Average Error: 0.2 → 0.2
Time: 16.8s
Precision: 64
\[0.0 \lt m \land 0.0 \lt v \land v \lt 0.25\]
\[\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot m\]
↓
\[m \cdot \left(\frac{m}{\frac{v}{1 - m}} - 1\right)\]
\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot m
↓
m \cdot \left(\frac{m}{\frac{v}{1 - m}} - 1\right)
double f(double m, double v) {
        double r17745 = m;
        double r17746 = 1.0;
        double r17747 = r17746 - r17745;
        double r17748 = r17745 * r17747;
        double r17749 = v;
        double r17750 = r17748 / r17749;
        double r17751 = r17750 - r17746;
        double r17752 = r17751 * r17745;
        return r17752;
}
↓
double f(double m, double v) {
        double r17753 = m;
        double r17754 = v;
        double r17755 = 1.0;
        double r17756 = r17755 - r17753;
        double r17757 = r17754 / r17756;
        double r17758 = r17753 / r17757;
        double r17759 = r17758 - r17755;
        double r17760 = r17753 * r17759;
        return r17760;
}

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

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

  4. Final simplification0.2

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

Reproduce

herbie shell --seed 2019294 
(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 m)) v) 1) m))