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

Percentage Accurate: 83.8% → 97.0%

Time: 9.4s

Alternatives: 11

Speedup: 1.0×

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: 83.8% 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: 97.0% accurate, 1.0× speedup

Math

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

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(x * Float64(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[(x * N[(N[(y - z), $MachinePrecision] / N[(t - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 85.6%

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

   1. associate-*r/ 98.1%

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

3. Simplified 98.1%

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

4. Final simplification 98.1%

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

Alternative 2: 67.5% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x \cdot \frac{y}{t - z}\\ t_2 := x \cdot \frac{y - z}{t}\\ \mathbf{if}\;z \leq -8.5 \cdot 10^{+98}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq -1.7 \cdot 10^{-21}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;z \leq -3.3 \cdot 10^{-113}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 8.4 \cdot 10^{-128}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;z \leq 5.4 \cdot 10^{+180}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (* x (/ y (- t z)))) (t_2 (* x (/ (- y z) t))))
   (if (<= z -8.5e+98)
     x
     (if (<= z -1.7e-21)
       t_2
       (if (<= z -3.3e-113)
         t_1
         (if (<= z 8.4e-128) t_2 (if (<= z 5.4e+180) t_1 x)))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = x * (y / (t - z));
	double t_2 = x * ((y - z) / t);
	double tmp;
	if (z <= -8.5e+98) {
		tmp = x;
	} else if (z <= -1.7e-21) {
		tmp = t_2;
	} else if (z <= -3.3e-113) {
		tmp = t_1;
	} else if (z <= 8.4e-128) {
		tmp = t_2;
	} else if (z <= 5.4e+180) {
		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) :: t_2
    real(8) :: tmp
    t_1 = x * (y / (t - z))
    t_2 = x * ((y - z) / t)
    if (z <= (-8.5d+98)) then
        tmp = x
    else if (z <= (-1.7d-21)) then
        tmp = t_2
    else if (z <= (-3.3d-113)) then
        tmp = t_1
    else if (z <= 8.4d-128) then
        tmp = t_2
    else if (z <= 5.4d+180) 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 = x * (y / (t - z));
	double t_2 = x * ((y - z) / t);
	double tmp;
	if (z <= -8.5e+98) {
		tmp = x;
	} else if (z <= -1.7e-21) {
		tmp = t_2;
	} else if (z <= -3.3e-113) {
		tmp = t_1;
	} else if (z <= 8.4e-128) {
		tmp = t_2;
	} else if (z <= 5.4e+180) {
		tmp = t_1;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = x * (y / (t - z))
	t_2 = x * ((y - z) / t)
	tmp = 0
	if z <= -8.5e+98:
		tmp = x
	elif z <= -1.7e-21:
		tmp = t_2
	elif z <= -3.3e-113:
		tmp = t_1
	elif z <= 8.4e-128:
		tmp = t_2
	elif z <= 5.4e+180:
		tmp = t_1
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(x * Float64(y / Float64(t - z)))
	t_2 = Float64(x * Float64(Float64(y - z) / t))
	tmp = 0.0
	if (z <= -8.5e+98)
		tmp = x;
	elseif (z <= -1.7e-21)
		tmp = t_2;
	elseif (z <= -3.3e-113)
		tmp = t_1;
	elseif (z <= 8.4e-128)
		tmp = t_2;
	elseif (z <= 5.4e+180)
		tmp = t_1;
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = x * (y / (t - z));
	t_2 = x * ((y - z) / t);
	tmp = 0.0;
	if (z <= -8.5e+98)
		tmp = x;
	elseif (z <= -1.7e-21)
		tmp = t_2;
	elseif (z <= -3.3e-113)
		tmp = t_1;
	elseif (z <= 8.4e-128)
		tmp = t_2;
	elseif (z <= 5.4e+180)
		tmp = t_1;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(x * N[(y / N[(t - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(x * N[(N[(y - z), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -8.5e+98], x, If[LessEqual[z, -1.7e-21], t$95$2, If[LessEqual[z, -3.3e-113], t$95$1, If[LessEqual[z, 8.4e-128], t$95$2, If[LessEqual[z, 5.4e+180], t$95$1, x]]]]]]]

Derivation

1. Split input into 3 regimes

2. if z < -8.4999999999999996e98 or 5.40000000000000033e180 < z

   1. Initial program 71.3%

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

      1. associate-*r/ 99.9%

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

   3. Simplified 99.9%

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

   4. Taylor expanded in z around inf 74.7%

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

   if -8.4999999999999996e98 < z < -1.7e-21 or -3.3000000000000002e-113 < z < 8.4000000000000004e-128

   1. Initial program 89.7%

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

      1. associate-*r/ 96.7%

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

   3. Simplified 96.7%

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

   4. Taylor expanded in t around inf 85.9%

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

   if -1.7e-21 < z < -3.3000000000000002e-113 or 8.4000000000000004e-128 < z < 5.40000000000000033e180

   1. Initial program 93.6%

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

      1. associate-*r/ 98.5%

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

   3. Simplified 98.5%

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

   4. Taylor expanded in y around inf 70.4%

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

4. Final simplification 78.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -8.5 \cdot 10^{+98}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq -1.7 \cdot 10^{-21}:\\ \;\;\;\;x \cdot \frac{y - z}{t}\\ \mathbf{elif}\;z \leq -3.3 \cdot 10^{-113}:\\ \;\;\;\;x \cdot \frac{y}{t - z}\\ \mathbf{elif}\;z \leq 8.4 \cdot 10^{-128}:\\ \;\;\;\;x \cdot \frac{y - z}{t}\\ \mathbf{elif}\;z \leq 5.4 \cdot 10^{+180}:\\ \;\;\;\;x \cdot \frac{y}{t - z}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]

Alternative 3: 74.5% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{y}{\frac{t - z}{x}}\\ \mathbf{if}\;z \leq -7.4 \cdot 10^{-23}:\\ \;\;\;\;\frac{x}{1 - \frac{t}{z}}\\ \mathbf{elif}\;z \leq -4 \cdot 10^{-159}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 1.6 \cdot 10^{-124}:\\ \;\;\;\;x \cdot \frac{y - z}{t}\\ \mathbf{elif}\;z \leq 9.5 \cdot 10^{+42}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{\frac{-z}{y - z}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (/ y (/ (- t z) x))))
   (if (<= z -7.4e-23)
     (/ x (- 1.0 (/ t z)))
     (if (<= z -4e-159)
       t_1
       (if (<= z 1.6e-124)
         (* x (/ (- y z) t))
         (if (<= z 9.5e+42) t_1 (/ x (/ (- z) (- y z)))))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = y / ((t - z) / x);
	double tmp;
	if (z <= -7.4e-23) {
		tmp = x / (1.0 - (t / z));
	} else if (z <= -4e-159) {
		tmp = t_1;
	} else if (z <= 1.6e-124) {
		tmp = x * ((y - z) / t);
	} else if (z <= 9.5e+42) {
		tmp = t_1;
	} else {
		tmp = x / (-z / (y - 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) :: t_1
    real(8) :: tmp
    t_1 = y / ((t - z) / x)
    if (z <= (-7.4d-23)) then
        tmp = x / (1.0d0 - (t / z))
    else if (z <= (-4d-159)) then
        tmp = t_1
    else if (z <= 1.6d-124) then
        tmp = x * ((y - z) / t)
    else if (z <= 9.5d+42) then
        tmp = t_1
    else
        tmp = x / (-z / (y - z))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = y / ((t - z) / x);
	double tmp;
	if (z <= -7.4e-23) {
		tmp = x / (1.0 - (t / z));
	} else if (z <= -4e-159) {
		tmp = t_1;
	} else if (z <= 1.6e-124) {
		tmp = x * ((y - z) / t);
	} else if (z <= 9.5e+42) {
		tmp = t_1;
	} else {
		tmp = x / (-z / (y - z));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = y / ((t - z) / x)
	tmp = 0
	if z <= -7.4e-23:
		tmp = x / (1.0 - (t / z))
	elif z <= -4e-159:
		tmp = t_1
	elif z <= 1.6e-124:
		tmp = x * ((y - z) / t)
	elif z <= 9.5e+42:
		tmp = t_1
	else:
		tmp = x / (-z / (y - z))
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(y / Float64(Float64(t - z) / x))
	tmp = 0.0
	if (z <= -7.4e-23)
		tmp = Float64(x / Float64(1.0 - Float64(t / z)));
	elseif (z <= -4e-159)
		tmp = t_1;
	elseif (z <= 1.6e-124)
		tmp = Float64(x * Float64(Float64(y - z) / t));
	elseif (z <= 9.5e+42)
		tmp = t_1;
	else
		tmp = Float64(x / Float64(Float64(-z) / Float64(y - z)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = y / ((t - z) / x);
	tmp = 0.0;
	if (z <= -7.4e-23)
		tmp = x / (1.0 - (t / z));
	elseif (z <= -4e-159)
		tmp = t_1;
	elseif (z <= 1.6e-124)
		tmp = x * ((y - z) / t);
	elseif (z <= 9.5e+42)
		tmp = t_1;
	else
		tmp = x / (-z / (y - z));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(y / N[(N[(t - z), $MachinePrecision] / x), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -7.4e-23], N[(x / N[(1.0 - N[(t / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, -4e-159], t$95$1, If[LessEqual[z, 1.6e-124], N[(x * N[(N[(y - z), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 9.5e+42], t$95$1, N[(x / N[((-z) / N[(y - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

1. Split input into 4 regimes

2. if z < -7.4000000000000005e-23

   1. Initial program 76.9%

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

      1. associate-/l* 98.6%

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

   3. Simplified 98.6%

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

      1. div-sub 98.6%

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

   5. Applied egg-rr 98.6%

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

   6. Taylor expanded in y around 0 78.5%

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

      1. mul-1-neg 78.5%

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

      2. unsub-neg 78.5%

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

   8. Simplified 78.5%

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

   if -7.4000000000000005e-23 < z < -3.99999999999999995e-159 or 1.60000000000000002e-124 < z < 9.50000000000000019e42

   1. Initial program 93.3%

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

      1. associate-/l* 96.5%

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

   3. Simplified 96.5%

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

      1. div-sub 96.5%

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

   5. Applied egg-rr 96.5%

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

   6. Taylor expanded in y around inf 75.2%

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

      1. associate-/l* 80.1%

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

   8. Simplified 80.1%

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

   if -3.99999999999999995e-159 < z < 1.60000000000000002e-124

   1. Initial program 91.2%

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

      1. associate-*r/ 97.1%

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

   3. Simplified 97.1%

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

   4. Taylor expanded in t around inf 93.3%

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

   if 9.50000000000000019e42 < z

   1. Initial program 81.8%

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

      1. associate-/l* 99.9%

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

   3. Simplified 99.9%

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

   4. Taylor expanded in t around 0 82.0%

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

      1. neg-mul-1 82.0%

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

      2. distribute-neg-frac 82.0%

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

   6. Simplified 82.0%

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

4. Final simplification 83.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -7.4 \cdot 10^{-23}:\\ \;\;\;\;\frac{x}{1 - \frac{t}{z}}\\ \mathbf{elif}\;z \leq -4 \cdot 10^{-159}:\\ \;\;\;\;\frac{y}{\frac{t - z}{x}}\\ \mathbf{elif}\;z \leq 1.6 \cdot 10^{-124}:\\ \;\;\;\;x \cdot \frac{y - z}{t}\\ \mathbf{elif}\;z \leq 9.5 \cdot 10^{+42}:\\ \;\;\;\;\frac{y}{\frac{t - z}{x}}\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{\frac{-z}{y - z}}\\ \end{array} \]

Alternative 4: 75.4% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x \cdot \frac{y}{t - z}\\ t_2 := \frac{x}{1 - \frac{t}{z}}\\ \mathbf{if}\;z \leq -5.8 \cdot 10^{-22}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;z \leq -3.2 \cdot 10^{-113}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 5.6 \cdot 10^{-125}:\\ \;\;\;\;x \cdot \frac{y - z}{t}\\ \mathbf{elif}\;z \leq 5.2 \cdot 10^{+43}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (* x (/ y (- t z)))) (t_2 (/ x (- 1.0 (/ t z)))))
   (if (<= z -5.8e-22)
     t_2
     (if (<= z -3.2e-113)
       t_1
       (if (<= z 5.6e-125) (* x (/ (- y z) t)) (if (<= z 5.2e+43) t_1 t_2))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = x * (y / (t - z));
	double t_2 = x / (1.0 - (t / z));
	double tmp;
	if (z <= -5.8e-22) {
		tmp = t_2;
	} else if (z <= -3.2e-113) {
		tmp = t_1;
	} else if (z <= 5.6e-125) {
		tmp = x * ((y - z) / t);
	} else if (z <= 5.2e+43) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	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 / (t - z))
    t_2 = x / (1.0d0 - (t / z))
    if (z <= (-5.8d-22)) then
        tmp = t_2
    else if (z <= (-3.2d-113)) then
        tmp = t_1
    else if (z <= 5.6d-125) then
        tmp = x * ((y - z) / t)
    else if (z <= 5.2d+43) then
        tmp = t_1
    else
        tmp = t_2
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = x * (y / (t - z));
	double t_2 = x / (1.0 - (t / z));
	double tmp;
	if (z <= -5.8e-22) {
		tmp = t_2;
	} else if (z <= -3.2e-113) {
		tmp = t_1;
	} else if (z <= 5.6e-125) {
		tmp = x * ((y - z) / t);
	} else if (z <= 5.2e+43) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = x * (y / (t - z))
	t_2 = x / (1.0 - (t / z))
	tmp = 0
	if z <= -5.8e-22:
		tmp = t_2
	elif z <= -3.2e-113:
		tmp = t_1
	elif z <= 5.6e-125:
		tmp = x * ((y - z) / t)
	elif z <= 5.2e+43:
		tmp = t_1
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(x * Float64(y / Float64(t - z)))
	t_2 = Float64(x / Float64(1.0 - Float64(t / z)))
	tmp = 0.0
	if (z <= -5.8e-22)
		tmp = t_2;
	elseif (z <= -3.2e-113)
		tmp = t_1;
	elseif (z <= 5.6e-125)
		tmp = Float64(x * Float64(Float64(y - z) / t));
	elseif (z <= 5.2e+43)
		tmp = t_1;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = x * (y / (t - z));
	t_2 = x / (1.0 - (t / z));
	tmp = 0.0;
	if (z <= -5.8e-22)
		tmp = t_2;
	elseif (z <= -3.2e-113)
		tmp = t_1;
	elseif (z <= 5.6e-125)
		tmp = x * ((y - z) / t);
	elseif (z <= 5.2e+43)
		tmp = t_1;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(x * N[(y / N[(t - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(x / N[(1.0 - N[(t / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -5.8e-22], t$95$2, If[LessEqual[z, -3.2e-113], t$95$1, If[LessEqual[z, 5.6e-125], N[(x * N[(N[(y - z), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 5.2e+43], t$95$1, t$95$2]]]]]]

Derivation

1. Split input into 3 regimes

2. if z < -5.8000000000000003e-22 or 5.20000000000000042e43 < z

   1. Initial program 78.8%

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

      1. associate-/l* 99.1%

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

   3. Simplified 99.1%

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

      1. div-sub 99.1%

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

   5. Applied egg-rr 99.1%

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

   6. Taylor expanded in y around 0 76.2%

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

      1. mul-1-neg 76.2%

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

      2. unsub-neg 76.2%

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

   8. Simplified 76.2%

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

   if -5.8000000000000003e-22 < z < -3.2000000000000002e-113 or 5.6e-125 < z < 5.20000000000000042e43

   1. Initial program 94.2%

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

      1. associate-*r/ 97.9%

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

   3. Simplified 97.9%

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

   4. Taylor expanded in y around inf 80.7%

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

   if -3.2000000000000002e-113 < z < 5.6e-125

   1. Initial program 90.9%

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

      1. associate-*r/ 96.2%

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

   3. Simplified 96.2%

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

   4. Taylor expanded in t around inf 91.5%

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

4. Final simplification 82.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -5.8 \cdot 10^{-22}:\\ \;\;\;\;\frac{x}{1 - \frac{t}{z}}\\ \mathbf{elif}\;z \leq -3.2 \cdot 10^{-113}:\\ \;\;\;\;x \cdot \frac{y}{t - z}\\ \mathbf{elif}\;z \leq 5.6 \cdot 10^{-125}:\\ \;\;\;\;x \cdot \frac{y - z}{t}\\ \mathbf{elif}\;z \leq 5.2 \cdot 10^{+43}:\\ \;\;\;\;x \cdot \frac{y}{t - z}\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{1 - \frac{t}{z}}\\ \end{array} \]

Alternative 5: 74.7% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{y}{\frac{t - z}{x}}\\ t_2 := \frac{x}{1 - \frac{t}{z}}\\ \mathbf{if}\;z \leq -3.8 \cdot 10^{-21}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;z \leq -9 \cdot 10^{-160}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 4.6 \cdot 10^{-128}:\\ \;\;\;\;x \cdot \frac{y - z}{t}\\ \mathbf{elif}\;z \leq 8 \cdot 10^{+43}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (/ y (/ (- t z) x))) (t_2 (/ x (- 1.0 (/ t z)))))
   (if (<= z -3.8e-21)
     t_2
     (if (<= z -9e-160)
       t_1
       (if (<= z 4.6e-128) (* x (/ (- y z) t)) (if (<= z 8e+43) t_1 t_2))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = y / ((t - z) / x);
	double t_2 = x / (1.0 - (t / z));
	double tmp;
	if (z <= -3.8e-21) {
		tmp = t_2;
	} else if (z <= -9e-160) {
		tmp = t_1;
	} else if (z <= 4.6e-128) {
		tmp = x * ((y - z) / t);
	} else if (z <= 8e+43) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	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 = y / ((t - z) / x)
    t_2 = x / (1.0d0 - (t / z))
    if (z <= (-3.8d-21)) then
        tmp = t_2
    else if (z <= (-9d-160)) then
        tmp = t_1
    else if (z <= 4.6d-128) then
        tmp = x * ((y - z) / t)
    else if (z <= 8d+43) then
        tmp = t_1
    else
        tmp = t_2
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = y / ((t - z) / x);
	double t_2 = x / (1.0 - (t / z));
	double tmp;
	if (z <= -3.8e-21) {
		tmp = t_2;
	} else if (z <= -9e-160) {
		tmp = t_1;
	} else if (z <= 4.6e-128) {
		tmp = x * ((y - z) / t);
	} else if (z <= 8e+43) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = y / ((t - z) / x)
	t_2 = x / (1.0 - (t / z))
	tmp = 0
	if z <= -3.8e-21:
		tmp = t_2
	elif z <= -9e-160:
		tmp = t_1
	elif z <= 4.6e-128:
		tmp = x * ((y - z) / t)
	elif z <= 8e+43:
		tmp = t_1
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(y / Float64(Float64(t - z) / x))
	t_2 = Float64(x / Float64(1.0 - Float64(t / z)))
	tmp = 0.0
	if (z <= -3.8e-21)
		tmp = t_2;
	elseif (z <= -9e-160)
		tmp = t_1;
	elseif (z <= 4.6e-128)
		tmp = Float64(x * Float64(Float64(y - z) / t));
	elseif (z <= 8e+43)
		tmp = t_1;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = y / ((t - z) / x);
	t_2 = x / (1.0 - (t / z));
	tmp = 0.0;
	if (z <= -3.8e-21)
		tmp = t_2;
	elseif (z <= -9e-160)
		tmp = t_1;
	elseif (z <= 4.6e-128)
		tmp = x * ((y - z) / t);
	elseif (z <= 8e+43)
		tmp = t_1;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(y / N[(N[(t - z), $MachinePrecision] / x), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(x / N[(1.0 - N[(t / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -3.8e-21], t$95$2, If[LessEqual[z, -9e-160], t$95$1, If[LessEqual[z, 4.6e-128], N[(x * N[(N[(y - z), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 8e+43], t$95$1, t$95$2]]]]]]

Derivation

1. Split input into 3 regimes

2. if z < -3.7999999999999998e-21 or 8.00000000000000011e43 < z

   1. Initial program 78.8%

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

      1. associate-/l* 99.1%

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

   3. Simplified 99.1%

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

      1. div-sub 99.1%

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

   5. Applied egg-rr 99.1%

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

   6. Taylor expanded in y around 0 76.2%

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

      1. mul-1-neg 76.2%

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

      2. unsub-neg 76.2%

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

   8. Simplified 76.2%

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

   if -3.7999999999999998e-21 < z < -9.00000000000000053e-160 or 4.6000000000000002e-128 < z < 8.00000000000000011e43

   1. Initial program 93.3%

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

      1. associate-/l* 96.5%

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

   3. Simplified 96.5%

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

      1. div-sub 96.5%

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

   5. Applied egg-rr 96.5%

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

   6. Taylor expanded in y around inf 75.2%

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

      1. associate-/l* 80.1%

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

   8. Simplified 80.1%

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

   if -9.00000000000000053e-160 < z < 4.6000000000000002e-128

   1. Initial program 91.2%

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

      1. associate-*r/ 97.1%

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

   3. Simplified 97.1%

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

   4. Taylor expanded in t around inf 93.3%

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

4. Final simplification 82.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -3.8 \cdot 10^{-21}:\\ \;\;\;\;\frac{x}{1 - \frac{t}{z}}\\ \mathbf{elif}\;z \leq -9 \cdot 10^{-160}:\\ \;\;\;\;\frac{y}{\frac{t - z}{x}}\\ \mathbf{elif}\;z \leq 4.6 \cdot 10^{-128}:\\ \;\;\;\;x \cdot \frac{y - z}{t}\\ \mathbf{elif}\;z \leq 8 \cdot 10^{+43}:\\ \;\;\;\;\frac{y}{\frac{t - z}{x}}\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{1 - \frac{t}{z}}\\ \end{array} \]

Alternative 6: 59.4% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -5.2 \cdot 10^{+98}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq -1.3 \cdot 10^{-32}:\\ \;\;\;\;x \cdot \frac{-z}{t}\\ \mathbf{elif}\;z \leq -1.1 \cdot 10^{-131}:\\ \;\;\;\;\frac{x}{\frac{-z}{y}}\\ \mathbf{elif}\;z \leq 7.5 \cdot 10^{+43}:\\ \;\;\;\;\frac{x}{\frac{t}{y}}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= z -5.2e+98)
   x
   (if (<= z -1.3e-32)
     (* x (/ (- z) t))
     (if (<= z -1.1e-131)
       (/ x (/ (- z) y))
       (if (<= z 7.5e+43) (/ x (/ t y)) x)))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (z <= -5.2e+98) {
		tmp = x;
	} else if (z <= -1.3e-32) {
		tmp = x * (-z / t);
	} else if (z <= -1.1e-131) {
		tmp = x / (-z / y);
	} else if (z <= 7.5e+43) {
		tmp = x / (t / y);
	} 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 <= (-5.2d+98)) then
        tmp = x
    else if (z <= (-1.3d-32)) then
        tmp = x * (-z / t)
    else if (z <= (-1.1d-131)) then
        tmp = x / (-z / y)
    else if (z <= 7.5d+43) then
        tmp = x / (t / y)
    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 <= -5.2e+98) {
		tmp = x;
	} else if (z <= -1.3e-32) {
		tmp = x * (-z / t);
	} else if (z <= -1.1e-131) {
		tmp = x / (-z / y);
	} else if (z <= 7.5e+43) {
		tmp = x / (t / y);
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if z <= -5.2e+98:
		tmp = x
	elif z <= -1.3e-32:
		tmp = x * (-z / t)
	elif z <= -1.1e-131:
		tmp = x / (-z / y)
	elif z <= 7.5e+43:
		tmp = x / (t / y)
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (z <= -5.2e+98)
		tmp = x;
	elseif (z <= -1.3e-32)
		tmp = Float64(x * Float64(Float64(-z) / t));
	elseif (z <= -1.1e-131)
		tmp = Float64(x / Float64(Float64(-z) / y));
	elseif (z <= 7.5e+43)
		tmp = Float64(x / Float64(t / y));
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (z <= -5.2e+98)
		tmp = x;
	elseif (z <= -1.3e-32)
		tmp = x * (-z / t);
	elseif (z <= -1.1e-131)
		tmp = x / (-z / y);
	elseif (z <= 7.5e+43)
		tmp = x / (t / y);
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[z, -5.2e+98], x, If[LessEqual[z, -1.3e-32], N[(x * N[((-z) / t), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, -1.1e-131], N[(x / N[((-z) / y), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 7.5e+43], N[(x / N[(t / y), $MachinePrecision]), $MachinePrecision], x]]]]

Derivation

1. Split input into 4 regimes

2. if z < -5.1999999999999999e98 or 7.49999999999999967e43 < z

   1. Initial program 76.7%

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

      1. associate-*r/ 99.9%

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

   3. Simplified 99.9%

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

   4. Taylor expanded in z around inf 66.8%

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

   if -5.1999999999999999e98 < z < -1.2999999999999999e-32

   1. Initial program 87.5%

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

      1. associate-*r/ 98.3%

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

   3. Simplified 98.3%

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

   4. Taylor expanded in t around inf 68.6%

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

   5. Taylor expanded in y around 0 45.3%

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

      1. mul-1-neg 45.3%

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

      2. associate-/l* 43.2%

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

      3. associate-/r/ 48.5%

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

      4. distribute-rgt-neg-in 48.5%

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

   7. Simplified 48.5%

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

   if -1.2999999999999999e-32 < z < -1.1e-131

   1. Initial program 94.8%

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

      1. associate-/l* 99.6%

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

   3. Simplified 99.6%

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

      1. div-sub 99.6%

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

   5. Applied egg-rr 99.6%

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

   6. Taylor expanded in z around 0 74.2%

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

   7. Taylor expanded in t around 0 55.2%

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

      1. associate-*r/ 55.2%

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

      2. mul-1-neg 55.2%

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

   9. Simplified 55.2%

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

   if -1.1e-131 < z < 7.49999999999999967e43

   1. Initial program 91.4%

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

      1. associate-/l* 96.3%

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

   3. Simplified 96.3%

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

   4. Taylor expanded in z around 0 75.5%

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

4. Final simplification 67.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -5.2 \cdot 10^{+98}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq -1.3 \cdot 10^{-32}:\\ \;\;\;\;x \cdot \frac{-z}{t}\\ \mathbf{elif}\;z \leq -1.1 \cdot 10^{-131}:\\ \;\;\;\;\frac{x}{\frac{-z}{y}}\\ \mathbf{elif}\;z \leq 7.5 \cdot 10^{+43}:\\ \;\;\;\;\frac{x}{\frac{t}{y}}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]

Alternative 7: 61.0% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -5.2 \cdot 10^{+98}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq -3.9 \cdot 10^{-56}:\\ \;\;\;\;z \cdot \frac{-x}{t}\\ \mathbf{elif}\;z \leq 9.5 \cdot 10^{+42}:\\ \;\;\;\;\frac{x}{\frac{t}{y}}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= z -5.2e+98)
   x
   (if (<= z -3.9e-56) (* z (/ (- x) t)) (if (<= z 9.5e+42) (/ x (/ t y)) x))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (z <= -5.2e+98) {
		tmp = x;
	} else if (z <= -3.9e-56) {
		tmp = z * (-x / t);
	} else if (z <= 9.5e+42) {
		tmp = x / (t / y);
	} 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 <= (-5.2d+98)) then
        tmp = x
    else if (z <= (-3.9d-56)) then
        tmp = z * (-x / t)
    else if (z <= 9.5d+42) then
        tmp = x / (t / y)
    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 <= -5.2e+98) {
		tmp = x;
	} else if (z <= -3.9e-56) {
		tmp = z * (-x / t);
	} else if (z <= 9.5e+42) {
		tmp = x / (t / y);
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if z <= -5.2e+98:
		tmp = x
	elif z <= -3.9e-56:
		tmp = z * (-x / t)
	elif z <= 9.5e+42:
		tmp = x / (t / y)
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (z <= -5.2e+98)
		tmp = x;
	elseif (z <= -3.9e-56)
		tmp = Float64(z * Float64(Float64(-x) / t));
	elseif (z <= 9.5e+42)
		tmp = Float64(x / Float64(t / y));
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (z <= -5.2e+98)
		tmp = x;
	elseif (z <= -3.9e-56)
		tmp = z * (-x / t);
	elseif (z <= 9.5e+42)
		tmp = x / (t / y);
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[z, -5.2e+98], x, If[LessEqual[z, -3.9e-56], N[(z * N[((-x) / t), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 9.5e+42], N[(x / N[(t / y), $MachinePrecision]), $MachinePrecision], x]]]

Derivation

1. Split input into 3 regimes

2. if z < -5.1999999999999999e98 or 9.50000000000000019e42 < z

   1. Initial program 76.7%

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

      1. associate-*r/ 99.9%

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

   3. Simplified 99.9%

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

   4. Taylor expanded in z around inf 66.8%

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

   if -5.1999999999999999e98 < z < -3.9e-56

   1. Initial program 88.5%

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

      1. associate-*r/ 98.5%

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

   3. Simplified 98.5%

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

   4. Taylor expanded in t around inf 64.2%

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

   5. Taylor expanded in y around 0 42.9%

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

      1. associate-*r/ 42.9%

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

      2. associate-*r* 42.9%

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

      3. neg-mul-1 42.9%

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

   7. Simplified 42.9%

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

   8. Taylor expanded in z around 0 42.9%

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

      1. mul-1-neg 42.9%

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

      2. associate-*r/ 40.9%

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

      3. *-commutative 40.9%

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

      4. distribute-rgt-neg-in 40.9%

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

   10. Simplified 40.9%

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

   if -3.9e-56 < z < 9.50000000000000019e42

   1. Initial program 91.8%

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

      1. associate-/l* 96.7%

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

   3. Simplified 96.7%

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

   4. Taylor expanded in z around 0 70.1%

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

4. Final simplification 65.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -5.2 \cdot 10^{+98}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq -3.9 \cdot 10^{-56}:\\ \;\;\;\;z \cdot \frac{-x}{t}\\ \mathbf{elif}\;z \leq 9.5 \cdot 10^{+42}:\\ \;\;\;\;\frac{x}{\frac{t}{y}}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]

Alternative 8: 61.0% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -5.8 \cdot 10^{+98}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq -4 \cdot 10^{-57}:\\ \;\;\;\;x \cdot \frac{-z}{t}\\ \mathbf{elif}\;z \leq 1.5 \cdot 10^{+43}:\\ \;\;\;\;\frac{x}{\frac{t}{y}}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= z -5.8e+98)
   x
   (if (<= z -4e-57) (* x (/ (- z) t)) (if (<= z 1.5e+43) (/ x (/ t y)) x))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (z <= -5.8e+98) {
		tmp = x;
	} else if (z <= -4e-57) {
		tmp = x * (-z / t);
	} else if (z <= 1.5e+43) {
		tmp = x / (t / y);
	} 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 <= (-5.8d+98)) then
        tmp = x
    else if (z <= (-4d-57)) then
        tmp = x * (-z / t)
    else if (z <= 1.5d+43) then
        tmp = x / (t / y)
    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 <= -5.8e+98) {
		tmp = x;
	} else if (z <= -4e-57) {
		tmp = x * (-z / t);
	} else if (z <= 1.5e+43) {
		tmp = x / (t / y);
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if z <= -5.8e+98:
		tmp = x
	elif z <= -4e-57:
		tmp = x * (-z / t)
	elif z <= 1.5e+43:
		tmp = x / (t / y)
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (z <= -5.8e+98)
		tmp = x;
	elseif (z <= -4e-57)
		tmp = Float64(x * Float64(Float64(-z) / t));
	elseif (z <= 1.5e+43)
		tmp = Float64(x / Float64(t / y));
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (z <= -5.8e+98)
		tmp = x;
	elseif (z <= -4e-57)
		tmp = x * (-z / t);
	elseif (z <= 1.5e+43)
		tmp = x / (t / y);
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[z, -5.8e+98], x, If[LessEqual[z, -4e-57], N[(x * N[((-z) / t), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 1.5e+43], N[(x / N[(t / y), $MachinePrecision]), $MachinePrecision], x]]]

Derivation

1. Split input into 3 regimes

2. if z < -5.8000000000000002e98 or 1.50000000000000008e43 < z

   1. Initial program 76.7%

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

      1. associate-*r/ 99.9%

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

   3. Simplified 99.9%

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

   4. Taylor expanded in z around inf 66.8%

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

   if -5.8000000000000002e98 < z < -3.99999999999999982e-57

   1. Initial program 88.5%

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

      1. associate-*r/ 98.5%

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

   3. Simplified 98.5%

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

   4. Taylor expanded in t around inf 64.2%

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

   5. Taylor expanded in y around 0 42.9%

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

      1. mul-1-neg 42.9%

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

      2. associate-/l* 40.9%

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

      3. associate-/r/ 45.7%

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

      4. distribute-rgt-neg-in 45.7%

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

   7. Simplified 45.7%

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

   if -3.99999999999999982e-57 < z < 1.50000000000000008e43

   1. Initial program 91.8%

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

      1. associate-/l* 96.7%

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

   3. Simplified 96.7%

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

   4. Taylor expanded in z around 0 70.1%

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

4. Final simplification 65.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -5.8 \cdot 10^{+98}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq -4 \cdot 10^{-57}:\\ \;\;\;\;x \cdot \frac{-z}{t}\\ \mathbf{elif}\;z \leq 1.5 \cdot 10^{+43}:\\ \;\;\;\;\frac{x}{\frac{t}{y}}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]

Alternative 9: 68.5% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -3.5 \cdot 10^{+101}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq 5.4 \cdot 10^{+180}:\\ \;\;\;\;x \cdot \frac{y}{t - z}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= z -3.5e+101) x (if (<= z 5.4e+180) (* x (/ y (- t z))) x)))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (z <= -3.5e+101) {
		tmp = x;
	} else if (z <= 5.4e+180) {
		tmp = x * (y / (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 <= (-3.5d+101)) then
        tmp = x
    else if (z <= 5.4d+180) then
        tmp = x * (y / (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 <= -3.5e+101) {
		tmp = x;
	} else if (z <= 5.4e+180) {
		tmp = x * (y / (t - z));
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if z <= -3.5e+101:
		tmp = x
	elif z <= 5.4e+180:
		tmp = x * (y / (t - z))
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (z <= -3.5e+101)
		tmp = x;
	elseif (z <= 5.4e+180)
		tmp = Float64(x * Float64(y / Float64(t - z)));
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (z <= -3.5e+101)
		tmp = x;
	elseif (z <= 5.4e+180)
		tmp = x * (y / (t - z));
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[z, -3.5e+101], x, If[LessEqual[z, 5.4e+180], N[(x * N[(y / N[(t - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], x]]

Derivation

1. Split input into 2 regimes

2. if z < -3.50000000000000023e101 or 5.40000000000000033e180 < z

   1. Initial program 71.3%

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

      1. associate-*r/ 99.9%

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

   3. Simplified 99.9%

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

   4. Taylor expanded in z around inf 74.7%

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

   if -3.50000000000000023e101 < z < 5.40000000000000033e180

   1. Initial program 91.3%

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

      1. associate-*r/ 97.4%

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

   3. Simplified 97.4%

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

   4. Taylor expanded in y around inf 70.9%

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

4. Final simplification 72.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -3.5 \cdot 10^{+101}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq 5.4 \cdot 10^{+180}:\\ \;\;\;\;x \cdot \frac{y}{t - z}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]

Alternative 10: 61.6% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -5.4 \cdot 10^{+98}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq 3 \cdot 10^{+44}:\\ \;\;\;\;x \cdot \frac{y}{t}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= z -5.4e+98) x (if (<= z 3e+44) (* x (/ y t)) x)))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (z <= -5.4e+98) {
		tmp = x;
	} else if (z <= 3e+44) {
		tmp = x * (y / 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 <= (-5.4d+98)) then
        tmp = x
    else if (z <= 3d+44) then
        tmp = x * (y / 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 <= -5.4e+98) {
		tmp = x;
	} else if (z <= 3e+44) {
		tmp = x * (y / t);
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if z <= -5.4e+98:
		tmp = x
	elif z <= 3e+44:
		tmp = x * (y / t)
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (z <= -5.4e+98)
		tmp = x;
	elseif (z <= 3e+44)
		tmp = Float64(x * Float64(y / t));
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (z <= -5.4e+98)
		tmp = x;
	elseif (z <= 3e+44)
		tmp = x * (y / t);
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[z, -5.4e+98], x, If[LessEqual[z, 3e+44], N[(x * N[(y / t), $MachinePrecision]), $MachinePrecision], x]]

Derivation

1. Split input into 2 regimes

2. if z < -5.4e98 or 2.99999999999999987e44 < z

   1. Initial program 76.7%

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

      1. associate-*r/ 99.9%

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

   3. Simplified 99.9%

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

   4. Taylor expanded in z around inf 66.8%

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

   if -5.4e98 < z < 2.99999999999999987e44

   1. Initial program 91.1%

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

      1. associate-*r/ 97.1%

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

   3. Simplified 97.1%

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

   4. Taylor expanded in z around 0 60.8%

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

4. Final simplification 63.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -5.4 \cdot 10^{+98}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq 3 \cdot 10^{+44}:\\ \;\;\;\;x \cdot \frac{y}{t}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]

Alternative 11: 35.7% 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 85.6%

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

   1. associate-*r/ 98.1%

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

3. Simplified 98.1%

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

4. Taylor expanded in z around inf 31.6%

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

5. Final simplification 31.6%

   \[\leadsto x \]

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 2023257 
(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)))