Average Error: 0.2 → 0.2
Time: 25.1s
Precision: 64
\[0.0 \lt m \land 0.0 \lt v \land v \lt 0.25\]
\[\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot m\]
↓
\[\left(\frac{m}{\frac{v}{1 - m}} - 1\right) \cdot m\]
\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot m
↓
\left(\frac{m}{\frac{v}{1 - m}} - 1\right) \cdot m
double f(double m, double v) {
        double r21713 = m;
        double r21714 = 1.0;
        double r21715 = r21714 - r21713;
        double r21716 = r21713 * r21715;
        double r21717 = v;
        double r21718 = r21716 / r21717;
        double r21719 = r21718 - r21714;
        double r21720 = r21719 * r21713;
        return r21720;
}
↓
double f(double m, double v) {
        double r21721 = m;
        double r21722 = v;
        double r21723 = 1.0;
        double r21724 = r21723 - r21721;
        double r21725 = r21722 / r21724;
        double r21726 = r21721 / r21725;
        double r21727 = r21726 - r21723;
        double r21728 = r21727 * r21721;
        return r21728;
}

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

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

  6. Final simplification0.2

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

Reproduce

herbie shell --seed 2019199 
(FPCore (m v)
  :name "a parameter of renormalized beta distribution"
  :pre (and (< 0.0 m) (< 0.0 v) (< v 0.25))
  (* (- (/ (* m (- 1.0 m)) v) 1.0) m))