Average Error: 0.1 → 0.1
Time: 10.8s
Precision: 64
\[0.0 \lt m \land 0.0 \lt v \land v \lt 0.25\]
\[\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot \left(1 - m\right)\]
↓
\[\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot \left(1 - m\right)\]
\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot \left(1 - m\right)
↓
\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot \left(1 - m\right)
double f(double m, double v) {
        double r20777 = m;
        double r20778 = 1.0;
        double r20779 = r20778 - r20777;
        double r20780 = r20777 * r20779;
        double r20781 = v;
        double r20782 = r20780 / r20781;
        double r20783 = r20782 - r20778;
        double r20784 = r20783 * r20779;
        return r20784;
}
↓
double f(double m, double v) {
        double r20785 = m;
        double r20786 = 1.0;
        double r20787 = r20786 - r20785;
        double r20788 = r20785 * r20787;
        double r20789 = v;
        double r20790 = r20788 / r20789;
        double r20791 = r20790 - r20786;
        double r20792 = r20791 * r20787;
        return r20792;
}

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 0.1

    \[\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot \left(1 - m\right)\]
  2. Using strategy rm

  3. Applied *-un-lft-identity0.1

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

  4. Final simplification0.1

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

Reproduce

herbie shell --seed 2019351 
(FPCore (m v)
  :name "b parameter of renormalized beta distribution"
  :precision binary64
  :pre (and (< 0.0 m) (< 0.0 v) (< v 0.25))
  (* (- (/ (* m (- 1 m)) v) 1) (- 1 m)))