Data.HashTable.ST.Basic:computeOverhead from hashtables-1.2.0.2

Percentage Accurate: 86.6% → 99.4%

Time: 10.8s

Alternatives: 14

Speedup: 0.8×

• 23.6% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\begin{array}{l} \\ \frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (+ (/ x y) (/ (+ 2.0 (* (* z 2.0) (- 1.0 t))) (* t z))))

C

double code(double x, double y, double z, double t) {
	return (x / y) + ((2.0 + ((z * 2.0) * (1.0 - t))) / (t * z));
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    code = (x / y) + ((2.0d0 + ((z * 2.0d0) * (1.0d0 - t))) / (t * z))
end function

Java

public static double code(double x, double y, double z, double t) {
	return (x / y) + ((2.0 + ((z * 2.0) * (1.0 - t))) / (t * z));
}

Python

def code(x, y, z, t):
	return (x / y) + ((2.0 + ((z * 2.0) * (1.0 - t))) / (t * z))

Julia

function code(x, y, z, t)
	return Float64(Float64(x / y) + Float64(Float64(2.0 + Float64(Float64(z * 2.0) * Float64(1.0 - t))) / Float64(t * z)))
end

MATLAB

function tmp = code(x, y, z, t)
	tmp = (x / y) + ((2.0 + ((z * 2.0) * (1.0 - t))) / (t * z));
end

Wolfram

code[x_, y_, z_, t_] := N[(N[(x / y), $MachinePrecision] + N[(N[(2.0 + N[(N[(z * 2.0), $MachinePrecision] * N[(1.0 - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(t * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Initial Program: 86.6% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (+ (/ x y) (/ (+ 2.0 (* (* z 2.0) (- 1.0 t))) (* t z))))

C

double code(double x, double y, double z, double t) {
	return (x / y) + ((2.0 + ((z * 2.0) * (1.0 - t))) / (t * z));
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    code = (x / y) + ((2.0d0 + ((z * 2.0d0) * (1.0d0 - t))) / (t * z))
end function

Java

public static double code(double x, double y, double z, double t) {
	return (x / y) + ((2.0 + ((z * 2.0) * (1.0 - t))) / (t * z));
}

Python

def code(x, y, z, t):
	return (x / y) + ((2.0 + ((z * 2.0) * (1.0 - t))) / (t * z))

Julia

function code(x, y, z, t)
	return Float64(Float64(x / y) + Float64(Float64(2.0 + Float64(Float64(z * 2.0) * Float64(1.0 - t))) / Float64(t * z)))
end

MATLAB

function tmp = code(x, y, z, t)
	tmp = (x / y) + ((2.0 + ((z * 2.0) * (1.0 - t))) / (t * z));
end

Wolfram

code[x_, y_, z_, t_] := N[(N[(x / y), $MachinePrecision] + N[(N[(2.0 + N[(N[(z * 2.0), $MachinePrecision] * N[(1.0 - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(t * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Alternative 1: 99.4% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x}{y} + \frac{2 + \left(1 - t\right) \cdot \left(2 \cdot z\right)}{z \cdot t}\\ \mathbf{if}\;t\_1 \leq \infty:\\ \;\;\;\;t\_1\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{y} - 2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (+ (/ x y) (/ (+ 2.0 (* (- 1.0 t) (* 2.0 z))) (* z t)))))
   (if (<= t_1 INFINITY) t_1 (- (/ x y) 2.0))))

C

double code(double x, double y, double z, double t) {
	double t_1 = (x / y) + ((2.0 + ((1.0 - t) * (2.0 * z))) / (z * t));
	double tmp;
	if (t_1 <= ((double) INFINITY)) {
		tmp = t_1;
	} else {
		tmp = (x / y) - 2.0;
	}
	return tmp;
}

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = (x / y) + ((2.0 + ((1.0 - t) * (2.0 * z))) / (z * t));
	double tmp;
	if (t_1 <= Double.POSITIVE_INFINITY) {
		tmp = t_1;
	} else {
		tmp = (x / y) - 2.0;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = (x / y) + ((2.0 + ((1.0 - t) * (2.0 * z))) / (z * t))
	tmp = 0
	if t_1 <= math.inf:
		tmp = t_1
	else:
		tmp = (x / y) - 2.0
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(x / y) + Float64(Float64(2.0 + Float64(Float64(1.0 - t) * Float64(2.0 * z))) / Float64(z * t)))
	tmp = 0.0
	if (t_1 <= Inf)
		tmp = t_1;
	else
		tmp = Float64(Float64(x / y) - 2.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = (x / y) + ((2.0 + ((1.0 - t) * (2.0 * z))) / (z * t));
	tmp = 0.0;
	if (t_1 <= Inf)
		tmp = t_1;
	else
		tmp = (x / y) - 2.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(x / y), $MachinePrecision] + N[(N[(2.0 + N[(N[(1.0 - t), $MachinePrecision] * N[(2.0 * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(z * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, Infinity], t$95$1, N[(N[(x / y), $MachinePrecision] - 2.0), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if (+.f64 (/.f64 x y) (/.f64 (+.f64 #s(literal 2 binary64) (*.f64 (*.f64 z #s(literal 2 binary64)) (-.f64 #s(literal 1 binary64) t))) (*.f64 t z))) < +inf.0

   1. Initial program 99.8%

      \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]
   2. Add Preprocessing

   if +inf.0 < (+.f64 (/.f64 x y) (/.f64 (+.f64 #s(literal 2 binary64) (*.f64 (*.f64 z #s(literal 2 binary64)) (-.f64 #s(literal 1 binary64) t))) (*.f64 t z)))

   1. Initial program 0.0%

      \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]
   2. Add Preprocessing

   3. Taylor expanded in t around inf 100.0%

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

4. Final simplification 99.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{x}{y} + \frac{2 + \left(1 - t\right) \cdot \left(2 \cdot z\right)}{z \cdot t} \leq \infty:\\ \;\;\;\;\frac{x}{y} + \frac{2 + \left(1 - t\right) \cdot \left(2 \cdot z\right)}{z \cdot t}\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{y} - 2\\ \end{array} \]
5. Add Preprocessing

Alternative 2: 81.4% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x}{y} - 2\\ t_2 := \frac{2}{z \cdot t}\\ \mathbf{if}\;t \leq -5.3 \cdot 10^{+132}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t \leq -6.2 \cdot 10^{-91}:\\ \;\;\;\;\frac{x}{y} + \frac{\frac{2}{t}}{z}\\ \mathbf{elif}\;t \leq 6.1 \cdot 10^{-18}:\\ \;\;\;\;t\_2 + \frac{2}{t}\\ \mathbf{elif}\;t \leq 3.4 \cdot 10^{+103}:\\ \;\;\;\;\frac{x}{y} + t\_2\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (- (/ x y) 2.0)) (t_2 (/ 2.0 (* z t))))
   (if (<= t -5.3e+132)
     t_1
     (if (<= t -6.2e-91)
       (+ (/ x y) (/ (/ 2.0 t) z))
       (if (<= t 6.1e-18)
         (+ t_2 (/ 2.0 t))
         (if (<= t 3.4e+103) (+ (/ x y) t_2) t_1))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = (x / y) - 2.0;
	double t_2 = 2.0 / (z * t);
	double tmp;
	if (t <= -5.3e+132) {
		tmp = t_1;
	} else if (t <= -6.2e-91) {
		tmp = (x / y) + ((2.0 / t) / z);
	} else if (t <= 6.1e-18) {
		tmp = t_2 + (2.0 / t);
	} else if (t <= 3.4e+103) {
		tmp = (x / y) + t_2;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = (x / y) - 2.0d0
    t_2 = 2.0d0 / (z * t)
    if (t <= (-5.3d+132)) then
        tmp = t_1
    else if (t <= (-6.2d-91)) then
        tmp = (x / y) + ((2.0d0 / t) / z)
    else if (t <= 6.1d-18) then
        tmp = t_2 + (2.0d0 / t)
    else if (t <= 3.4d+103) then
        tmp = (x / y) + t_2
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = (x / y) - 2.0;
	double t_2 = 2.0 / (z * t);
	double tmp;
	if (t <= -5.3e+132) {
		tmp = t_1;
	} else if (t <= -6.2e-91) {
		tmp = (x / y) + ((2.0 / t) / z);
	} else if (t <= 6.1e-18) {
		tmp = t_2 + (2.0 / t);
	} else if (t <= 3.4e+103) {
		tmp = (x / y) + t_2;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = (x / y) - 2.0
	t_2 = 2.0 / (z * t)
	tmp = 0
	if t <= -5.3e+132:
		tmp = t_1
	elif t <= -6.2e-91:
		tmp = (x / y) + ((2.0 / t) / z)
	elif t <= 6.1e-18:
		tmp = t_2 + (2.0 / t)
	elif t <= 3.4e+103:
		tmp = (x / y) + t_2
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(x / y) - 2.0)
	t_2 = Float64(2.0 / Float64(z * t))
	tmp = 0.0
	if (t <= -5.3e+132)
		tmp = t_1;
	elseif (t <= -6.2e-91)
		tmp = Float64(Float64(x / y) + Float64(Float64(2.0 / t) / z));
	elseif (t <= 6.1e-18)
		tmp = Float64(t_2 + Float64(2.0 / t));
	elseif (t <= 3.4e+103)
		tmp = Float64(Float64(x / y) + t_2);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = (x / y) - 2.0;
	t_2 = 2.0 / (z * t);
	tmp = 0.0;
	if (t <= -5.3e+132)
		tmp = t_1;
	elseif (t <= -6.2e-91)
		tmp = (x / y) + ((2.0 / t) / z);
	elseif (t <= 6.1e-18)
		tmp = t_2 + (2.0 / t);
	elseif (t <= 3.4e+103)
		tmp = (x / y) + t_2;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(x / y), $MachinePrecision] - 2.0), $MachinePrecision]}, Block[{t$95$2 = N[(2.0 / N[(z * t), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -5.3e+132], t$95$1, If[LessEqual[t, -6.2e-91], N[(N[(x / y), $MachinePrecision] + N[(N[(2.0 / t), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 6.1e-18], N[(t$95$2 + N[(2.0 / t), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 3.4e+103], N[(N[(x / y), $MachinePrecision] + t$95$2), $MachinePrecision], t$95$1]]]]]]

Derivation

1. Split input into 4 regimes

2. if t < -5.3e132 or 3.3999999999999998e103 < t

   1. Initial program 65.8%

      \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]
   2. Add Preprocessing

   3. Taylor expanded in t around inf 91.8%

      \[\leadsto \color{blue}{\frac{x}{y} - 2} \]

   if -5.3e132 < t < -6.19999999999999962e-91

   1. Initial program 83.1%

      \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0 91.9%

      \[\leadsto \color{blue}{\frac{x + y \cdot \left(2 \cdot \frac{1 - t}{t} + 2 \cdot \frac{1}{t \cdot z}\right)}{y}} \]

   4. Taylor expanded in z around 0 71.5%

      \[\leadsto \frac{x + y \cdot \color{blue}{\frac{2}{t \cdot z}}}{y} \]
   5. Step-by-step derivation

      1. *-commutative 71.5%

         \[\leadsto \frac{x + y \cdot \frac{2}{\color{blue}{z \cdot t}}}{y} \]

   6. Simplified 71.5%

      \[\leadsto \frac{x + y \cdot \color{blue}{\frac{2}{z \cdot t}}}{y} \]

   7. Taylor expanded in x around 0 77.4%

      \[\leadsto \color{blue}{2 \cdot \frac{1}{t \cdot z} + \frac{x}{y}} \]
   8. Step-by-step derivation

      1. associate-*r/ 77.4%

         \[\leadsto \color{blue}{\frac{2 \cdot 1}{t \cdot z}} + \frac{x}{y} \]

      2. metadata-eval 77.4%

         \[\leadsto \frac{\color{blue}{2}}{t \cdot z} + \frac{x}{y} \]

      3. associate-/l/ 77.5%

         \[\leadsto \color{blue}{\frac{\frac{2}{z}}{t}} + \frac{x}{y} \]

      4. +-commutative 77.5%

         \[\leadsto \color{blue}{\frac{x}{y} + \frac{\frac{2}{z}}{t}} \]

      5. associate-/l/ 77.4%

         \[\leadsto \frac{x}{y} + \color{blue}{\frac{2}{t \cdot z}} \]

      6. associate-/r* 77.4%

         \[\leadsto \frac{x}{y} + \color{blue}{\frac{\frac{2}{t}}{z}} \]

   9. Simplified 77.4%

      \[\leadsto \color{blue}{\frac{x}{y} + \frac{\frac{2}{t}}{z}} \]

   if -6.19999999999999962e-91 < t < 6.0999999999999999e-18

   1. Initial program 97.9%

      \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]
   2. Add Preprocessing

   3. Taylor expanded in t around 0 97.9%

      \[\leadsto \frac{x}{y} + \color{blue}{\frac{2 + 2 \cdot z}{t \cdot z}} \]

   4. Taylor expanded in z around 0 70.6%

      \[\leadsto \frac{x}{y} + \color{blue}{\frac{2 \cdot \frac{z}{t} + 2 \cdot \frac{1}{t}}{z}} \]

   5. Taylor expanded in x around 0 85.4%

      \[\leadsto \color{blue}{2 \cdot \frac{1}{t} + 2 \cdot \frac{1}{t \cdot z}} \]
   6. Step-by-step derivation

      1. associate-*r/ 85.4%

         \[\leadsto 2 \cdot \frac{1}{t} + \color{blue}{\frac{2 \cdot 1}{t \cdot z}} \]

      2. metadata-eval 85.4%

         \[\leadsto 2 \cdot \frac{1}{t} + \frac{\color{blue}{2}}{t \cdot z} \]

      3. associate-*r/ 85.4%

         \[\leadsto \color{blue}{\frac{2 \cdot 1}{t}} + \frac{2}{t \cdot z} \]

      4. metadata-eval 85.4%

         \[\leadsto \frac{\color{blue}{2}}{t} + \frac{2}{t \cdot z} \]

   7. Simplified 85.4%

      \[\leadsto \color{blue}{\frac{2}{t} + \frac{2}{t \cdot z}} \]

   if 6.0999999999999999e-18 < t < 3.3999999999999998e103

   1. Initial program 95.9%

      \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0 85.3%

      \[\leadsto \color{blue}{\frac{x + y \cdot \left(2 \cdot \frac{1 - t}{t} + 2 \cdot \frac{1}{t \cdot z}\right)}{y}} \]

   4. Taylor expanded in z around 0 70.5%

      \[\leadsto \frac{x + y \cdot \color{blue}{\frac{2}{t \cdot z}}}{y} \]
   5. Step-by-step derivation

      1. *-commutative 70.5%

         \[\leadsto \frac{x + y \cdot \frac{2}{\color{blue}{z \cdot t}}}{y} \]

   6. Simplified 70.5%

      \[\leadsto \frac{x + y \cdot \color{blue}{\frac{2}{z \cdot t}}}{y} \]

   7. Taylor expanded in x around 0 85.0%

      \[\leadsto \color{blue}{2 \cdot \frac{1}{t \cdot z} + \frac{x}{y}} \]
   8. Step-by-step derivation

      1. associate-*r/ 85.0%

         \[\leadsto \color{blue}{\frac{2 \cdot 1}{t \cdot z}} + \frac{x}{y} \]

      2. metadata-eval 85.0%

         \[\leadsto \frac{\color{blue}{2}}{t \cdot z} + \frac{x}{y} \]

      3. associate-/l/ 85.1%

         \[\leadsto \color{blue}{\frac{\frac{2}{z}}{t}} + \frac{x}{y} \]

      4. +-commutative 85.1%

         \[\leadsto \color{blue}{\frac{x}{y} + \frac{\frac{2}{z}}{t}} \]

      5. associate-/l/ 85.0%

         \[\leadsto \frac{x}{y} + \color{blue}{\frac{2}{t \cdot z}} \]

   9. Simplified 85.0%

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

4. Final simplification 85.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq -5.3 \cdot 10^{+132}:\\ \;\;\;\;\frac{x}{y} - 2\\ \mathbf{elif}\;t \leq -6.2 \cdot 10^{-91}:\\ \;\;\;\;\frac{x}{y} + \frac{\frac{2}{t}}{z}\\ \mathbf{elif}\;t \leq 6.1 \cdot 10^{-18}:\\ \;\;\;\;\frac{2}{z \cdot t} + \frac{2}{t}\\ \mathbf{elif}\;t \leq 3.4 \cdot 10^{+103}:\\ \;\;\;\;\frac{x}{y} + \frac{2}{z \cdot t}\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{y} - 2\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 81.3% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{2}{z \cdot t}\\ t_2 := \frac{x}{y} + t\_1\\ t_3 := \frac{x}{y} - 2\\ \mathbf{if}\;t \leq -6.5 \cdot 10^{+134}:\\ \;\;\;\;t\_3\\ \mathbf{elif}\;t \leq -3.85 \cdot 10^{-91}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;t \leq 6 \cdot 10^{-18}:\\ \;\;\;\;t\_1 + \frac{2}{t}\\ \mathbf{elif}\;t \leq 6 \cdot 10^{+103}:\\ \;\;\;\;t\_2\\ \mathbf{else}:\\ \;\;\;\;t\_3\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (/ 2.0 (* z t))) (t_2 (+ (/ x y) t_1)) (t_3 (- (/ x y) 2.0)))
   (if (<= t -6.5e+134)
     t_3
     (if (<= t -3.85e-91)
       t_2
       (if (<= t 6e-18) (+ t_1 (/ 2.0 t)) (if (<= t 6e+103) t_2 t_3))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = 2.0 / (z * t);
	double t_2 = (x / y) + t_1;
	double t_3 = (x / y) - 2.0;
	double tmp;
	if (t <= -6.5e+134) {
		tmp = t_3;
	} else if (t <= -3.85e-91) {
		tmp = t_2;
	} else if (t <= 6e-18) {
		tmp = t_1 + (2.0 / t);
	} else if (t <= 6e+103) {
		tmp = t_2;
	} else {
		tmp = t_3;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: t_3
    real(8) :: tmp
    t_1 = 2.0d0 / (z * t)
    t_2 = (x / y) + t_1
    t_3 = (x / y) - 2.0d0
    if (t <= (-6.5d+134)) then
        tmp = t_3
    else if (t <= (-3.85d-91)) then
        tmp = t_2
    else if (t <= 6d-18) then
        tmp = t_1 + (2.0d0 / t)
    else if (t <= 6d+103) then
        tmp = t_2
    else
        tmp = t_3
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = 2.0 / (z * t);
	double t_2 = (x / y) + t_1;
	double t_3 = (x / y) - 2.0;
	double tmp;
	if (t <= -6.5e+134) {
		tmp = t_3;
	} else if (t <= -3.85e-91) {
		tmp = t_2;
	} else if (t <= 6e-18) {
		tmp = t_1 + (2.0 / t);
	} else if (t <= 6e+103) {
		tmp = t_2;
	} else {
		tmp = t_3;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = 2.0 / (z * t)
	t_2 = (x / y) + t_1
	t_3 = (x / y) - 2.0
	tmp = 0
	if t <= -6.5e+134:
		tmp = t_3
	elif t <= -3.85e-91:
		tmp = t_2
	elif t <= 6e-18:
		tmp = t_1 + (2.0 / t)
	elif t <= 6e+103:
		tmp = t_2
	else:
		tmp = t_3
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(2.0 / Float64(z * t))
	t_2 = Float64(Float64(x / y) + t_1)
	t_3 = Float64(Float64(x / y) - 2.0)
	tmp = 0.0
	if (t <= -6.5e+134)
		tmp = t_3;
	elseif (t <= -3.85e-91)
		tmp = t_2;
	elseif (t <= 6e-18)
		tmp = Float64(t_1 + Float64(2.0 / t));
	elseif (t <= 6e+103)
		tmp = t_2;
	else
		tmp = t_3;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = 2.0 / (z * t);
	t_2 = (x / y) + t_1;
	t_3 = (x / y) - 2.0;
	tmp = 0.0;
	if (t <= -6.5e+134)
		tmp = t_3;
	elseif (t <= -3.85e-91)
		tmp = t_2;
	elseif (t <= 6e-18)
		tmp = t_1 + (2.0 / t);
	elseif (t <= 6e+103)
		tmp = t_2;
	else
		tmp = t_3;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(2.0 / N[(z * t), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(x / y), $MachinePrecision] + t$95$1), $MachinePrecision]}, Block[{t$95$3 = N[(N[(x / y), $MachinePrecision] - 2.0), $MachinePrecision]}, If[LessEqual[t, -6.5e+134], t$95$3, If[LessEqual[t, -3.85e-91], t$95$2, If[LessEqual[t, 6e-18], N[(t$95$1 + N[(2.0 / t), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 6e+103], t$95$2, t$95$3]]]]]]]

Derivation

1. Split input into 3 regimes

2. if t < -6.5e134 or 6e103 < t

   1. Initial program 65.8%

      \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]
   2. Add Preprocessing

   3. Taylor expanded in t around inf 91.8%

      \[\leadsto \color{blue}{\frac{x}{y} - 2} \]

   if -6.5e134 < t < -3.8499999999999999e-91 or 5.99999999999999966e-18 < t < 6e103

   1. Initial program 87.6%

      \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0 89.6%

      \[\leadsto \color{blue}{\frac{x + y \cdot \left(2 \cdot \frac{1 - t}{t} + 2 \cdot \frac{1}{t \cdot z}\right)}{y}} \]

   4. Taylor expanded in z around 0 71.2%

      \[\leadsto \frac{x + y \cdot \color{blue}{\frac{2}{t \cdot z}}}{y} \]
   5. Step-by-step derivation

      1. *-commutative 71.2%

         \[\leadsto \frac{x + y \cdot \frac{2}{\color{blue}{z \cdot t}}}{y} \]

   6. Simplified 71.2%

      \[\leadsto \frac{x + y \cdot \color{blue}{\frac{2}{z \cdot t}}}{y} \]

   7. Taylor expanded in x around 0 80.1%

      \[\leadsto \color{blue}{2 \cdot \frac{1}{t \cdot z} + \frac{x}{y}} \]
   8. Step-by-step derivation

      1. associate-*r/ 80.1%

         \[\leadsto \color{blue}{\frac{2 \cdot 1}{t \cdot z}} + \frac{x}{y} \]

      2. metadata-eval 80.1%

         \[\leadsto \frac{\color{blue}{2}}{t \cdot z} + \frac{x}{y} \]

      3. associate-/l/ 80.1%

         \[\leadsto \color{blue}{\frac{\frac{2}{z}}{t}} + \frac{x}{y} \]

      4. +-commutative 80.1%

         \[\leadsto \color{blue}{\frac{x}{y} + \frac{\frac{2}{z}}{t}} \]

      5. associate-/l/ 80.1%

         \[\leadsto \frac{x}{y} + \color{blue}{\frac{2}{t \cdot z}} \]

   9. Simplified 80.1%

      \[\leadsto \color{blue}{\frac{x}{y} + \frac{2}{t \cdot z}} \]

   if -3.8499999999999999e-91 < t < 5.99999999999999966e-18

   1. Initial program 97.9%

      \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]
   2. Add Preprocessing

   3. Taylor expanded in t around 0 97.9%

      \[\leadsto \frac{x}{y} + \color{blue}{\frac{2 + 2 \cdot z}{t \cdot z}} \]

   4. Taylor expanded in z around 0 70.6%

      \[\leadsto \frac{x}{y} + \color{blue}{\frac{2 \cdot \frac{z}{t} + 2 \cdot \frac{1}{t}}{z}} \]

   5. Taylor expanded in x around 0 85.4%

      \[\leadsto \color{blue}{2 \cdot \frac{1}{t} + 2 \cdot \frac{1}{t \cdot z}} \]
   6. Step-by-step derivation

      1. associate-*r/ 85.4%

         \[\leadsto 2 \cdot \frac{1}{t} + \color{blue}{\frac{2 \cdot 1}{t \cdot z}} \]

      2. metadata-eval 85.4%

         \[\leadsto 2 \cdot \frac{1}{t} + \frac{\color{blue}{2}}{t \cdot z} \]

      3. associate-*r/ 85.4%

         \[\leadsto \color{blue}{\frac{2 \cdot 1}{t}} + \frac{2}{t \cdot z} \]

      4. metadata-eval 85.4%

         \[\leadsto \frac{\color{blue}{2}}{t} + \frac{2}{t \cdot z} \]

   7. Simplified 85.4%

      \[\leadsto \color{blue}{\frac{2}{t} + \frac{2}{t \cdot z}} \]
3. Recombined 3 regimes into one program.

4. Final simplification 85.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq -6.5 \cdot 10^{+134}:\\ \;\;\;\;\frac{x}{y} - 2\\ \mathbf{elif}\;t \leq -3.85 \cdot 10^{-91}:\\ \;\;\;\;\frac{x}{y} + \frac{2}{z \cdot t}\\ \mathbf{elif}\;t \leq 6 \cdot 10^{-18}:\\ \;\;\;\;\frac{2}{z \cdot t} + \frac{2}{t}\\ \mathbf{elif}\;t \leq 6 \cdot 10^{+103}:\\ \;\;\;\;\frac{x}{y} + \frac{2}{z \cdot t}\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{y} - 2\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 98.7% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\frac{x}{y} \leq -1 \cdot 10^{+28} \lor \neg \left(\frac{x}{y} \leq 200000\right):\\ \;\;\;\;\frac{x}{y} + \frac{2 + 2 \cdot z}{z \cdot t}\\ \mathbf{else}:\\ \;\;\;\;\frac{\left(2 + \frac{2}{z}\right) + t \cdot \left(\frac{x}{y} + -2\right)}{t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= (/ x y) -1e+28) (not (<= (/ x y) 200000.0)))
   (+ (/ x y) (/ (+ 2.0 (* 2.0 z)) (* z t)))
   (/ (+ (+ 2.0 (/ 2.0 z)) (* t (+ (/ x y) -2.0))) t)))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (((x / y) <= -1e+28) || !((x / y) <= 200000.0)) {
		tmp = (x / y) + ((2.0 + (2.0 * z)) / (z * t));
	} else {
		tmp = ((2.0 + (2.0 / z)) + (t * ((x / y) + -2.0))) / t;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if (((x / y) <= (-1d+28)) .or. (.not. ((x / y) <= 200000.0d0))) then
        tmp = (x / y) + ((2.0d0 + (2.0d0 * z)) / (z * t))
    else
        tmp = ((2.0d0 + (2.0d0 / z)) + (t * ((x / y) + (-2.0d0)))) / t
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if (((x / y) <= -1e+28) || !((x / y) <= 200000.0)) {
		tmp = (x / y) + ((2.0 + (2.0 * z)) / (z * t));
	} else {
		tmp = ((2.0 + (2.0 / z)) + (t * ((x / y) + -2.0))) / t;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if ((x / y) <= -1e+28) or not ((x / y) <= 200000.0):
		tmp = (x / y) + ((2.0 + (2.0 * z)) / (z * t))
	else:
		tmp = ((2.0 + (2.0 / z)) + (t * ((x / y) + -2.0))) / t
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((Float64(x / y) <= -1e+28) || !(Float64(x / y) <= 200000.0))
		tmp = Float64(Float64(x / y) + Float64(Float64(2.0 + Float64(2.0 * z)) / Float64(z * t)));
	else
		tmp = Float64(Float64(Float64(2.0 + Float64(2.0 / z)) + Float64(t * Float64(Float64(x / y) + -2.0))) / t);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (((x / y) <= -1e+28) || ~(((x / y) <= 200000.0)))
		tmp = (x / y) + ((2.0 + (2.0 * z)) / (z * t));
	else
		tmp = ((2.0 + (2.0 / z)) + (t * ((x / y) + -2.0))) / t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[N[(x / y), $MachinePrecision], -1e+28], N[Not[LessEqual[N[(x / y), $MachinePrecision], 200000.0]], $MachinePrecision]], N[(N[(x / y), $MachinePrecision] + N[(N[(2.0 + N[(2.0 * z), $MachinePrecision]), $MachinePrecision] / N[(z * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(N[(2.0 + N[(2.0 / z), $MachinePrecision]), $MachinePrecision] + N[(t * N[(N[(x / y), $MachinePrecision] + -2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / t), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 x y) < -9.99999999999999958e27 or 2e5 < (/.f64 x y)

   1. Initial program 87.5%

      \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]
   2. Add Preprocessing

   3. Taylor expanded in t around 0 98.3%

      \[\leadsto \frac{x}{y} + \color{blue}{\frac{2 + 2 \cdot z}{t \cdot z}} \]

   if -9.99999999999999958e27 < (/.f64 x y) < 2e5

   1. Initial program 83.2%

      \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]
   2. Add Preprocessing

   3. Taylor expanded in t around 0 99.9%

      \[\leadsto \color{blue}{\frac{2 + \left(2 \cdot \frac{1}{z} + t \cdot \left(\frac{x}{y} - 2\right)\right)}{t}} \]
   4. Step-by-step derivation

      1. associate-+r+ 99.9%

         \[\leadsto \frac{\color{blue}{\left(2 + 2 \cdot \frac{1}{z}\right) + t \cdot \left(\frac{x}{y} - 2\right)}}{t} \]

      2. associate-*r/ 99.9%

         \[\leadsto \frac{\left(2 + \color{blue}{\frac{2 \cdot 1}{z}}\right) + t \cdot \left(\frac{x}{y} - 2\right)}{t} \]

      3. metadata-eval 99.9%

         \[\leadsto \frac{\left(2 + \frac{\color{blue}{2}}{z}\right) + t \cdot \left(\frac{x}{y} - 2\right)}{t} \]

      4. sub-neg 99.9%

         \[\leadsto \frac{\left(2 + \frac{2}{z}\right) + t \cdot \color{blue}{\left(\frac{x}{y} + \left(-2\right)\right)}}{t} \]

      5. metadata-eval 99.9%

         \[\leadsto \frac{\left(2 + \frac{2}{z}\right) + t \cdot \left(\frac{x}{y} + \color{blue}{-2}\right)}{t} \]

   5. Simplified 99.9%

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

4. Final simplification 99.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{x}{y} \leq -1 \cdot 10^{+28} \lor \neg \left(\frac{x}{y} \leq 200000\right):\\ \;\;\;\;\frac{x}{y} + \frac{2 + 2 \cdot z}{z \cdot t}\\ \mathbf{else}:\\ \;\;\;\;\frac{\left(2 + \frac{2}{z}\right) + t \cdot \left(\frac{x}{y} + -2\right)}{t}\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 97.8% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\frac{x}{y} \leq -5000000000000 \lor \neg \left(\frac{x}{y} \leq 0.0005\right):\\ \;\;\;\;\frac{x}{y} + \frac{2 + 2 \cdot z}{z \cdot t}\\ \mathbf{else}:\\ \;\;\;\;\frac{\left(2 + \frac{2}{z}\right) + t \cdot -2}{t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= (/ x y) -5000000000000.0) (not (<= (/ x y) 0.0005)))
   (+ (/ x y) (/ (+ 2.0 (* 2.0 z)) (* z t)))
   (/ (+ (+ 2.0 (/ 2.0 z)) (* t -2.0)) t)))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (((x / y) <= -5000000000000.0) || !((x / y) <= 0.0005)) {
		tmp = (x / y) + ((2.0 + (2.0 * z)) / (z * t));
	} else {
		tmp = ((2.0 + (2.0 / z)) + (t * -2.0)) / t;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if (((x / y) <= (-5000000000000.0d0)) .or. (.not. ((x / y) <= 0.0005d0))) then
        tmp = (x / y) + ((2.0d0 + (2.0d0 * z)) / (z * t))
    else
        tmp = ((2.0d0 + (2.0d0 / z)) + (t * (-2.0d0))) / t
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if (((x / y) <= -5000000000000.0) || !((x / y) <= 0.0005)) {
		tmp = (x / y) + ((2.0 + (2.0 * z)) / (z * t));
	} else {
		tmp = ((2.0 + (2.0 / z)) + (t * -2.0)) / t;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if ((x / y) <= -5000000000000.0) or not ((x / y) <= 0.0005):
		tmp = (x / y) + ((2.0 + (2.0 * z)) / (z * t))
	else:
		tmp = ((2.0 + (2.0 / z)) + (t * -2.0)) / t
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((Float64(x / y) <= -5000000000000.0) || !(Float64(x / y) <= 0.0005))
		tmp = Float64(Float64(x / y) + Float64(Float64(2.0 + Float64(2.0 * z)) / Float64(z * t)));
	else
		tmp = Float64(Float64(Float64(2.0 + Float64(2.0 / z)) + Float64(t * -2.0)) / t);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (((x / y) <= -5000000000000.0) || ~(((x / y) <= 0.0005)))
		tmp = (x / y) + ((2.0 + (2.0 * z)) / (z * t));
	else
		tmp = ((2.0 + (2.0 / z)) + (t * -2.0)) / t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[N[(x / y), $MachinePrecision], -5000000000000.0], N[Not[LessEqual[N[(x / y), $MachinePrecision], 0.0005]], $MachinePrecision]], N[(N[(x / y), $MachinePrecision] + N[(N[(2.0 + N[(2.0 * z), $MachinePrecision]), $MachinePrecision] / N[(z * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(N[(2.0 + N[(2.0 / z), $MachinePrecision]), $MachinePrecision] + N[(t * -2.0), $MachinePrecision]), $MachinePrecision] / t), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 x y) < -5e12 or 5.0000000000000001e-4 < (/.f64 x y)

   1. Initial program 87.3%

      \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]
   2. Add Preprocessing

   3. Taylor expanded in t around 0 97.9%

      \[\leadsto \frac{x}{y} + \color{blue}{\frac{2 + 2 \cdot z}{t \cdot z}} \]

   if -5e12 < (/.f64 x y) < 5.0000000000000001e-4

   1. Initial program 83.2%

      \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]
   2. Add Preprocessing

   3. Taylor expanded in t around 0 99.9%

      \[\leadsto \color{blue}{\frac{2 + \left(2 \cdot \frac{1}{z} + t \cdot \left(\frac{x}{y} - 2\right)\right)}{t}} \]
   4. Step-by-step derivation

      1. associate-+r+ 99.9%

         \[\leadsto \frac{\color{blue}{\left(2 + 2 \cdot \frac{1}{z}\right) + t \cdot \left(\frac{x}{y} - 2\right)}}{t} \]

      2. associate-*r/ 99.9%

         \[\leadsto \frac{\left(2 + \color{blue}{\frac{2 \cdot 1}{z}}\right) + t \cdot \left(\frac{x}{y} - 2\right)}{t} \]

      3. metadata-eval 99.9%

         \[\leadsto \frac{\left(2 + \frac{\color{blue}{2}}{z}\right) + t \cdot \left(\frac{x}{y} - 2\right)}{t} \]

      4. sub-neg 99.9%

         \[\leadsto \frac{\left(2 + \frac{2}{z}\right) + t \cdot \color{blue}{\left(\frac{x}{y} + \left(-2\right)\right)}}{t} \]

      5. metadata-eval 99.9%

         \[\leadsto \frac{\left(2 + \frac{2}{z}\right) + t \cdot \left(\frac{x}{y} + \color{blue}{-2}\right)}{t} \]

   5. Simplified 99.9%

      \[\leadsto \color{blue}{\frac{\left(2 + \frac{2}{z}\right) + t \cdot \left(\frac{x}{y} + -2\right)}{t}} \]

   6. Taylor expanded in x around 0 99.0%

      \[\leadsto \frac{\left(2 + \frac{2}{z}\right) + \color{blue}{-2 \cdot t}}{t} \]
   7. Step-by-step derivation

      1. *-commutative 99.0%

         \[\leadsto \frac{\left(2 + \frac{2}{z}\right) + \color{blue}{t \cdot -2}}{t} \]

   8. Simplified 99.0%

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

4. Final simplification 98.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{x}{y} \leq -5000000000000 \lor \neg \left(\frac{x}{y} \leq 0.0005\right):\\ \;\;\;\;\frac{x}{y} + \frac{2 + 2 \cdot z}{z \cdot t}\\ \mathbf{else}:\\ \;\;\;\;\frac{\left(2 + \frac{2}{z}\right) + t \cdot -2}{t}\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 63.2% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x}{y} - 2\\ \mathbf{if}\;z \leq -4 \cdot 10^{+185}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;z \leq -1.65 \cdot 10^{+49}:\\ \;\;\;\;-2 + \frac{2}{t}\\ \mathbf{elif}\;z \leq -1.4 \cdot 10^{-37} \lor \neg \left(z \leq 3.6 \cdot 10^{-135}\right):\\ \;\;\;\;t\_1\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{2}{z}}{t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (- (/ x y) 2.0)))
   (if (<= z -4e+185)
     t_1
     (if (<= z -1.65e+49)
       (+ -2.0 (/ 2.0 t))
       (if (or (<= z -1.4e-37) (not (<= z 3.6e-135))) t_1 (/ (/ 2.0 z) t))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = (x / y) - 2.0;
	double tmp;
	if (z <= -4e+185) {
		tmp = t_1;
	} else if (z <= -1.65e+49) {
		tmp = -2.0 + (2.0 / t);
	} else if ((z <= -1.4e-37) || !(z <= 3.6e-135)) {
		tmp = t_1;
	} else {
		tmp = (2.0 / z) / t;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: t_1
    real(8) :: tmp
    t_1 = (x / y) - 2.0d0
    if (z <= (-4d+185)) then
        tmp = t_1
    else if (z <= (-1.65d+49)) then
        tmp = (-2.0d0) + (2.0d0 / t)
    else if ((z <= (-1.4d-37)) .or. (.not. (z <= 3.6d-135))) then
        tmp = t_1
    else
        tmp = (2.0d0 / z) / t
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = (x / y) - 2.0;
	double tmp;
	if (z <= -4e+185) {
		tmp = t_1;
	} else if (z <= -1.65e+49) {
		tmp = -2.0 + (2.0 / t);
	} else if ((z <= -1.4e-37) || !(z <= 3.6e-135)) {
		tmp = t_1;
	} else {
		tmp = (2.0 / z) / t;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = (x / y) - 2.0
	tmp = 0
	if z <= -4e+185:
		tmp = t_1
	elif z <= -1.65e+49:
		tmp = -2.0 + (2.0 / t)
	elif (z <= -1.4e-37) or not (z <= 3.6e-135):
		tmp = t_1
	else:
		tmp = (2.0 / z) / t
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(x / y) - 2.0)
	tmp = 0.0
	if (z <= -4e+185)
		tmp = t_1;
	elseif (z <= -1.65e+49)
		tmp = Float64(-2.0 + Float64(2.0 / t));
	elseif ((z <= -1.4e-37) || !(z <= 3.6e-135))
		tmp = t_1;
	else
		tmp = Float64(Float64(2.0 / z) / t);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = (x / y) - 2.0;
	tmp = 0.0;
	if (z <= -4e+185)
		tmp = t_1;
	elseif (z <= -1.65e+49)
		tmp = -2.0 + (2.0 / t);
	elseif ((z <= -1.4e-37) || ~((z <= 3.6e-135)))
		tmp = t_1;
	else
		tmp = (2.0 / z) / t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(x / y), $MachinePrecision] - 2.0), $MachinePrecision]}, If[LessEqual[z, -4e+185], t$95$1, If[LessEqual[z, -1.65e+49], N[(-2.0 + N[(2.0 / t), $MachinePrecision]), $MachinePrecision], If[Or[LessEqual[z, -1.4e-37], N[Not[LessEqual[z, 3.6e-135]], $MachinePrecision]], t$95$1, N[(N[(2.0 / z), $MachinePrecision] / t), $MachinePrecision]]]]]

Derivation

1. Split input into 3 regimes

2. if z < -3.9999999999999999e185 or -1.6499999999999999e49 < z < -1.4000000000000001e-37 or 3.59999999999999978e-135 < z

   1. Initial program 77.7%

      \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]
   2. Add Preprocessing

   3. Taylor expanded in t around inf 69.9%

      \[\leadsto \color{blue}{\frac{x}{y} - 2} \]

   if -3.9999999999999999e185 < z < -1.6499999999999999e49

   1. Initial program 79.8%

      \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]
   2. Add Preprocessing

   3. Taylor expanded in z around inf 100.0%

      \[\leadsto \frac{x}{y} + \color{blue}{2 \cdot \frac{1 - t}{t}} \]
   4. Step-by-step derivation

      1. associate-*r/ 100.0%

         \[\leadsto \frac{x}{y} + \color{blue}{\frac{2 \cdot \left(1 - t\right)}{t}} \]

   5. Simplified 100.0%

      \[\leadsto \frac{x}{y} + \color{blue}{\frac{2 \cdot \left(1 - t\right)}{t}} \]

   6. Taylor expanded in x around 0 73.2%

      \[\leadsto \color{blue}{2 \cdot \frac{1 - t}{t}} \]
   7. Step-by-step derivation

      1. div-sub 73.2%

         \[\leadsto 2 \cdot \color{blue}{\left(\frac{1}{t} - \frac{t}{t}\right)} \]

      2. sub-neg 73.2%

         \[\leadsto 2 \cdot \color{blue}{\left(\frac{1}{t} + \left(-\frac{t}{t}\right)\right)} \]

      3. *-inverses 73.2%

         \[\leadsto 2 \cdot \left(\frac{1}{t} + \left(-\color{blue}{1}\right)\right) \]

      4. metadata-eval 73.2%

         \[\leadsto 2 \cdot \left(\frac{1}{t} + \color{blue}{-1}\right) \]

      5. distribute-lft-in 73.2%

         \[\leadsto \color{blue}{2 \cdot \frac{1}{t} + 2 \cdot -1} \]

      6. associate-*r/ 73.2%

         \[\leadsto \color{blue}{\frac{2 \cdot 1}{t}} + 2 \cdot -1 \]

      7. metadata-eval 73.2%

         \[\leadsto \frac{\color{blue}{2}}{t} + 2 \cdot -1 \]

      8. metadata-eval 73.2%

         \[\leadsto \frac{2}{t} + \color{blue}{-2} \]

   8. Simplified 73.2%

      \[\leadsto \color{blue}{\frac{2}{t} + -2} \]

   if -1.4000000000000001e-37 < z < 3.59999999999999978e-135

   1. Initial program 98.7%

      \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]
   2. Add Preprocessing

   3. Taylor expanded in t around 0 70.7%

      \[\leadsto \color{blue}{\frac{2 + 2 \cdot \frac{1}{z}}{t}} \]
   4. Step-by-step derivation

      1. associate-*r/ 70.7%

         \[\leadsto \frac{2 + \color{blue}{\frac{2 \cdot 1}{z}}}{t} \]

      2. metadata-eval 70.7%

         \[\leadsto \frac{2 + \frac{\color{blue}{2}}{z}}{t} \]

   5. Simplified 70.7%

      \[\leadsto \color{blue}{\frac{2 + \frac{2}{z}}{t}} \]

   6. Taylor expanded in z around 0 70.7%

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

4. Final simplification 70.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -4 \cdot 10^{+185}:\\ \;\;\;\;\frac{x}{y} - 2\\ \mathbf{elif}\;z \leq -1.65 \cdot 10^{+49}:\\ \;\;\;\;-2 + \frac{2}{t}\\ \mathbf{elif}\;z \leq -1.4 \cdot 10^{-37} \lor \neg \left(z \leq 3.6 \cdot 10^{-135}\right):\\ \;\;\;\;\frac{x}{y} - 2\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{2}{z}}{t}\\ \end{array} \]
5. Add Preprocessing

Alternative 7: 63.6% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x}{y} - 2\\ \mathbf{if}\;z \leq -5.5 \cdot 10^{+185}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;z \leq -1.8 \cdot 10^{+51}:\\ \;\;\;\;-2 + \frac{2}{t}\\ \mathbf{elif}\;z \leq -3.8 \cdot 10^{-60} \lor \neg \left(z \leq 7.6 \cdot 10^{-136}\right):\\ \;\;\;\;t\_1\\ \mathbf{else}:\\ \;\;\;\;\frac{2}{z \cdot t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (- (/ x y) 2.0)))
   (if (<= z -5.5e+185)
     t_1
     (if (<= z -1.8e+51)
       (+ -2.0 (/ 2.0 t))
       (if (or (<= z -3.8e-60) (not (<= z 7.6e-136))) t_1 (/ 2.0 (* z t)))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = (x / y) - 2.0;
	double tmp;
	if (z <= -5.5e+185) {
		tmp = t_1;
	} else if (z <= -1.8e+51) {
		tmp = -2.0 + (2.0 / t);
	} else if ((z <= -3.8e-60) || !(z <= 7.6e-136)) {
		tmp = t_1;
	} else {
		tmp = 2.0 / (z * t);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: t_1
    real(8) :: tmp
    t_1 = (x / y) - 2.0d0
    if (z <= (-5.5d+185)) then
        tmp = t_1
    else if (z <= (-1.8d+51)) then
        tmp = (-2.0d0) + (2.0d0 / t)
    else if ((z <= (-3.8d-60)) .or. (.not. (z <= 7.6d-136))) then
        tmp = t_1
    else
        tmp = 2.0d0 / (z * t)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = (x / y) - 2.0;
	double tmp;
	if (z <= -5.5e+185) {
		tmp = t_1;
	} else if (z <= -1.8e+51) {
		tmp = -2.0 + (2.0 / t);
	} else if ((z <= -3.8e-60) || !(z <= 7.6e-136)) {
		tmp = t_1;
	} else {
		tmp = 2.0 / (z * t);
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = (x / y) - 2.0
	tmp = 0
	if z <= -5.5e+185:
		tmp = t_1
	elif z <= -1.8e+51:
		tmp = -2.0 + (2.0 / t)
	elif (z <= -3.8e-60) or not (z <= 7.6e-136):
		tmp = t_1
	else:
		tmp = 2.0 / (z * t)
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(x / y) - 2.0)
	tmp = 0.0
	if (z <= -5.5e+185)
		tmp = t_1;
	elseif (z <= -1.8e+51)
		tmp = Float64(-2.0 + Float64(2.0 / t));
	elseif ((z <= -3.8e-60) || !(z <= 7.6e-136))
		tmp = t_1;
	else
		tmp = Float64(2.0 / Float64(z * t));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = (x / y) - 2.0;
	tmp = 0.0;
	if (z <= -5.5e+185)
		tmp = t_1;
	elseif (z <= -1.8e+51)
		tmp = -2.0 + (2.0 / t);
	elseif ((z <= -3.8e-60) || ~((z <= 7.6e-136)))
		tmp = t_1;
	else
		tmp = 2.0 / (z * t);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(x / y), $MachinePrecision] - 2.0), $MachinePrecision]}, If[LessEqual[z, -5.5e+185], t$95$1, If[LessEqual[z, -1.8e+51], N[(-2.0 + N[(2.0 / t), $MachinePrecision]), $MachinePrecision], If[Or[LessEqual[z, -3.8e-60], N[Not[LessEqual[z, 7.6e-136]], $MachinePrecision]], t$95$1, N[(2.0 / N[(z * t), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

1. Split input into 3 regimes

2. if z < -5.4999999999999996e185 or -1.80000000000000005e51 < z < -3.79999999999999994e-60 or 7.6000000000000005e-136 < z

   1. Initial program 78.1%

      \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]
   2. Add Preprocessing

   3. Taylor expanded in t around inf 69.6%

      \[\leadsto \color{blue}{\frac{x}{y} - 2} \]

   if -5.4999999999999996e185 < z < -1.80000000000000005e51

   1. Initial program 79.8%

      \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]
   2. Add Preprocessing

   3. Taylor expanded in z around inf 100.0%

      \[\leadsto \frac{x}{y} + \color{blue}{2 \cdot \frac{1 - t}{t}} \]
   4. Step-by-step derivation

      1. associate-*r/ 100.0%

         \[\leadsto \frac{x}{y} + \color{blue}{\frac{2 \cdot \left(1 - t\right)}{t}} \]

   5. Simplified 100.0%

      \[\leadsto \frac{x}{y} + \color{blue}{\frac{2 \cdot \left(1 - t\right)}{t}} \]

   6. Taylor expanded in x around 0 73.2%

      \[\leadsto \color{blue}{2 \cdot \frac{1 - t}{t}} \]
   7. Step-by-step derivation

      1. div-sub 73.2%

         \[\leadsto 2 \cdot \color{blue}{\left(\frac{1}{t} - \frac{t}{t}\right)} \]

      2. sub-neg 73.2%

         \[\leadsto 2 \cdot \color{blue}{\left(\frac{1}{t} + \left(-\frac{t}{t}\right)\right)} \]

      3. *-inverses 73.2%

         \[\leadsto 2 \cdot \left(\frac{1}{t} + \left(-\color{blue}{1}\right)\right) \]

      4. metadata-eval 73.2%

         \[\leadsto 2 \cdot \left(\frac{1}{t} + \color{blue}{-1}\right) \]

      5. distribute-lft-in 73.2%

         \[\leadsto \color{blue}{2 \cdot \frac{1}{t} + 2 \cdot -1} \]

      6. associate-*r/ 73.2%

         \[\leadsto \color{blue}{\frac{2 \cdot 1}{t}} + 2 \cdot -1 \]

      7. metadata-eval 73.2%

         \[\leadsto \frac{\color{blue}{2}}{t} + 2 \cdot -1 \]

      8. metadata-eval 73.2%

         \[\leadsto \frac{2}{t} + \color{blue}{-2} \]

   8. Simplified 73.2%

      \[\leadsto \color{blue}{\frac{2}{t} + -2} \]

   if -3.79999999999999994e-60 < z < 7.6000000000000005e-136

   1. Initial program 98.7%

      \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]
   2. Add Preprocessing

   3. Taylor expanded in z around 0 71.1%

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

4. Final simplification 70.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -5.5 \cdot 10^{+185}:\\ \;\;\;\;\frac{x}{y} - 2\\ \mathbf{elif}\;z \leq -1.8 \cdot 10^{+51}:\\ \;\;\;\;-2 + \frac{2}{t}\\ \mathbf{elif}\;z \leq -3.8 \cdot 10^{-60} \lor \neg \left(z \leq 7.6 \cdot 10^{-136}\right):\\ \;\;\;\;\frac{x}{y} - 2\\ \mathbf{else}:\\ \;\;\;\;\frac{2}{z \cdot t}\\ \end{array} \]
5. Add Preprocessing

Alternative 8: 50.4% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\frac{x}{y} \leq -24500000000:\\ \;\;\;\;\frac{x}{y}\\ \mathbf{elif}\;\frac{x}{y} \leq 1.28 \cdot 10^{-200}:\\ \;\;\;\;-2\\ \mathbf{elif}\;\frac{x}{y} \leq 2.6 \cdot 10^{+55}:\\ \;\;\;\;\frac{2}{t}\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= (/ x y) -24500000000.0)
   (/ x y)
   (if (<= (/ x y) 1.28e-200)
     -2.0
     (if (<= (/ x y) 2.6e+55) (/ 2.0 t) (/ x y)))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((x / y) <= -24500000000.0) {
		tmp = x / y;
	} else if ((x / y) <= 1.28e-200) {
		tmp = -2.0;
	} else if ((x / y) <= 2.6e+55) {
		tmp = 2.0 / t;
	} else {
		tmp = x / y;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if ((x / y) <= (-24500000000.0d0)) then
        tmp = x / y
    else if ((x / y) <= 1.28d-200) then
        tmp = -2.0d0
    else if ((x / y) <= 2.6d+55) then
        tmp = 2.0d0 / t
    else
        tmp = x / y
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((x / y) <= -24500000000.0) {
		tmp = x / y;
	} else if ((x / y) <= 1.28e-200) {
		tmp = -2.0;
	} else if ((x / y) <= 2.6e+55) {
		tmp = 2.0 / t;
	} else {
		tmp = x / y;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (x / y) <= -24500000000.0:
		tmp = x / y
	elif (x / y) <= 1.28e-200:
		tmp = -2.0
	elif (x / y) <= 2.6e+55:
		tmp = 2.0 / t
	else:
		tmp = x / y
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (Float64(x / y) <= -24500000000.0)
		tmp = Float64(x / y);
	elseif (Float64(x / y) <= 1.28e-200)
		tmp = -2.0;
	elseif (Float64(x / y) <= 2.6e+55)
		tmp = Float64(2.0 / t);
	else
		tmp = Float64(x / y);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((x / y) <= -24500000000.0)
		tmp = x / y;
	elseif ((x / y) <= 1.28e-200)
		tmp = -2.0;
	elseif ((x / y) <= 2.6e+55)
		tmp = 2.0 / t;
	else
		tmp = x / y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[N[(x / y), $MachinePrecision], -24500000000.0], N[(x / y), $MachinePrecision], If[LessEqual[N[(x / y), $MachinePrecision], 1.28e-200], -2.0, If[LessEqual[N[(x / y), $MachinePrecision], 2.6e+55], N[(2.0 / t), $MachinePrecision], N[(x / y), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if (/.f64 x y) < -2.45e10 or 2.6e55 < (/.f64 x y)

   1. Initial program 87.0%

      \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf 72.9%

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

   if -2.45e10 < (/.f64 x y) < 1.28e-200

   1. Initial program 78.5%

      \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]
   2. Add Preprocessing

   3. Taylor expanded in t around inf 43.4%

      \[\leadsto \color{blue}{\frac{x}{y} - 2} \]

   4. Taylor expanded in x around 0 42.9%

      \[\leadsto \color{blue}{-2} \]

   if 1.28e-200 < (/.f64 x y) < 2.6e55

   1. Initial program 97.2%

      \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]
   2. Add Preprocessing

   3. Taylor expanded in z around inf 54.2%

      \[\leadsto \frac{x}{y} + \color{blue}{2 \cdot \frac{1 - t}{t}} \]
   4. Step-by-step derivation

      1. associate-*r/ 54.2%

         \[\leadsto \frac{x}{y} + \color{blue}{\frac{2 \cdot \left(1 - t\right)}{t}} \]

   5. Simplified 54.2%

      \[\leadsto \frac{x}{y} + \color{blue}{\frac{2 \cdot \left(1 - t\right)}{t}} \]

   6. Taylor expanded in t around 0 36.2%

      \[\leadsto \color{blue}{\frac{2}{t}} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 9: 91.3% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -3.8 \cdot 10^{-53} \lor \neg \left(z \leq 1.05 \cdot 10^{-30}\right):\\ \;\;\;\;\frac{x}{y} + \frac{2 \cdot \left(1 - t\right)}{t}\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{y} + \frac{2}{z \cdot t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= z -3.8e-53) (not (<= z 1.05e-30)))
   (+ (/ x y) (/ (* 2.0 (- 1.0 t)) t))
   (+ (/ x y) (/ 2.0 (* z t)))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((z <= -3.8e-53) || !(z <= 1.05e-30)) {
		tmp = (x / y) + ((2.0 * (1.0 - t)) / t);
	} else {
		tmp = (x / y) + (2.0 / (z * t));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if ((z <= (-3.8d-53)) .or. (.not. (z <= 1.05d-30))) then
        tmp = (x / y) + ((2.0d0 * (1.0d0 - t)) / t)
    else
        tmp = (x / y) + (2.0d0 / (z * t))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((z <= -3.8e-53) || !(z <= 1.05e-30)) {
		tmp = (x / y) + ((2.0 * (1.0 - t)) / t);
	} else {
		tmp = (x / y) + (2.0 / (z * t));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (z <= -3.8e-53) or not (z <= 1.05e-30):
		tmp = (x / y) + ((2.0 * (1.0 - t)) / t)
	else:
		tmp = (x / y) + (2.0 / (z * t))
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((z <= -3.8e-53) || !(z <= 1.05e-30))
		tmp = Float64(Float64(x / y) + Float64(Float64(2.0 * Float64(1.0 - t)) / t));
	else
		tmp = Float64(Float64(x / y) + Float64(2.0 / Float64(z * t)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((z <= -3.8e-53) || ~((z <= 1.05e-30)))
		tmp = (x / y) + ((2.0 * (1.0 - t)) / t);
	else
		tmp = (x / y) + (2.0 / (z * t));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[z, -3.8e-53], N[Not[LessEqual[z, 1.05e-30]], $MachinePrecision]], N[(N[(x / y), $MachinePrecision] + N[(N[(2.0 * N[(1.0 - t), $MachinePrecision]), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision], N[(N[(x / y), $MachinePrecision] + N[(2.0 / N[(z * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if z < -3.7999999999999998e-53 or 1.0500000000000001e-30 < z

   1. Initial program 75.4%

      \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]
   2. Add Preprocessing

   3. Taylor expanded in z around inf 97.1%

      \[\leadsto \frac{x}{y} + \color{blue}{2 \cdot \frac{1 - t}{t}} \]
   4. Step-by-step derivation

      1. associate-*r/ 97.1%

         \[\leadsto \frac{x}{y} + \color{blue}{\frac{2 \cdot \left(1 - t\right)}{t}} \]

   5. Simplified 97.1%

      \[\leadsto \frac{x}{y} + \color{blue}{\frac{2 \cdot \left(1 - t\right)}{t}} \]

   if -3.7999999999999998e-53 < z < 1.0500000000000001e-30

   1. Initial program 98.9%

      \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0 90.3%

      \[\leadsto \color{blue}{\frac{x + y \cdot \left(2 \cdot \frac{1 - t}{t} + 2 \cdot \frac{1}{t \cdot z}\right)}{y}} \]

   4. Taylor expanded in z around 0 82.5%

      \[\leadsto \frac{x + y \cdot \color{blue}{\frac{2}{t \cdot z}}}{y} \]
   5. Step-by-step derivation

      1. *-commutative 82.5%

         \[\leadsto \frac{x + y \cdot \frac{2}{\color{blue}{z \cdot t}}}{y} \]

   6. Simplified 82.5%

      \[\leadsto \frac{x + y \cdot \color{blue}{\frac{2}{z \cdot t}}}{y} \]

   7. Taylor expanded in x around 0 91.1%

      \[\leadsto \color{blue}{2 \cdot \frac{1}{t \cdot z} + \frac{x}{y}} \]
   8. Step-by-step derivation

      1. associate-*r/ 91.1%

         \[\leadsto \color{blue}{\frac{2 \cdot 1}{t \cdot z}} + \frac{x}{y} \]

      2. metadata-eval 91.1%

         \[\leadsto \frac{\color{blue}{2}}{t \cdot z} + \frac{x}{y} \]

      3. associate-/l/ 91.1%

         \[\leadsto \color{blue}{\frac{\frac{2}{z}}{t}} + \frac{x}{y} \]

      4. +-commutative 91.1%

         \[\leadsto \color{blue}{\frac{x}{y} + \frac{\frac{2}{z}}{t}} \]

      5. associate-/l/ 91.1%

         \[\leadsto \frac{x}{y} + \color{blue}{\frac{2}{t \cdot z}} \]

   9. Simplified 91.1%

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

4. Final simplification 94.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -3.8 \cdot 10^{-53} \lor \neg \left(z \leq 1.05 \cdot 10^{-30}\right):\\ \;\;\;\;\frac{x}{y} + \frac{2 \cdot \left(1 - t\right)}{t}\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{y} + \frac{2}{z \cdot t}\\ \end{array} \]
5. Add Preprocessing

Alternative 10: 63.9% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\frac{x}{y} \leq -3.5 \cdot 10^{+26} \lor \neg \left(\frac{x}{y} \leq 2.6 \cdot 10^{+55}\right):\\ \;\;\;\;\frac{x}{y}\\ \mathbf{else}:\\ \;\;\;\;-2 + \frac{2}{t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= (/ x y) -3.5e+26) (not (<= (/ x y) 2.6e+55)))
   (/ x y)
   (+ -2.0 (/ 2.0 t))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (((x / y) <= -3.5e+26) || !((x / y) <= 2.6e+55)) {
		tmp = x / y;
	} else {
		tmp = -2.0 + (2.0 / t);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if (((x / y) <= (-3.5d+26)) .or. (.not. ((x / y) <= 2.6d+55))) then
        tmp = x / y
    else
        tmp = (-2.0d0) + (2.0d0 / t)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if (((x / y) <= -3.5e+26) || !((x / y) <= 2.6e+55)) {
		tmp = x / y;
	} else {
		tmp = -2.0 + (2.0 / t);
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if ((x / y) <= -3.5e+26) or not ((x / y) <= 2.6e+55):
		tmp = x / y
	else:
		tmp = -2.0 + (2.0 / t)
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((Float64(x / y) <= -3.5e+26) || !(Float64(x / y) <= 2.6e+55))
		tmp = Float64(x / y);
	else
		tmp = Float64(-2.0 + Float64(2.0 / t));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (((x / y) <= -3.5e+26) || ~(((x / y) <= 2.6e+55)))
		tmp = x / y;
	else
		tmp = -2.0 + (2.0 / t);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[N[(x / y), $MachinePrecision], -3.5e+26], N[Not[LessEqual[N[(x / y), $MachinePrecision], 2.6e+55]], $MachinePrecision]], N[(x / y), $MachinePrecision], N[(-2.0 + N[(2.0 / t), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 x y) < -3.4999999999999999e26 or 2.6e55 < (/.f64 x y)

   1. Initial program 86.5%

      \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf 74.6%

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

   if -3.4999999999999999e26 < (/.f64 x y) < 2.6e55

   1. Initial program 84.4%

      \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]
   2. Add Preprocessing

   3. Taylor expanded in z around inf 63.8%

      \[\leadsto \frac{x}{y} + \color{blue}{2 \cdot \frac{1 - t}{t}} \]
   4. Step-by-step derivation

      1. associate-*r/ 63.8%

         \[\leadsto \frac{x}{y} + \color{blue}{\frac{2 \cdot \left(1 - t\right)}{t}} \]

   5. Simplified 63.8%

      \[\leadsto \frac{x}{y} + \color{blue}{\frac{2 \cdot \left(1 - t\right)}{t}} \]

   6. Taylor expanded in x around 0 61.0%

      \[\leadsto \color{blue}{2 \cdot \frac{1 - t}{t}} \]
   7. Step-by-step derivation

      1. div-sub 61.0%

         \[\leadsto 2 \cdot \color{blue}{\left(\frac{1}{t} - \frac{t}{t}\right)} \]

      2. sub-neg 61.0%

         \[\leadsto 2 \cdot \color{blue}{\left(\frac{1}{t} + \left(-\frac{t}{t}\right)\right)} \]

      3. *-inverses 61.0%

         \[\leadsto 2 \cdot \left(\frac{1}{t} + \left(-\color{blue}{1}\right)\right) \]

      4. metadata-eval 61.0%

         \[\leadsto 2 \cdot \left(\frac{1}{t} + \color{blue}{-1}\right) \]

      5. distribute-lft-in 61.0%

         \[\leadsto \color{blue}{2 \cdot \frac{1}{t} + 2 \cdot -1} \]

      6. associate-*r/ 61.0%

         \[\leadsto \color{blue}{\frac{2 \cdot 1}{t}} + 2 \cdot -1 \]

      7. metadata-eval 61.0%

         \[\leadsto \frac{\color{blue}{2}}{t} + 2 \cdot -1 \]

      8. metadata-eval 61.0%

         \[\leadsto \frac{2}{t} + \color{blue}{-2} \]

   8. Simplified 61.0%

      \[\leadsto \color{blue}{\frac{2}{t} + -2} \]
3. Recombined 2 regimes into one program.

4. Final simplification 67.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{x}{y} \leq -3.5 \cdot 10^{+26} \lor \neg \left(\frac{x}{y} \leq 2.6 \cdot 10^{+55}\right):\\ \;\;\;\;\frac{x}{y}\\ \mathbf{else}:\\ \;\;\;\;-2 + \frac{2}{t}\\ \end{array} \]
5. Add Preprocessing

Alternative 11: 80.2% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -2.1 \cdot 10^{-11} \lor \neg \left(t \leq 8 \cdot 10^{-18}\right):\\ \;\;\;\;\frac{x}{y} - 2\\ \mathbf{else}:\\ \;\;\;\;\frac{2 + \frac{2}{z}}{t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= t -2.1e-11) (not (<= t 8e-18)))
   (- (/ x y) 2.0)
   (/ (+ 2.0 (/ 2.0 z)) t)))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((t <= -2.1e-11) || !(t <= 8e-18)) {
		tmp = (x / y) - 2.0;
	} else {
		tmp = (2.0 + (2.0 / z)) / t;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if ((t <= (-2.1d-11)) .or. (.not. (t <= 8d-18))) then
        tmp = (x / y) - 2.0d0
    else
        tmp = (2.0d0 + (2.0d0 / z)) / t
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((t <= -2.1e-11) || !(t <= 8e-18)) {
		tmp = (x / y) - 2.0;
	} else {
		tmp = (2.0 + (2.0 / z)) / t;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (t <= -2.1e-11) or not (t <= 8e-18):
		tmp = (x / y) - 2.0
	else:
		tmp = (2.0 + (2.0 / z)) / t
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((t <= -2.1e-11) || !(t <= 8e-18))
		tmp = Float64(Float64(x / y) - 2.0);
	else
		tmp = Float64(Float64(2.0 + Float64(2.0 / z)) / t);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((t <= -2.1e-11) || ~((t <= 8e-18)))
		tmp = (x / y) - 2.0;
	else
		tmp = (2.0 + (2.0 / z)) / t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[t, -2.1e-11], N[Not[LessEqual[t, 8e-18]], $MachinePrecision]], N[(N[(x / y), $MachinePrecision] - 2.0), $MachinePrecision], N[(N[(2.0 + N[(2.0 / z), $MachinePrecision]), $MachinePrecision] / t), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if t < -2.0999999999999999e-11 or 8.0000000000000006e-18 < t

   1. Initial program 74.5%

      \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]
   2. Add Preprocessing

   3. Taylor expanded in t around inf 82.0%

      \[\leadsto \color{blue}{\frac{x}{y} - 2} \]

   if -2.0999999999999999e-11 < t < 8.0000000000000006e-18

   1. Initial program 98.1%

      \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]
   2. Add Preprocessing

   3. Taylor expanded in t around 0 82.8%

      \[\leadsto \color{blue}{\frac{2 + 2 \cdot \frac{1}{z}}{t}} \]
   4. Step-by-step derivation

      1. associate-*r/ 82.8%

         \[\leadsto \frac{2 + \color{blue}{\frac{2 \cdot 1}{z}}}{t} \]

      2. metadata-eval 82.8%

         \[\leadsto \frac{2 + \frac{\color{blue}{2}}{z}}{t} \]

   5. Simplified 82.8%

      \[\leadsto \color{blue}{\frac{2 + \frac{2}{z}}{t}} \]
3. Recombined 2 regimes into one program.

4. Final simplification 82.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq -2.1 \cdot 10^{-11} \lor \neg \left(t \leq 8 \cdot 10^{-18}\right):\\ \;\;\;\;\frac{x}{y} - 2\\ \mathbf{else}:\\ \;\;\;\;\frac{2 + \frac{2}{z}}{t}\\ \end{array} \]
5. Add Preprocessing

Alternative 12: 69.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -1250000000 \lor \neg \left(t \leq 8 \cdot 10^{-18}\right):\\ \;\;\;\;\frac{x}{y} - 2\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{y} + \frac{2}{t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= t -1250000000.0) (not (<= t 8e-18)))
   (- (/ x y) 2.0)
   (+ (/ x y) (/ 2.0 t))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((t <= -1250000000.0) || !(t <= 8e-18)) {
		tmp = (x / y) - 2.0;
	} else {
		tmp = (x / y) + (2.0 / t);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if ((t <= (-1250000000.0d0)) .or. (.not. (t <= 8d-18))) then
        tmp = (x / y) - 2.0d0
    else
        tmp = (x / y) + (2.0d0 / t)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((t <= -1250000000.0) || !(t <= 8e-18)) {
		tmp = (x / y) - 2.0;
	} else {
		tmp = (x / y) + (2.0 / t);
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (t <= -1250000000.0) or not (t <= 8e-18):
		tmp = (x / y) - 2.0
	else:
		tmp = (x / y) + (2.0 / t)
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((t <= -1250000000.0) || !(t <= 8e-18))
		tmp = Float64(Float64(x / y) - 2.0);
	else
		tmp = Float64(Float64(x / y) + Float64(2.0 / t));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((t <= -1250000000.0) || ~((t <= 8e-18)))
		tmp = (x / y) - 2.0;
	else
		tmp = (x / y) + (2.0 / t);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[t, -1250000000.0], N[Not[LessEqual[t, 8e-18]], $MachinePrecision]], N[(N[(x / y), $MachinePrecision] - 2.0), $MachinePrecision], N[(N[(x / y), $MachinePrecision] + N[(2.0 / t), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if t < -1.25e9 or 8.0000000000000006e-18 < t

   1. Initial program 73.5%

      \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]
   2. Add Preprocessing

   3. Taylor expanded in t around inf 81.9%

      \[\leadsto \color{blue}{\frac{x}{y} - 2} \]

   if -1.25e9 < t < 8.0000000000000006e-18

   1. Initial program 98.2%

      \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]
   2. Add Preprocessing

   3. Taylor expanded in t around 0 97.8%

      \[\leadsto \frac{x}{y} + \color{blue}{\frac{2 + 2 \cdot z}{t \cdot z}} \]

   4. Taylor expanded in z around inf 62.3%

      \[\leadsto \color{blue}{2 \cdot \frac{1}{t} + \frac{x}{y}} \]
   5. Step-by-step derivation

      1. associate-*r/ 62.3%

         \[\leadsto \color{blue}{\frac{2 \cdot 1}{t}} + \frac{x}{y} \]

      2. metadata-eval 62.3%

         \[\leadsto \frac{\color{blue}{2}}{t} + \frac{x}{y} \]

      3. +-commutative 62.3%

         \[\leadsto \color{blue}{\frac{x}{y} + \frac{2}{t}} \]

   6. Simplified 62.3%

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

4. Final simplification 72.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq -1250000000 \lor \neg \left(t \leq 8 \cdot 10^{-18}\right):\\ \;\;\;\;\frac{x}{y} - 2\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{y} + \frac{2}{t}\\ \end{array} \]
5. Add Preprocessing

Alternative 13: 35.9% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -1250000000:\\ \;\;\;\;-2\\ \mathbf{elif}\;t \leq 8 \cdot 10^{-18}:\\ \;\;\;\;\frac{2}{t}\\ \mathbf{else}:\\ \;\;\;\;-2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= t -1250000000.0) -2.0 (if (<= t 8e-18) (/ 2.0 t) -2.0)))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (t <= -1250000000.0) {
		tmp = -2.0;
	} else if (t <= 8e-18) {
		tmp = 2.0 / t;
	} else {
		tmp = -2.0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if (t <= (-1250000000.0d0)) then
        tmp = -2.0d0
    else if (t <= 8d-18) then
        tmp = 2.0d0 / t
    else
        tmp = -2.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if (t <= -1250000000.0) {
		tmp = -2.0;
	} else if (t <= 8e-18) {
		tmp = 2.0 / t;
	} else {
		tmp = -2.0;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if t <= -1250000000.0:
		tmp = -2.0
	elif t <= 8e-18:
		tmp = 2.0 / t
	else:
		tmp = -2.0
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (t <= -1250000000.0)
		tmp = -2.0;
	elseif (t <= 8e-18)
		tmp = Float64(2.0 / t);
	else
		tmp = -2.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (t <= -1250000000.0)
		tmp = -2.0;
	elseif (t <= 8e-18)
		tmp = 2.0 / t;
	else
		tmp = -2.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[t, -1250000000.0], -2.0, If[LessEqual[t, 8e-18], N[(2.0 / t), $MachinePrecision], -2.0]]

Derivation

1. Split input into 2 regimes

2. if t < -1.25e9 or 8.0000000000000006e-18 < t

   1. Initial program 73.5%

      \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]
   2. Add Preprocessing

   3. Taylor expanded in t around inf 81.9%

      \[\leadsto \color{blue}{\frac{x}{y} - 2} \]

   4. Taylor expanded in x around 0 34.3%

      \[\leadsto \color{blue}{-2} \]

   if -1.25e9 < t < 8.0000000000000006e-18

   1. Initial program 98.2%

      \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]
   2. Add Preprocessing

   3. Taylor expanded in z around inf 62.7%

      \[\leadsto \frac{x}{y} + \color{blue}{2 \cdot \frac{1 - t}{t}} \]
   4. Step-by-step derivation

      1. associate-*r/ 62.7%

         \[\leadsto \frac{x}{y} + \color{blue}{\frac{2 \cdot \left(1 - t\right)}{t}} \]

   5. Simplified 62.7%

      \[\leadsto \frac{x}{y} + \color{blue}{\frac{2 \cdot \left(1 - t\right)}{t}} \]

   6. Taylor expanded in t around 0 40.1%

      \[\leadsto \color{blue}{\frac{2}{t}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 14: 19.7% accurate, 17.0× speedup

Math

\[\begin{array}{l} \\ -2 \end{array} \]

FPCore

(FPCore (x y z t) :precision binary64 -2.0)

C

double code(double x, double y, double z, double t) {
	return -2.0;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    code = -2.0d0
end function

Java

public static double code(double x, double y, double z, double t) {
	return -2.0;
}

Python

def code(x, y, z, t):
	return -2.0

Julia

function code(x, y, z, t)
	return -2.0
end

MATLAB

function tmp = code(x, y, z, t)
	tmp = -2.0;
end

Wolfram

code[x_, y_, z_, t_] := -2.0

Derivation

1. Initial program 85.4%

   \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]
2. Add Preprocessing

3. Taylor expanded in t around inf 54.4%

   \[\leadsto \color{blue}{\frac{x}{y} - 2} \]

4. Taylor expanded in x around 0 19.1%

   \[\leadsto \color{blue}{-2} \]
5. Add Preprocessing

Developer Target 1: 98.9% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \frac{\frac{2}{z} + 2}{t} - \left(2 - \frac{x}{y}\right) \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (- (/ (+ (/ 2.0 z) 2.0) t) (- 2.0 (/ x y))))

C

double code(double x, double y, double z, double t) {
	return (((2.0 / z) + 2.0) / t) - (2.0 - (x / y));
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    code = (((2.0d0 / z) + 2.0d0) / t) - (2.0d0 - (x / y))
end function

Java

public static double code(double x, double y, double z, double t) {
	return (((2.0 / z) + 2.0) / t) - (2.0 - (x / y));
}

Python

def code(x, y, z, t):
	return (((2.0 / z) + 2.0) / t) - (2.0 - (x / y))

Julia

function code(x, y, z, t)
	return Float64(Float64(Float64(Float64(2.0 / z) + 2.0) / t) - Float64(2.0 - Float64(x / y)))
end

MATLAB

function tmp = code(x, y, z, t)
	tmp = (((2.0 / z) + 2.0) / t) - (2.0 - (x / y));
end

Wolfram

code[x_, y_, z_, t_] := N[(N[(N[(N[(2.0 / z), $MachinePrecision] + 2.0), $MachinePrecision] / t), $MachinePrecision] - N[(2.0 - N[(x / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Reproduce

herbie shell --seed 2024170 
(FPCore (x y z t)
  :name "Data.HashTable.ST.Basic:computeOverhead from hashtables-1.2.0.2"
  :precision binary64

  :alt
  (! :herbie-platform default (- (/ (+ (/ 2 z) 2) t) (- 2 (/ x y))))

  (+ (/ x y) (/ (+ 2.0 (* (* z 2.0) (- 1.0 t))) (* t z))))