Diagrams.Backend.Cairo.Internal:setTexture from diagrams-cairo-1.3.0.3

Percentage Accurate: 84.8% → 94.4%

Time: 5.4s

Alternatives: 8

Speedup: 1.0×

Specification

Math

\[\begin{array}{l} \\ \frac{x \cdot \left(y - z\right)}{y} \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (/ (* x (- y z)) y))

C

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

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 - z)) / y
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 84.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{x \cdot \left(y - z\right)}{y} \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (/ (* x (- y z)) y))

C

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

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 - z)) / y
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 94.4% accurate, 1.0× speedup

Math

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

FPCore

(FPCore (x y z) :precision binary64 (- x (/ (* x z) y)))

C

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

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 - ((x * z) / y)
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 88.5%

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

   1. associate-*r/ 96.6%

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

3. Simplified 96.6%

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

4. Taylor expanded in y around 0 96.9%

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

5. Taylor expanded in x around 0 96.6%

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

   1. mul-1-neg 96.6%

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

   2. unsub-neg 96.6%

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

   3. distribute-lft-out-- 96.6%

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

   4. *-rgt-identity 96.6%

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

7. Simplified 96.6%

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

8. Taylor expanded in x around 0 96.9%

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

9. Final simplification 96.9%

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

Alternative 2: 71.4% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -4.4 \cdot 10^{-122}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 5.8 \cdot 10^{-23}:\\ \;\;\;\;z \cdot \left(-\frac{x}{y}\right)\\ \mathbf{elif}\;y \leq 2.5 \cdot 10^{+16}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 2.1 \cdot 10^{+44} \lor \neg \left(y \leq 3.1 \cdot 10^{+150}\right) \land y \leq 1.55 \cdot 10^{+170}:\\ \;\;\;\;\frac{z}{y} \cdot \left(-x\right)\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y -4.4e-122)
   x
   (if (<= y 5.8e-23)
     (* z (- (/ x y)))
     (if (<= y 2.5e+16)
       x
       (if (or (<= y 2.1e+44) (and (not (<= y 3.1e+150)) (<= y 1.55e+170)))
         (* (/ z y) (- x))
         x)))))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= -4.4e-122) {
		tmp = x;
	} else if (y <= 5.8e-23) {
		tmp = z * -(x / y);
	} else if (y <= 2.5e+16) {
		tmp = x;
	} else if ((y <= 2.1e+44) || (!(y <= 3.1e+150) && (y <= 1.55e+170))) {
		tmp = (z / y) * -x;
	} 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 <= (-4.4d-122)) then
        tmp = x
    else if (y <= 5.8d-23) then
        tmp = z * -(x / y)
    else if (y <= 2.5d+16) then
        tmp = x
    else if ((y <= 2.1d+44) .or. (.not. (y <= 3.1d+150)) .and. (y <= 1.55d+170)) then
        tmp = (z / y) * -x
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (y <= -4.4e-122) {
		tmp = x;
	} else if (y <= 5.8e-23) {
		tmp = z * -(x / y);
	} else if (y <= 2.5e+16) {
		tmp = x;
	} else if ((y <= 2.1e+44) || (!(y <= 3.1e+150) && (y <= 1.55e+170))) {
		tmp = (z / y) * -x;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if y <= -4.4e-122:
		tmp = x
	elif y <= 5.8e-23:
		tmp = z * -(x / y)
	elif y <= 2.5e+16:
		tmp = x
	elif (y <= 2.1e+44) or (not (y <= 3.1e+150) and (y <= 1.55e+170)):
		tmp = (z / y) * -x
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= -4.4e-122)
		tmp = x;
	elseif (y <= 5.8e-23)
		tmp = Float64(z * Float64(-Float64(x / y)));
	elseif (y <= 2.5e+16)
		tmp = x;
	elseif ((y <= 2.1e+44) || (!(y <= 3.1e+150) && (y <= 1.55e+170)))
		tmp = Float64(Float64(z / y) * Float64(-x));
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (y <= -4.4e-122)
		tmp = x;
	elseif (y <= 5.8e-23)
		tmp = z * -(x / y);
	elseif (y <= 2.5e+16)
		tmp = x;
	elseif ((y <= 2.1e+44) || (~((y <= 3.1e+150)) && (y <= 1.55e+170)))
		tmp = (z / y) * -x;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, -4.4e-122], x, If[LessEqual[y, 5.8e-23], N[(z * (-N[(x / y), $MachinePrecision])), $MachinePrecision], If[LessEqual[y, 2.5e+16], x, If[Or[LessEqual[y, 2.1e+44], And[N[Not[LessEqual[y, 3.1e+150]], $MachinePrecision], LessEqual[y, 1.55e+170]]], N[(N[(z / y), $MachinePrecision] * (-x)), $MachinePrecision], x]]]]

Derivation

1. Split input into 3 regimes

2. if y < -4.4e-122 or 5.8000000000000003e-23 < y < 2.5e16 or 2.09999999999999987e44 < y < 3.10000000000000014e150 or 1.55e170 < y

   1. Initial program 79.9%

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

      1. associate-*r/ 99.9%

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

   3. Simplified 99.9%

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

   4. Taylor expanded in y around inf 82.4%

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

   if -4.4e-122 < y < 5.8000000000000003e-23

   1. Initial program 97.9%

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

      1. associate-*r/ 91.6%

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

   3. Simplified 91.6%

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

   4. Taylor expanded in y around 0 86.4%

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

      1. associate-*l/ 81.9%

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

      2. neg-mul-1 81.9%

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

      3. distribute-rgt-neg-out 81.9%

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

   6. Simplified 81.9%

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

   if 2.5e16 < y < 2.09999999999999987e44 or 3.10000000000000014e150 < y < 1.55e170

   1. Initial program 99.7%

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

      1. associate-*r/ 99.7%

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

   3. Simplified 99.7%

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

   4. Taylor expanded in y around 0 80.6%

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

      1. mul-1-neg 80.6%

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

      2. *-commutative 80.6%

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

      3. associate-/l* 52.4%

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

      4. associate-/r/ 80.5%

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

      5. distribute-rgt-neg-in 80.5%

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

   6. Simplified 80.5%

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

4. Final simplification 82.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -4.4 \cdot 10^{-122}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 5.8 \cdot 10^{-23}:\\ \;\;\;\;z \cdot \left(-\frac{x}{y}\right)\\ \mathbf{elif}\;y \leq 2.5 \cdot 10^{+16}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 2.1 \cdot 10^{+44} \lor \neg \left(y \leq 3.1 \cdot 10^{+150}\right) \land y \leq 1.55 \cdot 10^{+170}:\\ \;\;\;\;\frac{z}{y} \cdot \left(-x\right)\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]

Alternative 3: 71.4% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -4.4 \cdot 10^{-122}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 4.5 \cdot 10^{-22}:\\ \;\;\;\;z \cdot \left(-\frac{x}{y}\right)\\ \mathbf{elif}\;y \leq 1.9 \cdot 10^{+16}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 2.8 \cdot 10^{+45}:\\ \;\;\;\;\frac{z}{y} \cdot \left(-x\right)\\ \mathbf{elif}\;y \leq 3.1 \cdot 10^{+150}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 1.55 \cdot 10^{+170}:\\ \;\;\;\;\frac{x}{\frac{-y}{z}}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y -4.4e-122)
   x
   (if (<= y 4.5e-22)
     (* z (- (/ x y)))
     (if (<= y 1.9e+16)
       x
       (if (<= y 2.8e+45)
         (* (/ z y) (- x))
         (if (<= y 3.1e+150) x (if (<= y 1.55e+170) (/ x (/ (- y) z)) x)))))))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= -4.4e-122) {
		tmp = x;
	} else if (y <= 4.5e-22) {
		tmp = z * -(x / y);
	} else if (y <= 1.9e+16) {
		tmp = x;
	} else if (y <= 2.8e+45) {
		tmp = (z / y) * -x;
	} else if (y <= 3.1e+150) {
		tmp = x;
	} else if (y <= 1.55e+170) {
		tmp = x / (-y / 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 <= (-4.4d-122)) then
        tmp = x
    else if (y <= 4.5d-22) then
        tmp = z * -(x / y)
    else if (y <= 1.9d+16) then
        tmp = x
    else if (y <= 2.8d+45) then
        tmp = (z / y) * -x
    else if (y <= 3.1d+150) then
        tmp = x
    else if (y <= 1.55d+170) then
        tmp = x / (-y / 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 <= -4.4e-122) {
		tmp = x;
	} else if (y <= 4.5e-22) {
		tmp = z * -(x / y);
	} else if (y <= 1.9e+16) {
		tmp = x;
	} else if (y <= 2.8e+45) {
		tmp = (z / y) * -x;
	} else if (y <= 3.1e+150) {
		tmp = x;
	} else if (y <= 1.55e+170) {
		tmp = x / (-y / z);
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if y <= -4.4e-122:
		tmp = x
	elif y <= 4.5e-22:
		tmp = z * -(x / y)
	elif y <= 1.9e+16:
		tmp = x
	elif y <= 2.8e+45:
		tmp = (z / y) * -x
	elif y <= 3.1e+150:
		tmp = x
	elif y <= 1.55e+170:
		tmp = x / (-y / z)
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= -4.4e-122)
		tmp = x;
	elseif (y <= 4.5e-22)
		tmp = Float64(z * Float64(-Float64(x / y)));
	elseif (y <= 1.9e+16)
		tmp = x;
	elseif (y <= 2.8e+45)
		tmp = Float64(Float64(z / y) * Float64(-x));
	elseif (y <= 3.1e+150)
		tmp = x;
	elseif (y <= 1.55e+170)
		tmp = Float64(x / Float64(Float64(-y) / z));
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (y <= -4.4e-122)
		tmp = x;
	elseif (y <= 4.5e-22)
		tmp = z * -(x / y);
	elseif (y <= 1.9e+16)
		tmp = x;
	elseif (y <= 2.8e+45)
		tmp = (z / y) * -x;
	elseif (y <= 3.1e+150)
		tmp = x;
	elseif (y <= 1.55e+170)
		tmp = x / (-y / z);
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, -4.4e-122], x, If[LessEqual[y, 4.5e-22], N[(z * (-N[(x / y), $MachinePrecision])), $MachinePrecision], If[LessEqual[y, 1.9e+16], x, If[LessEqual[y, 2.8e+45], N[(N[(z / y), $MachinePrecision] * (-x)), $MachinePrecision], If[LessEqual[y, 3.1e+150], x, If[LessEqual[y, 1.55e+170], N[(x / N[((-y) / z), $MachinePrecision]), $MachinePrecision], x]]]]]]

Derivation

1. Split input into 4 regimes

2. if y < -4.4e-122 or 4.49999999999999987e-22 < y < 1.9e16 or 2.7999999999999999e45 < y < 3.10000000000000014e150 or 1.55e170 < y

   1. Initial program 79.9%

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

      1. associate-*r/ 99.9%

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

   3. Simplified 99.9%

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

   4. Taylor expanded in y around inf 82.4%

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

   if -4.4e-122 < y < 4.49999999999999987e-22

   1. Initial program 97.9%

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

      1. associate-*r/ 91.6%

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

   3. Simplified 91.6%

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

   4. Taylor expanded in y around 0 86.4%

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

      1. associate-*l/ 81.9%

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

      2. neg-mul-1 81.9%

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

      3. distribute-rgt-neg-out 81.9%

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

   6. Simplified 81.9%

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

   if 1.9e16 < y < 2.7999999999999999e45

   1. Initial program 99.7%

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

      1. associate-*r/ 100.0%

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

   3. Simplified 100.0%

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

   4. Taylor expanded in y around 0 81.5%

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

      1. mul-1-neg 81.5%

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

      2. *-commutative 81.5%

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

      3. associate-/l* 71.8%

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

      4. associate-/r/ 81.7%

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

      5. distribute-rgt-neg-in 81.7%

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

   6. Simplified 81.7%

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

   if 3.10000000000000014e150 < y < 1.55e170

   1. Initial program 99.8%

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

      1. associate-*r/ 99.4%

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

   3. Simplified 99.4%

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

   4. Taylor expanded in y around 0 79.5%

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

      1. associate-*l/ 28.2%

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

      2. neg-mul-1 28.2%

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

      3. distribute-rgt-neg-out 28.2%

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

   6. Simplified 28.2%

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

      1. add-sqr-sqrt 14.4%

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

      2. sqrt-unprod 1.6%

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

      3. sqr-neg 1.6%

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

      4. sqrt-unprod 2.1%

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

      5. add-sqr-sqrt 4.3%

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

      6. associate-/r/ 3.4%

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

      7. frac-2neg 3.4%

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

      8. add-sqr-sqrt 1.0%

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

      9. sqrt-unprod 28.0%

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

      10. sqr-neg 28.0%

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

      11. sqrt-unprod 54.1%

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

      12. add-sqr-sqrt 79.3%

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

   8. Applied egg-rr 79.3%

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

4. Final simplification 82.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -4.4 \cdot 10^{-122}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 4.5 \cdot 10^{-22}:\\ \;\;\;\;z \cdot \left(-\frac{x}{y}\right)\\ \mathbf{elif}\;y \leq 1.9 \cdot 10^{+16}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 2.8 \cdot 10^{+45}:\\ \;\;\;\;\frac{z}{y} \cdot \left(-x\right)\\ \mathbf{elif}\;y \leq 3.1 \cdot 10^{+150}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 1.55 \cdot 10^{+170}:\\ \;\;\;\;\frac{x}{\frac{-y}{z}}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]

Alternative 4: 71.4% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{x \cdot \left(-z\right)}{y}\\ \mathbf{if}\;y \leq -4 \cdot 10^{-122}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 3.5 \cdot 10^{-22}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;y \leq 8.4 \cdot 10^{+15}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 2.9 \cdot 10^{+41}:\\ \;\;\;\;\frac{z}{y} \cdot \left(-x\right)\\ \mathbf{elif}\;y \leq 3.1 \cdot 10^{+150}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 1.55 \cdot 10^{+170}:\\ \;\;\;\;t_0\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (/ (* x (- z)) y)))
   (if (<= y -4e-122)
     x
     (if (<= y 3.5e-22)
       t_0
       (if (<= y 8.4e+15)
         x
         (if (<= y 2.9e+41)
           (* (/ z y) (- x))
           (if (<= y 3.1e+150) x (if (<= y 1.55e+170) t_0 x))))))))

C

double code(double x, double y, double z) {
	double t_0 = (x * -z) / y;
	double tmp;
	if (y <= -4e-122) {
		tmp = x;
	} else if (y <= 3.5e-22) {
		tmp = t_0;
	} else if (y <= 8.4e+15) {
		tmp = x;
	} else if (y <= 2.9e+41) {
		tmp = (z / y) * -x;
	} else if (y <= 3.1e+150) {
		tmp = x;
	} else if (y <= 1.55e+170) {
		tmp = t_0;
	} 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) :: t_0
    real(8) :: tmp
    t_0 = (x * -z) / y
    if (y <= (-4d-122)) then
        tmp = x
    else if (y <= 3.5d-22) then
        tmp = t_0
    else if (y <= 8.4d+15) then
        tmp = x
    else if (y <= 2.9d+41) then
        tmp = (z / y) * -x
    else if (y <= 3.1d+150) then
        tmp = x
    else if (y <= 1.55d+170) then
        tmp = t_0
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = (x * -z) / y;
	double tmp;
	if (y <= -4e-122) {
		tmp = x;
	} else if (y <= 3.5e-22) {
		tmp = t_0;
	} else if (y <= 8.4e+15) {
		tmp = x;
	} else if (y <= 2.9e+41) {
		tmp = (z / y) * -x;
	} else if (y <= 3.1e+150) {
		tmp = x;
	} else if (y <= 1.55e+170) {
		tmp = t_0;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = (x * -z) / y
	tmp = 0
	if y <= -4e-122:
		tmp = x
	elif y <= 3.5e-22:
		tmp = t_0
	elif y <= 8.4e+15:
		tmp = x
	elif y <= 2.9e+41:
		tmp = (z / y) * -x
	elif y <= 3.1e+150:
		tmp = x
	elif y <= 1.55e+170:
		tmp = t_0
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(Float64(x * Float64(-z)) / y)
	tmp = 0.0
	if (y <= -4e-122)
		tmp = x;
	elseif (y <= 3.5e-22)
		tmp = t_0;
	elseif (y <= 8.4e+15)
		tmp = x;
	elseif (y <= 2.9e+41)
		tmp = Float64(Float64(z / y) * Float64(-x));
	elseif (y <= 3.1e+150)
		tmp = x;
	elseif (y <= 1.55e+170)
		tmp = t_0;
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = (x * -z) / y;
	tmp = 0.0;
	if (y <= -4e-122)
		tmp = x;
	elseif (y <= 3.5e-22)
		tmp = t_0;
	elseif (y <= 8.4e+15)
		tmp = x;
	elseif (y <= 2.9e+41)
		tmp = (z / y) * -x;
	elseif (y <= 3.1e+150)
		tmp = x;
	elseif (y <= 1.55e+170)
		tmp = t_0;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(x * (-z)), $MachinePrecision] / y), $MachinePrecision]}, If[LessEqual[y, -4e-122], x, If[LessEqual[y, 3.5e-22], t$95$0, If[LessEqual[y, 8.4e+15], x, If[LessEqual[y, 2.9e+41], N[(N[(z / y), $MachinePrecision] * (-x)), $MachinePrecision], If[LessEqual[y, 3.1e+150], x, If[LessEqual[y, 1.55e+170], t$95$0, x]]]]]]]

Derivation

1. Split input into 3 regimes

2. if y < -4.00000000000000024e-122 or 3.50000000000000005e-22 < y < 8.4e15 or 2.89999999999999988e41 < y < 3.10000000000000014e150 or 1.55e170 < y

   1. Initial program 79.9%

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

      1. associate-*r/ 99.9%

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

   3. Simplified 99.9%

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

   4. Taylor expanded in y around inf 82.4%

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

   if -4.00000000000000024e-122 < y < 3.50000000000000005e-22 or 3.10000000000000014e150 < y < 1.55e170

   1. Initial program 98.1%

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

   2. Taylor expanded in y around 0 85.9%

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

      1. mul-1-neg 85.9%

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

      2. distribute-rgt-neg-out 85.9%

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

   4. Simplified 85.9%

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

   if 8.4e15 < y < 2.89999999999999988e41

   1. Initial program 99.7%

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

      1. associate-*r/ 100.0%

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

   3. Simplified 100.0%

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

   4. Taylor expanded in y around 0 81.5%

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

      1. mul-1-neg 81.5%

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

      2. *-commutative 81.5%

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

      3. associate-/l* 71.8%

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

      4. associate-/r/ 81.7%

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

      5. distribute-rgt-neg-in 81.7%

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

   6. Simplified 81.7%

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

4. Final simplification 83.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -4 \cdot 10^{-122}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 3.5 \cdot 10^{-22}:\\ \;\;\;\;\frac{x \cdot \left(-z\right)}{y}\\ \mathbf{elif}\;y \leq 8.4 \cdot 10^{+15}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 2.9 \cdot 10^{+41}:\\ \;\;\;\;\frac{z}{y} \cdot \left(-x\right)\\ \mathbf{elif}\;y \leq 3.1 \cdot 10^{+150}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 1.55 \cdot 10^{+170}:\\ \;\;\;\;\frac{x \cdot \left(-z\right)}{y}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]

Alternative 5: 71.9% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -4.4 \cdot 10^{-122}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 1.9 \cdot 10^{-21} \lor \neg \left(y \leq 4 \cdot 10^{+15}\right) \land y \leq 4.4 \cdot 10^{+41}:\\ \;\;\;\;z \cdot \left(-\frac{x}{y}\right)\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y -4.4e-122)
   x
   (if (or (<= y 1.9e-21) (and (not (<= y 4e+15)) (<= y 4.4e+41)))
     (* z (- (/ x y)))
     x)))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= -4.4e-122) {
		tmp = x;
	} else if ((y <= 1.9e-21) || (!(y <= 4e+15) && (y <= 4.4e+41))) {
		tmp = z * -(x / 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 (y <= (-4.4d-122)) then
        tmp = x
    else if ((y <= 1.9d-21) .or. (.not. (y <= 4d+15)) .and. (y <= 4.4d+41)) then
        tmp = z * -(x / y)
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (y <= -4.4e-122) {
		tmp = x;
	} else if ((y <= 1.9e-21) || (!(y <= 4e+15) && (y <= 4.4e+41))) {
		tmp = z * -(x / y);
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if y <= -4.4e-122:
		tmp = x
	elif (y <= 1.9e-21) or (not (y <= 4e+15) and (y <= 4.4e+41)):
		tmp = z * -(x / y)
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= -4.4e-122)
		tmp = x;
	elseif ((y <= 1.9e-21) || (!(y <= 4e+15) && (y <= 4.4e+41)))
		tmp = Float64(z * Float64(-Float64(x / y)));
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (y <= -4.4e-122)
		tmp = x;
	elseif ((y <= 1.9e-21) || (~((y <= 4e+15)) && (y <= 4.4e+41)))
		tmp = z * -(x / y);
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, -4.4e-122], x, If[Or[LessEqual[y, 1.9e-21], And[N[Not[LessEqual[y, 4e+15]], $MachinePrecision], LessEqual[y, 4.4e+41]]], N[(z * (-N[(x / y), $MachinePrecision])), $MachinePrecision], x]]

Derivation

1. Split input into 2 regimes

2. if y < -4.4e-122 or 1.8999999999999999e-21 < y < 4e15 or 4.3999999999999998e41 < y

   1. Initial program 81.0%

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

      1. associate-*r/ 99.9%

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

   3. Simplified 99.9%

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

   4. Taylor expanded in y around inf 78.8%

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

   if -4.4e-122 < y < 1.8999999999999999e-21 or 4e15 < y < 4.3999999999999998e41

   1. Initial program 98.1%

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

      1. associate-*r/ 92.3%

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

   3. Simplified 92.3%

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

   4. Taylor expanded in y around 0 85.9%

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

      1. associate-*l/ 81.6%

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

      2. neg-mul-1 81.6%

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

      3. distribute-rgt-neg-out 81.6%

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

   6. Simplified 81.6%

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

4. Final simplification 80.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -4.4 \cdot 10^{-122}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 1.9 \cdot 10^{-21} \lor \neg \left(y \leq 4 \cdot 10^{+15}\right) \land y \leq 4.4 \cdot 10^{+41}:\\ \;\;\;\;z \cdot \left(-\frac{x}{y}\right)\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]

Alternative 6: 52.6% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.55 \cdot 10^{-129}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 2.55 \cdot 10^{-203}:\\ \;\;\;\;y \cdot \frac{x}{y}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y -1.55e-129) x (if (<= y 2.55e-203) (* y (/ x y)) x)))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= -1.55e-129) {
		tmp = x;
	} else if (y <= 2.55e-203) {
		tmp = y * (x / 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 (y <= (-1.55d-129)) then
        tmp = x
    else if (y <= 2.55d-203) then
        tmp = y * (x / y)
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (y <= -1.55e-129) {
		tmp = x;
	} else if (y <= 2.55e-203) {
		tmp = y * (x / y);
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if y <= -1.55e-129:
		tmp = x
	elif y <= 2.55e-203:
		tmp = y * (x / y)
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= -1.55e-129)
		tmp = x;
	elseif (y <= 2.55e-203)
		tmp = Float64(y * Float64(x / y));
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (y <= -1.55e-129)
		tmp = x;
	elseif (y <= 2.55e-203)
		tmp = y * (x / y);
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, -1.55e-129], x, If[LessEqual[y, 2.55e-203], N[(y * N[(x / y), $MachinePrecision]), $MachinePrecision], x]]

Derivation

1. Split input into 2 regimes

2. if y < -1.55e-129 or 2.54999999999999992e-203 < y

   1. Initial program 85.0%

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

      1. associate-*r/ 99.4%

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

   3. Simplified 99.4%

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

   4. Taylor expanded in y around inf 66.8%

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

   if -1.55e-129 < y < 2.54999999999999992e-203

   1. Initial program 97.2%

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

   2. Taylor expanded in y around inf 11.4%

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

      1. associate-/l* 11.4%

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

      2. associate-/r/ 29.2%

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

   4. Applied egg-rr 29.2%

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

4. Final simplification 56.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1.55 \cdot 10^{-129}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 2.55 \cdot 10^{-203}:\\ \;\;\;\;y \cdot \frac{x}{y}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]

Alternative 7: 95.8% accurate, 1.0× speedup

Math

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

FPCore

(FPCore (x y z) :precision binary64 (* x (/ (- y z) y)))

C

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

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 - z) / y)
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 88.5%

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

   1. associate-*r/ 96.6%

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

3. Simplified 96.6%

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

4. Final simplification 96.6%

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

Alternative 8: 50.6% accurate, 7.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_, y_, z_] := x

Derivation

1. Initial program 88.5%

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

   1. associate-*r/ 96.6%

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

3. Simplified 96.6%

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

4. Taylor expanded in y around inf 51.0%

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

5. Final simplification 51.0%

   \[\leadsto x \]

Developer target: 96.1% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z < -2.060202331921739 \cdot 10^{+104}:\\ \;\;\;\;x - \frac{z \cdot x}{y}\\ \mathbf{elif}\;z < 1.6939766013828526 \cdot 10^{+213}:\\ \;\;\;\;\frac{x}{\frac{y}{y - z}}\\ \mathbf{else}:\\ \;\;\;\;\left(y - z\right) \cdot \frac{x}{y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (< z -2.060202331921739e+104)
   (- x (/ (* z x) y))
   (if (< z 1.6939766013828526e+213) (/ x (/ y (- y z))) (* (- y z) (/ x y)))))

C

double code(double x, double y, double z) {
	double tmp;
	if (z < -2.060202331921739e+104) {
		tmp = x - ((z * x) / y);
	} else if (z < 1.6939766013828526e+213) {
		tmp = x / (y / (y - z));
	} else {
		tmp = (y - z) * (x / y);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (z < (-2.060202331921739d+104)) then
        tmp = x - ((z * x) / y)
    else if (z < 1.6939766013828526d+213) then
        tmp = x / (y / (y - z))
    else
        tmp = (y - z) * (x / y)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (z < -2.060202331921739e+104) {
		tmp = x - ((z * x) / y);
	} else if (z < 1.6939766013828526e+213) {
		tmp = x / (y / (y - z));
	} else {
		tmp = (y - z) * (x / y);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if z < -2.060202331921739e+104:
		tmp = x - ((z * x) / y)
	elif z < 1.6939766013828526e+213:
		tmp = x / (y / (y - z))
	else:
		tmp = (y - z) * (x / y)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (z < -2.060202331921739e+104)
		tmp = Float64(x - Float64(Float64(z * x) / y));
	elseif (z < 1.6939766013828526e+213)
		tmp = Float64(x / Float64(y / Float64(y - z)));
	else
		tmp = Float64(Float64(y - z) * Float64(x / y));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (z < -2.060202331921739e+104)
		tmp = x - ((z * x) / y);
	elseif (z < 1.6939766013828526e+213)
		tmp = x / (y / (y - z));
	else
		tmp = (y - z) * (x / y);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Less[z, -2.060202331921739e+104], N[(x - N[(N[(z * x), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision], If[Less[z, 1.6939766013828526e+213], N[(x / N[(y / N[(y - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(y - z), $MachinePrecision] * N[(x / y), $MachinePrecision]), $MachinePrecision]]]

Reproduce

herbie shell --seed 2023318 
(FPCore (x y z)
  :name "Diagrams.Backend.Cairo.Internal:setTexture from diagrams-cairo-1.3.0.3"
  :precision binary64

  :herbie-target
  (if (< z -2.060202331921739e+104) (- x (/ (* z x) y)) (if (< z 1.6939766013828526e+213) (/ x (/ y (- y z))) (* (- y z) (/ x y))))

  (/ (* x (- y z)) y))