sintan (problem 3.4.5)

Average Accuracy: 51.4% → 100.0%

Time: 19.0s

Precision: binary64

Cost: 13768

Math

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

↓

\[\begin{array}{l} t_0 := \tan x - x\\ t_1 := \sin x - x\\ \mathbf{if}\;x \leq -0.025:\\ \;\;\;\;\frac{1}{\frac{t_0}{t_1}}\\ \mathbf{elif}\;x \leq 0.025:\\ \;\;\;\;\left(x \cdot x\right) \cdot \left(\left(x \cdot x\right) \cdot -0.009642857142857142 + 0.225\right) + -0.5\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{1}{t_0}}{\frac{1}{t_1}}\\ \end{array} \]

FPCore

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

↓

(FPCore (x)
 :precision binary64
 (let* ((t_0 (- (tan x) x)) (t_1 (- (sin x) x)))
   (if (<= x -0.025)
     (/ 1.0 (/ t_0 t_1))
     (if (<= x 0.025)
       (+ (* (* x x) (+ (* (* x x) -0.009642857142857142) 0.225)) -0.5)
       (/ (/ 1.0 t_0) (/ 1.0 t_1))))))

C

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

↓

double code(double x) {
	double t_0 = tan(x) - x;
	double t_1 = sin(x) - x;
	double tmp;
	if (x <= -0.025) {
		tmp = 1.0 / (t_0 / t_1);
	} else if (x <= 0.025) {
		tmp = ((x * x) * (((x * x) * -0.009642857142857142) + 0.225)) + -0.5;
	} else {
		tmp = (1.0 / t_0) / (1.0 / t_1);
	}
	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) :: t_1
    real(8) :: tmp
    t_0 = tan(x) - x
    t_1 = sin(x) - x
    if (x <= (-0.025d0)) then
        tmp = 1.0d0 / (t_0 / t_1)
    else if (x <= 0.025d0) then
        tmp = ((x * x) * (((x * x) * (-0.009642857142857142d0)) + 0.225d0)) + (-0.5d0)
    else
        tmp = (1.0d0 / t_0) / (1.0d0 / t_1)
    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 = Math.tan(x) - x;
	double t_1 = Math.sin(x) - x;
	double tmp;
	if (x <= -0.025) {
		tmp = 1.0 / (t_0 / t_1);
	} else if (x <= 0.025) {
		tmp = ((x * x) * (((x * x) * -0.009642857142857142) + 0.225)) + -0.5;
	} else {
		tmp = (1.0 / t_0) / (1.0 / t_1);
	}
	return tmp;
}

Python

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

↓

def code(x):
	t_0 = math.tan(x) - x
	t_1 = math.sin(x) - x
	tmp = 0
	if x <= -0.025:
		tmp = 1.0 / (t_0 / t_1)
	elif x <= 0.025:
		tmp = ((x * x) * (((x * x) * -0.009642857142857142) + 0.225)) + -0.5
	else:
		tmp = (1.0 / t_0) / (1.0 / t_1)
	return tmp

Julia

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

↓

function code(x)
	t_0 = Float64(tan(x) - x)
	t_1 = Float64(sin(x) - x)
	tmp = 0.0
	if (x <= -0.025)
		tmp = Float64(1.0 / Float64(t_0 / t_1));
	elseif (x <= 0.025)
		tmp = Float64(Float64(Float64(x * x) * Float64(Float64(Float64(x * x) * -0.009642857142857142) + 0.225)) + -0.5);
	else
		tmp = Float64(Float64(1.0 / t_0) / Float64(1.0 / t_1));
	end
	return tmp
end

MATLAB

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

↓

function tmp_2 = code(x)
	t_0 = tan(x) - x;
	t_1 = sin(x) - x;
	tmp = 0.0;
	if (x <= -0.025)
		tmp = 1.0 / (t_0 / t_1);
	elseif (x <= 0.025)
		tmp = ((x * x) * (((x * x) * -0.009642857142857142) + 0.225)) + -0.5;
	else
		tmp = (1.0 / t_0) / (1.0 / t_1);
	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[(N[Tan[x], $MachinePrecision] - x), $MachinePrecision]}, Block[{t$95$1 = N[(N[Sin[x], $MachinePrecision] - x), $MachinePrecision]}, If[LessEqual[x, -0.025], N[(1.0 / N[(t$95$0 / t$95$1), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 0.025], N[(N[(N[(x * x), $MachinePrecision] * N[(N[(N[(x * x), $MachinePrecision] * -0.009642857142857142), $MachinePrecision] + 0.225), $MachinePrecision]), $MachinePrecision] + -0.5), $MachinePrecision], N[(N[(1.0 / t$95$0), $MachinePrecision] / N[(1.0 / t$95$1), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

1. Split input into 3 regimes

2. if x < -0.025000000000000001

   1. Initial program 99.9%

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

   2. Simplified 99.9%

      \[\leadsto \color{blue}{\frac{\sin x - x}{\tan x - x}} \]
      Proof

      [Start] 99.9	\[ \frac{x - \sin x}{x - \tan x} \]
      sub-neg [=>] 99.9	\[ \frac{\color{blue}{x + \left(-\sin x\right)}}{x - \tan x} \]
      +-commutative [=>] 99.9	\[ \frac{\color{blue}{\left(-\sin x\right) + x}}{x - \tan x} \]
      neg-sub0 [=>] 99.9	\[ \frac{\color{blue}{\left(0 - \sin x\right)} + x}{x - \tan x} \]
      associate-+l- [=>] 99.9	\[ \frac{\color{blue}{0 - \left(\sin x - x\right)}}{x - \tan x} \]
      sub0-neg [=>] 99.9	\[ \frac{\color{blue}{-\left(\sin x - x\right)}}{x - \tan x} \]
      neg-mul-1 [=>] 99.9	\[ \frac{\color{blue}{-1 \cdot \left(\sin x - x\right)}}{x - \tan x} \]
      sub-neg [=>] 99.9	\[ \frac{-1 \cdot \left(\sin x - x\right)}{\color{blue}{x + \left(-\tan x\right)}} \]
      +-commutative [=>] 99.9	\[ \frac{-1 \cdot \left(\sin x - x\right)}{\color{blue}{\left(-\tan x\right) + x}} \]
      neg-sub0 [=>] 99.9	\[ \frac{-1 \cdot \left(\sin x - x\right)}{\color{blue}{\left(0 - \tan x\right)} + x} \]
      associate-+l- [=>] 99.9	\[ \frac{-1 \cdot \left(\sin x - x\right)}{\color{blue}{0 - \left(\tan x - x\right)}} \]
      sub0-neg [=>] 99.9	\[ \frac{-1 \cdot \left(\sin x - x\right)}{\color{blue}{-\left(\tan x - x\right)}} \]
      neg-mul-1 [=>] 99.9	\[ \frac{-1 \cdot \left(\sin x - x\right)}{\color{blue}{-1 \cdot \left(\tan x - x\right)}} \]
      times-frac [=>] 99.9	\[ \color{blue}{\frac{-1}{-1} \cdot \frac{\sin x - x}{\tan x - x}} \]
      metadata-eval [=>] 99.9	\[ \color{blue}{1} \cdot \frac{\sin x - x}{\tan x - x} \]
      *-lft-identity [=>] 99.9	\[ \color{blue}{\frac{\sin x - x}{\tan x - x}} \]

   3. Applied egg-rr 99.7%

      \[\leadsto \color{blue}{\frac{1}{\tan x - x} \cdot \left(\sin x - x\right)} \]

   4. Applied egg-rr 99.9%

      \[\leadsto \color{blue}{\frac{1}{\frac{\tan x - x}{\sin x - x}}} \]

   if -0.025000000000000001 < x < 0.025000000000000001

   1. Initial program 1.5%

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

   2. Simplified 1.5%

      \[\leadsto \color{blue}{\frac{\sin x - x}{\tan x - x}} \]
      Proof

      [Start] 1.5	\[ \frac{x - \sin x}{x - \tan x} \]
      sub-neg [=>] 1.5	\[ \frac{\color{blue}{x + \left(-\sin x\right)}}{x - \tan x} \]
      +-commutative [=>] 1.5	\[ \frac{\color{blue}{\left(-\sin x\right) + x}}{x - \tan x} \]
      neg-sub0 [=>] 1.5	\[ \frac{\color{blue}{\left(0 - \sin x\right)} + x}{x - \tan x} \]
      associate-+l- [=>] 1.5	\[ \frac{\color{blue}{0 - \left(\sin x - x\right)}}{x - \tan x} \]
      sub0-neg [=>] 1.5	\[ \frac{\color{blue}{-\left(\sin x - x\right)}}{x - \tan x} \]
      neg-mul-1 [=>] 1.5	\[ \frac{\color{blue}{-1 \cdot \left(\sin x - x\right)}}{x - \tan x} \]
      sub-neg [=>] 1.5	\[ \frac{-1 \cdot \left(\sin x - x\right)}{\color{blue}{x + \left(-\tan x\right)}} \]
      +-commutative [=>] 1.5	\[ \frac{-1 \cdot \left(\sin x - x\right)}{\color{blue}{\left(-\tan x\right) + x}} \]
      neg-sub0 [=>] 1.5	\[ \frac{-1 \cdot \left(\sin x - x\right)}{\color{blue}{\left(0 - \tan x\right)} + x} \]
      associate-+l- [=>] 1.5	\[ \frac{-1 \cdot \left(\sin x - x\right)}{\color{blue}{0 - \left(\tan x - x\right)}} \]
      sub0-neg [=>] 1.5	\[ \frac{-1 \cdot \left(\sin x - x\right)}{\color{blue}{-\left(\tan x - x\right)}} \]
      neg-mul-1 [=>] 1.5	\[ \frac{-1 \cdot \left(\sin x - x\right)}{\color{blue}{-1 \cdot \left(\tan x - x\right)}} \]
      times-frac [=>] 1.5	\[ \color{blue}{\frac{-1}{-1} \cdot \frac{\sin x - x}{\tan x - x}} \]
      metadata-eval [=>] 1.5	\[ \color{blue}{1} \cdot \frac{\sin x - x}{\tan x - x} \]
      *-lft-identity [=>] 1.5	\[ \color{blue}{\frac{\sin x - x}{\tan x - x}} \]

   3. Taylor expanded in x around 0 100.0%

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

   4. Simplified 100.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(0.225, x \cdot x, -0.009642857142857142 \cdot {x}^{4}\right) + -0.5} \]
      Proof

      [Start] 100.0	\[ \left(0.225 \cdot {x}^{2} + -0.009642857142857142 \cdot {x}^{4}\right) - 0.5 \]
      sub-neg [=>] 100.0	\[ \color{blue}{\left(0.225 \cdot {x}^{2} + -0.009642857142857142 \cdot {x}^{4}\right) + \left(-0.5\right)} \]
      unpow2 [=>] 100.0	\[ \left(0.225 \cdot \color{blue}{\left(x \cdot x\right)} + -0.009642857142857142 \cdot {x}^{4}\right) + \left(-0.5\right) \]
      fma-def [=>] 100.0	\[ \color{blue}{\mathsf{fma}\left(0.225, x \cdot x, -0.009642857142857142 \cdot {x}^{4}\right)} + \left(-0.5\right) \]
      metadata-eval [=>] 100.0	\[ \mathsf{fma}\left(0.225, x \cdot x, -0.009642857142857142 \cdot {x}^{4}\right) + \color{blue}{-0.5} \]

   5. Applied egg-rr 100.0%

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

   6. Applied egg-rr 100.0%

      \[\leadsto \color{blue}{\left(x \cdot x\right) \cdot \left(-0.009642857142857142 \cdot \left(x \cdot x\right) + 0.225\right)} + -0.5 \]

   if 0.025000000000000001 < x

   1. Initial program 99.9%

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

   2. Simplified 99.9%

      \[\leadsto \color{blue}{\frac{\sin x - x}{\tan x - x}} \]
      Proof

      [Start] 99.9	\[ \frac{x - \sin x}{x - \tan x} \]
      sub-neg [=>] 99.9	\[ \frac{\color{blue}{x + \left(-\sin x\right)}}{x - \tan x} \]
      +-commutative [=>] 99.9	\[ \frac{\color{blue}{\left(-\sin x\right) + x}}{x - \tan x} \]
      neg-sub0 [=>] 99.9	\[ \frac{\color{blue}{\left(0 - \sin x\right)} + x}{x - \tan x} \]
      associate-+l- [=>] 99.9	\[ \frac{\color{blue}{0 - \left(\sin x - x\right)}}{x - \tan x} \]
      sub0-neg [=>] 99.9	\[ \frac{\color{blue}{-\left(\sin x - x\right)}}{x - \tan x} \]
      neg-mul-1 [=>] 99.9	\[ \frac{\color{blue}{-1 \cdot \left(\sin x - x\right)}}{x - \tan x} \]
      sub-neg [=>] 99.9	\[ \frac{-1 \cdot \left(\sin x - x\right)}{\color{blue}{x + \left(-\tan x\right)}} \]
      +-commutative [=>] 99.9	\[ \frac{-1 \cdot \left(\sin x - x\right)}{\color{blue}{\left(-\tan x\right) + x}} \]
      neg-sub0 [=>] 99.9	\[ \frac{-1 \cdot \left(\sin x - x\right)}{\color{blue}{\left(0 - \tan x\right)} + x} \]
      associate-+l- [=>] 99.9	\[ \frac{-1 \cdot \left(\sin x - x\right)}{\color{blue}{0 - \left(\tan x - x\right)}} \]
      sub0-neg [=>] 99.9	\[ \frac{-1 \cdot \left(\sin x - x\right)}{\color{blue}{-\left(\tan x - x\right)}} \]
      neg-mul-1 [=>] 99.9	\[ \frac{-1 \cdot \left(\sin x - x\right)}{\color{blue}{-1 \cdot \left(\tan x - x\right)}} \]
      times-frac [=>] 99.9	\[ \color{blue}{\frac{-1}{-1} \cdot \frac{\sin x - x}{\tan x - x}} \]
      metadata-eval [=>] 99.9	\[ \color{blue}{1} \cdot \frac{\sin x - x}{\tan x - x} \]
      *-lft-identity [=>] 99.9	\[ \color{blue}{\frac{\sin x - x}{\tan x - x}} \]

   3. Applied egg-rr 99.7%

      \[\leadsto \color{blue}{\frac{1}{\tan x - x} \cdot \left(\sin x - x\right)} \]

   4. Applied egg-rr 99.9%

      \[\leadsto \color{blue}{\frac{\frac{1}{\tan x - x}}{\frac{1}{\sin x - x}}} \]
3. Recombined 3 regimes into one program.

4. Final simplification 100.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -0.025:\\ \;\;\;\;\frac{1}{\frac{\tan x - x}{\sin x - x}}\\ \mathbf{elif}\;x \leq 0.025:\\ \;\;\;\;\left(x \cdot x\right) \cdot \left(\left(x \cdot x\right) \cdot -0.009642857142857142 + 0.225\right) + -0.5\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{1}{\tan x - x}}{\frac{1}{\sin x - x}}\\ \end{array} \]

Alternatives

Alternative 1
Accuracy	100.0%
Cost	13513

\[\begin{array}{l} \mathbf{if}\;x \leq -0.025 \lor \neg \left(x \leq 0.025\right):\\ \;\;\;\;\frac{x - \sin x}{x - \tan x}\\ \mathbf{else}:\\ \;\;\;\;\left(x \cdot x\right) \cdot \left(\left(x \cdot x\right) \cdot -0.009642857142857142 + 0.225\right) + -0.5\\ \end{array} \]

Alternative 2
Accuracy	100.0%
Cost	13512

\[\begin{array}{l} \mathbf{if}\;x \leq -0.025:\\ \;\;\;\;\frac{1}{\frac{\tan x - x}{\sin x - x}}\\ \mathbf{elif}\;x \leq 0.025:\\ \;\;\;\;\left(x \cdot x\right) \cdot \left(\left(x \cdot x\right) \cdot -0.009642857142857142 + 0.225\right) + -0.5\\ \mathbf{else}:\\ \;\;\;\;\frac{x - \sin x}{x - \tan x}\\ \end{array} \]

Alternative 3
Accuracy	98.8%
Cost	6985

\[\begin{array}{l} \mathbf{if}\;x \leq -2.8 \lor \neg \left(x \leq 2.8\right):\\ \;\;\;\;\frac{x}{x - \tan x}\\ \mathbf{else}:\\ \;\;\;\;\left(x \cdot x\right) \cdot \left(\left(x \cdot x\right) \cdot -0.009642857142857142 + 0.225\right) + -0.5\\ \end{array} \]

Alternative 4
Accuracy	98.8%
Cost	6984

\[\begin{array}{l} t_0 := x - \tan x\\ \mathbf{if}\;x \leq -2.8:\\ \;\;\;\;\frac{1}{\frac{t_0}{x}}\\ \mathbf{elif}\;x \leq 2.8:\\ \;\;\;\;\left(x \cdot x\right) \cdot \left(\left(x \cdot x\right) \cdot -0.009642857142857142 + 0.225\right) + -0.5\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{t_0}\\ \end{array} \]

Alternative 5
Accuracy	98.8%
Cost	1096

\[\begin{array}{l} \mathbf{if}\;x \leq -2.9:\\ \;\;\;\;1\\ \mathbf{elif}\;x \leq 2.9:\\ \;\;\;\;\left(x \cdot x\right) \cdot \left(\left(x \cdot x\right) \cdot -0.009642857142857142 + 0.225\right) + -0.5\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \]

Alternative 6
Accuracy	98.7%
Cost	712

\[\begin{array}{l} \mathbf{if}\;x \leq -2.6:\\ \;\;\;\;1\\ \mathbf{elif}\;x \leq 4.5:\\ \;\;\;\;-0.5 + x \cdot \left(x \cdot 0.225\right)\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \]

Alternative 7
Accuracy	98.4%
Cost	328

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

Alternative 8
Accuracy	49.8%
Cost	64

\[-0.5 \]

Reproduce

herbie shell --seed 2023141 
(FPCore (x)
  :name "sintan (problem 3.4.5)"
  :precision binary64
  (/ (- x (sin x)) (- x (tan x))))