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

Percentage Accurate: 84.4% → 96.1%

Time: 6.5s

Alternatives: 10

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: 84.4% 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: 96.1% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -2.15 \cdot 10^{+50} \lor \neg \left(y \leq 3.2 \cdot 10^{-92}\right):\\ \;\;\;\;x \cdot \frac{y - z}{y}\\ \mathbf{else}:\\ \;\;\;\;\left(y - z\right) \cdot \frac{x}{y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= y -2.15e+50) (not (<= y 3.2e-92)))
   (* x (/ (- y z) y))
   (* (- y z) (/ x y))))

C

double code(double x, double y, double z) {
	double tmp;
	if ((y <= -2.15e+50) || !(y <= 3.2e-92)) {
		tmp = x * ((y - z) / y);
	} 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 ((y <= (-2.15d+50)) .or. (.not. (y <= 3.2d-92))) then
        tmp = x * ((y - z) / y)
    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 ((y <= -2.15e+50) || !(y <= 3.2e-92)) {
		tmp = x * ((y - z) / y);
	} else {
		tmp = (y - z) * (x / y);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (y <= -2.15e+50) or not (y <= 3.2e-92):
		tmp = x * ((y - z) / y)
	else:
		tmp = (y - z) * (x / y)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((y <= -2.15e+50) || !(y <= 3.2e-92))
		tmp = Float64(x * Float64(Float64(y - z) / y));
	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 ((y <= -2.15e+50) || ~((y <= 3.2e-92)))
		tmp = x * ((y - z) / y);
	else
		tmp = (y - z) * (x / y);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[y, -2.15e+50], N[Not[LessEqual[y, 3.2e-92]], $MachinePrecision]], N[(x * N[(N[(y - z), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision], N[(N[(y - z), $MachinePrecision] * N[(x / y), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -2.1499999999999999e50 or 3.1999999999999997e-92 < y

   1. Initial program 78.3%

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

      1. associate-/l* 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. Add Preprocessing

   if -2.1499999999999999e50 < y < 3.1999999999999997e-92

   1. Initial program 90.9%

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

      1. associate-/l* 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. Add Preprocessing

   5. Taylor expanded in x around 0 90.9%

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

      1. *-commutative 90.9%

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

      2. associate-*r/ 96.2%

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

   7. Simplified 96.2%

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

4. Final simplification 98.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -2.15 \cdot 10^{+50} \lor \neg \left(y \leq 3.2 \cdot 10^{-92}\right):\\ \;\;\;\;x \cdot \frac{y - z}{y}\\ \mathbf{else}:\\ \;\;\;\;\left(y - z\right) \cdot \frac{x}{y}\\ \end{array} \]
5. Add Preprocessing

Alternative 2: 95.9% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\frac{x \cdot \left(y - z\right)}{y} \leq 5 \cdot 10^{-68}:\\ \;\;\;\;\frac{x}{\frac{y}{y - z}}\\ \mathbf{else}:\\ \;\;\;\;\frac{y - z}{\frac{y}{x}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= (/ (* x (- y z)) y) 5e-68) (/ x (/ y (- y z))) (/ (- y z) (/ y x))))

C

double code(double x, double y, double z) {
	double tmp;
	if (((x * (y - z)) / y) <= 5e-68) {
		tmp = x / (y / (y - z));
	} else {
		tmp = (y - 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 * (y - z)) / y) <= 5d-68) then
        tmp = x / (y / (y - z))
    else
        tmp = (y - z) / (y / x)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (((x * (y - z)) / y) <= 5e-68) {
		tmp = x / (y / (y - z));
	} else {
		tmp = (y - z) / (y / x);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if ((x * (y - z)) / y) <= 5e-68:
		tmp = x / (y / (y - z))
	else:
		tmp = (y - z) / (y / x)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (Float64(Float64(x * Float64(y - z)) / y) <= 5e-68)
		tmp = Float64(x / Float64(y / Float64(y - z)));
	else
		tmp = Float64(Float64(y - z) / Float64(y / x));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (((x * (y - z)) / y) <= 5e-68)
		tmp = x / (y / (y - z));
	else
		tmp = (y - z) / (y / x);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[N[(N[(x * N[(y - z), $MachinePrecision]), $MachinePrecision] / y), $MachinePrecision], 5e-68], N[(x / N[(y / N[(y - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(y - z), $MachinePrecision] / N[(y / x), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (*.f64 x (-.f64 y z)) y) < 4.99999999999999971e-68

   1. Initial program 81.2%

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

      1. associate-/l* 95.3%

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

   3. Simplified 95.3%

      \[\leadsto \color{blue}{x \cdot \frac{y - z}{y}} \]
   4. Add Preprocessing

   5. Taylor expanded in x around 0 81.2%

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

      1. associate-*l/ 80.4%

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

      2. associate-/r/ 96.7%

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

   7. Simplified 96.7%

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

   if 4.99999999999999971e-68 < (/.f64 (*.f64 x (-.f64 y z)) y)

   1. Initial program 91.3%

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

      1. associate-/l* 93.4%

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

   3. Simplified 93.4%

      \[\leadsto \color{blue}{x \cdot \frac{y - z}{y}} \]
   4. Add Preprocessing

   5. Taylor expanded in x around 0 91.3%

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

      1. *-commutative 91.3%

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

      2. associate-*r/ 96.5%

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

   7. Simplified 96.5%

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

      1. clear-num 96.4%

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

      2. un-div-inv 97.1%

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

   9. Applied egg-rr 97.1%

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

4. Final simplification 96.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{x \cdot \left(y - z\right)}{y} \leq 5 \cdot 10^{-68}:\\ \;\;\;\;\frac{x}{\frac{y}{y - z}}\\ \mathbf{else}:\\ \;\;\;\;\frac{y - z}{\frac{y}{x}}\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 73.4% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -1.25 \cdot 10^{-10} \lor \neg \left(z \leq 3900000000\right):\\ \;\;\;\;\frac{x}{y} \cdot \left(-z\right)\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= z -1.25e-10) (not (<= z 3900000000.0))) (* (/ x y) (- z)) x))

C

double code(double x, double y, double z) {
	double tmp;
	if ((z <= -1.25e-10) || !(z <= 3900000000.0)) {
		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 <= (-1.25d-10)) .or. (.not. (z <= 3900000000.0d0))) 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 <= -1.25e-10) || !(z <= 3900000000.0)) {
		tmp = (x / y) * -z;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (z <= -1.25e-10) or not (z <= 3900000000.0):
		tmp = (x / y) * -z
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((z <= -1.25e-10) || !(z <= 3900000000.0))
		tmp = Float64(Float64(x / y) * Float64(-z));
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((z <= -1.25e-10) || ~((z <= 3900000000.0)))
		tmp = (x / y) * -z;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[z, -1.25e-10], N[Not[LessEqual[z, 3900000000.0]], $MachinePrecision]], N[(N[(x / y), $MachinePrecision] * (-z)), $MachinePrecision], x]

Derivation

1. Split input into 2 regimes

2. if z < -1.25000000000000008e-10 or 3.9e9 < z

   1. Initial program 92.0%

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

      1. associate-/l* 89.8%

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

   3. Simplified 89.8%

      \[\leadsto \color{blue}{x \cdot \frac{y - z}{y}} \]
   4. Add Preprocessing

   5. Taylor expanded in x around 0 92.0%

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

      1. *-commutative 92.0%

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

      2. associate-*r/ 88.6%

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

   7. Simplified 88.6%

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

      1. clear-num 87.8%

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

      2. un-div-inv 87.9%

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

   9. Applied egg-rr 87.9%

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

   10. Taylor expanded in y around 0 79.1%

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

       1. mul-1-neg 79.1%

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

       2. associate-*l/ 79.9%

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

       3. distribute-rgt-neg-in 79.9%

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

   12. Simplified 79.9%

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

   if -1.25000000000000008e-10 < z < 3.9e9

   1. Initial program 76.8%

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

      1. associate-/l* 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. Add Preprocessing

   5. Taylor expanded in y around inf 80.9%

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

4. Final simplification 80.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -1.25 \cdot 10^{-10} \lor \neg \left(z \leq 3900000000\right):\\ \;\;\;\;\frac{x}{y} \cdot \left(-z\right)\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 73.3% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -5.2 \cdot 10^{-10}:\\ \;\;\;\;\frac{z}{\frac{y}{-x}}\\ \mathbf{elif}\;z \leq 2100000000:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{y} \cdot \left(-z\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= z -5.2e-10)
   (/ z (/ y (- x)))
   (if (<= z 2100000000.0) x (* (/ x y) (- z)))))

C

double code(double x, double y, double z) {
	double tmp;
	if (z <= -5.2e-10) {
		tmp = z / (y / -x);
	} else if (z <= 2100000000.0) {
		tmp = x;
	} else {
		tmp = (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 (z <= (-5.2d-10)) then
        tmp = z / (y / -x)
    else if (z <= 2100000000.0d0) then
        tmp = x
    else
        tmp = (x / y) * -z
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (z <= -5.2e-10) {
		tmp = z / (y / -x);
	} else if (z <= 2100000000.0) {
		tmp = x;
	} else {
		tmp = (x / y) * -z;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if z <= -5.2e-10:
		tmp = z / (y / -x)
	elif z <= 2100000000.0:
		tmp = x
	else:
		tmp = (x / y) * -z
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (z <= -5.2e-10)
		tmp = Float64(z / Float64(y / Float64(-x)));
	elseif (z <= 2100000000.0)
		tmp = x;
	else
		tmp = Float64(Float64(x / y) * Float64(-z));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (z <= -5.2e-10)
		tmp = z / (y / -x);
	elseif (z <= 2100000000.0)
		tmp = x;
	else
		tmp = (x / y) * -z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[z, -5.2e-10], N[(z / N[(y / (-x)), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 2100000000.0], x, N[(N[(x / y), $MachinePrecision] * (-z)), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if z < -5.19999999999999962e-10

   1. Initial program 94.7%

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

      1. associate-/l* 90.8%

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

   3. Simplified 90.8%

      \[\leadsto \color{blue}{x \cdot \frac{y - z}{y}} \]
   4. Add Preprocessing

   5. Taylor expanded in y around 0 81.6%

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

      1. mul-1-neg 81.6%

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

      2. associate-/l* 73.8%

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

      3. *-commutative 73.8%

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

      4. distribute-rgt-neg-in 73.8%

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

   7. Simplified 73.8%

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

      1. *-commutative 73.8%

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

      2. clear-num 73.8%

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

      3. un-div-inv 75.3%

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

      4. add-sqr-sqrt 31.3%

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

      5. sqrt-unprod 28.2%

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

      6. sqr-neg 28.2%

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

      7. sqrt-unprod 1.0%

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

      8. add-sqr-sqrt 1.7%

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

   9. Applied egg-rr 1.7%

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

       1. associate-/r/ 1.5%

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

   11. Simplified 1.5%

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

       1. *-commutative 1.5%

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

       2. clear-num 1.5%

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

       3. div-inv 1.5%

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

       4. frac-2neg 1.5%

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

       5. distribute-neg-frac2 1.5%

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

       6. add-sqr-sqrt 0.8%

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

       7. sqrt-unprod 39.1%

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

       8. sqr-neg 39.1%

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

       9. sqrt-unprod 49.0%

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

       10. add-sqr-sqrt 81.4%

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

   13. Applied egg-rr 81.4%

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

   if -5.19999999999999962e-10 < z < 2.1e9

   1. Initial program 76.8%

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

      1. associate-/l* 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. Add Preprocessing

   5. Taylor expanded in y around inf 80.9%

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

   if 2.1e9 < z

   1. Initial program 88.7%

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

      1. associate-/l* 88.7%

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

   3. Simplified 88.7%

      \[\leadsto \color{blue}{x \cdot \frac{y - z}{y}} \]
   4. Add Preprocessing

   5. Taylor expanded in x around 0 88.7%

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

      1. *-commutative 88.7%

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

      2. associate-*r/ 90.4%

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

   7. Simplified 90.4%

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

      1. clear-num 88.8%

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

      2. un-div-inv 88.8%

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

   9. Applied egg-rr 88.8%

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

   10. Taylor expanded in y around 0 76.0%

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

       1. mul-1-neg 76.0%

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

       2. associate-*l/ 78.0%

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

       3. distribute-rgt-neg-in 78.0%

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

   12. Simplified 78.0%

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

4. Final simplification 80.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -5.2 \cdot 10^{-10}:\\ \;\;\;\;\frac{z}{\frac{y}{-x}}\\ \mathbf{elif}\;z \leq 2100000000:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{y} \cdot \left(-z\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 73.2% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -1.6 \cdot 10^{-11}:\\ \;\;\;\;\frac{x \cdot \left(-z\right)}{y}\\ \mathbf{elif}\;z \leq 33000000000:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{y} \cdot \left(-z\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= z -1.6e-11)
   (/ (* x (- z)) y)
   (if (<= z 33000000000.0) x (* (/ x y) (- z)))))

C

double code(double x, double y, double z) {
	double tmp;
	if (z <= -1.6e-11) {
		tmp = (x * -z) / y;
	} else if (z <= 33000000000.0) {
		tmp = x;
	} else {
		tmp = (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 (z <= (-1.6d-11)) then
        tmp = (x * -z) / y
    else if (z <= 33000000000.0d0) then
        tmp = x
    else
        tmp = (x / y) * -z
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (z <= -1.6e-11) {
		tmp = (x * -z) / y;
	} else if (z <= 33000000000.0) {
		tmp = x;
	} else {
		tmp = (x / y) * -z;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if z <= -1.6e-11:
		tmp = (x * -z) / y
	elif z <= 33000000000.0:
		tmp = x
	else:
		tmp = (x / y) * -z
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (z <= -1.6e-11)
		tmp = Float64(Float64(x * Float64(-z)) / y);
	elseif (z <= 33000000000.0)
		tmp = x;
	else
		tmp = Float64(Float64(x / y) * Float64(-z));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (z <= -1.6e-11)
		tmp = (x * -z) / y;
	elseif (z <= 33000000000.0)
		tmp = x;
	else
		tmp = (x / y) * -z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[z, -1.6e-11], N[(N[(x * (-z)), $MachinePrecision] / y), $MachinePrecision], If[LessEqual[z, 33000000000.0], x, N[(N[(x / y), $MachinePrecision] * (-z)), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if z < -1.59999999999999997e-11

   1. Initial program 94.7%

      \[\frac{x \cdot \left(y - z\right)}{y} \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0 81.6%

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

      1. associate-*r* 81.6%

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

      2. *-commutative 81.6%

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

      3. mul-1-neg 81.6%

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

   5. Simplified 81.6%

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

   if -1.59999999999999997e-11 < z < 3.3e10

   1. Initial program 76.8%

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

      1. associate-/l* 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. Add Preprocessing

   5. Taylor expanded in y around inf 80.9%

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

   if 3.3e10 < z

   1. Initial program 88.7%

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

      1. associate-/l* 88.7%

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

   3. Simplified 88.7%

      \[\leadsto \color{blue}{x \cdot \frac{y - z}{y}} \]
   4. Add Preprocessing

   5. Taylor expanded in x around 0 88.7%

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

      1. *-commutative 88.7%

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

      2. associate-*r/ 90.4%

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

   7. Simplified 90.4%

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

      1. clear-num 88.8%

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

      2. un-div-inv 88.8%

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

   9. Applied egg-rr 88.8%

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

   10. Taylor expanded in y around 0 76.0%

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

       1. mul-1-neg 76.0%

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

       2. associate-*l/ 78.0%

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

       3. distribute-rgt-neg-in 78.0%

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

   12. Simplified 78.0%

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

4. Final simplification 80.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -1.6 \cdot 10^{-11}:\\ \;\;\;\;\frac{x \cdot \left(-z\right)}{y}\\ \mathbf{elif}\;z \leq 33000000000:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{y} \cdot \left(-z\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 89.7% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq 5 \cdot 10^{-212}:\\ \;\;\;\;\frac{x \cdot \left(y - z\right)}{y}\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{\frac{y}{y - z}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= x 5e-212) (/ (* x (- y z)) y) (/ x (/ y (- y z)))))

C

double code(double x, double y, double z) {
	double tmp;
	if (x <= 5e-212) {
		tmp = (x * (y - z)) / y;
	} else {
		tmp = x / (y / (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 (x <= 5d-212) then
        tmp = (x * (y - z)) / y
    else
        tmp = x / (y / (y - z))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (x <= 5e-212) {
		tmp = (x * (y - z)) / y;
	} else {
		tmp = x / (y / (y - z));
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if x <= 5e-212:
		tmp = (x * (y - z)) / y
	else:
		tmp = x / (y / (y - z))
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (x <= 5e-212)
		tmp = Float64(Float64(x * Float64(y - z)) / y);
	else
		tmp = Float64(x / Float64(y / Float64(y - z)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (x <= 5e-212)
		tmp = (x * (y - z)) / y;
	else
		tmp = x / (y / (y - z));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[x, 5e-212], N[(N[(x * N[(y - z), $MachinePrecision]), $MachinePrecision] / y), $MachinePrecision], N[(x / N[(y / N[(y - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < 5.00000000000000043e-212

   1. Initial program 82.2%

      \[\frac{x \cdot \left(y - z\right)}{y} \]
   2. Add Preprocessing

   if 5.00000000000000043e-212 < x

   1. Initial program 88.3%

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

      1. associate-/l* 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. Add Preprocessing

   5. Taylor expanded in x around 0 88.3%

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

      1. associate-*l/ 90.3%

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

      2. associate-/r/ 99.8%

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

   7. Simplified 99.8%

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

4. Final simplification 89.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq 5 \cdot 10^{-212}:\\ \;\;\;\;\frac{x \cdot \left(y - z\right)}{y}\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{\frac{y}{y - z}}\\ \end{array} \]
5. Add Preprocessing

Alternative 7: 52.4% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq 10^{+84}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;y \cdot \frac{x}{y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (if (<= x 1e+84) x (* y (/ x y))))

C

double code(double x, double y, double z) {
	double tmp;
	if (x <= 1e+84) {
		tmp = x;
	} else {
		tmp = y * (x / y);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (x <= 1d+84) then
        tmp = x
    else
        tmp = y * (x / y)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (x <= 1e+84) {
		tmp = x;
	} else {
		tmp = y * (x / y);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if x <= 1e+84:
		tmp = x
	else:
		tmp = y * (x / y)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (x <= 1e+84)
		tmp = x;
	else
		tmp = Float64(y * Float64(x / y));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (x <= 1e+84)
		tmp = x;
	else
		tmp = y * (x / y);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[x, 1e+84], x, N[(y * N[(x / y), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < 1.00000000000000006e84

   1. Initial program 84.9%

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

      1. associate-/l* 93.7%

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

   3. Simplified 93.7%

      \[\leadsto \color{blue}{x \cdot \frac{y - z}{y}} \]
   4. Add Preprocessing

   5. Taylor expanded in y around inf 49.2%

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

   if 1.00000000000000006e84 < x

   1. Initial program 83.6%

      \[\frac{x \cdot \left(y - z\right)}{y} \]
   2. Add Preprocessing

   3. Taylor expanded in y around inf 36.9%

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

      1. *-commutative 36.9%

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

      2. associate-/l* 46.2%

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

   5. Applied egg-rr 46.2%

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

4. Final simplification 48.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq 10^{+84}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;y \cdot \frac{x}{y}\\ \end{array} \]
5. Add Preprocessing

Alternative 8: 96.0% 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 84.7%

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

   1. associate-/l* 94.7%

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

3. Simplified 94.7%

   \[\leadsto \color{blue}{x \cdot \frac{y - z}{y}} \]
4. Add Preprocessing

5. Final simplification 94.7%

   \[\leadsto x \cdot \frac{y - z}{y} \]
6. Add Preprocessing

Alternative 9: 96.3% accurate, 1.0× speedup

Math

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

FPCore

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

C

double code(double x, double y, double z) {
	return x / (y / (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 / (y - z))
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 84.7%

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

   1. associate-/l* 94.7%

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

3. Simplified 94.7%

   \[\leadsto \color{blue}{x \cdot \frac{y - z}{y}} \]
4. Add Preprocessing

5. Taylor expanded in x around 0 84.7%

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

   1. associate-*l/ 85.9%

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

   2. associate-/r/ 95.9%

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

7. Simplified 95.9%

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

8. Final simplification 95.9%

   \[\leadsto \frac{x}{\frac{y}{y - z}} \]
9. Add Preprocessing

Alternative 10: 51.2% 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 84.7%

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

   1. associate-/l* 94.7%

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

3. Simplified 94.7%

   \[\leadsto \color{blue}{x \cdot \frac{y - z}{y}} \]
4. Add Preprocessing

5. Taylor expanded in y around inf 48.8%

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

6. Final simplification 48.8%

   \[\leadsto x \]
7. Add Preprocessing

Developer target: 96.0% accurate, 0.4× 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 2024077 
(FPCore (x y z)
  :name "Diagrams.Backend.Cairo.Internal:setTexture from diagrams-cairo-1.3.0.3"
  :precision binary64

  :alt
  (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))