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

Percentage Accurate: 84.2% → 97.0%

Time: 8.1s

Alternatives: 9

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: 84.2% 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: 97.0% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -3.5 \cdot 10^{-262} \lor \neg \left(x \leq 5 \cdot 10^{-47}\right):\\ \;\;\;\;x - x \cdot \frac{z}{y}\\ \mathbf{else}:\\ \;\;\;\;x - \frac{z}{\frac{y}{x}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= x -3.5e-262) (not (<= x 5e-47)))
   (- x (* x (/ z y)))
   (- x (/ z (/ y x)))))

C

double code(double x, double y, double z) {
	double tmp;
	if ((x <= -3.5e-262) || !(x <= 5e-47)) {
		tmp = x - (x * (z / y));
	} else {
		tmp = x - (z / (y / 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 <= (-3.5d-262)) .or. (.not. (x <= 5d-47))) then
        tmp = x - (x * (z / y))
    else
        tmp = x - (z / (y / x))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((x <= -3.5e-262) || !(x <= 5e-47)) {
		tmp = x - (x * (z / y));
	} else {
		tmp = x - (z / (y / x));
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (x <= -3.5e-262) or not (x <= 5e-47):
		tmp = x - (x * (z / y))
	else:
		tmp = x - (z / (y / x))
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((x <= -3.5e-262) || !(x <= 5e-47))
		tmp = Float64(x - Float64(x * Float64(z / y)));
	else
		tmp = Float64(x - Float64(z / Float64(y / x)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((x <= -3.5e-262) || ~((x <= 5e-47)))
		tmp = x - (x * (z / y));
	else
		tmp = x - (z / (y / x));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[x, -3.5e-262], N[Not[LessEqual[x, 5e-47]], $MachinePrecision]], N[(x - N[(x * N[(z / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x - N[(z / N[(y / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -3.50000000000000011e-262 or 5.00000000000000011e-47 < x

   1. Initial program 83.9%

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

      1. --rgt-identity 83.9%

         \[\leadsto \color{blue}{\frac{x \cdot \left(y - z\right)}{y} - 0} \]

      2. associate-*l/ 86.4%

         \[\leadsto \color{blue}{\frac{x}{y} \cdot \left(y - z\right)} - 0 \]

      3. sub-neg 86.4%

         \[\leadsto \frac{x}{y} \cdot \color{blue}{\left(y + \left(-z\right)\right)} - 0 \]

      4. distribute-rgt-in 78.2%

         \[\leadsto \color{blue}{\left(y \cdot \frac{x}{y} + \left(-z\right) \cdot \frac{x}{y}\right)} - 0 \]

      5. *-commutative 78.2%

         \[\leadsto \left(\color{blue}{\frac{x}{y} \cdot y} + \left(-z\right) \cdot \frac{x}{y}\right) - 0 \]

      6. distribute-lft-neg-out 78.2%

         \[\leadsto \left(\frac{x}{y} \cdot y + \color{blue}{\left(-z \cdot \frac{x}{y}\right)}\right) - 0 \]

      7. unsub-neg 78.2%

         \[\leadsto \color{blue}{\left(\frac{x}{y} \cdot y - z \cdot \frac{x}{y}\right)} - 0 \]

      8. associate--r+ 78.2%

         \[\leadsto \color{blue}{\frac{x}{y} \cdot y - \left(z \cdot \frac{x}{y} + 0\right)} \]

      9. associate-*l/ 75.8%

         \[\leadsto \color{blue}{\frac{x \cdot y}{y}} - \left(z \cdot \frac{x}{y} + 0\right) \]

      10. associate-/l* 90.9%

          \[\leadsto \color{blue}{\frac{x}{\frac{y}{y}}} - \left(z \cdot \frac{x}{y} + 0\right) \]

      11. *-inverses 90.9%

          \[\leadsto \frac{x}{\color{blue}{1}} - \left(z \cdot \frac{x}{y} + 0\right) \]

      12. /-rgt-identity 90.9%

          \[\leadsto \color{blue}{x} - \left(z \cdot \frac{x}{y} + 0\right) \]

      13. +-rgt-identity 90.9%

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

      14. *-commutative 90.9%

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

      15. associate-/r/ 98.9%

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

   3. Simplified 98.9%

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

      1. clear-num 98.9%

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

      2. associate-/r/ 98.9%

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

      3. clear-num 99.0%

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

   5. Applied egg-rr 99.0%

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

   if -3.50000000000000011e-262 < x < 5.00000000000000011e-47

   1. Initial program 93.1%

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

      1. --rgt-identity 93.1%

         \[\leadsto \color{blue}{\frac{x \cdot \left(y - z\right)}{y} - 0} \]

      2. associate-*l/ 73.9%

         \[\leadsto \color{blue}{\frac{x}{y} \cdot \left(y - z\right)} - 0 \]

      3. sub-neg 73.9%

         \[\leadsto \frac{x}{y} \cdot \color{blue}{\left(y + \left(-z\right)\right)} - 0 \]

      4. distribute-rgt-in 73.9%

         \[\leadsto \color{blue}{\left(y \cdot \frac{x}{y} + \left(-z\right) \cdot \frac{x}{y}\right)} - 0 \]

      5. *-commutative 73.9%

         \[\leadsto \left(\color{blue}{\frac{x}{y} \cdot y} + \left(-z\right) \cdot \frac{x}{y}\right) - 0 \]

      6. distribute-lft-neg-out 73.9%

         \[\leadsto \left(\frac{x}{y} \cdot y + \color{blue}{\left(-z \cdot \frac{x}{y}\right)}\right) - 0 \]

      7. unsub-neg 73.9%

         \[\leadsto \color{blue}{\left(\frac{x}{y} \cdot y - z \cdot \frac{x}{y}\right)} - 0 \]

      8. associate--r+ 73.9%

         \[\leadsto \color{blue}{\frac{x}{y} \cdot y - \left(z \cdot \frac{x}{y} + 0\right)} \]

      9. associate-*l/ 95.0%

         \[\leadsto \color{blue}{\frac{x \cdot y}{y}} - \left(z \cdot \frac{x}{y} + 0\right) \]

      10. associate-/l* 98.2%

          \[\leadsto \color{blue}{\frac{x}{\frac{y}{y}}} - \left(z \cdot \frac{x}{y} + 0\right) \]

      11. *-inverses 98.2%

          \[\leadsto \frac{x}{\color{blue}{1}} - \left(z \cdot \frac{x}{y} + 0\right) \]

      12. /-rgt-identity 98.2%

          \[\leadsto \color{blue}{x} - \left(z \cdot \frac{x}{y} + 0\right) \]

      13. +-rgt-identity 98.2%

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

      14. *-commutative 98.2%

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

      15. associate-/r/ 83.6%

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

   3. Simplified 83.6%

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

      1. clear-num 83.6%

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

      2. associate-/r/ 83.6%

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

      3. clear-num 83.7%

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

   5. Applied egg-rr 83.7%

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

      1. associate-*l/ 96.4%

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

      2. associate-/l* 98.1%

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

   7. Applied egg-rr 98.1%

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

4. Final simplification 98.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -3.5 \cdot 10^{-262} \lor \neg \left(x \leq 5 \cdot 10^{-47}\right):\\ \;\;\;\;x - x \cdot \frac{z}{y}\\ \mathbf{else}:\\ \;\;\;\;x - \frac{z}{\frac{y}{x}}\\ \end{array} \]

Alternative 2: 71.3% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -8.8 \cdot 10^{+88} \lor \neg \left(z \leq 2.25 \cdot 10^{+27}\right):\\ \;\;\;\;\frac{-x}{\frac{y}{z}}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= z -8.8e+88) (not (<= z 2.25e+27))) (/ (- x) (/ y z)) x))

C

double code(double x, double y, double z) {
	double tmp;
	if ((z <= -8.8e+88) || !(z <= 2.25e+27)) {
		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 ((z <= (-8.8d+88)) .or. (.not. (z <= 2.25d+27))) 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 ((z <= -8.8e+88) || !(z <= 2.25e+27)) {
		tmp = -x / (y / z);
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (z <= -8.8e+88) or not (z <= 2.25e+27):
		tmp = -x / (y / z)
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((z <= -8.8e+88) || !(z <= 2.25e+27))
		tmp = Float64(Float64(-x) / Float64(y / z));
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((z <= -8.8e+88) || ~((z <= 2.25e+27)))
		tmp = -x / (y / z);
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[z, -8.8e+88], N[Not[LessEqual[z, 2.25e+27]], $MachinePrecision]], N[((-x) / N[(y / z), $MachinePrecision]), $MachinePrecision], x]

Derivation

1. Split input into 2 regimes

2. if z < -8.80000000000000035e88 or 2.25e27 < z

   1. Initial program 88.0%

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

   2. Taylor expanded in y around 0 77.3%

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

      1. mul-1-neg 77.3%

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

      2. distribute-rgt-neg-in 77.3%

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

   4. Simplified 77.3%

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

      1. frac-2neg 77.3%

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

      2. div-inv 77.2%

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

      3. distribute-rgt-neg-out 77.2%

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

      4. remove-double-neg 77.2%

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

   6. Applied egg-rr 77.2%

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

      1. un-div-inv 77.3%

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

      2. add-sqr-sqrt 38.8%

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

      3. sqrt-unprod 28.7%

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

      4. sqr-neg 28.7%

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

      5. sqrt-unprod 0.5%

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

      6. add-sqr-sqrt 1.2%

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

      7. associate-*l/ 1.2%

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

      8. associate-/r/ 1.3%

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

      9. frac-2neg 1.3%

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

      10. distribute-frac-neg 1.3%

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

      11. distribute-neg-frac 1.3%

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

      12. associate-/l* 1.2%

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

      13. *-commutative 1.2%

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

      14. associate-/l* 1.2%

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

      15. add-sqr-sqrt 0.7%

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

      16. sqrt-unprod 35.5%

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

      17. sqr-neg 35.5%

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

      18. sqrt-unprod 36.0%

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

      19. add-sqr-sqrt 72.6%

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

   8. Applied egg-rr 72.6%

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

   if -8.80000000000000035e88 < z < 2.25e27

   1. Initial program 84.5%

      \[\frac{x \cdot \left(y - z\right)}{y} \]
   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 75.4%

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

4. Final simplification 74.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -8.8 \cdot 10^{+88} \lor \neg \left(z \leq 2.25 \cdot 10^{+27}\right):\\ \;\;\;\;\frac{-x}{\frac{y}{z}}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]

Alternative 3: 71.0% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -9 \cdot 10^{+88} \lor \neg \left(z \leq 2.5 \cdot 10^{+28}\right):\\ \;\;\;\;x \cdot \frac{-z}{y}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= z -9e+88) (not (<= z 2.5e+28))) (* x (/ (- z) y)) x))

C

double code(double x, double y, double z) {
	double tmp;
	if ((z <= -9e+88) || !(z <= 2.5e+28)) {
		tmp = x * (-z / 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 ((z <= (-9d+88)) .or. (.not. (z <= 2.5d+28))) then
        tmp = x * (-z / y)
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((z <= -9e+88) || !(z <= 2.5e+28)) {
		tmp = x * (-z / y);
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (z <= -9e+88) or not (z <= 2.5e+28):
		tmp = x * (-z / y)
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((z <= -9e+88) || !(z <= 2.5e+28))
		tmp = Float64(x * Float64(Float64(-z) / y));
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((z <= -9e+88) || ~((z <= 2.5e+28)))
		tmp = x * (-z / y);
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[z, -9e+88], N[Not[LessEqual[z, 2.5e+28]], $MachinePrecision]], N[(x * N[((-z) / y), $MachinePrecision]), $MachinePrecision], x]

Derivation

1. Split input into 2 regimes

2. if z < -9e88 or 2.49999999999999979e28 < z

   1. Initial program 88.0%

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

      1. associate-*r/ 89.6%

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

   3. Simplified 89.6%

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

   4. Taylor expanded in y around 0 72.6%

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

      1. neg-mul-1 72.6%

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

      2. distribute-neg-frac 72.6%

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

   6. Simplified 72.6%

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

   if -9e88 < z < 2.49999999999999979e28

   1. Initial program 84.5%

      \[\frac{x \cdot \left(y - z\right)}{y} \]
   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 75.4%

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

4. Final simplification 74.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -9 \cdot 10^{+88} \lor \neg \left(z \leq 2.5 \cdot 10^{+28}\right):\\ \;\;\;\;x \cdot \frac{-z}{y}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]

Alternative 4: 72.7% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -8.8 \cdot 10^{+88} \lor \neg \left(z \leq 2.25 \cdot 10^{+27}\right):\\ \;\;\;\;z \cdot \frac{-x}{y}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= z -8.8e+88) (not (<= z 2.25e+27))) (* z (/ (- x) y)) x))

C

double code(double x, double y, double z) {
	double tmp;
	if ((z <= -8.8e+88) || !(z <= 2.25e+27)) {
		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 ((z <= (-8.8d+88)) .or. (.not. (z <= 2.25d+27))) 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 ((z <= -8.8e+88) || !(z <= 2.25e+27)) {
		tmp = z * (-x / y);
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (z <= -8.8e+88) or not (z <= 2.25e+27):
		tmp = z * (-x / y)
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((z <= -8.8e+88) || !(z <= 2.25e+27))
		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 ((z <= -8.8e+88) || ~((z <= 2.25e+27)))
		tmp = z * (-x / y);
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[z, -8.8e+88], N[Not[LessEqual[z, 2.25e+27]], $MachinePrecision]], N[(z * N[((-x) / y), $MachinePrecision]), $MachinePrecision], x]

Derivation

1. Split input into 2 regimes

2. if z < -8.80000000000000035e88 or 2.25e27 < z

   1. Initial program 88.0%

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

      1. associate-*r/ 89.6%

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

   3. Simplified 89.6%

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

   4. Taylor expanded in y around 0 77.3%

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

      1. associate-*r/ 78.4%

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

      2. associate-*r* 78.4%

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

      3. neg-mul-1 78.4%

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

      4. *-commutative 78.4%

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

   6. Simplified 78.4%

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

   if -8.80000000000000035e88 < z < 2.25e27

   1. Initial program 84.5%

      \[\frac{x \cdot \left(y - z\right)}{y} \]
   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 75.4%

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

4. Final simplification 76.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -8.8 \cdot 10^{+88} \lor \neg \left(z \leq 2.25 \cdot 10^{+27}\right):\\ \;\;\;\;z \cdot \frac{-x}{y}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]

Alternative 5: 72.6% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -1 \cdot 10^{+89}:\\ \;\;\;\;\frac{x \cdot \left(-z\right)}{y}\\ \mathbf{elif}\;z \leq 2 \cdot 10^{+27}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;z \cdot \frac{-x}{y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= z -1e+89) (/ (* x (- z)) y) (if (<= z 2e+27) x (* z (/ (- x) y)))))

C

double code(double x, double y, double z) {
	double tmp;
	if (z <= -1e+89) {
		tmp = (x * -z) / y;
	} else if (z <= 2e+27) {
		tmp = x;
	} else {
		tmp = 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 <= (-1d+89)) then
        tmp = (x * -z) / y
    else if (z <= 2d+27) then
        tmp = x
    else
        tmp = z * (-x / y)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (z <= -1e+89) {
		tmp = (x * -z) / y;
	} else if (z <= 2e+27) {
		tmp = x;
	} else {
		tmp = z * (-x / y);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if z <= -1e+89:
		tmp = (x * -z) / y
	elif z <= 2e+27:
		tmp = x
	else:
		tmp = z * (-x / y)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (z <= -1e+89)
		tmp = Float64(Float64(x * Float64(-z)) / y);
	elseif (z <= 2e+27)
		tmp = x;
	else
		tmp = Float64(z * Float64(Float64(-x) / y));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (z <= -1e+89)
		tmp = (x * -z) / y;
	elseif (z <= 2e+27)
		tmp = x;
	else
		tmp = z * (-x / y);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[z, -1e+89], N[(N[(x * (-z)), $MachinePrecision] / y), $MachinePrecision], If[LessEqual[z, 2e+27], x, N[(z * N[((-x) / y), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if z < -9.99999999999999995e88

   1. Initial program 88.6%

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

   2. Taylor expanded in y around 0 76.5%

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

      1. mul-1-neg 76.5%

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

      2. distribute-rgt-neg-in 76.5%

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

   4. Simplified 76.5%

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

   if -9.99999999999999995e88 < z < 2e27

   1. Initial program 84.5%

      \[\frac{x \cdot \left(y - z\right)}{y} \]
   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 75.4%

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

   if 2e27 < z

   1. Initial program 87.4%

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

      1. associate-*r/ 90.5%

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

   3. Simplified 90.5%

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

   4. Taylor expanded in y around 0 77.9%

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

      1. associate-*r/ 81.2%

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

      2. associate-*r* 81.2%

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

      3. neg-mul-1 81.2%

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

      4. *-commutative 81.2%

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

   6. Simplified 81.2%

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

4. Final simplification 77.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -1 \cdot 10^{+89}:\\ \;\;\;\;\frac{x \cdot \left(-z\right)}{y}\\ \mathbf{elif}\;z \leq 2 \cdot 10^{+27}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;z \cdot \frac{-x}{y}\\ \end{array} \]

Alternative 6: 52.3% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.55 \cdot 10^{-121}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 4.6 \cdot 10^{-245}:\\ \;\;\;\;y \cdot \frac{x}{y}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y -1.55e-121) x (if (<= y 4.6e-245) (* y (/ x y)) x)))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= -1.55e-121) {
		tmp = x;
	} else if (y <= 4.6e-245) {
		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-121)) then
        tmp = x
    else if (y <= 4.6d-245) 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-121) {
		tmp = x;
	} else if (y <= 4.6e-245) {
		tmp = y * (x / y);
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if y <= -1.55e-121:
		tmp = x
	elif y <= 4.6e-245:
		tmp = y * (x / y)
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= -1.55e-121)
		tmp = x;
	elseif (y <= 4.6e-245)
		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-121)
		tmp = x;
	elseif (y <= 4.6e-245)
		tmp = y * (x / y);
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, -1.55e-121], x, If[LessEqual[y, 4.6e-245], N[(y * N[(x / y), $MachinePrecision]), $MachinePrecision], x]]

Derivation

1. Split input into 2 regimes

2. if y < -1.5499999999999999e-121 or 4.6000000000000003e-245 < y

   1. Initial program 83.9%

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

      1. associate-*r/ 98.5%

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

   3. Simplified 98.5%

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

   4. Taylor expanded in y around inf 61.9%

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

   if -1.5499999999999999e-121 < y < 4.6000000000000003e-245

   1. Initial program 93.1%

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

   2. Taylor expanded in y around inf 9.0%

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

      1. associate-/l* 24.8%

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

      2. div-inv 24.8%

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

      3. clear-num 24.9%

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

   4. Applied egg-rr 24.9%

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

4. Final simplification 53.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1.55 \cdot 10^{-121}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 4.6 \cdot 10^{-245}:\\ \;\;\;\;y \cdot \frac{x}{y}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]

Alternative 7: 96.2% 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 86.0%

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

   1. associate-*r/ 95.5%

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

3. Simplified 95.5%

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

4. Final simplification 95.5%

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

Alternative 8: 96.2% accurate, 1.0× speedup

Math

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

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(x * Float64(z / y)))
end

MATLAB

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

Wolfram

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

Derivation

1. Initial program 86.0%

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

   1. --rgt-identity 86.0%

      \[\leadsto \color{blue}{\frac{x \cdot \left(y - z\right)}{y} - 0} \]

   2. associate-*l/ 83.6%

      \[\leadsto \color{blue}{\frac{x}{y} \cdot \left(y - z\right)} - 0 \]

   3. sub-neg 83.6%

      \[\leadsto \frac{x}{y} \cdot \color{blue}{\left(y + \left(-z\right)\right)} - 0 \]

   4. distribute-rgt-in 77.2%

      \[\leadsto \color{blue}{\left(y \cdot \frac{x}{y} + \left(-z\right) \cdot \frac{x}{y}\right)} - 0 \]

   5. *-commutative 77.2%

      \[\leadsto \left(\color{blue}{\frac{x}{y} \cdot y} + \left(-z\right) \cdot \frac{x}{y}\right) - 0 \]

   6. distribute-lft-neg-out 77.2%

      \[\leadsto \left(\frac{x}{y} \cdot y + \color{blue}{\left(-z \cdot \frac{x}{y}\right)}\right) - 0 \]

   7. unsub-neg 77.2%

      \[\leadsto \color{blue}{\left(\frac{x}{y} \cdot y - z \cdot \frac{x}{y}\right)} - 0 \]

   8. associate--r+ 77.2%

      \[\leadsto \color{blue}{\frac{x}{y} \cdot y - \left(z \cdot \frac{x}{y} + 0\right)} \]

   9. associate-*l/ 80.2%

      \[\leadsto \color{blue}{\frac{x \cdot y}{y}} - \left(z \cdot \frac{x}{y} + 0\right) \]

   10. associate-/l* 92.6%

       \[\leadsto \color{blue}{\frac{x}{\frac{y}{y}}} - \left(z \cdot \frac{x}{y} + 0\right) \]

   11. *-inverses 92.6%

       \[\leadsto \frac{x}{\color{blue}{1}} - \left(z \cdot \frac{x}{y} + 0\right) \]

   12. /-rgt-identity 92.6%

       \[\leadsto \color{blue}{x} - \left(z \cdot \frac{x}{y} + 0\right) \]

   13. +-rgt-identity 92.6%

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

   14. *-commutative 92.6%

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

   15. associate-/r/ 95.5%

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

3. Simplified 95.5%

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

   1. clear-num 95.4%

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

   2. associate-/r/ 95.4%

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

   3. clear-num 95.5%

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

5. Applied egg-rr 95.5%

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

6. Final simplification 95.5%

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

Alternative 9: 50.8% 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 86.0%

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

   1. associate-*r/ 95.5%

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

3. Simplified 95.5%

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

4. Taylor expanded in y around inf 50.5%

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

5. Final simplification 50.5%

   \[\leadsto x \]

Developer target: 96.0% 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 2023279 
(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))