Average Error: 0.2 → 0.2
Time: 4.3s
Precision: binary64
\[0 < m \land 0 < v \land v < 0.25\]
\[\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot m\]
\[m \cdot \left(-1 + \frac{m}{\frac{v}{1 - m}}\right)\]
\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot m
m \cdot \left(-1 + \frac{m}{\frac{v}{1 - m}}\right)
(FPCore (m v) :precision binary64 (* (- (/ (* m (- 1.0 m)) v) 1.0) m))
(FPCore (m v) :precision binary64 (* m (+ -1.0 (/ m (/ v (- 1.0 m))))))
double code(double m, double v) {
	return (((m * (1.0 - m)) / v) - 1.0) * m;
}
double code(double m, double v) {
	return m * (-1.0 + (m / (v / (1.0 - m))));
}

Error

Bits error versus m

Bits error versus v

Try it out

Your Program's Arguments

Results

Enter valid numbers for all inputs

Derivation

  1. Initial program 0.2

    \[\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot m\]
  2. Using strategy rm
  3. Applied *-un-lft-identity_binary640.2

    \[\leadsto \left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot \color{blue}{\left(1 \cdot m\right)}\]
  4. Applied associate-*r*_binary640.2

    \[\leadsto \color{blue}{\left(\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot 1\right) \cdot m}\]
  5. Simplified0.2

    \[\leadsto \color{blue}{\left(-1 + \frac{m - m \cdot m}{v}\right)} \cdot m\]
  6. Using strategy rm
  7. Applied *-un-lft-identity_binary640.2

    \[\leadsto \left(-1 + \frac{\color{blue}{1 \cdot m} - m \cdot m}{v}\right) \cdot m\]
  8. Applied distribute-rgt-out--_binary640.2

    \[\leadsto \left(-1 + \frac{\color{blue}{m \cdot \left(1 - m\right)}}{v}\right) \cdot m\]
  9. Applied associate-/l*_binary640.2

    \[\leadsto \left(-1 + \color{blue}{\frac{m}{\frac{v}{1 - m}}}\right) \cdot m\]
  10. Final simplification0.2

    \[\leadsto m \cdot \left(-1 + \frac{m}{\frac{v}{1 - m}}\right)\]

Reproduce

herbie shell --seed 2021174 
(FPCore (m v)
  :name "a parameter of renormalized beta distribution"
  :precision binary64
  :pre (and (< 0.0 m) (< 0.0 v) (< v 0.25))
  (* (- (/ (* m (- 1.0 m)) v) 1.0) m))