
(FPCore (f) :precision binary64 (let* ((t_0 (* (/ PI 4.0) f)) (t_1 (exp t_0)) (t_2 (exp (- t_0)))) (- (* (/ 1.0 (/ PI 4.0)) (log (/ (+ t_1 t_2) (- t_1 t_2)))))))
double code(double f) {
double t_0 = (((double) M_PI) / 4.0) * f;
double t_1 = exp(t_0);
double t_2 = exp(-t_0);
return -((1.0 / (((double) M_PI) / 4.0)) * log(((t_1 + t_2) / (t_1 - t_2))));
}
public static double code(double f) {
double t_0 = (Math.PI / 4.0) * f;
double t_1 = Math.exp(t_0);
double t_2 = Math.exp(-t_0);
return -((1.0 / (Math.PI / 4.0)) * Math.log(((t_1 + t_2) / (t_1 - t_2))));
}
def code(f): t_0 = (math.pi / 4.0) * f t_1 = math.exp(t_0) t_2 = math.exp(-t_0) return -((1.0 / (math.pi / 4.0)) * math.log(((t_1 + t_2) / (t_1 - t_2))))
function code(f) t_0 = Float64(Float64(pi / 4.0) * f) t_1 = exp(t_0) t_2 = exp(Float64(-t_0)) return Float64(-Float64(Float64(1.0 / Float64(pi / 4.0)) * log(Float64(Float64(t_1 + t_2) / Float64(t_1 - t_2))))) end
function tmp = code(f) t_0 = (pi / 4.0) * f; t_1 = exp(t_0); t_2 = exp(-t_0); tmp = -((1.0 / (pi / 4.0)) * log(((t_1 + t_2) / (t_1 - t_2)))); end
code[f_] := Block[{t$95$0 = N[(N[(Pi / 4.0), $MachinePrecision] * f), $MachinePrecision]}, Block[{t$95$1 = N[Exp[t$95$0], $MachinePrecision]}, Block[{t$95$2 = N[Exp[(-t$95$0)], $MachinePrecision]}, (-N[(N[(1.0 / N[(Pi / 4.0), $MachinePrecision]), $MachinePrecision] * N[Log[N[(N[(t$95$1 + t$95$2), $MachinePrecision] / N[(t$95$1 - t$95$2), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision])]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \frac{\pi}{4} \cdot f\\
t_1 := e^{t\_0}\\
t_2 := e^{-t\_0}\\
-\frac{1}{\frac{\pi}{4}} \cdot \log \left(\frac{t\_1 + t\_2}{t\_1 - t\_2}\right)
\end{array}
\end{array}
Sampling outcomes in binary64 precision:
Herbie found 5 alternatives:
| Alternative | Accuracy | Speedup |
|---|
(FPCore (f) :precision binary64 (let* ((t_0 (* (/ PI 4.0) f)) (t_1 (exp t_0)) (t_2 (exp (- t_0)))) (- (* (/ 1.0 (/ PI 4.0)) (log (/ (+ t_1 t_2) (- t_1 t_2)))))))
double code(double f) {
double t_0 = (((double) M_PI) / 4.0) * f;
double t_1 = exp(t_0);
double t_2 = exp(-t_0);
return -((1.0 / (((double) M_PI) / 4.0)) * log(((t_1 + t_2) / (t_1 - t_2))));
}
public static double code(double f) {
double t_0 = (Math.PI / 4.0) * f;
double t_1 = Math.exp(t_0);
double t_2 = Math.exp(-t_0);
return -((1.0 / (Math.PI / 4.0)) * Math.log(((t_1 + t_2) / (t_1 - t_2))));
}
def code(f): t_0 = (math.pi / 4.0) * f t_1 = math.exp(t_0) t_2 = math.exp(-t_0) return -((1.0 / (math.pi / 4.0)) * math.log(((t_1 + t_2) / (t_1 - t_2))))
function code(f) t_0 = Float64(Float64(pi / 4.0) * f) t_1 = exp(t_0) t_2 = exp(Float64(-t_0)) return Float64(-Float64(Float64(1.0 / Float64(pi / 4.0)) * log(Float64(Float64(t_1 + t_2) / Float64(t_1 - t_2))))) end
function tmp = code(f) t_0 = (pi / 4.0) * f; t_1 = exp(t_0); t_2 = exp(-t_0); tmp = -((1.0 / (pi / 4.0)) * log(((t_1 + t_2) / (t_1 - t_2)))); end
code[f_] := Block[{t$95$0 = N[(N[(Pi / 4.0), $MachinePrecision] * f), $MachinePrecision]}, Block[{t$95$1 = N[Exp[t$95$0], $MachinePrecision]}, Block[{t$95$2 = N[Exp[(-t$95$0)], $MachinePrecision]}, (-N[(N[(1.0 / N[(Pi / 4.0), $MachinePrecision]), $MachinePrecision] * N[Log[N[(N[(t$95$1 + t$95$2), $MachinePrecision] / N[(t$95$1 - t$95$2), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision])]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \frac{\pi}{4} \cdot f\\
t_1 := e^{t\_0}\\
t_2 := e^{-t\_0}\\
-\frac{1}{\frac{\pi}{4}} \cdot \log \left(\frac{t\_1 + t\_2}{t\_1 - t\_2}\right)
\end{array}
\end{array}
(FPCore (f) :precision binary64 (/ (log (tanh (/ PI (/ 4.0 f)))) (/ PI 4.0)))
double code(double f) {
return log(tanh((((double) M_PI) / (4.0 / f)))) / (((double) M_PI) / 4.0);
}
public static double code(double f) {
return Math.log(Math.tanh((Math.PI / (4.0 / f)))) / (Math.PI / 4.0);
}
def code(f): return math.log(math.tanh((math.pi / (4.0 / f)))) / (math.pi / 4.0)
function code(f) return Float64(log(tanh(Float64(pi / Float64(4.0 / f)))) / Float64(pi / 4.0)) end
function tmp = code(f) tmp = log(tanh((pi / (4.0 / f)))) / (pi / 4.0); end
code[f_] := N[(N[Log[N[Tanh[N[(Pi / N[(4.0 / f), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision] / N[(Pi / 4.0), $MachinePrecision]), $MachinePrecision]
\begin{array}{l}
\\
\frac{\log \tanh \left(\frac{\pi}{\frac{4}{f}}\right)}{\frac{\pi}{4}}
\end{array}
Initial program 8.5%
*-commutativeN/A
un-div-invN/A
distribute-neg-fracN/A
/-lowering-/.f64N/A
Applied egg-rr99.1%
(FPCore (f)
:precision binary64
(/
(log
(/
(+
(/ 2.0 (* PI 0.5))
(*
f
(*
f
(+
(* 0.0625 (* PI 2.0))
(* -2.0 (* (* PI (* PI 2.0)) (/ 0.010416666666666666 PI)))))))
f))
(/ PI -4.0)))
double code(double f) {
return log((((2.0 / (((double) M_PI) * 0.5)) + (f * (f * ((0.0625 * (((double) M_PI) * 2.0)) + (-2.0 * ((((double) M_PI) * (((double) M_PI) * 2.0)) * (0.010416666666666666 / ((double) M_PI)))))))) / f)) / (((double) M_PI) / -4.0);
}
public static double code(double f) {
return Math.log((((2.0 / (Math.PI * 0.5)) + (f * (f * ((0.0625 * (Math.PI * 2.0)) + (-2.0 * ((Math.PI * (Math.PI * 2.0)) * (0.010416666666666666 / Math.PI))))))) / f)) / (Math.PI / -4.0);
}
def code(f): return math.log((((2.0 / (math.pi * 0.5)) + (f * (f * ((0.0625 * (math.pi * 2.0)) + (-2.0 * ((math.pi * (math.pi * 2.0)) * (0.010416666666666666 / math.pi))))))) / f)) / (math.pi / -4.0)
function code(f) return Float64(log(Float64(Float64(Float64(2.0 / Float64(pi * 0.5)) + Float64(f * Float64(f * Float64(Float64(0.0625 * Float64(pi * 2.0)) + Float64(-2.0 * Float64(Float64(pi * Float64(pi * 2.0)) * Float64(0.010416666666666666 / pi))))))) / f)) / Float64(pi / -4.0)) end
function tmp = code(f) tmp = log((((2.0 / (pi * 0.5)) + (f * (f * ((0.0625 * (pi * 2.0)) + (-2.0 * ((pi * (pi * 2.0)) * (0.010416666666666666 / pi))))))) / f)) / (pi / -4.0); end
code[f_] := N[(N[Log[N[(N[(N[(2.0 / N[(Pi * 0.5), $MachinePrecision]), $MachinePrecision] + N[(f * N[(f * N[(N[(0.0625 * N[(Pi * 2.0), $MachinePrecision]), $MachinePrecision] + N[(-2.0 * N[(N[(Pi * N[(Pi * 2.0), $MachinePrecision]), $MachinePrecision] * N[(0.010416666666666666 / Pi), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / f), $MachinePrecision]], $MachinePrecision] / N[(Pi / -4.0), $MachinePrecision]), $MachinePrecision]
\begin{array}{l}
\\
\frac{\log \left(\frac{\frac{2}{\pi \cdot 0.5} + f \cdot \left(f \cdot \left(0.0625 \cdot \left(\pi \cdot 2\right) + -2 \cdot \left(\left(\pi \cdot \left(\pi \cdot 2\right)\right) \cdot \frac{0.010416666666666666}{\pi}\right)\right)\right)}{f}\right)}{\frac{\pi}{-4}}
\end{array}
Initial program 8.5%
distribute-lft-neg-inN/A
distribute-neg-frac2N/A
associate-*l/N/A
*-lft-identityN/A
/-lowering-/.f64N/A
Simplified8.5%
Taylor expanded in f around 0
Simplified97.1%
(FPCore (f) :precision binary64 (/ (log (* f (* PI 0.25))) (/ PI 4.0)))
double code(double f) {
return log((f * (((double) M_PI) * 0.25))) / (((double) M_PI) / 4.0);
}
public static double code(double f) {
return Math.log((f * (Math.PI * 0.25))) / (Math.PI / 4.0);
}
def code(f): return math.log((f * (math.pi * 0.25))) / (math.pi / 4.0)
function code(f) return Float64(log(Float64(f * Float64(pi * 0.25))) / Float64(pi / 4.0)) end
function tmp = code(f) tmp = log((f * (pi * 0.25))) / (pi / 4.0); end
code[f_] := N[(N[Log[N[(f * N[(Pi * 0.25), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[(Pi / 4.0), $MachinePrecision]), $MachinePrecision]
\begin{array}{l}
\\
\frac{\log \left(f \cdot \left(\pi \cdot 0.25\right)\right)}{\frac{\pi}{4}}
\end{array}
Initial program 8.5%
*-commutativeN/A
un-div-invN/A
distribute-neg-fracN/A
/-lowering-/.f64N/A
Applied egg-rr99.1%
Taylor expanded in f around 0
*-commutativeN/A
associate-*r*N/A
*-commutativeN/A
*-lowering-*.f64N/A
*-lowering-*.f64N/A
PI-lowering-PI.f6497.0%
Simplified97.0%
Final simplification97.0%
(FPCore (f) :precision binary64 (/ -4.0 (/ PI (log (/ 4.0 (* PI f))))))
double code(double f) {
return -4.0 / (((double) M_PI) / log((4.0 / (((double) M_PI) * f))));
}
public static double code(double f) {
return -4.0 / (Math.PI / Math.log((4.0 / (Math.PI * f))));
}
def code(f): return -4.0 / (math.pi / math.log((4.0 / (math.pi * f))))
function code(f) return Float64(-4.0 / Float64(pi / log(Float64(4.0 / Float64(pi * f))))) end
function tmp = code(f) tmp = -4.0 / (pi / log((4.0 / (pi * f)))); end
code[f_] := N[(-4.0 / N[(Pi / N[Log[N[(4.0 / N[(Pi * f), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]
\begin{array}{l}
\\
\frac{-4}{\frac{\pi}{\log \left(\frac{4}{\pi \cdot f}\right)}}
\end{array}
Initial program 8.5%
distribute-lft-neg-inN/A
distribute-neg-frac2N/A
associate-*l/N/A
*-lft-identityN/A
/-lowering-/.f64N/A
Simplified8.5%
Taylor expanded in f around 0
/-lowering-/.f64N/A
*-commutativeN/A
distribute-rgt-out--N/A
metadata-evalN/A
associate-*l*N/A
metadata-evalN/A
*-inversesN/A
metadata-evalN/A
times-fracN/A
*-commutativeN/A
metadata-evalN/A
distribute-rgt-out--N/A
associate-*r/N/A
*-lowering-*.f64N/A
PI-lowering-PI.f64N/A
Simplified96.6%
associate-/r*N/A
associate-/l/N/A
associate-/r*N/A
diff-logN/A
frac-2negN/A
div-invN/A
distribute-neg-frac2N/A
metadata-evalN/A
clear-numN/A
*-lowering-*.f64N/A
Applied egg-rr96.4%
clear-numN/A
add-cbrt-cubeN/A
associate-*r*N/A
un-div-invN/A
clear-numN/A
neg-logN/A
diff-logN/A
associate-*r*N/A
add-cbrt-cubeN/A
metadata-evalN/A
distribute-neg-frac2N/A
frac-2negN/A
associate-/r/N/A
*-commutativeN/A
Applied egg-rr96.5%
(FPCore (f) :precision binary64 (* (log (/ PI (/ 4.0 f))) (/ 4.0 PI)))
double code(double f) {
return log((((double) M_PI) / (4.0 / f))) * (4.0 / ((double) M_PI));
}
public static double code(double f) {
return Math.log((Math.PI / (4.0 / f))) * (4.0 / Math.PI);
}
def code(f): return math.log((math.pi / (4.0 / f))) * (4.0 / math.pi)
function code(f) return Float64(log(Float64(pi / Float64(4.0 / f))) * Float64(4.0 / pi)) end
function tmp = code(f) tmp = log((pi / (4.0 / f))) * (4.0 / pi); end
code[f_] := N[(N[Log[N[(Pi / N[(4.0 / f), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] * N[(4.0 / Pi), $MachinePrecision]), $MachinePrecision]
\begin{array}{l}
\\
\log \left(\frac{\pi}{\frac{4}{f}}\right) \cdot \frac{4}{\pi}
\end{array}
Initial program 8.5%
distribute-lft-neg-inN/A
distribute-neg-frac2N/A
associate-*l/N/A
*-lft-identityN/A
/-lowering-/.f64N/A
Simplified8.5%
Taylor expanded in f around 0
/-lowering-/.f64N/A
*-commutativeN/A
distribute-rgt-out--N/A
metadata-evalN/A
associate-*l*N/A
metadata-evalN/A
*-inversesN/A
metadata-evalN/A
times-fracN/A
*-commutativeN/A
metadata-evalN/A
distribute-rgt-out--N/A
associate-*r/N/A
*-lowering-*.f64N/A
PI-lowering-PI.f64N/A
Simplified96.6%
associate-/r*N/A
associate-/l/N/A
associate-/r*N/A
diff-logN/A
frac-2negN/A
div-invN/A
distribute-neg-frac2N/A
metadata-evalN/A
clear-numN/A
*-lowering-*.f64N/A
Applied egg-rr96.4%
herbie shell --seed 2024149
(FPCore (f)
:name "VandenBroeck and Keller, Equation (20)"
:precision binary64
(- (* (/ 1.0 (/ PI 4.0)) (log (/ (+ (exp (* (/ PI 4.0) f)) (exp (- (* (/ PI 4.0) f)))) (- (exp (* (/ PI 4.0) f)) (exp (- (* (/ PI 4.0) f)))))))))