Graphics.Rendering.Chart.Plot.AreaSpots:renderAreaSpots4D from Chart-1.5.3

Percentage Accurate: 84.6% → 96.6%

Time: 7.3s

Alternatives: 10

Speedup: 0.7×

Specification

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 84.6% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 96.6% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -1.2 \cdot 10^{-22} \lor \neg \left(z \leq 4.8 \cdot 10^{-101}\right):\\ \;\;\;\;\frac{x}{\frac{t - z}{y - z}}\\ \mathbf{else}:\\ \;\;\;\;\left(y - z\right) \cdot \frac{x}{t - z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= z -1.2e-22) (not (<= z 4.8e-101)))
   (/ x (/ (- t z) (- y z)))
   (* (- y z) (/ x (- t z)))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((z <= -1.2e-22) || !(z <= 4.8e-101)) {
		tmp = x / ((t - z) / (y - z));
	} else {
		tmp = (y - z) * (x / (t - z));
	}
	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 <= (-1.2d-22)) .or. (.not. (z <= 4.8d-101))) then
        tmp = x / ((t - z) / (y - z))
    else
        tmp = (y - z) * (x / (t - z))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((z <= -1.2e-22) || !(z <= 4.8e-101)) {
		tmp = x / ((t - z) / (y - z));
	} else {
		tmp = (y - z) * (x / (t - z));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (z <= -1.2e-22) or not (z <= 4.8e-101):
		tmp = x / ((t - z) / (y - z))
	else:
		tmp = (y - z) * (x / (t - z))
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((z <= -1.2e-22) || !(z <= 4.8e-101))
		tmp = Float64(x / Float64(Float64(t - z) / Float64(y - z)));
	else
		tmp = Float64(Float64(y - z) * Float64(x / Float64(t - z)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((z <= -1.2e-22) || ~((z <= 4.8e-101)))
		tmp = x / ((t - z) / (y - z));
	else
		tmp = (y - z) * (x / (t - z));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[z, -1.2e-22], N[Not[LessEqual[z, 4.8e-101]], $MachinePrecision]], N[(x / N[(N[(t - z), $MachinePrecision] / N[(y - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(y - z), $MachinePrecision] * N[(x / N[(t - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if z < -1.20000000000000001e-22 or 4.8e-101 < z

   1. Initial program 82.0%

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

      1. associate-/l* 99.9%

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

   3. Simplified 99.9%

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

   if -1.20000000000000001e-22 < z < 4.8e-101

   1. Initial program 91.2%

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

      1. associate-*l/ 97.1%

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

   3. Simplified 97.1%

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

4. Final simplification 98.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -1.2 \cdot 10^{-22} \lor \neg \left(z \leq 4.8 \cdot 10^{-101}\right):\\ \;\;\;\;\frac{x}{\frac{t - z}{y - z}}\\ \mathbf{else}:\\ \;\;\;\;\left(y - z\right) \cdot \frac{x}{t - z}\\ \end{array} \]

Alternative 2: 58.7% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -3 \cdot 10^{+27}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq -2.9 \cdot 10^{-8}:\\ \;\;\;\;x \cdot \frac{y}{-z}\\ \mathbf{elif}\;z \leq -5.5 \cdot 10^{-50}:\\ \;\;\;\;\frac{x}{\frac{t}{y}}\\ \mathbf{elif}\;z \leq -3.3 \cdot 10^{-123}:\\ \;\;\;\;z \cdot \frac{-x}{t}\\ \mathbf{elif}\;z \leq 8 \cdot 10^{+52}:\\ \;\;\;\;\frac{y}{\frac{t}{x}}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= z -3e+27)
   x
   (if (<= z -2.9e-8)
     (* x (/ y (- z)))
     (if (<= z -5.5e-50)
       (/ x (/ t y))
       (if (<= z -3.3e-123)
         (* z (/ (- x) t))
         (if (<= z 8e+52) (/ y (/ t x)) x))))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (z <= -3e+27) {
		tmp = x;
	} else if (z <= -2.9e-8) {
		tmp = x * (y / -z);
	} else if (z <= -5.5e-50) {
		tmp = x / (t / y);
	} else if (z <= -3.3e-123) {
		tmp = z * (-x / t);
	} else if (z <= 8e+52) {
		tmp = y / (t / x);
	} else {
		tmp = x;
	}
	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 <= (-3d+27)) then
        tmp = x
    else if (z <= (-2.9d-8)) then
        tmp = x * (y / -z)
    else if (z <= (-5.5d-50)) then
        tmp = x / (t / y)
    else if (z <= (-3.3d-123)) then
        tmp = z * (-x / t)
    else if (z <= 8d+52) then
        tmp = y / (t / x)
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if (z <= -3e+27) {
		tmp = x;
	} else if (z <= -2.9e-8) {
		tmp = x * (y / -z);
	} else if (z <= -5.5e-50) {
		tmp = x / (t / y);
	} else if (z <= -3.3e-123) {
		tmp = z * (-x / t);
	} else if (z <= 8e+52) {
		tmp = y / (t / x);
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if z <= -3e+27:
		tmp = x
	elif z <= -2.9e-8:
		tmp = x * (y / -z)
	elif z <= -5.5e-50:
		tmp = x / (t / y)
	elif z <= -3.3e-123:
		tmp = z * (-x / t)
	elif z <= 8e+52:
		tmp = y / (t / x)
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (z <= -3e+27)
		tmp = x;
	elseif (z <= -2.9e-8)
		tmp = Float64(x * Float64(y / Float64(-z)));
	elseif (z <= -5.5e-50)
		tmp = Float64(x / Float64(t / y));
	elseif (z <= -3.3e-123)
		tmp = Float64(z * Float64(Float64(-x) / t));
	elseif (z <= 8e+52)
		tmp = Float64(y / Float64(t / x));
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (z <= -3e+27)
		tmp = x;
	elseif (z <= -2.9e-8)
		tmp = x * (y / -z);
	elseif (z <= -5.5e-50)
		tmp = x / (t / y);
	elseif (z <= -3.3e-123)
		tmp = z * (-x / t);
	elseif (z <= 8e+52)
		tmp = y / (t / x);
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[z, -3e+27], x, If[LessEqual[z, -2.9e-8], N[(x * N[(y / (-z)), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, -5.5e-50], N[(x / N[(t / y), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, -3.3e-123], N[(z * N[((-x) / t), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 8e+52], N[(y / N[(t / x), $MachinePrecision]), $MachinePrecision], x]]]]]

Derivation

1. Split input into 5 regimes

2. if z < -2.99999999999999976e27 or 7.9999999999999999e52 < z

   1. Initial program 78.9%

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

      1. associate-*l/ 67.8%

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

   3. Simplified 67.8%

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

   4. Taylor expanded in z around inf 67.2%

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

   if -2.99999999999999976e27 < z < -2.9000000000000002e-8

   1. Initial program 88.0%

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

      1. associate-*l/ 100.0%

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

   3. Simplified 100.0%

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

   4. Taylor expanded in t around 0 77.2%

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

      1. mul-1-neg 77.2%

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

      2. associate-/l* 77.2%

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

      3. distribute-neg-frac 77.2%

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

   6. Simplified 77.2%

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

   7. Taylor expanded in z around 0 75.9%

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

   8. Taylor expanded in x around 0 75.9%

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

      1. associate-*r/ 75.9%

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

      2. *-commutative 75.9%

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

      3. associate-*l/ 75.9%

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

      4. associate-*r/ 75.9%

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

      5. associate-*l/ 75.9%

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

      6. metadata-eval 75.9%

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

      7. associate-/r* 75.9%

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

      8. neg-mul-1 75.9%

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

      9. associate-*l* 75.9%

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

      10. associate-/r/ 75.7%

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

      11. *-commutative 75.7%

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

      12. associate-/r/ 75.9%

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

      13. associate-*l/ 75.9%

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

      14. *-lft-identity 75.9%

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

   10. Simplified 75.9%

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

   if -2.9000000000000002e-8 < z < -5.49999999999999975e-50

   1. Initial program 90.2%

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

      1. associate-*l/ 84.0%

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

   3. Simplified 84.0%

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

   4. Taylor expanded in z around 0 41.8%

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

      1. associate-/l* 51.3%

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

   6. Simplified 51.3%

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

   if -5.49999999999999975e-50 < z < -3.3000000000000003e-123

   1. Initial program 76.0%

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

      1. associate-*l/ 92.0%

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

   3. Simplified 92.0%

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

   4. Taylor expanded in t around inf 75.7%

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

   5. Taylor expanded in y around 0 53.2%

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

      1. mul-1-neg 53.2%

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

      2. distribute-neg-frac 53.2%

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

      3. *-commutative 53.2%

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

      4. distribute-lft-neg-out 53.2%

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

      5. associate-*r/ 68.4%

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

      6. distribute-lft-neg-out 68.4%

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

      7. distribute-rgt-neg-in 68.4%

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

      8. distribute-neg-frac 68.4%

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

   7. Simplified 68.4%

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

   if -3.3000000000000003e-123 < z < 7.9999999999999999e52

   1. Initial program 92.8%

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

      1. associate-*l/ 98.1%

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

   3. Simplified 98.1%

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

   4. Taylor expanded in z around 0 65.5%

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

      1. associate-/l* 63.0%

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

   6. Simplified 63.0%

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

      1. associate-/r/ 69.0%

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

   8. Applied egg-rr 69.0%

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

      1. *-commutative 69.0%

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

      2. clear-num 69.0%

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

      3. div-inv 69.1%

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

   10. Applied egg-rr 69.1%

       \[\leadsto \color{blue}{\frac{y}{\frac{t}{x}}} \]
3. Recombined 5 regimes into one program.

4. Final simplification 67.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -3 \cdot 10^{+27}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq -2.9 \cdot 10^{-8}:\\ \;\;\;\;x \cdot \frac{y}{-z}\\ \mathbf{elif}\;z \leq -5.5 \cdot 10^{-50}:\\ \;\;\;\;\frac{x}{\frac{t}{y}}\\ \mathbf{elif}\;z \leq -3.3 \cdot 10^{-123}:\\ \;\;\;\;z \cdot \frac{-x}{t}\\ \mathbf{elif}\;z \leq 8 \cdot 10^{+52}:\\ \;\;\;\;\frac{y}{\frac{t}{x}}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]

Alternative 3: 67.3% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y \cdot \frac{x}{t - z}\\ \mathbf{if}\;z \leq -7.6 \cdot 10^{+27}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq -1.3 \cdot 10^{-41}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq -1.18 \cdot 10^{-172}:\\ \;\;\;\;\left(y - z\right) \cdot \frac{x}{t}\\ \mathbf{elif}\;z \leq 1.3 \cdot 10^{+65}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (* y (/ x (- t z)))))
   (if (<= z -7.6e+27)
     x
     (if (<= z -1.3e-41)
       t_1
       (if (<= z -1.18e-172) (* (- y z) (/ x t)) (if (<= z 1.3e+65) t_1 x))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = y * (x / (t - z));
	double tmp;
	if (z <= -7.6e+27) {
		tmp = x;
	} else if (z <= -1.3e-41) {
		tmp = t_1;
	} else if (z <= -1.18e-172) {
		tmp = (y - z) * (x / t);
	} else if (z <= 1.3e+65) {
		tmp = t_1;
	} else {
		tmp = x;
	}
	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 = y * (x / (t - z))
    if (z <= (-7.6d+27)) then
        tmp = x
    else if (z <= (-1.3d-41)) then
        tmp = t_1
    else if (z <= (-1.18d-172)) then
        tmp = (y - z) * (x / t)
    else if (z <= 1.3d+65) then
        tmp = t_1
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = y * (x / (t - z));
	double tmp;
	if (z <= -7.6e+27) {
		tmp = x;
	} else if (z <= -1.3e-41) {
		tmp = t_1;
	} else if (z <= -1.18e-172) {
		tmp = (y - z) * (x / t);
	} else if (z <= 1.3e+65) {
		tmp = t_1;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = y * (x / (t - z))
	tmp = 0
	if z <= -7.6e+27:
		tmp = x
	elif z <= -1.3e-41:
		tmp = t_1
	elif z <= -1.18e-172:
		tmp = (y - z) * (x / t)
	elif z <= 1.3e+65:
		tmp = t_1
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(y * Float64(x / Float64(t - z)))
	tmp = 0.0
	if (z <= -7.6e+27)
		tmp = x;
	elseif (z <= -1.3e-41)
		tmp = t_1;
	elseif (z <= -1.18e-172)
		tmp = Float64(Float64(y - z) * Float64(x / t));
	elseif (z <= 1.3e+65)
		tmp = t_1;
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = y * (x / (t - z));
	tmp = 0.0;
	if (z <= -7.6e+27)
		tmp = x;
	elseif (z <= -1.3e-41)
		tmp = t_1;
	elseif (z <= -1.18e-172)
		tmp = (y - z) * (x / t);
	elseif (z <= 1.3e+65)
		tmp = t_1;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(y * N[(x / N[(t - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -7.6e+27], x, If[LessEqual[z, -1.3e-41], t$95$1, If[LessEqual[z, -1.18e-172], N[(N[(y - z), $MachinePrecision] * N[(x / t), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 1.3e+65], t$95$1, x]]]]]

Derivation

1. Split input into 3 regimes

2. if z < -7.60000000000000043e27 or 1.30000000000000001e65 < z

   1. Initial program 77.9%

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

      1. associate-*l/ 66.2%

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

   3. Simplified 66.2%

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

   4. Taylor expanded in z around inf 68.4%

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

   if -7.60000000000000043e27 < z < -1.3e-41 or -1.17999999999999999e-172 < z < 1.30000000000000001e65

   1. Initial program 93.0%

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

      1. associate-*l/ 97.2%

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

   3. Simplified 97.2%

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

   4. Taylor expanded in y around inf 77.9%

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

      1. associate-*l/ 81.2%

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

      2. *-commutative 81.2%

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

   6. Simplified 81.2%

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

   if -1.3e-41 < z < -1.17999999999999999e-172

   1. Initial program 77.5%

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

      1. associate-*l/ 94.3%

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

   3. Simplified 94.3%

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

   4. Taylor expanded in t around inf 82.9%

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

4. Final simplification 76.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -7.6 \cdot 10^{+27}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq -1.3 \cdot 10^{-41}:\\ \;\;\;\;y \cdot \frac{x}{t - z}\\ \mathbf{elif}\;z \leq -1.18 \cdot 10^{-172}:\\ \;\;\;\;\left(y - z\right) \cdot \frac{x}{t}\\ \mathbf{elif}\;z \leq 1.3 \cdot 10^{+65}:\\ \;\;\;\;y \cdot \frac{x}{t - z}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]

Alternative 4: 74.3% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{-x}{\frac{z}{y - z}}\\ \mathbf{if}\;z \leq -4.1 \cdot 10^{-41}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq -8.6 \cdot 10^{-173}:\\ \;\;\;\;\left(y - z\right) \cdot \frac{x}{t}\\ \mathbf{elif}\;z \leq 7.6 \cdot 10^{+48}:\\ \;\;\;\;y \cdot \frac{x}{t - z}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (/ (- x) (/ z (- y z)))))
   (if (<= z -4.1e-41)
     t_1
     (if (<= z -8.6e-173)
       (* (- y z) (/ x t))
       (if (<= z 7.6e+48) (* y (/ x (- t z))) t_1)))))

C

double code(double x, double y, double z, double t) {
	double t_1 = -x / (z / (y - z));
	double tmp;
	if (z <= -4.1e-41) {
		tmp = t_1;
	} else if (z <= -8.6e-173) {
		tmp = (y - z) * (x / t);
	} else if (z <= 7.6e+48) {
		tmp = y * (x / (t - z));
	} 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) :: tmp
    t_1 = -x / (z / (y - z))
    if (z <= (-4.1d-41)) then
        tmp = t_1
    else if (z <= (-8.6d-173)) then
        tmp = (y - z) * (x / t)
    else if (z <= 7.6d+48) then
        tmp = y * (x / (t - z))
    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 / (z / (y - z));
	double tmp;
	if (z <= -4.1e-41) {
		tmp = t_1;
	} else if (z <= -8.6e-173) {
		tmp = (y - z) * (x / t);
	} else if (z <= 7.6e+48) {
		tmp = y * (x / (t - z));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = -x / (z / (y - z))
	tmp = 0
	if z <= -4.1e-41:
		tmp = t_1
	elif z <= -8.6e-173:
		tmp = (y - z) * (x / t)
	elif z <= 7.6e+48:
		tmp = y * (x / (t - z))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(-x) / Float64(z / Float64(y - z)))
	tmp = 0.0
	if (z <= -4.1e-41)
		tmp = t_1;
	elseif (z <= -8.6e-173)
		tmp = Float64(Float64(y - z) * Float64(x / t));
	elseif (z <= 7.6e+48)
		tmp = Float64(y * Float64(x / Float64(t - z)));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = -x / (z / (y - z));
	tmp = 0.0;
	if (z <= -4.1e-41)
		tmp = t_1;
	elseif (z <= -8.6e-173)
		tmp = (y - z) * (x / t);
	elseif (z <= 7.6e+48)
		tmp = y * (x / (t - z));
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[((-x) / N[(z / N[(y - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -4.1e-41], t$95$1, If[LessEqual[z, -8.6e-173], N[(N[(y - z), $MachinePrecision] * N[(x / t), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 7.6e+48], N[(y * N[(x / N[(t - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

1. Split input into 3 regimes

2. if z < -4.10000000000000014e-41 or 7.60000000000000001e48 < z

   1. Initial program 80.4%

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

      1. associate-*l/ 71.1%

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

   3. Simplified 71.1%

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

   4. Taylor expanded in t around 0 63.9%

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

      1. mul-1-neg 63.9%

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

      2. associate-/l* 80.0%

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

      3. distribute-neg-frac 80.0%

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

   6. Simplified 80.0%

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

   if -4.10000000000000014e-41 < z < -8.6000000000000006e-173

   1. Initial program 77.5%

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

      1. associate-*l/ 94.3%

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

   3. Simplified 94.3%

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

   4. Taylor expanded in t around inf 82.9%

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

   if -8.6000000000000006e-173 < z < 7.60000000000000001e48

   1. Initial program 93.3%

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

      1. associate-*l/ 98.0%

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

   3. Simplified 98.0%

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

   4. Taylor expanded in y around inf 81.1%

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

      1. associate-*l/ 84.7%

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

      2. *-commutative 84.7%

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

   6. Simplified 84.7%

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

4. Final simplification 82.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -4.1 \cdot 10^{-41}:\\ \;\;\;\;\frac{-x}{\frac{z}{y - z}}\\ \mathbf{elif}\;z \leq -8.6 \cdot 10^{-173}:\\ \;\;\;\;\left(y - z\right) \cdot \frac{x}{t}\\ \mathbf{elif}\;z \leq 7.6 \cdot 10^{+48}:\\ \;\;\;\;y \cdot \frac{x}{t - z}\\ \mathbf{else}:\\ \;\;\;\;\frac{-x}{\frac{z}{y - z}}\\ \end{array} \]

Alternative 5: 59.8% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -2.3 \cdot 10^{+27}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq -4.6 \cdot 10^{-7}:\\ \;\;\;\;x \cdot \frac{y}{-z}\\ \mathbf{elif}\;z \leq -2.2 \cdot 10^{-72}:\\ \;\;\;\;\frac{x}{\frac{t}{y}}\\ \mathbf{elif}\;z \leq 7.6 \cdot 10^{+48}:\\ \;\;\;\;\frac{y}{\frac{t}{x}}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= z -2.3e+27)
   x
   (if (<= z -4.6e-7)
     (* x (/ y (- z)))
     (if (<= z -2.2e-72) (/ x (/ t y)) (if (<= z 7.6e+48) (/ y (/ t x)) x)))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (z <= -2.3e+27) {
		tmp = x;
	} else if (z <= -4.6e-7) {
		tmp = x * (y / -z);
	} else if (z <= -2.2e-72) {
		tmp = x / (t / y);
	} else if (z <= 7.6e+48) {
		tmp = y / (t / x);
	} else {
		tmp = x;
	}
	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 <= (-2.3d+27)) then
        tmp = x
    else if (z <= (-4.6d-7)) then
        tmp = x * (y / -z)
    else if (z <= (-2.2d-72)) then
        tmp = x / (t / y)
    else if (z <= 7.6d+48) then
        tmp = y / (t / x)
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if (z <= -2.3e+27) {
		tmp = x;
	} else if (z <= -4.6e-7) {
		tmp = x * (y / -z);
	} else if (z <= -2.2e-72) {
		tmp = x / (t / y);
	} else if (z <= 7.6e+48) {
		tmp = y / (t / x);
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if z <= -2.3e+27:
		tmp = x
	elif z <= -4.6e-7:
		tmp = x * (y / -z)
	elif z <= -2.2e-72:
		tmp = x / (t / y)
	elif z <= 7.6e+48:
		tmp = y / (t / x)
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (z <= -2.3e+27)
		tmp = x;
	elseif (z <= -4.6e-7)
		tmp = Float64(x * Float64(y / Float64(-z)));
	elseif (z <= -2.2e-72)
		tmp = Float64(x / Float64(t / y));
	elseif (z <= 7.6e+48)
		tmp = Float64(y / Float64(t / x));
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (z <= -2.3e+27)
		tmp = x;
	elseif (z <= -4.6e-7)
		tmp = x * (y / -z);
	elseif (z <= -2.2e-72)
		tmp = x / (t / y);
	elseif (z <= 7.6e+48)
		tmp = y / (t / x);
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[z, -2.3e+27], x, If[LessEqual[z, -4.6e-7], N[(x * N[(y / (-z)), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, -2.2e-72], N[(x / N[(t / y), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 7.6e+48], N[(y / N[(t / x), $MachinePrecision]), $MachinePrecision], x]]]]

Derivation

1. Split input into 4 regimes

2. if z < -2.3000000000000001e27 or 7.60000000000000001e48 < z

   1. Initial program 78.9%

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

      1. associate-*l/ 67.8%

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

   3. Simplified 67.8%

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

   4. Taylor expanded in z around inf 67.2%

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

   if -2.3000000000000001e27 < z < -4.5999999999999999e-7

   1. Initial program 88.0%

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

      1. associate-*l/ 100.0%

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

   3. Simplified 100.0%

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

   4. Taylor expanded in t around 0 77.2%

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

      1. mul-1-neg 77.2%

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

      2. associate-/l* 77.2%

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

      3. distribute-neg-frac 77.2%

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

   6. Simplified 77.2%

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

   7. Taylor expanded in z around 0 75.9%

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

   8. Taylor expanded in x around 0 75.9%

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

      1. associate-*r/ 75.9%

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

      2. *-commutative 75.9%

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

      3. associate-*l/ 75.9%

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

      4. associate-*r/ 75.9%

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

      5. associate-*l/ 75.9%

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

      6. metadata-eval 75.9%

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

      7. associate-/r* 75.9%

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

      8. neg-mul-1 75.9%

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

      9. associate-*l* 75.9%

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

      10. associate-/r/ 75.7%

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

      11. *-commutative 75.7%

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

      12. associate-/r/ 75.9%

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

      13. associate-*l/ 75.9%

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

      14. *-lft-identity 75.9%

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

   10. Simplified 75.9%

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

   if -4.5999999999999999e-7 < z < -2.20000000000000002e-72

   1. Initial program 86.8%

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

      1. associate-*l/ 89.4%

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

   3. Simplified 89.4%

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

   4. Taylor expanded in z around 0 29.6%

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

      1. associate-/l* 42.4%

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

   6. Simplified 42.4%

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

   if -2.20000000000000002e-72 < z < 7.60000000000000001e48

   1. Initial program 91.7%

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

      1. associate-*l/ 97.5%

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

   3. Simplified 97.5%

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

   4. Taylor expanded in z around 0 63.6%

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

      1. associate-/l* 62.1%

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

   6. Simplified 62.1%

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

      1. associate-/r/ 66.9%

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

   8. Applied egg-rr 66.9%

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

      1. *-commutative 66.9%

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

      2. clear-num 66.9%

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

      3. div-inv 67.0%

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

   10. Applied egg-rr 67.0%

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

4. Final simplification 65.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -2.3 \cdot 10^{+27}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq -4.6 \cdot 10^{-7}:\\ \;\;\;\;x \cdot \frac{y}{-z}\\ \mathbf{elif}\;z \leq -2.2 \cdot 10^{-72}:\\ \;\;\;\;\frac{x}{\frac{t}{y}}\\ \mathbf{elif}\;z \leq 7.6 \cdot 10^{+48}:\\ \;\;\;\;\frac{y}{\frac{t}{x}}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]

Alternative 6: 72.8% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x - y \cdot \frac{x}{z}\\ \mathbf{if}\;z \leq -2.05 \cdot 10^{-42}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq -1.65 \cdot 10^{-172}:\\ \;\;\;\;\left(y - z\right) \cdot \frac{x}{t}\\ \mathbf{elif}\;z \leq 3 \cdot 10^{+53}:\\ \;\;\;\;y \cdot \frac{x}{t - z}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (- x (* y (/ x z)))))
   (if (<= z -2.05e-42)
     t_1
     (if (<= z -1.65e-172)
       (* (- y z) (/ x t))
       (if (<= z 3e+53) (* y (/ x (- t z))) t_1)))))

C

double code(double x, double y, double z, double t) {
	double t_1 = x - (y * (x / z));
	double tmp;
	if (z <= -2.05e-42) {
		tmp = t_1;
	} else if (z <= -1.65e-172) {
		tmp = (y - z) * (x / t);
	} else if (z <= 3e+53) {
		tmp = y * (x / (t - z));
	} 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) :: tmp
    t_1 = x - (y * (x / z))
    if (z <= (-2.05d-42)) then
        tmp = t_1
    else if (z <= (-1.65d-172)) then
        tmp = (y - z) * (x / t)
    else if (z <= 3d+53) then
        tmp = y * (x / (t - z))
    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 * (x / z));
	double tmp;
	if (z <= -2.05e-42) {
		tmp = t_1;
	} else if (z <= -1.65e-172) {
		tmp = (y - z) * (x / t);
	} else if (z <= 3e+53) {
		tmp = y * (x / (t - z));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = x - (y * (x / z))
	tmp = 0
	if z <= -2.05e-42:
		tmp = t_1
	elif z <= -1.65e-172:
		tmp = (y - z) * (x / t)
	elif z <= 3e+53:
		tmp = y * (x / (t - z))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(x - Float64(y * Float64(x / z)))
	tmp = 0.0
	if (z <= -2.05e-42)
		tmp = t_1;
	elseif (z <= -1.65e-172)
		tmp = Float64(Float64(y - z) * Float64(x / t));
	elseif (z <= 3e+53)
		tmp = Float64(y * Float64(x / Float64(t - z)));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = x - (y * (x / z));
	tmp = 0.0;
	if (z <= -2.05e-42)
		tmp = t_1;
	elseif (z <= -1.65e-172)
		tmp = (y - z) * (x / t);
	elseif (z <= 3e+53)
		tmp = y * (x / (t - z));
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(x - N[(y * N[(x / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -2.05e-42], t$95$1, If[LessEqual[z, -1.65e-172], N[(N[(y - z), $MachinePrecision] * N[(x / t), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 3e+53], N[(y * N[(x / N[(t - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

1. Split input into 3 regimes

2. if z < -2.0500000000000001e-42 or 2.99999999999999998e53 < z

   1. Initial program 80.4%

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

      1. associate-*l/ 71.1%

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

   3. Simplified 71.1%

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

   4. Taylor expanded in t around 0 63.9%

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

      1. mul-1-neg 63.9%

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

      2. associate-/l* 80.0%

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

      3. distribute-neg-frac 80.0%

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

   6. Simplified 80.0%

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

   7. Taylor expanded in z around 0 74.6%

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

      1. mul-1-neg 74.6%

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

      2. unsub-neg 74.6%

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

      3. associate-*l/ 78.5%

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

      4. *-commutative 78.5%

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

   9. Simplified 78.5%

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

   if -2.0500000000000001e-42 < z < -1.65e-172

   1. Initial program 77.5%

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

      1. associate-*l/ 94.3%

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

   3. Simplified 94.3%

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

   4. Taylor expanded in t around inf 82.9%

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

   if -1.65e-172 < z < 2.99999999999999998e53

   1. Initial program 93.3%

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

      1. associate-*l/ 98.0%

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

   3. Simplified 98.0%

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

   4. Taylor expanded in y around inf 81.1%

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

      1. associate-*l/ 84.7%

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

      2. *-commutative 84.7%

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

   6. Simplified 84.7%

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

4. Final simplification 81.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -2.05 \cdot 10^{-42}:\\ \;\;\;\;x - y \cdot \frac{x}{z}\\ \mathbf{elif}\;z \leq -1.65 \cdot 10^{-172}:\\ \;\;\;\;\left(y - z\right) \cdot \frac{x}{t}\\ \mathbf{elif}\;z \leq 3 \cdot 10^{+53}:\\ \;\;\;\;y \cdot \frac{x}{t - z}\\ \mathbf{else}:\\ \;\;\;\;x - y \cdot \frac{x}{z}\\ \end{array} \]

Alternative 7: 89.4% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -2.45 \cdot 10^{+188} \lor \neg \left(z \leq 4 \cdot 10^{+97}\right):\\ \;\;\;\;\frac{-x}{\frac{z}{y - z}}\\ \mathbf{else}:\\ \;\;\;\;\left(y - z\right) \cdot \frac{x}{t - z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= z -2.45e+188) (not (<= z 4e+97)))
   (/ (- x) (/ z (- y z)))
   (* (- y z) (/ x (- t z)))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((z <= -2.45e+188) || !(z <= 4e+97)) {
		tmp = -x / (z / (y - z));
	} else {
		tmp = (y - z) * (x / (t - z));
	}
	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 <= (-2.45d+188)) .or. (.not. (z <= 4d+97))) then
        tmp = -x / (z / (y - z))
    else
        tmp = (y - z) * (x / (t - z))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((z <= -2.45e+188) || !(z <= 4e+97)) {
		tmp = -x / (z / (y - z));
	} else {
		tmp = (y - z) * (x / (t - z));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (z <= -2.45e+188) or not (z <= 4e+97):
		tmp = -x / (z / (y - z))
	else:
		tmp = (y - z) * (x / (t - z))
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((z <= -2.45e+188) || !(z <= 4e+97))
		tmp = Float64(Float64(-x) / Float64(z / Float64(y - z)));
	else
		tmp = Float64(Float64(y - z) * Float64(x / Float64(t - z)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((z <= -2.45e+188) || ~((z <= 4e+97)))
		tmp = -x / (z / (y - z));
	else
		tmp = (y - z) * (x / (t - z));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[z, -2.45e+188], N[Not[LessEqual[z, 4e+97]], $MachinePrecision]], N[((-x) / N[(z / N[(y - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(y - z), $MachinePrecision] * N[(x / N[(t - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if z < -2.45e188 or 4.0000000000000003e97 < z

   1. Initial program 71.1%

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

      1. associate-*l/ 53.8%

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

   3. Simplified 53.8%

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

   4. Taylor expanded in t around 0 65.4%

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

      1. mul-1-neg 65.4%

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

      2. associate-/l* 91.3%

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

      3. distribute-neg-frac 91.3%

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

   6. Simplified 91.3%

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

   if -2.45e188 < z < 4.0000000000000003e97

   1. Initial program 91.0%

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

      1. associate-*l/ 95.1%

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

   3. Simplified 95.1%

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

4. Final simplification 94.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -2.45 \cdot 10^{+188} \lor \neg \left(z \leq 4 \cdot 10^{+97}\right):\\ \;\;\;\;\frac{-x}{\frac{z}{y - z}}\\ \mathbf{else}:\\ \;\;\;\;\left(y - z\right) \cdot \frac{x}{t - z}\\ \end{array} \]

Alternative 8: 67.6% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -8.5 \cdot 10^{+27}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq 8 \cdot 10^{+66}:\\ \;\;\;\;y \cdot \frac{x}{t - z}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= z -8.5e+27) x (if (<= z 8e+66) (* y (/ x (- t z))) x)))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (z <= -8.5e+27) {
		tmp = x;
	} else if (z <= 8e+66) {
		tmp = y * (x / (t - z));
	} else {
		tmp = x;
	}
	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 <= (-8.5d+27)) then
        tmp = x
    else if (z <= 8d+66) then
        tmp = y * (x / (t - z))
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if (z <= -8.5e+27) {
		tmp = x;
	} else if (z <= 8e+66) {
		tmp = y * (x / (t - z));
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if z <= -8.5e+27:
		tmp = x
	elif z <= 8e+66:
		tmp = y * (x / (t - z))
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (z <= -8.5e+27)
		tmp = x;
	elseif (z <= 8e+66)
		tmp = Float64(y * Float64(x / Float64(t - z)));
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (z <= -8.5e+27)
		tmp = x;
	elseif (z <= 8e+66)
		tmp = y * (x / (t - z));
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[z, -8.5e+27], x, If[LessEqual[z, 8e+66], N[(y * N[(x / N[(t - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], x]]

Derivation

1. Split input into 2 regimes

2. if z < -8.5e27 or 7.99999999999999956e66 < z

   1. Initial program 77.9%

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

      1. associate-*l/ 66.2%

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

   3. Simplified 66.2%

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

   4. Taylor expanded in z around inf 68.4%

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

   if -8.5e27 < z < 7.99999999999999956e66

   1. Initial program 91.3%

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

      1. associate-*l/ 96.9%

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

   3. Simplified 96.9%

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

   4. Taylor expanded in y around inf 74.2%

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

      1. associate-*l/ 77.7%

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

      2. *-commutative 77.7%

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

   6. Simplified 77.7%

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

4. Final simplification 74.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -8.5 \cdot 10^{+27}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq 8 \cdot 10^{+66}:\\ \;\;\;\;y \cdot \frac{x}{t - z}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]

Alternative 9: 59.5% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -1.5 \cdot 10^{+18}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq 4.8 \cdot 10^{+53}:\\ \;\;\;\;y \cdot \frac{x}{t}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= z -1.5e+18) x (if (<= z 4.8e+53) (* y (/ x t)) x)))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (z <= -1.5e+18) {
		tmp = x;
	} else if (z <= 4.8e+53) {
		tmp = y * (x / t);
	} else {
		tmp = x;
	}
	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 <= (-1.5d+18)) then
        tmp = x
    else if (z <= 4.8d+53) then
        tmp = y * (x / t)
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if (z <= -1.5e+18) {
		tmp = x;
	} else if (z <= 4.8e+53) {
		tmp = y * (x / t);
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if z <= -1.5e+18:
		tmp = x
	elif z <= 4.8e+53:
		tmp = y * (x / t)
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (z <= -1.5e+18)
		tmp = x;
	elseif (z <= 4.8e+53)
		tmp = Float64(y * Float64(x / t));
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (z <= -1.5e+18)
		tmp = x;
	elseif (z <= 4.8e+53)
		tmp = y * (x / t);
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[z, -1.5e+18], x, If[LessEqual[z, 4.8e+53], N[(y * N[(x / t), $MachinePrecision]), $MachinePrecision], x]]

Derivation

1. Split input into 2 regimes

2. if z < -1.5e18 or 4.8e53 < z

   1. Initial program 79.1%

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

      1. associate-*l/ 68.1%

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

   3. Simplified 68.1%

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

   4. Taylor expanded in z around inf 66.6%

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

   if -1.5e18 < z < 4.8e53

   1. Initial program 90.9%

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

      1. associate-*l/ 96.8%

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

   3. Simplified 96.8%

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

   4. Taylor expanded in z around 0 58.6%

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

      1. associate-/l* 59.3%

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

   6. Simplified 59.3%

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

      1. associate-/r/ 62.3%

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

   8. Applied egg-rr 62.3%

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

4. Final simplification 64.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -1.5 \cdot 10^{+18}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq 4.8 \cdot 10^{+53}:\\ \;\;\;\;y \cdot \frac{x}{t}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]

Alternative 10: 33.8% accurate, 9.0× speedup

Math

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

FPCore

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

C

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

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
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_, y_, z_, t_] := x

Derivation

1. Initial program 85.9%

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

   1. associate-*l/ 84.7%

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

3. Simplified 84.7%

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

4. Taylor expanded in z around inf 34.3%

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

5. Final simplification 34.3%

   \[\leadsto x \]

Developer target: 97.0% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Reproduce

herbie shell --seed 2023318 
(FPCore (x y z t)
  :name "Graphics.Rendering.Chart.Plot.AreaSpots:renderAreaSpots4D from Chart-1.5.3"
  :precision binary64

  :herbie-target
  (/ x (/ (- t z) (- y z)))

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