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

Percentage Accurate: 44.1% → 55.7%

Time: 14.4s

Alternatives: 5

Speedup: 211.0×

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: 44.1% 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: 55.7% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \frac{1}{\cos \left(\frac{\frac{0.5 \cdot x}{{\left(\sqrt[3]{y}\right)}^{2}}}{\sqrt[3]{y}}\right)} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (/ 1.0 (cos (/ (/ (* 0.5 x) (pow (cbrt y) 2.0)) (cbrt y)))))

C

double code(double x, double y) {
	return 1.0 / cos((((0.5 * x) / pow(cbrt(y), 2.0)) / cbrt(y)));
}

Java

public static double code(double x, double y) {
	return 1.0 / Math.cos((((0.5 * x) / Math.pow(Math.cbrt(y), 2.0)) / Math.cbrt(y)));
}

Julia

function code(x, y)
	return Float64(1.0 / cos(Float64(Float64(Float64(0.5 * x) / (cbrt(y) ^ 2.0)) / cbrt(y))))
end

Wolfram

code[x_, y_] := N[(1.0 / N[Cos[N[(N[(N[(0.5 * x), $MachinePrecision] / N[Power[N[Power[y, 1/3], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision] / N[Power[y, 1/3], $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 44.7%

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

2. Taylor expanded in x around inf 55.7%

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

   1. add-cube-cbrt 55.5%

      \[\leadsto \frac{1}{\cos \color{blue}{\left(\left(\sqrt[3]{0.5 \cdot \frac{x}{y}} \cdot \sqrt[3]{0.5 \cdot \frac{x}{y}}\right) \cdot \sqrt[3]{0.5 \cdot \frac{x}{y}}\right)}} \]

   2. pow3 55.5%

      \[\leadsto \frac{1}{\cos \color{blue}{\left({\left(\sqrt[3]{0.5 \cdot \frac{x}{y}}\right)}^{3}\right)}} \]

4. Applied egg-rr 55.5%

   \[\leadsto \frac{1}{\cos \color{blue}{\left({\left(\sqrt[3]{0.5 \cdot \frac{x}{y}}\right)}^{3}\right)}} \]
5. Step-by-step derivation

   1. rem-cube-cbrt 55.7%

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

   2. associate-*r/ 55.7%

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

   3. *-commutative 55.7%

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

   4. add-cube-cbrt 55.4%

      \[\leadsto \frac{1}{\cos \left(\frac{x \cdot 0.5}{\color{blue}{\left(\sqrt[3]{y} \cdot \sqrt[3]{y}\right) \cdot \sqrt[3]{y}}}\right)} \]

   5. associate-/r* 55.9%

      \[\leadsto \frac{1}{\cos \color{blue}{\left(\frac{\frac{x \cdot 0.5}{\sqrt[3]{y} \cdot \sqrt[3]{y}}}{\sqrt[3]{y}}\right)}} \]

   6. *-commutative 55.9%

      \[\leadsto \frac{1}{\cos \left(\frac{\frac{\color{blue}{0.5 \cdot x}}{\sqrt[3]{y} \cdot \sqrt[3]{y}}}{\sqrt[3]{y}}\right)} \]

   7. pow2 55.9%

      \[\leadsto \frac{1}{\cos \left(\frac{\frac{0.5 \cdot x}{\color{blue}{{\left(\sqrt[3]{y}\right)}^{2}}}}{\sqrt[3]{y}}\right)} \]

6. Applied egg-rr 55.9%

   \[\leadsto \frac{1}{\cos \color{blue}{\left(\frac{\frac{0.5 \cdot x}{{\left(\sqrt[3]{y}\right)}^{2}}}{\sqrt[3]{y}}\right)}} \]

7. Final simplification 55.9%

   \[\leadsto \frac{1}{\cos \left(\frac{\frac{0.5 \cdot x}{{\left(\sqrt[3]{y}\right)}^{2}}}{\sqrt[3]{y}}\right)} \]

Alternative 2: 55.6% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \frac{1}{\cos \left({\left(\sqrt[3]{\frac{0.5}{y}} \cdot \sqrt[3]{x}\right)}^{3}\right)} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (/ 1.0 (cos (pow (* (cbrt (/ 0.5 y)) (cbrt x)) 3.0))))

C

double code(double x, double y) {
	return 1.0 / cos(pow((cbrt((0.5 / y)) * cbrt(x)), 3.0));
}

Java

public static double code(double x, double y) {
	return 1.0 / Math.cos(Math.pow((Math.cbrt((0.5 / y)) * Math.cbrt(x)), 3.0));
}

Julia

function code(x, y)
	return Float64(1.0 / cos((Float64(cbrt(Float64(0.5 / y)) * cbrt(x)) ^ 3.0)))
end

Wolfram

code[x_, y_] := N[(1.0 / N[Cos[N[Power[N[(N[Power[N[(0.5 / y), $MachinePrecision], 1/3], $MachinePrecision] * N[Power[x, 1/3], $MachinePrecision]), $MachinePrecision], 3.0], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 44.7%

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

2. Taylor expanded in x around inf 55.7%

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

   1. add-cube-cbrt 55.5%

      \[\leadsto \frac{1}{\cos \color{blue}{\left(\left(\sqrt[3]{0.5 \cdot \frac{x}{y}} \cdot \sqrt[3]{0.5 \cdot \frac{x}{y}}\right) \cdot \sqrt[3]{0.5 \cdot \frac{x}{y}}\right)}} \]

   2. pow3 55.5%

      \[\leadsto \frac{1}{\cos \color{blue}{\left({\left(\sqrt[3]{0.5 \cdot \frac{x}{y}}\right)}^{3}\right)}} \]

4. Applied egg-rr 55.5%

   \[\leadsto \frac{1}{\cos \color{blue}{\left({\left(\sqrt[3]{0.5 \cdot \frac{x}{y}}\right)}^{3}\right)}} \]
5. Step-by-step derivation

   1. clear-num 55.2%

      \[\leadsto \frac{1}{\cos \left({\left(\sqrt[3]{0.5 \cdot \color{blue}{\frac{1}{\frac{y}{x}}}}\right)}^{3}\right)} \]

   2. div-inv 55.2%

      \[\leadsto \frac{1}{\cos \left({\left(\sqrt[3]{\color{blue}{\frac{0.5}{\frac{y}{x}}}}\right)}^{3}\right)} \]

   3. associate-/r/ 55.2%

      \[\leadsto \frac{1}{\cos \left({\left(\sqrt[3]{\color{blue}{\frac{0.5}{y} \cdot x}}\right)}^{3}\right)} \]

   4. cbrt-prod 55.7%

      \[\leadsto \frac{1}{\cos \left({\color{blue}{\left(\sqrt[3]{\frac{0.5}{y}} \cdot \sqrt[3]{x}\right)}}^{3}\right)} \]

6. Applied egg-rr 55.7%

   \[\leadsto \frac{1}{\cos \left({\color{blue}{\left(\sqrt[3]{\frac{0.5}{y}} \cdot \sqrt[3]{x}\right)}}^{3}\right)} \]

7. Final simplification 55.7%

   \[\leadsto \frac{1}{\cos \left({\left(\sqrt[3]{\frac{0.5}{y}} \cdot \sqrt[3]{x}\right)}^{3}\right)} \]

Alternative 3: 55.7% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \frac{1}{\cos \left({\left(\frac{1}{\sqrt[3]{\frac{y}{0.5 \cdot x}}}\right)}^{3}\right)} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (/ 1.0 (cos (pow (/ 1.0 (cbrt (/ y (* 0.5 x)))) 3.0))))

C

double code(double x, double y) {
	return 1.0 / cos(pow((1.0 / cbrt((y / (0.5 * x)))), 3.0));
}

Java

public static double code(double x, double y) {
	return 1.0 / Math.cos(Math.pow((1.0 / Math.cbrt((y / (0.5 * x)))), 3.0));
}

Julia

function code(x, y)
	return Float64(1.0 / cos((Float64(1.0 / cbrt(Float64(y / Float64(0.5 * x)))) ^ 3.0)))
end

Wolfram

code[x_, y_] := N[(1.0 / N[Cos[N[Power[N[(1.0 / N[Power[N[(y / N[(0.5 * x), $MachinePrecision]), $MachinePrecision], 1/3], $MachinePrecision]), $MachinePrecision], 3.0], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 44.7%

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

2. Taylor expanded in x around inf 55.7%

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

   1. add-cube-cbrt 55.5%

      \[\leadsto \frac{1}{\cos \color{blue}{\left(\left(\sqrt[3]{0.5 \cdot \frac{x}{y}} \cdot \sqrt[3]{0.5 \cdot \frac{x}{y}}\right) \cdot \sqrt[3]{0.5 \cdot \frac{x}{y}}\right)}} \]

   2. pow3 55.5%

      \[\leadsto \frac{1}{\cos \color{blue}{\left({\left(\sqrt[3]{0.5 \cdot \frac{x}{y}}\right)}^{3}\right)}} \]

4. Applied egg-rr 55.5%

   \[\leadsto \frac{1}{\cos \color{blue}{\left({\left(\sqrt[3]{0.5 \cdot \frac{x}{y}}\right)}^{3}\right)}} \]
5. Step-by-step derivation

   1. clear-num 55.2%

      \[\leadsto \frac{1}{\cos \left({\left(\sqrt[3]{0.5 \cdot \color{blue}{\frac{1}{\frac{y}{x}}}}\right)}^{3}\right)} \]

   2. div-inv 55.2%

      \[\leadsto \frac{1}{\cos \left({\left(\sqrt[3]{\color{blue}{\frac{0.5}{\frac{y}{x}}}}\right)}^{3}\right)} \]

   3. clear-num 55.2%

      \[\leadsto \frac{1}{\cos \left({\left(\sqrt[3]{\color{blue}{\frac{1}{\frac{\frac{y}{x}}{0.5}}}}\right)}^{3}\right)} \]

   4. cbrt-div 55.7%

      \[\leadsto \frac{1}{\cos \left({\color{blue}{\left(\frac{\sqrt[3]{1}}{\sqrt[3]{\frac{\frac{y}{x}}{0.5}}}\right)}}^{3}\right)} \]

   5. metadata-eval 55.7%

      \[\leadsto \frac{1}{\cos \left({\left(\frac{\color{blue}{1}}{\sqrt[3]{\frac{\frac{y}{x}}{0.5}}}\right)}^{3}\right)} \]

   6. associate-/l/ 55.7%

      \[\leadsto \frac{1}{\cos \left({\left(\frac{1}{\sqrt[3]{\color{blue}{\frac{y}{0.5 \cdot x}}}}\right)}^{3}\right)} \]

6. Applied egg-rr 55.7%

   \[\leadsto \frac{1}{\cos \left({\color{blue}{\left(\frac{1}{\sqrt[3]{\frac{y}{0.5 \cdot x}}}\right)}}^{3}\right)} \]
7. Step-by-step derivation

   1. *-commutative 55.7%

      \[\leadsto \frac{1}{\cos \left({\left(\frac{1}{\sqrt[3]{\frac{y}{\color{blue}{x \cdot 0.5}}}}\right)}^{3}\right)} \]

8. Simplified 55.7%

   \[\leadsto \frac{1}{\cos \left({\color{blue}{\left(\frac{1}{\sqrt[3]{\frac{y}{x \cdot 0.5}}}\right)}}^{3}\right)} \]

9. Final simplification 55.7%

   \[\leadsto \frac{1}{\cos \left({\left(\frac{1}{\sqrt[3]{\frac{y}{0.5 \cdot x}}}\right)}^{3}\right)} \]

Alternative 4: 55.7% accurate, 2.0× speedup

Math

\[\begin{array}{l} \\ \frac{1}{\cos \left(0.5 \cdot \frac{x}{y}\right)} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (/ 1.0 (cos (* 0.5 (/ x y)))))

C

double code(double x, double y) {
	return 1.0 / cos((0.5 * (x / y)));
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = 1.0d0 / cos((0.5d0 * (x / y)))
end function

Java

public static double code(double x, double y) {
	return 1.0 / Math.cos((0.5 * (x / y)));
}

Python

def code(x, y):
	return 1.0 / math.cos((0.5 * (x / y)))

Julia

function code(x, y)
	return Float64(1.0 / cos(Float64(0.5 * Float64(x / y))))
end

MATLAB

function tmp = code(x, y)
	tmp = 1.0 / cos((0.5 * (x / y)));
end

Wolfram

code[x_, y_] := N[(1.0 / N[Cos[N[(0.5 * N[(x / y), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 44.7%

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

2. Taylor expanded in x around inf 55.7%

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

3. Final simplification 55.7%

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

Alternative 5: 55.9% accurate, 211.0× speedup

Math

\[\begin{array}{l} \\ 1 \end{array} \]

FPCore

(FPCore (x y) :precision binary64 1.0)

C

double code(double x, double y) {
	return 1.0;
}

Fortran

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

Java

public static double code(double x, double y) {
	return 1.0;
}

Python

def code(x, y):
	return 1.0

Julia

function code(x, y)
	return 1.0
end

MATLAB

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

Wolfram

code[x_, y_] := 1.0

Derivation

1. Initial program 44.7%

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

2. Taylor expanded in x around 0 54.0%

   \[\leadsto \color{blue}{1} \]

3. Final simplification 54.0%

   \[\leadsto 1 \]

Developer target: 55.1% 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 2023200 
(FPCore (x y)
  :name "Diagrams.TwoD.Layout.CirclePacking:approxRadius from diagrams-contrib-1.3.0.5"
  :precision binary64

  :herbie-target
  (if (< y -1.2303690911306994e+114) 1.0 (if (< y -9.102852406811914e-222) (/ (sin (/ x (* y 2.0))) (* (sin (/ x (* y 2.0))) (log (exp (cos (/ x (* y 2.0))))))) 1.0))

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