invcot (example 3.9)

Percentage Accurate: 6.3% → 99.6%

Time: 13.8s

Alternatives: 6

Speedup: 21.0×

Specification

\[-0.026 < x \land x < 0.026\]

Math

\[\begin{array}{l} \\ \frac{1}{x} - \frac{1}{\tan x} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (- (/ 1.0 x) (/ 1.0 (tan x))))

C

double code(double x) {
	return (1.0 / x) - (1.0 / tan(x));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = (1.0d0 / x) - (1.0d0 / tan(x))
end function

Java

public static double code(double x) {
	return (1.0 / x) - (1.0 / Math.tan(x));
}

Python

def code(x):
	return (1.0 / x) - (1.0 / math.tan(x))

Julia

function code(x)
	return Float64(Float64(1.0 / x) - Float64(1.0 / tan(x)))
end

MATLAB

function tmp = code(x)
	tmp = (1.0 / x) - (1.0 / tan(x));
end

Wolfram

code[x_] := N[(N[(1.0 / x), $MachinePrecision] - N[(1.0 / N[Tan[x], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Initial Program: 6.3% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{1}{x} - \frac{1}{\tan x} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (- (/ 1.0 x) (/ 1.0 (tan x))))

C

double code(double x) {
	return (1.0 / x) - (1.0 / tan(x));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = (1.0d0 / x) - (1.0d0 / tan(x))
end function

Java

public static double code(double x) {
	return (1.0 / x) - (1.0 / Math.tan(x));
}

Python

def code(x):
	return (1.0 / x) - (1.0 / math.tan(x))

Julia

function code(x)
	return Float64(Float64(1.0 / x) - Float64(1.0 / tan(x)))
end

MATLAB

function tmp = code(x)
	tmp = (1.0 / x) - (1.0 / tan(x));
end

Wolfram

code[x_] := N[(N[(1.0 / x), $MachinePrecision] - N[(1.0 / N[Tan[x], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Alternative 1: 99.6% accurate, 4.2× speedup

Math

\[\begin{array}{l} \\ -0.1111111111111111 \cdot \frac{x}{0.022222222222222223 \cdot \left(x \cdot x\right) - 0.3333333333333333} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (*
  -0.1111111111111111
  (/ x (- (* 0.022222222222222223 (* x x)) 0.3333333333333333))))

C

double code(double x) {
	return -0.1111111111111111 * (x / ((0.022222222222222223 * (x * x)) - 0.3333333333333333));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = (-0.1111111111111111d0) * (x / ((0.022222222222222223d0 * (x * x)) - 0.3333333333333333d0))
end function

Java

public static double code(double x) {
	return -0.1111111111111111 * (x / ((0.022222222222222223 * (x * x)) - 0.3333333333333333));
}

Python

def code(x):
	return -0.1111111111111111 * (x / ((0.022222222222222223 * (x * x)) - 0.3333333333333333))

Julia

function code(x)
	return Float64(-0.1111111111111111 * Float64(x / Float64(Float64(0.022222222222222223 * Float64(x * x)) - 0.3333333333333333)))
end

MATLAB

function tmp = code(x)
	tmp = -0.1111111111111111 * (x / ((0.022222222222222223 * (x * x)) - 0.3333333333333333));
end

Wolfram

code[x_] := N[(-0.1111111111111111 * N[(x / N[(N[(0.022222222222222223 * N[(x * x), $MachinePrecision]), $MachinePrecision] - 0.3333333333333333), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 6.3%

   \[\frac{1}{x} - \frac{1}{\tan x} \]
2. Add Preprocessing

3. Taylor expanded in x around 0

   \[\leadsto \color{blue}{x \cdot \left(\frac{1}{3} + \frac{1}{45} \cdot {x}^{2}\right)} \]
4. Step-by-step derivation

   1. *-commutative N/A

      \[\leadsto \color{blue}{\left(\frac{1}{3} + \frac{1}{45} \cdot {x}^{2}\right) \cdot x} \]

   2. lower-*.f64 N/A

      \[\leadsto \color{blue}{\left(\frac{1}{3} + \frac{1}{45} \cdot {x}^{2}\right) \cdot x} \]

   3. +-commutative N/A

      \[\leadsto \color{blue}{\left(\frac{1}{45} \cdot {x}^{2} + \frac{1}{3}\right)} \cdot x \]

   4. *-commutative N/A

      \[\leadsto \left(\color{blue}{{x}^{2} \cdot \frac{1}{45}} + \frac{1}{3}\right) \cdot x \]

   5. lower-fma.f64 N/A

      \[\leadsto \color{blue}{\mathsf{fma}\left({x}^{2}, \frac{1}{45}, \frac{1}{3}\right)} \cdot x \]

   6. unpow2 N/A

      \[\leadsto \mathsf{fma}\left(\color{blue}{x \cdot x}, \frac{1}{45}, \frac{1}{3}\right) \cdot x \]

   7. lower-*.f64 99.4

      \[\leadsto \mathsf{fma}\left(\color{blue}{x \cdot x}, 0.022222222222222223, 0.3333333333333333\right) \cdot x \]

5. Applied rewrites 99.4%

   \[\leadsto \color{blue}{\mathsf{fma}\left(x \cdot x, 0.022222222222222223, 0.3333333333333333\right) \cdot x} \]
6. Step-by-step derivation

7. Applied rewrites 99.7%

   \[\leadsto \left(0.0004938271604938272 \cdot {x}^{4} - 0.1111111111111111\right) \cdot \color{blue}{\frac{x}{0.022222222222222223 \cdot \left(x \cdot x\right) - 0.3333333333333333}} \]

8. Taylor expanded in x around 0

   \[\leadsto \frac{-1}{9} \cdot \frac{\color{blue}{x}}{\frac{1}{45} \cdot \left(x \cdot x\right) - \frac{1}{3}} \]
9. Step-by-step derivation

10. Applied rewrites 99.7%

    \[\leadsto -0.1111111111111111 \cdot \frac{\color{blue}{x}}{0.022222222222222223 \cdot \left(x \cdot x\right) - 0.3333333333333333} \]
11. Add Preprocessing

Alternative 2: 99.4% accurate, 7.4× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(x \cdot x, 0.022222222222222223, 0.3333333333333333\right) \cdot x \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (* (fma (* x x) 0.022222222222222223 0.3333333333333333) x))

C

double code(double x) {
	return fma((x * x), 0.022222222222222223, 0.3333333333333333) * x;
}

Julia

function code(x)
	return Float64(fma(Float64(x * x), 0.022222222222222223, 0.3333333333333333) * x)
end

Wolfram

code[x_] := N[(N[(N[(x * x), $MachinePrecision] * 0.022222222222222223 + 0.3333333333333333), $MachinePrecision] * x), $MachinePrecision]

Derivation

1. Initial program 6.3%

   \[\frac{1}{x} - \frac{1}{\tan x} \]
2. Add Preprocessing

3. Taylor expanded in x around 0

   \[\leadsto \color{blue}{x \cdot \left(\frac{1}{3} + \frac{1}{45} \cdot {x}^{2}\right)} \]
4. Step-by-step derivation

   1. *-commutative N/A

      \[\leadsto \color{blue}{\left(\frac{1}{3} + \frac{1}{45} \cdot {x}^{2}\right) \cdot x} \]

   2. lower-*.f64 N/A

      \[\leadsto \color{blue}{\left(\frac{1}{3} + \frac{1}{45} \cdot {x}^{2}\right) \cdot x} \]

   3. +-commutative N/A

      \[\leadsto \color{blue}{\left(\frac{1}{45} \cdot {x}^{2} + \frac{1}{3}\right)} \cdot x \]

   4. *-commutative N/A

      \[\leadsto \left(\color{blue}{{x}^{2} \cdot \frac{1}{45}} + \frac{1}{3}\right) \cdot x \]

   5. lower-fma.f64 N/A

      \[\leadsto \color{blue}{\mathsf{fma}\left({x}^{2}, \frac{1}{45}, \frac{1}{3}\right)} \cdot x \]

   6. unpow2 N/A

      \[\leadsto \mathsf{fma}\left(\color{blue}{x \cdot x}, \frac{1}{45}, \frac{1}{3}\right) \cdot x \]

   7. lower-*.f64 99.4

      \[\leadsto \mathsf{fma}\left(\color{blue}{x \cdot x}, 0.022222222222222223, 0.3333333333333333\right) \cdot x \]

5. Applied rewrites 99.4%

   \[\leadsto \color{blue}{\mathsf{fma}\left(x \cdot x, 0.022222222222222223, 0.3333333333333333\right) \cdot x} \]
6. Add Preprocessing

Alternative 3: 99.1% accurate, 7.4× speedup

Math

\[\begin{array}{l} \\ -0.1111111111111111 \cdot \frac{x}{-0.3333333333333333} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (* -0.1111111111111111 (/ x -0.3333333333333333)))

C

double code(double x) {
	return -0.1111111111111111 * (x / -0.3333333333333333);
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = (-0.1111111111111111d0) * (x / (-0.3333333333333333d0))
end function

Java

public static double code(double x) {
	return -0.1111111111111111 * (x / -0.3333333333333333);
}

Python

def code(x):
	return -0.1111111111111111 * (x / -0.3333333333333333)

Julia

function code(x)
	return Float64(-0.1111111111111111 * Float64(x / -0.3333333333333333))
end

MATLAB

function tmp = code(x)
	tmp = -0.1111111111111111 * (x / -0.3333333333333333);
end

Wolfram

code[x_] := N[(-0.1111111111111111 * N[(x / -0.3333333333333333), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 6.3%

   \[\frac{1}{x} - \frac{1}{\tan x} \]
2. Add Preprocessing

3. Taylor expanded in x around 0

   \[\leadsto \color{blue}{x \cdot \left(\frac{1}{3} + \frac{1}{45} \cdot {x}^{2}\right)} \]
4. Step-by-step derivation

   1. *-commutative N/A

      \[\leadsto \color{blue}{\left(\frac{1}{3} + \frac{1}{45} \cdot {x}^{2}\right) \cdot x} \]

   2. lower-*.f64 N/A

      \[\leadsto \color{blue}{\left(\frac{1}{3} + \frac{1}{45} \cdot {x}^{2}\right) \cdot x} \]

   3. +-commutative N/A

      \[\leadsto \color{blue}{\left(\frac{1}{45} \cdot {x}^{2} + \frac{1}{3}\right)} \cdot x \]

   4. *-commutative N/A

      \[\leadsto \left(\color{blue}{{x}^{2} \cdot \frac{1}{45}} + \frac{1}{3}\right) \cdot x \]

   5. lower-fma.f64 N/A

      \[\leadsto \color{blue}{\mathsf{fma}\left({x}^{2}, \frac{1}{45}, \frac{1}{3}\right)} \cdot x \]

   6. unpow2 N/A

      \[\leadsto \mathsf{fma}\left(\color{blue}{x \cdot x}, \frac{1}{45}, \frac{1}{3}\right) \cdot x \]

   7. lower-*.f64 99.4

      \[\leadsto \mathsf{fma}\left(\color{blue}{x \cdot x}, 0.022222222222222223, 0.3333333333333333\right) \cdot x \]

5. Applied rewrites 99.4%

   \[\leadsto \color{blue}{\mathsf{fma}\left(x \cdot x, 0.022222222222222223, 0.3333333333333333\right) \cdot x} \]
6. Step-by-step derivation

7. Applied rewrites 99.7%

   \[\leadsto \left(0.0004938271604938272 \cdot {x}^{4} - 0.1111111111111111\right) \cdot \color{blue}{\frac{x}{0.022222222222222223 \cdot \left(x \cdot x\right) - 0.3333333333333333}} \]

8. Taylor expanded in x around 0

   \[\leadsto \frac{-1}{9} \cdot \frac{\color{blue}{x}}{\frac{1}{45} \cdot \left(x \cdot x\right) - \frac{1}{3}} \]
9. Step-by-step derivation

10. Applied rewrites 99.7%

    \[\leadsto -0.1111111111111111 \cdot \frac{\color{blue}{x}}{0.022222222222222223 \cdot \left(x \cdot x\right) - 0.3333333333333333} \]

11. Taylor expanded in x around 0

    \[\leadsto \frac{-1}{9} \cdot \frac{x}{\frac{-1}{3}} \]
12. Step-by-step derivation

13. Applied rewrites 99.2%

    \[\leadsto -0.1111111111111111 \cdot \frac{x}{-0.3333333333333333} \]
14. Add Preprocessing

Alternative 4: 99.1% accurate, 7.4× speedup

Math

\[\begin{array}{l} \\ 0.037037037037037035 \cdot \frac{x}{0.1111111111111111} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (* 0.037037037037037035 (/ x 0.1111111111111111)))

C

double code(double x) {
	return 0.037037037037037035 * (x / 0.1111111111111111);
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = 0.037037037037037035d0 * (x / 0.1111111111111111d0)
end function

Java

public static double code(double x) {
	return 0.037037037037037035 * (x / 0.1111111111111111);
}

Python

def code(x):
	return 0.037037037037037035 * (x / 0.1111111111111111)

Julia

function code(x)
	return Float64(0.037037037037037035 * Float64(x / 0.1111111111111111))
end

MATLAB

function tmp = code(x)
	tmp = 0.037037037037037035 * (x / 0.1111111111111111);
end

Wolfram

code[x_] := N[(0.037037037037037035 * N[(x / 0.1111111111111111), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 6.3%

   \[\frac{1}{x} - \frac{1}{\tan x} \]
2. Add Preprocessing

3. Taylor expanded in x around 0

   \[\leadsto \color{blue}{x \cdot \left(\frac{1}{3} + \frac{1}{45} \cdot {x}^{2}\right)} \]
4. Step-by-step derivation

   1. *-commutative N/A

      \[\leadsto \color{blue}{\left(\frac{1}{3} + \frac{1}{45} \cdot {x}^{2}\right) \cdot x} \]

   2. lower-*.f64 N/A

      \[\leadsto \color{blue}{\left(\frac{1}{3} + \frac{1}{45} \cdot {x}^{2}\right) \cdot x} \]

   3. +-commutative N/A

      \[\leadsto \color{blue}{\left(\frac{1}{45} \cdot {x}^{2} + \frac{1}{3}\right)} \cdot x \]

   4. *-commutative N/A

      \[\leadsto \left(\color{blue}{{x}^{2} \cdot \frac{1}{45}} + \frac{1}{3}\right) \cdot x \]

   5. lower-fma.f64 N/A

      \[\leadsto \color{blue}{\mathsf{fma}\left({x}^{2}, \frac{1}{45}, \frac{1}{3}\right)} \cdot x \]

   6. unpow2 N/A

      \[\leadsto \mathsf{fma}\left(\color{blue}{x \cdot x}, \frac{1}{45}, \frac{1}{3}\right) \cdot x \]

   7. lower-*.f64 99.4

      \[\leadsto \mathsf{fma}\left(\color{blue}{x \cdot x}, 0.022222222222222223, 0.3333333333333333\right) \cdot x \]

5. Applied rewrites 99.4%

   \[\leadsto \color{blue}{\mathsf{fma}\left(x \cdot x, 0.022222222222222223, 0.3333333333333333\right) \cdot x} \]
6. Step-by-step derivation

7. Applied rewrites 99.6%

   \[\leadsto \mathsf{fma}\left(1.0973936899862826 \cdot 10^{-5}, {x}^{6}, 0.037037037037037035\right) \cdot \color{blue}{\frac{x}{\mathsf{fma}\left(0.0004938271604938272, {x}^{4}, 0.1111111111111111 - 0.007407407407407408 \cdot \left(x \cdot x\right)\right)}} \]

8. Taylor expanded in x around 0

   \[\leadsto \mathsf{fma}\left(\frac{1}{91125}, {x}^{6}, \frac{1}{27}\right) \cdot \frac{x}{\frac{1}{9}} \]
9. Step-by-step derivation

10. Applied rewrites 99.1%

    \[\leadsto \mathsf{fma}\left(1.0973936899862826 \cdot 10^{-5}, {x}^{6}, 0.037037037037037035\right) \cdot \frac{x}{0.1111111111111111} \]

11. Taylor expanded in x around 0

    \[\leadsto \frac{1}{27} \cdot \frac{\color{blue}{x}}{\frac{1}{9}} \]
12. Step-by-step derivation

13. Applied rewrites 99.1%

    \[\leadsto 0.037037037037037035 \cdot \frac{\color{blue}{x}}{0.1111111111111111} \]
14. Add Preprocessing

Alternative 5: 98.9% accurate, 11.5× speedup

Math

\[\begin{array}{l} \\ -0.1111111111111111 \cdot \left(-3 \cdot x\right) \end{array} \]

FPCore

(FPCore (x) :precision binary64 (* -0.1111111111111111 (* -3.0 x)))

C

double code(double x) {
	return -0.1111111111111111 * (-3.0 * x);
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = (-0.1111111111111111d0) * ((-3.0d0) * x)
end function

Java

public static double code(double x) {
	return -0.1111111111111111 * (-3.0 * x);
}

Python

def code(x):
	return -0.1111111111111111 * (-3.0 * x)

Julia

function code(x)
	return Float64(-0.1111111111111111 * Float64(-3.0 * x))
end

MATLAB

function tmp = code(x)
	tmp = -0.1111111111111111 * (-3.0 * x);
end

Wolfram

code[x_] := N[(-0.1111111111111111 * N[(-3.0 * x), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 6.3%

   \[\frac{1}{x} - \frac{1}{\tan x} \]
2. Add Preprocessing

3. Taylor expanded in x around 0

   \[\leadsto \color{blue}{x \cdot \left(\frac{1}{3} + \frac{1}{45} \cdot {x}^{2}\right)} \]
4. Step-by-step derivation

   1. *-commutative N/A

      \[\leadsto \color{blue}{\left(\frac{1}{3} + \frac{1}{45} \cdot {x}^{2}\right) \cdot x} \]

   2. lower-*.f64 N/A

      \[\leadsto \color{blue}{\left(\frac{1}{3} + \frac{1}{45} \cdot {x}^{2}\right) \cdot x} \]

   3. +-commutative N/A

      \[\leadsto \color{blue}{\left(\frac{1}{45} \cdot {x}^{2} + \frac{1}{3}\right)} \cdot x \]

   4. *-commutative N/A

      \[\leadsto \left(\color{blue}{{x}^{2} \cdot \frac{1}{45}} + \frac{1}{3}\right) \cdot x \]

   5. lower-fma.f64 N/A

      \[\leadsto \color{blue}{\mathsf{fma}\left({x}^{2}, \frac{1}{45}, \frac{1}{3}\right)} \cdot x \]

   6. unpow2 N/A

      \[\leadsto \mathsf{fma}\left(\color{blue}{x \cdot x}, \frac{1}{45}, \frac{1}{3}\right) \cdot x \]

   7. lower-*.f64 99.4

      \[\leadsto \mathsf{fma}\left(\color{blue}{x \cdot x}, 0.022222222222222223, 0.3333333333333333\right) \cdot x \]

5. Applied rewrites 99.4%

   \[\leadsto \color{blue}{\mathsf{fma}\left(x \cdot x, 0.022222222222222223, 0.3333333333333333\right) \cdot x} \]
6. Step-by-step derivation

7. Applied rewrites 99.7%

   \[\leadsto \left(0.0004938271604938272 \cdot {x}^{4} - 0.1111111111111111\right) \cdot \color{blue}{\frac{x}{0.022222222222222223 \cdot \left(x \cdot x\right) - 0.3333333333333333}} \]

8. Taylor expanded in x around 0

   \[\leadsto \frac{-1}{9} \cdot \frac{\color{blue}{x}}{\frac{1}{45} \cdot \left(x \cdot x\right) - \frac{1}{3}} \]
9. Step-by-step derivation

10. Applied rewrites 99.7%

    \[\leadsto -0.1111111111111111 \cdot \frac{\color{blue}{x}}{0.022222222222222223 \cdot \left(x \cdot x\right) - 0.3333333333333333} \]

11. Taylor expanded in x around 0

    \[\leadsto \frac{-1}{9} \cdot \left(-3 \cdot \color{blue}{x}\right) \]
12. Step-by-step derivation

13. Applied rewrites 99.0%

    \[\leadsto -0.1111111111111111 \cdot \left(-3 \cdot \color{blue}{x}\right) \]
14. Add Preprocessing

Alternative 6: 98.9% accurate, 21.0× speedup

Math

\[\begin{array}{l} \\ 0.3333333333333333 \cdot x \end{array} \]

FPCore

(FPCore (x) :precision binary64 (* 0.3333333333333333 x))

C

double code(double x) {
	return 0.3333333333333333 * x;
}

Fortran

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

Java

public static double code(double x) {
	return 0.3333333333333333 * x;
}

Python

def code(x):
	return 0.3333333333333333 * x

Julia

function code(x)
	return Float64(0.3333333333333333 * x)
end

MATLAB

function tmp = code(x)
	tmp = 0.3333333333333333 * x;
end

Wolfram

code[x_] := N[(0.3333333333333333 * x), $MachinePrecision]

Derivation

1. Initial program 6.3%

   \[\frac{1}{x} - \frac{1}{\tan x} \]
2. Add Preprocessing

3. Taylor expanded in x around 0

   \[\leadsto \color{blue}{\frac{1}{3} \cdot x} \]
4. Step-by-step derivation

   1. lower-*.f64 99.0

      \[\leadsto \color{blue}{0.3333333333333333 \cdot x} \]

5. Applied rewrites 99.0%

   \[\leadsto \color{blue}{0.3333333333333333 \cdot x} \]
6. Add Preprocessing

Developer Target 1: 99.9% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\left|x\right| < 0.026:\\ \;\;\;\;\frac{x}{3} \cdot \left(1 + \frac{x \cdot x}{15}\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{x} - \frac{1}{\tan x}\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (< (fabs x) 0.026)
   (* (/ x 3.0) (+ 1.0 (/ (* x x) 15.0)))
   (- (/ 1.0 x) (/ 1.0 (tan x)))))

C

double code(double x) {
	double tmp;
	if (fabs(x) < 0.026) {
		tmp = (x / 3.0) * (1.0 + ((x * x) / 15.0));
	} else {
		tmp = (1.0 / x) - (1.0 / tan(x));
	}
	return tmp;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: tmp
    if (abs(x) < 0.026d0) then
        tmp = (x / 3.0d0) * (1.0d0 + ((x * x) / 15.0d0))
    else
        tmp = (1.0d0 / x) - (1.0d0 / tan(x))
    end if
    code = tmp
end function

Java

public static double code(double x) {
	double tmp;
	if (Math.abs(x) < 0.026) {
		tmp = (x / 3.0) * (1.0 + ((x * x) / 15.0));
	} else {
		tmp = (1.0 / x) - (1.0 / Math.tan(x));
	}
	return tmp;
}

Python

def code(x):
	tmp = 0
	if math.fabs(x) < 0.026:
		tmp = (x / 3.0) * (1.0 + ((x * x) / 15.0))
	else:
		tmp = (1.0 / x) - (1.0 / math.tan(x))
	return tmp

Julia

function code(x)
	tmp = 0.0
	if (abs(x) < 0.026)
		tmp = Float64(Float64(x / 3.0) * Float64(1.0 + Float64(Float64(x * x) / 15.0)));
	else
		tmp = Float64(Float64(1.0 / x) - Float64(1.0 / tan(x)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x)
	tmp = 0.0;
	if (abs(x) < 0.026)
		tmp = (x / 3.0) * (1.0 + ((x * x) / 15.0));
	else
		tmp = (1.0 / x) - (1.0 / tan(x));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := If[Less[N[Abs[x], $MachinePrecision], 0.026], N[(N[(x / 3.0), $MachinePrecision] * N[(1.0 + N[(N[(x * x), $MachinePrecision] / 15.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(1.0 / x), $MachinePrecision] - N[(1.0 / N[Tan[x], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Reproduce

herbie shell --seed 2024339 
(FPCore (x)
  :name "invcot (example 3.9)"
  :precision binary64
  :pre (and (< -0.026 x) (< x 0.026))

  :alt
  (! :herbie-platform default (if (< (fabs x) 13/500) (* (/ x 3) (+ 1 (/ (* x x) 15))) (- (/ 1 x) (/ 1 (tan x)))))

  (- (/ 1.0 x) (/ 1.0 (tan x))))