Graphics.Rendering.Plot.Render.Plot.Axis:tickPosition from plot-0.2.3.4

Percentage Accurate: 97.8% → 97.8%

Time: 6.3s

Alternatives: 13

Speedup: 1.0×

• 25.6% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

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

FPCore

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

C

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

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 - x) * (z / t))
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 97.8% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

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 - x) * (z / t))
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 97.8% accurate, 0.1× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 96.5%

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

   1. +-commutative 96.5%

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

   2. fma-def 96.5%

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

3. Simplified 96.5%

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

4. Final simplification 96.5%

   \[\leadsto \mathsf{fma}\left(y - x, \frac{z}{t}, x\right) \]

Alternative 2: 63.5% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y \cdot \frac{z}{t}\\ \mathbf{if}\;\frac{z}{t} \leq -1 \cdot 10^{-93}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;\frac{z}{t} \leq 0.01:\\ \;\;\;\;x\\ \mathbf{elif}\;\frac{z}{t} \leq 10^{+77} \lor \neg \left(\frac{z}{t} \leq 2 \cdot 10^{+208}\right):\\ \;\;\;\;\frac{x}{t} \cdot \left(-z\right)\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (* y (/ z t))))
   (if (<= (/ z t) -1e-93)
     t_1
     (if (<= (/ z t) 0.01)
       x
       (if (or (<= (/ z t) 1e+77) (not (<= (/ z t) 2e+208)))
         (* (/ x t) (- z))
         t_1)))))

C

double code(double x, double y, double z, double t) {
	double t_1 = y * (z / t);
	double tmp;
	if ((z / t) <= -1e-93) {
		tmp = t_1;
	} else if ((z / t) <= 0.01) {
		tmp = x;
	} else if (((z / t) <= 1e+77) || !((z / t) <= 2e+208)) {
		tmp = (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 = y * (z / t)
    if ((z / t) <= (-1d-93)) then
        tmp = t_1
    else if ((z / t) <= 0.01d0) then
        tmp = x
    else if (((z / t) <= 1d+77) .or. (.not. ((z / t) <= 2d+208))) then
        tmp = (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 = y * (z / t);
	double tmp;
	if ((z / t) <= -1e-93) {
		tmp = t_1;
	} else if ((z / t) <= 0.01) {
		tmp = x;
	} else if (((z / t) <= 1e+77) || !((z / t) <= 2e+208)) {
		tmp = (x / t) * -z;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = y * (z / t)
	tmp = 0
	if (z / t) <= -1e-93:
		tmp = t_1
	elif (z / t) <= 0.01:
		tmp = x
	elif ((z / t) <= 1e+77) or not ((z / t) <= 2e+208):
		tmp = (x / t) * -z
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(y * Float64(z / t))
	tmp = 0.0
	if (Float64(z / t) <= -1e-93)
		tmp = t_1;
	elseif (Float64(z / t) <= 0.01)
		tmp = x;
	elseif ((Float64(z / t) <= 1e+77) || !(Float64(z / t) <= 2e+208))
		tmp = Float64(Float64(x / t) * Float64(-z));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = y * (z / t);
	tmp = 0.0;
	if ((z / t) <= -1e-93)
		tmp = t_1;
	elseif ((z / t) <= 0.01)
		tmp = x;
	elseif (((z / t) <= 1e+77) || ~(((z / t) <= 2e+208)))
		tmp = (x / t) * -z;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(y * N[(z / t), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(z / t), $MachinePrecision], -1e-93], t$95$1, If[LessEqual[N[(z / t), $MachinePrecision], 0.01], x, If[Or[LessEqual[N[(z / t), $MachinePrecision], 1e+77], N[Not[LessEqual[N[(z / t), $MachinePrecision], 2e+208]], $MachinePrecision]], N[(N[(x / t), $MachinePrecision] * (-z)), $MachinePrecision], t$95$1]]]]

Derivation

1. Split input into 3 regimes

2. if (/.f64 z t) < -9.999999999999999e-94 or 9.99999999999999983e76 < (/.f64 z t) < 2e208

   1. Initial program 99.8%

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

   2. Taylor expanded in z around inf 81.0%

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

   3. Taylor expanded in y around inf 56.1%

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

   4. Taylor expanded in z around 0 58.0%

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

      1. *-commutative 58.0%

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

      2. associate-*l/ 61.9%

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

      3. *-commutative 61.9%

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

   6. Simplified 61.9%

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

   if -9.999999999999999e-94 < (/.f64 z t) < 0.0100000000000000002

   1. Initial program 95.6%

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

   2. Taylor expanded in z around 0 72.3%

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

   if 0.0100000000000000002 < (/.f64 z t) < 9.99999999999999983e76 or 2e208 < (/.f64 z t)

   1. Initial program 92.5%

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

   2. Taylor expanded in z around inf 79.1%

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

   3. Taylor expanded in y around 0 61.7%

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

      1. mul-1-neg 61.7%

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

      2. associate-*l/ 58.4%

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

      3. distribute-lft-neg-in 58.4%

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

      4. distribute-frac-neg 58.4%

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

      5. *-commutative 58.4%

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

   5. Simplified 58.4%

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

4. Final simplification 65.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{z}{t} \leq -1 \cdot 10^{-93}:\\ \;\;\;\;y \cdot \frac{z}{t}\\ \mathbf{elif}\;\frac{z}{t} \leq 0.01:\\ \;\;\;\;x\\ \mathbf{elif}\;\frac{z}{t} \leq 10^{+77} \lor \neg \left(\frac{z}{t} \leq 2 \cdot 10^{+208}\right):\\ \;\;\;\;\frac{x}{t} \cdot \left(-z\right)\\ \mathbf{else}:\\ \;\;\;\;y \cdot \frac{z}{t}\\ \end{array} \]

Alternative 3: 63.9% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y \cdot \frac{z}{t}\\ \mathbf{if}\;\frac{z}{t} \leq -1 \cdot 10^{-93}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;\frac{z}{t} \leq 0.01:\\ \;\;\;\;x\\ \mathbf{elif}\;\frac{z}{t} \leq 10^{+77}:\\ \;\;\;\;x \cdot \frac{z}{-t}\\ \mathbf{elif}\;\frac{z}{t} \leq 2 \cdot 10^{+208}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{t} \cdot \left(-z\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (* y (/ z t))))
   (if (<= (/ z t) -1e-93)
     t_1
     (if (<= (/ z t) 0.01)
       x
       (if (<= (/ z t) 1e+77)
         (* x (/ z (- t)))
         (if (<= (/ z t) 2e+208) t_1 (* (/ x t) (- z))))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = y * (z / t);
	double tmp;
	if ((z / t) <= -1e-93) {
		tmp = t_1;
	} else if ((z / t) <= 0.01) {
		tmp = x;
	} else if ((z / t) <= 1e+77) {
		tmp = x * (z / -t);
	} else if ((z / t) <= 2e+208) {
		tmp = t_1;
	} else {
		tmp = (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) :: t_1
    real(8) :: tmp
    t_1 = y * (z / t)
    if ((z / t) <= (-1d-93)) then
        tmp = t_1
    else if ((z / t) <= 0.01d0) then
        tmp = x
    else if ((z / t) <= 1d+77) then
        tmp = x * (z / -t)
    else if ((z / t) <= 2d+208) then
        tmp = t_1
    else
        tmp = (x / t) * -z
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = y * (z / t);
	double tmp;
	if ((z / t) <= -1e-93) {
		tmp = t_1;
	} else if ((z / t) <= 0.01) {
		tmp = x;
	} else if ((z / t) <= 1e+77) {
		tmp = x * (z / -t);
	} else if ((z / t) <= 2e+208) {
		tmp = t_1;
	} else {
		tmp = (x / t) * -z;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = y * (z / t)
	tmp = 0
	if (z / t) <= -1e-93:
		tmp = t_1
	elif (z / t) <= 0.01:
		tmp = x
	elif (z / t) <= 1e+77:
		tmp = x * (z / -t)
	elif (z / t) <= 2e+208:
		tmp = t_1
	else:
		tmp = (x / t) * -z
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(y * Float64(z / t))
	tmp = 0.0
	if (Float64(z / t) <= -1e-93)
		tmp = t_1;
	elseif (Float64(z / t) <= 0.01)
		tmp = x;
	elseif (Float64(z / t) <= 1e+77)
		tmp = Float64(x * Float64(z / Float64(-t)));
	elseif (Float64(z / t) <= 2e+208)
		tmp = t_1;
	else
		tmp = Float64(Float64(x / t) * Float64(-z));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = y * (z / t);
	tmp = 0.0;
	if ((z / t) <= -1e-93)
		tmp = t_1;
	elseif ((z / t) <= 0.01)
		tmp = x;
	elseif ((z / t) <= 1e+77)
		tmp = x * (z / -t);
	elseif ((z / t) <= 2e+208)
		tmp = t_1;
	else
		tmp = (x / t) * -z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(y * N[(z / t), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(z / t), $MachinePrecision], -1e-93], t$95$1, If[LessEqual[N[(z / t), $MachinePrecision], 0.01], x, If[LessEqual[N[(z / t), $MachinePrecision], 1e+77], N[(x * N[(z / (-t)), $MachinePrecision]), $MachinePrecision], If[LessEqual[N[(z / t), $MachinePrecision], 2e+208], t$95$1, N[(N[(x / t), $MachinePrecision] * (-z)), $MachinePrecision]]]]]]

Derivation

1. Split input into 4 regimes

2. if (/.f64 z t) < -9.999999999999999e-94 or 9.99999999999999983e76 < (/.f64 z t) < 2e208

   1. Initial program 99.8%

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

   2. Taylor expanded in z around inf 81.0%

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

   3. Taylor expanded in y around inf 56.1%

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

   4. Taylor expanded in z around 0 58.0%

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

      1. *-commutative 58.0%

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

      2. associate-*l/ 61.9%

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

      3. *-commutative 61.9%

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

   6. Simplified 61.9%

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

   if -9.999999999999999e-94 < (/.f64 z t) < 0.0100000000000000002

   1. Initial program 95.6%

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

   2. Taylor expanded in z around 0 72.3%

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

   if 0.0100000000000000002 < (/.f64 z t) < 9.99999999999999983e76

   1. Initial program 99.6%

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

   2. Taylor expanded in z around inf 59.9%

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

   3. Taylor expanded in y around 0 62.9%

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

      1. mul-1-neg 62.9%

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

      2. associate-*l/ 54.2%

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

      3. distribute-lft-neg-in 54.2%

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

      4. distribute-frac-neg 54.2%

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

      5. *-commutative 54.2%

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

   5. Simplified 54.2%

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

      1. frac-2neg 54.2%

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

      2. remove-double-neg 54.2%

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

      3. associate-*r/ 62.9%

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

   7. Applied egg-rr 62.9%

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

      1. associate-/l* 54.3%

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

      2. associate-/r/ 72.8%

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

   9. Simplified 72.8%

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

   if 2e208 < (/.f64 z t)

   1. Initial program 88.3%

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

   2. Taylor expanded in z around inf 90.5%

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

   3. Taylor expanded in y around 0 60.9%

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

      1. mul-1-neg 60.9%

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

      2. associate-*l/ 60.9%

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

      3. distribute-lft-neg-in 60.9%

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

      4. distribute-frac-neg 60.9%

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

      5. *-commutative 60.9%

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

   5. Simplified 60.9%

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

4. Final simplification 67.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{z}{t} \leq -1 \cdot 10^{-93}:\\ \;\;\;\;y \cdot \frac{z}{t}\\ \mathbf{elif}\;\frac{z}{t} \leq 0.01:\\ \;\;\;\;x\\ \mathbf{elif}\;\frac{z}{t} \leq 10^{+77}:\\ \;\;\;\;x \cdot \frac{z}{-t}\\ \mathbf{elif}\;\frac{z}{t} \leq 2 \cdot 10^{+208}:\\ \;\;\;\;y \cdot \frac{z}{t}\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{t} \cdot \left(-z\right)\\ \end{array} \]

Alternative 4: 63.9% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y \cdot \frac{z}{t}\\ \mathbf{if}\;\frac{z}{t} \leq -1 \cdot 10^{-93}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;\frac{z}{t} \leq 0.01:\\ \;\;\;\;x\\ \mathbf{elif}\;\frac{z}{t} \leq 10^{+77}:\\ \;\;\;\;x \cdot \frac{z}{-t}\\ \mathbf{elif}\;\frac{z}{t} \leq 2 \cdot 10^{+208}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;\frac{z}{\frac{-t}{x}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (* y (/ z t))))
   (if (<= (/ z t) -1e-93)
     t_1
     (if (<= (/ z t) 0.01)
       x
       (if (<= (/ z t) 1e+77)
         (* x (/ z (- t)))
         (if (<= (/ z t) 2e+208) t_1 (/ z (/ (- t) x))))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = y * (z / t);
	double tmp;
	if ((z / t) <= -1e-93) {
		tmp = t_1;
	} else if ((z / t) <= 0.01) {
		tmp = x;
	} else if ((z / t) <= 1e+77) {
		tmp = x * (z / -t);
	} else if ((z / t) <= 2e+208) {
		tmp = t_1;
	} else {
		tmp = z / (-t / 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 * (z / t)
    if ((z / t) <= (-1d-93)) then
        tmp = t_1
    else if ((z / t) <= 0.01d0) then
        tmp = x
    else if ((z / t) <= 1d+77) then
        tmp = x * (z / -t)
    else if ((z / t) <= 2d+208) then
        tmp = t_1
    else
        tmp = z / (-t / x)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = y * (z / t);
	double tmp;
	if ((z / t) <= -1e-93) {
		tmp = t_1;
	} else if ((z / t) <= 0.01) {
		tmp = x;
	} else if ((z / t) <= 1e+77) {
		tmp = x * (z / -t);
	} else if ((z / t) <= 2e+208) {
		tmp = t_1;
	} else {
		tmp = z / (-t / x);
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = y * (z / t)
	tmp = 0
	if (z / t) <= -1e-93:
		tmp = t_1
	elif (z / t) <= 0.01:
		tmp = x
	elif (z / t) <= 1e+77:
		tmp = x * (z / -t)
	elif (z / t) <= 2e+208:
		tmp = t_1
	else:
		tmp = z / (-t / x)
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(y * Float64(z / t))
	tmp = 0.0
	if (Float64(z / t) <= -1e-93)
		tmp = t_1;
	elseif (Float64(z / t) <= 0.01)
		tmp = x;
	elseif (Float64(z / t) <= 1e+77)
		tmp = Float64(x * Float64(z / Float64(-t)));
	elseif (Float64(z / t) <= 2e+208)
		tmp = t_1;
	else
		tmp = Float64(z / Float64(Float64(-t) / x));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = y * (z / t);
	tmp = 0.0;
	if ((z / t) <= -1e-93)
		tmp = t_1;
	elseif ((z / t) <= 0.01)
		tmp = x;
	elseif ((z / t) <= 1e+77)
		tmp = x * (z / -t);
	elseif ((z / t) <= 2e+208)
		tmp = t_1;
	else
		tmp = z / (-t / x);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(y * N[(z / t), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(z / t), $MachinePrecision], -1e-93], t$95$1, If[LessEqual[N[(z / t), $MachinePrecision], 0.01], x, If[LessEqual[N[(z / t), $MachinePrecision], 1e+77], N[(x * N[(z / (-t)), $MachinePrecision]), $MachinePrecision], If[LessEqual[N[(z / t), $MachinePrecision], 2e+208], t$95$1, N[(z / N[((-t) / x), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

1. Split input into 4 regimes

2. if (/.f64 z t) < -9.999999999999999e-94 or 9.99999999999999983e76 < (/.f64 z t) < 2e208

   1. Initial program 99.8%

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

   2. Taylor expanded in z around inf 81.0%

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

   3. Taylor expanded in y around inf 56.1%

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

   4. Taylor expanded in z around 0 58.0%

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

      1. *-commutative 58.0%

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

      2. associate-*l/ 61.9%

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

      3. *-commutative 61.9%

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

   6. Simplified 61.9%

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

   if -9.999999999999999e-94 < (/.f64 z t) < 0.0100000000000000002

   1. Initial program 95.6%

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

   2. Taylor expanded in z around 0 72.3%

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

   if 0.0100000000000000002 < (/.f64 z t) < 9.99999999999999983e76

   1. Initial program 99.6%

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

   2. Taylor expanded in z around inf 59.9%

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

   3. Taylor expanded in y around 0 62.9%

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

      1. mul-1-neg 62.9%

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

      2. associate-*l/ 54.2%

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

      3. distribute-lft-neg-in 54.2%

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

      4. distribute-frac-neg 54.2%

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

      5. *-commutative 54.2%

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

   5. Simplified 54.2%

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

      1. frac-2neg 54.2%

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

      2. remove-double-neg 54.2%

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

      3. associate-*r/ 62.9%

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

   7. Applied egg-rr 62.9%

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

      1. associate-/l* 54.3%

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

      2. associate-/r/ 72.8%

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

   9. Simplified 72.8%

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

   if 2e208 < (/.f64 z t)

   1. Initial program 88.3%

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

   2. Taylor expanded in z around inf 90.5%

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

   3. Taylor expanded in y around 0 60.9%

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

      1. mul-1-neg 60.9%

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

      2. associate-*l/ 60.9%

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

      3. distribute-lft-neg-in 60.9%

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

      4. distribute-frac-neg 60.9%

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

      5. *-commutative 60.9%

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

   5. Simplified 60.9%

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

      1. frac-2neg 60.9%

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

      2. remove-double-neg 60.9%

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

      3. associate-*r/ 60.9%

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

   7. Applied egg-rr 60.9%

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

      1. associate-/l* 60.9%

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

   9. Simplified 60.9%

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

4. Final simplification 67.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{z}{t} \leq -1 \cdot 10^{-93}:\\ \;\;\;\;y \cdot \frac{z}{t}\\ \mathbf{elif}\;\frac{z}{t} \leq 0.01:\\ \;\;\;\;x\\ \mathbf{elif}\;\frac{z}{t} \leq 10^{+77}:\\ \;\;\;\;x \cdot \frac{z}{-t}\\ \mathbf{elif}\;\frac{z}{t} \leq 2 \cdot 10^{+208}:\\ \;\;\;\;y \cdot \frac{z}{t}\\ \mathbf{else}:\\ \;\;\;\;\frac{z}{\frac{-t}{x}}\\ \end{array} \]

Alternative 5: 94.9% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\frac{z}{t} \leq -10000000 \lor \neg \left(\frac{z}{t} \leq 0.01\right):\\ \;\;\;\;\frac{\left(y - x\right) \cdot z}{t}\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot \frac{z}{t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= (/ z t) -10000000.0) (not (<= (/ z t) 0.01)))
   (/ (* (- y x) z) t)
   (+ x (* y (/ z t)))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (((z / t) <= -10000000.0) || !((z / t) <= 0.01)) {
		tmp = ((y - x) * z) / t;
	} else {
		tmp = x + (y * (z / t));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if (((z / t) <= (-10000000.0d0)) .or. (.not. ((z / t) <= 0.01d0))) then
        tmp = ((y - x) * z) / t
    else
        tmp = x + (y * (z / t))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if (((z / t) <= -10000000.0) || !((z / t) <= 0.01)) {
		tmp = ((y - x) * z) / t;
	} else {
		tmp = x + (y * (z / t));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if ((z / t) <= -10000000.0) or not ((z / t) <= 0.01):
		tmp = ((y - x) * z) / t
	else:
		tmp = x + (y * (z / t))
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((Float64(z / t) <= -10000000.0) || !(Float64(z / t) <= 0.01))
		tmp = Float64(Float64(Float64(y - x) * z) / t);
	else
		tmp = Float64(x + Float64(y * Float64(z / t)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (((z / t) <= -10000000.0) || ~(((z / t) <= 0.01)))
		tmp = ((y - x) * z) / t;
	else
		tmp = x + (y * (z / t));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[N[(z / t), $MachinePrecision], -10000000.0], N[Not[LessEqual[N[(z / t), $MachinePrecision], 0.01]], $MachinePrecision]], N[(N[(N[(y - x), $MachinePrecision] * z), $MachinePrecision] / t), $MachinePrecision], N[(x + N[(y * N[(z / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 z t) < -1e7 or 0.0100000000000000002 < (/.f64 z t)

   1. Initial program 96.9%

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

   2. Taylor expanded in z around inf 84.7%

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

      1. *-commutative 84.7%

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

      2. sub-div 89.4%

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

      3. associate-*l/ 93.8%

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

   4. Applied egg-rr 93.8%

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

   if -1e7 < (/.f64 z t) < 0.0100000000000000002

   1. Initial program 96.2%

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

   2. Taylor expanded in y around inf 94.0%

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

      1. associate-*r/ 94.2%

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

   4. Simplified 94.2%

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

4. Final simplification 94.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{z}{t} \leq -10000000 \lor \neg \left(\frac{z}{t} \leq 0.01\right):\\ \;\;\;\;\frac{\left(y - x\right) \cdot z}{t}\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot \frac{z}{t}\\ \end{array} \]

Alternative 6: 64.7% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\frac{z}{t} \leq -1 \cdot 10^{-93} \lor \neg \left(\frac{z}{t} \leq 5 \cdot 10^{-17}\right):\\ \;\;\;\;y \cdot \frac{z}{t}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= (/ z t) -1e-93) (not (<= (/ z t) 5e-17))) (* y (/ z t)) x))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (((z / t) <= -1e-93) || !((z / t) <= 5e-17)) {
		tmp = y * (z / 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 / t) <= (-1d-93)) .or. (.not. ((z / t) <= 5d-17))) then
        tmp = y * (z / 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 / t) <= -1e-93) || !((z / t) <= 5e-17)) {
		tmp = y * (z / t);
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if ((z / t) <= -1e-93) or not ((z / t) <= 5e-17):
		tmp = y * (z / t)
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((Float64(z / t) <= -1e-93) || !(Float64(z / t) <= 5e-17))
		tmp = Float64(y * Float64(z / t));
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (((z / t) <= -1e-93) || ~(((z / t) <= 5e-17)))
		tmp = y * (z / t);
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[N[(z / t), $MachinePrecision], -1e-93], N[Not[LessEqual[N[(z / t), $MachinePrecision], 5e-17]], $MachinePrecision]], N[(y * N[(z / t), $MachinePrecision]), $MachinePrecision], x]

Derivation

1. Split input into 2 regimes

2. if (/.f64 z t) < -9.999999999999999e-94 or 4.9999999999999999e-17 < (/.f64 z t)

   1. Initial program 97.3%

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

   2. Taylor expanded in z around inf 78.9%

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

   3. Taylor expanded in y around inf 49.0%

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

   4. Taylor expanded in z around 0 52.6%

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

      1. *-commutative 52.6%

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

      2. associate-*l/ 53.8%

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

      3. *-commutative 53.8%

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

   6. Simplified 53.8%

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

   if -9.999999999999999e-94 < (/.f64 z t) < 4.9999999999999999e-17

   1. Initial program 95.5%

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

   2. Taylor expanded in z around 0 73.9%

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

4. Final simplification 62.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{z}{t} \leq -1 \cdot 10^{-93} \lor \neg \left(\frac{z}{t} \leq 5 \cdot 10^{-17}\right):\\ \;\;\;\;y \cdot \frac{z}{t}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]

Alternative 7: 63.5% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\frac{z}{t} \leq -1 \cdot 10^{-93}:\\ \;\;\;\;y \cdot \frac{z}{t}\\ \mathbf{elif}\;\frac{z}{t} \leq 5 \cdot 10^{-17}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;\frac{y \cdot z}{t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= (/ z t) -1e-93)
   (* y (/ z t))
   (if (<= (/ z t) 5e-17) x (/ (* y z) t))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((z / t) <= -1e-93) {
		tmp = y * (z / t);
	} else if ((z / t) <= 5e-17) {
		tmp = x;
	} else {
		tmp = (y * z) / t;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if ((z / t) <= (-1d-93)) then
        tmp = y * (z / t)
    else if ((z / t) <= 5d-17) then
        tmp = x
    else
        tmp = (y * z) / t
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((z / t) <= -1e-93) {
		tmp = y * (z / t);
	} else if ((z / t) <= 5e-17) {
		tmp = x;
	} else {
		tmp = (y * z) / t;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (z / t) <= -1e-93:
		tmp = y * (z / t)
	elif (z / t) <= 5e-17:
		tmp = x
	else:
		tmp = (y * z) / t
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (Float64(z / t) <= -1e-93)
		tmp = Float64(y * Float64(z / t));
	elseif (Float64(z / t) <= 5e-17)
		tmp = x;
	else
		tmp = Float64(Float64(y * z) / t);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((z / t) <= -1e-93)
		tmp = y * (z / t);
	elseif ((z / t) <= 5e-17)
		tmp = x;
	else
		tmp = (y * z) / t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[N[(z / t), $MachinePrecision], -1e-93], N[(y * N[(z / t), $MachinePrecision]), $MachinePrecision], If[LessEqual[N[(z / t), $MachinePrecision], 5e-17], x, N[(N[(y * z), $MachinePrecision] / t), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if (/.f64 z t) < -9.999999999999999e-94

   1. Initial program 99.7%

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

   2. Taylor expanded in z around inf 79.3%

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

   3. Taylor expanded in y around inf 53.4%

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

   4. Taylor expanded in z around 0 54.6%

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

      1. *-commutative 54.6%

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

      2. associate-*l/ 58.5%

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

      3. *-commutative 58.5%

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

   6. Simplified 58.5%

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

   if -9.999999999999999e-94 < (/.f64 z t) < 4.9999999999999999e-17

   1. Initial program 95.5%

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

   2. Taylor expanded in z around 0 73.9%

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

   if 4.9999999999999999e-17 < (/.f64 z t)

   1. Initial program 94.9%

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

   2. Taylor expanded in z around inf 78.6%

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

   3. Taylor expanded in y around inf 44.9%

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

      1. associate-*r/ 50.7%

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

   5. Applied egg-rr 50.7%

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

4. Final simplification 62.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{z}{t} \leq -1 \cdot 10^{-93}:\\ \;\;\;\;y \cdot \frac{z}{t}\\ \mathbf{elif}\;\frac{z}{t} \leq 5 \cdot 10^{-17}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;\frac{y \cdot z}{t}\\ \end{array} \]

Alternative 8: 73.5% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -2.45 \cdot 10^{-114} \lor \neg \left(x \leq 2.15 \cdot 10^{-35}\right):\\ \;\;\;\;x \cdot \left(1 - \frac{z}{t}\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{y \cdot z}{t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= x -2.45e-114) (not (<= x 2.15e-35)))
   (* x (- 1.0 (/ z t)))
   (/ (* y z) t)))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((x <= -2.45e-114) || !(x <= 2.15e-35)) {
		tmp = x * (1.0 - (z / t));
	} else {
		tmp = (y * z) / t;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if ((x <= (-2.45d-114)) .or. (.not. (x <= 2.15d-35))) then
        tmp = x * (1.0d0 - (z / t))
    else
        tmp = (y * z) / t
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((x <= -2.45e-114) || !(x <= 2.15e-35)) {
		tmp = x * (1.0 - (z / t));
	} else {
		tmp = (y * z) / t;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (x <= -2.45e-114) or not (x <= 2.15e-35):
		tmp = x * (1.0 - (z / t))
	else:
		tmp = (y * z) / t
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((x <= -2.45e-114) || !(x <= 2.15e-35))
		tmp = Float64(x * Float64(1.0 - Float64(z / t)));
	else
		tmp = Float64(Float64(y * z) / t);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((x <= -2.45e-114) || ~((x <= 2.15e-35)))
		tmp = x * (1.0 - (z / t));
	else
		tmp = (y * z) / t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[x, -2.45e-114], N[Not[LessEqual[x, 2.15e-35]], $MachinePrecision]], N[(x * N[(1.0 - N[(z / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(y * z), $MachinePrecision] / t), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -2.4499999999999999e-114 or 2.1500000000000001e-35 < x

   1. Initial program 98.7%

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

   2. Taylor expanded in x around inf 82.2%

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

      1. mul-1-neg 82.2%

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

      2. unsub-neg 82.2%

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

   4. Simplified 82.2%

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

   if -2.4499999999999999e-114 < x < 2.1500000000000001e-35

   1. Initial program 93.0%

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

   2. Taylor expanded in z around inf 75.5%

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

   3. Taylor expanded in y around inf 68.0%

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

      1. associate-*r/ 71.5%

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

   5. Applied egg-rr 71.5%

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

4. Final simplification 78.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -2.45 \cdot 10^{-114} \lor \neg \left(x \leq 2.15 \cdot 10^{-35}\right):\\ \;\;\;\;x \cdot \left(1 - \frac{z}{t}\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{y \cdot z}{t}\\ \end{array} \]

Alternative 9: 85.8% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.7 \cdot 10^{+20} \lor \neg \left(y \leq 1.66 \cdot 10^{-34}\right):\\ \;\;\;\;x + y \cdot \frac{z}{t}\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(1 - \frac{z}{t}\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= y -1.7e+20) (not (<= y 1.66e-34)))
   (+ x (* y (/ z t)))
   (* x (- 1.0 (/ z t)))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((y <= -1.7e+20) || !(y <= 1.66e-34)) {
		tmp = x + (y * (z / t));
	} else {
		tmp = x * (1.0 - (z / t));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if ((y <= (-1.7d+20)) .or. (.not. (y <= 1.66d-34))) then
        tmp = x + (y * (z / t))
    else
        tmp = x * (1.0d0 - (z / t))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((y <= -1.7e+20) || !(y <= 1.66e-34)) {
		tmp = x + (y * (z / t));
	} else {
		tmp = x * (1.0 - (z / t));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (y <= -1.7e+20) or not (y <= 1.66e-34):
		tmp = x + (y * (z / t))
	else:
		tmp = x * (1.0 - (z / t))
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((y <= -1.7e+20) || !(y <= 1.66e-34))
		tmp = Float64(x + Float64(y * Float64(z / t)));
	else
		tmp = Float64(x * Float64(1.0 - Float64(z / t)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((y <= -1.7e+20) || ~((y <= 1.66e-34)))
		tmp = x + (y * (z / t));
	else
		tmp = x * (1.0 - (z / t));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[y, -1.7e+20], N[Not[LessEqual[y, 1.66e-34]], $MachinePrecision]], N[(x + N[(y * N[(z / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x * N[(1.0 - N[(z / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -1.7e20 or 1.6599999999999999e-34 < y

   1. Initial program 96.4%

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

   2. Taylor expanded in y around inf 83.9%

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

      1. associate-*r/ 85.4%

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

   4. Simplified 85.4%

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

   if -1.7e20 < y < 1.6599999999999999e-34

   1. Initial program 96.6%

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

   2. Taylor expanded in x around inf 87.2%

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

      1. mul-1-neg 87.2%

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

      2. unsub-neg 87.2%

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

   4. Simplified 87.2%

      \[\leadsto \color{blue}{x \cdot \left(1 - \frac{z}{t}\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 86.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1.7 \cdot 10^{+20} \lor \neg \left(y \leq 1.66 \cdot 10^{-34}\right):\\ \;\;\;\;x + y \cdot \frac{z}{t}\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(1 - \frac{z}{t}\right)\\ \end{array} \]

Alternative 10: 84.1% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -8.5 \cdot 10^{-55} \lor \neg \left(x \leq 750\right):\\ \;\;\;\;x \cdot \left(1 - \frac{z}{t}\right)\\ \mathbf{else}:\\ \;\;\;\;x + \frac{y \cdot z}{t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= x -8.5e-55) (not (<= x 750.0)))
   (* x (- 1.0 (/ z t)))
   (+ x (/ (* y z) t))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((x <= -8.5e-55) || !(x <= 750.0)) {
		tmp = x * (1.0 - (z / t));
	} else {
		tmp = x + ((y * z) / t);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if ((x <= (-8.5d-55)) .or. (.not. (x <= 750.0d0))) then
        tmp = x * (1.0d0 - (z / t))
    else
        tmp = x + ((y * z) / t)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((x <= -8.5e-55) || !(x <= 750.0)) {
		tmp = x * (1.0 - (z / t));
	} else {
		tmp = x + ((y * z) / t);
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (x <= -8.5e-55) or not (x <= 750.0):
		tmp = x * (1.0 - (z / t))
	else:
		tmp = x + ((y * z) / t)
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((x <= -8.5e-55) || !(x <= 750.0))
		tmp = Float64(x * Float64(1.0 - Float64(z / t)));
	else
		tmp = Float64(x + Float64(Float64(y * z) / t));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((x <= -8.5e-55) || ~((x <= 750.0)))
		tmp = x * (1.0 - (z / t));
	else
		tmp = x + ((y * z) / t);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[x, -8.5e-55], N[Not[LessEqual[x, 750.0]], $MachinePrecision]], N[(x * N[(1.0 - N[(z / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(N[(y * z), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -8.49999999999999968e-55 or 750 < x

   1. Initial program 99.9%

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

   2. Taylor expanded in x around inf 86.0%

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

      1. mul-1-neg 86.0%

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

      2. unsub-neg 86.0%

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

   4. Simplified 86.0%

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

   if -8.49999999999999968e-55 < x < 750

   1. Initial program 92.7%

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

   2. Taylor expanded in y around inf 87.4%

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

4. Final simplification 86.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -8.5 \cdot 10^{-55} \lor \neg \left(x \leq 750\right):\\ \;\;\;\;x \cdot \left(1 - \frac{z}{t}\right)\\ \mathbf{else}:\\ \;\;\;\;x + \frac{y \cdot z}{t}\\ \end{array} \]

Alternative 11: 85.7% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -5.3 \cdot 10^{+19}:\\ \;\;\;\;x + \frac{y}{\frac{t}{z}}\\ \mathbf{elif}\;y \leq 4.05 \cdot 10^{-35}:\\ \;\;\;\;x \cdot \left(1 - \frac{z}{t}\right)\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot \frac{z}{t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= y -5.3e+19)
   (+ x (/ y (/ t z)))
   (if (<= y 4.05e-35) (* x (- 1.0 (/ z t))) (+ x (* y (/ z t))))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (y <= -5.3e+19) {
		tmp = x + (y / (t / z));
	} else if (y <= 4.05e-35) {
		tmp = x * (1.0 - (z / t));
	} else {
		tmp = x + (y * (z / t));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if (y <= (-5.3d+19)) then
        tmp = x + (y / (t / z))
    else if (y <= 4.05d-35) then
        tmp = x * (1.0d0 - (z / t))
    else
        tmp = x + (y * (z / t))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if (y <= -5.3e+19) {
		tmp = x + (y / (t / z));
	} else if (y <= 4.05e-35) {
		tmp = x * (1.0 - (z / t));
	} else {
		tmp = x + (y * (z / t));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if y <= -5.3e+19:
		tmp = x + (y / (t / z))
	elif y <= 4.05e-35:
		tmp = x * (1.0 - (z / t))
	else:
		tmp = x + (y * (z / t))
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (y <= -5.3e+19)
		tmp = Float64(x + Float64(y / Float64(t / z)));
	elseif (y <= 4.05e-35)
		tmp = Float64(x * Float64(1.0 - Float64(z / t)));
	else
		tmp = Float64(x + Float64(y * Float64(z / t)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (y <= -5.3e+19)
		tmp = x + (y / (t / z));
	elseif (y <= 4.05e-35)
		tmp = x * (1.0 - (z / t));
	else
		tmp = x + (y * (z / t));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[y, -5.3e+19], N[(x + N[(y / N[(t / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 4.05e-35], N[(x * N[(1.0 - N[(z / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(y * N[(z / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if y < -5.3e19

   1. Initial program 98.1%

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

   2. Taylor expanded in y around inf 86.8%

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

      1. associate-*r/ 91.7%

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

   4. Simplified 91.7%

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

      1. associate-*r/ 86.8%

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

      2. associate-/l* 91.7%

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

   6. Applied egg-rr 91.7%

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

   if -5.3e19 < y < 4.05000000000000015e-35

   1. Initial program 96.6%

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

   2. Taylor expanded in x around inf 87.2%

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

      1. mul-1-neg 87.2%

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

      2. unsub-neg 87.2%

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

   4. Simplified 87.2%

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

   if 4.05000000000000015e-35 < y

   1. Initial program 95.3%

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

   2. Taylor expanded in y around inf 81.9%

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

      1. associate-*r/ 80.9%

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

   4. Simplified 80.9%

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

4. Final simplification 86.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -5.3 \cdot 10^{+19}:\\ \;\;\;\;x + \frac{y}{\frac{t}{z}}\\ \mathbf{elif}\;y \leq 4.05 \cdot 10^{-35}:\\ \;\;\;\;x \cdot \left(1 - \frac{z}{t}\right)\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot \frac{z}{t}\\ \end{array} \]

Alternative 12: 97.8% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

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 - x) * (z / t))
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 96.5%

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

2. Final simplification 96.5%

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

Alternative 13: 37.7% 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 96.5%

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

2. Taylor expanded in z around 0 34.8%

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

3. Final simplification 34.8%

   \[\leadsto x \]

Developer target: 97.6% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(y - x\right) \cdot \frac{z}{t}\\ t_2 := x + \frac{y - x}{\frac{t}{z}}\\ \mathbf{if}\;t_1 < -1013646692435.8867:\\ \;\;\;\;t_2\\ \mathbf{elif}\;t_1 < 0:\\ \;\;\;\;x + \frac{\left(y - x\right) \cdot z}{t}\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (* (- y x) (/ z t))) (t_2 (+ x (/ (- y x) (/ t z)))))
   (if (< t_1 -1013646692435.8867)
     t_2
     (if (< t_1 0.0) (+ x (/ (* (- y x) z) t)) t_2))))

C

double code(double x, double y, double z, double t) {
	double t_1 = (y - x) * (z / t);
	double t_2 = x + ((y - x) / (t / z));
	double tmp;
	if (t_1 < -1013646692435.8867) {
		tmp = t_2;
	} else if (t_1 < 0.0) {
		tmp = x + (((y - x) * z) / t);
	} 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 - x) * (z / t)
    t_2 = x + ((y - x) / (t / z))
    if (t_1 < (-1013646692435.8867d0)) then
        tmp = t_2
    else if (t_1 < 0.0d0) then
        tmp = x + (((y - x) * z) / t)
    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 - x) * (z / t);
	double t_2 = x + ((y - x) / (t / z));
	double tmp;
	if (t_1 < -1013646692435.8867) {
		tmp = t_2;
	} else if (t_1 < 0.0) {
		tmp = x + (((y - x) * z) / t);
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = (y - x) * (z / t)
	t_2 = x + ((y - x) / (t / z))
	tmp = 0
	if t_1 < -1013646692435.8867:
		tmp = t_2
	elif t_1 < 0.0:
		tmp = x + (((y - x) * z) / t)
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(y - x) * Float64(z / t))
	t_2 = Float64(x + Float64(Float64(y - x) / Float64(t / z)))
	tmp = 0.0
	if (t_1 < -1013646692435.8867)
		tmp = t_2;
	elseif (t_1 < 0.0)
		tmp = Float64(x + Float64(Float64(Float64(y - x) * z) / t));
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = (y - x) * (z / t);
	t_2 = x + ((y - x) / (t / z));
	tmp = 0.0;
	if (t_1 < -1013646692435.8867)
		tmp = t_2;
	elseif (t_1 < 0.0)
		tmp = x + (((y - x) * z) / t);
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(y - x), $MachinePrecision] * N[(z / t), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(x + N[(N[(y - x), $MachinePrecision] / N[(t / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[Less[t$95$1, -1013646692435.8867], t$95$2, If[Less[t$95$1, 0.0], N[(x + N[(N[(N[(y - x), $MachinePrecision] * z), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision], t$95$2]]]]

Reproduce

herbie shell --seed 2023320 
(FPCore (x y z t)
  :name "Graphics.Rendering.Plot.Render.Plot.Axis:tickPosition from plot-0.2.3.4"
  :precision binary64

  :herbie-target
  (if (< (* (- y x) (/ z t)) -1013646692435.8867) (+ x (/ (- y x) (/ t z))) (if (< (* (- y x) (/ z t)) 0.0) (+ x (/ (* (- y x) z) t)) (+ x (/ (- y x) (/ t z)))))

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