Harley's example

Percentage Accurate: 91.1% → 99.5%

Time: 55.5s

Alternatives: 7

Speedup: 896.0×

• could not determine a ground truth

  1. c_p = 4.745352187237148e-119
  2. c_n = 6.850596230485641e-296
  3. t = 8426851.891737785
  4. s = 6.921785116252826e+222

Specification

\[0 < c\_p \land 0 < c\_n\]

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{1}{1 + e^{-t}}\\ t_2 := \frac{1}{1 + e^{-s}}\\ \frac{{t\_2}^{c\_p} \cdot {\left(1 - t\_2\right)}^{c\_n}}{{t\_1}^{c\_p} \cdot {\left(1 - t\_1\right)}^{c\_n}} \end{array} \end{array} \]

FPCore

(FPCore (c_p c_n t s)
 :precision binary64
 (let* ((t_1 (/ 1.0 (+ 1.0 (exp (- t))))) (t_2 (/ 1.0 (+ 1.0 (exp (- s))))))
   (/
    (* (pow t_2 c_p) (pow (- 1.0 t_2) c_n))
    (* (pow t_1 c_p) (pow (- 1.0 t_1) c_n)))))

C

double code(double c_p, double c_n, double t, double s) {
	double t_1 = 1.0 / (1.0 + exp(-t));
	double t_2 = 1.0 / (1.0 + exp(-s));
	return (pow(t_2, c_p) * pow((1.0 - t_2), c_n)) / (pow(t_1, c_p) * pow((1.0 - t_1), c_n));
}

Fortran

real(8) function code(c_p, c_n, t, s)
    real(8), intent (in) :: c_p
    real(8), intent (in) :: c_n
    real(8), intent (in) :: t
    real(8), intent (in) :: s
    real(8) :: t_1
    real(8) :: t_2
    t_1 = 1.0d0 / (1.0d0 + exp(-t))
    t_2 = 1.0d0 / (1.0d0 + exp(-s))
    code = ((t_2 ** c_p) * ((1.0d0 - t_2) ** c_n)) / ((t_1 ** c_p) * ((1.0d0 - t_1) ** c_n))
end function

Java

public static double code(double c_p, double c_n, double t, double s) {
	double t_1 = 1.0 / (1.0 + Math.exp(-t));
	double t_2 = 1.0 / (1.0 + Math.exp(-s));
	return (Math.pow(t_2, c_p) * Math.pow((1.0 - t_2), c_n)) / (Math.pow(t_1, c_p) * Math.pow((1.0 - t_1), c_n));
}

Python

def code(c_p, c_n, t, s):
	t_1 = 1.0 / (1.0 + math.exp(-t))
	t_2 = 1.0 / (1.0 + math.exp(-s))
	return (math.pow(t_2, c_p) * math.pow((1.0 - t_2), c_n)) / (math.pow(t_1, c_p) * math.pow((1.0 - t_1), c_n))

Julia

function code(c_p, c_n, t, s)
	t_1 = Float64(1.0 / Float64(1.0 + exp(Float64(-t))))
	t_2 = Float64(1.0 / Float64(1.0 + exp(Float64(-s))))
	return Float64(Float64((t_2 ^ c_p) * (Float64(1.0 - t_2) ^ c_n)) / Float64((t_1 ^ c_p) * (Float64(1.0 - t_1) ^ c_n)))
end

MATLAB

function tmp = code(c_p, c_n, t, s)
	t_1 = 1.0 / (1.0 + exp(-t));
	t_2 = 1.0 / (1.0 + exp(-s));
	tmp = ((t_2 ^ c_p) * ((1.0 - t_2) ^ c_n)) / ((t_1 ^ c_p) * ((1.0 - t_1) ^ c_n));
end

Wolfram

code[c$95$p_, c$95$n_, t_, s_] := Block[{t$95$1 = N[(1.0 / N[(1.0 + N[Exp[(-t)], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(1.0 / N[(1.0 + N[Exp[(-s)], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, N[(N[(N[Power[t$95$2, c$95$p], $MachinePrecision] * N[Power[N[(1.0 - t$95$2), $MachinePrecision], c$95$n], $MachinePrecision]), $MachinePrecision] / N[(N[Power[t$95$1, c$95$p], $MachinePrecision] * N[Power[N[(1.0 - t$95$1), $MachinePrecision], c$95$n], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Initial Program: 91.1% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{1}{1 + e^{-t}}\\ t_2 := \frac{1}{1 + e^{-s}}\\ \frac{{t\_2}^{c\_p} \cdot {\left(1 - t\_2\right)}^{c\_n}}{{t\_1}^{c\_p} \cdot {\left(1 - t\_1\right)}^{c\_n}} \end{array} \end{array} \]

FPCore

(FPCore (c_p c_n t s)
 :precision binary64
 (let* ((t_1 (/ 1.0 (+ 1.0 (exp (- t))))) (t_2 (/ 1.0 (+ 1.0 (exp (- s))))))
   (/
    (* (pow t_2 c_p) (pow (- 1.0 t_2) c_n))
    (* (pow t_1 c_p) (pow (- 1.0 t_1) c_n)))))

C

double code(double c_p, double c_n, double t, double s) {
	double t_1 = 1.0 / (1.0 + exp(-t));
	double t_2 = 1.0 / (1.0 + exp(-s));
	return (pow(t_2, c_p) * pow((1.0 - t_2), c_n)) / (pow(t_1, c_p) * pow((1.0 - t_1), c_n));
}

Fortran

real(8) function code(c_p, c_n, t, s)
    real(8), intent (in) :: c_p
    real(8), intent (in) :: c_n
    real(8), intent (in) :: t
    real(8), intent (in) :: s
    real(8) :: t_1
    real(8) :: t_2
    t_1 = 1.0d0 / (1.0d0 + exp(-t))
    t_2 = 1.0d0 / (1.0d0 + exp(-s))
    code = ((t_2 ** c_p) * ((1.0d0 - t_2) ** c_n)) / ((t_1 ** c_p) * ((1.0d0 - t_1) ** c_n))
end function

Java

public static double code(double c_p, double c_n, double t, double s) {
	double t_1 = 1.0 / (1.0 + Math.exp(-t));
	double t_2 = 1.0 / (1.0 + Math.exp(-s));
	return (Math.pow(t_2, c_p) * Math.pow((1.0 - t_2), c_n)) / (Math.pow(t_1, c_p) * Math.pow((1.0 - t_1), c_n));
}

Python

def code(c_p, c_n, t, s):
	t_1 = 1.0 / (1.0 + math.exp(-t))
	t_2 = 1.0 / (1.0 + math.exp(-s))
	return (math.pow(t_2, c_p) * math.pow((1.0 - t_2), c_n)) / (math.pow(t_1, c_p) * math.pow((1.0 - t_1), c_n))

Julia

function code(c_p, c_n, t, s)
	t_1 = Float64(1.0 / Float64(1.0 + exp(Float64(-t))))
	t_2 = Float64(1.0 / Float64(1.0 + exp(Float64(-s))))
	return Float64(Float64((t_2 ^ c_p) * (Float64(1.0 - t_2) ^ c_n)) / Float64((t_1 ^ c_p) * (Float64(1.0 - t_1) ^ c_n)))
end

MATLAB

function tmp = code(c_p, c_n, t, s)
	t_1 = 1.0 / (1.0 + exp(-t));
	t_2 = 1.0 / (1.0 + exp(-s));
	tmp = ((t_2 ^ c_p) * ((1.0 - t_2) ^ c_n)) / ((t_1 ^ c_p) * ((1.0 - t_1) ^ c_n));
end

Wolfram

code[c$95$p_, c$95$n_, t_, s_] := Block[{t$95$1 = N[(1.0 / N[(1.0 + N[Exp[(-t)], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(1.0 / N[(1.0 + N[Exp[(-s)], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, N[(N[(N[Power[t$95$2, c$95$p], $MachinePrecision] * N[Power[N[(1.0 - t$95$2), $MachinePrecision], c$95$n], $MachinePrecision]), $MachinePrecision] / N[(N[Power[t$95$1, c$95$p], $MachinePrecision] * N[Power[N[(1.0 - t$95$1), $MachinePrecision], c$95$n], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Alternative 1: 99.5% accurate, 5.6× speedup

Math

\[\begin{array}{l} \\ e^{\mathsf{fma}\left(\mathsf{fma}\left(-0.5, c\_n, 0.5 \cdot c\_p\right), s, \mathsf{fma}\left(-0.5, c\_p, \mathsf{fma}\left(\left(c\_n + c\_p\right) \cdot \mathsf{fma}\left(t \cdot t, -0.005208333333333333, 0.125\right), t, 0.5 \cdot c\_n\right)\right) \cdot t\right)} \end{array} \]

FPCore

(FPCore (c_p c_n t s)
 :precision binary64
 (exp
  (fma
   (fma -0.5 c_n (* 0.5 c_p))
   s
   (*
    (fma
     -0.5
     c_p
     (fma
      (* (+ c_n c_p) (fma (* t t) -0.005208333333333333 0.125))
      t
      (* 0.5 c_n)))
    t))))

C

double code(double c_p, double c_n, double t, double s) {
	return exp(fma(fma(-0.5, c_n, (0.5 * c_p)), s, (fma(-0.5, c_p, fma(((c_n + c_p) * fma((t * t), -0.005208333333333333, 0.125)), t, (0.5 * c_n))) * t)));
}

Julia

function code(c_p, c_n, t, s)
	return exp(fma(fma(-0.5, c_n, Float64(0.5 * c_p)), s, Float64(fma(-0.5, c_p, fma(Float64(Float64(c_n + c_p) * fma(Float64(t * t), -0.005208333333333333, 0.125)), t, Float64(0.5 * c_n))) * t)))
end

Wolfram

code[c$95$p_, c$95$n_, t_, s_] := N[Exp[N[(N[(-0.5 * c$95$n + N[(0.5 * c$95$p), $MachinePrecision]), $MachinePrecision] * s + N[(N[(-0.5 * c$95$p + N[(N[(N[(c$95$n + c$95$p), $MachinePrecision] * N[(N[(t * t), $MachinePrecision] * -0.005208333333333333 + 0.125), $MachinePrecision]), $MachinePrecision] * t + N[(0.5 * c$95$n), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * t), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]

Derivation

1. Initial program 91.4%

   \[\frac{{\left(\frac{1}{1 + e^{-s}}\right)}^{c\_p} \cdot {\left(1 - \frac{1}{1 + e^{-s}}\right)}^{c\_n}}{{\left(\frac{1}{1 + e^{-t}}\right)}^{c\_p} \cdot {\left(1 - \frac{1}{1 + e^{-t}}\right)}^{c\_n}} \]
2. Add Preprocessing

4. Applied rewrites 96.2%

   \[\leadsto \color{blue}{e^{\mathsf{fma}\left(c\_p, \left(-\mathsf{log1p}\left(e^{-s}\right)\right) - \left(-\mathsf{log1p}\left(e^{-t}\right)\right), c\_n \cdot \left(\mathsf{log1p}\left({\left(-1 - e^{-s}\right)}^{-1}\right) - \mathsf{log1p}\left({\left(-1 - e^{-t}\right)}^{-1}\right)\right)\right)}} \]

5. Taylor expanded in t around 0

   \[\leadsto e^{\color{blue}{c\_n \cdot \left(\log \left(1 - \frac{1}{1 + e^{\mathsf{neg}\left(s\right)}}\right) - \log \frac{1}{2}\right) + \left(c\_p \cdot \left(\log 2 - \log \left(1 + e^{\mathsf{neg}\left(s\right)}\right)\right) + t \cdot \left(\frac{-1}{2} \cdot c\_p + \left(\frac{1}{2} \cdot c\_n + t \cdot \left(\frac{1}{8} \cdot c\_n + \left(\frac{1}{8} \cdot c\_p + {t}^{2} \cdot \left(\frac{-1}{192} \cdot c\_n + \frac{-1}{192} \cdot c\_p\right)\right)\right)\right)\right)\right)}} \]

7. Applied rewrites 98.5%

   \[\leadsto e^{\color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(-0.005208333333333333 \cdot \left(c\_p + c\_n\right), t \cdot t, 0.125 \cdot \left(c\_p + c\_n\right)\right), t, \mathsf{fma}\left(-0.5, c\_p, 0.5 \cdot c\_n\right)\right), t, \mathsf{fma}\left(\mathsf{log1p}\left(\frac{-1}{e^{-s} + 1}\right) - \log 0.5, c\_n, \left(\log 2 - \mathsf{log1p}\left(e^{-s}\right)\right) \cdot c\_p\right)\right)}} \]

8. Taylor expanded in s around 0

   \[\leadsto e^{s \cdot \left(\frac{-1}{2} \cdot c\_n + \frac{1}{2} \cdot c\_p\right) + \color{blue}{t \cdot \left(\frac{-1}{2} \cdot c\_p + \left(\frac{1}{2} \cdot c\_n + t \cdot \left(\frac{-1}{192} \cdot \left({t}^{2} \cdot \left(c\_n + c\_p\right)\right) + \frac{1}{8} \cdot \left(c\_n + c\_p\right)\right)\right)\right)}} \]

10. Applied rewrites 99.8%

    \[\leadsto e^{\mathsf{fma}\left(\mathsf{fma}\left(-0.5, c\_n, 0.5 \cdot c\_p\right), \color{blue}{s}, \mathsf{fma}\left(-0.5, c\_p, \mathsf{fma}\left(\left(c\_n + c\_p\right) \cdot \mathsf{fma}\left(t \cdot t, -0.005208333333333333, 0.125\right), t, 0.5 \cdot c\_n\right)\right) \cdot t\right)} \]
11. Add Preprocessing

Alternative 2: 98.5% accurate, 7.7× speedup

Math

\[\begin{array}{l} \\ e^{\mathsf{fma}\left(0.5, c\_p, -0.5 \cdot c\_n\right) \cdot s} \end{array} \]

FPCore

(FPCore (c_p c_n t s)
 :precision binary64
 (exp (* (fma 0.5 c_p (* -0.5 c_n)) s)))

C

double code(double c_p, double c_n, double t, double s) {
	return exp((fma(0.5, c_p, (-0.5 * c_n)) * s));
}

Julia

function code(c_p, c_n, t, s)
	return exp(Float64(fma(0.5, c_p, Float64(-0.5 * c_n)) * s))
end

Wolfram

code[c$95$p_, c$95$n_, t_, s_] := N[Exp[N[(N[(0.5 * c$95$p + N[(-0.5 * c$95$n), $MachinePrecision]), $MachinePrecision] * s), $MachinePrecision]], $MachinePrecision]

Derivation

1. Initial program 91.4%

   \[\frac{{\left(\frac{1}{1 + e^{-s}}\right)}^{c\_p} \cdot {\left(1 - \frac{1}{1 + e^{-s}}\right)}^{c\_n}}{{\left(\frac{1}{1 + e^{-t}}\right)}^{c\_p} \cdot {\left(1 - \frac{1}{1 + e^{-t}}\right)}^{c\_n}} \]
2. Add Preprocessing

4. Applied rewrites 96.2%

   \[\leadsto \color{blue}{e^{\mathsf{fma}\left(c\_p, \left(-\mathsf{log1p}\left(e^{-s}\right)\right) - \left(-\mathsf{log1p}\left(e^{-t}\right)\right), c\_n \cdot \left(\mathsf{log1p}\left({\left(-1 - e^{-s}\right)}^{-1}\right) - \mathsf{log1p}\left({\left(-1 - e^{-t}\right)}^{-1}\right)\right)\right)}} \]

5. Taylor expanded in t around 0

   \[\leadsto e^{\color{blue}{c\_n \cdot \left(\log \left(1 - \frac{1}{1 + e^{\mathsf{neg}\left(s\right)}}\right) - \log \frac{1}{2}\right) + \left(c\_p \cdot \left(\log 2 - \log \left(1 + e^{\mathsf{neg}\left(s\right)}\right)\right) + t \cdot \left(\frac{-1}{2} \cdot c\_p + \left(\frac{1}{2} \cdot c\_n + t \cdot \left(\frac{1}{8} \cdot c\_n + \left(\frac{1}{8} \cdot c\_p + {t}^{2} \cdot \left(\frac{-1}{192} \cdot c\_n + \frac{-1}{192} \cdot c\_p\right)\right)\right)\right)\right)\right)}} \]

7. Applied rewrites 98.5%

   \[\leadsto e^{\color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(-0.005208333333333333 \cdot \left(c\_p + c\_n\right), t \cdot t, 0.125 \cdot \left(c\_p + c\_n\right)\right), t, \mathsf{fma}\left(-0.5, c\_p, 0.5 \cdot c\_n\right)\right), t, \mathsf{fma}\left(\mathsf{log1p}\left(\frac{-1}{e^{-s} + 1}\right) - \log 0.5, c\_n, \left(\log 2 - \mathsf{log1p}\left(e^{-s}\right)\right) \cdot c\_p\right)\right)}} \]

8. Taylor expanded in s around 0

   \[\leadsto e^{s \cdot \left(\frac{-1}{2} \cdot c\_n + \frac{1}{2} \cdot c\_p\right) + \color{blue}{t \cdot \left(\frac{-1}{2} \cdot c\_p + \left(\frac{1}{2} \cdot c\_n + t \cdot \left(\frac{-1}{192} \cdot \left({t}^{2} \cdot \left(c\_n + c\_p\right)\right) + \frac{1}{8} \cdot \left(c\_n + c\_p\right)\right)\right)\right)}} \]

10. Applied rewrites 99.8%

    \[\leadsto e^{\mathsf{fma}\left(\mathsf{fma}\left(-0.5, c\_n, 0.5 \cdot c\_p\right), \color{blue}{s}, \mathsf{fma}\left(-0.5, c\_p, \mathsf{fma}\left(\left(c\_n + c\_p\right) \cdot \mathsf{fma}\left(t \cdot t, -0.005208333333333333, 0.125\right), t, 0.5 \cdot c\_n\right)\right) \cdot t\right)} \]

11. Taylor expanded in t around 0

    \[\leadsto e^{s \cdot \left(\frac{-1}{2} \cdot c\_n + \color{blue}{\frac{1}{2} \cdot c\_p}\right)} \]
12. Step-by-step derivation

13. Applied rewrites 98.8%

    \[\leadsto e^{\mathsf{fma}\left(0.5, c\_p, -0.5 \cdot c\_n\right) \cdot s} \]
14. Add Preprocessing

Alternative 3: 94.8% accurate, 15.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;-s \leq 500000:\\ \;\;\;\;\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(-0.25 \cdot c\_p, c\_n, \mathsf{fma}\left(c\_n \cdot c\_n, 0.125, -0.125 \cdot c\_n\right)\right), s, \mathsf{fma}\left(-0.5, c\_n, 0.5 \cdot c\_p\right)\right), s, 1\right)\\ \mathbf{else}:\\ \;\;\;\;\left(\left(\left(c\_n \cdot c\_n\right) \cdot 0.125\right) \cdot s\right) \cdot s\\ \end{array} \end{array} \]

FPCore

(FPCore (c_p c_n t s)
 :precision binary64
 (if (<= (- s) 500000.0)
   (fma
    (fma
     (fma (* -0.25 c_p) c_n (fma (* c_n c_n) 0.125 (* -0.125 c_n)))
     s
     (fma -0.5 c_n (* 0.5 c_p)))
    s
    1.0)
   (* (* (* (* c_n c_n) 0.125) s) s)))

C

double code(double c_p, double c_n, double t, double s) {
	double tmp;
	if (-s <= 500000.0) {
		tmp = fma(fma(fma((-0.25 * c_p), c_n, fma((c_n * c_n), 0.125, (-0.125 * c_n))), s, fma(-0.5, c_n, (0.5 * c_p))), s, 1.0);
	} else {
		tmp = (((c_n * c_n) * 0.125) * s) * s;
	}
	return tmp;
}

Julia

function code(c_p, c_n, t, s)
	tmp = 0.0
	if (Float64(-s) <= 500000.0)
		tmp = fma(fma(fma(Float64(-0.25 * c_p), c_n, fma(Float64(c_n * c_n), 0.125, Float64(-0.125 * c_n))), s, fma(-0.5, c_n, Float64(0.5 * c_p))), s, 1.0);
	else
		tmp = Float64(Float64(Float64(Float64(c_n * c_n) * 0.125) * s) * s);
	end
	return tmp
end

Wolfram

code[c$95$p_, c$95$n_, t_, s_] := If[LessEqual[(-s), 500000.0], N[(N[(N[(N[(-0.25 * c$95$p), $MachinePrecision] * c$95$n + N[(N[(c$95$n * c$95$n), $MachinePrecision] * 0.125 + N[(-0.125 * c$95$n), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * s + N[(-0.5 * c$95$n + N[(0.5 * c$95$p), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * s + 1.0), $MachinePrecision], N[(N[(N[(N[(c$95$n * c$95$n), $MachinePrecision] * 0.125), $MachinePrecision] * s), $MachinePrecision] * s), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (neg.f64 s) < 5e5

   1. Initial program 93.1%

      \[\frac{{\left(\frac{1}{1 + e^{-s}}\right)}^{c\_p} \cdot {\left(1 - \frac{1}{1 + e^{-s}}\right)}^{c\_n}}{{\left(\frac{1}{1 + e^{-t}}\right)}^{c\_p} \cdot {\left(1 - \frac{1}{1 + e^{-t}}\right)}^{c\_n}} \]
   2. Add Preprocessing

   3. Taylor expanded in t around 0

      \[\leadsto \color{blue}{\frac{{\left(1 - \frac{1}{1 + e^{\mathsf{neg}\left(s\right)}}\right)}^{c\_n} \cdot {\left(\frac{1}{1 + e^{\mathsf{neg}\left(s\right)}}\right)}^{c\_p}}{{\frac{1}{2}}^{c\_n} \cdot {\frac{1}{2}}^{c\_p}}} \]
   4. Step-by-step derivation

      1. associate-/l* N/A

         \[\leadsto \color{blue}{{\left(1 - \frac{1}{1 + e^{\mathsf{neg}\left(s\right)}}\right)}^{c\_n} \cdot \frac{{\left(\frac{1}{1 + e^{\mathsf{neg}\left(s\right)}}\right)}^{c\_p}}{{\frac{1}{2}}^{c\_n} \cdot {\frac{1}{2}}^{c\_p}}} \]

      2. *-commutative N/A

         \[\leadsto \color{blue}{\frac{{\left(\frac{1}{1 + e^{\mathsf{neg}\left(s\right)}}\right)}^{c\_p}}{{\frac{1}{2}}^{c\_n} \cdot {\frac{1}{2}}^{c\_p}} \cdot {\left(1 - \frac{1}{1 + e^{\mathsf{neg}\left(s\right)}}\right)}^{c\_n}} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\frac{{\left(\frac{1}{1 + e^{\mathsf{neg}\left(s\right)}}\right)}^{c\_p}}{{\frac{1}{2}}^{c\_n} \cdot {\frac{1}{2}}^{c\_p}} \cdot {\left(1 - \frac{1}{1 + e^{\mathsf{neg}\left(s\right)}}\right)}^{c\_n}} \]

   5. Applied rewrites 93.6%

      \[\leadsto \color{blue}{\frac{{\left(\frac{1}{e^{-s} + 1}\right)}^{c\_p}}{{0.5}^{c\_p} \cdot {0.5}^{c\_n}} \cdot {\left(1 - \frac{1}{e^{-s} + 1}\right)}^{c\_n}} \]

   6. Taylor expanded in s around 0

      \[\leadsto 1 + \color{blue}{s \cdot \left(\frac{-1}{2} \cdot c\_n + \left(\frac{1}{2} \cdot c\_p + s \cdot \left(\frac{-1}{4} \cdot \left(c\_n \cdot c\_p\right) + \left(\frac{-1}{8} \cdot c\_n + \left(\frac{-1}{8} \cdot c\_p + \left(\frac{1}{8} \cdot {c\_n}^{2} + \frac{1}{8} \cdot {c\_p}^{2}\right)\right)\right)\right)\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 94.3%

      \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(-0.25 \cdot c\_p, c\_n, \mathsf{fma}\left(0.125, \mathsf{fma}\left(c\_p, c\_p, c\_n \cdot c\_n\right), -0.125 \cdot \left(c\_p + c\_n\right)\right)\right), s, \mathsf{fma}\left(-0.5, c\_n, 0.5 \cdot c\_p\right)\right), \color{blue}{s}, 1\right) \]

   9. Taylor expanded in c_p around 0

      \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{-1}{4} \cdot c\_p, c\_n, \frac{-1}{8} \cdot c\_n + \frac{1}{8} \cdot {c\_n}^{2}\right), s, \mathsf{fma}\left(\frac{-1}{2}, c\_n, \frac{1}{2} \cdot c\_p\right)\right), s, 1\right) \]
   10. Step-by-step derivation

   11. Applied rewrites 94.4%

       \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(-0.25 \cdot c\_p, c\_n, \mathsf{fma}\left(c\_n \cdot c\_n, 0.125, -0.125 \cdot c\_n\right)\right), s, \mathsf{fma}\left(-0.5, c\_n, 0.5 \cdot c\_p\right)\right), s, 1\right) \]

   if 5e5 < (neg.f64 s)

   1. Initial program 50.0%

      \[\frac{{\left(\frac{1}{1 + e^{-s}}\right)}^{c\_p} \cdot {\left(1 - \frac{1}{1 + e^{-s}}\right)}^{c\_n}}{{\left(\frac{1}{1 + e^{-t}}\right)}^{c\_p} \cdot {\left(1 - \frac{1}{1 + e^{-t}}\right)}^{c\_n}} \]
   2. Add Preprocessing

   3. Taylor expanded in t around 0

      \[\leadsto \color{blue}{\frac{{\left(1 - \frac{1}{1 + e^{\mathsf{neg}\left(s\right)}}\right)}^{c\_n} \cdot {\left(\frac{1}{1 + e^{\mathsf{neg}\left(s\right)}}\right)}^{c\_p}}{{\frac{1}{2}}^{c\_n} \cdot {\frac{1}{2}}^{c\_p}}} \]
   4. Step-by-step derivation

      1. associate-/l* N/A

         \[\leadsto \color{blue}{{\left(1 - \frac{1}{1 + e^{\mathsf{neg}\left(s\right)}}\right)}^{c\_n} \cdot \frac{{\left(\frac{1}{1 + e^{\mathsf{neg}\left(s\right)}}\right)}^{c\_p}}{{\frac{1}{2}}^{c\_n} \cdot {\frac{1}{2}}^{c\_p}}} \]

      2. *-commutative N/A

         \[\leadsto \color{blue}{\frac{{\left(\frac{1}{1 + e^{\mathsf{neg}\left(s\right)}}\right)}^{c\_p}}{{\frac{1}{2}}^{c\_n} \cdot {\frac{1}{2}}^{c\_p}} \cdot {\left(1 - \frac{1}{1 + e^{\mathsf{neg}\left(s\right)}}\right)}^{c\_n}} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\frac{{\left(\frac{1}{1 + e^{\mathsf{neg}\left(s\right)}}\right)}^{c\_p}}{{\frac{1}{2}}^{c\_n} \cdot {\frac{1}{2}}^{c\_p}} \cdot {\left(1 - \frac{1}{1 + e^{\mathsf{neg}\left(s\right)}}\right)}^{c\_n}} \]

   5. Applied rewrites 50.0%

      \[\leadsto \color{blue}{\frac{{\left(\frac{1}{e^{-s} + 1}\right)}^{c\_p}}{{0.5}^{c\_p} \cdot {0.5}^{c\_n}} \cdot {\left(1 - \frac{1}{e^{-s} + 1}\right)}^{c\_n}} \]

   6. Taylor expanded in s around 0

      \[\leadsto 1 + \color{blue}{s \cdot \left(\frac{-1}{2} \cdot c\_n + \left(\frac{1}{2} \cdot c\_p + s \cdot \left(\frac{-1}{4} \cdot \left(c\_n \cdot c\_p\right) + \left(\frac{-1}{8} \cdot c\_n + \left(\frac{-1}{8} \cdot c\_p + \left(\frac{1}{8} \cdot {c\_n}^{2} + \frac{1}{8} \cdot {c\_p}^{2}\right)\right)\right)\right)\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 1.9%

      \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(-0.25 \cdot c\_p, c\_n, \mathsf{fma}\left(0.125, \mathsf{fma}\left(c\_p, c\_p, c\_n \cdot c\_n\right), -0.125 \cdot \left(c\_p + c\_n\right)\right)\right), s, \mathsf{fma}\left(-0.5, c\_n, 0.5 \cdot c\_p\right)\right), \color{blue}{s}, 1\right) \]

   9. Taylor expanded in c_n around inf

      \[\leadsto \frac{1}{8} \cdot \left({c\_n}^{2} \cdot \color{blue}{{s}^{2}}\right) \]
   10. Step-by-step derivation

   11. Applied rewrites 20.6%

       \[\leadsto \left(\left(c\_n \cdot c\_n\right) \cdot 0.125\right) \cdot \left(s \cdot \color{blue}{s}\right) \]
   12. Step-by-step derivation

   13. Applied rewrites 70.7%

       \[\leadsto \left(\left(\left(c\_n \cdot c\_n\right) \cdot 0.125\right) \cdot s\right) \cdot s \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 4: 95.1% accurate, 22.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;s \leq -750000000:\\ \;\;\;\;\left(\left(\left(c\_n \cdot c\_n\right) \cdot 0.125\right) \cdot s\right) \cdot s\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\mathsf{fma}\left(-0.5, c\_n, \mathsf{fma}\left(c\_n \cdot c\_n, 0.125, -0.125 \cdot c\_n\right) \cdot s\right), s, 1\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (c_p c_n t s)
 :precision binary64
 (if (<= s -750000000.0)
   (* (* (* (* c_n c_n) 0.125) s) s)
   (fma (fma -0.5 c_n (* (fma (* c_n c_n) 0.125 (* -0.125 c_n)) s)) s 1.0)))

C

double code(double c_p, double c_n, double t, double s) {
	double tmp;
	if (s <= -750000000.0) {
		tmp = (((c_n * c_n) * 0.125) * s) * s;
	} else {
		tmp = fma(fma(-0.5, c_n, (fma((c_n * c_n), 0.125, (-0.125 * c_n)) * s)), s, 1.0);
	}
	return tmp;
}

Julia

function code(c_p, c_n, t, s)
	tmp = 0.0
	if (s <= -750000000.0)
		tmp = Float64(Float64(Float64(Float64(c_n * c_n) * 0.125) * s) * s);
	else
		tmp = fma(fma(-0.5, c_n, Float64(fma(Float64(c_n * c_n), 0.125, Float64(-0.125 * c_n)) * s)), s, 1.0);
	end
	return tmp
end

Wolfram

code[c$95$p_, c$95$n_, t_, s_] := If[LessEqual[s, -750000000.0], N[(N[(N[(N[(c$95$n * c$95$n), $MachinePrecision] * 0.125), $MachinePrecision] * s), $MachinePrecision] * s), $MachinePrecision], N[(N[(-0.5 * c$95$n + N[(N[(N[(c$95$n * c$95$n), $MachinePrecision] * 0.125 + N[(-0.125 * c$95$n), $MachinePrecision]), $MachinePrecision] * s), $MachinePrecision]), $MachinePrecision] * s + 1.0), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if s < -7.5e8

   1. Initial program 50.0%

      \[\frac{{\left(\frac{1}{1 + e^{-s}}\right)}^{c\_p} \cdot {\left(1 - \frac{1}{1 + e^{-s}}\right)}^{c\_n}}{{\left(\frac{1}{1 + e^{-t}}\right)}^{c\_p} \cdot {\left(1 - \frac{1}{1 + e^{-t}}\right)}^{c\_n}} \]
   2. Add Preprocessing

   3. Taylor expanded in t around 0

      \[\leadsto \color{blue}{\frac{{\left(1 - \frac{1}{1 + e^{\mathsf{neg}\left(s\right)}}\right)}^{c\_n} \cdot {\left(\frac{1}{1 + e^{\mathsf{neg}\left(s\right)}}\right)}^{c\_p}}{{\frac{1}{2}}^{c\_n} \cdot {\frac{1}{2}}^{c\_p}}} \]
   4. Step-by-step derivation

      1. associate-/l* N/A

         \[\leadsto \color{blue}{{\left(1 - \frac{1}{1 + e^{\mathsf{neg}\left(s\right)}}\right)}^{c\_n} \cdot \frac{{\left(\frac{1}{1 + e^{\mathsf{neg}\left(s\right)}}\right)}^{c\_p}}{{\frac{1}{2}}^{c\_n} \cdot {\frac{1}{2}}^{c\_p}}} \]

      2. *-commutative N/A

         \[\leadsto \color{blue}{\frac{{\left(\frac{1}{1 + e^{\mathsf{neg}\left(s\right)}}\right)}^{c\_p}}{{\frac{1}{2}}^{c\_n} \cdot {\frac{1}{2}}^{c\_p}} \cdot {\left(1 - \frac{1}{1 + e^{\mathsf{neg}\left(s\right)}}\right)}^{c\_n}} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\frac{{\left(\frac{1}{1 + e^{\mathsf{neg}\left(s\right)}}\right)}^{c\_p}}{{\frac{1}{2}}^{c\_n} \cdot {\frac{1}{2}}^{c\_p}} \cdot {\left(1 - \frac{1}{1 + e^{\mathsf{neg}\left(s\right)}}\right)}^{c\_n}} \]

   5. Applied rewrites 50.0%

      \[\leadsto \color{blue}{\frac{{\left(\frac{1}{e^{-s} + 1}\right)}^{c\_p}}{{0.5}^{c\_p} \cdot {0.5}^{c\_n}} \cdot {\left(1 - \frac{1}{e^{-s} + 1}\right)}^{c\_n}} \]

   6. Taylor expanded in s around 0

      \[\leadsto 1 + \color{blue}{s \cdot \left(\frac{-1}{2} \cdot c\_n + \left(\frac{1}{2} \cdot c\_p + s \cdot \left(\frac{-1}{4} \cdot \left(c\_n \cdot c\_p\right) + \left(\frac{-1}{8} \cdot c\_n + \left(\frac{-1}{8} \cdot c\_p + \left(\frac{1}{8} \cdot {c\_n}^{2} + \frac{1}{8} \cdot {c\_p}^{2}\right)\right)\right)\right)\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 1.9%

      \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(-0.25 \cdot c\_p, c\_n, \mathsf{fma}\left(0.125, \mathsf{fma}\left(c\_p, c\_p, c\_n \cdot c\_n\right), -0.125 \cdot \left(c\_p + c\_n\right)\right)\right), s, \mathsf{fma}\left(-0.5, c\_n, 0.5 \cdot c\_p\right)\right), \color{blue}{s}, 1\right) \]

   9. Taylor expanded in c_n around inf

      \[\leadsto \frac{1}{8} \cdot \left({c\_n}^{2} \cdot \color{blue}{{s}^{2}}\right) \]
   10. Step-by-step derivation

   11. Applied rewrites 20.6%

       \[\leadsto \left(\left(c\_n \cdot c\_n\right) \cdot 0.125\right) \cdot \left(s \cdot \color{blue}{s}\right) \]
   12. Step-by-step derivation

   13. Applied rewrites 70.7%

       \[\leadsto \left(\left(\left(c\_n \cdot c\_n\right) \cdot 0.125\right) \cdot s\right) \cdot s \]

   if -7.5e8 < s

   1. Initial program 93.1%

      \[\frac{{\left(\frac{1}{1 + e^{-s}}\right)}^{c\_p} \cdot {\left(1 - \frac{1}{1 + e^{-s}}\right)}^{c\_n}}{{\left(\frac{1}{1 + e^{-t}}\right)}^{c\_p} \cdot {\left(1 - \frac{1}{1 + e^{-t}}\right)}^{c\_n}} \]
   2. Add Preprocessing

   3. Taylor expanded in t around 0

      \[\leadsto \color{blue}{\frac{{\left(1 - \frac{1}{1 + e^{\mathsf{neg}\left(s\right)}}\right)}^{c\_n} \cdot {\left(\frac{1}{1 + e^{\mathsf{neg}\left(s\right)}}\right)}^{c\_p}}{{\frac{1}{2}}^{c\_n} \cdot {\frac{1}{2}}^{c\_p}}} \]
   4. Step-by-step derivation

      1. associate-/l* N/A

         \[\leadsto \color{blue}{{\left(1 - \frac{1}{1 + e^{\mathsf{neg}\left(s\right)}}\right)}^{c\_n} \cdot \frac{{\left(\frac{1}{1 + e^{\mathsf{neg}\left(s\right)}}\right)}^{c\_p}}{{\frac{1}{2}}^{c\_n} \cdot {\frac{1}{2}}^{c\_p}}} \]

      2. *-commutative N/A

         \[\leadsto \color{blue}{\frac{{\left(\frac{1}{1 + e^{\mathsf{neg}\left(s\right)}}\right)}^{c\_p}}{{\frac{1}{2}}^{c\_n} \cdot {\frac{1}{2}}^{c\_p}} \cdot {\left(1 - \frac{1}{1 + e^{\mathsf{neg}\left(s\right)}}\right)}^{c\_n}} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\frac{{\left(\frac{1}{1 + e^{\mathsf{neg}\left(s\right)}}\right)}^{c\_p}}{{\frac{1}{2}}^{c\_n} \cdot {\frac{1}{2}}^{c\_p}} \cdot {\left(1 - \frac{1}{1 + e^{\mathsf{neg}\left(s\right)}}\right)}^{c\_n}} \]

   5. Applied rewrites 93.6%

      \[\leadsto \color{blue}{\frac{{\left(\frac{1}{e^{-s} + 1}\right)}^{c\_p}}{{0.5}^{c\_p} \cdot {0.5}^{c\_n}} \cdot {\left(1 - \frac{1}{e^{-s} + 1}\right)}^{c\_n}} \]

   6. Taylor expanded in s around 0

      \[\leadsto 1 + \color{blue}{s \cdot \left(\frac{-1}{2} \cdot c\_n + \left(\frac{1}{2} \cdot c\_p + s \cdot \left(\frac{-1}{4} \cdot \left(c\_n \cdot c\_p\right) + \left(\frac{-1}{8} \cdot c\_n + \left(\frac{-1}{8} \cdot c\_p + \left(\frac{1}{8} \cdot {c\_n}^{2} + \frac{1}{8} \cdot {c\_p}^{2}\right)\right)\right)\right)\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 94.3%

      \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(-0.25 \cdot c\_p, c\_n, \mathsf{fma}\left(0.125, \mathsf{fma}\left(c\_p, c\_p, c\_n \cdot c\_n\right), -0.125 \cdot \left(c\_p + c\_n\right)\right)\right), s, \mathsf{fma}\left(-0.5, c\_n, 0.5 \cdot c\_p\right)\right), \color{blue}{s}, 1\right) \]

   9. Taylor expanded in c_p around 0

      \[\leadsto \mathsf{fma}\left(\frac{-1}{2} \cdot c\_n + s \cdot \left(\frac{-1}{8} \cdot c\_n + \frac{1}{8} \cdot {c\_n}^{2}\right), s, 1\right) \]
   10. Step-by-step derivation

   11. Applied rewrites 94.4%

       \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(-0.5, c\_n, \mathsf{fma}\left(c\_n \cdot c\_n, 0.125, -0.125 \cdot c\_n\right) \cdot s\right), s, 1\right) \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 5: 95.1% accurate, 33.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;s \leq -7.3 \cdot 10^{+21}:\\ \;\;\;\;\left(\left(\left(c\_n \cdot c\_n\right) \cdot 0.125\right) \cdot s\right) \cdot s\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\mathsf{fma}\left(-0.5, c\_n, 0.5 \cdot c\_p\right), s, 1\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (c_p c_n t s)
 :precision binary64
 (if (<= s -7.3e+21)
   (* (* (* (* c_n c_n) 0.125) s) s)
   (fma (fma -0.5 c_n (* 0.5 c_p)) s 1.0)))

C

double code(double c_p, double c_n, double t, double s) {
	double tmp;
	if (s <= -7.3e+21) {
		tmp = (((c_n * c_n) * 0.125) * s) * s;
	} else {
		tmp = fma(fma(-0.5, c_n, (0.5 * c_p)), s, 1.0);
	}
	return tmp;
}

Julia

function code(c_p, c_n, t, s)
	tmp = 0.0
	if (s <= -7.3e+21)
		tmp = Float64(Float64(Float64(Float64(c_n * c_n) * 0.125) * s) * s);
	else
		tmp = fma(fma(-0.5, c_n, Float64(0.5 * c_p)), s, 1.0);
	end
	return tmp
end

Wolfram

code[c$95$p_, c$95$n_, t_, s_] := If[LessEqual[s, -7.3e+21], N[(N[(N[(N[(c$95$n * c$95$n), $MachinePrecision] * 0.125), $MachinePrecision] * s), $MachinePrecision] * s), $MachinePrecision], N[(N[(-0.5 * c$95$n + N[(0.5 * c$95$p), $MachinePrecision]), $MachinePrecision] * s + 1.0), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if s < -7.3e21

   1. Initial program 44.4%

      \[\frac{{\left(\frac{1}{1 + e^{-s}}\right)}^{c\_p} \cdot {\left(1 - \frac{1}{1 + e^{-s}}\right)}^{c\_n}}{{\left(\frac{1}{1 + e^{-t}}\right)}^{c\_p} \cdot {\left(1 - \frac{1}{1 + e^{-t}}\right)}^{c\_n}} \]
   2. Add Preprocessing

   3. Taylor expanded in t around 0

      \[\leadsto \color{blue}{\frac{{\left(1 - \frac{1}{1 + e^{\mathsf{neg}\left(s\right)}}\right)}^{c\_n} \cdot {\left(\frac{1}{1 + e^{\mathsf{neg}\left(s\right)}}\right)}^{c\_p}}{{\frac{1}{2}}^{c\_n} \cdot {\frac{1}{2}}^{c\_p}}} \]
   4. Step-by-step derivation

      1. associate-/l* N/A

         \[\leadsto \color{blue}{{\left(1 - \frac{1}{1 + e^{\mathsf{neg}\left(s\right)}}\right)}^{c\_n} \cdot \frac{{\left(\frac{1}{1 + e^{\mathsf{neg}\left(s\right)}}\right)}^{c\_p}}{{\frac{1}{2}}^{c\_n} \cdot {\frac{1}{2}}^{c\_p}}} \]

      2. *-commutative N/A

         \[\leadsto \color{blue}{\frac{{\left(\frac{1}{1 + e^{\mathsf{neg}\left(s\right)}}\right)}^{c\_p}}{{\frac{1}{2}}^{c\_n} \cdot {\frac{1}{2}}^{c\_p}} \cdot {\left(1 - \frac{1}{1 + e^{\mathsf{neg}\left(s\right)}}\right)}^{c\_n}} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\frac{{\left(\frac{1}{1 + e^{\mathsf{neg}\left(s\right)}}\right)}^{c\_p}}{{\frac{1}{2}}^{c\_n} \cdot {\frac{1}{2}}^{c\_p}} \cdot {\left(1 - \frac{1}{1 + e^{\mathsf{neg}\left(s\right)}}\right)}^{c\_n}} \]

   5. Applied rewrites 44.4%

      \[\leadsto \color{blue}{\frac{{\left(\frac{1}{e^{-s} + 1}\right)}^{c\_p}}{{0.5}^{c\_p} \cdot {0.5}^{c\_n}} \cdot {\left(1 - \frac{1}{e^{-s} + 1}\right)}^{c\_n}} \]

   6. Taylor expanded in s around 0

      \[\leadsto 1 + \color{blue}{s \cdot \left(\frac{-1}{2} \cdot c\_n + \left(\frac{1}{2} \cdot c\_p + s \cdot \left(\frac{-1}{4} \cdot \left(c\_n \cdot c\_p\right) + \left(\frac{-1}{8} \cdot c\_n + \left(\frac{-1}{8} \cdot c\_p + \left(\frac{1}{8} \cdot {c\_n}^{2} + \frac{1}{8} \cdot {c\_p}^{2}\right)\right)\right)\right)\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 1.8%

      \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(-0.25 \cdot c\_p, c\_n, \mathsf{fma}\left(0.125, \mathsf{fma}\left(c\_p, c\_p, c\_n \cdot c\_n\right), -0.125 \cdot \left(c\_p + c\_n\right)\right)\right), s, \mathsf{fma}\left(-0.5, c\_n, 0.5 \cdot c\_p\right)\right), \color{blue}{s}, 1\right) \]

   9. Taylor expanded in c_n around inf

      \[\leadsto \frac{1}{8} \cdot \left({c\_n}^{2} \cdot \color{blue}{{s}^{2}}\right) \]
   10. Step-by-step derivation

   11. Applied rewrites 22.6%

       \[\leadsto \left(\left(c\_n \cdot c\_n\right) \cdot 0.125\right) \cdot \left(s \cdot \color{blue}{s}\right) \]
   12. Step-by-step derivation

   13. Applied rewrites 78.3%

       \[\leadsto \left(\left(\left(c\_n \cdot c\_n\right) \cdot 0.125\right) \cdot s\right) \cdot s \]

   if -7.3e21 < s

   1. Initial program 93.2%

      \[\frac{{\left(\frac{1}{1 + e^{-s}}\right)}^{c\_p} \cdot {\left(1 - \frac{1}{1 + e^{-s}}\right)}^{c\_n}}{{\left(\frac{1}{1 + e^{-t}}\right)}^{c\_p} \cdot {\left(1 - \frac{1}{1 + e^{-t}}\right)}^{c\_n}} \]
   2. Add Preprocessing

   3. Taylor expanded in t around 0

      \[\leadsto \color{blue}{\frac{{\left(1 - \frac{1}{1 + e^{\mathsf{neg}\left(s\right)}}\right)}^{c\_n} \cdot {\left(\frac{1}{1 + e^{\mathsf{neg}\left(s\right)}}\right)}^{c\_p}}{{\frac{1}{2}}^{c\_n} \cdot {\frac{1}{2}}^{c\_p}}} \]
   4. Step-by-step derivation

      1. associate-/l* N/A

         \[\leadsto \color{blue}{{\left(1 - \frac{1}{1 + e^{\mathsf{neg}\left(s\right)}}\right)}^{c\_n} \cdot \frac{{\left(\frac{1}{1 + e^{\mathsf{neg}\left(s\right)}}\right)}^{c\_p}}{{\frac{1}{2}}^{c\_n} \cdot {\frac{1}{2}}^{c\_p}}} \]

      2. *-commutative N/A

         \[\leadsto \color{blue}{\frac{{\left(\frac{1}{1 + e^{\mathsf{neg}\left(s\right)}}\right)}^{c\_p}}{{\frac{1}{2}}^{c\_n} \cdot {\frac{1}{2}}^{c\_p}} \cdot {\left(1 - \frac{1}{1 + e^{\mathsf{neg}\left(s\right)}}\right)}^{c\_n}} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\frac{{\left(\frac{1}{1 + e^{\mathsf{neg}\left(s\right)}}\right)}^{c\_p}}{{\frac{1}{2}}^{c\_n} \cdot {\frac{1}{2}}^{c\_p}} \cdot {\left(1 - \frac{1}{1 + e^{\mathsf{neg}\left(s\right)}}\right)}^{c\_n}} \]

   5. Applied rewrites 93.7%

      \[\leadsto \color{blue}{\frac{{\left(\frac{1}{e^{-s} + 1}\right)}^{c\_p}}{{0.5}^{c\_p} \cdot {0.5}^{c\_n}} \cdot {\left(1 - \frac{1}{e^{-s} + 1}\right)}^{c\_n}} \]

   6. Taylor expanded in s around 0

      \[\leadsto 1 + \color{blue}{s \cdot \left(\frac{-1}{2} \cdot c\_n + \frac{1}{2} \cdot c\_p\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 94.0%

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

Alternative 6: 94.1% accurate, 74.7× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(s \cdot c\_n, -0.5, 1\right) \end{array} \]

FPCore

(FPCore (c_p c_n t s) :precision binary64 (fma (* s c_n) -0.5 1.0))

C

double code(double c_p, double c_n, double t, double s) {
	return fma((s * c_n), -0.5, 1.0);
}

Julia

function code(c_p, c_n, t, s)
	return fma(Float64(s * c_n), -0.5, 1.0)
end

Wolfram

code[c$95$p_, c$95$n_, t_, s_] := N[(N[(s * c$95$n), $MachinePrecision] * -0.5 + 1.0), $MachinePrecision]

Derivation

1. Initial program 91.4%

   \[\frac{{\left(\frac{1}{1 + e^{-s}}\right)}^{c\_p} \cdot {\left(1 - \frac{1}{1 + e^{-s}}\right)}^{c\_n}}{{\left(\frac{1}{1 + e^{-t}}\right)}^{c\_p} \cdot {\left(1 - \frac{1}{1 + e^{-t}}\right)}^{c\_n}} \]
2. Add Preprocessing

3. Taylor expanded in t around 0

   \[\leadsto \color{blue}{\frac{{\left(1 - \frac{1}{1 + e^{\mathsf{neg}\left(s\right)}}\right)}^{c\_n} \cdot {\left(\frac{1}{1 + e^{\mathsf{neg}\left(s\right)}}\right)}^{c\_p}}{{\frac{1}{2}}^{c\_n} \cdot {\frac{1}{2}}^{c\_p}}} \]
4. Step-by-step derivation

   1. associate-/l* N/A

      \[\leadsto \color{blue}{{\left(1 - \frac{1}{1 + e^{\mathsf{neg}\left(s\right)}}\right)}^{c\_n} \cdot \frac{{\left(\frac{1}{1 + e^{\mathsf{neg}\left(s\right)}}\right)}^{c\_p}}{{\frac{1}{2}}^{c\_n} \cdot {\frac{1}{2}}^{c\_p}}} \]

   2. *-commutative N/A

      \[\leadsto \color{blue}{\frac{{\left(\frac{1}{1 + e^{\mathsf{neg}\left(s\right)}}\right)}^{c\_p}}{{\frac{1}{2}}^{c\_n} \cdot {\frac{1}{2}}^{c\_p}} \cdot {\left(1 - \frac{1}{1 + e^{\mathsf{neg}\left(s\right)}}\right)}^{c\_n}} \]

   3. lower-*.f64 N/A

      \[\leadsto \color{blue}{\frac{{\left(\frac{1}{1 + e^{\mathsf{neg}\left(s\right)}}\right)}^{c\_p}}{{\frac{1}{2}}^{c\_n} \cdot {\frac{1}{2}}^{c\_p}} \cdot {\left(1 - \frac{1}{1 + e^{\mathsf{neg}\left(s\right)}}\right)}^{c\_n}} \]

5. Applied rewrites 91.9%

   \[\leadsto \color{blue}{\frac{{\left(\frac{1}{e^{-s} + 1}\right)}^{c\_p}}{{0.5}^{c\_p} \cdot {0.5}^{c\_n}} \cdot {\left(1 - \frac{1}{e^{-s} + 1}\right)}^{c\_n}} \]

6. Taylor expanded in c_p around 0

   \[\leadsto \frac{{\left(1 - \frac{1}{1 + e^{\mathsf{neg}\left(s\right)}}\right)}^{c\_n}}{\color{blue}{{\frac{1}{2}}^{c\_n}}} \]
7. Step-by-step derivation

8. Applied rewrites 91.5%

   \[\leadsto \frac{{\left(1 - \frac{1}{e^{-s} + 1}\right)}^{c\_n}}{\color{blue}{{0.5}^{c\_n}}} \]

9. Taylor expanded in s around 0

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

11. Applied rewrites 90.8%

    \[\leadsto \mathsf{fma}\left(s \cdot c\_n, -0.5, 1\right) \]
12. Add Preprocessing

Alternative 7: 94.2% accurate, 896.0× speedup

Math

\[\begin{array}{l} \\ 1 \end{array} \]

FPCore

(FPCore (c_p c_n t s) :precision binary64 1.0)

C

double code(double c_p, double c_n, double t, double s) {
	return 1.0;
}

Fortran

real(8) function code(c_p, c_n, t, s)
    real(8), intent (in) :: c_p
    real(8), intent (in) :: c_n
    real(8), intent (in) :: t
    real(8), intent (in) :: s
    code = 1.0d0
end function

Java

public static double code(double c_p, double c_n, double t, double s) {
	return 1.0;
}

Python

def code(c_p, c_n, t, s):
	return 1.0

Julia

function code(c_p, c_n, t, s)
	return 1.0
end

MATLAB

function tmp = code(c_p, c_n, t, s)
	tmp = 1.0;
end

Wolfram

code[c$95$p_, c$95$n_, t_, s_] := 1.0

Derivation

1. Initial program 91.4%

   \[\frac{{\left(\frac{1}{1 + e^{-s}}\right)}^{c\_p} \cdot {\left(1 - \frac{1}{1 + e^{-s}}\right)}^{c\_n}}{{\left(\frac{1}{1 + e^{-t}}\right)}^{c\_p} \cdot {\left(1 - \frac{1}{1 + e^{-t}}\right)}^{c\_n}} \]
2. Add Preprocessing

3. Taylor expanded in c_n around 0

   \[\leadsto \color{blue}{\frac{{\left(\frac{1}{1 + e^{\mathsf{neg}\left(s\right)}}\right)}^{c\_p}}{{\left(\frac{1}{1 + e^{\mathsf{neg}\left(t\right)}}\right)}^{c\_p}}} \]
4. Step-by-step derivation

   1. lower-/.f64 N/A

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

   2. lower-pow.f64 N/A

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

   3. lower-/.f64 N/A

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

   4. +-commutative N/A

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

   5. neg-mul-1 N/A

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

   6. lower-+.f64 N/A

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

   7. lower-exp.f64 N/A

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

   8. neg-mul-1 N/A

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

   9. lower-neg.f64 N/A

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

   10. lower-pow.f64 N/A

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

   11. lower-/.f64 N/A

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

   12. +-commutative N/A

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

   13. lower-+.f64 N/A

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

   14. lower-exp.f64 N/A

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

   15. lower-neg.f64 90.7

       \[\leadsto \frac{{\left(\frac{1}{e^{-s} + 1}\right)}^{c\_p}}{{\left(\frac{1}{e^{\color{blue}{-t}} + 1}\right)}^{c\_p}} \]

5. Applied rewrites 90.7%

   \[\leadsto \color{blue}{\frac{{\left(\frac{1}{e^{-s} + 1}\right)}^{c\_p}}{{\left(\frac{1}{e^{-t} + 1}\right)}^{c\_p}}} \]

6. Taylor expanded in c_p around 0

   \[\leadsto 1 \]
7. Step-by-step derivation

8. Applied rewrites 90.7%

   \[\leadsto 1 \]
9. Add Preprocessing

Developer Target 1: 96.5% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ {\left(\frac{1 + e^{-t}}{1 + e^{-s}}\right)}^{c\_p} \cdot {\left(\frac{1 + e^{t}}{1 + e^{s}}\right)}^{c\_n} \end{array} \]

FPCore

(FPCore (c_p c_n t s)
 :precision binary64
 (*
  (pow (/ (+ 1.0 (exp (- t))) (+ 1.0 (exp (- s)))) c_p)
  (pow (/ (+ 1.0 (exp t)) (+ 1.0 (exp s))) c_n)))

C

double code(double c_p, double c_n, double t, double s) {
	return pow(((1.0 + exp(-t)) / (1.0 + exp(-s))), c_p) * pow(((1.0 + exp(t)) / (1.0 + exp(s))), c_n);
}

Fortran

real(8) function code(c_p, c_n, t, s)
    real(8), intent (in) :: c_p
    real(8), intent (in) :: c_n
    real(8), intent (in) :: t
    real(8), intent (in) :: s
    code = (((1.0d0 + exp(-t)) / (1.0d0 + exp(-s))) ** c_p) * (((1.0d0 + exp(t)) / (1.0d0 + exp(s))) ** c_n)
end function

Java

public static double code(double c_p, double c_n, double t, double s) {
	return Math.pow(((1.0 + Math.exp(-t)) / (1.0 + Math.exp(-s))), c_p) * Math.pow(((1.0 + Math.exp(t)) / (1.0 + Math.exp(s))), c_n);
}

Python

def code(c_p, c_n, t, s):
	return math.pow(((1.0 + math.exp(-t)) / (1.0 + math.exp(-s))), c_p) * math.pow(((1.0 + math.exp(t)) / (1.0 + math.exp(s))), c_n)

Julia

function code(c_p, c_n, t, s)
	return Float64((Float64(Float64(1.0 + exp(Float64(-t))) / Float64(1.0 + exp(Float64(-s)))) ^ c_p) * (Float64(Float64(1.0 + exp(t)) / Float64(1.0 + exp(s))) ^ c_n))
end

MATLAB

function tmp = code(c_p, c_n, t, s)
	tmp = (((1.0 + exp(-t)) / (1.0 + exp(-s))) ^ c_p) * (((1.0 + exp(t)) / (1.0 + exp(s))) ^ c_n);
end

Wolfram

code[c$95$p_, c$95$n_, t_, s_] := N[(N[Power[N[(N[(1.0 + N[Exp[(-t)], $MachinePrecision]), $MachinePrecision] / N[(1.0 + N[Exp[(-s)], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], c$95$p], $MachinePrecision] * N[Power[N[(N[(1.0 + N[Exp[t], $MachinePrecision]), $MachinePrecision] / N[(1.0 + N[Exp[s], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], c$95$n], $MachinePrecision]), $MachinePrecision]

Reproduce

herbie shell --seed 2024296 
(FPCore (c_p c_n t s)
  :name "Harley's example"
  :precision binary64
  :pre (and (< 0.0 c_p) (< 0.0 c_n))

  :alt
  (! :herbie-platform default (* (pow (/ (+ 1 (exp (- t))) (+ 1 (exp (- s)))) c_p) (pow (/ (+ 1 (exp t)) (+ 1 (exp s))) c_n)))

  (/ (* (pow (/ 1.0 (+ 1.0 (exp (- s)))) c_p) (pow (- 1.0 (/ 1.0 (+ 1.0 (exp (- s))))) c_n)) (* (pow (/ 1.0 (+ 1.0 (exp (- t)))) c_p) (pow (- 1.0 (/ 1.0 (+ 1.0 (exp (- t))))) c_n))))