Average Error: 0.2 → 0.2
Time: 7.3s
Precision: binary64
\[0.0 \lt m \land 0.0 \lt v \land v \lt 0.25\]
\[\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot m\]
↓
\[\left(\left(1 - m\right) \cdot \frac{m}{v} - 1\right) \cdot m\]
\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot m
↓
\left(\left(1 - m\right) \cdot \frac{m}{v} - 1\right) \cdot m
double code(double m, double v) {
	return ((double) (((double) ((((double) (m * ((double) (1.0 - m)))) / v) - 1.0)) * m));
}
↓
double code(double m, double v) {
	return ((double) (((double) (((double) (((double) (1.0 - m)) * (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-identity0.2

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

  4. Applied associate-*r*0.2

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

  5. Simplified0.2

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

  6. Final simplification0.2

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

Reproduce

herbie shell --seed 2020182 
(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))