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

Percentage Accurate: 95.7% → 98.5%

Time: 3.0s

Alternatives: 9

Speedup: 1.1×

• 20.6% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

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

FPCore

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

C

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

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 95.7% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 98.5% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left(\left(y - 1\right) \cdot x\right) \cdot z\\ \mathbf{if}\;z \leq -15600000000:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;z \leq 2.5 \cdot 10^{-7}:\\ \;\;\;\;\mathsf{fma}\left(y \cdot z, x, x\right)\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* (* (- y 1.0) x) z)))
   (if (<= z -15600000000.0) t_0 (if (<= z 2.5e-7) (fma (* y z) x x) t_0))))

C

double code(double x, double y, double z) {
	double t_0 = ((y - 1.0) * x) * z;
	double tmp;
	if (z <= -15600000000.0) {
		tmp = t_0;
	} else if (z <= 2.5e-7) {
		tmp = fma((y * z), x, x);
	} else {
		tmp = t_0;
	}
	return tmp;
}

Julia

function code(x, y, z)
	t_0 = Float64(Float64(Float64(y - 1.0) * x) * z)
	tmp = 0.0
	if (z <= -15600000000.0)
		tmp = t_0;
	elseif (z <= 2.5e-7)
		tmp = fma(Float64(y * z), x, x);
	else
		tmp = t_0;
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(N[(y - 1.0), $MachinePrecision] * x), $MachinePrecision] * z), $MachinePrecision]}, If[LessEqual[z, -15600000000.0], t$95$0, If[LessEqual[z, 2.5e-7], N[(N[(y * z), $MachinePrecision] * x + x), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if z < -1.56e10 or 2.49999999999999989e-7 < z

   1. Initial program 91.7%

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

      1. lift-*.f64 N/A

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

      2. lift--.f64 N/A

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

      3. lift-*.f64 N/A

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

      4. lift--.f64 N/A

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

      5. *-commutative N/A

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

   3. Applied rewrites 99.9%

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

   4. Taylor expanded in z around inf

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

      1. associate-*l* N/A

         \[\leadsto x \cdot \left(z \cdot \left(y - 1\right)\right) \]

      2. *-commutative N/A

         \[\leadsto x \cdot \left(z \cdot \left(y - 1\right)\right) \]

      3. *-commutative N/A

         \[\leadsto x \cdot \left(z \cdot \left(y - 1\right)\right) \]

      4. *-commutative N/A

         \[\leadsto x \cdot \left(z \cdot \left(y - 1\right)\right) \]

      5. associate-*r* N/A

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

      6. *-commutative N/A

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

      7. associate-*l* N/A

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

      8. *-commutative N/A

         \[\leadsto \left(x \cdot \left(y - 1\right)\right) \cdot z \]

      9. lower-*.f64 N/A

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

      10. *-commutative N/A

          \[\leadsto \left(\left(y - 1\right) \cdot x\right) \cdot z \]

      11. lift--.f64 N/A

          \[\leadsto \left(\left(y - 1\right) \cdot x\right) \cdot z \]

      12. lift-*.f64 98.8

          \[\leadsto \left(\left(y - 1\right) \cdot x\right) \cdot z \]

   6. Applied rewrites 98.8%

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

   if -1.56e10 < z < 2.49999999999999989e-7

   1. Initial program 99.9%

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

      1. lift-*.f64 N/A

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

      2. lift--.f64 N/A

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

      3. lift-*.f64 N/A

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

      4. lift--.f64 N/A

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

      5. *-commutative N/A

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

   3. Applied rewrites 96.7%

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

   4. Taylor expanded in y around inf

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

   6. Applied rewrites 95.1%

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

      1. lift-*.f64 N/A

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

      2. lift-fma.f64 N/A

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

      3. associate-*r* N/A

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

      4. lower-fma.f64 N/A

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

      5. lower-*.f64 98.3

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

   8. Applied rewrites 98.3%

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

Alternative 2: 98.3% accurate, 1.1× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 95.7%

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

   1. lift-*.f64 N/A

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

   2. lift--.f64 N/A

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

   3. lift-*.f64 N/A

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

   4. lift--.f64 N/A

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

   5. *-commutative N/A

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

3. Applied rewrites 98.3%

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

Alternative 3: 97.5% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \mathsf{fma}\left(y, z \cdot x, x\right)\\ \mathbf{if}\;y \leq -550:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;y \leq 1:\\ \;\;\;\;x - z \cdot x\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (fma y (* z x) x)))
   (if (<= y -550.0) t_0 (if (<= y 1.0) (- x (* z x)) t_0))))

C

double code(double x, double y, double z) {
	double t_0 = fma(y, (z * x), x);
	double tmp;
	if (y <= -550.0) {
		tmp = t_0;
	} else if (y <= 1.0) {
		tmp = x - (z * x);
	} else {
		tmp = t_0;
	}
	return tmp;
}

Julia

function code(x, y, z)
	t_0 = fma(y, Float64(z * x), x)
	tmp = 0.0
	if (y <= -550.0)
		tmp = t_0;
	elseif (y <= 1.0)
		tmp = Float64(x - Float64(z * x));
	else
		tmp = t_0;
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(y * N[(z * x), $MachinePrecision] + x), $MachinePrecision]}, If[LessEqual[y, -550.0], t$95$0, If[LessEqual[y, 1.0], N[(x - N[(z * x), $MachinePrecision]), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if y < -550 or 1 < y

   1. Initial program 91.3%

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

      1. lift-*.f64 N/A

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

      2. lift--.f64 N/A

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

      3. lift-*.f64 N/A

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

      4. lift--.f64 N/A

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

      5. *-commutative N/A

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

   3. Applied rewrites 96.6%

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

   4. Taylor expanded in y around inf

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

   6. Applied rewrites 95.7%

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

   if -550 < y < 1

   1. Initial program 100.0%

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

      1. lift-*.f64 N/A

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

      2. lift--.f64 N/A

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

      3. lift-*.f64 N/A

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

      4. lift--.f64 N/A

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

      5. *-commutative N/A

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

   3. Applied rewrites 100.0%

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

   4. Taylor expanded in y around 0

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

      1. +-commutative N/A

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

      2. lower-+.f64 N/A

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

      3. mul-1-neg N/A

         \[\leadsto \left(\mathsf{neg}\left(x \cdot z\right)\right) + x \]

      4. lower-neg.f64 N/A

         \[\leadsto \left(-x \cdot z\right) + x \]

      5. lower-*.f64 99.2

         \[\leadsto \left(-x \cdot z\right) + x \]

   6. Applied rewrites 99.2%

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

      1. lift-+.f64 N/A

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

      2. lift-*.f64 N/A

         \[\leadsto \left(-x \cdot z\right) + x \]

      3. lift-neg.f64 N/A

         \[\leadsto \left(\mathsf{neg}\left(x \cdot z\right)\right) + x \]

      4. mul-1-neg N/A

         \[\leadsto -1 \cdot \left(x \cdot z\right) + x \]

      5. +-commutative N/A

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

      6. mul-1-neg N/A

         \[\leadsto x + \left(\mathsf{neg}\left(x \cdot z\right)\right) \]

      7. distribute-lft-neg-in N/A

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

      8. fp-cancel-sub-sign N/A

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

      9. lower--.f64 N/A

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

      10. *-commutative N/A

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

      11. lift-*.f64 99.2

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

   8. Applied rewrites 99.2%

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

Alternative 4: 96.0% accurate, 1.1× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 95.7%

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

   1. lift-*.f64 N/A

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

   2. lift--.f64 N/A

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

   3. lift-*.f64 N/A

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

   4. lift--.f64 N/A

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

   5. *-commutative N/A

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

3. Applied rewrites 96.0%

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

Alternative 5: 95.2% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \mathsf{fma}\left(x \cdot y, z, x\right)\\ \mathbf{if}\;y \leq -550:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;y \leq 1:\\ \;\;\;\;x - z \cdot x\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (fma (* x y) z x)))
   (if (<= y -550.0) t_0 (if (<= y 1.0) (- x (* z x)) t_0))))

C

double code(double x, double y, double z) {
	double t_0 = fma((x * y), z, x);
	double tmp;
	if (y <= -550.0) {
		tmp = t_0;
	} else if (y <= 1.0) {
		tmp = x - (z * x);
	} else {
		tmp = t_0;
	}
	return tmp;
}

Julia

function code(x, y, z)
	t_0 = fma(Float64(x * y), z, x)
	tmp = 0.0
	if (y <= -550.0)
		tmp = t_0;
	elseif (y <= 1.0)
		tmp = Float64(x - Float64(z * x));
	else
		tmp = t_0;
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(x * y), $MachinePrecision] * z + x), $MachinePrecision]}, If[LessEqual[y, -550.0], t$95$0, If[LessEqual[y, 1.0], N[(x - N[(z * x), $MachinePrecision]), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if y < -550 or 1 < y

   1. Initial program 91.3%

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

      1. lift-*.f64 N/A

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

      2. lift--.f64 N/A

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

      3. lift-*.f64 N/A

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

      4. lift--.f64 N/A

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

      5. *-commutative N/A

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

   3. Applied rewrites 91.8%

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

   4. Taylor expanded in y around inf

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

      1. lower-*.f64 91.0

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

   6. Applied rewrites 91.0%

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

   if -550 < y < 1

   1. Initial program 100.0%

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

      1. lift-*.f64 N/A

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

      2. lift--.f64 N/A

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

      3. lift-*.f64 N/A

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

      4. lift--.f64 N/A

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

      5. *-commutative N/A

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

   3. Applied rewrites 100.0%

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

   4. Taylor expanded in y around 0

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

      1. +-commutative N/A

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

      2. lower-+.f64 N/A

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

      3. mul-1-neg N/A

         \[\leadsto \left(\mathsf{neg}\left(x \cdot z\right)\right) + x \]

      4. lower-neg.f64 N/A

         \[\leadsto \left(-x \cdot z\right) + x \]

      5. lower-*.f64 99.2

         \[\leadsto \left(-x \cdot z\right) + x \]

   6. Applied rewrites 99.2%

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

      1. lift-+.f64 N/A

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

      2. lift-*.f64 N/A

         \[\leadsto \left(-x \cdot z\right) + x \]

      3. lift-neg.f64 N/A

         \[\leadsto \left(\mathsf{neg}\left(x \cdot z\right)\right) + x \]

      4. mul-1-neg N/A

         \[\leadsto -1 \cdot \left(x \cdot z\right) + x \]

      5. +-commutative N/A

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

      6. mul-1-neg N/A

         \[\leadsto x + \left(\mathsf{neg}\left(x \cdot z\right)\right) \]

      7. distribute-lft-neg-in N/A

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

      8. fp-cancel-sub-sign N/A

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

      9. lower--.f64 N/A

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

      10. *-commutative N/A

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

      11. lift-*.f64 99.2

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

   8. Applied rewrites 99.2%

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

Alternative 6: 83.4% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x \cdot \left(z \cdot y\right)\\ \mathbf{if}\;y \leq -1.15 \cdot 10^{+49}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;y \leq 5 \cdot 10^{+14}:\\ \;\;\;\;x - z \cdot x\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* x (* z y))))
   (if (<= y -1.15e+49) t_0 (if (<= y 5e+14) (- x (* z x)) t_0))))

C

double code(double x, double y, double z) {
	double t_0 = x * (z * y);
	double tmp;
	if (y <= -1.15e+49) {
		tmp = t_0;
	} else if (y <= 5e+14) {
		tmp = x - (z * x);
	} else {
		tmp = t_0;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x, y, z)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: tmp
    t_0 = x * (z * y)
    if (y <= (-1.15d+49)) then
        tmp = t_0
    else if (y <= 5d+14) then
        tmp = x - (z * 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 = x * (z * y);
	double tmp;
	if (y <= -1.15e+49) {
		tmp = t_0;
	} else if (y <= 5e+14) {
		tmp = x - (z * x);
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = x * (z * y)
	tmp = 0
	if y <= -1.15e+49:
		tmp = t_0
	elif y <= 5e+14:
		tmp = x - (z * x)
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(x * Float64(z * y))
	tmp = 0.0
	if (y <= -1.15e+49)
		tmp = t_0;
	elseif (y <= 5e+14)
		tmp = Float64(x - Float64(z * x));
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = x * (z * y);
	tmp = 0.0;
	if (y <= -1.15e+49)
		tmp = t_0;
	elseif (y <= 5e+14)
		tmp = x - (z * x);
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(x * N[(z * y), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -1.15e+49], t$95$0, If[LessEqual[y, 5e+14], N[(x - N[(z * x), $MachinePrecision]), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if y < -1.15000000000000001e49 or 5e14 < y

   1. Initial program 90.7%

      \[x \cdot \left(1 - \left(1 - y\right) \cdot z\right) \]

   2. Taylor expanded in y around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 68.3

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

   4. Applied rewrites 68.3%

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

   if -1.15000000000000001e49 < y < 5e14

   1. Initial program 99.8%

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

      1. lift-*.f64 N/A

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

      2. lift--.f64 N/A

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

      3. lift-*.f64 N/A

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

      4. lift--.f64 N/A

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

      5. *-commutative N/A

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

   3. Applied rewrites 99.9%

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

   4. Taylor expanded in y around 0

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

      1. +-commutative N/A

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

      2. lower-+.f64 N/A

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

      3. mul-1-neg N/A

         \[\leadsto \left(\mathsf{neg}\left(x \cdot z\right)\right) + x \]

      4. lower-neg.f64 N/A

         \[\leadsto \left(-x \cdot z\right) + x \]

      5. lower-*.f64 95.4

         \[\leadsto \left(-x \cdot z\right) + x \]

   6. Applied rewrites 95.4%

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

      1. lift-+.f64 N/A

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

      2. lift-*.f64 N/A

         \[\leadsto \left(-x \cdot z\right) + x \]

      3. lift-neg.f64 N/A

         \[\leadsto \left(\mathsf{neg}\left(x \cdot z\right)\right) + x \]

      4. mul-1-neg N/A

         \[\leadsto -1 \cdot \left(x \cdot z\right) + x \]

      5. +-commutative N/A

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

      6. mul-1-neg N/A

         \[\leadsto x + \left(\mathsf{neg}\left(x \cdot z\right)\right) \]

      7. distribute-lft-neg-in N/A

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

      8. fp-cancel-sub-sign N/A

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

      9. lower--.f64 N/A

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

      10. *-commutative N/A

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

      11. lift-*.f64 95.4

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

   8. Applied rewrites 95.4%

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

Alternative 7: 66.3% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ x - z \cdot x \end{array} \]

FPCore

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

C

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

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 95.7%

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

   1. lift-*.f64 N/A

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

   2. lift--.f64 N/A

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

   3. lift-*.f64 N/A

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

   4. lift--.f64 N/A

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

   5. *-commutative N/A

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

3. Applied rewrites 98.3%

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

4. Taylor expanded in y around 0

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

   1. +-commutative N/A

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

   2. lower-+.f64 N/A

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

   3. mul-1-neg N/A

      \[\leadsto \left(\mathsf{neg}\left(x \cdot z\right)\right) + x \]

   4. lower-neg.f64 N/A

      \[\leadsto \left(-x \cdot z\right) + x \]

   5. lower-*.f64 66.3

      \[\leadsto \left(-x \cdot z\right) + x \]

6. Applied rewrites 66.3%

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

   1. lift-+.f64 N/A

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

   2. lift-*.f64 N/A

      \[\leadsto \left(-x \cdot z\right) + x \]

   3. lift-neg.f64 N/A

      \[\leadsto \left(\mathsf{neg}\left(x \cdot z\right)\right) + x \]

   4. mul-1-neg N/A

      \[\leadsto -1 \cdot \left(x \cdot z\right) + x \]

   5. +-commutative N/A

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

   6. mul-1-neg N/A

      \[\leadsto x + \left(\mathsf{neg}\left(x \cdot z\right)\right) \]

   7. distribute-lft-neg-in N/A

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

   8. fp-cancel-sub-sign N/A

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

   9. lower--.f64 N/A

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

   10. *-commutative N/A

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

   11. lift-*.f64 66.3

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

8. Applied rewrites 66.3%

   \[\leadsto \color{blue}{x - z \cdot x} \]
9. Add Preprocessing

Alternative 8: 64.3% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x \cdot \left(-z\right)\\ \mathbf{if}\;z \leq -15600000000:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;z \leq 2.05 \cdot 10^{+22}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* x (- z))))
   (if (<= z -15600000000.0) t_0 (if (<= z 2.05e+22) x t_0))))

C

double code(double x, double y, double z) {
	double t_0 = x * -z;
	double tmp;
	if (z <= -15600000000.0) {
		tmp = t_0;
	} else if (z <= 2.05e+22) {
		tmp = x;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x, y, z)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: tmp
    t_0 = x * -z
    if (z <= (-15600000000.0d0)) then
        tmp = t_0
    else if (z <= 2.05d+22) 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 = x * -z;
	double tmp;
	if (z <= -15600000000.0) {
		tmp = t_0;
	} else if (z <= 2.05e+22) {
		tmp = x;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = x * -z
	tmp = 0
	if z <= -15600000000.0:
		tmp = t_0
	elif z <= 2.05e+22:
		tmp = x
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(x * Float64(-z))
	tmp = 0.0
	if (z <= -15600000000.0)
		tmp = t_0;
	elseif (z <= 2.05e+22)
		tmp = x;
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = x * -z;
	tmp = 0.0;
	if (z <= -15600000000.0)
		tmp = t_0;
	elseif (z <= 2.05e+22)
		tmp = x;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(x * (-z)), $MachinePrecision]}, If[LessEqual[z, -15600000000.0], t$95$0, If[LessEqual[z, 2.05e+22], x, t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if z < -1.56e10 or 2.0499999999999999e22 < z

   1. Initial program 91.3%

      \[x \cdot \left(1 - \left(1 - y\right) \cdot z\right) \]

   2. Taylor expanded in z around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. lower--.f64 91.2

         \[\leadsto x \cdot \left(\left(y - 1\right) \cdot z\right) \]

   4. Applied rewrites 91.2%

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

   5. Taylor expanded in y around 0

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

      1. mul-1-neg N/A

         \[\leadsto x \cdot \left(\mathsf{neg}\left(z\right)\right) \]

      2. lower-neg.f64 57.0

         \[\leadsto x \cdot \left(-z\right) \]

   7. Applied rewrites 57.0%

      \[\leadsto x \cdot \left(-z\right) \]

   if -1.56e10 < z < 2.0499999999999999e22

   1. Initial program 99.8%

      \[x \cdot \left(1 - \left(1 - y\right) \cdot z\right) \]

   2. Taylor expanded in z around 0

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

   4. Applied rewrites 71.1%

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

Alternative 9: 38.4% accurate, 12.1× 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

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x, y, z)
use fmin_fmax_functions
    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 95.7%

   \[x \cdot \left(1 - \left(1 - y\right) \cdot z\right) \]

2. Taylor expanded in z around 0

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

4. Applied rewrites 38.4%

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

Reproduce

herbie shell --seed 2025112 
(FPCore (x y z)
  :name "Data.Colour.RGBSpace.HSV:hsv from colour-2.3.3, J"
  :precision binary64
  (* x (- 1.0 (* (- 1.0 y) z))))