Diagrams.TwoD.Layout.CirclePacking:approxRadius from diagrams-contrib-1.3.0.5

Percentage Accurate: 44.5% → 57.6%

Time: 13.1s

Alternatives: 6

Speedup: 244.0×

Specification

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{x}{y \cdot 2}\\ \frac{\tan t\_0}{\sin t\_0} \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (/ x (* y 2.0)))) (/ (tan t_0) (sin t_0))))

C

double code(double x, double y) {
	double t_0 = x / (y * 2.0);
	return tan(t_0) / sin(t_0);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: t_0
    t_0 = x / (y * 2.0d0)
    code = tan(t_0) / sin(t_0)
end function

Java

public static double code(double x, double y) {
	double t_0 = x / (y * 2.0);
	return Math.tan(t_0) / Math.sin(t_0);
}

Python

def code(x, y):
	t_0 = x / (y * 2.0)
	return math.tan(t_0) / math.sin(t_0)

Julia

function code(x, y)
	t_0 = Float64(x / Float64(y * 2.0))
	return Float64(tan(t_0) / sin(t_0))
end

MATLAB

function tmp = code(x, y)
	t_0 = x / (y * 2.0);
	tmp = tan(t_0) / sin(t_0);
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(x / N[(y * 2.0), $MachinePrecision]), $MachinePrecision]}, N[(N[Tan[t$95$0], $MachinePrecision] / N[Sin[t$95$0], $MachinePrecision]), $MachinePrecision]]

Initial Program: 44.5% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{x}{y \cdot 2}\\ \frac{\tan t\_0}{\sin t\_0} \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (/ x (* y 2.0)))) (/ (tan t_0) (sin t_0))))

C

double code(double x, double y) {
	double t_0 = x / (y * 2.0);
	return tan(t_0) / sin(t_0);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: t_0
    t_0 = x / (y * 2.0d0)
    code = tan(t_0) / sin(t_0)
end function

Java

public static double code(double x, double y) {
	double t_0 = x / (y * 2.0);
	return Math.tan(t_0) / Math.sin(t_0);
}

Python

def code(x, y):
	t_0 = x / (y * 2.0)
	return math.tan(t_0) / math.sin(t_0)

Julia

function code(x, y)
	t_0 = Float64(x / Float64(y * 2.0))
	return Float64(tan(t_0) / sin(t_0))
end

MATLAB

function tmp = code(x, y)
	t_0 = x / (y * 2.0);
	tmp = tan(t_0) / sin(t_0);
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(x / N[(y * 2.0), $MachinePrecision]), $MachinePrecision]}, N[(N[Tan[t$95$0], $MachinePrecision] / N[Sin[t$95$0], $MachinePrecision]), $MachinePrecision]]

Alternative 1: 57.6% accurate, 1.4× speedup

Math

\[\begin{array}{l} y_m = \left|y\right| \\ x_m = \left|x\right| \\ \begin{array}{l} \mathbf{if}\;\frac{x\_m}{y\_m \cdot 2} \leq 2 \cdot 10^{+126}:\\ \;\;\;\;\frac{1}{\cos \left(\frac{-0.5}{y\_m} \cdot \left(y\_m \cdot \left(x\_m \cdot \frac{-1}{y\_m}\right)\right)\right)}\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \end{array} \]

FPCore

y_m = (fabs.f64 y)
x_m = (fabs.f64 x)
(FPCore (x_m y_m)
 :precision binary64
 (if (<= (/ x_m (* y_m 2.0)) 2e+126)
   (/ 1.0 (cos (* (/ -0.5 y_m) (* y_m (* x_m (/ -1.0 y_m))))))
   1.0))

C

y_m = fabs(y);
x_m = fabs(x);
double code(double x_m, double y_m) {
	double tmp;
	if ((x_m / (y_m * 2.0)) <= 2e+126) {
		tmp = 1.0 / cos(((-0.5 / y_m) * (y_m * (x_m * (-1.0 / y_m)))));
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Fortran

y_m = abs(y)
x_m = abs(x)
real(8) function code(x_m, y_m)
    real(8), intent (in) :: x_m
    real(8), intent (in) :: y_m
    real(8) :: tmp
    if ((x_m / (y_m * 2.0d0)) <= 2d+126) then
        tmp = 1.0d0 / cos((((-0.5d0) / y_m) * (y_m * (x_m * ((-1.0d0) / y_m)))))
    else
        tmp = 1.0d0
    end if
    code = tmp
end function

Java

y_m = Math.abs(y);
x_m = Math.abs(x);
public static double code(double x_m, double y_m) {
	double tmp;
	if ((x_m / (y_m * 2.0)) <= 2e+126) {
		tmp = 1.0 / Math.cos(((-0.5 / y_m) * (y_m * (x_m * (-1.0 / y_m)))));
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Python

y_m = math.fabs(y)
x_m = math.fabs(x)
def code(x_m, y_m):
	tmp = 0
	if (x_m / (y_m * 2.0)) <= 2e+126:
		tmp = 1.0 / math.cos(((-0.5 / y_m) * (y_m * (x_m * (-1.0 / y_m)))))
	else:
		tmp = 1.0
	return tmp

Julia

y_m = abs(y)
x_m = abs(x)
function code(x_m, y_m)
	tmp = 0.0
	if (Float64(x_m / Float64(y_m * 2.0)) <= 2e+126)
		tmp = Float64(1.0 / cos(Float64(Float64(-0.5 / y_m) * Float64(y_m * Float64(x_m * Float64(-1.0 / y_m))))));
	else
		tmp = 1.0;
	end
	return tmp
end

MATLAB

y_m = abs(y);
x_m = abs(x);
function tmp_2 = code(x_m, y_m)
	tmp = 0.0;
	if ((x_m / (y_m * 2.0)) <= 2e+126)
		tmp = 1.0 / cos(((-0.5 / y_m) * (y_m * (x_m * (-1.0 / y_m)))));
	else
		tmp = 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

y_m = N[Abs[y], $MachinePrecision]
x_m = N[Abs[x], $MachinePrecision]
code[x$95$m_, y$95$m_] := If[LessEqual[N[(x$95$m / N[(y$95$m * 2.0), $MachinePrecision]), $MachinePrecision], 2e+126], N[(1.0 / N[Cos[N[(N[(-0.5 / y$95$m), $MachinePrecision] * N[(y$95$m * N[(x$95$m * N[(-1.0 / y$95$m), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision], 1.0]

Derivation

1. Split input into 2 regimes

2. if (/.f64 x (*.f64 y #s(literal 2 binary64))) < 1.99999999999999985e126

   1. Initial program 54.4%

      \[\frac{\tan \left(\frac{x}{y \cdot 2}\right)}{\sin \left(\frac{x}{y \cdot 2}\right)} \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{\frac{1}{\cos \left(\frac{1}{2} \cdot \frac{x}{y}\right)}} \]
   4. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \color{blue}{\frac{1}{\cos \left(\frac{1}{2} \cdot \frac{x}{y}\right)}} \]

      2. associate-*r/ N/A

         \[\leadsto \frac{1}{\cos \color{blue}{\left(\frac{\frac{1}{2} \cdot x}{y}\right)}} \]

      3. *-commutative N/A

         \[\leadsto \frac{1}{\cos \left(\frac{\color{blue}{x \cdot \frac{1}{2}}}{y}\right)} \]

      4. associate-*r/ N/A

         \[\leadsto \frac{1}{\cos \color{blue}{\left(x \cdot \frac{\frac{1}{2}}{y}\right)}} \]

      5. metadata-eval N/A

         \[\leadsto \frac{1}{\cos \left(x \cdot \frac{\color{blue}{\frac{1}{2} \cdot 1}}{y}\right)} \]

      6. associate-*r/ N/A

         \[\leadsto \frac{1}{\cos \left(x \cdot \color{blue}{\left(\frac{1}{2} \cdot \frac{1}{y}\right)}\right)} \]

      7. lower-cos.f64 N/A

         \[\leadsto \frac{1}{\color{blue}{\cos \left(x \cdot \left(\frac{1}{2} \cdot \frac{1}{y}\right)\right)}} \]

      8. associate-*r/ N/A

         \[\leadsto \frac{1}{\cos \left(x \cdot \color{blue}{\frac{\frac{1}{2} \cdot 1}{y}}\right)} \]

      9. metadata-eval N/A

         \[\leadsto \frac{1}{\cos \left(x \cdot \frac{\color{blue}{\frac{1}{2}}}{y}\right)} \]

      10. associate-*r/ N/A

          \[\leadsto \frac{1}{\cos \color{blue}{\left(\frac{x \cdot \frac{1}{2}}{y}\right)}} \]

      11. *-commutative N/A

          \[\leadsto \frac{1}{\cos \left(\frac{\color{blue}{\frac{1}{2} \cdot x}}{y}\right)} \]

      12. lower-/.f64 N/A

          \[\leadsto \frac{1}{\cos \color{blue}{\left(\frac{\frac{1}{2} \cdot x}{y}\right)}} \]

      13. *-commutative N/A

          \[\leadsto \frac{1}{\cos \left(\frac{\color{blue}{x \cdot \frac{1}{2}}}{y}\right)} \]

      14. lower-*.f64 65.0

          \[\leadsto \frac{1}{\cos \left(\frac{\color{blue}{x \cdot 0.5}}{y}\right)} \]

   5. Applied rewrites 65.0%

      \[\leadsto \color{blue}{\frac{1}{\cos \left(\frac{x \cdot 0.5}{y}\right)}} \]
   6. Step-by-step derivation

      1. metadata-eval N/A

         \[\leadsto \frac{1}{\cos \left(\frac{x \cdot \color{blue}{\frac{1}{2}}}{y}\right)} \]

      2. div-inv N/A

         \[\leadsto \frac{1}{\cos \left(\frac{\color{blue}{\frac{x}{2}}}{y}\right)} \]

      3. associate-/r* N/A

         \[\leadsto \frac{1}{\cos \color{blue}{\left(\frac{x}{2 \cdot y}\right)}} \]

      4. metadata-eval N/A

         \[\leadsto \frac{1}{\cos \left(\frac{x}{\color{blue}{\left(\mathsf{neg}\left(-2\right)\right)} \cdot y}\right)} \]

      5. distribute-lft-neg-in N/A

         \[\leadsto \frac{1}{\cos \left(\frac{x}{\color{blue}{\mathsf{neg}\left(-2 \cdot y\right)}}\right)} \]

      6. *-commutative N/A

         \[\leadsto \frac{1}{\cos \left(\frac{x}{\mathsf{neg}\left(\color{blue}{y \cdot -2}\right)}\right)} \]

      7. lift-*.f64 N/A

         \[\leadsto \frac{1}{\cos \left(\frac{x}{\mathsf{neg}\left(\color{blue}{y \cdot -2}\right)}\right)} \]

      8. frac-2neg N/A

         \[\leadsto \frac{1}{\cos \color{blue}{\left(\frac{\mathsf{neg}\left(x\right)}{\mathsf{neg}\left(\left(\mathsf{neg}\left(y \cdot -2\right)\right)\right)}\right)}} \]

      9. distribute-frac-neg2 N/A

         \[\leadsto \frac{1}{\cos \color{blue}{\left(\mathsf{neg}\left(\frac{\mathsf{neg}\left(x\right)}{\mathsf{neg}\left(y \cdot -2\right)}\right)\right)}} \]

      10. frac-2neg N/A

          \[\leadsto \frac{1}{\cos \left(\mathsf{neg}\left(\color{blue}{\frac{x}{y \cdot -2}}\right)\right)} \]

      11. lift-/.f64 N/A

          \[\leadsto \frac{1}{\cos \left(\mathsf{neg}\left(\color{blue}{\frac{x}{y \cdot -2}}\right)\right)} \]

      12. neg-mul-1 N/A

          \[\leadsto \frac{1}{\cos \color{blue}{\left(-1 \cdot \frac{x}{y \cdot -2}\right)}} \]

      13. metadata-eval N/A

          \[\leadsto \frac{1}{\cos \left(\color{blue}{\left(0 - 1\right)} \cdot \frac{x}{y \cdot -2}\right)} \]

      14. *-inverses N/A

          \[\leadsto \frac{1}{\cos \left(\left(0 - \color{blue}{\frac{y \cdot -2}{y \cdot -2}}\right) \cdot \frac{x}{y \cdot -2}\right)} \]

      15. mul0-lft N/A

          \[\leadsto \frac{1}{\cos \left(\left(\color{blue}{0 \cdot \frac{x}{y \cdot -2}} - \frac{y \cdot -2}{y \cdot -2}\right) \cdot \frac{x}{y \cdot -2}\right)} \]

      16. lift-/.f64 N/A

          \[\leadsto \frac{1}{\cos \left(\left(0 \cdot \color{blue}{\frac{x}{y \cdot -2}} - \frac{y \cdot -2}{y \cdot -2}\right) \cdot \frac{x}{y \cdot -2}\right)} \]

      17. associate-*r/ N/A

          \[\leadsto \frac{1}{\cos \left(\left(\color{blue}{\frac{0 \cdot x}{y \cdot -2}} - \frac{y \cdot -2}{y \cdot -2}\right) \cdot \frac{x}{y \cdot -2}\right)} \]

      18. mul0-lft N/A

          \[\leadsto \frac{1}{\cos \left(\left(\frac{\color{blue}{0}}{y \cdot -2} - \frac{y \cdot -2}{y \cdot -2}\right) \cdot \frac{x}{y \cdot -2}\right)} \]

      19. div-sub N/A

          \[\leadsto \frac{1}{\cos \left(\color{blue}{\frac{0 - y \cdot -2}{y \cdot -2}} \cdot \frac{x}{y \cdot -2}\right)} \]

      20. mul0-lft N/A

          \[\leadsto \frac{1}{\cos \left(\frac{\color{blue}{0 \cdot \frac{y \cdot -2}{x}} - y \cdot -2}{y \cdot -2} \cdot \frac{x}{y \cdot -2}\right)} \]

      21. lift-*.f64 N/A

          \[\leadsto \frac{1}{\cos \left(\frac{\color{blue}{0 \cdot \frac{y \cdot -2}{x}} - y \cdot -2}{y \cdot -2} \cdot \frac{x}{y \cdot -2}\right)} \]

      22. lift--.f64 N/A

          \[\leadsto \frac{1}{\cos \left(\frac{\color{blue}{0 \cdot \frac{y \cdot -2}{x} - y \cdot -2}}{y \cdot -2} \cdot \frac{x}{y \cdot -2}\right)} \]

      23. /-rgt-identity N/A

          \[\leadsto \frac{1}{\cos \left(\color{blue}{\frac{\frac{0 \cdot \frac{y \cdot -2}{x} - y \cdot -2}{y \cdot -2}}{1}} \cdot \frac{x}{y \cdot -2}\right)} \]

   7. Applied rewrites 65.3%

      \[\leadsto \frac{1}{\cos \color{blue}{\left(\frac{-0.5}{y} \cdot {\left(\frac{\frac{y \cdot -2}{x}}{y \cdot 2}\right)}^{-1}\right)}} \]

   9. Applied rewrites 65.5%

      \[\leadsto \frac{1}{\cos \left(\frac{-0.5}{y} \cdot \color{blue}{\left(\left(\frac{1}{y} \cdot \left(-x\right)\right) \cdot y\right)}\right)} \]

   if 1.99999999999999985e126 < (/.f64 x (*.f64 y #s(literal 2 binary64)))

   1. Initial program 4.9%

      \[\frac{\tan \left(\frac{x}{y \cdot 2}\right)}{\sin \left(\frac{x}{y \cdot 2}\right)} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{1} \]
   4. Step-by-step derivation

   5. Applied rewrites 16.2%

      \[\leadsto \color{blue}{1} \]
3. Recombined 2 regimes into one program.

4. Final simplification 60.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{x}{y \cdot 2} \leq 2 \cdot 10^{+126}:\\ \;\;\;\;\frac{1}{\cos \left(\frac{-0.5}{y} \cdot \left(y \cdot \left(x \cdot \frac{-1}{y}\right)\right)\right)}\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \]
5. Add Preprocessing

Alternative 2: 57.7% accurate, 1.6× speedup

Math

\[\begin{array}{l} y_m = \left|y\right| \\ x_m = \left|x\right| \\ \begin{array}{l} \mathbf{if}\;\frac{x\_m}{y\_m \cdot 2} \leq 2.055 \cdot 10^{+50}:\\ \;\;\;\;\frac{1}{\cos \left(\frac{0.5}{\frac{y\_m}{x\_m}}\right)}\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \end{array} \]

FPCore

y_m = (fabs.f64 y)
x_m = (fabs.f64 x)
(FPCore (x_m y_m)
 :precision binary64
 (if (<= (/ x_m (* y_m 2.0)) 2.055e+50) (/ 1.0 (cos (/ 0.5 (/ y_m x_m)))) 1.0))

C

y_m = fabs(y);
x_m = fabs(x);
double code(double x_m, double y_m) {
	double tmp;
	if ((x_m / (y_m * 2.0)) <= 2.055e+50) {
		tmp = 1.0 / cos((0.5 / (y_m / x_m)));
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Fortran

y_m = abs(y)
x_m = abs(x)
real(8) function code(x_m, y_m)
    real(8), intent (in) :: x_m
    real(8), intent (in) :: y_m
    real(8) :: tmp
    if ((x_m / (y_m * 2.0d0)) <= 2.055d+50) then
        tmp = 1.0d0 / cos((0.5d0 / (y_m / x_m)))
    else
        tmp = 1.0d0
    end if
    code = tmp
end function

Java

y_m = Math.abs(y);
x_m = Math.abs(x);
public static double code(double x_m, double y_m) {
	double tmp;
	if ((x_m / (y_m * 2.0)) <= 2.055e+50) {
		tmp = 1.0 / Math.cos((0.5 / (y_m / x_m)));
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Python

y_m = math.fabs(y)
x_m = math.fabs(x)
def code(x_m, y_m):
	tmp = 0
	if (x_m / (y_m * 2.0)) <= 2.055e+50:
		tmp = 1.0 / math.cos((0.5 / (y_m / x_m)))
	else:
		tmp = 1.0
	return tmp

Julia

y_m = abs(y)
x_m = abs(x)
function code(x_m, y_m)
	tmp = 0.0
	if (Float64(x_m / Float64(y_m * 2.0)) <= 2.055e+50)
		tmp = Float64(1.0 / cos(Float64(0.5 / Float64(y_m / x_m))));
	else
		tmp = 1.0;
	end
	return tmp
end

MATLAB

y_m = abs(y);
x_m = abs(x);
function tmp_2 = code(x_m, y_m)
	tmp = 0.0;
	if ((x_m / (y_m * 2.0)) <= 2.055e+50)
		tmp = 1.0 / cos((0.5 / (y_m / x_m)));
	else
		tmp = 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

y_m = N[Abs[y], $MachinePrecision]
x_m = N[Abs[x], $MachinePrecision]
code[x$95$m_, y$95$m_] := If[LessEqual[N[(x$95$m / N[(y$95$m * 2.0), $MachinePrecision]), $MachinePrecision], 2.055e+50], N[(1.0 / N[Cos[N[(0.5 / N[(y$95$m / x$95$m), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision], 1.0]

Derivation

1. Split input into 2 regimes

2. if (/.f64 x (*.f64 y #s(literal 2 binary64))) < 2.055e50

   1. Initial program 57.9%

      \[\frac{\tan \left(\frac{x}{y \cdot 2}\right)}{\sin \left(\frac{x}{y \cdot 2}\right)} \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{\frac{1}{\cos \left(\frac{1}{2} \cdot \frac{x}{y}\right)}} \]
   4. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \color{blue}{\frac{1}{\cos \left(\frac{1}{2} \cdot \frac{x}{y}\right)}} \]

      2. associate-*r/ N/A

         \[\leadsto \frac{1}{\cos \color{blue}{\left(\frac{\frac{1}{2} \cdot x}{y}\right)}} \]

      3. *-commutative N/A

         \[\leadsto \frac{1}{\cos \left(\frac{\color{blue}{x \cdot \frac{1}{2}}}{y}\right)} \]

      4. associate-*r/ N/A

         \[\leadsto \frac{1}{\cos \color{blue}{\left(x \cdot \frac{\frac{1}{2}}{y}\right)}} \]

      5. metadata-eval N/A

         \[\leadsto \frac{1}{\cos \left(x \cdot \frac{\color{blue}{\frac{1}{2} \cdot 1}}{y}\right)} \]

      6. associate-*r/ N/A

         \[\leadsto \frac{1}{\cos \left(x \cdot \color{blue}{\left(\frac{1}{2} \cdot \frac{1}{y}\right)}\right)} \]

      7. lower-cos.f64 N/A

         \[\leadsto \frac{1}{\color{blue}{\cos \left(x \cdot \left(\frac{1}{2} \cdot \frac{1}{y}\right)\right)}} \]

      8. associate-*r/ N/A

         \[\leadsto \frac{1}{\cos \left(x \cdot \color{blue}{\frac{\frac{1}{2} \cdot 1}{y}}\right)} \]

      9. metadata-eval N/A

         \[\leadsto \frac{1}{\cos \left(x \cdot \frac{\color{blue}{\frac{1}{2}}}{y}\right)} \]

      10. associate-*r/ N/A

          \[\leadsto \frac{1}{\cos \color{blue}{\left(\frac{x \cdot \frac{1}{2}}{y}\right)}} \]

      11. *-commutative N/A

          \[\leadsto \frac{1}{\cos \left(\frac{\color{blue}{\frac{1}{2} \cdot x}}{y}\right)} \]

      12. lower-/.f64 N/A

          \[\leadsto \frac{1}{\cos \color{blue}{\left(\frac{\frac{1}{2} \cdot x}{y}\right)}} \]

      13. *-commutative N/A

          \[\leadsto \frac{1}{\cos \left(\frac{\color{blue}{x \cdot \frac{1}{2}}}{y}\right)} \]

      14. lower-*.f64 69.3

          \[\leadsto \frac{1}{\cos \left(\frac{\color{blue}{x \cdot 0.5}}{y}\right)} \]

   5. Applied rewrites 69.3%

      \[\leadsto \color{blue}{\frac{1}{\cos \left(\frac{x \cdot 0.5}{y}\right)}} \]
   6. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{1}{\cos \left(\frac{\color{blue}{\frac{1}{2} \cdot x}}{y}\right)} \]

      2. associate-/l* N/A

         \[\leadsto \frac{1}{\cos \color{blue}{\left(\frac{1}{2} \cdot \frac{x}{y}\right)}} \]

      3. clear-num N/A

         \[\leadsto \frac{1}{\cos \left(\frac{1}{2} \cdot \color{blue}{\frac{1}{\frac{y}{x}}}\right)} \]

      4. un-div-inv N/A

         \[\leadsto \frac{1}{\cos \color{blue}{\left(\frac{\frac{1}{2}}{\frac{y}{x}}\right)}} \]

      5. lower-/.f64 N/A

         \[\leadsto \frac{1}{\cos \color{blue}{\left(\frac{\frac{1}{2}}{\frac{y}{x}}\right)}} \]

      6. lower-/.f64 69.3

         \[\leadsto \frac{1}{\cos \left(\frac{0.5}{\color{blue}{\frac{y}{x}}}\right)} \]

   7. Applied rewrites 69.3%

      \[\leadsto \frac{1}{\cos \color{blue}{\left(\frac{0.5}{\frac{y}{x}}\right)}} \]

   if 2.055e50 < (/.f64 x (*.f64 y #s(literal 2 binary64)))

   1. Initial program 7.3%

      \[\frac{\tan \left(\frac{x}{y \cdot 2}\right)}{\sin \left(\frac{x}{y \cdot 2}\right)} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{1} \]
   4. Step-by-step derivation

   5. Applied rewrites 14.3%

      \[\leadsto \color{blue}{1} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 3: 57.8% accurate, 1.6× speedup

Math

\[\begin{array}{l} y_m = \left|y\right| \\ x_m = \left|x\right| \\ \begin{array}{l} \mathbf{if}\;\frac{x\_m}{y\_m \cdot 2} \leq 2 \cdot 10^{+45}:\\ \;\;\;\;\frac{1}{\cos \left(\frac{x\_m \cdot 0.5}{y\_m}\right)}\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \end{array} \]

FPCore

y_m = (fabs.f64 y)
x_m = (fabs.f64 x)
(FPCore (x_m y_m)
 :precision binary64
 (if (<= (/ x_m (* y_m 2.0)) 2e+45) (/ 1.0 (cos (/ (* x_m 0.5) y_m))) 1.0))

C

y_m = fabs(y);
x_m = fabs(x);
double code(double x_m, double y_m) {
	double tmp;
	if ((x_m / (y_m * 2.0)) <= 2e+45) {
		tmp = 1.0 / cos(((x_m * 0.5) / y_m));
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Fortran

y_m = abs(y)
x_m = abs(x)
real(8) function code(x_m, y_m)
    real(8), intent (in) :: x_m
    real(8), intent (in) :: y_m
    real(8) :: tmp
    if ((x_m / (y_m * 2.0d0)) <= 2d+45) then
        tmp = 1.0d0 / cos(((x_m * 0.5d0) / y_m))
    else
        tmp = 1.0d0
    end if
    code = tmp
end function

Java

y_m = Math.abs(y);
x_m = Math.abs(x);
public static double code(double x_m, double y_m) {
	double tmp;
	if ((x_m / (y_m * 2.0)) <= 2e+45) {
		tmp = 1.0 / Math.cos(((x_m * 0.5) / y_m));
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Python

y_m = math.fabs(y)
x_m = math.fabs(x)
def code(x_m, y_m):
	tmp = 0
	if (x_m / (y_m * 2.0)) <= 2e+45:
		tmp = 1.0 / math.cos(((x_m * 0.5) / y_m))
	else:
		tmp = 1.0
	return tmp

Julia

y_m = abs(y)
x_m = abs(x)
function code(x_m, y_m)
	tmp = 0.0
	if (Float64(x_m / Float64(y_m * 2.0)) <= 2e+45)
		tmp = Float64(1.0 / cos(Float64(Float64(x_m * 0.5) / y_m)));
	else
		tmp = 1.0;
	end
	return tmp
end

MATLAB

y_m = abs(y);
x_m = abs(x);
function tmp_2 = code(x_m, y_m)
	tmp = 0.0;
	if ((x_m / (y_m * 2.0)) <= 2e+45)
		tmp = 1.0 / cos(((x_m * 0.5) / y_m));
	else
		tmp = 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

y_m = N[Abs[y], $MachinePrecision]
x_m = N[Abs[x], $MachinePrecision]
code[x$95$m_, y$95$m_] := If[LessEqual[N[(x$95$m / N[(y$95$m * 2.0), $MachinePrecision]), $MachinePrecision], 2e+45], N[(1.0 / N[Cos[N[(N[(x$95$m * 0.5), $MachinePrecision] / y$95$m), $MachinePrecision]], $MachinePrecision]), $MachinePrecision], 1.0]

Derivation

1. Split input into 2 regimes

2. if (/.f64 x (*.f64 y #s(literal 2 binary64))) < 1.9999999999999999e45

   1. Initial program 58.2%

      \[\frac{\tan \left(\frac{x}{y \cdot 2}\right)}{\sin \left(\frac{x}{y \cdot 2}\right)} \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{\frac{1}{\cos \left(\frac{1}{2} \cdot \frac{x}{y}\right)}} \]
   4. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \color{blue}{\frac{1}{\cos \left(\frac{1}{2} \cdot \frac{x}{y}\right)}} \]

      2. associate-*r/ N/A

         \[\leadsto \frac{1}{\cos \color{blue}{\left(\frac{\frac{1}{2} \cdot x}{y}\right)}} \]

      3. *-commutative N/A

         \[\leadsto \frac{1}{\cos \left(\frac{\color{blue}{x \cdot \frac{1}{2}}}{y}\right)} \]

      4. associate-*r/ N/A

         \[\leadsto \frac{1}{\cos \color{blue}{\left(x \cdot \frac{\frac{1}{2}}{y}\right)}} \]

      5. metadata-eval N/A

         \[\leadsto \frac{1}{\cos \left(x \cdot \frac{\color{blue}{\frac{1}{2} \cdot 1}}{y}\right)} \]

      6. associate-*r/ N/A

         \[\leadsto \frac{1}{\cos \left(x \cdot \color{blue}{\left(\frac{1}{2} \cdot \frac{1}{y}\right)}\right)} \]

      7. lower-cos.f64 N/A

         \[\leadsto \frac{1}{\color{blue}{\cos \left(x \cdot \left(\frac{1}{2} \cdot \frac{1}{y}\right)\right)}} \]

      8. associate-*r/ N/A

         \[\leadsto \frac{1}{\cos \left(x \cdot \color{blue}{\frac{\frac{1}{2} \cdot 1}{y}}\right)} \]

      9. metadata-eval N/A

         \[\leadsto \frac{1}{\cos \left(x \cdot \frac{\color{blue}{\frac{1}{2}}}{y}\right)} \]

      10. associate-*r/ N/A

          \[\leadsto \frac{1}{\cos \color{blue}{\left(\frac{x \cdot \frac{1}{2}}{y}\right)}} \]

      11. *-commutative N/A

          \[\leadsto \frac{1}{\cos \left(\frac{\color{blue}{\frac{1}{2} \cdot x}}{y}\right)} \]

      12. lower-/.f64 N/A

          \[\leadsto \frac{1}{\cos \color{blue}{\left(\frac{\frac{1}{2} \cdot x}{y}\right)}} \]

      13. *-commutative N/A

          \[\leadsto \frac{1}{\cos \left(\frac{\color{blue}{x \cdot \frac{1}{2}}}{y}\right)} \]

      14. lower-*.f64 69.6

          \[\leadsto \frac{1}{\cos \left(\frac{\color{blue}{x \cdot 0.5}}{y}\right)} \]

   5. Applied rewrites 69.6%

      \[\leadsto \color{blue}{\frac{1}{\cos \left(\frac{x \cdot 0.5}{y}\right)}} \]

   if 1.9999999999999999e45 < (/.f64 x (*.f64 y #s(literal 2 binary64)))

   1. Initial program 7.2%

      \[\frac{\tan \left(\frac{x}{y \cdot 2}\right)}{\sin \left(\frac{x}{y \cdot 2}\right)} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{1} \]
   4. Step-by-step derivation

   5. Applied rewrites 14.1%

      \[\leadsto \color{blue}{1} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 4: 57.6% accurate, 1.6× speedup

Math

\[\begin{array}{l} y_m = \left|y\right| \\ x_m = \left|x\right| \\ \begin{array}{l} \mathbf{if}\;\frac{x\_m}{y\_m \cdot 2} \leq 2 \cdot 10^{+126}:\\ \;\;\;\;\frac{1}{\cos \left(x\_m \cdot \frac{0.5}{y\_m}\right)}\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \end{array} \]

FPCore

y_m = (fabs.f64 y)
x_m = (fabs.f64 x)
(FPCore (x_m y_m)
 :precision binary64
 (if (<= (/ x_m (* y_m 2.0)) 2e+126) (/ 1.0 (cos (* x_m (/ 0.5 y_m)))) 1.0))

C

y_m = fabs(y);
x_m = fabs(x);
double code(double x_m, double y_m) {
	double tmp;
	if ((x_m / (y_m * 2.0)) <= 2e+126) {
		tmp = 1.0 / cos((x_m * (0.5 / y_m)));
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Fortran

y_m = abs(y)
x_m = abs(x)
real(8) function code(x_m, y_m)
    real(8), intent (in) :: x_m
    real(8), intent (in) :: y_m
    real(8) :: tmp
    if ((x_m / (y_m * 2.0d0)) <= 2d+126) then
        tmp = 1.0d0 / cos((x_m * (0.5d0 / y_m)))
    else
        tmp = 1.0d0
    end if
    code = tmp
end function

Java

y_m = Math.abs(y);
x_m = Math.abs(x);
public static double code(double x_m, double y_m) {
	double tmp;
	if ((x_m / (y_m * 2.0)) <= 2e+126) {
		tmp = 1.0 / Math.cos((x_m * (0.5 / y_m)));
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Python

y_m = math.fabs(y)
x_m = math.fabs(x)
def code(x_m, y_m):
	tmp = 0
	if (x_m / (y_m * 2.0)) <= 2e+126:
		tmp = 1.0 / math.cos((x_m * (0.5 / y_m)))
	else:
		tmp = 1.0
	return tmp

Julia

y_m = abs(y)
x_m = abs(x)
function code(x_m, y_m)
	tmp = 0.0
	if (Float64(x_m / Float64(y_m * 2.0)) <= 2e+126)
		tmp = Float64(1.0 / cos(Float64(x_m * Float64(0.5 / y_m))));
	else
		tmp = 1.0;
	end
	return tmp
end

MATLAB

y_m = abs(y);
x_m = abs(x);
function tmp_2 = code(x_m, y_m)
	tmp = 0.0;
	if ((x_m / (y_m * 2.0)) <= 2e+126)
		tmp = 1.0 / cos((x_m * (0.5 / y_m)));
	else
		tmp = 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

y_m = N[Abs[y], $MachinePrecision]
x_m = N[Abs[x], $MachinePrecision]
code[x$95$m_, y$95$m_] := If[LessEqual[N[(x$95$m / N[(y$95$m * 2.0), $MachinePrecision]), $MachinePrecision], 2e+126], N[(1.0 / N[Cos[N[(x$95$m * N[(0.5 / y$95$m), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision], 1.0]

Derivation

1. Split input into 2 regimes

2. if (/.f64 x (*.f64 y #s(literal 2 binary64))) < 1.99999999999999985e126

   1. Initial program 54.4%

      \[\frac{\tan \left(\frac{x}{y \cdot 2}\right)}{\sin \left(\frac{x}{y \cdot 2}\right)} \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{\frac{1}{\cos \left(\frac{1}{2} \cdot \frac{x}{y}\right)}} \]
   4. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \color{blue}{\frac{1}{\cos \left(\frac{1}{2} \cdot \frac{x}{y}\right)}} \]

      2. associate-*r/ N/A

         \[\leadsto \frac{1}{\cos \color{blue}{\left(\frac{\frac{1}{2} \cdot x}{y}\right)}} \]

      3. *-commutative N/A

         \[\leadsto \frac{1}{\cos \left(\frac{\color{blue}{x \cdot \frac{1}{2}}}{y}\right)} \]

      4. associate-*r/ N/A

         \[\leadsto \frac{1}{\cos \color{blue}{\left(x \cdot \frac{\frac{1}{2}}{y}\right)}} \]

      5. metadata-eval N/A

         \[\leadsto \frac{1}{\cos \left(x \cdot \frac{\color{blue}{\frac{1}{2} \cdot 1}}{y}\right)} \]

      6. associate-*r/ N/A

         \[\leadsto \frac{1}{\cos \left(x \cdot \color{blue}{\left(\frac{1}{2} \cdot \frac{1}{y}\right)}\right)} \]

      7. lower-cos.f64 N/A

         \[\leadsto \frac{1}{\color{blue}{\cos \left(x \cdot \left(\frac{1}{2} \cdot \frac{1}{y}\right)\right)}} \]

      8. associate-*r/ N/A

         \[\leadsto \frac{1}{\cos \left(x \cdot \color{blue}{\frac{\frac{1}{2} \cdot 1}{y}}\right)} \]

      9. metadata-eval N/A

         \[\leadsto \frac{1}{\cos \left(x \cdot \frac{\color{blue}{\frac{1}{2}}}{y}\right)} \]

      10. associate-*r/ N/A

          \[\leadsto \frac{1}{\cos \color{blue}{\left(\frac{x \cdot \frac{1}{2}}{y}\right)}} \]

      11. *-commutative N/A

          \[\leadsto \frac{1}{\cos \left(\frac{\color{blue}{\frac{1}{2} \cdot x}}{y}\right)} \]

      12. lower-/.f64 N/A

          \[\leadsto \frac{1}{\cos \color{blue}{\left(\frac{\frac{1}{2} \cdot x}{y}\right)}} \]

      13. *-commutative N/A

          \[\leadsto \frac{1}{\cos \left(\frac{\color{blue}{x \cdot \frac{1}{2}}}{y}\right)} \]

      14. lower-*.f64 65.0

          \[\leadsto \frac{1}{\cos \left(\frac{\color{blue}{x \cdot 0.5}}{y}\right)} \]

   5. Applied rewrites 65.0%

      \[\leadsto \color{blue}{\frac{1}{\cos \left(\frac{x \cdot 0.5}{y}\right)}} \]
   6. Step-by-step derivation

      1. associate-/l* N/A

         \[\leadsto \frac{1}{\cos \color{blue}{\left(x \cdot \frac{\frac{1}{2}}{y}\right)}} \]

      2. *-commutative N/A

         \[\leadsto \frac{1}{\cos \color{blue}{\left(\frac{\frac{1}{2}}{y} \cdot x\right)}} \]

      3. lower-*.f64 N/A

         \[\leadsto \frac{1}{\cos \color{blue}{\left(\frac{\frac{1}{2}}{y} \cdot x\right)}} \]

      4. lower-/.f64 65.1

         \[\leadsto \frac{1}{\cos \left(\color{blue}{\frac{0.5}{y}} \cdot x\right)} \]

   7. Applied rewrites 65.1%

      \[\leadsto \frac{1}{\cos \color{blue}{\left(\frac{0.5}{y} \cdot x\right)}} \]

   if 1.99999999999999985e126 < (/.f64 x (*.f64 y #s(literal 2 binary64)))

   1. Initial program 4.9%

      \[\frac{\tan \left(\frac{x}{y \cdot 2}\right)}{\sin \left(\frac{x}{y \cdot 2}\right)} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{1} \]
   4. Step-by-step derivation

   5. Applied rewrites 16.2%

      \[\leadsto \color{blue}{1} \]
3. Recombined 2 regimes into one program.

4. Final simplification 59.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{x}{y \cdot 2} \leq 2 \cdot 10^{+126}:\\ \;\;\;\;\frac{1}{\cos \left(x \cdot \frac{0.5}{y}\right)}\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 55.8% accurate, 244.0× speedup

Math

\[\begin{array}{l} y_m = \left|y\right| \\ x_m = \left|x\right| \\ 1 \end{array} \]

FPCore

y_m = (fabs.f64 y)
x_m = (fabs.f64 x)
(FPCore (x_m y_m) :precision binary64 1.0)

C

y_m = fabs(y);
x_m = fabs(x);
double code(double x_m, double y_m) {
	return 1.0;
}

Fortran

y_m = abs(y)
x_m = abs(x)
real(8) function code(x_m, y_m)
    real(8), intent (in) :: x_m
    real(8), intent (in) :: y_m
    code = 1.0d0
end function

Java

y_m = Math.abs(y);
x_m = Math.abs(x);
public static double code(double x_m, double y_m) {
	return 1.0;
}

Python

y_m = math.fabs(y)
x_m = math.fabs(x)
def code(x_m, y_m):
	return 1.0

Julia

y_m = abs(y)
x_m = abs(x)
function code(x_m, y_m)
	return 1.0
end

MATLAB

y_m = abs(y);
x_m = abs(x);
function tmp = code(x_m, y_m)
	tmp = 1.0;
end

Wolfram

y_m = N[Abs[y], $MachinePrecision]
x_m = N[Abs[x], $MachinePrecision]
code[x$95$m_, y$95$m_] := 1.0

Derivation

1. Initial program 49.0%

   \[\frac{\tan \left(\frac{x}{y \cdot 2}\right)}{\sin \left(\frac{x}{y \cdot 2}\right)} \]
2. Add Preprocessing

3. Taylor expanded in x around 0

   \[\leadsto \color{blue}{1} \]
4. Step-by-step derivation

5. Applied rewrites 58.5%

   \[\leadsto \color{blue}{1} \]
6. Add Preprocessing

Alternative 6: 6.5% accurate, 244.0× speedup

Math

\[\begin{array}{l} y_m = \left|y\right| \\ x_m = \left|x\right| \\ -1 \end{array} \]

FPCore

y_m = (fabs.f64 y)
x_m = (fabs.f64 x)
(FPCore (x_m y_m) :precision binary64 -1.0)

C

y_m = fabs(y);
x_m = fabs(x);
double code(double x_m, double y_m) {
	return -1.0;
}

Fortran

y_m = abs(y)
x_m = abs(x)
real(8) function code(x_m, y_m)
    real(8), intent (in) :: x_m
    real(8), intent (in) :: y_m
    code = -1.0d0
end function

Java

y_m = Math.abs(y);
x_m = Math.abs(x);
public static double code(double x_m, double y_m) {
	return -1.0;
}

Python

y_m = math.fabs(y)
x_m = math.fabs(x)
def code(x_m, y_m):
	return -1.0

Julia

y_m = abs(y)
x_m = abs(x)
function code(x_m, y_m)
	return -1.0
end

MATLAB

y_m = abs(y);
x_m = abs(x);
function tmp = code(x_m, y_m)
	tmp = -1.0;
end

Wolfram

y_m = N[Abs[y], $MachinePrecision]
x_m = N[Abs[x], $MachinePrecision]
code[x$95$m_, y$95$m_] := -1.0

Derivation

1. Initial program 49.0%

   \[\frac{\tan \left(\frac{x}{y \cdot 2}\right)}{\sin \left(\frac{x}{y \cdot 2}\right)} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. lift-*.f64 N/A

      \[\leadsto \frac{\tan \left(\frac{x}{\color{blue}{y \cdot 2}}\right)}{\sin \left(\frac{x}{y \cdot 2}\right)} \]

   2. clear-num N/A

      \[\leadsto \frac{\tan \color{blue}{\left(\frac{1}{\frac{y \cdot 2}{x}}\right)}}{\sin \left(\frac{x}{y \cdot 2}\right)} \]

   3. inv-pow N/A

      \[\leadsto \frac{\tan \color{blue}{\left({\left(\frac{y \cdot 2}{x}\right)}^{-1}\right)}}{\sin \left(\frac{x}{y \cdot 2}\right)} \]

   4. metadata-eval N/A

      \[\leadsto \frac{\tan \left({\left(\frac{y \cdot 2}{x}\right)}^{\color{blue}{\left(2 \cdot \frac{-1}{2}\right)}}\right)}{\sin \left(\frac{x}{y \cdot 2}\right)} \]

   5. metadata-eval N/A

      \[\leadsto \frac{\tan \left({\left(\frac{y \cdot 2}{x}\right)}^{\left(2 \cdot \color{blue}{\left(\mathsf{neg}\left(\frac{1}{2}\right)\right)}\right)}\right)}{\sin \left(\frac{x}{y \cdot 2}\right)} \]

   6. metadata-eval N/A

      \[\leadsto \frac{\tan \left({\left(\frac{y \cdot 2}{x}\right)}^{\left(2 \cdot \left(\mathsf{neg}\left(\color{blue}{\frac{1}{2}}\right)\right)\right)}\right)}{\sin \left(\frac{x}{y \cdot 2}\right)} \]

   7. pow-sqr N/A

      \[\leadsto \frac{\tan \color{blue}{\left({\left(\frac{y \cdot 2}{x}\right)}^{\left(\mathsf{neg}\left(\frac{1}{2}\right)\right)} \cdot {\left(\frac{y \cdot 2}{x}\right)}^{\left(\mathsf{neg}\left(\frac{1}{2}\right)\right)}\right)}}{\sin \left(\frac{x}{y \cdot 2}\right)} \]

   8. pow-prod-down N/A

      \[\leadsto \frac{\tan \color{blue}{\left({\left(\frac{y \cdot 2}{x} \cdot \frac{y \cdot 2}{x}\right)}^{\left(\mathsf{neg}\left(\frac{1}{2}\right)\right)}\right)}}{\sin \left(\frac{x}{y \cdot 2}\right)} \]

   9. lower-pow.f64 N/A

      \[\leadsto \frac{\tan \color{blue}{\left({\left(\frac{y \cdot 2}{x} \cdot \frac{y \cdot 2}{x}\right)}^{\left(\mathsf{neg}\left(\frac{1}{2}\right)\right)}\right)}}{\sin \left(\frac{x}{y \cdot 2}\right)} \]

   10. lower-*.f64 N/A

       \[\leadsto \frac{\tan \left({\color{blue}{\left(\frac{y \cdot 2}{x} \cdot \frac{y \cdot 2}{x}\right)}}^{\left(\mathsf{neg}\left(\frac{1}{2}\right)\right)}\right)}{\sin \left(\frac{x}{y \cdot 2}\right)} \]

   11. lift-*.f64 N/A

       \[\leadsto \frac{\tan \left({\left(\frac{\color{blue}{y \cdot 2}}{x} \cdot \frac{y \cdot 2}{x}\right)}^{\left(\mathsf{neg}\left(\frac{1}{2}\right)\right)}\right)}{\sin \left(\frac{x}{y \cdot 2}\right)} \]

   12. associate-*l/ N/A

       \[\leadsto \frac{\tan \left({\left(\color{blue}{\left(\frac{y}{x} \cdot 2\right)} \cdot \frac{y \cdot 2}{x}\right)}^{\left(\mathsf{neg}\left(\frac{1}{2}\right)\right)}\right)}{\sin \left(\frac{x}{y \cdot 2}\right)} \]

   13. associate-/r/ N/A

       \[\leadsto \frac{\tan \left({\left(\color{blue}{\frac{y}{\frac{x}{2}}} \cdot \frac{y \cdot 2}{x}\right)}^{\left(\mathsf{neg}\left(\frac{1}{2}\right)\right)}\right)}{\sin \left(\frac{x}{y \cdot 2}\right)} \]

   14. lower-/.f64 N/A

       \[\leadsto \frac{\tan \left({\left(\color{blue}{\frac{y}{\frac{x}{2}}} \cdot \frac{y \cdot 2}{x}\right)}^{\left(\mathsf{neg}\left(\frac{1}{2}\right)\right)}\right)}{\sin \left(\frac{x}{y \cdot 2}\right)} \]

   15. div-inv N/A

       \[\leadsto \frac{\tan \left({\left(\frac{y}{\color{blue}{x \cdot \frac{1}{2}}} \cdot \frac{y \cdot 2}{x}\right)}^{\left(\mathsf{neg}\left(\frac{1}{2}\right)\right)}\right)}{\sin \left(\frac{x}{y \cdot 2}\right)} \]

   16. lower-*.f64 N/A

       \[\leadsto \frac{\tan \left({\left(\frac{y}{\color{blue}{x \cdot \frac{1}{2}}} \cdot \frac{y \cdot 2}{x}\right)}^{\left(\mathsf{neg}\left(\frac{1}{2}\right)\right)}\right)}{\sin \left(\frac{x}{y \cdot 2}\right)} \]

   17. metadata-eval N/A

       \[\leadsto \frac{\tan \left({\left(\frac{y}{x \cdot \color{blue}{\frac{1}{2}}} \cdot \frac{y \cdot 2}{x}\right)}^{\left(\mathsf{neg}\left(\frac{1}{2}\right)\right)}\right)}{\sin \left(\frac{x}{y \cdot 2}\right)} \]

   18. lift-*.f64 N/A

       \[\leadsto \frac{\tan \left({\left(\frac{y}{x \cdot \frac{1}{2}} \cdot \frac{\color{blue}{y \cdot 2}}{x}\right)}^{\left(\mathsf{neg}\left(\frac{1}{2}\right)\right)}\right)}{\sin \left(\frac{x}{y \cdot 2}\right)} \]

   19. associate-*l/ N/A

       \[\leadsto \frac{\tan \left({\left(\frac{y}{x \cdot \frac{1}{2}} \cdot \color{blue}{\left(\frac{y}{x} \cdot 2\right)}\right)}^{\left(\mathsf{neg}\left(\frac{1}{2}\right)\right)}\right)}{\sin \left(\frac{x}{y \cdot 2}\right)} \]

   20. associate-/r/ N/A

       \[\leadsto \frac{\tan \left({\left(\frac{y}{x \cdot \frac{1}{2}} \cdot \color{blue}{\frac{y}{\frac{x}{2}}}\right)}^{\left(\mathsf{neg}\left(\frac{1}{2}\right)\right)}\right)}{\sin \left(\frac{x}{y \cdot 2}\right)} \]

   21. lower-/.f64 N/A

       \[\leadsto \frac{\tan \left({\left(\frac{y}{x \cdot \frac{1}{2}} \cdot \color{blue}{\frac{y}{\frac{x}{2}}}\right)}^{\left(\mathsf{neg}\left(\frac{1}{2}\right)\right)}\right)}{\sin \left(\frac{x}{y \cdot 2}\right)} \]

   22. div-inv N/A

       \[\leadsto \frac{\tan \left({\left(\frac{y}{x \cdot \frac{1}{2}} \cdot \frac{y}{\color{blue}{x \cdot \frac{1}{2}}}\right)}^{\left(\mathsf{neg}\left(\frac{1}{2}\right)\right)}\right)}{\sin \left(\frac{x}{y \cdot 2}\right)} \]

   23. lower-*.f64 N/A

       \[\leadsto \frac{\tan \left({\left(\frac{y}{x \cdot \frac{1}{2}} \cdot \frac{y}{\color{blue}{x \cdot \frac{1}{2}}}\right)}^{\left(\mathsf{neg}\left(\frac{1}{2}\right)\right)}\right)}{\sin \left(\frac{x}{y \cdot 2}\right)} \]

   24. metadata-eval N/A

       \[\leadsto \frac{\tan \left({\left(\frac{y}{x \cdot \frac{1}{2}} \cdot \frac{y}{x \cdot \color{blue}{\frac{1}{2}}}\right)}^{\left(\mathsf{neg}\left(\frac{1}{2}\right)\right)}\right)}{\sin \left(\frac{x}{y \cdot 2}\right)} \]

   25. metadata-eval N/A

       \[\leadsto \frac{\tan \left({\left(\frac{y}{x \cdot \frac{1}{2}} \cdot \frac{y}{x \cdot \frac{1}{2}}\right)}^{\left(\mathsf{neg}\left(\color{blue}{\frac{1}{2}}\right)\right)}\right)}{\sin \left(\frac{x}{y \cdot 2}\right)} \]

   26. metadata-eval 17.3

       \[\leadsto \frac{\tan \left({\left(\frac{y}{x \cdot 0.5} \cdot \frac{y}{x \cdot 0.5}\right)}^{\color{blue}{-0.5}}\right)}{\sin \left(\frac{x}{y \cdot 2}\right)} \]

4. Applied rewrites 17.3%

   \[\leadsto \frac{\tan \color{blue}{\left({\left(\frac{y}{x \cdot 0.5} \cdot \frac{y}{x \cdot 0.5}\right)}^{-0.5}\right)}}{\sin \left(\frac{x}{y \cdot 2}\right)} \]

5. Taylor expanded in y around -inf

   \[\leadsto \color{blue}{-1} \]
6. Step-by-step derivation

7. Applied rewrites 6.3%

   \[\leadsto \color{blue}{-1} \]
8. Add Preprocessing

Developer Target 1: 55.8% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{x}{y \cdot 2}\\ t_1 := \sin t\_0\\ \mathbf{if}\;y < -1.2303690911306994 \cdot 10^{+114}:\\ \;\;\;\;1\\ \mathbf{elif}\;y < -9.102852406811914 \cdot 10^{-222}:\\ \;\;\;\;\frac{t\_1}{t\_1 \cdot \log \left(e^{\cos t\_0}\right)}\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (/ x (* y 2.0))) (t_1 (sin t_0)))
   (if (< y -1.2303690911306994e+114)
     1.0
     (if (< y -9.102852406811914e-222)
       (/ t_1 (* t_1 (log (exp (cos t_0)))))
       1.0))))

C

double code(double x, double y) {
	double t_0 = x / (y * 2.0);
	double t_1 = sin(t_0);
	double tmp;
	if (y < -1.2303690911306994e+114) {
		tmp = 1.0;
	} else if (y < -9.102852406811914e-222) {
		tmp = t_1 / (t_1 * log(exp(cos(t_0))));
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: tmp
    t_0 = x / (y * 2.0d0)
    t_1 = sin(t_0)
    if (y < (-1.2303690911306994d+114)) then
        tmp = 1.0d0
    else if (y < (-9.102852406811914d-222)) then
        tmp = t_1 / (t_1 * log(exp(cos(t_0))))
    else
        tmp = 1.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double t_0 = x / (y * 2.0);
	double t_1 = Math.sin(t_0);
	double tmp;
	if (y < -1.2303690911306994e+114) {
		tmp = 1.0;
	} else if (y < -9.102852406811914e-222) {
		tmp = t_1 / (t_1 * Math.log(Math.exp(Math.cos(t_0))));
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = x / (y * 2.0)
	t_1 = math.sin(t_0)
	tmp = 0
	if y < -1.2303690911306994e+114:
		tmp = 1.0
	elif y < -9.102852406811914e-222:
		tmp = t_1 / (t_1 * math.log(math.exp(math.cos(t_0))))
	else:
		tmp = 1.0
	return tmp

Julia

function code(x, y)
	t_0 = Float64(x / Float64(y * 2.0))
	t_1 = sin(t_0)
	tmp = 0.0
	if (y < -1.2303690911306994e+114)
		tmp = 1.0;
	elseif (y < -9.102852406811914e-222)
		tmp = Float64(t_1 / Float64(t_1 * log(exp(cos(t_0)))));
	else
		tmp = 1.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = x / (y * 2.0);
	t_1 = sin(t_0);
	tmp = 0.0;
	if (y < -1.2303690911306994e+114)
		tmp = 1.0;
	elseif (y < -9.102852406811914e-222)
		tmp = t_1 / (t_1 * log(exp(cos(t_0))));
	else
		tmp = 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(x / N[(y * 2.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[Sin[t$95$0], $MachinePrecision]}, If[Less[y, -1.2303690911306994e+114], 1.0, If[Less[y, -9.102852406811914e-222], N[(t$95$1 / N[(t$95$1 * N[Log[N[Exp[N[Cos[t$95$0], $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 1.0]]]]

Reproduce

herbie shell --seed 2024216 
(FPCore (x y)
  :name "Diagrams.TwoD.Layout.CirclePacking:approxRadius from diagrams-contrib-1.3.0.5"
  :precision binary64

  :alt
  (! :herbie-platform default (if (< y -1230369091130699400000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000) 1 (if (< y -4551426203405957/500000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000) (/ (sin (/ x (* y 2))) (* (sin (/ x (* y 2))) (log (exp (cos (/ x (* y 2))))))) 1)))

  (/ (tan (/ x (* y 2.0))) (sin (/ x (* y 2.0)))))