Average Error: 0.2 → 0.2
Time: 4.8s
Precision: 64
\[0.0 \lt m \land 0.0 \lt v \land v \lt 0.25\]
\[\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot m\]
↓
\[\left(\frac{1}{\frac{\frac{v}{1 - m}}{m}} - 1\right) \cdot m\]
\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot m
↓
\left(\frac{1}{\frac{\frac{v}{1 - m}}{m}} - 1\right) \cdot m
double f(double m, double v) {
        double r11608 = m;
        double r11609 = 1.0;
        double r11610 = r11609 - r11608;
        double r11611 = r11608 * r11610;
        double r11612 = v;
        double r11613 = r11611 / r11612;
        double r11614 = r11613 - r11609;
        double r11615 = r11614 * r11608;
        return r11615;
}
↓
double f(double m, double v) {
        double r11616 = 1.0;
        double r11617 = v;
        double r11618 = 1.0;
        double r11619 = m;
        double r11620 = r11618 - r11619;
        double r11621 = r11617 / r11620;
        double r11622 = r11621 / r11619;
        double r11623 = r11616 / r11622;
        double r11624 = r11623 - r11618;
        double r11625 = r11624 * r11619;
        return r11625;
}

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. Using strategy rm

  5. Applied clear-num0.2

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

  6. Final simplification0.2

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

Reproduce

herbie shell --seed 2020018 
(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))