Data.Colour.SRGB:transferFunction from colour-2.3.3

Percentage Accurate: 100.0% → 100.0%

Time: 4.8s

Alternatives: 7

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

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

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. Add Preprocessing

3. Final simplification 100.0%

   \[\leadsto y \cdot \left(x + 1\right) - x \]
4. Add Preprocessing

Alternative 2: 61.9% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.85 \cdot 10^{+244}:\\ \;\;\;\;x \cdot y\\ \mathbf{elif}\;y \leq -6.6 \cdot 10^{+152}:\\ \;\;\;\;y\\ \mathbf{elif}\;y \leq -1:\\ \;\;\;\;x \cdot y\\ \mathbf{elif}\;y \leq 4.3 \cdot 10^{-78}:\\ \;\;\;\;-x\\ \mathbf{elif}\;y \leq 1.46 \cdot 10^{+113} \lor \neg \left(y \leq 2.9 \cdot 10^{+200}\right) \land y \leq 5.4 \cdot 10^{+262}:\\ \;\;\;\;y\\ \mathbf{else}:\\ \;\;\;\;x \cdot y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y -1.85e+244)
   (* x y)
   (if (<= y -6.6e+152)
     y
     (if (<= y -1.0)
       (* x y)
       (if (<= y 4.3e-78)
         (- x)
         (if (or (<= y 1.46e+113) (and (not (<= y 2.9e+200)) (<= y 5.4e+262)))
           y
           (* x y)))))))

C

double code(double x, double y) {
	double tmp;
	if (y <= -1.85e+244) {
		tmp = x * y;
	} else if (y <= -6.6e+152) {
		tmp = y;
	} else if (y <= -1.0) {
		tmp = x * y;
	} else if (y <= 4.3e-78) {
		tmp = -x;
	} else if ((y <= 1.46e+113) || (!(y <= 2.9e+200) && (y <= 5.4e+262))) {
		tmp = y;
	} else {
		tmp = 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 <= (-1.85d+244)) then
        tmp = x * y
    else if (y <= (-6.6d+152)) then
        tmp = y
    else if (y <= (-1.0d0)) then
        tmp = x * y
    else if (y <= 4.3d-78) then
        tmp = -x
    else if ((y <= 1.46d+113) .or. (.not. (y <= 2.9d+200)) .and. (y <= 5.4d+262)) then
        tmp = y
    else
        tmp = x * y
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (y <= -1.85e+244) {
		tmp = x * y;
	} else if (y <= -6.6e+152) {
		tmp = y;
	} else if (y <= -1.0) {
		tmp = x * y;
	} else if (y <= 4.3e-78) {
		tmp = -x;
	} else if ((y <= 1.46e+113) || (!(y <= 2.9e+200) && (y <= 5.4e+262))) {
		tmp = y;
	} else {
		tmp = x * y;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= -1.85e+244:
		tmp = x * y
	elif y <= -6.6e+152:
		tmp = y
	elif y <= -1.0:
		tmp = x * y
	elif y <= 4.3e-78:
		tmp = -x
	elif (y <= 1.46e+113) or (not (y <= 2.9e+200) and (y <= 5.4e+262)):
		tmp = y
	else:
		tmp = x * y
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= -1.85e+244)
		tmp = Float64(x * y);
	elseif (y <= -6.6e+152)
		tmp = y;
	elseif (y <= -1.0)
		tmp = Float64(x * y);
	elseif (y <= 4.3e-78)
		tmp = Float64(-x);
	elseif ((y <= 1.46e+113) || (!(y <= 2.9e+200) && (y <= 5.4e+262)))
		tmp = y;
	else
		tmp = Float64(x * y);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= -1.85e+244)
		tmp = x * y;
	elseif (y <= -6.6e+152)
		tmp = y;
	elseif (y <= -1.0)
		tmp = x * y;
	elseif (y <= 4.3e-78)
		tmp = -x;
	elseif ((y <= 1.46e+113) || (~((y <= 2.9e+200)) && (y <= 5.4e+262)))
		tmp = y;
	else
		tmp = x * y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, -1.85e+244], N[(x * y), $MachinePrecision], If[LessEqual[y, -6.6e+152], y, If[LessEqual[y, -1.0], N[(x * y), $MachinePrecision], If[LessEqual[y, 4.3e-78], (-x), If[Or[LessEqual[y, 1.46e+113], And[N[Not[LessEqual[y, 2.9e+200]], $MachinePrecision], LessEqual[y, 5.4e+262]]], y, N[(x * y), $MachinePrecision]]]]]]

Derivation

1. Split input into 3 regimes

2. if y < -1.8500000000000001e244 or -6.6000000000000003e152 < y < -1 or 1.46e113 < y < 2.8999999999999999e200 or 5.4000000000000002e262 < y

   1. Initial program 100.0%

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

   3. Taylor expanded in x around inf 74.1%

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

      1. *-commutative 74.1%

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

   5. Simplified 74.1%

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

   6. Taylor expanded in y around inf 70.0%

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

   if -1.8500000000000001e244 < y < -6.6000000000000003e152 or 4.29999999999999994e-78 < y < 1.46e113 or 2.8999999999999999e200 < y < 5.4000000000000002e262

   1. Initial program 100.0%

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

   3. Taylor expanded in x around 0 64.8%

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

   if -1 < y < 4.29999999999999994e-78

   1. Initial program 100.0%

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

   3. Taylor expanded in y around 0 85.2%

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

      1. neg-mul-1 85.2%

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

   5. Simplified 85.2%

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

4. Final simplification 75.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1.85 \cdot 10^{+244}:\\ \;\;\;\;x \cdot y\\ \mathbf{elif}\;y \leq -6.6 \cdot 10^{+152}:\\ \;\;\;\;y\\ \mathbf{elif}\;y \leq -1:\\ \;\;\;\;x \cdot y\\ \mathbf{elif}\;y \leq 4.3 \cdot 10^{-78}:\\ \;\;\;\;-x\\ \mathbf{elif}\;y \leq 1.46 \cdot 10^{+113} \lor \neg \left(y \leq 2.9 \cdot 10^{+200}\right) \land y \leq 5.4 \cdot 10^{+262}:\\ \;\;\;\;y\\ \mathbf{else}:\\ \;\;\;\;x \cdot y\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 85.3% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -2.7 \cdot 10^{-70} \lor \neg \left(x \leq 1.4 \cdot 10^{-122} \lor \neg \left(x \leq 7.8 \cdot 10^{-104}\right) \land x \leq 3.6 \cdot 10^{-19}\right):\\ \;\;\;\;x \cdot \left(y + -1\right)\\ \mathbf{else}:\\ \;\;\;\;y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (or (<= x -2.7e-70)
         (not (or (<= x 1.4e-122) (and (not (<= x 7.8e-104)) (<= x 3.6e-19)))))
   (* x (+ y -1.0))
   y))

C

double code(double x, double y) {
	double tmp;
	if ((x <= -2.7e-70) || !((x <= 1.4e-122) || (!(x <= 7.8e-104) && (x <= 3.6e-19)))) {
		tmp = x * (y + -1.0);
	} 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 ((x <= (-2.7d-70)) .or. (.not. (x <= 1.4d-122) .or. (.not. (x <= 7.8d-104)) .and. (x <= 3.6d-19))) then
        tmp = x * (y + (-1.0d0))
    else
        tmp = y
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if ((x <= -2.7e-70) || !((x <= 1.4e-122) || (!(x <= 7.8e-104) && (x <= 3.6e-19)))) {
		tmp = x * (y + -1.0);
	} else {
		tmp = y;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (x <= -2.7e-70) or not ((x <= 1.4e-122) or (not (x <= 7.8e-104) and (x <= 3.6e-19))):
		tmp = x * (y + -1.0)
	else:
		tmp = y
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if ((x <= -2.7e-70) || !((x <= 1.4e-122) || (!(x <= 7.8e-104) && (x <= 3.6e-19))))
		tmp = Float64(x * Float64(y + -1.0));
	else
		tmp = y;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if ((x <= -2.7e-70) || ~(((x <= 1.4e-122) || (~((x <= 7.8e-104)) && (x <= 3.6e-19)))))
		tmp = x * (y + -1.0);
	else
		tmp = y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[Or[LessEqual[x, -2.7e-70], N[Not[Or[LessEqual[x, 1.4e-122], And[N[Not[LessEqual[x, 7.8e-104]], $MachinePrecision], LessEqual[x, 3.6e-19]]]], $MachinePrecision]], N[(x * N[(y + -1.0), $MachinePrecision]), $MachinePrecision], y]

Derivation

1. Split input into 2 regimes

2. if x < -2.7000000000000001e-70 or 1.3999999999999999e-122 < x < 7.8000000000000004e-104 or 3.6000000000000001e-19 < x

   1. Initial program 100.0%

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

   3. Taylor expanded in x around inf 96.4%

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

   if -2.7000000000000001e-70 < x < 1.3999999999999999e-122 or 7.8000000000000004e-104 < x < 3.6000000000000001e-19

   1. Initial program 100.0%

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

   3. Taylor expanded in x around 0 85.3%

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

4. Final simplification 92.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -2.7 \cdot 10^{-70} \lor \neg \left(x \leq 1.4 \cdot 10^{-122} \lor \neg \left(x \leq 7.8 \cdot 10^{-104}\right) \land x \leq 3.6 \cdot 10^{-19}\right):\\ \;\;\;\;x \cdot \left(y + -1\right)\\ \mathbf{else}:\\ \;\;\;\;y\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 86.4% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -41000 \lor \neg \left(y \leq 4.3 \cdot 10^{-78}\right):\\ \;\;\;\;y \cdot \left(x + 1\right)\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(y + -1\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (or (<= y -41000.0) (not (<= y 4.3e-78)))
   (* y (+ x 1.0))
   (* x (+ y -1.0))))

C

double code(double x, double y) {
	double tmp;
	if ((y <= -41000.0) || !(y <= 4.3e-78)) {
		tmp = y * (x + 1.0);
	} else {
		tmp = x * (y + -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 <= (-41000.0d0)) .or. (.not. (y <= 4.3d-78))) then
        tmp = y * (x + 1.0d0)
    else
        tmp = x * (y + (-1.0d0))
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if ((y <= -41000.0) || !(y <= 4.3e-78)) {
		tmp = y * (x + 1.0);
	} else {
		tmp = x * (y + -1.0);
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (y <= -41000.0) or not (y <= 4.3e-78):
		tmp = y * (x + 1.0)
	else:
		tmp = x * (y + -1.0)
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if ((y <= -41000.0) || !(y <= 4.3e-78))
		tmp = Float64(y * Float64(x + 1.0));
	else
		tmp = Float64(x * Float64(y + -1.0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if ((y <= -41000.0) || ~((y <= 4.3e-78)))
		tmp = y * (x + 1.0);
	else
		tmp = x * (y + -1.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[Or[LessEqual[y, -41000.0], N[Not[LessEqual[y, 4.3e-78]], $MachinePrecision]], N[(y * N[(x + 1.0), $MachinePrecision]), $MachinePrecision], N[(x * N[(y + -1.0), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -41000 or 4.29999999999999994e-78 < y

   1. Initial program 100.0%

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

   3. Taylor expanded in y around inf 93.3%

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

   if -41000 < y < 4.29999999999999994e-78

   1. Initial program 100.0%

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

   3. Taylor expanded in x around inf 87.0%

      \[\leadsto \color{blue}{x \cdot \left(y - 1\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 90.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -41000 \lor \neg \left(y \leq 4.3 \cdot 10^{-78}\right):\\ \;\;\;\;y \cdot \left(x + 1\right)\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(y + -1\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 86.4% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -44000 \lor \neg \left(y \leq 4.3 \cdot 10^{-78}\right):\\ \;\;\;\;y \cdot \left(x + 1\right)\\ \mathbf{else}:\\ \;\;\;\;x \cdot y - x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (or (<= y -44000.0) (not (<= y 4.3e-78))) (* y (+ x 1.0)) (- (* x y) x)))

C

double code(double x, double y) {
	double tmp;
	if ((y <= -44000.0) || !(y <= 4.3e-78)) {
		tmp = y * (x + 1.0);
	} else {
		tmp = (x * 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 <= (-44000.0d0)) .or. (.not. (y <= 4.3d-78))) then
        tmp = y * (x + 1.0d0)
    else
        tmp = (x * y) - x
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if ((y <= -44000.0) || !(y <= 4.3e-78)) {
		tmp = y * (x + 1.0);
	} else {
		tmp = (x * y) - x;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (y <= -44000.0) or not (y <= 4.3e-78):
		tmp = y * (x + 1.0)
	else:
		tmp = (x * y) - x
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if ((y <= -44000.0) || !(y <= 4.3e-78))
		tmp = Float64(y * Float64(x + 1.0));
	else
		tmp = Float64(Float64(x * y) - x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if ((y <= -44000.0) || ~((y <= 4.3e-78)))
		tmp = y * (x + 1.0);
	else
		tmp = (x * y) - x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[Or[LessEqual[y, -44000.0], N[Not[LessEqual[y, 4.3e-78]], $MachinePrecision]], N[(y * N[(x + 1.0), $MachinePrecision]), $MachinePrecision], N[(N[(x * y), $MachinePrecision] - x), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -44000 or 4.29999999999999994e-78 < y

   1. Initial program 100.0%

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

   3. Taylor expanded in y around inf 93.3%

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

   if -44000 < y < 4.29999999999999994e-78

   1. Initial program 100.0%

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

   3. Taylor expanded in x around inf 87.0%

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

      1. *-commutative 87.0%

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

   5. Simplified 87.0%

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

4. Final simplification 90.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -44000 \lor \neg \left(y \leq 4.3 \cdot 10^{-78}\right):\\ \;\;\;\;y \cdot \left(x + 1\right)\\ \mathbf{else}:\\ \;\;\;\;x \cdot y - x\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 61.7% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -41000:\\ \;\;\;\;y\\ \mathbf{elif}\;y \leq 4.3 \cdot 10^{-78}:\\ \;\;\;\;-x\\ \mathbf{else}:\\ \;\;\;\;y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y -41000.0) y (if (<= y 4.3e-78) (- x) y)))

C

double code(double x, double y) {
	double tmp;
	if (y <= -41000.0) {
		tmp = y;
	} else if (y <= 4.3e-78) {
		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 <= (-41000.0d0)) then
        tmp = y
    else if (y <= 4.3d-78) 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 <= -41000.0) {
		tmp = y;
	} else if (y <= 4.3e-78) {
		tmp = -x;
	} else {
		tmp = y;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= -41000.0:
		tmp = y
	elif y <= 4.3e-78:
		tmp = -x
	else:
		tmp = y
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= -41000.0)
		tmp = y;
	elseif (y <= 4.3e-78)
		tmp = Float64(-x);
	else
		tmp = y;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= -41000.0)
		tmp = y;
	elseif (y <= 4.3e-78)
		tmp = -x;
	else
		tmp = y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, -41000.0], y, If[LessEqual[y, 4.3e-78], (-x), y]]

Derivation

1. Split input into 2 regimes

2. if y < -41000 or 4.29999999999999994e-78 < y

   1. Initial program 100.0%

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

   3. Taylor expanded in x around 0 49.1%

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

   if -41000 < y < 4.29999999999999994e-78

   1. Initial program 100.0%

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

   3. Taylor expanded in y around 0 82.7%

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

      1. neg-mul-1 82.7%

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

   5. Simplified 82.7%

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

4. Final simplification 64.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -41000:\\ \;\;\;\;y\\ \mathbf{elif}\;y \leq 4.3 \cdot 10^{-78}:\\ \;\;\;\;-x\\ \mathbf{else}:\\ \;\;\;\;y\\ \end{array} \]
5. Add Preprocessing

Alternative 7: 38.5% 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. Add Preprocessing

3. Taylor expanded in x around 0 33.6%

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

4. Final simplification 33.6%

   \[\leadsto y \]
5. Add Preprocessing

Reproduce

herbie shell --seed 2024034 
(FPCore (x y)
  :name "Data.Colour.SRGB:transferFunction from colour-2.3.3"
  :precision binary64
  (- (* (+ x 1.0) y) x))