Data.Colour.RGB:hslsv from colour-2.3.3, D

Percentage Accurate: 100.0% → 100.0%

Time: 7.1s

Alternatives: 6

Speedup: 1.0×

Specification

Math

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

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

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

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, 0.5× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 100.0%

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

   1. div-sub 99.9%

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

   2. div-inv 99.9%

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

   3. flip-+ 64.3%

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

   4. associate-/r/ 64.2%

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

   5. prod-diff 64.1%

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

3. Applied egg-rr 64.1%

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

   1. +-commutative 64.1%

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

   2. fma-udef 64.1%

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

   3. associate-+r+ 64.1%

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

5. Simplified 99.9%

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

   1. frac-times 60.8%

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

   2. clear-num 60.8%

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

   3. *-commutative 60.8%

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

   4. associate-/l/ 68.4%

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

   5. difference-of-squares 64.3%

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

   6. flip-+ 100.0%

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

7. Applied egg-rr 100.0%

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

8. Taylor expanded in x around 0 100.0%

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

9. Final simplification 100.0%

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

Alternative 2: 74.0% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -9.5 \cdot 10^{+88} \lor \neg \left(y \leq -4.2 \cdot 10^{+43} \lor \neg \left(y \leq -40\right) \land y \leq 1.75 \cdot 10^{+20}\right):\\ \;\;\;\;\frac{x}{y} \cdot 2 + -1\\ \mathbf{else}:\\ \;\;\;\;1 + -2 \cdot \frac{y}{x}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (or (<= y -9.5e+88)
         (not (or (<= y -4.2e+43) (and (not (<= y -40.0)) (<= y 1.75e+20)))))
   (+ (* (/ x y) 2.0) -1.0)
   (+ 1.0 (* -2.0 (/ y x)))))

C

double code(double x, double y) {
	double tmp;
	if ((y <= -9.5e+88) || !((y <= -4.2e+43) || (!(y <= -40.0) && (y <= 1.75e+20)))) {
		tmp = ((x / y) * 2.0) + -1.0;
	} else {
		tmp = 1.0 + (-2.0 * (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 <= (-9.5d+88)) .or. (.not. (y <= (-4.2d+43)) .or. (.not. (y <= (-40.0d0))) .and. (y <= 1.75d+20))) then
        tmp = ((x / y) * 2.0d0) + (-1.0d0)
    else
        tmp = 1.0d0 + ((-2.0d0) * (y / x))
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if ((y <= -9.5e+88) || !((y <= -4.2e+43) || (!(y <= -40.0) && (y <= 1.75e+20)))) {
		tmp = ((x / y) * 2.0) + -1.0;
	} else {
		tmp = 1.0 + (-2.0 * (y / x));
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (y <= -9.5e+88) or not ((y <= -4.2e+43) or (not (y <= -40.0) and (y <= 1.75e+20))):
		tmp = ((x / y) * 2.0) + -1.0
	else:
		tmp = 1.0 + (-2.0 * (y / x))
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if ((y <= -9.5e+88) || !((y <= -4.2e+43) || (!(y <= -40.0) && (y <= 1.75e+20))))
		tmp = Float64(Float64(Float64(x / y) * 2.0) + -1.0);
	else
		tmp = Float64(1.0 + Float64(-2.0 * Float64(y / x)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if ((y <= -9.5e+88) || ~(((y <= -4.2e+43) || (~((y <= -40.0)) && (y <= 1.75e+20)))))
		tmp = ((x / y) * 2.0) + -1.0;
	else
		tmp = 1.0 + (-2.0 * (y / x));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[Or[LessEqual[y, -9.5e+88], N[Not[Or[LessEqual[y, -4.2e+43], And[N[Not[LessEqual[y, -40.0]], $MachinePrecision], LessEqual[y, 1.75e+20]]]], $MachinePrecision]], N[(N[(N[(x / y), $MachinePrecision] * 2.0), $MachinePrecision] + -1.0), $MachinePrecision], N[(1.0 + N[(-2.0 * N[(y / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -9.50000000000000059e88 or -4.20000000000000003e43 < y < -40 or 1.75e20 < y

   1. Initial program 100.0%

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

   2. Taylor expanded in x around 0 81.2%

      \[\leadsto \color{blue}{2 \cdot \frac{x}{y} - 1} \]

   if -9.50000000000000059e88 < y < -4.20000000000000003e43 or -40 < y < 1.75e20

   1. Initial program 99.9%

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

   2. Taylor expanded in y around 0 76.4%

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

4. Final simplification 78.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -9.5 \cdot 10^{+88} \lor \neg \left(y \leq -4.2 \cdot 10^{+43} \lor \neg \left(y \leq -40\right) \land y \leq 1.75 \cdot 10^{+20}\right):\\ \;\;\;\;\frac{x}{y} \cdot 2 + -1\\ \mathbf{else}:\\ \;\;\;\;1 + -2 \cdot \frac{y}{x}\\ \end{array} \]

Alternative 3: 73.6% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -4.3 \cdot 10^{+88}:\\ \;\;\;\;-1\\ \mathbf{elif}\;y \leq -6.8 \cdot 10^{+43} \lor \neg \left(y \leq -1800000000\right) \land y \leq 1.75 \cdot 10^{+20}:\\ \;\;\;\;1 + -2 \cdot \frac{y}{x}\\ \mathbf{else}:\\ \;\;\;\;-1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y -4.3e+88)
   -1.0
   (if (or (<= y -6.8e+43) (and (not (<= y -1800000000.0)) (<= y 1.75e+20)))
     (+ 1.0 (* -2.0 (/ y x)))
     -1.0)))

C

double code(double x, double y) {
	double tmp;
	if (y <= -4.3e+88) {
		tmp = -1.0;
	} else if ((y <= -6.8e+43) || (!(y <= -1800000000.0) && (y <= 1.75e+20))) {
		tmp = 1.0 + (-2.0 * (y / 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 <= (-4.3d+88)) then
        tmp = -1.0d0
    else if ((y <= (-6.8d+43)) .or. (.not. (y <= (-1800000000.0d0))) .and. (y <= 1.75d+20)) then
        tmp = 1.0d0 + ((-2.0d0) * (y / x))
    else
        tmp = -1.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (y <= -4.3e+88) {
		tmp = -1.0;
	} else if ((y <= -6.8e+43) || (!(y <= -1800000000.0) && (y <= 1.75e+20))) {
		tmp = 1.0 + (-2.0 * (y / x));
	} else {
		tmp = -1.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= -4.3e+88:
		tmp = -1.0
	elif (y <= -6.8e+43) or (not (y <= -1800000000.0) and (y <= 1.75e+20)):
		tmp = 1.0 + (-2.0 * (y / x))
	else:
		tmp = -1.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= -4.3e+88)
		tmp = -1.0;
	elseif ((y <= -6.8e+43) || (!(y <= -1800000000.0) && (y <= 1.75e+20)))
		tmp = Float64(1.0 + Float64(-2.0 * Float64(y / x)));
	else
		tmp = -1.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= -4.3e+88)
		tmp = -1.0;
	elseif ((y <= -6.8e+43) || (~((y <= -1800000000.0)) && (y <= 1.75e+20)))
		tmp = 1.0 + (-2.0 * (y / x));
	else
		tmp = -1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, -4.3e+88], -1.0, If[Or[LessEqual[y, -6.8e+43], And[N[Not[LessEqual[y, -1800000000.0]], $MachinePrecision], LessEqual[y, 1.75e+20]]], N[(1.0 + N[(-2.0 * N[(y / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], -1.0]]

Derivation

1. Split input into 2 regimes

2. if y < -4.29999999999999974e88 or -6.80000000000000024e43 < y < -1.8e9 or 1.75e20 < y

   1. Initial program 100.0%

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

   2. Taylor expanded in x around 0 80.1%

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

   if -4.29999999999999974e88 < y < -6.80000000000000024e43 or -1.8e9 < y < 1.75e20

   1. Initial program 99.9%

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

   2. Taylor expanded in y around 0 76.0%

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

4. Final simplification 78.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -4.3 \cdot 10^{+88}:\\ \;\;\;\;-1\\ \mathbf{elif}\;y \leq -6.8 \cdot 10^{+43} \lor \neg \left(y \leq -1800000000\right) \land y \leq 1.75 \cdot 10^{+20}:\\ \;\;\;\;1 + -2 \cdot \frac{y}{x}\\ \mathbf{else}:\\ \;\;\;\;-1\\ \end{array} \]

Alternative 4: 72.8% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -4.3 \cdot 10^{+88}:\\ \;\;\;\;-1\\ \mathbf{elif}\;y \leq -1.56 \cdot 10^{+43}:\\ \;\;\;\;1\\ \mathbf{elif}\;y \leq -18000000000:\\ \;\;\;\;-1\\ \mathbf{elif}\;y \leq 1.75 \cdot 10^{+20}:\\ \;\;\;\;1\\ \mathbf{else}:\\ \;\;\;\;-1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y -4.3e+88)
   -1.0
   (if (<= y -1.56e+43)
     1.0
     (if (<= y -18000000000.0) -1.0 (if (<= y 1.75e+20) 1.0 -1.0)))))

C

double code(double x, double y) {
	double tmp;
	if (y <= -4.3e+88) {
		tmp = -1.0;
	} else if (y <= -1.56e+43) {
		tmp = 1.0;
	} else if (y <= -18000000000.0) {
		tmp = -1.0;
	} else if (y <= 1.75e+20) {
		tmp = 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 <= (-4.3d+88)) then
        tmp = -1.0d0
    else if (y <= (-1.56d+43)) then
        tmp = 1.0d0
    else if (y <= (-18000000000.0d0)) then
        tmp = -1.0d0
    else if (y <= 1.75d+20) then
        tmp = 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 <= -4.3e+88) {
		tmp = -1.0;
	} else if (y <= -1.56e+43) {
		tmp = 1.0;
	} else if (y <= -18000000000.0) {
		tmp = -1.0;
	} else if (y <= 1.75e+20) {
		tmp = 1.0;
	} else {
		tmp = -1.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= -4.3e+88:
		tmp = -1.0
	elif y <= -1.56e+43:
		tmp = 1.0
	elif y <= -18000000000.0:
		tmp = -1.0
	elif y <= 1.75e+20:
		tmp = 1.0
	else:
		tmp = -1.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= -4.3e+88)
		tmp = -1.0;
	elseif (y <= -1.56e+43)
		tmp = 1.0;
	elseif (y <= -18000000000.0)
		tmp = -1.0;
	elseif (y <= 1.75e+20)
		tmp = 1.0;
	else
		tmp = -1.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= -4.3e+88)
		tmp = -1.0;
	elseif (y <= -1.56e+43)
		tmp = 1.0;
	elseif (y <= -18000000000.0)
		tmp = -1.0;
	elseif (y <= 1.75e+20)
		tmp = 1.0;
	else
		tmp = -1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, -4.3e+88], -1.0, If[LessEqual[y, -1.56e+43], 1.0, If[LessEqual[y, -18000000000.0], -1.0, If[LessEqual[y, 1.75e+20], 1.0, -1.0]]]]

Derivation

1. Split input into 2 regimes

2. if y < -4.29999999999999974e88 or -1.55999999999999988e43 < y < -1.8e10 or 1.75e20 < y

   1. Initial program 100.0%

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

   2. Taylor expanded in x around 0 80.1%

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

   if -4.29999999999999974e88 < y < -1.55999999999999988e43 or -1.8e10 < y < 1.75e20

   1. Initial program 99.9%

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

   2. Taylor expanded in x around inf 75.4%

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

4. Final simplification 77.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -4.3 \cdot 10^{+88}:\\ \;\;\;\;-1\\ \mathbf{elif}\;y \leq -1.56 \cdot 10^{+43}:\\ \;\;\;\;1\\ \mathbf{elif}\;y \leq -18000000000:\\ \;\;\;\;-1\\ \mathbf{elif}\;y \leq 1.75 \cdot 10^{+20}:\\ \;\;\;\;1\\ \mathbf{else}:\\ \;\;\;\;-1\\ \end{array} \]

Alternative 5: 100.0% accurate, 1.0× speedup

Math

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

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]

Derivation

1. Initial program 100.0%

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

2. Final simplification 100.0%

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

Alternative 6: 50.4% 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%

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

2. Taylor expanded in x around 0 51.6%

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

3. Final simplification 51.6%

   \[\leadsto -1 \]

Developer target: 100.0% accurate, 0.6× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Reproduce

herbie shell --seed 2023221 
(FPCore (x y)
  :name "Data.Colour.RGB:hslsv from colour-2.3.3, D"
  :precision binary64

  :herbie-target
  (- (/ x (+ x y)) (/ y (+ x y)))

  (/ (- x y) (+ x y)))