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

Percentage Accurate: 98.0% → 98.0%

Time: 7.5s

Alternatives: 7

Speedup: 1.0×

• 24.8% 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: 98.0% 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: 98.0% 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 99.5%

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

3. Final simplification 99.5%

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

Alternative 2: 85.8% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.28 \cdot 10^{+92} \lor \neg \left(x \leq 1.8 \cdot 10^{-11}\right):\\ \;\;\;\;x \cdot \left(1 - \frac{z}{t}\right)\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot \frac{z}{t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= x -1.28e+92) (not (<= x 1.8e-11)))
   (* x (- 1.0 (/ z t)))
   (+ x (* y (/ z t)))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((x <= -1.28e+92) || !(x <= 1.8e-11)) {
		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 <= (-1.28d+92)) .or. (.not. (x <= 1.8d-11))) 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 <= -1.28e+92) || !(x <= 1.8e-11)) {
		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 <= -1.28e+92) or not (x <= 1.8e-11):
		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 <= -1.28e+92) || !(x <= 1.8e-11))
		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 ((x <= -1.28e+92) || ~((x <= 1.8e-11)))
		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, -1.28e+92], N[Not[LessEqual[x, 1.8e-11]], $MachinePrecision]], 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 2 regimes

2. if x < -1.27999999999999996e92 or 1.79999999999999992e-11 < x

   1. Initial program 99.9%

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

   3. Taylor expanded in y around 0 84.4%

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

      1. mul-1-neg 84.4%

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

      2. associate-/l* 93.9%

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

      3. distribute-lft-neg-out 93.9%

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

      4. *-commutative 93.9%

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

   5. Simplified 93.9%

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

   6. Taylor expanded in x around 0 93.8%

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

      1. mul-1-neg 93.8%

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

      2. unsub-neg 93.8%

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

   8. Simplified 93.8%

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

   if -1.27999999999999996e92 < x < 1.79999999999999992e-11

   1. Initial program 99.2%

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

   3. Taylor expanded in y around inf 84.1%

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

      1. associate-*r/ 90.9%

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

   5. Simplified 90.9%

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

4. Final simplification 92.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -1.28 \cdot 10^{+92} \lor \neg \left(x \leq 1.8 \cdot 10^{-11}\right):\\ \;\;\;\;x \cdot \left(1 - \frac{z}{t}\right)\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot \frac{z}{t}\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 85.6% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -3.9 \cdot 10^{+92}:\\ \;\;\;\;x \cdot \left(1 - \frac{z}{t}\right)\\ \mathbf{elif}\;x \leq 3.8 \cdot 10^{-21}:\\ \;\;\;\;x + y \cdot \frac{z}{t}\\ \mathbf{else}:\\ \;\;\;\;x - \frac{x}{\frac{t}{z}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= x -3.9e+92)
   (* x (- 1.0 (/ z t)))
   (if (<= x 3.8e-21) (+ x (* y (/ z t))) (- x (/ x (/ t z))))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (x <= -3.9e+92) {
		tmp = x * (1.0 - (z / t));
	} else if (x <= 3.8e-21) {
		tmp = x + (y * (z / t));
	} else {
		tmp = x - (x / (t / z));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if (x <= (-3.9d+92)) then
        tmp = x * (1.0d0 - (z / t))
    else if (x <= 3.8d-21) then
        tmp = x + (y * (z / t))
    else
        tmp = x - (x / (t / z))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if (x <= -3.9e+92) {
		tmp = x * (1.0 - (z / t));
	} else if (x <= 3.8e-21) {
		tmp = x + (y * (z / t));
	} else {
		tmp = x - (x / (t / z));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if x <= -3.9e+92:
		tmp = x * (1.0 - (z / t))
	elif x <= 3.8e-21:
		tmp = x + (y * (z / t))
	else:
		tmp = x - (x / (t / z))
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (x <= -3.9e+92)
		tmp = Float64(x * Float64(1.0 - Float64(z / t)));
	elseif (x <= 3.8e-21)
		tmp = Float64(x + Float64(y * Float64(z / t)));
	else
		tmp = Float64(x - Float64(x / Float64(t / z)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (x <= -3.9e+92)
		tmp = x * (1.0 - (z / t));
	elseif (x <= 3.8e-21)
		tmp = x + (y * (z / t));
	else
		tmp = x - (x / (t / z));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[x, -3.9e+92], N[(x * N[(1.0 - N[(z / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 3.8e-21], N[(x + N[(y * N[(z / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x - N[(x / N[(t / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < -3.90000000000000011e92

   1. Initial program 100.0%

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

   3. Taylor expanded in y around 0 87.1%

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

      1. mul-1-neg 87.1%

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

      2. associate-/l* 96.2%

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

      3. distribute-lft-neg-out 96.2%

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

      4. *-commutative 96.2%

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

   5. Simplified 96.2%

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

   6. Taylor expanded in x around 0 96.2%

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

      1. mul-1-neg 96.2%

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

      2. unsub-neg 96.2%

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

   8. Simplified 96.2%

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

   if -3.90000000000000011e92 < x < 3.7999999999999998e-21

   1. Initial program 99.2%

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

   3. Taylor expanded in y around inf 84.1%

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

      1. associate-*r/ 90.9%

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

   5. Simplified 90.9%

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

   if 3.7999999999999998e-21 < x

   1. Initial program 99.9%

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

   3. Taylor expanded in y around 0 82.5%

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

      1. mul-1-neg 82.5%

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

      2. associate-/l* 92.2%

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

      3. distribute-lft-neg-out 92.2%

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

      4. *-commutative 92.2%

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

   5. Simplified 92.2%

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

      1. clear-num 92.2%

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

      2. associate-*l/ 92.3%

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

      3. *-un-lft-identity 92.3%

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

      4. add-sqr-sqrt 0.0%

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

      5. sqrt-unprod 21.3%

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

      6. sqr-neg 21.3%

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

      7. sqrt-prod 46.7%

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

      8. add-sqr-sqrt 46.7%

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

      9. frac-2neg 46.7%

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

      10. add-sqr-sqrt 0.0%

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

      11. sqrt-unprod 59.7%

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

      12. sqr-neg 59.7%

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

      13. sqrt-prod 92.2%

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

      14. add-sqr-sqrt 92.3%

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

      15. distribute-neg-frac 92.3%

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

   7. Applied egg-rr 92.3%

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

      1. frac-2neg 92.3%

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

      2. div-inv 92.2%

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

      3. distribute-frac-neg 92.2%

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

      4. remove-double-neg 92.2%

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

      5. clear-num 92.2%

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

      6. add-sqr-sqrt 43.4%

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

      7. sqrt-unprod 57.2%

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

      8. sqr-neg 57.2%

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

      9. sqrt-unprod 25.7%

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

      10. add-sqr-sqrt 46.7%

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

      11. cancel-sign-sub-inv 46.7%

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

      12. clear-num 46.7%

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

      13. div-inv 46.7%

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

      14. associate-/r/ 40.4%

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

      15. *-commutative 40.4%

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

      16. add-sqr-sqrt 24.8%

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

      17. sqrt-unprod 57.3%

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

      18. sqr-neg 57.3%

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

      19. sqrt-unprod 37.8%

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

      20. add-sqr-sqrt 84.1%

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

   9. Applied egg-rr 84.1%

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

       1. associate-*r/ 82.5%

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

       2. *-commutative 82.5%

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

       3. associate-/l* 92.2%

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

       4. clear-num 92.2%

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

       5. un-div-inv 92.3%

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

   11. Applied egg-rr 92.3%

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

4. Final simplification 92.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -3.9 \cdot 10^{+92}:\\ \;\;\;\;x \cdot \left(1 - \frac{z}{t}\right)\\ \mathbf{elif}\;x \leq 3.8 \cdot 10^{-21}:\\ \;\;\;\;x + y \cdot \frac{z}{t}\\ \mathbf{else}:\\ \;\;\;\;x - \frac{x}{\frac{t}{z}}\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 85.8% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.12 \cdot 10^{+92}:\\ \;\;\;\;x - x \cdot \frac{z}{t}\\ \mathbf{elif}\;x \leq 2.6 \cdot 10^{-12}:\\ \;\;\;\;x + y \cdot \frac{z}{t}\\ \mathbf{else}:\\ \;\;\;\;x - \frac{x}{\frac{t}{z}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= x -1.12e+92)
   (- x (* x (/ z t)))
   (if (<= x 2.6e-12) (+ x (* y (/ z t))) (- x (/ x (/ t z))))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (x <= -1.12e+92) {
		tmp = x - (x * (z / t));
	} else if (x <= 2.6e-12) {
		tmp = x + (y * (z / t));
	} else {
		tmp = x - (x / (t / z));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if (x <= (-1.12d+92)) then
        tmp = x - (x * (z / t))
    else if (x <= 2.6d-12) then
        tmp = x + (y * (z / t))
    else
        tmp = x - (x / (t / z))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if (x <= -1.12e+92) {
		tmp = x - (x * (z / t));
	} else if (x <= 2.6e-12) {
		tmp = x + (y * (z / t));
	} else {
		tmp = x - (x / (t / z));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if x <= -1.12e+92:
		tmp = x - (x * (z / t))
	elif x <= 2.6e-12:
		tmp = x + (y * (z / t))
	else:
		tmp = x - (x / (t / z))
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (x <= -1.12e+92)
		tmp = Float64(x - Float64(x * Float64(z / t)));
	elseif (x <= 2.6e-12)
		tmp = Float64(x + Float64(y * Float64(z / t)));
	else
		tmp = Float64(x - Float64(x / Float64(t / z)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (x <= -1.12e+92)
		tmp = x - (x * (z / t));
	elseif (x <= 2.6e-12)
		tmp = x + (y * (z / t));
	else
		tmp = x - (x / (t / z));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[x, -1.12e+92], N[(x - N[(x * N[(z / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 2.6e-12], N[(x + N[(y * N[(z / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x - N[(x / N[(t / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < -1.1199999999999999e92

   1. Initial program 100.0%

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

   3. Taylor expanded in y around 0 87.1%

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

      1. mul-1-neg 87.1%

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

      2. associate-/l* 96.2%

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

      3. distribute-lft-neg-out 96.2%

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

      4. *-commutative 96.2%

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

   5. Simplified 96.2%

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

   if -1.1199999999999999e92 < x < 2.59999999999999983e-12

   1. Initial program 99.2%

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

   3. Taylor expanded in y around inf 84.1%

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

      1. associate-*r/ 90.9%

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

   5. Simplified 90.9%

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

   if 2.59999999999999983e-12 < x

   1. Initial program 99.9%

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

   3. Taylor expanded in y around 0 82.5%

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

      1. mul-1-neg 82.5%

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

      2. associate-/l* 92.2%

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

      3. distribute-lft-neg-out 92.2%

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

      4. *-commutative 92.2%

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

   5. Simplified 92.2%

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

      1. clear-num 92.2%

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

      2. associate-*l/ 92.3%

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

      3. *-un-lft-identity 92.3%

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

      4. add-sqr-sqrt 0.0%

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

      5. sqrt-unprod 21.3%

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

      6. sqr-neg 21.3%

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

      7. sqrt-prod 46.7%

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

      8. add-sqr-sqrt 46.7%

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

      9. frac-2neg 46.7%

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

      10. add-sqr-sqrt 0.0%

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

      11. sqrt-unprod 59.7%

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

      12. sqr-neg 59.7%

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

      13. sqrt-prod 92.2%

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

      14. add-sqr-sqrt 92.3%

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

      15. distribute-neg-frac 92.3%

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

   7. Applied egg-rr 92.3%

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

      1. frac-2neg 92.3%

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

      2. div-inv 92.2%

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

      3. distribute-frac-neg 92.2%

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

      4. remove-double-neg 92.2%

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

      5. clear-num 92.2%

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

      6. add-sqr-sqrt 43.4%

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

      7. sqrt-unprod 57.2%

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

      8. sqr-neg 57.2%

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

      9. sqrt-unprod 25.7%

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

      10. add-sqr-sqrt 46.7%

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

      11. cancel-sign-sub-inv 46.7%

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

      12. clear-num 46.7%

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

      13. div-inv 46.7%

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

      14. associate-/r/ 40.4%

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

      15. *-commutative 40.4%

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

      16. add-sqr-sqrt 24.8%

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

      17. sqrt-unprod 57.3%

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

      18. sqr-neg 57.3%

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

      19. sqrt-unprod 37.8%

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

      20. add-sqr-sqrt 84.1%

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

   9. Applied egg-rr 84.1%

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

       1. associate-*r/ 82.5%

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

       2. *-commutative 82.5%

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

       3. associate-/l* 92.2%

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

       4. clear-num 92.2%

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

       5. un-div-inv 92.3%

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

   11. Applied egg-rr 92.3%

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

4. Final simplification 92.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -1.12 \cdot 10^{+92}:\\ \;\;\;\;x - x \cdot \frac{z}{t}\\ \mathbf{elif}\;x \leq 2.6 \cdot 10^{-12}:\\ \;\;\;\;x + y \cdot \frac{z}{t}\\ \mathbf{else}:\\ \;\;\;\;x - \frac{x}{\frac{t}{z}}\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 51.6% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -1.02 \cdot 10^{+101} \lor \neg \left(z \leq 6.5 \cdot 10^{+24}\right):\\ \;\;\;\;x \cdot \frac{z}{-t}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= z -1.02e+101) (not (<= z 6.5e+24))) (* x (/ z (- t))) x))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((z <= -1.02e+101) || !(z <= 6.5e+24)) {
		tmp = x * (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 <= (-1.02d+101)) .or. (.not. (z <= 6.5d+24))) then
        tmp = x * (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 <= -1.02e+101) || !(z <= 6.5e+24)) {
		tmp = x * (z / -t);
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (z <= -1.02e+101) or not (z <= 6.5e+24):
		tmp = x * (z / -t)
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((z <= -1.02e+101) || !(z <= 6.5e+24))
		tmp = Float64(x * Float64(z / Float64(-t)));
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((z <= -1.02e+101) || ~((z <= 6.5e+24)))
		tmp = x * (z / -t);
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[z, -1.02e+101], N[Not[LessEqual[z, 6.5e+24]], $MachinePrecision]], N[(x * N[(z / (-t)), $MachinePrecision]), $MachinePrecision], x]

Derivation

1. Split input into 2 regimes

2. if z < -1.02000000000000002e101 or 6.4999999999999996e24 < z

   1. Initial program 99.9%

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

   3. Taylor expanded in y around 0 52.7%

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

      1. mul-1-neg 52.7%

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

      2. associate-/l* 64.9%

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

      3. distribute-lft-neg-out 64.9%

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

      4. *-commutative 64.9%

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

   5. Simplified 64.9%

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

   6. Taylor expanded in x around 0 64.9%

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

      1. mul-1-neg 64.9%

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

      2. unsub-neg 64.9%

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

   8. Simplified 64.9%

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

   9. Taylor expanded in z around inf 51.9%

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

       1. mul-1-neg 51.9%

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

       2. distribute-frac-neg2 51.9%

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

   11. Simplified 51.9%

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

   if -1.02000000000000002e101 < z < 6.4999999999999996e24

   1. Initial program 99.3%

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

   3. Taylor expanded in y around inf 86.2%

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

      1. associate-*r/ 86.4%

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

   5. Simplified 86.4%

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

   6. Taylor expanded in x around inf 59.8%

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

4. Final simplification 56.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -1.02 \cdot 10^{+101} \lor \neg \left(z \leq 6.5 \cdot 10^{+24}\right):\\ \;\;\;\;x \cdot \frac{z}{-t}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 66.3% accurate, 1.3× speedup

Math

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

FPCore

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

C

double code(double x, double y, double z, double t) {
	return x * (1.0 - (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 * (1.0d0 - (z / t))
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 99.5%

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

3. Taylor expanded in y around 0 64.3%

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

   1. mul-1-neg 64.3%

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

   2. associate-/l* 70.7%

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

   3. distribute-lft-neg-out 70.7%

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

   4. *-commutative 70.7%

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

5. Simplified 70.7%

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

6. Taylor expanded in x around 0 70.7%

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

   1. mul-1-neg 70.7%

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

   2. unsub-neg 70.7%

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

8. Simplified 70.7%

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

9. Final simplification 70.7%

   \[\leadsto x \cdot \left(1 - \frac{z}{t}\right) \]
10. Add Preprocessing

Alternative 7: 39.6% 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 99.5%

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

3. Taylor expanded in y around inf 74.5%

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

   1. associate-*r/ 78.3%

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

5. Simplified 78.3%

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

6. Taylor expanded in x around inf 43.0%

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

7. Final simplification 43.0%

   \[\leadsto x \]
8. Add Preprocessing

Developer target: 97.9% accurate, 0.3× 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 2024044 
(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))))