Graphics.Rendering.Plot.Render.Plot.Axis:renderAxisLine from plot-0.2.3.4, B

Percentage Accurate: 98.3% → 98.4%

Time: 8.5s

Alternatives: 15

Speedup: 1.0×

Specification

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 98.3% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 98.4% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 98.0%

   \[x + y \cdot \frac{z - t}{a - t} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. +-commutative 98.0%

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

   2. clear-num 98.0%

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

   3. un-div-inv 98.1%

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

4. Applied egg-rr 98.1%

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

Alternative 2: 73.5% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -9.2 \cdot 10^{+76}:\\ \;\;\;\;y + x\\ \mathbf{elif}\;t \leq 2.65 \cdot 10^{-45}:\\ \;\;\;\;x + \frac{y}{\frac{a}{z}}\\ \mathbf{elif}\;t \leq 2.3 \cdot 10^{+57}:\\ \;\;\;\;y \cdot \left(1 - \frac{z}{t}\right)\\ \mathbf{elif}\;t \leq 3.6 \cdot 10^{+84}:\\ \;\;\;\;x + \frac{y \cdot z}{a}\\ \mathbf{else}:\\ \;\;\;\;y + x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= t -9.2e+76)
   (+ y x)
   (if (<= t 2.65e-45)
     (+ x (/ y (/ a z)))
     (if (<= t 2.3e+57)
       (* y (- 1.0 (/ z t)))
       (if (<= t 3.6e+84) (+ x (/ (* y z) a)) (+ y x))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (t <= -9.2e+76) {
		tmp = y + x;
	} else if (t <= 2.65e-45) {
		tmp = x + (y / (a / z));
	} else if (t <= 2.3e+57) {
		tmp = y * (1.0 - (z / t));
	} else if (t <= 3.6e+84) {
		tmp = x + ((y * z) / a);
	} else {
		tmp = y + x;
	}
	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 (t <= (-9.2d+76)) then
        tmp = y + x
    else if (t <= 2.65d-45) then
        tmp = x + (y / (a / z))
    else if (t <= 2.3d+57) then
        tmp = y * (1.0d0 - (z / t))
    else if (t <= 3.6d+84) then
        tmp = x + ((y * z) / a)
    else
        tmp = y + x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (t <= -9.2e+76) {
		tmp = y + x;
	} else if (t <= 2.65e-45) {
		tmp = x + (y / (a / z));
	} else if (t <= 2.3e+57) {
		tmp = y * (1.0 - (z / t));
	} else if (t <= 3.6e+84) {
		tmp = x + ((y * z) / a);
	} else {
		tmp = y + x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if t <= -9.2e+76:
		tmp = y + x
	elif t <= 2.65e-45:
		tmp = x + (y / (a / z))
	elif t <= 2.3e+57:
		tmp = y * (1.0 - (z / t))
	elif t <= 3.6e+84:
		tmp = x + ((y * z) / a)
	else:
		tmp = y + x
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (t <= -9.2e+76)
		tmp = Float64(y + x);
	elseif (t <= 2.65e-45)
		tmp = Float64(x + Float64(y / Float64(a / z)));
	elseif (t <= 2.3e+57)
		tmp = Float64(y * Float64(1.0 - Float64(z / t)));
	elseif (t <= 3.6e+84)
		tmp = Float64(x + Float64(Float64(y * z) / a));
	else
		tmp = Float64(y + x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (t <= -9.2e+76)
		tmp = y + x;
	elseif (t <= 2.65e-45)
		tmp = x + (y / (a / z));
	elseif (t <= 2.3e+57)
		tmp = y * (1.0 - (z / t));
	elseif (t <= 3.6e+84)
		tmp = x + ((y * z) / a);
	else
		tmp = y + x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[t, -9.2e+76], N[(y + x), $MachinePrecision], If[LessEqual[t, 2.65e-45], N[(x + N[(y / N[(a / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 2.3e+57], N[(y * N[(1.0 - N[(z / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 3.6e+84], N[(x + N[(N[(y * z), $MachinePrecision] / a), $MachinePrecision]), $MachinePrecision], N[(y + x), $MachinePrecision]]]]]

Derivation

1. Split input into 4 regimes

2. if t < -9.20000000000000005e76 or 3.5999999999999999e84 < t

   1. Initial program 100.0%

      \[x + y \cdot \frac{z - t}{a - t} \]
   2. Add Preprocessing

   3. Taylor expanded in t around inf 88.3%

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

      1. +-commutative 88.3%

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

   5. Simplified 88.3%

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

   if -9.20000000000000005e76 < t < 2.6499999999999999e-45

   1. Initial program 96.1%

      \[x + y \cdot \frac{z - t}{a - t} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. +-commutative 96.1%

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

      2. clear-num 96.1%

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

      3. un-div-inv 96.2%

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

   4. Applied egg-rr 96.2%

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

   5. Taylor expanded in t around 0 79.9%

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

   if 2.6499999999999999e-45 < t < 2.2999999999999999e57

   1. Initial program 99.7%

      \[x + y \cdot \frac{z - t}{a - t} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. +-commutative 99.7%

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

      2. clear-num 99.8%

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

      3. un-div-inv 99.9%

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

   4. Applied egg-rr 99.9%

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

   5. Taylor expanded in a around 0 85.6%

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

      1. mul-1-neg 85.6%

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

      2. unsub-neg 85.6%

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

      3. associate-/l* 85.3%

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

      4. div-sub 85.3%

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

      5. *-inverses 85.3%

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

   7. Simplified 85.3%

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

   8. Taylor expanded in x around 0 58.8%

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

      1. mul-1-neg 58.8%

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

      2. sub-neg 58.8%

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

      3. metadata-eval 58.8%

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

      4. distribute-rgt-neg-in 58.8%

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

      5. +-commutative 58.8%

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

      6. distribute-neg-in 58.8%

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

      7. metadata-eval 58.8%

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

      8. sub-neg 58.8%

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

   10. Simplified 58.8%

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

   if 2.2999999999999999e57 < t < 3.5999999999999999e84

   1. Initial program 99.9%

      \[x + y \cdot \frac{z - t}{a - t} \]
   2. Add Preprocessing

   3. Taylor expanded in t around 0 71.7%

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

4. Final simplification 81.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq -9.2 \cdot 10^{+76}:\\ \;\;\;\;y + x\\ \mathbf{elif}\;t \leq 2.65 \cdot 10^{-45}:\\ \;\;\;\;x + \frac{y}{\frac{a}{z}}\\ \mathbf{elif}\;t \leq 2.3 \cdot 10^{+57}:\\ \;\;\;\;y \cdot \left(1 - \frac{z}{t}\right)\\ \mathbf{elif}\;t \leq 3.6 \cdot 10^{+84}:\\ \;\;\;\;x + \frac{y \cdot z}{a}\\ \mathbf{else}:\\ \;\;\;\;y + x\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 73.4% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -2 \cdot 10^{+77}:\\ \;\;\;\;y + x\\ \mathbf{elif}\;t \leq 2.65 \cdot 10^{-45}:\\ \;\;\;\;x + y \cdot \frac{z}{a}\\ \mathbf{elif}\;t \leq 2.7 \cdot 10^{+54}:\\ \;\;\;\;y \cdot \left(1 - \frac{z}{t}\right)\\ \mathbf{elif}\;t \leq 1.7 \cdot 10^{+85}:\\ \;\;\;\;x + \frac{y \cdot z}{a}\\ \mathbf{else}:\\ \;\;\;\;y + x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= t -2e+77)
   (+ y x)
   (if (<= t 2.65e-45)
     (+ x (* y (/ z a)))
     (if (<= t 2.7e+54)
       (* y (- 1.0 (/ z t)))
       (if (<= t 1.7e+85) (+ x (/ (* y z) a)) (+ y x))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (t <= -2e+77) {
		tmp = y + x;
	} else if (t <= 2.65e-45) {
		tmp = x + (y * (z / a));
	} else if (t <= 2.7e+54) {
		tmp = y * (1.0 - (z / t));
	} else if (t <= 1.7e+85) {
		tmp = x + ((y * z) / a);
	} else {
		tmp = y + x;
	}
	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 (t <= (-2d+77)) then
        tmp = y + x
    else if (t <= 2.65d-45) then
        tmp = x + (y * (z / a))
    else if (t <= 2.7d+54) then
        tmp = y * (1.0d0 - (z / t))
    else if (t <= 1.7d+85) then
        tmp = x + ((y * z) / a)
    else
        tmp = y + x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (t <= -2e+77) {
		tmp = y + x;
	} else if (t <= 2.65e-45) {
		tmp = x + (y * (z / a));
	} else if (t <= 2.7e+54) {
		tmp = y * (1.0 - (z / t));
	} else if (t <= 1.7e+85) {
		tmp = x + ((y * z) / a);
	} else {
		tmp = y + x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if t <= -2e+77:
		tmp = y + x
	elif t <= 2.65e-45:
		tmp = x + (y * (z / a))
	elif t <= 2.7e+54:
		tmp = y * (1.0 - (z / t))
	elif t <= 1.7e+85:
		tmp = x + ((y * z) / a)
	else:
		tmp = y + x
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (t <= -2e+77)
		tmp = Float64(y + x);
	elseif (t <= 2.65e-45)
		tmp = Float64(x + Float64(y * Float64(z / a)));
	elseif (t <= 2.7e+54)
		tmp = Float64(y * Float64(1.0 - Float64(z / t)));
	elseif (t <= 1.7e+85)
		tmp = Float64(x + Float64(Float64(y * z) / a));
	else
		tmp = Float64(y + x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (t <= -2e+77)
		tmp = y + x;
	elseif (t <= 2.65e-45)
		tmp = x + (y * (z / a));
	elseif (t <= 2.7e+54)
		tmp = y * (1.0 - (z / t));
	elseif (t <= 1.7e+85)
		tmp = x + ((y * z) / a);
	else
		tmp = y + x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[t, -2e+77], N[(y + x), $MachinePrecision], If[LessEqual[t, 2.65e-45], N[(x + N[(y * N[(z / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 2.7e+54], N[(y * N[(1.0 - N[(z / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 1.7e+85], N[(x + N[(N[(y * z), $MachinePrecision] / a), $MachinePrecision]), $MachinePrecision], N[(y + x), $MachinePrecision]]]]]

Derivation

1. Split input into 4 regimes

2. if t < -1.99999999999999997e77 or 1.7000000000000002e85 < t

   1. Initial program 100.0%

      \[x + y \cdot \frac{z - t}{a - t} \]
   2. Add Preprocessing

   3. Taylor expanded in t around inf 88.3%

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

      1. +-commutative 88.3%

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

   5. Simplified 88.3%

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

   if -1.99999999999999997e77 < t < 2.6499999999999999e-45

   1. Initial program 96.1%

      \[x + y \cdot \frac{z - t}{a - t} \]
   2. Add Preprocessing

   3. Taylor expanded in t around 0 79.9%

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

   if 2.6499999999999999e-45 < t < 2.70000000000000011e54

   1. Initial program 99.7%

      \[x + y \cdot \frac{z - t}{a - t} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. +-commutative 99.7%

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

      2. clear-num 99.8%

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

      3. un-div-inv 99.9%

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

   4. Applied egg-rr 99.9%

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

   5. Taylor expanded in a around 0 85.6%

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

      1. mul-1-neg 85.6%

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

      2. unsub-neg 85.6%

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

      3. associate-/l* 85.3%

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

      4. div-sub 85.3%

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

      5. *-inverses 85.3%

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

   7. Simplified 85.3%

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

   8. Taylor expanded in x around 0 58.8%

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

      1. mul-1-neg 58.8%

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

      2. sub-neg 58.8%

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

      3. metadata-eval 58.8%

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

      4. distribute-rgt-neg-in 58.8%

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

      5. +-commutative 58.8%

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

      6. distribute-neg-in 58.8%

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

      7. metadata-eval 58.8%

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

      8. sub-neg 58.8%

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

   10. Simplified 58.8%

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

   if 2.70000000000000011e54 < t < 1.7000000000000002e85

   1. Initial program 99.9%

      \[x + y \cdot \frac{z - t}{a - t} \]
   2. Add Preprocessing

   3. Taylor expanded in t around 0 71.7%

      \[\leadsto x + \color{blue}{\frac{y \cdot z}{a}} \]
3. Recombined 4 regimes into one program.
4. Add Preprocessing

Alternative 4: 73.6% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + y \cdot \frac{z}{a}\\ \mathbf{if}\;t \leq -3.75 \cdot 10^{+77}:\\ \;\;\;\;y + x\\ \mathbf{elif}\;t \leq 2.25 \cdot 10^{-45}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t \leq 9 \cdot 10^{+54}:\\ \;\;\;\;y \cdot \left(1 - \frac{z}{t}\right)\\ \mathbf{elif}\;t \leq 4.1 \cdot 10^{+77}:\\ \;\;\;\;t\_1\\ \mathbf{else}:\\ \;\;\;\;y + x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ x (* y (/ z a)))))
   (if (<= t -3.75e+77)
     (+ y x)
     (if (<= t 2.25e-45)
       t_1
       (if (<= t 9e+54)
         (* y (- 1.0 (/ z t)))
         (if (<= t 4.1e+77) t_1 (+ y x)))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x + (y * (z / a));
	double tmp;
	if (t <= -3.75e+77) {
		tmp = y + x;
	} else if (t <= 2.25e-45) {
		tmp = t_1;
	} else if (t <= 9e+54) {
		tmp = y * (1.0 - (z / t));
	} else if (t <= 4.1e+77) {
		tmp = t_1;
	} else {
		tmp = y + x;
	}
	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))
    if (t <= (-3.75d+77)) then
        tmp = y + x
    else if (t <= 2.25d-45) then
        tmp = t_1
    else if (t <= 9d+54) then
        tmp = y * (1.0d0 - (z / t))
    else if (t <= 4.1d+77) then
        tmp = t_1
    else
        tmp = y + x
    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));
	double tmp;
	if (t <= -3.75e+77) {
		tmp = y + x;
	} else if (t <= 2.25e-45) {
		tmp = t_1;
	} else if (t <= 9e+54) {
		tmp = y * (1.0 - (z / t));
	} else if (t <= 4.1e+77) {
		tmp = t_1;
	} else {
		tmp = y + x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x + (y * (z / a))
	tmp = 0
	if t <= -3.75e+77:
		tmp = y + x
	elif t <= 2.25e-45:
		tmp = t_1
	elif t <= 9e+54:
		tmp = y * (1.0 - (z / t))
	elif t <= 4.1e+77:
		tmp = t_1
	else:
		tmp = y + x
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x + Float64(y * Float64(z / a)))
	tmp = 0.0
	if (t <= -3.75e+77)
		tmp = Float64(y + x);
	elseif (t <= 2.25e-45)
		tmp = t_1;
	elseif (t <= 9e+54)
		tmp = Float64(y * Float64(1.0 - Float64(z / t)));
	elseif (t <= 4.1e+77)
		tmp = t_1;
	else
		tmp = Float64(y + x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x + (y * (z / a));
	tmp = 0.0;
	if (t <= -3.75e+77)
		tmp = y + x;
	elseif (t <= 2.25e-45)
		tmp = t_1;
	elseif (t <= 9e+54)
		tmp = y * (1.0 - (z / t));
	elseif (t <= 4.1e+77)
		tmp = t_1;
	else
		tmp = y + x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x + N[(y * N[(z / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -3.75e+77], N[(y + x), $MachinePrecision], If[LessEqual[t, 2.25e-45], t$95$1, If[LessEqual[t, 9e+54], N[(y * N[(1.0 - N[(z / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 4.1e+77], t$95$1, N[(y + x), $MachinePrecision]]]]]]

Derivation

1. Split input into 3 regimes

2. if t < -3.74999999999999977e77 or 4.1000000000000001e77 < t

   1. Initial program 100.0%

      \[x + y \cdot \frac{z - t}{a - t} \]
   2. Add Preprocessing

   3. Taylor expanded in t around inf 87.7%

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

      1. +-commutative 87.7%

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

   5. Simplified 87.7%

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

   if -3.74999999999999977e77 < t < 2.2499999999999999e-45 or 8.99999999999999968e54 < t < 4.1000000000000001e77

   1. Initial program 96.3%

      \[x + y \cdot \frac{z - t}{a - t} \]
   2. Add Preprocessing

   3. Taylor expanded in t around 0 79.5%

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

   if 2.2499999999999999e-45 < t < 8.99999999999999968e54

   1. Initial program 99.7%

      \[x + y \cdot \frac{z - t}{a - t} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. +-commutative 99.7%

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

      2. clear-num 99.8%

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

      3. un-div-inv 99.9%

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

   4. Applied egg-rr 99.9%

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

   5. Taylor expanded in a around 0 85.6%

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

      1. mul-1-neg 85.6%

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

      2. unsub-neg 85.6%

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

      3. associate-/l* 85.3%

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

      4. div-sub 85.3%

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

      5. *-inverses 85.3%

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

   7. Simplified 85.3%

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

   8. Taylor expanded in x around 0 58.8%

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

      1. mul-1-neg 58.8%

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

      2. sub-neg 58.8%

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

      3. metadata-eval 58.8%

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

      4. distribute-rgt-neg-in 58.8%

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

      5. +-commutative 58.8%

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

      6. distribute-neg-in 58.8%

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

      7. metadata-eval 58.8%

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

      8. sub-neg 58.8%

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

   10. Simplified 58.8%

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

Alternative 5: 76.0% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -1.55 \cdot 10^{+77}:\\ \;\;\;\;y + x\\ \mathbf{elif}\;t \leq 1.25 \cdot 10^{-89}:\\ \;\;\;\;x + \frac{y}{\frac{a}{z}}\\ \mathbf{elif}\;t \leq 3.2 \cdot 10^{+87}:\\ \;\;\;\;x - \frac{y \cdot z}{t}\\ \mathbf{else}:\\ \;\;\;\;y + x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= t -1.55e+77)
   (+ y x)
   (if (<= t 1.25e-89)
     (+ x (/ y (/ a z)))
     (if (<= t 3.2e+87) (- x (/ (* y z) t)) (+ y x)))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (t <= -1.55e+77) {
		tmp = y + x;
	} else if (t <= 1.25e-89) {
		tmp = x + (y / (a / z));
	} else if (t <= 3.2e+87) {
		tmp = x - ((y * z) / t);
	} else {
		tmp = y + x;
	}
	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 (t <= (-1.55d+77)) then
        tmp = y + x
    else if (t <= 1.25d-89) then
        tmp = x + (y / (a / z))
    else if (t <= 3.2d+87) then
        tmp = x - ((y * z) / t)
    else
        tmp = y + x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (t <= -1.55e+77) {
		tmp = y + x;
	} else if (t <= 1.25e-89) {
		tmp = x + (y / (a / z));
	} else if (t <= 3.2e+87) {
		tmp = x - ((y * z) / t);
	} else {
		tmp = y + x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if t <= -1.55e+77:
		tmp = y + x
	elif t <= 1.25e-89:
		tmp = x + (y / (a / z))
	elif t <= 3.2e+87:
		tmp = x - ((y * z) / t)
	else:
		tmp = y + x
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (t <= -1.55e+77)
		tmp = Float64(y + x);
	elseif (t <= 1.25e-89)
		tmp = Float64(x + Float64(y / Float64(a / z)));
	elseif (t <= 3.2e+87)
		tmp = Float64(x - Float64(Float64(y * z) / t));
	else
		tmp = Float64(y + x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (t <= -1.55e+77)
		tmp = y + x;
	elseif (t <= 1.25e-89)
		tmp = x + (y / (a / z));
	elseif (t <= 3.2e+87)
		tmp = x - ((y * z) / t);
	else
		tmp = y + x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[t, -1.55e+77], N[(y + x), $MachinePrecision], If[LessEqual[t, 1.25e-89], N[(x + N[(y / N[(a / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 3.2e+87], N[(x - N[(N[(y * z), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision], N[(y + x), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if t < -1.54999999999999999e77 or 3.2e87 < t

   1. Initial program 100.0%

      \[x + y \cdot \frac{z - t}{a - t} \]
   2. Add Preprocessing

   3. Taylor expanded in t around inf 88.3%

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

      1. +-commutative 88.3%

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

   5. Simplified 88.3%

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

   if -1.54999999999999999e77 < t < 1.24999999999999992e-89

   1. Initial program 95.9%

      \[x + y \cdot \frac{z - t}{a - t} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. +-commutative 95.9%

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

      2. clear-num 95.9%

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

      3. un-div-inv 96.0%

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

   4. Applied egg-rr 96.0%

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

   5. Taylor expanded in t around 0 80.4%

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

   if 1.24999999999999992e-89 < t < 3.2e87

   1. Initial program 99.8%

      \[x + y \cdot \frac{z - t}{a - t} \]
   2. Add Preprocessing

   3. Taylor expanded in z around inf 86.8%

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

   4. Taylor expanded in a around 0 79.3%

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

      1. mul-1-neg 79.3%

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

      2. unsub-neg 79.3%

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

   6. Simplified 79.3%

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

4. Final simplification 83.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq -1.55 \cdot 10^{+77}:\\ \;\;\;\;y + x\\ \mathbf{elif}\;t \leq 1.25 \cdot 10^{-89}:\\ \;\;\;\;x + \frac{y}{\frac{a}{z}}\\ \mathbf{elif}\;t \leq 3.2 \cdot 10^{+87}:\\ \;\;\;\;x - \frac{y \cdot z}{t}\\ \mathbf{else}:\\ \;\;\;\;y + x\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 76.0% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -1 \cdot 10^{+77}:\\ \;\;\;\;y + x\\ \mathbf{elif}\;t \leq 1.25 \cdot 10^{-89}:\\ \;\;\;\;x + \frac{y}{\frac{a}{z}}\\ \mathbf{elif}\;t \leq 4 \cdot 10^{+87}:\\ \;\;\;\;x - \frac{y}{\frac{t}{z}}\\ \mathbf{else}:\\ \;\;\;\;y + x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= t -1e+77)
   (+ y x)
   (if (<= t 1.25e-89)
     (+ x (/ y (/ a z)))
     (if (<= t 4e+87) (- x (/ y (/ t z))) (+ y x)))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (t <= -1e+77) {
		tmp = y + x;
	} else if (t <= 1.25e-89) {
		tmp = x + (y / (a / z));
	} else if (t <= 4e+87) {
		tmp = x - (y / (t / z));
	} else {
		tmp = y + x;
	}
	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 (t <= (-1d+77)) then
        tmp = y + x
    else if (t <= 1.25d-89) then
        tmp = x + (y / (a / z))
    else if (t <= 4d+87) then
        tmp = x - (y / (t / z))
    else
        tmp = y + x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (t <= -1e+77) {
		tmp = y + x;
	} else if (t <= 1.25e-89) {
		tmp = x + (y / (a / z));
	} else if (t <= 4e+87) {
		tmp = x - (y / (t / z));
	} else {
		tmp = y + x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if t <= -1e+77:
		tmp = y + x
	elif t <= 1.25e-89:
		tmp = x + (y / (a / z))
	elif t <= 4e+87:
		tmp = x - (y / (t / z))
	else:
		tmp = y + x
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (t <= -1e+77)
		tmp = Float64(y + x);
	elseif (t <= 1.25e-89)
		tmp = Float64(x + Float64(y / Float64(a / z)));
	elseif (t <= 4e+87)
		tmp = Float64(x - Float64(y / Float64(t / z)));
	else
		tmp = Float64(y + x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (t <= -1e+77)
		tmp = y + x;
	elseif (t <= 1.25e-89)
		tmp = x + (y / (a / z));
	elseif (t <= 4e+87)
		tmp = x - (y / (t / z));
	else
		tmp = y + x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[t, -1e+77], N[(y + x), $MachinePrecision], If[LessEqual[t, 1.25e-89], N[(x + N[(y / N[(a / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 4e+87], N[(x - N[(y / N[(t / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(y + x), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if t < -9.99999999999999983e76 or 3.9999999999999998e87 < t

   1. Initial program 100.0%

      \[x + y \cdot \frac{z - t}{a - t} \]
   2. Add Preprocessing

   3. Taylor expanded in t around inf 88.3%

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

      1. +-commutative 88.3%

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

   5. Simplified 88.3%

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

   if -9.99999999999999983e76 < t < 1.24999999999999992e-89

   1. Initial program 95.9%

      \[x + y \cdot \frac{z - t}{a - t} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. +-commutative 95.9%

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

      2. clear-num 95.9%

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

      3. un-div-inv 96.0%

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

   4. Applied egg-rr 96.0%

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

   5. Taylor expanded in t around 0 80.4%

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

   if 1.24999999999999992e-89 < t < 3.9999999999999998e87

   1. Initial program 99.8%

      \[x + y \cdot \frac{z - t}{a - t} \]
   2. Add Preprocessing

   3. Taylor expanded in z around inf 86.8%

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

   4. Taylor expanded in a around 0 79.3%

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

      1. mul-1-neg 79.3%

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

      2. unsub-neg 79.3%

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

   6. Simplified 79.3%

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

      1. associate-/l* 79.1%

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

      2. clear-num 79.2%

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

      3. un-div-inv 79.3%

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

   8. Applied egg-rr 79.3%

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

4. Final simplification 83.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq -1 \cdot 10^{+77}:\\ \;\;\;\;y + x\\ \mathbf{elif}\;t \leq 1.25 \cdot 10^{-89}:\\ \;\;\;\;x + \frac{y}{\frac{a}{z}}\\ \mathbf{elif}\;t \leq 4 \cdot 10^{+87}:\\ \;\;\;\;x - \frac{y}{\frac{t}{z}}\\ \mathbf{else}:\\ \;\;\;\;y + x\\ \end{array} \]
5. Add Preprocessing

Alternative 7: 83.4% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -8 \cdot 10^{+180} \lor \neg \left(t \leq 3.3 \cdot 10^{+89}\right):\\ \;\;\;\;y + x\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot \frac{z}{a - t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= t -8e+180) (not (<= t 3.3e+89)))
   (+ y x)
   (+ x (* y (/ z (- a t))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((t <= -8e+180) || !(t <= 3.3e+89)) {
		tmp = y + x;
	} else {
		tmp = x + (y * (z / (a - 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 ((t <= (-8d+180)) .or. (.not. (t <= 3.3d+89))) then
        tmp = y + x
    else
        tmp = x + (y * (z / (a - 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 ((t <= -8e+180) || !(t <= 3.3e+89)) {
		tmp = y + x;
	} else {
		tmp = x + (y * (z / (a - t)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (t <= -8e+180) or not (t <= 3.3e+89):
		tmp = y + x
	else:
		tmp = x + (y * (z / (a - t)))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((t <= -8e+180) || !(t <= 3.3e+89))
		tmp = Float64(y + x);
	else
		tmp = Float64(x + Float64(y * Float64(z / Float64(a - t))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((t <= -8e+180) || ~((t <= 3.3e+89)))
		tmp = y + x;
	else
		tmp = x + (y * (z / (a - t)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[t, -8e+180], N[Not[LessEqual[t, 3.3e+89]], $MachinePrecision]], N[(y + x), $MachinePrecision], N[(x + N[(y * N[(z / N[(a - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if t < -8.0000000000000001e180 or 3.29999999999999974e89 < t

   1. Initial program 100.0%

      \[x + y \cdot \frac{z - t}{a - t} \]
   2. Add Preprocessing

   3. Taylor expanded in t around inf 90.9%

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

      1. +-commutative 90.9%

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

   5. Simplified 90.9%

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

   if -8.0000000000000001e180 < t < 3.29999999999999974e89

   1. Initial program 97.1%

      \[x + y \cdot \frac{z - t}{a - t} \]
   2. Add Preprocessing

   3. Taylor expanded in z around inf 85.6%

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

      1. associate-/l* 86.7%

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

   5. Simplified 86.7%

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

4. Final simplification 88.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq -8 \cdot 10^{+180} \lor \neg \left(t \leq 3.3 \cdot 10^{+89}\right):\\ \;\;\;\;y + x\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot \frac{z}{a - t}\\ \end{array} \]
5. Add Preprocessing

Alternative 8: 87.3% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -9 \cdot 10^{+61}:\\ \;\;\;\;x + \frac{y}{1 - \frac{a}{t}}\\ \mathbf{elif}\;t \leq 1.25 \cdot 10^{+87}:\\ \;\;\;\;x + \frac{y}{\frac{a - t}{z}}\\ \mathbf{else}:\\ \;\;\;\;x - y \cdot \left(\frac{z}{t} + -1\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= t -9e+61)
   (+ x (/ y (- 1.0 (/ a t))))
   (if (<= t 1.25e+87)
     (+ x (/ y (/ (- a t) z)))
     (- x (* y (+ (/ z t) -1.0))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (t <= -9e+61) {
		tmp = x + (y / (1.0 - (a / t)));
	} else if (t <= 1.25e+87) {
		tmp = x + (y / ((a - t) / z));
	} else {
		tmp = x - (y * ((z / t) + -1.0));
	}
	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 (t <= (-9d+61)) then
        tmp = x + (y / (1.0d0 - (a / t)))
    else if (t <= 1.25d+87) then
        tmp = x + (y / ((a - t) / z))
    else
        tmp = x - (y * ((z / t) + (-1.0d0)))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (t <= -9e+61) {
		tmp = x + (y / (1.0 - (a / t)));
	} else if (t <= 1.25e+87) {
		tmp = x + (y / ((a - t) / z));
	} else {
		tmp = x - (y * ((z / t) + -1.0));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if t <= -9e+61:
		tmp = x + (y / (1.0 - (a / t)))
	elif t <= 1.25e+87:
		tmp = x + (y / ((a - t) / z))
	else:
		tmp = x - (y * ((z / t) + -1.0))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (t <= -9e+61)
		tmp = Float64(x + Float64(y / Float64(1.0 - Float64(a / t))));
	elseif (t <= 1.25e+87)
		tmp = Float64(x + Float64(y / Float64(Float64(a - t) / z)));
	else
		tmp = Float64(x - Float64(y * Float64(Float64(z / t) + -1.0)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (t <= -9e+61)
		tmp = x + (y / (1.0 - (a / t)));
	elseif (t <= 1.25e+87)
		tmp = x + (y / ((a - t) / z));
	else
		tmp = x - (y * ((z / t) + -1.0));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[t, -9e+61], N[(x + N[(y / N[(1.0 - N[(a / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 1.25e+87], N[(x + N[(y / N[(N[(a - t), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x - N[(y * N[(N[(z / t), $MachinePrecision] + -1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if t < -9e61

   1. Initial program 100.0%

      \[x + y \cdot \frac{z - t}{a - t} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. +-commutative 100.0%

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

      2. clear-num 99.9%

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

      3. un-div-inv 100.0%

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

   4. Applied egg-rr 100.0%

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

   5. Taylor expanded in z around 0 88.4%

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

      1. mul-1-neg 88.4%

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

      2. div-sub 88.4%

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

      3. sub-neg 88.4%

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

      4. *-inverses 88.4%

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

      5. metadata-eval 88.4%

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

   7. Simplified 88.4%

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

   8. Taylor expanded in y around 0 88.4%

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

   if -9e61 < t < 1.24999999999999995e87

   1. Initial program 96.8%

      \[x + y \cdot \frac{z - t}{a - t} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. +-commutative 96.8%

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

      2. clear-num 96.8%

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

      3. un-div-inv 96.9%

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

   4. Applied egg-rr 96.9%

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

   5. Taylor expanded in z around inf 89.0%

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

   if 1.24999999999999995e87 < t

   1. Initial program 100.0%

      \[x + y \cdot \frac{z - t}{a - t} \]
   2. Add Preprocessing

   3. Taylor expanded in a around 0 74.3%

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

      1. mul-1-neg 74.3%

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

      2. unsub-neg 74.3%

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

      3. associate-/l* 100.0%

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

      4. div-sub 100.0%

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

      5. sub-neg 100.0%

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

      6. *-inverses 100.0%

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

      7. metadata-eval 100.0%

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

   5. Simplified 100.0%

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

4. Final simplification 91.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq -9 \cdot 10^{+61}:\\ \;\;\;\;x + \frac{y}{1 - \frac{a}{t}}\\ \mathbf{elif}\;t \leq 1.25 \cdot 10^{+87}:\\ \;\;\;\;x + \frac{y}{\frac{a - t}{z}}\\ \mathbf{else}:\\ \;\;\;\;x - y \cdot \left(\frac{z}{t} + -1\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 9: 85.7% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -1.55 \cdot 10^{+63}:\\ \;\;\;\;x + \frac{y}{1 - \frac{a}{t}}\\ \mathbf{elif}\;t \leq 1.04 \cdot 10^{+86}:\\ \;\;\;\;x + \frac{y}{\frac{a - t}{z}}\\ \mathbf{else}:\\ \;\;\;\;y + x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= t -1.55e+63)
   (+ x (/ y (- 1.0 (/ a t))))
   (if (<= t 1.04e+86) (+ x (/ y (/ (- a t) z))) (+ y x))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (t <= -1.55e+63) {
		tmp = x + (y / (1.0 - (a / t)));
	} else if (t <= 1.04e+86) {
		tmp = x + (y / ((a - t) / z));
	} else {
		tmp = y + x;
	}
	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 (t <= (-1.55d+63)) then
        tmp = x + (y / (1.0d0 - (a / t)))
    else if (t <= 1.04d+86) then
        tmp = x + (y / ((a - t) / z))
    else
        tmp = y + x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (t <= -1.55e+63) {
		tmp = x + (y / (1.0 - (a / t)));
	} else if (t <= 1.04e+86) {
		tmp = x + (y / ((a - t) / z));
	} else {
		tmp = y + x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if t <= -1.55e+63:
		tmp = x + (y / (1.0 - (a / t)))
	elif t <= 1.04e+86:
		tmp = x + (y / ((a - t) / z))
	else:
		tmp = y + x
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (t <= -1.55e+63)
		tmp = Float64(x + Float64(y / Float64(1.0 - Float64(a / t))));
	elseif (t <= 1.04e+86)
		tmp = Float64(x + Float64(y / Float64(Float64(a - t) / z)));
	else
		tmp = Float64(y + x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (t <= -1.55e+63)
		tmp = x + (y / (1.0 - (a / t)));
	elseif (t <= 1.04e+86)
		tmp = x + (y / ((a - t) / z));
	else
		tmp = y + x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[t, -1.55e+63], N[(x + N[(y / N[(1.0 - N[(a / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 1.04e+86], N[(x + N[(y / N[(N[(a - t), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(y + x), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if t < -1.55e63

   1. Initial program 100.0%

      \[x + y \cdot \frac{z - t}{a - t} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. +-commutative 100.0%

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

      2. clear-num 99.9%

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

      3. un-div-inv 100.0%

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

   4. Applied egg-rr 100.0%

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

   5. Taylor expanded in z around 0 88.4%

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

      1. mul-1-neg 88.4%

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

      2. div-sub 88.4%

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

      3. sub-neg 88.4%

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

      4. *-inverses 88.4%

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

      5. metadata-eval 88.4%

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

   7. Simplified 88.4%

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

   8. Taylor expanded in y around 0 88.4%

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

   if -1.55e63 < t < 1.04000000000000004e86

   1. Initial program 96.8%

      \[x + y \cdot \frac{z - t}{a - t} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. +-commutative 96.8%

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

      2. clear-num 96.8%

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

      3. un-div-inv 96.9%

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

   4. Applied egg-rr 96.9%

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

   5. Taylor expanded in z around inf 89.0%

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

   if 1.04000000000000004e86 < t

   1. Initial program 100.0%

      \[x + y \cdot \frac{z - t}{a - t} \]
   2. Add Preprocessing

   3. Taylor expanded in t around inf 92.8%

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

      1. +-commutative 92.8%

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

   5. Simplified 92.8%

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

4. Final simplification 89.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq -1.55 \cdot 10^{+63}:\\ \;\;\;\;x + \frac{y}{1 - \frac{a}{t}}\\ \mathbf{elif}\;t \leq 1.04 \cdot 10^{+86}:\\ \;\;\;\;x + \frac{y}{\frac{a - t}{z}}\\ \mathbf{else}:\\ \;\;\;\;y + x\\ \end{array} \]
5. Add Preprocessing

Alternative 10: 84.3% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -2.5 \cdot 10^{+62}:\\ \;\;\;\;x + \frac{y}{1 - \frac{a}{t}}\\ \mathbf{elif}\;t \leq 1.65 \cdot 10^{+88}:\\ \;\;\;\;x + \frac{y \cdot z}{a - t}\\ \mathbf{else}:\\ \;\;\;\;y + x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= t -2.5e+62)
   (+ x (/ y (- 1.0 (/ a t))))
   (if (<= t 1.65e+88) (+ x (/ (* y z) (- a t))) (+ y x))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (t <= -2.5e+62) {
		tmp = x + (y / (1.0 - (a / t)));
	} else if (t <= 1.65e+88) {
		tmp = x + ((y * z) / (a - t));
	} else {
		tmp = y + x;
	}
	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 (t <= (-2.5d+62)) then
        tmp = x + (y / (1.0d0 - (a / t)))
    else if (t <= 1.65d+88) then
        tmp = x + ((y * z) / (a - t))
    else
        tmp = y + x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (t <= -2.5e+62) {
		tmp = x + (y / (1.0 - (a / t)));
	} else if (t <= 1.65e+88) {
		tmp = x + ((y * z) / (a - t));
	} else {
		tmp = y + x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if t <= -2.5e+62:
		tmp = x + (y / (1.0 - (a / t)))
	elif t <= 1.65e+88:
		tmp = x + ((y * z) / (a - t))
	else:
		tmp = y + x
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (t <= -2.5e+62)
		tmp = Float64(x + Float64(y / Float64(1.0 - Float64(a / t))));
	elseif (t <= 1.65e+88)
		tmp = Float64(x + Float64(Float64(y * z) / Float64(a - t)));
	else
		tmp = Float64(y + x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (t <= -2.5e+62)
		tmp = x + (y / (1.0 - (a / t)));
	elseif (t <= 1.65e+88)
		tmp = x + ((y * z) / (a - t));
	else
		tmp = y + x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[t, -2.5e+62], N[(x + N[(y / N[(1.0 - N[(a / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 1.65e+88], N[(x + N[(N[(y * z), $MachinePrecision] / N[(a - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(y + x), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if t < -2.50000000000000014e62

   1. Initial program 100.0%

      \[x + y \cdot \frac{z - t}{a - t} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. +-commutative 100.0%

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

      2. clear-num 99.9%

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

      3. un-div-inv 100.0%

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

   4. Applied egg-rr 100.0%

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

   5. Taylor expanded in z around 0 88.4%

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

      1. mul-1-neg 88.4%

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

      2. div-sub 88.4%

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

      3. sub-neg 88.4%

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

      4. *-inverses 88.4%

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

      5. metadata-eval 88.4%

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

   7. Simplified 88.4%

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

   8. Taylor expanded in y around 0 88.4%

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

   if -2.50000000000000014e62 < t < 1.6500000000000002e88

   1. Initial program 96.8%

      \[x + y \cdot \frac{z - t}{a - t} \]
   2. Add Preprocessing

   3. Taylor expanded in z around inf 88.9%

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

   if 1.6500000000000002e88 < t

   1. Initial program 100.0%

      \[x + y \cdot \frac{z - t}{a - t} \]
   2. Add Preprocessing

   3. Taylor expanded in t around inf 92.8%

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

      1. +-commutative 92.8%

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

   5. Simplified 92.8%

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

4. Final simplification 89.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq -2.5 \cdot 10^{+62}:\\ \;\;\;\;x + \frac{y}{1 - \frac{a}{t}}\\ \mathbf{elif}\;t \leq 1.65 \cdot 10^{+88}:\\ \;\;\;\;x + \frac{y \cdot z}{a - t}\\ \mathbf{else}:\\ \;\;\;\;y + x\\ \end{array} \]
5. Add Preprocessing

Alternative 11: 62.1% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -7.4 \cdot 10^{+84} \lor \neg \left(y \leq 3.4 \cdot 10^{+169}\right):\\ \;\;\;\;y \cdot \left(1 - \frac{z}{t}\right)\\ \mathbf{else}:\\ \;\;\;\;y + x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= y -7.4e+84) (not (<= y 3.4e+169))) (* y (- 1.0 (/ z t))) (+ y x)))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((y <= -7.4e+84) || !(y <= 3.4e+169)) {
		tmp = y * (1.0 - (z / t));
	} else {
		tmp = y + x;
	}
	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 <= (-7.4d+84)) .or. (.not. (y <= 3.4d+169))) then
        tmp = y * (1.0d0 - (z / t))
    else
        tmp = y + x
    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 <= -7.4e+84) || !(y <= 3.4e+169)) {
		tmp = y * (1.0 - (z / t));
	} else {
		tmp = y + x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (y <= -7.4e+84) or not (y <= 3.4e+169):
		tmp = y * (1.0 - (z / t))
	else:
		tmp = y + x
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((y <= -7.4e+84) || !(y <= 3.4e+169))
		tmp = Float64(y * Float64(1.0 - Float64(z / t)));
	else
		tmp = Float64(y + x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((y <= -7.4e+84) || ~((y <= 3.4e+169)))
		tmp = y * (1.0 - (z / t));
	else
		tmp = y + x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[y, -7.4e+84], N[Not[LessEqual[y, 3.4e+169]], $MachinePrecision]], N[(y * N[(1.0 - N[(z / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(y + x), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -7.4e84 or 3.40000000000000028e169 < y

   1. Initial program 98.6%

      \[x + y \cdot \frac{z - t}{a - t} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. +-commutative 98.6%

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

      2. clear-num 98.6%

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

      3. un-div-inv 98.7%

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

   4. Applied egg-rr 98.7%

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

   5. Taylor expanded in a around 0 39.4%

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

      1. mul-1-neg 39.4%

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

      2. unsub-neg 39.4%

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

      3. associate-/l* 65.8%

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

      4. div-sub 65.8%

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

      5. *-inverses 65.8%

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

   7. Simplified 65.8%

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

   8. Taylor expanded in x around 0 60.1%

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

      1. mul-1-neg 60.1%

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

      2. sub-neg 60.1%

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

      3. metadata-eval 60.1%

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

      4. distribute-rgt-neg-in 60.1%

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

      5. +-commutative 60.1%

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

      6. distribute-neg-in 60.1%

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

      7. metadata-eval 60.1%

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

      8. sub-neg 60.1%

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

   10. Simplified 60.1%

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

   if -7.4e84 < y < 3.40000000000000028e169

   1. Initial program 97.8%

      \[x + y \cdot \frac{z - t}{a - t} \]
   2. Add Preprocessing

   3. Taylor expanded in t around inf 67.0%

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

      1. +-commutative 67.0%

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

   5. Simplified 67.0%

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

4. Final simplification 64.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -7.4 \cdot 10^{+84} \lor \neg \left(y \leq 3.4 \cdot 10^{+169}\right):\\ \;\;\;\;y \cdot \left(1 - \frac{z}{t}\right)\\ \mathbf{else}:\\ \;\;\;\;y + x\\ \end{array} \]
5. Add Preprocessing

Alternative 12: 60.4% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -4.2 \cdot 10^{+220} \lor \neg \left(z \leq 8.6 \cdot 10^{+253}\right):\\ \;\;\;\;z \cdot \frac{-y}{t}\\ \mathbf{else}:\\ \;\;\;\;y + x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= z -4.2e+220) (not (<= z 8.6e+253))) (* z (/ (- y) t)) (+ y x)))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((z <= -4.2e+220) || !(z <= 8.6e+253)) {
		tmp = z * (-y / t);
	} else {
		tmp = y + x;
	}
	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+220)) .or. (.not. (z <= 8.6d+253))) then
        tmp = z * (-y / t)
    else
        tmp = y + x
    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+220) || !(z <= 8.6e+253)) {
		tmp = z * (-y / t);
	} else {
		tmp = y + x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (z <= -4.2e+220) or not (z <= 8.6e+253):
		tmp = z * (-y / t)
	else:
		tmp = y + x
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((z <= -4.2e+220) || !(z <= 8.6e+253))
		tmp = Float64(z * Float64(Float64(-y) / t));
	else
		tmp = Float64(y + x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((z <= -4.2e+220) || ~((z <= 8.6e+253)))
		tmp = z * (-y / t);
	else
		tmp = y + x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[z, -4.2e+220], N[Not[LessEqual[z, 8.6e+253]], $MachinePrecision]], N[(z * N[((-y) / t), $MachinePrecision]), $MachinePrecision], N[(y + x), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if z < -4.20000000000000014e220 or 8.5999999999999997e253 < z

   1. Initial program 91.7%

      \[x + y \cdot \frac{z - t}{a - t} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. +-commutative 91.7%

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

      2. clear-num 91.7%

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

      3. un-div-inv 91.8%

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

   4. Applied egg-rr 91.8%

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

   5. Taylor expanded in a around 0 65.9%

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

      1. mul-1-neg 65.9%

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

      2. unsub-neg 65.9%

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

      3. associate-/l* 62.8%

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

      4. div-sub 62.8%

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

      5. *-inverses 62.8%

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

   7. Simplified 62.8%

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

   8. Taylor expanded in x around 0 54.4%

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

      1. mul-1-neg 54.4%

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

      2. sub-neg 54.4%

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

      3. metadata-eval 54.4%

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

      4. distribute-rgt-neg-in 54.4%

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

      5. +-commutative 54.4%

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

      6. distribute-neg-in 54.4%

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

      7. metadata-eval 54.4%

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

      8. sub-neg 54.4%

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

   10. Simplified 54.4%

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

   11. Taylor expanded in z around inf 54.6%

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

       1. mul-1-neg 54.6%

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

       2. associate-*l/ 56.8%

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

       3. distribute-lft-neg-in 56.8%

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

       4. distribute-neg-frac2 56.8%

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

   13. Simplified 56.8%

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

   if -4.20000000000000014e220 < z < 8.5999999999999997e253

   1. Initial program 99.0%

      \[x + y \cdot \frac{z - t}{a - t} \]
   2. Add Preprocessing

   3. Taylor expanded in t around inf 65.5%

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

      1. +-commutative 65.5%

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

   5. Simplified 65.5%

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

4. Final simplification 64.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -4.2 \cdot 10^{+220} \lor \neg \left(z \leq 8.6 \cdot 10^{+253}\right):\\ \;\;\;\;z \cdot \frac{-y}{t}\\ \mathbf{else}:\\ \;\;\;\;y + x\\ \end{array} \]
5. Add Preprocessing

Alternative 13: 62.5% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -2.9 \cdot 10^{-126} \lor \neg \left(t \leq 7.8 \cdot 10^{-161}\right):\\ \;\;\;\;y + x\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= t -2.9e-126) (not (<= t 7.8e-161))) (+ y x) x))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((t <= -2.9e-126) || !(t <= 7.8e-161)) {
		tmp = y + x;
	} else {
		tmp = x;
	}
	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 ((t <= (-2.9d-126)) .or. (.not. (t <= 7.8d-161))) then
        tmp = y + x
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((t <= -2.9e-126) || !(t <= 7.8e-161)) {
		tmp = y + x;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (t <= -2.9e-126) or not (t <= 7.8e-161):
		tmp = y + x
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((t <= -2.9e-126) || !(t <= 7.8e-161))
		tmp = Float64(y + x);
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((t <= -2.9e-126) || ~((t <= 7.8e-161)))
		tmp = y + x;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[t, -2.9e-126], N[Not[LessEqual[t, 7.8e-161]], $MachinePrecision]], N[(y + x), $MachinePrecision], x]

Derivation

1. Split input into 2 regimes

2. if t < -2.89999999999999988e-126 or 7.79999999999999947e-161 < t

   1. Initial program 98.9%

      \[x + y \cdot \frac{z - t}{a - t} \]
   2. Add Preprocessing

   3. Taylor expanded in t around inf 68.8%

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

      1. +-commutative 68.8%

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

   5. Simplified 68.8%

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

   if -2.89999999999999988e-126 < t < 7.79999999999999947e-161

   1. Initial program 95.7%

      \[x + y \cdot \frac{z - t}{a - t} \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf 46.3%

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

4. Final simplification 62.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq -2.9 \cdot 10^{-126} \lor \neg \left(t \leq 7.8 \cdot 10^{-161}\right):\\ \;\;\;\;y + x\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]
5. Add Preprocessing

Alternative 14: 98.3% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 98.0%

   \[x + y \cdot \frac{z - t}{a - t} \]
2. Add Preprocessing
3. Add Preprocessing

Alternative 15: 49.9% 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 98.0%

   \[x + y \cdot \frac{z - t}{a - t} \]
2. Add Preprocessing

3. Taylor expanded in x around inf 48.0%

   \[\leadsto \color{blue}{x} \]
4. Add Preprocessing

Developer target: 99.4% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + y \cdot \frac{z - t}{a - t}\\ \mathbf{if}\;y < -8.508084860551241 \cdot 10^{-17}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;y < 2.894426862792089 \cdot 10^{-49}:\\ \;\;\;\;x + \left(y \cdot \left(z - t\right)\right) \cdot \frac{1}{a - t}\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ x (* y (/ (- z t) (- a t))))))
   (if (< y -8.508084860551241e-17)
     t_1
     (if (< y 2.894426862792089e-49)
       (+ x (* (* y (- z t)) (/ 1.0 (- a t))))
       t_1))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x + (y * ((z - t) / (a - t)));
	double tmp;
	if (y < -8.508084860551241e-17) {
		tmp = t_1;
	} else if (y < 2.894426862792089e-49) {
		tmp = x + ((y * (z - t)) * (1.0 / (a - t)));
	} 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 - t) / (a - t)))
    if (y < (-8.508084860551241d-17)) then
        tmp = t_1
    else if (y < 2.894426862792089d-49) then
        tmp = x + ((y * (z - t)) * (1.0d0 / (a - t)))
    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 - t) / (a - t)));
	double tmp;
	if (y < -8.508084860551241e-17) {
		tmp = t_1;
	} else if (y < 2.894426862792089e-49) {
		tmp = x + ((y * (z - t)) * (1.0 / (a - t)));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x + (y * ((z - t) / (a - t)))
	tmp = 0
	if y < -8.508084860551241e-17:
		tmp = t_1
	elif y < 2.894426862792089e-49:
		tmp = x + ((y * (z - t)) * (1.0 / (a - t)))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x + Float64(y * Float64(Float64(z - t) / Float64(a - t))))
	tmp = 0.0
	if (y < -8.508084860551241e-17)
		tmp = t_1;
	elseif (y < 2.894426862792089e-49)
		tmp = Float64(x + Float64(Float64(y * Float64(z - t)) * Float64(1.0 / Float64(a - t))));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x + (y * ((z - t) / (a - t)));
	tmp = 0.0;
	if (y < -8.508084860551241e-17)
		tmp = t_1;
	elseif (y < 2.894426862792089e-49)
		tmp = x + ((y * (z - t)) * (1.0 / (a - t)));
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x + N[(y * N[(N[(z - t), $MachinePrecision] / N[(a - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[Less[y, -8.508084860551241e-17], t$95$1, If[Less[y, 2.894426862792089e-49], N[(x + N[(N[(y * N[(z - t), $MachinePrecision]), $MachinePrecision] * N[(1.0 / N[(a - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]

Reproduce

herbie shell --seed 2024095 
(FPCore (x y z t a)
  :name "Graphics.Rendering.Plot.Render.Plot.Axis:renderAxisLine from plot-0.2.3.4, B"
  :precision binary64

  :alt
  (if (< y -8.508084860551241e-17) (+ x (* y (/ (- z t) (- a t)))) (if (< y 2.894426862792089e-49) (+ x (* (* y (- z t)) (/ 1.0 (- a t)))) (+ x (* y (/ (- z t) (- a t))))))

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