Average Error: 0.2 → 0.2
Time: 21.6s
Precision: 64
\[0.0 \lt m \land 0.0 \lt v \land v \lt 0.25\]
\[\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot m\]
↓
\[m \cdot \left(\frac{1 - m}{v} \cdot m - 1\right)\]
\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot m
↓
m \cdot \left(\frac{1 - m}{v} \cdot m - 1\right)
double f(double m, double v) {
        double r30686 = m;
        double r30687 = 1.0;
        double r30688 = r30687 - r30686;
        double r30689 = r30686 * r30688;
        double r30690 = v;
        double r30691 = r30689 / r30690;
        double r30692 = r30691 - r30687;
        double r30693 = r30692 * r30686;
        return r30693;
}
↓
double f(double m, double v) {
        double r30694 = m;
        double r30695 = 1.0;
        double r30696 = r30695 - r30694;
        double r30697 = v;
        double r30698 = r30696 / r30697;
        double r30699 = r30698 * r30694;
        double r30700 = r30699 - r30695;
        double r30701 = r30694 * r30700;
        return r30701;
}

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

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

  4. Applied times-frac0.2

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

  5. Simplified0.2

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

  6. Final simplification0.2

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

Reproduce

herbie shell --seed 2019195 
(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))