sintan (problem 3.4.5)

Average Error: 31.0 → 0.0

Time: 22.9s

Precision: binary64

Cost: 20488

Math

\[\frac{x - \sin x}{x - \tan x} \]

↓

\[\begin{array}{l} t_0 := x - \sin x\\ \mathbf{if}\;x \leq -0.092:\\ \;\;\;\;\frac{t_0}{x - \tan x}\\ \mathbf{elif}\;x \leq 0.095:\\ \;\;\;\;\left(0.225 \cdot {x}^{2} + \left(-0.009642857142857142 \cdot {x}^{4} + 0.00024107142857142857 \cdot {x}^{6}\right)\right) - 0.5\\ \mathbf{else}:\\ \;\;\;\;\frac{t_0}{\left(x + \tan x\right) + \left(-\tan x \cdot 2\right)}\\ \end{array} \]

FPCore

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

↓

(FPCore (x)
 :precision binary64
 (let* ((t_0 (- x (sin x))))
   (if (<= x -0.092)
     (/ t_0 (- x (tan x)))
     (if (<= x 0.095)
       (-
        (+
         (* 0.225 (pow x 2.0))
         (+
          (* -0.009642857142857142 (pow x 4.0))
          (* 0.00024107142857142857 (pow x 6.0))))
        0.5)
       (/ t_0 (+ (+ x (tan x)) (- (* (tan x) 2.0))))))))

C

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

↓

double code(double x) {
	double t_0 = x - sin(x);
	double tmp;
	if (x <= -0.092) {
		tmp = t_0 / (x - tan(x));
	} else if (x <= 0.095) {
		tmp = ((0.225 * pow(x, 2.0)) + ((-0.009642857142857142 * pow(x, 4.0)) + (0.00024107142857142857 * pow(x, 6.0)))) - 0.5;
	} else {
		tmp = t_0 / ((x + tan(x)) + -(tan(x) * 2.0));
	}
	return tmp;
}

Fortran

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

↓

real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: t_0
    real(8) :: tmp
    t_0 = x - sin(x)
    if (x <= (-0.092d0)) then
        tmp = t_0 / (x - tan(x))
    else if (x <= 0.095d0) then
        tmp = ((0.225d0 * (x ** 2.0d0)) + (((-0.009642857142857142d0) * (x ** 4.0d0)) + (0.00024107142857142857d0 * (x ** 6.0d0)))) - 0.5d0
    else
        tmp = t_0 / ((x + tan(x)) + -(tan(x) * 2.0d0))
    end if
    code = tmp
end function

Java

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

↓

public static double code(double x) {
	double t_0 = x - Math.sin(x);
	double tmp;
	if (x <= -0.092) {
		tmp = t_0 / (x - Math.tan(x));
	} else if (x <= 0.095) {
		tmp = ((0.225 * Math.pow(x, 2.0)) + ((-0.009642857142857142 * Math.pow(x, 4.0)) + (0.00024107142857142857 * Math.pow(x, 6.0)))) - 0.5;
	} else {
		tmp = t_0 / ((x + Math.tan(x)) + -(Math.tan(x) * 2.0));
	}
	return tmp;
}

Python

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

↓

def code(x):
	t_0 = x - math.sin(x)
	tmp = 0
	if x <= -0.092:
		tmp = t_0 / (x - math.tan(x))
	elif x <= 0.095:
		tmp = ((0.225 * math.pow(x, 2.0)) + ((-0.009642857142857142 * math.pow(x, 4.0)) + (0.00024107142857142857 * math.pow(x, 6.0)))) - 0.5
	else:
		tmp = t_0 / ((x + math.tan(x)) + -(math.tan(x) * 2.0))
	return tmp

Julia

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

↓

function code(x)
	t_0 = Float64(x - sin(x))
	tmp = 0.0
	if (x <= -0.092)
		tmp = Float64(t_0 / Float64(x - tan(x)));
	elseif (x <= 0.095)
		tmp = Float64(Float64(Float64(0.225 * (x ^ 2.0)) + Float64(Float64(-0.009642857142857142 * (x ^ 4.0)) + Float64(0.00024107142857142857 * (x ^ 6.0)))) - 0.5);
	else
		tmp = Float64(t_0 / Float64(Float64(x + tan(x)) + Float64(-Float64(tan(x) * 2.0))));
	end
	return tmp
end

MATLAB

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

↓

function tmp_2 = code(x)
	t_0 = x - sin(x);
	tmp = 0.0;
	if (x <= -0.092)
		tmp = t_0 / (x - tan(x));
	elseif (x <= 0.095)
		tmp = ((0.225 * (x ^ 2.0)) + ((-0.009642857142857142 * (x ^ 4.0)) + (0.00024107142857142857 * (x ^ 6.0)))) - 0.5;
	else
		tmp = t_0 / ((x + tan(x)) + -(tan(x) * 2.0));
	end
	tmp_2 = tmp;
end

Wolfram

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

↓

code[x_] := Block[{t$95$0 = N[(x - N[Sin[x], $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -0.092], N[(t$95$0 / N[(x - N[Tan[x], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 0.095], N[(N[(N[(0.225 * N[Power[x, 2.0], $MachinePrecision]), $MachinePrecision] + N[(N[(-0.009642857142857142 * N[Power[x, 4.0], $MachinePrecision]), $MachinePrecision] + N[(0.00024107142857142857 * N[Power[x, 6.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - 0.5), $MachinePrecision], N[(t$95$0 / N[(N[(x + N[Tan[x], $MachinePrecision]), $MachinePrecision] + (-N[(N[Tan[x], $MachinePrecision] * 2.0), $MachinePrecision])), $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if x < -0.091999999999999998

   1. Initial program 0.0

      \[\frac{x - \sin x}{x - \tan x} \]

   if -0.091999999999999998 < x < 0.095000000000000001

   1. Initial program 63.2

      \[\frac{x - \sin x}{x - \tan x} \]

   2. Taylor expanded in x around 0 0.0

      \[\leadsto \color{blue}{\left(0.225 \cdot {x}^{2} + \left(-0.009642857142857142 \cdot {x}^{4} + 0.00024107142857142857 \cdot {x}^{6}\right)\right) - 0.5} \]

   if 0.095000000000000001 < x

   1. Initial program 0.0

      \[\frac{x - \sin x}{x - \tan x} \]

   2. Applied egg-rr 0.0

      \[\leadsto \frac{x - \sin x}{\color{blue}{\left(x + \tan x\right) + \left(-\tan x \cdot 2\right)}} \]
3. Recombined 3 regimes into one program.

4. Final simplification 0.0

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -0.092:\\ \;\;\;\;\frac{x - \sin x}{x - \tan x}\\ \mathbf{elif}\;x \leq 0.095:\\ \;\;\;\;\left(0.225 \cdot {x}^{2} + \left(-0.009642857142857142 \cdot {x}^{4} + 0.00024107142857142857 \cdot {x}^{6}\right)\right) - 0.5\\ \mathbf{else}:\\ \;\;\;\;\frac{x - \sin x}{\left(x + \tan x\right) + \left(-\tan x \cdot 2\right)}\\ \end{array} \]

Alternatives

Alternative 1
Error	0.0
Cost	20232

\[\begin{array}{l} t_0 := x - \sin x\\ \mathbf{if}\;x \leq -0.026:\\ \;\;\;\;\frac{t_0}{x - \tan x}\\ \mathbf{elif}\;x \leq 0.029:\\ \;\;\;\;\left(0.225 \cdot {x}^{2} + -0.009642857142857142 \cdot {x}^{4}\right) - 0.5\\ \mathbf{else}:\\ \;\;\;\;\frac{t_0}{\left(x + \tan x\right) + \left(-\tan x \cdot 2\right)}\\ \end{array} \]

Alternative 2
Error	0.0
Cost	13768

\[\begin{array}{l} t_0 := \frac{x - \sin x}{x - \tan x}\\ \mathbf{if}\;x \leq -0.026:\\ \;\;\;\;t_0\\ \mathbf{elif}\;x \leq 0.0275:\\ \;\;\;\;\left(0.225 \cdot {x}^{2} + -0.009642857142857142 \cdot {x}^{4}\right) - 0.5\\ \mathbf{else}:\\ \;\;\;\;t_0\\ \end{array} \]

Alternative 3
Error	0.1
Cost	13512

\[\begin{array}{l} t_0 := \frac{x - \sin x}{x - \tan x}\\ \mathbf{if}\;x \leq -0.005:\\ \;\;\;\;t_0\\ \mathbf{elif}\;x \leq 0.0052:\\ \;\;\;\;0.225 \cdot {x}^{2} - 0.5\\ \mathbf{else}:\\ \;\;\;\;t_0\\ \end{array} \]

Alternative 4
Error	0.8
Cost	7048

\[\begin{array}{l} \mathbf{if}\;x \leq -2.6:\\ \;\;\;\;1\\ \mathbf{elif}\;x \leq 2.3:\\ \;\;\;\;0.225 \cdot {x}^{2} - 0.5\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{x - \tan x}\\ \end{array} \]

Alternative 5
Error	1.0
Cost	6984

\[\begin{array}{l} \mathbf{if}\;x \leq -1.55:\\ \;\;\;\;1\\ \mathbf{elif}\;x \leq 1.3:\\ \;\;\;\;-0.5\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{x - \tan x}\\ \end{array} \]

Alternative 6
Error	1.0
Cost	328

\[\begin{array}{l} \mathbf{if}\;x \leq -1.55:\\ \;\;\;\;1\\ \mathbf{elif}\;x \leq 1.6:\\ \;\;\;\;-0.5\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \]

Alternative 7
Error	32.3
Cost	64

\[-0.5 \]

Reproduce

herbie shell --seed 2023094 
(FPCore (x)
  :name "sintan (problem 3.4.5)"
  :precision binary64
  (/ (- x (sin x)) (- x (tan x))))