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

Percentage Accurate: 85.7% → 98.4%

Time: 8.7s

Alternatives: 11

Speedup: 1.0×

Specification

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 85.7% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 98.4% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 88.1%

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

   1. associate-*l/ 98.1%

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

3. Simplified 98.1%

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

4. Final simplification 98.1%

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

Alternative 2: 81.3% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -6.9 \cdot 10^{-63} \lor \neg \left(a \leq 3.45 \cdot 10^{-136}\right):\\ \;\;\;\;x + \frac{y}{\frac{a - z}{t}}\\ \mathbf{else}:\\ \;\;\;\;x + t \cdot \left(1 - \frac{y}{z}\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= a -6.9e-63) (not (<= a 3.45e-136)))
   (+ x (/ y (/ (- a z) t)))
   (+ x (* t (- 1.0 (/ y z))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((a <= -6.9e-63) || !(a <= 3.45e-136)) {
		tmp = x + (y / ((a - z) / t));
	} else {
		tmp = x + (t * (1.0 - (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 ((a <= (-6.9d-63)) .or. (.not. (a <= 3.45d-136))) then
        tmp = x + (y / ((a - z) / t))
    else
        tmp = x + (t * (1.0d0 - (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 ((a <= -6.9e-63) || !(a <= 3.45e-136)) {
		tmp = x + (y / ((a - z) / t));
	} else {
		tmp = x + (t * (1.0 - (y / z)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (a <= -6.9e-63) or not (a <= 3.45e-136):
		tmp = x + (y / ((a - z) / t))
	else:
		tmp = x + (t * (1.0 - (y / z)))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((a <= -6.9e-63) || !(a <= 3.45e-136))
		tmp = Float64(x + Float64(y / Float64(Float64(a - z) / t)));
	else
		tmp = Float64(x + Float64(t * Float64(1.0 - Float64(y / z))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((a <= -6.9e-63) || ~((a <= 3.45e-136)))
		tmp = x + (y / ((a - z) / t));
	else
		tmp = x + (t * (1.0 - (y / z)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[a, -6.9e-63], N[Not[LessEqual[a, 3.45e-136]], $MachinePrecision]], N[(x + N[(y / N[(N[(a - z), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(t * N[(1.0 - N[(y / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if a < -6.9e-63 or 3.4500000000000002e-136 < a

   1. Initial program 87.5%

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

      1. associate-*l/ 98.3%

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

   3. Simplified 98.3%

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

   4. Taylor expanded in y around inf 81.1%

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

      1. associate-/l* 86.6%

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

   6. Simplified 86.6%

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

   if -6.9e-63 < a < 3.4500000000000002e-136

   1. Initial program 89.3%

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

      1. associate-*l/ 97.8%

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

   3. Simplified 97.8%

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

   4. Taylor expanded in a around 0 91.3%

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

      1. associate-*r/ 91.3%

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

      2. neg-mul-1 91.3%

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

      3. neg-sub0 91.3%

         \[\leadsto x + \frac{\color{blue}{0 - \left(y - z\right)}}{z} \cdot t \]

      4. associate--r- 91.3%

         \[\leadsto x + \frac{\color{blue}{\left(0 - y\right) + z}}{z} \cdot t \]

      5. neg-sub0 91.3%

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

   6. Simplified 91.3%

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

   7. Taylor expanded in y around 0 91.3%

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

      1. +-commutative 91.3%

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

      2. mul-1-neg 91.3%

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

      3. unsub-neg 91.3%

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

   9. Simplified 91.3%

      \[\leadsto x + \color{blue}{\left(1 - \frac{y}{z}\right)} \cdot t \]
3. Recombined 2 regimes into one program.

4. Final simplification 88.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;a \leq -6.9 \cdot 10^{-63} \lor \neg \left(a \leq 3.45 \cdot 10^{-136}\right):\\ \;\;\;\;x + \frac{y}{\frac{a - z}{t}}\\ \mathbf{else}:\\ \;\;\;\;x + t \cdot \left(1 - \frac{y}{z}\right)\\ \end{array} \]

Alternative 3: 79.6% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -2.45 \cdot 10^{+76}:\\ \;\;\;\;x + y \cdot \frac{t}{a}\\ \mathbf{elif}\;a \leq 2050000000:\\ \;\;\;\;x + t \cdot \left(1 - \frac{y}{z}\right)\\ \mathbf{else}:\\ \;\;\;\;x + t \cdot \frac{y}{a}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= a -2.45e+76)
   (+ x (* y (/ t a)))
   (if (<= a 2050000000.0) (+ x (* t (- 1.0 (/ y z)))) (+ x (* t (/ y a))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (a <= -2.45e+76) {
		tmp = x + (y * (t / a));
	} else if (a <= 2050000000.0) {
		tmp = x + (t * (1.0 - (y / 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 (a <= (-2.45d+76)) then
        tmp = x + (y * (t / a))
    else if (a <= 2050000000.0d0) then
        tmp = x + (t * (1.0d0 - (y / 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 (a <= -2.45e+76) {
		tmp = x + (y * (t / a));
	} else if (a <= 2050000000.0) {
		tmp = x + (t * (1.0 - (y / z)));
	} else {
		tmp = x + (t * (y / a));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if a <= -2.45e+76:
		tmp = x + (y * (t / a))
	elif a <= 2050000000.0:
		tmp = x + (t * (1.0 - (y / z)))
	else:
		tmp = x + (t * (y / a))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (a <= -2.45e+76)
		tmp = Float64(x + Float64(y * Float64(t / a)));
	elseif (a <= 2050000000.0)
		tmp = Float64(x + Float64(t * Float64(1.0 - Float64(y / 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 (a <= -2.45e+76)
		tmp = x + (y * (t / a));
	elseif (a <= 2050000000.0)
		tmp = x + (t * (1.0 - (y / z)));
	else
		tmp = x + (t * (y / a));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[a, -2.45e+76], N[(x + N[(y * N[(t / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 2050000000.0], N[(x + N[(t * N[(1.0 - N[(y / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(t * N[(y / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if a < -2.45000000000000013e76

   1. Initial program 90.9%

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

      1. associate-*l/ 96.5%

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

   3. Simplified 96.5%

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

   4. Taylor expanded in z around 0 83.7%

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

      1. associate-/l* 88.3%

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

   6. Simplified 88.3%

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

      1. clear-num 88.2%

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

      2. associate-/r/ 88.2%

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

      3. clear-num 88.3%

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

   8. Applied egg-rr 88.3%

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

   if -2.45000000000000013e76 < a < 2.05e9

   1. Initial program 89.4%

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

      1. associate-*l/ 97.9%

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

   3. Simplified 97.9%

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

   4. Taylor expanded in a around 0 87.3%

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

      1. associate-*r/ 87.3%

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

      2. neg-mul-1 87.3%

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

      3. neg-sub0 87.3%

         \[\leadsto x + \frac{\color{blue}{0 - \left(y - z\right)}}{z} \cdot t \]

      4. associate--r- 87.3%

         \[\leadsto x + \frac{\color{blue}{\left(0 - y\right) + z}}{z} \cdot t \]

      5. neg-sub0 87.3%

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

   6. Simplified 87.3%

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

   7. Taylor expanded in y around 0 87.3%

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

      1. +-commutative 87.3%

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

      2. mul-1-neg 87.3%

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

      3. unsub-neg 87.3%

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

   9. Simplified 87.3%

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

   if 2.05e9 < a

   1. Initial program 83.7%

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

      1. associate-*l/ 99.5%

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

   3. Simplified 99.5%

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

   4. Taylor expanded in z around 0 82.5%

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

4. Final simplification 86.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;a \leq -2.45 \cdot 10^{+76}:\\ \;\;\;\;x + y \cdot \frac{t}{a}\\ \mathbf{elif}\;a \leq 2050000000:\\ \;\;\;\;x + t \cdot \left(1 - \frac{y}{z}\right)\\ \mathbf{else}:\\ \;\;\;\;x + t \cdot \frac{y}{a}\\ \end{array} \]

Alternative 4: 80.4% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -4.4 \cdot 10^{+76}:\\ \;\;\;\;x + y \cdot \frac{t}{a}\\ \mathbf{elif}\;a \leq 5.3 \cdot 10^{-136}:\\ \;\;\;\;x + t \cdot \left(1 - \frac{y}{z}\right)\\ \mathbf{else}:\\ \;\;\;\;x + t \cdot \frac{y}{a - z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= a -4.4e+76)
   (+ x (* y (/ t a)))
   (if (<= a 5.3e-136) (+ x (* t (- 1.0 (/ y z)))) (+ x (* t (/ y (- a z)))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (a <= -4.4e+76) {
		tmp = x + (y * (t / a));
	} else if (a <= 5.3e-136) {
		tmp = x + (t * (1.0 - (y / z)));
	} else {
		tmp = x + (t * (y / (a - z)));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if (a <= (-4.4d+76)) then
        tmp = x + (y * (t / a))
    else if (a <= 5.3d-136) then
        tmp = x + (t * (1.0d0 - (y / z)))
    else
        tmp = x + (t * (y / (a - z)))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (a <= -4.4e+76) {
		tmp = x + (y * (t / a));
	} else if (a <= 5.3e-136) {
		tmp = x + (t * (1.0 - (y / z)));
	} else {
		tmp = x + (t * (y / (a - z)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if a <= -4.4e+76:
		tmp = x + (y * (t / a))
	elif a <= 5.3e-136:
		tmp = x + (t * (1.0 - (y / z)))
	else:
		tmp = x + (t * (y / (a - z)))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (a <= -4.4e+76)
		tmp = Float64(x + Float64(y * Float64(t / a)));
	elseif (a <= 5.3e-136)
		tmp = Float64(x + Float64(t * Float64(1.0 - Float64(y / z))));
	else
		tmp = Float64(x + Float64(t * Float64(y / Float64(a - z))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (a <= -4.4e+76)
		tmp = x + (y * (t / a));
	elseif (a <= 5.3e-136)
		tmp = x + (t * (1.0 - (y / z)));
	else
		tmp = x + (t * (y / (a - z)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[a, -4.4e+76], N[(x + N[(y * N[(t / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 5.3e-136], N[(x + N[(t * N[(1.0 - N[(y / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(t * N[(y / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if a < -4.4000000000000001e76

   1. Initial program 90.9%

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

      1. associate-*l/ 96.5%

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

   3. Simplified 96.5%

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

   4. Taylor expanded in z around 0 83.7%

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

      1. associate-/l* 88.3%

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

   6. Simplified 88.3%

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

      1. clear-num 88.2%

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

      2. associate-/r/ 88.2%

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

      3. clear-num 88.3%

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

   8. Applied egg-rr 88.3%

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

   if -4.4000000000000001e76 < a < 5.30000000000000018e-136

   1. Initial program 89.2%

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

      1. associate-*l/ 98.3%

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

   3. Simplified 98.3%

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

   4. Taylor expanded in a around 0 90.0%

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

      1. associate-*r/ 90.0%

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

      2. neg-mul-1 90.0%

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

      3. neg-sub0 90.0%

         \[\leadsto x + \frac{\color{blue}{0 - \left(y - z\right)}}{z} \cdot t \]

      4. associate--r- 90.0%

         \[\leadsto x + \frac{\color{blue}{\left(0 - y\right) + z}}{z} \cdot t \]

      5. neg-sub0 90.0%

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

   6. Simplified 90.0%

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

   7. Taylor expanded in y around 0 90.0%

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

      1. +-commutative 90.0%

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

      2. mul-1-neg 90.0%

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

      3. unsub-neg 90.0%

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

   9. Simplified 90.0%

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

   if 5.30000000000000018e-136 < a

   1. Initial program 85.6%

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

      1. associate-*l/ 98.6%

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

   3. Simplified 98.6%

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

   4. Taylor expanded in y around inf 85.3%

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

4. Final simplification 87.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;a \leq -4.4 \cdot 10^{+76}:\\ \;\;\;\;x + y \cdot \frac{t}{a}\\ \mathbf{elif}\;a \leq 5.3 \cdot 10^{-136}:\\ \;\;\;\;x + t \cdot \left(1 - \frac{y}{z}\right)\\ \mathbf{else}:\\ \;\;\;\;x + t \cdot \frac{y}{a - z}\\ \end{array} \]

Alternative 5: 81.8% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -3.8 \cdot 10^{+76}:\\ \;\;\;\;x + \frac{t}{\frac{a}{y - z}}\\ \mathbf{elif}\;a \leq 5.6 \cdot 10^{-135}:\\ \;\;\;\;x + t \cdot \left(1 - \frac{y}{z}\right)\\ \mathbf{else}:\\ \;\;\;\;x + t \cdot \frac{y}{a - z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= a -3.8e+76)
   (+ x (/ t (/ a (- y z))))
   (if (<= a 5.6e-135) (+ x (* t (- 1.0 (/ y z)))) (+ x (* t (/ y (- a z)))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (a <= -3.8e+76) {
		tmp = x + (t / (a / (y - z)));
	} else if (a <= 5.6e-135) {
		tmp = x + (t * (1.0 - (y / z)));
	} else {
		tmp = x + (t * (y / (a - z)));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if (a <= (-3.8d+76)) then
        tmp = x + (t / (a / (y - z)))
    else if (a <= 5.6d-135) then
        tmp = x + (t * (1.0d0 - (y / z)))
    else
        tmp = x + (t * (y / (a - z)))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (a <= -3.8e+76) {
		tmp = x + (t / (a / (y - z)));
	} else if (a <= 5.6e-135) {
		tmp = x + (t * (1.0 - (y / z)));
	} else {
		tmp = x + (t * (y / (a - z)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if a <= -3.8e+76:
		tmp = x + (t / (a / (y - z)))
	elif a <= 5.6e-135:
		tmp = x + (t * (1.0 - (y / z)))
	else:
		tmp = x + (t * (y / (a - z)))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (a <= -3.8e+76)
		tmp = Float64(x + Float64(t / Float64(a / Float64(y - z))));
	elseif (a <= 5.6e-135)
		tmp = Float64(x + Float64(t * Float64(1.0 - Float64(y / z))));
	else
		tmp = Float64(x + Float64(t * Float64(y / Float64(a - z))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (a <= -3.8e+76)
		tmp = x + (t / (a / (y - z)));
	elseif (a <= 5.6e-135)
		tmp = x + (t * (1.0 - (y / z)));
	else
		tmp = x + (t * (y / (a - z)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[a, -3.8e+76], N[(x + N[(t / N[(a / N[(y - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 5.6e-135], N[(x + N[(t * N[(1.0 - N[(y / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(t * N[(y / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if a < -3.80000000000000024e76

   1. Initial program 90.9%

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

      1. associate-*l/ 96.5%

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

   3. Simplified 96.5%

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

   4. Taylor expanded in a around inf 86.3%

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

      1. associate-/l* 88.6%

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

   6. Simplified 88.6%

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

   if -3.80000000000000024e76 < a < 5.60000000000000047e-135

   1. Initial program 89.2%

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

      1. associate-*l/ 98.3%

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

   3. Simplified 98.3%

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

   4. Taylor expanded in a around 0 90.0%

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

      1. associate-*r/ 90.0%

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

      2. neg-mul-1 90.0%

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

      3. neg-sub0 90.0%

         \[\leadsto x + \frac{\color{blue}{0 - \left(y - z\right)}}{z} \cdot t \]

      4. associate--r- 90.0%

         \[\leadsto x + \frac{\color{blue}{\left(0 - y\right) + z}}{z} \cdot t \]

      5. neg-sub0 90.0%

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

   6. Simplified 90.0%

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

   7. Taylor expanded in y around 0 90.0%

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

      1. +-commutative 90.0%

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

      2. mul-1-neg 90.0%

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

      3. unsub-neg 90.0%

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

   9. Simplified 90.0%

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

   if 5.60000000000000047e-135 < a

   1. Initial program 85.6%

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

      1. associate-*l/ 98.6%

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

   3. Simplified 98.6%

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

   4. Taylor expanded in y around inf 85.3%

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

4. Final simplification 88.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;a \leq -3.8 \cdot 10^{+76}:\\ \;\;\;\;x + \frac{t}{\frac{a}{y - z}}\\ \mathbf{elif}\;a \leq 5.6 \cdot 10^{-135}:\\ \;\;\;\;x + t \cdot \left(1 - \frac{y}{z}\right)\\ \mathbf{else}:\\ \;\;\;\;x + t \cdot \frac{y}{a - z}\\ \end{array} \]

Alternative 6: 76.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -1.9 \cdot 10^{-35} \lor \neg \left(z \leq 1.25 \cdot 10^{+51}\right):\\ \;\;\;\;x + t\\ \mathbf{else}:\\ \;\;\;\;x + \frac{y}{\frac{a}{t}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= z -1.9e-35) (not (<= z 1.25e+51))) (+ x t) (+ x (/ y (/ a t)))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((z <= -1.9e-35) || !(z <= 1.25e+51)) {
		tmp = x + t;
	} else {
		tmp = x + (y / (a / t));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if ((z <= (-1.9d-35)) .or. (.not. (z <= 1.25d+51))) then
        tmp = x + t
    else
        tmp = x + (y / (a / t))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((z <= -1.9e-35) || !(z <= 1.25e+51)) {
		tmp = x + t;
	} else {
		tmp = x + (y / (a / t));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (z <= -1.9e-35) or not (z <= 1.25e+51):
		tmp = x + t
	else:
		tmp = x + (y / (a / t))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((z <= -1.9e-35) || !(z <= 1.25e+51))
		tmp = Float64(x + t);
	else
		tmp = Float64(x + Float64(y / Float64(a / t)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((z <= -1.9e-35) || ~((z <= 1.25e+51)))
		tmp = x + t;
	else
		tmp = x + (y / (a / t));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[z, -1.9e-35], N[Not[LessEqual[z, 1.25e+51]], $MachinePrecision]], N[(x + t), $MachinePrecision], N[(x + N[(y / N[(a / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if z < -1.9000000000000001e-35 or 1.25e51 < z

   1. Initial program 79.8%

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

      1. associate-*l/ 100.0%

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

   3. Simplified 100.0%

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

   4. Taylor expanded in z around inf 79.9%

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

   if -1.9000000000000001e-35 < z < 1.25e51

   1. Initial program 96.9%

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

      1. associate-*l/ 96.2%

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

   3. Simplified 96.2%

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

   4. Taylor expanded in z around 0 77.4%

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

      1. associate-/l* 78.5%

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

   6. Simplified 78.5%

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

4. Final simplification 79.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -1.9 \cdot 10^{-35} \lor \neg \left(z \leq 1.25 \cdot 10^{+51}\right):\\ \;\;\;\;x + t\\ \mathbf{else}:\\ \;\;\;\;x + \frac{y}{\frac{a}{t}}\\ \end{array} \]

Alternative 7: 76.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -8.5 \cdot 10^{-38}:\\ \;\;\;\;x + t\\ \mathbf{elif}\;z \leq 2.8 \cdot 10^{+50}:\\ \;\;\;\;x + y \cdot \frac{t}{a}\\ \mathbf{else}:\\ \;\;\;\;x + t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= z -8.5e-38) (+ x t) (if (<= z 2.8e+50) (+ x (* y (/ t a))) (+ x t))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -8.5e-38) {
		tmp = x + t;
	} else if (z <= 2.8e+50) {
		tmp = x + (y * (t / a));
	} else {
		tmp = x + t;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if (z <= (-8.5d-38)) then
        tmp = x + t
    else if (z <= 2.8d+50) then
        tmp = x + (y * (t / a))
    else
        tmp = x + t
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -8.5e-38) {
		tmp = x + t;
	} else if (z <= 2.8e+50) {
		tmp = x + (y * (t / a));
	} else {
		tmp = x + t;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if z <= -8.5e-38:
		tmp = x + t
	elif z <= 2.8e+50:
		tmp = x + (y * (t / a))
	else:
		tmp = x + t
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (z <= -8.5e-38)
		tmp = Float64(x + t);
	elseif (z <= 2.8e+50)
		tmp = Float64(x + Float64(y * Float64(t / a)));
	else
		tmp = Float64(x + t);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (z <= -8.5e-38)
		tmp = x + t;
	elseif (z <= 2.8e+50)
		tmp = x + (y * (t / a));
	else
		tmp = x + t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[z, -8.5e-38], N[(x + t), $MachinePrecision], If[LessEqual[z, 2.8e+50], N[(x + N[(y * N[(t / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + t), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if z < -8.50000000000000046e-38 or 2.7999999999999998e50 < z

   1. Initial program 79.8%

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

      1. associate-*l/ 100.0%

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

   3. Simplified 100.0%

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

   4. Taylor expanded in z around inf 79.9%

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

   if -8.50000000000000046e-38 < z < 2.7999999999999998e50

   1. Initial program 96.9%

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

      1. associate-*l/ 96.2%

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

   3. Simplified 96.2%

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

   4. Taylor expanded in z around 0 77.4%

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

      1. associate-/l* 78.5%

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

   6. Simplified 78.5%

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

      1. clear-num 78.5%

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

      2. associate-/r/ 78.5%

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

      3. clear-num 78.5%

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

   8. Applied egg-rr 78.5%

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

4. Final simplification 79.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -8.5 \cdot 10^{-38}:\\ \;\;\;\;x + t\\ \mathbf{elif}\;z \leq 2.8 \cdot 10^{+50}:\\ \;\;\;\;x + y \cdot \frac{t}{a}\\ \mathbf{else}:\\ \;\;\;\;x + t\\ \end{array} \]

Alternative 8: 63.3% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -4.2 \cdot 10^{+201}:\\ \;\;\;\;x + t \cdot \frac{z}{a}\\ \mathbf{elif}\;a \leq 1.15 \cdot 10^{+156}:\\ \;\;\;\;x + t\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= a -4.2e+201) (+ x (* t (/ z a))) (if (<= a 1.15e+156) (+ x t) x)))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (a <= -4.2e+201) {
		tmp = x + (t * (z / a));
	} else if (a <= 1.15e+156) {
		tmp = x + t;
	} 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 <= (-4.2d+201)) then
        tmp = x + (t * (z / a))
    else if (a <= 1.15d+156) then
        tmp = x + t
    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 <= -4.2e+201) {
		tmp = x + (t * (z / a));
	} else if (a <= 1.15e+156) {
		tmp = x + t;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if a <= -4.2e+201:
		tmp = x + (t * (z / a))
	elif a <= 1.15e+156:
		tmp = x + t
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (a <= -4.2e+201)
		tmp = Float64(x + Float64(t * Float64(z / a)));
	elseif (a <= 1.15e+156)
		tmp = Float64(x + t);
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (a <= -4.2e+201)
		tmp = x + (t * (z / a));
	elseif (a <= 1.15e+156)
		tmp = x + t;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[a, -4.2e+201], N[(x + N[(t * N[(z / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 1.15e+156], N[(x + t), $MachinePrecision], x]]

Derivation

1. Split input into 3 regimes

2. if a < -4.1999999999999998e201

   1. Initial program 94.7%

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

      1. associate-*l/ 91.7%

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

   3. Simplified 91.7%

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

   4. Taylor expanded in a around inf 94.7%

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

   5. Taylor expanded in y around 0 62.3%

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

      1. associate-*r/ 62.3%

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

      2. associate-*r* 62.3%

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

      3. neg-mul-1 62.3%

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

   7. Simplified 62.3%

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

      1. expm1-log1p-u 62.3%

         \[\leadsto x + \color{blue}{\mathsf{expm1}\left(\mathsf{log1p}\left(\frac{\left(-t\right) \cdot z}{a}\right)\right)} \]

      2. expm1-udef 62.2%

         \[\leadsto x + \color{blue}{\left(e^{\mathsf{log1p}\left(\frac{\left(-t\right) \cdot z}{a}\right)} - 1\right)} \]

      3. associate-/l* 62.2%

         \[\leadsto x + \left(e^{\mathsf{log1p}\left(\color{blue}{\frac{-t}{\frac{a}{z}}}\right)} - 1\right) \]

      4. add-sqr-sqrt 39.4%

         \[\leadsto x + \left(e^{\mathsf{log1p}\left(\frac{\color{blue}{\sqrt{-t} \cdot \sqrt{-t}}}{\frac{a}{z}}\right)} - 1\right) \]

      5. sqrt-unprod 56.1%

         \[\leadsto x + \left(e^{\mathsf{log1p}\left(\frac{\color{blue}{\sqrt{\left(-t\right) \cdot \left(-t\right)}}}{\frac{a}{z}}\right)} - 1\right) \]

      6. sqr-neg 56.1%

         \[\leadsto x + \left(e^{\mathsf{log1p}\left(\frac{\sqrt{\color{blue}{t \cdot t}}}{\frac{a}{z}}\right)} - 1\right) \]

      7. sqrt-unprod 22.7%

         \[\leadsto x + \left(e^{\mathsf{log1p}\left(\frac{\color{blue}{\sqrt{t} \cdot \sqrt{t}}}{\frac{a}{z}}\right)} - 1\right) \]

      8. add-sqr-sqrt 62.2%

         \[\leadsto x + \left(e^{\mathsf{log1p}\left(\frac{\color{blue}{t}}{\frac{a}{z}}\right)} - 1\right) \]

   9. Applied egg-rr 62.2%

      \[\leadsto x + \color{blue}{\left(e^{\mathsf{log1p}\left(\frac{t}{\frac{a}{z}}\right)} - 1\right)} \]
   10. Step-by-step derivation

       1. expm1-def 62.2%

          \[\leadsto x + \color{blue}{\mathsf{expm1}\left(\mathsf{log1p}\left(\frac{t}{\frac{a}{z}}\right)\right)} \]

       2. expm1-log1p 62.6%

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

       3. associate-/l* 62.7%

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

       4. associate-*r/ 62.6%

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

   11. Simplified 62.6%

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

   if -4.1999999999999998e201 < a < 1.1499999999999999e156

   1. Initial program 87.9%

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

      1. associate-*l/ 98.5%

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

   3. Simplified 98.5%

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

   4. Taylor expanded in z around inf 67.2%

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

   if 1.1499999999999999e156 < a

   1. Initial program 86.1%

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

      1. associate-*l/ 99.2%

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

   3. Simplified 99.2%

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

   4. Taylor expanded in z around 0 83.6%

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

      1. associate-/l* 90.6%

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

   6. Simplified 90.6%

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

   7. Taylor expanded in x around inf 80.2%

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

4. Final simplification 69.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;a \leq -4.2 \cdot 10^{+201}:\\ \;\;\;\;x + t \cdot \frac{z}{a}\\ \mathbf{elif}\;a \leq 1.15 \cdot 10^{+156}:\\ \;\;\;\;x + t\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]

Alternative 9: 63.3% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -2.6 \cdot 10^{+203}:\\ \;\;\;\;x + z \cdot \frac{t}{a}\\ \mathbf{elif}\;a \leq 10^{+156}:\\ \;\;\;\;x + t\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= a -2.6e+203) (+ x (* z (/ t a))) (if (<= a 1e+156) (+ x t) x)))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (a <= -2.6e+203) {
		tmp = x + (z * (t / a));
	} else if (a <= 1e+156) {
		tmp = x + t;
	} 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 <= (-2.6d+203)) then
        tmp = x + (z * (t / a))
    else if (a <= 1d+156) then
        tmp = x + t
    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 <= -2.6e+203) {
		tmp = x + (z * (t / a));
	} else if (a <= 1e+156) {
		tmp = x + t;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if a <= -2.6e+203:
		tmp = x + (z * (t / a))
	elif a <= 1e+156:
		tmp = x + t
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (a <= -2.6e+203)
		tmp = Float64(x + Float64(z * Float64(t / a)));
	elseif (a <= 1e+156)
		tmp = Float64(x + t);
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (a <= -2.6e+203)
		tmp = x + (z * (t / a));
	elseif (a <= 1e+156)
		tmp = x + t;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[a, -2.6e+203], N[(x + N[(z * N[(t / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 1e+156], N[(x + t), $MachinePrecision], x]]

Derivation

1. Split input into 3 regimes

2. if a < -2.5999999999999998e203

   1. Initial program 94.7%

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

      1. associate-*l/ 91.7%

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

   3. Simplified 91.7%

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

   4. Taylor expanded in a around inf 94.7%

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

   5. Taylor expanded in y around 0 62.3%

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

      1. associate-*r/ 62.3%

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

      2. associate-*r* 62.3%

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

      3. neg-mul-1 62.3%

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

   7. Simplified 62.3%

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

      1. div-inv 62.3%

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

      2. *-commutative 62.3%

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

      3. associate-*l* 62.3%

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

      4. add-sqr-sqrt 39.4%

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

      5. sqrt-unprod 56.7%

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

      6. sqr-neg 56.7%

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

      7. sqrt-unprod 23.3%

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

      8. add-sqr-sqrt 62.7%

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

      9. div-inv 62.7%

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

   9. Applied egg-rr 62.7%

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

   if -2.5999999999999998e203 < a < 9.9999999999999998e155

   1. Initial program 87.9%

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

      1. associate-*l/ 98.5%

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

   3. Simplified 98.5%

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

   4. Taylor expanded in z around inf 67.2%

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

   if 9.9999999999999998e155 < a

   1. Initial program 86.1%

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

      1. associate-*l/ 99.2%

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

   3. Simplified 99.2%

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

   4. Taylor expanded in z around 0 83.6%

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

      1. associate-/l* 90.6%

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

   6. Simplified 90.6%

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

   7. Taylor expanded in x around inf 80.2%

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

4. Final simplification 69.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;a \leq -2.6 \cdot 10^{+203}:\\ \;\;\;\;x + z \cdot \frac{t}{a}\\ \mathbf{elif}\;a \leq 10^{+156}:\\ \;\;\;\;x + t\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]

Alternative 10: 63.2% accurate, 1.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -3.6 \cdot 10^{+222}:\\ \;\;\;\;x\\ \mathbf{elif}\;a \leq 4.2 \cdot 10^{+156}:\\ \;\;\;\;x + t\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= a -3.6e+222) x (if (<= a 4.2e+156) (+ x t) x)))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (a <= -3.6e+222) {
		tmp = x;
	} else if (a <= 4.2e+156) {
		tmp = x + t;
	} 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 <= (-3.6d+222)) then
        tmp = x
    else if (a <= 4.2d+156) then
        tmp = x + t
    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 <= -3.6e+222) {
		tmp = x;
	} else if (a <= 4.2e+156) {
		tmp = x + t;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if a <= -3.6e+222:
		tmp = x
	elif a <= 4.2e+156:
		tmp = x + t
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (a <= -3.6e+222)
		tmp = x;
	elseif (a <= 4.2e+156)
		tmp = Float64(x + t);
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (a <= -3.6e+222)
		tmp = x;
	elseif (a <= 4.2e+156)
		tmp = x + t;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[a, -3.6e+222], x, If[LessEqual[a, 4.2e+156], N[(x + t), $MachinePrecision], x]]

Derivation

1. Split input into 2 regimes

2. if a < -3.6000000000000002e222 or 4.19999999999999963e156 < a

   1. Initial program 87.2%

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

      1. associate-*l/ 96.5%

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

   3. Simplified 96.5%

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

   4. Taylor expanded in z around 0 84.9%

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

      1. associate-/l* 92.2%

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

   6. Simplified 92.2%

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

   7. Taylor expanded in x around inf 76.9%

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

   if -3.6000000000000002e222 < a < 4.19999999999999963e156

   1. Initial program 88.4%

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

      1. associate-*l/ 98.5%

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

   3. Simplified 98.5%

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

   4. Taylor expanded in z around inf 67.0%

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

4. Final simplification 69.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;a \leq -3.6 \cdot 10^{+222}:\\ \;\;\;\;x\\ \mathbf{elif}\;a \leq 4.2 \cdot 10^{+156}:\\ \;\;\;\;x + t\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]

Alternative 11: 50.4% 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 88.1%

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

   1. associate-*l/ 98.1%

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

3. Simplified 98.1%

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

4. Taylor expanded in z around 0 61.6%

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

   1. associate-/l* 62.6%

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

6. Simplified 62.6%

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

7. Taylor expanded in x around inf 56.3%

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

8. Final simplification 56.3%

   \[\leadsto x \]

Developer target: 99.2% accurate, 0.7× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Reproduce

herbie shell --seed 2023257 
(FPCore (x y z t a)
  :name "Graphics.Rendering.Plot.Render.Plot.Axis:renderAxisTick from plot-0.2.3.4, A"
  :precision binary64

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

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