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

Percentage Accurate: 98.2% → 98.7%

Time: 10.9s

Alternatives: 13

Speedup: 1.0×

Specification

Math

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

FPCore

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

C

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

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) / (z - a)))
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 98.2% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

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) / (z - a)))
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 98.7% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{z - t}{z - a}\\ \mathbf{if}\;t_1 \leq 5 \cdot 10^{+88}:\\ \;\;\;\;x + t_1 \cdot y\\ \mathbf{else}:\\ \;\;\;\;x + \left(z - t\right) \cdot \frac{y}{z - a}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (/ (- z t) (- z a))))
   (if (<= t_1 5e+88) (+ x (* t_1 y)) (+ x (* (- z t) (/ y (- z a)))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = (z - t) / (z - a);
	double tmp;
	if (t_1 <= 5e+88) {
		tmp = x + (t_1 * y);
	} else {
		tmp = x + ((z - t) * (y / (z - a)));
	}
	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 = (z - t) / (z - a)
    if (t_1 <= 5d+88) then
        tmp = x + (t_1 * y)
    else
        tmp = x + ((z - t) * (y / (z - a)))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = (z - t) / (z - a);
	double tmp;
	if (t_1 <= 5e+88) {
		tmp = x + (t_1 * y);
	} else {
		tmp = x + ((z - t) * (y / (z - a)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = (z - t) / (z - a)
	tmp = 0
	if t_1 <= 5e+88:
		tmp = x + (t_1 * y)
	else:
		tmp = x + ((z - t) * (y / (z - a)))
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(Float64(z - t) / Float64(z - a))
	tmp = 0.0
	if (t_1 <= 5e+88)
		tmp = Float64(x + Float64(t_1 * y));
	else
		tmp = Float64(x + Float64(Float64(z - t) * Float64(y / Float64(z - a))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = (z - t) / (z - a);
	tmp = 0.0;
	if (t_1 <= 5e+88)
		tmp = x + (t_1 * y);
	else
		tmp = x + ((z - t) * (y / (z - a)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(N[(z - t), $MachinePrecision] / N[(z - a), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, 5e+88], N[(x + N[(t$95$1 * y), $MachinePrecision]), $MachinePrecision], N[(x + N[(N[(z - t), $MachinePrecision] * N[(y / N[(z - a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (-.f64 z t) (-.f64 z a)) < 4.99999999999999997e88

   1. Initial program 99.1%

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

   if 4.99999999999999997e88 < (/.f64 (-.f64 z t) (-.f64 z a))

   1. Initial program 86.5%

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

   2. Taylor expanded in y around 0 99.9%

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

      1. associate-*l/ 99.9%

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

   4. Simplified 99.9%

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

4. Final simplification 99.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{z - t}{z - a} \leq 5 \cdot 10^{+88}:\\ \;\;\;\;x + \frac{z - t}{z - a} \cdot y\\ \mathbf{else}:\\ \;\;\;\;x + \left(z - t\right) \cdot \frac{y}{z - a}\\ \end{array} \]

Alternative 2: 81.1% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + y \cdot \frac{z}{z - a}\\ \mathbf{if}\;z \leq -2.7 \cdot 10^{-87}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 4.1 \cdot 10^{-123}:\\ \;\;\;\;x + t \cdot \frac{y}{a}\\ \mathbf{elif}\;z \leq 9 \cdot 10^{-49} \lor \neg \left(z \leq 10^{+113}\right):\\ \;\;\;\;x + y \cdot \left(1 - \frac{t}{z}\right)\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ x (* y (/ z (- z a))))))
   (if (<= z -2.7e-87)
     t_1
     (if (<= z 4.1e-123)
       (+ x (* t (/ y a)))
       (if (or (<= z 9e-49) (not (<= z 1e+113)))
         (+ x (* y (- 1.0 (/ t z))))
         t_1)))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x + (y * (z / (z - a)));
	double tmp;
	if (z <= -2.7e-87) {
		tmp = t_1;
	} else if (z <= 4.1e-123) {
		tmp = x + (t * (y / a));
	} else if ((z <= 9e-49) || !(z <= 1e+113)) {
		tmp = x + (y * (1.0 - (t / 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 / (z - a)))
    if (z <= (-2.7d-87)) then
        tmp = t_1
    else if (z <= 4.1d-123) then
        tmp = x + (t * (y / a))
    else if ((z <= 9d-49) .or. (.not. (z <= 1d+113))) then
        tmp = x + (y * (1.0d0 - (t / z)))
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = x + (y * (z / (z - a)));
	double tmp;
	if (z <= -2.7e-87) {
		tmp = t_1;
	} else if (z <= 4.1e-123) {
		tmp = x + (t * (y / a));
	} else if ((z <= 9e-49) || !(z <= 1e+113)) {
		tmp = x + (y * (1.0 - (t / z)));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x + (y * (z / (z - a)))
	tmp = 0
	if z <= -2.7e-87:
		tmp = t_1
	elif z <= 4.1e-123:
		tmp = x + (t * (y / a))
	elif (z <= 9e-49) or not (z <= 1e+113):
		tmp = x + (y * (1.0 - (t / z)))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x + Float64(y * Float64(z / Float64(z - a))))
	tmp = 0.0
	if (z <= -2.7e-87)
		tmp = t_1;
	elseif (z <= 4.1e-123)
		tmp = Float64(x + Float64(t * Float64(y / a)));
	elseif ((z <= 9e-49) || !(z <= 1e+113))
		tmp = Float64(x + Float64(y * Float64(1.0 - Float64(t / z))));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x + (y * (z / (z - a)));
	tmp = 0.0;
	if (z <= -2.7e-87)
		tmp = t_1;
	elseif (z <= 4.1e-123)
		tmp = x + (t * (y / a));
	elseif ((z <= 9e-49) || ~((z <= 1e+113)))
		tmp = x + (y * (1.0 - (t / z)));
	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[(z / N[(z - a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -2.7e-87], t$95$1, If[LessEqual[z, 4.1e-123], N[(x + N[(t * N[(y / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[Or[LessEqual[z, 9e-49], N[Not[LessEqual[z, 1e+113]], $MachinePrecision]], N[(x + N[(y * N[(1.0 - N[(t / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

1. Split input into 3 regimes

2. if z < -2.69999999999999984e-87 or 9.0000000000000004e-49 < z < 1e113

   1. Initial program 98.4%

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

   2. Taylor expanded in t around 0 83.7%

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

   if -2.69999999999999984e-87 < z < 4.1e-123

   1. Initial program 95.2%

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

   2. Taylor expanded in z around 0 89.0%

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

      1. associate-/l* 90.1%

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

   4. Simplified 90.1%

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

      1. associate-/r/ 90.5%

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

   6. Applied egg-rr 90.5%

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

   if 4.1e-123 < z < 9.0000000000000004e-49 or 1e113 < z

   1. Initial program 98.4%

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

   2. Taylor expanded in a around 0 93.8%

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

      1. div-sub 93.8%

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

      2. *-inverses 93.8%

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

   4. Simplified 93.8%

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

4. Final simplification 88.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -2.7 \cdot 10^{-87}:\\ \;\;\;\;x + y \cdot \frac{z}{z - a}\\ \mathbf{elif}\;z \leq 4.1 \cdot 10^{-123}:\\ \;\;\;\;x + t \cdot \frac{y}{a}\\ \mathbf{elif}\;z \leq 9 \cdot 10^{-49} \lor \neg \left(z \leq 10^{+113}\right):\\ \;\;\;\;x + y \cdot \left(1 - \frac{t}{z}\right)\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot \frac{z}{z - a}\\ \end{array} \]

Alternative 3: 81.1% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -2.8 \cdot 10^{-87}:\\ \;\;\;\;x + y \cdot \frac{z}{z - a}\\ \mathbf{elif}\;z \leq 2 \cdot 10^{-124}:\\ \;\;\;\;x + t \cdot \frac{y}{a}\\ \mathbf{elif}\;z \leq 2.05 \cdot 10^{-68} \lor \neg \left(z \leq 1.04 \cdot 10^{+111}\right):\\ \;\;\;\;x + y \cdot \left(1 - \frac{t}{z}\right)\\ \mathbf{else}:\\ \;\;\;\;x + z \cdot \frac{y}{z - a}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= z -2.8e-87)
   (+ x (* y (/ z (- z a))))
   (if (<= z 2e-124)
     (+ x (* t (/ y a)))
     (if (or (<= z 2.05e-68) (not (<= z 1.04e+111)))
       (+ x (* y (- 1.0 (/ t z))))
       (+ x (* z (/ y (- z a))))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -2.8e-87) {
		tmp = x + (y * (z / (z - a)));
	} else if (z <= 2e-124) {
		tmp = x + (t * (y / a));
	} else if ((z <= 2.05e-68) || !(z <= 1.04e+111)) {
		tmp = x + (y * (1.0 - (t / z)));
	} else {
		tmp = x + (z * (y / (z - a)));
	}
	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 <= (-2.8d-87)) then
        tmp = x + (y * (z / (z - a)))
    else if (z <= 2d-124) then
        tmp = x + (t * (y / a))
    else if ((z <= 2.05d-68) .or. (.not. (z <= 1.04d+111))) then
        tmp = x + (y * (1.0d0 - (t / z)))
    else
        tmp = x + (z * (y / (z - a)))
    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 <= -2.8e-87) {
		tmp = x + (y * (z / (z - a)));
	} else if (z <= 2e-124) {
		tmp = x + (t * (y / a));
	} else if ((z <= 2.05e-68) || !(z <= 1.04e+111)) {
		tmp = x + (y * (1.0 - (t / z)));
	} else {
		tmp = x + (z * (y / (z - a)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if z <= -2.8e-87:
		tmp = x + (y * (z / (z - a)))
	elif z <= 2e-124:
		tmp = x + (t * (y / a))
	elif (z <= 2.05e-68) or not (z <= 1.04e+111):
		tmp = x + (y * (1.0 - (t / z)))
	else:
		tmp = x + (z * (y / (z - a)))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (z <= -2.8e-87)
		tmp = Float64(x + Float64(y * Float64(z / Float64(z - a))));
	elseif (z <= 2e-124)
		tmp = Float64(x + Float64(t * Float64(y / a)));
	elseif ((z <= 2.05e-68) || !(z <= 1.04e+111))
		tmp = Float64(x + Float64(y * Float64(1.0 - Float64(t / z))));
	else
		tmp = Float64(x + Float64(z * Float64(y / Float64(z - a))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (z <= -2.8e-87)
		tmp = x + (y * (z / (z - a)));
	elseif (z <= 2e-124)
		tmp = x + (t * (y / a));
	elseif ((z <= 2.05e-68) || ~((z <= 1.04e+111)))
		tmp = x + (y * (1.0 - (t / z)));
	else
		tmp = x + (z * (y / (z - a)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[z, -2.8e-87], N[(x + N[(y * N[(z / N[(z - a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 2e-124], N[(x + N[(t * N[(y / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[Or[LessEqual[z, 2.05e-68], N[Not[LessEqual[z, 1.04e+111]], $MachinePrecision]], N[(x + N[(y * N[(1.0 - N[(t / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(z * N[(y / N[(z - a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 4 regimes

2. if z < -2.8000000000000001e-87

   1. Initial program 98.8%

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

   2. Taylor expanded in t around 0 84.2%

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

   if -2.8000000000000001e-87 < z < 1.99999999999999987e-124

   1. Initial program 95.2%

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

   2. Taylor expanded in z around 0 89.0%

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

      1. associate-/l* 90.1%

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

   4. Simplified 90.1%

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

      1. associate-/r/ 90.5%

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

   6. Applied egg-rr 90.5%

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

   if 1.99999999999999987e-124 < z < 2.05000000000000011e-68 or 1.04e111 < z

   1. Initial program 98.3%

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

   2. Taylor expanded in a around 0 93.4%

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

      1. div-sub 93.4%

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

      2. *-inverses 93.4%

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

   4. Simplified 93.4%

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

   if 2.05000000000000011e-68 < z < 1.04e111

   1. Initial program 97.6%

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

   2. Taylor expanded in t around 0 76.7%

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

      1. associate-*l/ 84.4%

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

      2. *-commutative 84.4%

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

   4. Simplified 84.4%

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

4. Final simplification 88.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -2.8 \cdot 10^{-87}:\\ \;\;\;\;x + y \cdot \frac{z}{z - a}\\ \mathbf{elif}\;z \leq 2 \cdot 10^{-124}:\\ \;\;\;\;x + t \cdot \frac{y}{a}\\ \mathbf{elif}\;z \leq 2.05 \cdot 10^{-68} \lor \neg \left(z \leq 1.04 \cdot 10^{+111}\right):\\ \;\;\;\;x + y \cdot \left(1 - \frac{t}{z}\right)\\ \mathbf{else}:\\ \;\;\;\;x + z \cdot \frac{y}{z - a}\\ \end{array} \]

Alternative 4: 76.3% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -1.7 \cdot 10^{+46}:\\ \;\;\;\;x + y\\ \mathbf{elif}\;z \leq 3.95 \cdot 10^{-128}:\\ \;\;\;\;x + y \cdot \frac{t}{a}\\ \mathbf{elif}\;z \leq 2.9 \cdot 10^{+133}:\\ \;\;\;\;x - t \cdot \frac{y}{z}\\ \mathbf{else}:\\ \;\;\;\;x + y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= z -1.7e+46)
   (+ x y)
   (if (<= z 3.95e-128)
     (+ x (* y (/ t a)))
     (if (<= z 2.9e+133) (- x (* t (/ y z))) (+ x y)))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -1.7e+46) {
		tmp = x + y;
	} else if (z <= 3.95e-128) {
		tmp = x + (y * (t / a));
	} else if (z <= 2.9e+133) {
		tmp = x - (t * (y / z));
	} else {
		tmp = x + y;
	}
	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 <= (-1.7d+46)) then
        tmp = x + y
    else if (z <= 3.95d-128) then
        tmp = x + (y * (t / a))
    else if (z <= 2.9d+133) then
        tmp = x - (t * (y / z))
    else
        tmp = x + y
    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 <= -1.7e+46) {
		tmp = x + y;
	} else if (z <= 3.95e-128) {
		tmp = x + (y * (t / a));
	} else if (z <= 2.9e+133) {
		tmp = x - (t * (y / z));
	} else {
		tmp = x + y;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if z <= -1.7e+46:
		tmp = x + y
	elif z <= 3.95e-128:
		tmp = x + (y * (t / a))
	elif z <= 2.9e+133:
		tmp = x - (t * (y / z))
	else:
		tmp = x + y
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (z <= -1.7e+46)
		tmp = Float64(x + y);
	elseif (z <= 3.95e-128)
		tmp = Float64(x + Float64(y * Float64(t / a)));
	elseif (z <= 2.9e+133)
		tmp = Float64(x - Float64(t * Float64(y / z)));
	else
		tmp = Float64(x + y);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (z <= -1.7e+46)
		tmp = x + y;
	elseif (z <= 3.95e-128)
		tmp = x + (y * (t / a));
	elseif (z <= 2.9e+133)
		tmp = x - (t * (y / z));
	else
		tmp = x + y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[z, -1.7e+46], N[(x + y), $MachinePrecision], If[LessEqual[z, 3.95e-128], N[(x + N[(y * N[(t / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 2.9e+133], N[(x - N[(t * N[(y / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + y), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if z < -1.6999999999999999e46 or 2.9000000000000001e133 < z

   1. Initial program 100.0%

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

   2. Taylor expanded in z around inf 85.4%

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

   if -1.6999999999999999e46 < z < 3.9500000000000001e-128

   1. Initial program 95.5%

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

   2. Taylor expanded in z around 0 83.3%

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

   if 3.9500000000000001e-128 < z < 2.9000000000000001e133

   1. Initial program 97.1%

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

   2. Taylor expanded in a around 0 75.3%

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

      1. div-sub 75.3%

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

      2. *-inverses 75.3%

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

   4. Simplified 75.3%

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

   5. Taylor expanded in t around inf 74.9%

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

      1. associate-*r/ 74.9%

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

      2. mul-1-neg 74.9%

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

      3. distribute-rgt-neg-in 74.9%

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

      4. associate-/l* 72.1%

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

   7. Simplified 72.1%

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

   8. Taylor expanded in x around 0 74.9%

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

      1. +-commutative 74.9%

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

      2. mul-1-neg 74.9%

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

      3. associate-*r/ 70.5%

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

      4. sub-neg 70.5%

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

   10. Simplified 70.5%

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

   11. Taylor expanded in x around 0 74.9%

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

       1. neg-mul-1 74.9%

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

       2. +-commutative 74.9%

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

       3. sub-neg 74.9%

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

       4. *-commutative 74.9%

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

       5. associate-*r/ 74.6%

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

   13. Simplified 74.6%

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

4. Final simplification 81.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -1.7 \cdot 10^{+46}:\\ \;\;\;\;x + y\\ \mathbf{elif}\;z \leq 3.95 \cdot 10^{-128}:\\ \;\;\;\;x + y \cdot \frac{t}{a}\\ \mathbf{elif}\;z \leq 2.9 \cdot 10^{+133}:\\ \;\;\;\;x - t \cdot \frac{y}{z}\\ \mathbf{else}:\\ \;\;\;\;x + y\\ \end{array} \]

Alternative 5: 76.3% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -1.75 \cdot 10^{+44}:\\ \;\;\;\;x + y\\ \mathbf{elif}\;z \leq 3.9 \cdot 10^{-128}:\\ \;\;\;\;x + y \cdot \frac{t}{a}\\ \mathbf{elif}\;z \leq 3.8 \cdot 10^{+134}:\\ \;\;\;\;x - \frac{t}{\frac{z}{y}}\\ \mathbf{else}:\\ \;\;\;\;x + y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= z -1.75e+44)
   (+ x y)
   (if (<= z 3.9e-128)
     (+ x (* y (/ t a)))
     (if (<= z 3.8e+134) (- x (/ t (/ z y))) (+ x y)))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -1.75e+44) {
		tmp = x + y;
	} else if (z <= 3.9e-128) {
		tmp = x + (y * (t / a));
	} else if (z <= 3.8e+134) {
		tmp = x - (t / (z / y));
	} else {
		tmp = x + y;
	}
	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 <= (-1.75d+44)) then
        tmp = x + y
    else if (z <= 3.9d-128) then
        tmp = x + (y * (t / a))
    else if (z <= 3.8d+134) then
        tmp = x - (t / (z / y))
    else
        tmp = x + y
    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 <= -1.75e+44) {
		tmp = x + y;
	} else if (z <= 3.9e-128) {
		tmp = x + (y * (t / a));
	} else if (z <= 3.8e+134) {
		tmp = x - (t / (z / y));
	} else {
		tmp = x + y;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if z <= -1.75e+44:
		tmp = x + y
	elif z <= 3.9e-128:
		tmp = x + (y * (t / a))
	elif z <= 3.8e+134:
		tmp = x - (t / (z / y))
	else:
		tmp = x + y
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (z <= -1.75e+44)
		tmp = Float64(x + y);
	elseif (z <= 3.9e-128)
		tmp = Float64(x + Float64(y * Float64(t / a)));
	elseif (z <= 3.8e+134)
		tmp = Float64(x - Float64(t / Float64(z / y)));
	else
		tmp = Float64(x + y);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (z <= -1.75e+44)
		tmp = x + y;
	elseif (z <= 3.9e-128)
		tmp = x + (y * (t / a));
	elseif (z <= 3.8e+134)
		tmp = x - (t / (z / y));
	else
		tmp = x + y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[z, -1.75e+44], N[(x + y), $MachinePrecision], If[LessEqual[z, 3.9e-128], N[(x + N[(y * N[(t / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 3.8e+134], N[(x - N[(t / N[(z / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + y), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if z < -1.75e44 or 3.79999999999999998e134 < z

   1. Initial program 100.0%

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

   2. Taylor expanded in z around inf 85.4%

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

   if -1.75e44 < z < 3.89999999999999997e-128

   1. Initial program 95.5%

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

   2. Taylor expanded in z around 0 83.3%

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

   if 3.89999999999999997e-128 < z < 3.79999999999999998e134

   1. Initial program 97.1%

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

   2. Taylor expanded in a around 0 75.3%

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

      1. div-sub 75.3%

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

      2. *-inverses 75.3%

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

   4. Simplified 75.3%

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

   5. Taylor expanded in t around inf 74.9%

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

      1. associate-*r/ 74.9%

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

      2. mul-1-neg 74.9%

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

      3. distribute-rgt-neg-in 74.9%

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

      4. associate-/l* 72.1%

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

   7. Simplified 72.1%

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

   8. Taylor expanded in x around 0 74.9%

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

      1. +-commutative 74.9%

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

      2. mul-1-neg 74.9%

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

      3. associate-*r/ 70.5%

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

      4. sub-neg 70.5%

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

   10. Simplified 70.5%

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

       1. associate-*r/ 74.9%

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

       2. associate-*l/ 74.6%

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

       3. clear-num 74.6%

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

       4. associate-*l/ 74.6%

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

       5. *-un-lft-identity 74.6%

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

   12. Applied egg-rr 74.6%

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

4. Final simplification 81.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -1.75 \cdot 10^{+44}:\\ \;\;\;\;x + y\\ \mathbf{elif}\;z \leq 3.9 \cdot 10^{-128}:\\ \;\;\;\;x + y \cdot \frac{t}{a}\\ \mathbf{elif}\;z \leq 3.8 \cdot 10^{+134}:\\ \;\;\;\;x - \frac{t}{\frac{z}{y}}\\ \mathbf{else}:\\ \;\;\;\;x + y\\ \end{array} \]

Alternative 6: 76.2% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -6.6 \cdot 10^{+45}:\\ \;\;\;\;x + y\\ \mathbf{elif}\;z \leq 3.95 \cdot 10^{-128}:\\ \;\;\;\;x + y \cdot \frac{t}{a}\\ \mathbf{elif}\;z \leq 1.85 \cdot 10^{+132}:\\ \;\;\;\;x - \frac{t \cdot y}{z}\\ \mathbf{else}:\\ \;\;\;\;x + y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= z -6.6e+45)
   (+ x y)
   (if (<= z 3.95e-128)
     (+ x (* y (/ t a)))
     (if (<= z 1.85e+132) (- x (/ (* t y) z)) (+ x y)))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -6.6e+45) {
		tmp = x + y;
	} else if (z <= 3.95e-128) {
		tmp = x + (y * (t / a));
	} else if (z <= 1.85e+132) {
		tmp = x - ((t * y) / z);
	} else {
		tmp = x + y;
	}
	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 <= (-6.6d+45)) then
        tmp = x + y
    else if (z <= 3.95d-128) then
        tmp = x + (y * (t / a))
    else if (z <= 1.85d+132) then
        tmp = x - ((t * y) / z)
    else
        tmp = x + y
    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 <= -6.6e+45) {
		tmp = x + y;
	} else if (z <= 3.95e-128) {
		tmp = x + (y * (t / a));
	} else if (z <= 1.85e+132) {
		tmp = x - ((t * y) / z);
	} else {
		tmp = x + y;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if z <= -6.6e+45:
		tmp = x + y
	elif z <= 3.95e-128:
		tmp = x + (y * (t / a))
	elif z <= 1.85e+132:
		tmp = x - ((t * y) / z)
	else:
		tmp = x + y
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (z <= -6.6e+45)
		tmp = Float64(x + y);
	elseif (z <= 3.95e-128)
		tmp = Float64(x + Float64(y * Float64(t / a)));
	elseif (z <= 1.85e+132)
		tmp = Float64(x - Float64(Float64(t * y) / z));
	else
		tmp = Float64(x + y);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (z <= -6.6e+45)
		tmp = x + y;
	elseif (z <= 3.95e-128)
		tmp = x + (y * (t / a));
	elseif (z <= 1.85e+132)
		tmp = x - ((t * y) / z);
	else
		tmp = x + y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[z, -6.6e+45], N[(x + y), $MachinePrecision], If[LessEqual[z, 3.95e-128], N[(x + N[(y * N[(t / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 1.85e+132], N[(x - N[(N[(t * y), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision], N[(x + y), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if z < -6.6000000000000001e45 or 1.85000000000000005e132 < z

   1. Initial program 100.0%

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

   2. Taylor expanded in z around inf 85.4%

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

   if -6.6000000000000001e45 < z < 3.9500000000000001e-128

   1. Initial program 95.5%

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

   2. Taylor expanded in z around 0 83.3%

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

   if 3.9500000000000001e-128 < z < 1.85000000000000005e132

   1. Initial program 97.1%

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

   2. Taylor expanded in a around 0 75.3%

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

      1. div-sub 75.3%

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

      2. *-inverses 75.3%

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

   4. Simplified 75.3%

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

   5. Taylor expanded in t around inf 74.9%

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

      1. associate-*r/ 74.9%

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

      2. associate-*r* 74.9%

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

      3. neg-mul-1 74.9%

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

   7. Simplified 74.9%

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

4. Final simplification 82.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -6.6 \cdot 10^{+45}:\\ \;\;\;\;x + y\\ \mathbf{elif}\;z \leq 3.95 \cdot 10^{-128}:\\ \;\;\;\;x + y \cdot \frac{t}{a}\\ \mathbf{elif}\;z \leq 1.85 \cdot 10^{+132}:\\ \;\;\;\;x - \frac{t \cdot y}{z}\\ \mathbf{else}:\\ \;\;\;\;x + y\\ \end{array} \]

Alternative 7: 81.2% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -1.35 \cdot 10^{-65} \lor \neg \left(z \leq 3.9 \cdot 10^{-123}\right):\\ \;\;\;\;x + y \cdot \left(1 - \frac{t}{z}\right)\\ \mathbf{else}:\\ \;\;\;\;x + t \cdot \frac{y}{a}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= z -1.35e-65) (not (<= z 3.9e-123)))
   (+ x (* y (- 1.0 (/ t z))))
   (+ x (* t (/ y a)))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((z <= -1.35e-65) || !(z <= 3.9e-123)) {
		tmp = x + (y * (1.0 - (t / z)));
	} else {
		tmp = x + (t * (y / a));
	}
	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 <= (-1.35d-65)) .or. (.not. (z <= 3.9d-123))) then
        tmp = x + (y * (1.0d0 - (t / z)))
    else
        tmp = x + (t * (y / a))
    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 <= -1.35e-65) || !(z <= 3.9e-123)) {
		tmp = x + (y * (1.0 - (t / z)));
	} else {
		tmp = x + (t * (y / a));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (z <= -1.35e-65) or not (z <= 3.9e-123):
		tmp = x + (y * (1.0 - (t / z)))
	else:
		tmp = x + (t * (y / a))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((z <= -1.35e-65) || !(z <= 3.9e-123))
		tmp = Float64(x + Float64(y * Float64(1.0 - Float64(t / z))));
	else
		tmp = Float64(x + Float64(t * Float64(y / a)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((z <= -1.35e-65) || ~((z <= 3.9e-123)))
		tmp = x + (y * (1.0 - (t / z)));
	else
		tmp = x + (t * (y / a));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[z, -1.35e-65], N[Not[LessEqual[z, 3.9e-123]], $MachinePrecision]], N[(x + N[(y * N[(1.0 - N[(t / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(t * N[(y / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if z < -1.3499999999999999e-65 or 3.89999999999999976e-123 < z

   1. Initial program 98.3%

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

   2. Taylor expanded in a around 0 82.9%

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

      1. div-sub 82.9%

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

      2. *-inverses 82.9%

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

   4. Simplified 82.9%

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

   if -1.3499999999999999e-65 < z < 3.89999999999999976e-123

   1. Initial program 95.5%

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

   2. Taylor expanded in z around 0 88.5%

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

      1. associate-/l* 89.6%

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

   4. Simplified 89.6%

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

      1. associate-/r/ 89.9%

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

   6. Applied egg-rr 89.9%

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

4. Final simplification 85.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -1.35 \cdot 10^{-65} \lor \neg \left(z \leq 3.9 \cdot 10^{-123}\right):\\ \;\;\;\;x + y \cdot \left(1 - \frac{t}{z}\right)\\ \mathbf{else}:\\ \;\;\;\;x + t \cdot \frac{y}{a}\\ \end{array} \]

Alternative 8: 75.7% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -7.5 \cdot 10^{+47} \lor \neg \left(z \leq 5.8 \cdot 10^{-113}\right):\\ \;\;\;\;x + y\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot \frac{t}{a}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= z -7.5e+47) (not (<= z 5.8e-113))) (+ x y) (+ x (* y (/ t a)))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((z <= -7.5e+47) || !(z <= 5.8e-113)) {
		tmp = x + y;
	} else {
		tmp = x + (y * (t / a));
	}
	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 <= (-7.5d+47)) .or. (.not. (z <= 5.8d-113))) then
        tmp = x + y
    else
        tmp = x + (y * (t / a))
    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 <= -7.5e+47) || !(z <= 5.8e-113)) {
		tmp = x + y;
	} else {
		tmp = x + (y * (t / a));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (z <= -7.5e+47) or not (z <= 5.8e-113):
		tmp = x + y
	else:
		tmp = x + (y * (t / a))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((z <= -7.5e+47) || !(z <= 5.8e-113))
		tmp = Float64(x + y);
	else
		tmp = Float64(x + Float64(y * Float64(t / a)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((z <= -7.5e+47) || ~((z <= 5.8e-113)))
		tmp = x + y;
	else
		tmp = x + (y * (t / a));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[z, -7.5e+47], N[Not[LessEqual[z, 5.8e-113]], $MachinePrecision]], N[(x + y), $MachinePrecision], N[(x + N[(y * N[(t / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if z < -7.4999999999999999e47 or 5.80000000000000008e-113 < z

   1. Initial program 98.7%

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

   2. Taylor expanded in z around inf 75.7%

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

   if -7.4999999999999999e47 < z < 5.80000000000000008e-113

   1. Initial program 95.6%

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

   2. Taylor expanded in z around 0 82.2%

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

4. Final simplification 78.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -7.5 \cdot 10^{+47} \lor \neg \left(z \leq 5.8 \cdot 10^{-113}\right):\\ \;\;\;\;x + y\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot \frac{t}{a}\\ \end{array} \]

Alternative 9: 75.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -1.3 \cdot 10^{+46}:\\ \;\;\;\;x + y\\ \mathbf{elif}\;z \leq 6 \cdot 10^{-113}:\\ \;\;\;\;x + y \cdot \frac{t}{a}\\ \mathbf{else}:\\ \;\;\;\;x + z \cdot \frac{y}{z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= z -1.3e+46)
   (+ x y)
   (if (<= z 6e-113) (+ x (* y (/ t a))) (+ x (* z (/ y z))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -1.3e+46) {
		tmp = x + y;
	} else if (z <= 6e-113) {
		tmp = x + (y * (t / a));
	} else {
		tmp = x + (z * (y / 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 (z <= (-1.3d+46)) then
        tmp = x + y
    else if (z <= 6d-113) then
        tmp = x + (y * (t / a))
    else
        tmp = x + (z * (y / z))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -1.3e+46) {
		tmp = x + y;
	} else if (z <= 6e-113) {
		tmp = x + (y * (t / a));
	} else {
		tmp = x + (z * (y / z));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if z <= -1.3e+46:
		tmp = x + y
	elif z <= 6e-113:
		tmp = x + (y * (t / a))
	else:
		tmp = x + (z * (y / z))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (z <= -1.3e+46)
		tmp = Float64(x + y);
	elseif (z <= 6e-113)
		tmp = Float64(x + Float64(y * Float64(t / a)));
	else
		tmp = Float64(x + Float64(z * Float64(y / z)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (z <= -1.3e+46)
		tmp = x + y;
	elseif (z <= 6e-113)
		tmp = x + (y * (t / a));
	else
		tmp = x + (z * (y / z));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[z, -1.3e+46], N[(x + y), $MachinePrecision], If[LessEqual[z, 6e-113], N[(x + N[(y * N[(t / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(z * N[(y / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if z < -1.30000000000000007e46

   1. Initial program 100.0%

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

   2. Taylor expanded in z around inf 82.9%

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

   if -1.30000000000000007e46 < z < 6.0000000000000002e-113

   1. Initial program 95.6%

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

   2. Taylor expanded in z around 0 82.2%

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

   if 6.0000000000000002e-113 < z

   1. Initial program 98.0%

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

   2. Taylor expanded in t around 0 67.4%

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

      1. associate-*l/ 81.5%

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

      2. *-commutative 81.5%

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

   4. Simplified 81.5%

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

   5. Taylor expanded in z around inf 72.0%

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

4. Final simplification 78.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -1.3 \cdot 10^{+46}:\\ \;\;\;\;x + y\\ \mathbf{elif}\;z \leq 6 \cdot 10^{-113}:\\ \;\;\;\;x + y \cdot \frac{t}{a}\\ \mathbf{else}:\\ \;\;\;\;x + z \cdot \frac{y}{z}\\ \end{array} \]

Alternative 10: 75.4% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -9.5 \cdot 10^{+43}:\\ \;\;\;\;x + y\\ \mathbf{elif}\;z \leq 3.2 \cdot 10^{-90}:\\ \;\;\;\;x + \frac{y}{\frac{a}{t}}\\ \mathbf{else}:\\ \;\;\;\;x + z \cdot \frac{y}{z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= z -9.5e+43)
   (+ x y)
   (if (<= z 3.2e-90) (+ x (/ y (/ a t))) (+ x (* z (/ y z))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -9.5e+43) {
		tmp = x + y;
	} else if (z <= 3.2e-90) {
		tmp = x + (y / (a / t));
	} else {
		tmp = x + (z * (y / 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 (z <= (-9.5d+43)) then
        tmp = x + y
    else if (z <= 3.2d-90) then
        tmp = x + (y / (a / t))
    else
        tmp = x + (z * (y / z))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -9.5e+43) {
		tmp = x + y;
	} else if (z <= 3.2e-90) {
		tmp = x + (y / (a / t));
	} else {
		tmp = x + (z * (y / z));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if z <= -9.5e+43:
		tmp = x + y
	elif z <= 3.2e-90:
		tmp = x + (y / (a / t))
	else:
		tmp = x + (z * (y / z))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (z <= -9.5e+43)
		tmp = Float64(x + y);
	elseif (z <= 3.2e-90)
		tmp = Float64(x + Float64(y / Float64(a / t)));
	else
		tmp = Float64(x + Float64(z * Float64(y / z)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (z <= -9.5e+43)
		tmp = x + y;
	elseif (z <= 3.2e-90)
		tmp = x + (y / (a / t));
	else
		tmp = x + (z * (y / z));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[z, -9.5e+43], N[(x + y), $MachinePrecision], If[LessEqual[z, 3.2e-90], N[(x + N[(y / N[(a / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(z * N[(y / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if z < -9.5000000000000004e43

   1. Initial program 100.0%

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

   2. Taylor expanded in z around inf 82.9%

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

   if -9.5000000000000004e43 < z < 3.20000000000000007e-90

   1. Initial program 95.7%

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

   2. Taylor expanded in z around 0 80.0%

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

      1. associate-/l* 80.8%

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

   4. Simplified 80.8%

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

   if 3.20000000000000007e-90 < z

   1. Initial program 97.9%

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

   2. Taylor expanded in t around 0 68.8%

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

      1. associate-*l/ 83.3%

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

      2. *-commutative 83.3%

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

   4. Simplified 83.3%

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

   5. Taylor expanded in z around inf 73.6%

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

4. Final simplification 78.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -9.5 \cdot 10^{+43}:\\ \;\;\;\;x + y\\ \mathbf{elif}\;z \leq 3.2 \cdot 10^{-90}:\\ \;\;\;\;x + \frac{y}{\frac{a}{t}}\\ \mathbf{else}:\\ \;\;\;\;x + z \cdot \frac{y}{z}\\ \end{array} \]

Alternative 11: 98.2% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

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 + (((z - t) / (z - a)) * y)
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 97.4%

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

2. Final simplification 97.4%

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

Alternative 12: 63.1% accurate, 1.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -6.2 \cdot 10^{+91}:\\ \;\;\;\;x\\ \mathbf{elif}\;a \leq 1.7 \cdot 10^{+113}:\\ \;\;\;\;x + y\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= a -6.2e+91) x (if (<= a 1.7e+113) (+ x y) x)))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (a <= -6.2e+91) {
		tmp = x;
	} else if (a <= 1.7e+113) {
		tmp = x + y;
	} 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 (a <= (-6.2d+91)) then
        tmp = x
    else if (a <= 1.7d+113) then
        tmp = x + y
    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 (a <= -6.2e+91) {
		tmp = x;
	} else if (a <= 1.7e+113) {
		tmp = x + y;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if a <= -6.2e+91:
		tmp = x
	elif a <= 1.7e+113:
		tmp = x + y
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (a <= -6.2e+91)
		tmp = x;
	elseif (a <= 1.7e+113)
		tmp = Float64(x + y);
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (a <= -6.2e+91)
		tmp = x;
	elseif (a <= 1.7e+113)
		tmp = x + y;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[a, -6.2e+91], x, If[LessEqual[a, 1.7e+113], N[(x + y), $MachinePrecision], x]]

Derivation

1. Split input into 2 regimes

2. if a < -6.19999999999999995e91 or 1.70000000000000009e113 < a

   1. Initial program 98.9%

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

   2. Taylor expanded in x around inf 71.1%

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

   if -6.19999999999999995e91 < a < 1.70000000000000009e113

   1. Initial program 96.6%

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

   2. Taylor expanded in z around inf 66.9%

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

4. Final simplification 68.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;a \leq -6.2 \cdot 10^{+91}:\\ \;\;\;\;x\\ \mathbf{elif}\;a \leq 1.7 \cdot 10^{+113}:\\ \;\;\;\;x + y\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]

Alternative 13: 51.0% 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 97.4%

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

2. Taylor expanded in x around inf 51.8%

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

3. Final simplification 51.8%

   \[\leadsto x \]

Developer target: 98.2% accurate, 1.0× speedup

Math

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

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

Reproduce

herbie shell --seed 2023274 
(FPCore (x y z t a)
  :name "Graphics.Rendering.Plot.Render.Plot.Axis:renderAxisLine from plot-0.2.3.4, A"
  :precision binary64

  :herbie-target
  (+ x (/ y (/ (- z a) (- z t))))

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