Data.Colour.RGB:hslsv from colour-2.3.3, B

Percentage Accurate: 99.4% → 99.8%

Time: 20.4s

Alternatives: 15

Speedup: 1.0×

Specification

Math

\[\begin{array}{l} \\ \frac{60 \cdot \left(x - y\right)}{z - t} + a \cdot 120 \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (+ (/ (* 60.0 (- x y)) (- z t)) (* a 120.0)))

C

double code(double x, double y, double z, double t, double a) {
	return ((60.0 * (x - y)) / (z - t)) + (a * 120.0);
}

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 = ((60.0d0 * (x - y)) / (z - t)) + (a * 120.0d0)
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	return ((60.0 * (x - y)) / (z - t)) + (a * 120.0);
}

Python

def code(x, y, z, t, a):
	return ((60.0 * (x - y)) / (z - t)) + (a * 120.0)

Julia

function code(x, y, z, t, a)
	return Float64(Float64(Float64(60.0 * Float64(x - y)) / Float64(z - t)) + Float64(a * 120.0))
end

MATLAB

function tmp = code(x, y, z, t, a)
	tmp = ((60.0 * (x - y)) / (z - t)) + (a * 120.0);
end

Wolfram

code[x_, y_, z_, t_, a_] := N[(N[(N[(60.0 * N[(x - y), $MachinePrecision]), $MachinePrecision] / N[(z - t), $MachinePrecision]), $MachinePrecision] + N[(a * 120.0), $MachinePrecision]), $MachinePrecision]

Initial Program: 99.4% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{60 \cdot \left(x - y\right)}{z - t} + a \cdot 120 \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (+ (/ (* 60.0 (- x y)) (- z t)) (* a 120.0)))

C

double code(double x, double y, double z, double t, double a) {
	return ((60.0 * (x - y)) / (z - t)) + (a * 120.0);
}

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 = ((60.0d0 * (x - y)) / (z - t)) + (a * 120.0d0)
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	return ((60.0 * (x - y)) / (z - t)) + (a * 120.0);
}

Python

def code(x, y, z, t, a):
	return ((60.0 * (x - y)) / (z - t)) + (a * 120.0)

Julia

function code(x, y, z, t, a)
	return Float64(Float64(Float64(60.0 * Float64(x - y)) / Float64(z - t)) + Float64(a * 120.0))
end

MATLAB

function tmp = code(x, y, z, t, a)
	tmp = ((60.0 * (x - y)) / (z - t)) + (a * 120.0);
end

Wolfram

code[x_, y_, z_, t_, a_] := N[(N[(N[(60.0 * N[(x - y), $MachinePrecision]), $MachinePrecision] / N[(z - t), $MachinePrecision]), $MachinePrecision] + N[(a * 120.0), $MachinePrecision]), $MachinePrecision]

Alternative 1: 99.8% accurate, 0.1× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(x - y, \frac{60}{z - t}, a \cdot 120\right) \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (fma (- x y) (/ 60.0 (- z t)) (* a 120.0)))

C

double code(double x, double y, double z, double t, double a) {
	return fma((x - y), (60.0 / (z - t)), (a * 120.0));
}

Julia

function code(x, y, z, t, a)
	return fma(Float64(x - y), Float64(60.0 / Float64(z - t)), Float64(a * 120.0))
end

Wolfram

code[x_, y_, z_, t_, a_] := N[(N[(x - y), $MachinePrecision] * N[(60.0 / N[(z - t), $MachinePrecision]), $MachinePrecision] + N[(a * 120.0), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 98.7%

   \[\frac{60 \cdot \left(x - y\right)}{z - t} + a \cdot 120 \]
2. Step-by-step derivation

   1. associate-*l/ 99.8%

      \[\leadsto \color{blue}{\frac{60}{z - t} \cdot \left(x - y\right)} + a \cdot 120 \]

   2. *-commutative 99.8%

      \[\leadsto \color{blue}{\left(x - y\right) \cdot \frac{60}{z - t}} + a \cdot 120 \]

   3. fma-def 99.8%

      \[\leadsto \color{blue}{\mathsf{fma}\left(x - y, \frac{60}{z - t}, a \cdot 120\right)} \]

3. Simplified 99.8%

   \[\leadsto \color{blue}{\mathsf{fma}\left(x - y, \frac{60}{z - t}, a \cdot 120\right)} \]

4. Final simplification 99.8%

   \[\leadsto \mathsf{fma}\left(x - y, \frac{60}{z - t}, a \cdot 120\right) \]

Alternative 2: 75.1% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{\left(x - y\right) \cdot 60}{z - t}\\ \mathbf{if}\;t_1 \leq -5 \lor \neg \left(t_1 \leq 10000000\right):\\ \;\;\;\;60 \cdot \frac{x - y}{z - t}\\ \mathbf{else}:\\ \;\;\;\;a \cdot 120\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (/ (* (- x y) 60.0) (- z t))))
   (if (or (<= t_1 -5.0) (not (<= t_1 10000000.0)))
     (* 60.0 (/ (- x y) (- z t)))
     (* a 120.0))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = ((x - y) * 60.0) / (z - t);
	double tmp;
	if ((t_1 <= -5.0) || !(t_1 <= 10000000.0)) {
		tmp = 60.0 * ((x - y) / (z - t));
	} else {
		tmp = a * 120.0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: t_1
    real(8) :: tmp
    t_1 = ((x - y) * 60.0d0) / (z - t)
    if ((t_1 <= (-5.0d0)) .or. (.not. (t_1 <= 10000000.0d0))) then
        tmp = 60.0d0 * ((x - y) / (z - t))
    else
        tmp = a * 120.0d0
    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) * 60.0) / (z - t);
	double tmp;
	if ((t_1 <= -5.0) || !(t_1 <= 10000000.0)) {
		tmp = 60.0 * ((x - y) / (z - t));
	} else {
		tmp = a * 120.0;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = ((x - y) * 60.0) / (z - t)
	tmp = 0
	if (t_1 <= -5.0) or not (t_1 <= 10000000.0):
		tmp = 60.0 * ((x - y) / (z - t))
	else:
		tmp = a * 120.0
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(Float64(Float64(x - y) * 60.0) / Float64(z - t))
	tmp = 0.0
	if ((t_1 <= -5.0) || !(t_1 <= 10000000.0))
		tmp = Float64(60.0 * Float64(Float64(x - y) / Float64(z - t)));
	else
		tmp = Float64(a * 120.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = ((x - y) * 60.0) / (z - t);
	tmp = 0.0;
	if ((t_1 <= -5.0) || ~((t_1 <= 10000000.0)))
		tmp = 60.0 * ((x - y) / (z - t));
	else
		tmp = a * 120.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(N[(N[(x - y), $MachinePrecision] * 60.0), $MachinePrecision] / N[(z - t), $MachinePrecision]), $MachinePrecision]}, If[Or[LessEqual[t$95$1, -5.0], N[Not[LessEqual[t$95$1, 10000000.0]], $MachinePrecision]], N[(60.0 * N[(N[(x - y), $MachinePrecision] / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(a * 120.0), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (*.f64 60 (-.f64 x y)) (-.f64 z t)) < -5 or 1e7 < (/.f64 (*.f64 60 (-.f64 x y)) (-.f64 z t))

   1. Initial program 97.8%

      \[\frac{60 \cdot \left(x - y\right)}{z - t} + a \cdot 120 \]
   2. Step-by-step derivation

      1. associate-/l* 99.7%

         \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}}} + a \cdot 120 \]

   3. Simplified 99.7%

      \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}} + a \cdot 120} \]
   4. Step-by-step derivation

      1. clear-num 99.5%

         \[\leadsto \frac{60}{\color{blue}{\frac{1}{\frac{x - y}{z - t}}}} + a \cdot 120 \]

      2. associate-/r/ 99.7%

         \[\leadsto \frac{60}{\color{blue}{\frac{1}{x - y} \cdot \left(z - t\right)}} + a \cdot 120 \]

   5. Applied egg-rr 99.7%

      \[\leadsto \frac{60}{\color{blue}{\frac{1}{x - y} \cdot \left(z - t\right)}} + a \cdot 120 \]

   6. Taylor expanded in a around 0 78.1%

      \[\leadsto \color{blue}{60 \cdot \frac{x - y}{z - t}} \]

   if -5 < (/.f64 (*.f64 60 (-.f64 x y)) (-.f64 z t)) < 1e7

   1. Initial program 99.9%

      \[\frac{60 \cdot \left(x - y\right)}{z - t} + a \cdot 120 \]
   2. Step-by-step derivation

      1. associate-/l* 99.9%

         \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}}} + a \cdot 120 \]

   3. Simplified 99.9%

      \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}} + a \cdot 120} \]

   4. Taylor expanded in z around inf 81.3%

      \[\leadsto \color{blue}{120 \cdot a} \]
3. Recombined 2 regimes into one program.

4. Final simplification 79.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{\left(x - y\right) \cdot 60}{z - t} \leq -5 \lor \neg \left(\frac{\left(x - y\right) \cdot 60}{z - t} \leq 10000000\right):\\ \;\;\;\;60 \cdot \frac{x - y}{z - t}\\ \mathbf{else}:\\ \;\;\;\;a \cdot 120\\ \end{array} \]

Alternative 3: 56.8% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := 60 \cdot \frac{x}{z - t}\\ \mathbf{if}\;x \leq -1.95 \cdot 10^{+92}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq 8 \cdot 10^{-100}:\\ \;\;\;\;a \cdot 120\\ \mathbf{elif}\;x \leq 4.4 \cdot 10^{-82}:\\ \;\;\;\;-60 \cdot \frac{y}{z - t}\\ \mathbf{elif}\;x \leq 3.9 \cdot 10^{+148}:\\ \;\;\;\;a \cdot 120\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* 60.0 (/ x (- z t)))))
   (if (<= x -1.95e+92)
     t_1
     (if (<= x 8e-100)
       (* a 120.0)
       (if (<= x 4.4e-82)
         (* -60.0 (/ y (- z t)))
         (if (<= x 3.9e+148) (* a 120.0) t_1))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = 60.0 * (x / (z - t));
	double tmp;
	if (x <= -1.95e+92) {
		tmp = t_1;
	} else if (x <= 8e-100) {
		tmp = a * 120.0;
	} else if (x <= 4.4e-82) {
		tmp = -60.0 * (y / (z - t));
	} else if (x <= 3.9e+148) {
		tmp = a * 120.0;
	} 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 = 60.0d0 * (x / (z - t))
    if (x <= (-1.95d+92)) then
        tmp = t_1
    else if (x <= 8d-100) then
        tmp = a * 120.0d0
    else if (x <= 4.4d-82) then
        tmp = (-60.0d0) * (y / (z - t))
    else if (x <= 3.9d+148) then
        tmp = a * 120.0d0
    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 = 60.0 * (x / (z - t));
	double tmp;
	if (x <= -1.95e+92) {
		tmp = t_1;
	} else if (x <= 8e-100) {
		tmp = a * 120.0;
	} else if (x <= 4.4e-82) {
		tmp = -60.0 * (y / (z - t));
	} else if (x <= 3.9e+148) {
		tmp = a * 120.0;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = 60.0 * (x / (z - t))
	tmp = 0
	if x <= -1.95e+92:
		tmp = t_1
	elif x <= 8e-100:
		tmp = a * 120.0
	elif x <= 4.4e-82:
		tmp = -60.0 * (y / (z - t))
	elif x <= 3.9e+148:
		tmp = a * 120.0
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(60.0 * Float64(x / Float64(z - t)))
	tmp = 0.0
	if (x <= -1.95e+92)
		tmp = t_1;
	elseif (x <= 8e-100)
		tmp = Float64(a * 120.0);
	elseif (x <= 4.4e-82)
		tmp = Float64(-60.0 * Float64(y / Float64(z - t)));
	elseif (x <= 3.9e+148)
		tmp = Float64(a * 120.0);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = 60.0 * (x / (z - t));
	tmp = 0.0;
	if (x <= -1.95e+92)
		tmp = t_1;
	elseif (x <= 8e-100)
		tmp = a * 120.0;
	elseif (x <= 4.4e-82)
		tmp = -60.0 * (y / (z - t));
	elseif (x <= 3.9e+148)
		tmp = a * 120.0;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(60.0 * N[(x / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -1.95e+92], t$95$1, If[LessEqual[x, 8e-100], N[(a * 120.0), $MachinePrecision], If[LessEqual[x, 4.4e-82], N[(-60.0 * N[(y / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 3.9e+148], N[(a * 120.0), $MachinePrecision], t$95$1]]]]]

Derivation

1. Split input into 3 regimes

2. if x < -1.95000000000000006e92 or 3.90000000000000002e148 < x

   1. Initial program 97.6%

      \[\frac{60 \cdot \left(x - y\right)}{z - t} + a \cdot 120 \]
   2. Step-by-step derivation

      1. associate-/l* 99.7%

         \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}}} + a \cdot 120 \]

   3. Simplified 99.7%

      \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}} + a \cdot 120} \]
   4. Step-by-step derivation

      1. clear-num 99.5%

         \[\leadsto \frac{60}{\color{blue}{\frac{1}{\frac{x - y}{z - t}}}} + a \cdot 120 \]

      2. associate-/r/ 99.7%

         \[\leadsto \frac{60}{\color{blue}{\frac{1}{x - y} \cdot \left(z - t\right)}} + a \cdot 120 \]

   5. Applied egg-rr 99.7%

      \[\leadsto \frac{60}{\color{blue}{\frac{1}{x - y} \cdot \left(z - t\right)}} + a \cdot 120 \]

   6. Taylor expanded in a around 0 78.4%

      \[\leadsto \color{blue}{60 \cdot \frac{x - y}{z - t}} \]

   7. Taylor expanded in x around inf 71.7%

      \[\leadsto 60 \cdot \color{blue}{\frac{x}{z - t}} \]

   if -1.95000000000000006e92 < x < 8.0000000000000002e-100 or 4.39999999999999971e-82 < x < 3.90000000000000002e148

   1. Initial program 99.3%

      \[\frac{60 \cdot \left(x - y\right)}{z - t} + a \cdot 120 \]
   2. Step-by-step derivation

      1. associate-/l* 99.9%

         \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}}} + a \cdot 120 \]

   3. Simplified 99.9%

      \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}} + a \cdot 120} \]

   4. Taylor expanded in z around inf 65.9%

      \[\leadsto \color{blue}{120 \cdot a} \]

   if 8.0000000000000002e-100 < x < 4.39999999999999971e-82

   1. Initial program 100.0%

      \[\frac{60 \cdot \left(x - y\right)}{z - t} + a \cdot 120 \]
   2. Step-by-step derivation

      1. associate-/l* 100.0%

         \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}}} + a \cdot 120 \]

   3. Simplified 100.0%

      \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}} + a \cdot 120} \]
   4. Step-by-step derivation

      1. clear-num 99.5%

         \[\leadsto \frac{60}{\color{blue}{\frac{1}{\frac{x - y}{z - t}}}} + a \cdot 120 \]

      2. associate-/r/ 99.7%

         \[\leadsto \frac{60}{\color{blue}{\frac{1}{x - y} \cdot \left(z - t\right)}} + a \cdot 120 \]

   5. Applied egg-rr 99.7%

      \[\leadsto \frac{60}{\color{blue}{\frac{1}{x - y} \cdot \left(z - t\right)}} + a \cdot 120 \]

   6. Taylor expanded in a around 0 99.5%

      \[\leadsto \color{blue}{60 \cdot \frac{x - y}{z - t}} \]
   7. Step-by-step derivation

      1. associate-*r/ 100.0%

         \[\leadsto \color{blue}{\frac{60 \cdot \left(x - y\right)}{z - t}} \]

   8. Applied egg-rr 100.0%

      \[\leadsto \color{blue}{\frac{60 \cdot \left(x - y\right)}{z - t}} \]

   9. Taylor expanded in x around 0 99.5%

      \[\leadsto \color{blue}{-60 \cdot \frac{y}{z - t}} \]
3. Recombined 3 regimes into one program.

4. Final simplification 68.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -1.95 \cdot 10^{+92}:\\ \;\;\;\;60 \cdot \frac{x}{z - t}\\ \mathbf{elif}\;x \leq 8 \cdot 10^{-100}:\\ \;\;\;\;a \cdot 120\\ \mathbf{elif}\;x \leq 4.4 \cdot 10^{-82}:\\ \;\;\;\;-60 \cdot \frac{y}{z - t}\\ \mathbf{elif}\;x \leq 3.9 \cdot 10^{+148}:\\ \;\;\;\;a \cdot 120\\ \mathbf{else}:\\ \;\;\;\;60 \cdot \frac{x}{z - t}\\ \end{array} \]

Alternative 4: 56.8% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.32 \cdot 10^{+94}:\\ \;\;\;\;60 \cdot \frac{x}{z - t}\\ \mathbf{elif}\;x \leq 4.8 \cdot 10^{-100}:\\ \;\;\;\;a \cdot 120\\ \mathbf{elif}\;x \leq 5.5 \cdot 10^{-82}:\\ \;\;\;\;-60 \cdot \frac{y}{z - t}\\ \mathbf{elif}\;x \leq 4.2 \cdot 10^{+140}:\\ \;\;\;\;a \cdot 120\\ \mathbf{else}:\\ \;\;\;\;\frac{60}{\frac{z - t}{x}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= x -1.32e+94)
   (* 60.0 (/ x (- z t)))
   (if (<= x 4.8e-100)
     (* a 120.0)
     (if (<= x 5.5e-82)
       (* -60.0 (/ y (- z t)))
       (if (<= x 4.2e+140) (* a 120.0) (/ 60.0 (/ (- z t) x)))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (x <= -1.32e+94) {
		tmp = 60.0 * (x / (z - t));
	} else if (x <= 4.8e-100) {
		tmp = a * 120.0;
	} else if (x <= 5.5e-82) {
		tmp = -60.0 * (y / (z - t));
	} else if (x <= 4.2e+140) {
		tmp = a * 120.0;
	} else {
		tmp = 60.0 / ((z - t) / 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 (x <= (-1.32d+94)) then
        tmp = 60.0d0 * (x / (z - t))
    else if (x <= 4.8d-100) then
        tmp = a * 120.0d0
    else if (x <= 5.5d-82) then
        tmp = (-60.0d0) * (y / (z - t))
    else if (x <= 4.2d+140) then
        tmp = a * 120.0d0
    else
        tmp = 60.0d0 / ((z - t) / 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 (x <= -1.32e+94) {
		tmp = 60.0 * (x / (z - t));
	} else if (x <= 4.8e-100) {
		tmp = a * 120.0;
	} else if (x <= 5.5e-82) {
		tmp = -60.0 * (y / (z - t));
	} else if (x <= 4.2e+140) {
		tmp = a * 120.0;
	} else {
		tmp = 60.0 / ((z - t) / x);
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if x <= -1.32e+94:
		tmp = 60.0 * (x / (z - t))
	elif x <= 4.8e-100:
		tmp = a * 120.0
	elif x <= 5.5e-82:
		tmp = -60.0 * (y / (z - t))
	elif x <= 4.2e+140:
		tmp = a * 120.0
	else:
		tmp = 60.0 / ((z - t) / x)
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (x <= -1.32e+94)
		tmp = Float64(60.0 * Float64(x / Float64(z - t)));
	elseif (x <= 4.8e-100)
		tmp = Float64(a * 120.0);
	elseif (x <= 5.5e-82)
		tmp = Float64(-60.0 * Float64(y / Float64(z - t)));
	elseif (x <= 4.2e+140)
		tmp = Float64(a * 120.0);
	else
		tmp = Float64(60.0 / Float64(Float64(z - t) / x));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (x <= -1.32e+94)
		tmp = 60.0 * (x / (z - t));
	elseif (x <= 4.8e-100)
		tmp = a * 120.0;
	elseif (x <= 5.5e-82)
		tmp = -60.0 * (y / (z - t));
	elseif (x <= 4.2e+140)
		tmp = a * 120.0;
	else
		tmp = 60.0 / ((z - t) / x);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[x, -1.32e+94], N[(60.0 * N[(x / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 4.8e-100], N[(a * 120.0), $MachinePrecision], If[LessEqual[x, 5.5e-82], N[(-60.0 * N[(y / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 4.2e+140], N[(a * 120.0), $MachinePrecision], N[(60.0 / N[(N[(z - t), $MachinePrecision] / x), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

1. Split input into 4 regimes

2. if x < -1.32000000000000003e94

   1. Initial program 99.8%

      \[\frac{60 \cdot \left(x - y\right)}{z - t} + a \cdot 120 \]
   2. Step-by-step derivation

      1. associate-/l* 99.6%

         \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}}} + a \cdot 120 \]

   3. Simplified 99.6%

      \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}} + a \cdot 120} \]
   4. Step-by-step derivation

      1. clear-num 99.4%

         \[\leadsto \frac{60}{\color{blue}{\frac{1}{\frac{x - y}{z - t}}}} + a \cdot 120 \]

      2. associate-/r/ 99.6%

         \[\leadsto \frac{60}{\color{blue}{\frac{1}{x - y} \cdot \left(z - t\right)}} + a \cdot 120 \]

   5. Applied egg-rr 99.6%

      \[\leadsto \frac{60}{\color{blue}{\frac{1}{x - y} \cdot \left(z - t\right)}} + a \cdot 120 \]

   6. Taylor expanded in a around 0 73.5%

      \[\leadsto \color{blue}{60 \cdot \frac{x - y}{z - t}} \]

   7. Taylor expanded in x around inf 65.9%

      \[\leadsto 60 \cdot \color{blue}{\frac{x}{z - t}} \]

   if -1.32000000000000003e94 < x < 4.8000000000000005e-100 or 5.4999999999999998e-82 < x < 4.2000000000000004e140

   1. Initial program 99.3%

      \[\frac{60 \cdot \left(x - y\right)}{z - t} + a \cdot 120 \]
   2. Step-by-step derivation

      1. associate-/l* 99.9%

         \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}}} + a \cdot 120 \]

   3. Simplified 99.9%

      \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}} + a \cdot 120} \]

   4. Taylor expanded in z around inf 65.9%

      \[\leadsto \color{blue}{120 \cdot a} \]

   if 4.8000000000000005e-100 < x < 5.4999999999999998e-82

   1. Initial program 100.0%

      \[\frac{60 \cdot \left(x - y\right)}{z - t} + a \cdot 120 \]
   2. Step-by-step derivation

      1. associate-/l* 100.0%

         \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}}} + a \cdot 120 \]

   3. Simplified 100.0%

      \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}} + a \cdot 120} \]
   4. Step-by-step derivation

      1. clear-num 99.5%

         \[\leadsto \frac{60}{\color{blue}{\frac{1}{\frac{x - y}{z - t}}}} + a \cdot 120 \]

      2. associate-/r/ 99.7%

         \[\leadsto \frac{60}{\color{blue}{\frac{1}{x - y} \cdot \left(z - t\right)}} + a \cdot 120 \]

   5. Applied egg-rr 99.7%

      \[\leadsto \frac{60}{\color{blue}{\frac{1}{x - y} \cdot \left(z - t\right)}} + a \cdot 120 \]

   6. Taylor expanded in a around 0 99.5%

      \[\leadsto \color{blue}{60 \cdot \frac{x - y}{z - t}} \]
   7. Step-by-step derivation

      1. associate-*r/ 100.0%

         \[\leadsto \color{blue}{\frac{60 \cdot \left(x - y\right)}{z - t}} \]

   8. Applied egg-rr 100.0%

      \[\leadsto \color{blue}{\frac{60 \cdot \left(x - y\right)}{z - t}} \]

   9. Taylor expanded in x around 0 99.5%

      \[\leadsto \color{blue}{-60 \cdot \frac{y}{z - t}} \]

   if 4.2000000000000004e140 < x

   1. Initial program 94.8%

      \[\frac{60 \cdot \left(x - y\right)}{z - t} + a \cdot 120 \]
   2. Step-by-step derivation

      1. associate-/l* 99.7%

         \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}}} + a \cdot 120 \]

   3. Simplified 99.7%

      \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}} + a \cdot 120} \]
   4. Step-by-step derivation

      1. clear-num 99.6%

         \[\leadsto \frac{60}{\color{blue}{\frac{1}{\frac{x - y}{z - t}}}} + a \cdot 120 \]

      2. associate-/r/ 99.7%

         \[\leadsto \frac{60}{\color{blue}{\frac{1}{x - y} \cdot \left(z - t\right)}} + a \cdot 120 \]

   5. Applied egg-rr 99.7%

      \[\leadsto \frac{60}{\color{blue}{\frac{1}{x - y} \cdot \left(z - t\right)}} + a \cdot 120 \]

   6. Taylor expanded in a around 0 84.8%

      \[\leadsto \color{blue}{60 \cdot \frac{x - y}{z - t}} \]

   7. Taylor expanded in x around inf 79.2%

      \[\leadsto 60 \cdot \color{blue}{\frac{x}{z - t}} \]
   8. Step-by-step derivation

      1. clear-num 79.1%

         \[\leadsto 60 \cdot \color{blue}{\frac{1}{\frac{z - t}{x}}} \]

      2. div-inv 79.2%

         \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x}}} \]

   9. Applied egg-rr 79.2%

      \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x}}} \]
3. Recombined 4 regimes into one program.

4. Final simplification 68.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -1.32 \cdot 10^{+94}:\\ \;\;\;\;60 \cdot \frac{x}{z - t}\\ \mathbf{elif}\;x \leq 4.8 \cdot 10^{-100}:\\ \;\;\;\;a \cdot 120\\ \mathbf{elif}\;x \leq 5.5 \cdot 10^{-82}:\\ \;\;\;\;-60 \cdot \frac{y}{z - t}\\ \mathbf{elif}\;x \leq 4.2 \cdot 10^{+140}:\\ \;\;\;\;a \cdot 120\\ \mathbf{else}:\\ \;\;\;\;\frac{60}{\frac{z - t}{x}}\\ \end{array} \]

Alternative 5: 56.7% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.05 \cdot 10^{+93}:\\ \;\;\;\;\frac{x \cdot 60}{z - t}\\ \mathbf{elif}\;x \leq 8 \cdot 10^{-100}:\\ \;\;\;\;a \cdot 120\\ \mathbf{elif}\;x \leq 4.6 \cdot 10^{-82}:\\ \;\;\;\;-60 \cdot \frac{y}{z - t}\\ \mathbf{elif}\;x \leq 1.86 \cdot 10^{+140}:\\ \;\;\;\;a \cdot 120\\ \mathbf{else}:\\ \;\;\;\;\frac{60}{\frac{z - t}{x}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= x -1.05e+93)
   (/ (* x 60.0) (- z t))
   (if (<= x 8e-100)
     (* a 120.0)
     (if (<= x 4.6e-82)
       (* -60.0 (/ y (- z t)))
       (if (<= x 1.86e+140) (* a 120.0) (/ 60.0 (/ (- z t) x)))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (x <= -1.05e+93) {
		tmp = (x * 60.0) / (z - t);
	} else if (x <= 8e-100) {
		tmp = a * 120.0;
	} else if (x <= 4.6e-82) {
		tmp = -60.0 * (y / (z - t));
	} else if (x <= 1.86e+140) {
		tmp = a * 120.0;
	} else {
		tmp = 60.0 / ((z - t) / 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 (x <= (-1.05d+93)) then
        tmp = (x * 60.0d0) / (z - t)
    else if (x <= 8d-100) then
        tmp = a * 120.0d0
    else if (x <= 4.6d-82) then
        tmp = (-60.0d0) * (y / (z - t))
    else if (x <= 1.86d+140) then
        tmp = a * 120.0d0
    else
        tmp = 60.0d0 / ((z - t) / 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 (x <= -1.05e+93) {
		tmp = (x * 60.0) / (z - t);
	} else if (x <= 8e-100) {
		tmp = a * 120.0;
	} else if (x <= 4.6e-82) {
		tmp = -60.0 * (y / (z - t));
	} else if (x <= 1.86e+140) {
		tmp = a * 120.0;
	} else {
		tmp = 60.0 / ((z - t) / x);
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if x <= -1.05e+93:
		tmp = (x * 60.0) / (z - t)
	elif x <= 8e-100:
		tmp = a * 120.0
	elif x <= 4.6e-82:
		tmp = -60.0 * (y / (z - t))
	elif x <= 1.86e+140:
		tmp = a * 120.0
	else:
		tmp = 60.0 / ((z - t) / x)
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (x <= -1.05e+93)
		tmp = Float64(Float64(x * 60.0) / Float64(z - t));
	elseif (x <= 8e-100)
		tmp = Float64(a * 120.0);
	elseif (x <= 4.6e-82)
		tmp = Float64(-60.0 * Float64(y / Float64(z - t)));
	elseif (x <= 1.86e+140)
		tmp = Float64(a * 120.0);
	else
		tmp = Float64(60.0 / Float64(Float64(z - t) / x));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (x <= -1.05e+93)
		tmp = (x * 60.0) / (z - t);
	elseif (x <= 8e-100)
		tmp = a * 120.0;
	elseif (x <= 4.6e-82)
		tmp = -60.0 * (y / (z - t));
	elseif (x <= 1.86e+140)
		tmp = a * 120.0;
	else
		tmp = 60.0 / ((z - t) / x);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[x, -1.05e+93], N[(N[(x * 60.0), $MachinePrecision] / N[(z - t), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 8e-100], N[(a * 120.0), $MachinePrecision], If[LessEqual[x, 4.6e-82], N[(-60.0 * N[(y / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 1.86e+140], N[(a * 120.0), $MachinePrecision], N[(60.0 / N[(N[(z - t), $MachinePrecision] / x), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

1. Split input into 4 regimes

2. if x < -1.0499999999999999e93

   1. Initial program 99.8%

      \[\frac{60 \cdot \left(x - y\right)}{z - t} + a \cdot 120 \]
   2. Step-by-step derivation

      1. associate-/l* 99.6%

         \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}}} + a \cdot 120 \]

   3. Simplified 99.6%

      \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}} + a \cdot 120} \]
   4. Step-by-step derivation

      1. clear-num 99.4%

         \[\leadsto \frac{60}{\color{blue}{\frac{1}{\frac{x - y}{z - t}}}} + a \cdot 120 \]

      2. associate-/r/ 99.6%

         \[\leadsto \frac{60}{\color{blue}{\frac{1}{x - y} \cdot \left(z - t\right)}} + a \cdot 120 \]

   5. Applied egg-rr 99.6%

      \[\leadsto \frac{60}{\color{blue}{\frac{1}{x - y} \cdot \left(z - t\right)}} + a \cdot 120 \]

   6. Taylor expanded in a around 0 73.5%

      \[\leadsto \color{blue}{60 \cdot \frac{x - y}{z - t}} \]
   7. Step-by-step derivation

      1. associate-*r/ 73.6%

         \[\leadsto \color{blue}{\frac{60 \cdot \left(x - y\right)}{z - t}} \]

   8. Applied egg-rr 73.6%

      \[\leadsto \color{blue}{\frac{60 \cdot \left(x - y\right)}{z - t}} \]

   9. Taylor expanded in x around inf 65.9%

      \[\leadsto \frac{\color{blue}{60 \cdot x}}{z - t} \]
   10. Step-by-step derivation

       1. *-commutative 65.9%

          \[\leadsto \frac{\color{blue}{x \cdot 60}}{z - t} \]

   11. Simplified 65.9%

       \[\leadsto \frac{\color{blue}{x \cdot 60}}{z - t} \]

   if -1.0499999999999999e93 < x < 8.0000000000000002e-100 or 4.59999999999999994e-82 < x < 1.86e140

   1. Initial program 99.3%

      \[\frac{60 \cdot \left(x - y\right)}{z - t} + a \cdot 120 \]
   2. Step-by-step derivation

      1. associate-/l* 99.9%

         \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}}} + a \cdot 120 \]

   3. Simplified 99.9%

      \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}} + a \cdot 120} \]

   4. Taylor expanded in z around inf 65.9%

      \[\leadsto \color{blue}{120 \cdot a} \]

   if 8.0000000000000002e-100 < x < 4.59999999999999994e-82

   1. Initial program 100.0%

      \[\frac{60 \cdot \left(x - y\right)}{z - t} + a \cdot 120 \]
   2. Step-by-step derivation

      1. associate-/l* 100.0%

         \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}}} + a \cdot 120 \]

   3. Simplified 100.0%

      \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}} + a \cdot 120} \]
   4. Step-by-step derivation

      1. clear-num 99.5%

         \[\leadsto \frac{60}{\color{blue}{\frac{1}{\frac{x - y}{z - t}}}} + a \cdot 120 \]

      2. associate-/r/ 99.7%

         \[\leadsto \frac{60}{\color{blue}{\frac{1}{x - y} \cdot \left(z - t\right)}} + a \cdot 120 \]

   5. Applied egg-rr 99.7%

      \[\leadsto \frac{60}{\color{blue}{\frac{1}{x - y} \cdot \left(z - t\right)}} + a \cdot 120 \]

   6. Taylor expanded in a around 0 99.5%

      \[\leadsto \color{blue}{60 \cdot \frac{x - y}{z - t}} \]
   7. Step-by-step derivation

      1. associate-*r/ 100.0%

         \[\leadsto \color{blue}{\frac{60 \cdot \left(x - y\right)}{z - t}} \]

   8. Applied egg-rr 100.0%

      \[\leadsto \color{blue}{\frac{60 \cdot \left(x - y\right)}{z - t}} \]

   9. Taylor expanded in x around 0 99.5%

      \[\leadsto \color{blue}{-60 \cdot \frac{y}{z - t}} \]

   if 1.86e140 < x

   1. Initial program 94.8%

      \[\frac{60 \cdot \left(x - y\right)}{z - t} + a \cdot 120 \]
   2. Step-by-step derivation

      1. associate-/l* 99.7%

         \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}}} + a \cdot 120 \]

   3. Simplified 99.7%

      \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}} + a \cdot 120} \]
   4. Step-by-step derivation

      1. clear-num 99.6%

         \[\leadsto \frac{60}{\color{blue}{\frac{1}{\frac{x - y}{z - t}}}} + a \cdot 120 \]

      2. associate-/r/ 99.7%

         \[\leadsto \frac{60}{\color{blue}{\frac{1}{x - y} \cdot \left(z - t\right)}} + a \cdot 120 \]

   5. Applied egg-rr 99.7%

      \[\leadsto \frac{60}{\color{blue}{\frac{1}{x - y} \cdot \left(z - t\right)}} + a \cdot 120 \]

   6. Taylor expanded in a around 0 84.8%

      \[\leadsto \color{blue}{60 \cdot \frac{x - y}{z - t}} \]

   7. Taylor expanded in x around inf 79.2%

      \[\leadsto 60 \cdot \color{blue}{\frac{x}{z - t}} \]
   8. Step-by-step derivation

      1. clear-num 79.1%

         \[\leadsto 60 \cdot \color{blue}{\frac{1}{\frac{z - t}{x}}} \]

      2. div-inv 79.2%

         \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x}}} \]

   9. Applied egg-rr 79.2%

      \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x}}} \]
3. Recombined 4 regimes into one program.

4. Final simplification 68.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -1.05 \cdot 10^{+93}:\\ \;\;\;\;\frac{x \cdot 60}{z - t}\\ \mathbf{elif}\;x \leq 8 \cdot 10^{-100}:\\ \;\;\;\;a \cdot 120\\ \mathbf{elif}\;x \leq 4.6 \cdot 10^{-82}:\\ \;\;\;\;-60 \cdot \frac{y}{z - t}\\ \mathbf{elif}\;x \leq 1.86 \cdot 10^{+140}:\\ \;\;\;\;a \cdot 120\\ \mathbf{else}:\\ \;\;\;\;\frac{60}{\frac{z - t}{x}}\\ \end{array} \]

Alternative 6: 87.8% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -3.6 \cdot 10^{+78} \lor \neg \left(x \leq 1.8 \cdot 10^{-65}\right):\\ \;\;\;\;x \cdot \frac{60}{z - t} + a \cdot 120\\ \mathbf{else}:\\ \;\;\;\;a \cdot 120 + -60 \cdot \frac{y}{z - t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= x -3.6e+78) (not (<= x 1.8e-65)))
   (+ (* x (/ 60.0 (- z t))) (* a 120.0))
   (+ (* a 120.0) (* -60.0 (/ y (- z t))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((x <= -3.6e+78) || !(x <= 1.8e-65)) {
		tmp = (x * (60.0 / (z - t))) + (a * 120.0);
	} else {
		tmp = (a * 120.0) + (-60.0 * (y / (z - 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 ((x <= (-3.6d+78)) .or. (.not. (x <= 1.8d-65))) then
        tmp = (x * (60.0d0 / (z - t))) + (a * 120.0d0)
    else
        tmp = (a * 120.0d0) + ((-60.0d0) * (y / (z - 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 ((x <= -3.6e+78) || !(x <= 1.8e-65)) {
		tmp = (x * (60.0 / (z - t))) + (a * 120.0);
	} else {
		tmp = (a * 120.0) + (-60.0 * (y / (z - t)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (x <= -3.6e+78) or not (x <= 1.8e-65):
		tmp = (x * (60.0 / (z - t))) + (a * 120.0)
	else:
		tmp = (a * 120.0) + (-60.0 * (y / (z - t)))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((x <= -3.6e+78) || !(x <= 1.8e-65))
		tmp = Float64(Float64(x * Float64(60.0 / Float64(z - t))) + Float64(a * 120.0));
	else
		tmp = Float64(Float64(a * 120.0) + Float64(-60.0 * Float64(y / Float64(z - t))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((x <= -3.6e+78) || ~((x <= 1.8e-65)))
		tmp = (x * (60.0 / (z - t))) + (a * 120.0);
	else
		tmp = (a * 120.0) + (-60.0 * (y / (z - t)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[x, -3.6e+78], N[Not[LessEqual[x, 1.8e-65]], $MachinePrecision]], N[(N[(x * N[(60.0 / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(a * 120.0), $MachinePrecision]), $MachinePrecision], N[(N[(a * 120.0), $MachinePrecision] + N[(-60.0 * N[(y / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -3.6000000000000002e78 or 1.7999999999999999e-65 < x

   1. Initial program 98.3%

      \[\frac{60 \cdot \left(x - y\right)}{z - t} + a \cdot 120 \]
   2. Step-by-step derivation

      1. associate-/l* 99.7%

         \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}}} + a \cdot 120 \]

   3. Simplified 99.7%

      \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}} + a \cdot 120} \]

   4. Taylor expanded in x around inf 91.1%

      \[\leadsto \color{blue}{60 \cdot \frac{x}{z - t}} + a \cdot 120 \]
   5. Step-by-step derivation

      1. associate-*r/ 90.5%

         \[\leadsto \color{blue}{\frac{60 \cdot x}{z - t}} + a \cdot 120 \]

      2. associate-*l/ 91.2%

         \[\leadsto \color{blue}{\frac{60}{z - t} \cdot x} + a \cdot 120 \]

      3. *-commutative 91.2%

         \[\leadsto \color{blue}{x \cdot \frac{60}{z - t}} + a \cdot 120 \]

   6. Simplified 91.2%

      \[\leadsto \color{blue}{x \cdot \frac{60}{z - t}} + a \cdot 120 \]

   if -3.6000000000000002e78 < x < 1.7999999999999999e-65

   1. Initial program 99.1%

      \[\frac{60 \cdot \left(x - y\right)}{z - t} + a \cdot 120 \]
   2. Step-by-step derivation

      1. associate-/l* 99.9%

         \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}}} + a \cdot 120 \]

   3. Simplified 99.9%

      \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}} + a \cdot 120} \]

   4. Taylor expanded in x around 0 95.5%

      \[\leadsto \color{blue}{120 \cdot a + -60 \cdot \frac{y}{z - t}} \]
3. Recombined 2 regimes into one program.

4. Final simplification 93.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -3.6 \cdot 10^{+78} \lor \neg \left(x \leq 1.8 \cdot 10^{-65}\right):\\ \;\;\;\;x \cdot \frac{60}{z - t} + a \cdot 120\\ \mathbf{else}:\\ \;\;\;\;a \cdot 120 + -60 \cdot \frac{y}{z - t}\\ \end{array} \]

Alternative 7: 87.8% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -4 \cdot 10^{+77} \lor \neg \left(x \leq 2 \cdot 10^{-65}\right):\\ \;\;\;\;x \cdot \frac{60}{z - t} + a \cdot 120\\ \mathbf{else}:\\ \;\;\;\;\frac{-60}{\frac{z - t}{y}} + a \cdot 120\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= x -4e+77) (not (<= x 2e-65)))
   (+ (* x (/ 60.0 (- z t))) (* a 120.0))
   (+ (/ -60.0 (/ (- z t) y)) (* a 120.0))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((x <= -4e+77) || !(x <= 2e-65)) {
		tmp = (x * (60.0 / (z - t))) + (a * 120.0);
	} else {
		tmp = (-60.0 / ((z - t) / y)) + (a * 120.0);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if ((x <= (-4d+77)) .or. (.not. (x <= 2d-65))) then
        tmp = (x * (60.0d0 / (z - t))) + (a * 120.0d0)
    else
        tmp = ((-60.0d0) / ((z - t) / y)) + (a * 120.0d0)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((x <= -4e+77) || !(x <= 2e-65)) {
		tmp = (x * (60.0 / (z - t))) + (a * 120.0);
	} else {
		tmp = (-60.0 / ((z - t) / y)) + (a * 120.0);
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (x <= -4e+77) or not (x <= 2e-65):
		tmp = (x * (60.0 / (z - t))) + (a * 120.0)
	else:
		tmp = (-60.0 / ((z - t) / y)) + (a * 120.0)
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((x <= -4e+77) || !(x <= 2e-65))
		tmp = Float64(Float64(x * Float64(60.0 / Float64(z - t))) + Float64(a * 120.0));
	else
		tmp = Float64(Float64(-60.0 / Float64(Float64(z - t) / y)) + Float64(a * 120.0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((x <= -4e+77) || ~((x <= 2e-65)))
		tmp = (x * (60.0 / (z - t))) + (a * 120.0);
	else
		tmp = (-60.0 / ((z - t) / y)) + (a * 120.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[x, -4e+77], N[Not[LessEqual[x, 2e-65]], $MachinePrecision]], N[(N[(x * N[(60.0 / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(a * 120.0), $MachinePrecision]), $MachinePrecision], N[(N[(-60.0 / N[(N[(z - t), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision] + N[(a * 120.0), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -3.99999999999999993e77 or 1.99999999999999985e-65 < x

   1. Initial program 98.3%

      \[\frac{60 \cdot \left(x - y\right)}{z - t} + a \cdot 120 \]
   2. Step-by-step derivation

      1. associate-/l* 99.7%

         \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}}} + a \cdot 120 \]

   3. Simplified 99.7%

      \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}} + a \cdot 120} \]

   4. Taylor expanded in x around inf 91.1%

      \[\leadsto \color{blue}{60 \cdot \frac{x}{z - t}} + a \cdot 120 \]
   5. Step-by-step derivation

      1. associate-*r/ 90.5%

         \[\leadsto \color{blue}{\frac{60 \cdot x}{z - t}} + a \cdot 120 \]

      2. associate-*l/ 91.2%

         \[\leadsto \color{blue}{\frac{60}{z - t} \cdot x} + a \cdot 120 \]

      3. *-commutative 91.2%

         \[\leadsto \color{blue}{x \cdot \frac{60}{z - t}} + a \cdot 120 \]

   6. Simplified 91.2%

      \[\leadsto \color{blue}{x \cdot \frac{60}{z - t}} + a \cdot 120 \]

   if -3.99999999999999993e77 < x < 1.99999999999999985e-65

   1. Initial program 99.1%

      \[\frac{60 \cdot \left(x - y\right)}{z - t} + a \cdot 120 \]
   2. Step-by-step derivation

      1. associate-/l* 99.9%

         \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}}} + a \cdot 120 \]

   3. Simplified 99.9%

      \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}} + a \cdot 120} \]

   4. Taylor expanded in x around 0 95.5%

      \[\leadsto \color{blue}{-60 \cdot \frac{y}{z - t}} + a \cdot 120 \]
   5. Step-by-step derivation

      1. associate-*r/ 94.8%

         \[\leadsto \color{blue}{\frac{-60 \cdot y}{z - t}} + a \cdot 120 \]

      2. associate-/l* 95.6%

         \[\leadsto \color{blue}{\frac{-60}{\frac{z - t}{y}}} + a \cdot 120 \]

   6. Simplified 95.6%

      \[\leadsto \color{blue}{\frac{-60}{\frac{z - t}{y}}} + a \cdot 120 \]
3. Recombined 2 regimes into one program.

4. Final simplification 93.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -4 \cdot 10^{+77} \lor \neg \left(x \leq 2 \cdot 10^{-65}\right):\\ \;\;\;\;x \cdot \frac{60}{z - t} + a \cdot 120\\ \mathbf{else}:\\ \;\;\;\;\frac{-60}{\frac{z - t}{y}} + a \cdot 120\\ \end{array} \]

Alternative 8: 82.6% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -5 \cdot 10^{+90}:\\ \;\;\;\;\frac{\left(x - y\right) \cdot 60}{z - t}\\ \mathbf{elif}\;x \leq 2.75 \cdot 10^{+147}:\\ \;\;\;\;a \cdot 120 + -60 \cdot \frac{y}{z - t}\\ \mathbf{else}:\\ \;\;\;\;60 \cdot \frac{x - y}{z - t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= x -5e+90)
   (/ (* (- x y) 60.0) (- z t))
   (if (<= x 2.75e+147)
     (+ (* a 120.0) (* -60.0 (/ y (- z t))))
     (* 60.0 (/ (- x y) (- z t))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (x <= -5e+90) {
		tmp = ((x - y) * 60.0) / (z - t);
	} else if (x <= 2.75e+147) {
		tmp = (a * 120.0) + (-60.0 * (y / (z - t)));
	} else {
		tmp = 60.0 * ((x - y) / (z - 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 (x <= (-5d+90)) then
        tmp = ((x - y) * 60.0d0) / (z - t)
    else if (x <= 2.75d+147) then
        tmp = (a * 120.0d0) + ((-60.0d0) * (y / (z - t)))
    else
        tmp = 60.0d0 * ((x - y) / (z - 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 (x <= -5e+90) {
		tmp = ((x - y) * 60.0) / (z - t);
	} else if (x <= 2.75e+147) {
		tmp = (a * 120.0) + (-60.0 * (y / (z - t)));
	} else {
		tmp = 60.0 * ((x - y) / (z - t));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if x <= -5e+90:
		tmp = ((x - y) * 60.0) / (z - t)
	elif x <= 2.75e+147:
		tmp = (a * 120.0) + (-60.0 * (y / (z - t)))
	else:
		tmp = 60.0 * ((x - y) / (z - t))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (x <= -5e+90)
		tmp = Float64(Float64(Float64(x - y) * 60.0) / Float64(z - t));
	elseif (x <= 2.75e+147)
		tmp = Float64(Float64(a * 120.0) + Float64(-60.0 * Float64(y / Float64(z - t))));
	else
		tmp = Float64(60.0 * Float64(Float64(x - y) / Float64(z - t)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (x <= -5e+90)
		tmp = ((x - y) * 60.0) / (z - t);
	elseif (x <= 2.75e+147)
		tmp = (a * 120.0) + (-60.0 * (y / (z - t)));
	else
		tmp = 60.0 * ((x - y) / (z - t));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[x, -5e+90], N[(N[(N[(x - y), $MachinePrecision] * 60.0), $MachinePrecision] / N[(z - t), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 2.75e+147], N[(N[(a * 120.0), $MachinePrecision] + N[(-60.0 * N[(y / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(60.0 * N[(N[(x - y), $MachinePrecision] / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < -5.0000000000000004e90

   1. Initial program 99.8%

      \[\frac{60 \cdot \left(x - y\right)}{z - t} + a \cdot 120 \]
   2. Step-by-step derivation

      1. associate-/l* 99.6%

         \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}}} + a \cdot 120 \]

   3. Simplified 99.6%

      \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}} + a \cdot 120} \]
   4. Step-by-step derivation

      1. clear-num 99.4%

         \[\leadsto \frac{60}{\color{blue}{\frac{1}{\frac{x - y}{z - t}}}} + a \cdot 120 \]

      2. associate-/r/ 99.6%

         \[\leadsto \frac{60}{\color{blue}{\frac{1}{x - y} \cdot \left(z - t\right)}} + a \cdot 120 \]

   5. Applied egg-rr 99.6%

      \[\leadsto \frac{60}{\color{blue}{\frac{1}{x - y} \cdot \left(z - t\right)}} + a \cdot 120 \]

   6. Taylor expanded in a around 0 73.5%

      \[\leadsto \color{blue}{60 \cdot \frac{x - y}{z - t}} \]
   7. Step-by-step derivation

      1. associate-*r/ 73.6%

         \[\leadsto \color{blue}{\frac{60 \cdot \left(x - y\right)}{z - t}} \]

   8. Applied egg-rr 73.6%

      \[\leadsto \color{blue}{\frac{60 \cdot \left(x - y\right)}{z - t}} \]

   if -5.0000000000000004e90 < x < 2.7499999999999999e147

   1. Initial program 99.3%

      \[\frac{60 \cdot \left(x - y\right)}{z - t} + a \cdot 120 \]
   2. Step-by-step derivation

      1. associate-/l* 99.9%

         \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}}} + a \cdot 120 \]

   3. Simplified 99.9%

      \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}} + a \cdot 120} \]

   4. Taylor expanded in x around 0 91.3%

      \[\leadsto \color{blue}{120 \cdot a + -60 \cdot \frac{y}{z - t}} \]

   if 2.7499999999999999e147 < x

   1. Initial program 94.8%

      \[\frac{60 \cdot \left(x - y\right)}{z - t} + a \cdot 120 \]
   2. Step-by-step derivation

      1. associate-/l* 99.7%

         \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}}} + a \cdot 120 \]

   3. Simplified 99.7%

      \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}} + a \cdot 120} \]
   4. Step-by-step derivation

      1. clear-num 99.6%

         \[\leadsto \frac{60}{\color{blue}{\frac{1}{\frac{x - y}{z - t}}}} + a \cdot 120 \]

      2. associate-/r/ 99.7%

         \[\leadsto \frac{60}{\color{blue}{\frac{1}{x - y} \cdot \left(z - t\right)}} + a \cdot 120 \]

   5. Applied egg-rr 99.7%

      \[\leadsto \frac{60}{\color{blue}{\frac{1}{x - y} \cdot \left(z - t\right)}} + a \cdot 120 \]

   6. Taylor expanded in a around 0 84.8%

      \[\leadsto \color{blue}{60 \cdot \frac{x - y}{z - t}} \]
3. Recombined 3 regimes into one program.

4. Final simplification 86.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -5 \cdot 10^{+90}:\\ \;\;\;\;\frac{\left(x - y\right) \cdot 60}{z - t}\\ \mathbf{elif}\;x \leq 2.75 \cdot 10^{+147}:\\ \;\;\;\;a \cdot 120 + -60 \cdot \frac{y}{z - t}\\ \mathbf{else}:\\ \;\;\;\;60 \cdot \frac{x - y}{z - t}\\ \end{array} \]

Alternative 9: 87.7% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -4.6 \cdot 10^{+77}:\\ \;\;\;\;\frac{x \cdot 60}{z - t} + a \cdot 120\\ \mathbf{elif}\;x \leq 2 \cdot 10^{-65}:\\ \;\;\;\;\frac{-60}{\frac{z - t}{y}} + a \cdot 120\\ \mathbf{else}:\\ \;\;\;\;x \cdot \frac{60}{z - t} + a \cdot 120\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= x -4.6e+77)
   (+ (/ (* x 60.0) (- z t)) (* a 120.0))
   (if (<= x 2e-65)
     (+ (/ -60.0 (/ (- z t) y)) (* a 120.0))
     (+ (* x (/ 60.0 (- z t))) (* a 120.0)))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (x <= -4.6e+77) {
		tmp = ((x * 60.0) / (z - t)) + (a * 120.0);
	} else if (x <= 2e-65) {
		tmp = (-60.0 / ((z - t) / y)) + (a * 120.0);
	} else {
		tmp = (x * (60.0 / (z - t))) + (a * 120.0);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if (x <= (-4.6d+77)) then
        tmp = ((x * 60.0d0) / (z - t)) + (a * 120.0d0)
    else if (x <= 2d-65) then
        tmp = ((-60.0d0) / ((z - t) / y)) + (a * 120.0d0)
    else
        tmp = (x * (60.0d0 / (z - t))) + (a * 120.0d0)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (x <= -4.6e+77) {
		tmp = ((x * 60.0) / (z - t)) + (a * 120.0);
	} else if (x <= 2e-65) {
		tmp = (-60.0 / ((z - t) / y)) + (a * 120.0);
	} else {
		tmp = (x * (60.0 / (z - t))) + (a * 120.0);
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if x <= -4.6e+77:
		tmp = ((x * 60.0) / (z - t)) + (a * 120.0)
	elif x <= 2e-65:
		tmp = (-60.0 / ((z - t) / y)) + (a * 120.0)
	else:
		tmp = (x * (60.0 / (z - t))) + (a * 120.0)
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (x <= -4.6e+77)
		tmp = Float64(Float64(Float64(x * 60.0) / Float64(z - t)) + Float64(a * 120.0));
	elseif (x <= 2e-65)
		tmp = Float64(Float64(-60.0 / Float64(Float64(z - t) / y)) + Float64(a * 120.0));
	else
		tmp = Float64(Float64(x * Float64(60.0 / Float64(z - t))) + Float64(a * 120.0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (x <= -4.6e+77)
		tmp = ((x * 60.0) / (z - t)) + (a * 120.0);
	elseif (x <= 2e-65)
		tmp = (-60.0 / ((z - t) / y)) + (a * 120.0);
	else
		tmp = (x * (60.0 / (z - t))) + (a * 120.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[x, -4.6e+77], N[(N[(N[(x * 60.0), $MachinePrecision] / N[(z - t), $MachinePrecision]), $MachinePrecision] + N[(a * 120.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 2e-65], N[(N[(-60.0 / N[(N[(z - t), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision] + N[(a * 120.0), $MachinePrecision]), $MachinePrecision], N[(N[(x * N[(60.0 / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(a * 120.0), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < -4.5999999999999999e77

   1. Initial program 99.8%

      \[\frac{60 \cdot \left(x - y\right)}{z - t} + a \cdot 120 \]

   2. Taylor expanded in x around inf 92.5%

      \[\leadsto \frac{\color{blue}{60 \cdot x}}{z - t} + a \cdot 120 \]
   3. Step-by-step derivation

      1. *-commutative 64.2%

         \[\leadsto \frac{\color{blue}{x \cdot 60}}{z - t} \]

   4. Simplified 92.5%

      \[\leadsto \frac{\color{blue}{x \cdot 60}}{z - t} + a \cdot 120 \]

   if -4.5999999999999999e77 < x < 1.99999999999999985e-65

   1. Initial program 99.1%

      \[\frac{60 \cdot \left(x - y\right)}{z - t} + a \cdot 120 \]
   2. Step-by-step derivation

      1. associate-/l* 99.9%

         \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}}} + a \cdot 120 \]

   3. Simplified 99.9%

      \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}} + a \cdot 120} \]

   4. Taylor expanded in x around 0 95.5%

      \[\leadsto \color{blue}{-60 \cdot \frac{y}{z - t}} + a \cdot 120 \]
   5. Step-by-step derivation

      1. associate-*r/ 94.8%

         \[\leadsto \color{blue}{\frac{-60 \cdot y}{z - t}} + a \cdot 120 \]

      2. associate-/l* 95.6%

         \[\leadsto \color{blue}{\frac{-60}{\frac{z - t}{y}}} + a \cdot 120 \]

   6. Simplified 95.6%

      \[\leadsto \color{blue}{\frac{-60}{\frac{z - t}{y}}} + a \cdot 120 \]

   if 1.99999999999999985e-65 < x

   1. Initial program 97.2%

      \[\frac{60 \cdot \left(x - y\right)}{z - t} + a \cdot 120 \]
   2. Step-by-step derivation

      1. associate-/l* 99.8%

         \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}}} + a \cdot 120 \]

   3. Simplified 99.8%

      \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}} + a \cdot 120} \]

   4. Taylor expanded in x around inf 90.1%

      \[\leadsto \color{blue}{60 \cdot \frac{x}{z - t}} + a \cdot 120 \]
   5. Step-by-step derivation

      1. associate-*r/ 89.0%

         \[\leadsto \color{blue}{\frac{60 \cdot x}{z - t}} + a \cdot 120 \]

      2. associate-*l/ 90.2%

         \[\leadsto \color{blue}{\frac{60}{z - t} \cdot x} + a \cdot 120 \]

      3. *-commutative 90.2%

         \[\leadsto \color{blue}{x \cdot \frac{60}{z - t}} + a \cdot 120 \]

   6. Simplified 90.2%

      \[\leadsto \color{blue}{x \cdot \frac{60}{z - t}} + a \cdot 120 \]
3. Recombined 3 regimes into one program.

4. Final simplification 93.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -4.6 \cdot 10^{+77}:\\ \;\;\;\;\frac{x \cdot 60}{z - t} + a \cdot 120\\ \mathbf{elif}\;x \leq 2 \cdot 10^{-65}:\\ \;\;\;\;\frac{-60}{\frac{z - t}{y}} + a \cdot 120\\ \mathbf{else}:\\ \;\;\;\;x \cdot \frac{60}{z - t} + a \cdot 120\\ \end{array} \]

Alternative 10: 50.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := 60 \cdot \frac{x}{z}\\ \mathbf{if}\;x \leq -4.1 \cdot 10^{+99}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq 4 \cdot 10^{-93}:\\ \;\;\;\;a \cdot 120\\ \mathbf{elif}\;x \leq 4.4 \cdot 10^{-82}:\\ \;\;\;\;60 \cdot \frac{y}{t}\\ \mathbf{elif}\;x \leq 1.8 \cdot 10^{+148}:\\ \;\;\;\;a \cdot 120\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* 60.0 (/ x z))))
   (if (<= x -4.1e+99)
     t_1
     (if (<= x 4e-93)
       (* a 120.0)
       (if (<= x 4.4e-82)
         (* 60.0 (/ y t))
         (if (<= x 1.8e+148) (* a 120.0) t_1))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = 60.0 * (x / z);
	double tmp;
	if (x <= -4.1e+99) {
		tmp = t_1;
	} else if (x <= 4e-93) {
		tmp = a * 120.0;
	} else if (x <= 4.4e-82) {
		tmp = 60.0 * (y / t);
	} else if (x <= 1.8e+148) {
		tmp = a * 120.0;
	} 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 = 60.0d0 * (x / z)
    if (x <= (-4.1d+99)) then
        tmp = t_1
    else if (x <= 4d-93) then
        tmp = a * 120.0d0
    else if (x <= 4.4d-82) then
        tmp = 60.0d0 * (y / t)
    else if (x <= 1.8d+148) then
        tmp = a * 120.0d0
    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 = 60.0 * (x / z);
	double tmp;
	if (x <= -4.1e+99) {
		tmp = t_1;
	} else if (x <= 4e-93) {
		tmp = a * 120.0;
	} else if (x <= 4.4e-82) {
		tmp = 60.0 * (y / t);
	} else if (x <= 1.8e+148) {
		tmp = a * 120.0;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = 60.0 * (x / z)
	tmp = 0
	if x <= -4.1e+99:
		tmp = t_1
	elif x <= 4e-93:
		tmp = a * 120.0
	elif x <= 4.4e-82:
		tmp = 60.0 * (y / t)
	elif x <= 1.8e+148:
		tmp = a * 120.0
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(60.0 * Float64(x / z))
	tmp = 0.0
	if (x <= -4.1e+99)
		tmp = t_1;
	elseif (x <= 4e-93)
		tmp = Float64(a * 120.0);
	elseif (x <= 4.4e-82)
		tmp = Float64(60.0 * Float64(y / t));
	elseif (x <= 1.8e+148)
		tmp = Float64(a * 120.0);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = 60.0 * (x / z);
	tmp = 0.0;
	if (x <= -4.1e+99)
		tmp = t_1;
	elseif (x <= 4e-93)
		tmp = a * 120.0;
	elseif (x <= 4.4e-82)
		tmp = 60.0 * (y / t);
	elseif (x <= 1.8e+148)
		tmp = a * 120.0;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(60.0 * N[(x / z), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -4.1e+99], t$95$1, If[LessEqual[x, 4e-93], N[(a * 120.0), $MachinePrecision], If[LessEqual[x, 4.4e-82], N[(60.0 * N[(y / t), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 1.8e+148], N[(a * 120.0), $MachinePrecision], t$95$1]]]]]

Derivation

1. Split input into 3 regimes

2. if x < -4.09999999999999979e99 or 1.80000000000000003e148 < x

   1. Initial program 97.6%

      \[\frac{60 \cdot \left(x - y\right)}{z - t} + a \cdot 120 \]
   2. Step-by-step derivation

      1. associate-/l* 99.7%

         \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}}} + a \cdot 120 \]

   3. Simplified 99.7%

      \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}} + a \cdot 120} \]
   4. Step-by-step derivation

      1. clear-num 99.5%

         \[\leadsto \frac{60}{\color{blue}{\frac{1}{\frac{x - y}{z - t}}}} + a \cdot 120 \]

      2. associate-/r/ 99.7%

         \[\leadsto \frac{60}{\color{blue}{\frac{1}{x - y} \cdot \left(z - t\right)}} + a \cdot 120 \]

   5. Applied egg-rr 99.7%

      \[\leadsto \frac{60}{\color{blue}{\frac{1}{x - y} \cdot \left(z - t\right)}} + a \cdot 120 \]

   6. Taylor expanded in a around 0 77.9%

      \[\leadsto \color{blue}{60 \cdot \frac{x - y}{z - t}} \]

   7. Taylor expanded in x around inf 71.0%

      \[\leadsto 60 \cdot \color{blue}{\frac{x}{z - t}} \]

   8. Taylor expanded in z around inf 40.1%

      \[\leadsto 60 \cdot \color{blue}{\frac{x}{z}} \]

   if -4.09999999999999979e99 < x < 3.9999999999999996e-93 or 4.39999999999999971e-82 < x < 1.80000000000000003e148

   1. Initial program 99.3%

      \[\frac{60 \cdot \left(x - y\right)}{z - t} + a \cdot 120 \]
   2. Step-by-step derivation

      1. associate-/l* 99.9%

         \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}}} + a \cdot 120 \]

   3. Simplified 99.9%

      \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}} + a \cdot 120} \]

   4. Taylor expanded in z around inf 64.8%

      \[\leadsto \color{blue}{120 \cdot a} \]

   if 3.9999999999999996e-93 < x < 4.39999999999999971e-82

   1. Initial program 100.0%

      \[\frac{60 \cdot \left(x - y\right)}{z - t} + a \cdot 120 \]
   2. Step-by-step derivation

      1. associate-/l* 100.0%

         \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}}} + a \cdot 120 \]

   3. Simplified 100.0%

      \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}} + a \cdot 120} \]
   4. Step-by-step derivation

      1. clear-num 99.7%

         \[\leadsto \frac{60}{\color{blue}{\frac{1}{\frac{x - y}{z - t}}}} + a \cdot 120 \]

      2. associate-/r/ 99.7%

         \[\leadsto \frac{60}{\color{blue}{\frac{1}{x - y} \cdot \left(z - t\right)}} + a \cdot 120 \]

   5. Applied egg-rr 99.7%

      \[\leadsto \frac{60}{\color{blue}{\frac{1}{x - y} \cdot \left(z - t\right)}} + a \cdot 120 \]

   6. Taylor expanded in a around 0 99.7%

      \[\leadsto \color{blue}{60 \cdot \frac{x - y}{z - t}} \]

   7. Taylor expanded in z around 0 99.7%

      \[\leadsto 60 \cdot \color{blue}{\left(-1 \cdot \frac{x - y}{t}\right)} \]
   8. Step-by-step derivation

      1. associate-*r/ 99.7%

         \[\leadsto 60 \cdot \color{blue}{\frac{-1 \cdot \left(x - y\right)}{t}} \]

      2. mul-1-neg 99.7%

         \[\leadsto 60 \cdot \frac{\color{blue}{-\left(x - y\right)}}{t} \]

   9. Simplified 99.7%

      \[\leadsto 60 \cdot \color{blue}{\frac{-\left(x - y\right)}{t}} \]

   10. Taylor expanded in x around 0 99.7%

       \[\leadsto \color{blue}{60 \cdot \frac{y}{t}} \]
3. Recombined 3 regimes into one program.

4. Final simplification 57.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -4.1 \cdot 10^{+99}:\\ \;\;\;\;60 \cdot \frac{x}{z}\\ \mathbf{elif}\;x \leq 4 \cdot 10^{-93}:\\ \;\;\;\;a \cdot 120\\ \mathbf{elif}\;x \leq 4.4 \cdot 10^{-82}:\\ \;\;\;\;60 \cdot \frac{y}{t}\\ \mathbf{elif}\;x \leq 1.8 \cdot 10^{+148}:\\ \;\;\;\;a \cdot 120\\ \mathbf{else}:\\ \;\;\;\;60 \cdot \frac{x}{z}\\ \end{array} \]

Alternative 11: 99.7% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{60}{\frac{z - t}{x - y}} + a \cdot 120 \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (+ (/ 60.0 (/ (- z t) (- x y))) (* a 120.0)))

C

double code(double x, double y, double z, double t, double a) {
	return (60.0 / ((z - t) / (x - y))) + (a * 120.0);
}

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 = (60.0d0 / ((z - t) / (x - y))) + (a * 120.0d0)
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	return (60.0 / ((z - t) / (x - y))) + (a * 120.0);
}

Python

def code(x, y, z, t, a):
	return (60.0 / ((z - t) / (x - y))) + (a * 120.0)

Julia

function code(x, y, z, t, a)
	return Float64(Float64(60.0 / Float64(Float64(z - t) / Float64(x - y))) + Float64(a * 120.0))
end

MATLAB

function tmp = code(x, y, z, t, a)
	tmp = (60.0 / ((z - t) / (x - y))) + (a * 120.0);
end

Wolfram

code[x_, y_, z_, t_, a_] := N[(N[(60.0 / N[(N[(z - t), $MachinePrecision] / N[(x - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(a * 120.0), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 98.7%

   \[\frac{60 \cdot \left(x - y\right)}{z - t} + a \cdot 120 \]
2. Step-by-step derivation

   1. associate-/l* 99.8%

      \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}}} + a \cdot 120 \]

3. Simplified 99.8%

   \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}} + a \cdot 120} \]

4. Final simplification 99.8%

   \[\leadsto \frac{60}{\frac{z - t}{x - y}} + a \cdot 120 \]

Alternative 12: 56.5% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.8 \cdot 10^{+237} \lor \neg \left(y \leq 3.4 \cdot 10^{+192}\right):\\ \;\;\;\;-60 \cdot \frac{y}{z - t}\\ \mathbf{else}:\\ \;\;\;\;a \cdot 120\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= y -1.8e+237) (not (<= y 3.4e+192)))
   (* -60.0 (/ y (- z t)))
   (* a 120.0)))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((y <= -1.8e+237) || !(y <= 3.4e+192)) {
		tmp = -60.0 * (y / (z - t));
	} else {
		tmp = a * 120.0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if ((y <= (-1.8d+237)) .or. (.not. (y <= 3.4d+192))) then
        tmp = (-60.0d0) * (y / (z - t))
    else
        tmp = a * 120.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((y <= -1.8e+237) || !(y <= 3.4e+192)) {
		tmp = -60.0 * (y / (z - t));
	} else {
		tmp = a * 120.0;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (y <= -1.8e+237) or not (y <= 3.4e+192):
		tmp = -60.0 * (y / (z - t))
	else:
		tmp = a * 120.0
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((y <= -1.8e+237) || !(y <= 3.4e+192))
		tmp = Float64(-60.0 * Float64(y / Float64(z - t)));
	else
		tmp = Float64(a * 120.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((y <= -1.8e+237) || ~((y <= 3.4e+192)))
		tmp = -60.0 * (y / (z - t));
	else
		tmp = a * 120.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[y, -1.8e+237], N[Not[LessEqual[y, 3.4e+192]], $MachinePrecision]], N[(-60.0 * N[(y / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(a * 120.0), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -1.80000000000000007e237 or 3.39999999999999996e192 < y

   1. Initial program 95.8%

      \[\frac{60 \cdot \left(x - y\right)}{z - t} + a \cdot 120 \]
   2. Step-by-step derivation

      1. associate-/l* 99.8%

         \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}}} + a \cdot 120 \]

   3. Simplified 99.8%

      \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}} + a \cdot 120} \]
   4. Step-by-step derivation

      1. clear-num 99.8%

         \[\leadsto \frac{60}{\color{blue}{\frac{1}{\frac{x - y}{z - t}}}} + a \cdot 120 \]

      2. associate-/r/ 99.8%

         \[\leadsto \frac{60}{\color{blue}{\frac{1}{x - y} \cdot \left(z - t\right)}} + a \cdot 120 \]

   5. Applied egg-rr 99.8%

      \[\leadsto \frac{60}{\color{blue}{\frac{1}{x - y} \cdot \left(z - t\right)}} + a \cdot 120 \]

   6. Taylor expanded in a around 0 79.7%

      \[\leadsto \color{blue}{60 \cdot \frac{x - y}{z - t}} \]
   7. Step-by-step derivation

      1. associate-*r/ 77.7%

         \[\leadsto \color{blue}{\frac{60 \cdot \left(x - y\right)}{z - t}} \]

   8. Applied egg-rr 77.7%

      \[\leadsto \color{blue}{\frac{60 \cdot \left(x - y\right)}{z - t}} \]

   9. Taylor expanded in x around 0 75.4%

      \[\leadsto \color{blue}{-60 \cdot \frac{y}{z - t}} \]

   if -1.80000000000000007e237 < y < 3.39999999999999996e192

   1. Initial program 99.4%

      \[\frac{60 \cdot \left(x - y\right)}{z - t} + a \cdot 120 \]
   2. Step-by-step derivation

      1. associate-/l* 99.8%

         \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}}} + a \cdot 120 \]

   3. Simplified 99.8%

      \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}} + a \cdot 120} \]

   4. Taylor expanded in z around inf 55.3%

      \[\leadsto \color{blue}{120 \cdot a} \]
3. Recombined 2 regimes into one program.

4. Final simplification 59.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1.8 \cdot 10^{+237} \lor \neg \left(y \leq 3.4 \cdot 10^{+192}\right):\\ \;\;\;\;-60 \cdot \frac{y}{z - t}\\ \mathbf{else}:\\ \;\;\;\;a \cdot 120\\ \end{array} \]

Alternative 13: 50.7% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -9.8 \cdot 10^{-61} \lor \neg \left(a \leq 3.7 \cdot 10^{-131}\right):\\ \;\;\;\;a \cdot 120\\ \mathbf{else}:\\ \;\;\;\;60 \cdot \frac{-x}{t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= a -9.8e-61) (not (<= a 3.7e-131)))
   (* a 120.0)
   (* 60.0 (/ (- x) t))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((a <= -9.8e-61) || !(a <= 3.7e-131)) {
		tmp = a * 120.0;
	} else {
		tmp = 60.0 * (-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 ((a <= (-9.8d-61)) .or. (.not. (a <= 3.7d-131))) then
        tmp = a * 120.0d0
    else
        tmp = 60.0d0 * (-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 ((a <= -9.8e-61) || !(a <= 3.7e-131)) {
		tmp = a * 120.0;
	} else {
		tmp = 60.0 * (-x / t);
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (a <= -9.8e-61) or not (a <= 3.7e-131):
		tmp = a * 120.0
	else:
		tmp = 60.0 * (-x / t)
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((a <= -9.8e-61) || !(a <= 3.7e-131))
		tmp = Float64(a * 120.0);
	else
		tmp = Float64(60.0 * Float64(Float64(-x) / t));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((a <= -9.8e-61) || ~((a <= 3.7e-131)))
		tmp = a * 120.0;
	else
		tmp = 60.0 * (-x / t);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[a, -9.8e-61], N[Not[LessEqual[a, 3.7e-131]], $MachinePrecision]], N[(a * 120.0), $MachinePrecision], N[(60.0 * N[((-x) / t), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if a < -9.80000000000000004e-61 or 3.7000000000000002e-131 < a

   1. Initial program 98.8%

      \[\frac{60 \cdot \left(x - y\right)}{z - t} + a \cdot 120 \]
   2. Step-by-step derivation

      1. associate-/l* 99.9%

         \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}}} + a \cdot 120 \]

   3. Simplified 99.9%

      \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}} + a \cdot 120} \]

   4. Taylor expanded in z around inf 67.9%

      \[\leadsto \color{blue}{120 \cdot a} \]

   if -9.80000000000000004e-61 < a < 3.7000000000000002e-131

   1. Initial program 98.6%

      \[\frac{60 \cdot \left(x - y\right)}{z - t} + a \cdot 120 \]
   2. Step-by-step derivation

      1. associate-/l* 99.6%

         \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}}} + a \cdot 120 \]

   3. Simplified 99.6%

      \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}} + a \cdot 120} \]
   4. Step-by-step derivation

      1. clear-num 99.5%

         \[\leadsto \frac{60}{\color{blue}{\frac{1}{\frac{x - y}{z - t}}}} + a \cdot 120 \]

      2. associate-/r/ 99.6%

         \[\leadsto \frac{60}{\color{blue}{\frac{1}{x - y} \cdot \left(z - t\right)}} + a \cdot 120 \]

   5. Applied egg-rr 99.6%

      \[\leadsto \frac{60}{\color{blue}{\frac{1}{x - y} \cdot \left(z - t\right)}} + a \cdot 120 \]

   6. Taylor expanded in a around 0 87.3%

      \[\leadsto \color{blue}{60 \cdot \frac{x - y}{z - t}} \]

   7. Taylor expanded in x around inf 50.5%

      \[\leadsto 60 \cdot \color{blue}{\frac{x}{z - t}} \]

   8. Taylor expanded in z around 0 30.2%

      \[\leadsto 60 \cdot \color{blue}{\left(-1 \cdot \frac{x}{t}\right)} \]
   9. Step-by-step derivation

      1. associate-*r/ 30.2%

         \[\leadsto 60 \cdot \color{blue}{\frac{-1 \cdot x}{t}} \]

      2. neg-mul-1 30.2%

         \[\leadsto 60 \cdot \frac{\color{blue}{-x}}{t} \]

   10. Simplified 30.2%

       \[\leadsto 60 \cdot \color{blue}{\frac{-x}{t}} \]
3. Recombined 2 regimes into one program.

4. Final simplification 54.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;a \leq -9.8 \cdot 10^{-61} \lor \neg \left(a \leq 3.7 \cdot 10^{-131}\right):\\ \;\;\;\;a \cdot 120\\ \mathbf{else}:\\ \;\;\;\;60 \cdot \frac{-x}{t}\\ \end{array} \]

Alternative 14: 50.5% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -4.5 \cdot 10^{+99} \lor \neg \left(x \leq 4.2 \cdot 10^{+151}\right):\\ \;\;\;\;60 \cdot \frac{x}{z}\\ \mathbf{else}:\\ \;\;\;\;a \cdot 120\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= x -4.5e+99) (not (<= x 4.2e+151))) (* 60.0 (/ x z)) (* a 120.0)))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((x <= -4.5e+99) || !(x <= 4.2e+151)) {
		tmp = 60.0 * (x / z);
	} else {
		tmp = a * 120.0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if ((x <= (-4.5d+99)) .or. (.not. (x <= 4.2d+151))) then
        tmp = 60.0d0 * (x / z)
    else
        tmp = a * 120.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((x <= -4.5e+99) || !(x <= 4.2e+151)) {
		tmp = 60.0 * (x / z);
	} else {
		tmp = a * 120.0;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (x <= -4.5e+99) or not (x <= 4.2e+151):
		tmp = 60.0 * (x / z)
	else:
		tmp = a * 120.0
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((x <= -4.5e+99) || !(x <= 4.2e+151))
		tmp = Float64(60.0 * Float64(x / z));
	else
		tmp = Float64(a * 120.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((x <= -4.5e+99) || ~((x <= 4.2e+151)))
		tmp = 60.0 * (x / z);
	else
		tmp = a * 120.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[x, -4.5e+99], N[Not[LessEqual[x, 4.2e+151]], $MachinePrecision]], N[(60.0 * N[(x / z), $MachinePrecision]), $MachinePrecision], N[(a * 120.0), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -4.5e99 or 4.2000000000000001e151 < x

   1. Initial program 97.6%

      \[\frac{60 \cdot \left(x - y\right)}{z - t} + a \cdot 120 \]
   2. Step-by-step derivation

      1. associate-/l* 99.7%

         \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}}} + a \cdot 120 \]

   3. Simplified 99.7%

      \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}} + a \cdot 120} \]
   4. Step-by-step derivation

      1. clear-num 99.5%

         \[\leadsto \frac{60}{\color{blue}{\frac{1}{\frac{x - y}{z - t}}}} + a \cdot 120 \]

      2. associate-/r/ 99.7%

         \[\leadsto \frac{60}{\color{blue}{\frac{1}{x - y} \cdot \left(z - t\right)}} + a \cdot 120 \]

   5. Applied egg-rr 99.7%

      \[\leadsto \frac{60}{\color{blue}{\frac{1}{x - y} \cdot \left(z - t\right)}} + a \cdot 120 \]

   6. Taylor expanded in a around 0 77.9%

      \[\leadsto \color{blue}{60 \cdot \frac{x - y}{z - t}} \]

   7. Taylor expanded in x around inf 71.0%

      \[\leadsto 60 \cdot \color{blue}{\frac{x}{z - t}} \]

   8. Taylor expanded in z around inf 40.1%

      \[\leadsto 60 \cdot \color{blue}{\frac{x}{z}} \]

   if -4.5e99 < x < 4.2000000000000001e151

   1. Initial program 99.3%

      \[\frac{60 \cdot \left(x - y\right)}{z - t} + a \cdot 120 \]
   2. Step-by-step derivation

      1. associate-/l* 99.9%

         \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}}} + a \cdot 120 \]

   3. Simplified 99.9%

      \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}} + a \cdot 120} \]

   4. Taylor expanded in z around inf 63.0%

      \[\leadsto \color{blue}{120 \cdot a} \]
3. Recombined 2 regimes into one program.

4. Final simplification 55.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -4.5 \cdot 10^{+99} \lor \neg \left(x \leq 4.2 \cdot 10^{+151}\right):\\ \;\;\;\;60 \cdot \frac{x}{z}\\ \mathbf{else}:\\ \;\;\;\;a \cdot 120\\ \end{array} \]

Alternative 15: 50.2% accurate, 4.3× speedup

Math

\[\begin{array}{l} \\ a \cdot 120 \end{array} \]

FPCore

(FPCore (x y z t a) :precision binary64 (* a 120.0))

C

double code(double x, double y, double z, double t, double a) {
	return a * 120.0;
}

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 = a * 120.0d0
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	return a * 120.0;
}

Python

def code(x, y, z, t, a):
	return a * 120.0

Julia

function code(x, y, z, t, a)
	return Float64(a * 120.0)
end

MATLAB

function tmp = code(x, y, z, t, a)
	tmp = a * 120.0;
end

Wolfram

code[x_, y_, z_, t_, a_] := N[(a * 120.0), $MachinePrecision]

Derivation

1. Initial program 98.7%

   \[\frac{60 \cdot \left(x - y\right)}{z - t} + a \cdot 120 \]
2. Step-by-step derivation

   1. associate-/l* 99.8%

      \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}}} + a \cdot 120 \]

3. Simplified 99.8%

   \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}} + a \cdot 120} \]

4. Taylor expanded in z around inf 49.2%

   \[\leadsto \color{blue}{120 \cdot a} \]

5. Final simplification 49.2%

   \[\leadsto a \cdot 120 \]

Developer target: 99.7% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{60}{\frac{z - t}{x - y}} + a \cdot 120 \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (+ (/ 60.0 (/ (- z t) (- x y))) (* a 120.0)))

C

double code(double x, double y, double z, double t, double a) {
	return (60.0 / ((z - t) / (x - y))) + (a * 120.0);
}

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 = (60.0d0 / ((z - t) / (x - y))) + (a * 120.0d0)
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	return (60.0 / ((z - t) / (x - y))) + (a * 120.0);
}

Python

def code(x, y, z, t, a):
	return (60.0 / ((z - t) / (x - y))) + (a * 120.0)

Julia

function code(x, y, z, t, a)
	return Float64(Float64(60.0 / Float64(Float64(z - t) / Float64(x - y))) + Float64(a * 120.0))
end

MATLAB

function tmp = code(x, y, z, t, a)
	tmp = (60.0 / ((z - t) / (x - y))) + (a * 120.0);
end

Wolfram

code[x_, y_, z_, t_, a_] := N[(N[(60.0 / N[(N[(z - t), $MachinePrecision] / N[(x - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(a * 120.0), $MachinePrecision]), $MachinePrecision]

Reproduce

herbie shell --seed 2023274 
(FPCore (x y z t a)
  :name "Data.Colour.RGB:hslsv from colour-2.3.3, B"
  :precision binary64

  :herbie-target
  (+ (/ 60.0 (/ (- z t) (- x y))) (* a 120.0))

  (+ (/ (* 60.0 (- x y)) (- z t)) (* a 120.0)))