Data.Colour.SRGB:transferFunction from colour-2.3.3

Percentage Accurate: 100.0% → 100.0%

Time: 2.6s

Alternatives: 8

Speedup: 1.0×

Specification

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 100.0% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 100.0% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 100.0%

   \[\left(x + 1\right) \cdot y - x \]

2. Taylor expanded in x around 0 100.0%

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

3. Final simplification 100.0%

   \[\leadsto \left(y + y \cdot x\right) - x \]

Alternative 2: 61.7% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -2.65 \cdot 10^{+98}:\\ \;\;\;\;y \cdot x\\ \mathbf{elif}\;y \leq -1.6 \cdot 10^{+51}:\\ \;\;\;\;y\\ \mathbf{elif}\;y \leq -420000000:\\ \;\;\;\;y \cdot x\\ \mathbf{elif}\;y \leq 1.45 \cdot 10^{-29}:\\ \;\;\;\;-x\\ \mathbf{elif}\;y \leq 8.5 \cdot 10^{+178}:\\ \;\;\;\;y\\ \mathbf{elif}\;y \leq 3.3 \cdot 10^{+220}:\\ \;\;\;\;y \cdot x\\ \mathbf{else}:\\ \;\;\;\;y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y -2.65e+98)
   (* y x)
   (if (<= y -1.6e+51)
     y
     (if (<= y -420000000.0)
       (* y x)
       (if (<= y 1.45e-29)
         (- x)
         (if (<= y 8.5e+178) y (if (<= y 3.3e+220) (* y x) y)))))))

C

double code(double x, double y) {
	double tmp;
	if (y <= -2.65e+98) {
		tmp = y * x;
	} else if (y <= -1.6e+51) {
		tmp = y;
	} else if (y <= -420000000.0) {
		tmp = y * x;
	} else if (y <= 1.45e-29) {
		tmp = -x;
	} else if (y <= 8.5e+178) {
		tmp = y;
	} else if (y <= 3.3e+220) {
		tmp = y * x;
	} else {
		tmp = 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 <= (-2.65d+98)) then
        tmp = y * x
    else if (y <= (-1.6d+51)) then
        tmp = y
    else if (y <= (-420000000.0d0)) then
        tmp = y * x
    else if (y <= 1.45d-29) then
        tmp = -x
    else if (y <= 8.5d+178) then
        tmp = y
    else if (y <= 3.3d+220) then
        tmp = y * x
    else
        tmp = y
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (y <= -2.65e+98) {
		tmp = y * x;
	} else if (y <= -1.6e+51) {
		tmp = y;
	} else if (y <= -420000000.0) {
		tmp = y * x;
	} else if (y <= 1.45e-29) {
		tmp = -x;
	} else if (y <= 8.5e+178) {
		tmp = y;
	} else if (y <= 3.3e+220) {
		tmp = y * x;
	} else {
		tmp = y;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= -2.65e+98:
		tmp = y * x
	elif y <= -1.6e+51:
		tmp = y
	elif y <= -420000000.0:
		tmp = y * x
	elif y <= 1.45e-29:
		tmp = -x
	elif y <= 8.5e+178:
		tmp = y
	elif y <= 3.3e+220:
		tmp = y * x
	else:
		tmp = y
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= -2.65e+98)
		tmp = Float64(y * x);
	elseif (y <= -1.6e+51)
		tmp = y;
	elseif (y <= -420000000.0)
		tmp = Float64(y * x);
	elseif (y <= 1.45e-29)
		tmp = Float64(-x);
	elseif (y <= 8.5e+178)
		tmp = y;
	elseif (y <= 3.3e+220)
		tmp = Float64(y * x);
	else
		tmp = y;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= -2.65e+98)
		tmp = y * x;
	elseif (y <= -1.6e+51)
		tmp = y;
	elseif (y <= -420000000.0)
		tmp = y * x;
	elseif (y <= 1.45e-29)
		tmp = -x;
	elseif (y <= 8.5e+178)
		tmp = y;
	elseif (y <= 3.3e+220)
		tmp = y * x;
	else
		tmp = y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, -2.65e+98], N[(y * x), $MachinePrecision], If[LessEqual[y, -1.6e+51], y, If[LessEqual[y, -420000000.0], N[(y * x), $MachinePrecision], If[LessEqual[y, 1.45e-29], (-x), If[LessEqual[y, 8.5e+178], y, If[LessEqual[y, 3.3e+220], N[(y * x), $MachinePrecision], y]]]]]]

Derivation

1. Split input into 3 regimes

2. if y < -2.64999999999999999e98 or -1.6000000000000001e51 < y < -4.2e8 or 8.49999999999999991e178 < y < 3.30000000000000021e220

   1. Initial program 100.0%

      \[\left(x + 1\right) \cdot y - x \]

   2. Taylor expanded in x around inf 66.9%

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

   3. Taylor expanded in y around inf 66.9%

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

   if -2.64999999999999999e98 < y < -1.6000000000000001e51 or 1.45000000000000012e-29 < y < 8.49999999999999991e178 or 3.30000000000000021e220 < y

   1. Initial program 100.0%

      \[\left(x + 1\right) \cdot y - x \]

   2. Taylor expanded in x around 0 100.0%

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

   3. Taylor expanded in x around 0 64.8%

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

   if -4.2e8 < y < 1.45000000000000012e-29

   1. Initial program 100.0%

      \[\left(x + 1\right) \cdot y - x \]

   2. Taylor expanded in y around 0 85.0%

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

      1. neg-mul-1 85.0%

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

   4. Simplified 85.0%

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

4. Final simplification 74.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -2.65 \cdot 10^{+98}:\\ \;\;\;\;y \cdot x\\ \mathbf{elif}\;y \leq -1.6 \cdot 10^{+51}:\\ \;\;\;\;y\\ \mathbf{elif}\;y \leq -420000000:\\ \;\;\;\;y \cdot x\\ \mathbf{elif}\;y \leq 1.45 \cdot 10^{-29}:\\ \;\;\;\;-x\\ \mathbf{elif}\;y \leq 8.5 \cdot 10^{+178}:\\ \;\;\;\;y\\ \mathbf{elif}\;y \leq 3.3 \cdot 10^{+220}:\\ \;\;\;\;y \cdot x\\ \mathbf{else}:\\ \;\;\;\;y\\ \end{array} \]

Alternative 3: 97.9% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -420000000 \lor \neg \left(y \leq 1.45 \cdot 10^{-29}\right):\\ \;\;\;\;y \cdot \left(x + 1\right)\\ \mathbf{else}:\\ \;\;\;\;y - x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (or (<= y -420000000.0) (not (<= y 1.45e-29))) (* y (+ x 1.0)) (- y x)))

C

double code(double x, double y) {
	double tmp;
	if ((y <= -420000000.0) || !(y <= 1.45e-29)) {
		tmp = y * (x + 1.0);
	} else {
		tmp = y - 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 <= (-420000000.0d0)) .or. (.not. (y <= 1.45d-29))) then
        tmp = y * (x + 1.0d0)
    else
        tmp = y - x
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if ((y <= -420000000.0) || !(y <= 1.45e-29)) {
		tmp = y * (x + 1.0);
	} else {
		tmp = y - x;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (y <= -420000000.0) or not (y <= 1.45e-29):
		tmp = y * (x + 1.0)
	else:
		tmp = y - x
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if ((y <= -420000000.0) || !(y <= 1.45e-29))
		tmp = Float64(y * Float64(x + 1.0));
	else
		tmp = Float64(y - x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if ((y <= -420000000.0) || ~((y <= 1.45e-29)))
		tmp = y * (x + 1.0);
	else
		tmp = y - x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[Or[LessEqual[y, -420000000.0], N[Not[LessEqual[y, 1.45e-29]], $MachinePrecision]], N[(y * N[(x + 1.0), $MachinePrecision]), $MachinePrecision], N[(y - x), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -4.2e8 or 1.45000000000000012e-29 < y

   1. Initial program 100.0%

      \[\left(x + 1\right) \cdot y - x \]

   2. Taylor expanded in x around 0 100.0%

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

   3. Taylor expanded in y around inf 99.8%

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

   if -4.2e8 < y < 1.45000000000000012e-29

   1. Initial program 100.0%

      \[\left(x + 1\right) \cdot y - x \]

   2. Taylor expanded in x around 0 99.4%

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

4. Final simplification 99.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -420000000 \lor \neg \left(y \leq 1.45 \cdot 10^{-29}\right):\\ \;\;\;\;y \cdot \left(x + 1\right)\\ \mathbf{else}:\\ \;\;\;\;y - x\\ \end{array} \]

Alternative 4: 97.9% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -420000000:\\ \;\;\;\;y + y \cdot x\\ \mathbf{elif}\;y \leq 1.45 \cdot 10^{-29}:\\ \;\;\;\;y - x\\ \mathbf{else}:\\ \;\;\;\;y \cdot \left(x + 1\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y -420000000.0)
   (+ y (* y x))
   (if (<= y 1.45e-29) (- y x) (* y (+ x 1.0)))))

C

double code(double x, double y) {
	double tmp;
	if (y <= -420000000.0) {
		tmp = y + (y * x);
	} else if (y <= 1.45e-29) {
		tmp = y - x;
	} else {
		tmp = y * (x + 1.0);
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (y <= (-420000000.0d0)) then
        tmp = y + (y * x)
    else if (y <= 1.45d-29) then
        tmp = y - x
    else
        tmp = y * (x + 1.0d0)
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (y <= -420000000.0) {
		tmp = y + (y * x);
	} else if (y <= 1.45e-29) {
		tmp = y - x;
	} else {
		tmp = y * (x + 1.0);
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= -420000000.0:
		tmp = y + (y * x)
	elif y <= 1.45e-29:
		tmp = y - x
	else:
		tmp = y * (x + 1.0)
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= -420000000.0)
		tmp = Float64(y + Float64(y * x));
	elseif (y <= 1.45e-29)
		tmp = Float64(y - x);
	else
		tmp = Float64(y * Float64(x + 1.0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= -420000000.0)
		tmp = y + (y * x);
	elseif (y <= 1.45e-29)
		tmp = y - x;
	else
		tmp = y * (x + 1.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, -420000000.0], N[(y + N[(y * x), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 1.45e-29], N[(y - x), $MachinePrecision], N[(y * N[(x + 1.0), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if y < -4.2e8

   1. Initial program 100.0%

      \[\left(x + 1\right) \cdot y - x \]

   2. Taylor expanded in x around 0 100.0%

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

   3. Taylor expanded in y around inf 100.0%

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

   4. Taylor expanded in x around 0 100.0%

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

   if -4.2e8 < y < 1.45000000000000012e-29

   1. Initial program 100.0%

      \[\left(x + 1\right) \cdot y - x \]

   2. Taylor expanded in x around 0 99.4%

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

   if 1.45000000000000012e-29 < y

   1. Initial program 100.0%

      \[\left(x + 1\right) \cdot y - x \]

   2. Taylor expanded in x around 0 100.0%

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

   3. Taylor expanded in y around inf 99.7%

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

4. Final simplification 99.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -420000000:\\ \;\;\;\;y + y \cdot x\\ \mathbf{elif}\;y \leq 1.45 \cdot 10^{-29}:\\ \;\;\;\;y - x\\ \mathbf{else}:\\ \;\;\;\;y \cdot \left(x + 1\right)\\ \end{array} \]

Alternative 5: 75.2% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -6.6 \cdot 10^{+130}:\\ \;\;\;\;y \cdot x\\ \mathbf{elif}\;x \leq 1.35 \cdot 10^{+105}:\\ \;\;\;\;y - x\\ \mathbf{else}:\\ \;\;\;\;y \cdot x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= x -6.6e+130) (* y x) (if (<= x 1.35e+105) (- y x) (* y x))))

C

double code(double x, double y) {
	double tmp;
	if (x <= -6.6e+130) {
		tmp = y * x;
	} else if (x <= 1.35e+105) {
		tmp = y - x;
	} else {
		tmp = y * 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 <= (-6.6d+130)) then
        tmp = y * x
    else if (x <= 1.35d+105) then
        tmp = y - x
    else
        tmp = y * x
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (x <= -6.6e+130) {
		tmp = y * x;
	} else if (x <= 1.35e+105) {
		tmp = y - x;
	} else {
		tmp = y * x;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if x <= -6.6e+130:
		tmp = y * x
	elif x <= 1.35e+105:
		tmp = y - x
	else:
		tmp = y * x
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (x <= -6.6e+130)
		tmp = Float64(y * x);
	elseif (x <= 1.35e+105)
		tmp = Float64(y - x);
	else
		tmp = Float64(y * x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (x <= -6.6e+130)
		tmp = y * x;
	elseif (x <= 1.35e+105)
		tmp = y - x;
	else
		tmp = y * x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[x, -6.6e+130], N[(y * x), $MachinePrecision], If[LessEqual[x, 1.35e+105], N[(y - x), $MachinePrecision], N[(y * x), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if x < -6.6e130 or 1.35000000000000008e105 < x

   1. Initial program 100.0%

      \[\left(x + 1\right) \cdot y - x \]

   2. Taylor expanded in x around inf 100.0%

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

   3. Taylor expanded in y around inf 62.2%

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

   if -6.6e130 < x < 1.35000000000000008e105

   1. Initial program 100.0%

      \[\left(x + 1\right) \cdot y - x \]

   2. Taylor expanded in x around 0 86.6%

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

4. Final simplification 79.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -6.6 \cdot 10^{+130}:\\ \;\;\;\;y \cdot x\\ \mathbf{elif}\;x \leq 1.35 \cdot 10^{+105}:\\ \;\;\;\;y - x\\ \mathbf{else}:\\ \;\;\;\;y \cdot x\\ \end{array} \]

Alternative 6: 100.0% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 100.0%

   \[\left(x + 1\right) \cdot y - x \]

2. Final simplification 100.0%

   \[\leadsto y \cdot \left(x + 1\right) - x \]

Alternative 7: 62.7% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -0.00046:\\ \;\;\;\;y\\ \mathbf{elif}\;y \leq 1.35 \cdot 10^{-29}:\\ \;\;\;\;-x\\ \mathbf{else}:\\ \;\;\;\;y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y -0.00046) y (if (<= y 1.35e-29) (- x) y)))

C

double code(double x, double y) {
	double tmp;
	if (y <= -0.00046) {
		tmp = y;
	} else if (y <= 1.35e-29) {
		tmp = -x;
	} else {
		tmp = 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 <= (-0.00046d0)) then
        tmp = y
    else if (y <= 1.35d-29) then
        tmp = -x
    else
        tmp = y
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (y <= -0.00046) {
		tmp = y;
	} else if (y <= 1.35e-29) {
		tmp = -x;
	} else {
		tmp = y;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= -0.00046:
		tmp = y
	elif y <= 1.35e-29:
		tmp = -x
	else:
		tmp = y
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= -0.00046)
		tmp = y;
	elseif (y <= 1.35e-29)
		tmp = Float64(-x);
	else
		tmp = y;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= -0.00046)
		tmp = y;
	elseif (y <= 1.35e-29)
		tmp = -x;
	else
		tmp = y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, -0.00046], y, If[LessEqual[y, 1.35e-29], (-x), y]]

Derivation

1. Split input into 2 regimes

2. if y < -4.6000000000000001e-4 or 1.35000000000000011e-29 < y

   1. Initial program 100.0%

      \[\left(x + 1\right) \cdot y - x \]

   2. Taylor expanded in x around 0 100.0%

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

   3. Taylor expanded in x around 0 51.5%

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

   if -4.6000000000000001e-4 < y < 1.35000000000000011e-29

   1. Initial program 100.0%

      \[\left(x + 1\right) \cdot y - x \]

   2. Taylor expanded in y around 0 85.7%

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

      1. neg-mul-1 85.7%

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

   4. Simplified 85.7%

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

4. Final simplification 66.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -0.00046:\\ \;\;\;\;y\\ \mathbf{elif}\;y \leq 1.35 \cdot 10^{-29}:\\ \;\;\;\;-x\\ \mathbf{else}:\\ \;\;\;\;y\\ \end{array} \]

Alternative 8: 39.2% accurate, 7.0× speedup

Math

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

FPCore

(FPCore (x y) :precision binary64 y)

C

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

Fortran

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

Java

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

Python

def code(x, y):
	return y

Julia

function code(x, y)
	return y
end

MATLAB

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

Wolfram

code[x_, y_] := y

Derivation

1. Initial program 100.0%

   \[\left(x + 1\right) \cdot y - x \]

2. Taylor expanded in x around 0 100.0%

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

3. Taylor expanded in x around 0 36.1%

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

4. Final simplification 36.1%

   \[\leadsto y \]

Reproduce

herbie shell --seed 2023196 
(FPCore (x y)
  :name "Data.Colour.SRGB:transferFunction from colour-2.3.3"
  :precision binary64
  (- (* (+ x 1.0) y) x))