Graphics.Rendering.Plot.Render.Plot.Axis:renderAxisTick from plot-0.2.3.4, A

Percentage Accurate: 85.4% → 98.2%

Time: 7.1s

Alternatives: 8

Speedup: 1.0×

Specification

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 85.4% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 98.2% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 84.1%

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

   1. associate-*l/ 98.8%

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

3. Simplified 98.8%

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

4. Final simplification 98.8%

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

Alternative 2: 88.4% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -4.9 \cdot 10^{+17} \lor \neg \left(y \leq 13000000\right):\\ \;\;\;\;x + t \cdot \frac{y}{a - z}\\ \mathbf{else}:\\ \;\;\;\;x + \frac{t}{1 - \frac{a}{z}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= y -4.9e+17) (not (<= y 13000000.0)))
   (+ x (* t (/ y (- a z))))
   (+ x (/ t (- 1.0 (/ a z))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((y <= -4.9e+17) || !(y <= 13000000.0)) {
		tmp = x + (t * (y / (a - z)));
	} else {
		tmp = x + (t / (1.0 - (a / z)));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if ((y <= (-4.9d+17)) .or. (.not. (y <= 13000000.0d0))) then
        tmp = x + (t * (y / (a - z)))
    else
        tmp = x + (t / (1.0d0 - (a / z)))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((y <= -4.9e+17) || !(y <= 13000000.0)) {
		tmp = x + (t * (y / (a - z)));
	} else {
		tmp = x + (t / (1.0 - (a / z)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (y <= -4.9e+17) or not (y <= 13000000.0):
		tmp = x + (t * (y / (a - z)))
	else:
		tmp = x + (t / (1.0 - (a / z)))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((y <= -4.9e+17) || !(y <= 13000000.0))
		tmp = Float64(x + Float64(t * Float64(y / Float64(a - z))));
	else
		tmp = Float64(x + Float64(t / Float64(1.0 - Float64(a / z))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((y <= -4.9e+17) || ~((y <= 13000000.0)))
		tmp = x + (t * (y / (a - z)));
	else
		tmp = x + (t / (1.0 - (a / z)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[y, -4.9e+17], N[Not[LessEqual[y, 13000000.0]], $MachinePrecision]], N[(x + N[(t * N[(y / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(t / N[(1.0 - N[(a / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -4.9e17 or 1.3e7 < y

   1. Initial program 81.1%

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

      1. associate-*l/ 98.3%

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

   3. Simplified 98.3%

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

   4. Taylor expanded in y around inf 87.8%

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

   if -4.9e17 < y < 1.3e7

   1. Initial program 86.8%

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

      1. associate-*l/ 99.2%

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

   3. Simplified 99.2%

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

   4. Taylor expanded in y around 0 91.3%

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

      1. neg-mul-1 91.3%

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

      2. distribute-neg-frac 91.3%

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

   6. Simplified 91.3%

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

      1. associate-*l/ 78.2%

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

      2. frac-2neg 78.2%

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

      3. add-sqr-sqrt 38.0%

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

      4. sqrt-unprod 57.5%

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

      5. sqr-neg 57.5%

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

      6. sqrt-unprod 29.6%

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

      7. add-sqr-sqrt 57.2%

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

      8. distribute-lft-neg-out 57.2%

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

      9. add-sqr-sqrt 27.6%

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

      10. sqrt-unprod 55.5%

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

      11. sqr-neg 55.5%

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

      12. sqrt-unprod 40.0%

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

      13. add-sqr-sqrt 78.2%

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

      14. sub-neg 78.2%

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

      15. distribute-neg-in 78.2%

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

      16. remove-double-neg 78.2%

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

   8. Applied egg-rr 78.2%

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

      1. *-commutative 78.2%

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

      2. associate-/l* 91.3%

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

      3. +-commutative 91.3%

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

      4. unsub-neg 91.3%

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

   10. Simplified 91.3%

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

   11. Taylor expanded in z around 0 91.3%

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

       1. mul-1-neg 91.3%

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

       2. sub-neg 91.3%

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

   13. Simplified 91.3%

       \[\leadsto x + \frac{t}{\color{blue}{1 - \frac{a}{z}}} \]
3. Recombined 2 regimes into one program.

4. Final simplification 89.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -4.9 \cdot 10^{+17} \lor \neg \left(y \leq 13000000\right):\\ \;\;\;\;x + t \cdot \frac{y}{a - z}\\ \mathbf{else}:\\ \;\;\;\;x + \frac{t}{1 - \frac{a}{z}}\\ \end{array} \]

Alternative 3: 84.3% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -4.2 \cdot 10^{+90}:\\ \;\;\;\;x + t\\ \mathbf{elif}\;z \leq 1.3 \cdot 10^{+81}:\\ \;\;\;\;x + t \cdot \frac{y}{a - z}\\ \mathbf{else}:\\ \;\;\;\;x + t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= z -4.2e+90)
   (+ x t)
   (if (<= z 1.3e+81) (+ x (* t (/ y (- a z)))) (+ x t))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -4.2e+90) {
		tmp = x + t;
	} else if (z <= 1.3e+81) {
		tmp = x + (t * (y / (a - z)));
	} else {
		tmp = x + t;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if (z <= (-4.2d+90)) then
        tmp = x + t
    else if (z <= 1.3d+81) then
        tmp = x + (t * (y / (a - z)))
    else
        tmp = x + t
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -4.2e+90) {
		tmp = x + t;
	} else if (z <= 1.3e+81) {
		tmp = x + (t * (y / (a - z)));
	} else {
		tmp = x + t;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if z <= -4.2e+90:
		tmp = x + t
	elif z <= 1.3e+81:
		tmp = x + (t * (y / (a - z)))
	else:
		tmp = x + t
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (z <= -4.2e+90)
		tmp = Float64(x + t);
	elseif (z <= 1.3e+81)
		tmp = Float64(x + Float64(t * Float64(y / Float64(a - z))));
	else
		tmp = Float64(x + t);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (z <= -4.2e+90)
		tmp = x + t;
	elseif (z <= 1.3e+81)
		tmp = x + (t * (y / (a - z)));
	else
		tmp = x + t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[z, -4.2e+90], N[(x + t), $MachinePrecision], If[LessEqual[z, 1.3e+81], N[(x + N[(t * N[(y / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + t), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if z < -4.19999999999999961e90 or 1.29999999999999996e81 < z

   1. Initial program 69.6%

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

      1. associate-*l/ 99.9%

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

   3. Simplified 99.9%

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

   4. Taylor expanded in z around inf 81.1%

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

   if -4.19999999999999961e90 < z < 1.29999999999999996e81

   1. Initial program 93.3%

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

      1. associate-*l/ 98.1%

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

   3. Simplified 98.1%

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

   4. Taylor expanded in y around inf 88.0%

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

4. Final simplification 85.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -4.2 \cdot 10^{+90}:\\ \;\;\;\;x + t\\ \mathbf{elif}\;z \leq 1.3 \cdot 10^{+81}:\\ \;\;\;\;x + t \cdot \frac{y}{a - z}\\ \mathbf{else}:\\ \;\;\;\;x + t\\ \end{array} \]

Alternative 4: 88.6% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.92 \cdot 10^{+18}:\\ \;\;\;\;x + \frac{y}{\frac{a - z}{t}}\\ \mathbf{elif}\;y \leq 2600000:\\ \;\;\;\;x + \frac{t}{1 - \frac{a}{z}}\\ \mathbf{else}:\\ \;\;\;\;x + t \cdot \frac{y}{a - z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= y -1.92e+18)
   (+ x (/ y (/ (- a z) t)))
   (if (<= y 2600000.0)
     (+ x (/ t (- 1.0 (/ a z))))
     (+ x (* t (/ y (- a z)))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (y <= -1.92e+18) {
		tmp = x + (y / ((a - z) / t));
	} else if (y <= 2600000.0) {
		tmp = x + (t / (1.0 - (a / z)));
	} else {
		tmp = x + (t * (y / (a - z)));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if (y <= (-1.92d+18)) then
        tmp = x + (y / ((a - z) / t))
    else if (y <= 2600000.0d0) then
        tmp = x + (t / (1.0d0 - (a / z)))
    else
        tmp = x + (t * (y / (a - z)))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (y <= -1.92e+18) {
		tmp = x + (y / ((a - z) / t));
	} else if (y <= 2600000.0) {
		tmp = x + (t / (1.0 - (a / z)));
	} else {
		tmp = x + (t * (y / (a - z)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if y <= -1.92e+18:
		tmp = x + (y / ((a - z) / t))
	elif y <= 2600000.0:
		tmp = x + (t / (1.0 - (a / z)))
	else:
		tmp = x + (t * (y / (a - z)))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (y <= -1.92e+18)
		tmp = Float64(x + Float64(y / Float64(Float64(a - z) / t)));
	elseif (y <= 2600000.0)
		tmp = Float64(x + Float64(t / Float64(1.0 - Float64(a / z))));
	else
		tmp = Float64(x + Float64(t * Float64(y / Float64(a - z))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (y <= -1.92e+18)
		tmp = x + (y / ((a - z) / t));
	elseif (y <= 2600000.0)
		tmp = x + (t / (1.0 - (a / z)));
	else
		tmp = x + (t * (y / (a - z)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[y, -1.92e+18], N[(x + N[(y / N[(N[(a - z), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 2600000.0], N[(x + N[(t / N[(1.0 - N[(a / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(t * N[(y / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if y < -1.92e18

   1. Initial program 74.0%

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

      1. associate-*l/ 97.0%

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

   3. Simplified 97.0%

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

   4. Taylor expanded in y around inf 69.7%

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

      1. associate-/l* 84.9%

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

   6. Simplified 84.9%

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

   if -1.92e18 < y < 2.6e6

   1. Initial program 86.8%

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

      1. associate-*l/ 99.2%

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

   3. Simplified 99.2%

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

   4. Taylor expanded in y around 0 91.3%

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

      1. neg-mul-1 91.3%

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

      2. distribute-neg-frac 91.3%

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

   6. Simplified 91.3%

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

      1. associate-*l/ 78.2%

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

      2. frac-2neg 78.2%

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

      3. add-sqr-sqrt 38.0%

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

      4. sqrt-unprod 57.5%

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

      5. sqr-neg 57.5%

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

      6. sqrt-unprod 29.6%

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

      7. add-sqr-sqrt 57.2%

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

      8. distribute-lft-neg-out 57.2%

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

      9. add-sqr-sqrt 27.6%

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

      10. sqrt-unprod 55.5%

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

      11. sqr-neg 55.5%

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

      12. sqrt-unprod 40.0%

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

      13. add-sqr-sqrt 78.2%

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

      14. sub-neg 78.2%

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

      15. distribute-neg-in 78.2%

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

      16. remove-double-neg 78.2%

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

   8. Applied egg-rr 78.2%

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

      1. *-commutative 78.2%

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

      2. associate-/l* 91.3%

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

      3. +-commutative 91.3%

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

      4. unsub-neg 91.3%

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

   10. Simplified 91.3%

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

   11. Taylor expanded in z around 0 91.3%

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

       1. mul-1-neg 91.3%

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

       2. sub-neg 91.3%

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

   13. Simplified 91.3%

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

   if 2.6e6 < y

   1. Initial program 89.6%

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

      1. associate-*l/ 99.9%

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

   3. Simplified 99.9%

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

   4. Taylor expanded in y around inf 94.6%

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

4. Final simplification 90.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1.92 \cdot 10^{+18}:\\ \;\;\;\;x + \frac{y}{\frac{a - z}{t}}\\ \mathbf{elif}\;y \leq 2600000:\\ \;\;\;\;x + \frac{t}{1 - \frac{a}{z}}\\ \mathbf{else}:\\ \;\;\;\;x + t \cdot \frac{y}{a - z}\\ \end{array} \]

Alternative 5: 88.6% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -2 \cdot 10^{+18}:\\ \;\;\;\;x + \frac{y}{\frac{a - z}{t}}\\ \mathbf{elif}\;y \leq 185000000:\\ \;\;\;\;x - t \cdot \frac{z}{a - z}\\ \mathbf{else}:\\ \;\;\;\;x + t \cdot \frac{y}{a - z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= y -2e+18)
   (+ x (/ y (/ (- a z) t)))
   (if (<= y 185000000.0)
     (- x (* t (/ z (- a z))))
     (+ x (* t (/ y (- a z)))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (y <= -2e+18) {
		tmp = x + (y / ((a - z) / t));
	} else if (y <= 185000000.0) {
		tmp = x - (t * (z / (a - z)));
	} else {
		tmp = x + (t * (y / (a - z)));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if (y <= (-2d+18)) then
        tmp = x + (y / ((a - z) / t))
    else if (y <= 185000000.0d0) then
        tmp = x - (t * (z / (a - z)))
    else
        tmp = x + (t * (y / (a - z)))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (y <= -2e+18) {
		tmp = x + (y / ((a - z) / t));
	} else if (y <= 185000000.0) {
		tmp = x - (t * (z / (a - z)));
	} else {
		tmp = x + (t * (y / (a - z)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if y <= -2e+18:
		tmp = x + (y / ((a - z) / t))
	elif y <= 185000000.0:
		tmp = x - (t * (z / (a - z)))
	else:
		tmp = x + (t * (y / (a - z)))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (y <= -2e+18)
		tmp = Float64(x + Float64(y / Float64(Float64(a - z) / t)));
	elseif (y <= 185000000.0)
		tmp = Float64(x - Float64(t * Float64(z / Float64(a - z))));
	else
		tmp = Float64(x + Float64(t * Float64(y / Float64(a - z))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (y <= -2e+18)
		tmp = x + (y / ((a - z) / t));
	elseif (y <= 185000000.0)
		tmp = x - (t * (z / (a - z)));
	else
		tmp = x + (t * (y / (a - z)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[y, -2e+18], N[(x + N[(y / N[(N[(a - z), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 185000000.0], N[(x - N[(t * N[(z / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(t * N[(y / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if y < -2e18

   1. Initial program 74.0%

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

      1. associate-*l/ 97.0%

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

   3. Simplified 97.0%

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

   4. Taylor expanded in y around inf 69.7%

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

      1. associate-/l* 84.9%

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

   6. Simplified 84.9%

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

   if -2e18 < y < 1.85e8

   1. Initial program 86.8%

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

      1. associate-*l/ 99.2%

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

   3. Simplified 99.2%

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

   4. Taylor expanded in y around 0 91.3%

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

      1. neg-mul-1 91.3%

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

      2. distribute-neg-frac 91.3%

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

   6. Simplified 91.3%

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

   if 1.85e8 < y

   1. Initial program 89.6%

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

      1. associate-*l/ 99.9%

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

   3. Simplified 99.9%

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

   4. Taylor expanded in y around inf 94.6%

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

4. Final simplification 90.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -2 \cdot 10^{+18}:\\ \;\;\;\;x + \frac{y}{\frac{a - z}{t}}\\ \mathbf{elif}\;y \leq 185000000:\\ \;\;\;\;x - t \cdot \frac{z}{a - z}\\ \mathbf{else}:\\ \;\;\;\;x + t \cdot \frac{y}{a - z}\\ \end{array} \]

Alternative 6: 76.3% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -1.9 \cdot 10^{+34}:\\ \;\;\;\;x + t\\ \mathbf{elif}\;z \leq 4.6 \cdot 10^{+81}:\\ \;\;\;\;x + t \cdot \frac{y}{a}\\ \mathbf{else}:\\ \;\;\;\;x + t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= z -1.9e+34) (+ x t) (if (<= z 4.6e+81) (+ x (* t (/ y a))) (+ x t))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -1.9e+34) {
		tmp = x + t;
	} else if (z <= 4.6e+81) {
		tmp = x + (t * (y / a));
	} else {
		tmp = x + t;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if (z <= (-1.9d+34)) then
        tmp = x + t
    else if (z <= 4.6d+81) then
        tmp = x + (t * (y / a))
    else
        tmp = x + t
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -1.9e+34) {
		tmp = x + t;
	} else if (z <= 4.6e+81) {
		tmp = x + (t * (y / a));
	} else {
		tmp = x + t;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if z <= -1.9e+34:
		tmp = x + t
	elif z <= 4.6e+81:
		tmp = x + (t * (y / a))
	else:
		tmp = x + t
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (z <= -1.9e+34)
		tmp = Float64(x + t);
	elseif (z <= 4.6e+81)
		tmp = Float64(x + Float64(t * Float64(y / a)));
	else
		tmp = Float64(x + t);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (z <= -1.9e+34)
		tmp = x + t;
	elseif (z <= 4.6e+81)
		tmp = x + (t * (y / a));
	else
		tmp = x + t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[z, -1.9e+34], N[(x + t), $MachinePrecision], If[LessEqual[z, 4.6e+81], N[(x + N[(t * N[(y / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + t), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if z < -1.9000000000000001e34 or 4.5999999999999998e81 < z

   1. Initial program 72.8%

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

      1. associate-*l/ 99.9%

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

   3. Simplified 99.9%

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

   4. Taylor expanded in z around inf 78.5%

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

   if -1.9000000000000001e34 < z < 4.5999999999999998e81

   1. Initial program 93.3%

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

      1. associate-*l/ 97.9%

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

   3. Simplified 97.9%

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

   4. Taylor expanded in z around 0 76.2%

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

4. Final simplification 77.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -1.9 \cdot 10^{+34}:\\ \;\;\;\;x + t\\ \mathbf{elif}\;z \leq 4.6 \cdot 10^{+81}:\\ \;\;\;\;x + t \cdot \frac{y}{a}\\ \mathbf{else}:\\ \;\;\;\;x + t\\ \end{array} \]

Alternative 7: 63.8% accurate, 1.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -3.4 \cdot 10^{-81}:\\ \;\;\;\;x + t\\ \mathbf{elif}\;z \leq 10^{-10}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;x + t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= z -3.4e-81) (+ x t) (if (<= z 1e-10) x (+ x t))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -3.4e-81) {
		tmp = x + t;
	} else if (z <= 1e-10) {
		tmp = x;
	} else {
		tmp = x + t;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if (z <= (-3.4d-81)) then
        tmp = x + t
    else if (z <= 1d-10) then
        tmp = x
    else
        tmp = x + t
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -3.4e-81) {
		tmp = x + t;
	} else if (z <= 1e-10) {
		tmp = x;
	} else {
		tmp = x + t;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if z <= -3.4e-81:
		tmp = x + t
	elif z <= 1e-10:
		tmp = x
	else:
		tmp = x + t
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (z <= -3.4e-81)
		tmp = Float64(x + t);
	elseif (z <= 1e-10)
		tmp = x;
	else
		tmp = Float64(x + t);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (z <= -3.4e-81)
		tmp = x + t;
	elseif (z <= 1e-10)
		tmp = x;
	else
		tmp = x + t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[z, -3.4e-81], N[(x + t), $MachinePrecision], If[LessEqual[z, 1e-10], x, N[(x + t), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if z < -3.3999999999999999e-81 or 1.00000000000000004e-10 < z

   1. Initial program 76.7%

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

      1. associate-*l/ 99.3%

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

   3. Simplified 99.3%

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

   4. Taylor expanded in z around inf 71.6%

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

   if -3.3999999999999999e-81 < z < 1.00000000000000004e-10

   1. Initial program 94.6%

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

      1. associate-*l/ 98.1%

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

   3. Simplified 98.1%

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

   4. Taylor expanded in z around 0 80.0%

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

      1. associate-/l* 82.3%

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

   6. Simplified 82.3%

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

   7. Taylor expanded in x around inf 53.3%

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

4. Final simplification 64.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -3.4 \cdot 10^{-81}:\\ \;\;\;\;x + t\\ \mathbf{elif}\;z \leq 10^{-10}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;x + t\\ \end{array} \]

Alternative 8: 50.9% accurate, 11.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 84.1%

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

   1. associate-*l/ 98.8%

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

3. Simplified 98.8%

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

4. Taylor expanded in z around 0 59.3%

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

   1. associate-/l* 61.1%

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

6. Simplified 61.1%

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

7. Taylor expanded in x around inf 49.6%

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

8. Final simplification 49.6%

   \[\leadsto x \]

Developer target: 99.3% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + \frac{y - z}{a - z} \cdot t\\ \mathbf{if}\;t < -1.0682974490174067 \cdot 10^{-39}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t < 3.9110949887586375 \cdot 10^{-141}:\\ \;\;\;\;x + \frac{\left(y - z\right) \cdot t}{a - z}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ x (* (/ (- y z) (- a z)) t))))
   (if (< t -1.0682974490174067e-39)
     t_1
     (if (< t 3.9110949887586375e-141) (+ x (/ (* (- y z) t) (- a z))) t_1))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x + (((y - z) / (a - z)) * t);
	double tmp;
	if (t < -1.0682974490174067e-39) {
		tmp = t_1;
	} else if (t < 3.9110949887586375e-141) {
		tmp = x + (((y - z) * t) / (a - z));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: t_1
    real(8) :: tmp
    t_1 = x + (((y - z) / (a - z)) * t)
    if (t < (-1.0682974490174067d-39)) then
        tmp = t_1
    else if (t < 3.9110949887586375d-141) then
        tmp = x + (((y - z) * t) / (a - 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 a) {
	double t_1 = x + (((y - z) / (a - z)) * t);
	double tmp;
	if (t < -1.0682974490174067e-39) {
		tmp = t_1;
	} else if (t < 3.9110949887586375e-141) {
		tmp = x + (((y - z) * t) / (a - z));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x + (((y - z) / (a - z)) * t)
	tmp = 0
	if t < -1.0682974490174067e-39:
		tmp = t_1
	elif t < 3.9110949887586375e-141:
		tmp = x + (((y - z) * t) / (a - z))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x + Float64(Float64(Float64(y - z) / Float64(a - z)) * t))
	tmp = 0.0
	if (t < -1.0682974490174067e-39)
		tmp = t_1;
	elseif (t < 3.9110949887586375e-141)
		tmp = Float64(x + Float64(Float64(Float64(y - z) * t) / Float64(a - z)));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x + (((y - z) / (a - z)) * t);
	tmp = 0.0;
	if (t < -1.0682974490174067e-39)
		tmp = t_1;
	elseif (t < 3.9110949887586375e-141)
		tmp = x + (((y - z) * t) / (a - z));
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x + N[(N[(N[(y - z), $MachinePrecision] / N[(a - z), $MachinePrecision]), $MachinePrecision] * t), $MachinePrecision]), $MachinePrecision]}, If[Less[t, -1.0682974490174067e-39], t$95$1, If[Less[t, 3.9110949887586375e-141], N[(x + N[(N[(N[(y - z), $MachinePrecision] * t), $MachinePrecision] / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]

Reproduce

herbie shell --seed 2023171 
(FPCore (x y z t a)
  :name "Graphics.Rendering.Plot.Render.Plot.Axis:renderAxisTick from plot-0.2.3.4, A"
  :precision binary64

  :herbie-target
  (if (< t -1.0682974490174067e-39) (+ x (* (/ (- y z) (- a z)) t)) (if (< t 3.9110949887586375e-141) (+ x (/ (* (- y z) t) (- a z))) (+ x (* (/ (- y z) (- a z)) t))))

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