Linear.Matrix:fromQuaternion from linear-1.19.1.3, B

Percentage Accurate: 95.2% → 100.0%

Time: 3.2s

Alternatives: 3

Speedup: 1.4×

• 27.3% 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: 95.2% 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, 1.4× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 95.3%

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

   1. lift-*.f64 N/A

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

   2. lift-+.f64 N/A

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

   3. lift-*.f64 N/A

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

   4. lift-*.f64 N/A

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

   5. distribute-lft-out N/A

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

   6. associate-*r* N/A

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

   7. *-commutative N/A

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

   8. lower-*.f64 N/A

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

   9. +-commutative N/A

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

   10. lower-+.f64 N/A

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

   11. lower-*.f64 100.0

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

4. Applied rewrites 100.0%

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

Alternative 2: 79.6% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -5.2 \cdot 10^{+90} \lor \neg \left(y \leq 6 \cdot 10^{-129}\right):\\ \;\;\;\;2 \cdot \left(y \cdot x\right)\\ \mathbf{else}:\\ \;\;\;\;\left(x \cdot x\right) \cdot 2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (or (<= y -5.2e+90) (not (<= y 6e-129))) (* 2.0 (* y x)) (* (* x x) 2.0)))

C

double code(double x, double y) {
	double tmp;
	if ((y <= -5.2e+90) || !(y <= 6e-129)) {
		tmp = 2.0 * (y * x);
	} else {
		tmp = (x * x) * 2.0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if ((y <= (-5.2d+90)) .or. (.not. (y <= 6d-129))) then
        tmp = 2.0d0 * (y * x)
    else
        tmp = (x * x) * 2.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if ((y <= -5.2e+90) || !(y <= 6e-129)) {
		tmp = 2.0 * (y * x);
	} else {
		tmp = (x * x) * 2.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (y <= -5.2e+90) or not (y <= 6e-129):
		tmp = 2.0 * (y * x)
	else:
		tmp = (x * x) * 2.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if ((y <= -5.2e+90) || !(y <= 6e-129))
		tmp = Float64(2.0 * Float64(y * x));
	else
		tmp = Float64(Float64(x * x) * 2.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if ((y <= -5.2e+90) || ~((y <= 6e-129)))
		tmp = 2.0 * (y * x);
	else
		tmp = (x * x) * 2.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[Or[LessEqual[y, -5.2e+90], N[Not[LessEqual[y, 6e-129]], $MachinePrecision]], N[(2.0 * N[(y * x), $MachinePrecision]), $MachinePrecision], N[(N[(x * x), $MachinePrecision] * 2.0), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -5.1999999999999997e90 or 5.9999999999999996e-129 < y

   1. Initial program 92.2%

      \[2 \cdot \left(x \cdot x + x \cdot y\right) \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

      1. *-commutative N/A

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

      2. lower-*.f64 82.1

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

   5. Applied rewrites 82.1%

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

   if -5.1999999999999997e90 < y < 5.9999999999999996e-129

   1. Initial program 98.4%

      \[2 \cdot \left(x \cdot x + x \cdot y\right) \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

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

      1. *-commutative N/A

         \[\leadsto \color{blue}{{x}^{2} \cdot 2} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{{x}^{2} \cdot 2} \]

      3. unpow2 N/A

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

      4. lower-*.f64 89.8

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

   5. Applied rewrites 89.8%

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

4. Final simplification 86.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -5.2 \cdot 10^{+90} \lor \neg \left(y \leq 6 \cdot 10^{-129}\right):\\ \;\;\;\;2 \cdot \left(y \cdot x\right)\\ \mathbf{else}:\\ \;\;\;\;\left(x \cdot x\right) \cdot 2\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 58.4% accurate, 1.7× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 95.3%

   \[2 \cdot \left(x \cdot x + x \cdot y\right) \]
2. Add Preprocessing

3. Taylor expanded in x around inf

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

   1. *-commutative N/A

      \[\leadsto \color{blue}{{x}^{2} \cdot 2} \]

   2. lower-*.f64 N/A

      \[\leadsto \color{blue}{{x}^{2} \cdot 2} \]

   3. unpow2 N/A

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

   4. lower-*.f64 62.0

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

5. Applied rewrites 62.0%

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

Developer Target 1: 100.0% accurate, 1.4× 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 2024313 
(FPCore (x y)
  :name "Linear.Matrix:fromQuaternion from linear-1.19.1.3, B"
  :precision binary64

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

  (* 2.0 (+ (* x x) (* x y))))