Hyperbolic tangent

Percentage Accurate: 8.9% → 97.4%
Time: 7.9s
Alternatives: 5
Speedup: 409.0×

Specification

?
\[\begin{array}{l} \\ \begin{array}{l} t_0 := e^{-x}\\ \frac{e^{x} - t_0}{e^{x} + t_0} \end{array} \end{array} \]
(FPCore (x)
 :precision binary64
 (let* ((t_0 (exp (- x)))) (/ (- (exp x) t_0) (+ (exp x) t_0))))
double code(double x) {
	double t_0 = exp(-x);
	return (exp(x) - t_0) / (exp(x) + t_0);
}

real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: t_0
    t_0 = exp(-x)
    code = (exp(x) - t_0) / (exp(x) + t_0)
end function

public static double code(double x) {
	double t_0 = Math.exp(-x);
	return (Math.exp(x) - t_0) / (Math.exp(x) + t_0);
}

def code(x):
	t_0 = math.exp(-x)
	return (math.exp(x) - t_0) / (math.exp(x) + t_0)

function code(x)
	t_0 = exp(Float64(-x))
	return Float64(Float64(exp(x) - t_0) / Float64(exp(x) + t_0))
end

function tmp = code(x)
	t_0 = exp(-x);
	tmp = (exp(x) - t_0) / (exp(x) + t_0);
end

code[x_] := Block[{t$95$0 = N[Exp[(-x)], $MachinePrecision]}, N[(N[(N[Exp[x], $MachinePrecision] - t$95$0), $MachinePrecision] / N[(N[Exp[x], $MachinePrecision] + t$95$0), $MachinePrecision]), $MachinePrecision]]

\begin{array}{l}

\\
\begin{array}{l}
t_0 := e^{-x}\\
\frac{e^{x} - t_0}{e^{x} + t_0}
\end{array}
\end{array}

Sampling outcomes in binary64 precision:

Local Percentage Accuracy vs ?

The average percentage accuracy by input value. Horizontal axis shows value of an input variable; the variable is choosen in the title. Vertical axis is accuracy; higher is better. Red represent the original program, while blue represents Herbie's suggestion. These can be toggled with buttons below the plot. The line is an average while dots represent individual samples.

Accuracy vs Speed?

Herbie found 5 alternatives:

AlternativeAccuracySpeedup
The accuracy (vertical axis) and speed (horizontal axis) of each alternatives. Up and to the right is better. The red square shows the initial program, and each blue circle shows an alternative.The line shows the best available speed-accuracy tradeoffs.

Initial Program: 8.9% accurate, 1.0× speedup?

\[\begin{array}{l} \\ \begin{array}{l} t_0 := e^{-x}\\ \frac{e^{x} - t_0}{e^{x} + t_0} \end{array} \end{array} \]
(FPCore (x)
 :precision binary64
 (let* ((t_0 (exp (- x)))) (/ (- (exp x) t_0) (+ (exp x) t_0))))
double code(double x) {
	double t_0 = exp(-x);
	return (exp(x) - t_0) / (exp(x) + t_0);
}

real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: t_0
    t_0 = exp(-x)
    code = (exp(x) - t_0) / (exp(x) + t_0)
end function

public static double code(double x) {
	double t_0 = Math.exp(-x);
	return (Math.exp(x) - t_0) / (Math.exp(x) + t_0);
}

def code(x):
	t_0 = math.exp(-x)
	return (math.exp(x) - t_0) / (math.exp(x) + t_0)

function code(x)
	t_0 = exp(Float64(-x))
	return Float64(Float64(exp(x) - t_0) / Float64(exp(x) + t_0))
end

function tmp = code(x)
	t_0 = exp(-x);
	tmp = (exp(x) - t_0) / (exp(x) + t_0);
end

code[x_] := Block[{t$95$0 = N[Exp[(-x)], $MachinePrecision]}, N[(N[(N[Exp[x], $MachinePrecision] - t$95$0), $MachinePrecision] / N[(N[Exp[x], $MachinePrecision] + t$95$0), $MachinePrecision]), $MachinePrecision]]

\begin{array}{l}

\\
\begin{array}{l}
t_0 := e^{-x}\\
\frac{e^{x} - t_0}{e^{x} + t_0}
\end{array}
\end{array}

Alternative 1: 97.4% accurate, 0.8× speedup?

\[\begin{array}{l} \\ \frac{2 \cdot x + \left(0.3333333333333333 \cdot {x}^{3} + \left(0.0003968253968253968 \cdot {x}^{7} + 0.016666666666666666 \cdot {x}^{5}\right)\right)}{e^{x} + e^{-x}} \end{array} \]
(FPCore (x)
 :precision binary64
 (/
  (+
   (* 2.0 x)
   (+
    (* 0.3333333333333333 (pow x 3.0))
    (+
     (* 0.0003968253968253968 (pow x 7.0))
     (* 0.016666666666666666 (pow x 5.0)))))
  (+ (exp x) (exp (- x)))))
double code(double x) {
	return ((2.0 * x) + ((0.3333333333333333 * pow(x, 3.0)) + ((0.0003968253968253968 * pow(x, 7.0)) + (0.016666666666666666 * pow(x, 5.0))))) / (exp(x) + exp(-x));
}

real(8) function code(x)
    real(8), intent (in) :: x
    code = ((2.0d0 * x) + ((0.3333333333333333d0 * (x ** 3.0d0)) + ((0.0003968253968253968d0 * (x ** 7.0d0)) + (0.016666666666666666d0 * (x ** 5.0d0))))) / (exp(x) + exp(-x))
end function

public static double code(double x) {
	return ((2.0 * x) + ((0.3333333333333333 * Math.pow(x, 3.0)) + ((0.0003968253968253968 * Math.pow(x, 7.0)) + (0.016666666666666666 * Math.pow(x, 5.0))))) / (Math.exp(x) + Math.exp(-x));
}

def code(x):
	return ((2.0 * x) + ((0.3333333333333333 * math.pow(x, 3.0)) + ((0.0003968253968253968 * math.pow(x, 7.0)) + (0.016666666666666666 * math.pow(x, 5.0))))) / (math.exp(x) + math.exp(-x))

function code(x)
	return Float64(Float64(Float64(2.0 * x) + Float64(Float64(0.3333333333333333 * (x ^ 3.0)) + Float64(Float64(0.0003968253968253968 * (x ^ 7.0)) + Float64(0.016666666666666666 * (x ^ 5.0))))) / Float64(exp(x) + exp(Float64(-x))))
end

function tmp = code(x)
	tmp = ((2.0 * x) + ((0.3333333333333333 * (x ^ 3.0)) + ((0.0003968253968253968 * (x ^ 7.0)) + (0.016666666666666666 * (x ^ 5.0))))) / (exp(x) + exp(-x));
end

code[x_] := N[(N[(N[(2.0 * x), $MachinePrecision] + N[(N[(0.3333333333333333 * N[Power[x, 3.0], $MachinePrecision]), $MachinePrecision] + N[(N[(0.0003968253968253968 * N[Power[x, 7.0], $MachinePrecision]), $MachinePrecision] + N[(0.016666666666666666 * N[Power[x, 5.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(N[Exp[x], $MachinePrecision] + N[Exp[(-x)], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

\begin{array}{l}

\\
\frac{2 \cdot x + \left(0.3333333333333333 \cdot {x}^{3} + \left(0.0003968253968253968 \cdot {x}^{7} + 0.016666666666666666 \cdot {x}^{5}\right)\right)}{e^{x} + e^{-x}}
\end{array}
Derivation

  1. Initial program 8.6%

    \[\frac{e^{x} - e^{-x}}{e^{x} + e^{-x}} \]

  2. Taylor expanded in x around 0 97.5%

    \[\leadsto \frac{\color{blue}{2 \cdot x + \left(0.3333333333333333 \cdot {x}^{3} + \left(0.0003968253968253968 \cdot {x}^{7} + 0.016666666666666666 \cdot {x}^{5}\right)\right)}}{e^{x} + e^{-x}} \]

  3. Final simplification97.5%

    \[\leadsto \frac{2 \cdot x + \left(0.3333333333333333 \cdot {x}^{3} + \left(0.0003968253968253968 \cdot {x}^{7} + 0.016666666666666666 \cdot {x}^{5}\right)\right)}{e^{x} + e^{-x}} \]

Alternative 2: 97.3% accurate, 1.9× speedup?

\[\begin{array}{l} \\ {x}^{3} \cdot -0.3333333333333333 + \left(x + {x}^{5} \cdot 0.13333333333333333\right) \end{array} \]
(FPCore (x)
 :precision binary64
 (+
  (* (pow x 3.0) -0.3333333333333333)
  (+ x (* (pow x 5.0) 0.13333333333333333))))
double code(double x) {
	return (pow(x, 3.0) * -0.3333333333333333) + (x + (pow(x, 5.0) * 0.13333333333333333));
}

real(8) function code(x)
    real(8), intent (in) :: x
    code = ((x ** 3.0d0) * (-0.3333333333333333d0)) + (x + ((x ** 5.0d0) * 0.13333333333333333d0))
end function

public static double code(double x) {
	return (Math.pow(x, 3.0) * -0.3333333333333333) + (x + (Math.pow(x, 5.0) * 0.13333333333333333));
}

def code(x):
	return (math.pow(x, 3.0) * -0.3333333333333333) + (x + (math.pow(x, 5.0) * 0.13333333333333333))

function code(x)
	return Float64(Float64((x ^ 3.0) * -0.3333333333333333) + Float64(x + Float64((x ^ 5.0) * 0.13333333333333333)))
end

function tmp = code(x)
	tmp = ((x ^ 3.0) * -0.3333333333333333) + (x + ((x ^ 5.0) * 0.13333333333333333));
end

code[x_] := N[(N[(N[Power[x, 3.0], $MachinePrecision] * -0.3333333333333333), $MachinePrecision] + N[(x + N[(N[Power[x, 5.0], $MachinePrecision] * 0.13333333333333333), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

\begin{array}{l}

\\
{x}^{3} \cdot -0.3333333333333333 + \left(x + {x}^{5} \cdot 0.13333333333333333\right)
\end{array}
Derivation

  1. Initial program 8.6%

    \[\frac{e^{x} - e^{-x}}{e^{x} + e^{-x}} \]

  2. Taylor expanded in x around 0 97.4%

    \[\leadsto \color{blue}{-0.3333333333333333 \cdot {x}^{3} + \left(0.13333333333333333 \cdot {x}^{5} + x\right)} \]

  3. Final simplification97.4%

    \[\leadsto {x}^{3} \cdot -0.3333333333333333 + \left(x + {x}^{5} \cdot 0.13333333333333333\right) \]

Alternative 3: 96.9% accurate, 3.9× speedup?

\[\begin{array}{l} \\ x + {x}^{3} \cdot -0.3333333333333333 \end{array} \]
(FPCore (x) :precision binary64 (+ x (* (pow x 3.0) -0.3333333333333333)))
double code(double x) {
	return x + (pow(x, 3.0) * -0.3333333333333333);
}

real(8) function code(x)
    real(8), intent (in) :: x
    code = x + ((x ** 3.0d0) * (-0.3333333333333333d0))
end function

public static double code(double x) {
	return x + (Math.pow(x, 3.0) * -0.3333333333333333);
}

def code(x):
	return x + (math.pow(x, 3.0) * -0.3333333333333333)

function code(x)
	return Float64(x + Float64((x ^ 3.0) * -0.3333333333333333))
end

function tmp = code(x)
	tmp = x + ((x ^ 3.0) * -0.3333333333333333);
end

code[x_] := N[(x + N[(N[Power[x, 3.0], $MachinePrecision] * -0.3333333333333333), $MachinePrecision]), $MachinePrecision]

\begin{array}{l}

\\
x + {x}^{3} \cdot -0.3333333333333333
\end{array}
Derivation

  1. Initial program 8.6%

    \[\frac{e^{x} - e^{-x}}{e^{x} + e^{-x}} \]

  2. Taylor expanded in x around 0 97.1%

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

  3. Final simplification97.1%

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

Alternative 4: 4.0% accurate, 409.0× speedup?

\[\begin{array}{l} \\ -2 \end{array} \]
(FPCore (x) :precision binary64 -2.0)
double code(double x) {
	return -2.0;
}

real(8) function code(x)
    real(8), intent (in) :: x
    code = -2.0d0
end function

public static double code(double x) {
	return -2.0;
}

def code(x):
	return -2.0

function code(x)
	return -2.0
end

function tmp = code(x)
	tmp = -2.0;
end

code[x_] := -2.0

\begin{array}{l}

\\
-2
\end{array}
Derivation

  1. Initial program 8.6%

    \[\frac{e^{x} - e^{-x}}{e^{x} + e^{-x}} \]

  2. Taylor expanded in x around 0 7.2%

    \[\leadsto \frac{e^{x} - e^{-x}}{\color{blue}{1} + e^{-x}} \]
  3. Step-by-step derivation

    1. clear-num7.2%

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

    2. inv-pow7.2%

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

    3. +-commutative7.2%

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

    4. sinh-undef96.3%

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

  4. Applied egg-rr96.3%

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

  5. Taylor expanded in x around 0 96.1%

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

  6. Taylor expanded in x around inf 3.9%

    \[\leadsto \color{blue}{-2} \]

  7. Final simplification3.9%

    \[\leadsto -2 \]

Alternative 5: 96.6% accurate, 409.0× speedup?

\[\begin{array}{l} \\ x \end{array} \]
(FPCore (x) :precision binary64 x)
double code(double x) {
	return x;
}

real(8) function code(x)
    real(8), intent (in) :: x
    code = x
end function

public static double code(double x) {
	return x;
}

def code(x):
	return x

function code(x)
	return x
end

function tmp = code(x)
	tmp = x;
end

code[x_] := x

\begin{array}{l}

\\
x
\end{array}
Derivation

  1. Initial program 8.6%

    \[\frac{e^{x} - e^{-x}}{e^{x} + e^{-x}} \]

  2. Taylor expanded in x around 0 96.9%

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

  3. Final simplification96.9%

    \[\leadsto x \]

Reproduce

?
herbie shell --seed 2023193 
(FPCore (x)
  :name "Hyperbolic tangent"
  :precision binary64
  (/ (- (exp x) (exp (- x))) (+ (exp x) (exp (- x)))))