000000000001e-162 < x < 4.3e-117

   1. Initial program 91.1%

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

   2. Taylor expanded in x around 0 80.5%

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

   3. Taylor expanded in y around 0 47.7%

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

4. Final simplification 39.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -2.05 \cdot 10^{-162}:\\ \;\;\;\;x\\ \mathbf{elif}\;x \leq 4.3 \cdot 10^{-117}:\\ \;\;\;\;y\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]

Alternative 13: 35.0% 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 87.8%

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

2. Taylor expanded in y around 0 29.1%

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

3. Final simplification 29.1%

   \[\leadsto x \]

Developer target: 94.0% 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 2023308 
(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))))