
(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 7 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) 0.25))
double code(double f) {
return (log(tanh((((double) M_PI) / (4.0 / f)))) / ((double) M_PI)) / 0.25;
}
public static double code(double f) {
return (Math.log(Math.tanh((Math.PI / (4.0 / f)))) / Math.PI) / 0.25;
}
def code(f): return (math.log(math.tanh((math.pi / (4.0 / f)))) / math.pi) / 0.25
function code(f) return Float64(Float64(log(tanh(Float64(pi / Float64(4.0 / f)))) / pi) / 0.25) end
function tmp = code(f) tmp = (log(tanh((pi / (4.0 / f)))) / pi) / 0.25; end
code[f_] := N[(N[(N[Log[N[Tanh[N[(Pi / N[(4.0 / f), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision] / Pi), $MachinePrecision] / 0.25), $MachinePrecision]
\begin{array}{l}
\\
\frac{\frac{\log \tanh \left(\frac{\pi}{\frac{4}{f}}\right)}{\pi}}{0.25}
\end{array}
Initial program 7.7%
*-commutativeN/A
un-div-invN/A
distribute-neg-frac2N/A
div-invN/A
Applied egg-rr99.1%
(FPCore (f) :precision binary64 (/ 4.0 (/ PI (log (tanh (/ PI (/ 4.0 f)))))))
double code(double f) {
return 4.0 / (((double) M_PI) / log(tanh((((double) M_PI) / (4.0 / f)))));
}
public static double code(double f) {
return 4.0 / (Math.PI / Math.log(Math.tanh((Math.PI / (4.0 / f)))));
}
def code(f): return 4.0 / (math.pi / math.log(math.tanh((math.pi / (4.0 / f)))))
function code(f) return Float64(4.0 / Float64(pi / log(tanh(Float64(pi / Float64(4.0 / f)))))) end
function tmp = code(f) tmp = 4.0 / (pi / log(tanh((pi / (4.0 / f))))); end
code[f_] := N[(4.0 / N[(Pi / N[Log[N[Tanh[N[(Pi / N[(4.0 / f), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]
\begin{array}{l}
\\
\frac{4}{\frac{\pi}{\log \tanh \left(\frac{\pi}{\frac{4}{f}}\right)}}
\end{array}
Initial program 7.7%
*-commutativeN/A
un-div-invN/A
distribute-neg-frac2N/A
div-invN/A
Applied egg-rr99.1%
Applied egg-rr98.9%
(FPCore (f) :precision binary64 (/ (/ (log (/ (+ (/ 4.0 PI) (* PI (* 0.08333333333333333 (* f f)))) f)) PI) -0.25))
double code(double f) {
return (log((((4.0 / ((double) M_PI)) + (((double) M_PI) * (0.08333333333333333 * (f * f)))) / f)) / ((double) M_PI)) / -0.25;
}
public static double code(double f) {
return (Math.log((((4.0 / Math.PI) + (Math.PI * (0.08333333333333333 * (f * f)))) / f)) / Math.PI) / -0.25;
}
def code(f): return (math.log((((4.0 / math.pi) + (math.pi * (0.08333333333333333 * (f * f)))) / f)) / math.pi) / -0.25
function code(f) return Float64(Float64(log(Float64(Float64(Float64(4.0 / pi) + Float64(pi * Float64(0.08333333333333333 * Float64(f * f)))) / f)) / pi) / -0.25) end
function tmp = code(f) tmp = (log((((4.0 / pi) + (pi * (0.08333333333333333 * (f * f)))) / f)) / pi) / -0.25; end
code[f_] := N[(N[(N[Log[N[(N[(N[(4.0 / Pi), $MachinePrecision] + N[(Pi * N[(0.08333333333333333 * N[(f * f), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / f), $MachinePrecision]], $MachinePrecision] / Pi), $MachinePrecision] / -0.25), $MachinePrecision]
\begin{array}{l}
\\
\frac{\frac{\log \left(\frac{\frac{4}{\pi} + \pi \cdot \left(0.08333333333333333 \cdot \left(f \cdot f\right)\right)}{f}\right)}{\pi}}{-0.25}
\end{array}
Initial program 7.7%
distribute-lft-neg-inN/A
distribute-neg-frac2N/A
associate-*l/N/A
*-lft-identityN/A
/-lowering-/.f64N/A
Simplified7.7%
Taylor expanded in f around 0
Simplified95.0%
Taylor expanded in f around 0
*-commutativeN/A
distribute-rgt-outN/A
associate-*l*N/A
*-lowering-*.f64N/A
PI-lowering-PI.f64N/A
*-lowering-*.f64N/A
metadata-eval95.0%
Simplified95.0%
div-invN/A
metadata-evalN/A
metadata-evalN/A
associate-/r*N/A
/-lowering-/.f64N/A
Applied egg-rr95.0%
(FPCore (f) :precision binary64 (/ -4.0 (/ PI (log (/ (+ (/ 4.0 PI) (* PI (* 0.08333333333333333 (* f f)))) f)))))
double code(double f) {
return -4.0 / (((double) M_PI) / log((((4.0 / ((double) M_PI)) + (((double) M_PI) * (0.08333333333333333 * (f * f)))) / f)));
}
public static double code(double f) {
return -4.0 / (Math.PI / Math.log((((4.0 / Math.PI) + (Math.PI * (0.08333333333333333 * (f * f)))) / f)));
}
def code(f): return -4.0 / (math.pi / math.log((((4.0 / math.pi) + (math.pi * (0.08333333333333333 * (f * f)))) / f)))
function code(f) return Float64(-4.0 / Float64(pi / log(Float64(Float64(Float64(4.0 / pi) + Float64(pi * Float64(0.08333333333333333 * Float64(f * f)))) / f)))) end
function tmp = code(f) tmp = -4.0 / (pi / log((((4.0 / pi) + (pi * (0.08333333333333333 * (f * f)))) / f))); end
code[f_] := N[(-4.0 / N[(Pi / N[Log[N[(N[(N[(4.0 / Pi), $MachinePrecision] + N[(Pi * N[(0.08333333333333333 * N[(f * f), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / f), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]
\begin{array}{l}
\\
\frac{-4}{\frac{\pi}{\log \left(\frac{\frac{4}{\pi} + \pi \cdot \left(0.08333333333333333 \cdot \left(f \cdot f\right)\right)}{f}\right)}}
\end{array}
Initial program 7.7%
distribute-lft-neg-inN/A
distribute-neg-frac2N/A
associate-*l/N/A
*-lft-identityN/A
/-lowering-/.f64N/A
Simplified7.7%
Taylor expanded in f around 0
Simplified95.0%
Taylor expanded in f around 0
*-commutativeN/A
distribute-rgt-outN/A
associate-*l*N/A
*-lowering-*.f64N/A
PI-lowering-PI.f64N/A
*-lowering-*.f64N/A
metadata-eval95.0%
Simplified95.0%
associate-/r/N/A
*-commutativeN/A
clear-numN/A
un-div-invN/A
/-lowering-/.f64N/A
/-lowering-/.f64N/A
PI-lowering-PI.f64N/A
log-lowering-log.f64N/A
/-lowering-/.f64N/A
Applied egg-rr94.8%
(FPCore (f) :precision binary64 (/ (/ (log (* (/ PI 2.0) (/ 0.5 (/ 1.0 f)))) PI) 0.25))
double code(double f) {
return (log(((((double) M_PI) / 2.0) * (0.5 / (1.0 / f)))) / ((double) M_PI)) / 0.25;
}
public static double code(double f) {
return (Math.log(((Math.PI / 2.0) * (0.5 / (1.0 / f)))) / Math.PI) / 0.25;
}
def code(f): return (math.log(((math.pi / 2.0) * (0.5 / (1.0 / f)))) / math.pi) / 0.25
function code(f) return Float64(Float64(log(Float64(Float64(pi / 2.0) * Float64(0.5 / Float64(1.0 / f)))) / pi) / 0.25) end
function tmp = code(f) tmp = (log(((pi / 2.0) * (0.5 / (1.0 / f)))) / pi) / 0.25; end
code[f_] := N[(N[(N[Log[N[(N[(Pi / 2.0), $MachinePrecision] * N[(0.5 / N[(1.0 / f), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / Pi), $MachinePrecision] / 0.25), $MachinePrecision]
\begin{array}{l}
\\
\frac{\frac{\log \left(\frac{\pi}{2} \cdot \frac{0.5}{\frac{1}{f}}\right)}{\pi}}{0.25}
\end{array}
Initial program 7.7%
distribute-lft-neg-inN/A
distribute-neg-frac2N/A
associate-*l/N/A
*-lft-identityN/A
/-lowering-/.f64N/A
Simplified7.7%
Taylor expanded in f around 0
/-lowering-/.f64N/A
*-commutativeN/A
*-lowering-*.f64N/A
distribute-rgt-out--N/A
metadata-evalN/A
*-lowering-*.f64N/A
PI-lowering-PI.f6494.3%
Simplified94.3%
clear-numN/A
associate-/r/N/A
clear-numN/A
*-lowering-*.f64N/A
/-lowering-/.f64N/A
PI-lowering-PI.f64N/A
associate-/r*N/A
div-invN/A
*-commutativeN/A
associate-/r*N/A
metadata-evalN/A
frac-timesN/A
metadata-evalN/A
associate-/l/N/A
log-lowering-log.f64N/A
div-invN/A
frac-timesN/A
metadata-evalN/A
/-lowering-/.f64N/A
*-lowering-*.f64N/A
PI-lowering-PI.f6494.2%
Applied egg-rr94.2%
Applied egg-rr94.3%
div-invN/A
associate-/r*N/A
div-invN/A
metadata-evalN/A
metadata-evalN/A
associate-*l*N/A
div-invN/A
times-fracN/A
/-rgt-identityN/A
*-lowering-*.f64N/A
metadata-evalN/A
div-invN/A
/-lowering-/.f64N/A
PI-lowering-PI.f64N/A
/-lowering-/.f64N/A
/-lowering-/.f6494.3%
Applied egg-rr94.3%
(FPCore (f) :precision binary64 (/ (/ (log (/ 4.0 (* PI f))) PI) -0.25))
double code(double f) {
return (log((4.0 / (((double) M_PI) * f))) / ((double) M_PI)) / -0.25;
}
public static double code(double f) {
return (Math.log((4.0 / (Math.PI * f))) / Math.PI) / -0.25;
}
def code(f): return (math.log((4.0 / (math.pi * f))) / math.pi) / -0.25
function code(f) return Float64(Float64(log(Float64(4.0 / Float64(pi * f))) / pi) / -0.25) end
function tmp = code(f) tmp = (log((4.0 / (pi * f))) / pi) / -0.25; end
code[f_] := N[(N[(N[Log[N[(4.0 / N[(Pi * f), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / Pi), $MachinePrecision] / -0.25), $MachinePrecision]
\begin{array}{l}
\\
\frac{\frac{\log \left(\frac{4}{\pi \cdot f}\right)}{\pi}}{-0.25}
\end{array}
Initial program 7.7%
distribute-lft-neg-inN/A
distribute-neg-frac2N/A
associate-*l/N/A
*-lft-identityN/A
/-lowering-/.f64N/A
Simplified7.7%
Taylor expanded in f around 0
/-lowering-/.f64N/A
*-commutativeN/A
*-lowering-*.f64N/A
distribute-rgt-out--N/A
metadata-evalN/A
*-lowering-*.f64N/A
PI-lowering-PI.f6494.3%
Simplified94.3%
div-invN/A
metadata-evalN/A
metadata-evalN/A
associate-/r*N/A
/-lowering-/.f64N/A
Applied egg-rr94.3%
Final simplification94.3%
(FPCore (f) :precision binary64 (* (log (/ 4.0 (* PI f))) (/ -4.0 PI)))
double code(double f) {
return log((4.0 / (((double) M_PI) * f))) * (-4.0 / ((double) M_PI));
}
public static double code(double f) {
return Math.log((4.0 / (Math.PI * f))) * (-4.0 / Math.PI);
}
def code(f): return math.log((4.0 / (math.pi * f))) * (-4.0 / math.pi)
function code(f) return Float64(log(Float64(4.0 / Float64(pi * f))) * Float64(-4.0 / pi)) end
function tmp = code(f) tmp = log((4.0 / (pi * f))) * (-4.0 / pi); end
code[f_] := N[(N[Log[N[(4.0 / N[(Pi * f), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] * N[(-4.0 / Pi), $MachinePrecision]), $MachinePrecision]
\begin{array}{l}
\\
\log \left(\frac{4}{\pi \cdot f}\right) \cdot \frac{-4}{\pi}
\end{array}
Initial program 7.7%
distribute-lft-neg-inN/A
distribute-neg-frac2N/A
associate-*l/N/A
*-lft-identityN/A
/-lowering-/.f64N/A
Simplified7.7%
Taylor expanded in f around 0
/-lowering-/.f64N/A
*-commutativeN/A
*-lowering-*.f64N/A
distribute-rgt-out--N/A
metadata-evalN/A
*-lowering-*.f64N/A
PI-lowering-PI.f6494.3%
Simplified94.3%
clear-numN/A
associate-/r/N/A
clear-numN/A
*-lowering-*.f64N/A
/-lowering-/.f64N/A
PI-lowering-PI.f64N/A
associate-/r*N/A
div-invN/A
*-commutativeN/A
associate-/r*N/A
metadata-evalN/A
frac-timesN/A
metadata-evalN/A
associate-/l/N/A
log-lowering-log.f64N/A
div-invN/A
frac-timesN/A
metadata-evalN/A
/-lowering-/.f64N/A
*-lowering-*.f64N/A
PI-lowering-PI.f6494.2%
Applied egg-rr94.2%
Final simplification94.2%
herbie shell --seed 2024164
(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)))))))))