Data.Colour.RGBSpace.HSL:hsl from colour-2.3.3, A

Percentage Accurate: 100.0% → 100.0%

Time: 3.4s

Alternatives: 7

Speedup: 1.0×

Specification

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 100.0% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 100.0% accurate, 0.1× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(y, 1 - x, x\right) \end{array} \]

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 100.0%

   \[\left(x + y\right) - x \cdot y \]
2. Step-by-step derivation

   1. associate--l+ 100.0%

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

   2. +-commutative 100.0%

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

   3. *-lft-identity 100.0%

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

   4. metadata-eval 100.0%

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

   5. distribute-rgt-out-- 100.0%

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

   6. fma-define 100.0%

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

   7. metadata-eval 100.0%

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

3. Simplified 100.0%

   \[\leadsto \color{blue}{\mathsf{fma}\left(y, 1 - x, x\right)} \]
4. Add Preprocessing
5. Add Preprocessing

Alternative 2: 59.5% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x \cdot \left(-y\right)\\ \mathbf{if}\;y \leq -1:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;y \leq 5.1 \cdot 10^{-166}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 7.6 \cdot 10^{-116}:\\ \;\;\;\;y\\ \mathbf{elif}\;y \leq 1.1 \cdot 10^{-73}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 7 \cdot 10^{+249}:\\ \;\;\;\;y\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (* x (- y))))
   (if (<= y -1.0)
     t_0
     (if (<= y 5.1e-166)
       x
       (if (<= y 7.6e-116)
         y
         (if (<= y 1.1e-73) x (if (<= y 7e+249) y t_0)))))))

C

double code(double x, double y) {
	double t_0 = x * -y;
	double tmp;
	if (y <= -1.0) {
		tmp = t_0;
	} else if (y <= 5.1e-166) {
		tmp = x;
	} else if (y <= 7.6e-116) {
		tmp = y;
	} else if (y <= 1.1e-73) {
		tmp = x;
	} else if (y <= 7e+249) {
		tmp = 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 = x * -y
    if (y <= (-1.0d0)) then
        tmp = t_0
    else if (y <= 5.1d-166) then
        tmp = x
    else if (y <= 7.6d-116) then
        tmp = y
    else if (y <= 1.1d-73) then
        tmp = x
    else if (y <= 7d+249) then
        tmp = y
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double t_0 = x * -y;
	double tmp;
	if (y <= -1.0) {
		tmp = t_0;
	} else if (y <= 5.1e-166) {
		tmp = x;
	} else if (y <= 7.6e-116) {
		tmp = y;
	} else if (y <= 1.1e-73) {
		tmp = x;
	} else if (y <= 7e+249) {
		tmp = y;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = x * -y
	tmp = 0
	if y <= -1.0:
		tmp = t_0
	elif y <= 5.1e-166:
		tmp = x
	elif y <= 7.6e-116:
		tmp = y
	elif y <= 1.1e-73:
		tmp = x
	elif y <= 7e+249:
		tmp = y
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y)
	t_0 = Float64(x * Float64(-y))
	tmp = 0.0
	if (y <= -1.0)
		tmp = t_0;
	elseif (y <= 5.1e-166)
		tmp = x;
	elseif (y <= 7.6e-116)
		tmp = y;
	elseif (y <= 1.1e-73)
		tmp = x;
	elseif (y <= 7e+249)
		tmp = y;
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = x * -y;
	tmp = 0.0;
	if (y <= -1.0)
		tmp = t_0;
	elseif (y <= 5.1e-166)
		tmp = x;
	elseif (y <= 7.6e-116)
		tmp = y;
	elseif (y <= 1.1e-73)
		tmp = x;
	elseif (y <= 7e+249)
		tmp = y;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(x * (-y)), $MachinePrecision]}, If[LessEqual[y, -1.0], t$95$0, If[LessEqual[y, 5.1e-166], x, If[LessEqual[y, 7.6e-116], y, If[LessEqual[y, 1.1e-73], x, If[LessEqual[y, 7e+249], y, t$95$0]]]]]]

Derivation

1. Split input into 3 regimes

2. if y < -1 or 7.00000000000000024e249 < y

   1. Initial program 99.9%

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

   3. Taylor expanded in y around inf 99.9%

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

   4. Taylor expanded in x around inf 53.0%

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

      1. mul-1-neg 53.0%

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

      2. *-commutative 53.0%

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

      3. distribute-rgt-neg-in 53.0%

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

   6. Simplified 53.0%

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

   if -1 < y < 5.1000000000000002e-166 or 7.6000000000000003e-116 < y < 1.1e-73

   1. Initial program 100.0%

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

   3. Taylor expanded in y around 0 80.2%

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

   if 5.1000000000000002e-166 < y < 7.6000000000000003e-116 or 1.1e-73 < y < 7.00000000000000024e249

   1. Initial program 100.0%

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

   3. Taylor expanded in x around 0 49.5%

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

4. Final simplification 63.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1:\\ \;\;\;\;x \cdot \left(-y\right)\\ \mathbf{elif}\;y \leq 5.1 \cdot 10^{-166}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 7.6 \cdot 10^{-116}:\\ \;\;\;\;y\\ \mathbf{elif}\;y \leq 1.1 \cdot 10^{-73}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 7 \cdot 10^{+249}:\\ \;\;\;\;y\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(-y\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 72.0% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x \cdot \left(1 - y\right)\\ \mathbf{if}\;x \leq -4.4 \cdot 10^{-63}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;x \leq -1.35 \cdot 10^{-117}:\\ \;\;\;\;y\\ \mathbf{elif}\;x \leq -9 \cdot 10^{-176}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;x \leq 1:\\ \;\;\;\;y\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(-y\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (* x (- 1.0 y))))
   (if (<= x -4.4e-63)
     t_0
     (if (<= x -1.35e-117)
       y
       (if (<= x -9e-176) t_0 (if (<= x 1.0) y (* x (- y))))))))

C

double code(double x, double y) {
	double t_0 = x * (1.0 - y);
	double tmp;
	if (x <= -4.4e-63) {
		tmp = t_0;
	} else if (x <= -1.35e-117) {
		tmp = y;
	} else if (x <= -9e-176) {
		tmp = t_0;
	} else if (x <= 1.0) {
		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) :: t_0
    real(8) :: tmp
    t_0 = x * (1.0d0 - y)
    if (x <= (-4.4d-63)) then
        tmp = t_0
    else if (x <= (-1.35d-117)) then
        tmp = y
    else if (x <= (-9d-176)) then
        tmp = t_0
    else if (x <= 1.0d0) then
        tmp = y
    else
        tmp = x * -y
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double t_0 = x * (1.0 - y);
	double tmp;
	if (x <= -4.4e-63) {
		tmp = t_0;
	} else if (x <= -1.35e-117) {
		tmp = y;
	} else if (x <= -9e-176) {
		tmp = t_0;
	} else if (x <= 1.0) {
		tmp = y;
	} else {
		tmp = x * -y;
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = x * (1.0 - y)
	tmp = 0
	if x <= -4.4e-63:
		tmp = t_0
	elif x <= -1.35e-117:
		tmp = y
	elif x <= -9e-176:
		tmp = t_0
	elif x <= 1.0:
		tmp = y
	else:
		tmp = x * -y
	return tmp

Julia

function code(x, y)
	t_0 = Float64(x * Float64(1.0 - y))
	tmp = 0.0
	if (x <= -4.4e-63)
		tmp = t_0;
	elseif (x <= -1.35e-117)
		tmp = y;
	elseif (x <= -9e-176)
		tmp = t_0;
	elseif (x <= 1.0)
		tmp = y;
	else
		tmp = Float64(x * Float64(-y));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = x * (1.0 - y);
	tmp = 0.0;
	if (x <= -4.4e-63)
		tmp = t_0;
	elseif (x <= -1.35e-117)
		tmp = y;
	elseif (x <= -9e-176)
		tmp = t_0;
	elseif (x <= 1.0)
		tmp = y;
	else
		tmp = x * -y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(x * N[(1.0 - y), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -4.4e-63], t$95$0, If[LessEqual[x, -1.35e-117], y, If[LessEqual[x, -9e-176], t$95$0, If[LessEqual[x, 1.0], y, N[(x * (-y)), $MachinePrecision]]]]]]

Derivation

1. Split input into 3 regimes

2. if x < -4.3999999999999999e-63 or -1.35000000000000001e-117 < x < -9e-176

   1. Initial program 100.0%

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

   3. Taylor expanded in x around inf 87.2%

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

   if -4.3999999999999999e-63 < x < -1.35000000000000001e-117 or -9e-176 < x < 1

   1. Initial program 100.0%

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

   3. Taylor expanded in x around 0 75.7%

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

   if 1 < x

   1. Initial program 99.9%

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

   3. Taylor expanded in y around inf 48.0%

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

   4. Taylor expanded in x around inf 45.6%

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

      1. mul-1-neg 45.6%

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

      2. *-commutative 45.6%

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

      3. distribute-rgt-neg-in 45.6%

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

   6. Simplified 45.6%

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

4. Final simplification 73.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -4.4 \cdot 10^{-63}:\\ \;\;\;\;x \cdot \left(1 - y\right)\\ \mathbf{elif}\;x \leq -1.35 \cdot 10^{-117}:\\ \;\;\;\;y\\ \mathbf{elif}\;x \leq -9 \cdot 10^{-176}:\\ \;\;\;\;x \cdot \left(1 - y\right)\\ \mathbf{elif}\;x \leq 1:\\ \;\;\;\;y\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(-y\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 72.2% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq 5.1 \cdot 10^{-166}:\\ \;\;\;\;x \cdot \left(1 - y\right)\\ \mathbf{elif}\;y \leq 7.6 \cdot 10^{-116}:\\ \;\;\;\;y\\ \mathbf{elif}\;y \leq 3.6 \cdot 10^{-74}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;y \cdot \left(1 - x\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y 5.1e-166)
   (* x (- 1.0 y))
   (if (<= y 7.6e-116) y (if (<= y 3.6e-74) x (* y (- 1.0 x))))))

C

double code(double x, double y) {
	double tmp;
	if (y <= 5.1e-166) {
		tmp = x * (1.0 - y);
	} else if (y <= 7.6e-116) {
		tmp = y;
	} else if (y <= 3.6e-74) {
		tmp = x;
	} else {
		tmp = 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 <= 5.1d-166) then
        tmp = x * (1.0d0 - y)
    else if (y <= 7.6d-116) then
        tmp = y
    else if (y <= 3.6d-74) then
        tmp = x
    else
        tmp = y * (1.0d0 - x)
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (y <= 5.1e-166) {
		tmp = x * (1.0 - y);
	} else if (y <= 7.6e-116) {
		tmp = y;
	} else if (y <= 3.6e-74) {
		tmp = x;
	} else {
		tmp = y * (1.0 - x);
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= 5.1e-166:
		tmp = x * (1.0 - y)
	elif y <= 7.6e-116:
		tmp = y
	elif y <= 3.6e-74:
		tmp = x
	else:
		tmp = y * (1.0 - x)
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= 5.1e-166)
		tmp = Float64(x * Float64(1.0 - y));
	elseif (y <= 7.6e-116)
		tmp = y;
	elseif (y <= 3.6e-74)
		tmp = x;
	else
		tmp = Float64(y * Float64(1.0 - x));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= 5.1e-166)
		tmp = x * (1.0 - y);
	elseif (y <= 7.6e-116)
		tmp = y;
	elseif (y <= 3.6e-74)
		tmp = x;
	else
		tmp = y * (1.0 - x);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, 5.1e-166], N[(x * N[(1.0 - y), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 7.6e-116], y, If[LessEqual[y, 3.6e-74], x, N[(y * N[(1.0 - x), $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 4 regimes

2. if y < 5.1000000000000002e-166

   1. Initial program 100.0%

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

   3. Taylor expanded in x around inf 69.6%

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

   if 5.1000000000000002e-166 < y < 7.6000000000000003e-116

   1. Initial program 100.0%

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

   3. Taylor expanded in x around 0 55.7%

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

   if 7.6000000000000003e-116 < y < 3.6000000000000002e-74

   1. Initial program 100.0%

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

   3. Taylor expanded in y around 0 100.0%

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

   if 3.6000000000000002e-74 < y

   1. Initial program 100.0%

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

   3. Taylor expanded in y around inf 85.8%

      \[\leadsto \color{blue}{y \cdot \left(1 - x\right)} \]
3. Recombined 4 regimes into one program.
4. Add Preprocessing

Alternative 5: 100.0% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 100.0%

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

3. Final simplification 100.0%

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

Alternative 6: 47.7% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq 5.1 \cdot 10^{-166}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (if (<= y 5.1e-166) x y))

C

double code(double x, double y) {
	double tmp;
	if (y <= 5.1e-166) {
		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 <= 5.1d-166) 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 <= 5.1e-166) {
		tmp = x;
	} else {
		tmp = y;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= 5.1e-166:
		tmp = x
	else:
		tmp = y
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= 5.1e-166)
		tmp = x;
	else
		tmp = y;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= 5.1e-166)
		tmp = x;
	else
		tmp = y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, 5.1e-166], x, y]

Derivation

1. Split input into 2 regimes

2. if y < 5.1000000000000002e-166

   1. Initial program 100.0%

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

   3. Taylor expanded in y around 0 49.9%

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

   if 5.1000000000000002e-166 < y

   1. Initial program 100.0%

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

   3. Taylor expanded in x around 0 45.5%

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

Alternative 7: 38.5% accurate, 7.0× speedup

Math

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

FPCore

(FPCore (x y) :precision binary64 x)

C

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

Fortran

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

Java

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

Python

def code(x, y):
	return x

Julia

function code(x, y)
	return x
end

MATLAB

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

Wolfram

code[x_, y_] := x

Derivation

1. Initial program 100.0%

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

3. Taylor expanded in y around 0 40.3%

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

Reproduce

herbie shell --seed 2024097 
(FPCore (x y)
  :name "Data.Colour.RGBSpace.HSL:hsl from colour-2.3.3, A"
  :precision binary64
  (- (+ x y) (* x y)))