Data.Colour.RGBSpace.HSV:hsv from colour-2.3.3, I

Percentage Accurate: 95.9% → 97.9%

Time: 4.7s

Alternatives: 6

Speedup: 0.5×

Specification

Math

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

FPCore

(FPCore (x y z) :precision binary64 (* x (- 1.0 (* y z))))

C

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

Fortran

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

Java

public static double code(double x, double y, double z) {
	return x * (1.0 - (y * z));
}

Python

def code(x, y, z):
	return x * (1.0 - (y * z))

Julia

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

MATLAB

function tmp = code(x, y, z)
	tmp = x * (1.0 - (y * z));
end

Wolfram

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

Initial Program: 95.9% accurate, 1.0× speedup

Math

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

FPCore

(FPCore (x y z) :precision binary64 (* x (- 1.0 (* y z))))

C

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

Fortran

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

Java

public static double code(double x, double y, double z) {
	return x * (1.0 - (y * z));
}

Python

def code(x, y, z):
	return x * (1.0 - (y * z))

Julia

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

MATLAB

function tmp = code(x, y, z)
	tmp = x * (1.0 - (y * z));
end

Wolfram

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

Alternative 1: 97.9% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \cdot z \leq -\infty:\\ \;\;\;\;-z \cdot \left(y \cdot x\right)\\ \mathbf{else}:\\ \;\;\;\;x - x \cdot \left(y \cdot z\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= (* y z) (- INFINITY)) (- (* z (* y x))) (- x (* x (* y z)))))

C

double code(double x, double y, double z) {
	double tmp;
	if ((y * z) <= -((double) INFINITY)) {
		tmp = -(z * (y * x));
	} else {
		tmp = x - (x * (y * z));
	}
	return tmp;
}

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((y * z) <= -Double.POSITIVE_INFINITY) {
		tmp = -(z * (y * x));
	} else {
		tmp = x - (x * (y * z));
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (y * z) <= -math.inf:
		tmp = -(z * (y * x))
	else:
		tmp = x - (x * (y * z))
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (Float64(y * z) <= Float64(-Inf))
		tmp = Float64(-Float64(z * Float64(y * x)));
	else
		tmp = Float64(x - Float64(x * Float64(y * z)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((y * z) <= -Inf)
		tmp = -(z * (y * x));
	else
		tmp = x - (x * (y * z));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[N[(y * z), $MachinePrecision], (-Infinity)], (-N[(z * N[(y * x), $MachinePrecision]), $MachinePrecision]), N[(x - N[(x * N[(y * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 y z) < -inf.0

   1. Initial program 36.9%

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

   3. Taylor expanded in y around inf 99.8%

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

   4. Taylor expanded in z around inf 99.8%

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

      1. mul-1-neg 99.8%

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

      2. distribute-rgt-neg-out 99.8%

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

      3. associate-*l* 99.9%

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

      4. *-commutative 99.9%

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

   6. Applied egg-rr 99.9%

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

   if -inf.0 < (*.f64 y z)

   1. Initial program 98.7%

      \[x \cdot \left(1 - y \cdot z\right) \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. sub-neg 98.7%

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

      2. distribute-rgt-in 98.7%

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

      3. *-un-lft-identity 98.7%

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

      4. distribute-rgt-neg-in 98.7%

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

   4. Applied egg-rr 98.7%

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

      1. add-sqr-sqrt 47.7%

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

      2. sqrt-unprod 65.5%

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

      3. sqr-neg 65.5%

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

      4. sqrt-unprod 25.3%

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

      5. add-sqr-sqrt 52.2%

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

      6. cancel-sign-sub 52.2%

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

      7. distribute-rgt-neg-out 52.2%

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

      8. *-commutative 52.2%

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

      9. *-commutative 52.2%

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

      10. associate-*r* 50.6%

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

      11. distribute-rgt-neg-in 50.6%

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

      12. *-commutative 50.6%

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

      13. distribute-rgt-neg-in 50.6%

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

      14. associate-*r* 49.0%

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

      15. distribute-lft-neg-in 49.0%

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

      16. mul-1-neg 49.0%

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

      17. add-sqr-sqrt 29.9%

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

      18. sqrt-unprod 59.0%

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

      19. mul-1-neg 59.0%

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

      20. mul-1-neg 59.0%

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

      21. sqr-neg 59.0%

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

   6. Applied egg-rr 89.0%

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

   7. Taylor expanded in z around 0 98.7%

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

Alternative 2: 73.4% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := -z \cdot \left(y \cdot x\right)\\ \mathbf{if}\;z \leq -1.2 \cdot 10^{-19}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;z \leq 2.7 \cdot 10^{+55}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (- (* z (* y x)))))
   (if (<= z -1.2e-19) t_0 (if (<= z 2.7e+55) x t_0))))

C

double code(double x, double y, double z) {
	double t_0 = -(z * (y * x));
	double tmp;
	if (z <= -1.2e-19) {
		tmp = t_0;
	} else if (z <= 2.7e+55) {
		tmp = x;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: tmp
    t_0 = -(z * (y * x))
    if (z <= (-1.2d-19)) then
        tmp = t_0
    else if (z <= 2.7d+55) then
        tmp = x
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = -(z * (y * x));
	double tmp;
	if (z <= -1.2e-19) {
		tmp = t_0;
	} else if (z <= 2.7e+55) {
		tmp = x;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = -(z * (y * x))
	tmp = 0
	if z <= -1.2e-19:
		tmp = t_0
	elif z <= 2.7e+55:
		tmp = x
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(-Float64(z * Float64(y * x)))
	tmp = 0.0
	if (z <= -1.2e-19)
		tmp = t_0;
	elseif (z <= 2.7e+55)
		tmp = x;
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = -(z * (y * x));
	tmp = 0.0;
	if (z <= -1.2e-19)
		tmp = t_0;
	elseif (z <= 2.7e+55)
		tmp = x;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = (-N[(z * N[(y * x), $MachinePrecision]), $MachinePrecision])}, If[LessEqual[z, -1.2e-19], t$95$0, If[LessEqual[z, 2.7e+55], x, t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if z < -1.20000000000000011e-19 or 2.69999999999999977e55 < z

   1. Initial program 89.7%

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

   3. Taylor expanded in y around inf 90.5%

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

   4. Taylor expanded in z around inf 75.7%

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

      1. mul-1-neg 75.7%

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

      2. distribute-rgt-neg-out 75.7%

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

      3. associate-*l* 70.9%

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

      4. *-commutative 70.9%

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

   6. Applied egg-rr 70.9%

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

   if -1.20000000000000011e-19 < z < 2.69999999999999977e55

   1. Initial program 99.9%

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

   3. Taylor expanded in y around 0 75.7%

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

Alternative 3: 97.9% accurate, 0.5× speedup

Math

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

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= (* y z) (- INFINITY)) (- (* z (* y x))) (* x (- 1.0 (* y z)))))

C

double code(double x, double y, double z) {
	double tmp;
	if ((y * z) <= -((double) INFINITY)) {
		tmp = -(z * (y * x));
	} else {
		tmp = x * (1.0 - (y * z));
	}
	return tmp;
}

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((y * z) <= -Double.POSITIVE_INFINITY) {
		tmp = -(z * (y * x));
	} else {
		tmp = x * (1.0 - (y * z));
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (y * z) <= -math.inf:
		tmp = -(z * (y * x))
	else:
		tmp = x * (1.0 - (y * z))
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (Float64(y * z) <= Float64(-Inf))
		tmp = Float64(-Float64(z * Float64(y * x)));
	else
		tmp = Float64(x * Float64(1.0 - Float64(y * z)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((y * z) <= -Inf)
		tmp = -(z * (y * x));
	else
		tmp = x * (1.0 - (y * z));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[N[(y * z), $MachinePrecision], (-Infinity)], (-N[(z * N[(y * x), $MachinePrecision]), $MachinePrecision]), N[(x * N[(1.0 - N[(y * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 y z) < -inf.0

   1. Initial program 36.9%

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

   3. Taylor expanded in y around inf 99.8%

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

   4. Taylor expanded in z around inf 99.8%

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

      1. mul-1-neg 99.8%

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

      2. distribute-rgt-neg-out 99.8%

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

      3. associate-*l* 99.9%

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

      4. *-commutative 99.9%

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

   6. Applied egg-rr 99.9%

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

   if -inf.0 < (*.f64 y z)

   1. Initial program 98.7%

      \[x \cdot \left(1 - y \cdot z\right) \]
   2. Add Preprocessing
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 4: 52.8% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -6.2 \cdot 10^{+215}:\\ \;\;\;\;\frac{y \cdot x}{y}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (if (<= y -6.2e+215) (/ (* y x) y) x))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= -6.2e+215) {
		tmp = (y * x) / y;
	} else {
		tmp = x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (y <= (-6.2d+215)) then
        tmp = (y * x) / y
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (y <= -6.2e+215) {
		tmp = (y * x) / y;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if y <= -6.2e+215:
		tmp = (y * x) / y
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= -6.2e+215)
		tmp = Float64(Float64(y * x) / y);
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (y <= -6.2e+215)
		tmp = (y * x) / y;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, -6.2e+215], N[(N[(y * x), $MachinePrecision] / y), $MachinePrecision], x]

Derivation

1. Split input into 2 regimes

2. if y < -6.1999999999999998e215

   1. Initial program 86.2%

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

   3. Taylor expanded in y around inf 99.4%

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

   4. Taylor expanded in z around 0 10.0%

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

      1. associate-*r/ 23.9%

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

   6. Applied egg-rr 23.9%

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

   if -6.1999999999999998e215 < y

   1. Initial program 95.6%

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

   3. Taylor expanded in y around 0 53.1%

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

Alternative 5: 52.9% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq 1.6 \cdot 10^{+185}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;\frac{z \cdot x}{z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (if (<= z 1.6e+185) x (/ (* z x) z)))

C

double code(double x, double y, double z) {
	double tmp;
	if (z <= 1.6e+185) {
		tmp = x;
	} else {
		tmp = (z * x) / z;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (z <= 1.6d+185) then
        tmp = x
    else
        tmp = (z * x) / z
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (z <= 1.6e+185) {
		tmp = x;
	} else {
		tmp = (z * x) / z;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if z <= 1.6e+185:
		tmp = x
	else:
		tmp = (z * x) / z
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (z <= 1.6e+185)
		tmp = x;
	else
		tmp = Float64(Float64(z * x) / z);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (z <= 1.6e+185)
		tmp = x;
	else
		tmp = (z * x) / z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[z, 1.6e+185], x, N[(N[(z * x), $MachinePrecision] / z), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if z < 1.60000000000000003e185

   1. Initial program 95.0%

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

   3. Taylor expanded in y around 0 54.9%

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

   if 1.60000000000000003e185 < z

   1. Initial program 96.0%

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

   3. Taylor expanded in z around inf 69.6%

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

   4. Taylor expanded in y around 0 4.7%

      \[\leadsto z \cdot \color{blue}{\frac{x}{z}} \]
   5. Step-by-step derivation

      1. associate-*r/ 26.2%

         \[\leadsto \color{blue}{\frac{z \cdot x}{z}} \]

   6. Applied egg-rr 26.2%

      \[\leadsto \color{blue}{\frac{z \cdot x}{z}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 6: 52.1% accurate, 7.0× speedup

Math

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

FPCore

(FPCore (x y z) :precision binary64 x)

C

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

Fortran

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

Java

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

Python

def code(x, y, z):
	return x

Julia

function code(x, y, z)
	return x
end

MATLAB

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

Wolfram

code[x_, y_, z_] := x

Derivation

1. Initial program 95.1%

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

3. Taylor expanded in y around 0 50.7%

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

Reproduce

herbie shell --seed 2024076 -o generate:simplify
(FPCore (x y z)
  :name "Data.Colour.RGBSpace.HSV:hsv from colour-2.3.3, I"
  :precision binary64
  (* x (- 1.0 (* y z))))