Logistic function

Percentage Accurate: 99.8% → 99.9%

Time: 3.4s

Alternatives: 12

Speedup: 1.0×

Specification

\[0 \leq s \land s \leq 1.0651631\]

Math

\[\begin{array}{l} \\ \frac{1}{1 + e^{\frac{-x}{s}}} \end{array} \]

FPCore

(FPCore (x s) :precision binary32 (/ 1.0 (+ 1.0 (exp (/ (- x) s)))))

C

float code(float x, float s) {
	return 1.0f / (1.0f + expf((-x / s)));
}

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(4) function code(x, s)
use fmin_fmax_functions
    real(4), intent (in) :: x
    real(4), intent (in) :: s
    code = 1.0e0 / (1.0e0 + exp((-x / s)))
end function

Julia

function code(x, s)
	return Float32(Float32(1.0) / Float32(Float32(1.0) + exp(Float32(Float32(-x) / s))))
end

MATLAB

function tmp = code(x, s)
	tmp = single(1.0) / (single(1.0) + exp((-x / s)));
end

Initial Program: 99.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{1}{1 + e^{\frac{-x}{s}}} \end{array} \]

FPCore

(FPCore (x s) :precision binary32 (/ 1.0 (+ 1.0 (exp (/ (- x) s)))))

C

float code(float x, float s) {
	return 1.0f / (1.0f + expf((-x / s)));
}

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(4) function code(x, s)
use fmin_fmax_functions
    real(4), intent (in) :: x
    real(4), intent (in) :: s
    code = 1.0e0 / (1.0e0 + exp((-x / s)))
end function

Julia

function code(x, s)
	return Float32(Float32(1.0) / Float32(Float32(1.0) + exp(Float32(Float32(-x) / s))))
end

MATLAB

function tmp = code(x, s)
	tmp = single(1.0) / (single(1.0) + exp((-x / s)));
end

Alternative 1: 99.9% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ e^{-\mathsf{log1p}\left(e^{\frac{-x}{s}}\right)} \end{array} \]

FPCore

(FPCore (x s) :precision binary32 (exp (- (log1p (exp (/ (- x) s))))))

C

float code(float x, float s) {
	return expf(-log1pf(expf((-x / s))));
}

Julia

function code(x, s)
	return exp(Float32(-log1p(exp(Float32(Float32(-x) / s)))))
end

Derivation

1. Initial program 99.8%

   \[\frac{1}{1 + e^{\frac{-x}{s}}} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. lift-/.f32 N/A

      \[\leadsto \color{blue}{\frac{1}{1 + e^{\frac{-x}{s}}}} \]

   2. lift-+.f32 N/A

      \[\leadsto \frac{1}{\color{blue}{1 + e^{\frac{-x}{s}}}} \]

   3. lift-exp.f32 N/A

      \[\leadsto \frac{1}{1 + \color{blue}{e^{\frac{-x}{s}}}} \]

   4. lift-neg.f32 N/A

      \[\leadsto \frac{1}{1 + e^{\frac{\color{blue}{\mathsf{neg}\left(x\right)}}{s}}} \]

   5. lift-/.f32 N/A

      \[\leadsto \frac{1}{1 + e^{\color{blue}{\frac{\mathsf{neg}\left(x\right)}{s}}}} \]

   6. inv-pow N/A

      \[\leadsto \color{blue}{{\left(1 + e^{\frac{\mathsf{neg}\left(x\right)}{s}}\right)}^{-1}} \]

   7. pow-to-exp N/A

      \[\leadsto \color{blue}{e^{\log \left(1 + e^{\frac{\mathsf{neg}\left(x\right)}{s}}\right) \cdot -1}} \]

   8. lower-exp.f32 N/A

      \[\leadsto \color{blue}{e^{\log \left(1 + e^{\frac{\mathsf{neg}\left(x\right)}{s}}\right) \cdot -1}} \]

   9. lower-*.f32 N/A

      \[\leadsto e^{\color{blue}{\log \left(1 + e^{\frac{\mathsf{neg}\left(x\right)}{s}}\right) \cdot -1}} \]

   10. lower-log1p.f32 N/A

       \[\leadsto e^{\color{blue}{\mathsf{log1p}\left(e^{\frac{\mathsf{neg}\left(x\right)}{s}}\right)} \cdot -1} \]

   11. lift-/.f32 N/A

       \[\leadsto e^{\mathsf{log1p}\left(e^{\color{blue}{\frac{\mathsf{neg}\left(x\right)}{s}}}\right) \cdot -1} \]

   12. lift-neg.f32 N/A

       \[\leadsto e^{\mathsf{log1p}\left(e^{\frac{\color{blue}{-x}}{s}}\right) \cdot -1} \]

   13. lift-exp.f32 99.9

       \[\leadsto e^{\mathsf{log1p}\left(\color{blue}{e^{\frac{-x}{s}}}\right) \cdot -1} \]

4. Applied rewrites 99.9%

   \[\leadsto \color{blue}{e^{\mathsf{log1p}\left(e^{\frac{-x}{s}}\right) \cdot -1}} \]
5. Step-by-step derivation

   1. lift-*.f32 N/A

      \[\leadsto e^{\color{blue}{\mathsf{log1p}\left(e^{\frac{-x}{s}}\right) \cdot -1}} \]

   2. lift-log1p.f32 N/A

      \[\leadsto e^{\color{blue}{\log \left(1 + e^{\frac{-x}{s}}\right)} \cdot -1} \]

   3. lift-exp.f32 N/A

      \[\leadsto e^{\log \left(1 + \color{blue}{e^{\frac{-x}{s}}}\right) \cdot -1} \]

   4. lift-neg.f32 N/A

      \[\leadsto e^{\log \left(1 + e^{\frac{\color{blue}{\mathsf{neg}\left(x\right)}}{s}}\right) \cdot -1} \]

   5. lift-/.f32 N/A

      \[\leadsto e^{\log \left(1 + e^{\color{blue}{\frac{\mathsf{neg}\left(x\right)}{s}}}\right) \cdot -1} \]

   6. *-commutative N/A

      \[\leadsto e^{\color{blue}{-1 \cdot \log \left(1 + e^{\frac{\mathsf{neg}\left(x\right)}{s}}\right)}} \]

   7. log-pow-rev N/A

      \[\leadsto e^{\color{blue}{\log \left({\left(1 + e^{\frac{\mathsf{neg}\left(x\right)}{s}}\right)}^{-1}\right)}} \]

   8. inv-pow N/A

      \[\leadsto e^{\log \color{blue}{\left(\frac{1}{1 + e^{\frac{\mathsf{neg}\left(x\right)}{s}}}\right)}} \]

   9. neg-log N/A

      \[\leadsto e^{\color{blue}{\mathsf{neg}\left(\log \left(1 + e^{\frac{\mathsf{neg}\left(x\right)}{s}}\right)\right)}} \]

   10. lower-neg.f32 N/A

       \[\leadsto e^{\color{blue}{-\log \left(1 + e^{\frac{\mathsf{neg}\left(x\right)}{s}}\right)}} \]

   11. lift-/.f32 N/A

       \[\leadsto e^{-\log \left(1 + e^{\color{blue}{\frac{\mathsf{neg}\left(x\right)}{s}}}\right)} \]

   12. lift-neg.f32 N/A

       \[\leadsto e^{-\log \left(1 + e^{\frac{\color{blue}{-x}}{s}}\right)} \]

   13. lift-exp.f32 N/A

       \[\leadsto e^{-\log \left(1 + \color{blue}{e^{\frac{-x}{s}}}\right)} \]

   14. lift-log1p.f32 99.9

       \[\leadsto e^{-\color{blue}{\mathsf{log1p}\left(e^{\frac{-x}{s}}\right)}} \]

6. Applied rewrites 99.9%

   \[\leadsto \color{blue}{e^{-\mathsf{log1p}\left(e^{\frac{-x}{s}}\right)}} \]
7. Add Preprocessing

Alternative 2: 66.0% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 1 + e^{\frac{-x}{s}}\\ \mathbf{if}\;t\_0 \leq 1.5:\\ \;\;\;\;0.5\\ \mathbf{elif}\;t\_0 \leq 5:\\ \;\;\;\;\mathsf{fma}\left(\frac{x}{s}, 0.25, 0.5\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{\mathsf{fma}\left(\frac{0.5 \cdot x}{s \cdot s}, x, 2\right)}\\ \end{array} \end{array} \]

FPCore

(FPCore (x s)
 :precision binary32
 (let* ((t_0 (+ 1.0 (exp (/ (- x) s)))))
   (if (<= t_0 1.5)
     0.5
     (if (<= t_0 5.0)
       (fma (/ x s) 0.25 0.5)
       (/ 1.0 (fma (/ (* 0.5 x) (* s s)) x 2.0))))))

C

float code(float x, float s) {
	float t_0 = 1.0f + expf((-x / s));
	float tmp;
	if (t_0 <= 1.5f) {
		tmp = 0.5f;
	} else if (t_0 <= 5.0f) {
		tmp = fmaf((x / s), 0.25f, 0.5f);
	} else {
		tmp = 1.0f / fmaf(((0.5f * x) / (s * s)), x, 2.0f);
	}
	return tmp;
}

Julia

function code(x, s)
	t_0 = Float32(Float32(1.0) + exp(Float32(Float32(-x) / s)))
	tmp = Float32(0.0)
	if (t_0 <= Float32(1.5))
		tmp = Float32(0.5);
	elseif (t_0 <= Float32(5.0))
		tmp = fma(Float32(x / s), Float32(0.25), Float32(0.5));
	else
		tmp = Float32(Float32(1.0) / fma(Float32(Float32(Float32(0.5) * x) / Float32(s * s)), x, Float32(2.0)));
	end
	return tmp
end

Derivation

1. Split input into 3 regimes

2. if (+.f32 #s(literal 1 binary32) (exp.f32 (/.f32 (neg.f32 x) s))) < 1.5

   1. Initial program 99.9%

      \[\frac{1}{1 + e^{\frac{-x}{s}}} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\frac{1}{2}} \]
   4. Step-by-step derivation

   5. Applied rewrites 28.1%

      \[\leadsto \color{blue}{0.5} \]

   if 1.5 < (+.f32 #s(literal 1 binary32) (exp.f32 (/.f32 (neg.f32 x) s))) < 5

   1. Initial program 99.5%

      \[\frac{1}{1 + e^{\frac{-x}{s}}} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\frac{1}{2} + \frac{1}{4} \cdot \frac{x}{s}} \]
   4. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \frac{1}{4} \cdot \frac{x}{s} + \color{blue}{\frac{1}{2}} \]

      2. *-commutative N/A

         \[\leadsto \frac{x}{s} \cdot \frac{1}{4} + \frac{1}{2} \]

      3. lower-fma.f32 N/A

         \[\leadsto \mathsf{fma}\left(\frac{x}{s}, \color{blue}{\frac{1}{4}}, \frac{1}{2}\right) \]

      4. lower-/.f32 94.9

         \[\leadsto \mathsf{fma}\left(\frac{x}{s}, 0.25, 0.5\right) \]

   5. Applied rewrites 94.9%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{x}{s}, 0.25, 0.5\right)} \]

   if 5 < (+.f32 #s(literal 1 binary32) (exp.f32 (/.f32 (neg.f32 x) s)))

   1. Initial program 99.9%

      \[\frac{1}{1 + e^{\frac{-x}{s}}} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \frac{1}{\color{blue}{2 + x \cdot \left(\frac{1}{2} \cdot \frac{x}{{s}^{2}} - \frac{1}{s}\right)}} \]
   4. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \frac{1}{x \cdot \left(\frac{1}{2} \cdot \frac{x}{{s}^{2}} - \frac{1}{s}\right) + \color{blue}{2}} \]

      2. *-commutative N/A

         \[\leadsto \frac{1}{\left(\frac{1}{2} \cdot \frac{x}{{s}^{2}} - \frac{1}{s}\right) \cdot x + 2} \]

      3. lower-fma.f32 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{1}{2} \cdot \frac{x}{{s}^{2}} - \frac{1}{s}, \color{blue}{x}, 2\right)} \]

      4. lower--.f32 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{1}{2} \cdot \frac{x}{{s}^{2}} - \frac{1}{s}, x, 2\right)} \]

      5. *-commutative N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{x}{{s}^{2}} \cdot \frac{1}{2} - \frac{1}{s}, x, 2\right)} \]

      6. lower-*.f32 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{x}{{s}^{2}} \cdot \frac{1}{2} - \frac{1}{s}, x, 2\right)} \]

      7. lower-/.f32 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{x}{{s}^{2}} \cdot \frac{1}{2} - \frac{1}{s}, x, 2\right)} \]

      8. unpow2 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{x}{s \cdot s} \cdot \frac{1}{2} - \frac{1}{s}, x, 2\right)} \]

      9. lower-*.f32 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{x}{s \cdot s} \cdot \frac{1}{2} - \frac{1}{s}, x, 2\right)} \]

      10. lower-/.f32 79.1

          \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{x}{s \cdot s} \cdot 0.5 - \frac{1}{s}, x, 2\right)} \]

   5. Applied rewrites 79.1%

      \[\leadsto \frac{1}{\color{blue}{\mathsf{fma}\left(\frac{x}{s \cdot s} \cdot 0.5 - \frac{1}{s}, x, 2\right)}} \]

   6. Taylor expanded in s around 0

      \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{-1 \cdot s + \frac{1}{2} \cdot x}{{s}^{2}}, x, 2\right)} \]
   7. Step-by-step derivation

      1. lower-/.f32 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{-1 \cdot s + \frac{1}{2} \cdot x}{{s}^{2}}, x, 2\right)} \]

      2. mul-1-neg N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{\left(\mathsf{neg}\left(s\right)\right) + \frac{1}{2} \cdot x}{{s}^{2}}, x, 2\right)} \]

      3. +-commutative N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{\frac{1}{2} \cdot x + \left(\mathsf{neg}\left(s\right)\right)}{{s}^{2}}, x, 2\right)} \]

      4. lower-fma.f32 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{\mathsf{fma}\left(\frac{1}{2}, x, \mathsf{neg}\left(s\right)\right)}{{s}^{2}}, x, 2\right)} \]

      5. lower-neg.f32 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{\mathsf{fma}\left(\frac{1}{2}, x, -s\right)}{{s}^{2}}, x, 2\right)} \]

      6. pow2 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{\mathsf{fma}\left(\frac{1}{2}, x, -s\right)}{s \cdot s}, x, 2\right)} \]

      7. lift-*.f32 79.1

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{\mathsf{fma}\left(0.5, x, -s\right)}{s \cdot s}, x, 2\right)} \]

   8. Applied rewrites 79.1%

      \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{\mathsf{fma}\left(0.5, x, -s\right)}{s \cdot s}, x, 2\right)} \]

   9. Taylor expanded in x around inf

      \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{\frac{1}{2} \cdot x}{s \cdot s}, x, 2\right)} \]
   10. Step-by-step derivation

       1. lower-*.f32 79.1

          \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{0.5 \cdot x}{s \cdot s}, x, 2\right)} \]

   11. Applied rewrites 79.1%

       \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{0.5 \cdot x}{s \cdot s}, x, 2\right)} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 3: 64.2% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 1 + e^{\frac{-x}{s}}\\ \mathbf{if}\;t\_0 \leq 1.5:\\ \;\;\;\;0.5\\ \mathbf{elif}\;t\_0 \leq 5:\\ \;\;\;\;\mathsf{fma}\left(\frac{x}{s}, 0.25, 0.5\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{\frac{\left(x \cdot x\right) \cdot 0.5}{s \cdot s}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x s)
 :precision binary32
 (let* ((t_0 (+ 1.0 (exp (/ (- x) s)))))
   (if (<= t_0 1.5)
     0.5
     (if (<= t_0 5.0)
       (fma (/ x s) 0.25 0.5)
       (/ 1.0 (/ (* (* x x) 0.5) (* s s)))))))

C

float code(float x, float s) {
	float t_0 = 1.0f + expf((-x / s));
	float tmp;
	if (t_0 <= 1.5f) {
		tmp = 0.5f;
	} else if (t_0 <= 5.0f) {
		tmp = fmaf((x / s), 0.25f, 0.5f);
	} else {
		tmp = 1.0f / (((x * x) * 0.5f) / (s * s));
	}
	return tmp;
}

Julia

function code(x, s)
	t_0 = Float32(Float32(1.0) + exp(Float32(Float32(-x) / s)))
	tmp = Float32(0.0)
	if (t_0 <= Float32(1.5))
		tmp = Float32(0.5);
	elseif (t_0 <= Float32(5.0))
		tmp = fma(Float32(x / s), Float32(0.25), Float32(0.5));
	else
		tmp = Float32(Float32(1.0) / Float32(Float32(Float32(x * x) * Float32(0.5)) / Float32(s * s)));
	end
	return tmp
end

Derivation

1. Split input into 3 regimes

2. if (+.f32 #s(literal 1 binary32) (exp.f32 (/.f32 (neg.f32 x) s))) < 1.5

   1. Initial program 99.9%

      \[\frac{1}{1 + e^{\frac{-x}{s}}} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\frac{1}{2}} \]
   4. Step-by-step derivation

   5. Applied rewrites 28.1%

      \[\leadsto \color{blue}{0.5} \]

   if 1.5 < (+.f32 #s(literal 1 binary32) (exp.f32 (/.f32 (neg.f32 x) s))) < 5

   1. Initial program 99.5%

      \[\frac{1}{1 + e^{\frac{-x}{s}}} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\frac{1}{2} + \frac{1}{4} \cdot \frac{x}{s}} \]
   4. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \frac{1}{4} \cdot \frac{x}{s} + \color{blue}{\frac{1}{2}} \]

      2. *-commutative N/A

         \[\leadsto \frac{x}{s} \cdot \frac{1}{4} + \frac{1}{2} \]

      3. lower-fma.f32 N/A

         \[\leadsto \mathsf{fma}\left(\frac{x}{s}, \color{blue}{\frac{1}{4}}, \frac{1}{2}\right) \]

      4. lower-/.f32 94.9

         \[\leadsto \mathsf{fma}\left(\frac{x}{s}, 0.25, 0.5\right) \]

   5. Applied rewrites 94.9%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{x}{s}, 0.25, 0.5\right)} \]

   if 5 < (+.f32 #s(literal 1 binary32) (exp.f32 (/.f32 (neg.f32 x) s)))

   1. Initial program 99.9%

      \[\frac{1}{1 + e^{\frac{-x}{s}}} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \frac{1}{\color{blue}{2 + -1 \cdot \frac{x}{s}}} \]
   4. Step-by-step derivation

      1. +-commutative N/A

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

      2. lower-fma.f32 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(-1, \color{blue}{\frac{x}{s}}, 2\right)} \]

      3. lower-/.f32 35.2

         \[\leadsto \frac{1}{\mathsf{fma}\left(-1, \frac{x}{\color{blue}{s}}, 2\right)} \]

   5. Applied rewrites 35.2%

      \[\leadsto \frac{1}{\color{blue}{\mathsf{fma}\left(-1, \frac{x}{s}, 2\right)}} \]

   6. Taylor expanded in s around -inf

      \[\leadsto \frac{1}{\color{blue}{2 + -1 \cdot \frac{x + \frac{-1}{2} \cdot \frac{{x}^{2}}{s}}{s}}} \]
   7. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \frac{1}{-1 \cdot \frac{x + \frac{-1}{2} \cdot \frac{{x}^{2}}{s}}{s} + \color{blue}{2}} \]

      2. *-commutative N/A

         \[\leadsto \frac{1}{\frac{x + \frac{-1}{2} \cdot \frac{{x}^{2}}{s}}{s} \cdot -1 + 2} \]

      3. lower-fma.f32 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{x + \frac{-1}{2} \cdot \frac{{x}^{2}}{s}}{s}, \color{blue}{-1}, 2\right)} \]

      4. lower-/.f32 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{x + \frac{-1}{2} \cdot \frac{{x}^{2}}{s}}{s}, -1, 2\right)} \]

      5. +-commutative N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{\frac{-1}{2} \cdot \frac{{x}^{2}}{s} + x}{s}, -1, 2\right)} \]

      6. *-commutative N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{\frac{{x}^{2}}{s} \cdot \frac{-1}{2} + x}{s}, -1, 2\right)} \]

      7. lower-fma.f32 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{\mathsf{fma}\left(\frac{{x}^{2}}{s}, \frac{-1}{2}, x\right)}{s}, -1, 2\right)} \]

      8. lower-/.f32 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{\mathsf{fma}\left(\frac{{x}^{2}}{s}, \frac{-1}{2}, x\right)}{s}, -1, 2\right)} \]

      9. pow2 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{\mathsf{fma}\left(\frac{x \cdot x}{s}, \frac{-1}{2}, x\right)}{s}, -1, 2\right)} \]

      10. lift-*.f32 69.1

          \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{\mathsf{fma}\left(\frac{x \cdot x}{s}, -0.5, x\right)}{s}, -1, 2\right)} \]

   8. Applied rewrites 69.1%

      \[\leadsto \frac{1}{\color{blue}{\mathsf{fma}\left(\frac{\mathsf{fma}\left(\frac{x \cdot x}{s}, -0.5, x\right)}{s}, -1, 2\right)}} \]

   9. Taylor expanded in x around inf

      \[\leadsto \frac{1}{\frac{1}{2} \cdot \color{blue}{\frac{{x}^{2}}{{s}^{2}}}} \]
   10. Step-by-step derivation

       1. associate-*r/ N/A

          \[\leadsto \frac{1}{\frac{\frac{1}{2} \cdot {x}^{2}}{{s}^{\color{blue}{2}}}} \]

       2. lower-/.f32 N/A

          \[\leadsto \frac{1}{\frac{\frac{1}{2} \cdot {x}^{2}}{{s}^{\color{blue}{2}}}} \]

       3. *-commutative N/A

          \[\leadsto \frac{1}{\frac{{x}^{2} \cdot \frac{1}{2}}{{s}^{2}}} \]

       4. lower-*.f32 N/A

          \[\leadsto \frac{1}{\frac{{x}^{2} \cdot \frac{1}{2}}{{s}^{2}}} \]

       5. pow2 N/A

          \[\leadsto \frac{1}{\frac{\left(x \cdot x\right) \cdot \frac{1}{2}}{{s}^{2}}} \]

       6. lift-*.f32 N/A

          \[\leadsto \frac{1}{\frac{\left(x \cdot x\right) \cdot \frac{1}{2}}{{s}^{2}}} \]

       7. pow2 N/A

          \[\leadsto \frac{1}{\frac{\left(x \cdot x\right) \cdot \frac{1}{2}}{s \cdot s}} \]

       8. lift-*.f32 74.9

          \[\leadsto \frac{1}{\frac{\left(x \cdot x\right) \cdot 0.5}{s \cdot s}} \]

   11. Applied rewrites 74.9%

       \[\leadsto \frac{1}{\frac{\left(x \cdot x\right) \cdot 0.5}{\color{blue}{s \cdot s}}} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 4: 59.3% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 1 + e^{\frac{-x}{s}}\\ \mathbf{if}\;t\_0 \leq 1.5:\\ \;\;\;\;0.5\\ \mathbf{elif}\;t\_0 \leq 5:\\ \;\;\;\;\mathsf{fma}\left(\frac{x}{s}, 0.25, 0.5\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{\mathsf{fma}\left(\frac{-s}{s \cdot s}, x, 2\right)}\\ \end{array} \end{array} \]

FPCore

(FPCore (x s)
 :precision binary32
 (let* ((t_0 (+ 1.0 (exp (/ (- x) s)))))
   (if (<= t_0 1.5)
     0.5
     (if (<= t_0 5.0)
       (fma (/ x s) 0.25 0.5)
       (/ 1.0 (fma (/ (- s) (* s s)) x 2.0))))))

C

float code(float x, float s) {
	float t_0 = 1.0f + expf((-x / s));
	float tmp;
	if (t_0 <= 1.5f) {
		tmp = 0.5f;
	} else if (t_0 <= 5.0f) {
		tmp = fmaf((x / s), 0.25f, 0.5f);
	} else {
		tmp = 1.0f / fmaf((-s / (s * s)), x, 2.0f);
	}
	return tmp;
}

Julia

function code(x, s)
	t_0 = Float32(Float32(1.0) + exp(Float32(Float32(-x) / s)))
	tmp = Float32(0.0)
	if (t_0 <= Float32(1.5))
		tmp = Float32(0.5);
	elseif (t_0 <= Float32(5.0))
		tmp = fma(Float32(x / s), Float32(0.25), Float32(0.5));
	else
		tmp = Float32(Float32(1.0) / fma(Float32(Float32(-s) / Float32(s * s)), x, Float32(2.0)));
	end
	return tmp
end

Derivation

1. Split input into 3 regimes

2. if (+.f32 #s(literal 1 binary32) (exp.f32 (/.f32 (neg.f32 x) s))) < 1.5

   1. Initial program 99.9%

      \[\frac{1}{1 + e^{\frac{-x}{s}}} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\frac{1}{2}} \]
   4. Step-by-step derivation

   5. Applied rewrites 28.1%

      \[\leadsto \color{blue}{0.5} \]

   if 1.5 < (+.f32 #s(literal 1 binary32) (exp.f32 (/.f32 (neg.f32 x) s))) < 5

   1. Initial program 99.5%

      \[\frac{1}{1 + e^{\frac{-x}{s}}} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\frac{1}{2} + \frac{1}{4} \cdot \frac{x}{s}} \]
   4. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \frac{1}{4} \cdot \frac{x}{s} + \color{blue}{\frac{1}{2}} \]

      2. *-commutative N/A

         \[\leadsto \frac{x}{s} \cdot \frac{1}{4} + \frac{1}{2} \]

      3. lower-fma.f32 N/A

         \[\leadsto \mathsf{fma}\left(\frac{x}{s}, \color{blue}{\frac{1}{4}}, \frac{1}{2}\right) \]

      4. lower-/.f32 94.9

         \[\leadsto \mathsf{fma}\left(\frac{x}{s}, 0.25, 0.5\right) \]

   5. Applied rewrites 94.9%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{x}{s}, 0.25, 0.5\right)} \]

   if 5 < (+.f32 #s(literal 1 binary32) (exp.f32 (/.f32 (neg.f32 x) s)))

   1. Initial program 99.9%

      \[\frac{1}{1 + e^{\frac{-x}{s}}} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \frac{1}{\color{blue}{2 + x \cdot \left(\frac{1}{2} \cdot \frac{x}{{s}^{2}} - \frac{1}{s}\right)}} \]
   4. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \frac{1}{x \cdot \left(\frac{1}{2} \cdot \frac{x}{{s}^{2}} - \frac{1}{s}\right) + \color{blue}{2}} \]

      2. *-commutative N/A

         \[\leadsto \frac{1}{\left(\frac{1}{2} \cdot \frac{x}{{s}^{2}} - \frac{1}{s}\right) \cdot x + 2} \]

      3. lower-fma.f32 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{1}{2} \cdot \frac{x}{{s}^{2}} - \frac{1}{s}, \color{blue}{x}, 2\right)} \]

      4. lower--.f32 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{1}{2} \cdot \frac{x}{{s}^{2}} - \frac{1}{s}, x, 2\right)} \]

      5. *-commutative N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{x}{{s}^{2}} \cdot \frac{1}{2} - \frac{1}{s}, x, 2\right)} \]

      6. lower-*.f32 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{x}{{s}^{2}} \cdot \frac{1}{2} - \frac{1}{s}, x, 2\right)} \]

      7. lower-/.f32 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{x}{{s}^{2}} \cdot \frac{1}{2} - \frac{1}{s}, x, 2\right)} \]

      8. unpow2 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{x}{s \cdot s} \cdot \frac{1}{2} - \frac{1}{s}, x, 2\right)} \]

      9. lower-*.f32 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{x}{s \cdot s} \cdot \frac{1}{2} - \frac{1}{s}, x, 2\right)} \]

      10. lower-/.f32 79.1

          \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{x}{s \cdot s} \cdot 0.5 - \frac{1}{s}, x, 2\right)} \]

   5. Applied rewrites 79.1%

      \[\leadsto \frac{1}{\color{blue}{\mathsf{fma}\left(\frac{x}{s \cdot s} \cdot 0.5 - \frac{1}{s}, x, 2\right)}} \]

   6. Taylor expanded in s around 0

      \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{-1 \cdot s + \frac{1}{2} \cdot x}{{s}^{2}}, x, 2\right)} \]
   7. Step-by-step derivation

      1. lower-/.f32 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{-1 \cdot s + \frac{1}{2} \cdot x}{{s}^{2}}, x, 2\right)} \]

      2. mul-1-neg N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{\left(\mathsf{neg}\left(s\right)\right) + \frac{1}{2} \cdot x}{{s}^{2}}, x, 2\right)} \]

      3. +-commutative N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{\frac{1}{2} \cdot x + \left(\mathsf{neg}\left(s\right)\right)}{{s}^{2}}, x, 2\right)} \]

      4. lower-fma.f32 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{\mathsf{fma}\left(\frac{1}{2}, x, \mathsf{neg}\left(s\right)\right)}{{s}^{2}}, x, 2\right)} \]

      5. lower-neg.f32 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{\mathsf{fma}\left(\frac{1}{2}, x, -s\right)}{{s}^{2}}, x, 2\right)} \]

      6. pow2 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{\mathsf{fma}\left(\frac{1}{2}, x, -s\right)}{s \cdot s}, x, 2\right)} \]

      7. lift-*.f32 79.1

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{\mathsf{fma}\left(0.5, x, -s\right)}{s \cdot s}, x, 2\right)} \]

   8. Applied rewrites 79.1%

      \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{\mathsf{fma}\left(0.5, x, -s\right)}{s \cdot s}, x, 2\right)} \]

   9. Taylor expanded in x around 0

      \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{-1 \cdot s}{s \cdot s}, x, 2\right)} \]
   10. Step-by-step derivation

       1. mul-1-neg N/A

          \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{\mathsf{neg}\left(s\right)}{s \cdot s}, x, 2\right)} \]

       2. lift-neg.f32 62.6

          \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{-s}{s \cdot s}, x, 2\right)} \]

   11. Applied rewrites 62.6%

       \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{-s}{s \cdot s}, x, 2\right)} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 5: 50.3% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{-x}{s}\\ t_1 := 1 + e^{t\_0}\\ \mathbf{if}\;t\_1 \leq 1.5:\\ \;\;\;\;0.5\\ \mathbf{elif}\;t\_1 \leq 5:\\ \;\;\;\;\mathsf{fma}\left(\frac{x}{s}, 0.25, 0.5\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{t\_0}\\ \end{array} \end{array} \]

FPCore

(FPCore (x s)
 :precision binary32
 (let* ((t_0 (/ (- x) s)) (t_1 (+ 1.0 (exp t_0))))
   (if (<= t_1 1.5) 0.5 (if (<= t_1 5.0) (fma (/ x s) 0.25 0.5) (/ 1.0 t_0)))))

C

float code(float x, float s) {
	float t_0 = -x / s;
	float t_1 = 1.0f + expf(t_0);
	float tmp;
	if (t_1 <= 1.5f) {
		tmp = 0.5f;
	} else if (t_1 <= 5.0f) {
		tmp = fmaf((x / s), 0.25f, 0.5f);
	} else {
		tmp = 1.0f / t_0;
	}
	return tmp;
}

Julia

function code(x, s)
	t_0 = Float32(Float32(-x) / s)
	t_1 = Float32(Float32(1.0) + exp(t_0))
	tmp = Float32(0.0)
	if (t_1 <= Float32(1.5))
		tmp = Float32(0.5);
	elseif (t_1 <= Float32(5.0))
		tmp = fma(Float32(x / s), Float32(0.25), Float32(0.5));
	else
		tmp = Float32(Float32(1.0) / t_0);
	end
	return tmp
end

Derivation

1. Split input into 3 regimes

2. if (+.f32 #s(literal 1 binary32) (exp.f32 (/.f32 (neg.f32 x) s))) < 1.5

   1. Initial program 99.9%

      \[\frac{1}{1 + e^{\frac{-x}{s}}} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\frac{1}{2}} \]
   4. Step-by-step derivation

   5. Applied rewrites 28.1%

      \[\leadsto \color{blue}{0.5} \]

   if 1.5 < (+.f32 #s(literal 1 binary32) (exp.f32 (/.f32 (neg.f32 x) s))) < 5

   1. Initial program 99.5%

      \[\frac{1}{1 + e^{\frac{-x}{s}}} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\frac{1}{2} + \frac{1}{4} \cdot \frac{x}{s}} \]
   4. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \frac{1}{4} \cdot \frac{x}{s} + \color{blue}{\frac{1}{2}} \]

      2. *-commutative N/A

         \[\leadsto \frac{x}{s} \cdot \frac{1}{4} + \frac{1}{2} \]

      3. lower-fma.f32 N/A

         \[\leadsto \mathsf{fma}\left(\frac{x}{s}, \color{blue}{\frac{1}{4}}, \frac{1}{2}\right) \]

      4. lower-/.f32 94.9

         \[\leadsto \mathsf{fma}\left(\frac{x}{s}, 0.25, 0.5\right) \]

   5. Applied rewrites 94.9%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{x}{s}, 0.25, 0.5\right)} \]

   if 5 < (+.f32 #s(literal 1 binary32) (exp.f32 (/.f32 (neg.f32 x) s)))

   1. Initial program 99.9%

      \[\frac{1}{1 + e^{\frac{-x}{s}}} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \frac{1}{\color{blue}{2 + -1 \cdot \frac{x}{s}}} \]
   4. Step-by-step derivation

      1. +-commutative N/A

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

      2. lower-fma.f32 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(-1, \color{blue}{\frac{x}{s}}, 2\right)} \]

      3. lower-/.f32 35.2

         \[\leadsto \frac{1}{\mathsf{fma}\left(-1, \frac{x}{\color{blue}{s}}, 2\right)} \]

   5. Applied rewrites 35.2%

      \[\leadsto \frac{1}{\color{blue}{\mathsf{fma}\left(-1, \frac{x}{s}, 2\right)}} \]

   6. Taylor expanded in x around inf

      \[\leadsto \frac{1}{-1 \cdot \color{blue}{\frac{x}{s}}} \]
   7. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \frac{1}{\mathsf{neg}\left(\frac{x}{s}\right)} \]

      2. distribute-frac-neg N/A

         \[\leadsto \frac{1}{\frac{\mathsf{neg}\left(x\right)}{s}} \]

      3. lift-/.f32 N/A

         \[\leadsto \frac{1}{\frac{\mathsf{neg}\left(x\right)}{s}} \]

      4. lift-neg.f32 35.2

         \[\leadsto \frac{1}{\frac{-x}{s}} \]

   8. Applied rewrites 35.2%

      \[\leadsto \frac{1}{\frac{-x}{\color{blue}{s}}} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 6: 89.9% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\frac{1}{1 + e^{\frac{-x}{s}}} \leq 0.5600000023841858:\\ \;\;\;\;\frac{1}{\mathsf{fma}\left(\frac{\frac{x}{s} \cdot 0.5 - 1}{s}, x, 2\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{1 + \frac{1}{\mathsf{fma}\left(\mathsf{fma}\left(\frac{x}{s \cdot s}, 0.5, \frac{1}{s}\right), x, 1\right)}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x s)
 :precision binary32
 (if (<= (/ 1.0 (+ 1.0 (exp (/ (- x) s)))) 0.5600000023841858)
   (/ 1.0 (fma (/ (- (* (/ x s) 0.5) 1.0) s) x 2.0))
   (/ 1.0 (+ 1.0 (/ 1.0 (fma (fma (/ x (* s s)) 0.5 (/ 1.0 s)) x 1.0))))))

C

float code(float x, float s) {
	float tmp;
	if ((1.0f / (1.0f + expf((-x / s)))) <= 0.5600000023841858f) {
		tmp = 1.0f / fmaf(((((x / s) * 0.5f) - 1.0f) / s), x, 2.0f);
	} else {
		tmp = 1.0f / (1.0f + (1.0f / fmaf(fmaf((x / (s * s)), 0.5f, (1.0f / s)), x, 1.0f)));
	}
	return tmp;
}

Julia

function code(x, s)
	tmp = Float32(0.0)
	if (Float32(Float32(1.0) / Float32(Float32(1.0) + exp(Float32(Float32(-x) / s)))) <= Float32(0.5600000023841858))
		tmp = Float32(Float32(1.0) / fma(Float32(Float32(Float32(Float32(x / s) * Float32(0.5)) - Float32(1.0)) / s), x, Float32(2.0)));
	else
		tmp = Float32(Float32(1.0) / Float32(Float32(1.0) + Float32(Float32(1.0) / fma(fma(Float32(x / Float32(s * s)), Float32(0.5), Float32(Float32(1.0) / s)), x, Float32(1.0)))));
	end
	return tmp
end

Derivation

1. Split input into 2 regimes

2. if (/.f32 #s(literal 1 binary32) (+.f32 #s(literal 1 binary32) (exp.f32 (/.f32 (neg.f32 x) s)))) < 0.560000002

   1. Initial program 99.7%

      \[\frac{1}{1 + e^{\frac{-x}{s}}} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \frac{1}{\color{blue}{2 + x \cdot \left(\frac{1}{2} \cdot \frac{x}{{s}^{2}} - \frac{1}{s}\right)}} \]
   4. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \frac{1}{x \cdot \left(\frac{1}{2} \cdot \frac{x}{{s}^{2}} - \frac{1}{s}\right) + \color{blue}{2}} \]

      2. *-commutative N/A

         \[\leadsto \frac{1}{\left(\frac{1}{2} \cdot \frac{x}{{s}^{2}} - \frac{1}{s}\right) \cdot x + 2} \]

      3. lower-fma.f32 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{1}{2} \cdot \frac{x}{{s}^{2}} - \frac{1}{s}, \color{blue}{x}, 2\right)} \]

      4. lower--.f32 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{1}{2} \cdot \frac{x}{{s}^{2}} - \frac{1}{s}, x, 2\right)} \]

      5. *-commutative N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{x}{{s}^{2}} \cdot \frac{1}{2} - \frac{1}{s}, x, 2\right)} \]

      6. lower-*.f32 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{x}{{s}^{2}} \cdot \frac{1}{2} - \frac{1}{s}, x, 2\right)} \]

      7. lower-/.f32 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{x}{{s}^{2}} \cdot \frac{1}{2} - \frac{1}{s}, x, 2\right)} \]

      8. unpow2 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{x}{s \cdot s} \cdot \frac{1}{2} - \frac{1}{s}, x, 2\right)} \]

      9. lower-*.f32 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{x}{s \cdot s} \cdot \frac{1}{2} - \frac{1}{s}, x, 2\right)} \]

      10. lower-/.f32 76.7

          \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{x}{s \cdot s} \cdot 0.5 - \frac{1}{s}, x, 2\right)} \]

   5. Applied rewrites 76.7%

      \[\leadsto \frac{1}{\color{blue}{\mathsf{fma}\left(\frac{x}{s \cdot s} \cdot 0.5 - \frac{1}{s}, x, 2\right)}} \]

   6. Taylor expanded in s around inf

      \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{\frac{1}{2} \cdot \frac{x}{s} - 1}{s}, x, 2\right)} \]
   7. Step-by-step derivation

      1. lower-/.f32 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{\frac{1}{2} \cdot \frac{x}{s} - 1}{s}, x, 2\right)} \]

      2. lower--.f32 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{\frac{1}{2} \cdot \frac{x}{s} - 1}{s}, x, 2\right)} \]

      3. *-commutative N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{\frac{x}{s} \cdot \frac{1}{2} - 1}{s}, x, 2\right)} \]

      4. lower-*.f32 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{\frac{x}{s} \cdot \frac{1}{2} - 1}{s}, x, 2\right)} \]

      5. lift-/.f32 83.9

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{\frac{x}{s} \cdot 0.5 - 1}{s}, x, 2\right)} \]

   8. Applied rewrites 83.9%

      \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{\frac{x}{s} \cdot 0.5 - 1}{s}, x, 2\right)} \]

   if 0.560000002 < (/.f32 #s(literal 1 binary32) (+.f32 #s(literal 1 binary32) (exp.f32 (/.f32 (neg.f32 x) s))))

   1. Initial program 99.9%

      \[\frac{1}{1 + e^{\frac{-x}{s}}} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-exp.f32 N/A

         \[\leadsto \frac{1}{1 + \color{blue}{e^{\frac{-x}{s}}}} \]

      2. lift-neg.f32 N/A

         \[\leadsto \frac{1}{1 + e^{\frac{\color{blue}{\mathsf{neg}\left(x\right)}}{s}}} \]

      3. lift-/.f32 N/A

         \[\leadsto \frac{1}{1 + e^{\color{blue}{\frac{\mathsf{neg}\left(x\right)}{s}}}} \]

      4. distribute-frac-neg N/A

         \[\leadsto \frac{1}{1 + e^{\color{blue}{\mathsf{neg}\left(\frac{x}{s}\right)}}} \]

      5. exp-neg N/A

         \[\leadsto \frac{1}{1 + \color{blue}{\frac{1}{e^{\frac{x}{s}}}}} \]

      6. lower-/.f32 N/A

         \[\leadsto \frac{1}{1 + \color{blue}{\frac{1}{e^{\frac{x}{s}}}}} \]

      7. lower-exp.f32 N/A

         \[\leadsto \frac{1}{1 + \frac{1}{\color{blue}{e^{\frac{x}{s}}}}} \]

      8. lower-/.f32 99.9

         \[\leadsto \frac{1}{1 + \frac{1}{e^{\color{blue}{\frac{x}{s}}}}} \]

   4. Applied rewrites 99.9%

      \[\leadsto \frac{1}{1 + \color{blue}{\frac{1}{e^{\frac{x}{s}}}}} \]

   5. Taylor expanded in x around 0

      \[\leadsto \frac{1}{1 + \frac{1}{\color{blue}{1 + x \cdot \left(\frac{1}{2} \cdot \frac{x}{{s}^{2}} + \frac{1}{s}\right)}}} \]
   6. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \frac{1}{1 + \frac{1}{x \cdot \left(\frac{1}{2} \cdot \frac{x}{{s}^{2}} + \frac{1}{s}\right) + \color{blue}{1}}} \]

      2. *-commutative N/A

         \[\leadsto \frac{1}{1 + \frac{1}{\left(\frac{1}{2} \cdot \frac{x}{{s}^{2}} + \frac{1}{s}\right) \cdot x + 1}} \]

      3. lower-fma.f32 N/A

         \[\leadsto \frac{1}{1 + \frac{1}{\mathsf{fma}\left(\frac{1}{2} \cdot \frac{x}{{s}^{2}} + \frac{1}{s}, \color{blue}{x}, 1\right)}} \]

      4. *-commutative N/A

         \[\leadsto \frac{1}{1 + \frac{1}{\mathsf{fma}\left(\frac{x}{{s}^{2}} \cdot \frac{1}{2} + \frac{1}{s}, x, 1\right)}} \]

      5. lower-fma.f32 N/A

         \[\leadsto \frac{1}{1 + \frac{1}{\mathsf{fma}\left(\mathsf{fma}\left(\frac{x}{{s}^{2}}, \frac{1}{2}, \frac{1}{s}\right), x, 1\right)}} \]

      6. lower-/.f32 N/A

         \[\leadsto \frac{1}{1 + \frac{1}{\mathsf{fma}\left(\mathsf{fma}\left(\frac{x}{{s}^{2}}, \frac{1}{2}, \frac{1}{s}\right), x, 1\right)}} \]

      7. pow2 N/A

         \[\leadsto \frac{1}{1 + \frac{1}{\mathsf{fma}\left(\mathsf{fma}\left(\frac{x}{s \cdot s}, \frac{1}{2}, \frac{1}{s}\right), x, 1\right)}} \]

      8. lift-*.f32 N/A

         \[\leadsto \frac{1}{1 + \frac{1}{\mathsf{fma}\left(\mathsf{fma}\left(\frac{x}{s \cdot s}, \frac{1}{2}, \frac{1}{s}\right), x, 1\right)}} \]

      9. lower-/.f32 97.2

         \[\leadsto \frac{1}{1 + \frac{1}{\mathsf{fma}\left(\mathsf{fma}\left(\frac{x}{s \cdot s}, 0.5, \frac{1}{s}\right), x, 1\right)}} \]

   7. Applied rewrites 97.2%

      \[\leadsto \frac{1}{1 + \frac{1}{\color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(\frac{x}{s \cdot s}, 0.5, \frac{1}{s}\right), x, 1\right)}}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 7: 89.0% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\frac{1}{1 + e^{\frac{-x}{s}}} \leq 0.6000000238418579:\\ \;\;\;\;\frac{1}{\mathsf{fma}\left(\frac{\frac{x}{s} \cdot 0.5 - 1}{s}, x, 2\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{1 + \frac{1}{\frac{x}{s} + 1}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x s)
 :precision binary32
 (if (<= (/ 1.0 (+ 1.0 (exp (/ (- x) s)))) 0.6000000238418579)
   (/ 1.0 (fma (/ (- (* (/ x s) 0.5) 1.0) s) x 2.0))
   (/ 1.0 (+ 1.0 (/ 1.0 (+ (/ x s) 1.0))))))

C

float code(float x, float s) {
	float tmp;
	if ((1.0f / (1.0f + expf((-x / s)))) <= 0.6000000238418579f) {
		tmp = 1.0f / fmaf(((((x / s) * 0.5f) - 1.0f) / s), x, 2.0f);
	} else {
		tmp = 1.0f / (1.0f + (1.0f / ((x / s) + 1.0f)));
	}
	return tmp;
}

Julia

function code(x, s)
	tmp = Float32(0.0)
	if (Float32(Float32(1.0) / Float32(Float32(1.0) + exp(Float32(Float32(-x) / s)))) <= Float32(0.6000000238418579))
		tmp = Float32(Float32(1.0) / fma(Float32(Float32(Float32(Float32(x / s) * Float32(0.5)) - Float32(1.0)) / s), x, Float32(2.0)));
	else
		tmp = Float32(Float32(1.0) / Float32(Float32(1.0) + Float32(Float32(1.0) / Float32(Float32(x / s) + Float32(1.0)))));
	end
	return tmp
end

Derivation

1. Split input into 2 regimes

2. if (/.f32 #s(literal 1 binary32) (+.f32 #s(literal 1 binary32) (exp.f32 (/.f32 (neg.f32 x) s)))) < 0.600000024

   1. Initial program 99.7%

      \[\frac{1}{1 + e^{\frac{-x}{s}}} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \frac{1}{\color{blue}{2 + x \cdot \left(\frac{1}{2} \cdot \frac{x}{{s}^{2}} - \frac{1}{s}\right)}} \]
   4. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \frac{1}{x \cdot \left(\frac{1}{2} \cdot \frac{x}{{s}^{2}} - \frac{1}{s}\right) + \color{blue}{2}} \]

      2. *-commutative N/A

         \[\leadsto \frac{1}{\left(\frac{1}{2} \cdot \frac{x}{{s}^{2}} - \frac{1}{s}\right) \cdot x + 2} \]

      3. lower-fma.f32 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{1}{2} \cdot \frac{x}{{s}^{2}} - \frac{1}{s}, \color{blue}{x}, 2\right)} \]

      4. lower--.f32 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{1}{2} \cdot \frac{x}{{s}^{2}} - \frac{1}{s}, x, 2\right)} \]

      5. *-commutative N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{x}{{s}^{2}} \cdot \frac{1}{2} - \frac{1}{s}, x, 2\right)} \]

      6. lower-*.f32 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{x}{{s}^{2}} \cdot \frac{1}{2} - \frac{1}{s}, x, 2\right)} \]

      7. lower-/.f32 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{x}{{s}^{2}} \cdot \frac{1}{2} - \frac{1}{s}, x, 2\right)} \]

      8. unpow2 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{x}{s \cdot s} \cdot \frac{1}{2} - \frac{1}{s}, x, 2\right)} \]

      9. lower-*.f32 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{x}{s \cdot s} \cdot \frac{1}{2} - \frac{1}{s}, x, 2\right)} \]

      10. lower-/.f32 76.6

          \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{x}{s \cdot s} \cdot 0.5 - \frac{1}{s}, x, 2\right)} \]

   5. Applied rewrites 76.6%

      \[\leadsto \frac{1}{\color{blue}{\mathsf{fma}\left(\frac{x}{s \cdot s} \cdot 0.5 - \frac{1}{s}, x, 2\right)}} \]

   6. Taylor expanded in s around inf

      \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{\frac{1}{2} \cdot \frac{x}{s} - 1}{s}, x, 2\right)} \]
   7. Step-by-step derivation

      1. lower-/.f32 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{\frac{1}{2} \cdot \frac{x}{s} - 1}{s}, x, 2\right)} \]

      2. lower--.f32 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{\frac{1}{2} \cdot \frac{x}{s} - 1}{s}, x, 2\right)} \]

      3. *-commutative N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{\frac{x}{s} \cdot \frac{1}{2} - 1}{s}, x, 2\right)} \]

      4. lower-*.f32 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{\frac{x}{s} \cdot \frac{1}{2} - 1}{s}, x, 2\right)} \]

      5. lift-/.f32 83.8

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{\frac{x}{s} \cdot 0.5 - 1}{s}, x, 2\right)} \]

   8. Applied rewrites 83.8%

      \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{\frac{x}{s} \cdot 0.5 - 1}{s}, x, 2\right)} \]

   if 0.600000024 < (/.f32 #s(literal 1 binary32) (+.f32 #s(literal 1 binary32) (exp.f32 (/.f32 (neg.f32 x) s))))

   1. Initial program 99.9%

      \[\frac{1}{1 + e^{\frac{-x}{s}}} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-exp.f32 N/A

         \[\leadsto \frac{1}{1 + \color{blue}{e^{\frac{-x}{s}}}} \]

      2. lift-neg.f32 N/A

         \[\leadsto \frac{1}{1 + e^{\frac{\color{blue}{\mathsf{neg}\left(x\right)}}{s}}} \]

      3. lift-/.f32 N/A

         \[\leadsto \frac{1}{1 + e^{\color{blue}{\frac{\mathsf{neg}\left(x\right)}{s}}}} \]

      4. distribute-frac-neg N/A

         \[\leadsto \frac{1}{1 + e^{\color{blue}{\mathsf{neg}\left(\frac{x}{s}\right)}}} \]

      5. exp-neg N/A

         \[\leadsto \frac{1}{1 + \color{blue}{\frac{1}{e^{\frac{x}{s}}}}} \]

      6. lower-/.f32 N/A

         \[\leadsto \frac{1}{1 + \color{blue}{\frac{1}{e^{\frac{x}{s}}}}} \]

      7. lower-exp.f32 N/A

         \[\leadsto \frac{1}{1 + \frac{1}{\color{blue}{e^{\frac{x}{s}}}}} \]

      8. lower-/.f32 99.9

         \[\leadsto \frac{1}{1 + \frac{1}{e^{\color{blue}{\frac{x}{s}}}}} \]

   4. Applied rewrites 99.9%

      \[\leadsto \frac{1}{1 + \color{blue}{\frac{1}{e^{\frac{x}{s}}}}} \]

   5. Taylor expanded in x around 0

      \[\leadsto \frac{1}{1 + \frac{1}{\color{blue}{1 + \frac{x}{s}}}} \]
   6. Step-by-step derivation

      1. +-commutative N/A

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

      2. lower-+.f32 N/A

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

      3. lift-/.f32 94.3

         \[\leadsto \frac{1}{1 + \frac{1}{\frac{x}{s} + 1}} \]

   7. Applied rewrites 94.3%

      \[\leadsto \frac{1}{1 + \frac{1}{\color{blue}{\frac{x}{s} + 1}}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 8: 90.0% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\frac{1}{1 + e^{\frac{-x}{s}}} \leq 0.10000000149011612:\\ \;\;\;\;\frac{1}{\mathsf{fma}\left(\frac{\mathsf{fma}\left(0.5, x, -s\right)}{s \cdot s}, x, 2\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{1 + \frac{1}{\frac{x}{s} + 1}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x s)
 :precision binary32
 (if (<= (/ 1.0 (+ 1.0 (exp (/ (- x) s)))) 0.10000000149011612)
   (/ 1.0 (fma (/ (fma 0.5 x (- s)) (* s s)) x 2.0))
   (/ 1.0 (+ 1.0 (/ 1.0 (+ (/ x s) 1.0))))))

C

float code(float x, float s) {
	float tmp;
	if ((1.0f / (1.0f + expf((-x / s)))) <= 0.10000000149011612f) {
		tmp = 1.0f / fmaf((fmaf(0.5f, x, -s) / (s * s)), x, 2.0f);
	} else {
		tmp = 1.0f / (1.0f + (1.0f / ((x / s) + 1.0f)));
	}
	return tmp;
}

Julia

function code(x, s)
	tmp = Float32(0.0)
	if (Float32(Float32(1.0) / Float32(Float32(1.0) + exp(Float32(Float32(-x) / s)))) <= Float32(0.10000000149011612))
		tmp = Float32(Float32(1.0) / fma(Float32(fma(Float32(0.5), x, Float32(-s)) / Float32(s * s)), x, Float32(2.0)));
	else
		tmp = Float32(Float32(1.0) / Float32(Float32(1.0) + Float32(Float32(1.0) / Float32(Float32(x / s) + Float32(1.0)))));
	end
	return tmp
end

Derivation

1. Split input into 2 regimes

2. if (/.f32 #s(literal 1 binary32) (+.f32 #s(literal 1 binary32) (exp.f32 (/.f32 (neg.f32 x) s)))) < 0.100000001

   1. Initial program 99.9%

      \[\frac{1}{1 + e^{\frac{-x}{s}}} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \frac{1}{\color{blue}{2 + x \cdot \left(\frac{1}{2} \cdot \frac{x}{{s}^{2}} - \frac{1}{s}\right)}} \]
   4. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \frac{1}{x \cdot \left(\frac{1}{2} \cdot \frac{x}{{s}^{2}} - \frac{1}{s}\right) + \color{blue}{2}} \]

      2. *-commutative N/A

         \[\leadsto \frac{1}{\left(\frac{1}{2} \cdot \frac{x}{{s}^{2}} - \frac{1}{s}\right) \cdot x + 2} \]

      3. lower-fma.f32 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{1}{2} \cdot \frac{x}{{s}^{2}} - \frac{1}{s}, \color{blue}{x}, 2\right)} \]

      4. lower--.f32 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{1}{2} \cdot \frac{x}{{s}^{2}} - \frac{1}{s}, x, 2\right)} \]

      5. *-commutative N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{x}{{s}^{2}} \cdot \frac{1}{2} - \frac{1}{s}, x, 2\right)} \]

      6. lower-*.f32 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{x}{{s}^{2}} \cdot \frac{1}{2} - \frac{1}{s}, x, 2\right)} \]

      7. lower-/.f32 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{x}{{s}^{2}} \cdot \frac{1}{2} - \frac{1}{s}, x, 2\right)} \]

      8. unpow2 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{x}{s \cdot s} \cdot \frac{1}{2} - \frac{1}{s}, x, 2\right)} \]

      9. lower-*.f32 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{x}{s \cdot s} \cdot \frac{1}{2} - \frac{1}{s}, x, 2\right)} \]

      10. lower-/.f32 79.1

          \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{x}{s \cdot s} \cdot 0.5 - \frac{1}{s}, x, 2\right)} \]

   5. Applied rewrites 79.1%

      \[\leadsto \frac{1}{\color{blue}{\mathsf{fma}\left(\frac{x}{s \cdot s} \cdot 0.5 - \frac{1}{s}, x, 2\right)}} \]

   6. Taylor expanded in s around 0

      \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{-1 \cdot s + \frac{1}{2} \cdot x}{{s}^{2}}, x, 2\right)} \]
   7. Step-by-step derivation

      1. lower-/.f32 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{-1 \cdot s + \frac{1}{2} \cdot x}{{s}^{2}}, x, 2\right)} \]

      2. mul-1-neg N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{\left(\mathsf{neg}\left(s\right)\right) + \frac{1}{2} \cdot x}{{s}^{2}}, x, 2\right)} \]

      3. +-commutative N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{\frac{1}{2} \cdot x + \left(\mathsf{neg}\left(s\right)\right)}{{s}^{2}}, x, 2\right)} \]

      4. lower-fma.f32 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{\mathsf{fma}\left(\frac{1}{2}, x, \mathsf{neg}\left(s\right)\right)}{{s}^{2}}, x, 2\right)} \]

      5. lower-neg.f32 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{\mathsf{fma}\left(\frac{1}{2}, x, -s\right)}{{s}^{2}}, x, 2\right)} \]

      6. pow2 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{\mathsf{fma}\left(\frac{1}{2}, x, -s\right)}{s \cdot s}, x, 2\right)} \]

      7. lift-*.f32 79.1

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{\mathsf{fma}\left(0.5, x, -s\right)}{s \cdot s}, x, 2\right)} \]

   8. Applied rewrites 79.1%

      \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{\mathsf{fma}\left(0.5, x, -s\right)}{s \cdot s}, x, 2\right)} \]

   if 0.100000001 < (/.f32 #s(literal 1 binary32) (+.f32 #s(literal 1 binary32) (exp.f32 (/.f32 (neg.f32 x) s))))

   1. Initial program 99.8%

      \[\frac{1}{1 + e^{\frac{-x}{s}}} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-exp.f32 N/A

         \[\leadsto \frac{1}{1 + \color{blue}{e^{\frac{-x}{s}}}} \]

      2. lift-neg.f32 N/A

         \[\leadsto \frac{1}{1 + e^{\frac{\color{blue}{\mathsf{neg}\left(x\right)}}{s}}} \]

      3. lift-/.f32 N/A

         \[\leadsto \frac{1}{1 + e^{\color{blue}{\frac{\mathsf{neg}\left(x\right)}{s}}}} \]

      4. distribute-frac-neg N/A

         \[\leadsto \frac{1}{1 + e^{\color{blue}{\mathsf{neg}\left(\frac{x}{s}\right)}}} \]

      5. exp-neg N/A

         \[\leadsto \frac{1}{1 + \color{blue}{\frac{1}{e^{\frac{x}{s}}}}} \]

      6. lower-/.f32 N/A

         \[\leadsto \frac{1}{1 + \color{blue}{\frac{1}{e^{\frac{x}{s}}}}} \]

      7. lower-exp.f32 N/A

         \[\leadsto \frac{1}{1 + \frac{1}{\color{blue}{e^{\frac{x}{s}}}}} \]

      8. lower-/.f32 99.7

         \[\leadsto \frac{1}{1 + \frac{1}{e^{\color{blue}{\frac{x}{s}}}}} \]

   4. Applied rewrites 99.7%

      \[\leadsto \frac{1}{1 + \color{blue}{\frac{1}{e^{\frac{x}{s}}}}} \]

   5. Taylor expanded in x around 0

      \[\leadsto \frac{1}{1 + \frac{1}{\color{blue}{1 + \frac{x}{s}}}} \]
   6. Step-by-step derivation

      1. +-commutative N/A

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

      2. lower-+.f32 N/A

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

      3. lift-/.f32 92.2

         \[\leadsto \frac{1}{1 + \frac{1}{\frac{x}{s} + 1}} \]

   7. Applied rewrites 92.2%

      \[\leadsto \frac{1}{1 + \frac{1}{\color{blue}{\frac{x}{s} + 1}}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 9: 90.0% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;1 + e^{\frac{-x}{s}} \leq 5:\\ \;\;\;\;\frac{1}{1 + \frac{1}{\frac{x}{s} + 1}}\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{\mathsf{fma}\left(\frac{0.5 \cdot x}{s \cdot s}, x, 2\right)}\\ \end{array} \end{array} \]

FPCore

(FPCore (x s)
 :precision binary32
 (if (<= (+ 1.0 (exp (/ (- x) s))) 5.0)
   (/ 1.0 (+ 1.0 (/ 1.0 (+ (/ x s) 1.0))))
   (/ 1.0 (fma (/ (* 0.5 x) (* s s)) x 2.0))))

C

float code(float x, float s) {
	float tmp;
	if ((1.0f + expf((-x / s))) <= 5.0f) {
		tmp = 1.0f / (1.0f + (1.0f / ((x / s) + 1.0f)));
	} else {
		tmp = 1.0f / fmaf(((0.5f * x) / (s * s)), x, 2.0f);
	}
	return tmp;
}

Julia

function code(x, s)
	tmp = Float32(0.0)
	if (Float32(Float32(1.0) + exp(Float32(Float32(-x) / s))) <= Float32(5.0))
		tmp = Float32(Float32(1.0) / Float32(Float32(1.0) + Float32(Float32(1.0) / Float32(Float32(x / s) + Float32(1.0)))));
	else
		tmp = Float32(Float32(1.0) / fma(Float32(Float32(Float32(0.5) * x) / Float32(s * s)), x, Float32(2.0)));
	end
	return tmp
end

Derivation

1. Split input into 2 regimes

2. if (+.f32 #s(literal 1 binary32) (exp.f32 (/.f32 (neg.f32 x) s))) < 5

   1. Initial program 99.8%

      \[\frac{1}{1 + e^{\frac{-x}{s}}} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-exp.f32 N/A

         \[\leadsto \frac{1}{1 + \color{blue}{e^{\frac{-x}{s}}}} \]

      2. lift-neg.f32 N/A

         \[\leadsto \frac{1}{1 + e^{\frac{\color{blue}{\mathsf{neg}\left(x\right)}}{s}}} \]

      3. lift-/.f32 N/A

         \[\leadsto \frac{1}{1 + e^{\color{blue}{\frac{\mathsf{neg}\left(x\right)}{s}}}} \]

      4. distribute-frac-neg N/A

         \[\leadsto \frac{1}{1 + e^{\color{blue}{\mathsf{neg}\left(\frac{x}{s}\right)}}} \]

      5. exp-neg N/A

         \[\leadsto \frac{1}{1 + \color{blue}{\frac{1}{e^{\frac{x}{s}}}}} \]

      6. lower-/.f32 N/A

         \[\leadsto \frac{1}{1 + \color{blue}{\frac{1}{e^{\frac{x}{s}}}}} \]

      7. lower-exp.f32 N/A

         \[\leadsto \frac{1}{1 + \frac{1}{\color{blue}{e^{\frac{x}{s}}}}} \]

      8. lower-/.f32 99.7

         \[\leadsto \frac{1}{1 + \frac{1}{e^{\color{blue}{\frac{x}{s}}}}} \]

   4. Applied rewrites 99.7%

      \[\leadsto \frac{1}{1 + \color{blue}{\frac{1}{e^{\frac{x}{s}}}}} \]

   5. Taylor expanded in x around 0

      \[\leadsto \frac{1}{1 + \frac{1}{\color{blue}{1 + \frac{x}{s}}}} \]
   6. Step-by-step derivation

      1. +-commutative N/A

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

      2. lower-+.f32 N/A

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

      3. lift-/.f32 92.2

         \[\leadsto \frac{1}{1 + \frac{1}{\frac{x}{s} + 1}} \]

   7. Applied rewrites 92.2%

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

   if 5 < (+.f32 #s(literal 1 binary32) (exp.f32 (/.f32 (neg.f32 x) s)))

   1. Initial program 99.9%

      \[\frac{1}{1 + e^{\frac{-x}{s}}} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \frac{1}{\color{blue}{2 + x \cdot \left(\frac{1}{2} \cdot \frac{x}{{s}^{2}} - \frac{1}{s}\right)}} \]
   4. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \frac{1}{x \cdot \left(\frac{1}{2} \cdot \frac{x}{{s}^{2}} - \frac{1}{s}\right) + \color{blue}{2}} \]

      2. *-commutative N/A

         \[\leadsto \frac{1}{\left(\frac{1}{2} \cdot \frac{x}{{s}^{2}} - \frac{1}{s}\right) \cdot x + 2} \]

      3. lower-fma.f32 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{1}{2} \cdot \frac{x}{{s}^{2}} - \frac{1}{s}, \color{blue}{x}, 2\right)} \]

      4. lower--.f32 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{1}{2} \cdot \frac{x}{{s}^{2}} - \frac{1}{s}, x, 2\right)} \]

      5. *-commutative N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{x}{{s}^{2}} \cdot \frac{1}{2} - \frac{1}{s}, x, 2\right)} \]

      6. lower-*.f32 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{x}{{s}^{2}} \cdot \frac{1}{2} - \frac{1}{s}, x, 2\right)} \]

      7. lower-/.f32 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{x}{{s}^{2}} \cdot \frac{1}{2} - \frac{1}{s}, x, 2\right)} \]

      8. unpow2 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{x}{s \cdot s} \cdot \frac{1}{2} - \frac{1}{s}, x, 2\right)} \]

      9. lower-*.f32 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{x}{s \cdot s} \cdot \frac{1}{2} - \frac{1}{s}, x, 2\right)} \]

      10. lower-/.f32 79.1

          \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{x}{s \cdot s} \cdot 0.5 - \frac{1}{s}, x, 2\right)} \]

   5. Applied rewrites 79.1%

      \[\leadsto \frac{1}{\color{blue}{\mathsf{fma}\left(\frac{x}{s \cdot s} \cdot 0.5 - \frac{1}{s}, x, 2\right)}} \]

   6. Taylor expanded in s around 0

      \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{-1 \cdot s + \frac{1}{2} \cdot x}{{s}^{2}}, x, 2\right)} \]
   7. Step-by-step derivation

      1. lower-/.f32 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{-1 \cdot s + \frac{1}{2} \cdot x}{{s}^{2}}, x, 2\right)} \]

      2. mul-1-neg N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{\left(\mathsf{neg}\left(s\right)\right) + \frac{1}{2} \cdot x}{{s}^{2}}, x, 2\right)} \]

      3. +-commutative N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{\frac{1}{2} \cdot x + \left(\mathsf{neg}\left(s\right)\right)}{{s}^{2}}, x, 2\right)} \]

      4. lower-fma.f32 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{\mathsf{fma}\left(\frac{1}{2}, x, \mathsf{neg}\left(s\right)\right)}{{s}^{2}}, x, 2\right)} \]

      5. lower-neg.f32 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{\mathsf{fma}\left(\frac{1}{2}, x, -s\right)}{{s}^{2}}, x, 2\right)} \]

      6. pow2 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{\mathsf{fma}\left(\frac{1}{2}, x, -s\right)}{s \cdot s}, x, 2\right)} \]

      7. lift-*.f32 79.1

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{\mathsf{fma}\left(0.5, x, -s\right)}{s \cdot s}, x, 2\right)} \]

   8. Applied rewrites 79.1%

      \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{\mathsf{fma}\left(0.5, x, -s\right)}{s \cdot s}, x, 2\right)} \]

   9. Taylor expanded in x around inf

      \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{\frac{1}{2} \cdot x}{s \cdot s}, x, 2\right)} \]
   10. Step-by-step derivation

       1. lower-*.f32 79.1

          \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{0.5 \cdot x}{s \cdot s}, x, 2\right)} \]

   11. Applied rewrites 79.1%

       \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{0.5 \cdot x}{s \cdot s}, x, 2\right)} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 10: 49.6% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{-x}{s}\\ \mathbf{if}\;1 + e^{t\_0} \leq 1.5:\\ \;\;\;\;0.5\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{t\_0 + 2}\\ \end{array} \end{array} \]

FPCore

(FPCore (x s)
 :precision binary32
 (let* ((t_0 (/ (- x) s)))
   (if (<= (+ 1.0 (exp t_0)) 1.5) 0.5 (/ 1.0 (+ t_0 2.0)))))

C

float code(float x, float s) {
	float t_0 = -x / s;
	float tmp;
	if ((1.0f + expf(t_0)) <= 1.5f) {
		tmp = 0.5f;
	} else {
		tmp = 1.0f / (t_0 + 2.0f);
	}
	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(4) function code(x, s)
use fmin_fmax_functions
    real(4), intent (in) :: x
    real(4), intent (in) :: s
    real(4) :: t_0
    real(4) :: tmp
    t_0 = -x / s
    if ((1.0e0 + exp(t_0)) <= 1.5e0) then
        tmp = 0.5e0
    else
        tmp = 1.0e0 / (t_0 + 2.0e0)
    end if
    code = tmp
end function

Julia

function code(x, s)
	t_0 = Float32(Float32(-x) / s)
	tmp = Float32(0.0)
	if (Float32(Float32(1.0) + exp(t_0)) <= Float32(1.5))
		tmp = Float32(0.5);
	else
		tmp = Float32(Float32(1.0) / Float32(t_0 + Float32(2.0)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, s)
	t_0 = -x / s;
	tmp = single(0.0);
	if ((single(1.0) + exp(t_0)) <= single(1.5))
		tmp = single(0.5);
	else
		tmp = single(1.0) / (t_0 + single(2.0));
	end
	tmp_2 = tmp;
end

Derivation

1. Split input into 2 regimes

2. if (+.f32 #s(literal 1 binary32) (exp.f32 (/.f32 (neg.f32 x) s))) < 1.5

   1. Initial program 99.9%

      \[\frac{1}{1 + e^{\frac{-x}{s}}} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\frac{1}{2}} \]
   4. Step-by-step derivation

   5. Applied rewrites 28.1%

      \[\leadsto \color{blue}{0.5} \]

   if 1.5 < (+.f32 #s(literal 1 binary32) (exp.f32 (/.f32 (neg.f32 x) s)))

   1. Initial program 99.7%

      \[\frac{1}{1 + e^{\frac{-x}{s}}} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \frac{1}{\color{blue}{2 + -1 \cdot \frac{x}{s}}} \]
   4. Step-by-step derivation

      1. +-commutative N/A

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

      2. lower-fma.f32 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(-1, \color{blue}{\frac{x}{s}}, 2\right)} \]

      3. lower-/.f32 56.5

         \[\leadsto \frac{1}{\mathsf{fma}\left(-1, \frac{x}{\color{blue}{s}}, 2\right)} \]

   5. Applied rewrites 56.5%

      \[\leadsto \frac{1}{\color{blue}{\mathsf{fma}\left(-1, \frac{x}{s}, 2\right)}} \]
   6. Step-by-step derivation

      1. lift-/.f32 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(-1, \frac{x}{\color{blue}{s}}, 2\right)} \]

      2. lift-fma.f32 N/A

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

      3. lower-+.f32 N/A

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

      4. mul-1-neg N/A

         \[\leadsto \frac{1}{\left(\mathsf{neg}\left(\frac{x}{s}\right)\right) + 2} \]

      5. distribute-frac-neg N/A

         \[\leadsto \frac{1}{\frac{\mathsf{neg}\left(x\right)}{s} + 2} \]

      6. lift-/.f32 N/A

         \[\leadsto \frac{1}{\frac{\mathsf{neg}\left(x\right)}{s} + 2} \]

      7. lift-neg.f32 56.5

         \[\leadsto \frac{1}{\frac{-x}{s} + 2} \]

   7. Applied rewrites 56.5%

      \[\leadsto \frac{1}{\frac{-x}{s} + \color{blue}{2}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 11: 99.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{1}{1 + e^{\frac{-x}{s}}} \end{array} \]

FPCore

(FPCore (x s) :precision binary32 (/ 1.0 (+ 1.0 (exp (/ (- x) s)))))

C

float code(float x, float s) {
	return 1.0f / (1.0f + expf((-x / s)));
}

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(4) function code(x, s)
use fmin_fmax_functions
    real(4), intent (in) :: x
    real(4), intent (in) :: s
    code = 1.0e0 / (1.0e0 + exp((-x / s)))
end function

Julia

function code(x, s)
	return Float32(Float32(1.0) / Float32(Float32(1.0) + exp(Float32(Float32(-x) / s))))
end

MATLAB

function tmp = code(x, s)
	tmp = single(1.0) / (single(1.0) + exp((-x / s)));
end

Derivation

1. Initial program 99.8%

   \[\frac{1}{1 + e^{\frac{-x}{s}}} \]
2. Add Preprocessing
3. Add Preprocessing

Alternative 12: 35.4% accurate, 128.0× speedup

Math

\[\begin{array}{l} \\ 0.5 \end{array} \]

FPCore

(FPCore (x s) :precision binary32 0.5)

C

float code(float x, float s) {
	return 0.5f;
}

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(4) function code(x, s)
use fmin_fmax_functions
    real(4), intent (in) :: x
    real(4), intent (in) :: s
    code = 0.5e0
end function

Julia

function code(x, s)
	return Float32(0.5)
end

MATLAB

function tmp = code(x, s)
	tmp = single(0.5);
end

Derivation

1. Initial program 99.8%

   \[\frac{1}{1 + e^{\frac{-x}{s}}} \]
2. Add Preprocessing

3. Taylor expanded in x around 0

   \[\leadsto \color{blue}{\frac{1}{2}} \]
4. Step-by-step derivation

5. Applied rewrites 32.9%

   \[\leadsto \color{blue}{0.5} \]
6. Add Preprocessing

Reproduce

herbie shell --seed 2025082 
(FPCore (x s)
  :name "Logistic function"
  :precision binary32
  :pre (and (<= 0.0 s) (<= s 1.0651631))
  (/ 1.0 (+ 1.0 (exp (/ (- x) s)))))