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

Percentage Accurate: 99.4% → 99.8%

Time: 12.0s

Alternatives: 13

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

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 99.4%

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

   1. +-commutative 99.4%

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

   2. fma-def 99.5%

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

   3. associate-*l/ 99.8%

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

3. Simplified 99.8%

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

4. Final simplification 99.8%

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

Alternative 2: 62.5% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := a \cdot 120 + \frac{-60}{\frac{z}{y}}\\ t_2 := a \cdot 120 + -60 \cdot \frac{x}{t}\\ t_3 := 60 \cdot \frac{x}{z - t}\\ \mathbf{if}\;t \leq -5.6 \cdot 10^{+17}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;t \leq 6.2 \cdot 10^{-189}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq 1.45 \cdot 10^{-103}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;t \leq 5 \cdot 10^{-81}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq 0.022:\\ \;\;\;\;t_3\\ \mathbf{elif}\;t \leq 3.1 \cdot 10^{+29}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq 5.6 \cdot 10^{+68}:\\ \;\;\;\;\frac{60}{z - t} \cdot x\\ \mathbf{elif}\;t \leq 7 \cdot 10^{+218}:\\ \;\;\;\;a \cdot 120 + 60 \cdot \frac{y}{t}\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ (* a 120.0) (/ -60.0 (/ z y))))
        (t_2 (+ (* a 120.0) (* -60.0 (/ x t))))
        (t_3 (* 60.0 (/ x (- z t)))))
   (if (<= t -5.6e+17)
     t_2
     (if (<= t 6.2e-189)
       t_1
       (if (<= t 1.45e-103)
         t_3
         (if (<= t 5e-81)
           t_1
           (if (<= t 0.022)
             t_3
             (if (<= t 3.1e+29)
               t_1
               (if (<= t 5.6e+68)
                 (* (/ 60.0 (- z t)) x)
                 (if (<= t 7e+218)
                   (+ (* a 120.0) (* 60.0 (/ y t)))
                   t_2))))))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = (a * 120.0) + (-60.0 / (z / y));
	double t_2 = (a * 120.0) + (-60.0 * (x / t));
	double t_3 = 60.0 * (x / (z - t));
	double tmp;
	if (t <= -5.6e+17) {
		tmp = t_2;
	} else if (t <= 6.2e-189) {
		tmp = t_1;
	} else if (t <= 1.45e-103) {
		tmp = t_3;
	} else if (t <= 5e-81) {
		tmp = t_1;
	} else if (t <= 0.022) {
		tmp = t_3;
	} else if (t <= 3.1e+29) {
		tmp = t_1;
	} else if (t <= 5.6e+68) {
		tmp = (60.0 / (z - t)) * x;
	} else if (t <= 7e+218) {
		tmp = (a * 120.0) + (60.0 * (y / t));
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: t_3
    real(8) :: tmp
    t_1 = (a * 120.0d0) + ((-60.0d0) / (z / y))
    t_2 = (a * 120.0d0) + ((-60.0d0) * (x / t))
    t_3 = 60.0d0 * (x / (z - t))
    if (t <= (-5.6d+17)) then
        tmp = t_2
    else if (t <= 6.2d-189) then
        tmp = t_1
    else if (t <= 1.45d-103) then
        tmp = t_3
    else if (t <= 5d-81) then
        tmp = t_1
    else if (t <= 0.022d0) then
        tmp = t_3
    else if (t <= 3.1d+29) then
        tmp = t_1
    else if (t <= 5.6d+68) then
        tmp = (60.0d0 / (z - t)) * x
    else if (t <= 7d+218) then
        tmp = (a * 120.0d0) + (60.0d0 * (y / t))
    else
        tmp = t_2
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = (a * 120.0) + (-60.0 / (z / y));
	double t_2 = (a * 120.0) + (-60.0 * (x / t));
	double t_3 = 60.0 * (x / (z - t));
	double tmp;
	if (t <= -5.6e+17) {
		tmp = t_2;
	} else if (t <= 6.2e-189) {
		tmp = t_1;
	} else if (t <= 1.45e-103) {
		tmp = t_3;
	} else if (t <= 5e-81) {
		tmp = t_1;
	} else if (t <= 0.022) {
		tmp = t_3;
	} else if (t <= 3.1e+29) {
		tmp = t_1;
	} else if (t <= 5.6e+68) {
		tmp = (60.0 / (z - t)) * x;
	} else if (t <= 7e+218) {
		tmp = (a * 120.0) + (60.0 * (y / t));
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = (a * 120.0) + (-60.0 / (z / y))
	t_2 = (a * 120.0) + (-60.0 * (x / t))
	t_3 = 60.0 * (x / (z - t))
	tmp = 0
	if t <= -5.6e+17:
		tmp = t_2
	elif t <= 6.2e-189:
		tmp = t_1
	elif t <= 1.45e-103:
		tmp = t_3
	elif t <= 5e-81:
		tmp = t_1
	elif t <= 0.022:
		tmp = t_3
	elif t <= 3.1e+29:
		tmp = t_1
	elif t <= 5.6e+68:
		tmp = (60.0 / (z - t)) * x
	elif t <= 7e+218:
		tmp = (a * 120.0) + (60.0 * (y / t))
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(Float64(a * 120.0) + Float64(-60.0 / Float64(z / y)))
	t_2 = Float64(Float64(a * 120.0) + Float64(-60.0 * Float64(x / t)))
	t_3 = Float64(60.0 * Float64(x / Float64(z - t)))
	tmp = 0.0
	if (t <= -5.6e+17)
		tmp = t_2;
	elseif (t <= 6.2e-189)
		tmp = t_1;
	elseif (t <= 1.45e-103)
		tmp = t_3;
	elseif (t <= 5e-81)
		tmp = t_1;
	elseif (t <= 0.022)
		tmp = t_3;
	elseif (t <= 3.1e+29)
		tmp = t_1;
	elseif (t <= 5.6e+68)
		tmp = Float64(Float64(60.0 / Float64(z - t)) * x);
	elseif (t <= 7e+218)
		tmp = Float64(Float64(a * 120.0) + Float64(60.0 * Float64(y / t)));
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = (a * 120.0) + (-60.0 / (z / y));
	t_2 = (a * 120.0) + (-60.0 * (x / t));
	t_3 = 60.0 * (x / (z - t));
	tmp = 0.0;
	if (t <= -5.6e+17)
		tmp = t_2;
	elseif (t <= 6.2e-189)
		tmp = t_1;
	elseif (t <= 1.45e-103)
		tmp = t_3;
	elseif (t <= 5e-81)
		tmp = t_1;
	elseif (t <= 0.022)
		tmp = t_3;
	elseif (t <= 3.1e+29)
		tmp = t_1;
	elseif (t <= 5.6e+68)
		tmp = (60.0 / (z - t)) * x;
	elseif (t <= 7e+218)
		tmp = (a * 120.0) + (60.0 * (y / t));
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(N[(a * 120.0), $MachinePrecision] + N[(-60.0 / N[(z / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(a * 120.0), $MachinePrecision] + N[(-60.0 * N[(x / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(60.0 * N[(x / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -5.6e+17], t$95$2, If[LessEqual[t, 6.2e-189], t$95$1, If[LessEqual[t, 1.45e-103], t$95$3, If[LessEqual[t, 5e-81], t$95$1, If[LessEqual[t, 0.022], t$95$3, If[LessEqual[t, 3.1e+29], t$95$1, If[LessEqual[t, 5.6e+68], N[(N[(60.0 / N[(z - t), $MachinePrecision]), $MachinePrecision] * x), $MachinePrecision], If[LessEqual[t, 7e+218], N[(N[(a * 120.0), $MachinePrecision] + N[(60.0 * N[(y / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$2]]]]]]]]]]]

Derivation

1. Split input into 5 regimes

2. if t < -5.6e17 or 7.00000000000000038e218 < t

   1. Initial program 99.8%

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

   2. Taylor expanded in x around inf 90.1%

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

      1. associate-*r/ 90.2%

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

   4. Simplified 90.2%

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

   5. Taylor expanded in z around 0 84.8%

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

   if -5.6e17 < t < 6.2000000000000001e-189 or 1.4499999999999999e-103 < t < 4.99999999999999981e-81 or 0.021999999999999999 < t < 3.0999999999999999e29

   1. Initial program 99.9%

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

   2. Taylor expanded in z around inf 88.9%

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

   3. Taylor expanded in x around 0 71.0%

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

      1. associate-*r/ 71.1%

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

      2. associate-/l* 71.0%

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

   5. Simplified 71.0%

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

   if 6.2000000000000001e-189 < t < 1.4499999999999999e-103 or 4.99999999999999981e-81 < t < 0.021999999999999999

   1. Initial program 99.7%

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

      1. associate-*l/ 99.8%

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

   3. Applied egg-rr 99.8%

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

   4. Taylor expanded in x around inf 71.8%

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

   if 3.0999999999999999e29 < t < 5.6e68

   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/ 100.0%

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

   3. Applied egg-rr 100.0%

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

   4. Taylor expanded in x around inf 75.8%

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

      1. associate-*r/ 75.4%

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

      2. associate-*l/ 76.0%

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

      3. *-commutative 76.0%

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

   6. Simplified 76.0%

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

   if 5.6e68 < t < 7.00000000000000038e218

   1. Initial program 96.5%

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

      1. associate-*l/ 99.7%

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

   3. Applied egg-rr 99.7%

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

      1. clear-num 99.7%

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

      2. associate-*l/ 99.8%

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

      3. div-inv 99.8%

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

      4. *-un-lft-identity 99.8%

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

      5. metadata-eval 99.8%

         \[\leadsto \frac{x - y}{\left(z - t\right) \cdot \color{blue}{0.016666666666666666}} + a \cdot 120 \]

   5. Applied egg-rr 99.8%

      \[\leadsto \color{blue}{\frac{x - y}{\left(z - t\right) \cdot 0.016666666666666666}} + a \cdot 120 \]

   6. Taylor expanded in z around 0 86.4%

      \[\leadsto \frac{x - y}{\color{blue}{-0.016666666666666666 \cdot t}} + a \cdot 120 \]
   7. Step-by-step derivation

      1. *-commutative 86.4%

         \[\leadsto \frac{x - y}{\color{blue}{t \cdot -0.016666666666666666}} + a \cdot 120 \]

   8. Simplified 86.4%

      \[\leadsto \frac{x - y}{\color{blue}{t \cdot -0.016666666666666666}} + a \cdot 120 \]

   9. Taylor expanded in x around 0 76.6%

      \[\leadsto \color{blue}{60 \cdot \frac{y}{t}} + a \cdot 120 \]
3. Recombined 5 regimes into one program.

4. Final simplification 75.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq -5.6 \cdot 10^{+17}:\\ \;\;\;\;a \cdot 120 + -60 \cdot \frac{x}{t}\\ \mathbf{elif}\;t \leq 6.2 \cdot 10^{-189}:\\ \;\;\;\;a \cdot 120 + \frac{-60}{\frac{z}{y}}\\ \mathbf{elif}\;t \leq 1.45 \cdot 10^{-103}:\\ \;\;\;\;60 \cdot \frac{x}{z - t}\\ \mathbf{elif}\;t \leq 5 \cdot 10^{-81}:\\ \;\;\;\;a \cdot 120 + \frac{-60}{\frac{z}{y}}\\ \mathbf{elif}\;t \leq 0.022:\\ \;\;\;\;60 \cdot \frac{x}{z - t}\\ \mathbf{elif}\;t \leq 3.1 \cdot 10^{+29}:\\ \;\;\;\;a \cdot 120 + \frac{-60}{\frac{z}{y}}\\ \mathbf{elif}\;t \leq 5.6 \cdot 10^{+68}:\\ \;\;\;\;\frac{60}{z - t} \cdot x\\ \mathbf{elif}\;t \leq 7 \cdot 10^{+218}:\\ \;\;\;\;a \cdot 120 + 60 \cdot \frac{y}{t}\\ \mathbf{else}:\\ \;\;\;\;a \cdot 120 + -60 \cdot \frac{x}{t}\\ \end{array} \]

Alternative 3: 71.7% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := a \cdot 120 + \frac{-60}{\frac{z}{y}}\\ t_2 := a \cdot 120 + -60 \cdot \frac{x - y}{t}\\ \mathbf{if}\;t \leq -5.6 \cdot 10^{+17}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;t \leq 6.2 \cdot 10^{-189}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq 1.55 \cdot 10^{-103}:\\ \;\;\;\;60 \cdot \frac{x}{z - t}\\ \mathbf{elif}\;t \leq 1.25 \cdot 10^{-83} \lor \neg \left(t \leq 0.6\right) \land t \leq 1.4 \cdot 10^{+29}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ (* a 120.0) (/ -60.0 (/ z y))))
        (t_2 (+ (* a 120.0) (* -60.0 (/ (- x y) t)))))
   (if (<= t -5.6e+17)
     t_2
     (if (<= t 6.2e-189)
       t_1
       (if (<= t 1.55e-103)
         (* 60.0 (/ x (- z t)))
         (if (or (<= t 1.25e-83) (and (not (<= t 0.6)) (<= t 1.4e+29)))
           t_1
           t_2))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = (a * 120.0) + (-60.0 / (z / y));
	double t_2 = (a * 120.0) + (-60.0 * ((x - y) / t));
	double tmp;
	if (t <= -5.6e+17) {
		tmp = t_2;
	} else if (t <= 6.2e-189) {
		tmp = t_1;
	} else if (t <= 1.55e-103) {
		tmp = 60.0 * (x / (z - t));
	} else if ((t <= 1.25e-83) || (!(t <= 0.6) && (t <= 1.4e+29))) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = (a * 120.0d0) + ((-60.0d0) / (z / y))
    t_2 = (a * 120.0d0) + ((-60.0d0) * ((x - y) / t))
    if (t <= (-5.6d+17)) then
        tmp = t_2
    else if (t <= 6.2d-189) then
        tmp = t_1
    else if (t <= 1.55d-103) then
        tmp = 60.0d0 * (x / (z - t))
    else if ((t <= 1.25d-83) .or. (.not. (t <= 0.6d0)) .and. (t <= 1.4d+29)) then
        tmp = t_1
    else
        tmp = t_2
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = (a * 120.0) + (-60.0 / (z / y));
	double t_2 = (a * 120.0) + (-60.0 * ((x - y) / t));
	double tmp;
	if (t <= -5.6e+17) {
		tmp = t_2;
	} else if (t <= 6.2e-189) {
		tmp = t_1;
	} else if (t <= 1.55e-103) {
		tmp = 60.0 * (x / (z - t));
	} else if ((t <= 1.25e-83) || (!(t <= 0.6) && (t <= 1.4e+29))) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = (a * 120.0) + (-60.0 / (z / y))
	t_2 = (a * 120.0) + (-60.0 * ((x - y) / t))
	tmp = 0
	if t <= -5.6e+17:
		tmp = t_2
	elif t <= 6.2e-189:
		tmp = t_1
	elif t <= 1.55e-103:
		tmp = 60.0 * (x / (z - t))
	elif (t <= 1.25e-83) or (not (t <= 0.6) and (t <= 1.4e+29)):
		tmp = t_1
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(Float64(a * 120.0) + Float64(-60.0 / Float64(z / y)))
	t_2 = Float64(Float64(a * 120.0) + Float64(-60.0 * Float64(Float64(x - y) / t)))
	tmp = 0.0
	if (t <= -5.6e+17)
		tmp = t_2;
	elseif (t <= 6.2e-189)
		tmp = t_1;
	elseif (t <= 1.55e-103)
		tmp = Float64(60.0 * Float64(x / Float64(z - t)));
	elseif ((t <= 1.25e-83) || (!(t <= 0.6) && (t <= 1.4e+29)))
		tmp = t_1;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = (a * 120.0) + (-60.0 / (z / y));
	t_2 = (a * 120.0) + (-60.0 * ((x - y) / t));
	tmp = 0.0;
	if (t <= -5.6e+17)
		tmp = t_2;
	elseif (t <= 6.2e-189)
		tmp = t_1;
	elseif (t <= 1.55e-103)
		tmp = 60.0 * (x / (z - t));
	elseif ((t <= 1.25e-83) || (~((t <= 0.6)) && (t <= 1.4e+29)))
		tmp = t_1;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(N[(a * 120.0), $MachinePrecision] + N[(-60.0 / N[(z / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(a * 120.0), $MachinePrecision] + N[(-60.0 * N[(N[(x - y), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -5.6e+17], t$95$2, If[LessEqual[t, 6.2e-189], t$95$1, If[LessEqual[t, 1.55e-103], N[(60.0 * N[(x / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[Or[LessEqual[t, 1.25e-83], And[N[Not[LessEqual[t, 0.6]], $MachinePrecision], LessEqual[t, 1.4e+29]]], t$95$1, t$95$2]]]]]]

Derivation

1. Split input into 3 regimes

2. if t < -5.6e17 or 1.25e-83 < t < 0.599999999999999978 or 1.4e29 < t

   1. Initial program 99.0%

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

   2. Taylor expanded in z around 0 88.4%

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

   if -5.6e17 < t < 6.2000000000000001e-189 or 1.5500000000000001e-103 < t < 1.25e-83 or 0.599999999999999978 < t < 1.4e29

   1. Initial program 99.9%

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

   2. Taylor expanded in z around inf 88.9%

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

   3. Taylor expanded in x around 0 71.0%

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

      1. associate-*r/ 71.1%

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

      2. associate-/l* 71.0%

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

   5. Simplified 71.0%

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

   if 6.2000000000000001e-189 < t < 1.5500000000000001e-103

   1. Initial program 99.6%

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

      1. associate-*l/ 99.8%

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

   3. Applied egg-rr 99.8%

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

   4. Taylor expanded in x around inf 74.4%

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

4. Final simplification 79.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq -5.6 \cdot 10^{+17}:\\ \;\;\;\;a \cdot 120 + -60 \cdot \frac{x - y}{t}\\ \mathbf{elif}\;t \leq 6.2 \cdot 10^{-189}:\\ \;\;\;\;a \cdot 120 + \frac{-60}{\frac{z}{y}}\\ \mathbf{elif}\;t \leq 1.55 \cdot 10^{-103}:\\ \;\;\;\;60 \cdot \frac{x}{z - t}\\ \mathbf{elif}\;t \leq 1.25 \cdot 10^{-83} \lor \neg \left(t \leq 0.6\right) \land t \leq 1.4 \cdot 10^{+29}:\\ \;\;\;\;a \cdot 120 + \frac{-60}{\frac{z}{y}}\\ \mathbf{else}:\\ \;\;\;\;a \cdot 120 + -60 \cdot \frac{x - y}{t}\\ \end{array} \]

Alternative 4: 54.2% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := -60 \cdot \frac{y}{z - t}\\ t_2 := 60 \cdot \frac{x}{z - t}\\ \mathbf{if}\;x \leq -2.1 \cdot 10^{+61}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;x \leq -7.2 \cdot 10^{-110}:\\ \;\;\;\;a \cdot 120\\ \mathbf{elif}\;x \leq -5.6 \cdot 10^{-149}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq 4.8 \cdot 10^{-18}:\\ \;\;\;\;a \cdot 120\\ \mathbf{elif}\;x \leq 3.4 \cdot 10^{+78}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* -60.0 (/ y (- z t)))) (t_2 (* 60.0 (/ x (- z t)))))
   (if (<= x -2.1e+61)
     t_2
     (if (<= x -7.2e-110)
       (* a 120.0)
       (if (<= x -5.6e-149)
         t_1
         (if (<= x 4.8e-18) (* a 120.0) (if (<= x 3.4e+78) t_1 t_2)))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = -60.0 * (y / (z - t));
	double t_2 = 60.0 * (x / (z - t));
	double tmp;
	if (x <= -2.1e+61) {
		tmp = t_2;
	} else if (x <= -7.2e-110) {
		tmp = a * 120.0;
	} else if (x <= -5.6e-149) {
		tmp = t_1;
	} else if (x <= 4.8e-18) {
		tmp = a * 120.0;
	} else if (x <= 3.4e+78) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = (-60.0d0) * (y / (z - t))
    t_2 = 60.0d0 * (x / (z - t))
    if (x <= (-2.1d+61)) then
        tmp = t_2
    else if (x <= (-7.2d-110)) then
        tmp = a * 120.0d0
    else if (x <= (-5.6d-149)) then
        tmp = t_1
    else if (x <= 4.8d-18) then
        tmp = a * 120.0d0
    else if (x <= 3.4d+78) then
        tmp = t_1
    else
        tmp = t_2
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = -60.0 * (y / (z - t));
	double t_2 = 60.0 * (x / (z - t));
	double tmp;
	if (x <= -2.1e+61) {
		tmp = t_2;
	} else if (x <= -7.2e-110) {
		tmp = a * 120.0;
	} else if (x <= -5.6e-149) {
		tmp = t_1;
	} else if (x <= 4.8e-18) {
		tmp = a * 120.0;
	} else if (x <= 3.4e+78) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = -60.0 * (y / (z - t))
	t_2 = 60.0 * (x / (z - t))
	tmp = 0
	if x <= -2.1e+61:
		tmp = t_2
	elif x <= -7.2e-110:
		tmp = a * 120.0
	elif x <= -5.6e-149:
		tmp = t_1
	elif x <= 4.8e-18:
		tmp = a * 120.0
	elif x <= 3.4e+78:
		tmp = t_1
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(-60.0 * Float64(y / Float64(z - t)))
	t_2 = Float64(60.0 * Float64(x / Float64(z - t)))
	tmp = 0.0
	if (x <= -2.1e+61)
		tmp = t_2;
	elseif (x <= -7.2e-110)
		tmp = Float64(a * 120.0);
	elseif (x <= -5.6e-149)
		tmp = t_1;
	elseif (x <= 4.8e-18)
		tmp = Float64(a * 120.0);
	elseif (x <= 3.4e+78)
		tmp = t_1;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = -60.0 * (y / (z - t));
	t_2 = 60.0 * (x / (z - t));
	tmp = 0.0;
	if (x <= -2.1e+61)
		tmp = t_2;
	elseif (x <= -7.2e-110)
		tmp = a * 120.0;
	elseif (x <= -5.6e-149)
		tmp = t_1;
	elseif (x <= 4.8e-18)
		tmp = a * 120.0;
	elseif (x <= 3.4e+78)
		tmp = t_1;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(-60.0 * N[(y / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(60.0 * N[(x / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -2.1e+61], t$95$2, If[LessEqual[x, -7.2e-110], N[(a * 120.0), $MachinePrecision], If[LessEqual[x, -5.6e-149], t$95$1, If[LessEqual[x, 4.8e-18], N[(a * 120.0), $MachinePrecision], If[LessEqual[x, 3.4e+78], t$95$1, t$95$2]]]]]]]

Derivation

1. Split input into 3 regimes

2. if x < -2.1000000000000001e61 or 3.40000000000000007e78 < x

   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/ 99.7%

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

   3. Applied egg-rr 99.7%

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

   4. Taylor expanded in x around inf 69.1%

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

   if -2.1000000000000001e61 < x < -7.1999999999999999e-110 or -5.5999999999999997e-149 < x < 4.79999999999999988e-18

   1. Initial program 99.9%

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

   2. Taylor expanded in z around inf 64.0%

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

   if -7.1999999999999999e-110 < x < -5.5999999999999997e-149 or 4.79999999999999988e-18 < x < 3.40000000000000007e78

   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/ 99.7%

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

   3. Applied egg-rr 99.7%

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

   4. Taylor expanded in y around inf 68.0%

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

4. Final simplification 66.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -2.1 \cdot 10^{+61}:\\ \;\;\;\;60 \cdot \frac{x}{z - t}\\ \mathbf{elif}\;x \leq -7.2 \cdot 10^{-110}:\\ \;\;\;\;a \cdot 120\\ \mathbf{elif}\;x \leq -5.6 \cdot 10^{-149}:\\ \;\;\;\;-60 \cdot \frac{y}{z - t}\\ \mathbf{elif}\;x \leq 4.8 \cdot 10^{-18}:\\ \;\;\;\;a \cdot 120\\ \mathbf{elif}\;x \leq 3.4 \cdot 10^{+78}:\\ \;\;\;\;-60 \cdot \frac{y}{z - t}\\ \mathbf{else}:\\ \;\;\;\;60 \cdot \frac{x}{z - t}\\ \end{array} \]

Alternative 5: 54.3% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{-60}{\frac{z - t}{y}}\\ t_2 := 60 \cdot \frac{x}{z - t}\\ \mathbf{if}\;x \leq -2.9 \cdot 10^{+60}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;x \leq -6.4 \cdot 10^{-108}:\\ \;\;\;\;a \cdot 120\\ \mathbf{elif}\;x \leq -1.8 \cdot 10^{-147}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq 3.45 \cdot 10^{-18}:\\ \;\;\;\;a \cdot 120\\ \mathbf{elif}\;x \leq 3.3 \cdot 10^{+78}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (/ -60.0 (/ (- z t) y))) (t_2 (* 60.0 (/ x (- z t)))))
   (if (<= x -2.9e+60)
     t_2
     (if (<= x -6.4e-108)
       (* a 120.0)
       (if (<= x -1.8e-147)
         t_1
         (if (<= x 3.45e-18) (* a 120.0) (if (<= x 3.3e+78) t_1 t_2)))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = -60.0 / ((z - t) / y);
	double t_2 = 60.0 * (x / (z - t));
	double tmp;
	if (x <= -2.9e+60) {
		tmp = t_2;
	} else if (x <= -6.4e-108) {
		tmp = a * 120.0;
	} else if (x <= -1.8e-147) {
		tmp = t_1;
	} else if (x <= 3.45e-18) {
		tmp = a * 120.0;
	} else if (x <= 3.3e+78) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = (-60.0d0) / ((z - t) / y)
    t_2 = 60.0d0 * (x / (z - t))
    if (x <= (-2.9d+60)) then
        tmp = t_2
    else if (x <= (-6.4d-108)) then
        tmp = a * 120.0d0
    else if (x <= (-1.8d-147)) then
        tmp = t_1
    else if (x <= 3.45d-18) then
        tmp = a * 120.0d0
    else if (x <= 3.3d+78) then
        tmp = t_1
    else
        tmp = t_2
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = -60.0 / ((z - t) / y);
	double t_2 = 60.0 * (x / (z - t));
	double tmp;
	if (x <= -2.9e+60) {
		tmp = t_2;
	} else if (x <= -6.4e-108) {
		tmp = a * 120.0;
	} else if (x <= -1.8e-147) {
		tmp = t_1;
	} else if (x <= 3.45e-18) {
		tmp = a * 120.0;
	} else if (x <= 3.3e+78) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = -60.0 / ((z - t) / y)
	t_2 = 60.0 * (x / (z - t))
	tmp = 0
	if x <= -2.9e+60:
		tmp = t_2
	elif x <= -6.4e-108:
		tmp = a * 120.0
	elif x <= -1.8e-147:
		tmp = t_1
	elif x <= 3.45e-18:
		tmp = a * 120.0
	elif x <= 3.3e+78:
		tmp = t_1
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(-60.0 / Float64(Float64(z - t) / y))
	t_2 = Float64(60.0 * Float64(x / Float64(z - t)))
	tmp = 0.0
	if (x <= -2.9e+60)
		tmp = t_2;
	elseif (x <= -6.4e-108)
		tmp = Float64(a * 120.0);
	elseif (x <= -1.8e-147)
		tmp = t_1;
	elseif (x <= 3.45e-18)
		tmp = Float64(a * 120.0);
	elseif (x <= 3.3e+78)
		tmp = t_1;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = -60.0 / ((z - t) / y);
	t_2 = 60.0 * (x / (z - t));
	tmp = 0.0;
	if (x <= -2.9e+60)
		tmp = t_2;
	elseif (x <= -6.4e-108)
		tmp = a * 120.0;
	elseif (x <= -1.8e-147)
		tmp = t_1;
	elseif (x <= 3.45e-18)
		tmp = a * 120.0;
	elseif (x <= 3.3e+78)
		tmp = t_1;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(-60.0 / N[(N[(z - t), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(60.0 * N[(x / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -2.9e+60], t$95$2, If[LessEqual[x, -6.4e-108], N[(a * 120.0), $MachinePrecision], If[LessEqual[x, -1.8e-147], t$95$1, If[LessEqual[x, 3.45e-18], N[(a * 120.0), $MachinePrecision], If[LessEqual[x, 3.3e+78], t$95$1, t$95$2]]]]]]]

Derivation

1. Split input into 3 regimes

2. if x < -2.9e60 or 3.3e78 < x

   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/ 99.7%

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

   3. Applied egg-rr 99.7%

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

   4. Taylor expanded in x around inf 69.1%

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

   if -2.9e60 < x < -6.3999999999999999e-108 or -1.80000000000000006e-147 < x < 3.4500000000000001e-18

   1. Initial program 99.9%

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

   2. Taylor expanded in z around inf 64.0%

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

   if -6.3999999999999999e-108 < x < -1.80000000000000006e-147 or 3.4500000000000001e-18 < x < 3.3e78

   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/ 99.7%

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

   3. Applied egg-rr 99.7%

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

   4. Taylor expanded in y around inf 68.0%

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

      1. associate-*r/ 68.0%

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

      2. associate-/l* 68.1%

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

   6. Applied egg-rr 68.1%

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

4. Final simplification 66.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -2.9 \cdot 10^{+60}:\\ \;\;\;\;60 \cdot \frac{x}{z - t}\\ \mathbf{elif}\;x \leq -6.4 \cdot 10^{-108}:\\ \;\;\;\;a \cdot 120\\ \mathbf{elif}\;x \leq -1.8 \cdot 10^{-147}:\\ \;\;\;\;\frac{-60}{\frac{z - t}{y}}\\ \mathbf{elif}\;x \leq 3.45 \cdot 10^{-18}:\\ \;\;\;\;a \cdot 120\\ \mathbf{elif}\;x \leq 3.3 \cdot 10^{+78}:\\ \;\;\;\;\frac{-60}{\frac{z - t}{y}}\\ \mathbf{else}:\\ \;\;\;\;60 \cdot \frac{x}{z - t}\\ \end{array} \]

Alternative 6: 53.8% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := -60 \cdot \frac{y}{z - t}\\ \mathbf{if}\;y \leq -2.3 \cdot 10^{+54}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq -0.48:\\ \;\;\;\;a \cdot 120\\ \mathbf{elif}\;y \leq -2.1 \cdot 10^{-63}:\\ \;\;\;\;60 \cdot \frac{x}{z}\\ \mathbf{elif}\;y \leq 1.3 \cdot 10^{+77}:\\ \;\;\;\;a \cdot 120\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* -60.0 (/ y (- z t)))))
   (if (<= y -2.3e+54)
     t_1
     (if (<= y -0.48)
       (* a 120.0)
       (if (<= y -2.1e-63)
         (* 60.0 (/ x z))
         (if (<= y 1.3e+77) (* a 120.0) t_1))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = -60.0 * (y / (z - t));
	double tmp;
	if (y <= -2.3e+54) {
		tmp = t_1;
	} else if (y <= -0.48) {
		tmp = a * 120.0;
	} else if (y <= -2.1e-63) {
		tmp = 60.0 * (x / z);
	} else if (y <= 1.3e+77) {
		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) * (y / (z - t))
    if (y <= (-2.3d+54)) then
        tmp = t_1
    else if (y <= (-0.48d0)) then
        tmp = a * 120.0d0
    else if (y <= (-2.1d-63)) then
        tmp = 60.0d0 * (x / z)
    else if (y <= 1.3d+77) 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 * (y / (z - t));
	double tmp;
	if (y <= -2.3e+54) {
		tmp = t_1;
	} else if (y <= -0.48) {
		tmp = a * 120.0;
	} else if (y <= -2.1e-63) {
		tmp = 60.0 * (x / z);
	} else if (y <= 1.3e+77) {
		tmp = a * 120.0;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = -60.0 * (y / (z - t))
	tmp = 0
	if y <= -2.3e+54:
		tmp = t_1
	elif y <= -0.48:
		tmp = a * 120.0
	elif y <= -2.1e-63:
		tmp = 60.0 * (x / z)
	elif y <= 1.3e+77:
		tmp = a * 120.0
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(-60.0 * Float64(y / Float64(z - t)))
	tmp = 0.0
	if (y <= -2.3e+54)
		tmp = t_1;
	elseif (y <= -0.48)
		tmp = Float64(a * 120.0);
	elseif (y <= -2.1e-63)
		tmp = Float64(60.0 * Float64(x / z));
	elseif (y <= 1.3e+77)
		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 * (y / (z - t));
	tmp = 0.0;
	if (y <= -2.3e+54)
		tmp = t_1;
	elseif (y <= -0.48)
		tmp = a * 120.0;
	elseif (y <= -2.1e-63)
		tmp = 60.0 * (x / z);
	elseif (y <= 1.3e+77)
		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[(y / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -2.3e+54], t$95$1, If[LessEqual[y, -0.48], N[(a * 120.0), $MachinePrecision], If[LessEqual[y, -2.1e-63], N[(60.0 * N[(x / z), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 1.3e+77], N[(a * 120.0), $MachinePrecision], t$95$1]]]]]

Derivation

1. Split input into 3 regimes

2. if y < -2.29999999999999994e54 or 1.3000000000000001e77 < y

   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/ 99.7%

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

   3. Applied egg-rr 99.7%

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

   4. Taylor expanded in y around inf 59.1%

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

   if -2.29999999999999994e54 < y < -0.47999999999999998 or -2.1e-63 < y < 1.3000000000000001e77

   1. Initial program 99.2%

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

   2. Taylor expanded in z around inf 56.9%

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

   if -0.47999999999999998 < y < -2.1e-63

   1. Initial program 99.7%

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

      1. associate-*l/ 99.7%

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

   3. Applied egg-rr 99.7%

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

   4. Taylor expanded in x around inf 80.0%

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

      1. associate-*r/ 80.2%

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

      2. associate-*l/ 80.2%

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

      3. *-commutative 80.2%

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

   6. Simplified 80.2%

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

   7. Taylor expanded in z around inf 60.9%

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

4. Final simplification 57.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -2.3 \cdot 10^{+54}:\\ \;\;\;\;-60 \cdot \frac{y}{z - t}\\ \mathbf{elif}\;y \leq -0.48:\\ \;\;\;\;a \cdot 120\\ \mathbf{elif}\;y \leq -2.1 \cdot 10^{-63}:\\ \;\;\;\;60 \cdot \frac{x}{z}\\ \mathbf{elif}\;y \leq 1.3 \cdot 10^{+77}:\\ \;\;\;\;a \cdot 120\\ \mathbf{else}:\\ \;\;\;\;-60 \cdot \frac{y}{z - t}\\ \end{array} \]

Alternative 7: 83.9% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -5.6 \cdot 10^{+17} \lor \neg \left(t \leq 8.8 \cdot 10^{+18}\right):\\ \;\;\;\;a \cdot 120 + -60 \cdot \frac{x - y}{t}\\ \mathbf{else}:\\ \;\;\;\;a \cdot 120 + 60 \cdot \frac{x - y}{z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= t -5.6e+17) (not (<= t 8.8e+18)))
   (+ (* a 120.0) (* -60.0 (/ (- x y) t)))
   (+ (* a 120.0) (* 60.0 (/ (- x y) z)))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((t <= -5.6e+17) || !(t <= 8.8e+18)) {
		tmp = (a * 120.0) + (-60.0 * ((x - y) / t));
	} else {
		tmp = (a * 120.0) + (60.0 * ((x - 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 ((t <= (-5.6d+17)) .or. (.not. (t <= 8.8d+18))) then
        tmp = (a * 120.0d0) + ((-60.0d0) * ((x - y) / t))
    else
        tmp = (a * 120.0d0) + (60.0d0 * ((x - 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 ((t <= -5.6e+17) || !(t <= 8.8e+18)) {
		tmp = (a * 120.0) + (-60.0 * ((x - y) / t));
	} else {
		tmp = (a * 120.0) + (60.0 * ((x - y) / z));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (t <= -5.6e+17) or not (t <= 8.8e+18):
		tmp = (a * 120.0) + (-60.0 * ((x - y) / t))
	else:
		tmp = (a * 120.0) + (60.0 * ((x - y) / z))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((t <= -5.6e+17) || !(t <= 8.8e+18))
		tmp = Float64(Float64(a * 120.0) + Float64(-60.0 * Float64(Float64(x - y) / t)));
	else
		tmp = Float64(Float64(a * 120.0) + Float64(60.0 * Float64(Float64(x - y) / z)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((t <= -5.6e+17) || ~((t <= 8.8e+18)))
		tmp = (a * 120.0) + (-60.0 * ((x - y) / t));
	else
		tmp = (a * 120.0) + (60.0 * ((x - y) / z));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[t, -5.6e+17], N[Not[LessEqual[t, 8.8e+18]], $MachinePrecision]], N[(N[(a * 120.0), $MachinePrecision] + N[(-60.0 * N[(N[(x - y), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(a * 120.0), $MachinePrecision] + N[(60.0 * N[(N[(x - y), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if t < -5.6e17 or 8.8e18 < t

   1. Initial program 98.9%

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

   2. Taylor expanded in z around 0 90.1%

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

   if -5.6e17 < t < 8.8e18

   1. Initial program 99.8%

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

   2. Taylor expanded in z around inf 82.6%

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

4. Final simplification 85.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq -5.6 \cdot 10^{+17} \lor \neg \left(t \leq 8.8 \cdot 10^{+18}\right):\\ \;\;\;\;a \cdot 120 + -60 \cdot \frac{x - y}{t}\\ \mathbf{else}:\\ \;\;\;\;a \cdot 120 + 60 \cdot \frac{x - y}{z}\\ \end{array} \]

Alternative 8: 88.9% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -960 \lor \neg \left(x \leq 1.75 \cdot 10^{+75}\right):\\ \;\;\;\;a \cdot 120 + \frac{60 \cdot x}{z - t}\\ \mathbf{else}:\\ \;\;\;\;a \cdot 120 + \frac{y \cdot -60}{z - t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= x -960.0) (not (<= x 1.75e+75)))
   (+ (* a 120.0) (/ (* 60.0 x) (- z t)))
   (+ (* a 120.0) (/ (* y -60.0) (- z t)))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((x <= -960.0) || !(x <= 1.75e+75)) {
		tmp = (a * 120.0) + ((60.0 * x) / (z - t));
	} else {
		tmp = (a * 120.0) + ((y * -60.0) / (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 <= (-960.0d0)) .or. (.not. (x <= 1.75d+75))) then
        tmp = (a * 120.0d0) + ((60.0d0 * x) / (z - t))
    else
        tmp = (a * 120.0d0) + ((y * (-60.0d0)) / (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 <= -960.0) || !(x <= 1.75e+75)) {
		tmp = (a * 120.0) + ((60.0 * x) / (z - t));
	} else {
		tmp = (a * 120.0) + ((y * -60.0) / (z - t));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (x <= -960.0) or not (x <= 1.75e+75):
		tmp = (a * 120.0) + ((60.0 * x) / (z - t))
	else:
		tmp = (a * 120.0) + ((y * -60.0) / (z - t))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((x <= -960.0) || !(x <= 1.75e+75))
		tmp = Float64(Float64(a * 120.0) + Float64(Float64(60.0 * x) / Float64(z - t)));
	else
		tmp = Float64(Float64(a * 120.0) + Float64(Float64(y * -60.0) / Float64(z - t)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((x <= -960.0) || ~((x <= 1.75e+75)))
		tmp = (a * 120.0) + ((60.0 * x) / (z - t));
	else
		tmp = (a * 120.0) + ((y * -60.0) / (z - t));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[x, -960.0], N[Not[LessEqual[x, 1.75e+75]], $MachinePrecision]], N[(N[(a * 120.0), $MachinePrecision] + N[(N[(60.0 * x), $MachinePrecision] / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(a * 120.0), $MachinePrecision] + N[(N[(y * -60.0), $MachinePrecision] / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -960 or 1.7499999999999999e75 < x

   1. Initial program 98.9%

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

   2. Taylor expanded in x around inf 89.7%

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

      1. associate-*r/ 88.8%

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

   4. Simplified 88.8%

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

   if -960 < x < 1.7499999999999999e75

   1. Initial program 99.9%

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

   2. Taylor expanded in x around 0 93.2%

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

      1. associate-*r/ 93.2%

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

   4. Simplified 93.2%

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

4. Final simplification 91.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -960 \lor \neg \left(x \leq 1.75 \cdot 10^{+75}\right):\\ \;\;\;\;a \cdot 120 + \frac{60 \cdot x}{z - t}\\ \mathbf{else}:\\ \;\;\;\;a \cdot 120 + \frac{y \cdot -60}{z - t}\\ \end{array} \]

Alternative 9: 99.8% accurate, 1.0× speedup

Math

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

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(Float64(60.0 / 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[(N[(60.0 / N[(z - t), $MachinePrecision]), $MachinePrecision] * N[(x - y), $MachinePrecision]), $MachinePrecision] + N[(a * 120.0), $MachinePrecision]), $MachinePrecision]

Derivation

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%

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

3. Applied egg-rr 99.8%

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

4. Final simplification 99.8%

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

Alternative 10: 51.1% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := 60 \cdot \frac{x}{z}\\ \mathbf{if}\;x \leq -2.2 \cdot 10^{+121}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq 2.5 \cdot 10^{+159}:\\ \;\;\;\;a \cdot 120\\ \mathbf{elif}\;x \leq 1.85 \cdot 10^{+282}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;x \cdot \frac{-60}{t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* 60.0 (/ x z))))
   (if (<= x -2.2e+121)
     t_1
     (if (<= x 2.5e+159)
       (* a 120.0)
       (if (<= x 1.85e+282) t_1 (* x (/ -60.0 t)))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = 60.0 * (x / z);
	double tmp;
	if (x <= -2.2e+121) {
		tmp = t_1;
	} else if (x <= 2.5e+159) {
		tmp = a * 120.0;
	} else if (x <= 1.85e+282) {
		tmp = t_1;
	} else {
		tmp = x * (-60.0 / 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) :: t_1
    real(8) :: tmp
    t_1 = 60.0d0 * (x / z)
    if (x <= (-2.2d+121)) then
        tmp = t_1
    else if (x <= 2.5d+159) then
        tmp = a * 120.0d0
    else if (x <= 1.85d+282) then
        tmp = t_1
    else
        tmp = x * ((-60.0d0) / t)
    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 <= -2.2e+121) {
		tmp = t_1;
	} else if (x <= 2.5e+159) {
		tmp = a * 120.0;
	} else if (x <= 1.85e+282) {
		tmp = t_1;
	} else {
		tmp = x * (-60.0 / t);
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = 60.0 * (x / z)
	tmp = 0
	if x <= -2.2e+121:
		tmp = t_1
	elif x <= 2.5e+159:
		tmp = a * 120.0
	elif x <= 1.85e+282:
		tmp = t_1
	else:
		tmp = x * (-60.0 / t)
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(60.0 * Float64(x / z))
	tmp = 0.0
	if (x <= -2.2e+121)
		tmp = t_1;
	elseif (x <= 2.5e+159)
		tmp = Float64(a * 120.0);
	elseif (x <= 1.85e+282)
		tmp = t_1;
	else
		tmp = Float64(x * Float64(-60.0 / t));
	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 <= -2.2e+121)
		tmp = t_1;
	elseif (x <= 2.5e+159)
		tmp = a * 120.0;
	elseif (x <= 1.85e+282)
		tmp = t_1;
	else
		tmp = x * (-60.0 / t);
	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, -2.2e+121], t$95$1, If[LessEqual[x, 2.5e+159], N[(a * 120.0), $MachinePrecision], If[LessEqual[x, 1.85e+282], t$95$1, N[(x * N[(-60.0 / t), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

1. Split input into 3 regimes

2. if x < -2.20000000000000001e121 or 2.50000000000000002e159 < x < 1.8500000000000001e282

   1. Initial program 99.7%

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

      1. associate-*l/ 99.7%

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

   3. Applied egg-rr 99.7%

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

   4. Taylor expanded in x around inf 83.5%

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

      1. associate-*r/ 83.4%

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

      2. associate-*l/ 83.5%

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

      3. *-commutative 83.5%

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

   6. Simplified 83.5%

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

   7. Taylor expanded in z around inf 55.7%

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

   if -2.20000000000000001e121 < x < 2.50000000000000002e159

   1. Initial program 99.8%

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

   2. Taylor expanded in z around inf 52.6%

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

   if 1.8500000000000001e282 < x

   1. Initial program 86.3%

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

      1. associate-*l/ 100.0%

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

   3. Applied egg-rr 100.0%

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

   4. Taylor expanded in x around inf 73.3%

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

      1. associate-*r/ 59.6%

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

      2. associate-*l/ 73.3%

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

      3. *-commutative 73.3%

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

   6. Simplified 73.3%

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

   7. Taylor expanded in z around 0 72.1%

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

4. Final simplification 53.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -2.2 \cdot 10^{+121}:\\ \;\;\;\;60 \cdot \frac{x}{z}\\ \mathbf{elif}\;x \leq 2.5 \cdot 10^{+159}:\\ \;\;\;\;a \cdot 120\\ \mathbf{elif}\;x \leq 1.85 \cdot 10^{+282}:\\ \;\;\;\;60 \cdot \frac{x}{z}\\ \mathbf{else}:\\ \;\;\;\;x \cdot \frac{-60}{t}\\ \end{array} \]

Alternative 11: 49.2% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -3.5 \cdot 10^{+47} \lor \neg \left(y \leq 2.7 \cdot 10^{+200}\right):\\ \;\;\;\;-60 \cdot \frac{y}{z}\\ \mathbf{else}:\\ \;\;\;\;a \cdot 120\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= y -3.5e+47) (not (<= y 2.7e+200))) (* -60.0 (/ y z)) (* a 120.0)))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((y <= -3.5e+47) || !(y <= 2.7e+200)) {
		tmp = -60.0 * (y / 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 ((y <= (-3.5d+47)) .or. (.not. (y <= 2.7d+200))) then
        tmp = (-60.0d0) * (y / 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 ((y <= -3.5e+47) || !(y <= 2.7e+200)) {
		tmp = -60.0 * (y / z);
	} else {
		tmp = a * 120.0;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (y <= -3.5e+47) or not (y <= 2.7e+200):
		tmp = -60.0 * (y / z)
	else:
		tmp = a * 120.0
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((y <= -3.5e+47) || !(y <= 2.7e+200))
		tmp = Float64(-60.0 * Float64(y / 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 ((y <= -3.5e+47) || ~((y <= 2.7e+200)))
		tmp = -60.0 * (y / z);
	else
		tmp = a * 120.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[y, -3.5e+47], N[Not[LessEqual[y, 2.7e+200]], $MachinePrecision]], N[(-60.0 * N[(y / z), $MachinePrecision]), $MachinePrecision], N[(a * 120.0), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -3.50000000000000015e47 or 2.70000000000000016e200 < y

   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/ 99.7%

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

   3. Applied egg-rr 99.7%

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

   4. Taylor expanded in y around inf 62.4%

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

   5. Taylor expanded in z around inf 45.5%

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

   if -3.50000000000000015e47 < y < 2.70000000000000016e200

   1. Initial program 99.3%

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

   2. Taylor expanded in z around inf 52.8%

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

4. Final simplification 50.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -3.5 \cdot 10^{+47} \lor \neg \left(y \leq 2.7 \cdot 10^{+200}\right):\\ \;\;\;\;-60 \cdot \frac{y}{z}\\ \mathbf{else}:\\ \;\;\;\;a \cdot 120\\ \end{array} \]

Alternative 12: 51.1% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.8 \cdot 10^{+119} \lor \neg \left(x \leq 5 \cdot 10^{+159}\right):\\ \;\;\;\;60 \cdot \frac{x}{z}\\ \mathbf{else}:\\ \;\;\;\;a \cdot 120\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= x -1.8e+119) (not (<= x 5e+159))) (* 60.0 (/ x z)) (* a 120.0)))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((x <= -1.8e+119) || !(x <= 5e+159)) {
		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 <= (-1.8d+119)) .or. (.not. (x <= 5d+159))) 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 <= -1.8e+119) || !(x <= 5e+159)) {
		tmp = 60.0 * (x / z);
	} else {
		tmp = a * 120.0;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (x <= -1.8e+119) or not (x <= 5e+159):
		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 <= -1.8e+119) || !(x <= 5e+159))
		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 <= -1.8e+119) || ~((x <= 5e+159)))
		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, -1.8e+119], N[Not[LessEqual[x, 5e+159]], $MachinePrecision]], N[(60.0 * N[(x / z), $MachinePrecision]), $MachinePrecision], N[(a * 120.0), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -1.80000000000000001e119 or 5.00000000000000003e159 < x

   1. Initial program 98.2%

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

      1. associate-*l/ 99.7%

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

   3. Applied egg-rr 99.7%

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

   4. Taylor expanded in x around inf 82.4%

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

      1. associate-*r/ 80.7%

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

      2. associate-*l/ 82.3%

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

      3. *-commutative 82.3%

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

   6. Simplified 82.3%

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

   7. Taylor expanded in z around inf 49.8%

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

   if -1.80000000000000001e119 < x < 5.00000000000000003e159

   1. Initial program 99.8%

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

   2. Taylor expanded in z around inf 52.6%

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

4. Final simplification 51.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -1.8 \cdot 10^{+119} \lor \neg \left(x \leq 5 \cdot 10^{+159}\right):\\ \;\;\;\;60 \cdot \frac{x}{z}\\ \mathbf{else}:\\ \;\;\;\;a \cdot 120\\ \end{array} \]

Alternative 13: 50.3% 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 99.4%

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

2. Taylor expanded in z around inf 43.7%

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

3. Final simplification 43.7%

   \[\leadsto a \cdot 120 \]

Developer target: 99.8% 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 2023311 
(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)))