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

Percentage Accurate: 99.4% → 99.8%

Time: 15.1s

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

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

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* 99.8%

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

3. Simplified 99.8%

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

   1. +-commutative 99.8%

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

   2. fma-define 99.8%

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

   3. associate-*r/ 98.3%

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

   4. *-commutative 98.3%

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

   5. associate-/l* 99.8%

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

6. Applied egg-rr 99.8%

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

7. Final simplification 99.8%

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

Alternative 2: 73.3% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \cdot 120 \leq -0.0004 \lor \neg \left(a \cdot 120 \leq -2 \cdot 10^{-69} \lor \neg \left(a \cdot 120 \leq -1 \cdot 10^{-137}\right) \land a \cdot 120 \leq 2 \cdot 10^{-26}\right):\\ \;\;\;\;a \cdot 120\\ \mathbf{else}:\\ \;\;\;\;60 \cdot \frac{y - x}{t - z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= (* a 120.0) -0.0004)
         (not
          (or (<= (* a 120.0) -2e-69)
              (and (not (<= (* a 120.0) -1e-137)) (<= (* a 120.0) 2e-26)))))
   (* a 120.0)
   (* 60.0 (/ (- y x) (- t z)))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (((a * 120.0) <= -0.0004) || !(((a * 120.0) <= -2e-69) || (!((a * 120.0) <= -1e-137) && ((a * 120.0) <= 2e-26)))) {
		tmp = a * 120.0;
	} else {
		tmp = 60.0 * ((y - x) / (t - z));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if (((a * 120.0d0) <= (-0.0004d0)) .or. (.not. ((a * 120.0d0) <= (-2d-69)) .or. (.not. ((a * 120.0d0) <= (-1d-137))) .and. ((a * 120.0d0) <= 2d-26))) then
        tmp = a * 120.0d0
    else
        tmp = 60.0d0 * ((y - x) / (t - z))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (((a * 120.0) <= -0.0004) || !(((a * 120.0) <= -2e-69) || (!((a * 120.0) <= -1e-137) && ((a * 120.0) <= 2e-26)))) {
		tmp = a * 120.0;
	} else {
		tmp = 60.0 * ((y - x) / (t - z));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if ((a * 120.0) <= -0.0004) or not (((a * 120.0) <= -2e-69) or (not ((a * 120.0) <= -1e-137) and ((a * 120.0) <= 2e-26))):
		tmp = a * 120.0
	else:
		tmp = 60.0 * ((y - x) / (t - z))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((Float64(a * 120.0) <= -0.0004) || !((Float64(a * 120.0) <= -2e-69) || (!(Float64(a * 120.0) <= -1e-137) && (Float64(a * 120.0) <= 2e-26))))
		tmp = Float64(a * 120.0);
	else
		tmp = Float64(60.0 * Float64(Float64(y - x) / Float64(t - z)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (((a * 120.0) <= -0.0004) || ~((((a * 120.0) <= -2e-69) || (~(((a * 120.0) <= -1e-137)) && ((a * 120.0) <= 2e-26)))))
		tmp = a * 120.0;
	else
		tmp = 60.0 * ((y - x) / (t - z));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[N[(a * 120.0), $MachinePrecision], -0.0004], N[Not[Or[LessEqual[N[(a * 120.0), $MachinePrecision], -2e-69], And[N[Not[LessEqual[N[(a * 120.0), $MachinePrecision], -1e-137]], $MachinePrecision], LessEqual[N[(a * 120.0), $MachinePrecision], 2e-26]]]], $MachinePrecision]], N[(a * 120.0), $MachinePrecision], N[(60.0 * N[(N[(y - x), $MachinePrecision] / N[(t - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 a #s(literal 120 binary64)) < -4.00000000000000019e-4 or -1.9999999999999999e-69 < (*.f64 a #s(literal 120 binary64)) < -9.99999999999999978e-138 or 2.0000000000000001e-26 < (*.f64 a #s(literal 120 binary64))

   1. Initial program 97.9%

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

      1. associate-/l* 99.9%

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

   3. Simplified 99.9%

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

   5. Taylor expanded in z around inf 70.5%

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

   if -4.00000000000000019e-4 < (*.f64 a #s(literal 120 binary64)) < -1.9999999999999999e-69 or -9.99999999999999978e-138 < (*.f64 a #s(literal 120 binary64)) < 2.0000000000000001e-26

   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%

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

   3. Simplified 99.7%

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

   5. Taylor expanded in a around 0 84.1%

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

4. Final simplification 76.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;a \cdot 120 \leq -0.0004 \lor \neg \left(a \cdot 120 \leq -2 \cdot 10^{-69} \lor \neg \left(a \cdot 120 \leq -1 \cdot 10^{-137}\right) \land a \cdot 120 \leq 2 \cdot 10^{-26}\right):\\ \;\;\;\;a \cdot 120\\ \mathbf{else}:\\ \;\;\;\;60 \cdot \frac{y - x}{t - z}\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 72.1% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := 60 \cdot \frac{y - x}{t - z}\\ \mathbf{if}\;a \cdot 120 \leq -0.0004:\\ \;\;\;\;a \cdot 120\\ \mathbf{elif}\;a \cdot 120 \leq -2 \cdot 10^{-69}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;a \cdot 120 \leq -1 \cdot 10^{-137}:\\ \;\;\;\;a \cdot 120\\ \mathbf{elif}\;a \cdot 120 \leq 2 \cdot 10^{-26}:\\ \;\;\;\;t\_1\\ \mathbf{else}:\\ \;\;\;\;a \cdot 120 + -60 \cdot \frac{y}{z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* 60.0 (/ (- y x) (- t z)))))
   (if (<= (* a 120.0) -0.0004)
     (* a 120.0)
     (if (<= (* a 120.0) -2e-69)
       t_1
       (if (<= (* a 120.0) -1e-137)
         (* a 120.0)
         (if (<= (* a 120.0) 2e-26) t_1 (+ (* a 120.0) (* -60.0 (/ y z)))))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = 60.0 * ((y - x) / (t - z));
	double tmp;
	if ((a * 120.0) <= -0.0004) {
		tmp = a * 120.0;
	} else if ((a * 120.0) <= -2e-69) {
		tmp = t_1;
	} else if ((a * 120.0) <= -1e-137) {
		tmp = a * 120.0;
	} else if ((a * 120.0) <= 2e-26) {
		tmp = t_1;
	} else {
		tmp = (a * 120.0) + (-60.0 * (y / z));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: t_1
    real(8) :: tmp
    t_1 = 60.0d0 * ((y - x) / (t - z))
    if ((a * 120.0d0) <= (-0.0004d0)) then
        tmp = a * 120.0d0
    else if ((a * 120.0d0) <= (-2d-69)) then
        tmp = t_1
    else if ((a * 120.0d0) <= (-1d-137)) then
        tmp = a * 120.0d0
    else if ((a * 120.0d0) <= 2d-26) then
        tmp = t_1
    else
        tmp = (a * 120.0d0) + ((-60.0d0) * (y / z))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = 60.0 * ((y - x) / (t - z));
	double tmp;
	if ((a * 120.0) <= -0.0004) {
		tmp = a * 120.0;
	} else if ((a * 120.0) <= -2e-69) {
		tmp = t_1;
	} else if ((a * 120.0) <= -1e-137) {
		tmp = a * 120.0;
	} else if ((a * 120.0) <= 2e-26) {
		tmp = t_1;
	} else {
		tmp = (a * 120.0) + (-60.0 * (y / z));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = 60.0 * ((y - x) / (t - z))
	tmp = 0
	if (a * 120.0) <= -0.0004:
		tmp = a * 120.0
	elif (a * 120.0) <= -2e-69:
		tmp = t_1
	elif (a * 120.0) <= -1e-137:
		tmp = a * 120.0
	elif (a * 120.0) <= 2e-26:
		tmp = t_1
	else:
		tmp = (a * 120.0) + (-60.0 * (y / z))
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(60.0 * Float64(Float64(y - x) / Float64(t - z)))
	tmp = 0.0
	if (Float64(a * 120.0) <= -0.0004)
		tmp = Float64(a * 120.0);
	elseif (Float64(a * 120.0) <= -2e-69)
		tmp = t_1;
	elseif (Float64(a * 120.0) <= -1e-137)
		tmp = Float64(a * 120.0);
	elseif (Float64(a * 120.0) <= 2e-26)
		tmp = t_1;
	else
		tmp = Float64(Float64(a * 120.0) + Float64(-60.0 * Float64(y / z)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = 60.0 * ((y - x) / (t - z));
	tmp = 0.0;
	if ((a * 120.0) <= -0.0004)
		tmp = a * 120.0;
	elseif ((a * 120.0) <= -2e-69)
		tmp = t_1;
	elseif ((a * 120.0) <= -1e-137)
		tmp = a * 120.0;
	elseif ((a * 120.0) <= 2e-26)
		tmp = t_1;
	else
		tmp = (a * 120.0) + (-60.0 * (y / z));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(60.0 * N[(N[(y - x), $MachinePrecision] / N[(t - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(a * 120.0), $MachinePrecision], -0.0004], N[(a * 120.0), $MachinePrecision], If[LessEqual[N[(a * 120.0), $MachinePrecision], -2e-69], t$95$1, If[LessEqual[N[(a * 120.0), $MachinePrecision], -1e-137], N[(a * 120.0), $MachinePrecision], If[LessEqual[N[(a * 120.0), $MachinePrecision], 2e-26], t$95$1, N[(N[(a * 120.0), $MachinePrecision] + N[(-60.0 * N[(y / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

1. Split input into 3 regimes

2. if (*.f64 a #s(literal 120 binary64)) < -4.00000000000000019e-4 or -1.9999999999999999e-69 < (*.f64 a #s(literal 120 binary64)) < -9.99999999999999978e-138

   1. Initial program 98.5%

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

      1. associate-/l* 99.8%

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

   3. Simplified 99.8%

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

   5. Taylor expanded in z around inf 71.5%

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

   if -4.00000000000000019e-4 < (*.f64 a #s(literal 120 binary64)) < -1.9999999999999999e-69 or -9.99999999999999978e-138 < (*.f64 a #s(literal 120 binary64)) < 2.0000000000000001e-26

   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%

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

   3. Simplified 99.7%

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

   5. Taylor expanded in a around 0 84.1%

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

   if 2.0000000000000001e-26 < (*.f64 a #s(literal 120 binary64))

   1. Initial program 97.1%

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

      1. associate-/l* 99.9%

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

   3. Simplified 99.9%

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

      1. clear-num 99.9%

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

      2. un-div-inv 100.0%

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

   6. Applied egg-rr 100.0%

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

      1. clear-num 99.9%

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

      2. inv-pow 99.9%

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

   8. Applied egg-rr 99.9%

      \[\leadsto \frac{60}{\color{blue}{{\left(\frac{x - y}{z - t}\right)}^{-1}}} + a \cdot 120 \]
   9. Step-by-step derivation

      1. unpow-1 99.9%

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

   10. Simplified 99.9%

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

   11. Taylor expanded in x around 0 82.5%

       \[\leadsto \frac{60}{\frac{1}{\color{blue}{-1 \cdot \frac{y}{z - t}}}} + a \cdot 120 \]
   12. Step-by-step derivation

       1. neg-mul-1 82.5%

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

       2. distribute-neg-frac2 82.5%

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

       3. neg-sub0 82.5%

          \[\leadsto \frac{60}{\frac{1}{\frac{y}{\color{blue}{0 - \left(z - t\right)}}}} + a \cdot 120 \]

       4. associate--r- 82.5%

          \[\leadsto \frac{60}{\frac{1}{\frac{y}{\color{blue}{\left(0 - z\right) + t}}}} + a \cdot 120 \]

       5. neg-sub0 82.5%

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

   13. Simplified 82.5%

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

   14. Taylor expanded in z around inf 72.5%

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

4. Final simplification 77.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;a \cdot 120 \leq -0.0004:\\ \;\;\;\;a \cdot 120\\ \mathbf{elif}\;a \cdot 120 \leq -2 \cdot 10^{-69}:\\ \;\;\;\;60 \cdot \frac{y - x}{t - z}\\ \mathbf{elif}\;a \cdot 120 \leq -1 \cdot 10^{-137}:\\ \;\;\;\;a \cdot 120\\ \mathbf{elif}\;a \cdot 120 \leq 2 \cdot 10^{-26}:\\ \;\;\;\;60 \cdot \frac{y - x}{t - z}\\ \mathbf{else}:\\ \;\;\;\;a \cdot 120 + -60 \cdot \frac{y}{z}\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 71.5% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := 60 \cdot \frac{y - x}{t - z}\\ t_2 := a \cdot 120 + 60 \cdot \frac{y}{t}\\ \mathbf{if}\;a \cdot 120 \leq -0.0004:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;a \cdot 120 \leq -1 \cdot 10^{-76}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;a \cdot 120 \leq -1 \cdot 10^{-137}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;a \cdot 120 \leq 2 \cdot 10^{-26}:\\ \;\;\;\;t\_1\\ \mathbf{else}:\\ \;\;\;\;a \cdot 120 + -60 \cdot \frac{y}{z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* 60.0 (/ (- y x) (- t z))))
        (t_2 (+ (* a 120.0) (* 60.0 (/ y t)))))
   (if (<= (* a 120.0) -0.0004)
     t_2
     (if (<= (* a 120.0) -1e-76)
       t_1
       (if (<= (* a 120.0) -1e-137)
         t_2
         (if (<= (* a 120.0) 2e-26) t_1 (+ (* a 120.0) (* -60.0 (/ y z)))))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = 60.0 * ((y - x) / (t - z));
	double t_2 = (a * 120.0) + (60.0 * (y / t));
	double tmp;
	if ((a * 120.0) <= -0.0004) {
		tmp = t_2;
	} else if ((a * 120.0) <= -1e-76) {
		tmp = t_1;
	} else if ((a * 120.0) <= -1e-137) {
		tmp = t_2;
	} else if ((a * 120.0) <= 2e-26) {
		tmp = t_1;
	} else {
		tmp = (a * 120.0) + (-60.0 * (y / z));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = 60.0d0 * ((y - x) / (t - z))
    t_2 = (a * 120.0d0) + (60.0d0 * (y / t))
    if ((a * 120.0d0) <= (-0.0004d0)) then
        tmp = t_2
    else if ((a * 120.0d0) <= (-1d-76)) then
        tmp = t_1
    else if ((a * 120.0d0) <= (-1d-137)) then
        tmp = t_2
    else if ((a * 120.0d0) <= 2d-26) then
        tmp = t_1
    else
        tmp = (a * 120.0d0) + ((-60.0d0) * (y / z))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = 60.0 * ((y - x) / (t - z));
	double t_2 = (a * 120.0) + (60.0 * (y / t));
	double tmp;
	if ((a * 120.0) <= -0.0004) {
		tmp = t_2;
	} else if ((a * 120.0) <= -1e-76) {
		tmp = t_1;
	} else if ((a * 120.0) <= -1e-137) {
		tmp = t_2;
	} else if ((a * 120.0) <= 2e-26) {
		tmp = t_1;
	} else {
		tmp = (a * 120.0) + (-60.0 * (y / z));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = 60.0 * ((y - x) / (t - z))
	t_2 = (a * 120.0) + (60.0 * (y / t))
	tmp = 0
	if (a * 120.0) <= -0.0004:
		tmp = t_2
	elif (a * 120.0) <= -1e-76:
		tmp = t_1
	elif (a * 120.0) <= -1e-137:
		tmp = t_2
	elif (a * 120.0) <= 2e-26:
		tmp = t_1
	else:
		tmp = (a * 120.0) + (-60.0 * (y / z))
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(60.0 * Float64(Float64(y - x) / Float64(t - z)))
	t_2 = Float64(Float64(a * 120.0) + Float64(60.0 * Float64(y / t)))
	tmp = 0.0
	if (Float64(a * 120.0) <= -0.0004)
		tmp = t_2;
	elseif (Float64(a * 120.0) <= -1e-76)
		tmp = t_1;
	elseif (Float64(a * 120.0) <= -1e-137)
		tmp = t_2;
	elseif (Float64(a * 120.0) <= 2e-26)
		tmp = t_1;
	else
		tmp = Float64(Float64(a * 120.0) + Float64(-60.0 * Float64(y / z)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = 60.0 * ((y - x) / (t - z));
	t_2 = (a * 120.0) + (60.0 * (y / t));
	tmp = 0.0;
	if ((a * 120.0) <= -0.0004)
		tmp = t_2;
	elseif ((a * 120.0) <= -1e-76)
		tmp = t_1;
	elseif ((a * 120.0) <= -1e-137)
		tmp = t_2;
	elseif ((a * 120.0) <= 2e-26)
		tmp = t_1;
	else
		tmp = (a * 120.0) + (-60.0 * (y / z));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(60.0 * N[(N[(y - x), $MachinePrecision] / N[(t - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(a * 120.0), $MachinePrecision] + N[(60.0 * N[(y / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(a * 120.0), $MachinePrecision], -0.0004], t$95$2, If[LessEqual[N[(a * 120.0), $MachinePrecision], -1e-76], t$95$1, If[LessEqual[N[(a * 120.0), $MachinePrecision], -1e-137], t$95$2, If[LessEqual[N[(a * 120.0), $MachinePrecision], 2e-26], t$95$1, N[(N[(a * 120.0), $MachinePrecision] + N[(-60.0 * N[(y / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]]

Derivation

1. Split input into 3 regimes

2. if (*.f64 a #s(literal 120 binary64)) < -4.00000000000000019e-4 or -9.99999999999999927e-77 < (*.f64 a #s(literal 120 binary64)) < -9.99999999999999978e-138

   1. Initial program 98.5%

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

      1. associate-/l* 99.8%

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

   3. Simplified 99.8%

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

   5. Taylor expanded in z around 0 75.3%

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

      1. associate-*r/ 75.3%

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

      2. *-commutative 75.3%

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

      3. associate-/l* 75.3%

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

   7. Simplified 75.3%

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

   8. Taylor expanded in x around 0 73.8%

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

   if -4.00000000000000019e-4 < (*.f64 a #s(literal 120 binary64)) < -9.99999999999999927e-77 or -9.99999999999999978e-138 < (*.f64 a #s(literal 120 binary64)) < 2.0000000000000001e-26

   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%

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

   3. Simplified 99.7%

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

   5. Taylor expanded in a around 0 83.4%

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

   if 2.0000000000000001e-26 < (*.f64 a #s(literal 120 binary64))

   1. Initial program 97.1%

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

      1. associate-/l* 99.9%

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

   3. Simplified 99.9%

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

      1. clear-num 99.9%

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

      2. un-div-inv 100.0%

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

   6. Applied egg-rr 100.0%

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

      1. clear-num 99.9%

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

      2. inv-pow 99.9%

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

   8. Applied egg-rr 99.9%

      \[\leadsto \frac{60}{\color{blue}{{\left(\frac{x - y}{z - t}\right)}^{-1}}} + a \cdot 120 \]
   9. Step-by-step derivation

      1. unpow-1 99.9%

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

   10. Simplified 99.9%

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

   11. Taylor expanded in x around 0 82.5%

       \[\leadsto \frac{60}{\frac{1}{\color{blue}{-1 \cdot \frac{y}{z - t}}}} + a \cdot 120 \]
   12. Step-by-step derivation

       1. neg-mul-1 82.5%

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

       2. distribute-neg-frac2 82.5%

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

       3. neg-sub0 82.5%

          \[\leadsto \frac{60}{\frac{1}{\frac{y}{\color{blue}{0 - \left(z - t\right)}}}} + a \cdot 120 \]

       4. associate--r- 82.5%

          \[\leadsto \frac{60}{\frac{1}{\frac{y}{\color{blue}{\left(0 - z\right) + t}}}} + a \cdot 120 \]

       5. neg-sub0 82.5%

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

   13. Simplified 82.5%

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

   14. Taylor expanded in z around inf 72.5%

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

4. Final simplification 77.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;a \cdot 120 \leq -0.0004:\\ \;\;\;\;a \cdot 120 + 60 \cdot \frac{y}{t}\\ \mathbf{elif}\;a \cdot 120 \leq -1 \cdot 10^{-76}:\\ \;\;\;\;60 \cdot \frac{y - x}{t - z}\\ \mathbf{elif}\;a \cdot 120 \leq -1 \cdot 10^{-137}:\\ \;\;\;\;a \cdot 120 + 60 \cdot \frac{y}{t}\\ \mathbf{elif}\;a \cdot 120 \leq 2 \cdot 10^{-26}:\\ \;\;\;\;60 \cdot \frac{y - x}{t - z}\\ \mathbf{else}:\\ \;\;\;\;a \cdot 120 + -60 \cdot \frac{y}{z}\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 71.5% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := 60 \cdot \frac{y - x}{t - z}\\ \mathbf{if}\;a \cdot 120 \leq -0.0004:\\ \;\;\;\;a \cdot 120 + 60 \cdot \frac{y}{t}\\ \mathbf{elif}\;a \cdot 120 \leq -1 \cdot 10^{-76}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;a \cdot 120 \leq -1 \cdot 10^{-137}:\\ \;\;\;\;a \cdot 120 + \frac{60}{\frac{t}{y}}\\ \mathbf{elif}\;a \cdot 120 \leq 2 \cdot 10^{-26}:\\ \;\;\;\;t\_1\\ \mathbf{else}:\\ \;\;\;\;a \cdot 120 + -60 \cdot \frac{y}{z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* 60.0 (/ (- y x) (- t z)))))
   (if (<= (* a 120.0) -0.0004)
     (+ (* a 120.0) (* 60.0 (/ y t)))
     (if (<= (* a 120.0) -1e-76)
       t_1
       (if (<= (* a 120.0) -1e-137)
         (+ (* a 120.0) (/ 60.0 (/ t y)))
         (if (<= (* a 120.0) 2e-26) t_1 (+ (* a 120.0) (* -60.0 (/ y z)))))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = 60.0 * ((y - x) / (t - z));
	double tmp;
	if ((a * 120.0) <= -0.0004) {
		tmp = (a * 120.0) + (60.0 * (y / t));
	} else if ((a * 120.0) <= -1e-76) {
		tmp = t_1;
	} else if ((a * 120.0) <= -1e-137) {
		tmp = (a * 120.0) + (60.0 / (t / y));
	} else if ((a * 120.0) <= 2e-26) {
		tmp = t_1;
	} else {
		tmp = (a * 120.0) + (-60.0 * (y / z));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: t_1
    real(8) :: tmp
    t_1 = 60.0d0 * ((y - x) / (t - z))
    if ((a * 120.0d0) <= (-0.0004d0)) then
        tmp = (a * 120.0d0) + (60.0d0 * (y / t))
    else if ((a * 120.0d0) <= (-1d-76)) then
        tmp = t_1
    else if ((a * 120.0d0) <= (-1d-137)) then
        tmp = (a * 120.0d0) + (60.0d0 / (t / y))
    else if ((a * 120.0d0) <= 2d-26) then
        tmp = t_1
    else
        tmp = (a * 120.0d0) + ((-60.0d0) * (y / z))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = 60.0 * ((y - x) / (t - z));
	double tmp;
	if ((a * 120.0) <= -0.0004) {
		tmp = (a * 120.0) + (60.0 * (y / t));
	} else if ((a * 120.0) <= -1e-76) {
		tmp = t_1;
	} else if ((a * 120.0) <= -1e-137) {
		tmp = (a * 120.0) + (60.0 / (t / y));
	} else if ((a * 120.0) <= 2e-26) {
		tmp = t_1;
	} else {
		tmp = (a * 120.0) + (-60.0 * (y / z));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = 60.0 * ((y - x) / (t - z))
	tmp = 0
	if (a * 120.0) <= -0.0004:
		tmp = (a * 120.0) + (60.0 * (y / t))
	elif (a * 120.0) <= -1e-76:
		tmp = t_1
	elif (a * 120.0) <= -1e-137:
		tmp = (a * 120.0) + (60.0 / (t / y))
	elif (a * 120.0) <= 2e-26:
		tmp = t_1
	else:
		tmp = (a * 120.0) + (-60.0 * (y / z))
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(60.0 * Float64(Float64(y - x) / Float64(t - z)))
	tmp = 0.0
	if (Float64(a * 120.0) <= -0.0004)
		tmp = Float64(Float64(a * 120.0) + Float64(60.0 * Float64(y / t)));
	elseif (Float64(a * 120.0) <= -1e-76)
		tmp = t_1;
	elseif (Float64(a * 120.0) <= -1e-137)
		tmp = Float64(Float64(a * 120.0) + Float64(60.0 / Float64(t / y)));
	elseif (Float64(a * 120.0) <= 2e-26)
		tmp = t_1;
	else
		tmp = Float64(Float64(a * 120.0) + Float64(-60.0 * Float64(y / z)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = 60.0 * ((y - x) / (t - z));
	tmp = 0.0;
	if ((a * 120.0) <= -0.0004)
		tmp = (a * 120.0) + (60.0 * (y / t));
	elseif ((a * 120.0) <= -1e-76)
		tmp = t_1;
	elseif ((a * 120.0) <= -1e-137)
		tmp = (a * 120.0) + (60.0 / (t / y));
	elseif ((a * 120.0) <= 2e-26)
		tmp = t_1;
	else
		tmp = (a * 120.0) + (-60.0 * (y / z));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(60.0 * N[(N[(y - x), $MachinePrecision] / N[(t - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(a * 120.0), $MachinePrecision], -0.0004], N[(N[(a * 120.0), $MachinePrecision] + N[(60.0 * N[(y / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[N[(a * 120.0), $MachinePrecision], -1e-76], t$95$1, If[LessEqual[N[(a * 120.0), $MachinePrecision], -1e-137], N[(N[(a * 120.0), $MachinePrecision] + N[(60.0 / N[(t / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[N[(a * 120.0), $MachinePrecision], 2e-26], t$95$1, N[(N[(a * 120.0), $MachinePrecision] + N[(-60.0 * N[(y / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

1. Split input into 4 regimes

2. if (*.f64 a #s(literal 120 binary64)) < -4.00000000000000019e-4

   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* 99.8%

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

   3. Simplified 99.8%

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

   5. Taylor expanded in z around 0 74.3%

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

      1. associate-*r/ 74.2%

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

      2. *-commutative 74.2%

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

      3. associate-/l* 74.3%

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

   7. Simplified 74.3%

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

   8. Taylor expanded in x around 0 73.0%

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

   if -4.00000000000000019e-4 < (*.f64 a #s(literal 120 binary64)) < -9.99999999999999927e-77 or -9.99999999999999978e-138 < (*.f64 a #s(literal 120 binary64)) < 2.0000000000000001e-26

   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%

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

   3. Simplified 99.7%

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

   5. Taylor expanded in a around 0 83.4%

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

   if -9.99999999999999927e-77 < (*.f64 a #s(literal 120 binary64)) < -9.99999999999999978e-138

   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* 99.9%

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

   3. Simplified 99.9%

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

      1. clear-num 99.9%

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

      2. un-div-inv 99.7%

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

   6. Applied egg-rr 99.7%

      \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}}} + a \cdot 120 \]
   7. 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. inv-pow 99.7%

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

   8. Applied egg-rr 99.7%

      \[\leadsto \frac{60}{\color{blue}{{\left(\frac{x - y}{z - t}\right)}^{-1}}} + a \cdot 120 \]
   9. Step-by-step derivation

      1. unpow-1 99.7%

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

   10. Simplified 99.7%

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

   11. Taylor expanded in x around 0 86.9%

       \[\leadsto \frac{60}{\frac{1}{\color{blue}{-1 \cdot \frac{y}{z - t}}}} + a \cdot 120 \]
   12. Step-by-step derivation

       1. neg-mul-1 86.9%

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

       2. distribute-neg-frac2 86.9%

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

       3. neg-sub0 86.9%

          \[\leadsto \frac{60}{\frac{1}{\frac{y}{\color{blue}{0 - \left(z - t\right)}}}} + a \cdot 120 \]

       4. associate--r- 86.9%

          \[\leadsto \frac{60}{\frac{1}{\frac{y}{\color{blue}{\left(0 - z\right) + t}}}} + a \cdot 120 \]

       5. neg-sub0 86.9%

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

   13. Simplified 86.9%

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

   14. Taylor expanded in z around 0 78.6%

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

   if 2.0000000000000001e-26 < (*.f64 a #s(literal 120 binary64))

   1. Initial program 97.1%

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

      1. associate-/l* 99.9%

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

   3. Simplified 99.9%

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

      1. clear-num 99.9%

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

      2. un-div-inv 100.0%

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

   6. Applied egg-rr 100.0%

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

      1. clear-num 99.9%

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

      2. inv-pow 99.9%

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

   8. Applied egg-rr 99.9%

      \[\leadsto \frac{60}{\color{blue}{{\left(\frac{x - y}{z - t}\right)}^{-1}}} + a \cdot 120 \]
   9. Step-by-step derivation

      1. unpow-1 99.9%

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

   10. Simplified 99.9%

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

   11. Taylor expanded in x around 0 82.5%

       \[\leadsto \frac{60}{\frac{1}{\color{blue}{-1 \cdot \frac{y}{z - t}}}} + a \cdot 120 \]
   12. Step-by-step derivation

       1. neg-mul-1 82.5%

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

       2. distribute-neg-frac2 82.5%

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

       3. neg-sub0 82.5%

          \[\leadsto \frac{60}{\frac{1}{\frac{y}{\color{blue}{0 - \left(z - t\right)}}}} + a \cdot 120 \]

       4. associate--r- 82.5%

          \[\leadsto \frac{60}{\frac{1}{\frac{y}{\color{blue}{\left(0 - z\right) + t}}}} + a \cdot 120 \]

       5. neg-sub0 82.5%

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

   13. Simplified 82.5%

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

   14. Taylor expanded in z around inf 72.5%

       \[\leadsto \color{blue}{-60 \cdot \frac{y}{z}} + a \cdot 120 \]
3. Recombined 4 regimes into one program.

4. Final simplification 77.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;a \cdot 120 \leq -0.0004:\\ \;\;\;\;a \cdot 120 + 60 \cdot \frac{y}{t}\\ \mathbf{elif}\;a \cdot 120 \leq -1 \cdot 10^{-76}:\\ \;\;\;\;60 \cdot \frac{y - x}{t - z}\\ \mathbf{elif}\;a \cdot 120 \leq -1 \cdot 10^{-137}:\\ \;\;\;\;a \cdot 120 + \frac{60}{\frac{t}{y}}\\ \mathbf{elif}\;a \cdot 120 \leq 2 \cdot 10^{-26}:\\ \;\;\;\;60 \cdot \frac{y - x}{t - z}\\ \mathbf{else}:\\ \;\;\;\;a \cdot 120 + -60 \cdot \frac{y}{z}\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 66.2% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z - t \leq -1.2 \cdot 10^{+190}:\\ \;\;\;\;a \cdot 120\\ \mathbf{elif}\;z - t \leq -5 \cdot 10^{+87}:\\ \;\;\;\;a \cdot 120 + \frac{x \cdot 60}{z}\\ \mathbf{elif}\;z - t \leq 2 \cdot 10^{+67}:\\ \;\;\;\;60 \cdot \frac{y - x}{t - z}\\ \mathbf{elif}\;z - t \leq 2 \cdot 10^{+211}:\\ \;\;\;\;a \cdot 120 + -60 \cdot \frac{y}{z}\\ \mathbf{else}:\\ \;\;\;\;a \cdot 120 + \frac{60}{\frac{t}{y}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= (- z t) -1.2e+190)
   (* a 120.0)
   (if (<= (- z t) -5e+87)
     (+ (* a 120.0) (/ (* x 60.0) z))
     (if (<= (- z t) 2e+67)
       (* 60.0 (/ (- y x) (- t z)))
       (if (<= (- z t) 2e+211)
         (+ (* a 120.0) (* -60.0 (/ y z)))
         (+ (* a 120.0) (/ 60.0 (/ t y))))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((z - t) <= -1.2e+190) {
		tmp = a * 120.0;
	} else if ((z - t) <= -5e+87) {
		tmp = (a * 120.0) + ((x * 60.0) / z);
	} else if ((z - t) <= 2e+67) {
		tmp = 60.0 * ((y - x) / (t - z));
	} else if ((z - t) <= 2e+211) {
		tmp = (a * 120.0) + (-60.0 * (y / z));
	} else {
		tmp = (a * 120.0) + (60.0 / (t / y));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if ((z - t) <= (-1.2d+190)) then
        tmp = a * 120.0d0
    else if ((z - t) <= (-5d+87)) then
        tmp = (a * 120.0d0) + ((x * 60.0d0) / z)
    else if ((z - t) <= 2d+67) then
        tmp = 60.0d0 * ((y - x) / (t - z))
    else if ((z - t) <= 2d+211) then
        tmp = (a * 120.0d0) + ((-60.0d0) * (y / z))
    else
        tmp = (a * 120.0d0) + (60.0d0 / (t / y))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((z - t) <= -1.2e+190) {
		tmp = a * 120.0;
	} else if ((z - t) <= -5e+87) {
		tmp = (a * 120.0) + ((x * 60.0) / z);
	} else if ((z - t) <= 2e+67) {
		tmp = 60.0 * ((y - x) / (t - z));
	} else if ((z - t) <= 2e+211) {
		tmp = (a * 120.0) + (-60.0 * (y / z));
	} else {
		tmp = (a * 120.0) + (60.0 / (t / y));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (z - t) <= -1.2e+190:
		tmp = a * 120.0
	elif (z - t) <= -5e+87:
		tmp = (a * 120.0) + ((x * 60.0) / z)
	elif (z - t) <= 2e+67:
		tmp = 60.0 * ((y - x) / (t - z))
	elif (z - t) <= 2e+211:
		tmp = (a * 120.0) + (-60.0 * (y / z))
	else:
		tmp = (a * 120.0) + (60.0 / (t / y))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (Float64(z - t) <= -1.2e+190)
		tmp = Float64(a * 120.0);
	elseif (Float64(z - t) <= -5e+87)
		tmp = Float64(Float64(a * 120.0) + Float64(Float64(x * 60.0) / z));
	elseif (Float64(z - t) <= 2e+67)
		tmp = Float64(60.0 * Float64(Float64(y - x) / Float64(t - z)));
	elseif (Float64(z - t) <= 2e+211)
		tmp = Float64(Float64(a * 120.0) + Float64(-60.0 * Float64(y / z)));
	else
		tmp = Float64(Float64(a * 120.0) + Float64(60.0 / Float64(t / y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((z - t) <= -1.2e+190)
		tmp = a * 120.0;
	elseif ((z - t) <= -5e+87)
		tmp = (a * 120.0) + ((x * 60.0) / z);
	elseif ((z - t) <= 2e+67)
		tmp = 60.0 * ((y - x) / (t - z));
	elseif ((z - t) <= 2e+211)
		tmp = (a * 120.0) + (-60.0 * (y / z));
	else
		tmp = (a * 120.0) + (60.0 / (t / y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[N[(z - t), $MachinePrecision], -1.2e+190], N[(a * 120.0), $MachinePrecision], If[LessEqual[N[(z - t), $MachinePrecision], -5e+87], N[(N[(a * 120.0), $MachinePrecision] + N[(N[(x * 60.0), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision], If[LessEqual[N[(z - t), $MachinePrecision], 2e+67], N[(60.0 * N[(N[(y - x), $MachinePrecision] / N[(t - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[N[(z - t), $MachinePrecision], 2e+211], N[(N[(a * 120.0), $MachinePrecision] + N[(-60.0 * N[(y / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(a * 120.0), $MachinePrecision] + N[(60.0 / N[(t / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

1. Split input into 5 regimes

2. if (-.f64 z t) < -1.1999999999999999e190

   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%

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

   3. Simplified 99.9%

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

   5. Taylor expanded in z around inf 79.2%

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

   if -1.1999999999999999e190 < (-.f64 z t) < -4.9999999999999998e87

   1. Initial program 96.0%

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

      1. associate-/l* 99.8%

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

   3. Simplified 99.8%

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

   5. Taylor expanded in x around inf 87.9%

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

      1. associate-*r/ 87.9%

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

      2. *-commutative 87.9%

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

      3. associate-*r/ 87.9%

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

   7. Simplified 87.9%

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

   8. Taylor expanded in z around inf 84.1%

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

      1. associate-*r/ 84.1%

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

      2. *-commutative 84.1%

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

   10. Simplified 84.1%

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

   if -4.9999999999999998e87 < (-.f64 z t) < 1.99999999999999997e67

   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%

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

   3. Simplified 99.8%

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

   5. Taylor expanded in a around 0 79.9%

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

   if 1.99999999999999997e67 < (-.f64 z t) < 1.9999999999999999e211

   1. Initial program 97.7%

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

      1. associate-/l* 99.8%

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

   3. Simplified 99.8%

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

      1. clear-num 99.7%

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

      2. un-div-inv 99.7%

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

   6. Applied egg-rr 99.7%

      \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}}} + a \cdot 120 \]
   7. 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. inv-pow 99.8%

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

   8. Applied egg-rr 99.8%

      \[\leadsto \frac{60}{\color{blue}{{\left(\frac{x - y}{z - t}\right)}^{-1}}} + a \cdot 120 \]
   9. Step-by-step derivation

      1. unpow-1 99.8%

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

   10. Simplified 99.8%

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

   11. Taylor expanded in x around 0 83.7%

       \[\leadsto \frac{60}{\frac{1}{\color{blue}{-1 \cdot \frac{y}{z - t}}}} + a \cdot 120 \]
   12. Step-by-step derivation

       1. neg-mul-1 83.7%

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

       2. distribute-neg-frac2 83.7%

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

       3. neg-sub0 83.7%

          \[\leadsto \frac{60}{\frac{1}{\frac{y}{\color{blue}{0 - \left(z - t\right)}}}} + a \cdot 120 \]

       4. associate--r- 83.7%

          \[\leadsto \frac{60}{\frac{1}{\frac{y}{\color{blue}{\left(0 - z\right) + t}}}} + a \cdot 120 \]

       5. neg-sub0 83.7%

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

   13. Simplified 83.7%

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

   14. Taylor expanded in z around inf 71.7%

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

   if 1.9999999999999999e211 < (-.f64 z t)

   1. Initial program 94.3%

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

      1. associate-/l* 99.8%

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

   3. Simplified 99.8%

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

      1. clear-num 99.8%

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

      2. un-div-inv 99.8%

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

   6. Applied egg-rr 99.8%

      \[\leadsto \color{blue}{\frac{60}{\frac{z - t}{x - y}}} + a \cdot 120 \]
   7. 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. inv-pow 99.6%

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

   8. Applied egg-rr 99.6%

      \[\leadsto \frac{60}{\color{blue}{{\left(\frac{x - y}{z - t}\right)}^{-1}}} + a \cdot 120 \]
   9. Step-by-step derivation

      1. unpow-1 99.6%

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

   10. Simplified 99.6%

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

   11. Taylor expanded in x around 0 85.0%

       \[\leadsto \frac{60}{\frac{1}{\color{blue}{-1 \cdot \frac{y}{z - t}}}} + a \cdot 120 \]
   12. Step-by-step derivation

       1. neg-mul-1 85.0%

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

       2. distribute-neg-frac2 85.0%

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

       3. neg-sub0 85.0%

          \[\leadsto \frac{60}{\frac{1}{\frac{y}{\color{blue}{0 - \left(z - t\right)}}}} + a \cdot 120 \]

       4. associate--r- 85.0%

          \[\leadsto \frac{60}{\frac{1}{\frac{y}{\color{blue}{\left(0 - z\right) + t}}}} + a \cdot 120 \]

       5. neg-sub0 85.0%

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

   13. Simplified 85.0%

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

   14. Taylor expanded in z around 0 74.0%

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

4. Final simplification 77.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z - t \leq -1.2 \cdot 10^{+190}:\\ \;\;\;\;a \cdot 120\\ \mathbf{elif}\;z - t \leq -5 \cdot 10^{+87}:\\ \;\;\;\;a \cdot 120 + \frac{x \cdot 60}{z}\\ \mathbf{elif}\;z - t \leq 2 \cdot 10^{+67}:\\ \;\;\;\;60 \cdot \frac{y - x}{t - z}\\ \mathbf{elif}\;z - t \leq 2 \cdot 10^{+211}:\\ \;\;\;\;a \cdot 120 + -60 \cdot \frac{y}{z}\\ \mathbf{else}:\\ \;\;\;\;a \cdot 120 + \frac{60}{\frac{t}{y}}\\ \end{array} \]
5. Add Preprocessing

Alternative 7: 58.5% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -2.8 \cdot 10^{-12} \lor \neg \left(a \leq -5.2 \cdot 10^{-75}\right) \land \left(a \leq -3.6 \cdot 10^{-143} \lor \neg \left(a \leq 5 \cdot 10^{-44}\right)\right):\\ \;\;\;\;a \cdot 120\\ \mathbf{else}:\\ \;\;\;\;60 \cdot \frac{x - y}{z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= a -2.8e-12)
         (and (not (<= a -5.2e-75)) (or (<= a -3.6e-143) (not (<= a 5e-44)))))
   (* a 120.0)
   (* 60.0 (/ (- x y) z))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((a <= -2.8e-12) || (!(a <= -5.2e-75) && ((a <= -3.6e-143) || !(a <= 5e-44)))) {
		tmp = a * 120.0;
	} else {
		tmp = 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 ((a <= (-2.8d-12)) .or. (.not. (a <= (-5.2d-75))) .and. (a <= (-3.6d-143)) .or. (.not. (a <= 5d-44))) then
        tmp = a * 120.0d0
    else
        tmp = 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 ((a <= -2.8e-12) || (!(a <= -5.2e-75) && ((a <= -3.6e-143) || !(a <= 5e-44)))) {
		tmp = a * 120.0;
	} else {
		tmp = 60.0 * ((x - y) / z);
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (a <= -2.8e-12) or (not (a <= -5.2e-75) and ((a <= -3.6e-143) or not (a <= 5e-44))):
		tmp = a * 120.0
	else:
		tmp = 60.0 * ((x - y) / z)
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((a <= -2.8e-12) || (!(a <= -5.2e-75) && ((a <= -3.6e-143) || !(a <= 5e-44))))
		tmp = Float64(a * 120.0);
	else
		tmp = 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 ((a <= -2.8e-12) || (~((a <= -5.2e-75)) && ((a <= -3.6e-143) || ~((a <= 5e-44)))))
		tmp = a * 120.0;
	else
		tmp = 60.0 * ((x - y) / z);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[a, -2.8e-12], And[N[Not[LessEqual[a, -5.2e-75]], $MachinePrecision], Or[LessEqual[a, -3.6e-143], N[Not[LessEqual[a, 5e-44]], $MachinePrecision]]]], N[(a * 120.0), $MachinePrecision], N[(60.0 * N[(N[(x - y), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if a < -2.8000000000000002e-12 or -5.2e-75 < a < -3.5999999999999998e-143 or 5.00000000000000039e-44 < a

   1. Initial program 97.9%

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

      1. associate-/l* 99.9%

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

   3. Simplified 99.9%

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

   5. Taylor expanded in z around inf 69.3%

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

   if -2.8000000000000002e-12 < a < -5.2e-75 or -3.5999999999999998e-143 < a < 5.00000000000000039e-44

   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%

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

   3. Simplified 99.7%

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

   5. Taylor expanded in a around 0 84.5%

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

   6. Taylor expanded in z around inf 58.2%

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

4. Final simplification 64.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;a \leq -2.8 \cdot 10^{-12} \lor \neg \left(a \leq -5.2 \cdot 10^{-75}\right) \land \left(a \leq -3.6 \cdot 10^{-143} \lor \neg \left(a \leq 5 \cdot 10^{-44}\right)\right):\\ \;\;\;\;a \cdot 120\\ \mathbf{else}:\\ \;\;\;\;60 \cdot \frac{x - y}{z}\\ \end{array} \]
5. Add Preprocessing

Alternative 8: 83.3% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -2.1 \cdot 10^{+26} \lor \neg \left(z \leq 3.75 \cdot 10^{-47}\right):\\ \;\;\;\;a \cdot 120 + \frac{60}{\frac{z}{x - y}}\\ \mathbf{else}:\\ \;\;\;\;a \cdot 120 - \frac{-60}{t} \cdot \left(y - x\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= z -2.1e+26) (not (<= z 3.75e-47)))
   (+ (* a 120.0) (/ 60.0 (/ z (- x y))))
   (- (* a 120.0) (* (/ -60.0 t) (- y x)))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((z <= -2.1e+26) || !(z <= 3.75e-47)) {
		tmp = (a * 120.0) + (60.0 / (z / (x - y)));
	} else {
		tmp = (a * 120.0) - ((-60.0 / t) * (y - 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 ((z <= (-2.1d+26)) .or. (.not. (z <= 3.75d-47))) then
        tmp = (a * 120.0d0) + (60.0d0 / (z / (x - y)))
    else
        tmp = (a * 120.0d0) - (((-60.0d0) / t) * (y - 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 ((z <= -2.1e+26) || !(z <= 3.75e-47)) {
		tmp = (a * 120.0) + (60.0 / (z / (x - y)));
	} else {
		tmp = (a * 120.0) - ((-60.0 / t) * (y - x));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (z <= -2.1e+26) or not (z <= 3.75e-47):
		tmp = (a * 120.0) + (60.0 / (z / (x - y)))
	else:
		tmp = (a * 120.0) - ((-60.0 / t) * (y - x))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((z <= -2.1e+26) || !(z <= 3.75e-47))
		tmp = Float64(Float64(a * 120.0) + Float64(60.0 / Float64(z / Float64(x - y))));
	else
		tmp = Float64(Float64(a * 120.0) - Float64(Float64(-60.0 / t) * Float64(y - x)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((z <= -2.1e+26) || ~((z <= 3.75e-47)))
		tmp = (a * 120.0) + (60.0 / (z / (x - y)));
	else
		tmp = (a * 120.0) - ((-60.0 / t) * (y - x));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[z, -2.1e+26], N[Not[LessEqual[z, 3.75e-47]], $MachinePrecision]], N[(N[(a * 120.0), $MachinePrecision] + N[(60.0 / N[(z / N[(x - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(a * 120.0), $MachinePrecision] - N[(N[(-60.0 / t), $MachinePrecision] * N[(y - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if z < -2.1000000000000001e26 or 3.74999999999999984e-47 < z

   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* 99.8%

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

   3. Simplified 99.8%

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

      1. clear-num 99.7%

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

      2. un-div-inv 99.7%

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

   6. Applied egg-rr 99.7%

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

   7. Taylor expanded in z around inf 91.8%

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

   if -2.1000000000000001e26 < z < 3.74999999999999984e-47

   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* 99.8%

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

   3. Simplified 99.8%

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

   5. Taylor expanded in z around 0 84.2%

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

      1. associate-*r/ 82.6%

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

      2. *-commutative 82.6%

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

      3. associate-/l* 84.3%

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

   7. Simplified 84.3%

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

4. Final simplification 88.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -2.1 \cdot 10^{+26} \lor \neg \left(z \leq 3.75 \cdot 10^{-47}\right):\\ \;\;\;\;a \cdot 120 + \frac{60}{\frac{z}{x - y}}\\ \mathbf{else}:\\ \;\;\;\;a \cdot 120 - \frac{-60}{t} \cdot \left(y - x\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 9: 89.5% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.45 \cdot 10^{+94} \lor \neg \left(y \leq 7.6 \cdot 10^{+38}\right):\\ \;\;\;\;a \cdot 120 + \frac{y \cdot -60}{z - t}\\ \mathbf{else}:\\ \;\;\;\;a \cdot 120 + x \cdot \frac{60}{z - t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= y -1.45e+94) (not (<= y 7.6e+38)))
   (+ (* a 120.0) (/ (* y -60.0) (- z t)))
   (+ (* a 120.0) (* x (/ 60.0 (- z t))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((y <= -1.45e+94) || !(y <= 7.6e+38)) {
		tmp = (a * 120.0) + ((y * -60.0) / (z - t));
	} else {
		tmp = (a * 120.0) + (x * (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 ((y <= (-1.45d+94)) .or. (.not. (y <= 7.6d+38))) then
        tmp = (a * 120.0d0) + ((y * (-60.0d0)) / (z - t))
    else
        tmp = (a * 120.0d0) + (x * (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 ((y <= -1.45e+94) || !(y <= 7.6e+38)) {
		tmp = (a * 120.0) + ((y * -60.0) / (z - t));
	} else {
		tmp = (a * 120.0) + (x * (60.0 / (z - t)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (y <= -1.45e+94) or not (y <= 7.6e+38):
		tmp = (a * 120.0) + ((y * -60.0) / (z - t))
	else:
		tmp = (a * 120.0) + (x * (60.0 / (z - t)))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((y <= -1.45e+94) || !(y <= 7.6e+38))
		tmp = Float64(Float64(a * 120.0) + Float64(Float64(y * -60.0) / Float64(z - t)));
	else
		tmp = Float64(Float64(a * 120.0) + Float64(x * Float64(60.0 / Float64(z - t))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((y <= -1.45e+94) || ~((y <= 7.6e+38)))
		tmp = (a * 120.0) + ((y * -60.0) / (z - t));
	else
		tmp = (a * 120.0) + (x * (60.0 / (z - t)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[y, -1.45e+94], N[Not[LessEqual[y, 7.6e+38]], $MachinePrecision]], N[(N[(a * 120.0), $MachinePrecision] + N[(N[(y * -60.0), $MachinePrecision] / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(a * 120.0), $MachinePrecision] + N[(x * N[(60.0 / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -1.4499999999999999e94 or 7.5999999999999996e38 < y

   1. Initial program 96.8%

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

      1. associate-/l* 99.8%

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

   3. Simplified 99.8%

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

   5. Taylor expanded in x around 0 88.0%

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

      1. associate-*r/ 84.9%

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

   7. Simplified 84.9%

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

   if -1.4499999999999999e94 < y < 7.5999999999999996e38

   1. Initial program 99.2%

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

      1. associate-/l* 99.8%

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

   3. Simplified 99.8%

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

   5. Taylor expanded in x around inf 94.5%

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

      1. associate-*r/ 93.9%

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

      2. *-commutative 93.9%

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

      3. associate-*r/ 94.5%

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

   7. Simplified 94.5%

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

4. Final simplification 90.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1.45 \cdot 10^{+94} \lor \neg \left(y \leq 7.6 \cdot 10^{+38}\right):\\ \;\;\;\;a \cdot 120 + \frac{y \cdot -60}{z - t}\\ \mathbf{else}:\\ \;\;\;\;a \cdot 120 + x \cdot \frac{60}{z - t}\\ \end{array} \]
5. Add Preprocessing

Alternative 10: 89.7% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -4.8 \cdot 10^{+93} \lor \neg \left(y \leq 4.5 \cdot 10^{+37}\right):\\ \;\;\;\;a \cdot 120 + \frac{60}{\frac{t - z}{y}}\\ \mathbf{else}:\\ \;\;\;\;a \cdot 120 + x \cdot \frac{60}{z - t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= y -4.8e+93) (not (<= y 4.5e+37)))
   (+ (* a 120.0) (/ 60.0 (/ (- t z) y)))
   (+ (* a 120.0) (* x (/ 60.0 (- z t))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((y <= -4.8e+93) || !(y <= 4.5e+37)) {
		tmp = (a * 120.0) + (60.0 / ((t - z) / y));
	} else {
		tmp = (a * 120.0) + (x * (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 ((y <= (-4.8d+93)) .or. (.not. (y <= 4.5d+37))) then
        tmp = (a * 120.0d0) + (60.0d0 / ((t - z) / y))
    else
        tmp = (a * 120.0d0) + (x * (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 ((y <= -4.8e+93) || !(y <= 4.5e+37)) {
		tmp = (a * 120.0) + (60.0 / ((t - z) / y));
	} else {
		tmp = (a * 120.0) + (x * (60.0 / (z - t)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (y <= -4.8e+93) or not (y <= 4.5e+37):
		tmp = (a * 120.0) + (60.0 / ((t - z) / y))
	else:
		tmp = (a * 120.0) + (x * (60.0 / (z - t)))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((y <= -4.8e+93) || !(y <= 4.5e+37))
		tmp = Float64(Float64(a * 120.0) + Float64(60.0 / Float64(Float64(t - z) / y)));
	else
		tmp = Float64(Float64(a * 120.0) + Float64(x * Float64(60.0 / Float64(z - t))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((y <= -4.8e+93) || ~((y <= 4.5e+37)))
		tmp = (a * 120.0) + (60.0 / ((t - z) / y));
	else
		tmp = (a * 120.0) + (x * (60.0 / (z - t)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[y, -4.8e+93], N[Not[LessEqual[y, 4.5e+37]], $MachinePrecision]], N[(N[(a * 120.0), $MachinePrecision] + N[(60.0 / N[(N[(t - z), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(a * 120.0), $MachinePrecision] + N[(x * N[(60.0 / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -4.80000000000000021e93 or 4.49999999999999962e37 < y

   1. Initial program 96.8%

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

      1. associate-/l* 99.8%

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

   3. Simplified 99.8%

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

      1. clear-num 99.7%

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

      2. un-div-inv 99.7%

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

   6. Applied egg-rr 99.7%

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

   7. Taylor expanded in x around 0 87.9%

      \[\leadsto \frac{60}{\color{blue}{-1 \cdot \frac{z - t}{y}}} + a \cdot 120 \]
   8. Step-by-step derivation

      1. associate-*r/ 87.9%

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

      2. neg-mul-1 87.9%

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

   9. Simplified 87.9%

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

   if -4.80000000000000021e93 < y < 4.49999999999999962e37

   1. Initial program 99.2%

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

      1. associate-/l* 99.8%

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

   3. Simplified 99.8%

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

   5. Taylor expanded in x around inf 94.5%

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

      1. associate-*r/ 93.9%

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

      2. *-commutative 93.9%

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

      3. associate-*r/ 94.5%

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

   7. Simplified 94.5%

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

4. Final simplification 92.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -4.8 \cdot 10^{+93} \lor \neg \left(y \leq 4.5 \cdot 10^{+37}\right):\\ \;\;\;\;a \cdot 120 + \frac{60}{\frac{t - z}{y}}\\ \mathbf{else}:\\ \;\;\;\;a \cdot 120 + x \cdot \frac{60}{z - t}\\ \end{array} \]
5. Add Preprocessing

Alternative 11: 80.8% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.7 \cdot 10^{+36}:\\ \;\;\;\;60 \cdot \frac{y - x}{t - z}\\ \mathbf{elif}\;y \leq 1.3 \cdot 10^{+123}:\\ \;\;\;\;a \cdot 120 + x \cdot \frac{60}{z - t}\\ \mathbf{else}:\\ \;\;\;\;\frac{60}{\frac{z - t}{x - y}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= y -1.7e+36)
   (* 60.0 (/ (- y x) (- t z)))
   (if (<= y 1.3e+123)
     (+ (* a 120.0) (* x (/ 60.0 (- z t))))
     (/ 60.0 (/ (- z t) (- x y))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (y <= -1.7e+36) {
		tmp = 60.0 * ((y - x) / (t - z));
	} else if (y <= 1.3e+123) {
		tmp = (a * 120.0) + (x * (60.0 / (z - t)));
	} else {
		tmp = 60.0 / ((z - t) / (x - y));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if (y <= (-1.7d+36)) then
        tmp = 60.0d0 * ((y - x) / (t - z))
    else if (y <= 1.3d+123) then
        tmp = (a * 120.0d0) + (x * (60.0d0 / (z - t)))
    else
        tmp = 60.0d0 / ((z - t) / (x - y))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (y <= -1.7e+36) {
		tmp = 60.0 * ((y - x) / (t - z));
	} else if (y <= 1.3e+123) {
		tmp = (a * 120.0) + (x * (60.0 / (z - t)));
	} else {
		tmp = 60.0 / ((z - t) / (x - y));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if y <= -1.7e+36:
		tmp = 60.0 * ((y - x) / (t - z))
	elif y <= 1.3e+123:
		tmp = (a * 120.0) + (x * (60.0 / (z - t)))
	else:
		tmp = 60.0 / ((z - t) / (x - y))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (y <= -1.7e+36)
		tmp = Float64(60.0 * Float64(Float64(y - x) / Float64(t - z)));
	elseif (y <= 1.3e+123)
		tmp = Float64(Float64(a * 120.0) + Float64(x * Float64(60.0 / Float64(z - t))));
	else
		tmp = Float64(60.0 / Float64(Float64(z - t) / Float64(x - y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (y <= -1.7e+36)
		tmp = 60.0 * ((y - x) / (t - z));
	elseif (y <= 1.3e+123)
		tmp = (a * 120.0) + (x * (60.0 / (z - t)));
	else
		tmp = 60.0 / ((z - t) / (x - y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[y, -1.7e+36], N[(60.0 * N[(N[(y - x), $MachinePrecision] / N[(t - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 1.3e+123], N[(N[(a * 120.0), $MachinePrecision] + N[(x * N[(60.0 / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(60.0 / N[(N[(z - t), $MachinePrecision] / N[(x - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if y < -1.6999999999999999e36

   1. Initial program 96.3%

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

      1. associate-/l* 99.8%

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

   3. Simplified 99.8%

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

   5. Taylor expanded in a around 0 75.1%

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

   if -1.6999999999999999e36 < y < 1.29999999999999993e123

   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* 99.8%

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

   3. Simplified 99.8%

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

   5. Taylor expanded in x around inf 93.7%

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

      1. associate-*r/ 93.7%

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

      2. *-commutative 93.7%

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

      3. associate-*r/ 93.7%

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

   7. Simplified 93.7%

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

   if 1.29999999999999993e123 < y

   1. Initial program 94.1%

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

      1. associate-/l* 99.6%

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

   3. Simplified 99.6%

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

   5. Taylor expanded in a around 0 78.8%

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

      1. clear-num 99.6%

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

      2. un-div-inv 99.6%

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

   7. Applied egg-rr 78.8%

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

4. Final simplification 87.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1.7 \cdot 10^{+36}:\\ \;\;\;\;60 \cdot \frac{y - x}{t - z}\\ \mathbf{elif}\;y \leq 1.3 \cdot 10^{+123}:\\ \;\;\;\;a \cdot 120 + x \cdot \frac{60}{z - t}\\ \mathbf{else}:\\ \;\;\;\;\frac{60}{\frac{z - t}{x - y}}\\ \end{array} \]
5. Add Preprocessing

Alternative 12: 54.9% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -9.5 \cdot 10^{-85} \lor \neg \left(z \leq 2.05 \cdot 10^{-112}\right):\\ \;\;\;\;a \cdot 120\\ \mathbf{else}:\\ \;\;\;\;-60 \cdot \frac{x - y}{t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= z -9.5e-85) (not (<= z 2.05e-112)))
   (* a 120.0)
   (* -60.0 (/ (- x y) t))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((z <= -9.5e-85) || !(z <= 2.05e-112)) {
		tmp = a * 120.0;
	} else {
		tmp = -60.0 * ((x - y) / t);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if ((z <= (-9.5d-85)) .or. (.not. (z <= 2.05d-112))) then
        tmp = a * 120.0d0
    else
        tmp = (-60.0d0) * ((x - y) / t)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((z <= -9.5e-85) || !(z <= 2.05e-112)) {
		tmp = a * 120.0;
	} else {
		tmp = -60.0 * ((x - y) / t);
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (z <= -9.5e-85) or not (z <= 2.05e-112):
		tmp = a * 120.0
	else:
		tmp = -60.0 * ((x - y) / t)
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((z <= -9.5e-85) || !(z <= 2.05e-112))
		tmp = Float64(a * 120.0);
	else
		tmp = Float64(-60.0 * Float64(Float64(x - y) / t));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((z <= -9.5e-85) || ~((z <= 2.05e-112)))
		tmp = a * 120.0;
	else
		tmp = -60.0 * ((x - y) / t);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[z, -9.5e-85], N[Not[LessEqual[z, 2.05e-112]], $MachinePrecision]], N[(a * 120.0), $MachinePrecision], N[(-60.0 * N[(N[(x - y), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if z < -9.49999999999999964e-85 or 2.04999999999999998e-112 < z

   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%

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

   3. Simplified 99.8%

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

   5. Taylor expanded in z around inf 58.7%

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

   if -9.49999999999999964e-85 < z < 2.04999999999999998e-112

   1. Initial program 97.5%

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

      1. associate-/l* 99.7%

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

   3. Simplified 99.7%

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

   5. Taylor expanded in a around 0 71.3%

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

   6. Taylor expanded in z around 0 57.2%

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

4. Final simplification 58.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -9.5 \cdot 10^{-85} \lor \neg \left(z \leq 2.05 \cdot 10^{-112}\right):\\ \;\;\;\;a \cdot 120\\ \mathbf{else}:\\ \;\;\;\;-60 \cdot \frac{x - y}{t}\\ \end{array} \]
5. Add Preprocessing

Alternative 13: 52.0% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -3.6 \cdot 10^{+108} \lor \neg \left(y \leq 1.7 \cdot 10^{+217}\right):\\ \;\;\;\;-60 \cdot \frac{y}{-t}\\ \mathbf{else}:\\ \;\;\;\;a \cdot 120\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= y -3.6e+108) (not (<= y 1.7e+217)))
   (* -60.0 (/ y (- t)))
   (* a 120.0)))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((y <= -3.6e+108) || !(y <= 1.7e+217)) {
		tmp = -60.0 * (y / -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 <= (-3.6d+108)) .or. (.not. (y <= 1.7d+217))) then
        tmp = (-60.0d0) * (y / -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 <= -3.6e+108) || !(y <= 1.7e+217)) {
		tmp = -60.0 * (y / -t);
	} else {
		tmp = a * 120.0;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (y <= -3.6e+108) or not (y <= 1.7e+217):
		tmp = -60.0 * (y / -t)
	else:
		tmp = a * 120.0
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((y <= -3.6e+108) || !(y <= 1.7e+217))
		tmp = Float64(-60.0 * Float64(y / Float64(-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 <= -3.6e+108) || ~((y <= 1.7e+217)))
		tmp = -60.0 * (y / -t);
	else
		tmp = a * 120.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[y, -3.6e+108], N[Not[LessEqual[y, 1.7e+217]], $MachinePrecision]], N[(-60.0 * N[(y / (-t)), $MachinePrecision]), $MachinePrecision], N[(a * 120.0), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -3.6e108 or 1.6999999999999999e217 < y

   1. Initial program 94.7%

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

      1. associate-/l* 99.8%

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

   3. Simplified 99.8%

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

   5. Taylor expanded in a around 0 78.2%

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

   6. Taylor expanded in z around 0 50.6%

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

   7. Taylor expanded in x around 0 46.6%

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

      1. neg-mul-1 46.6%

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

      2. distribute-neg-frac 46.6%

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

   9. Simplified 46.6%

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

   if -3.6e108 < y < 1.6999999999999999e217

   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* 99.8%

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

   3. Simplified 99.8%

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

   5. Taylor expanded in z around inf 55.9%

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

4. Final simplification 53.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -3.6 \cdot 10^{+108} \lor \neg \left(y \leq 1.7 \cdot 10^{+217}\right):\\ \;\;\;\;-60 \cdot \frac{y}{-t}\\ \mathbf{else}:\\ \;\;\;\;a \cdot 120\\ \end{array} \]
5. Add Preprocessing

Alternative 14: 99.8% accurate, 1.0× speedup

Math

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

FPCore

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

C

double code(double x, double y, double z, double t, double a) {
	return (60.0 * ((y - x) / (t - z))) + (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 * ((y - x) / (t - z))) + (a * 120.0d0)
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

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* 99.8%

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

3. Simplified 99.8%

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

5. Final simplification 99.8%

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

Alternative 15: 50.9% 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 98.3%

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

   1. associate-/l* 99.8%

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

3. Simplified 99.8%

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

5. Taylor expanded in z around inf 48.3%

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

6. Final simplification 48.3%

   \[\leadsto a \cdot 120 \]
7. Add Preprocessing

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 2024077 
(FPCore (x y z t a)
  :name "Data.Colour.RGB:hslsv from colour-2.3.3, B"
  :precision binary64

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

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