Average Error: 0.1 → 0.1
Time: 16.6s
Precision: 64
\[0.0 \lt m \land 0.0 \lt v \land v \lt 0.25\]
\[\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot \left(1 - m\right)\]
↓
\[\left(\frac{m}{v} \cdot \left(1 - m\right) - 1\right) \cdot \left(1 - m\right)\]
\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot \left(1 - m\right)
↓
\left(\frac{m}{v} \cdot \left(1 - m\right) - 1\right) \cdot \left(1 - m\right)
double f(double m, double v) {
        double r18568 = m;
        double r18569 = 1.0;
        double r18570 = r18569 - r18568;
        double r18571 = r18568 * r18570;
        double r18572 = v;
        double r18573 = r18571 / r18572;
        double r18574 = r18573 - r18569;
        double r18575 = r18574 * r18570;
        return r18575;
}
↓
double f(double m, double v) {
        double r18576 = m;
        double r18577 = v;
        double r18578 = r18576 / r18577;
        double r18579 = 1.0;
        double r18580 = r18579 - r18576;
        double r18581 = r18578 * r18580;
        double r18582 = r18581 - r18579;
        double r18583 = r18582 * r18580;
        return r18583;
}

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 flip--0.1

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

  4. Applied associate-*r/0.1

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

  5. Applied associate-/l/0.1

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

  7. Applied flip-+0.1

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

  8. Applied associate-*r/0.1

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

  9. Applied associate-/r/0.1

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

  10. Simplified0.1

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

  11. Final simplification0.1

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

Reproduce

herbie shell --seed 2019325 +o rules:numerics
(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)))