Numeric.SpecFunctions:choose from math-functions-0.1.5.2

Percentage Accurate: 84.4% → 96.2%

Time: 7.7s

Alternatives: 7

Speedup: 1.0×

Specification

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 84.4% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 96.2% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 85.1%

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

3. Simplified 0

   \[\leadsto expr\]
4. Add Preprocessing

6. Applied egg-rr 0

   \[\leadsto expr\]
7. Add Preprocessing

Alternative 2: 68.9% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{x}{\frac{z}{y}}\\ \mathbf{if}\;y \leq -2.1 \cdot 10^{-129}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;y \leq 8 \cdot 10^{-35}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 2.4 \cdot 10^{+114}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;y \leq 4.5 \cdot 10^{+129}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{z} \cdot y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (/ x (/ z y))))
   (if (<= y -2.1e-129)
     t_0
     (if (<= y 8e-35)
       x
       (if (<= y 2.4e+114) t_0 (if (<= y 4.5e+129) x (* (/ x z) y)))))))

C

double code(double x, double y, double z) {
	double t_0 = x / (z / y);
	double tmp;
	if (y <= -2.1e-129) {
		tmp = t_0;
	} else if (y <= 8e-35) {
		tmp = x;
	} else if (y <= 2.4e+114) {
		tmp = t_0;
	} else if (y <= 4.5e+129) {
		tmp = x;
	} else {
		tmp = (x / z) * y;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: tmp
    t_0 = x / (z / y)
    if (y <= (-2.1d-129)) then
        tmp = t_0
    else if (y <= 8d-35) then
        tmp = x
    else if (y <= 2.4d+114) then
        tmp = t_0
    else if (y <= 4.5d+129) then
        tmp = x
    else
        tmp = (x / z) * y
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = x / (z / y);
	double tmp;
	if (y <= -2.1e-129) {
		tmp = t_0;
	} else if (y <= 8e-35) {
		tmp = x;
	} else if (y <= 2.4e+114) {
		tmp = t_0;
	} else if (y <= 4.5e+129) {
		tmp = x;
	} else {
		tmp = (x / z) * y;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = x / (z / y)
	tmp = 0
	if y <= -2.1e-129:
		tmp = t_0
	elif y <= 8e-35:
		tmp = x
	elif y <= 2.4e+114:
		tmp = t_0
	elif y <= 4.5e+129:
		tmp = x
	else:
		tmp = (x / z) * y
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(x / Float64(z / y))
	tmp = 0.0
	if (y <= -2.1e-129)
		tmp = t_0;
	elseif (y <= 8e-35)
		tmp = x;
	elseif (y <= 2.4e+114)
		tmp = t_0;
	elseif (y <= 4.5e+129)
		tmp = x;
	else
		tmp = Float64(Float64(x / z) * y);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = x / (z / y);
	tmp = 0.0;
	if (y <= -2.1e-129)
		tmp = t_0;
	elseif (y <= 8e-35)
		tmp = x;
	elseif (y <= 2.4e+114)
		tmp = t_0;
	elseif (y <= 4.5e+129)
		tmp = x;
	else
		tmp = (x / z) * y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(x / N[(z / y), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -2.1e-129], t$95$0, If[LessEqual[y, 8e-35], x, If[LessEqual[y, 2.4e+114], t$95$0, If[LessEqual[y, 4.5e+129], x, N[(N[(x / z), $MachinePrecision] * y), $MachinePrecision]]]]]]

Derivation

1. Split input into 3 regimes

2. if y < -2.1e-129 or 8.00000000000000006e-35 < y < 2.4e114

   1. Initial program 90.3%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in y around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   9. Applied egg-rr 0

      \[\leadsto expr\]

   if -2.1e-129 < y < 8.00000000000000006e-35 or 2.4e114 < y < 4.5000000000000001e129

   1. Initial program 77.4%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in y around 0 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if 4.5000000000000001e129 < y

   1. Initial program 86.4%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in y around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   9. Applied egg-rr 0

      \[\leadsto expr\]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 3: 68.0% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x \cdot \frac{y}{z}\\ \mathbf{if}\;y \leq -1.7 \cdot 10^{-129}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;y \leq 7.2 \cdot 10^{-35}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 4.4 \cdot 10^{+114}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;y \leq 4.5 \cdot 10^{+129}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* x (/ y z))))
   (if (<= y -1.7e-129)
     t_0
     (if (<= y 7.2e-35)
       x
       (if (<= y 4.4e+114) t_0 (if (<= y 4.5e+129) x t_0))))))

C

double code(double x, double y, double z) {
	double t_0 = x * (y / z);
	double tmp;
	if (y <= -1.7e-129) {
		tmp = t_0;
	} else if (y <= 7.2e-35) {
		tmp = x;
	} else if (y <= 4.4e+114) {
		tmp = t_0;
	} else if (y <= 4.5e+129) {
		tmp = x;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: tmp
    t_0 = x * (y / z)
    if (y <= (-1.7d-129)) then
        tmp = t_0
    else if (y <= 7.2d-35) then
        tmp = x
    else if (y <= 4.4d+114) then
        tmp = t_0
    else if (y <= 4.5d+129) then
        tmp = x
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = x * (y / z);
	double tmp;
	if (y <= -1.7e-129) {
		tmp = t_0;
	} else if (y <= 7.2e-35) {
		tmp = x;
	} else if (y <= 4.4e+114) {
		tmp = t_0;
	} else if (y <= 4.5e+129) {
		tmp = x;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = x * (y / z)
	tmp = 0
	if y <= -1.7e-129:
		tmp = t_0
	elif y <= 7.2e-35:
		tmp = x
	elif y <= 4.4e+114:
		tmp = t_0
	elif y <= 4.5e+129:
		tmp = x
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(x * Float64(y / z))
	tmp = 0.0
	if (y <= -1.7e-129)
		tmp = t_0;
	elseif (y <= 7.2e-35)
		tmp = x;
	elseif (y <= 4.4e+114)
		tmp = t_0;
	elseif (y <= 4.5e+129)
		tmp = x;
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = x * (y / z);
	tmp = 0.0;
	if (y <= -1.7e-129)
		tmp = t_0;
	elseif (y <= 7.2e-35)
		tmp = x;
	elseif (y <= 4.4e+114)
		tmp = t_0;
	elseif (y <= 4.5e+129)
		tmp = x;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(x * N[(y / z), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -1.7e-129], t$95$0, If[LessEqual[y, 7.2e-35], x, If[LessEqual[y, 4.4e+114], t$95$0, If[LessEqual[y, 4.5e+129], x, t$95$0]]]]]

Derivation

1. Split input into 2 regimes

2. if y < -1.70000000000000007e-129 or 7.20000000000000038e-35 < y < 4.4000000000000001e114 or 4.5000000000000001e129 < y

   1. Initial program 89.5%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in y around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if -1.70000000000000007e-129 < y < 7.20000000000000038e-35 or 4.4000000000000001e114 < y < 4.5000000000000001e129

   1. Initial program 77.4%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in y around 0 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 4: 71.6% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{x \cdot y}{z}\\ \mathbf{if}\;y \leq -3.3 \cdot 10^{-106}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;y \leq 6 \cdot 10^{-35}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (/ (* x y) z)))
   (if (<= y -3.3e-106) t_0 (if (<= y 6e-35) x t_0))))

C

double code(double x, double y, double z) {
	double t_0 = (x * y) / z;
	double tmp;
	if (y <= -3.3e-106) {
		tmp = t_0;
	} else if (y <= 6e-35) {
		tmp = x;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: tmp
    t_0 = (x * y) / z
    if (y <= (-3.3d-106)) then
        tmp = t_0
    else if (y <= 6d-35) then
        tmp = x
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = (x * y) / z;
	double tmp;
	if (y <= -3.3e-106) {
		tmp = t_0;
	} else if (y <= 6e-35) {
		tmp = x;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = (x * y) / z
	tmp = 0
	if y <= -3.3e-106:
		tmp = t_0
	elif y <= 6e-35:
		tmp = x
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(Float64(x * y) / z)
	tmp = 0.0
	if (y <= -3.3e-106)
		tmp = t_0;
	elseif (y <= 6e-35)
		tmp = x;
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = (x * y) / z;
	tmp = 0.0;
	if (y <= -3.3e-106)
		tmp = t_0;
	elseif (y <= 6e-35)
		tmp = x;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(x * y), $MachinePrecision] / z), $MachinePrecision]}, If[LessEqual[y, -3.3e-106], t$95$0, If[LessEqual[y, 6e-35], x, t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if y < -3.30000000000000016e-106 or 5.99999999999999978e-35 < y

   1. Initial program 89.5%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in y around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if -3.30000000000000016e-106 < y < 5.99999999999999978e-35

   1. Initial program 77.4%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in y around 0 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 5: 70.3% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{x}{z} \cdot y\\ \mathbf{if}\;y \leq -2.1 \cdot 10^{-129}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;y \leq 1.2 \cdot 10^{-34}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* (/ x z) y)))
   (if (<= y -2.1e-129) t_0 (if (<= y 1.2e-34) x t_0))))

C

double code(double x, double y, double z) {
	double t_0 = (x / z) * y;
	double tmp;
	if (y <= -2.1e-129) {
		tmp = t_0;
	} else if (y <= 1.2e-34) {
		tmp = x;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: tmp
    t_0 = (x / z) * y
    if (y <= (-2.1d-129)) then
        tmp = t_0
    else if (y <= 1.2d-34) then
        tmp = x
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = (x / z) * y;
	double tmp;
	if (y <= -2.1e-129) {
		tmp = t_0;
	} else if (y <= 1.2e-34) {
		tmp = x;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = (x / z) * y
	tmp = 0
	if y <= -2.1e-129:
		tmp = t_0
	elif y <= 1.2e-34:
		tmp = x
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(Float64(x / z) * y)
	tmp = 0.0
	if (y <= -2.1e-129)
		tmp = t_0;
	elseif (y <= 1.2e-34)
		tmp = x;
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = (x / z) * y;
	tmp = 0.0;
	if (y <= -2.1e-129)
		tmp = t_0;
	elseif (y <= 1.2e-34)
		tmp = x;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(x / z), $MachinePrecision] * y), $MachinePrecision]}, If[LessEqual[y, -2.1e-129], t$95$0, If[LessEqual[y, 1.2e-34], x, t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if y < -2.1e-129 or 1.19999999999999996e-34 < y

   1. Initial program 88.7%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in y around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   9. Applied egg-rr 0

      \[\leadsto expr\]

   if -2.1e-129 < y < 1.19999999999999996e-34

   1. Initial program 78.0%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in y around 0 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 6: 95.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ x \cdot \left(\frac{y}{z} + 1\right) \end{array} \]

FPCore

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

C

double code(double x, double y, double z) {
	return x * ((y / z) + 1.0);
}

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) + 1.0d0)
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 85.1%

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

3. Simplified 0

   \[\leadsto expr\]
4. Add Preprocessing
5. Add Preprocessing

Alternative 7: 50.0% 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 85.1%

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

3. Simplified 0

   \[\leadsto expr\]
4. Add Preprocessing

5. Taylor expanded in y around 0 0

   \[\leadsto expr\]

7. Simplified 0

   \[\leadsto expr\]
8. Add Preprocessing

Developer target: 96.2% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Reproduce

herbie shell --seed 2024110 
(FPCore (x y z)
  :name "Numeric.SpecFunctions:choose from math-functions-0.1.5.2"
  :precision binary64

  :alt
  (/ x (/ z (+ y z)))

  (/ (* x (+ y z)) z))