Average Error: 0.2 → 0.2
Time: 20.2s
Precision: 64
\[0.0 \lt m \land 0.0 \lt v \land v \lt 0.25\]
\[\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot m\]
↓
\[m \cdot \left(\frac{\frac{\left(1 \cdot 1 - m \cdot m\right) \cdot m}{v}}{1 + m} - 1\right)\]
\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot m
↓
m \cdot \left(\frac{\frac{\left(1 \cdot 1 - m \cdot m\right) \cdot m}{v}}{1 + m} - 1\right)
double f(double m, double v) {
        double r1052793 = m;
        double r1052794 = 1.0;
        double r1052795 = r1052794 - r1052793;
        double r1052796 = r1052793 * r1052795;
        double r1052797 = v;
        double r1052798 = r1052796 / r1052797;
        double r1052799 = r1052798 - r1052794;
        double r1052800 = r1052799 * r1052793;
        return r1052800;
}
↓
double f(double m, double v) {
        double r1052801 = m;
        double r1052802 = 1.0;
        double r1052803 = r1052802 * r1052802;
        double r1052804 = r1052801 * r1052801;
        double r1052805 = r1052803 - r1052804;
        double r1052806 = r1052805 * r1052801;
        double r1052807 = v;
        double r1052808 = r1052806 / r1052807;
        double r1052809 = r1052802 + r1052801;
        double r1052810 = r1052808 / r1052809;
        double r1052811 = r1052810 - r1052802;
        double r1052812 = r1052801 * r1052811;
        return r1052812;
}

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 flip--0.2

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

  4. Applied associate-*r/0.2

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

  5. Applied associate-/l/0.2

    \[\leadsto \left(\color{blue}{\frac{m \cdot \left(1 \cdot 1 - m \cdot m\right)}{v \cdot \left(1 + m\right)}} - 1\right) \cdot m\]
  6. Using strategy rm

  7. Applied associate-/r*0.2

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

  8. Final simplification0.2

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

Reproduce

herbie shell --seed 2019192 
(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))