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

Percentage Accurate: 99.4% → 99.8%

Time: 14.8s

Alternatives: 14

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

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 99.0%

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

   1. +-commutative 99.0%

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

   2. fma-def 99.1%

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

   3. *-commutative 99.1%

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

   4. associate-*l/ 99.8%

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

3. Simplified 99.8%

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

5. Final simplification 99.8%

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

Alternative 2: 53.0% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := -60 \cdot \frac{y}{z - t}\\ t_2 := 60 \cdot \frac{x}{z - t}\\ \mathbf{if}\;z - t \leq -2 \cdot 10^{+127}:\\ \;\;\;\;a \cdot 120\\ \mathbf{elif}\;z - t \leq -5 \cdot 10^{+70}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z - t \leq -20000000:\\ \;\;\;\;t_2\\ \mathbf{elif}\;z - t \leq 10^{-17}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z - t \leq 2 \cdot 10^{+58}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;z - t \leq 5 \cdot 10^{+82}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;a \cdot 120\\ \end{array} \end{array} \]

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 (<= (- z t) -2e+127)
     (* a 120.0)
     (if (<= (- z t) -5e+70)
       t_1
       (if (<= (- z t) -20000000.0)
         t_2
         (if (<= (- z t) 1e-17)
           t_1
           (if (<= (- z t) 2e+58)
             t_2
             (if (<= (- z t) 5e+82) t_1 (* a 120.0)))))))))

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 ((z - t) <= -2e+127) {
		tmp = a * 120.0;
	} else if ((z - t) <= -5e+70) {
		tmp = t_1;
	} else if ((z - t) <= -20000000.0) {
		tmp = t_2;
	} else if ((z - t) <= 1e-17) {
		tmp = t_1;
	} else if ((z - t) <= 2e+58) {
		tmp = t_2;
	} else if ((z - t) <= 5e+82) {
		tmp = t_1;
	} else {
		tmp = a * 120.0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = (-60.0d0) * (y / (z - t))
    t_2 = 60.0d0 * (x / (z - t))
    if ((z - t) <= (-2d+127)) then
        tmp = a * 120.0d0
    else if ((z - t) <= (-5d+70)) then
        tmp = t_1
    else if ((z - t) <= (-20000000.0d0)) then
        tmp = t_2
    else if ((z - t) <= 1d-17) then
        tmp = t_1
    else if ((z - t) <= 2d+58) then
        tmp = t_2
    else if ((z - t) <= 5d+82) then
        tmp = t_1
    else
        tmp = a * 120.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = -60.0 * (y / (z - t));
	double t_2 = 60.0 * (x / (z - t));
	double tmp;
	if ((z - t) <= -2e+127) {
		tmp = a * 120.0;
	} else if ((z - t) <= -5e+70) {
		tmp = t_1;
	} else if ((z - t) <= -20000000.0) {
		tmp = t_2;
	} else if ((z - t) <= 1e-17) {
		tmp = t_1;
	} else if ((z - t) <= 2e+58) {
		tmp = t_2;
	} else if ((z - t) <= 5e+82) {
		tmp = t_1;
	} else {
		tmp = a * 120.0;
	}
	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 (z - t) <= -2e+127:
		tmp = a * 120.0
	elif (z - t) <= -5e+70:
		tmp = t_1
	elif (z - t) <= -20000000.0:
		tmp = t_2
	elif (z - t) <= 1e-17:
		tmp = t_1
	elif (z - t) <= 2e+58:
		tmp = t_2
	elif (z - t) <= 5e+82:
		tmp = t_1
	else:
		tmp = a * 120.0
	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 (Float64(z - t) <= -2e+127)
		tmp = Float64(a * 120.0);
	elseif (Float64(z - t) <= -5e+70)
		tmp = t_1;
	elseif (Float64(z - t) <= -20000000.0)
		tmp = t_2;
	elseif (Float64(z - t) <= 1e-17)
		tmp = t_1;
	elseif (Float64(z - t) <= 2e+58)
		tmp = t_2;
	elseif (Float64(z - t) <= 5e+82)
		tmp = t_1;
	else
		tmp = Float64(a * 120.0);
	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 ((z - t) <= -2e+127)
		tmp = a * 120.0;
	elseif ((z - t) <= -5e+70)
		tmp = t_1;
	elseif ((z - t) <= -20000000.0)
		tmp = t_2;
	elseif ((z - t) <= 1e-17)
		tmp = t_1;
	elseif ((z - t) <= 2e+58)
		tmp = t_2;
	elseif ((z - t) <= 5e+82)
		tmp = t_1;
	else
		tmp = a * 120.0;
	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[N[(z - t), $MachinePrecision], -2e+127], N[(a * 120.0), $MachinePrecision], If[LessEqual[N[(z - t), $MachinePrecision], -5e+70], t$95$1, If[LessEqual[N[(z - t), $MachinePrecision], -20000000.0], t$95$2, If[LessEqual[N[(z - t), $MachinePrecision], 1e-17], t$95$1, If[LessEqual[N[(z - t), $MachinePrecision], 2e+58], t$95$2, If[LessEqual[N[(z - t), $MachinePrecision], 5e+82], t$95$1, N[(a * 120.0), $MachinePrecision]]]]]]]]]

Derivation

1. Split input into 3 regimes

2. if (-.f64 z t) < -1.99999999999999991e127 or 5.00000000000000015e82 < (-.f64 z t)

   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%

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

   3. Simplified 99.8%

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

   5. Taylor expanded in z around inf 72.4%

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

   if -1.99999999999999991e127 < (-.f64 z t) < -5.0000000000000002e70 or -2e7 < (-.f64 z t) < 1.00000000000000007e-17 or 1.99999999999999989e58 < (-.f64 z t) < 5.00000000000000015e82

   1. Initial program 98.7%

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

      1. associate-/l* 99.8%

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

   3. Simplified 99.8%

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

      1. associate-/r/ 99.8%

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

   6. Applied egg-rr 99.8%

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

      1. associate-*l/ 98.7%

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

      2. *-un-lft-identity 98.7%

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

      3. times-frac 99.8%

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

      4. metadata-eval 99.8%

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

      5. metadata-eval 99.8%

         \[\leadsto \color{blue}{\frac{1}{0.016666666666666666}} \cdot \frac{x - y}{z - t} + a \cdot 120 \]

      6. times-frac 99.7%

         \[\leadsto \color{blue}{\frac{1 \cdot \left(x - y\right)}{0.016666666666666666 \cdot \left(z - t\right)}} + a \cdot 120 \]

      7. *-un-lft-identity 99.7%

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

      8. *-commutative 99.7%

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

   8. Applied egg-rr 99.7%

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

   9. Taylor expanded in y around inf 60.7%

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

   if -5.0000000000000002e70 < (-.f64 z t) < -2e7 or 1.00000000000000007e-17 < (-.f64 z t) < 1.99999999999999989e58

   1. Initial program 95.4%

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

      1. associate-/l* 99.4%

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

   3. Simplified 99.4%

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

      1. associate-/r/ 99.7%

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

   6. Applied egg-rr 99.7%

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

      1. associate-*l/ 95.4%

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

      2. *-un-lft-identity 95.4%

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

      3. times-frac 99.9%

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

      4. metadata-eval 99.9%

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

      5. metadata-eval 99.9%

         \[\leadsto \color{blue}{\frac{1}{0.016666666666666666}} \cdot \frac{x - y}{z - t} + a \cdot 120 \]

      6. times-frac 99.6%

         \[\leadsto \color{blue}{\frac{1 \cdot \left(x - y\right)}{0.016666666666666666 \cdot \left(z - t\right)}} + a \cdot 120 \]

      7. *-un-lft-identity 99.6%

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

      8. *-commutative 99.6%

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

   8. Applied egg-rr 99.6%

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

   9. Taylor expanded in x around inf 69.0%

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

4. Final simplification 68.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z - t \leq -2 \cdot 10^{+127}:\\ \;\;\;\;a \cdot 120\\ \mathbf{elif}\;z - t \leq -5 \cdot 10^{+70}:\\ \;\;\;\;-60 \cdot \frac{y}{z - t}\\ \mathbf{elif}\;z - t \leq -20000000:\\ \;\;\;\;60 \cdot \frac{x}{z - t}\\ \mathbf{elif}\;z - t \leq 10^{-17}:\\ \;\;\;\;-60 \cdot \frac{y}{z - t}\\ \mathbf{elif}\;z - t \leq 2 \cdot 10^{+58}:\\ \;\;\;\;60 \cdot \frac{x}{z - t}\\ \mathbf{elif}\;z - t \leq 5 \cdot 10^{+82}:\\ \;\;\;\;-60 \cdot \frac{y}{z - t}\\ \mathbf{else}:\\ \;\;\;\;a \cdot 120\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 53.0% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := -60 \cdot \frac{y}{z - t}\\ \mathbf{if}\;z - t \leq -2 \cdot 10^{+127}:\\ \;\;\;\;a \cdot 120\\ \mathbf{elif}\;z - t \leq -5 \cdot 10^{+70}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z - t \leq -20000000:\\ \;\;\;\;60 \cdot \frac{x}{z - t}\\ \mathbf{elif}\;z - t \leq 10^{-90}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z - t \leq 5 \cdot 10^{+82}:\\ \;\;\;\;60 \cdot \frac{x - y}{z}\\ \mathbf{else}:\\ \;\;\;\;a \cdot 120\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* -60.0 (/ y (- z t)))))
   (if (<= (- z t) -2e+127)
     (* a 120.0)
     (if (<= (- z t) -5e+70)
       t_1
       (if (<= (- z t) -20000000.0)
         (* 60.0 (/ x (- z t)))
         (if (<= (- z t) 1e-90)
           t_1
           (if (<= (- z t) 5e+82) (* 60.0 (/ (- x y) z)) (* a 120.0))))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = -60.0 * (y / (z - t));
	double tmp;
	if ((z - t) <= -2e+127) {
		tmp = a * 120.0;
	} else if ((z - t) <= -5e+70) {
		tmp = t_1;
	} else if ((z - t) <= -20000000.0) {
		tmp = 60.0 * (x / (z - t));
	} else if ((z - t) <= 1e-90) {
		tmp = t_1;
	} else if ((z - t) <= 5e+82) {
		tmp = 60.0 * ((x - 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) :: t_1
    real(8) :: tmp
    t_1 = (-60.0d0) * (y / (z - t))
    if ((z - t) <= (-2d+127)) then
        tmp = a * 120.0d0
    else if ((z - t) <= (-5d+70)) then
        tmp = t_1
    else if ((z - t) <= (-20000000.0d0)) then
        tmp = 60.0d0 * (x / (z - t))
    else if ((z - t) <= 1d-90) then
        tmp = t_1
    else if ((z - t) <= 5d+82) then
        tmp = 60.0d0 * ((x - 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 t_1 = -60.0 * (y / (z - t));
	double tmp;
	if ((z - t) <= -2e+127) {
		tmp = a * 120.0;
	} else if ((z - t) <= -5e+70) {
		tmp = t_1;
	} else if ((z - t) <= -20000000.0) {
		tmp = 60.0 * (x / (z - t));
	} else if ((z - t) <= 1e-90) {
		tmp = t_1;
	} else if ((z - t) <= 5e+82) {
		tmp = 60.0 * ((x - y) / z);
	} else {
		tmp = a * 120.0;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = -60.0 * (y / (z - t))
	tmp = 0
	if (z - t) <= -2e+127:
		tmp = a * 120.0
	elif (z - t) <= -5e+70:
		tmp = t_1
	elif (z - t) <= -20000000.0:
		tmp = 60.0 * (x / (z - t))
	elif (z - t) <= 1e-90:
		tmp = t_1
	elif (z - t) <= 5e+82:
		tmp = 60.0 * ((x - y) / z)
	else:
		tmp = a * 120.0
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(-60.0 * Float64(y / Float64(z - t)))
	tmp = 0.0
	if (Float64(z - t) <= -2e+127)
		tmp = Float64(a * 120.0);
	elseif (Float64(z - t) <= -5e+70)
		tmp = t_1;
	elseif (Float64(z - t) <= -20000000.0)
		tmp = Float64(60.0 * Float64(x / Float64(z - t)));
	elseif (Float64(z - t) <= 1e-90)
		tmp = t_1;
	elseif (Float64(z - t) <= 5e+82)
		tmp = Float64(60.0 * Float64(Float64(x - y) / z));
	else
		tmp = Float64(a * 120.0);
	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 ((z - t) <= -2e+127)
		tmp = a * 120.0;
	elseif ((z - t) <= -5e+70)
		tmp = t_1;
	elseif ((z - t) <= -20000000.0)
		tmp = 60.0 * (x / (z - t));
	elseif ((z - t) <= 1e-90)
		tmp = t_1;
	elseif ((z - t) <= 5e+82)
		tmp = 60.0 * ((x - y) / z);
	else
		tmp = a * 120.0;
	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[N[(z - t), $MachinePrecision], -2e+127], N[(a * 120.0), $MachinePrecision], If[LessEqual[N[(z - t), $MachinePrecision], -5e+70], t$95$1, If[LessEqual[N[(z - t), $MachinePrecision], -20000000.0], N[(60.0 * N[(x / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[N[(z - t), $MachinePrecision], 1e-90], t$95$1, If[LessEqual[N[(z - t), $MachinePrecision], 5e+82], N[(60.0 * N[(N[(x - y), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision], N[(a * 120.0), $MachinePrecision]]]]]]]

Derivation

1. Split input into 4 regimes

2. if (-.f64 z t) < -1.99999999999999991e127 or 5.00000000000000015e82 < (-.f64 z t)

   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%

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

   3. Simplified 99.8%

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

   5. Taylor expanded in z around inf 72.4%

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

   if -1.99999999999999991e127 < (-.f64 z t) < -5.0000000000000002e70 or -2e7 < (-.f64 z t) < 9.99999999999999995e-91

   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* 99.9%

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

   3. Simplified 99.9%

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

      1. associate-/r/ 99.8%

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

   6. Applied egg-rr 99.8%

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

      1. associate-*l/ 99.8%

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

      2. *-un-lft-identity 99.8%

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

      3. times-frac 99.8%

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

      4. metadata-eval 99.8%

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

      5. metadata-eval 99.8%

         \[\leadsto \color{blue}{\frac{1}{0.016666666666666666}} \cdot \frac{x - y}{z - t} + a \cdot 120 \]

      6. times-frac 99.7%

         \[\leadsto \color{blue}{\frac{1 \cdot \left(x - y\right)}{0.016666666666666666 \cdot \left(z - t\right)}} + a \cdot 120 \]

      7. *-un-lft-identity 99.7%

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

      8. *-commutative 99.7%

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

   8. Applied egg-rr 99.7%

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

   9. Taylor expanded in y around inf 57.9%

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

   if -5.0000000000000002e70 < (-.f64 z t) < -2e7

   1. Initial program 89.6%

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

      1. associate-/l* 99.3%

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

   3. Simplified 99.3%

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

      1. associate-/r/ 99.8%

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

   6. Applied egg-rr 99.8%

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

      1. associate-*l/ 89.6%

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

      2. *-un-lft-identity 89.6%

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

      3. times-frac 99.8%

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

      4. metadata-eval 99.8%

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

      5. metadata-eval 99.8%

         \[\leadsto \color{blue}{\frac{1}{0.016666666666666666}} \cdot \frac{x - y}{z - t} + a \cdot 120 \]

      6. times-frac 99.7%

         \[\leadsto \color{blue}{\frac{1 \cdot \left(x - y\right)}{0.016666666666666666 \cdot \left(z - t\right)}} + a \cdot 120 \]

      7. *-un-lft-identity 99.7%

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

      8. *-commutative 99.7%

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

   8. Applied egg-rr 99.7%

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

   9. Taylor expanded in x around inf 56.7%

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

   if 9.99999999999999995e-91 < (-.f64 z t) < 5.00000000000000015e82

   1. Initial program 97.0%

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

      1. associate-/l* 99.6%

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

   3. Simplified 99.6%

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

   5. Taylor expanded in a around 0 91.9%

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

   6. Taylor expanded in z around inf 62.2%

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

4. Final simplification 66.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z - t \leq -2 \cdot 10^{+127}:\\ \;\;\;\;a \cdot 120\\ \mathbf{elif}\;z - t \leq -5 \cdot 10^{+70}:\\ \;\;\;\;-60 \cdot \frac{y}{z - t}\\ \mathbf{elif}\;z - t \leq -20000000:\\ \;\;\;\;60 \cdot \frac{x}{z - t}\\ \mathbf{elif}\;z - t \leq 10^{-90}:\\ \;\;\;\;-60 \cdot \frac{y}{z - t}\\ \mathbf{elif}\;z - t \leq 5 \cdot 10^{+82}:\\ \;\;\;\;60 \cdot \frac{x - y}{z}\\ \mathbf{else}:\\ \;\;\;\;a \cdot 120\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 53.0% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z - t \leq -2 \cdot 10^{+127}:\\ \;\;\;\;a \cdot 120\\ \mathbf{elif}\;z - t \leq -5 \cdot 10^{+70}:\\ \;\;\;\;y \cdot \frac{-60}{z - t}\\ \mathbf{elif}\;z - t \leq -20000000:\\ \;\;\;\;60 \cdot \frac{x}{z - t}\\ \mathbf{elif}\;z - t \leq 10^{-90}:\\ \;\;\;\;-60 \cdot \frac{y}{z - t}\\ \mathbf{elif}\;z - t \leq 5 \cdot 10^{+82}:\\ \;\;\;\;60 \cdot \frac{x - y}{z}\\ \mathbf{else}:\\ \;\;\;\;a \cdot 120\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= (- z t) -2e+127)
   (* a 120.0)
   (if (<= (- z t) -5e+70)
     (* y (/ -60.0 (- z t)))
     (if (<= (- z t) -20000000.0)
       (* 60.0 (/ x (- z t)))
       (if (<= (- z t) 1e-90)
         (* -60.0 (/ y (- z t)))
         (if (<= (- z t) 5e+82) (* 60.0 (/ (- x y) z)) (* a 120.0)))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((z - t) <= -2e+127) {
		tmp = a * 120.0;
	} else if ((z - t) <= -5e+70) {
		tmp = y * (-60.0 / (z - t));
	} else if ((z - t) <= -20000000.0) {
		tmp = 60.0 * (x / (z - t));
	} else if ((z - t) <= 1e-90) {
		tmp = -60.0 * (y / (z - t));
	} else if ((z - t) <= 5e+82) {
		tmp = 60.0 * ((x - 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 ((z - t) <= (-2d+127)) then
        tmp = a * 120.0d0
    else if ((z - t) <= (-5d+70)) then
        tmp = y * ((-60.0d0) / (z - t))
    else if ((z - t) <= (-20000000.0d0)) then
        tmp = 60.0d0 * (x / (z - t))
    else if ((z - t) <= 1d-90) then
        tmp = (-60.0d0) * (y / (z - t))
    else if ((z - t) <= 5d+82) then
        tmp = 60.0d0 * ((x - 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 ((z - t) <= -2e+127) {
		tmp = a * 120.0;
	} else if ((z - t) <= -5e+70) {
		tmp = y * (-60.0 / (z - t));
	} else if ((z - t) <= -20000000.0) {
		tmp = 60.0 * (x / (z - t));
	} else if ((z - t) <= 1e-90) {
		tmp = -60.0 * (y / (z - t));
	} else if ((z - t) <= 5e+82) {
		tmp = 60.0 * ((x - y) / z);
	} else {
		tmp = a * 120.0;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (z - t) <= -2e+127:
		tmp = a * 120.0
	elif (z - t) <= -5e+70:
		tmp = y * (-60.0 / (z - t))
	elif (z - t) <= -20000000.0:
		tmp = 60.0 * (x / (z - t))
	elif (z - t) <= 1e-90:
		tmp = -60.0 * (y / (z - t))
	elif (z - t) <= 5e+82:
		tmp = 60.0 * ((x - y) / z)
	else:
		tmp = a * 120.0
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (Float64(z - t) <= -2e+127)
		tmp = Float64(a * 120.0);
	elseif (Float64(z - t) <= -5e+70)
		tmp = Float64(y * Float64(-60.0 / Float64(z - t)));
	elseif (Float64(z - t) <= -20000000.0)
		tmp = Float64(60.0 * Float64(x / Float64(z - t)));
	elseif (Float64(z - t) <= 1e-90)
		tmp = Float64(-60.0 * Float64(y / Float64(z - t)));
	elseif (Float64(z - t) <= 5e+82)
		tmp = Float64(60.0 * Float64(Float64(x - 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 ((z - t) <= -2e+127)
		tmp = a * 120.0;
	elseif ((z - t) <= -5e+70)
		tmp = y * (-60.0 / (z - t));
	elseif ((z - t) <= -20000000.0)
		tmp = 60.0 * (x / (z - t));
	elseif ((z - t) <= 1e-90)
		tmp = -60.0 * (y / (z - t));
	elseif ((z - t) <= 5e+82)
		tmp = 60.0 * ((x - y) / z);
	else
		tmp = a * 120.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[N[(z - t), $MachinePrecision], -2e+127], N[(a * 120.0), $MachinePrecision], If[LessEqual[N[(z - t), $MachinePrecision], -5e+70], N[(y * N[(-60.0 / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[N[(z - t), $MachinePrecision], -20000000.0], N[(60.0 * N[(x / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[N[(z - t), $MachinePrecision], 1e-90], N[(-60.0 * N[(y / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[N[(z - t), $MachinePrecision], 5e+82], N[(60.0 * N[(N[(x - y), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision], N[(a * 120.0), $MachinePrecision]]]]]]

Derivation

1. Split input into 5 regimes

2. if (-.f64 z t) < -1.99999999999999991e127 or 5.00000000000000015e82 < (-.f64 z t)

   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%

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

   3. Simplified 99.8%

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

   5. Taylor expanded in z around inf 72.4%

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

   if -1.99999999999999991e127 < (-.f64 z t) < -5.0000000000000002e70

   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%

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

   3. Simplified 99.8%

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

      1. associate-/r/ 99.7%

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

   6. Applied egg-rr 99.7%

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

      1. associate-*l/ 99.7%

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

      2. *-un-lft-identity 99.7%

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

      3. times-frac 99.7%

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

      4. metadata-eval 99.7%

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

      5. metadata-eval 99.7%

         \[\leadsto \color{blue}{\frac{1}{0.016666666666666666}} \cdot \frac{x - y}{z - t} + a \cdot 120 \]

      6. times-frac 99.8%

         \[\leadsto \color{blue}{\frac{1 \cdot \left(x - y\right)}{0.016666666666666666 \cdot \left(z - t\right)}} + a \cdot 120 \]

      7. *-un-lft-identity 99.8%

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

      8. *-commutative 99.8%

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

   8. Applied egg-rr 99.8%

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

   9. Taylor expanded in a around 0 70.9%

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

       1. associate-*r/ 70.9%

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

       2. *-commutative 70.9%

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

       3. associate-/l* 71.0%

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

   11. Simplified 71.0%

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

   12. Taylor expanded in x around 0 59.0%

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

       1. associate-*r/ 59.0%

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

       2. associate-/l* 59.1%

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

       3. associate-/r/ 59.0%

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

       4. metadata-eval 59.0%

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

       5. distribute-neg-frac 59.0%

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

       6. distribute-lft-neg-in 59.0%

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

       7. *-commutative 59.0%

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

       8. distribute-rgt-neg-in 59.0%

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

       9. distribute-neg-frac 59.0%

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

       10. metadata-eval 59.0%

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

   14. Simplified 59.0%

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

   if -5.0000000000000002e70 < (-.f64 z t) < -2e7

   1. Initial program 89.6%

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

      1. associate-/l* 99.3%

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

   3. Simplified 99.3%

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

      1. associate-/r/ 99.8%

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

   6. Applied egg-rr 99.8%

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

      1. associate-*l/ 89.6%

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

      2. *-un-lft-identity 89.6%

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

      3. times-frac 99.8%

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

      4. metadata-eval 99.8%

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

      5. metadata-eval 99.8%

         \[\leadsto \color{blue}{\frac{1}{0.016666666666666666}} \cdot \frac{x - y}{z - t} + a \cdot 120 \]

      6. times-frac 99.7%

         \[\leadsto \color{blue}{\frac{1 \cdot \left(x - y\right)}{0.016666666666666666 \cdot \left(z - t\right)}} + a \cdot 120 \]

      7. *-un-lft-identity 99.7%

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

      8. *-commutative 99.7%

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

   8. Applied egg-rr 99.7%

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

   9. Taylor expanded in x around inf 56.7%

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

   if -2e7 < (-.f64 z t) < 9.99999999999999995e-91

   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* 99.9%

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

   3. Simplified 99.9%

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

      1. associate-/r/ 99.8%

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

   6. Applied egg-rr 99.8%

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

      1. associate-*l/ 99.8%

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

      2. *-un-lft-identity 99.8%

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

      3. times-frac 99.8%

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

      4. metadata-eval 99.8%

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

      5. metadata-eval 99.8%

         \[\leadsto \color{blue}{\frac{1}{0.016666666666666666}} \cdot \frac{x - y}{z - t} + a \cdot 120 \]

      6. times-frac 99.6%

         \[\leadsto \color{blue}{\frac{1 \cdot \left(x - y\right)}{0.016666666666666666 \cdot \left(z - t\right)}} + a \cdot 120 \]

      7. *-un-lft-identity 99.6%

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

      8. *-commutative 99.6%

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

   8. Applied egg-rr 99.6%

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

   9. Taylor expanded in y around inf 57.6%

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

   if 9.99999999999999995e-91 < (-.f64 z t) < 5.00000000000000015e82

   1. Initial program 97.0%

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

      1. associate-/l* 99.6%

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

   3. Simplified 99.6%

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

   5. Taylor expanded in a around 0 91.9%

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

   6. Taylor expanded in z around inf 62.2%

      \[\leadsto \color{blue}{60 \cdot \frac{x - y}{z}} \]
3. Recombined 5 regimes into one program.

4. Final simplification 66.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z - t \leq -2 \cdot 10^{+127}:\\ \;\;\;\;a \cdot 120\\ \mathbf{elif}\;z - t \leq -5 \cdot 10^{+70}:\\ \;\;\;\;y \cdot \frac{-60}{z - t}\\ \mathbf{elif}\;z - t \leq -20000000:\\ \;\;\;\;60 \cdot \frac{x}{z - t}\\ \mathbf{elif}\;z - t \leq 10^{-90}:\\ \;\;\;\;-60 \cdot \frac{y}{z - t}\\ \mathbf{elif}\;z - t \leq 5 \cdot 10^{+82}:\\ \;\;\;\;60 \cdot \frac{x - y}{z}\\ \mathbf{else}:\\ \;\;\;\;a \cdot 120\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 53.0% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z - t \leq -2 \cdot 10^{+127}:\\ \;\;\;\;a \cdot 120\\ \mathbf{elif}\;z - t \leq -5 \cdot 10^{+70}:\\ \;\;\;\;\frac{y \cdot -60}{z - t}\\ \mathbf{elif}\;z - t \leq -20000000:\\ \;\;\;\;60 \cdot \frac{x}{z - t}\\ \mathbf{elif}\;z - t \leq 10^{-90}:\\ \;\;\;\;-60 \cdot \frac{y}{z - t}\\ \mathbf{elif}\;z - t \leq 5 \cdot 10^{+82}:\\ \;\;\;\;60 \cdot \frac{x - y}{z}\\ \mathbf{else}:\\ \;\;\;\;a \cdot 120\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= (- z t) -2e+127)
   (* a 120.0)
   (if (<= (- z t) -5e+70)
     (/ (* y -60.0) (- z t))
     (if (<= (- z t) -20000000.0)
       (* 60.0 (/ x (- z t)))
       (if (<= (- z t) 1e-90)
         (* -60.0 (/ y (- z t)))
         (if (<= (- z t) 5e+82) (* 60.0 (/ (- x y) z)) (* a 120.0)))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((z - t) <= -2e+127) {
		tmp = a * 120.0;
	} else if ((z - t) <= -5e+70) {
		tmp = (y * -60.0) / (z - t);
	} else if ((z - t) <= -20000000.0) {
		tmp = 60.0 * (x / (z - t));
	} else if ((z - t) <= 1e-90) {
		tmp = -60.0 * (y / (z - t));
	} else if ((z - t) <= 5e+82) {
		tmp = 60.0 * ((x - 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 ((z - t) <= (-2d+127)) then
        tmp = a * 120.0d0
    else if ((z - t) <= (-5d+70)) then
        tmp = (y * (-60.0d0)) / (z - t)
    else if ((z - t) <= (-20000000.0d0)) then
        tmp = 60.0d0 * (x / (z - t))
    else if ((z - t) <= 1d-90) then
        tmp = (-60.0d0) * (y / (z - t))
    else if ((z - t) <= 5d+82) then
        tmp = 60.0d0 * ((x - 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 ((z - t) <= -2e+127) {
		tmp = a * 120.0;
	} else if ((z - t) <= -5e+70) {
		tmp = (y * -60.0) / (z - t);
	} else if ((z - t) <= -20000000.0) {
		tmp = 60.0 * (x / (z - t));
	} else if ((z - t) <= 1e-90) {
		tmp = -60.0 * (y / (z - t));
	} else if ((z - t) <= 5e+82) {
		tmp = 60.0 * ((x - y) / z);
	} else {
		tmp = a * 120.0;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (z - t) <= -2e+127:
		tmp = a * 120.0
	elif (z - t) <= -5e+70:
		tmp = (y * -60.0) / (z - t)
	elif (z - t) <= -20000000.0:
		tmp = 60.0 * (x / (z - t))
	elif (z - t) <= 1e-90:
		tmp = -60.0 * (y / (z - t))
	elif (z - t) <= 5e+82:
		tmp = 60.0 * ((x - y) / z)
	else:
		tmp = a * 120.0
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (Float64(z - t) <= -2e+127)
		tmp = Float64(a * 120.0);
	elseif (Float64(z - t) <= -5e+70)
		tmp = Float64(Float64(y * -60.0) / Float64(z - t));
	elseif (Float64(z - t) <= -20000000.0)
		tmp = Float64(60.0 * Float64(x / Float64(z - t)));
	elseif (Float64(z - t) <= 1e-90)
		tmp = Float64(-60.0 * Float64(y / Float64(z - t)));
	elseif (Float64(z - t) <= 5e+82)
		tmp = Float64(60.0 * Float64(Float64(x - 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 ((z - t) <= -2e+127)
		tmp = a * 120.0;
	elseif ((z - t) <= -5e+70)
		tmp = (y * -60.0) / (z - t);
	elseif ((z - t) <= -20000000.0)
		tmp = 60.0 * (x / (z - t));
	elseif ((z - t) <= 1e-90)
		tmp = -60.0 * (y / (z - t));
	elseif ((z - t) <= 5e+82)
		tmp = 60.0 * ((x - y) / z);
	else
		tmp = a * 120.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[N[(z - t), $MachinePrecision], -2e+127], N[(a * 120.0), $MachinePrecision], If[LessEqual[N[(z - t), $MachinePrecision], -5e+70], N[(N[(y * -60.0), $MachinePrecision] / N[(z - t), $MachinePrecision]), $MachinePrecision], If[LessEqual[N[(z - t), $MachinePrecision], -20000000.0], N[(60.0 * N[(x / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[N[(z - t), $MachinePrecision], 1e-90], N[(-60.0 * N[(y / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[N[(z - t), $MachinePrecision], 5e+82], N[(60.0 * N[(N[(x - y), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision], N[(a * 120.0), $MachinePrecision]]]]]]

Derivation

1. Split input into 5 regimes

2. if (-.f64 z t) < -1.99999999999999991e127 or 5.00000000000000015e82 < (-.f64 z t)

   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%

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

   3. Simplified 99.8%

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

   5. Taylor expanded in z around inf 72.4%

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

   if -1.99999999999999991e127 < (-.f64 z t) < -5.0000000000000002e70

   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%

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

   3. Simplified 99.8%

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

      1. associate-/r/ 99.7%

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

   6. Applied egg-rr 99.7%

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

      1. associate-*l/ 99.7%

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

      2. *-un-lft-identity 99.7%

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

      3. times-frac 99.7%

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

      4. metadata-eval 99.7%

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

      5. metadata-eval 99.7%

         \[\leadsto \color{blue}{\frac{1}{0.016666666666666666}} \cdot \frac{x - y}{z - t} + a \cdot 120 \]

      6. times-frac 99.8%

         \[\leadsto \color{blue}{\frac{1 \cdot \left(x - y\right)}{0.016666666666666666 \cdot \left(z - t\right)}} + a \cdot 120 \]

      7. *-un-lft-identity 99.8%

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

      8. *-commutative 99.8%

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

   8. Applied egg-rr 99.8%

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

   9. Taylor expanded in y around inf 59.0%

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

       1. associate-*r/ 59.0%

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

   11. Simplified 59.0%

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

   if -5.0000000000000002e70 < (-.f64 z t) < -2e7

   1. Initial program 89.6%

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

      1. associate-/l* 99.3%

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

   3. Simplified 99.3%

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

      1. associate-/r/ 99.8%

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

   6. Applied egg-rr 99.8%

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

      1. associate-*l/ 89.6%

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

      2. *-un-lft-identity 89.6%

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

      3. times-frac 99.8%

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

      4. metadata-eval 99.8%

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

      5. metadata-eval 99.8%

         \[\leadsto \color{blue}{\frac{1}{0.016666666666666666}} \cdot \frac{x - y}{z - t} + a \cdot 120 \]

      6. times-frac 99.7%

         \[\leadsto \color{blue}{\frac{1 \cdot \left(x - y\right)}{0.016666666666666666 \cdot \left(z - t\right)}} + a \cdot 120 \]

      7. *-un-lft-identity 99.7%

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

      8. *-commutative 99.7%

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

   8. Applied egg-rr 99.7%

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

   9. Taylor expanded in x around inf 56.7%

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

   if -2e7 < (-.f64 z t) < 9.99999999999999995e-91

   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* 99.9%

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

   3. Simplified 99.9%

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

      1. associate-/r/ 99.8%

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

   6. Applied egg-rr 99.8%

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

      1. associate-*l/ 99.8%

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

      2. *-un-lft-identity 99.8%

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

      3. times-frac 99.8%

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

      4. metadata-eval 99.8%

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

      5. metadata-eval 99.8%

         \[\leadsto \color{blue}{\frac{1}{0.016666666666666666}} \cdot \frac{x - y}{z - t} + a \cdot 120 \]

      6. times-frac 99.6%

         \[\leadsto \color{blue}{\frac{1 \cdot \left(x - y\right)}{0.016666666666666666 \cdot \left(z - t\right)}} + a \cdot 120 \]

      7. *-un-lft-identity 99.6%

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

      8. *-commutative 99.6%

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

   8. Applied egg-rr 99.6%

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

   9. Taylor expanded in y around inf 57.6%

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

   if 9.99999999999999995e-91 < (-.f64 z t) < 5.00000000000000015e82

   1. Initial program 97.0%

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

      1. associate-/l* 99.6%

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

   3. Simplified 99.6%

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

   5. Taylor expanded in a around 0 91.9%

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

   6. Taylor expanded in z around inf 62.2%

      \[\leadsto \color{blue}{60 \cdot \frac{x - y}{z}} \]
3. Recombined 5 regimes into one program.

4. Final simplification 66.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z - t \leq -2 \cdot 10^{+127}:\\ \;\;\;\;a \cdot 120\\ \mathbf{elif}\;z - t \leq -5 \cdot 10^{+70}:\\ \;\;\;\;\frac{y \cdot -60}{z - t}\\ \mathbf{elif}\;z - t \leq -20000000:\\ \;\;\;\;60 \cdot \frac{x}{z - t}\\ \mathbf{elif}\;z - t \leq 10^{-90}:\\ \;\;\;\;-60 \cdot \frac{y}{z - t}\\ \mathbf{elif}\;z - t \leq 5 \cdot 10^{+82}:\\ \;\;\;\;60 \cdot \frac{x - y}{z}\\ \mathbf{else}:\\ \;\;\;\;a \cdot 120\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 79.1% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{60}{\frac{z}{x - y}} + a \cdot 120\\ \mathbf{if}\;z \leq -5.2 \cdot 10^{-127}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 5 \cdot 10^{-165}:\\ \;\;\;\;\left(x - y\right) \cdot \frac{-60}{t} + a \cdot 120\\ \mathbf{elif}\;z \leq 1.42 \cdot 10^{+80}:\\ \;\;\;\;\frac{x - y}{z - t} \cdot 60\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ (/ 60.0 (/ z (- x y))) (* a 120.0))))
   (if (<= z -5.2e-127)
     t_1
     (if (<= z 5e-165)
       (+ (* (- x y) (/ -60.0 t)) (* a 120.0))
       (if (<= z 1.42e+80) (* (/ (- x y) (- z t)) 60.0) t_1)))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = (60.0 / (z / (x - y))) + (a * 120.0);
	double tmp;
	if (z <= -5.2e-127) {
		tmp = t_1;
	} else if (z <= 5e-165) {
		tmp = ((x - y) * (-60.0 / t)) + (a * 120.0);
	} else if (z <= 1.42e+80) {
		tmp = ((x - y) / (z - t)) * 60.0;
	} 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 / (z / (x - y))) + (a * 120.0d0)
    if (z <= (-5.2d-127)) then
        tmp = t_1
    else if (z <= 5d-165) then
        tmp = ((x - y) * ((-60.0d0) / t)) + (a * 120.0d0)
    else if (z <= 1.42d+80) then
        tmp = ((x - y) / (z - t)) * 60.0d0
    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 / (z / (x - y))) + (a * 120.0);
	double tmp;
	if (z <= -5.2e-127) {
		tmp = t_1;
	} else if (z <= 5e-165) {
		tmp = ((x - y) * (-60.0 / t)) + (a * 120.0);
	} else if (z <= 1.42e+80) {
		tmp = ((x - y) / (z - t)) * 60.0;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = (60.0 / (z / (x - y))) + (a * 120.0)
	tmp = 0
	if z <= -5.2e-127:
		tmp = t_1
	elif z <= 5e-165:
		tmp = ((x - y) * (-60.0 / t)) + (a * 120.0)
	elif z <= 1.42e+80:
		tmp = ((x - y) / (z - t)) * 60.0
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(Float64(60.0 / Float64(z / Float64(x - y))) + Float64(a * 120.0))
	tmp = 0.0
	if (z <= -5.2e-127)
		tmp = t_1;
	elseif (z <= 5e-165)
		tmp = Float64(Float64(Float64(x - y) * Float64(-60.0 / t)) + Float64(a * 120.0));
	elseif (z <= 1.42e+80)
		tmp = Float64(Float64(Float64(x - y) / Float64(z - t)) * 60.0);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = (60.0 / (z / (x - y))) + (a * 120.0);
	tmp = 0.0;
	if (z <= -5.2e-127)
		tmp = t_1;
	elseif (z <= 5e-165)
		tmp = ((x - y) * (-60.0 / t)) + (a * 120.0);
	elseif (z <= 1.42e+80)
		tmp = ((x - y) / (z - t)) * 60.0;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(N[(60.0 / N[(z / N[(x - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(a * 120.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -5.2e-127], t$95$1, If[LessEqual[z, 5e-165], N[(N[(N[(x - y), $MachinePrecision] * N[(-60.0 / t), $MachinePrecision]), $MachinePrecision] + N[(a * 120.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 1.42e+80], N[(N[(N[(x - y), $MachinePrecision] / N[(z - t), $MachinePrecision]), $MachinePrecision] * 60.0), $MachinePrecision], t$95$1]]]]

Derivation

1. Split input into 3 regimes

2. if z < -5.19999999999999982e-127 or 1.4200000000000001e80 < z

   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* 99.7%

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

   3. Simplified 99.7%

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

   5. Taylor expanded in z around inf 87.5%

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

   if -5.19999999999999982e-127 < z < 4.99999999999999981e-165

   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%

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

   3. Simplified 99.8%

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

   5. Taylor expanded in z around 0 95.1%

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

      1. associate-*r/ 95.1%

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

   7. Simplified 95.1%

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

      1. associate-/l* 95.1%

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

      2. associate-/r/ 95.1%

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

   9. Applied egg-rr 95.1%

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

   if 4.99999999999999981e-165 < z < 1.4200000000000001e80

   1. Initial program 97.6%

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

      1. associate-/l* 99.8%

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

   3. Simplified 99.8%

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

   5. Taylor expanded in a around 0 78.3%

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

4. Final simplification 87.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -5.2 \cdot 10^{-127}:\\ \;\;\;\;\frac{60}{\frac{z}{x - y}} + a \cdot 120\\ \mathbf{elif}\;z \leq 5 \cdot 10^{-165}:\\ \;\;\;\;\left(x - y\right) \cdot \frac{-60}{t} + a \cdot 120\\ \mathbf{elif}\;z \leq 1.42 \cdot 10^{+80}:\\ \;\;\;\;\frac{x - y}{z - t} \cdot 60\\ \mathbf{else}:\\ \;\;\;\;\frac{60}{\frac{z}{x - y}} + a \cdot 120\\ \end{array} \]
5. Add Preprocessing

Alternative 7: 76.6% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z - t \leq -2 \cdot 10^{+127} \lor \neg \left(z - t \leq 5 \cdot 10^{+82}\right):\\ \;\;\;\;x \cdot \frac{60}{z - t} + a \cdot 120\\ \mathbf{else}:\\ \;\;\;\;\frac{x - y}{z - t} \cdot 60\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= (- z t) -2e+127) (not (<= (- z t) 5e+82)))
   (+ (* x (/ 60.0 (- z t))) (* a 120.0))
   (* (/ (- x y) (- z t)) 60.0)))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (((z - t) <= -2e+127) || !((z - t) <= 5e+82)) {
		tmp = (x * (60.0 / (z - t))) + (a * 120.0);
	} else {
		tmp = ((x - y) / (z - t)) * 60.0;
	}
	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 (((z - t) <= (-2d+127)) .or. (.not. ((z - t) <= 5d+82))) then
        tmp = (x * (60.0d0 / (z - t))) + (a * 120.0d0)
    else
        tmp = ((x - y) / (z - t)) * 60.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (((z - t) <= -2e+127) || !((z - t) <= 5e+82)) {
		tmp = (x * (60.0 / (z - t))) + (a * 120.0);
	} else {
		tmp = ((x - y) / (z - t)) * 60.0;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if ((z - t) <= -2e+127) or not ((z - t) <= 5e+82):
		tmp = (x * (60.0 / (z - t))) + (a * 120.0)
	else:
		tmp = ((x - y) / (z - t)) * 60.0
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((Float64(z - t) <= -2e+127) || !(Float64(z - t) <= 5e+82))
		tmp = Float64(Float64(x * Float64(60.0 / Float64(z - t))) + Float64(a * 120.0));
	else
		tmp = Float64(Float64(Float64(x - y) / Float64(z - t)) * 60.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (((z - t) <= -2e+127) || ~(((z - t) <= 5e+82)))
		tmp = (x * (60.0 / (z - t))) + (a * 120.0);
	else
		tmp = ((x - y) / (z - t)) * 60.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[N[(z - t), $MachinePrecision], -2e+127], N[Not[LessEqual[N[(z - t), $MachinePrecision], 5e+82]], $MachinePrecision]], N[(N[(x * N[(60.0 / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(a * 120.0), $MachinePrecision]), $MachinePrecision], N[(N[(N[(x - y), $MachinePrecision] / N[(z - t), $MachinePrecision]), $MachinePrecision] * 60.0), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (-.f64 z t) < -1.99999999999999991e127 or 5.00000000000000015e82 < (-.f64 z t)

   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%

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

   3. Simplified 99.8%

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

   5. Taylor expanded in x around inf 87.0%

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

      1. associate-*r/ 87.0%

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

      2. associate-*l/ 87.0%

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

      3. *-commutative 87.0%

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

   7. Simplified 87.0%

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

   if -1.99999999999999991e127 < (-.f64 z t) < 5.00000000000000015e82

   1. Initial program 98.0%

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

      1. associate-/l* 99.7%

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

   3. Simplified 99.7%

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

   5. Taylor expanded in a around 0 83.1%

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

4. Final simplification 85.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z - t \leq -2 \cdot 10^{+127} \lor \neg \left(z - t \leq 5 \cdot 10^{+82}\right):\\ \;\;\;\;x \cdot \frac{60}{z - t} + a \cdot 120\\ \mathbf{else}:\\ \;\;\;\;\frac{x - y}{z - t} \cdot 60\\ \end{array} \]
5. Add Preprocessing

Alternative 8: 74.1% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \cdot 120 \leq -5 \cdot 10^{+48} \lor \neg \left(a \cdot 120 \leq 4 \cdot 10^{+57}\right):\\ \;\;\;\;a \cdot 120\\ \mathbf{else}:\\ \;\;\;\;\frac{x - y}{z - t} \cdot 60\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= (* a 120.0) -5e+48) (not (<= (* a 120.0) 4e+57)))
   (* a 120.0)
   (* (/ (- x y) (- z t)) 60.0)))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (((a * 120.0) <= -5e+48) || !((a * 120.0) <= 4e+57)) {
		tmp = a * 120.0;
	} else {
		tmp = ((x - y) / (z - t)) * 60.0;
	}
	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 (((a * 120.0d0) <= (-5d+48)) .or. (.not. ((a * 120.0d0) <= 4d+57))) then
        tmp = a * 120.0d0
    else
        tmp = ((x - y) / (z - t)) * 60.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (((a * 120.0) <= -5e+48) || !((a * 120.0) <= 4e+57)) {
		tmp = a * 120.0;
	} else {
		tmp = ((x - y) / (z - t)) * 60.0;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if ((a * 120.0) <= -5e+48) or not ((a * 120.0) <= 4e+57):
		tmp = a * 120.0
	else:
		tmp = ((x - y) / (z - t)) * 60.0
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((Float64(a * 120.0) <= -5e+48) || !(Float64(a * 120.0) <= 4e+57))
		tmp = Float64(a * 120.0);
	else
		tmp = Float64(Float64(Float64(x - y) / Float64(z - t)) * 60.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (((a * 120.0) <= -5e+48) || ~(((a * 120.0) <= 4e+57)))
		tmp = a * 120.0;
	else
		tmp = ((x - y) / (z - t)) * 60.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[N[(a * 120.0), $MachinePrecision], -5e+48], N[Not[LessEqual[N[(a * 120.0), $MachinePrecision], 4e+57]], $MachinePrecision]], N[(a * 120.0), $MachinePrecision], N[(N[(N[(x - y), $MachinePrecision] / N[(z - t), $MachinePrecision]), $MachinePrecision] * 60.0), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 a 120) < -4.99999999999999973e48 or 4.00000000000000019e57 < (*.f64 a 120)

   1. Initial program 99.0%

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

      1. associate-/l* 99.9%

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

   3. Simplified 99.9%

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

   5. Taylor expanded in z around inf 85.1%

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

   if -4.99999999999999973e48 < (*.f64 a 120) < 4.00000000000000019e57

   1. Initial program 99.0%

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

      1. associate-/l* 99.6%

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

   3. Simplified 99.6%

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

   5. Taylor expanded in a around 0 76.8%

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

4. Final simplification 80.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;a \cdot 120 \leq -5 \cdot 10^{+48} \lor \neg \left(a \cdot 120 \leq 4 \cdot 10^{+57}\right):\\ \;\;\;\;a \cdot 120\\ \mathbf{else}:\\ \;\;\;\;\frac{x - y}{z - t} \cdot 60\\ \end{array} \]
5. Add Preprocessing

Alternative 9: 89.3% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -6.5 \cdot 10^{+50} \lor \neg \left(y \leq 3.8 \cdot 10^{-14}\right):\\ \;\;\;\;\frac{y \cdot -60}{z - t} + a \cdot 120\\ \mathbf{else}:\\ \;\;\;\;x \cdot \frac{60}{z - t} + a \cdot 120\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= y -6.5e+50) (not (<= y 3.8e-14)))
   (+ (/ (* y -60.0) (- z t)) (* a 120.0))
   (+ (* x (/ 60.0 (- z t))) (* a 120.0))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((y <= -6.5e+50) || !(y <= 3.8e-14)) {
		tmp = ((y * -60.0) / (z - t)) + (a * 120.0);
	} else {
		tmp = (x * (60.0 / (z - t))) + (a * 120.0);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if ((y <= (-6.5d+50)) .or. (.not. (y <= 3.8d-14))) then
        tmp = ((y * (-60.0d0)) / (z - t)) + (a * 120.0d0)
    else
        tmp = (x * (60.0d0 / (z - t))) + (a * 120.0d0)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((y <= -6.5e+50) || !(y <= 3.8e-14)) {
		tmp = ((y * -60.0) / (z - t)) + (a * 120.0);
	} else {
		tmp = (x * (60.0 / (z - t))) + (a * 120.0);
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (y <= -6.5e+50) or not (y <= 3.8e-14):
		tmp = ((y * -60.0) / (z - t)) + (a * 120.0)
	else:
		tmp = (x * (60.0 / (z - t))) + (a * 120.0)
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((y <= -6.5e+50) || !(y <= 3.8e-14))
		tmp = Float64(Float64(Float64(y * -60.0) / Float64(z - t)) + Float64(a * 120.0));
	else
		tmp = Float64(Float64(x * Float64(60.0 / Float64(z - t))) + Float64(a * 120.0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((y <= -6.5e+50) || ~((y <= 3.8e-14)))
		tmp = ((y * -60.0) / (z - t)) + (a * 120.0);
	else
		tmp = (x * (60.0 / (z - t))) + (a * 120.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[y, -6.5e+50], N[Not[LessEqual[y, 3.8e-14]], $MachinePrecision]], N[(N[(N[(y * -60.0), $MachinePrecision] / N[(z - t), $MachinePrecision]), $MachinePrecision] + N[(a * 120.0), $MachinePrecision]), $MachinePrecision], N[(N[(x * N[(60.0 / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(a * 120.0), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -6.5000000000000003e50 or 3.8000000000000002e-14 < y

   1. Initial program 98.9%

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

   3. Taylor expanded in x around 0 89.3%

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

   if -6.5000000000000003e50 < y < 3.8000000000000002e-14

   1. Initial program 99.1%

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

      1. associate-/l* 99.8%

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

   3. Simplified 99.8%

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

   5. Taylor expanded in x around inf 92.6%

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

      1. associate-*r/ 91.8%

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

      2. associate-*l/ 92.6%

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

      3. *-commutative 92.6%

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

   7. Simplified 92.6%

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

4. Final simplification 91.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -6.5 \cdot 10^{+50} \lor \neg \left(y \leq 3.8 \cdot 10^{-14}\right):\\ \;\;\;\;\frac{y \cdot -60}{z - t} + a \cdot 120\\ \mathbf{else}:\\ \;\;\;\;x \cdot \frac{60}{z - t} + a \cdot 120\\ \end{array} \]
5. Add Preprocessing

Alternative 10: 52.6% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -7 \cdot 10^{-223}:\\ \;\;\;\;a \cdot 120\\ \mathbf{elif}\;a \leq 5.5 \cdot 10^{-261}:\\ \;\;\;\;-60 \cdot \frac{y}{z}\\ \mathbf{elif}\;a \leq 2.8 \cdot 10^{-121}:\\ \;\;\;\;60 \cdot \frac{x}{z}\\ \mathbf{else}:\\ \;\;\;\;a \cdot 120\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= a -7e-223)
   (* a 120.0)
   (if (<= a 5.5e-261)
     (* -60.0 (/ y z))
     (if (<= a 2.8e-121) (* 60.0 (/ x z)) (* a 120.0)))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (a <= -7e-223) {
		tmp = a * 120.0;
	} else if (a <= 5.5e-261) {
		tmp = -60.0 * (y / z);
	} else if (a <= 2.8e-121) {
		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 (a <= (-7d-223)) then
        tmp = a * 120.0d0
    else if (a <= 5.5d-261) then
        tmp = (-60.0d0) * (y / z)
    else if (a <= 2.8d-121) 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 (a <= -7e-223) {
		tmp = a * 120.0;
	} else if (a <= 5.5e-261) {
		tmp = -60.0 * (y / z);
	} else if (a <= 2.8e-121) {
		tmp = 60.0 * (x / z);
	} else {
		tmp = a * 120.0;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if a <= -7e-223:
		tmp = a * 120.0
	elif a <= 5.5e-261:
		tmp = -60.0 * (y / z)
	elif a <= 2.8e-121:
		tmp = 60.0 * (x / z)
	else:
		tmp = a * 120.0
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (a <= -7e-223)
		tmp = Float64(a * 120.0);
	elseif (a <= 5.5e-261)
		tmp = Float64(-60.0 * Float64(y / z));
	elseif (a <= 2.8e-121)
		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 (a <= -7e-223)
		tmp = a * 120.0;
	elseif (a <= 5.5e-261)
		tmp = -60.0 * (y / z);
	elseif (a <= 2.8e-121)
		tmp = 60.0 * (x / z);
	else
		tmp = a * 120.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[a, -7e-223], N[(a * 120.0), $MachinePrecision], If[LessEqual[a, 5.5e-261], N[(-60.0 * N[(y / z), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 2.8e-121], N[(60.0 * N[(x / z), $MachinePrecision]), $MachinePrecision], N[(a * 120.0), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if a < -7.00000000000000018e-223 or 2.8000000000000001e-121 < a

   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%

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

   3. Simplified 99.8%

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

   5. Taylor expanded in z around inf 61.8%

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

   if -7.00000000000000018e-223 < a < 5.50000000000000042e-261

   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* 99.4%

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

   3. Simplified 99.4%

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

      1. associate-/r/ 99.5%

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

   6. Applied egg-rr 99.5%

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

      1. associate-*l/ 99.4%

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

      2. *-un-lft-identity 99.4%

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

      3. times-frac 99.6%

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

      4. metadata-eval 99.6%

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

      5. metadata-eval 99.6%

         \[\leadsto \color{blue}{\frac{1}{0.016666666666666666}} \cdot \frac{x - y}{z - t} + a \cdot 120 \]

      6. times-frac 99.5%

         \[\leadsto \color{blue}{\frac{1 \cdot \left(x - y\right)}{0.016666666666666666 \cdot \left(z - t\right)}} + a \cdot 120 \]

      7. *-un-lft-identity 99.5%

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

      8. *-commutative 99.5%

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

   8. Applied egg-rr 99.5%

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

   9. Taylor expanded in y around inf 65.7%

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

   10. Taylor expanded in z around inf 46.1%

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

   if 5.50000000000000042e-261 < a < 2.8000000000000001e-121

   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* 99.6%

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

   3. Simplified 99.6%

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

   5. Taylor expanded in a around 0 88.8%

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

   6. Taylor expanded in z around inf 53.5%

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

   7. Taylor expanded in x around inf 43.1%

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

4. Final simplification 57.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;a \leq -7 \cdot 10^{-223}:\\ \;\;\;\;a \cdot 120\\ \mathbf{elif}\;a \leq 5.5 \cdot 10^{-261}:\\ \;\;\;\;-60 \cdot \frac{y}{z}\\ \mathbf{elif}\;a \leq 2.8 \cdot 10^{-121}:\\ \;\;\;\;60 \cdot \frac{x}{z}\\ \mathbf{else}:\\ \;\;\;\;a \cdot 120\\ \end{array} \]
5. Add Preprocessing

Alternative 11: 57.8% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -5.4 \cdot 10^{-92} \lor \neg \left(a \leq 10^{+21}\right):\\ \;\;\;\;a \cdot 120\\ \mathbf{else}:\\ \;\;\;\;-60 \cdot \frac{y}{z - t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= a -5.4e-92) (not (<= a 1e+21)))
   (* a 120.0)
   (* -60.0 (/ y (- z t)))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((a <= -5.4e-92) || !(a <= 1e+21)) {
		tmp = a * 120.0;
	} else {
		tmp = -60.0 * (y / (z - t));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if ((a <= (-5.4d-92)) .or. (.not. (a <= 1d+21))) then
        tmp = a * 120.0d0
    else
        tmp = (-60.0d0) * (y / (z - t))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((a <= -5.4e-92) || !(a <= 1e+21)) {
		tmp = a * 120.0;
	} else {
		tmp = -60.0 * (y / (z - t));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (a <= -5.4e-92) or not (a <= 1e+21):
		tmp = a * 120.0
	else:
		tmp = -60.0 * (y / (z - t))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((a <= -5.4e-92) || !(a <= 1e+21))
		tmp = Float64(a * 120.0);
	else
		tmp = Float64(-60.0 * Float64(y / Float64(z - t)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((a <= -5.4e-92) || ~((a <= 1e+21)))
		tmp = a * 120.0;
	else
		tmp = -60.0 * (y / (z - t));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[a, -5.4e-92], N[Not[LessEqual[a, 1e+21]], $MachinePrecision]], N[(a * 120.0), $MachinePrecision], N[(-60.0 * N[(y / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if a < -5.3999999999999999e-92 or 1e21 < a

   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* 99.9%

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

   3. Simplified 99.9%

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

   5. Taylor expanded in z around inf 74.1%

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

   if -5.3999999999999999e-92 < a < 1e21

   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* 99.6%

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

   3. Simplified 99.6%

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

      1. associate-/r/ 99.7%

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

   6. Applied egg-rr 99.7%

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

      1. associate-*l/ 98.8%

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

      2. *-un-lft-identity 98.8%

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

      3. times-frac 99.7%

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

      4. metadata-eval 99.7%

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

      5. metadata-eval 99.7%

         \[\leadsto \color{blue}{\frac{1}{0.016666666666666666}} \cdot \frac{x - y}{z - t} + a \cdot 120 \]

      6. times-frac 99.6%

         \[\leadsto \color{blue}{\frac{1 \cdot \left(x - y\right)}{0.016666666666666666 \cdot \left(z - t\right)}} + a \cdot 120 \]

      7. *-un-lft-identity 99.6%

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

      8. *-commutative 99.6%

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

   8. Applied egg-rr 99.6%

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

   9. Taylor expanded in y around inf 49.0%

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

4. Final simplification 62.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;a \leq -5.4 \cdot 10^{-92} \lor \neg \left(a \leq 10^{+21}\right):\\ \;\;\;\;a \cdot 120\\ \mathbf{else}:\\ \;\;\;\;-60 \cdot \frac{y}{z - t}\\ \end{array} \]
5. Add Preprocessing

Alternative 12: 52.6% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -7 \cdot 10^{-223} \lor \neg \left(a \leq 1.5 \cdot 10^{-143}\right):\\ \;\;\;\;a \cdot 120\\ \mathbf{else}:\\ \;\;\;\;-60 \cdot \frac{y}{z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= a -7e-223) (not (<= a 1.5e-143))) (* a 120.0) (* -60.0 (/ y z))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((a <= -7e-223) || !(a <= 1.5e-143)) {
		tmp = a * 120.0;
	} else {
		tmp = -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) :: tmp
    if ((a <= (-7d-223)) .or. (.not. (a <= 1.5d-143))) then
        tmp = a * 120.0d0
    else
        tmp = (-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 tmp;
	if ((a <= -7e-223) || !(a <= 1.5e-143)) {
		tmp = a * 120.0;
	} else {
		tmp = -60.0 * (y / z);
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (a <= -7e-223) or not (a <= 1.5e-143):
		tmp = a * 120.0
	else:
		tmp = -60.0 * (y / z)
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((a <= -7e-223) || !(a <= 1.5e-143))
		tmp = Float64(a * 120.0);
	else
		tmp = Float64(-60.0 * Float64(y / z));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((a <= -7e-223) || ~((a <= 1.5e-143)))
		tmp = a * 120.0;
	else
		tmp = -60.0 * (y / z);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[a, -7e-223], N[Not[LessEqual[a, 1.5e-143]], $MachinePrecision]], N[(a * 120.0), $MachinePrecision], N[(-60.0 * N[(y / z), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if a < -7.00000000000000018e-223 or 1.49999999999999993e-143 < a

   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%

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

   3. Simplified 99.8%

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

   5. Taylor expanded in z around inf 61.4%

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

   if -7.00000000000000018e-223 < a < 1.49999999999999993e-143

   1. Initial program 98.0%

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

      1. associate-/l* 99.5%

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

   3. Simplified 99.5%

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

      1. associate-/r/ 99.6%

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

   6. Applied egg-rr 99.6%

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

      1. associate-*l/ 98.0%

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

      2. *-un-lft-identity 98.0%

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

      3. times-frac 99.6%

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

      4. metadata-eval 99.6%

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

      5. metadata-eval 99.6%

         \[\leadsto \color{blue}{\frac{1}{0.016666666666666666}} \cdot \frac{x - y}{z - t} + a \cdot 120 \]

      6. times-frac 99.5%

         \[\leadsto \color{blue}{\frac{1 \cdot \left(x - y\right)}{0.016666666666666666 \cdot \left(z - t\right)}} + a \cdot 120 \]

      7. *-un-lft-identity 99.5%

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

      8. *-commutative 99.5%

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

   8. Applied egg-rr 99.5%

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

   9. Taylor expanded in y around inf 53.3%

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

   10. Taylor expanded in z around inf 32.3%

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

4. Final simplification 54.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;a \leq -7 \cdot 10^{-223} \lor \neg \left(a \leq 1.5 \cdot 10^{-143}\right):\\ \;\;\;\;a \cdot 120\\ \mathbf{else}:\\ \;\;\;\;-60 \cdot \frac{y}{z}\\ \end{array} \]
5. Add Preprocessing

Alternative 13: 99.8% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 99.0%

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

   1. associate-/l* 99.7%

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

3. Simplified 99.7%

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

   1. associate-/r/ 99.8%

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

6. Applied egg-rr 99.8%

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

7. Final simplification 99.8%

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

Alternative 14: 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 99.0%

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

   1. associate-/l* 99.7%

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

3. Simplified 99.7%

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

5. Taylor expanded in z around inf 49.1%

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

6. Final simplification 49.1%

   \[\leadsto a \cdot 120 \]
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 2024020 
(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)))