Numeric.SpecFunctions:invIncompleteBetaWorker from math-functions-0.1.5.2, E

Percentage Accurate: 99.9% → 99.9%

Time: 7.5s

Alternatives: 10

Speedup: 1.0×

Specification

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 99.9% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 99.9% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 99.9%

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

Alternative 2: 93.3% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := y \cdot \sqrt{x}\\ \mathbf{if}\;y \leq -1.8 \cdot 10^{+53}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;y \leq 3100000000000:\\ \;\;\;\;1 - x\\ \mathbf{else}:\\ \;\;\;\;1 + t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (* y (sqrt x))))
   (if (<= y -1.8e+53) t_0 (if (<= y 3100000000000.0) (- 1.0 x) (+ 1.0 t_0)))))

C

double code(double x, double y) {
	double t_0 = y * sqrt(x);
	double tmp;
	if (y <= -1.8e+53) {
		tmp = t_0;
	} else if (y <= 3100000000000.0) {
		tmp = 1.0 - x;
	} else {
		tmp = 1.0 + t_0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: t_0
    real(8) :: tmp
    t_0 = y * sqrt(x)
    if (y <= (-1.8d+53)) then
        tmp = t_0
    else if (y <= 3100000000000.0d0) then
        tmp = 1.0d0 - x
    else
        tmp = 1.0d0 + t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double t_0 = y * Math.sqrt(x);
	double tmp;
	if (y <= -1.8e+53) {
		tmp = t_0;
	} else if (y <= 3100000000000.0) {
		tmp = 1.0 - x;
	} else {
		tmp = 1.0 + t_0;
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = y * math.sqrt(x)
	tmp = 0
	if y <= -1.8e+53:
		tmp = t_0
	elif y <= 3100000000000.0:
		tmp = 1.0 - x
	else:
		tmp = 1.0 + t_0
	return tmp

Julia

function code(x, y)
	t_0 = Float64(y * sqrt(x))
	tmp = 0.0
	if (y <= -1.8e+53)
		tmp = t_0;
	elseif (y <= 3100000000000.0)
		tmp = Float64(1.0 - x);
	else
		tmp = Float64(1.0 + t_0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = y * sqrt(x);
	tmp = 0.0;
	if (y <= -1.8e+53)
		tmp = t_0;
	elseif (y <= 3100000000000.0)
		tmp = 1.0 - x;
	else
		tmp = 1.0 + t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(y * N[Sqrt[x], $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -1.8e+53], t$95$0, If[LessEqual[y, 3100000000000.0], N[(1.0 - x), $MachinePrecision], N[(1.0 + t$95$0), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if y < -1.8e53

   1. Initial program 99.5%

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

      1. +-commutative 99.5%

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

      2. *-commutative 99.5%

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

      3. add-sqr-sqrt 99.3%

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

      4. associate-*l* 99.4%

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

      5. fma-define 99.4%

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

      6. pow1/2 99.4%

         \[\leadsto \mathsf{fma}\left(\sqrt{\color{blue}{{x}^{0.5}}}, \sqrt{\sqrt{x}} \cdot y, 1 - x\right) \]

      7. sqrt-pow1 99.4%

         \[\leadsto \mathsf{fma}\left(\color{blue}{{x}^{\left(\frac{0.5}{2}\right)}}, \sqrt{\sqrt{x}} \cdot y, 1 - x\right) \]

      8. metadata-eval 99.4%

         \[\leadsto \mathsf{fma}\left({x}^{\color{blue}{0.25}}, \sqrt{\sqrt{x}} \cdot y, 1 - x\right) \]

      9. pow1/2 99.4%

         \[\leadsto \mathsf{fma}\left({x}^{0.25}, \sqrt{\color{blue}{{x}^{0.5}}} \cdot y, 1 - x\right) \]

      10. sqrt-pow1 99.4%

          \[\leadsto \mathsf{fma}\left({x}^{0.25}, \color{blue}{{x}^{\left(\frac{0.5}{2}\right)}} \cdot y, 1 - x\right) \]

      11. metadata-eval 99.4%

          \[\leadsto \mathsf{fma}\left({x}^{0.25}, {x}^{\color{blue}{0.25}} \cdot y, 1 - x\right) \]

   4. Applied egg-rr 99.4%

      \[\leadsto \color{blue}{\mathsf{fma}\left({x}^{0.25}, {x}^{0.25} \cdot y, 1 - x\right)} \]

   5. Taylor expanded in y around inf 91.6%

      \[\leadsto \color{blue}{\sqrt{x} \cdot y} \]

   if -1.8e53 < y < 3.1e12

   1. Initial program 100.0%

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

   3. Taylor expanded in y around 0 98.9%

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

   if 3.1e12 < y

   1. Initial program 99.8%

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

   3. Taylor expanded in x around 0 89.4%

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

4. Final simplification 95.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1.8 \cdot 10^{+53}:\\ \;\;\;\;y \cdot \sqrt{x}\\ \mathbf{elif}\;y \leq 3100000000000:\\ \;\;\;\;1 - x\\ \mathbf{else}:\\ \;\;\;\;1 + y \cdot \sqrt{x}\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 92.4% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -9.2 \cdot 10^{+55} \lor \neg \left(y \leq 2.45 \cdot 10^{+50}\right):\\ \;\;\;\;y \cdot \sqrt{x}\\ \mathbf{else}:\\ \;\;\;\;1 - x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (or (<= y -9.2e+55) (not (<= y 2.45e+50))) (* y (sqrt x)) (- 1.0 x)))

C

double code(double x, double y) {
	double tmp;
	if ((y <= -9.2e+55) || !(y <= 2.45e+50)) {
		tmp = y * sqrt(x);
	} else {
		tmp = 1.0 - x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if ((y <= (-9.2d+55)) .or. (.not. (y <= 2.45d+50))) then
        tmp = y * sqrt(x)
    else
        tmp = 1.0d0 - x
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if ((y <= -9.2e+55) || !(y <= 2.45e+50)) {
		tmp = y * Math.sqrt(x);
	} else {
		tmp = 1.0 - x;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (y <= -9.2e+55) or not (y <= 2.45e+50):
		tmp = y * math.sqrt(x)
	else:
		tmp = 1.0 - x
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if ((y <= -9.2e+55) || !(y <= 2.45e+50))
		tmp = Float64(y * sqrt(x));
	else
		tmp = Float64(1.0 - x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if ((y <= -9.2e+55) || ~((y <= 2.45e+50)))
		tmp = y * sqrt(x);
	else
		tmp = 1.0 - x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[Or[LessEqual[y, -9.2e+55], N[Not[LessEqual[y, 2.45e+50]], $MachinePrecision]], N[(y * N[Sqrt[x], $MachinePrecision]), $MachinePrecision], N[(1.0 - x), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -9.1999999999999995e55 or 2.4500000000000001e50 < y

   1. Initial program 99.7%

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

      1. +-commutative 99.7%

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

      2. *-commutative 99.7%

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

      3. add-sqr-sqrt 99.3%

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

      4. associate-*l* 99.4%

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

      5. fma-define 99.4%

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

      6. pow1/2 99.4%

         \[\leadsto \mathsf{fma}\left(\sqrt{\color{blue}{{x}^{0.5}}}, \sqrt{\sqrt{x}} \cdot y, 1 - x\right) \]

      7. sqrt-pow1 99.6%

         \[\leadsto \mathsf{fma}\left(\color{blue}{{x}^{\left(\frac{0.5}{2}\right)}}, \sqrt{\sqrt{x}} \cdot y, 1 - x\right) \]

      8. metadata-eval 99.6%

         \[\leadsto \mathsf{fma}\left({x}^{\color{blue}{0.25}}, \sqrt{\sqrt{x}} \cdot y, 1 - x\right) \]

      9. pow1/2 99.6%

         \[\leadsto \mathsf{fma}\left({x}^{0.25}, \sqrt{\color{blue}{{x}^{0.5}}} \cdot y, 1 - x\right) \]

      10. sqrt-pow1 99.4%

          \[\leadsto \mathsf{fma}\left({x}^{0.25}, \color{blue}{{x}^{\left(\frac{0.5}{2}\right)}} \cdot y, 1 - x\right) \]

      11. metadata-eval 99.4%

          \[\leadsto \mathsf{fma}\left({x}^{0.25}, {x}^{\color{blue}{0.25}} \cdot y, 1 - x\right) \]

   4. Applied egg-rr 99.4%

      \[\leadsto \color{blue}{\mathsf{fma}\left({x}^{0.25}, {x}^{0.25} \cdot y, 1 - x\right)} \]

   5. Taylor expanded in y around inf 91.5%

      \[\leadsto \color{blue}{\sqrt{x} \cdot y} \]

   if -9.1999999999999995e55 < y < 2.4500000000000001e50

   1. Initial program 100.0%

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

   3. Taylor expanded in y around 0 96.8%

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

4. Final simplification 94.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -9.2 \cdot 10^{+55} \lor \neg \left(y \leq 2.45 \cdot 10^{+50}\right):\\ \;\;\;\;y \cdot \sqrt{x}\\ \mathbf{else}:\\ \;\;\;\;1 - x\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 98.7% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := y \cdot \sqrt{x}\\ \mathbf{if}\;x \leq 0.09:\\ \;\;\;\;1 + t\_0\\ \mathbf{else}:\\ \;\;\;\;t\_0 - x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (* y (sqrt x)))) (if (<= x 0.09) (+ 1.0 t_0) (- t_0 x))))

C

double code(double x, double y) {
	double t_0 = y * sqrt(x);
	double tmp;
	if (x <= 0.09) {
		tmp = 1.0 + t_0;
	} else {
		tmp = t_0 - x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: t_0
    real(8) :: tmp
    t_0 = y * sqrt(x)
    if (x <= 0.09d0) then
        tmp = 1.0d0 + t_0
    else
        tmp = t_0 - x
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double t_0 = y * Math.sqrt(x);
	double tmp;
	if (x <= 0.09) {
		tmp = 1.0 + t_0;
	} else {
		tmp = t_0 - x;
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = y * math.sqrt(x)
	tmp = 0
	if x <= 0.09:
		tmp = 1.0 + t_0
	else:
		tmp = t_0 - x
	return tmp

Julia

function code(x, y)
	t_0 = Float64(y * sqrt(x))
	tmp = 0.0
	if (x <= 0.09)
		tmp = Float64(1.0 + t_0);
	else
		tmp = Float64(t_0 - x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = y * sqrt(x);
	tmp = 0.0;
	if (x <= 0.09)
		tmp = 1.0 + t_0;
	else
		tmp = t_0 - x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(y * N[Sqrt[x], $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, 0.09], N[(1.0 + t$95$0), $MachinePrecision], N[(t$95$0 - x), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if x < 0.089999999999999997

   1. Initial program 99.8%

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

   3. Taylor expanded in x around 0 98.0%

      \[\leadsto \color{blue}{1 + \sqrt{x} \cdot y} \]

   if 0.089999999999999997 < x

   1. Initial program 99.9%

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

   3. Taylor expanded in x around inf 99.7%

      \[\leadsto \color{blue}{-1 \cdot x} + y \cdot \sqrt{x} \]
   4. Step-by-step derivation

      1. neg-mul-1 99.7%

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

   5. Simplified 99.7%

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

   6. Taylor expanded in y around 0 99.7%

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

4. Final simplification 98.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq 0.09:\\ \;\;\;\;1 + y \cdot \sqrt{x}\\ \mathbf{else}:\\ \;\;\;\;y \cdot \sqrt{x} - x\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 69.8% accurate, 6.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -7 \cdot 10^{+114}:\\ \;\;\;\;\frac{y \cdot \left(-1 - x\right)}{y}\\ \mathbf{elif}\;y \leq 1.15 \cdot 10^{+104}:\\ \;\;\;\;1 - x\\ \mathbf{else}:\\ \;\;\;\;\frac{y \cdot \left(1 + x\right)}{y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y -7e+114)
   (/ (* y (- -1.0 x)) y)
   (if (<= y 1.15e+104) (- 1.0 x) (/ (* y (+ 1.0 x)) y))))

C

double code(double x, double y) {
	double tmp;
	if (y <= -7e+114) {
		tmp = (y * (-1.0 - x)) / y;
	} else if (y <= 1.15e+104) {
		tmp = 1.0 - x;
	} else {
		tmp = (y * (1.0 + 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 (y <= (-7d+114)) then
        tmp = (y * ((-1.0d0) - x)) / y
    else if (y <= 1.15d+104) then
        tmp = 1.0d0 - x
    else
        tmp = (y * (1.0d0 + x)) / y
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (y <= -7e+114) {
		tmp = (y * (-1.0 - x)) / y;
	} else if (y <= 1.15e+104) {
		tmp = 1.0 - x;
	} else {
		tmp = (y * (1.0 + x)) / y;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= -7e+114:
		tmp = (y * (-1.0 - x)) / y
	elif y <= 1.15e+104:
		tmp = 1.0 - x
	else:
		tmp = (y * (1.0 + x)) / y
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= -7e+114)
		tmp = Float64(Float64(y * Float64(-1.0 - x)) / y);
	elseif (y <= 1.15e+104)
		tmp = Float64(1.0 - x);
	else
		tmp = Float64(Float64(y * Float64(1.0 + x)) / y);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= -7e+114)
		tmp = (y * (-1.0 - x)) / y;
	elseif (y <= 1.15e+104)
		tmp = 1.0 - x;
	else
		tmp = (y * (1.0 + x)) / y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, -7e+114], N[(N[(y * N[(-1.0 - x), $MachinePrecision]), $MachinePrecision] / y), $MachinePrecision], If[LessEqual[y, 1.15e+104], N[(1.0 - x), $MachinePrecision], N[(N[(y * N[(1.0 + x), $MachinePrecision]), $MachinePrecision] / y), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if y < -7.0000000000000001e114

   1. Initial program 99.5%

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

   3. Taylor expanded in y around inf 99.5%

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

      1. associate--l+ 99.5%

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

      2. div-sub 99.5%

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

   5. Simplified 99.5%

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

   6. Taylor expanded in y around 0 2.9%

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

      1. *-commutative 2.9%

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

      2. frac-2neg 2.9%

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

      3. associate-*l/ 27.3%

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

   8. Applied egg-rr 29.8%

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

   if -7.0000000000000001e114 < y < 1.14999999999999992e104

   1. Initial program 100.0%

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

   3. Taylor expanded in y around 0 88.2%

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

   if 1.14999999999999992e104 < y

   1. Initial program 99.7%

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

   3. Taylor expanded in y around inf 99.7%

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

      1. associate--l+ 99.7%

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

      2. div-sub 99.7%

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

   5. Simplified 99.7%

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

   6. Taylor expanded in y around 0 3.5%

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

      1. associate-*r/ 3.5%

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

      2. sub-neg 3.5%

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

      3. pow1 3.5%

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

      4. metadata-eval 3.5%

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

      5. sqrt-pow1 16.3%

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

      6. pow2 16.3%

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

      7. sqr-neg 16.3%

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

      8. sqrt-prod 16.3%

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

      9. add-sqr-sqrt 16.3%

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

      10. +-commutative 16.3%

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

   8. Applied egg-rr 16.3%

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

4. Final simplification 67.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -7 \cdot 10^{+114}:\\ \;\;\;\;\frac{y \cdot \left(-1 - x\right)}{y}\\ \mathbf{elif}\;y \leq 1.15 \cdot 10^{+104}:\\ \;\;\;\;1 - x\\ \mathbf{else}:\\ \;\;\;\;\frac{y \cdot \left(1 + x\right)}{y}\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 69.6% accurate, 6.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.7 \cdot 10^{+114}:\\ \;\;\;\;\frac{-1}{y} \cdot \left(x \cdot y\right)\\ \mathbf{elif}\;y \leq 1.15 \cdot 10^{+104}:\\ \;\;\;\;1 - x\\ \mathbf{else}:\\ \;\;\;\;\frac{y \cdot \left(1 + x\right)}{y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y -1.7e+114)
   (* (/ -1.0 y) (* x y))
   (if (<= y 1.15e+104) (- 1.0 x) (/ (* y (+ 1.0 x)) y))))

C

double code(double x, double y) {
	double tmp;
	if (y <= -1.7e+114) {
		tmp = (-1.0 / y) * (x * y);
	} else if (y <= 1.15e+104) {
		tmp = 1.0 - x;
	} else {
		tmp = (y * (1.0 + 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 (y <= (-1.7d+114)) then
        tmp = ((-1.0d0) / y) * (x * y)
    else if (y <= 1.15d+104) then
        tmp = 1.0d0 - x
    else
        tmp = (y * (1.0d0 + x)) / y
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (y <= -1.7e+114) {
		tmp = (-1.0 / y) * (x * y);
	} else if (y <= 1.15e+104) {
		tmp = 1.0 - x;
	} else {
		tmp = (y * (1.0 + x)) / y;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= -1.7e+114:
		tmp = (-1.0 / y) * (x * y)
	elif y <= 1.15e+104:
		tmp = 1.0 - x
	else:
		tmp = (y * (1.0 + x)) / y
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= -1.7e+114)
		tmp = Float64(Float64(-1.0 / y) * Float64(x * y));
	elseif (y <= 1.15e+104)
		tmp = Float64(1.0 - x);
	else
		tmp = Float64(Float64(y * Float64(1.0 + x)) / y);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= -1.7e+114)
		tmp = (-1.0 / y) * (x * y);
	elseif (y <= 1.15e+104)
		tmp = 1.0 - x;
	else
		tmp = (y * (1.0 + x)) / y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, -1.7e+114], N[(N[(-1.0 / y), $MachinePrecision] * N[(x * y), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 1.15e+104], N[(1.0 - x), $MachinePrecision], N[(N[(y * N[(1.0 + x), $MachinePrecision]), $MachinePrecision] / y), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if y < -1.7e114

   1. Initial program 99.5%

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

   3. Taylor expanded in y around inf 99.5%

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

      1. associate--l+ 99.5%

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

      2. div-sub 99.5%

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

   5. Simplified 99.5%

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

   6. Taylor expanded in x around inf 75.2%

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

      1. associate-*r* 97.0%

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

      2. *-commutative 97.0%

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

      3. sub-neg 97.0%

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

      4. distribute-neg-frac 97.0%

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

      5. metadata-eval 97.0%

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

   8. Simplified 97.0%

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

   9. Taylor expanded in y around 0 28.6%

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

   if -1.7e114 < y < 1.14999999999999992e104

   1. Initial program 100.0%

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

   3. Taylor expanded in y around 0 88.2%

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

   if 1.14999999999999992e104 < y

   1. Initial program 99.7%

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

   3. Taylor expanded in y around inf 99.7%

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

      1. associate--l+ 99.7%

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

      2. div-sub 99.7%

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

   5. Simplified 99.7%

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

   6. Taylor expanded in y around 0 3.5%

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

      1. associate-*r/ 3.5%

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

      2. sub-neg 3.5%

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

      3. pow1 3.5%

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

      4. metadata-eval 3.5%

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

      5. sqrt-pow1 16.3%

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

      6. pow2 16.3%

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

      7. sqr-neg 16.3%

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

      8. sqrt-prod 16.3%

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

      9. add-sqr-sqrt 16.3%

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

      10. +-commutative 16.3%

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

   8. Applied egg-rr 16.3%

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

4. Final simplification 67.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1.7 \cdot 10^{+114}:\\ \;\;\;\;\frac{-1}{y} \cdot \left(x \cdot y\right)\\ \mathbf{elif}\;y \leq 1.15 \cdot 10^{+104}:\\ \;\;\;\;1 - x\\ \mathbf{else}:\\ \;\;\;\;\frac{y \cdot \left(1 + x\right)}{y}\\ \end{array} \]
5. Add Preprocessing

Alternative 7: 66.4% accurate, 8.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -7.6 \cdot 10^{+114}:\\ \;\;\;\;\frac{-1}{y} \cdot \left(x \cdot y\right)\\ \mathbf{else}:\\ \;\;\;\;1 - x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y -7.6e+114) (* (/ -1.0 y) (* x y)) (- 1.0 x)))

C

double code(double x, double y) {
	double tmp;
	if (y <= -7.6e+114) {
		tmp = (-1.0 / y) * (x * y);
	} else {
		tmp = 1.0 - x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (y <= (-7.6d+114)) then
        tmp = ((-1.0d0) / y) * (x * y)
    else
        tmp = 1.0d0 - x
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (y <= -7.6e+114) {
		tmp = (-1.0 / y) * (x * y);
	} else {
		tmp = 1.0 - x;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= -7.6e+114:
		tmp = (-1.0 / y) * (x * y)
	else:
		tmp = 1.0 - x
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= -7.6e+114)
		tmp = Float64(Float64(-1.0 / y) * Float64(x * y));
	else
		tmp = Float64(1.0 - x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= -7.6e+114)
		tmp = (-1.0 / y) * (x * y);
	else
		tmp = 1.0 - x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, -7.6e+114], N[(N[(-1.0 / y), $MachinePrecision] * N[(x * y), $MachinePrecision]), $MachinePrecision], N[(1.0 - x), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -7.6000000000000001e114

   1. Initial program 99.5%

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

   3. Taylor expanded in y around inf 99.5%

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

      1. associate--l+ 99.5%

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

      2. div-sub 99.5%

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

   5. Simplified 99.5%

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

   6. Taylor expanded in x around inf 75.2%

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

      1. associate-*r* 97.0%

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

      2. *-commutative 97.0%

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

      3. sub-neg 97.0%

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

      4. distribute-neg-frac 97.0%

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

      5. metadata-eval 97.0%

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

   8. Simplified 97.0%

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

   9. Taylor expanded in y around 0 28.6%

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

   if -7.6000000000000001e114 < y

   1. Initial program 99.9%

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

   3. Taylor expanded in y around 0 71.3%

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

Alternative 8: 61.9% accurate, 15.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq 65000000000:\\ \;\;\;\;1\\ \mathbf{else}:\\ \;\;\;\;-x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (if (<= x 65000000000.0) 1.0 (- x)))

C

double code(double x, double y) {
	double tmp;
	if (x <= 65000000000.0) {
		tmp = 1.0;
	} else {
		tmp = -x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (x <= 65000000000.0d0) then
        tmp = 1.0d0
    else
        tmp = -x
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (x <= 65000000000.0) {
		tmp = 1.0;
	} else {
		tmp = -x;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if x <= 65000000000.0:
		tmp = 1.0
	else:
		tmp = -x
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (x <= 65000000000.0)
		tmp = 1.0;
	else
		tmp = Float64(-x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (x <= 65000000000.0)
		tmp = 1.0;
	else
		tmp = -x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[x, 65000000000.0], 1.0, (-x)]

Derivation

1. Split input into 2 regimes

2. if x < 6.5e10

   1. Initial program 99.8%

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

   3. Taylor expanded in y around 0 58.9%

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

   4. Taylor expanded in x around 0 57.3%

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

   if 6.5e10 < x

   1. Initial program 99.9%

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

   3. Taylor expanded in x around inf 99.7%

      \[\leadsto \color{blue}{-1 \cdot x} + y \cdot \sqrt{x} \]
   4. Step-by-step derivation

      1. neg-mul-1 99.7%

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

   5. Simplified 99.7%

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

   6. Taylor expanded in y around 0 65.3%

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

      1. mul-1-neg 65.3%

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

   8. Simplified 65.3%

      \[\leadsto \color{blue}{-x} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 9: 63.6% accurate, 35.7× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 99.9%

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

3. Taylor expanded in y around 0 62.0%

   \[\leadsto \color{blue}{1 - x} \]
4. Add Preprocessing

Alternative 10: 31.2% accurate, 107.0× speedup

Math

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

FPCore

(FPCore (x y) :precision binary64 1.0)

C

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

Fortran

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

Java

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

Python

def code(x, y):
	return 1.0

Julia

function code(x, y)
	return 1.0
end

MATLAB

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

Wolfram

code[x_, y_] := 1.0

Derivation

1. Initial program 99.9%

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

3. Taylor expanded in y around 0 62.0%

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

4. Taylor expanded in x around 0 31.4%

   \[\leadsto \color{blue}{1} \]
5. Add Preprocessing

Reproduce

herbie shell --seed 2024137 
(FPCore (x y)
  :name "Numeric.SpecFunctions:invIncompleteBetaWorker from math-functions-0.1.5.2, E"
  :precision binary64
  (+ (- 1.0 x) (* y (sqrt x))))