Linear.Matrix:fromQuaternion from linear-1.19.1.3, A

Percentage Accurate: 95.4% → 100.0%

Time: 4.6s

Alternatives: 5

Speedup: 1.3×

• 27.5% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\begin{array}{l} \\ 2 \cdot \left(x \cdot x - x \cdot y\right) \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* 2.0 (- (* x x) (* x y))))

C

double code(double x, double y) {
	return 2.0 * ((x * x) - (x * y));
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = 2.0d0 * ((x * x) - (x * y))
end function

Java

public static double code(double x, double y) {
	return 2.0 * ((x * x) - (x * y));
}

Python

def code(x, y):
	return 2.0 * ((x * x) - (x * y))

Julia

function code(x, y)
	return Float64(2.0 * Float64(Float64(x * x) - Float64(x * y)))
end

MATLAB

function tmp = code(x, y)
	tmp = 2.0 * ((x * x) - (x * y));
end

Wolfram

code[x_, y_] := N[(2.0 * N[(N[(x * x), $MachinePrecision] - N[(x * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Initial Program: 95.4% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ 2 \cdot \left(x \cdot x - x \cdot y\right) \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* 2.0 (- (* x x) (* x y))))

C

double code(double x, double y) {
	return 2.0 * ((x * x) - (x * y));
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = 2.0d0 * ((x * x) - (x * y))
end function

Java

public static double code(double x, double y) {
	return 2.0 * ((x * x) - (x * y));
}

Python

def code(x, y):
	return 2.0 * ((x * x) - (x * y))

Julia

function code(x, y)
	return Float64(2.0 * Float64(Float64(x * x) - Float64(x * y)))
end

MATLAB

function tmp = code(x, y)
	tmp = 2.0 * ((x * x) - (x * y));
end

Wolfram

code[x_, y_] := N[(2.0 * N[(N[(x * x), $MachinePrecision] - N[(x * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Alternative 1: 100.0% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ 2 \cdot \left(x \cdot \left(x - y\right)\right) \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* 2.0 (* x (- x y))))

C

double code(double x, double y) {
	return 2.0 * (x * (x - y));
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = 2.0d0 * (x * (x - y))
end function

Java

public static double code(double x, double y) {
	return 2.0 * (x * (x - y));
}

Python

def code(x, y):
	return 2.0 * (x * (x - y))

Julia

function code(x, y)
	return Float64(2.0 * Float64(x * Float64(x - y)))
end

MATLAB

function tmp = code(x, y)
	tmp = 2.0 * (x * (x - y));
end

Wolfram

code[x_, y_] := N[(2.0 * N[(x * N[(x - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 93.7%

   \[2 \cdot \left(x \cdot x - x \cdot y\right) \]
2. Step-by-step derivation

   1. *-lowering-*.f64 N/A

      \[\leadsto \mathsf{*.f64}\left(2, \color{blue}{\left(x \cdot x - x \cdot y\right)}\right) \]

   2. distribute-lft-out-- N/A

      \[\leadsto \mathsf{*.f64}\left(2, \left(x \cdot \color{blue}{\left(x - y\right)}\right)\right) \]

   3. *-lowering-*.f64 N/A

      \[\leadsto \mathsf{*.f64}\left(2, \mathsf{*.f64}\left(x, \color{blue}{\left(x - y\right)}\right)\right) \]

   4. --lowering--.f64 100.0%

      \[\leadsto \mathsf{*.f64}\left(2, \mathsf{*.f64}\left(x, \mathsf{\_.f64}\left(x, \color{blue}{y}\right)\right)\right) \]

3. Simplified 100.0%

   \[\leadsto \color{blue}{2 \cdot \left(x \cdot \left(x - y\right)\right)} \]
4. Add Preprocessing
5. Add Preprocessing

Alternative 2: 82.4% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := y \cdot \left(x \cdot -2\right)\\ \mathbf{if}\;y \leq -1.55 \cdot 10^{+100}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;y \leq 1.6 \cdot 10^{+14}:\\ \;\;\;\;2 \cdot \left(x \cdot x\right)\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (* y (* x -2.0))))
   (if (<= y -1.55e+100) t_0 (if (<= y 1.6e+14) (* 2.0 (* x x)) t_0))))

C

double code(double x, double y) {
	double t_0 = y * (x * -2.0);
	double tmp;
	if (y <= -1.55e+100) {
		tmp = t_0;
	} else if (y <= 1.6e+14) {
		tmp = 2.0 * (x * x);
	} else {
		tmp = t_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) :: tmp
    t_0 = y * (x * (-2.0d0))
    if (y <= (-1.55d+100)) then
        tmp = t_0
    else if (y <= 1.6d+14) then
        tmp = 2.0d0 * (x * x)
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double t_0 = y * (x * -2.0);
	double tmp;
	if (y <= -1.55e+100) {
		tmp = t_0;
	} else if (y <= 1.6e+14) {
		tmp = 2.0 * (x * x);
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = y * (x * -2.0)
	tmp = 0
	if y <= -1.55e+100:
		tmp = t_0
	elif y <= 1.6e+14:
		tmp = 2.0 * (x * x)
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y)
	t_0 = Float64(y * Float64(x * -2.0))
	tmp = 0.0
	if (y <= -1.55e+100)
		tmp = t_0;
	elseif (y <= 1.6e+14)
		tmp = Float64(2.0 * Float64(x * x));
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = y * (x * -2.0);
	tmp = 0.0;
	if (y <= -1.55e+100)
		tmp = t_0;
	elseif (y <= 1.6e+14)
		tmp = 2.0 * (x * x);
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(y * N[(x * -2.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -1.55e+100], t$95$0, If[LessEqual[y, 1.6e+14], N[(2.0 * N[(x * x), $MachinePrecision]), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if y < -1.55000000000000003e100 or 1.6e14 < y

   1. Initial program 84.4%

      \[2 \cdot \left(x \cdot x - x \cdot y\right) \]
   2. Step-by-step derivation

      1. *-lowering-*.f64 N/A

         \[\leadsto \mathsf{*.f64}\left(2, \color{blue}{\left(x \cdot x - x \cdot y\right)}\right) \]

      2. distribute-lft-out-- N/A

         \[\leadsto \mathsf{*.f64}\left(2, \left(x \cdot \color{blue}{\left(x - y\right)}\right)\right) \]

      3. *-lowering-*.f64 N/A

         \[\leadsto \mathsf{*.f64}\left(2, \mathsf{*.f64}\left(x, \color{blue}{\left(x - y\right)}\right)\right) \]

      4. --lowering--.f64 100.0%

         \[\leadsto \mathsf{*.f64}\left(2, \mathsf{*.f64}\left(x, \mathsf{\_.f64}\left(x, \color{blue}{y}\right)\right)\right) \]

   3. Simplified 100.0%

      \[\leadsto \color{blue}{2 \cdot \left(x \cdot \left(x - y\right)\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in x around 0

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

      1. associate-*r* N/A

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

      2. *-commutative N/A

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

      3. *-lowering-*.f64 N/A

         \[\leadsto \mathsf{*.f64}\left(y, \color{blue}{\left(-2 \cdot x\right)}\right) \]

      4. *-lowering-*.f64 84.7%

         \[\leadsto \mathsf{*.f64}\left(y, \mathsf{*.f64}\left(-2, \color{blue}{x}\right)\right) \]

   7. Simplified 84.7%

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

   if -1.55000000000000003e100 < y < 1.6e14

   1. Initial program 99.3%

      \[2 \cdot \left(x \cdot x - x \cdot y\right) \]
   2. Step-by-step derivation

      1. *-lowering-*.f64 N/A

         \[\leadsto \mathsf{*.f64}\left(2, \color{blue}{\left(x \cdot x - x \cdot y\right)}\right) \]

      2. distribute-lft-out-- N/A

         \[\leadsto \mathsf{*.f64}\left(2, \left(x \cdot \color{blue}{\left(x - y\right)}\right)\right) \]

      3. *-lowering-*.f64 N/A

         \[\leadsto \mathsf{*.f64}\left(2, \mathsf{*.f64}\left(x, \color{blue}{\left(x - y\right)}\right)\right) \]

      4. --lowering--.f64 100.0%

         \[\leadsto \mathsf{*.f64}\left(2, \mathsf{*.f64}\left(x, \mathsf{\_.f64}\left(x, \color{blue}{y}\right)\right)\right) \]

   3. Simplified 100.0%

      \[\leadsto \color{blue}{2 \cdot \left(x \cdot \left(x - y\right)\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in x around inf

      \[\leadsto \color{blue}{2 \cdot {x}^{2}} \]
   6. Step-by-step derivation

      1. *-lowering-*.f64 N/A

         \[\leadsto \mathsf{*.f64}\left(2, \color{blue}{\left({x}^{2}\right)}\right) \]

      2. unpow2 N/A

         \[\leadsto \mathsf{*.f64}\left(2, \left(x \cdot \color{blue}{x}\right)\right) \]

      3. *-lowering-*.f64 85.0%

         \[\leadsto \mathsf{*.f64}\left(2, \mathsf{*.f64}\left(x, \color{blue}{x}\right)\right) \]

   7. Simplified 85.0%

      \[\leadsto \color{blue}{2 \cdot \left(x \cdot x\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 84.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1.55 \cdot 10^{+100}:\\ \;\;\;\;y \cdot \left(x \cdot -2\right)\\ \mathbf{elif}\;y \leq 1.6 \cdot 10^{+14}:\\ \;\;\;\;2 \cdot \left(x \cdot x\right)\\ \mathbf{else}:\\ \;\;\;\;y \cdot \left(x \cdot -2\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 58.6% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ 2 \cdot \left(x \cdot x\right) \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* 2.0 (* x x)))

C

double code(double x, double y) {
	return 2.0 * (x * x);
}

Fortran

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

Java

public static double code(double x, double y) {
	return 2.0 * (x * x);
}

Python

def code(x, y):
	return 2.0 * (x * x)

Julia

function code(x, y)
	return Float64(2.0 * Float64(x * x))
end

MATLAB

function tmp = code(x, y)
	tmp = 2.0 * (x * x);
end

Wolfram

code[x_, y_] := N[(2.0 * N[(x * x), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 93.7%

   \[2 \cdot \left(x \cdot x - x \cdot y\right) \]
2. Step-by-step derivation

   1. *-lowering-*.f64 N/A

      \[\leadsto \mathsf{*.f64}\left(2, \color{blue}{\left(x \cdot x - x \cdot y\right)}\right) \]

   2. distribute-lft-out-- N/A

      \[\leadsto \mathsf{*.f64}\left(2, \left(x \cdot \color{blue}{\left(x - y\right)}\right)\right) \]

   3. *-lowering-*.f64 N/A

      \[\leadsto \mathsf{*.f64}\left(2, \mathsf{*.f64}\left(x, \color{blue}{\left(x - y\right)}\right)\right) \]

   4. --lowering--.f64 100.0%

      \[\leadsto \mathsf{*.f64}\left(2, \mathsf{*.f64}\left(x, \mathsf{\_.f64}\left(x, \color{blue}{y}\right)\right)\right) \]

3. Simplified 100.0%

   \[\leadsto \color{blue}{2 \cdot \left(x \cdot \left(x - y\right)\right)} \]
4. Add Preprocessing

5. Taylor expanded in x around inf

   \[\leadsto \color{blue}{2 \cdot {x}^{2}} \]
6. Step-by-step derivation

   1. *-lowering-*.f64 N/A

      \[\leadsto \mathsf{*.f64}\left(2, \color{blue}{\left({x}^{2}\right)}\right) \]

   2. unpow2 N/A

      \[\leadsto \mathsf{*.f64}\left(2, \left(x \cdot \color{blue}{x}\right)\right) \]

   3. *-lowering-*.f64 63.9%

      \[\leadsto \mathsf{*.f64}\left(2, \mathsf{*.f64}\left(x, \color{blue}{x}\right)\right) \]

7. Simplified 63.9%

   \[\leadsto \color{blue}{2 \cdot \left(x \cdot x\right)} \]
8. Add Preprocessing

Alternative 4: 4.3% accurate, 3.0× speedup

Math

\[\begin{array}{l} \\ 2 \cdot x \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* 2.0 x))

C

double code(double x, double y) {
	return 2.0 * x;
}

Fortran

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

Java

public static double code(double x, double y) {
	return 2.0 * x;
}

Python

def code(x, y):
	return 2.0 * x

Julia

function code(x, y)
	return Float64(2.0 * x)
end

MATLAB

function tmp = code(x, y)
	tmp = 2.0 * x;
end

Wolfram

code[x_, y_] := N[(2.0 * x), $MachinePrecision]

Derivation

1. Initial program 93.7%

   \[2 \cdot \left(x \cdot x - x \cdot y\right) \]
2. Step-by-step derivation

   1. *-lowering-*.f64 N/A

      \[\leadsto \mathsf{*.f64}\left(2, \color{blue}{\left(x \cdot x - x \cdot y\right)}\right) \]

   2. distribute-lft-out-- N/A

      \[\leadsto \mathsf{*.f64}\left(2, \left(x \cdot \color{blue}{\left(x - y\right)}\right)\right) \]

   3. *-lowering-*.f64 N/A

      \[\leadsto \mathsf{*.f64}\left(2, \mathsf{*.f64}\left(x, \color{blue}{\left(x - y\right)}\right)\right) \]

   4. --lowering--.f64 100.0%

      \[\leadsto \mathsf{*.f64}\left(2, \mathsf{*.f64}\left(x, \mathsf{\_.f64}\left(x, \color{blue}{y}\right)\right)\right) \]

3. Simplified 100.0%

   \[\leadsto \color{blue}{2 \cdot \left(x \cdot \left(x - y\right)\right)} \]
4. Add Preprocessing

5. Taylor expanded in x around inf

   \[\leadsto \color{blue}{2 \cdot {x}^{2}} \]
6. Step-by-step derivation

   1. *-lowering-*.f64 N/A

      \[\leadsto \mathsf{*.f64}\left(2, \color{blue}{\left({x}^{2}\right)}\right) \]

   2. unpow2 N/A

      \[\leadsto \mathsf{*.f64}\left(2, \left(x \cdot \color{blue}{x}\right)\right) \]

   3. *-lowering-*.f64 63.9%

      \[\leadsto \mathsf{*.f64}\left(2, \mathsf{*.f64}\left(x, \color{blue}{x}\right)\right) \]

7. Simplified 63.9%

   \[\leadsto \color{blue}{2 \cdot \left(x \cdot x\right)} \]
8. Step-by-step derivation

   1. pow2 N/A

      \[\leadsto \mathsf{*.f64}\left(2, \left({x}^{\color{blue}{2}}\right)\right) \]

   2. pow-to-exp N/A

      \[\leadsto \mathsf{*.f64}\left(2, \left(e^{\log x \cdot 2}\right)\right) \]

   3. *-commutative N/A

      \[\leadsto \mathsf{*.f64}\left(2, \left(e^{2 \cdot \log x}\right)\right) \]

   4. count-2 N/A

      \[\leadsto \mathsf{*.f64}\left(2, \left(e^{\log x + \log x}\right)\right) \]

   5. flip-+ N/A

      \[\leadsto \mathsf{*.f64}\left(2, \left(e^{\frac{\log x \cdot \log x - \log x \cdot \log x}{\log x - \log x}}\right)\right) \]

   6. +-inverses N/A

      \[\leadsto \mathsf{*.f64}\left(2, \left(e^{\frac{0}{\log x - \log x}}\right)\right) \]

   7. +-inverses N/A

      \[\leadsto \mathsf{*.f64}\left(2, \left(e^{\frac{\log \left({x}^{\left(\frac{\frac{2}{2}}{2}\right)}\right) \cdot \log \left({x}^{\left(\frac{\frac{2}{2}}{2}\right)}\right) - \log \left({x}^{\left(\frac{\frac{2}{2}}{2}\right)}\right) \cdot \log \left({x}^{\left(\frac{\frac{2}{2}}{2}\right)}\right)}{\log x - \log x}}\right)\right) \]

   8. +-inverses N/A

      \[\leadsto \mathsf{*.f64}\left(2, \left(e^{\frac{\log \left({x}^{\left(\frac{\frac{2}{2}}{2}\right)}\right) \cdot \log \left({x}^{\left(\frac{\frac{2}{2}}{2}\right)}\right) - \log \left({x}^{\left(\frac{\frac{2}{2}}{2}\right)}\right) \cdot \log \left({x}^{\left(\frac{\frac{2}{2}}{2}\right)}\right)}{0}}\right)\right) \]

   9. +-inverses N/A

      \[\leadsto \mathsf{*.f64}\left(2, \left(e^{\frac{\log \left({x}^{\left(\frac{\frac{2}{2}}{2}\right)}\right) \cdot \log \left({x}^{\left(\frac{\frac{2}{2}}{2}\right)}\right) - \log \left({x}^{\left(\frac{\frac{2}{2}}{2}\right)}\right) \cdot \log \left({x}^{\left(\frac{\frac{2}{2}}{2}\right)}\right)}{\log \left({x}^{\left(\frac{\frac{2}{2}}{2}\right)}\right) - \log \left({x}^{\left(\frac{\frac{2}{2}}{2}\right)}\right)}}\right)\right) \]

   10. flip-+ N/A

       \[\leadsto \mathsf{*.f64}\left(2, \left(e^{\log \left({x}^{\left(\frac{\frac{2}{2}}{2}\right)}\right) + \log \left({x}^{\left(\frac{\frac{2}{2}}{2}\right)}\right)}\right)\right) \]

   11. log-prod N/A

       \[\leadsto \mathsf{*.f64}\left(2, \left(e^{\log \left({x}^{\left(\frac{\frac{2}{2}}{2}\right)} \cdot {x}^{\left(\frac{\frac{2}{2}}{2}\right)}\right)}\right)\right) \]

   12. sqr-pow N/A

       \[\leadsto \mathsf{*.f64}\left(2, \left(e^{\log \left({x}^{\left(\frac{2}{2}\right)}\right)}\right)\right) \]

   13. metadata-eval N/A

       \[\leadsto \mathsf{*.f64}\left(2, \left(e^{\log \left({x}^{1}\right)}\right)\right) \]

   14. unpow1 N/A

       \[\leadsto \mathsf{*.f64}\left(2, \left(e^{\log x}\right)\right) \]

   15. rem-exp-log 4.3%

       \[\leadsto \mathsf{*.f64}\left(2, x\right) \]

9. Applied egg-rr 4.3%

   \[\leadsto 2 \cdot \color{blue}{x} \]
10. Add Preprocessing

Alternative 5: 3.8% accurate, 9.0× speedup

Math

\[\begin{array}{l} \\ 2 \end{array} \]

FPCore

(FPCore (x y) :precision binary64 2.0)

C

double code(double x, double y) {
	return 2.0;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = 2.0d0
end function

Java

public static double code(double x, double y) {
	return 2.0;
}

Python

def code(x, y):
	return 2.0

Julia

function code(x, y)
	return 2.0
end

MATLAB

function tmp = code(x, y)
	tmp = 2.0;
end

Wolfram

code[x_, y_] := 2.0

Derivation

1. Initial program 93.7%

   \[2 \cdot \left(x \cdot x - x \cdot y\right) \]
2. Step-by-step derivation

   1. *-lowering-*.f64 N/A

      \[\leadsto \mathsf{*.f64}\left(2, \color{blue}{\left(x \cdot x - x \cdot y\right)}\right) \]

   2. distribute-lft-out-- N/A

      \[\leadsto \mathsf{*.f64}\left(2, \left(x \cdot \color{blue}{\left(x - y\right)}\right)\right) \]

   3. *-lowering-*.f64 N/A

      \[\leadsto \mathsf{*.f64}\left(2, \mathsf{*.f64}\left(x, \color{blue}{\left(x - y\right)}\right)\right) \]

   4. --lowering--.f64 100.0%

      \[\leadsto \mathsf{*.f64}\left(2, \mathsf{*.f64}\left(x, \mathsf{\_.f64}\left(x, \color{blue}{y}\right)\right)\right) \]

3. Simplified 100.0%

   \[\leadsto \color{blue}{2 \cdot \left(x \cdot \left(x - y\right)\right)} \]
4. Add Preprocessing

5. Taylor expanded in x around inf

   \[\leadsto \color{blue}{2 \cdot {x}^{2}} \]
6. Step-by-step derivation

   1. *-lowering-*.f64 N/A

      \[\leadsto \mathsf{*.f64}\left(2, \color{blue}{\left({x}^{2}\right)}\right) \]

   2. unpow2 N/A

      \[\leadsto \mathsf{*.f64}\left(2, \left(x \cdot \color{blue}{x}\right)\right) \]

   3. *-lowering-*.f64 63.9%

      \[\leadsto \mathsf{*.f64}\left(2, \mathsf{*.f64}\left(x, \color{blue}{x}\right)\right) \]

7. Simplified 63.9%

   \[\leadsto \color{blue}{2 \cdot \left(x \cdot x\right)} \]

9. Applied egg-rr 3.7%

   \[\leadsto \color{blue}{2} \]
10. Add Preprocessing

Developer Target 1: 100.0% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \left(x \cdot 2\right) \cdot \left(x - y\right) \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* (* x 2.0) (- x y)))

C

double code(double x, double y) {
	return (x * 2.0) * (x - y);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (x * 2.0d0) * (x - y)
end function

Java

public static double code(double x, double y) {
	return (x * 2.0) * (x - y);
}

Python

def code(x, y):
	return (x * 2.0) * (x - y)

Julia

function code(x, y)
	return Float64(Float64(x * 2.0) * Float64(x - y))
end

MATLAB

function tmp = code(x, y)
	tmp = (x * 2.0) * (x - y);
end

Wolfram

code[x_, y_] := N[(N[(x * 2.0), $MachinePrecision] * N[(x - y), $MachinePrecision]), $MachinePrecision]

Reproduce

herbie shell --seed 2024161 
(FPCore (x y)
  :name "Linear.Matrix:fromQuaternion from linear-1.19.1.3, A"
  :precision binary64

  :alt
  (! :herbie-platform default (* (* x 2) (- x y)))

  (* 2.0 (- (* x x) (* x y))))