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

Percentage Accurate: 98.2% → 95.9%

Time: 9.6s

Alternatives: 11

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: 95.9% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{z - t}{z - a}\\ \mathbf{if}\;t_1 \leq -10:\\ \;\;\;\;x - \frac{y \cdot t}{z - a}\\ \mathbf{elif}\;t_1 \leq 2 \cdot 10^{-8}:\\ \;\;\;\;x - \frac{y}{\frac{a}{z - t}}\\ \mathbf{elif}\;t_1 \leq 1.00000000000005:\\ \;\;\;\;x + y \cdot \frac{z - t}{z}\\ \mathbf{else}:\\ \;\;\;\;x - y \cdot \frac{t}{z - a}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (/ (- z t) (- z a))))
   (if (<= t_1 -10.0)
     (- x (/ (* y t) (- z a)))
     (if (<= t_1 2e-8)
       (- x (/ y (/ a (- z t))))
       (if (<= t_1 1.00000000000005)
         (+ x (* y (/ (- z t) z)))
         (- x (* y (/ t (- 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 <= -10.0) {
		tmp = x - ((y * t) / (z - a));
	} else if (t_1 <= 2e-8) {
		tmp = x - (y / (a / (z - t)));
	} else if (t_1 <= 1.00000000000005) {
		tmp = x + (y * ((z - t) / z));
	} else {
		tmp = x - (y * (t / (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 <= (-10.0d0)) then
        tmp = x - ((y * t) / (z - a))
    else if (t_1 <= 2d-8) then
        tmp = x - (y / (a / (z - t)))
    else if (t_1 <= 1.00000000000005d0) then
        tmp = x + (y * ((z - t) / z))
    else
        tmp = x - (y * (t / (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 <= -10.0) {
		tmp = x - ((y * t) / (z - a));
	} else if (t_1 <= 2e-8) {
		tmp = x - (y / (a / (z - t)));
	} else if (t_1 <= 1.00000000000005) {
		tmp = x + (y * ((z - t) / z));
	} else {
		tmp = x - (y * (t / (z - a)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = (z - t) / (z - a)
	tmp = 0
	if t_1 <= -10.0:
		tmp = x - ((y * t) / (z - a))
	elif t_1 <= 2e-8:
		tmp = x - (y / (a / (z - t)))
	elif t_1 <= 1.00000000000005:
		tmp = x + (y * ((z - t) / z))
	else:
		tmp = x - (y * (t / (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 <= -10.0)
		tmp = Float64(x - Float64(Float64(y * t) / Float64(z - a)));
	elseif (t_1 <= 2e-8)
		tmp = Float64(x - Float64(y / Float64(a / Float64(z - t))));
	elseif (t_1 <= 1.00000000000005)
		tmp = Float64(x + Float64(y * Float64(Float64(z - t) / z)));
	else
		tmp = Float64(x - Float64(y * Float64(t / 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 <= -10.0)
		tmp = x - ((y * t) / (z - a));
	elseif (t_1 <= 2e-8)
		tmp = x - (y / (a / (z - t)));
	elseif (t_1 <= 1.00000000000005)
		tmp = x + (y * ((z - t) / z));
	else
		tmp = x - (y * (t / (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, -10.0], N[(x - N[(N[(y * t), $MachinePrecision] / N[(z - a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$1, 2e-8], N[(x - N[(y / N[(a / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$1, 1.00000000000005], N[(x + N[(y * N[(N[(z - t), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x - N[(y * N[(t / N[(z - a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

1. Split input into 4 regimes

2. if (/.f64 (-.f64 z t) (-.f64 z a)) < -10

   1. Initial program 91.4%

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

      1. associate-*r/ 93.5%

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

      2. clear-num 93.5%

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

   3. Applied egg-rr 93.5%

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

      1. *-un-lft-identity 93.5%

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

      2. times-frac 91.1%

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

   5. Applied egg-rr 91.1%

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

   6. Taylor expanded in t around inf 91.9%

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

      1. associate-*r/ 91.9%

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

      2. mul-1-neg 91.9%

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

      3. *-commutative 91.9%

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

   8. Simplified 91.9%

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

   if -10 < (/.f64 (-.f64 z t) (-.f64 z a)) < 2e-8

   1. Initial program 99.8%

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

   2. Taylor expanded in a around inf 87.6%

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

      1. mul-1-neg 87.6%

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

      2. unsub-neg 87.6%

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

      3. associate-/l* 99.9%

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

   4. Simplified 99.9%

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

   if 2e-8 < (/.f64 (-.f64 z t) (-.f64 z a)) < 1.00000000000004996

   1. Initial program 100.0%

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

   2. Taylor expanded in a around 0 100.0%

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

   if 1.00000000000004996 < (/.f64 (-.f64 z t) (-.f64 z a))

   1. Initial program 99.8%

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

   2. Taylor expanded in t around inf 99.8%

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

      1. neg-mul-1 99.8%

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

      2. distribute-neg-frac 99.8%

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

   4. Simplified 99.8%

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

4. Final simplification 98.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{z - t}{z - a} \leq -10:\\ \;\;\;\;x - \frac{y \cdot t}{z - a}\\ \mathbf{elif}\;\frac{z - t}{z - a} \leq 2 \cdot 10^{-8}:\\ \;\;\;\;x - \frac{y}{\frac{a}{z - t}}\\ \mathbf{elif}\;\frac{z - t}{z - a} \leq 1.00000000000005:\\ \;\;\;\;x + y \cdot \frac{z - t}{z}\\ \mathbf{else}:\\ \;\;\;\;x - y \cdot \frac{t}{z - a}\\ \end{array} \]

Alternative 2: 82.1% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{z - t}{z - a}\\ \mathbf{if}\;t_1 \leq -4 \cdot 10^{+188}:\\ \;\;\;\;x + \frac{y \cdot t}{a}\\ \mathbf{elif}\;t_1 \leq -10:\\ \;\;\;\;x - \frac{t}{\frac{z}{y}}\\ \mathbf{elif}\;t_1 \leq -2 \cdot 10^{-155}:\\ \;\;\;\;x - \frac{y}{\frac{a}{z}}\\ \mathbf{elif}\;t_1 \leq 2 \cdot 10^{-26}:\\ \;\;\;\;x - \frac{y}{\frac{-a}{t}}\\ \mathbf{elif}\;t_1 \leq 4 \cdot 10^{+49}:\\ \;\;\;\;x + y\\ \mathbf{else}:\\ \;\;\;\;y \cdot t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (/ (- z t) (- z a))))
   (if (<= t_1 -4e+188)
     (+ x (/ (* y t) a))
     (if (<= t_1 -10.0)
       (- x (/ t (/ z y)))
       (if (<= t_1 -2e-155)
         (- x (/ y (/ a z)))
         (if (<= t_1 2e-26)
           (- x (/ y (/ (- a) t)))
           (if (<= t_1 4e+49) (+ x y) (* y t_1))))))))

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 <= -4e+188) {
		tmp = x + ((y * t) / a);
	} else if (t_1 <= -10.0) {
		tmp = x - (t / (z / y));
	} else if (t_1 <= -2e-155) {
		tmp = x - (y / (a / z));
	} else if (t_1 <= 2e-26) {
		tmp = x - (y / (-a / t));
	} else if (t_1 <= 4e+49) {
		tmp = x + y;
	} else {
		tmp = y * 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 = (z - t) / (z - a)
    if (t_1 <= (-4d+188)) then
        tmp = x + ((y * t) / a)
    else if (t_1 <= (-10.0d0)) then
        tmp = x - (t / (z / y))
    else if (t_1 <= (-2d-155)) then
        tmp = x - (y / (a / z))
    else if (t_1 <= 2d-26) then
        tmp = x - (y / (-a / t))
    else if (t_1 <= 4d+49) then
        tmp = x + y
    else
        tmp = y * 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 = (z - t) / (z - a);
	double tmp;
	if (t_1 <= -4e+188) {
		tmp = x + ((y * t) / a);
	} else if (t_1 <= -10.0) {
		tmp = x - (t / (z / y));
	} else if (t_1 <= -2e-155) {
		tmp = x - (y / (a / z));
	} else if (t_1 <= 2e-26) {
		tmp = x - (y / (-a / t));
	} else if (t_1 <= 4e+49) {
		tmp = x + y;
	} else {
		tmp = y * t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = (z - t) / (z - a)
	tmp = 0
	if t_1 <= -4e+188:
		tmp = x + ((y * t) / a)
	elif t_1 <= -10.0:
		tmp = x - (t / (z / y))
	elif t_1 <= -2e-155:
		tmp = x - (y / (a / z))
	elif t_1 <= 2e-26:
		tmp = x - (y / (-a / t))
	elif t_1 <= 4e+49:
		tmp = x + y
	else:
		tmp = y * t_1
	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 <= -4e+188)
		tmp = Float64(x + Float64(Float64(y * t) / a));
	elseif (t_1 <= -10.0)
		tmp = Float64(x - Float64(t / Float64(z / y)));
	elseif (t_1 <= -2e-155)
		tmp = Float64(x - Float64(y / Float64(a / z)));
	elseif (t_1 <= 2e-26)
		tmp = Float64(x - Float64(y / Float64(Float64(-a) / t)));
	elseif (t_1 <= 4e+49)
		tmp = Float64(x + y);
	else
		tmp = Float64(y * t_1);
	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 <= -4e+188)
		tmp = x + ((y * t) / a);
	elseif (t_1 <= -10.0)
		tmp = x - (t / (z / y));
	elseif (t_1 <= -2e-155)
		tmp = x - (y / (a / z));
	elseif (t_1 <= 2e-26)
		tmp = x - (y / (-a / t));
	elseif (t_1 <= 4e+49)
		tmp = x + y;
	else
		tmp = y * t_1;
	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, -4e+188], N[(x + N[(N[(y * t), $MachinePrecision] / a), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$1, -10.0], N[(x - N[(t / N[(z / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$1, -2e-155], N[(x - N[(y / N[(a / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$1, 2e-26], N[(x - N[(y / N[((-a) / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$1, 4e+49], N[(x + y), $MachinePrecision], N[(y * t$95$1), $MachinePrecision]]]]]]]

Derivation

1. Split input into 6 regimes

2. if (/.f64 (-.f64 z t) (-.f64 z a)) < -4.0000000000000001e188

   1. Initial program 80.8%

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

   2. Taylor expanded in z around 0 80.6%

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

   if -4.0000000000000001e188 < (/.f64 (-.f64 z t) (-.f64 z a)) < -10

   1. Initial program 99.9%

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

   2. Taylor expanded in a around 0 74.2%

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

      1. +-commutative 74.2%

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

      2. associate-/l* 74.1%

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

   4. Simplified 74.1%

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

   5. Taylor expanded in z around 0 73.4%

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

      1. mul-1-neg 73.4%

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

      2. associate-/l* 77.3%

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

   7. Simplified 77.3%

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

   if -10 < (/.f64 (-.f64 z t) (-.f64 z a)) < -2.00000000000000003e-155

   1. Initial program 99.9%

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

   2. Taylor expanded in a around inf 93.2%

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

      1. mul-1-neg 93.2%

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

      2. unsub-neg 93.2%

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

      3. associate-/l* 99.8%

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

   4. Simplified 99.8%

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

   5. Taylor expanded in z around inf 88.6%

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

   if -2.00000000000000003e-155 < (/.f64 (-.f64 z t) (-.f64 z a)) < 2.0000000000000001e-26

   1. Initial program 99.8%

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

   2. Taylor expanded in a around inf 84.2%

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

      1. mul-1-neg 84.2%

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

      2. unsub-neg 84.2%

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

      3. associate-/l* 99.9%

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

   4. Simplified 99.9%

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

   5. Taylor expanded in z around 0 83.3%

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

      1. associate-*r/ 83.3%

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

      2. neg-mul-1 83.3%

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

   7. Simplified 83.3%

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

   if 2.0000000000000001e-26 < (/.f64 (-.f64 z t) (-.f64 z a)) < 3.99999999999999979e49

   1. Initial program 100.0%

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

   2. Taylor expanded in z around inf 99.0%

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

      1. +-commutative 99.0%

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

   4. Simplified 99.0%

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

   if 3.99999999999999979e49 < (/.f64 (-.f64 z t) (-.f64 z a))

   1. Initial program 99.8%

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

      1. associate-*r/ 88.7%

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

      2. clear-num 88.8%

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

   3. Applied egg-rr 88.8%

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

      1. *-un-lft-identity 88.8%

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

      2. times-frac 99.7%

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

   5. Applied egg-rr 99.7%

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

   6. Taylor expanded in y around inf 88.2%

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

      1. div-sub 88.2%

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

   8. Simplified 88.2%

      \[\leadsto \color{blue}{y \cdot \frac{z - t}{z - a}} \]
3. Recombined 6 regimes into one program.

4. Final simplification 89.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{z - t}{z - a} \leq -4 \cdot 10^{+188}:\\ \;\;\;\;x + \frac{y \cdot t}{a}\\ \mathbf{elif}\;\frac{z - t}{z - a} \leq -10:\\ \;\;\;\;x - \frac{t}{\frac{z}{y}}\\ \mathbf{elif}\;\frac{z - t}{z - a} \leq -2 \cdot 10^{-155}:\\ \;\;\;\;x - \frac{y}{\frac{a}{z}}\\ \mathbf{elif}\;\frac{z - t}{z - a} \leq 2 \cdot 10^{-26}:\\ \;\;\;\;x - \frac{y}{\frac{-a}{t}}\\ \mathbf{elif}\;\frac{z - t}{z - a} \leq 4 \cdot 10^{+49}:\\ \;\;\;\;x + y\\ \mathbf{else}:\\ \;\;\;\;y \cdot \frac{z - t}{z - a}\\ \end{array} \]

Alternative 3: 86.9% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{z - t}{z - a}\\ \mathbf{if}\;t_1 \leq -2 \cdot 10^{+224}:\\ \;\;\;\;x + \frac{1}{\frac{a}{y \cdot t}}\\ \mathbf{elif}\;t_1 \leq -10:\\ \;\;\;\;x + \frac{y \cdot \left(z - t\right)}{z}\\ \mathbf{elif}\;t_1 \leq 2 \cdot 10^{-26}:\\ \;\;\;\;x - \frac{y}{\frac{a}{z - t}}\\ \mathbf{elif}\;t_1 \leq 4 \cdot 10^{+49}:\\ \;\;\;\;x + y \cdot \frac{z}{z - a}\\ \mathbf{else}:\\ \;\;\;\;y \cdot t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (/ (- z t) (- z a))))
   (if (<= t_1 -2e+224)
     (+ x (/ 1.0 (/ a (* y t))))
     (if (<= t_1 -10.0)
       (+ x (/ (* y (- z t)) z))
       (if (<= t_1 2e-26)
         (- x (/ y (/ a (- z t))))
         (if (<= t_1 4e+49) (+ x (* y (/ z (- z a)))) (* y t_1)))))))

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 <= -2e+224) {
		tmp = x + (1.0 / (a / (y * t)));
	} else if (t_1 <= -10.0) {
		tmp = x + ((y * (z - t)) / z);
	} else if (t_1 <= 2e-26) {
		tmp = x - (y / (a / (z - t)));
	} else if (t_1 <= 4e+49) {
		tmp = x + (y * (z / (z - a)));
	} else {
		tmp = y * 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 = (z - t) / (z - a)
    if (t_1 <= (-2d+224)) then
        tmp = x + (1.0d0 / (a / (y * t)))
    else if (t_1 <= (-10.0d0)) then
        tmp = x + ((y * (z - t)) / z)
    else if (t_1 <= 2d-26) then
        tmp = x - (y / (a / (z - t)))
    else if (t_1 <= 4d+49) then
        tmp = x + (y * (z / (z - a)))
    else
        tmp = y * 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 = (z - t) / (z - a);
	double tmp;
	if (t_1 <= -2e+224) {
		tmp = x + (1.0 / (a / (y * t)));
	} else if (t_1 <= -10.0) {
		tmp = x + ((y * (z - t)) / z);
	} else if (t_1 <= 2e-26) {
		tmp = x - (y / (a / (z - t)));
	} else if (t_1 <= 4e+49) {
		tmp = x + (y * (z / (z - a)));
	} else {
		tmp = y * t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = (z - t) / (z - a)
	tmp = 0
	if t_1 <= -2e+224:
		tmp = x + (1.0 / (a / (y * t)))
	elif t_1 <= -10.0:
		tmp = x + ((y * (z - t)) / z)
	elif t_1 <= 2e-26:
		tmp = x - (y / (a / (z - t)))
	elif t_1 <= 4e+49:
		tmp = x + (y * (z / (z - a)))
	else:
		tmp = y * t_1
	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 <= -2e+224)
		tmp = Float64(x + Float64(1.0 / Float64(a / Float64(y * t))));
	elseif (t_1 <= -10.0)
		tmp = Float64(x + Float64(Float64(y * Float64(z - t)) / z));
	elseif (t_1 <= 2e-26)
		tmp = Float64(x - Float64(y / Float64(a / Float64(z - t))));
	elseif (t_1 <= 4e+49)
		tmp = Float64(x + Float64(y * Float64(z / Float64(z - a))));
	else
		tmp = Float64(y * t_1);
	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 <= -2e+224)
		tmp = x + (1.0 / (a / (y * t)));
	elseif (t_1 <= -10.0)
		tmp = x + ((y * (z - t)) / z);
	elseif (t_1 <= 2e-26)
		tmp = x - (y / (a / (z - t)));
	elseif (t_1 <= 4e+49)
		tmp = x + (y * (z / (z - a)));
	else
		tmp = y * t_1;
	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, -2e+224], N[(x + N[(1.0 / N[(a / N[(y * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$1, -10.0], N[(x + N[(N[(y * N[(z - t), $MachinePrecision]), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$1, 2e-26], N[(x - N[(y / N[(a / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$1, 4e+49], N[(x + N[(y * N[(z / N[(z - a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(y * t$95$1), $MachinePrecision]]]]]]

Derivation

1. Split input into 5 regimes

2. if (/.f64 (-.f64 z t) (-.f64 z a)) < -1.99999999999999994e224

   1. Initial program 74.5%

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

   2. Taylor expanded in z around 0 86.9%

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

      1. +-commutative 86.9%

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

      2. associate-/l* 86.8%

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

   4. Simplified 86.8%

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

      1. clear-num 86.9%

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

      2. inv-pow 86.9%

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

   6. Applied egg-rr 86.9%

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

      1. unpow-1 86.9%

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

      2. associate-/l/ 87.0%

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

   8. Simplified 87.0%

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

   if -1.99999999999999994e224 < (/.f64 (-.f64 z t) (-.f64 z a)) < -10

   1. Initial program 99.9%

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

   2. Taylor expanded in a around 0 75.2%

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

   if -10 < (/.f64 (-.f64 z t) (-.f64 z a)) < 2.0000000000000001e-26

   1. Initial program 99.8%

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

   2. Taylor expanded in a around inf 87.3%

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

      1. mul-1-neg 87.3%

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

      2. unsub-neg 87.3%

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

      3. associate-/l* 99.8%

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

   4. Simplified 99.8%

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

   if 2.0000000000000001e-26 < (/.f64 (-.f64 z t) (-.f64 z a)) < 3.99999999999999979e49

   1. Initial program 100.0%

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

   2. Taylor expanded in t around 0 99.0%

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

   if 3.99999999999999979e49 < (/.f64 (-.f64 z t) (-.f64 z a))

   1. Initial program 99.8%

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

      1. associate-*r/ 88.7%

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

      2. clear-num 88.8%

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

   3. Applied egg-rr 88.8%

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

      1. *-un-lft-identity 88.8%

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

      2. times-frac 99.7%

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

   5. Applied egg-rr 99.7%

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

   6. Taylor expanded in y around inf 88.2%

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

      1. div-sub 88.2%

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

   8. Simplified 88.2%

      \[\leadsto \color{blue}{y \cdot \frac{z - t}{z - a}} \]
3. Recombined 5 regimes into one program.

4. Final simplification 94.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{z - t}{z - a} \leq -2 \cdot 10^{+224}:\\ \;\;\;\;x + \frac{1}{\frac{a}{y \cdot t}}\\ \mathbf{elif}\;\frac{z - t}{z - a} \leq -10:\\ \;\;\;\;x + \frac{y \cdot \left(z - t\right)}{z}\\ \mathbf{elif}\;\frac{z - t}{z - a} \leq 2 \cdot 10^{-26}:\\ \;\;\;\;x - \frac{y}{\frac{a}{z - t}}\\ \mathbf{elif}\;\frac{z - t}{z - a} \leq 4 \cdot 10^{+49}:\\ \;\;\;\;x + y \cdot \frac{z}{z - a}\\ \mathbf{else}:\\ \;\;\;\;y \cdot \frac{z - t}{z - a}\\ \end{array} \]

Alternative 4: 96.7% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{z - t}{z - a}\\ t_2 := x - y \cdot \frac{t}{z - a}\\ \mathbf{if}\;t_1 \leq -10:\\ \;\;\;\;t_2\\ \mathbf{elif}\;t_1 \leq 2 \cdot 10^{-8}:\\ \;\;\;\;x - \frac{y}{\frac{a}{z - t}}\\ \mathbf{elif}\;t_1 \leq 1.00000000000005:\\ \;\;\;\;x + y \cdot \frac{z - t}{z}\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (/ (- z t) (- z a))) (t_2 (- x (* y (/ t (- z a))))))
   (if (<= t_1 -10.0)
     t_2
     (if (<= t_1 2e-8)
       (- x (/ y (/ a (- z t))))
       (if (<= t_1 1.00000000000005) (+ x (* y (/ (- z t) z))) t_2)))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = (z - t) / (z - a);
	double t_2 = x - (y * (t / (z - a)));
	double tmp;
	if (t_1 <= -10.0) {
		tmp = t_2;
	} else if (t_1 <= 2e-8) {
		tmp = x - (y / (a / (z - t)));
	} else if (t_1 <= 1.00000000000005) {
		tmp = x + (y * ((z - t) / z));
	} else {
		tmp = t_2;
	}
	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) :: t_2
    real(8) :: tmp
    t_1 = (z - t) / (z - a)
    t_2 = x - (y * (t / (z - a)))
    if (t_1 <= (-10.0d0)) then
        tmp = t_2
    else if (t_1 <= 2d-8) then
        tmp = x - (y / (a / (z - t)))
    else if (t_1 <= 1.00000000000005d0) then
        tmp = x + (y * ((z - t) / z))
    else
        tmp = t_2
    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 t_2 = x - (y * (t / (z - a)));
	double tmp;
	if (t_1 <= -10.0) {
		tmp = t_2;
	} else if (t_1 <= 2e-8) {
		tmp = x - (y / (a / (z - t)));
	} else if (t_1 <= 1.00000000000005) {
		tmp = x + (y * ((z - t) / z));
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = (z - t) / (z - a)
	t_2 = x - (y * (t / (z - a)))
	tmp = 0
	if t_1 <= -10.0:
		tmp = t_2
	elif t_1 <= 2e-8:
		tmp = x - (y / (a / (z - t)))
	elif t_1 <= 1.00000000000005:
		tmp = x + (y * ((z - t) / z))
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(Float64(z - t) / Float64(z - a))
	t_2 = Float64(x - Float64(y * Float64(t / Float64(z - a))))
	tmp = 0.0
	if (t_1 <= -10.0)
		tmp = t_2;
	elseif (t_1 <= 2e-8)
		tmp = Float64(x - Float64(y / Float64(a / Float64(z - t))));
	elseif (t_1 <= 1.00000000000005)
		tmp = Float64(x + Float64(y * Float64(Float64(z - t) / z)));
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = (z - t) / (z - a);
	t_2 = x - (y * (t / (z - a)));
	tmp = 0.0;
	if (t_1 <= -10.0)
		tmp = t_2;
	elseif (t_1 <= 2e-8)
		tmp = x - (y / (a / (z - t)));
	elseif (t_1 <= 1.00000000000005)
		tmp = x + (y * ((z - t) / z));
	else
		tmp = t_2;
	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]}, Block[{t$95$2 = N[(x - N[(y * N[(t / N[(z - a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, -10.0], t$95$2, If[LessEqual[t$95$1, 2e-8], N[(x - N[(y / N[(a / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$1, 1.00000000000005], N[(x + N[(y * N[(N[(z - t), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$2]]]]]

Derivation

1. Split input into 3 regimes

2. if (/.f64 (-.f64 z t) (-.f64 z a)) < -10 or 1.00000000000004996 < (/.f64 (-.f64 z t) (-.f64 z a))

   1. Initial program 94.9%

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

   2. Taylor expanded in t around inf 94.0%

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

      1. neg-mul-1 94.0%

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

      2. distribute-neg-frac 94.0%

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

   4. Simplified 94.0%

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

   if -10 < (/.f64 (-.f64 z t) (-.f64 z a)) < 2e-8

   1. Initial program 99.8%

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

   2. Taylor expanded in a around inf 87.6%

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

      1. mul-1-neg 87.6%

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

      2. unsub-neg 87.6%

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

      3. associate-/l* 99.9%

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

   4. Simplified 99.9%

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

   if 2e-8 < (/.f64 (-.f64 z t) (-.f64 z a)) < 1.00000000000004996

   1. Initial program 100.0%

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

   2. Taylor expanded in a around 0 100.0%

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

4. Final simplification 98.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{z - t}{z - a} \leq -10:\\ \;\;\;\;x - y \cdot \frac{t}{z - a}\\ \mathbf{elif}\;\frac{z - t}{z - a} \leq 2 \cdot 10^{-8}:\\ \;\;\;\;x - \frac{y}{\frac{a}{z - t}}\\ \mathbf{elif}\;\frac{z - t}{z - a} \leq 1.00000000000005:\\ \;\;\;\;x + y \cdot \frac{z - t}{z}\\ \mathbf{else}:\\ \;\;\;\;x - y \cdot \frac{t}{z - a}\\ \end{array} \]

Alternative 5: 84.7% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y \cdot \frac{z - t}{z - a}\\ \mathbf{if}\;t_1 \leq -1 \cdot 10^{+187} \lor \neg \left(t_1 \leq 10^{+155}\right):\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot \frac{z}{z - a}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* y (/ (- z t) (- z a)))))
   (if (or (<= t_1 -1e+187) (not (<= t_1 1e+155)))
     t_1
     (+ x (* y (/ z (- z a)))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = y * ((z - t) / (z - a));
	double tmp;
	if ((t_1 <= -1e+187) || !(t_1 <= 1e+155)) {
		tmp = t_1;
	} else {
		tmp = x + (y * (z / (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 = y * ((z - t) / (z - a))
    if ((t_1 <= (-1d+187)) .or. (.not. (t_1 <= 1d+155))) then
        tmp = t_1
    else
        tmp = x + (y * (z / (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 = y * ((z - t) / (z - a));
	double tmp;
	if ((t_1 <= -1e+187) || !(t_1 <= 1e+155)) {
		tmp = t_1;
	} else {
		tmp = x + (y * (z / (z - a)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = y * ((z - t) / (z - a))
	tmp = 0
	if (t_1 <= -1e+187) or not (t_1 <= 1e+155):
		tmp = t_1
	else:
		tmp = x + (y * (z / (z - a)))
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(y * Float64(Float64(z - t) / Float64(z - a)))
	tmp = 0.0
	if ((t_1 <= -1e+187) || !(t_1 <= 1e+155))
		tmp = t_1;
	else
		tmp = Float64(x + Float64(y * Float64(z / Float64(z - a))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = y * ((z - t) / (z - a));
	tmp = 0.0;
	if ((t_1 <= -1e+187) || ~((t_1 <= 1e+155)))
		tmp = t_1;
	else
		tmp = x + (y * (z / (z - a)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(y * N[(N[(z - t), $MachinePrecision] / N[(z - a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[Or[LessEqual[t$95$1, -1e+187], N[Not[LessEqual[t$95$1, 1e+155]], $MachinePrecision]], t$95$1, N[(x + N[(y * N[(z / N[(z - a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 y (/.f64 (-.f64 z t) (-.f64 z a))) < -9.99999999999999907e186 or 1.00000000000000001e155 < (*.f64 y (/.f64 (-.f64 z t) (-.f64 z a)))

   1. Initial program 94.6%

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

      1. associate-*r/ 64.3%

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

      2. clear-num 64.2%

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

   3. Applied egg-rr 64.2%

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

      1. *-un-lft-identity 64.2%

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

      2. times-frac 94.3%

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

   5. Applied egg-rr 94.3%

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

   6. Taylor expanded in y around inf 86.7%

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

      1. div-sub 86.7%

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

   8. Simplified 86.7%

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

   if -9.99999999999999907e186 < (*.f64 y (/.f64 (-.f64 z t) (-.f64 z a))) < 1.00000000000000001e155

   1. Initial program 99.9%

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

   2. Taylor expanded in t around 0 86.4%

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

4. Final simplification 86.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \cdot \frac{z - t}{z - a} \leq -1 \cdot 10^{+187} \lor \neg \left(y \cdot \frac{z - t}{z - a} \leq 10^{+155}\right):\\ \;\;\;\;y \cdot \frac{z - t}{z - a}\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot \frac{z}{z - a}\\ \end{array} \]

Alternative 6: 76.6% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -5 \cdot 10^{+48}:\\ \;\;\;\;x + y\\ \mathbf{elif}\;z \leq 1.4 \cdot 10^{+66}:\\ \;\;\;\;x + t \cdot \frac{y}{a}\\ \mathbf{else}:\\ \;\;\;\;x + y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= z -5e+48) (+ x y) (if (<= z 1.4e+66) (+ x (* t (/ y a))) (+ x y))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -5e+48) {
		tmp = x + y;
	} else if (z <= 1.4e+66) {
		tmp = x + (t * (y / a));
	} 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 <= (-5d+48)) then
        tmp = x + y
    else if (z <= 1.4d+66) then
        tmp = x + (t * (y / a))
    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 <= -5e+48) {
		tmp = x + y;
	} else if (z <= 1.4e+66) {
		tmp = x + (t * (y / a));
	} else {
		tmp = x + y;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if z <= -5e+48:
		tmp = x + y
	elif z <= 1.4e+66:
		tmp = x + (t * (y / a))
	else:
		tmp = x + y
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (z <= -5e+48)
		tmp = Float64(x + y);
	elseif (z <= 1.4e+66)
		tmp = Float64(x + Float64(t * Float64(y / a)));
	else
		tmp = Float64(x + y);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (z <= -5e+48)
		tmp = x + y;
	elseif (z <= 1.4e+66)
		tmp = x + (t * (y / a));
	else
		tmp = x + y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[z, -5e+48], N[(x + y), $MachinePrecision], If[LessEqual[z, 1.4e+66], N[(x + N[(t * N[(y / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + y), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if z < -4.99999999999999973e48 or 1.4e66 < z

   1. Initial program 99.9%

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

   2. Taylor expanded in z around inf 80.6%

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

      1. +-commutative 80.6%

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

   4. Simplified 80.6%

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

   if -4.99999999999999973e48 < z < 1.4e66

   1. Initial program 97.3%

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

      1. associate-*r/ 94.2%

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

      2. clear-num 94.1%

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

   3. Applied egg-rr 94.1%

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

   4. Taylor expanded in z around 0 71.3%

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

      1. associate-*r/ 74.3%

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

   6. Simplified 74.3%

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

4. Final simplification 77.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -5 \cdot 10^{+48}:\\ \;\;\;\;x + y\\ \mathbf{elif}\;z \leq 1.4 \cdot 10^{+66}:\\ \;\;\;\;x + t \cdot \frac{y}{a}\\ \mathbf{else}:\\ \;\;\;\;x + y\\ \end{array} \]

Alternative 7: 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]

Derivation

1. Initial program 98.4%

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

2. Final simplification 98.4%

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

Alternative 8: 61.2% accurate, 1.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -1.48 \cdot 10^{+209}:\\ \;\;\;\;y \cdot \frac{t}{a}\\ \mathbf{else}:\\ \;\;\;\;x + y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= t -1.48e+209) (* y (/ t a)) (+ x y)))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (t <= -1.48e+209) {
		tmp = y * (t / a);
	} 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 (t <= (-1.48d+209)) then
        tmp = y * (t / a)
    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 (t <= -1.48e+209) {
		tmp = y * (t / a);
	} else {
		tmp = x + y;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if t <= -1.48e+209:
		tmp = y * (t / a)
	else:
		tmp = x + y
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (t <= -1.48e+209)
		tmp = Float64(y * Float64(t / a));
	else
		tmp = Float64(x + y);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (t <= -1.48e+209)
		tmp = y * (t / a);
	else
		tmp = x + y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[t, -1.48e+209], N[(y * N[(t / a), $MachinePrecision]), $MachinePrecision], N[(x + y), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if t < -1.47999999999999995e209

   1. Initial program 95.7%

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

   2. Taylor expanded in a around inf 53.9%

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

      1. mul-1-neg 53.9%

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

      2. unsub-neg 53.9%

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

      3. associate-/l* 64.2%

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

   4. Simplified 64.2%

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

   5. Taylor expanded in y around inf 51.0%

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

   6. Taylor expanded in t around inf 55.4%

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

   if -1.47999999999999995e209 < t

   1. Initial program 98.7%

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

   2. Taylor expanded in z around inf 66.0%

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

      1. +-commutative 66.0%

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

   4. Simplified 66.0%

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

4. Final simplification 65.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq -1.48 \cdot 10^{+209}:\\ \;\;\;\;y \cdot \frac{t}{a}\\ \mathbf{else}:\\ \;\;\;\;x + y\\ \end{array} \]

Alternative 9: 61.3% accurate, 1.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -2.15 \cdot 10^{+209}:\\ \;\;\;\;\frac{y}{\frac{a}{t}}\\ \mathbf{else}:\\ \;\;\;\;x + y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= t -2.15e+209) (/ y (/ a t)) (+ x y)))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (t <= -2.15e+209) {
		tmp = y / (a / t);
	} 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 (t <= (-2.15d+209)) then
        tmp = y / (a / t)
    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 (t <= -2.15e+209) {
		tmp = y / (a / t);
	} else {
		tmp = x + y;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if t <= -2.15e+209:
		tmp = y / (a / t)
	else:
		tmp = x + y
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (t <= -2.15e+209)
		tmp = Float64(y / Float64(a / t));
	else
		tmp = Float64(x + y);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (t <= -2.15e+209)
		tmp = y / (a / t);
	else
		tmp = x + y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[t, -2.15e+209], N[(y / N[(a / t), $MachinePrecision]), $MachinePrecision], N[(x + y), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if t < -2.14999999999999994e209

   1. Initial program 95.7%

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

   2. Taylor expanded in a around inf 53.9%

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

      1. mul-1-neg 53.9%

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

      2. unsub-neg 53.9%

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

      3. associate-/l* 64.2%

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

   4. Simplified 64.2%

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

   5. Taylor expanded in y around inf 51.0%

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

   6. Taylor expanded in t around inf 55.4%

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

      1. clear-num 55.4%

         \[\leadsto y \cdot \color{blue}{\frac{1}{\frac{a}{t}}} \]

      2. add-sqr-sqrt 25.2%

         \[\leadsto y \cdot \frac{1}{\frac{\color{blue}{\sqrt{a} \cdot \sqrt{a}}}{t}} \]

      3. sqrt-unprod 23.4%

         \[\leadsto y \cdot \frac{1}{\frac{\color{blue}{\sqrt{a \cdot a}}}{t}} \]

      4. sqr-neg 23.4%

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

      5. sqrt-unprod 0.6%

         \[\leadsto y \cdot \frac{1}{\frac{\color{blue}{\sqrt{-a} \cdot \sqrt{-a}}}{t}} \]

      6. add-sqr-sqrt 1.7%

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

      7. div-inv 1.7%

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

      8. add-sqr-sqrt 0.6%

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

      9. sqrt-unprod 23.4%

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

      10. sqr-neg 23.4%

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

      11. sqrt-unprod 25.2%

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

      12. add-sqr-sqrt 55.5%

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

   8. Applied egg-rr 55.5%

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

   if -2.14999999999999994e209 < t

   1. Initial program 98.7%

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

   2. Taylor expanded in z around inf 66.0%

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

      1. +-commutative 66.0%

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

   4. Simplified 66.0%

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

4. Final simplification 65.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq -2.15 \cdot 10^{+209}:\\ \;\;\;\;\frac{y}{\frac{a}{t}}\\ \mathbf{else}:\\ \;\;\;\;x + y\\ \end{array} \]

Alternative 10: 60.7% accurate, 3.7× speedup

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_, y_, z_, t_, a_] := N[(x + y), $MachinePrecision]

Derivation

1. Initial program 98.4%

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

2. Taylor expanded in z around inf 61.5%

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

   1. +-commutative 61.5%

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

4. Simplified 61.5%

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

5. Final simplification 61.5%

   \[\leadsto x + y \]

Alternative 11: 51.7% 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 98.4%

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

2. Taylor expanded in x around inf 49.3%

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

3. Final simplification 49.3%

   \[\leadsto x \]

Developer target: 98.4% 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 2023271 
(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)))))