Average Error: 0.2 → 0.2
Time: 24.9s
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 r27710 = m;
        double r27711 = 1.0;
        double r27712 = r27711 - r27710;
        double r27713 = r27710 * r27712;
        double r27714 = v;
        double r27715 = r27713 / r27714;
        double r27716 = r27715 - r27711;
        double r27717 = r27716 * r27710;
        return r27717;
}
↓
double f(double m, double v) {
        double r27718 = m;
        double r27719 = v;
        double r27720 = 1.0;
        double r27721 = r27720 - r27718;
        double r27722 = r27719 / r27721;
        double r27723 = r27718 / r27722;
        double r27724 = r27723 - r27720;
        double r27725 = r27724 * r27718;
        return r27725;
}

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 associate-/l*0.2

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

  4. Final simplification0.2

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

Reproduce

herbie shell --seed 2019306 +o rules:numerics
(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))