Diagrams.Solve.Polynomial:cubForm from diagrams-solve-0.1, C

Average Accuracy: 87.9% → 87.9%

Time: 4.3s

Precision: binary64

Cost: 320

Math

\[\frac{x}{y \cdot 3} \]

↓

\[\frac{x}{y \cdot 3} \]

FPCore

(FPCore (x y) :precision binary64 (/ x (* y 3.0)))

↓

(FPCore (x y) :precision binary64 (/ x (* y 3.0)))

C

double code(double x, double y) {
	return x / (y * 3.0);
}

↓

double code(double x, double y) {
	return x / (y * 3.0);
}

Fortran

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

↓

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

Java

public static double code(double x, double y) {
	return x / (y * 3.0);
}

↓

public static double code(double x, double y) {
	return x / (y * 3.0);
}

Python

def code(x, y):
	return x / (y * 3.0)

↓

def code(x, y):
	return x / (y * 3.0)

Julia

function code(x, y)
	return Float64(x / Float64(y * 3.0))
end

↓

function code(x, y)
	return Float64(x / Float64(y * 3.0))
end

MATLAB

function tmp = code(x, y)
	tmp = x / (y * 3.0);
end

↓

function tmp = code(x, y)
	tmp = x / (y * 3.0);
end

Wolfram

code[x_, y_] := N[(x / N[(y * 3.0), $MachinePrecision]), $MachinePrecision]

↓

code[x_, y_] := N[(x / N[(y * 3.0), $MachinePrecision]), $MachinePrecision]

Target

Original	87.9%
Target	87.9%
Herbie	87.9%

\[\frac{\frac{x}{y}}{3} \]

Derivation

1. Initial program 89.2%

   \[\frac{x}{y \cdot 3} \]

2. Final simplification 89.2%

   \[\leadsto \frac{x}{y \cdot 3} \]

Alternatives

Alternative 1
Accuracy	87.8%
Cost	320

\[x \cdot \frac{0.3333333333333333}{y} \]

Alternative 2
Accuracy	87.7%
Cost	320

\[\frac{x}{y} \cdot 0.3333333333333333 \]

Reproduce

herbie shell --seed 2023157 
(FPCore (x y)
  :name "Diagrams.Solve.Polynomial:cubForm  from diagrams-solve-0.1, C"
  :precision binary64

  :herbie-target
  (/ (/ x y) 3.0)

  (/ x (* y 3.0)))