Hyperbolic arc-(co)tangent

Percentage Accurate: 81.7% → 99.7%

Time: 7.3s

Alternatives: 5

Speedup: 18.5×

Specification

Math

\[\begin{array}{l} \\ \frac{1}{2} \cdot \log \left(\frac{1 + x}{1 - x}\right) \end{array} \]

FPCore

(FPCore (x) :precision binary64 (* (/ 1.0 2.0) (log (/ (+ 1.0 x) (- 1.0 x)))))

C

double code(double x) {
	return (1.0 / 2.0) * log(((1.0 + x) / (1.0 - x)));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = (1.0d0 / 2.0d0) * log(((1.0d0 + x) / (1.0d0 - x)))
end function

Java

public static double code(double x) {
	return (1.0 / 2.0) * Math.log(((1.0 + x) / (1.0 - x)));
}

Python

def code(x):
	return (1.0 / 2.0) * math.log(((1.0 + x) / (1.0 - x)))

Julia

function code(x)
	return Float64(Float64(1.0 / 2.0) * log(Float64(Float64(1.0 + x) / Float64(1.0 - x))))
end

MATLAB

function tmp = code(x)
	tmp = (1.0 / 2.0) * log(((1.0 + x) / (1.0 - x)));
end

Wolfram

code[x_] := N[(N[(1.0 / 2.0), $MachinePrecision] * N[Log[N[(N[(1.0 + x), $MachinePrecision] / N[(1.0 - x), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]

Initial Program: 81.7% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{1}{2} \cdot \log \left(\frac{1 + x}{1 - x}\right) \end{array} \]

FPCore

(FPCore (x) :precision binary64 (* (/ 1.0 2.0) (log (/ (+ 1.0 x) (- 1.0 x)))))

C

double code(double x) {
	return (1.0 / 2.0) * log(((1.0 + x) / (1.0 - x)));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = (1.0d0 / 2.0d0) * log(((1.0d0 + x) / (1.0d0 - x)))
end function

Java

public static double code(double x) {
	return (1.0 / 2.0) * Math.log(((1.0 + x) / (1.0 - x)));
}

Python

def code(x):
	return (1.0 / 2.0) * math.log(((1.0 + x) / (1.0 - x)))

Julia

function code(x)
	return Float64(Float64(1.0 / 2.0) * log(Float64(Float64(1.0 + x) / Float64(1.0 - x))))
end

MATLAB

function tmp = code(x)
	tmp = (1.0 / 2.0) * log(((1.0 + x) / (1.0 - x)));
end

Wolfram

code[x_] := N[(N[(1.0 / 2.0), $MachinePrecision] * N[Log[N[(N[(1.0 + x), $MachinePrecision] / N[(1.0 - x), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]

Alternative 1: 99.7% accurate, 0.3× speedup

Math

\[\begin{array}{l} x_m = \left|x\right| \\ x_s = \mathsf{copysign}\left(1, x\right) \\ x\_s \cdot \begin{array}{l} \mathbf{if}\;x\_m \leq 7.5 \cdot 10^{-68}:\\ \;\;\;\;0\\ \mathbf{else}:\\ \;\;\;\;x\_m + \left(0.14285714285714285 \cdot {x\_m}^{7} + \left(0.2 \cdot {x\_m}^{5} + 0.3333333333333333 \cdot {x\_m}^{3}\right)\right)\\ \end{array} \end{array} \]

FPCore

x_m = (fabs.f64 x)
x_s = (copysign.f64 1 x)
(FPCore (x_s x_m)
 :precision binary64
 (*
  x_s
  (if (<= x_m 7.5e-68)
    0.0
    (+
     x_m
     (+
      (* 0.14285714285714285 (pow x_m 7.0))
      (+ (* 0.2 (pow x_m 5.0)) (* 0.3333333333333333 (pow x_m 3.0))))))))

C

x_m = fabs(x);
x_s = copysign(1.0, x);
double code(double x_s, double x_m) {
	double tmp;
	if (x_m <= 7.5e-68) {
		tmp = 0.0;
	} else {
		tmp = x_m + ((0.14285714285714285 * pow(x_m, 7.0)) + ((0.2 * pow(x_m, 5.0)) + (0.3333333333333333 * pow(x_m, 3.0))));
	}
	return x_s * tmp;
}

Fortran

x_m = abs(x)
x_s = copysign(1.0d0, x)
real(8) function code(x_s, x_m)
    real(8), intent (in) :: x_s
    real(8), intent (in) :: x_m
    real(8) :: tmp
    if (x_m <= 7.5d-68) then
        tmp = 0.0d0
    else
        tmp = x_m + ((0.14285714285714285d0 * (x_m ** 7.0d0)) + ((0.2d0 * (x_m ** 5.0d0)) + (0.3333333333333333d0 * (x_m ** 3.0d0))))
    end if
    code = x_s * tmp
end function

Java

x_m = Math.abs(x);
x_s = Math.copySign(1.0, x);
public static double code(double x_s, double x_m) {
	double tmp;
	if (x_m <= 7.5e-68) {
		tmp = 0.0;
	} else {
		tmp = x_m + ((0.14285714285714285 * Math.pow(x_m, 7.0)) + ((0.2 * Math.pow(x_m, 5.0)) + (0.3333333333333333 * Math.pow(x_m, 3.0))));
	}
	return x_s * tmp;
}

Python

x_m = math.fabs(x)
x_s = math.copysign(1.0, x)
def code(x_s, x_m):
	tmp = 0
	if x_m <= 7.5e-68:
		tmp = 0.0
	else:
		tmp = x_m + ((0.14285714285714285 * math.pow(x_m, 7.0)) + ((0.2 * math.pow(x_m, 5.0)) + (0.3333333333333333 * math.pow(x_m, 3.0))))
	return x_s * tmp

Julia

x_m = abs(x)
x_s = copysign(1.0, x)
function code(x_s, x_m)
	tmp = 0.0
	if (x_m <= 7.5e-68)
		tmp = 0.0;
	else
		tmp = Float64(x_m + Float64(Float64(0.14285714285714285 * (x_m ^ 7.0)) + Float64(Float64(0.2 * (x_m ^ 5.0)) + Float64(0.3333333333333333 * (x_m ^ 3.0)))));
	end
	return Float64(x_s * tmp)
end

MATLAB

x_m = abs(x);
x_s = sign(x) * abs(1.0);
function tmp_2 = code(x_s, x_m)
	tmp = 0.0;
	if (x_m <= 7.5e-68)
		tmp = 0.0;
	else
		tmp = x_m + ((0.14285714285714285 * (x_m ^ 7.0)) + ((0.2 * (x_m ^ 5.0)) + (0.3333333333333333 * (x_m ^ 3.0))));
	end
	tmp_2 = x_s * tmp;
end

Wolfram

x_m = N[Abs[x], $MachinePrecision]
x_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[x]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[x$95$s_, x$95$m_] := N[(x$95$s * If[LessEqual[x$95$m, 7.5e-68], 0.0, N[(x$95$m + N[(N[(0.14285714285714285 * N[Power[x$95$m, 7.0], $MachinePrecision]), $MachinePrecision] + N[(N[(0.2 * N[Power[x$95$m, 5.0], $MachinePrecision]), $MachinePrecision] + N[(0.3333333333333333 * N[Power[x$95$m, 3.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

2. if x < 7.50000000000000081e-68

   1. Initial program 91.6%

      \[\frac{1}{2} \cdot \log \left(\frac{1 + x}{1 - x}\right) \]
   2. Step-by-step derivation

      1. metadata-eval 91.6%

         \[\leadsto \color{blue}{0.5} \cdot \log \left(\frac{1 + x}{1 - x}\right) \]

   3. Simplified 91.6%

      \[\leadsto \color{blue}{0.5 \cdot \log \left(\frac{1 + x}{1 - x}\right)} \]
   4. Add Preprocessing

   6. Applied egg-rr 89.7%

      \[\leadsto 0.5 \cdot \color{blue}{\left(\left(\mathsf{log1p}\left(x\right) - \mathsf{log1p}\left(x\right)\right) + \left(\mathsf{log1p}\left(x\right) - \mathsf{log1p}\left(x\right)\right)\right)} \]
   7. Step-by-step derivation

      1. count-2 89.7%

         \[\leadsto 0.5 \cdot \color{blue}{\left(2 \cdot \left(\mathsf{log1p}\left(x\right) - \mathsf{log1p}\left(x\right)\right)\right)} \]

      2. +-inverses 89.7%

         \[\leadsto 0.5 \cdot \left(2 \cdot \color{blue}{0}\right) \]

      3. metadata-eval 89.7%

         \[\leadsto 0.5 \cdot \color{blue}{0} \]

   8. Simplified 89.7%

      \[\leadsto 0.5 \cdot \color{blue}{0} \]

   if 7.50000000000000081e-68 < x

   1. Initial program 13.5%

      \[\frac{1}{2} \cdot \log \left(\frac{1 + x}{1 - x}\right) \]
   2. Step-by-step derivation

      1. metadata-eval 13.5%

         \[\leadsto \color{blue}{0.5} \cdot \log \left(\frac{1 + x}{1 - x}\right) \]

   3. Simplified 13.5%

      \[\leadsto \color{blue}{0.5 \cdot \log \left(\frac{1 + x}{1 - x}\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in x around 0 100.0%

      \[\leadsto \color{blue}{x + \left(0.14285714285714285 \cdot {x}^{7} + \left(0.2 \cdot {x}^{5} + 0.3333333333333333 \cdot {x}^{3}\right)\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 90.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq 7.5 \cdot 10^{-68}:\\ \;\;\;\;0\\ \mathbf{else}:\\ \;\;\;\;x + \left(0.14285714285714285 \cdot {x}^{7} + \left(0.2 \cdot {x}^{5} + 0.3333333333333333 \cdot {x}^{3}\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 2: 100.0% accurate, 0.5× speedup

Math

\[\begin{array}{l} x_m = \left|x\right| \\ x_s = \mathsf{copysign}\left(1, x\right) \\ x\_s \cdot \begin{array}{l} \mathbf{if}\;x\_m \leq 7.5 \cdot 10^{-68}:\\ \;\;\;\;0\\ \mathbf{else}:\\ \;\;\;\;0.5 \cdot \left(\mathsf{log1p}\left(x\_m\right) - \mathsf{log1p}\left(-x\_m\right)\right)\\ \end{array} \end{array} \]

FPCore

x_m = (fabs.f64 x)
x_s = (copysign.f64 1 x)
(FPCore (x_s x_m)
 :precision binary64
 (* x_s (if (<= x_m 7.5e-68) 0.0 (* 0.5 (- (log1p x_m) (log1p (- x_m)))))))

C

x_m = fabs(x);
x_s = copysign(1.0, x);
double code(double x_s, double x_m) {
	double tmp;
	if (x_m <= 7.5e-68) {
		tmp = 0.0;
	} else {
		tmp = 0.5 * (log1p(x_m) - log1p(-x_m));
	}
	return x_s * tmp;
}

Java

x_m = Math.abs(x);
x_s = Math.copySign(1.0, x);
public static double code(double x_s, double x_m) {
	double tmp;
	if (x_m <= 7.5e-68) {
		tmp = 0.0;
	} else {
		tmp = 0.5 * (Math.log1p(x_m) - Math.log1p(-x_m));
	}
	return x_s * tmp;
}

Python

x_m = math.fabs(x)
x_s = math.copysign(1.0, x)
def code(x_s, x_m):
	tmp = 0
	if x_m <= 7.5e-68:
		tmp = 0.0
	else:
		tmp = 0.5 * (math.log1p(x_m) - math.log1p(-x_m))
	return x_s * tmp

Julia

x_m = abs(x)
x_s = copysign(1.0, x)
function code(x_s, x_m)
	tmp = 0.0
	if (x_m <= 7.5e-68)
		tmp = 0.0;
	else
		tmp = Float64(0.5 * Float64(log1p(x_m) - log1p(Float64(-x_m))));
	end
	return Float64(x_s * tmp)
end

Wolfram

x_m = N[Abs[x], $MachinePrecision]
x_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[x]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[x$95$s_, x$95$m_] := N[(x$95$s * If[LessEqual[x$95$m, 7.5e-68], 0.0, N[(0.5 * N[(N[Log[1 + x$95$m], $MachinePrecision] - N[Log[1 + (-x$95$m)], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

2. if x < 7.50000000000000081e-68

   1. Initial program 91.6%

      \[\frac{1}{2} \cdot \log \left(\frac{1 + x}{1 - x}\right) \]
   2. Step-by-step derivation

      1. metadata-eval 91.6%

         \[\leadsto \color{blue}{0.5} \cdot \log \left(\frac{1 + x}{1 - x}\right) \]

   3. Simplified 91.6%

      \[\leadsto \color{blue}{0.5 \cdot \log \left(\frac{1 + x}{1 - x}\right)} \]
   4. Add Preprocessing

   6. Applied egg-rr 89.7%

      \[\leadsto 0.5 \cdot \color{blue}{\left(\left(\mathsf{log1p}\left(x\right) - \mathsf{log1p}\left(x\right)\right) + \left(\mathsf{log1p}\left(x\right) - \mathsf{log1p}\left(x\right)\right)\right)} \]
   7. Step-by-step derivation

      1. count-2 89.7%

         \[\leadsto 0.5 \cdot \color{blue}{\left(2 \cdot \left(\mathsf{log1p}\left(x\right) - \mathsf{log1p}\left(x\right)\right)\right)} \]

      2. +-inverses 89.7%

         \[\leadsto 0.5 \cdot \left(2 \cdot \color{blue}{0}\right) \]

      3. metadata-eval 89.7%

         \[\leadsto 0.5 \cdot \color{blue}{0} \]

   8. Simplified 89.7%

      \[\leadsto 0.5 \cdot \color{blue}{0} \]

   if 7.50000000000000081e-68 < x

   1. Initial program 13.5%

      \[\frac{1}{2} \cdot \log \left(\frac{1 + x}{1 - x}\right) \]
   2. Step-by-step derivation

      1. metadata-eval 13.5%

         \[\leadsto \color{blue}{0.5} \cdot \log \left(\frac{1 + x}{1 - x}\right) \]

      2. log-div 13.6%

         \[\leadsto 0.5 \cdot \color{blue}{\left(\log \left(1 + x\right) - \log \left(1 - x\right)\right)} \]

      3. log1p-def 25.3%

         \[\leadsto 0.5 \cdot \left(\color{blue}{\mathsf{log1p}\left(x\right)} - \log \left(1 - x\right)\right) \]

      4. sub-neg 25.3%

         \[\leadsto 0.5 \cdot \left(\mathsf{log1p}\left(x\right) - \log \color{blue}{\left(1 + \left(-x\right)\right)}\right) \]

      5. log1p-def 99.9%

         \[\leadsto 0.5 \cdot \left(\mathsf{log1p}\left(x\right) - \color{blue}{\mathsf{log1p}\left(-x\right)}\right) \]

   3. Simplified 99.9%

      \[\leadsto \color{blue}{0.5 \cdot \left(\mathsf{log1p}\left(x\right) - \mathsf{log1p}\left(-x\right)\right)} \]
   4. Add Preprocessing
3. Recombined 2 regimes into one program.

4. Final simplification 90.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq 7.5 \cdot 10^{-68}:\\ \;\;\;\;0\\ \mathbf{else}:\\ \;\;\;\;0.5 \cdot \left(\mathsf{log1p}\left(x\right) - \mathsf{log1p}\left(-x\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 99.5% accurate, 1.0× speedup

Math

\[\begin{array}{l} x_m = \left|x\right| \\ x_s = \mathsf{copysign}\left(1, x\right) \\ x\_s \cdot \begin{array}{l} \mathbf{if}\;x\_m \leq 7.5 \cdot 10^{-68}:\\ \;\;\;\;0\\ \mathbf{else}:\\ \;\;\;\;x\_m + 0.3333333333333333 \cdot {x\_m}^{3}\\ \end{array} \end{array} \]

FPCore

x_m = (fabs.f64 x)
x_s = (copysign.f64 1 x)
(FPCore (x_s x_m)
 :precision binary64
 (*
  x_s
  (if (<= x_m 7.5e-68) 0.0 (+ x_m (* 0.3333333333333333 (pow x_m 3.0))))))

C

x_m = fabs(x);
x_s = copysign(1.0, x);
double code(double x_s, double x_m) {
	double tmp;
	if (x_m <= 7.5e-68) {
		tmp = 0.0;
	} else {
		tmp = x_m + (0.3333333333333333 * pow(x_m, 3.0));
	}
	return x_s * tmp;
}

Fortran

x_m = abs(x)
x_s = copysign(1.0d0, x)
real(8) function code(x_s, x_m)
    real(8), intent (in) :: x_s
    real(8), intent (in) :: x_m
    real(8) :: tmp
    if (x_m <= 7.5d-68) then
        tmp = 0.0d0
    else
        tmp = x_m + (0.3333333333333333d0 * (x_m ** 3.0d0))
    end if
    code = x_s * tmp
end function

Java

x_m = Math.abs(x);
x_s = Math.copySign(1.0, x);
public static double code(double x_s, double x_m) {
	double tmp;
	if (x_m <= 7.5e-68) {
		tmp = 0.0;
	} else {
		tmp = x_m + (0.3333333333333333 * Math.pow(x_m, 3.0));
	}
	return x_s * tmp;
}

Python

x_m = math.fabs(x)
x_s = math.copysign(1.0, x)
def code(x_s, x_m):
	tmp = 0
	if x_m <= 7.5e-68:
		tmp = 0.0
	else:
		tmp = x_m + (0.3333333333333333 * math.pow(x_m, 3.0))
	return x_s * tmp

Julia

x_m = abs(x)
x_s = copysign(1.0, x)
function code(x_s, x_m)
	tmp = 0.0
	if (x_m <= 7.5e-68)
		tmp = 0.0;
	else
		tmp = Float64(x_m + Float64(0.3333333333333333 * (x_m ^ 3.0)));
	end
	return Float64(x_s * tmp)
end

MATLAB

x_m = abs(x);
x_s = sign(x) * abs(1.0);
function tmp_2 = code(x_s, x_m)
	tmp = 0.0;
	if (x_m <= 7.5e-68)
		tmp = 0.0;
	else
		tmp = x_m + (0.3333333333333333 * (x_m ^ 3.0));
	end
	tmp_2 = x_s * tmp;
end

Wolfram

x_m = N[Abs[x], $MachinePrecision]
x_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[x]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[x$95$s_, x$95$m_] := N[(x$95$s * If[LessEqual[x$95$m, 7.5e-68], 0.0, N[(x$95$m + N[(0.3333333333333333 * N[Power[x$95$m, 3.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

2. if x < 7.50000000000000081e-68

   1. Initial program 91.6%

      \[\frac{1}{2} \cdot \log \left(\frac{1 + x}{1 - x}\right) \]
   2. Step-by-step derivation

      1. metadata-eval 91.6%

         \[\leadsto \color{blue}{0.5} \cdot \log \left(\frac{1 + x}{1 - x}\right) \]

   3. Simplified 91.6%

      \[\leadsto \color{blue}{0.5 \cdot \log \left(\frac{1 + x}{1 - x}\right)} \]
   4. Add Preprocessing

   6. Applied egg-rr 89.7%

      \[\leadsto 0.5 \cdot \color{blue}{\left(\left(\mathsf{log1p}\left(x\right) - \mathsf{log1p}\left(x\right)\right) + \left(\mathsf{log1p}\left(x\right) - \mathsf{log1p}\left(x\right)\right)\right)} \]
   7. Step-by-step derivation

      1. count-2 89.7%

         \[\leadsto 0.5 \cdot \color{blue}{\left(2 \cdot \left(\mathsf{log1p}\left(x\right) - \mathsf{log1p}\left(x\right)\right)\right)} \]

      2. +-inverses 89.7%

         \[\leadsto 0.5 \cdot \left(2 \cdot \color{blue}{0}\right) \]

      3. metadata-eval 89.7%

         \[\leadsto 0.5 \cdot \color{blue}{0} \]

   8. Simplified 89.7%

      \[\leadsto 0.5 \cdot \color{blue}{0} \]

   if 7.50000000000000081e-68 < x

   1. Initial program 13.5%

      \[\frac{1}{2} \cdot \log \left(\frac{1 + x}{1 - x}\right) \]
   2. Step-by-step derivation

      1. metadata-eval 13.5%

         \[\leadsto \color{blue}{0.5} \cdot \log \left(\frac{1 + x}{1 - x}\right) \]

   3. Simplified 13.5%

      \[\leadsto \color{blue}{0.5 \cdot \log \left(\frac{1 + x}{1 - x}\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in x around 0 100.0%

      \[\leadsto \color{blue}{x + 0.3333333333333333 \cdot {x}^{3}} \]
   6. Step-by-step derivation

      1. *-commutative 100.0%

         \[\leadsto x + \color{blue}{{x}^{3} \cdot 0.3333333333333333} \]

   7. Simplified 100.0%

      \[\leadsto \color{blue}{x + {x}^{3} \cdot 0.3333333333333333} \]
3. Recombined 2 regimes into one program.

4. Final simplification 90.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq 7.5 \cdot 10^{-68}:\\ \;\;\;\;0\\ \mathbf{else}:\\ \;\;\;\;x + 0.3333333333333333 \cdot {x}^{3}\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 99.0% accurate, 18.5× speedup

Math

\[\begin{array}{l} x_m = \left|x\right| \\ x_s = \mathsf{copysign}\left(1, x\right) \\ x\_s \cdot \begin{array}{l} \mathbf{if}\;x\_m \leq 7.5 \cdot 10^{-68}:\\ \;\;\;\;0\\ \mathbf{else}:\\ \;\;\;\;x\_m\\ \end{array} \end{array} \]

FPCore

x_m = (fabs.f64 x)
x_s = (copysign.f64 1 x)
(FPCore (x_s x_m) :precision binary64 (* x_s (if (<= x_m 7.5e-68) 0.0 x_m)))

C

x_m = fabs(x);
x_s = copysign(1.0, x);
double code(double x_s, double x_m) {
	double tmp;
	if (x_m <= 7.5e-68) {
		tmp = 0.0;
	} else {
		tmp = x_m;
	}
	return x_s * tmp;
}

Fortran

x_m = abs(x)
x_s = copysign(1.0d0, x)
real(8) function code(x_s, x_m)
    real(8), intent (in) :: x_s
    real(8), intent (in) :: x_m
    real(8) :: tmp
    if (x_m <= 7.5d-68) then
        tmp = 0.0d0
    else
        tmp = x_m
    end if
    code = x_s * tmp
end function

Java

x_m = Math.abs(x);
x_s = Math.copySign(1.0, x);
public static double code(double x_s, double x_m) {
	double tmp;
	if (x_m <= 7.5e-68) {
		tmp = 0.0;
	} else {
		tmp = x_m;
	}
	return x_s * tmp;
}

Python

x_m = math.fabs(x)
x_s = math.copysign(1.0, x)
def code(x_s, x_m):
	tmp = 0
	if x_m <= 7.5e-68:
		tmp = 0.0
	else:
		tmp = x_m
	return x_s * tmp

Julia

x_m = abs(x)
x_s = copysign(1.0, x)
function code(x_s, x_m)
	tmp = 0.0
	if (x_m <= 7.5e-68)
		tmp = 0.0;
	else
		tmp = x_m;
	end
	return Float64(x_s * tmp)
end

MATLAB

x_m = abs(x);
x_s = sign(x) * abs(1.0);
function tmp_2 = code(x_s, x_m)
	tmp = 0.0;
	if (x_m <= 7.5e-68)
		tmp = 0.0;
	else
		tmp = x_m;
	end
	tmp_2 = x_s * tmp;
end

Wolfram

x_m = N[Abs[x], $MachinePrecision]
x_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[x]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[x$95$s_, x$95$m_] := N[(x$95$s * If[LessEqual[x$95$m, 7.5e-68], 0.0, x$95$m]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

2. if x < 7.50000000000000081e-68

   1. Initial program 91.6%

      \[\frac{1}{2} \cdot \log \left(\frac{1 + x}{1 - x}\right) \]
   2. Step-by-step derivation

      1. metadata-eval 91.6%

         \[\leadsto \color{blue}{0.5} \cdot \log \left(\frac{1 + x}{1 - x}\right) \]

   3. Simplified 91.6%

      \[\leadsto \color{blue}{0.5 \cdot \log \left(\frac{1 + x}{1 - x}\right)} \]
   4. Add Preprocessing

   6. Applied egg-rr 89.7%

      \[\leadsto 0.5 \cdot \color{blue}{\left(\left(\mathsf{log1p}\left(x\right) - \mathsf{log1p}\left(x\right)\right) + \left(\mathsf{log1p}\left(x\right) - \mathsf{log1p}\left(x\right)\right)\right)} \]
   7. Step-by-step derivation

      1. count-2 89.7%

         \[\leadsto 0.5 \cdot \color{blue}{\left(2 \cdot \left(\mathsf{log1p}\left(x\right) - \mathsf{log1p}\left(x\right)\right)\right)} \]

      2. +-inverses 89.7%

         \[\leadsto 0.5 \cdot \left(2 \cdot \color{blue}{0}\right) \]

      3. metadata-eval 89.7%

         \[\leadsto 0.5 \cdot \color{blue}{0} \]

   8. Simplified 89.7%

      \[\leadsto 0.5 \cdot \color{blue}{0} \]

   if 7.50000000000000081e-68 < x

   1. Initial program 13.5%

      \[\frac{1}{2} \cdot \log \left(\frac{1 + x}{1 - x}\right) \]
   2. Step-by-step derivation

      1. metadata-eval 13.5%

         \[\leadsto \color{blue}{0.5} \cdot \log \left(\frac{1 + x}{1 - x}\right) \]

   3. Simplified 13.5%

      \[\leadsto \color{blue}{0.5 \cdot \log \left(\frac{1 + x}{1 - x}\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in x around 0 99.4%

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

4. Final simplification 90.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq 7.5 \cdot 10^{-68}:\\ \;\;\;\;0\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 25.7% accurate, 111.0× speedup

Math

\[\begin{array}{l} x_m = \left|x\right| \\ x_s = \mathsf{copysign}\left(1, x\right) \\ x\_s \cdot x\_m \end{array} \]

FPCore

x_m = (fabs.f64 x)
x_s = (copysign.f64 1 x)
(FPCore (x_s x_m) :precision binary64 (* x_s x_m))

C

x_m = fabs(x);
x_s = copysign(1.0, x);
double code(double x_s, double x_m) {
	return x_s * x_m;
}

Fortran

x_m = abs(x)
x_s = copysign(1.0d0, x)
real(8) function code(x_s, x_m)
    real(8), intent (in) :: x_s
    real(8), intent (in) :: x_m
    code = x_s * x_m
end function

Java

x_m = Math.abs(x);
x_s = Math.copySign(1.0, x);
public static double code(double x_s, double x_m) {
	return x_s * x_m;
}

Python

x_m = math.fabs(x)
x_s = math.copysign(1.0, x)
def code(x_s, x_m):
	return x_s * x_m

Julia

x_m = abs(x)
x_s = copysign(1.0, x)
function code(x_s, x_m)
	return Float64(x_s * x_m)
end

MATLAB

x_m = abs(x);
x_s = sign(x) * abs(1.0);
function tmp = code(x_s, x_m)
	tmp = x_s * x_m;
end

Wolfram

x_m = N[Abs[x], $MachinePrecision]
x_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[x]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[x$95$s_, x$95$m_] := N[(x$95$s * x$95$m), $MachinePrecision]

Derivation

1. Initial program 82.4%

   \[\frac{1}{2} \cdot \log \left(\frac{1 + x}{1 - x}\right) \]
2. Step-by-step derivation

   1. metadata-eval 82.4%

      \[\leadsto \color{blue}{0.5} \cdot \log \left(\frac{1 + x}{1 - x}\right) \]

3. Simplified 82.4%

   \[\leadsto \color{blue}{0.5 \cdot \log \left(\frac{1 + x}{1 - x}\right)} \]
4. Add Preprocessing

5. Taylor expanded in x around 0 25.2%

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

6. Final simplification 25.2%

   \[\leadsto x \]
7. Add Preprocessing

Reproduce

herbie shell --seed 2024031 
(FPCore (x)
  :name "Hyperbolic arc-(co)tangent"
  :precision binary64
  (* (/ 1.0 2.0) (log (/ (+ 1.0 x) (- 1.0 x)))))