Diagrams.TwoD.Apollonian:initialConfig from diagrams-contrib-1.3.0.5, B

Percentage Accurate: 68.1% → 99.4%

Time: 9.8s

Precision: binary64

Cost: 836

• Unsound rule application detected in e-graph. Results may not be sound.

Math

\[x \cdot \sqrt{y \cdot y - z \cdot z} \]

↓

\[\begin{array}{l} \mathbf{if}\;y \leq -4.1 \cdot 10^{-277}:\\ \;\;\;\;x \cdot \left(0.5 \cdot \frac{z}{\frac{y}{z}} - y\right)\\ \mathbf{else}:\\ \;\;\;\;y \cdot x\\ \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (* x (sqrt (- (* y y) (* z z)))))

↓

(FPCore (x y z)
 :precision binary64
 (if (<= y -4.1e-277) (* x (- (* 0.5 (/ z (/ y z))) y)) (* y x)))

C

double code(double x, double y, double z) {
	return x * sqrt(((y * y) - (z * z)));
}

↓

double code(double x, double y, double z) {
	double tmp;
	if (y <= -4.1e-277) {
		tmp = x * ((0.5 * (z / (y / z))) - y);
	} else {
		tmp = y * x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = x * sqrt(((y * y) - (z * z)))
end function

↓

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (y <= (-4.1d-277)) then
        tmp = x * ((0.5d0 * (z / (y / z))) - y)
    else
        tmp = y * x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	return x * Math.sqrt(((y * y) - (z * z)));
}

↓

public static double code(double x, double y, double z) {
	double tmp;
	if (y <= -4.1e-277) {
		tmp = x * ((0.5 * (z / (y / z))) - y);
	} else {
		tmp = y * x;
	}
	return tmp;
}

Python

def code(x, y, z):
	return x * math.sqrt(((y * y) - (z * z)))

↓

def code(x, y, z):
	tmp = 0
	if y <= -4.1e-277:
		tmp = x * ((0.5 * (z / (y / z))) - y)
	else:
		tmp = y * x
	return tmp

Julia

function code(x, y, z)
	return Float64(x * sqrt(Float64(Float64(y * y) - Float64(z * z))))
end

↓

function code(x, y, z)
	tmp = 0.0
	if (y <= -4.1e-277)
		tmp = Float64(x * Float64(Float64(0.5 * Float64(z / Float64(y / z))) - y));
	else
		tmp = Float64(y * x);
	end
	return tmp
end

MATLAB

function tmp = code(x, y, z)
	tmp = x * sqrt(((y * y) - (z * z)));
end

↓

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (y <= -4.1e-277)
		tmp = x * ((0.5 * (z / (y / z))) - y);
	else
		tmp = y * x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := N[(x * N[Sqrt[N[(N[(y * y), $MachinePrecision] - N[(z * z), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]

↓

code[x_, y_, z_] := If[LessEqual[y, -4.1e-277], N[(x * N[(N[(0.5 * N[(z / N[(y / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - y), $MachinePrecision]), $MachinePrecision], N[(y * x), $MachinePrecision]]

Target

Original	68.1%
Target	99.3%
Herbie	99.4%

\[\begin{array}{l} \mathbf{if}\;y < 2.5816096488251695 \cdot 10^{-278}:\\ \;\;\;\;-x \cdot y\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(\sqrt{y + z} \cdot \sqrt{y - z}\right)\\ \end{array} \]

Derivation

1. Split input into 2 regimes

2. if y < -4.09999999999999989e-277

   1. Initial program 68.8%

      \[x \cdot \sqrt{y \cdot y - z \cdot z} \]

   2. Taylor expanded in y around -inf 92.2%

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

   3. Simplified 99.8%

      \[\leadsto x \cdot \color{blue}{\left(0.5 \cdot \frac{z}{\frac{y}{z}} - y\right)} \]
      Step-by-step derivation

      [Start] 92.2%	\[ x \cdot \left(0.5 \cdot \frac{{z}^{2}}{y} + -1 \cdot y\right) \]
      mul-1-neg [=>] 92.2%	\[ x \cdot \left(0.5 \cdot \frac{{z}^{2}}{y} + \color{blue}{\left(-y\right)}\right) \]
      unsub-neg [=>] 92.2%	\[ x \cdot \color{blue}{\left(0.5 \cdot \frac{{z}^{2}}{y} - y\right)} \]
      unpow2 [=>] 92.2%	\[ x \cdot \left(0.5 \cdot \frac{\color{blue}{z \cdot z}}{y} - y\right) \]
      associate-/l* [=>] 99.8%	\[ x \cdot \left(0.5 \cdot \color{blue}{\frac{z}{\frac{y}{z}}} - y\right) \]

   if -4.09999999999999989e-277 < y

   1. Initial program 71.2%

      \[x \cdot \sqrt{y \cdot y - z \cdot z} \]

   2. Taylor expanded in y around inf 99.5%

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

4. Final simplification 99.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -4.1 \cdot 10^{-277}:\\ \;\;\;\;x \cdot \left(0.5 \cdot \frac{z}{\frac{y}{z}} - y\right)\\ \mathbf{else}:\\ \;\;\;\;y \cdot x\\ \end{array} \]

Alternatives

Alternative 1
Accuracy	99.4%
Cost	836

\[\begin{array}{l} \mathbf{if}\;y \leq -4.1 \cdot 10^{-277}:\\ \;\;\;\;x \cdot \left(0.5 \cdot \frac{z}{\frac{y}{z}} - y\right)\\ \mathbf{else}:\\ \;\;\;\;y \cdot x\\ \end{array} \]

Alternative 2
Accuracy	99.3%
Cost	388

\[\begin{array}{l} \mathbf{if}\;y \leq -4.1 \cdot 10^{-277}:\\ \;\;\;\;y \cdot \left(-x\right)\\ \mathbf{else}:\\ \;\;\;\;y \cdot x\\ \end{array} \]

Alternative 3
Accuracy	52.0%
Cost	192

\[y \cdot x \]

Reproduce

herbie shell --seed 2023272 
(FPCore (x y z)
  :name "Diagrams.TwoD.Apollonian:initialConfig from diagrams-contrib-1.3.0.5, B"
  :precision binary64

  :herbie-target
  (if (< y 2.5816096488251695e-278) (- (* x y)) (* x (* (sqrt (+ y z)) (sqrt (- y z)))))

  (* x (sqrt (- (* y y) (* z z)))))