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

Percentage Accurate: 85.0% → 96.3%

Time: 7.0s

Alternatives: 5

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: 85.0% 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: 96.3% accurate, 0.8× 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 85.3%

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

4. Applied rewrites 96.6%

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

Alternative 2: 89.0% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -8.5 \cdot 10^{+155}:\\ \;\;\;\;1 \cdot x\\ \mathbf{elif}\;y \leq 4 \cdot 10^{+230}:\\ \;\;\;\;\frac{x}{y} \cdot \left(y - z\right)\\ \mathbf{else}:\\ \;\;\;\;1 \cdot x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y -8.5e+155)
   (* 1.0 x)
   (if (<= y 4e+230) (* (/ x y) (- y z)) (* 1.0 x))))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= -8.5e+155) {
		tmp = 1.0 * x;
	} else if (y <= 4e+230) {
		tmp = (x / y) * (y - z);
	} else {
		tmp = 1.0 * 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 <= (-8.5d+155)) then
        tmp = 1.0d0 * x
    else if (y <= 4d+230) then
        tmp = (x / y) * (y - z)
    else
        tmp = 1.0d0 * x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (y <= -8.5e+155) {
		tmp = 1.0 * x;
	} else if (y <= 4e+230) {
		tmp = (x / y) * (y - z);
	} else {
		tmp = 1.0 * x;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if y <= -8.5e+155:
		tmp = 1.0 * x
	elif y <= 4e+230:
		tmp = (x / y) * (y - z)
	else:
		tmp = 1.0 * x
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= -8.5e+155)
		tmp = Float64(1.0 * x);
	elseif (y <= 4e+230)
		tmp = Float64(Float64(x / y) * Float64(y - z));
	else
		tmp = Float64(1.0 * x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (y <= -8.5e+155)
		tmp = 1.0 * x;
	elseif (y <= 4e+230)
		tmp = (x / y) * (y - z);
	else
		tmp = 1.0 * x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, -8.5e+155], N[(1.0 * x), $MachinePrecision], If[LessEqual[y, 4e+230], N[(N[(x / y), $MachinePrecision] * N[(y - z), $MachinePrecision]), $MachinePrecision], N[(1.0 * x), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if y < -8.5000000000000002e155 or 4.0000000000000004e230 < y

   1. Initial program 67.1%

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

   4. Applied rewrites 100.0%

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

      1. lift-/.f64 N/A

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

      2. lift--.f64 N/A

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

      3. div-sub N/A

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

      4. sub-neg N/A

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

      5. *-inverses N/A

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

      6. +-commutative N/A

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

      7. distribute-frac-neg2 N/A

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

      8. div-inv N/A

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

      9. lower-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left(z, \frac{1}{\mathsf{neg}\left(y\right)}, 1\right)} \cdot x \]

      10. neg-mul-1 N/A

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

      11. associate-/r* N/A

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

      12. metadata-eval N/A

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

      13. lower-/.f64 100.0

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

   6. Applied rewrites 100.0%

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

   7. Taylor expanded in y around inf

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

   9. Applied rewrites 96.1%

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

   if -8.5000000000000002e155 < y < 4.0000000000000004e230

   1. Initial program 90.5%

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

   4. Applied rewrites 91.3%

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

Alternative 3: 72.1% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -5.5 \cdot 10^{-28}:\\ \;\;\;\;1 \cdot x\\ \mathbf{elif}\;y \leq 1.18 \cdot 10^{-61}:\\ \;\;\;\;\frac{-x}{y} \cdot z\\ \mathbf{else}:\\ \;\;\;\;1 \cdot x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y -5.5e-28)
   (* 1.0 x)
   (if (<= y 1.18e-61) (* (/ (- x) y) z) (* 1.0 x))))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= -5.5e-28) {
		tmp = 1.0 * x;
	} else if (y <= 1.18e-61) {
		tmp = (-x / y) * z;
	} else {
		tmp = 1.0 * 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 <= (-5.5d-28)) then
        tmp = 1.0d0 * x
    else if (y <= 1.18d-61) then
        tmp = (-x / y) * z
    else
        tmp = 1.0d0 * x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (y <= -5.5e-28) {
		tmp = 1.0 * x;
	} else if (y <= 1.18e-61) {
		tmp = (-x / y) * z;
	} else {
		tmp = 1.0 * x;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if y <= -5.5e-28:
		tmp = 1.0 * x
	elif y <= 1.18e-61:
		tmp = (-x / y) * z
	else:
		tmp = 1.0 * x
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= -5.5e-28)
		tmp = Float64(1.0 * x);
	elseif (y <= 1.18e-61)
		tmp = Float64(Float64(Float64(-x) / y) * z);
	else
		tmp = Float64(1.0 * x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (y <= -5.5e-28)
		tmp = 1.0 * x;
	elseif (y <= 1.18e-61)
		tmp = (-x / y) * z;
	else
		tmp = 1.0 * x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, -5.5e-28], N[(1.0 * x), $MachinePrecision], If[LessEqual[y, 1.18e-61], N[(N[((-x) / y), $MachinePrecision] * z), $MachinePrecision], N[(1.0 * x), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if y < -5.49999999999999967e-28 or 1.1800000000000001e-61 < y

   1. Initial program 80.0%

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

   4. Applied rewrites 99.9%

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

      1. lift-/.f64 N/A

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

      2. lift--.f64 N/A

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

      3. div-sub N/A

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

      4. sub-neg N/A

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

      5. *-inverses N/A

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

      6. +-commutative N/A

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

      7. distribute-frac-neg2 N/A

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

      8. div-inv N/A

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

      9. lower-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left(z, \frac{1}{\mathsf{neg}\left(y\right)}, 1\right)} \cdot x \]

      10. neg-mul-1 N/A

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

      11. associate-/r* N/A

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

      12. metadata-eval N/A

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

      13. lower-/.f64 99.9

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

   6. Applied rewrites 99.9%

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

   7. Taylor expanded in y around inf

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

   9. Applied rewrites 80.4%

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

   if -5.49999999999999967e-28 < y < 1.1800000000000001e-61

   1. Initial program 92.8%

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

   3. Taylor expanded in y around 0

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

      1. associate-*l/ N/A

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

      2. associate-*l* N/A

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

      3. lower-*.f64 N/A

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

      4. associate-*r/ N/A

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

      5. lower-/.f64 N/A

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

      6. mul-1-neg N/A

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

      7. lower-neg.f64 76.4

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

   5. Applied rewrites 76.4%

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

Alternative 4: 95.9% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 85.3%

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

4. Applied rewrites 96.2%

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

Alternative 5: 50.5% accurate, 3.3× speedup

Math

\[\begin{array}{l} \\ 1 \cdot x \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (* 1.0 x))

C

double code(double x, double y, double z) {
	return 1.0 * 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 = 1.0d0 * x
end function

Java

public static double code(double x, double y, double z) {
	return 1.0 * x;
}

Python

def code(x, y, z):
	return 1.0 * x

Julia

function code(x, y, z)
	return Float64(1.0 * x)
end

MATLAB

function tmp = code(x, y, z)
	tmp = 1.0 * x;
end

Wolfram

code[x_, y_, z_] := N[(1.0 * x), $MachinePrecision]

Derivation

1. Initial program 85.3%

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

4. Applied rewrites 96.2%

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

   1. lift-/.f64 N/A

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

   2. lift--.f64 N/A

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

   3. div-sub N/A

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

   4. sub-neg N/A

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

   5. *-inverses N/A

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

   6. +-commutative N/A

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

   7. distribute-frac-neg2 N/A

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

   8. div-inv N/A

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

   9. lower-fma.f64 N/A

      \[\leadsto \color{blue}{\mathsf{fma}\left(z, \frac{1}{\mathsf{neg}\left(y\right)}, 1\right)} \cdot x \]

   10. neg-mul-1 N/A

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

   11. associate-/r* N/A

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

   12. metadata-eval N/A

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

   13. lower-/.f64 96.2

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

6. Applied rewrites 96.2%

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

7. Taylor expanded in y around inf

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

9. Applied rewrites 56.2%

   \[\leadsto \color{blue}{1} \cdot x \]
10. Add Preprocessing

Developer Target 1: 96.1% accurate, 0.5× 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 2024298 
(FPCore (x y z)
  :name "Diagrams.Backend.Cairo.Internal:setTexture from diagrams-cairo-1.3.0.3"
  :precision binary64

  :alt
  (! :herbie-platform default (if (< z -206020233192173900000000000000000000000000000000000000000000000000000000000000000000000000000000000000000) (- x (/ (* z x) y)) (if (< z 1693976601382852600000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000) (/ x (/ y (- y z))) (* (- y z) (/ x y)))))

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