Data.Colour.SRGB:invTransferFunction from colour-2.3.3

Percentage Accurate: 100.0% → 100.0%

Time: 3.5s

Alternatives: 7

Speedup: 1.0×

Specification

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 100.0% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 100.0% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 100.0%

   \[\frac{x + y}{y + 1} \]

2. Final simplification 100.0%

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

Alternative 2: 74.2% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{x}{y + 1}\\ t_1 := \frac{y}{y + 1}\\ \mathbf{if}\;x \leq -1.3 \cdot 10^{+89}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;x \leq -7.8 \cdot 10^{+18}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq -6.6 \cdot 10^{-12}:\\ \;\;\;\;x\\ \mathbf{elif}\;x \leq 9.8 \cdot 10^{-42}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (/ x (+ y 1.0))) (t_1 (/ y (+ y 1.0))))
   (if (<= x -1.3e+89)
     t_0
     (if (<= x -7.8e+18)
       t_1
       (if (<= x -6.6e-12) x (if (<= x 9.8e-42) t_1 t_0))))))

C

double code(double x, double y) {
	double t_0 = x / (y + 1.0);
	double t_1 = y / (y + 1.0);
	double tmp;
	if (x <= -1.3e+89) {
		tmp = t_0;
	} else if (x <= -7.8e+18) {
		tmp = t_1;
	} else if (x <= -6.6e-12) {
		tmp = x;
	} else if (x <= 9.8e-42) {
		tmp = t_1;
	} else {
		tmp = 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) :: t_1
    real(8) :: tmp
    t_0 = x / (y + 1.0d0)
    t_1 = y / (y + 1.0d0)
    if (x <= (-1.3d+89)) then
        tmp = t_0
    else if (x <= (-7.8d+18)) then
        tmp = t_1
    else if (x <= (-6.6d-12)) then
        tmp = x
    else if (x <= 9.8d-42) then
        tmp = t_1
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double t_0 = x / (y + 1.0);
	double t_1 = y / (y + 1.0);
	double tmp;
	if (x <= -1.3e+89) {
		tmp = t_0;
	} else if (x <= -7.8e+18) {
		tmp = t_1;
	} else if (x <= -6.6e-12) {
		tmp = x;
	} else if (x <= 9.8e-42) {
		tmp = t_1;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = x / (y + 1.0)
	t_1 = y / (y + 1.0)
	tmp = 0
	if x <= -1.3e+89:
		tmp = t_0
	elif x <= -7.8e+18:
		tmp = t_1
	elif x <= -6.6e-12:
		tmp = x
	elif x <= 9.8e-42:
		tmp = t_1
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y)
	t_0 = Float64(x / Float64(y + 1.0))
	t_1 = Float64(y / Float64(y + 1.0))
	tmp = 0.0
	if (x <= -1.3e+89)
		tmp = t_0;
	elseif (x <= -7.8e+18)
		tmp = t_1;
	elseif (x <= -6.6e-12)
		tmp = x;
	elseif (x <= 9.8e-42)
		tmp = t_1;
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = x / (y + 1.0);
	t_1 = y / (y + 1.0);
	tmp = 0.0;
	if (x <= -1.3e+89)
		tmp = t_0;
	elseif (x <= -7.8e+18)
		tmp = t_1;
	elseif (x <= -6.6e-12)
		tmp = x;
	elseif (x <= 9.8e-42)
		tmp = t_1;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(x / N[(y + 1.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(y / N[(y + 1.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -1.3e+89], t$95$0, If[LessEqual[x, -7.8e+18], t$95$1, If[LessEqual[x, -6.6e-12], x, If[LessEqual[x, 9.8e-42], t$95$1, t$95$0]]]]]]

Derivation

1. Split input into 3 regimes

2. if x < -1.3e89 or 9.8000000000000001e-42 < x

   1. Initial program 100.0%

      \[\frac{x + y}{y + 1} \]

   2. Taylor expanded in x around inf 80.9%

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

      1. +-commutative 80.9%

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

   4. Simplified 80.9%

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

   if -1.3e89 < x < -7.8e18 or -6.6000000000000001e-12 < x < 9.8000000000000001e-42

   1. Initial program 100.0%

      \[\frac{x + y}{y + 1} \]

   2. Taylor expanded in x around 0 81.7%

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

      1. +-commutative 81.7%

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

   4. Simplified 81.7%

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

   if -7.8e18 < x < -6.6000000000000001e-12

   1. Initial program 100.0%

      \[\frac{x + y}{y + 1} \]

   2. Taylor expanded in y around 0 100.0%

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

4. Final simplification 81.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -1.3 \cdot 10^{+89}:\\ \;\;\;\;\frac{x}{y + 1}\\ \mathbf{elif}\;x \leq -7.8 \cdot 10^{+18}:\\ \;\;\;\;\frac{y}{y + 1}\\ \mathbf{elif}\;x \leq -6.6 \cdot 10^{-12}:\\ \;\;\;\;x\\ \mathbf{elif}\;x \leq 9.8 \cdot 10^{-42}:\\ \;\;\;\;\frac{y}{y + 1}\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{y + 1}\\ \end{array} \]

Alternative 3: 86.7% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -8000000 \lor \neg \left(y \leq 820000\right):\\ \;\;\;\;\frac{x + y}{y}\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{y + 1}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (or (<= y -8000000.0) (not (<= y 820000.0)))
   (/ (+ x y) y)
   (/ x (+ y 1.0))))

C

double code(double x, double y) {
	double tmp;
	if ((y <= -8000000.0) || !(y <= 820000.0)) {
		tmp = (x + y) / y;
	} 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 <= (-8000000.0d0)) .or. (.not. (y <= 820000.0d0))) then
        tmp = (x + y) / y
    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 <= -8000000.0) || !(y <= 820000.0)) {
		tmp = (x + y) / y;
	} else {
		tmp = x / (y + 1.0);
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (y <= -8000000.0) or not (y <= 820000.0):
		tmp = (x + y) / y
	else:
		tmp = x / (y + 1.0)
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if ((y <= -8000000.0) || !(y <= 820000.0))
		tmp = Float64(Float64(x + y) / y);
	else
		tmp = Float64(x / Float64(y + 1.0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if ((y <= -8000000.0) || ~((y <= 820000.0)))
		tmp = (x + y) / y;
	else
		tmp = x / (y + 1.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[Or[LessEqual[y, -8000000.0], N[Not[LessEqual[y, 820000.0]], $MachinePrecision]], N[(N[(x + y), $MachinePrecision] / y), $MachinePrecision], N[(x / N[(y + 1.0), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -8e6 or 8.2e5 < y

   1. Initial program 100.0%

      \[\frac{x + y}{y + 1} \]
   2. Step-by-step derivation

      1. clear-num 99.8%

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

      2. associate-/r/ 99.7%

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

   3. Applied egg-rr 99.7%

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

   4. Taylor expanded in y around inf 99.0%

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

      1. *-commutative 99.0%

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

      2. div-inv 99.3%

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

      3. +-commutative 99.3%

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

   6. Applied egg-rr 99.3%

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

   if -8e6 < y < 8.2e5

   1. Initial program 100.0%

      \[\frac{x + y}{y + 1} \]

   2. Taylor expanded in x around inf 73.0%

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

      1. +-commutative 73.0%

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

   4. Simplified 73.0%

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

4. Final simplification 86.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -8000000 \lor \neg \left(y \leq 820000\right):\\ \;\;\;\;\frac{x + y}{y}\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{y + 1}\\ \end{array} \]

Alternative 4: 72.3% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.45 \cdot 10^{+102}:\\ \;\;\;\;1\\ \mathbf{elif}\;y \leq 3 \cdot 10^{+96}:\\ \;\;\;\;\frac{x}{y + 1}\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y -1.45e+102) 1.0 (if (<= y 3e+96) (/ x (+ y 1.0)) 1.0)))

C

double code(double x, double y) {
	double tmp;
	if (y <= -1.45e+102) {
		tmp = 1.0;
	} else if (y <= 3e+96) {
		tmp = x / (y + 1.0);
	} else {
		tmp = 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 <= (-1.45d+102)) then
        tmp = 1.0d0
    else if (y <= 3d+96) then
        tmp = x / (y + 1.0d0)
    else
        tmp = 1.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (y <= -1.45e+102) {
		tmp = 1.0;
	} else if (y <= 3e+96) {
		tmp = x / (y + 1.0);
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= -1.45e+102:
		tmp = 1.0
	elif y <= 3e+96:
		tmp = x / (y + 1.0)
	else:
		tmp = 1.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= -1.45e+102)
		tmp = 1.0;
	elseif (y <= 3e+96)
		tmp = Float64(x / Float64(y + 1.0));
	else
		tmp = 1.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= -1.45e+102)
		tmp = 1.0;
	elseif (y <= 3e+96)
		tmp = x / (y + 1.0);
	else
		tmp = 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, -1.45e+102], 1.0, If[LessEqual[y, 3e+96], N[(x / N[(y + 1.0), $MachinePrecision]), $MachinePrecision], 1.0]]

Derivation

1. Split input into 2 regimes

2. if y < -1.4500000000000001e102 or 3e96 < y

   1. Initial program 100.0%

      \[\frac{x + y}{y + 1} \]

   2. Taylor expanded in y around inf 77.2%

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

   if -1.4500000000000001e102 < y < 3e96

   1. Initial program 100.0%

      \[\frac{x + y}{y + 1} \]

   2. Taylor expanded in x around inf 70.6%

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

      1. +-commutative 70.6%

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

   4. Simplified 70.6%

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

4. Final simplification 73.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1.45 \cdot 10^{+102}:\\ \;\;\;\;1\\ \mathbf{elif}\;y \leq 3 \cdot 10^{+96}:\\ \;\;\;\;\frac{x}{y + 1}\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \]

Alternative 5: 70.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -3.5 \cdot 10^{+102}:\\ \;\;\;\;1\\ \mathbf{elif}\;y \leq -920000000000:\\ \;\;\;\;\frac{x}{y}\\ \mathbf{elif}\;y \leq 1.4 \cdot 10^{-29}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y -3.5e+102)
   1.0
   (if (<= y -920000000000.0) (/ x y) (if (<= y 1.4e-29) x 1.0))))

C

double code(double x, double y) {
	double tmp;
	if (y <= -3.5e+102) {
		tmp = 1.0;
	} else if (y <= -920000000000.0) {
		tmp = x / y;
	} else if (y <= 1.4e-29) {
		tmp = x;
	} else {
		tmp = 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 <= (-3.5d+102)) then
        tmp = 1.0d0
    else if (y <= (-920000000000.0d0)) then
        tmp = x / y
    else if (y <= 1.4d-29) then
        tmp = x
    else
        tmp = 1.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (y <= -3.5e+102) {
		tmp = 1.0;
	} else if (y <= -920000000000.0) {
		tmp = x / y;
	} else if (y <= 1.4e-29) {
		tmp = x;
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= -3.5e+102:
		tmp = 1.0
	elif y <= -920000000000.0:
		tmp = x / y
	elif y <= 1.4e-29:
		tmp = x
	else:
		tmp = 1.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= -3.5e+102)
		tmp = 1.0;
	elseif (y <= -920000000000.0)
		tmp = Float64(x / y);
	elseif (y <= 1.4e-29)
		tmp = x;
	else
		tmp = 1.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= -3.5e+102)
		tmp = 1.0;
	elseif (y <= -920000000000.0)
		tmp = x / y;
	elseif (y <= 1.4e-29)
		tmp = x;
	else
		tmp = 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, -3.5e+102], 1.0, If[LessEqual[y, -920000000000.0], N[(x / y), $MachinePrecision], If[LessEqual[y, 1.4e-29], x, 1.0]]]

Derivation

1. Split input into 3 regimes

2. if y < -3.50000000000000011e102 or 1.4000000000000001e-29 < y

   1. Initial program 100.0%

      \[\frac{x + y}{y + 1} \]

   2. Taylor expanded in y around inf 70.4%

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

   if -3.50000000000000011e102 < y < -9.2e11

   1. Initial program 99.9%

      \[\frac{x + y}{y + 1} \]

   2. Taylor expanded in x around inf 67.1%

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

      1. +-commutative 67.1%

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

   4. Simplified 67.1%

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

   5. Taylor expanded in y around inf 67.1%

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

   if -9.2e11 < y < 1.4000000000000001e-29

   1. Initial program 100.0%

      \[\frac{x + y}{y + 1} \]

   2. Taylor expanded in y around 0 73.9%

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

4. Final simplification 71.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -3.5 \cdot 10^{+102}:\\ \;\;\;\;1\\ \mathbf{elif}\;y \leq -920000000000:\\ \;\;\;\;\frac{x}{y}\\ \mathbf{elif}\;y \leq 1.4 \cdot 10^{-29}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \]

Alternative 6: 72.8% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1:\\ \;\;\;\;1\\ \mathbf{elif}\;y \leq 1.4 \cdot 10^{-29}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y -1.0) 1.0 (if (<= y 1.4e-29) x 1.0)))

C

double code(double x, double y) {
	double tmp;
	if (y <= -1.0) {
		tmp = 1.0;
	} else if (y <= 1.4e-29) {
		tmp = x;
	} else {
		tmp = 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 <= (-1.0d0)) then
        tmp = 1.0d0
    else if (y <= 1.4d-29) then
        tmp = x
    else
        tmp = 1.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (y <= -1.0) {
		tmp = 1.0;
	} else if (y <= 1.4e-29) {
		tmp = x;
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= -1.0:
		tmp = 1.0
	elif y <= 1.4e-29:
		tmp = x
	else:
		tmp = 1.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= -1.0)
		tmp = 1.0;
	elseif (y <= 1.4e-29)
		tmp = x;
	else
		tmp = 1.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= -1.0)
		tmp = 1.0;
	elseif (y <= 1.4e-29)
		tmp = x;
	else
		tmp = 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, -1.0], 1.0, If[LessEqual[y, 1.4e-29], x, 1.0]]

Derivation

1. Split input into 2 regimes

2. if y < -1 or 1.4000000000000001e-29 < y

   1. Initial program 100.0%

      \[\frac{x + y}{y + 1} \]

   2. Taylor expanded in y around inf 65.6%

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

   if -1 < y < 1.4000000000000001e-29

   1. Initial program 100.0%

      \[\frac{x + y}{y + 1} \]

   2. Taylor expanded in y around 0 74.4%

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

4. Final simplification 69.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1:\\ \;\;\;\;1\\ \mathbf{elif}\;y \leq 1.4 \cdot 10^{-29}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \]

Alternative 7: 38.7% accurate, 7.0× 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 100.0%

   \[\frac{x + y}{y + 1} \]

2. Taylor expanded in y around inf 36.2%

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

3. Final simplification 36.2%

   \[\leadsto 1 \]

Reproduce

herbie shell --seed 2023257 
(FPCore (x y)
  :name "Data.Colour.SRGB:invTransferFunction from colour-2.3.3"
  :precision binary64
  (/ (+ x y) (+ y 1.0)))