Numeric.SpecFunctions:incompleteGamma from math-functions-0.1.5.2, B

Percentage Accurate: 99.4% → 99.4%

Time: 10.4s

Alternatives: 14

Speedup: 1.2×

Specification

Math

\[\begin{array}{l} \\ \left(3 \cdot \sqrt{x}\right) \cdot \left(\left(y + \frac{1}{x \cdot 9}\right) - 1\right) \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (* (* 3.0 (sqrt x)) (- (+ y (/ 1.0 (* x 9.0))) 1.0)))

C

double code(double x, double y) {
	return (3.0 * sqrt(x)) * ((y + (1.0 / (x * 9.0))) - 1.0);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (3.0d0 * sqrt(x)) * ((y + (1.0d0 / (x * 9.0d0))) - 1.0d0)
end function

Java

public static double code(double x, double y) {
	return (3.0 * Math.sqrt(x)) * ((y + (1.0 / (x * 9.0))) - 1.0);
}

Python

def code(x, y):
	return (3.0 * math.sqrt(x)) * ((y + (1.0 / (x * 9.0))) - 1.0)

Julia

function code(x, y)
	return Float64(Float64(3.0 * sqrt(x)) * Float64(Float64(y + Float64(1.0 / Float64(x * 9.0))) - 1.0))
end

MATLAB

function tmp = code(x, y)
	tmp = (3.0 * sqrt(x)) * ((y + (1.0 / (x * 9.0))) - 1.0);
end

Wolfram

code[x_, y_] := N[(N[(3.0 * N[Sqrt[x], $MachinePrecision]), $MachinePrecision] * N[(N[(y + N[(1.0 / N[(x * 9.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - 1.0), $MachinePrecision]), $MachinePrecision]

Initial Program: 99.4% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(3 \cdot \sqrt{x}\right) \cdot \left(\left(y + \frac{1}{x \cdot 9}\right) - 1\right) \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (* (* 3.0 (sqrt x)) (- (+ y (/ 1.0 (* x 9.0))) 1.0)))

C

double code(double x, double y) {
	return (3.0 * sqrt(x)) * ((y + (1.0 / (x * 9.0))) - 1.0);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (3.0d0 * sqrt(x)) * ((y + (1.0d0 / (x * 9.0d0))) - 1.0d0)
end function

Java

public static double code(double x, double y) {
	return (3.0 * Math.sqrt(x)) * ((y + (1.0 / (x * 9.0))) - 1.0);
}

Python

def code(x, y):
	return (3.0 * math.sqrt(x)) * ((y + (1.0 / (x * 9.0))) - 1.0)

Julia

function code(x, y)
	return Float64(Float64(3.0 * sqrt(x)) * Float64(Float64(y + Float64(1.0 / Float64(x * 9.0))) - 1.0))
end

MATLAB

function tmp = code(x, y)
	tmp = (3.0 * sqrt(x)) * ((y + (1.0 / (x * 9.0))) - 1.0);
end

Wolfram

code[x_, y_] := N[(N[(3.0 * N[Sqrt[x], $MachinePrecision]), $MachinePrecision] * N[(N[(y + N[(1.0 / N[(x * 9.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - 1.0), $MachinePrecision]), $MachinePrecision]

Alternative 1: 99.4% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(0.3333333333333333, \sqrt{\frac{1}{x}}, \sqrt{x} \cdot \mathsf{fma}\left(3, y, -3\right)\right) \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (fma 0.3333333333333333 (sqrt (/ 1.0 x)) (* (sqrt x) (fma 3.0 y -3.0))))

C

double code(double x, double y) {
	return fma(0.3333333333333333, sqrt((1.0 / x)), (sqrt(x) * fma(3.0, y, -3.0)));
}

Julia

function code(x, y)
	return fma(0.3333333333333333, sqrt(Float64(1.0 / x)), Float64(sqrt(x) * fma(3.0, y, -3.0)))
end

Wolfram

code[x_, y_] := N[(0.3333333333333333 * N[Sqrt[N[(1.0 / x), $MachinePrecision]], $MachinePrecision] + N[(N[Sqrt[x], $MachinePrecision] * N[(3.0 * y + -3.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.4%

   \[\left(3 \cdot \sqrt{x}\right) \cdot \left(\left(y + \frac{1}{x \cdot 9}\right) - 1\right) \]
2. Add Preprocessing

3. Taylor expanded in x around 0

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

   1. lower-/.f64 N/A

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

   2. +-commutative N/A

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

   3. *-commutative N/A

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

   4. associate-*l* N/A

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

   5. lower-fma.f64 N/A

      \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(\sqrt{{x}^{3}}, \left(y - 1\right) \cdot 3, \frac{1}{3} \cdot \sqrt{x}\right)}}{x} \]

   6. lower-sqrt.f64 N/A

      \[\leadsto \frac{\mathsf{fma}\left(\color{blue}{\sqrt{{x}^{3}}}, \left(y - 1\right) \cdot 3, \frac{1}{3} \cdot \sqrt{x}\right)}{x} \]

   7. cube-mult N/A

      \[\leadsto \frac{\mathsf{fma}\left(\sqrt{\color{blue}{x \cdot \left(x \cdot x\right)}}, \left(y - 1\right) \cdot 3, \frac{1}{3} \cdot \sqrt{x}\right)}{x} \]

   8. lower-*.f64 N/A

      \[\leadsto \frac{\mathsf{fma}\left(\sqrt{\color{blue}{x \cdot \left(x \cdot x\right)}}, \left(y - 1\right) \cdot 3, \frac{1}{3} \cdot \sqrt{x}\right)}{x} \]

   9. lower-*.f64 N/A

      \[\leadsto \frac{\mathsf{fma}\left(\sqrt{x \cdot \color{blue}{\left(x \cdot x\right)}}, \left(y - 1\right) \cdot 3, \frac{1}{3} \cdot \sqrt{x}\right)}{x} \]

   10. *-commutative N/A

       \[\leadsto \frac{\mathsf{fma}\left(\sqrt{x \cdot \left(x \cdot x\right)}, \color{blue}{3 \cdot \left(y - 1\right)}, \frac{1}{3} \cdot \sqrt{x}\right)}{x} \]

   11. sub-neg N/A

       \[\leadsto \frac{\mathsf{fma}\left(\sqrt{x \cdot \left(x \cdot x\right)}, 3 \cdot \color{blue}{\left(y + \left(\mathsf{neg}\left(1\right)\right)\right)}, \frac{1}{3} \cdot \sqrt{x}\right)}{x} \]

   12. metadata-eval N/A

       \[\leadsto \frac{\mathsf{fma}\left(\sqrt{x \cdot \left(x \cdot x\right)}, 3 \cdot \left(y + \color{blue}{-1}\right), \frac{1}{3} \cdot \sqrt{x}\right)}{x} \]

   13. distribute-lft-in N/A

       \[\leadsto \frac{\mathsf{fma}\left(\sqrt{x \cdot \left(x \cdot x\right)}, \color{blue}{3 \cdot y + 3 \cdot -1}, \frac{1}{3} \cdot \sqrt{x}\right)}{x} \]

   14. metadata-eval N/A

       \[\leadsto \frac{\mathsf{fma}\left(\sqrt{x \cdot \left(x \cdot x\right)}, 3 \cdot y + \color{blue}{-3}, \frac{1}{3} \cdot \sqrt{x}\right)}{x} \]

   15. lower-fma.f64 N/A

       \[\leadsto \frac{\mathsf{fma}\left(\sqrt{x \cdot \left(x \cdot x\right)}, \color{blue}{\mathsf{fma}\left(3, y, -3\right)}, \frac{1}{3} \cdot \sqrt{x}\right)}{x} \]

   16. lower-*.f64 N/A

       \[\leadsto \frac{\mathsf{fma}\left(\sqrt{x \cdot \left(x \cdot x\right)}, \mathsf{fma}\left(3, y, -3\right), \color{blue}{\frac{1}{3} \cdot \sqrt{x}}\right)}{x} \]

   17. lower-sqrt.f64 63.6

       \[\leadsto \frac{\mathsf{fma}\left(\sqrt{x \cdot \left(x \cdot x\right)}, \mathsf{fma}\left(3, y, -3\right), 0.3333333333333333 \cdot \color{blue}{\sqrt{x}}\right)}{x} \]

5. Applied rewrites 63.6%

   \[\leadsto \color{blue}{\frac{\mathsf{fma}\left(\sqrt{x \cdot \left(x \cdot x\right)}, \mathsf{fma}\left(3, y, -3\right), 0.3333333333333333 \cdot \sqrt{x}\right)}{x}} \]

6. Taylor expanded in y around 0

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

8. Applied rewrites 99.5%

   \[\leadsto \mathsf{fma}\left(0.3333333333333333, \color{blue}{\sqrt{\frac{1}{x}}}, \sqrt{x} \cdot \mathsf{fma}\left(3, y, -3\right)\right) \]
9. Add Preprocessing

Alternative 2: 92.3% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 3 \cdot \sqrt{x}\\ t_1 := \left(\left(y + \frac{1}{x \cdot 9}\right) + -1\right) \cdot t\_0\\ \mathbf{if}\;t\_1 \leq -2 \cdot 10^{+27}:\\ \;\;\;\;\sqrt{x} \cdot \mathsf{fma}\left(3, y, -3\right)\\ \mathbf{elif}\;t\_1 \leq 2 \cdot 10^{+151}:\\ \;\;\;\;\sqrt{x} \cdot \left(-3 + \frac{3}{x \cdot 9}\right)\\ \mathbf{else}:\\ \;\;\;\;y \cdot t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (* 3.0 (sqrt x))) (t_1 (* (+ (+ y (/ 1.0 (* x 9.0))) -1.0) t_0)))
   (if (<= t_1 -2e+27)
     (* (sqrt x) (fma 3.0 y -3.0))
     (if (<= t_1 2e+151) (* (sqrt x) (+ -3.0 (/ 3.0 (* x 9.0)))) (* y t_0)))))

C

double code(double x, double y) {
	double t_0 = 3.0 * sqrt(x);
	double t_1 = ((y + (1.0 / (x * 9.0))) + -1.0) * t_0;
	double tmp;
	if (t_1 <= -2e+27) {
		tmp = sqrt(x) * fma(3.0, y, -3.0);
	} else if (t_1 <= 2e+151) {
		tmp = sqrt(x) * (-3.0 + (3.0 / (x * 9.0)));
	} else {
		tmp = y * t_0;
	}
	return tmp;
}

Julia

function code(x, y)
	t_0 = Float64(3.0 * sqrt(x))
	t_1 = Float64(Float64(Float64(y + Float64(1.0 / Float64(x * 9.0))) + -1.0) * t_0)
	tmp = 0.0
	if (t_1 <= -2e+27)
		tmp = Float64(sqrt(x) * fma(3.0, y, -3.0));
	elseif (t_1 <= 2e+151)
		tmp = Float64(sqrt(x) * Float64(-3.0 + Float64(3.0 / Float64(x * 9.0))));
	else
		tmp = Float64(y * t_0);
	end
	return tmp
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(3.0 * N[Sqrt[x], $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(N[(N[(y + N[(1.0 / N[(x * 9.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + -1.0), $MachinePrecision] * t$95$0), $MachinePrecision]}, If[LessEqual[t$95$1, -2e+27], N[(N[Sqrt[x], $MachinePrecision] * N[(3.0 * y + -3.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$1, 2e+151], N[(N[Sqrt[x], $MachinePrecision] * N[(-3.0 + N[(3.0 / N[(x * 9.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(y * t$95$0), $MachinePrecision]]]]]

Derivation

1. Split input into 3 regimes

2. if (*.f64 (*.f64 #s(literal 3 binary64) (sqrt.f64 x)) (-.f64 (+.f64 y (/.f64 #s(literal 1 binary64) (*.f64 x #s(literal 9 binary64)))) #s(literal 1 binary64))) < -2e27

   1. Initial program 99.5%

      \[\left(3 \cdot \sqrt{x}\right) \cdot \left(\left(y + \frac{1}{x \cdot 9}\right) - 1\right) \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

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

      1. *-commutative N/A

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

      2. associate-*l* N/A

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

      3. lower-*.f64 N/A

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

      4. lower-sqrt.f64 N/A

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

      5. *-commutative N/A

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

      6. sub-neg N/A

         \[\leadsto \sqrt{x} \cdot \left(3 \cdot \color{blue}{\left(y + \left(\mathsf{neg}\left(1\right)\right)\right)}\right) \]

      7. metadata-eval N/A

         \[\leadsto \sqrt{x} \cdot \left(3 \cdot \left(y + \color{blue}{-1}\right)\right) \]

      8. distribute-lft-in N/A

         \[\leadsto \sqrt{x} \cdot \color{blue}{\left(3 \cdot y + 3 \cdot -1\right)} \]

      9. metadata-eval N/A

         \[\leadsto \sqrt{x} \cdot \left(3 \cdot y + \color{blue}{-3}\right) \]

      10. lower-fma.f64 98.9

          \[\leadsto \sqrt{x} \cdot \color{blue}{\mathsf{fma}\left(3, y, -3\right)} \]

   5. Applied rewrites 98.9%

      \[\leadsto \color{blue}{\sqrt{x} \cdot \mathsf{fma}\left(3, y, -3\right)} \]

   if -2e27 < (*.f64 (*.f64 #s(literal 3 binary64) (sqrt.f64 x)) (-.f64 (+.f64 y (/.f64 #s(literal 1 binary64) (*.f64 x #s(literal 9 binary64)))) #s(literal 1 binary64))) < 2.00000000000000003e151

   1. Initial program 99.3%

      \[\left(3 \cdot \sqrt{x}\right) \cdot \left(\left(y + \frac{1}{x \cdot 9}\right) - 1\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0

      \[\leadsto \color{blue}{3 \cdot \left(\sqrt{x} \cdot \left(\frac{1}{9} \cdot \frac{1}{x} - 1\right)\right)} \]
   4. Step-by-step derivation

      1. associate-*r* N/A

         \[\leadsto \color{blue}{\left(3 \cdot \sqrt{x}\right) \cdot \left(\frac{1}{9} \cdot \frac{1}{x} - 1\right)} \]

      2. *-commutative N/A

         \[\leadsto \color{blue}{\left(\sqrt{x} \cdot 3\right)} \cdot \left(\frac{1}{9} \cdot \frac{1}{x} - 1\right) \]

      3. associate-*l* N/A

         \[\leadsto \color{blue}{\sqrt{x} \cdot \left(3 \cdot \left(\frac{1}{9} \cdot \frac{1}{x} - 1\right)\right)} \]

      4. lower-*.f64 N/A

         \[\leadsto \color{blue}{\sqrt{x} \cdot \left(3 \cdot \left(\frac{1}{9} \cdot \frac{1}{x} - 1\right)\right)} \]

      5. lower-sqrt.f64 N/A

         \[\leadsto \color{blue}{\sqrt{x}} \cdot \left(3 \cdot \left(\frac{1}{9} \cdot \frac{1}{x} - 1\right)\right) \]

      6. sub-neg N/A

         \[\leadsto \sqrt{x} \cdot \left(3 \cdot \color{blue}{\left(\frac{1}{9} \cdot \frac{1}{x} + \left(\mathsf{neg}\left(1\right)\right)\right)}\right) \]

      7. metadata-eval N/A

         \[\leadsto \sqrt{x} \cdot \left(3 \cdot \left(\frac{1}{9} \cdot \frac{1}{x} + \color{blue}{-1}\right)\right) \]

      8. +-commutative N/A

         \[\leadsto \sqrt{x} \cdot \left(3 \cdot \color{blue}{\left(-1 + \frac{1}{9} \cdot \frac{1}{x}\right)}\right) \]

      9. distribute-rgt-in N/A

         \[\leadsto \sqrt{x} \cdot \color{blue}{\left(-1 \cdot 3 + \left(\frac{1}{9} \cdot \frac{1}{x}\right) \cdot 3\right)} \]

      10. metadata-eval N/A

          \[\leadsto \sqrt{x} \cdot \left(\color{blue}{-3} + \left(\frac{1}{9} \cdot \frac{1}{x}\right) \cdot 3\right) \]

      11. lower-+.f64 N/A

          \[\leadsto \sqrt{x} \cdot \color{blue}{\left(-3 + \left(\frac{1}{9} \cdot \frac{1}{x}\right) \cdot 3\right)} \]

      12. associate-*r/ N/A

          \[\leadsto \sqrt{x} \cdot \left(-3 + \color{blue}{\frac{\frac{1}{9} \cdot 1}{x}} \cdot 3\right) \]

      13. metadata-eval N/A

          \[\leadsto \sqrt{x} \cdot \left(-3 + \frac{\color{blue}{\frac{1}{9}}}{x} \cdot 3\right) \]

      14. associate-*l/ N/A

          \[\leadsto \sqrt{x} \cdot \left(-3 + \color{blue}{\frac{\frac{1}{9} \cdot 3}{x}}\right) \]

      15. metadata-eval N/A

          \[\leadsto \sqrt{x} \cdot \left(-3 + \frac{\color{blue}{\frac{1}{3}}}{x}\right) \]

      16. lower-/.f64 86.0

          \[\leadsto \sqrt{x} \cdot \left(-3 + \color{blue}{\frac{0.3333333333333333}{x}}\right) \]

   5. Applied rewrites 86.0%

      \[\leadsto \color{blue}{\sqrt{x} \cdot \left(-3 + \frac{0.3333333333333333}{x}\right)} \]
   6. Step-by-step derivation

   7. Applied rewrites 86.0%

      \[\leadsto \sqrt{x} \cdot \left(-3 + \frac{3}{\color{blue}{x \cdot 9}}\right) \]

   if 2.00000000000000003e151 < (*.f64 (*.f64 #s(literal 3 binary64) (sqrt.f64 x)) (-.f64 (+.f64 y (/.f64 #s(literal 1 binary64) (*.f64 x #s(literal 9 binary64)))) #s(literal 1 binary64)))

   1. Initial program 99.6%

      \[\left(3 \cdot \sqrt{x}\right) \cdot \left(\left(y + \frac{1}{x \cdot 9}\right) - 1\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y around inf

      \[\leadsto \color{blue}{3 \cdot \left(\sqrt{x} \cdot y\right)} \]
   4. Step-by-step derivation

      1. associate-*r* N/A

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

      2. *-commutative N/A

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

      3. lower-*.f64 N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

      6. lower-sqrt.f64 94.6

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

   5. Applied rewrites 94.6%

      \[\leadsto \color{blue}{y \cdot \left(\sqrt{x} \cdot 3\right)} \]
3. Recombined 3 regimes into one program.

4. Final simplification 92.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\left(\left(y + \frac{1}{x \cdot 9}\right) + -1\right) \cdot \left(3 \cdot \sqrt{x}\right) \leq -2 \cdot 10^{+27}:\\ \;\;\;\;\sqrt{x} \cdot \mathsf{fma}\left(3, y, -3\right)\\ \mathbf{elif}\;\left(\left(y + \frac{1}{x \cdot 9}\right) + -1\right) \cdot \left(3 \cdot \sqrt{x}\right) \leq 2 \cdot 10^{+151}:\\ \;\;\;\;\sqrt{x} \cdot \left(-3 + \frac{3}{x \cdot 9}\right)\\ \mathbf{else}:\\ \;\;\;\;y \cdot \left(3 \cdot \sqrt{x}\right)\\ \end{array} \]
5. Add Preprocessing

Developer Target 1: 99.4% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ 3 \cdot \left(y \cdot \sqrt{x} + \left(\frac{1}{x \cdot 9} - 1\right) \cdot \sqrt{x}\right) \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (* 3.0 (+ (* y (sqrt x)) (* (- (/ 1.0 (* x 9.0)) 1.0) (sqrt x)))))

C

double code(double x, double y) {
	return 3.0 * ((y * sqrt(x)) + (((1.0 / (x * 9.0)) - 1.0) * sqrt(x)));
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = 3.0d0 * ((y * sqrt(x)) + (((1.0d0 / (x * 9.0d0)) - 1.0d0) * sqrt(x)))
end function

Java

public static double code(double x, double y) {
	return 3.0 * ((y * Math.sqrt(x)) + (((1.0 / (x * 9.0)) - 1.0) * Math.sqrt(x)));
}

Python

def code(x, y):
	return 3.0 * ((y * math.sqrt(x)) + (((1.0 / (x * 9.0)) - 1.0) * math.sqrt(x)))

Julia

function code(x, y)
	return Float64(3.0 * Float64(Float64(y * sqrt(x)) + Float64(Float64(Float64(1.0 / Float64(x * 9.0)) - 1.0) * sqrt(x))))
end

MATLAB

function tmp = code(x, y)
	tmp = 3.0 * ((y * sqrt(x)) + (((1.0 / (x * 9.0)) - 1.0) * sqrt(x)));
end

Wolfram

code[x_, y_] := N[(3.0 * N[(N[(y * N[Sqrt[x], $MachinePrecision]), $MachinePrecision] + N[(N[(N[(1.0 / N[(x * 9.0), $MachinePrecision]), $MachinePrecision] - 1.0), $MachinePrecision] * N[Sqrt[x], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Reproduce

herbie shell --seed 2024228 
(FPCore (x y)
  :name "Numeric.SpecFunctions:incompleteGamma from math-functions-0.1.5.2, B"
  :precision binary64

  :alt
  (! :herbie-platform default (* 3 (+ (* y (sqrt x)) (* (- (/ 1 (* x 9)) 1) (sqrt x)))))

  (* (* 3.0 (sqrt x)) (- (+ y (/ 1.0 (* x 9.0))) 1.0)))