Diagrams.TwoD.Arc:bezierFromSweepQ1 from diagrams-lib-1.3.0.3

Percentage Accurate: 93.9% → 99.8%

Time: 6.4s

Alternatives: 6

Speedup: 1.1×

• 25.2% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

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

FPCore

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

C

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

Fortran

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

Java

public static double code(double x, double y) {
	return ((1.0 - x) * (3.0 - x)) / (y * 3.0);
}

Python

def code(x, y):
	return ((1.0 - x) * (3.0 - x)) / (y * 3.0)

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 93.9% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

public static double code(double x, double y) {
	return ((1.0 - x) * (3.0 - x)) / (y * 3.0);
}

Python

def code(x, y):
	return ((1.0 - x) * (3.0 - x)) / (y * 3.0)

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 99.8% accurate, 1.1× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 95.2%

   \[\frac{\left(1 - x\right) \cdot \left(3 - x\right)}{y \cdot 3} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. lift-/.f64 N/A

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

   2. lift-*.f64 N/A

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

   3. associate-/r* N/A

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

   4. lower-/.f64 N/A

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

   5. lift-*.f64 N/A

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

   6. associate-/l* N/A

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

   7. *-commutative N/A

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

   8. lower-*.f64 N/A

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

   9. lower-/.f64 99.6

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

4. Applied rewrites 99.6%

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

   1. lift-/.f64 N/A

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

   2. frac-2neg N/A

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

   3. div-inv N/A

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

   4. lower-*.f64 N/A

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

   5. lift-*.f64 N/A

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

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

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

   7. lift-/.f64 N/A

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

   8. distribute-frac-neg2 N/A

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

   9. lower-*.f64 N/A

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

   10. frac-2neg N/A

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

   11. remove-double-neg N/A

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

   12. lower-/.f64 N/A

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

   13. neg-sub0 N/A

       \[\leadsto \left(\frac{\color{blue}{0 - \left(3 - x\right)}}{y} \cdot \left(1 - x\right)\right) \cdot \frac{1}{\mathsf{neg}\left(3\right)} \]

   14. lift--.f64 N/A

       \[\leadsto \left(\frac{0 - \color{blue}{\left(3 - x\right)}}{y} \cdot \left(1 - x\right)\right) \cdot \frac{1}{\mathsf{neg}\left(3\right)} \]

   15. sub-neg N/A

       \[\leadsto \left(\frac{0 - \color{blue}{\left(3 + \left(\mathsf{neg}\left(x\right)\right)\right)}}{y} \cdot \left(1 - x\right)\right) \cdot \frac{1}{\mathsf{neg}\left(3\right)} \]

   16. +-commutative N/A

       \[\leadsto \left(\frac{0 - \color{blue}{\left(\left(\mathsf{neg}\left(x\right)\right) + 3\right)}}{y} \cdot \left(1 - x\right)\right) \cdot \frac{1}{\mathsf{neg}\left(3\right)} \]

   17. associate--r+ N/A

       \[\leadsto \left(\frac{\color{blue}{\left(0 - \left(\mathsf{neg}\left(x\right)\right)\right) - 3}}{y} \cdot \left(1 - x\right)\right) \cdot \frac{1}{\mathsf{neg}\left(3\right)} \]

   18. neg-sub0 N/A

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

   19. remove-double-neg N/A

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

   20. lower--.f64 N/A

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

   21. metadata-eval N/A

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

   22. metadata-eval 99.5

       \[\leadsto \left(\frac{x - 3}{y} \cdot \left(1 - x\right)\right) \cdot \color{blue}{-0.3333333333333333} \]

6. Applied rewrites 99.5%

   \[\leadsto \color{blue}{\left(\frac{x - 3}{y} \cdot \left(1 - x\right)\right) \cdot -0.3333333333333333} \]

7. Taylor expanded in y around 0

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

   1. *-commutative N/A

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

   2. associate-/l* N/A

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

   3. associate-*r* N/A

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

   4. *-commutative N/A

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

   5. lower-*.f64 N/A

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

   6. lower-/.f64 N/A

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

   7. lower--.f64 N/A

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

   8. sub-neg N/A

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

   9. metadata-eval N/A

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

   10. distribute-lft-in N/A

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

   11. metadata-eval N/A

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

   12. lower-fma.f64 99.8

       \[\leadsto \frac{1 - x}{y} \cdot \color{blue}{\mathsf{fma}\left(-0.3333333333333333, x, 1\right)} \]

9. Applied rewrites 99.8%

   \[\leadsto \color{blue}{\frac{1 - x}{y} \cdot \mathsf{fma}\left(-0.3333333333333333, x, 1\right)} \]

10. Final simplification 99.8%

    \[\leadsto \mathsf{fma}\left(-0.3333333333333333, x, 1\right) \cdot \frac{1 - x}{y} \]
11. Add Preprocessing

Alternative 2: 98.4% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\left(3 - x\right) \cdot \left(1 - x\right) \leq 5:\\ \;\;\;\;\mathsf{fma}\left(-4, x, 3\right) \cdot \frac{0.3333333333333333}{y}\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(0.3333333333333333, x, -1.3333333333333333\right) \cdot \frac{x}{y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= (* (- 3.0 x) (- 1.0 x)) 5.0)
   (* (fma -4.0 x 3.0) (/ 0.3333333333333333 y))
   (* (fma 0.3333333333333333 x -1.3333333333333333) (/ x y))))

C

double code(double x, double y) {
	double tmp;
	if (((3.0 - x) * (1.0 - x)) <= 5.0) {
		tmp = fma(-4.0, x, 3.0) * (0.3333333333333333 / y);
	} else {
		tmp = fma(0.3333333333333333, x, -1.3333333333333333) * (x / y);
	}
	return tmp;
}

Julia

function code(x, y)
	tmp = 0.0
	if (Float64(Float64(3.0 - x) * Float64(1.0 - x)) <= 5.0)
		tmp = Float64(fma(-4.0, x, 3.0) * Float64(0.3333333333333333 / y));
	else
		tmp = Float64(fma(0.3333333333333333, x, -1.3333333333333333) * Float64(x / y));
	end
	return tmp
end

Wolfram

code[x_, y_] := If[LessEqual[N[(N[(3.0 - x), $MachinePrecision] * N[(1.0 - x), $MachinePrecision]), $MachinePrecision], 5.0], N[(N[(-4.0 * x + 3.0), $MachinePrecision] * N[(0.3333333333333333 / y), $MachinePrecision]), $MachinePrecision], N[(N[(0.3333333333333333 * x + -1.3333333333333333), $MachinePrecision] * N[(x / y), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (-.f64 #s(literal 1 binary64) x) (-.f64 #s(literal 3 binary64) x)) < 5

   1. Initial program 99.0%

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

   3. Taylor expanded in x around 0

      \[\leadsto \frac{\color{blue}{3 + -4 \cdot x}}{y \cdot 3} \]
   4. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \frac{\color{blue}{-4 \cdot x + 3}}{y \cdot 3} \]

      2. lower-fma.f64 97.0

         \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(-4, x, 3\right)}}{y \cdot 3} \]

   5. Applied rewrites 97.0%

      \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(-4, x, 3\right)}}{y \cdot 3} \]
   6. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \color{blue}{\frac{\mathsf{fma}\left(-4, x, 3\right)}{y \cdot 3}} \]

      2. clear-num N/A

         \[\leadsto \color{blue}{\frac{1}{\frac{y \cdot 3}{\mathsf{fma}\left(-4, x, 3\right)}}} \]

      3. associate-/r/ N/A

         \[\leadsto \color{blue}{\frac{1}{y \cdot 3} \cdot \mathsf{fma}\left(-4, x, 3\right)} \]

      4. lower-*.f64 N/A

         \[\leadsto \color{blue}{\frac{1}{y \cdot 3} \cdot \mathsf{fma}\left(-4, x, 3\right)} \]

      5. lift-*.f64 N/A

         \[\leadsto \frac{1}{\color{blue}{y \cdot 3}} \cdot \mathsf{fma}\left(-4, x, 3\right) \]

      6. *-commutative N/A

         \[\leadsto \frac{1}{\color{blue}{3 \cdot y}} \cdot \mathsf{fma}\left(-4, x, 3\right) \]

      7. associate-/r* N/A

         \[\leadsto \color{blue}{\frac{\frac{1}{3}}{y}} \cdot \mathsf{fma}\left(-4, x, 3\right) \]

      8. metadata-eval N/A

         \[\leadsto \frac{\color{blue}{\frac{1}{3}}}{y} \cdot \mathsf{fma}\left(-4, x, 3\right) \]

      9. lower-/.f64 97.3

         \[\leadsto \color{blue}{\frac{0.3333333333333333}{y}} \cdot \mathsf{fma}\left(-4, x, 3\right) \]

   7. Applied rewrites 97.3%

      \[\leadsto \color{blue}{\frac{0.3333333333333333}{y} \cdot \mathsf{fma}\left(-4, x, 3\right)} \]

   if 5 < (*.f64 (-.f64 #s(literal 1 binary64) x) (-.f64 #s(literal 3 binary64) x))

   1. Initial program 90.9%

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

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{{x}^{2} \cdot \left(\frac{1}{3} \cdot \frac{1}{y} - \frac{4}{3} \cdot \frac{1}{x \cdot y}\right)} \]
   4. Step-by-step derivation

      1. sub-neg N/A

         \[\leadsto {x}^{2} \cdot \color{blue}{\left(\frac{1}{3} \cdot \frac{1}{y} + \left(\mathsf{neg}\left(\frac{4}{3} \cdot \frac{1}{x \cdot y}\right)\right)\right)} \]

      2. associate-*r/ N/A

         \[\leadsto {x}^{2} \cdot \left(\frac{1}{3} \cdot \frac{1}{y} + \left(\mathsf{neg}\left(\color{blue}{\frac{\frac{4}{3} \cdot 1}{x \cdot y}}\right)\right)\right) \]

      3. metadata-eval N/A

         \[\leadsto {x}^{2} \cdot \left(\frac{1}{3} \cdot \frac{1}{y} + \left(\mathsf{neg}\left(\frac{\color{blue}{\frac{4}{3}}}{x \cdot y}\right)\right)\right) \]

      4. distribute-lft-in N/A

         \[\leadsto \color{blue}{{x}^{2} \cdot \left(\frac{1}{3} \cdot \frac{1}{y}\right) + {x}^{2} \cdot \left(\mathsf{neg}\left(\frac{\frac{4}{3}}{x \cdot y}\right)\right)} \]

      5. associate-*r/ N/A

         \[\leadsto {x}^{2} \cdot \color{blue}{\frac{\frac{1}{3} \cdot 1}{y}} + {x}^{2} \cdot \left(\mathsf{neg}\left(\frac{\frac{4}{3}}{x \cdot y}\right)\right) \]

      6. metadata-eval N/A

         \[\leadsto {x}^{2} \cdot \frac{\color{blue}{\frac{1}{3}}}{y} + {x}^{2} \cdot \left(\mathsf{neg}\left(\frac{\frac{4}{3}}{x \cdot y}\right)\right) \]

      7. associate-*r/ N/A

         \[\leadsto \color{blue}{\frac{{x}^{2} \cdot \frac{1}{3}}{y}} + {x}^{2} \cdot \left(\mathsf{neg}\left(\frac{\frac{4}{3}}{x \cdot y}\right)\right) \]

      8. unpow2 N/A

         \[\leadsto \frac{\color{blue}{\left(x \cdot x\right)} \cdot \frac{1}{3}}{y} + {x}^{2} \cdot \left(\mathsf{neg}\left(\frac{\frac{4}{3}}{x \cdot y}\right)\right) \]

      9. associate-*l* N/A

         \[\leadsto \frac{\color{blue}{x \cdot \left(x \cdot \frac{1}{3}\right)}}{y} + {x}^{2} \cdot \left(\mathsf{neg}\left(\frac{\frac{4}{3}}{x \cdot y}\right)\right) \]

      10. *-commutative N/A

          \[\leadsto \frac{x \cdot \color{blue}{\left(\frac{1}{3} \cdot x\right)}}{y} + {x}^{2} \cdot \left(\mathsf{neg}\left(\frac{\frac{4}{3}}{x \cdot y}\right)\right) \]

      11. associate-*l/ N/A

          \[\leadsto \color{blue}{\frac{x}{y} \cdot \left(\frac{1}{3} \cdot x\right)} + {x}^{2} \cdot \left(\mathsf{neg}\left(\frac{\frac{4}{3}}{x \cdot y}\right)\right) \]

      12. distribute-neg-frac N/A

          \[\leadsto \frac{x}{y} \cdot \left(\frac{1}{3} \cdot x\right) + {x}^{2} \cdot \color{blue}{\frac{\mathsf{neg}\left(\frac{4}{3}\right)}{x \cdot y}} \]

      13. metadata-eval N/A

          \[\leadsto \frac{x}{y} \cdot \left(\frac{1}{3} \cdot x\right) + {x}^{2} \cdot \frac{\color{blue}{\frac{-4}{3}}}{x \cdot y} \]

      14. associate-*r/ N/A

          \[\leadsto \frac{x}{y} \cdot \left(\frac{1}{3} \cdot x\right) + \color{blue}{\frac{{x}^{2} \cdot \frac{-4}{3}}{x \cdot y}} \]

      15. times-frac N/A

          \[\leadsto \frac{x}{y} \cdot \left(\frac{1}{3} \cdot x\right) + \color{blue}{\frac{{x}^{2}}{x} \cdot \frac{\frac{-4}{3}}{y}} \]

   5. Applied rewrites 97.7%

      \[\leadsto \color{blue}{\frac{x}{y} \cdot \mathsf{fma}\left(0.3333333333333333, x, -1.3333333333333333\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 97.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\left(3 - x\right) \cdot \left(1 - x\right) \leq 5:\\ \;\;\;\;\mathsf{fma}\left(-4, x, 3\right) \cdot \frac{0.3333333333333333}{y}\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(0.3333333333333333, x, -1.3333333333333333\right) \cdot \frac{x}{y}\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 98.1% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\left(3 - x\right) \cdot \left(1 - x\right) \leq 5:\\ \;\;\;\;\frac{1}{y}\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(0.3333333333333333, x, -1.3333333333333333\right) \cdot \frac{x}{y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= (* (- 3.0 x) (- 1.0 x)) 5.0)
   (/ 1.0 y)
   (* (fma 0.3333333333333333 x -1.3333333333333333) (/ x y))))

C

double code(double x, double y) {
	double tmp;
	if (((3.0 - x) * (1.0 - x)) <= 5.0) {
		tmp = 1.0 / y;
	} else {
		tmp = fma(0.3333333333333333, x, -1.3333333333333333) * (x / y);
	}
	return tmp;
}

Julia

function code(x, y)
	tmp = 0.0
	if (Float64(Float64(3.0 - x) * Float64(1.0 - x)) <= 5.0)
		tmp = Float64(1.0 / y);
	else
		tmp = Float64(fma(0.3333333333333333, x, -1.3333333333333333) * Float64(x / y));
	end
	return tmp
end

Wolfram

code[x_, y_] := If[LessEqual[N[(N[(3.0 - x), $MachinePrecision] * N[(1.0 - x), $MachinePrecision]), $MachinePrecision], 5.0], N[(1.0 / y), $MachinePrecision], N[(N[(0.3333333333333333 * x + -1.3333333333333333), $MachinePrecision] * N[(x / y), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (-.f64 #s(literal 1 binary64) x) (-.f64 #s(literal 3 binary64) x)) < 5

   1. Initial program 99.0%

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

   3. Taylor expanded in x around 0

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

      1. lower-/.f64 96.6

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

   5. Applied rewrites 96.6%

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

   if 5 < (*.f64 (-.f64 #s(literal 1 binary64) x) (-.f64 #s(literal 3 binary64) x))

   1. Initial program 90.9%

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

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{{x}^{2} \cdot \left(\frac{1}{3} \cdot \frac{1}{y} - \frac{4}{3} \cdot \frac{1}{x \cdot y}\right)} \]
   4. Step-by-step derivation

      1. sub-neg N/A

         \[\leadsto {x}^{2} \cdot \color{blue}{\left(\frac{1}{3} \cdot \frac{1}{y} + \left(\mathsf{neg}\left(\frac{4}{3} \cdot \frac{1}{x \cdot y}\right)\right)\right)} \]

      2. associate-*r/ N/A

         \[\leadsto {x}^{2} \cdot \left(\frac{1}{3} \cdot \frac{1}{y} + \left(\mathsf{neg}\left(\color{blue}{\frac{\frac{4}{3} \cdot 1}{x \cdot y}}\right)\right)\right) \]

      3. metadata-eval N/A

         \[\leadsto {x}^{2} \cdot \left(\frac{1}{3} \cdot \frac{1}{y} + \left(\mathsf{neg}\left(\frac{\color{blue}{\frac{4}{3}}}{x \cdot y}\right)\right)\right) \]

      4. distribute-lft-in N/A

         \[\leadsto \color{blue}{{x}^{2} \cdot \left(\frac{1}{3} \cdot \frac{1}{y}\right) + {x}^{2} \cdot \left(\mathsf{neg}\left(\frac{\frac{4}{3}}{x \cdot y}\right)\right)} \]

      5. associate-*r/ N/A

         \[\leadsto {x}^{2} \cdot \color{blue}{\frac{\frac{1}{3} \cdot 1}{y}} + {x}^{2} \cdot \left(\mathsf{neg}\left(\frac{\frac{4}{3}}{x \cdot y}\right)\right) \]

      6. metadata-eval N/A

         \[\leadsto {x}^{2} \cdot \frac{\color{blue}{\frac{1}{3}}}{y} + {x}^{2} \cdot \left(\mathsf{neg}\left(\frac{\frac{4}{3}}{x \cdot y}\right)\right) \]

      7. associate-*r/ N/A

         \[\leadsto \color{blue}{\frac{{x}^{2} \cdot \frac{1}{3}}{y}} + {x}^{2} \cdot \left(\mathsf{neg}\left(\frac{\frac{4}{3}}{x \cdot y}\right)\right) \]

      8. unpow2 N/A

         \[\leadsto \frac{\color{blue}{\left(x \cdot x\right)} \cdot \frac{1}{3}}{y} + {x}^{2} \cdot \left(\mathsf{neg}\left(\frac{\frac{4}{3}}{x \cdot y}\right)\right) \]

      9. associate-*l* N/A

         \[\leadsto \frac{\color{blue}{x \cdot \left(x \cdot \frac{1}{3}\right)}}{y} + {x}^{2} \cdot \left(\mathsf{neg}\left(\frac{\frac{4}{3}}{x \cdot y}\right)\right) \]

      10. *-commutative N/A

          \[\leadsto \frac{x \cdot \color{blue}{\left(\frac{1}{3} \cdot x\right)}}{y} + {x}^{2} \cdot \left(\mathsf{neg}\left(\frac{\frac{4}{3}}{x \cdot y}\right)\right) \]

      11. associate-*l/ N/A

          \[\leadsto \color{blue}{\frac{x}{y} \cdot \left(\frac{1}{3} \cdot x\right)} + {x}^{2} \cdot \left(\mathsf{neg}\left(\frac{\frac{4}{3}}{x \cdot y}\right)\right) \]

      12. distribute-neg-frac N/A

          \[\leadsto \frac{x}{y} \cdot \left(\frac{1}{3} \cdot x\right) + {x}^{2} \cdot \color{blue}{\frac{\mathsf{neg}\left(\frac{4}{3}\right)}{x \cdot y}} \]

      13. metadata-eval N/A

          \[\leadsto \frac{x}{y} \cdot \left(\frac{1}{3} \cdot x\right) + {x}^{2} \cdot \frac{\color{blue}{\frac{-4}{3}}}{x \cdot y} \]

      14. associate-*r/ N/A

          \[\leadsto \frac{x}{y} \cdot \left(\frac{1}{3} \cdot x\right) + \color{blue}{\frac{{x}^{2} \cdot \frac{-4}{3}}{x \cdot y}} \]

      15. times-frac N/A

          \[\leadsto \frac{x}{y} \cdot \left(\frac{1}{3} \cdot x\right) + \color{blue}{\frac{{x}^{2}}{x} \cdot \frac{\frac{-4}{3}}{y}} \]

   5. Applied rewrites 97.7%

      \[\leadsto \color{blue}{\frac{x}{y} \cdot \mathsf{fma}\left(0.3333333333333333, x, -1.3333333333333333\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 97.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\left(3 - x\right) \cdot \left(1 - x\right) \leq 5:\\ \;\;\;\;\frac{1}{y}\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(0.3333333333333333, x, -1.3333333333333333\right) \cdot \frac{x}{y}\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 97.6% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\left(3 - x\right) \cdot \left(1 - x\right) \leq 5:\\ \;\;\;\;\frac{1}{y}\\ \mathbf{else}:\\ \;\;\;\;\left(\frac{x}{y} \cdot 0.3333333333333333\right) \cdot x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= (* (- 3.0 x) (- 1.0 x)) 5.0)
   (/ 1.0 y)
   (* (* (/ x y) 0.3333333333333333) x)))

C

double code(double x, double y) {
	double tmp;
	if (((3.0 - x) * (1.0 - x)) <= 5.0) {
		tmp = 1.0 / y;
	} else {
		tmp = ((x / y) * 0.3333333333333333) * x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (((3.0d0 - x) * (1.0d0 - x)) <= 5.0d0) then
        tmp = 1.0d0 / y
    else
        tmp = ((x / y) * 0.3333333333333333d0) * x
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (((3.0 - x) * (1.0 - x)) <= 5.0) {
		tmp = 1.0 / y;
	} else {
		tmp = ((x / y) * 0.3333333333333333) * x;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if ((3.0 - x) * (1.0 - x)) <= 5.0:
		tmp = 1.0 / y
	else:
		tmp = ((x / y) * 0.3333333333333333) * x
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (Float64(Float64(3.0 - x) * Float64(1.0 - x)) <= 5.0)
		tmp = Float64(1.0 / y);
	else
		tmp = Float64(Float64(Float64(x / y) * 0.3333333333333333) * x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (((3.0 - x) * (1.0 - x)) <= 5.0)
		tmp = 1.0 / y;
	else
		tmp = ((x / y) * 0.3333333333333333) * x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[N[(N[(3.0 - x), $MachinePrecision] * N[(1.0 - x), $MachinePrecision]), $MachinePrecision], 5.0], N[(1.0 / y), $MachinePrecision], N[(N[(N[(x / y), $MachinePrecision] * 0.3333333333333333), $MachinePrecision] * x), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (-.f64 #s(literal 1 binary64) x) (-.f64 #s(literal 3 binary64) x)) < 5

   1. Initial program 99.0%

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

   3. Taylor expanded in x around 0

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

      1. lower-/.f64 96.6

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

   5. Applied rewrites 96.6%

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

   if 5 < (*.f64 (-.f64 #s(literal 1 binary64) x) (-.f64 #s(literal 3 binary64) x))

   1. Initial program 90.9%

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

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{\frac{1}{3} \cdot \frac{{x}^{2}}{y}} \]
   4. Step-by-step derivation

      1. unpow2 N/A

         \[\leadsto \frac{1}{3} \cdot \frac{\color{blue}{x \cdot x}}{y} \]

      2. associate-/l* N/A

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

      3. *-commutative N/A

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

      4. associate-*l* N/A

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

      5. lower-*.f64 N/A

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

      6. lower-*.f64 N/A

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

      7. lower-/.f64 95.9

         \[\leadsto \left(0.3333333333333333 \cdot \color{blue}{\frac{x}{y}}\right) \cdot x \]

   5. Applied rewrites 95.9%

      \[\leadsto \color{blue}{\left(0.3333333333333333 \cdot \frac{x}{y}\right) \cdot x} \]
3. Recombined 2 regimes into one program.

4. Final simplification 96.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\left(3 - x\right) \cdot \left(1 - x\right) \leq 5:\\ \;\;\;\;\frac{1}{y}\\ \mathbf{else}:\\ \;\;\;\;\left(\frac{x}{y} \cdot 0.3333333333333333\right) \cdot x\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 57.6% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -0.75:\\ \;\;\;\;\frac{-1.3333333333333333}{y} \cdot x\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= x -0.75) (* (/ -1.3333333333333333 y) x) (/ 1.0 y)))

C

double code(double x, double y) {
	double tmp;
	if (x <= -0.75) {
		tmp = (-1.3333333333333333 / y) * x;
	} else {
		tmp = 1.0 / y;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (x <= (-0.75d0)) then
        tmp = ((-1.3333333333333333d0) / y) * x
    else
        tmp = 1.0d0 / y
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (x <= -0.75) {
		tmp = (-1.3333333333333333 / y) * x;
	} else {
		tmp = 1.0 / y;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if x <= -0.75:
		tmp = (-1.3333333333333333 / y) * x
	else:
		tmp = 1.0 / y
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (x <= -0.75)
		tmp = Float64(Float64(-1.3333333333333333 / y) * x);
	else
		tmp = Float64(1.0 / y);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (x <= -0.75)
		tmp = (-1.3333333333333333 / y) * x;
	else
		tmp = 1.0 / y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[x, -0.75], N[(N[(-1.3333333333333333 / y), $MachinePrecision] * x), $MachinePrecision], N[(1.0 / y), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -0.75

   1. Initial program 90.7%

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

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{{x}^{2} \cdot \left(\frac{1}{3} \cdot \frac{1}{y} - \frac{4}{3} \cdot \frac{1}{x \cdot y}\right)} \]
   4. Step-by-step derivation

      1. sub-neg N/A

         \[\leadsto {x}^{2} \cdot \color{blue}{\left(\frac{1}{3} \cdot \frac{1}{y} + \left(\mathsf{neg}\left(\frac{4}{3} \cdot \frac{1}{x \cdot y}\right)\right)\right)} \]

      2. associate-*r/ N/A

         \[\leadsto {x}^{2} \cdot \left(\frac{1}{3} \cdot \frac{1}{y} + \left(\mathsf{neg}\left(\color{blue}{\frac{\frac{4}{3} \cdot 1}{x \cdot y}}\right)\right)\right) \]

      3. metadata-eval N/A

         \[\leadsto {x}^{2} \cdot \left(\frac{1}{3} \cdot \frac{1}{y} + \left(\mathsf{neg}\left(\frac{\color{blue}{\frac{4}{3}}}{x \cdot y}\right)\right)\right) \]

      4. distribute-lft-in N/A

         \[\leadsto \color{blue}{{x}^{2} \cdot \left(\frac{1}{3} \cdot \frac{1}{y}\right) + {x}^{2} \cdot \left(\mathsf{neg}\left(\frac{\frac{4}{3}}{x \cdot y}\right)\right)} \]

      5. associate-*r/ N/A

         \[\leadsto {x}^{2} \cdot \color{blue}{\frac{\frac{1}{3} \cdot 1}{y}} + {x}^{2} \cdot \left(\mathsf{neg}\left(\frac{\frac{4}{3}}{x \cdot y}\right)\right) \]

      6. metadata-eval N/A

         \[\leadsto {x}^{2} \cdot \frac{\color{blue}{\frac{1}{3}}}{y} + {x}^{2} \cdot \left(\mathsf{neg}\left(\frac{\frac{4}{3}}{x \cdot y}\right)\right) \]

      7. associate-*r/ N/A

         \[\leadsto \color{blue}{\frac{{x}^{2} \cdot \frac{1}{3}}{y}} + {x}^{2} \cdot \left(\mathsf{neg}\left(\frac{\frac{4}{3}}{x \cdot y}\right)\right) \]

      8. unpow2 N/A

         \[\leadsto \frac{\color{blue}{\left(x \cdot x\right)} \cdot \frac{1}{3}}{y} + {x}^{2} \cdot \left(\mathsf{neg}\left(\frac{\frac{4}{3}}{x \cdot y}\right)\right) \]

      9. associate-*l* N/A

         \[\leadsto \frac{\color{blue}{x \cdot \left(x \cdot \frac{1}{3}\right)}}{y} + {x}^{2} \cdot \left(\mathsf{neg}\left(\frac{\frac{4}{3}}{x \cdot y}\right)\right) \]

      10. *-commutative N/A

          \[\leadsto \frac{x \cdot \color{blue}{\left(\frac{1}{3} \cdot x\right)}}{y} + {x}^{2} \cdot \left(\mathsf{neg}\left(\frac{\frac{4}{3}}{x \cdot y}\right)\right) \]

      11. associate-*l/ N/A

          \[\leadsto \color{blue}{\frac{x}{y} \cdot \left(\frac{1}{3} \cdot x\right)} + {x}^{2} \cdot \left(\mathsf{neg}\left(\frac{\frac{4}{3}}{x \cdot y}\right)\right) \]

      12. distribute-neg-frac N/A

          \[\leadsto \frac{x}{y} \cdot \left(\frac{1}{3} \cdot x\right) + {x}^{2} \cdot \color{blue}{\frac{\mathsf{neg}\left(\frac{4}{3}\right)}{x \cdot y}} \]

      13. metadata-eval N/A

          \[\leadsto \frac{x}{y} \cdot \left(\frac{1}{3} \cdot x\right) + {x}^{2} \cdot \frac{\color{blue}{\frac{-4}{3}}}{x \cdot y} \]

      14. associate-*r/ N/A

          \[\leadsto \frac{x}{y} \cdot \left(\frac{1}{3} \cdot x\right) + \color{blue}{\frac{{x}^{2} \cdot \frac{-4}{3}}{x \cdot y}} \]

      15. times-frac N/A

          \[\leadsto \frac{x}{y} \cdot \left(\frac{1}{3} \cdot x\right) + \color{blue}{\frac{{x}^{2}}{x} \cdot \frac{\frac{-4}{3}}{y}} \]

   5. Applied rewrites 96.7%

      \[\leadsto \color{blue}{\frac{x}{y} \cdot \mathsf{fma}\left(0.3333333333333333, x, -1.3333333333333333\right)} \]

   6. Taylor expanded in x around 0

      \[\leadsto \frac{-4}{3} \cdot \color{blue}{\frac{x}{y}} \]
   7. Step-by-step derivation

   8. Applied rewrites 31.8%

      \[\leadsto \frac{-1.3333333333333333}{y} \cdot \color{blue}{x} \]

   if -0.75 < x

   1. Initial program 96.8%

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

   3. Taylor expanded in x around 0

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

      1. lower-/.f64 70.2

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

   5. Applied rewrites 70.2%

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

Alternative 6: 51.2% accurate, 2.3× speedup

Math

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

FPCore

(FPCore (x y) :precision binary64 (/ 1.0 y))

C

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

Fortran

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

Java

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

Python

def code(x, y):
	return 1.0 / y

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 95.2%

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

3. Taylor expanded in x around 0

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

   1. lower-/.f64 54.1

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

5. Applied rewrites 54.1%

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

Developer Target 1: 99.8% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \frac{1 - x}{y} \cdot \frac{3 - x}{3} \end{array} \]

FPCore

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

C

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

Fortran

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

Java

public static double code(double x, double y) {
	return ((1.0 - x) / y) * ((3.0 - x) / 3.0);
}

Python

def code(x, y):
	return ((1.0 - x) / y) * ((3.0 - x) / 3.0)

Julia

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

MATLAB

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

Wolfram

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

Reproduce

herbie shell --seed 2024325 
(FPCore (x y)
  :name "Diagrams.TwoD.Arc:bezierFromSweepQ1 from diagrams-lib-1.3.0.3"
  :precision binary64

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

  (/ (* (- 1.0 x) (- 3.0 x)) (* y 3.0)))