Hyperbolic secant

Percentage Accurate: 100.0% → 100.0%

Time: 7.2s

Alternatives: 7

Speedup: 1.0×

Specification

Math

\[\begin{array}{l} \\ \frac{2}{e^{x} + e^{-x}} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (/ 2.0 (+ (exp x) (exp (- x)))))

C

double code(double x) {
	return 2.0 / (exp(x) + exp(-x));
}

Fortran

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

Java

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

Python

def code(x):
	return 2.0 / (math.exp(x) + math.exp(-x))

Julia

function code(x)
	return Float64(2.0 / Float64(exp(x) + exp(Float64(-x))))
end

MATLAB

function tmp = code(x)
	tmp = 2.0 / (exp(x) + exp(-x));
end

Wolfram

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

Initial Program: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{2}{e^{x} + e^{-x}} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (/ 2.0 (+ (exp x) (exp (- x)))))

C

double code(double x) {
	return 2.0 / (exp(x) + exp(-x));
}

Fortran

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

Java

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

Python

def code(x):
	return 2.0 / (math.exp(x) + math.exp(-x))

Julia

function code(x)
	return Float64(2.0 / Float64(exp(x) + exp(Float64(-x))))
end

MATLAB

function tmp = code(x)
	tmp = 2.0 / (exp(x) + exp(-x));
end

Wolfram

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

Alternative 1: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{2}{e^{x} + e^{-x}} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (/ 2.0 (+ (exp x) (exp (- x)))))

C

double code(double x) {
	return 2.0 / (exp(x) + exp(-x));
}

Fortran

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

Java

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

Python

def code(x):
	return 2.0 / (math.exp(x) + math.exp(-x))

Julia

function code(x)
	return Float64(2.0 / Float64(exp(x) + exp(Float64(-x))))
end

MATLAB

function tmp = code(x)
	tmp = 2.0 / (exp(x) + exp(-x));
end

Wolfram

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

Derivation

1. Initial program 100.0%

   \[\frac{2}{e^{x} + e^{-x}} \]
2. Add Preprocessing
3. Add Preprocessing

Alternative 2: 91.7% accurate, 1.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq 500:\\ \;\;\;\;\frac{2}{e^{x} + \left(1 + x \cdot \left(x \cdot \left(0.5 + x \cdot -0.16666666666666666\right) + -1\right)\right)}\\ \mathbf{else}:\\ \;\;\;\;0\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (<= x 500.0)
   (/
    2.0
    (+
     (exp x)
     (+ 1.0 (* x (+ (* x (+ 0.5 (* x -0.16666666666666666))) -1.0)))))
   0.0))

C

double code(double x) {
	double tmp;
	if (x <= 500.0) {
		tmp = 2.0 / (exp(x) + (1.0 + (x * ((x * (0.5 + (x * -0.16666666666666666))) + -1.0))));
	} else {
		tmp = 0.0;
	}
	return tmp;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: tmp
    if (x <= 500.0d0) then
        tmp = 2.0d0 / (exp(x) + (1.0d0 + (x * ((x * (0.5d0 + (x * (-0.16666666666666666d0)))) + (-1.0d0)))))
    else
        tmp = 0.0d0
    end if
    code = tmp
end function

Java

public static double code(double x) {
	double tmp;
	if (x <= 500.0) {
		tmp = 2.0 / (Math.exp(x) + (1.0 + (x * ((x * (0.5 + (x * -0.16666666666666666))) + -1.0))));
	} else {
		tmp = 0.0;
	}
	return tmp;
}

Python

def code(x):
	tmp = 0
	if x <= 500.0:
		tmp = 2.0 / (math.exp(x) + (1.0 + (x * ((x * (0.5 + (x * -0.16666666666666666))) + -1.0))))
	else:
		tmp = 0.0
	return tmp

Julia

function code(x)
	tmp = 0.0
	if (x <= 500.0)
		tmp = Float64(2.0 / Float64(exp(x) + Float64(1.0 + Float64(x * Float64(Float64(x * Float64(0.5 + Float64(x * -0.16666666666666666))) + -1.0)))));
	else
		tmp = 0.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x)
	tmp = 0.0;
	if (x <= 500.0)
		tmp = 2.0 / (exp(x) + (1.0 + (x * ((x * (0.5 + (x * -0.16666666666666666))) + -1.0))));
	else
		tmp = 0.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := If[LessEqual[x, 500.0], N[(2.0 / N[(N[Exp[x], $MachinePrecision] + N[(1.0 + N[(x * N[(N[(x * N[(0.5 + N[(x * -0.16666666666666666), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + -1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 0.0]

Derivation

1. Split input into 2 regimes

2. if x < 500

   1. Initial program 100.0%

      \[\frac{2}{e^{x} + e^{-x}} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0 91.3%

      \[\leadsto \frac{2}{e^{x} + \color{blue}{\left(1 + x \cdot \left(x \cdot \left(0.5 + -0.16666666666666666 \cdot x\right) - 1\right)\right)}} \]

   if 500 < x

   1. Initial program 100.0%

      \[\frac{2}{e^{x} + e^{-x}} \]
   2. Add Preprocessing

   4. Applied egg-rr 100.0%

      \[\leadsto \color{blue}{0} \]
3. Recombined 2 regimes into one program.

4. Final simplification 93.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq 500:\\ \;\;\;\;\frac{2}{e^{x} + \left(1 + x \cdot \left(x \cdot \left(0.5 + x \cdot -0.16666666666666666\right) + -1\right)\right)}\\ \mathbf{else}:\\ \;\;\;\;0\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 87.7% accurate, 7.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq 350:\\ \;\;\;\;\frac{2}{\left(1 + x \cdot \left(1 + x \cdot 0.5\right)\right) + \left(1 + x \cdot \left(x \cdot 0.5 + -1\right)\right)}\\ \mathbf{else}:\\ \;\;\;\;0\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (<= x 350.0)
   (/ 2.0 (+ (+ 1.0 (* x (+ 1.0 (* x 0.5)))) (+ 1.0 (* x (+ (* x 0.5) -1.0)))))
   0.0))

C

double code(double x) {
	double tmp;
	if (x <= 350.0) {
		tmp = 2.0 / ((1.0 + (x * (1.0 + (x * 0.5)))) + (1.0 + (x * ((x * 0.5) + -1.0))));
	} else {
		tmp = 0.0;
	}
	return tmp;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: tmp
    if (x <= 350.0d0) then
        tmp = 2.0d0 / ((1.0d0 + (x * (1.0d0 + (x * 0.5d0)))) + (1.0d0 + (x * ((x * 0.5d0) + (-1.0d0)))))
    else
        tmp = 0.0d0
    end if
    code = tmp
end function

Java

public static double code(double x) {
	double tmp;
	if (x <= 350.0) {
		tmp = 2.0 / ((1.0 + (x * (1.0 + (x * 0.5)))) + (1.0 + (x * ((x * 0.5) + -1.0))));
	} else {
		tmp = 0.0;
	}
	return tmp;
}

Python

def code(x):
	tmp = 0
	if x <= 350.0:
		tmp = 2.0 / ((1.0 + (x * (1.0 + (x * 0.5)))) + (1.0 + (x * ((x * 0.5) + -1.0))))
	else:
		tmp = 0.0
	return tmp

Julia

function code(x)
	tmp = 0.0
	if (x <= 350.0)
		tmp = Float64(2.0 / Float64(Float64(1.0 + Float64(x * Float64(1.0 + Float64(x * 0.5)))) + Float64(1.0 + Float64(x * Float64(Float64(x * 0.5) + -1.0)))));
	else
		tmp = 0.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x)
	tmp = 0.0;
	if (x <= 350.0)
		tmp = 2.0 / ((1.0 + (x * (1.0 + (x * 0.5)))) + (1.0 + (x * ((x * 0.5) + -1.0))));
	else
		tmp = 0.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := If[LessEqual[x, 350.0], N[(2.0 / N[(N[(1.0 + N[(x * N[(1.0 + N[(x * 0.5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(1.0 + N[(x * N[(N[(x * 0.5), $MachinePrecision] + -1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 0.0]

Derivation

1. Split input into 2 regimes

2. if x < 350

   1. Initial program 100.0%

      \[\frac{2}{e^{x} + e^{-x}} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0 91.3%

      \[\leadsto \frac{2}{e^{x} + \color{blue}{\left(1 + x \cdot \left(x \cdot \left(0.5 + -0.16666666666666666 \cdot x\right) - 1\right)\right)}} \]

   4. Taylor expanded in x around 0 91.3%

      \[\leadsto \frac{2}{\color{blue}{\left(1 + x \cdot \left(1 + 0.5 \cdot x\right)\right)} + \left(1 + x \cdot \left(x \cdot \left(0.5 + -0.16666666666666666 \cdot x\right) - 1\right)\right)} \]
   5. Step-by-step derivation

      1. *-commutative 91.3%

         \[\leadsto \frac{2}{\left(1 + x \cdot \left(1 + \color{blue}{x \cdot 0.5}\right)\right) + \left(1 + x \cdot \left(x \cdot \left(0.5 + -0.16666666666666666 \cdot x\right) - 1\right)\right)} \]

   6. Simplified 91.3%

      \[\leadsto \frac{2}{\color{blue}{\left(1 + x \cdot \left(1 + x \cdot 0.5\right)\right)} + \left(1 + x \cdot \left(x \cdot \left(0.5 + -0.16666666666666666 \cdot x\right) - 1\right)\right)} \]

   7. Taylor expanded in x around 0 83.4%

      \[\leadsto \frac{2}{\left(1 + x \cdot \left(1 + x \cdot 0.5\right)\right) + \left(1 + x \cdot \left(\color{blue}{0.5 \cdot x} - 1\right)\right)} \]
   8. Step-by-step derivation

      1. *-commutative 83.4%

         \[\leadsto \frac{2}{\left(1 + x \cdot \left(1 + x \cdot 0.5\right)\right) + \left(1 + x \cdot \left(\color{blue}{x \cdot 0.5} - 1\right)\right)} \]

   9. Simplified 83.4%

      \[\leadsto \frac{2}{\left(1 + x \cdot \left(1 + x \cdot 0.5\right)\right) + \left(1 + x \cdot \left(\color{blue}{x \cdot 0.5} - 1\right)\right)} \]

   if 350 < x

   1. Initial program 100.0%

      \[\frac{2}{e^{x} + e^{-x}} \]
   2. Add Preprocessing

   4. Applied egg-rr 100.0%

      \[\leadsto \color{blue}{0} \]
3. Recombined 2 regimes into one program.

4. Final simplification 87.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq 350:\\ \;\;\;\;\frac{2}{\left(1 + x \cdot \left(1 + x \cdot 0.5\right)\right) + \left(1 + x \cdot \left(x \cdot 0.5 + -1\right)\right)}\\ \mathbf{else}:\\ \;\;\;\;0\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 91.3% accurate, 8.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq 370:\\ \;\;\;\;\frac{2}{\left(1 + x \cdot \left(1 + x \cdot \left(0.5 + x \cdot 0.16666666666666666\right)\right)\right) + \left(1 - x\right)}\\ \mathbf{else}:\\ \;\;\;\;0\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (<= x 370.0)
   (/
    2.0
    (+
     (+ 1.0 (* x (+ 1.0 (* x (+ 0.5 (* x 0.16666666666666666))))))
     (- 1.0 x)))
   0.0))

C

double code(double x) {
	double tmp;
	if (x <= 370.0) {
		tmp = 2.0 / ((1.0 + (x * (1.0 + (x * (0.5 + (x * 0.16666666666666666)))))) + (1.0 - x));
	} else {
		tmp = 0.0;
	}
	return tmp;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: tmp
    if (x <= 370.0d0) then
        tmp = 2.0d0 / ((1.0d0 + (x * (1.0d0 + (x * (0.5d0 + (x * 0.16666666666666666d0)))))) + (1.0d0 - x))
    else
        tmp = 0.0d0
    end if
    code = tmp
end function

Java

public static double code(double x) {
	double tmp;
	if (x <= 370.0) {
		tmp = 2.0 / ((1.0 + (x * (1.0 + (x * (0.5 + (x * 0.16666666666666666)))))) + (1.0 - x));
	} else {
		tmp = 0.0;
	}
	return tmp;
}

Python

def code(x):
	tmp = 0
	if x <= 370.0:
		tmp = 2.0 / ((1.0 + (x * (1.0 + (x * (0.5 + (x * 0.16666666666666666)))))) + (1.0 - x))
	else:
		tmp = 0.0
	return tmp

Julia

function code(x)
	tmp = 0.0
	if (x <= 370.0)
		tmp = Float64(2.0 / Float64(Float64(1.0 + Float64(x * Float64(1.0 + Float64(x * Float64(0.5 + Float64(x * 0.16666666666666666)))))) + Float64(1.0 - x)));
	else
		tmp = 0.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x)
	tmp = 0.0;
	if (x <= 370.0)
		tmp = 2.0 / ((1.0 + (x * (1.0 + (x * (0.5 + (x * 0.16666666666666666)))))) + (1.0 - x));
	else
		tmp = 0.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := If[LessEqual[x, 370.0], N[(2.0 / N[(N[(1.0 + N[(x * N[(1.0 + N[(x * N[(0.5 + N[(x * 0.16666666666666666), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(1.0 - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 0.0]

Derivation

1. Split input into 2 regimes

2. if x < 370

   1. Initial program 100.0%

      \[\frac{2}{e^{x} + e^{-x}} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0 91.3%

      \[\leadsto \frac{2}{e^{x} + \color{blue}{\left(1 + x \cdot \left(x \cdot \left(0.5 + -0.16666666666666666 \cdot x\right) - 1\right)\right)}} \]

   4. Taylor expanded in x around 0 67.7%

      \[\leadsto \frac{2}{\color{blue}{\left(1 + x \cdot \left(1 + x \cdot \left(0.5 + 0.16666666666666666 \cdot x\right)\right)\right)} + \left(1 + x \cdot \left(x \cdot \left(0.5 + -0.16666666666666666 \cdot x\right) - 1\right)\right)} \]
   5. Step-by-step derivation

      1. *-lft-identity 67.7%

         \[\leadsto \frac{2}{\left(1 + x \cdot \color{blue}{\left(1 \cdot \left(1 + x \cdot \left(0.5 + 0.16666666666666666 \cdot x\right)\right)\right)}\right) + \left(1 + x \cdot \left(x \cdot \left(0.5 + -0.16666666666666666 \cdot x\right) - 1\right)\right)} \]

      2. *-lft-identity 67.7%

         \[\leadsto \frac{2}{\left(1 + x \cdot \color{blue}{\left(1 + x \cdot \left(0.5 + 0.16666666666666666 \cdot x\right)\right)}\right) + \left(1 + x \cdot \left(x \cdot \left(0.5 + -0.16666666666666666 \cdot x\right) - 1\right)\right)} \]

      3. *-commutative 67.7%

         \[\leadsto \frac{2}{\left(1 + x \cdot \left(1 + x \cdot \left(0.5 + \color{blue}{x \cdot 0.16666666666666666}\right)\right)\right) + \left(1 + x \cdot \left(x \cdot \left(0.5 + -0.16666666666666666 \cdot x\right) - 1\right)\right)} \]

   6. Simplified 67.7%

      \[\leadsto \frac{2}{\color{blue}{\left(1 + x \cdot \left(1 + x \cdot \left(0.5 + x \cdot 0.16666666666666666\right)\right)\right)} + \left(1 + x \cdot \left(x \cdot \left(0.5 + -0.16666666666666666 \cdot x\right) - 1\right)\right)} \]

   7. Taylor expanded in x around 0 91.1%

      \[\leadsto \frac{2}{\left(1 + x \cdot \left(1 + x \cdot \left(0.5 + x \cdot 0.16666666666666666\right)\right)\right) + \left(1 + \color{blue}{-1 \cdot x}\right)} \]
   8. Step-by-step derivation

      1. mul-1-neg 91.1%

         \[\leadsto \frac{2}{\left(1 + x \cdot \left(1 + x \cdot \left(0.5 + x \cdot 0.16666666666666666\right)\right)\right) + \left(1 + \color{blue}{\left(-x\right)}\right)} \]

   9. Simplified 91.1%

      \[\leadsto \frac{2}{\left(1 + x \cdot \left(1 + x \cdot \left(0.5 + x \cdot 0.16666666666666666\right)\right)\right) + \left(1 + \color{blue}{\left(-x\right)}\right)} \]

   if 370 < x

   1. Initial program 100.0%

      \[\frac{2}{e^{x} + e^{-x}} \]
   2. Add Preprocessing

   4. Applied egg-rr 100.0%

      \[\leadsto \color{blue}{0} \]
3. Recombined 2 regimes into one program.

4. Final simplification 93.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq 370:\\ \;\;\;\;\frac{2}{\left(1 + x \cdot \left(1 + x \cdot \left(0.5 + x \cdot 0.16666666666666666\right)\right)\right) + \left(1 - x\right)}\\ \mathbf{else}:\\ \;\;\;\;0\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 87.4% accurate, 10.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq 350:\\ \;\;\;\;\frac{2}{\left(1 + x \cdot \left(x \cdot 0.5 + -1\right)\right) + \left(x + 1\right)}\\ \mathbf{else}:\\ \;\;\;\;0\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (<= x 350.0) (/ 2.0 (+ (+ 1.0 (* x (+ (* x 0.5) -1.0))) (+ x 1.0))) 0.0))

C

double code(double x) {
	double tmp;
	if (x <= 350.0) {
		tmp = 2.0 / ((1.0 + (x * ((x * 0.5) + -1.0))) + (x + 1.0));
	} else {
		tmp = 0.0;
	}
	return tmp;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: tmp
    if (x <= 350.0d0) then
        tmp = 2.0d0 / ((1.0d0 + (x * ((x * 0.5d0) + (-1.0d0)))) + (x + 1.0d0))
    else
        tmp = 0.0d0
    end if
    code = tmp
end function

Java

public static double code(double x) {
	double tmp;
	if (x <= 350.0) {
		tmp = 2.0 / ((1.0 + (x * ((x * 0.5) + -1.0))) + (x + 1.0));
	} else {
		tmp = 0.0;
	}
	return tmp;
}

Python

def code(x):
	tmp = 0
	if x <= 350.0:
		tmp = 2.0 / ((1.0 + (x * ((x * 0.5) + -1.0))) + (x + 1.0))
	else:
		tmp = 0.0
	return tmp

Julia

function code(x)
	tmp = 0.0
	if (x <= 350.0)
		tmp = Float64(2.0 / Float64(Float64(1.0 + Float64(x * Float64(Float64(x * 0.5) + -1.0))) + Float64(x + 1.0)));
	else
		tmp = 0.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x)
	tmp = 0.0;
	if (x <= 350.0)
		tmp = 2.0 / ((1.0 + (x * ((x * 0.5) + -1.0))) + (x + 1.0));
	else
		tmp = 0.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := If[LessEqual[x, 350.0], N[(2.0 / N[(N[(1.0 + N[(x * N[(N[(x * 0.5), $MachinePrecision] + -1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(x + 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 0.0]

Derivation

1. Split input into 2 regimes

2. if x < 350

   1. Initial program 100.0%

      \[\frac{2}{e^{x} + e^{-x}} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0 91.3%

      \[\leadsto \frac{2}{e^{x} + \color{blue}{\left(1 + x \cdot \left(x \cdot \left(0.5 + -0.16666666666666666 \cdot x\right) - 1\right)\right)}} \]

   4. Taylor expanded in x around 0 91.1%

      \[\leadsto \frac{2}{\color{blue}{\left(1 + x\right)} + \left(1 + x \cdot \left(x \cdot \left(0.5 + -0.16666666666666666 \cdot x\right) - 1\right)\right)} \]

   5. Taylor expanded in x around 0 83.2%

      \[\leadsto \frac{2}{\left(1 + x\right) + \left(1 + x \cdot \left(\color{blue}{0.5 \cdot x} - 1\right)\right)} \]
   6. Step-by-step derivation

      1. *-commutative 83.4%

         \[\leadsto \frac{2}{\left(1 + x \cdot \left(1 + x \cdot 0.5\right)\right) + \left(1 + x \cdot \left(\color{blue}{x \cdot 0.5} - 1\right)\right)} \]

   7. Simplified 83.2%

      \[\leadsto \frac{2}{\left(1 + x\right) + \left(1 + x \cdot \left(\color{blue}{x \cdot 0.5} - 1\right)\right)} \]

   if 350 < x

   1. Initial program 100.0%

      \[\frac{2}{e^{x} + e^{-x}} \]
   2. Add Preprocessing

   4. Applied egg-rr 100.0%

      \[\leadsto \color{blue}{0} \]
3. Recombined 2 regimes into one program.

4. Final simplification 87.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq 350:\\ \;\;\;\;\frac{2}{\left(1 + x \cdot \left(x \cdot 0.5 + -1\right)\right) + \left(x + 1\right)}\\ \mathbf{else}:\\ \;\;\;\;0\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 74.9% accurate, 34.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq 350:\\ \;\;\;\;1\\ \mathbf{else}:\\ \;\;\;\;0\\ \end{array} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (if (<= x 350.0) 1.0 0.0))

C

double code(double x) {
	double tmp;
	if (x <= 350.0) {
		tmp = 1.0;
	} else {
		tmp = 0.0;
	}
	return tmp;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: tmp
    if (x <= 350.0d0) then
        tmp = 1.0d0
    else
        tmp = 0.0d0
    end if
    code = tmp
end function

Java

public static double code(double x) {
	double tmp;
	if (x <= 350.0) {
		tmp = 1.0;
	} else {
		tmp = 0.0;
	}
	return tmp;
}

Python

def code(x):
	tmp = 0
	if x <= 350.0:
		tmp = 1.0
	else:
		tmp = 0.0
	return tmp

Julia

function code(x)
	tmp = 0.0
	if (x <= 350.0)
		tmp = 1.0;
	else
		tmp = 0.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x)
	tmp = 0.0;
	if (x <= 350.0)
		tmp = 1.0;
	else
		tmp = 0.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := If[LessEqual[x, 350.0], 1.0, 0.0]

Derivation

1. Split input into 2 regimes

2. if x < 350

   1. Initial program 100.0%

      \[\frac{2}{e^{x} + e^{-x}} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0 68.3%

      \[\leadsto \color{blue}{1} \]

   if 350 < x

   1. Initial program 100.0%

      \[\frac{2}{e^{x} + e^{-x}} \]
   2. Add Preprocessing

   4. Applied egg-rr 100.0%

      \[\leadsto \color{blue}{0} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 7: 51.4% accurate, 206.0× speedup

Math

\[\begin{array}{l} \\ 0 \end{array} \]

FPCore

(FPCore (x) :precision binary64 0.0)

C

double code(double x) {
	return 0.0;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = 0.0d0
end function

Java

public static double code(double x) {
	return 0.0;
}

Python

def code(x):
	return 0.0

Julia

function code(x)
	return 0.0
end

MATLAB

function tmp = code(x)
	tmp = 0.0;
end

Wolfram

code[x_] := 0.0

Derivation

1. Initial program 100.0%

   \[\frac{2}{e^{x} + e^{-x}} \]
2. Add Preprocessing

4. Applied egg-rr 51.2%

   \[\leadsto \color{blue}{0} \]
5. Add Preprocessing

Reproduce

herbie shell --seed 2024137 
(FPCore (x)
  :name "Hyperbolic secant"
  :precision binary64
  (/ 2.0 (+ (exp x) (exp (- x)))))