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

Percentage Accurate: 93.6% → 99.8%

Time: 8.5s

Alternatives: 15

Speedup: 1.0×

• 24.5% 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: 93.6% 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, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 92.1%

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

   1. *-commutative 92.1%

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

   2. associate-*l/ 99.7%

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

   3. *-commutative 99.7%

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

   4. associate-/l/ 99.8%

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

3. Simplified 99.8%

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

   1. associate-*r/ 92.6%

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

   2. associate-/l* 99.9%

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

   3. frac-2neg 99.9%

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

   4. metadata-eval 99.9%

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

   5. +-commutative 99.9%

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

   6. distribute-neg-in 99.9%

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

   7. metadata-eval 99.9%

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

   8. sub-neg 99.9%

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

   9. associate-/l* 92.6%

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

   10. associate-*l/ 99.8%

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

   11. *-commutative 99.8%

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

   12. clear-num 99.8%

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

   13. frac-times 99.9%

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

   14. *-un-lft-identity 99.9%

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

   15. metadata-eval 99.9%

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

   16. sub-neg 99.9%

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

   17. metadata-eval 99.9%

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

   18. distribute-neg-in 99.9%

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

   19. +-commutative 99.9%

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

   20. frac-2neg 99.9%

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

5. Applied egg-rr 99.9%

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

6. Final simplification 99.9%

   \[\leadsto \frac{1 - x}{\frac{-3}{x + -3} \cdot y} \]

Alternative 2: 98.0% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -3.8 \lor \neg \left(x \leq 3\right):\\ \;\;\;\;-0.3333333333333333 \cdot \frac{x}{\frac{y}{1 - x}}\\ \mathbf{else}:\\ \;\;\;\;\frac{x \cdot -1.3333333333333333 + 1}{y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (or (<= x -3.8) (not (<= x 3.0)))
   (* -0.3333333333333333 (/ x (/ y (- 1.0 x))))
   (/ (+ (* x -1.3333333333333333) 1.0) y)))

C

double code(double x, double y) {
	double tmp;
	if ((x <= -3.8) || !(x <= 3.0)) {
		tmp = -0.3333333333333333 * (x / (y / (1.0 - x)));
	} else {
		tmp = ((x * -1.3333333333333333) + 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 <= (-3.8d0)) .or. (.not. (x <= 3.0d0))) then
        tmp = (-0.3333333333333333d0) * (x / (y / (1.0d0 - x)))
    else
        tmp = ((x * (-1.3333333333333333d0)) + 1.0d0) / y
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if ((x <= -3.8) || !(x <= 3.0)) {
		tmp = -0.3333333333333333 * (x / (y / (1.0 - x)));
	} else {
		tmp = ((x * -1.3333333333333333) + 1.0) / y;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (x <= -3.8) or not (x <= 3.0):
		tmp = -0.3333333333333333 * (x / (y / (1.0 - x)))
	else:
		tmp = ((x * -1.3333333333333333) + 1.0) / y
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if ((x <= -3.8) || !(x <= 3.0))
		tmp = Float64(-0.3333333333333333 * Float64(x / Float64(y / Float64(1.0 - x))));
	else
		tmp = Float64(Float64(Float64(x * -1.3333333333333333) + 1.0) / y);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if ((x <= -3.8) || ~((x <= 3.0)))
		tmp = -0.3333333333333333 * (x / (y / (1.0 - x)));
	else
		tmp = ((x * -1.3333333333333333) + 1.0) / y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[Or[LessEqual[x, -3.8], N[Not[LessEqual[x, 3.0]], $MachinePrecision]], N[(-0.3333333333333333 * N[(x / N[(y / N[(1.0 - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(N[(x * -1.3333333333333333), $MachinePrecision] + 1.0), $MachinePrecision] / y), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -3.7999999999999998 or 3 < x

   1. Initial program 82.6%

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

      1. *-commutative 82.6%

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

      2. associate-*l/ 99.6%

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

      3. *-commutative 99.6%

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

      4. associate-/l/ 99.7%

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

   3. Simplified 99.7%

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

   4. Taylor expanded in x around inf 96.9%

      \[\leadsto \left(1 - x\right) \cdot \color{blue}{\left(-0.3333333333333333 \cdot \frac{x}{y}\right)} \]
   5. Step-by-step derivation

      1. *-commutative 96.9%

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

      2. metadata-eval 96.9%

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

      3. distribute-rgt-neg-in 96.9%

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

      4. associate-*l/ 96.9%

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

      5. associate-*r/ 96.9%

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

      6. distribute-rgt-neg-in 96.9%

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

      7. distribute-neg-frac 96.9%

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

      8. metadata-eval 96.9%

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

   6. Simplified 96.9%

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

   7. Taylor expanded in y around 0 79.8%

      \[\leadsto \color{blue}{-0.3333333333333333 \cdot \frac{x \cdot \left(1 - x\right)}{y}} \]
   8. Step-by-step derivation

      1. associate-/l* 96.9%

         \[\leadsto -0.3333333333333333 \cdot \color{blue}{\frac{x}{\frac{y}{1 - x}}} \]

   9. Simplified 96.9%

      \[\leadsto \color{blue}{-0.3333333333333333 \cdot \frac{x}{\frac{y}{1 - x}}} \]

   if -3.7999999999999998 < x < 3

   1. Initial program 99.7%

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

      1. *-commutative 99.7%

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

      2. associate-*l/ 99.7%

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

      3. *-commutative 99.7%

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

      4. associate-/l/ 100.0%

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

   3. Simplified 100.0%

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

   4. Taylor expanded in x around 0 98.1%

      \[\leadsto \color{blue}{-1.3333333333333333 \cdot \frac{x}{y} + \frac{1}{y}} \]

   5. Taylor expanded in y around 0 98.1%

      \[\leadsto \color{blue}{\frac{1 + -1.3333333333333333 \cdot x}{y}} \]
3. Recombined 2 regimes into one program.

4. Final simplification 97.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -3.8 \lor \neg \left(x \leq 3\right):\\ \;\;\;\;-0.3333333333333333 \cdot \frac{x}{\frac{y}{1 - x}}\\ \mathbf{else}:\\ \;\;\;\;\frac{x \cdot -1.3333333333333333 + 1}{y}\\ \end{array} \]

Alternative 3: 98.1% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -3.8 \lor \neg \left(x \leq 3\right):\\ \;\;\;\;\frac{1 - x}{y \cdot \frac{-3}{x}}\\ \mathbf{else}:\\ \;\;\;\;\frac{x \cdot -1.3333333333333333 + 1}{y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (or (<= x -3.8) (not (<= x 3.0)))
   (/ (- 1.0 x) (* y (/ -3.0 x)))
   (/ (+ (* x -1.3333333333333333) 1.0) y)))

C

double code(double x, double y) {
	double tmp;
	if ((x <= -3.8) || !(x <= 3.0)) {
		tmp = (1.0 - x) / (y * (-3.0 / x));
	} else {
		tmp = ((x * -1.3333333333333333) + 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 <= (-3.8d0)) .or. (.not. (x <= 3.0d0))) then
        tmp = (1.0d0 - x) / (y * ((-3.0d0) / x))
    else
        tmp = ((x * (-1.3333333333333333d0)) + 1.0d0) / y
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if ((x <= -3.8) || !(x <= 3.0)) {
		tmp = (1.0 - x) / (y * (-3.0 / x));
	} else {
		tmp = ((x * -1.3333333333333333) + 1.0) / y;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (x <= -3.8) or not (x <= 3.0):
		tmp = (1.0 - x) / (y * (-3.0 / x))
	else:
		tmp = ((x * -1.3333333333333333) + 1.0) / y
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if ((x <= -3.8) || !(x <= 3.0))
		tmp = Float64(Float64(1.0 - x) / Float64(y * Float64(-3.0 / x)));
	else
		tmp = Float64(Float64(Float64(x * -1.3333333333333333) + 1.0) / y);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if ((x <= -3.8) || ~((x <= 3.0)))
		tmp = (1.0 - x) / (y * (-3.0 / x));
	else
		tmp = ((x * -1.3333333333333333) + 1.0) / y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[Or[LessEqual[x, -3.8], N[Not[LessEqual[x, 3.0]], $MachinePrecision]], N[(N[(1.0 - x), $MachinePrecision] / N[(y * N[(-3.0 / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(N[(x * -1.3333333333333333), $MachinePrecision] + 1.0), $MachinePrecision] / y), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -3.7999999999999998 or 3 < x

   1. Initial program 82.6%

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

      1. *-commutative 82.6%

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

      2. associate-*l/ 99.6%

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

      3. *-commutative 99.6%

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

      4. associate-/l/ 99.7%

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

   3. Simplified 99.7%

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

      1. associate-*r/ 83.4%

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

      2. associate-/l* 99.7%

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

      3. frac-2neg 99.7%

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

      4. metadata-eval 99.7%

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

      5. +-commutative 99.7%

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

      6. distribute-neg-in 99.7%

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

      7. metadata-eval 99.7%

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

      8. sub-neg 99.7%

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

      9. associate-/l* 83.4%

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

      10. associate-*l/ 99.7%

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

      11. *-commutative 99.7%

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

      12. clear-num 99.6%

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

      13. frac-times 99.7%

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

      14. *-un-lft-identity 99.7%

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

      15. metadata-eval 99.7%

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

      16. sub-neg 99.7%

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

      17. metadata-eval 99.7%

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

      18. distribute-neg-in 99.7%

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

      19. +-commutative 99.7%

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

      20. frac-2neg 99.7%

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

   5. Applied egg-rr 99.7%

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

   6. Taylor expanded in x around inf 97.1%

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

   if -3.7999999999999998 < x < 3

   1. Initial program 99.7%

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

      1. *-commutative 99.7%

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

      2. associate-*l/ 99.7%

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

      3. *-commutative 99.7%

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

      4. associate-/l/ 100.0%

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

   3. Simplified 100.0%

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

   4. Taylor expanded in x around 0 98.1%

      \[\leadsto \color{blue}{-1.3333333333333333 \cdot \frac{x}{y} + \frac{1}{y}} \]

   5. Taylor expanded in y around 0 98.1%

      \[\leadsto \color{blue}{\frac{1 + -1.3333333333333333 \cdot x}{y}} \]
3. Recombined 2 regimes into one program.

4. Final simplification 97.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -3.8 \lor \neg \left(x \leq 3\right):\\ \;\;\;\;\frac{1 - x}{y \cdot \frac{-3}{x}}\\ \mathbf{else}:\\ \;\;\;\;\frac{x \cdot -1.3333333333333333 + 1}{y}\\ \end{array} \]

Alternative 4: 98.2% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -2.3 \lor \neg \left(x \leq 1.3\right):\\ \;\;\;\;\frac{x}{y} \cdot \frac{x - 3}{3}\\ \mathbf{else}:\\ \;\;\;\;\frac{x \cdot -1.3333333333333333 + 1}{y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (or (<= x -2.3) (not (<= x 1.3)))
   (* (/ x y) (/ (- x 3.0) 3.0))
   (/ (+ (* x -1.3333333333333333) 1.0) y)))

C

double code(double x, double y) {
	double tmp;
	if ((x <= -2.3) || !(x <= 1.3)) {
		tmp = (x / y) * ((x - 3.0) / 3.0);
	} else {
		tmp = ((x * -1.3333333333333333) + 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 <= (-2.3d0)) .or. (.not. (x <= 1.3d0))) then
        tmp = (x / y) * ((x - 3.0d0) / 3.0d0)
    else
        tmp = ((x * (-1.3333333333333333d0)) + 1.0d0) / y
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if ((x <= -2.3) || !(x <= 1.3)) {
		tmp = (x / y) * ((x - 3.0) / 3.0);
	} else {
		tmp = ((x * -1.3333333333333333) + 1.0) / y;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (x <= -2.3) or not (x <= 1.3):
		tmp = (x / y) * ((x - 3.0) / 3.0)
	else:
		tmp = ((x * -1.3333333333333333) + 1.0) / y
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if ((x <= -2.3) || !(x <= 1.3))
		tmp = Float64(Float64(x / y) * Float64(Float64(x - 3.0) / 3.0));
	else
		tmp = Float64(Float64(Float64(x * -1.3333333333333333) + 1.0) / y);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if ((x <= -2.3) || ~((x <= 1.3)))
		tmp = (x / y) * ((x - 3.0) / 3.0);
	else
		tmp = ((x * -1.3333333333333333) + 1.0) / y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[Or[LessEqual[x, -2.3], N[Not[LessEqual[x, 1.3]], $MachinePrecision]], N[(N[(x / y), $MachinePrecision] * N[(N[(x - 3.0), $MachinePrecision] / 3.0), $MachinePrecision]), $MachinePrecision], N[(N[(N[(x * -1.3333333333333333), $MachinePrecision] + 1.0), $MachinePrecision] / y), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -2.2999999999999998 or 1.30000000000000004 < x

   1. Initial program 82.6%

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

      1. times-frac 99.7%

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

   3. Simplified 99.7%

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

   4. Taylor expanded in x around inf 97.2%

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

      1. neg-mul-1 14.1%

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

      2. distribute-neg-frac 14.1%

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

   6. Simplified 97.2%

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

   if -2.2999999999999998 < x < 1.30000000000000004

   1. Initial program 99.7%

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

      1. *-commutative 99.7%

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

      2. associate-*l/ 99.7%

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

      3. *-commutative 99.7%

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

      4. associate-/l/ 100.0%

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

   3. Simplified 100.0%

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

   4. Taylor expanded in x around 0 98.1%

      \[\leadsto \color{blue}{-1.3333333333333333 \cdot \frac{x}{y} + \frac{1}{y}} \]

   5. Taylor expanded in y around 0 98.1%

      \[\leadsto \color{blue}{\frac{1 + -1.3333333333333333 \cdot x}{y}} \]
3. Recombined 2 regimes into one program.

4. Final simplification 97.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -2.3 \lor \neg \left(x \leq 1.3\right):\\ \;\;\;\;\frac{x}{y} \cdot \frac{x - 3}{3}\\ \mathbf{else}:\\ \;\;\;\;\frac{x \cdot -1.3333333333333333 + 1}{y}\\ \end{array} \]

Alternative 5: 98.0% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -3.8:\\ \;\;\;\;-0.3333333333333333 \cdot \frac{x}{\frac{y}{1 - x}}\\ \mathbf{elif}\;x \leq 3:\\ \;\;\;\;\frac{x \cdot -1.3333333333333333 + 1}{y}\\ \mathbf{else}:\\ \;\;\;\;\left(1 - x\right) \cdot \left(x \cdot \frac{-0.3333333333333333}{y}\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= x -3.8)
   (* -0.3333333333333333 (/ x (/ y (- 1.0 x))))
   (if (<= x 3.0)
     (/ (+ (* x -1.3333333333333333) 1.0) y)
     (* (- 1.0 x) (* x (/ -0.3333333333333333 y))))))

C

double code(double x, double y) {
	double tmp;
	if (x <= -3.8) {
		tmp = -0.3333333333333333 * (x / (y / (1.0 - x)));
	} else if (x <= 3.0) {
		tmp = ((x * -1.3333333333333333) + 1.0) / y;
	} else {
		tmp = (1.0 - x) * (x * (-0.3333333333333333 / 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 <= (-3.8d0)) then
        tmp = (-0.3333333333333333d0) * (x / (y / (1.0d0 - x)))
    else if (x <= 3.0d0) then
        tmp = ((x * (-1.3333333333333333d0)) + 1.0d0) / y
    else
        tmp = (1.0d0 - x) * (x * ((-0.3333333333333333d0) / y))
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (x <= -3.8) {
		tmp = -0.3333333333333333 * (x / (y / (1.0 - x)));
	} else if (x <= 3.0) {
		tmp = ((x * -1.3333333333333333) + 1.0) / y;
	} else {
		tmp = (1.0 - x) * (x * (-0.3333333333333333 / y));
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if x <= -3.8:
		tmp = -0.3333333333333333 * (x / (y / (1.0 - x)))
	elif x <= 3.0:
		tmp = ((x * -1.3333333333333333) + 1.0) / y
	else:
		tmp = (1.0 - x) * (x * (-0.3333333333333333 / y))
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (x <= -3.8)
		tmp = Float64(-0.3333333333333333 * Float64(x / Float64(y / Float64(1.0 - x))));
	elseif (x <= 3.0)
		tmp = Float64(Float64(Float64(x * -1.3333333333333333) + 1.0) / y);
	else
		tmp = Float64(Float64(1.0 - x) * Float64(x * Float64(-0.3333333333333333 / y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (x <= -3.8)
		tmp = -0.3333333333333333 * (x / (y / (1.0 - x)));
	elseif (x <= 3.0)
		tmp = ((x * -1.3333333333333333) + 1.0) / y;
	else
		tmp = (1.0 - x) * (x * (-0.3333333333333333 / y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[x, -3.8], N[(-0.3333333333333333 * N[(x / N[(y / N[(1.0 - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 3.0], N[(N[(N[(x * -1.3333333333333333), $MachinePrecision] + 1.0), $MachinePrecision] / y), $MachinePrecision], N[(N[(1.0 - x), $MachinePrecision] * N[(x * N[(-0.3333333333333333 / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < -3.7999999999999998

   1. Initial program 79.1%

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

      1. *-commutative 79.1%

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

      2. associate-*l/ 99.8%

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

      3. *-commutative 99.8%

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

      4. associate-/l/ 99.8%

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

   3. Simplified 99.8%

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

   4. Taylor expanded in x around inf 97.9%

      \[\leadsto \left(1 - x\right) \cdot \color{blue}{\left(-0.3333333333333333 \cdot \frac{x}{y}\right)} \]
   5. Step-by-step derivation

      1. *-commutative 97.9%

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

      2. metadata-eval 97.9%

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

      3. distribute-rgt-neg-in 97.9%

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

      4. associate-*l/ 98.0%

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

      5. associate-*r/ 97.9%

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

      6. distribute-rgt-neg-in 97.9%

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

      7. distribute-neg-frac 97.9%

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

      8. metadata-eval 97.9%

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

   6. Simplified 97.9%

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

   7. Taylor expanded in y around 0 77.4%

      \[\leadsto \color{blue}{-0.3333333333333333 \cdot \frac{x \cdot \left(1 - x\right)}{y}} \]
   8. Step-by-step derivation

      1. associate-/l* 98.1%

         \[\leadsto -0.3333333333333333 \cdot \color{blue}{\frac{x}{\frac{y}{1 - x}}} \]

   9. Simplified 98.1%

      \[\leadsto \color{blue}{-0.3333333333333333 \cdot \frac{x}{\frac{y}{1 - x}}} \]

   if -3.7999999999999998 < x < 3

   1. Initial program 99.7%

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

      1. *-commutative 99.7%

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

      2. associate-*l/ 99.7%

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

      3. *-commutative 99.7%

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

      4. associate-/l/ 100.0%

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

   3. Simplified 100.0%

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

   4. Taylor expanded in x around 0 98.1%

      \[\leadsto \color{blue}{-1.3333333333333333 \cdot \frac{x}{y} + \frac{1}{y}} \]

   5. Taylor expanded in y around 0 98.1%

      \[\leadsto \color{blue}{\frac{1 + -1.3333333333333333 \cdot x}{y}} \]

   if 3 < x

   1. Initial program 85.8%

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

      1. *-commutative 85.8%

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

      2. associate-*l/ 99.5%

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

      3. *-commutative 99.5%

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

      4. associate-/l/ 99.6%

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

   3. Simplified 99.6%

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

   4. Taylor expanded in x around inf 96.0%

      \[\leadsto \left(1 - x\right) \cdot \color{blue}{\left(-0.3333333333333333 \cdot \frac{x}{y}\right)} \]
   5. Step-by-step derivation

      1. *-commutative 96.0%

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

      2. metadata-eval 96.0%

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

      3. distribute-rgt-neg-in 96.0%

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

      4. associate-*l/ 96.0%

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

      5. associate-*r/ 95.9%

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

      6. distribute-rgt-neg-in 95.9%

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

      7. distribute-neg-frac 95.9%

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

      8. metadata-eval 95.9%

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

   6. Simplified 95.9%

      \[\leadsto \left(1 - x\right) \cdot \color{blue}{\left(x \cdot \frac{-0.3333333333333333}{y}\right)} \]
3. Recombined 3 regimes into one program.

4. Final simplification 97.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -3.8:\\ \;\;\;\;-0.3333333333333333 \cdot \frac{x}{\frac{y}{1 - x}}\\ \mathbf{elif}\;x \leq 3:\\ \;\;\;\;\frac{x \cdot -1.3333333333333333 + 1}{y}\\ \mathbf{else}:\\ \;\;\;\;\left(1 - x\right) \cdot \left(x \cdot \frac{-0.3333333333333333}{y}\right)\\ \end{array} \]

Alternative 6: 98.0% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -3.8:\\ \;\;\;\;-0.3333333333333333 \cdot \frac{x}{\frac{y}{1 - x}}\\ \mathbf{elif}\;x \leq 3:\\ \;\;\;\;\frac{x \cdot -1.3333333333333333 + 1}{y}\\ \mathbf{else}:\\ \;\;\;\;\left(1 - x\right) \cdot \left(-0.3333333333333333 \cdot \frac{x}{y}\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= x -3.8)
   (* -0.3333333333333333 (/ x (/ y (- 1.0 x))))
   (if (<= x 3.0)
     (/ (+ (* x -1.3333333333333333) 1.0) y)
     (* (- 1.0 x) (* -0.3333333333333333 (/ x y))))))

C

double code(double x, double y) {
	double tmp;
	if (x <= -3.8) {
		tmp = -0.3333333333333333 * (x / (y / (1.0 - x)));
	} else if (x <= 3.0) {
		tmp = ((x * -1.3333333333333333) + 1.0) / y;
	} else {
		tmp = (1.0 - x) * (-0.3333333333333333 * (x / 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 <= (-3.8d0)) then
        tmp = (-0.3333333333333333d0) * (x / (y / (1.0d0 - x)))
    else if (x <= 3.0d0) then
        tmp = ((x * (-1.3333333333333333d0)) + 1.0d0) / y
    else
        tmp = (1.0d0 - x) * ((-0.3333333333333333d0) * (x / y))
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (x <= -3.8) {
		tmp = -0.3333333333333333 * (x / (y / (1.0 - x)));
	} else if (x <= 3.0) {
		tmp = ((x * -1.3333333333333333) + 1.0) / y;
	} else {
		tmp = (1.0 - x) * (-0.3333333333333333 * (x / y));
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if x <= -3.8:
		tmp = -0.3333333333333333 * (x / (y / (1.0 - x)))
	elif x <= 3.0:
		tmp = ((x * -1.3333333333333333) + 1.0) / y
	else:
		tmp = (1.0 - x) * (-0.3333333333333333 * (x / y))
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (x <= -3.8)
		tmp = Float64(-0.3333333333333333 * Float64(x / Float64(y / Float64(1.0 - x))));
	elseif (x <= 3.0)
		tmp = Float64(Float64(Float64(x * -1.3333333333333333) + 1.0) / y);
	else
		tmp = Float64(Float64(1.0 - x) * Float64(-0.3333333333333333 * Float64(x / y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (x <= -3.8)
		tmp = -0.3333333333333333 * (x / (y / (1.0 - x)));
	elseif (x <= 3.0)
		tmp = ((x * -1.3333333333333333) + 1.0) / y;
	else
		tmp = (1.0 - x) * (-0.3333333333333333 * (x / y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[x, -3.8], N[(-0.3333333333333333 * N[(x / N[(y / N[(1.0 - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 3.0], N[(N[(N[(x * -1.3333333333333333), $MachinePrecision] + 1.0), $MachinePrecision] / y), $MachinePrecision], N[(N[(1.0 - x), $MachinePrecision] * N[(-0.3333333333333333 * N[(x / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < -3.7999999999999998

   1. Initial program 79.1%

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

      1. *-commutative 79.1%

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

      2. associate-*l/ 99.8%

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

      3. *-commutative 99.8%

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

      4. associate-/l/ 99.8%

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

   3. Simplified 99.8%

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

   4. Taylor expanded in x around inf 97.9%

      \[\leadsto \left(1 - x\right) \cdot \color{blue}{\left(-0.3333333333333333 \cdot \frac{x}{y}\right)} \]
   5. Step-by-step derivation

      1. *-commutative 97.9%

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

      2. metadata-eval 97.9%

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

      3. distribute-rgt-neg-in 97.9%

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

      4. associate-*l/ 98.0%

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

      5. associate-*r/ 97.9%

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

      6. distribute-rgt-neg-in 97.9%

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

      7. distribute-neg-frac 97.9%

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

      8. metadata-eval 97.9%

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

   6. Simplified 97.9%

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

   7. Taylor expanded in y around 0 77.4%

      \[\leadsto \color{blue}{-0.3333333333333333 \cdot \frac{x \cdot \left(1 - x\right)}{y}} \]
   8. Step-by-step derivation

      1. associate-/l* 98.1%

         \[\leadsto -0.3333333333333333 \cdot \color{blue}{\frac{x}{\frac{y}{1 - x}}} \]

   9. Simplified 98.1%

      \[\leadsto \color{blue}{-0.3333333333333333 \cdot \frac{x}{\frac{y}{1 - x}}} \]

   if -3.7999999999999998 < x < 3

   1. Initial program 99.7%

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

      1. *-commutative 99.7%

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

      2. associate-*l/ 99.7%

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

      3. *-commutative 99.7%

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

      4. associate-/l/ 100.0%

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

   3. Simplified 100.0%

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

   4. Taylor expanded in x around 0 98.1%

      \[\leadsto \color{blue}{-1.3333333333333333 \cdot \frac{x}{y} + \frac{1}{y}} \]

   5. Taylor expanded in y around 0 98.1%

      \[\leadsto \color{blue}{\frac{1 + -1.3333333333333333 \cdot x}{y}} \]

   if 3 < x

   1. Initial program 85.8%

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

      1. *-commutative 85.8%

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

      2. associate-*l/ 99.5%

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

      3. *-commutative 99.5%

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

      4. *-lft-identity 99.5%

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

      5. metadata-eval 99.5%

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

      6. distribute-lft-neg-in 99.5%

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

      7. metadata-eval 99.5%

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

      8. times-frac 99.5%

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

      9. *-commutative 99.5%

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

      10. neg-mul-1 99.5%

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

      11. distribute-rgt-neg-in 99.5%

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

      12. times-frac 99.6%

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

      13. metadata-eval 99.6%

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

      14. metadata-eval 99.6%

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

      15. metadata-eval 99.6%

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

      16. distribute-lft-neg-in 99.6%

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

      17. distribute-frac-neg 99.6%

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

      18. sub-neg 99.6%

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

      19. +-commutative 99.6%

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

      20. distribute-neg-in 99.6%

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

      21. remove-double-neg 99.6%

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

      22. metadata-eval 99.6%

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

      23. metadata-eval 99.6%

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

   3. Simplified 99.6%

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

   4. Taylor expanded in x around inf 96.0%

      \[\leadsto \left(1 - x\right) \cdot \left(\color{blue}{\frac{x}{y}} \cdot -0.3333333333333333\right) \]
3. Recombined 3 regimes into one program.

4. Final simplification 97.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -3.8:\\ \;\;\;\;-0.3333333333333333 \cdot \frac{x}{\frac{y}{1 - x}}\\ \mathbf{elif}\;x \leq 3:\\ \;\;\;\;\frac{x \cdot -1.3333333333333333 + 1}{y}\\ \mathbf{else}:\\ \;\;\;\;\left(1 - x\right) \cdot \left(-0.3333333333333333 \cdot \frac{x}{y}\right)\\ \end{array} \]

Alternative 7: 98.1% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -3.8:\\ \;\;\;\;\frac{1 - x}{y \cdot \frac{-3}{x}}\\ \mathbf{elif}\;x \leq 3:\\ \;\;\;\;\frac{x \cdot -1.3333333333333333 + 1}{y}\\ \mathbf{else}:\\ \;\;\;\;\frac{1 - x}{\frac{-3}{\frac{x}{y}}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= x -3.8)
   (/ (- 1.0 x) (* y (/ -3.0 x)))
   (if (<= x 3.0)
     (/ (+ (* x -1.3333333333333333) 1.0) y)
     (/ (- 1.0 x) (/ -3.0 (/ x y))))))

C

double code(double x, double y) {
	double tmp;
	if (x <= -3.8) {
		tmp = (1.0 - x) / (y * (-3.0 / x));
	} else if (x <= 3.0) {
		tmp = ((x * -1.3333333333333333) + 1.0) / y;
	} else {
		tmp = (1.0 - x) / (-3.0 / (x / 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 <= (-3.8d0)) then
        tmp = (1.0d0 - x) / (y * ((-3.0d0) / x))
    else if (x <= 3.0d0) then
        tmp = ((x * (-1.3333333333333333d0)) + 1.0d0) / y
    else
        tmp = (1.0d0 - x) / ((-3.0d0) / (x / y))
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (x <= -3.8) {
		tmp = (1.0 - x) / (y * (-3.0 / x));
	} else if (x <= 3.0) {
		tmp = ((x * -1.3333333333333333) + 1.0) / y;
	} else {
		tmp = (1.0 - x) / (-3.0 / (x / y));
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if x <= -3.8:
		tmp = (1.0 - x) / (y * (-3.0 / x))
	elif x <= 3.0:
		tmp = ((x * -1.3333333333333333) + 1.0) / y
	else:
		tmp = (1.0 - x) / (-3.0 / (x / y))
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (x <= -3.8)
		tmp = Float64(Float64(1.0 - x) / Float64(y * Float64(-3.0 / x)));
	elseif (x <= 3.0)
		tmp = Float64(Float64(Float64(x * -1.3333333333333333) + 1.0) / y);
	else
		tmp = Float64(Float64(1.0 - x) / Float64(-3.0 / Float64(x / y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (x <= -3.8)
		tmp = (1.0 - x) / (y * (-3.0 / x));
	elseif (x <= 3.0)
		tmp = ((x * -1.3333333333333333) + 1.0) / y;
	else
		tmp = (1.0 - x) / (-3.0 / (x / y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[x, -3.8], N[(N[(1.0 - x), $MachinePrecision] / N[(y * N[(-3.0 / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 3.0], N[(N[(N[(x * -1.3333333333333333), $MachinePrecision] + 1.0), $MachinePrecision] / y), $MachinePrecision], N[(N[(1.0 - x), $MachinePrecision] / N[(-3.0 / N[(x / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < -3.7999999999999998

   1. Initial program 79.1%

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

      1. *-commutative 79.1%

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

      2. associate-*l/ 99.8%

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

      3. *-commutative 99.8%

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

      4. associate-/l/ 99.8%

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

   3. Simplified 99.8%

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

      1. associate-*r/ 80.8%

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

      2. associate-/l* 99.7%

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

      3. frac-2neg 99.7%

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

      4. metadata-eval 99.7%

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

      5. +-commutative 99.7%

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

      6. distribute-neg-in 99.7%

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

      7. metadata-eval 99.7%

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

      8. sub-neg 99.7%

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

      9. associate-/l* 80.8%

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

      10. associate-*l/ 99.7%

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

      11. *-commutative 99.7%

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

      12. clear-num 99.7%

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

      13. frac-times 99.8%

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

      14. *-un-lft-identity 99.8%

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

      15. metadata-eval 99.8%

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

      16. sub-neg 99.8%

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

      17. metadata-eval 99.8%

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

      18. distribute-neg-in 99.8%

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

      19. +-commutative 99.8%

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

      20. frac-2neg 99.8%

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

   5. Applied egg-rr 99.8%

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

   6. Taylor expanded in x around inf 98.1%

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

   if -3.7999999999999998 < x < 3

   1. Initial program 99.7%

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

      1. *-commutative 99.7%

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

      2. associate-*l/ 99.7%

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

      3. *-commutative 99.7%

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

      4. associate-/l/ 100.0%

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

   3. Simplified 100.0%

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

   4. Taylor expanded in x around 0 98.1%

      \[\leadsto \color{blue}{-1.3333333333333333 \cdot \frac{x}{y} + \frac{1}{y}} \]

   5. Taylor expanded in y around 0 98.1%

      \[\leadsto \color{blue}{\frac{1 + -1.3333333333333333 \cdot x}{y}} \]

   if 3 < x

   1. Initial program 85.8%

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

      1. *-commutative 85.8%

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

      2. associate-*l/ 99.5%

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

      3. *-commutative 99.5%

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

      4. associate-/l/ 99.6%

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

   3. Simplified 99.6%

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

      1. associate-*r/ 85.8%

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

      2. associate-/l* 99.8%

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

      3. frac-2neg 99.8%

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

      4. metadata-eval 99.8%

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

      5. +-commutative 99.8%

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

      6. distribute-neg-in 99.8%

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

      7. metadata-eval 99.8%

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

      8. sub-neg 99.8%

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

      9. associate-/l* 85.8%

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

      10. associate-*l/ 99.6%

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

      11. *-commutative 99.6%

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

      12. clear-num 99.6%

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

      13. frac-times 99.7%

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

      14. *-un-lft-identity 99.7%

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

      15. metadata-eval 99.7%

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

      16. sub-neg 99.7%

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

      17. metadata-eval 99.7%

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

      18. distribute-neg-in 99.7%

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

      19. +-commutative 99.7%

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

      20. frac-2neg 99.7%

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

   5. Applied egg-rr 99.7%

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

   6. Taylor expanded in x around inf 96.1%

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

      1. /-rgt-identity 96.1%

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

      2. associate-/l* 96.2%

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

      3. associate-/r/ 96.2%

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

      4. associate-*l/ 96.2%

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

      5. *-lft-identity 96.2%

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

   8. Simplified 96.2%

      \[\leadsto \frac{1 - x}{\color{blue}{\frac{-3}{\frac{x}{y}}}} \]
3. Recombined 3 regimes into one program.

4. Final simplification 97.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -3.8:\\ \;\;\;\;\frac{1 - x}{y \cdot \frac{-3}{x}}\\ \mathbf{elif}\;x \leq 3:\\ \;\;\;\;\frac{x \cdot -1.3333333333333333 + 1}{y}\\ \mathbf{else}:\\ \;\;\;\;\frac{1 - x}{\frac{-3}{\frac{x}{y}}}\\ \end{array} \]

Alternative 8: 98.0% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -4.6 \lor \neg \left(x \leq 0.64\right):\\ \;\;\;\;-0.3333333333333333 \cdot \frac{x}{\frac{-y}{x}}\\ \mathbf{else}:\\ \;\;\;\;\frac{x \cdot -1.3333333333333333 + 1}{y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (or (<= x -4.6) (not (<= x 0.64)))
   (* -0.3333333333333333 (/ x (/ (- y) x)))
   (/ (+ (* x -1.3333333333333333) 1.0) y)))

C

double code(double x, double y) {
	double tmp;
	if ((x <= -4.6) || !(x <= 0.64)) {
		tmp = -0.3333333333333333 * (x / (-y / x));
	} else {
		tmp = ((x * -1.3333333333333333) + 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 <= (-4.6d0)) .or. (.not. (x <= 0.64d0))) then
        tmp = (-0.3333333333333333d0) * (x / (-y / x))
    else
        tmp = ((x * (-1.3333333333333333d0)) + 1.0d0) / y
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if ((x <= -4.6) || !(x <= 0.64)) {
		tmp = -0.3333333333333333 * (x / (-y / x));
	} else {
		tmp = ((x * -1.3333333333333333) + 1.0) / y;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (x <= -4.6) or not (x <= 0.64):
		tmp = -0.3333333333333333 * (x / (-y / x))
	else:
		tmp = ((x * -1.3333333333333333) + 1.0) / y
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if ((x <= -4.6) || !(x <= 0.64))
		tmp = Float64(-0.3333333333333333 * Float64(x / Float64(Float64(-y) / x)));
	else
		tmp = Float64(Float64(Float64(x * -1.3333333333333333) + 1.0) / y);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if ((x <= -4.6) || ~((x <= 0.64)))
		tmp = -0.3333333333333333 * (x / (-y / x));
	else
		tmp = ((x * -1.3333333333333333) + 1.0) / y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[Or[LessEqual[x, -4.6], N[Not[LessEqual[x, 0.64]], $MachinePrecision]], N[(-0.3333333333333333 * N[(x / N[((-y) / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(N[(x * -1.3333333333333333), $MachinePrecision] + 1.0), $MachinePrecision] / y), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -4.5999999999999996 or 0.640000000000000013 < x

   1. Initial program 82.6%

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

      1. *-commutative 82.6%

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

      2. associate-*l/ 99.6%

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

      3. *-commutative 99.6%

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

      4. associate-/l/ 99.7%

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

   3. Simplified 99.7%

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

   4. Taylor expanded in x around inf 96.9%

      \[\leadsto \left(1 - x\right) \cdot \color{blue}{\left(-0.3333333333333333 \cdot \frac{x}{y}\right)} \]
   5. Step-by-step derivation

      1. *-commutative 96.9%

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

      2. metadata-eval 96.9%

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

      3. distribute-rgt-neg-in 96.9%

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

      4. associate-*l/ 96.9%

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

      5. associate-*r/ 96.9%

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

      6. distribute-rgt-neg-in 96.9%

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

      7. distribute-neg-frac 96.9%

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

      8. metadata-eval 96.9%

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

   6. Simplified 96.9%

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

   7. Taylor expanded in y around 0 79.8%

      \[\leadsto \color{blue}{-0.3333333333333333 \cdot \frac{x \cdot \left(1 - x\right)}{y}} \]
   8. Step-by-step derivation

      1. associate-/l* 96.9%

         \[\leadsto -0.3333333333333333 \cdot \color{blue}{\frac{x}{\frac{y}{1 - x}}} \]

   9. Simplified 96.9%

      \[\leadsto \color{blue}{-0.3333333333333333 \cdot \frac{x}{\frac{y}{1 - x}}} \]

   10. Taylor expanded in x around inf 96.9%

       \[\leadsto -0.3333333333333333 \cdot \frac{x}{\color{blue}{-1 \cdot \frac{y}{x}}} \]
   11. Step-by-step derivation

       1. associate-*r/ 96.9%

          \[\leadsto -0.3333333333333333 \cdot \frac{x}{\color{blue}{\frac{-1 \cdot y}{x}}} \]

       2. neg-mul-1 96.9%

          \[\leadsto -0.3333333333333333 \cdot \frac{x}{\frac{\color{blue}{-y}}{x}} \]

   12. Simplified 96.9%

       \[\leadsto -0.3333333333333333 \cdot \frac{x}{\color{blue}{\frac{-y}{x}}} \]

   if -4.5999999999999996 < x < 0.640000000000000013

   1. Initial program 99.7%

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

      1. *-commutative 99.7%

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

      2. associate-*l/ 99.7%

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

      3. *-commutative 99.7%

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

      4. associate-/l/ 100.0%

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

   3. Simplified 100.0%

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

   4. Taylor expanded in x around 0 98.1%

      \[\leadsto \color{blue}{-1.3333333333333333 \cdot \frac{x}{y} + \frac{1}{y}} \]

   5. Taylor expanded in y around 0 98.1%

      \[\leadsto \color{blue}{\frac{1 + -1.3333333333333333 \cdot x}{y}} \]
3. Recombined 2 regimes into one program.

4. Final simplification 97.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -4.6 \lor \neg \left(x \leq 0.64\right):\\ \;\;\;\;-0.3333333333333333 \cdot \frac{x}{\frac{-y}{x}}\\ \mathbf{else}:\\ \;\;\;\;\frac{x \cdot -1.3333333333333333 + 1}{y}\\ \end{array} \]

Alternative 9: 99.5% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (1.0d0 - x) * (((x + (-3.0d0)) / y) * (-0.3333333333333333d0))
end function

Java

public static double code(double x, double y) {
	return (1.0 - x) * (((x + -3.0) / y) * -0.3333333333333333);
}

Python

def code(x, y):
	return (1.0 - x) * (((x + -3.0) / y) * -0.3333333333333333)

Julia

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

MATLAB

function tmp = code(x, y)
	tmp = (1.0 - x) * (((x + -3.0) / y) * -0.3333333333333333);
end

Wolfram

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

Derivation

1. Initial program 92.1%

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

   1. *-commutative 92.1%

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

   2. associate-*l/ 99.7%

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

   3. *-commutative 99.7%

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

   4. *-lft-identity 99.7%

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

   5. metadata-eval 99.7%

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

   6. distribute-lft-neg-in 99.7%

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

   7. metadata-eval 99.7%

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

   8. times-frac 99.7%

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

   9. *-commutative 99.7%

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

   10. neg-mul-1 99.7%

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

   11. distribute-rgt-neg-in 99.7%

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

   12. times-frac 99.5%

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

   13. metadata-eval 99.5%

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

   14. metadata-eval 99.5%

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

   15. metadata-eval 99.5%

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

   16. distribute-lft-neg-in 99.5%

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

   17. distribute-frac-neg 99.5%

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

   18. sub-neg 99.5%

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

   19. +-commutative 99.5%

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

   20. distribute-neg-in 99.5%

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

   21. remove-double-neg 99.5%

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

   22. metadata-eval 99.5%

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

   23. metadata-eval 99.5%

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

3. Simplified 99.5%

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

4. Final simplification 99.5%

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

Alternative 10: 99.8% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 92.1%

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

   1. *-commutative 92.1%

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

   2. associate-*l/ 99.7%

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

   3. *-commutative 99.7%

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

   4. associate-/l/ 99.8%

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

3. Simplified 99.8%

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

4. Final simplification 99.8%

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

Alternative 11: 57.6% accurate, 1.6× speedup

Math

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

FPCore

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

C

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

Python

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

Julia

function code(x, y)
	tmp = 0.0
	if (x <= -0.75)
		tmp = Float64(-1.3333333333333333 * Float64(x / y));
	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 * (x / y);
	else
		tmp = 1.0 / y;
	end
	tmp_2 = tmp;
end

Wolfram

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

Derivation

1. Split input into 2 regimes

2. if x < -0.75

   1. Initial program 79.1%

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

   2. Taylor expanded in x around 0 28.8%

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

      1. *-commutative 28.8%

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

   4. Simplified 28.8%

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

   5. Taylor expanded in x around inf 28.8%

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

   if -0.75 < x

   1. Initial program 95.5%

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

      1. *-commutative 95.5%

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

      2. associate-*l/ 99.6%

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

      3. *-commutative 99.6%

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

      4. associate-/l/ 99.8%

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

   3. Simplified 99.8%

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

      1. associate-*r/ 95.7%

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

      2. associate-/l* 99.9%

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

      3. frac-2neg 99.9%

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

      4. metadata-eval 99.9%

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

      5. +-commutative 99.9%

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

      6. distribute-neg-in 99.9%

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

      7. metadata-eval 99.9%

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

      8. sub-neg 99.9%

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

      9. associate-/l* 95.7%

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

      10. associate-*l/ 99.9%

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

      11. *-commutative 99.9%

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

      12. clear-num 99.8%

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

      13. frac-times 99.9%

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

      14. *-un-lft-identity 99.9%

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

      15. metadata-eval 99.9%

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

      16. sub-neg 99.9%

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

      17. metadata-eval 99.9%

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

      18. distribute-neg-in 99.9%

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

      19. +-commutative 99.9%

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

      20. frac-2neg 99.9%

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

   5. Applied egg-rr 99.9%

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

   6. Taylor expanded in x around 0 69.5%

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

4. Final simplification 60.9%

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

Alternative 12: 57.1% accurate, 1.6× speedup

Math

\[\begin{array}{l} \\ \frac{x \cdot -1.3333333333333333 + 1}{y} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (/ (+ (* x -1.3333333333333333) 1.0) y))

C

double code(double x, double y) {
	return ((x * -1.3333333333333333) + 1.0) / y;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = ((x * (-1.3333333333333333d0)) + 1.0d0) / y
end function

Java

public static double code(double x, double y) {
	return ((x * -1.3333333333333333) + 1.0) / y;
}

Python

def code(x, y):
	return ((x * -1.3333333333333333) + 1.0) / y

Julia

function code(x, y)
	return Float64(Float64(Float64(x * -1.3333333333333333) + 1.0) / y)
end

MATLAB

function tmp = code(x, y)
	tmp = ((x * -1.3333333333333333) + 1.0) / y;
end

Wolfram

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

Derivation

1. Initial program 92.1%

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

   1. *-commutative 92.1%

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

   2. associate-*l/ 99.7%

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

   3. *-commutative 99.7%

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

   4. associate-/l/ 99.8%

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

3. Simplified 99.8%

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

4. Taylor expanded in x around 0 60.7%

   \[\leadsto \color{blue}{-1.3333333333333333 \cdot \frac{x}{y} + \frac{1}{y}} \]

5. Taylor expanded in y around 0 60.7%

   \[\leadsto \color{blue}{\frac{1 + -1.3333333333333333 \cdot x}{y}} \]

6. Final simplification 60.7%

   \[\leadsto \frac{x \cdot -1.3333333333333333 + 1}{y} \]

Alternative 13: 57.6% accurate, 1.8× speedup

Math

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

FPCore

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

C

double code(double x, double y) {
	double tmp;
	if (x <= -1.0) {
		tmp = -x / y;
	} 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 <= (-1.0d0)) then
        tmp = -x / y
    else
        tmp = 1.0d0 / y
    end if
    code = tmp
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Split input into 2 regimes

2. if x < -1

   1. Initial program 79.1%

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

      1. *-commutative 79.1%

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

      2. associate-*l/ 99.8%

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

      3. *-commutative 99.8%

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

      4. associate-/l/ 99.8%

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

   3. Simplified 99.8%

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

      1. associate-*r/ 80.8%

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

      2. associate-/l* 99.7%

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

      3. frac-2neg 99.7%

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

      4. metadata-eval 99.7%

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

      5. +-commutative 99.7%

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

      6. distribute-neg-in 99.7%

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

      7. metadata-eval 99.7%

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

      8. sub-neg 99.7%

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

      9. associate-/l* 80.8%

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

      10. associate-*l/ 99.7%

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

      11. *-commutative 99.7%

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

      12. clear-num 99.7%

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

      13. frac-times 99.8%

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

      14. *-un-lft-identity 99.8%

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

      15. metadata-eval 99.8%

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

      16. sub-neg 99.8%

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

      17. metadata-eval 99.8%

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

      18. distribute-neg-in 99.8%

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

      19. +-commutative 99.8%

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

      20. frac-2neg 99.8%

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

   5. Applied egg-rr 99.8%

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

   6. Taylor expanded in x around 0 28.8%

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

   7. Taylor expanded in x around inf 28.8%

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

      1. neg-mul-1 28.8%

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

      2. distribute-neg-frac 28.8%

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

   9. Simplified 28.8%

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

   if -1 < x

   1. Initial program 95.5%

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

      1. *-commutative 95.5%

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

      2. associate-*l/ 99.6%

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

      3. *-commutative 99.6%

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

      4. associate-/l/ 99.8%

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

   3. Simplified 99.8%

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

      1. associate-*r/ 95.7%

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

      2. associate-/l* 99.9%

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

      3. frac-2neg 99.9%

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

      4. metadata-eval 99.9%

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

      5. +-commutative 99.9%

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

      6. distribute-neg-in 99.9%

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

      7. metadata-eval 99.9%

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

      8. sub-neg 99.9%

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

      9. associate-/l* 95.7%

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

      10. associate-*l/ 99.9%

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

      11. *-commutative 99.9%

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

      12. clear-num 99.8%

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

      13. frac-times 99.9%

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

      14. *-un-lft-identity 99.9%

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

      15. metadata-eval 99.9%

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

      16. sub-neg 99.9%

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

      17. metadata-eval 99.9%

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

      18. distribute-neg-in 99.9%

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

      19. +-commutative 99.9%

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

      20. frac-2neg 99.9%

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

   5. Applied egg-rr 99.9%

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

   6. Taylor expanded in x around 0 69.5%

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

4. Final simplification 60.9%

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

Alternative 14: 56.6% accurate, 2.2× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 92.1%

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

   1. *-commutative 92.1%

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

   2. associate-*l/ 99.7%

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

   3. *-commutative 99.7%

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

   4. associate-/l/ 99.8%

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

3. Simplified 99.8%

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

   1. associate-*r/ 92.6%

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

   2. associate-/l* 99.9%

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

   3. frac-2neg 99.9%

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

   4. metadata-eval 99.9%

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

   5. +-commutative 99.9%

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

   6. distribute-neg-in 99.9%

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

   7. metadata-eval 99.9%

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

   8. sub-neg 99.9%

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

   9. associate-/l* 92.6%

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

   10. associate-*l/ 99.8%

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

   11. *-commutative 99.8%

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

   12. clear-num 99.8%

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

   13. frac-times 99.9%

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

   14. *-un-lft-identity 99.9%

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

   15. metadata-eval 99.9%

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

   16. sub-neg 99.9%

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

   17. metadata-eval 99.9%

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

   18. distribute-neg-in 99.9%

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

   19. +-commutative 99.9%

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

   20. frac-2neg 99.9%

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

5. Applied egg-rr 99.9%

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

6. Taylor expanded in x around 0 60.1%

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

7. Final simplification 60.1%

   \[\leadsto \frac{1 - x}{y} \]

Alternative 15: 51.3% accurate, 3.7× 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 92.1%

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

   1. *-commutative 92.1%

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

   2. associate-*l/ 99.7%

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

   3. *-commutative 99.7%

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

   4. associate-/l/ 99.8%

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

3. Simplified 99.8%

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

   1. associate-*r/ 92.6%

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

   2. associate-/l* 99.9%

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

   3. frac-2neg 99.9%

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

   4. metadata-eval 99.9%

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

   5. +-commutative 99.9%

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

   6. distribute-neg-in 99.9%

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

   7. metadata-eval 99.9%

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

   8. sub-neg 99.9%

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

   9. associate-/l* 92.6%

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

   10. associate-*l/ 99.8%

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

   11. *-commutative 99.8%

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

   12. clear-num 99.8%

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

   13. frac-times 99.9%

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

   14. *-un-lft-identity 99.9%

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

   15. metadata-eval 99.9%

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

   16. sub-neg 99.9%

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

   17. metadata-eval 99.9%

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

   18. distribute-neg-in 99.9%

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

   19. +-commutative 99.9%

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

   20. frac-2neg 99.9%

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

5. Applied egg-rr 99.9%

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

6. Taylor expanded in x around 0 55.9%

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

7. Final simplification 55.9%

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

Developer target: 99.8% accurate, 1.0× 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 2023332 
(FPCore (x y)
  :name "Diagrams.TwoD.Arc:bezierFromSweepQ1 from diagrams-lib-1.3.0.3"
  :precision binary64

  :herbie-target
  (* (/ (- 1.0 x) y) (/ (- 3.0 x) 3.0))

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