Data.Colour.SRGB:invTransferFunction from colour-2.3.3

Percentage Accurate: 100.0% → 100.0%

Time: 5.6s

Alternatives: 10

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

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 2: 72.5% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{x}{y + 1}\\ t_1 := \frac{y}{y + 1}\\ \mathbf{if}\;x \leq -4.2 \cdot 10^{+64}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;x \leq -5.8 \cdot 10^{+18}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq -2.2 \cdot 10^{-39}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;x \leq 1.1 \cdot 10^{-70}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq 1.22 \cdot 10^{+89} \lor \neg \left(x \leq 1.6 \cdot 10^{+141}\right):\\ \;\;\;\;t_0\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (/ x (+ y 1.0))) (t_1 (/ y (+ y 1.0))))
   (if (<= x -4.2e+64)
     t_0
     (if (<= x -5.8e+18)
       t_1
       (if (<= x -2.2e-39)
         t_0
         (if (<= x 1.1e-70)
           t_1
           (if (or (<= x 1.22e+89) (not (<= x 1.6e+141))) t_0 1.0)))))))

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 <= -4.2e+64) {
		tmp = t_0;
	} else if (x <= -5.8e+18) {
		tmp = t_1;
	} else if (x <= -2.2e-39) {
		tmp = t_0;
	} else if (x <= 1.1e-70) {
		tmp = t_1;
	} else if ((x <= 1.22e+89) || !(x <= 1.6e+141)) {
		tmp = t_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) :: t_0
    real(8) :: t_1
    real(8) :: tmp
    t_0 = x / (y + 1.0d0)
    t_1 = y / (y + 1.0d0)
    if (x <= (-4.2d+64)) then
        tmp = t_0
    else if (x <= (-5.8d+18)) then
        tmp = t_1
    else if (x <= (-2.2d-39)) then
        tmp = t_0
    else if (x <= 1.1d-70) then
        tmp = t_1
    else if ((x <= 1.22d+89) .or. (.not. (x <= 1.6d+141))) then
        tmp = t_0
    else
        tmp = 1.0d0
    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 <= -4.2e+64) {
		tmp = t_0;
	} else if (x <= -5.8e+18) {
		tmp = t_1;
	} else if (x <= -2.2e-39) {
		tmp = t_0;
	} else if (x <= 1.1e-70) {
		tmp = t_1;
	} else if ((x <= 1.22e+89) || !(x <= 1.6e+141)) {
		tmp = t_0;
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = x / (y + 1.0)
	t_1 = y / (y + 1.0)
	tmp = 0
	if x <= -4.2e+64:
		tmp = t_0
	elif x <= -5.8e+18:
		tmp = t_1
	elif x <= -2.2e-39:
		tmp = t_0
	elif x <= 1.1e-70:
		tmp = t_1
	elif (x <= 1.22e+89) or not (x <= 1.6e+141):
		tmp = t_0
	else:
		tmp = 1.0
	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 <= -4.2e+64)
		tmp = t_0;
	elseif (x <= -5.8e+18)
		tmp = t_1;
	elseif (x <= -2.2e-39)
		tmp = t_0;
	elseif (x <= 1.1e-70)
		tmp = t_1;
	elseif ((x <= 1.22e+89) || !(x <= 1.6e+141))
		tmp = t_0;
	else
		tmp = 1.0;
	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 <= -4.2e+64)
		tmp = t_0;
	elseif (x <= -5.8e+18)
		tmp = t_1;
	elseif (x <= -2.2e-39)
		tmp = t_0;
	elseif (x <= 1.1e-70)
		tmp = t_1;
	elseif ((x <= 1.22e+89) || ~((x <= 1.6e+141)))
		tmp = t_0;
	else
		tmp = 1.0;
	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, -4.2e+64], t$95$0, If[LessEqual[x, -5.8e+18], t$95$1, If[LessEqual[x, -2.2e-39], t$95$0, If[LessEqual[x, 1.1e-70], t$95$1, If[Or[LessEqual[x, 1.22e+89], N[Not[LessEqual[x, 1.6e+141]], $MachinePrecision]], t$95$0, 1.0]]]]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 3: 73.4% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{x}{y + 1}\\ \mathbf{if}\;y \leq -9.2 \cdot 10^{+27}:\\ \;\;\;\;1\\ \mathbf{elif}\;y \leq -5.8 \cdot 10^{-15}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;y \leq -4 \cdot 10^{-41}:\\ \;\;\;\;y\\ \mathbf{elif}\;y \leq 4.2 \cdot 10^{-106}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 3.4 \cdot 10^{-60}:\\ \;\;\;\;y\\ \mathbf{elif}\;y \leq 1.12 \cdot 10^{+81}:\\ \;\;\;\;t_0\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (/ x (+ y 1.0))))
   (if (<= y -9.2e+27)
     1.0
     (if (<= y -5.8e-15)
       t_0
       (if (<= y -4e-41)
         y
         (if (<= y 4.2e-106)
           x
           (if (<= y 3.4e-60) y (if (<= y 1.12e+81) t_0 1.0))))))))

C

double code(double x, double y) {
	double t_0 = x / (y + 1.0);
	double tmp;
	if (y <= -9.2e+27) {
		tmp = 1.0;
	} else if (y <= -5.8e-15) {
		tmp = t_0;
	} else if (y <= -4e-41) {
		tmp = y;
	} else if (y <= 4.2e-106) {
		tmp = x;
	} else if (y <= 3.4e-60) {
		tmp = y;
	} else if (y <= 1.12e+81) {
		tmp = t_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) :: t_0
    real(8) :: tmp
    t_0 = x / (y + 1.0d0)
    if (y <= (-9.2d+27)) then
        tmp = 1.0d0
    else if (y <= (-5.8d-15)) then
        tmp = t_0
    else if (y <= (-4d-41)) then
        tmp = y
    else if (y <= 4.2d-106) then
        tmp = x
    else if (y <= 3.4d-60) then
        tmp = y
    else if (y <= 1.12d+81) then
        tmp = t_0
    else
        tmp = 1.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double t_0 = x / (y + 1.0);
	double tmp;
	if (y <= -9.2e+27) {
		tmp = 1.0;
	} else if (y <= -5.8e-15) {
		tmp = t_0;
	} else if (y <= -4e-41) {
		tmp = y;
	} else if (y <= 4.2e-106) {
		tmp = x;
	} else if (y <= 3.4e-60) {
		tmp = y;
	} else if (y <= 1.12e+81) {
		tmp = t_0;
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = x / (y + 1.0)
	tmp = 0
	if y <= -9.2e+27:
		tmp = 1.0
	elif y <= -5.8e-15:
		tmp = t_0
	elif y <= -4e-41:
		tmp = y
	elif y <= 4.2e-106:
		tmp = x
	elif y <= 3.4e-60:
		tmp = y
	elif y <= 1.12e+81:
		tmp = t_0
	else:
		tmp = 1.0
	return tmp

Julia

function code(x, y)
	t_0 = Float64(x / Float64(y + 1.0))
	tmp = 0.0
	if (y <= -9.2e+27)
		tmp = 1.0;
	elseif (y <= -5.8e-15)
		tmp = t_0;
	elseif (y <= -4e-41)
		tmp = y;
	elseif (y <= 4.2e-106)
		tmp = x;
	elseif (y <= 3.4e-60)
		tmp = y;
	elseif (y <= 1.12e+81)
		tmp = t_0;
	else
		tmp = 1.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = x / (y + 1.0);
	tmp = 0.0;
	if (y <= -9.2e+27)
		tmp = 1.0;
	elseif (y <= -5.8e-15)
		tmp = t_0;
	elseif (y <= -4e-41)
		tmp = y;
	elseif (y <= 4.2e-106)
		tmp = x;
	elseif (y <= 3.4e-60)
		tmp = y;
	elseif (y <= 1.12e+81)
		tmp = t_0;
	else
		tmp = 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(x / N[(y + 1.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -9.2e+27], 1.0, If[LessEqual[y, -5.8e-15], t$95$0, If[LessEqual[y, -4e-41], y, If[LessEqual[y, 4.2e-106], x, If[LessEqual[y, 3.4e-60], y, If[LessEqual[y, 1.12e+81], t$95$0, 1.0]]]]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 4: 73.1% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x - x \cdot y\\ \mathbf{if}\;y \leq -1:\\ \;\;\;\;1\\ \mathbf{elif}\;y \leq 4.2 \cdot 10^{-106}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;y \leq 2 \cdot 10^{-57}:\\ \;\;\;\;y\\ \mathbf{elif}\;y \leq 5.2 \cdot 10^{-14}:\\ \;\;\;\;t_0\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (- x (* x y))))
   (if (<= y -1.0)
     1.0
     (if (<= y 4.2e-106)
       t_0
       (if (<= y 2e-57) y (if (<= y 5.2e-14) t_0 1.0))))))

C

double code(double x, double y) {
	double t_0 = x - (x * y);
	double tmp;
	if (y <= -1.0) {
		tmp = 1.0;
	} else if (y <= 4.2e-106) {
		tmp = t_0;
	} else if (y <= 2e-57) {
		tmp = y;
	} else if (y <= 5.2e-14) {
		tmp = t_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) :: t_0
    real(8) :: tmp
    t_0 = x - (x * y)
    if (y <= (-1.0d0)) then
        tmp = 1.0d0
    else if (y <= 4.2d-106) then
        tmp = t_0
    else if (y <= 2d-57) then
        tmp = y
    else if (y <= 5.2d-14) then
        tmp = t_0
    else
        tmp = 1.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double t_0 = x - (x * y);
	double tmp;
	if (y <= -1.0) {
		tmp = 1.0;
	} else if (y <= 4.2e-106) {
		tmp = t_0;
	} else if (y <= 2e-57) {
		tmp = y;
	} else if (y <= 5.2e-14) {
		tmp = t_0;
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = x - (x * y)
	tmp = 0
	if y <= -1.0:
		tmp = 1.0
	elif y <= 4.2e-106:
		tmp = t_0
	elif y <= 2e-57:
		tmp = y
	elif y <= 5.2e-14:
		tmp = t_0
	else:
		tmp = 1.0
	return tmp

Julia

function code(x, y)
	t_0 = Float64(x - Float64(x * y))
	tmp = 0.0
	if (y <= -1.0)
		tmp = 1.0;
	elseif (y <= 4.2e-106)
		tmp = t_0;
	elseif (y <= 2e-57)
		tmp = y;
	elseif (y <= 5.2e-14)
		tmp = t_0;
	else
		tmp = 1.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = x - (x * y);
	tmp = 0.0;
	if (y <= -1.0)
		tmp = 1.0;
	elseif (y <= 4.2e-106)
		tmp = t_0;
	elseif (y <= 2e-57)
		tmp = y;
	elseif (y <= 5.2e-14)
		tmp = t_0;
	else
		tmp = 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(x - N[(x * y), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -1.0], 1.0, If[LessEqual[y, 4.2e-106], t$95$0, If[LessEqual[y, 2e-57], y, If[LessEqual[y, 5.2e-14], t$95$0, 1.0]]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 5: 85.5% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 1 + \frac{x}{y}\\ \mathbf{if}\;y \leq -3000:\\ \;\;\;\;t_0\\ \mathbf{elif}\;y \leq 4.2 \cdot 10^{-106}:\\ \;\;\;\;\frac{x}{y + 1}\\ \mathbf{elif}\;y \leq 3.2 \cdot 10^{-60}:\\ \;\;\;\;\frac{y}{y + 1}\\ \mathbf{elif}\;y \leq 5.2 \cdot 10^{-14}:\\ \;\;\;\;x - x \cdot y\\ \mathbf{else}:\\ \;\;\;\;t_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (+ 1.0 (/ x y))))
   (if (<= y -3000.0)
     t_0
     (if (<= y 4.2e-106)
       (/ x (+ y 1.0))
       (if (<= y 3.2e-60)
         (/ y (+ y 1.0))
         (if (<= y 5.2e-14) (- x (* x y)) t_0))))))

C

double code(double x, double y) {
	double t_0 = 1.0 + (x / y);
	double tmp;
	if (y <= -3000.0) {
		tmp = t_0;
	} else if (y <= 4.2e-106) {
		tmp = x / (y + 1.0);
	} else if (y <= 3.2e-60) {
		tmp = y / (y + 1.0);
	} else if (y <= 5.2e-14) {
		tmp = x - (x * y);
	} 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) :: tmp
    t_0 = 1.0d0 + (x / y)
    if (y <= (-3000.0d0)) then
        tmp = t_0
    else if (y <= 4.2d-106) then
        tmp = x / (y + 1.0d0)
    else if (y <= 3.2d-60) then
        tmp = y / (y + 1.0d0)
    else if (y <= 5.2d-14) then
        tmp = x - (x * y)
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double t_0 = 1.0 + (x / y);
	double tmp;
	if (y <= -3000.0) {
		tmp = t_0;
	} else if (y <= 4.2e-106) {
		tmp = x / (y + 1.0);
	} else if (y <= 3.2e-60) {
		tmp = y / (y + 1.0);
	} else if (y <= 5.2e-14) {
		tmp = x - (x * y);
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = 1.0 + (x / y)
	tmp = 0
	if y <= -3000.0:
		tmp = t_0
	elif y <= 4.2e-106:
		tmp = x / (y + 1.0)
	elif y <= 3.2e-60:
		tmp = y / (y + 1.0)
	elif y <= 5.2e-14:
		tmp = x - (x * y)
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y)
	t_0 = Float64(1.0 + Float64(x / y))
	tmp = 0.0
	if (y <= -3000.0)
		tmp = t_0;
	elseif (y <= 4.2e-106)
		tmp = Float64(x / Float64(y + 1.0));
	elseif (y <= 3.2e-60)
		tmp = Float64(y / Float64(y + 1.0));
	elseif (y <= 5.2e-14)
		tmp = Float64(x - Float64(x * y));
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = 1.0 + (x / y);
	tmp = 0.0;
	if (y <= -3000.0)
		tmp = t_0;
	elseif (y <= 4.2e-106)
		tmp = x / (y + 1.0);
	elseif (y <= 3.2e-60)
		tmp = y / (y + 1.0);
	elseif (y <= 5.2e-14)
		tmp = x - (x * y);
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(1.0 + N[(x / y), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -3000.0], t$95$0, If[LessEqual[y, 4.2e-106], N[(x / N[(y + 1.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 3.2e-60], N[(y / N[(y + 1.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 5.2e-14], N[(x - N[(x * y), $MachinePrecision]), $MachinePrecision], t$95$0]]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 6: 98.1% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1 \lor \neg \left(y \leq 0.78\right):\\ \;\;\;\;1 + \frac{x}{y}\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot \left(1 - x\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (or (<= y -1.0) (not (<= y 0.78))) (+ 1.0 (/ x y)) (+ x (* y (- 1.0 x)))))

C

double code(double x, double y) {
	double tmp;
	if ((y <= -1.0) || !(y <= 0.78)) {
		tmp = 1.0 + (x / y);
	} else {
		tmp = x + (y * (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 <= (-1.0d0)) .or. (.not. (y <= 0.78d0))) then
        tmp = 1.0d0 + (x / y)
    else
        tmp = x + (y * (1.0d0 - x))
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if ((y <= -1.0) || !(y <= 0.78)) {
		tmp = 1.0 + (x / y);
	} else {
		tmp = x + (y * (1.0 - x));
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (y <= -1.0) or not (y <= 0.78):
		tmp = 1.0 + (x / y)
	else:
		tmp = x + (y * (1.0 - x))
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if ((y <= -1.0) || !(y <= 0.78))
		tmp = Float64(1.0 + Float64(x / y));
	else
		tmp = Float64(x + Float64(y * Float64(1.0 - x)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if ((y <= -1.0) || ~((y <= 0.78)))
		tmp = 1.0 + (x / y);
	else
		tmp = x + (y * (1.0 - x));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[Or[LessEqual[y, -1.0], N[Not[LessEqual[y, 0.78]], $MachinePrecision]], N[(1.0 + N[(x / y), $MachinePrecision]), $MachinePrecision], N[(x + N[(y * N[(1.0 - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 7: 98.4% accurate, 0.6× speedup

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 8: 73.0% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1:\\ \;\;\;\;1\\ \mathbf{elif}\;y \leq 4.2 \cdot 10^{-106}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 3.2 \cdot 10^{-60}:\\ \;\;\;\;y\\ \mathbf{elif}\;y \leq 5.2 \cdot 10^{-14}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y -1.0)
   1.0
   (if (<= y 4.2e-106) x (if (<= y 3.2e-60) y (if (<= y 5.2e-14) x 1.0)))))

C

double code(double x, double y) {
	double tmp;
	if (y <= -1.0) {
		tmp = 1.0;
	} else if (y <= 4.2e-106) {
		tmp = x;
	} else if (y <= 3.2e-60) {
		tmp = y;
	} else if (y <= 5.2e-14) {
		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 <= 4.2d-106) then
        tmp = x
    else if (y <= 3.2d-60) then
        tmp = y
    else if (y <= 5.2d-14) 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 <= 4.2e-106) {
		tmp = x;
	} else if (y <= 3.2e-60) {
		tmp = y;
	} else if (y <= 5.2e-14) {
		tmp = x;
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= -1.0:
		tmp = 1.0
	elif y <= 4.2e-106:
		tmp = x
	elif y <= 3.2e-60:
		tmp = y
	elif y <= 5.2e-14:
		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 <= 4.2e-106)
		tmp = x;
	elseif (y <= 3.2e-60)
		tmp = y;
	elseif (y <= 5.2e-14)
		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 <= 4.2e-106)
		tmp = x;
	elseif (y <= 3.2e-60)
		tmp = y;
	elseif (y <= 5.2e-14)
		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, 4.2e-106], x, If[LessEqual[y, 3.2e-60], y, If[LessEqual[y, 5.2e-14], x, 1.0]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 9: 73.9% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1:\\ \;\;\;\;1\\ \mathbf{elif}\;y \leq 5.2 \cdot 10^{-14}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y -1.0) 1.0 (if (<= y 5.2e-14) x 1.0)))

C

double code(double x, double y) {
	double tmp;
	if (y <= -1.0) {
		tmp = 1.0;
	} else if (y <= 5.2e-14) {
		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 <= 5.2d-14) 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 <= 5.2e-14) {
		tmp = x;
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= -1.0:
		tmp = 1.0
	elif y <= 5.2e-14:
		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 <= 5.2e-14)
		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 <= 5.2e-14)
		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, 5.2e-14], x, 1.0]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 10: 39.7% 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

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Reproduce

herbie shell --seed 2023350 
(FPCore (x y)
  :name "Data.Colour.SRGB:invTransferFunction from colour-2.3.3"
  :precision binary64
  (/ (+ x y) (+ y 1.0)))